The addition of PPE preserved the spherical shape of the “Boba balls”, and as the PPE ratio increased, new junction zones were observed in SEM images. The in vitro digestibility of PPE was significantly (p < 0.05) improved by preserving PPE from the mouth and gastric medium, and “Boba balls” showed the highest release and bio-accessibility in the intestinal medium. Consequently, PPE as a by-product could be successfully used at 2% concentration for enhancing the functionality and bio-accessibility of “Boba balls” without affecting sensory properties.“Boba balls” or pearls have recently gained popularity for beverages or food toppings. “Boba balls” could be developed into functional foods by the encapsulation of bioactive compounds. In this study, gelatin/sodium alginate composite “Boba balls” enriched with pomegranate peel extract (PPE) at different concentrations  were prepared. They were characterized in terms of physical, rheological, textural, morphological, and sensory properties, as well as in vitro digestion, bio-accessibility, and release kinetic of PPE. Adding PPE improved the “Boba” mix’s viscoelasticity and decreased the “Boba balls”’ hardness. The increasing PPE ratio significantly ( Traditional bubble tea, also known as Boba tea, is a drink made from tea, milk, syrup, and tapioca pearls. The traditional “Boba balls” are made from gelatinized tapioca starch that has been formed into a pearl shape. Customarily, they acquire their color and flavor from brown sugar, but may incorporate other colors or flavors . “Boba bWhile there are many studies on alginate encapsulation by calcium alginate gels ,4,5 therThe spherical balls can be produced with fruits providing different aromas; these structures can also be produced by using herbal extracts with functional properties, such as phenolic compounds, antioxidants, anthocyanins, etc. Considering the consumption and market value of “Boba balls”, it is obvious that enriching these products with plant extracts rich in functional properties will significantly contribute to human health. In a study, Liu, Xiao, Feng, Zhang, Li, Tu and Niu  producedPunica granatum L.) peel is a standard by-product from the production of pomegranate juice, and the peel makes up 40–50% of the total weight of the pomegranate. There are numerous studies about the composition of pomegranate peel extract (PPE), and it was previously reported that PPE is rich in bioactive compounds, for example polyphenols, flavonoids, anthocyanin and tannins such as ellagic acid, tannins, gallic acid, and punicalagin A and punicalagin B [Pomegranate  peels were kindly provided by Bursa Gelatin  and Dohler , respectively. The pomegranate juice (PJ) was purchased from a local market in Bursa, Turkey, and it was used during the production of “Boba balls” for providing a pomegranate aroma. The peels were dried at 40 °C for 48 h in an air-circulated oven , and then dried peels were pulverized with a blender . The powdered pomegranate peels were filled in a Ziploc bags and stored at −18 °C.Sodium alginate (SA) (CAS number:9005-38-3), ethanol (absolute), Folin- Ciocalteu, 2,2’-azino-bis 3-ethylbenzothiazoline-6-sulphonic acid (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH), Neocuproine Cupric Ion Reducing Antioxidant Capacity (CUPRAC), and chemicals for simulating the in vitro digestion were acquired from Sigma-Aldrich . Bovine gelatin at ~225 bloom and pomegranate (v/v) was mixed with powdered peels at 1/10 (w/v) ratio, and then homogenized  for 5 min at 10,000 rpm. In order to increase the extraction yield, the powdered peels underwent a further 15 min of ultrasonic homogenization  at 50% amplitude using a 7 mm titanium probe. An ice-water bath was used to maintain a temperature below 25 °C throughout the ultrasonic homogenization. Solid particles were removed by the centrifugation  for 10 min at 9056× g, and the supernatant was evaporated at 45 °C using a vacuum rotary evaporator . The evaporated extract was stored at −20 °C for 2 days before the freeze dried  at −50 °C for 72 h and the resulting powders were stored at −18 °C in Ziploc bags.The preparation of pomegranate peel extract (PPE) was carried out following procedures from our previous study . Brieflyw/v) concentrations were dissolved in 70 mL of pomegranate juice (PJ) for 5 min at 40 °C on a magnetic stirrer, and separately, SA (1.5 g) was dissolved in distilled water (30 mL) for 5 min at 70 °C on a magnetic stirrer. After cooling the SA solution at 40 °C, gelatin solution was combined with the SA solution before mixing for an additional 15 min at 40 °C. Then, the solution was cooled down to 20 °C and immediately transferred to a 20 mL syringe. The syringe was settled on the syringe pump , and the solution was fed drop by drop from a 15 cm distance into a 10 cm high bath of cooled corn oil at −9 °C. The gel “Boba balls” were collected with a stainless-steel filter spoon and mixed with 10% CaCl2 solution on a magnetic stirrer for 2 min. Finally, the “Boba balls” were collected by the filter spoon and washed twice with distilled water. The “Boba balls” were coded as C , PPE1 (containing 1% PPE), PPE2 (containing 2% PPE), and PPE3 (containing 3% PPE), and samples of “Boba balls” were stored under refrigeration at 4 °C until further analysis. Prior to FT-IR and scanning electron microscopy, the “Boba balls” were stored at −20 °C for 2 days before the lyophilization at −50 °C for 72 h.The “Boba balls” production was performed by the methodology of Liu, Xiao, Feng, Zhang, Li, Tu and Niu , with thThe gelation properties of “Boba” mix were evaluated with a controlled stress rheometer . A cone-plate geometry (25 mm diameter and 2° cone angle) was utilized for rheological evaluation. The composite solutions were held at 20 °C for 120 s on the flat surface for temperature equilibrium. The storage v/v) in a falcon tube, and then the mixture was kept in a shaking water bath at 25 °C for 2 h. After that, solids were separated from the supernatants of the samples by centrifugation for 10 min at 9000× g. Then the supernatant was used for the TPC and antioxidant capacity analysis. Analysis of TPC was performed based on the Folin–Ciocalteu method. The calculation of TPC was performed using gallic acid to construct the calibration curve [The TPC and antioxidant capacity of the PPE and “Boba balls” samples were measured. For this purpose, samples (1 g) were vortexed with ethanol/water , ABTS) , free raThe total monomeric anthocyanin (TMA) content of “Boba balls” was established following the method and calculation of Lee, et al. , and resThe measurement of the encapsulation efficiency (EE) of PPE in “Boba balls” was determined based on the TPC of samples before and after the formation of “Boba balls”, and was calculated by the following formula:L*, a*, and b*) values of “Boba balls” was assessed via a color spectrophotometer . For measurements, ten different “Boba balls” were used, and they were directly placed on the measuring cell, and the color values were recorded. Prior to the color measurement of the samples, calibration of the instrument was performed using a white standard plate . The total color difference (ΔE) of “Boba balls” based on the control sample was calculated according to following formula:“Boba balls” were visually observed with a stereo zoom microscope  at 8× magnification, and the images were captured with an integrated 5-megapixel camera. The color  of “Boba balls” was measured with a texture analyzer  outfitted with a 36 mm diameter P/36R probe. “Boba balls” containing PPE extract were directly placed on the center of heavy-duty platform, and the probe was compressed to 50% of samples at a 5 mm/s test speed. The waiting time between the two cycles of the compression was 5 s, and 10 different “Boba balls” were subjected to testing at room temperature .−1 scan resolution between 4000 and 400 cm−1 wavenumber range.Lyophilized “Boba balls” and PPE extracts were converted into powder form with the help of a mortar, powdered samples (~20 mg) were directly placed in the sample chamber of the FT-IR device , and FT-IR spectra were taken directly with the help of an ATR diamond crystal at a 2 cmThe scanning electron microscope (SEM)  was used for the visualization of the microstructure of “Boba balls”. “Boba balls” that broke after being immersed in liquid nitrogen for 1 min were fixed on the aluminum stubs by double-sided tape, followed by gold-palladium coating of samples in a low vacuum chamber . Images were captured at an accelerating voltage of 20 kV. The surface microstructural images were captured at 100× and 500× magnifications.g, and then supernatants were stored at −18 °C. The bio-accessibility (%) was calculated by dividing the results obtained from the bio-accessible fraction by the sum of the extractable fraction and multiplying by 100.The in vitro gastrointestinal digestion of PPE and “Boba balls” was determined with the TPC and antioxidant capacity analyses  after tht and infinite times, respectively. k and n refer to the kinetic constant depending on the geometrical and structural properties of particles, and release exponent, which shows the release mechanism, respectively. m and For the release kinetic study, the “Boba balls” in the gastric condition were sampled every 15 min for 120 min, and then every 30 min until 240 min. For the intestinal condition, samples were held in the gastric condition for 120 min and the sampling procedure was repeated as above. The release kinetics of phenolic compounds from “Boba balls” in the stomach and intestine mediums were evaluated by the Korsmeyer–Peppas (Equation (3)) and Peppas–Sahlin (Equation (4)) kinetic models, which are widely used in release kinetic studies, as well as the Makoid–Banakar model (Equation (5)):The sensory panel was selected from the students and academicians of the Food Engineering Department at Bursa Technical University, Turkey. Panelists consisted of 15 women and 15 men between the ages of 19 and 42 years old that were semi-trained about the product and evaluation criteria before starting the evaluation. The products were evaluated by a five-point hedonic scale, where 1 = dislike very much, 2 = dislike slightly, 3 = neither like nor dislike, 4 = like slightly, and 5 = like very much. The evaluation was based on appearance, elasticity, taste, and aroma, general acceptability, and affordability. The samples were randomly numbered with three digits before serving to panelists. After the evaluation of each sample, the panelists rinsed their mouths with water.The production of “Boba balls” was performed in triplicate, and rheology, TPC, antioxidant capacity, EE, in vitro release, and sensory tests were conducted at least in triplicate, while texture and color analysis were measured for 10 samples per batch of “Boba balls”. The results of these tests are presented as mean ± standard deviation. A one-way analysis of variance was performed to determine any significant differences between treatments, followed by Duncan’s multiple range test at a 95% confidence interval with SPSS v21 . OriginPro v8.5  was used for the graphical visualization of the results. The release kinetic mathematical models were applied to experimental data by using MS Excel  with DDSolver.p > 0.05), whereas gelation temperature significantly increased (p < 0.05). The changes in gelation temperatures could be explained by the increased total soluble solid content of the “Boba” mix and the formation of triple-helix structures due to hydroxyl groups and hydrogen bonding. Additionally, the high molecular weight of PPE and SA provides a protective effect for the “Boba” mix gel structure [The production of “Boba balls” occurs from two different processes, mainly the heating-swelling and cooling-molding of the polymer mix, and therefore, temperature ramp analysis of the “Boba” mix can provide significant information about the simulation of production. The storage ( gelatin . The heaperature . The heaassembly . The addtructure . Similartructure  for a kop > 0.05). However, the TPC values of PPE were high ; the EE results of “Boba balls” may have decreased due to the high gelatin content of the “Boba” mix. The higher gelatin concentration resulted in the production of a porous, hydrophilic, and loose gel network structure, causing the encapsulated material to leach out during “Boba balls” production [+2 ions is needed. Due to the hydrophilic nature of PPE, the encapsulation efficiency may have decreased due to the possibility that a small quantity of the extract was emitted into the CaCl2 solution [The encapsulation efficiency (EE), total phenolic content (TPC), antioxidant capacity, and total monomeric anthocyanin content (TMA) of PPE and PPE-enriched “Boba balls” are summarized in oduction . In a production  stated tsolution .p < 0.05) was observed from PPE3 . CUPRAC, ABTS, and DPPH methods are the most widely used techniques for establishing the antioxidant capacity, and therefore these methods were used for PPE and “Boba balls”. Since the measurement principles of these methods are different from each other, the results obtained gave the expected results, but were not comparable with each other. As expected, the PPE was found to have the highest antioxidant capacity. Amongst the “Boba balls” samples, the control showed the lowest antioxidant capacity results due to the lower antioxidant capacity of pomegranate juice. The addition of PPE significantly improved the antioxidant capacity of “Boba balls” (p < 0.05). The increasing antioxidant capacity with PPE could be attributed to the secondary metabolites of pomegranate peels, such as phenolic compounds including flavonoids and tannins, which engage in metal chelating and scavenging activities [The TPC value of PPE was determined to be 234.18 mg gallic acid equivalent/g of extract, which was comparable to the quantity reported in the literature . The conp < 0.05) increased TMA value (p < 0.05).The major pigments of pomegranate peel are dependent on the anthocyanin content, which belongs to the flavonoid family. The color change and bioactive compounds in the pomegranate peel are mainly affected by the variety, environmental conditions , cultivation conditions, growing region, and ripening stage of the fruit to be harvested . The TMAp < 0.05) affected the L* and b* values but did not affect a* values (p > 0.05). The lowest L* value (16.97) was observed for the PPE1 sample, while PPE3 showed the highest L* value (29.72). The significant change in the L* values could be attributed to the transparency and opacity of the samples, which is caused by light transmission through control and PPE1 samples, whereas PPE2 and PPE3 reflected the light and hence showed higher L* values. The b* values of the “Boba ball” samples were not significantly (p > 0.05) altered by the addition of PPE up to 3%. At 3% PPE, the “Boba balls” showed the highest b* results, which meant increased yellowness. However, the lower PPE concentrations caused a more greenish color of “Boba balls”. As expected, the typical color of “Boba balls” was reddish due to the dominant color of pomegranate juice, and the a* values were not significantly (p > 0.05) impacted by the PPE addition.The visual appearance of “Boba balls” captured by a stereo microscope is shown in E) of “Boba balls” were evaluated based on the control sample, which was produced without PPE addition. The results showed that there was no significant difference between PPE1 and PPE2 (p > 0.05), whereas PPE3 showed a significantly higher ΔE value than others (p < 0.05). The increasing PPE ratio caused higher ΔE values probably due to increasing L*, a* and b* values. According to the classification of ΔE values by Cserhalmi, et al. [The total color differences  for the control and PPE1 samples; however, the further addition of PPE significantly (p < 0.05) decreased the hardness. The decreasing hardness values after the 0.25% purple-sweet-potato-peel anthocyanin addition were also reported by Liu, Xiao, Feng, Zhang, Li, Tu and Niu [p < 0.05) increased. The degree of difficulty required to break down the gel’s internal structure is an indication of the cohesiveness value. Although control, PPE1, and PPE2 showed the highest cohesiveness results, the lowest value was determined for PPE3. This means that the internal structure of “Boba balls” containing PPE at 1 and 2% was better than those containing 3% PPE. Resilience displays the degree to which a product strives to regain its initial height. PPE3 showed the lowest resilience and control, and PPE2 had the highest results.The hardness and elasticity of a hydrogel-type food product are crucial parameters that directly affect consumer acceptability. These parameters can easily and rapidly be determined by the texture profile analysis, and the results are provided in  and Niu . Springi−1, which can be allocated to hydroxylic groups’  stretching vibration bond; aliphatic C-H groups because of C-CH and C-CH2 bonds; COO groups of aldehyde and ketone, and carboxylic acid C=O groups; the C=O and C=C stretching vibration bonds; and the stretching bonding of CO and OH deformation in alcohols, respectively [−1 for the “Boba” mix was higher than PPE because the “Boba” mix consists of gelatin, and these peaks (Amide I and II) are fingerprints for protein molecules. In addition, a similar backbone structure was observed for the “Boba balls” enriched with PPE. However, a new peak, which was not observed in the PPE and “Boba” mix, was detected for PPE-enriched “Boba balls” at the 2361 cm−1 wavenumber, and the intensity of this peak increased with increasing PPE ratio. This peak is highly related to carbonyl specific absorption [The potential molecular interactions between the “Boba” mix and core material (pomegranate peel extract) were investigated with the FT-IR spectrum. For this purpose, freeze-dried “Boba” mix without PPE, neat PPE, and “Boba balls” enriched with PPE at different ratios were analyzed, and the related images are illustrated in ectively . The “Bosorption . OverallThe interactions between polymers used for “Boba ball” production can be further explained by morphological characterization, and for this purpose, the freeze-dried “Boba balls” were investigated with an electron microscope. The images were captured from the surface (50×) and cross-section (500×) of broken “Boba balls” by liquid nitrogen . The defThe in vitro digestibility of coated and/or encapsulated bioactive substances is very important because most of the bioactive substances are required to pass through the mouth and stomach without being damaged and to be released in the intestinal medium, thus increasing the bio-availability. In vitro bio-accessibility of PPE-loaded “Boba balls” was evaluated by the assessment of TPC and antioxidant capacity. The findings are depicted in 2 values (>0.950) both for the stomach and intestine medium. In the Korsmeyer–Peppas model, the exponent n describes the release mechanism and highly depends on the geometry of the micro particles. If n = 0.43, spherical-shaped micro particles show diffusion-controlled release or Fickian diffusion, whereas if n = 0.85, micro particles indicate swelling-controlled release or case II transport of active compounds. However, in some cases in which n values are between 0.43 and 0.85, the release behavior of micro particles can be defined as the superposition of both phenomena, called anomalous transport [n values lower than 0.43 both for gastric and intestinal conditions and showed a quasi-Fickian diffusion of TPC [The in vitro release kinetic of phenolic compounds in the “Boba balls” in the stomach and intestinal environments was evaluated with widely used release models. The cumulative percentage release vs. time graph of TPC in PPE and “Boba balls” is described in ransport . In our n of TPC . These rn of TPC ,39.p < 0.05) effects on some sensory properties of the final product but did not change other sensory properties such as taste and aroma, smell, acceptability, and affordability. However, the appearance, elasticity, and consistency scores of “Boba balls” decreased with an increasing PPE ratio. The changes in sensory scores are well supported by The sensory properties of “Boba balls” enriched with PPE at various concentrations are depicted in p > 0.05) effect was observed on the encapsulation efficiency and melting temperature. The highest bioactive properties, such as antioxidant capacity, total monomeric anthocyanin, and total phenolic content, were observed for PPE, and as expected, increasing PPE concentration in “Boba balls” significantly (p < 0.05) improved these results. The visual appearance of “Boba balls” changed and became more opaque as PPE concentration increased. The hardness and chewiness of “Boba balls” significantly decreased with PPE addition. FT-IR results clearly revealed the chemical bonds occurring between PPE and “Boba balls”. Additionally, more junction zones were observed from the microstructural images of PPE-incorporated samples. The in vitro bio-accessibility of “Boba balls” clearly revealed that PPE was successfully covered by the “Boba” mix and increased the gastrointestinal bio-accessibility. Moreover, the release kinetic of PPE from “Boba balls” was modeled by widely used drug-release kinetic models with a higher 2R than 0.85. Sensory scores clearly showed that the smell, taste, aroma, overall acceptability, and affordability of “Boba balls” did not change with the PPE addition. At the same time, appearance, elasticity, and consistency decreased. Considering all these results, PPE can be successfully encapsulated in the “Boba balls”. It can also be a valuable supplementary material for improving not only the bioactivity of “Boba balls” but also the bio-accessibility.In this study, “Boba balls” were successfully enriched with pomegranate peel extract (PPE), and then the bioactivity, bio-accessibility, release kinetic, physical, chemical, and morphological properties, as well as sensory properties, of “Boba balls” were characterized. PPE addition significantly increased the gelation temperature, whereas no significant (


Woody tissue carbon (C) concentration is a key wood trait necessary for accurately estimating forest C stocks and fluxes, which also varies widely across species and biomes. However, coarse approximations of woody tissue C  remain commonplace in forest C estimation and reporting protocols, despite leading to substantial errors in forest C estimates. Here, we describe the Global Woody Tissue Carbon Concentration Database (GLOWCAD): a database containing 3,676 individual records of woody tissue C concentrations from 864 tree species. Woody tissue C concentration data—i.e., the mass of C per unit dry mass—were obtained from live and dead woody tissues from 130 peer-reviewed sources published between 1980–2020. Auxiliary data for each observation include tissue type, as well as decay class and size characteristics for dead wood. In GLOWCAD, 1,242 data points are associated with geographic coordinates, and are therefore presented alongside 46 standardized bioclimatic variables extracted from climate databases. GLOWCAD represents the largest available woody tissue C concentration database, and informs studies on forest C estimation, as well as analyses evaluating the extent, causes, and consequences of inter- and intraspecific variation in wood chemical traits.  At the same time, C cycling in forested biomes is highly dynamic and transient, with estimates indicating that forests sequester between ~2.15 to 2.4 Pg C y−1 globally on average2. Throughout the 2000s, structurally intact old-growth forests accounted for ~0.85 Pg C y−1, while C sequestration was ~1.30 Pg C y−1 in secondary forests2. Tropical regions are particularly important in sequestering atmospheric carbon dioxide (CO2) in both regenerating5 and intact forests7. Nevertheless, recent analyses from both temperate8 and tropical regions7 have indicated that the magnitude of C sinks in old-growth forests are declining.Forests play a critical role in the global carbon (C) cycle, with the world’s forests storing an estimated 861 ± 66 Pg C across tropical (~471 Pg C), boreal (~272 Pg C), and temperate forest ecosystems (~119 Pg C)12. Estimates of AGB are then converted into C estimates by multiplying these values by a woody tissue C concentration, commonly referred to in the literature as a C fraction16 . Accurate woody tissue C concentration data are therefore critical in (1) accurately estimating terrestrial forest C budgets and sequestration rates17, (2) estimating the C emissions associated with land-use change18, and ultimately (3) informing decision-making related to the identification of forests with high C storage capacity11. Indeed, the Intergovernmental Panel on Climate Change’s (IPCC) Tier 3 C accounting protocols suggests that a “specific carbon fraction…should also be incorporated” when estimating C stocks and fluxes in AGB13. Moreover, woody tissue C concentration data can be employed in studies on the abiotic or biotic predictors of variation in – and possible adaptive significance of – wood chemical traits across tree species20, as well as evaluating the role that different sample extraction, preparation, and analytical methods have on wood C fractions17. Owing at least in part to a lack of large woody tissue C datasets, these research areas have received relatively little attention in comparison to other suites of plant traits21.The amount of C stored within, and transferred to and from, trees and forests have been estimated from field- or remote-sensing-based observations of tree attributes, which are used to obtain estimates of tree- or forest aboveground biomass (AGB)13), which has led to substantial systematic errors in forest C estimates. For example, our recent analyses indicated that generic woody tissue C fractions overestimate C stocks by approximately 8.9% in tropical forests19. Similar issues exist for the accounting of C stocks and fluxes in dead wood, with recent analyses indicating that generic dead wood C fractions may result in dead wood C pools being overestimated by ~3.0 Pg C globally22. Although multiple studies evaluating woody tissue C concentrations in trees globally through field- or meta-analyses now exist25, there is no single woody tissue C data repository to aid researchers in accessing and using these data.To date, most C estimation and reporting protocols use generic approximations of woody tissue C concentrations , which contains woody tissue C concentrations measured on live and dead tree tissues, spanning all forested biomes. By organizing and standardizing data from a range of taxonomic groups and woody tissue-types (described below), GLOWCAD represents a resource that helps improve our understanding of both global forest C dynamics and inter- and intraspecific variability in wood chemical traits. GLOWCAD only includes data from peer-reviewed sources. In addition to associated information on the taxonomic identities and woody tissue types for each woody tissue C data point, GLOWCAD includes geographical and associated bioclimatic data obtained from climate databases27.To address these issues, we created and describe here the “Global Woody Tissue Carbon Concentration Database”  variation in dead woody tissue C concentrations22; (3) relationships between woody tissue C concentrations and tree life-history strategies22; and (4) climate correlates of woody tissue C concentrations in trees28.Data records in GLOWCAD are stored in 3 easy-to-use Comma Separated Values (.csv) spreadsheets Fig. . All spr25, and more recently published in 201819 and 202129. GLOWCAD is therefore based on a systematic search on primary literature of all peer-reviewed papers that cited previously published studies on woody tissue C concentrations25. We searched key terms “carbon”, “tree”, “wood carbon”, “coarse woody debris”, “dead wood”, and “wood nutrient”, as well as “carbon” alongside major tree tissue types , within four web-based platforms , in order to identify peer-reviewed publications that present species- specific woody tissue C concentration data.Data compilation expanded earlier versions of the GLOWCAD first initiated in 201229 and the Global Root Traits (GRooT) Database28. The TRY datasets included are the Subarctic Plant Species Database (dataset ID 105), Plant Traits for Pinus and Juniperus Forests in Arizona (dataset ID 193), Plant Physiology Database (dataset ID 97), Panama Tree Traits Database (dataset ID 230), FRED database (dataset ID 339), and the ECOCRAFT Database (dataset ID 12). While most root C values in GRooT were included in the FRED database (v. 2.0)30, data from 10 papers in GRooT were added to GLOWCAD (n = 197 data points): Isaac et al.31; Liu et al.32; Wang et al.33; Minden et al.34; Alameda et al.35; Aubin et al.36; Fernández-García et al.37; Grechi et al.38; Ineson et al.39; and Pregitzer et al.40.In addition to peer-reviewed papers, other sources of data included in GLOWCAD include the TRY Plant Trait Database (v. 5.0)41. If species-by-tissue-specific woody tissue C data were not published, the corresponding authors were contacted to provide data.To be included in GLOWCAD, the species-specific binomial nomenclature and tissue-specific information for each woody tissue C sample was required. A detailed field and lab methodology was also necessary, in order to maximize our sample size while permitting reliable species- and tissue-specific analysis. Where a single paper contained multiple tissue- and species-specific woody tissue C records, all the published values were recorded. In the majority of cases, woody tissue C data were extracted directly from published tables or from supplementary data of the articles. In instances where woody tissue C data were published as figures, the data was extracted using the WebPlotDigitizer v4.2 software42) presented both boreal and temperate data, which were differentiated in our database based on the sampling location coupled with a consultation of species distribution maps. Species taxonomy was first recorded as presented as in published articles. A final list of taxa was then compared with, and resolved according to, the Taxonomic Name Resolution Service v. 4.043. Both original and resolved taxonomy is maintained in GLOWCAD. Inclusion of new published data was halted as of Dec. 31, 2020.Each published woody tissue C record was then classified according to the forest biome in which it was sampled. A small number of studies (e.g.44). When classifying position of dead wood, “standing” referred to snags and suspended woody debris, and “downed” referred to anything sampled from the forest floor. The default position was “downed” for the few publications that did not specify position.When classifying dead wood data, we considered three primary factors associated with woody tissue decomposition and related chemical change: A) decay class (DC), B) position, and C) size (diameter and length). In the majority of publications, dead woody tissue C values were reported along a conventional 1–5 DC scale. These values were included in GLOWCAD as published, while noting the DC scale employed. In cases where DC was reported as a two-category range (e.g. DC 1–2), the higher DC was included in GLOWCAD. In cases where a multiple category DC was presented (e.g. DC 3–5), the middle DC value was used in GLOWCAD. In the few instances DC was reported along a 0–5 point scale (where DC of 0 was defined as dead and not live wood), dead wood reported with a DC of 0 was classified as DC 1. Lastly, in a subset of papers the number of years since tree death (instead of DC) was reported. In these cases, years since death were converted to DC based on published decay class transition metrics  to label the study region’s dominant Whittaker biome45, and therefore categorize the region as one of Boreal forest, Subtropical desert, Temperate grassland/desert, Temperate rain forest, Temperate seasonal forest, Tropical rain forest, Tropical seasonal forest/savanna, or Woodland/shrubland. A list containing the details collected from each publication is presented  and mean annual precipitation (MAP) of the study region were recorded as an average. The study regions’ latitude and longitude were also used to further describe its climate with WorldClim v.2) data data27. n = 973 observations of dead wood C only, from 121 species; 2) the second version contained n = 1,145 observations of live woody tissue C only, from 415 species paired with geographic coordinates and climate data; and 3) the third version contained n = 2,432 observations of live woody tissue C only, from 636 species including all of the observations of the previous version .GLOWCAD is the fourth iteration of the woody tissue C dataset, though these earlier versions did not use the same acronym, and contained differing sets/ subsets of data based on different research questions. Three earlier versions are publicly hosted in the TRY Plant Trait Database, such that: 1) the first version contained n = 3,405), and includes 271 new woody tissue C observations from 10 additional publications46. In sum, n = 3,676 data points in the GLOWCAD version described here. Unlike previous versions, GLOWCAD also includes information on growth habit or ‘woodiness’ (described below) and the original binomial nomenclatures as listed in their publications.GLOWCAD is a single data product which consolidates the dead and live woody tissue C observations of all prior iterations  is derived from sources published in or after 2010 . Data outputs consist of a single database of 3,676 woody tissue C observations from 130 sourcesn = 927 data points), Fabaceae (n = 383 data points), Fagaceae (n = 335 data points), Cupressaceae (n = 159 data points) and Betulaceae (n = 146 data points) being most well represented , followed by temperate , subtropical/ Mediterranean (n = 518 data points) and boreal (n = 301 data points) forests. Across the entire database, woody tissue C ranged from 18.4–75.1% , while the remaining 27%  came from dead plant measurements. In regard to tissue types, stems , roots  and branches  were most well represented , and MAP ranging from 160–5,130 mm .In GLOWCAD, 73% of data points were obtained from woody tissue measurements of live plants , while the least common was the Minimizing the Loss of Carbon (MLC) method described by Jones and O’Hara . Drying temperatures ranged widely from 18–110 °C, with drying durations spanning 5–360 hours. The majority of publications made use of Elemental Analyzers  when estimating woody tissue C concentrations. In sum, 34% of observations in GLOWCAD  were associated with exact geographic coordinates of their sampling locations , and only these observations were assigned climate information from WorldClim (v.2)27.The foremost drying method employed by publications incorporated into GLOWCAD was conventional oven-drying  family (n = 8 species) were also included in GLOWCAD (n = 32 data points) since these 1) met the functional definition of “wood” and are 2) are important contributors to aboveground biomass C in Neotropical forests, relative to other biogeographic locations161, monocot species162 and non-conventional woody species (e.g. tree ferns)163.Growth habit or ‘woodiness’ was evaluated for all species included in GLOWCAD to ensure that woody tissue C data corresponded only to woody plant species, based on a functional definition of “woody”: i.e., having a persistent aboveground stemn = 1,241 data points). Linear regression models indicated that statistically significant positive relationships existed between (1) publication- vs. WorldClim-derived estimates of MAT ) and (2) publication- vs. WorldClim-derived estimates of MAP ).Bioclimatic variables were assigned to observations which were accompanied by the specific geographic coordinates (excluding ranges) of their sample location, using WorldClim (v.2). In GLOWCAD 34% of woody tissue C observations include a WorldClim-derived estimate of MAT and MAP (GLOWCAD is openly available for use in any application. It can be accessed via (1) the DRYAD Digital Repository (10.5061/dryad.18931zcxk), (2) a GitHub repository, (3) the TRY Plant Trait Database, and (4) upon request to the corresponding author. GLOWCAD is licensed under CC-BY 4.0.Supplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5


Evidence regarding associations of circulating saturated fatty acids (SFAs) with chronic diseases is mixed. The objective of this study was to determine the associations between total or individual SFA biomarkers and the risk of cardiometabolic diseases.Four electronic databases were searched from inception to March 2022. Three investigators independently assessed for inclusion and extracted data. Random-effects or fixed-effects models was used to estimate the pooled relative risks (RRs) and corresponding 95% confidence intervals (CIs) for the association of total or individual SFA biomarkers, including even-chain SFAs , odd-chain SFAs  and very-long-chain SFAs , with risk of incident type 2 diabetes (T2D), cardiovascular disease , CHD and stroke.A total of 49 prospective studies reported in 45 articles were included. Higher concentration of circulating total SFAs was associated with an increasing risk of cardiometabolic diseases, the risk increased significantly by 50% for CVD (95%CI:1.31–1.71), 63% for CHD (95%CI:1.38–1.94), 38% for stroke (95%CI:1.05–1.82), respectively. Similarly, levels of even-chain SFAs were positively associated with higher risk of chronic diseases, with RRs ranging from 1.15 to 1.43. In contrast, the risk of cardiometabolic diseases was reduced with increasing odd-chain SFA levels, with RRs ranging from 0.62 to 0.91. A higher level of VLCSFAs corresponded to 19% reduction in CVD. Further dose-response analysis indicated that each 50% increment in percentage of total SFAs in circulating was associated with an 8% higher risk of T2D  and trends toward higher risk of CVD . Inverse linear relationships were observed between 17:0 biomarker and T2D or CVD risk.Our findings support the current recommendations of reducing intake of saturated fat as part of healthy dietary patterns. Further studies are needed to confirm our findings on these SFAs in relation to cardiometabolic outcomes and to elucidate underlying mechanisms.https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022329182], identifier [CRD42022329182].[ In vitro and animal studies have demonstrated that SFAs can accelerate the atherosclerotic process, induce cellular inflammation and influence the insulin sensitivity (Limiting intake of saturated fatty acids (SFAs) has been considered as a key component to reduce the risk of chronic disorders for decades . The 202sitivity , 5. Resusitivity . TherefoAssociations of dietary SFAs with the incidence of major cardiometabolic diseases have been reported by some large prospective cohort studies, and findings of these studies were inconsistent –9. InterTo fill this knowledge gap, a dose-response meta-analysis of data from prospective studies was conducted to evaluate the associations between the circulating even-chain SFAs, odd-chain SFAs, VLCSFAs as well as the total SFAs and the incident of T2D, CVD, CHD, and stroke.This study was registered in PROSPERO (CRD42022329182) and reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines.A comprehensive literature search of Web of Science, PubMed, EMBASE, and Cochrane Library were conducted to identify relevant published articles from inception to March 2022. The following terms were used: “fatty acids” or “saturated fatty acid” or “saturated fatty acids” or “lauric acid” or “myristic acid” or “pentadecanoic acid” or “palmitic acid” or “margaric acid” or “stearic acid” or “arachidic acid” or “heneicosylic acid” or “behenic acid” or “tricosylic acid” or “lignoceric acid,” AND “type 2 diabetes” or “diabetes mellitus” or “impaired glucose” or “impaired fasting insulin” or “cardiovascular disease” or “coronary heart disease” or “heart disease” or “ischemic heart disease” or “coronary artery disease” or “myocardial infarction” or “stroke” or “ischemic stroke” or “haemorrhagic stroke,” AND “blood” or “marker” or “biomarker” or “serum” or “plasma” or “whole blood” or “adipose tissue” or “circulating” or “erythrocytes” or “red blood cell” or “cholesterol esters,” AND “prospective” or “nested case-control” or “cohort” or “case-cohort” or “follow-up” or “longitudinal.” The search was restricted to human studies without any language restriction. Additionally, reference lists of retrieved articles, review articles, and meta-analyses were manually scanned to identify any other relevant studies. Corresponding authors of eligible articles were contacted for any further relevant work, published or unpublished, to reduce risk of publication bias.Studies were included in the meta-analysis if they satisfied the following criteria: (1) the study design was prospective  with a follow-up more than 1 year; (2) the exposures of interest were total or individual SFA biomarkers  measured in any type of tissue [circulating blood  or adipose tissue]; (3) the endpoints of interest included incident T2D, CVD, CHD, and stroke; (4) the adjusted relative risks (RRs), odds ratios, or hazard ratios with corresponding 95% confidence intervals (CIs) or standard errors were presented or could be calculated. Whenever reports referred to identical outcomes from the same population, only those with the highest number of cases or the longest follow-up times were retained to avoid data duplication. Potentially eligible studies were assessed independently by three investigators , with discrepancies resolved by discussion until consensus was reached.Data extraction were conducted independently by 3 investigators . A standardized data collection form was applied to extract the following baseline characteristics from each study: first author name, publication year, study location, study name, study design, follow-up duration, participant characteristics , SFA subtype, biological sample type , assessment method , outcomes of interest (type and number of cases), covariates adjusted for in the multivariable model, and the risk estimate with 95%CIs for all categories of each biomarkers. When studies reported multiple results based on different numbers of covariates included in statistical adjustments, the results that adjusted for the most number of variables were extracted.Study quality was performed by the same authors according to the validated Newcastle-Ottawa Scale (NOS) , which aI2 statistic , prospective case-cohort study (PCC), or nested case-control study (NCC)], geographical location , assessment method , biomarker type , number of cases (< 300 or ≥ 300), as well as study quality (moderate or high). Dose-response analyses were conducted based on the method proposed by Greenland and Longnecker , GLC (n = 18), NMR (n = 2), and GC-tandem mass spectrometry (n = 1). 48 studies measured SFA biomarkers in circulating blood  and one in adipose tissue. Most studies were adjusted for age (n = 47) and smoking (n = 46), and alcohol drinking (n = 40), physical activity (n = 39) and body mass index (n = 38) were also controlled for in many studies. The results of the study quality assessment showed that 44 studies were graded as high quality, others were graded as moderate quality  higher risk of T2D. For every 50% increase in percentage of odd-chain SFAs concentration in total SFAs, the risk of T2D was reduced by 8% for 15:0 , 16% for 17:0 , respectively.Twenty-two studies were assessed to estimate risk between the concentrations of SFA biomarkers and T2D, including 166,745 participants and 21,252 T2D events , 27–44. P < 0.001; Pheterogeneity = 0.56; P = 0.002; Pheterogeneity < 0.001; P = 0.03; Pheterogeneity < 0.001; P < 0.001; Pheterogeneity = 0.15; P = 0.001; Pheterogeneity = 0.45; P = 0.09; Plinearity = 0.96) for each 50% increment in percentage of total SFAs in circulating. For every 50% increment in percentage of 17:0 and 24:0 concentrations of total SFAs, the RR of CVD decreased by 18% for 17:0 , 10% for 24:0 , respectively.Association between circulating SFAs and risk of CVD were investigated in 27 studies, with 53,845 participants and 14,613 CVD events , 45–66. P < 0.001; Pheterogeneity = 0.45; P = 0.05; Pheterogeneity = 0.003; P = 0.02; Pheterogeneity = 0.21; P = 0.03; Pheterogeneity = 0.22; P < 0.001; Pheterogeneity = 0.23; A total of 14 studies with 7,314 cases in 26,772 participants were included to investigate the relationship between circulating SFAs and CHD risk , 49–59. P = 0.02; Pheterogeneity = 0.76; P < 0.001; Pheterogeneity = 0.11; P = 0.004; Pheterogeneity = 0.10; The association between circulating SFAs with stroke risk were investigated in nine studies, comprising 16,589 participants and 3,176 stroke events , 60–66. P > 0.05 for both tests).Sensitivity analysis showed that no single study substantially changed the statistical significance or direction of the combined RR for any of the outcomes. Visual inspection of the funnel plot did not reveal substantial asymmetry –67. SimiIn the present meta-analysis, higher concentration of circulating total SFAs was associated with an increasing risk of CVD, CHD, and stroke, rather than T2D. Compared with the adverse effect of even-chain SFAs, odd-chain SFAs and VLCSFAs showed a significant inverse association with these cardiometabolic diseases. Our study suggested that individual circulating SFAs may not be equally associated with the risk of chronic diseases, which raises the possibility of generating public health recommendations and nutritional guidelines of SFAs in these cardiometabolic diseases prevention.Previous studies have found that higher dietary intake of SFAs was associated with an increasing risk of chronic health condition . Data frin vitro experiments, Pal et al. found that even-chain SFAs could induce proinflammatory cytokine expression in adipocytes through the FetA and TLR4 pathway, resulting in insulin resistance  stress –73. Systsistance . Animal sistance . Along wsistance , 78. Thesistance . Clinicasistance , 81. Measistance , which msistance  and initogenesis , ultimatSD increment of the sum of 15:0 and 17:0 was associated with a 10% decrement of this inflammatory marker (Compared with the deleterious effects of even-chain SFAs, the present results suggested that a higher level of odd-chain SFAs was associated with a lower risk of chronic conditions. Data from Northern Sweden Health and Disease Study suggested that each percent increase of the proportion of this fatty acid species was significantly associated with a risk reduction of a first myocardial infarction . The Väsy marker . Additioy marker . Moreovey marker , 88–93. y marker , 95. DesFor VLCSFAs, the present study of an inverse association between circulating VLCSFAs and CVD risk was in line with the results from previous epidemiological studies , 96. WhiSome limitations of this study should be mentioned. Firstly, individual blood SFAs were quantitatively measured once and changes of these SFAs over time might lead to potential random measurement error caused by within-person variation. However, among the Nurses’ Health Study and Nurses’ Health Study II participants, reasonable validity and reproducibility of concentration of SFAs such as 15:0, 16:0, and 24:0 have been demonstrated with intraclass correlation coefficients, indicating a single measurement of fatty acid biomarker levels can reliably represent long-term levels over time . SecondlOur meta-analysis of existing prospective studies indicated that a higher concentration of total SFAs and even-chain SFAs was associated with an increasing risk of cardiometabolic diseases, which supported the current recommendations of reducing intake of saturated fat as part of healthy dietary patterns. In addition, our finding of protective effects of odd-chain SFAs has potentially important clinical implications for preventing cardiometabolic diseases. Further studies are apparently needed to confirm our findings on these SFAs in relation to cardiometabolic outcomes and to elucidate underlying mechanisms.The original contributions presented in this study are included in the article/LM, JL, and MM generated the idea for the study, formulated an analytical plan, and supervised the study. ZL, HL, and HJ searched for relevant studies, conducted quality assessment, and extracted the data. ZL and HJ performed data analysis. ZL and HL wrote the manuscript and all other authors revised the manuscript. All authors approved the final manuscript for submission and acquired, analyzed, or interpreted the data.


There is currently no robust method to evaluate how built environment design affects our emotion. Understanding emotion is significant, as it influences cognitive processes, behavior, and wellbeing, and is linked to the functioning of physiological systems. As mental health problems are becoming more prevalent, and exposure to indoor environments is increasing, it is important we develop rigorous methods to understand whether design elements in our environment affect emotion. This study examines whether the scale of interior built environments modulate neural networks involved in emotion regulation. Using a Cave Automatic Virtual Environment (CAVE) and controlling for indoor environmental quality (IEQ), 66 adults  were exposed to context-neutral enclosed indoor room scenes to understand whether built environment scale affected self-report, autonomic nervous system, and central nervous system correlates of emotion. Our results revealed enlarged scale increased electroencephalography (EEG) power in the β bandwidth. Frontal midline low-γ and high-γ power were also found to increase with enlarged scale, but contrary to our hypothesis, scale did not modulate frontal midline power or lateralization in the θ or α bandwidths. We did not detect an effect of scale on autonomic indicators or self-reported emotion. However, we did find increased range in skin conductance response (SCR) and heart rate variability (HRV) to the built environment conditions. This study provides a rigorous empirical framework for assessing the environmental impact of a design characteristic on human emotion and suggests that measures of high-frequency oscillations may provide a useful marker of the response to built environment. Our empirical study provides a technique and approach for assessing the impact of built environment design on emotion. Using virtual reality (VR), we assessed autonomic nervous system, electroencephalography (EEG) correlates and self-report of emotion to built environments that vary in scale. Although we did not detect autonomic and EEG markers linked to emotional processing, we found evidence that enlarged scale of the built environment modulates high-frequency oscillatory activity, which may have further implications for attention and cognitive performance. This novel approach for measuring neural correlates and physiological indicators controlled the exposure through a Cave Automatic Virtual Environment (CAVE), while monitoring indoor environmental quality (IEQ). This research and technique enhance our understanding of how to predict, design, and optimize interior spaces for optimal mental health.There is currently no robust method to evaluate how building design affects our emotion. Emotion is recognized to play an important role in our mental and physical health . AccordiEnvironmental enrichment studies in animal models have suggested that features of the physical enclosure, including size of the environment , impact Scale has strong theoretical underpinnings in social and architectural history . The conEmerging empirical studies exploring design characteristics of interior built environments have approached the question using experimental designs where design aesthetics comprise a complex array of features and characteristics . HoweverIn this study, we investigated whether the scale of an interior room would result in modulation of autonomic, EEG, and self-report indicators of emotion. We defined emotion as a response to an environmental event involving multiple systems of cognitive, autonomic, and behavioral response . Here, wTo reduce the complexity of building design, the study used a Cave Automatic Virtual Environment (CAVE), to create an environmentally controlled, cost-effective simulation, and providing greater sensorimotor integration than virtual reality (VR) headsets . Scene nAs this field of research is early in development, in conjunction with our a priori hypotheses, we opted to perform exploratory analyses across the remaining EEG power spectra regions of interest (ROIs) and of the overall power spectral density. This approach was selected as we were interested in understanding whether the built environment may affect other cognitive functions such as perception, attention, and memory which have been associated with higher frequency oscillatory activity. Neural oscillations in the γ frequency range have been associated with visual tasks such as perception , attentiTo investigate our research questions, we examined whether there are detectable differences in autonomic, EEG, and self-report indices of emotion when changing the design characteristic of scale within a virtual built environment. Using EEG, we investigated frontal midline power and lateralization in the α and θ bandwidths. In addition to our primary hypothesis-driven analyses, we conducted exploratory data driven analysis of low-γ and high-γ, and overall power spectral density across electrodes. The study was approved by the Deakin University Human Research Ethics Committee and carried in accordance with relevant guidelines and regulations. On completion of the study participants were offered a $20.00 AUD gift voucher as reimbursement for their time. An overview of the experimental design and setup is illustrated in The sample size for this study was determined by an a priori power analysis using G*Power 3.1.9.3 . BecauseThe study took place over a consecutive five-week period at Deakin University Waurn Ponds Campus, Geelong, Victoria, Australia. We recruited 66 adults  aged between 18 and 55 years old; with no prior training or work experience in built environment design; and with no prior diagnosed psychiatric, neurologic, or neurodevelopmental conditions. A healthy adult sample was selected because of the experimental nature of the study and to reduce confounding variables. All participants were able to speak and read English and had normal or corrected-to-normal vision. 12 different languages were spoken at home by participants and 14 countries were identified as the location participants spent the most time growing up in.Participants were individually tested, and each session ran for ∼90 min. On arrival, participants completed a secure web-based survey , which took between 15 and 30 min. The survey included 13 questions regarding socio-demographic background, experience, and expertise for both VR and computer gaming. This was followed by a personality test using the abbreviated International Personality Inventory Pool  and cleaning the surface of any residue with an alcohol wipe. Three ECG electrodes were placed on the hand and wrists. The positive electrode was located on the left wrist and the negative electrode was placed on the right wrist. The reference was placed on the knuckle of the middle finger on the left-hand side. For SCR, finger electrodes were placed underneath the middle and index finger on the left hand and secured with a Velcro strap. A respiratory belt transducer was positioned on the sternum and secured firmly around the chest. A 10 min, three-lead ECG recording  was performed. Circuit zero was applied before the first recording and a subject zero was undertaken between each condition recording. A sampling rate of 1000 Hz and notch filter of 50 Hz was used.We used a 64-channel cap (Philips Hydrocel Geodesic Sensor Net 64-channel HCGSN) for acquiring EEG data. Net Station 5 Geodesic EEG software, version 5.4.2  was used to record EEG data. The cap was positioned on the head after being soaked in an electrolyte solution. Data were acquired at a sampling rate of 1000 Hz, with Cz as the online reference. The Cz electrode was not included in our analysis, however, for the purposes of visualization, we have interpolated the Cz site for figures. A continuous recording was created for each participant and the EEG trace was manually time stamped by the researcher at the start and end of each 2-min scene exposure. The majority of impedances were kept under 50 kΩ with an average value of 25.2 kΩ (SD = 7.75).Participants were then led into the VR lab containing the CAVE. Participants were assisted to step into the CAVE and take a seat while the researcher carefully took the cords leading from the attached EEG, ECG, and SCR sensors to connect to the monitoring equipment behind the participant. A pair of stereoscopic glasses to view the CAVE projection were then carefully fitted on top of the EEG cap. These remained on throughout the experiment. Impedances were checked and adjusted when necessary to ensure the quality of electrode-to-scalp contact.Participants were seated for the duration of the experiment and instructed to pay attention to the scene they were presented. We elected to run a static resting state study where participants sat immersed in the space, rather than setting a task involving movement through thresholds, as this has been thought to affect cognition and memory . By sittWe measured indoor environmental quality (IEQ) variables within the CAVE throughout the study. These measures were analyzed to ensure any fluctuations to these properties linked to data which may influence emotion and neurophysiological response were minimized. To reduce the chance of negative influence all data were collected over a consecutive period in spring to reduce heat/cool load on the building triggering changes in the heating, ventilation, and air conditioning system.The CAVE consisted of three walls (3 m wide × 2.4 m high) and a floor (2.4 m wide × 3 m long), each with Barco Galaxy NW-12 stereoscopic projectors. The projectors connect to a series of image generators (computers) each consisting of Nvidia Quadro P6000 graphic cards. The graphic cards are synced using Quadro Sync II cards at 120 Hz (60 Hz per eye) to frame lock the projectors to ensure rendered images are displayed at the same time. The CAVE uses an optical-based tracking system consisting of eight cameras that tracks active LED markers located on the stereoscopic glasses to track user movements. The tracking system operates at 240 Hz with sub millimeter accuracy and connects back to a Virtual Reality Peripheral Network (VRPN) server. The CAVE uses a custom-built Unity environment to run VR experiences with Vertical Sync (VSync) set to 60 frames per second. The Unity environment connects to the tracking systems via VRPN server using an ethernet connect and updates the tracked position on each rendered frame.Autodesk Revit was used to create a 3D model that represented a conventional cubic room that was then exported into the Unity game engine (2019.2.15) for CAVE integration. A matte plaster texture was applied to the three wall surfaces with a slight gloss texture of bumpy concrete applied to the floor. A matte wood texture was applied to the door, doorway and chair with a low gloss metal surface applied to the door handle. Once material color, texture settings and lighting had been applied to the model, the room was duplicated (Unity Prefabs) into three separately scaled rooms. Prebaked lightmaps were applied for each scaled room to ensure consistent lighting and texture relative to the scale and “realistic” as possible to view.The control condition was designed using Standards Australia measurements for a residential internal door (820 × 35 × 2040 mm) , and rooA wooden fixed chair with a seat pad for comfort and back support for posture consistency was positioned in the center of the CAVE, effectively within the center of each virtual room regardless of scale. The chair remained in the central position to ensure all participants were situated in the same location. Room lights were switched on for safety when a participant entered the CAVE, that displayed the resting state scene. After the participant was set up and briefed on the experiment procedure, the researcher turned off the room lights.CR100 Measurement and Control System with LoggerNet 4.6.2 software  was used to acquire and record data. Before the experiment, we completed a test recording and calibrated the recording equipment to ensure the readings were accurate in accordance with EN ISO 7730 Fanger Comfort Model .IEQ data were recorded at 1 min intervals which were date and time stamped. We averaged the 1 min readings from the corresponding time stamped data within each participants session to create an overall average per person and then determined the average across all participants. Although the VR lab was acoustically soundproof and no talking occurred during the scene recordings, a handheld sound level meter was used to capture fluctuating mechanical equipment noises from the CAVE projector lamp ventilation and cooling system which could not be controlled. Sound level recordings were conducted at different intervals during experiments to establish an overall range across the five-week period. Overall mean air and wet-bulb globe temperature was within the 21–25°C range for optimal performance , the carSelf-report of emotion was collected using the self-assessment manikin where three dimensions, pleasure, arousal, and dominance, are recorded by the participant using a visual 5-point scale . The parPhysiological data were acquired using PowerLab 4/35 (ADI Instruments PL3504) with a respiratory belt transducer (ADI Instruments TN1132/ST), Ag/AgCl ECG electrodes (Ambu Bluesensor N) and SCR finger plate electrodes (ADI Instruments MLT118F). Data for all physiological measures were acquired at 1000 Hz, and for SCR circuit zero was applied before the first recording and a subject zero was undertaken between each condition recording. Online filtering parameters differed between measures: ECG −100 to 100 mV; SCR −40 to 40 μS; and respiration −10 to 10 V. Five channels were set to record and calculate ECG, SCR and respiration. Results were divided into time segments  and one overall time block (10–110 s) to capture whether an effect occurred at onset but diminished because of habituation over the recording. Three datasets from participants were excluded in the SCR and respiration analysis because of equipment fault. In respiratory data, 10 s from the onset of recording was removed for the measure to be accurately detected. For consistency the last 10 s was also removed.Heart rate variability (HRV) settings used a beat classification for RR intervals between 600 to 1400 ms and complexity of 1 to 1.5. Ectopic heartbeats were excluded from analysis. Detection was adjusted to a minimum peak height of 1.2 SD and typical QRS width between 80 ms over a 350 ms minimum period. A low-pass filter of 30 Hz was used. We analyzed the root mean square successive difference (RMSSD) and the SD of the R-R interval (SDRR) time domain components of the QRS complex within the ECG recording in accordance with the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology . RespiraN = 2 HRV and breathing datasets were excluded from the analysis because of HRV arrythmia; however, data for SCR were still incorporated. To correct for distribution, a log transformation (log10) was applied to both HRV and SCR data. To correct for normality, we removed outliers which fell below [Q1 – (1.5 × IQR)] and were above [Q3 + (1.5 × IQR)]. A within subjects repeated measures ANOVA with the Greenhouse–Geisser correction for sphericity was used across the six physiological measures we analyzed. To control for multiple comparisons, a false discovery rate (FDR) correction was applied to the results .  results .The EEG data were preprocessed using EEGLab (v2019.1) , an openUsing the time-stamped event markers in the continuous recording, each file was then split into 120 s block files using the start marker for each condition. Data were segmented into 3-s epochs for subsequent analyses. Finally, additional artifact rejection was performed to remove any remaining noisy epochs with data exceeding ± 150 μV using the EEGLab ‘pop_eegthresh’ function. After cleaning we calculated the average epochs remaining for each condition and participant (mean number of epochs = 39.5 ± 1.46). Lastly, we converted data from each participant/electrode to the frequency domain using the fast Fourier transform (FFT) with Hanning taper in the FieldTrip toolbox for EEG/MEG-analysis , θ (4–7 Hz) α (8–12 Hz), β (13–29 Hz), low-γ (30–45 Hz), and high-γ (55–70 Hz). Power was then averaged over electrodes within three hypothesis-driven a priori ROIs: frontal midline , frontal right-hemispheric , and frontal left-hemispheric sites . During a posteriori analysis of γ lateralization, we selected sites from across the whole scalp to run an exploratory analysis . A lateralization index was generated to understand the power difference between the average over the frontal left and right ROIs, where higher values correspond to stronger power in the right compared with the left ROIs :(α)=. We removed extreme values which fell below [Q1 – (1.5 × IQR)] and were above [Q3 + (1.5 × IQR)]. Overall statistical analysis was conducted in RStudio using a repeated measures ANOVA with G-G correction. To correct for multiple comparisons where significance was detected within-subjects, the FDR method was used . The FDRhttps://doi.org/10.17605/OSF.IO/5MVN3.Source data and analysis code to accompany this manuscript submission are all available to be viewed on Open Science Framework: Six measures were preselected to analyze physiological response to robustly compare group differences in distribution, variability and skew . We calcF = 7.04, p ≤ 0.001, η2p = 0.110). Power was significantly lower in resting state than: small , control , large , and extra-large . There was also significant increase in power when comparing the small to the extra-large condition , the control to the extra-large , and the large to the extra-large .We found significant differences between the scale conditions for β power across the average of all channels  = 13.6, p ≤ 0.001, η2p = 0.229). During post hoc analysis we detected significantly lower power for resting state to: small , control , large , and extra-large . We also detected a significant increase from the control to the extra-large in the low-γ bandwidth, but this was lost after applying FDR correction for multiple comparisons. Lastly, an increase in high-γ power was detected in scale conditions when compared with the resting state  = 12.8, p ≤ 0.001, η2p = 0.217). These effects were only seen between resting and the scale conditions: small  = −4.949, pcorrected ≤ 0.001, 95% CI ], control , large , and extra-large .In the low-γ bandwidth we found significant differences  = 15.1, p ≤ 0.001, η2p = 0.229). With differences between resting and the scale conditions: small  = −5.001, pcorrected ≤ 0.001, 95% CI ], control , large , and extra-large . Similar effects were seen in the θ bandwidth  = 13.0, p ≤ 0.001, η2p = 0.203). Follow-up analysis indicated lower power was detected for resting state than small , control , large , and extra-large . Lastly, α waves, which are commonly found during awake rest, showed within-subject effects  = 5.00, p = 0.007, η2p = 0.089); however, follow-up analysis indicated that resting state α power was only significantly lower to the control condition . Results are shown in We also detected significant differences in the remaining bandwidths; however, 10.1523/ENEURO.0104-22.2022.f2-1Extended Data Figure 2-1Figure 2-1, DOCX file.Descriptives (mean/SD) for EEG analysis. Download 10.1523/ENEURO.0104-22.2022.f2-2Extended Data Figure 2-2p, p value; FDR, FDR correction. Download Figure 2-2, DOCX file.Statistical significance values for EEG analysis. Note: power spectral density, frontal midline, and θ and α lateralization statistics are derived from parametric one-way repeated measures ANOVAs. 10.1523/ENEURO.0104-22.2022.f2-3Extended Data Figure 2-3A), θ 4–7 Hz with amplitude range (B), and α 8–12 Hz (C). Download Figure 2-3, EPS file.EEG topographies and boxplots with quartile ranges and medians for the overall power spectra in the δ, θ, and α bandwidths. Note: the Cz site has been interpolated for this figure. δ 1–3 Hz  =25.7, p ≤ 0.001, η2p = 0.255). Post hoc comparisons showed significant differences between resting state and all conditions: small , control , large , and extra-large . We also detected an increase in power from the control to the extra-large , and the large to the extra-large .An exploratory analysis to further investigate frontal midline and lateralization in the low-γ and high-γ bandwidth was undertaken after analyzing the results of the overall power spectral density. Frontal midline power in the low-γ bandwidth increased with the scale of the room  =16.6, p ≤ 0.001, η2p = 0.211). Post hoc analysis revealed differences between the resting state and all conditions: small , control , large , and extra-large . We also found a difference between the control to extra-large , and large to extra-large .An effect of condition was also seen in the high-γ bandwidth  = 9.23, p ≤ 0.001, η2p = 0.156). Post hoc comparisons showed these differences were constrained to comparisons between the resting state to conditions, with a significant increase between resting state and all conditions: small , control , large , and extra-large . We also detected an effect for α frontal midline power  =8.58, p ≤ 0.001, η2p = 0.118). However, these effects were limited to comparisons between resting state to the built environment scale conditions, which were lost during correction for multiple comparisons.An effect of condition on frontal midline power was found in the θ band  = 10.2, p ≤ 0.001, η2p = 0.178). Post hoc analysis revealed the significant increases in θ lateralization was between resting state and the scale built environment conditions: small , control , large , and extra-large . We also detected difference in frontal α lateralization  = 3.71, p = 0.018, η2p = 0.072). Post hoc analysis revealed there were differences between resting state and the conditions, but these did not survive correction. Results are shown in Significant differences were also detected in the frontal hemispheric θ lateralization  = 3.89, p = 0.007, η2p = 0.064). RMSSD reflects changes to vagal tone and is less affected by changes in respiration  = 3.395, pcorrected = 0.010, 95% CI ], and the extra-large , but we did not detect significant difference to the small or large conditions. We detected an effect in the SDRR  = 2.79, p = 0.032, η2p = 0.047). However, post hoc analysis revealed these values did not survive correction for multiple comparisons.HRV within-subjects effects for time-domain showed an effect of condition in the RMSSD  = 2.22, p = 0.096, η2p = 0.042) and Mx-Mn  = 1.07, p = 0.368, η2p = 0.024) did not reveal significant differences between conditions.Respiration measures analyzed were the mean value and maximum minus minimum (Mx-Mn). Within-subjects comparisons for the mean  = 1.68, p = 0.170, η2p = 0.046). There was, however, a significant difference between conditions in Mx-Mn  = 10.7, p ≤ 0.001, η2p = 0.171). Post hoc comparisons for the Mx-Mn showed a significant increase from resting state to conditions with small , control , large  and extra-large . Results are shown in SCR measures were the mean and the Mx-Mn of the slope. The within-subjects analysis did not show significant differences between conditions in the mean  for physiological analysis. Download 10.1523/ENEURO.0104-22.2022.f4-2Extended Data Figure 4-2p, p value; FDR, FDR correction. Download Figure 4-2, DOCX file.Statistical significance values for physiological measures. Note: statistics are derived from one-way repeated measures ANOVAs. F = 12.0, p ≤ 0.001, η2 = 0.156). Post hoc comparisons showed significant positive increases between resting state and all conditions, small , control , large , and extra-large , but did not reveal significant differences between scale conditions. No significant effects were observed for self-reports of arousal  = 1.36, p = 0.251, η2 = 0.020) or dominance  = 1.78, p = 0.143, η2 = 0.027).It is important to understand whether participants can accurately identify changes to their emotional state. Currently, accepted practice during postoccupancy evaluations of buildings is to complete surveys with building users to understand whether their needs are being met. However, the degree to which subjective emotional judgments are associated with electrophysiological measures related to emotion is unclear. During the experiment, participants provided self-reports of their emotional state using the Self-Assessment Manikin. Self-report of pleasure showed an effect of condition , arousal , and dominance  shown in Using the baseline resting state scores as a comparator, we analyzed whether participants rated themselves higher or lower for each of the three measures and compared this to the direction of change in the most responsive physiological measure, SCR Mx-Mn. Using Pearson’s As each participant experienced the resting state before a randomized set of conditions, we checked for stimulus habituation by comparing results of SCR Mx-Mn, and the averaged γ EEG power spectra density with the order of exposure presented to each participant. We did not find a positive or negative linear relationship, which argues against the possibility that the difference between resting state and the scale conditions was because of the exposure order.We measured IEQ variables within the CAVE throughout the study. Air temperature (°C) was stratified across three height levels of low (M = 22.2 ± SEM = 0.108), mid (M = 22.2 ± SEM = 0.106), and high (M = 22.3 ± SEM = 0.105). Wet-bulb globe temperature (°C), which measures apparent temperature, was stratified across four height levels of low (M = 22.1 ± SEM = 0.106), mid (M = 22.2 ± SEM = 0.102), high (M = 22.2 ± SEM = 0.104), and approximate head height for standing position (M = 23.1 ± SEM = 0.111). Air velocity (m/s) was also stratified across four levels of low (M = 0.076 ± SEM = 0.003), mid (M = 0.069 ± SEM ≤ 0.001), high (M = 0.070 ± SEM = 0.001), and head (M = 0.006 ± SEM ≤ 0.001). We also recorded overall relative humidity  and carbon dioxide in parts per million . Noise levels (dB) fluctuated because of mechanical projector lamp ventilation (M = 46.7 ± SEM = 0.383). Results are shown in Extended Data To understand whether personality played a role in response to the built environment, we tested whether differences in participants’ personality accounted for differences in response to the built environment. Participants completed the abbreviated International Personality Inventory Pool (IPIP-NEO-120) before the experiment. To check for an association between autonomic reactivity in the built environment and personality we ran a correlation analysis using the most reactive physiological measure, SCR Mx-Mn. We did not observe any correlations as presented in With limited exploratory work conducted in the field , this stThe results of this study indicate that changes in HRV and SCR do occur during built environment exposures, which are modulated through the autonomic nervous system. This has been thought to correspond with limbic system activation, which is involved in our behavioral and emotional response. It is important to distinguish that these lowered levels of HRV and elevated SCR to the built environment scenes do not equate to a positive emotion or a better environment for our health. Likewise, we cannot rule out that these changes will result in long-term negative effects, however research shows elevated arousal and stress over a long period of time can be detrimental to our health . InsteadThere are multiple theories for hemispheric lateralization in emotional processing studies. Increased asymmetry of the right hemisphere has been associated with emotional stimuli, regardless of valence , 2000. FThe study also revealed that self-report of emotion was not an accurate indicator for increased autonomic nervous system response. Emotion processing studies investigating alignment between self-report and physiological indicators remain inconsistent. With some studies reporting consistency , while oThere is evidence widespread high-frequency activity is increased during a range of complex cognitive tasks . As we fAs the study is exploratory, further work understanding the interplay between design elements is required. It is expected that this technique and singular approach can be further used with different design elements with larger, more complex scenes. The scene created was purposefully designed to be context neutral. This meant it was devoid of color, materiality/excessive texture, atypical geometry, and furnishings, which may indicate to the participant the context/setting. However, this is not realistic as we do not experience environments that have so little visual information. This study also relied on visual information processing to understand the effect of scale. Work is required to understand whether similar physiological activity and neural encoding occurs when processing the built environment through other sensory modalities such as the auditory system through processing reverberation feedback to determine the scale of the space. Future research could steadily progress in complexity by exploring how these design elements of the environment interact with other enrichment components through studies involving motor activity, cognitive stimulation, and the presence of other people in the space.The study also limited participants to those self-reporting they had no underlying mental health conditions. This may mean a broader more inclusive sample will enable us to understand whether the built environment impacts those with preexisting psychological, psychiatric, neurodevelopmental, and neurodegenerative conditions to a greater extent than the study sample.Active debate continues over the ecological validity of virtual environments to simulate physical environments . VR enabThe ability for built environment design to modulate neural processing may have implications on our cognitive, attentional, perceptual, and emotional functioning. With the potential to deliver significant public health, economic and social benefits to the entire community. This work generates new knowledge for industry and policy makers to enable enhanced understanding, prediction, and optimization of built environment design. It is important that attention is drawn to pursuing future studies that investigate whether built environment design can provide a neuroprotective factor for individuals who are at increased risk of developing a psychological disorder because of other environmental and epigenetic stressors. This study provides a rigorous empirical framework for assessing the impact of the built environment on human emotion for future studies. The findings confirm that the buildings we inhabit play a role in determining our health.


The novel multivalued neutrosophic aggregation operators are proposed in this paper to handle the complicated decision-making situations with correlation between specific information and partitioned parameters at the same time, which are based on weighted power partitioned Hamy mean (WMNPPHAM) operators for multivalued neutrosophic sets (MNS) proposed by combining the Power Average and Hamy operators. Firstly, the power partitioned Hamy mean (PPHAM) is capable of capture the correlation between aggregation parameters and the relationship among attributes dividing several parts, where the attributes are dependent definitely within the interchangeable fragment, other attributes in divergent sections are irrelevant. Secondly, because MNS can effectively represent imprecise, insufficient, and uncertain information, we proposed the multivalued neutrosophic PMHAM (WMNPHAM) operator for MNS and its partitioned variant (WMNPPHAM) with the characteristics and examples. Finally, this multiple attribute group decision making (MAGDM) technique is proven to be feasible by comparing with the existing methods to confirm this method’s usefulness and validity. The world is full of partial, imprecise, inconsistent, and uncertain data that can’t be characterized with precise numbers –3. In orBecause the decision makers typically cannot generate precise assessment values for every membership due to limited knowledge and experience –20, the Aggregation operators, as a critical tool for solving MADM or MAGDM issues, may combine information from all qualities and decision makers and rate the options –29. HoweExisting MAGDM approaches are unable to capture complex relationship patterns between characteristics properly. However, actual MADM or MAGDM situations with many attribute correlations need the use of developing operators as Power average operator, Maclaurin symmetric mean operator –39. WhenIt is critical to expand the Hamy mean operator to handle complicated MAGDM issues with ambiguous information and interrelationships of attributes, the novel multi-valued neutrosophic power partitioned hamy mean (MNPPHM) operator is presented in this paper, which is based on the Hamy operator. As a result, our research concentrates on the nominated operators to build the MAGDM technique using MNS. The contributions are:The PPHAM operator is extended to improve the ability of existing operators to handle the specific information and partitioned parameters.The PHAM operator (WMNPHAM) is presented with MNS, as well as its partitioned operator (WMNPPHAM).The developed operators’ properties are demonstrated with numerous unique examples.A practical MAGDM approach is generalized with the WMNPPHAM operator.The applicability and efficacy of the provided MAGDM technique is demonstrated.The following is the structure of this paper. We introduce the fundamental principles and operating norms of MNS and Hamy mean operator operators in Section 2. Section 3 proposes the WMNPHAM and WMNPPHAM. In Section 4, we construct MAGDM technique with these operators. Section 5 provides an example that compares the proposed method to existing ways to demonstrate its viability. Finally, the findings are described in Section 6.q is a generic element in K, the single valued neutrosophic set Q in K is defined as:K(q), DK(q) and MK(q) denote the truth-membership, the indeterminacy-membership and the falsity-membership of the element x ∈ X to the set K respectively. For each point q in Q, we have UK(q), DK(q), MK(q) ∈ , 0 ≤ UK(q) + DK(q) + MK(q) ≤ 3.Definition 1. As a universe of objects K, where Q =  to represent single valued neutrosophic set, and the element q can be termed as a single valued neutrosophic number.For simplicity, we may utilize the simpler form Q could be characterized as:Definition 2. K is a nonempty fixed set, the multivalued neutrosophic set (MNS) in The I ∈ MNS, the complement of an MNS may be characterized as Ic stated as follows:Definition 3. Let k > 0, the basic operations can be defined as:Definition 4. Two MNS Definition 5. Such any MNS Definition 6. For any a MNS Assume If If If Definition 7. Assume i1, i2, …,ij) explores every j-tuple combination and Definition 8. The form of Hamy mean operator is:Obviously, the Hamy mean operator has several characteristics:HAMj) = 0, HAMj) = w;HAMj) ≤ HAMj), if wi ≤ yifor all i;wi} ≤ HAMj) ≤ max{wi}.min{Rn → R as:Sup is the support as αi from αj. And certain qualities are detailed as follows:Definition 9. The Power aggregation operator is the mapping Sup ∈ ;Sup = SupSup ≥ Sup, if |αe − αf|<|αi − αj|.i1, i2, …,ip) explores the whole p-tuple combination and Definition 10. The Partitioned Hamy mean operator (PHAM) is expressed in the form as:In summary, we will investigate the Hamy mean operator and Power aggregation operator to deal with MNS and build MNPPHAM operator and WMNPPHAM operator, as well as explain various attributes and specific circumstances of these new operators, with the operating regulations of MNS.x = 1,2,…,m The MNPHAM operator is described this way:Definition 11. n is the balancing coefficient in the preceding 1, σ2,…,σn). As a result, The denominator The following theorems could be derived from the operational rules of the MNS:Theorem 1. Let Proof. Sincewe haveIn addition, the NPHAM has some features as:i = 1, 2,…,n), then(1) Theorem 2 (Idempotency). i = 1, 2,…,n), thenProof. Since So, according to Theorem 1, we havei, then(2) Theorem 3 (monotonicity). Let i, thenProof. (1)Since i, then(2) Since i, then(3) Since (3) Theorem 4 (Boundedness). The MNHPHM operator situates within:Proof. Let Definition 12.ω = T is the weight vector of ωi ∈  and Theorem 5. Assume Proof. This theorem’s proof is analogous to that of Theorem 1.(1)Theorem 6 (Idempotency).i, then(2) Theorem 7 (monotonicity). If i, thenProof. (1) Since i, then(2) Since i, then(3) Since So, we can get(3) Theorem 8 (Boundedness). The WNPHAM operator situates within:Proof. Let l parts P = , where t = , Ps ⋂ Pt = ∅, Definition 13. Let Furthermore, the foregoing operators satisfy the Theorems of Idempotency, Commutativity, and Boundedness. However, the proofs are much like the proofs of the Theorems for MNPPHAM and WMNPPHAM operators, therefore the proof procedure is omitted here.A = {A1, A2,…,Am}, the decision makers D = {D1, D2,…,Dz} with their weight vector θ = T and a collection of n attributes C = {C1, C2,…,Cn} with ω = T matching θl ∈ ,ωn ∈ ,C, C can be divided into l parts P1, P2,…,Pl, where t = , Ps ⋂ Pt = ∅,Pt| represents the number in partition Pt. Assume that the DM Dh evaluates his/her assessment using MNS Cj regarding the alternative Ai. The decision matrix might indeed be compiled as follows:In this part, we will utilize the WMNPPHAM operators to solve the MAGDM issue. For example, a MAGDM issue concludes a set of m alternatives To overcome this problem, the following phases of the new MAGDM technique might be taken:Rh must be normalized into standard matrices SRh by converting the cost-type to the benefit-type.Step 1. The gathered choice matrices Step 2. Calculate the supports As Definition 7, Step 3. Estimate the weights SinceStep 4. Aggregate each expert’s evaluation information.To composite the MNS Step 5. Estimate the supports τijStep 6. Estimate the weights τij with the MN SincePt, using the WMNPHAM operator described by (27), where i = 1, 2,…, m as:Step 7. Evaluate each alternative inside each partition Step 8. Use the Eqs.  to calcuAi and pick the most accomplished one(s).Step 9. As shown by Definition 6, order the alternatives A1, A2, A3 and A4. Three characteristics are used to evaluate the four alternatives: (1) C1(the risk index), (2) C2(the growth index), (3) C3, where C1 and C2 belong to the benefit type, C3 is of the cost type with their weight is ω = T. Furthermore, there are three decision makers that can make quick choices based on their knowledge and experience. Their decisions may be written down in the form MNS by i = 1,2,3,4, j = 1,2,3,4, h = 1,2,3) with their weight is θ = T. The qualities may be separated into two portions based on their characteristics P1 = {C1, C2} and P2 = {C3}. In this situation, the decision maker can provide numerous possible values under each characteristic for the evaluations while adhering to the primary level of “excellent.” Then there is the decision matrix among the decision makers The investment firm seeks to choose the best option from the four agricultural brands, which are Step 1. Normalize the decision matrix.The normalized MN decision matrix Supkt. According to Supkt  can be calculated:Step 2. Compute the supports degree Step 3. Estimate the weights Tk :The σk  as follows:The weights Step 4. Combine the evaluation information of every expert.The collective multivalued neutrosophic decision matrix Step 5. Retrieve the supports τij with MNS Step 6. Compute the weights The weighted support τij  can be computed.So the weights Step 7. Evaluate the information within each partition of attributes.Pt by (20), which show in This step can estimate the collective evaluation values of each alternate within αi can be calculated by WMNPPHAM operator in The value Step 8. Use the Eqs.  to calcuSi can be calculated by Definition 5 as:Step 9. Order all the alternatives.S1 > S2 >S4 >S3. So, the final rank of all the alternatives could be shown as A1 ≻ A2 ≻ A4 ≻ A3.The results in Step 4, we can get The changing value of parameter x in the MNWHAM operator can be taken to demonstrate the effects on the ranking results in 1 ≻ α4 ≻ α2 ≻ α3 [1, whereas the worst alternative is α3.The efficacy and practicality of the suggested MAGDM technique by WMNPPHAM operators must be compared and verified, thus we perform a comparative analysis using the same illustrative case. The analysis might be made from the following aspects: techniques utilizing MNS with other operators and ways using the same operators with different discrete forms of neutrosophic numbers. Then these different ranking results could be shown as α α2 ≻ α3 . ClearlyWe summarize the reasons for variances in the final rankings of all the examined approaches and the suggested methodology in In this study, we propose the WMNPHAM and WMNPPHAM operators, which extend the Hamy mean and Power aggregation operator to the MNS. In addition, we describe the desirable qualities, create the score function, and use it to rank the choices. After that, based on the WMNPPHAM operator, we provide detailed procedures for solving MAGDM issues using multi-valued neutrosophic information. Furthermore, we compare the efficacy and practicality of the created technique to current methods.Therefore, for addressing complex decision-making situations, these proposed novel multivalued neutrosophic aggregation operators can aggregate fuzzy information and partitioned parameters meantime, which can be used as a practical tool to solve the MADM challenges more efficiently and effectively. In the future, further research can expand them to other aspects of MADM or MAGDM, such as clustering algorithms or consistency analysis. Meanwhile, other fuzzy sets can be combined to deal with the practical problems, such as q-rung orthopair fuzzy 2-tuple linguistic sets , interva


A need exists for reliable and valid swimming protocols that assess VO2maxsw in a flume environment. The purpose was to assess: (a) reliability and (b) “performance” validity of a VO2maxsw flume protocol using the 457-m freestyle pool performance swim (PS) test as the criterion. Nineteen males (n = 9) and females (n = 10)  performed two flume VO2maxsw tests (VO2maxswA and VO2maxswB) and one PS test [457 m (469.4 ± 94.7 s)]. For test–retest reliability , moderately strong relationships were established for VO2maxsw (mL·kg−1·min−1), O2pulse (mL O2·beat−1), VEmax (L·min−1) , final test time (sec) , and immediate post-test blood lactate (IPE (BLa)) . For performance validity, moderately strong relationships (p < 0.05) were found between VO2maxswA , O2pulse , VEmax (r = −0.509 p = 0.037), and 457-m swim times. The swimming flume protocol examined is a reliable and valid assessment of VO2maxsw., and offers an alternative for military, open water, or those seeking complementary forms of training to improve swimming performance.A mode-specific swimming protocol to assess maximal aerobic uptake (VO Competitive, fitness, and recreational swimming have grown in popularity with an increase in participation over the last 5 years ,2. The cA swimming flume directs water circulation through a controlled closed loop system where a “test basin” receives laminar and constant water flow, enabling a swimmer to remain stationary at velocities controlled by the investigator . By cont2maxsw; mL·kg−1·min−1) and other variables such as critical velocity to explore flume and swimming performance with mixed results. This was due, in part, to a lack of a standardized swimming protocol, as well as the criterion swimming distances chosen ), heart rate, and RPE were obtained following the performance trial. The cool-down at an ad libitum swimming pace occurred until recovery HR was to <110 beat∙minp < 0.05, two-sided. The test–retest reliability of the flume-based variables was assessed with an intraclass correlation ). A paired t-test was also conducted in order to examine differences between trials. The validity of the flume-based tests was assessed using Pearson or Spearman correlation coefficients as appropriate. VO2maxsw A trial data was only used to simulate application where little to no practice/orientation would typically occur for a participant prior to a swimming flume trial. Bland–Altman plots were graphed to assess concordance between VO2maxsw A and B trials including systematic bias, patterns of error, and 95% limits of agreement  , and final test time  determined by the two swimming flume experimental trials (2 (mL∙kg−1∙min−1) measures were reliable for Stages 2 through 6 (ICC = 0.465 to 0.669); p < 0.05). This sample was comprised of an approximately equal number of men and women and represented a distribution of the general population. Therefore, findings are reflected as a mixed sample of males and females to better reflect the general population. ICCs revealed moderate to strong reliability for VOl trials . In addit-test showed no significant differences (p > 0.05) between Trials A and B for physiological and perceptual variables except for RERmax and IPE-RPE (p < 0.05). Bland–Altman analyses showed that the VO2maxsw tests reflected a systematic error with a slightly positive mean bias (1.21 mL∙kg−1∙min−1), which specifies that participants had a slightly higher value during test 2 as compared to test 1; however, this difference did not demonstrate statistical significance. The 95% limits of agreement were , suggesting moderate reliability , as well as O2pulse  and VEmax . No significant correlations were found between 457-m PS time and IPE [BLa], HR max, RERmax, and IPE-RPE (p > 0.05).Validity of the VO PS time . The par2maxsw flume protocol demonstrated moderately strong test–retest reliability for both a health-fitness and competitive masters swimming sample of males and females. Results also established “performance validity” for swimming events of >6 min in duration. A standardized flume-based testing protocol can provide measures that, over time, reflect physiological adaptations of a prescriptive training program. Furthermore, our study’s results provide a robust application for those desiring to pursue a home-based swimming flume program  in addition to those undertaking aerobic training for vocational purposes or competitive swimming events.The present study found that a continuous graded intensity VO2maxsw, HRmax, VEmax, O2 pulse, and IPE [BLa]. These findings were somewhat lower than previous protocols that employed a self-regulated intensity strategy using both pool- and land-based settings  (9.3 mmol∙L−1) confirmed a maximal effort at the point of test termination. Furthermore, the VO2maxsw Trial A and Trial B difference (1.21 mL∙kg−1∙min−1) was lower than established endpoint criteria (<2.1 mL∙kg−1∙min−1) for VO2max. In the present study, the secondary criteria, which reflected attainment of maximal aerobic power, were consistent with two previous studies of shallow water running and pool swimming [Results indicated that the current VOsettings ,32,33,34swimming ,31.For the majority of participants, the point of exhaustion occurred between the fifth and sixth flume stage. This proposes a predominant aerobic energy pathway contribution consistent with previous studies examining an association between aerobic power and swimming performances greater than 400 m . While i2max values compared to protocols with more conservative, graded, or ramped characteristics [2maxsw.. The flume protocol would be advantageous to measure energy cost and swimming economy at submaximal velocities since it was shown to be a clear indicator of performance across a range of swimming distances [2maxsw alone [The regulatory functions of a swimming flume are to some degree standardized and should be viewed as analogous to a treadmill when used in a controlled laboratory environment. In the present study, the average time of flume test termination was greater than 7 min. The characteristics of the present study’s flume protocol allowed for systematic increases in velocity (aggressiveness and duration) similar to supramaximal paradigms that are shorter in duration and more aggressive. Such protocols have exhibited similar VOeristics ,37,38,39sw alone ,40,41,422maxsw flume protocol and experimental design did present several limitations. Although participants practiced swimming in a stationary position at higher flume velocities, they occasionally became disoriented by water turbulence during near maximal intensity efforts of 90% and greater. Furthermore, measurement technicalities involving use of indirect calorimetry in a swimming flume environment should also be considered. Flow characteristics from a swimming flume are also known to change stroke mechanics in swimming, particularly upper body stroke parameters [The VOrameters ,43. AlthIn lieu of indirect calorimetry, a cost-effective approach to assessing swimmers in a flume would also involve accelerometry or other performance analysis metrics  in water that could assist with the development of statistical models to predict swimming performance ,19,44,452maxsw swimming flume protocol examined was found to have moderately strong test–retest reliability when measuring maximal oxygen uptake and provided a valid measure of the 457-m swim. The current flume protocol can be adopted as a valuable alternative method to pool protocols when evaluating aerobic fitness. Furthermore, with frequent assessments, an accurate swimming flume protocol can provide important feedback for those who train to improve health-related fitness and/or to enhance competitive performance.The continuous VO