Furthermore, high values of all hematological ratios, such as the NLR, MLR, PLR, SII, SIRI, and AISI, are directly associated with the occurrence of pneumothorax (p < 0.001). Furthermore, increased values of the NLR, SII, SIRI, and AISI at admission predict a lengthier hospitalization (p = 0.003). (4) Conclusions: Increased neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic inflammatory index (SII), aggregate inflammatory systemic index (AISI), and systemic inflammatory response index (SIRI) levels at admission highly predict the occurrence of pneumothorax, according to our data. (1) Background: Trauma is one of the leading causes of death worldwide, with the chest being the third most frequent body part injured after abdominal and head trauma. Identifying and predicting injuries related to the trauma mechanism is the initial step in managing significant thoracic trauma. The purpose of this study is to assess the predictive capabilities of blood count-derived inflammatory markers at admission. (2) Materials and Methods: The current study was designed as an observational, analytical, retrospective cohort study. It included all patients over the age of 18 diagnosed with thoracic trauma, confirmed with a CT scan, and admitted to the Clinical Emergency Hospital of Targu Mureş, Romania. (3) Results: The occurrence of posttraumatic pneumothorax is highly linked to age ( Trauma is the world’s top cause of disability and death in the first four decades of life. In this age group, the number of young adults who die from trauma exceeds all deaths from cancer combined . ThoraciThe assessment of thoracic trauma severity determines the choice of first therapy and the subsequent clinical course when treating patients with polytrauma. Although thoracic trauma specifically has received little attention in the literature, there is a wealth of information on the mortality-associated risk factors following trauma in general ,6. Pathological inflammatory and anti-inflammatory responses that occur in the first hours following extensive trauma are one of the major contributing factors to mortality in post-traumatic patients and remain challenging to control and distinguish from a physiological immune reaction . The balThe neutrophil-to-lymphocyte ratio (NLR) is one of the most accessible markers. This ratio has been proven to significantly predict the outcomes of patients with COVID-19 infection ,13,14,15The monocyte-to-lymphocyte ratio (MLR) has been proven to be a valid predictor of the occurrence of complication in strokes , and theThe NLR, PLR, and MLR, for example, have been the subject of a growing number of studies in recent years. However, their findings suggest that a combination of these ratios would increase their predictive value ,35,36. TThe prognosis ratios calculated from routine blood tests appear to be a helpful and cost-effective resource in trauma management. Although there are mentions in the literature of the correlation between the NLR and the outcomes of thoracic trauma patients , there aThe purpose of this study is to establish the prognostic value of inflammatory biomarkers and the underlying risk factors in patients with thoracic trauma.The present study was designed to be an observational, retrospective, analytical cohort study where we included all patients over the age of 18 who presented, were diagnosed with thoracic trauma, and admitted to the County Emergency Clinical Hospital of Targu Mureş, Romania, between January 2015 and December 2022. All patients included in our study underwent a radiological examination of either a conventional X-ray or a CT scan, and all were diagnosed with thoracic trauma as the main diagnosis. We excluded patients who passed away within the first 24 h, suffered severe bone fractures with need for specialized orthopaedical care, had a history of hematological or oncological disorders, presented thromboembolic events in the last two months, and patients with pneumonia. We also excluded patients suffering from mediastinal hematoma and aortic dissection as such patients are referred to the cardiovascular surgery department, not the thoracic surgery department. All patients included in our study suffered from peacetime injuries.We initially split the patients in two categories: “Pneumothorax” and “No Pneumothorax” based on the findings at admission. We collected the following data from our patients: age, sex, medical history ) and length of hospital stay (LOS). Moreover, we were interested in the routine blood tests at admittance. From these results, we extracted the following data: hemoglobin levels, hematocrit, neutrophil count, monocyte count, lymphocyte count, platelet count, sodium, and potassium. We were also interested in the number and location of rib fractures. All data were collected from the hospital’s integrated electronic database. MLR = monocytes/lymphocytesNLR = neutrophils/lymphocytesPLR = platelets/lymphocytesSII = (neutrophils × platelets)/lymphocytesSIRI = (monocytes × platelets)/lymphocytesAISI = (neutrophils × monocytes × platelets)/lymphocytesFrom the results of the initial blood test at admittance, we managed to calculate the following ratios: The primary endpoint for our study was the risk of pneumothorax development. We also recorded the length of hospital stay as an outcome, making it our secondary endpoint. t-tests or Mann–Whitney tests. The receiver operating characteristic (ROC) curve analysis was used to determine the cut-off values for inflammatory markers and evaluate their predictive potential. Based on the Youden index , the suitable NLR, MLR, PLR, SII, SIRI, and AISI cut-off values were determined using the ROC curve analysis. Software-wise, we used SPSS for Mac OS (28.0.1.0) . All systemic inflammatory marker associations with category factors were evaluated using chi-square tests, whilst differences in continuous variables were evaluated using Student During our study period, we identified 611 patients suffering from thoracic trauma that met the inclusion criteria for our study. The mean age was 47.48 ± 18.66 (18–98) . The majp = 0.002), as well as a higher incidence of tobacco use (p = 0.019) and obesity (p = 0.038). As for the etiology of trauma, we found the majority of patients suffered from blunt trauma . In this category, we considered all patients who suffered from motor vehicle accidents, workplace accidents, accidental falls, sport-related injuries, and suicide attempts. In terms of patients who experienced penetrating trauma, we included all patients who experienced hetero-aggression and stabbings. They accounted for 11.78% of all patients and 41.93% of pneumothorax patients. Moreover, patients who suffered from posttraumatic pneumothorax showed higher sodium levels (p = 0.024), higher neutrophil (p < 0.0001), monocyte (p < 0.0001), and platelet (p = 0.009) counts, and lower lymphocyte (p < 0.0001) counts. All hematological ratios were higher in the “Pneumothorax” group (p < 0.0001). The length of hospital stay was also longer in the “Pneumothorax” group (p = 0.003). After splitting the patients into two lots depending on the occurrence of pneumothorax, we noticed an increase in the mean age for the “Pneumothorax” group to 51.68 ± 19.39 , as highlighted in In terms of systemic inflammatory makers and the length of hospital stay, we computed the Spearman correlation, and we identified a positive correlation between the NLR, SII, SIRI, and AISI and length of hospital stay , the presence of COPD , as well as tobacco , act as predictive factors for pneumothorax risk. In contrast, obesity acts as protective factor against pneumothorax . We considered an increased value of the NLR as being a value higher than the identified cut-off . This is similar for a high MLR , PLR , SII , SIRI , and AISI . We proceeded with the multivariate analysis of age, risk factors, all inflammatory ratios, and the occurrence of pneumothorax within the patients in the second group, as shown in According to the recent literature, thoracic trauma is a frequently occurring presentation in injured patients . Post-trHowever, it is a preventable cause of death. Early diagnosis of pneumothorax can aid in the management of such patients, prevent hemodynamic deterioration, or occurrence of other complications. n = 155/611), with a mean age of 47.48 ± 18.66, are similar findings to those found in the literature. In the present study, the incidence of pneumothorax was 25.37% (p = 0.009) and a lower rate of in-hospital mortality (p < 0.001). However, smokers appear to develop a higher rate of pneumonia (p < 0.001) [p = 0.01). A plausible reason for this discrepancy can be attributed to the high proportion of smokers included in the study of Grigorian et al. totaling 57,619 patients (20.4%). Most studies found in the recent literature report a negative impact on the outcomes of trauma patients among smokers ,47,48. I< 0.001) . In our p = 0.003). One of the reasons for such paradoxical findings can be attributed to the protective role of the adipose tissue upon blunt chest injuries. The role of obesity as a risk factor for the outcomes of trauma patients is a topic of debate in the current literature. There are plenty of papers, including complex meta-analyses, that advocate for poorer outcomes of obese patients following major trauma ,51,52,53The type of trauma appears to also play an important role in the development of pneumothorax. We notice that the majority of patients included in our study suffered from blunt chest injuries, which is to be expected as we did not have any wartime injuries reported in the past few years. We also notice that the majority of patients with penetrating trauma develop pneumothorax (65/72), but as the number of patients suffering from penetrating trauma is low, we can consider these data as purely observational.The predictive values of hematological ratios in trauma patients have reportedly been researched more and more, although with conflicting results. Additionally, there has been a significant rise in the need for prognostic tools in trauma patients with unfavorable evolution and decompensation.Our study included 611 patients diagnosed with thoracic trauma. We identified the inflammatory biomarkers in patient blood samples at admission and determined the presence of pneumothorax using CT scans at admission. Our study’s most important outcome is that the high baseline values for the NLR, MLR, PLR, AISI, SII, and SIRI are strong predictors for the development of post-traumatic pneumothorax. To the best of our knowledge, this is the first study to demonstrate that patients with high hematological ratios were more likely to develop pneumothorax and that the ratios predict a longer hospital stay. According to Soulaiman et al., there is a statistically proven association between the NLR at admission and the outcomes of trauma patients, where a higher NLR predicts an unfavorable outcome . AccordiIn contrast, other studies, such as the one conducted by Dilektasli et al., revealed no statistically significant association between the NLR calculated from the blood samples at admission and the outcomes of trauma patients . p = 0.002) and length of hospital stay (p < 0.001). The total number of patients included in their study appears to be more modest (207 patients); patients with all types of trauma were included, not just chest injuries. Despite all these limitations, the findings of their study appear to support ours. These controverted findings inspired another study, conducted by Younan et al. , to invep < 0.0001) [According to Jo et al., the PLR has significant prediction power for the outcomes of trauma patients ( 0.0001) ; howeverA recent study by Rau et al. , includip < 0.0001 in all cases). Moreover, we proved that some increased hematological ratios can indirectly predict the occurrence of complications through an increased length of hospital stay . Lastly, the present paper also revealed a major risk factor for traumatic pneumothorax development in tobacco use , whilst obesity is a protective factor . In the current study, according to the multivariate analysis, all the hematological ratios were able to predict the occurrence of pneumothorax (p = 0.02). The findings of the second paper revealed that the NLR, PLR, MLR, AISI, SII, and SIRI are powerful predictors of the development of acute kidney injury, mortality, and a composite endpoint of these two outcomes in abdominally injured patients .The findings of our previous studies on the role of hematological biomarkers as predictive factors in the outcomes of both specific splenic trauma  and abdoNevertheless, the present study has a set of limitations. The first limitation relies on the design of the study as a retrospective monocentric study. Further improvement could be brought by extending the research to a multicentric prospective study. Secondly, due to the retrospective nature of our study, we were unable to gather enough data on chronic medications administered before admission (corticosteroids or anti-inflammatory drugs), which prevented us from assessing how various medications affect inflammatory biomarkers. Lastly, the study only analyzed the inflammatory biomarkers at admission. Repeated determination throughout the hospitalization period may better reflect the dynamics of the inflammatory process and may improve the quality of our findings. In spite of all these limitations, we consider our findings to be a stepping stone toward the development of new risk scoring systems for the improvement of the overall management of thoracic trauma patients and the early identification of patients at risk. We consider these hematological ratios to be especially important, taking into account their ease of determination and the low cost of assessment.Our data show that patients with thoracic injuries, who have elevated NLRs, PLRs, MLRs, SIIs, SIRIs, and AISIs at admission at values that are above our calculated cutoff, are likely to have sustained severe thoracic trauma, are likely to have developed pneumothorax, and will likely follow a long evolution with a long duration of hospitalization. Additionally, we proved that tobacco use is a strong predictor of the development of post-traumatic pneumothorax in such patients, whilst obesity is a protective factor. Given the ease of use of such ratios and the low cost of these metrics, they can be used in clinical practice to categorize patient treatment groups, develop predictive patterns, and classify risk groups for admission.


This review article covers all types of paper-based humidity sensor, such as capacitive, resistive, impedance, fiber-optic, mass-sensitive, microwave, and RFID (radio-frequency identification) humidity sensors. The parameters of these sensors and the materials involved in their research and development, such as carbon nanotubes, graphene, semiconductors, and polymers, are comprehensively detailed, with a special focus on the advantages/disadvantages from an application perspective. Numerous technological/design approaches to the optimization of the performances of the sensors are considered, along with some non-conventional approaches. The review ends with a detailed analysis of the current problems encountered in the development of paper-based humidity sensors, supported by some solutions. The first part of this literature review  primaril2), M-Liner (cardboard) (230 g/m2), and PackPro (food-packaging paper) (50 g/m2), and Andersson et al. [2), Wawrzynek et al. [2) from Stora Enso .A wide variety of paper types have been tested to manufacture capacitive humidity sensors. In particular, Courbat et al.  used p_en et al.  tested Hn et al.  employedk et al.  fabricatk et al.  suggestek et al.  fabricat2), respectively. Metalized paper (MP) is a commodity in which a thin layer of aluminum offers both a decorative appearance and protective/controlled gas permeation. Rahimi et al. [2-based laser-ablation process.Although parallel-plate (PP) capacitive sensors generally have a higher sensitivity than IDE-based capacitive sensors , these si et al.  found thIn general, the humidity-sensing materials employed in PB sensors might be classified into two types: (i) the paper itself, and (ii) non-paper hydrophilic humidity-sensing materials, such as ceramics, polymers, carbon materials, and their composites. Below, we summarize the development status of PB humidity sensors according to the types of the materials used in their design.0) and sensitivity (ΔC/ΔRH) are usually used, which describe the change in capacitance upon interaction with humid air, as well as the response time (τres) and recovery time (τrec), which characterize the kinetics of the sensory response. The response/recovery time is usually defined as the time taken to reach 90% of the final equilibrium value. A parameter such as hysteresis is also used, which characterizes the possible differences between sensor readings when measuring in conditions of increasing or decreasing humidity. The methods for measuring these parameters do not differ from the methods used in the testing of gas sensors and described in many publications, including [ncluding ,5,6,9,10The typical characteristics of humidity sensors employing paper-based sensing elements are shown in It was also found that the capacitive response is reversible a, and th0) above 10%), low sensitivity , and slow response/recovery  [0) at 1 kHz, and a maximum change of 411.4% at 10 kHz, using robust and precise screen- and gravure-printing technologies. The sensors were highly repeatable, exhibiting a standard deviation in sensitivity of 1.05% for three continuous runs, stable, and insensitive to mild temperature changes (15–30 °C). It is worth noting their fast response, of ca. 0.8 s, and recovery, of ca. 0.78 s. To achieve these results, Zhang et al. [However, the analysis of published results indicates that despite the significant progress in paper-based humidity sensors, most sensors still exhibit poor repeatability, high drift  ,22. In aminutes) , developg et al.  subjecteg et al. , remarkag et al. , also shHowever, the information given in Some solid-state materials, such as carbon nanotubes (CNTs), graphene oxide (GO), porous silicon (PSi), and alumina oxide (AO), have been applied to produce capacitive paper-based humidity sensors. The parameters of these sensors are listed in 2O3-based detectors [2O3 and PSi encourage the capillary condensation of water vapor, which has a significant effect on the material capacitance (or dielectric permittivity) compared to the adsorption of H2O molecules on the surfaces of GO and CNTs only. Regarding the sensors developed by Song et al. [As can be seen from the results presented in etectors  and PSi etectors . Howeverg et al. , high se2O molecules. In addition, CNCs have large specific surface areas and high mechanical strength, which are important for sensing units. Cellulose nanocrystals are mainly derived from naturally occurring cellulose fibers by removing amorphous segments through strong-acid hydrolysis. In the study, to form a GO/CNC composite, the GO suspension was mixed with the CNC composite at the desired ratio, of 5 wt.% and 10 wt.% of CNC, followed by homogenization. Further, an electrode with an interdigital transducer (IDT) pattern was deposited on a PET substrate using a conventional lift-off process. Next, the CNC/GO solution was poured dropwise onto the patterned electrode and dried in an oven at 60 °C. It should be noted that no paper substrate was used in the manufacturing of this sensor, which distinguishes it from those considered above.Recently, a remarkably high sensitivity to humidity was reported for sensors based on GO/CNC composites; the data are displayed in 2O content of less than 5% RH. However, it was also clear that the sensors had a limited sensitivity range because the sensory response was rather low, in the RH < 70% range. In addition, these sensors had long response and recovery times. It was also found that the sensors had significant hysteresis characteristics.Previously, Jalkanen et al.  showed t2) was used. The ID-electrode capacitor was fabricated by an evaporation of 300-nm-thick aluminum onto the top of a thin AAO film through a Pt mask  or current (I/I0) to changes in humidity, the sensitivity calculated as the ratio of changes in resistance to changes in humidity (ΔR/ΔRH), and the response/recovery time determined as mentioned earlier in The parameters of humidity sensors which employ a paper as a detector element are presented in 2O vapors and can vary over a wide range, which makes it possible to develop highly sensitive conductometric (resistive) humidity sensors. However, the conductivity of paper normally appears to be very low, which makes the resistance of the corresponding sensors excessively high for ordinary electronic readout circuits. The conductivity begins to noticeably increase only at humidity levels above 40–50% RH [−2 and 185 g·m−2, respectively. Standard carbon paper has a density of 80 g·m−2. Gooder et al. [2O vapors below 20% RH. The Whatman 1 Chr had the lowest sensitivity and did not give a noticeable signal below 65% RH. The Whatman 3MM Chr was significantly more sensitive than the Whatman 1 Chr and detected the humidity at RH > 30%. Gooder et al. [The research data presented in 0–50% RH ,38. Thisr et al. ,41,44. Sr et al.  are draw3, making these sensors suitable for the accurate detection of H2O vapors in a given environment. As shown in According to Duane et al. , the con3NOH hydrogel film in 4 wt% KOH solution for over 6 h. To prepare the cellulose/BzMe3NOH hydrogel film, the cellulose sample was dispersed in an aqueous BzMe3NOH solution at room temperature following centrifugation. The as-prepared cellulose/KOH composite hydrogel film was air-dried at room temperature overnight. The cellulose substrate was fabricated through a water-evaporation-induced dense-packing strategy. As demonstrated in To expand the sensitivity range of PB humidity sensors, Wang et al.  suggeste−1. However, these sensors also had slow responses. The response and recovery times were about 60 s and 500 s, respectively. Notably, relatively long recovery times are a common problem for sensors employing paper as the humidity-detecting element. This is due to the hydrophilic nature of cellulose, which takes longer to desorb water molecules. Moreover, regularity is frequently observed regarding the effect of paper density: the greater the density of the paper, the slower the sensory response  in the HP paper and AlO(OH) in the Canon paper. Additionally, no differences in porosity were observed through the Hg-porosimetry measurements. Moreover, the HP paper was found to have significantly higher resistance in a dry environment  of the humidity sensor gradually reduced as the distance between the electrodes increased. This effect was also observed by Zhao et al. [2O molecules on paper surfaces creates a discontinuous layer when the gap between two electrodes is excessively large, breaking the percolation conduction channel even at high relative humidity values, which contributes to reductions in sensory response.It was also been found  that theo et al. . Accordio et al. , the adsWith regard to the bendability of resistive humidity sensors on paper, tests carried by Duan et al.  suggesteIn the manufacturing of paper-based humidity sensors using carbon-based materials as detecting elements, carbon nanotubes (CNTs or MWCNTs), graphene, graphene oxide (GO), reduced graphene (rG), and composites, including carbon and cellulose, have been applied. The parameters of these sensors are listed in The analysis of the presented results indicates that these sensors have (i) unique electrical and mechanical properties, (ii) high aspect ratios, and (iii) nanoscale structures due to carbon-based nanomaterials. However, they exhibit sensitivity that is significantly inferior to those of sensors employing paper only. This difference is usually explained by the inherent hydrophobicity of carbon nanomaterials and the very high conductivity of these materials. As shown in Studies have shown that the characteristics of conductometric humidity sensors, as well as capacitive humidity sensors, depend on the paper used. Beniwal et al.  compared2O vapors. The effects of the paper type and CNT-oxidation mode on sensor performance are presented in Recently, many efforts have been undertaken to improve the sensitivity of carbon-based humidity sensors. In particular, treating CNTs with a mixture of acids, such as nitric and sulfuric acids, oxidization, or plasma processing to increase their hydrophilicity have become universal methods for increasing the sensitivity of sensors based on CNTs to HChu et al.  proposed2O sensitivity of the sensor, albeit to a limited degree.As a result of the research, it was found that a more effective approach to optimizing the parameters of carbon-based humidity sensors is the application of composite structures. In particular, it was found that the formation of composites based on CNTs and cellulose nanofibers (CNFs) or nanofibrillated cellulose (NFC) can improve the performances of carbon-based sensors ,56. For A further study by Zhu et al.  showed t−1, and (iv) folding resistance, up to at least 50 times. As shown in 2O molecules. The presence of an additional sensitive layer on the surface of the paper substrate slows down this process even further. This conclusion is consistent with the observations above regarding the development of PB sensors with additional humidity-sensitive materials, according to which the simplest way to reduce the response/recovery time is to passivate the paper surface before applying the humidity-sensitive layer. This passivation layer should isolate the paper from contact with the atmosphere. Various methods can be used in this processing to supply the paper with superhydrophobic properties, such as laser ablation [According to Zhu et al. ,55,56, tablation , the useablation , coatingablation ,70, and ablation ,72.0, is linear, reaching 250% (It is interesting that humidity sensors based on graphene nanoplatelets (GNPs) have a much higher sensitivity to moisture than CNT-based sensors . The chaing 250% c, and thing 250% d. FurtheThe comparative data from the tests of resistive humidity sensors that employ solid materials as the detector elements are brought together in 2O molecules. For the conductivity of ZnO to efficiently control the resistance of the sensors, it is necessary to significantly reduce the resistance of ZnO, which can be achieved through doping or by changing the deposition technology.It can be seen that the conductometric responses of sensors can vary over a very wide range, depending on the material used and the method of processing. Sensors using CdS  and ZnO 2O in a wider range of relative humidity, albeit with a rather low response  groups are attached to long molecular chains of cellulose. Upon exposure to H2O, the molecular bonds in these acetyl groups are broken, releasing acetic acid [2 in this process is indicated by the fact that the current magnitude observed without the SiO2 NPs film is 30 times less than that with the SiO2 NPs film, at a relative humidity of 40%. Kano et al. [2 NP film under bending. However, the sensor remained functional even after a 500-fold bending test at r = 1.3 mm.In principle, SiOo et al.  develope0–93% RH a, with a0–93% RH b. The retic acid . This seo et al.  showed tThe characteristics of paper-based humidity sensors with polymer detector elements are listed in 2O vapors, the effective resistance of PANI films changes due to the appearance of hydrogen bonds between the water molecules and nitrogen present in the amino group [It can be seen that most of the studies were carried out using polyaniline (PANI) ,82. Amonno group . In addino group  and polyno group  were tesStudies have shown that polymer-based humidity sensors exhibit higher sensitivity than those based on carbon-based materials  sensors, surface acoustic wave (SAW) sensors, capacitive micromachined ultrasonic transducers (CMUTs), and microcantilevers. Mass-sensitive moisture detection is mainly based on the piezoelectric effect, which converts the change in mass of a humidity-sensitive material after the absorption of water molecules into a change in frequency. The sensitivity of these sensors largely depends on the properties of the paper used as a humidity sensitive material, namely the density of the paper and its adsorption capacity. Most studies consider cellulose nanocrystals (CNC) as humidity-sensitive materials in QCM devices ,108,109.2O vapors interact with a detector material deposited on the surface of the optical fiber [In principle, fiber-optic humidity sensors should be of increased interest because fiber sensors are cheaper than conventional electrical sensors and better suited to use in hazardous or explosive environments, or when immunity to electromagnetic interference is required . They alal fiber . Fiber-oal fiber ,115, caral fiber , liquid al fiber , or cellal fiber   technologies have been applied in many industries to automatically identify and track objects such as packaged goods during storage, handling, and transport in the supply chain. Radio-frequency identification with an additional sensory function  allow the improved monitoring of goods with more diverse and newer technologies. Therefore, they have received significant attention in both industry and academia ,125. OneD method ,132.The structures and principles of operation of RFID sensor tags, as well as the method used to perform humidity measurements, are discussed in detail elsewhere . The mos2O molecules more efficiently if a faster response is required.As sensitive elements in RFID sensor tags, electric LC-resonators are ordinarily employed; a typical structure of these resonators is shown in 2O input, while the external dipole arm was applied for chipless identification.Another variation of the paper-based resistive humidity sensors developed by Quddious et al.  for use 2, 88 μm thick) and Ag-based ID electrodes. The area of the designed sensor was 2 × 2 cm2. The width and the spacing of the ID electrodes were both 300 μm. A copper coil, 2 cm in diameter, was used as a readout antenna.The parameters of other RFID humidity-sensor tags are listed in In the literature, there are further examples of the design of humidity sensors beyond those reviewed above. Here, we survey some of the more remarkable cases, which are worthy of discussion.One of the non-traditional approaches to the development of PB humidity sensors is the use of shape deformation under the influence of moisture ,138. The−1 to 0.260 cm−1 when the relative humidity changed from 25% to 85%, and the response time was only 3–5 s. The excellent humidity response was due to the addition of GO, which actively interacted with the water, increasing the flexibility and sensitivity of the composite films to moisture.Research has shown that this simple shape-deformation-based humidity-detection method can be implemented using inexpensive paper materials. Various approaches can be used for this purpose. For example, one side of a paper-based cantilever may be coated with a moisture-swellable polymer . The mec−1). The thickness of the CNT-paper/BOPP composite film was 76 μm. The CNT-paper/BOPP actuator was manufactured at 60% RH and, therefore, the actuator was not bent at this humidity. When the relative humidity decreased to 14%, the actuator bent towards the CNT-paper. The curvature rapidly enlarged in the first 60 s. When the relative humidity increased to 60%, the CNT-paper/BOPP drive returned to its initial flat state. In addition, the relative humidity continued to enhance rapidly in the first 60 s. Cao et al. [3C2Tx) and polydopamine (PDA) (G-MXCP) is also sensitive to moisture. To form this structure, a layer-by-layer assembly method was employed, which was based on a vacuum-filtration process. Cao et al. [Zhou et al.  developeo et al.  showed to et al.  found thAn interesting approach to the development of a humidity sensor was proposed by Wu et al. . They ab2O levels with the naked eye. An example of the implementation of these sensors is shown in It is worth noting that the bending of sensors that are optimally designed is reversible and directional, and allows cyclicity. In addition, the bending of the cantilever can be controlled by the thickness of the films applied in the sensor. In very thick films, bending is not observed. It is clear that such sensors are not very accurate. However, it is an extremely simple method that allows the monitoring of humidity without external equipment or interface electronics. Thus, with the help of these devices, it is possible to control HAnother unusual approach to developing paper-based humidity sensors was proposed by He et al. . They usThe functional sensing characteristics of the devices were measured by applying an AC voltage. They are presented in Moist indicators are not strictly sensors. However, the basis of these indicators is usually paper. Therefore, we decided to include a description of moisture indicators in this review article.On some occasions, it is desirable to detect only the appearance of moisture in a sample, while the exact concentration is only of secondary importance. This situation can occur during the operation of drying chambers, in which a moisture breakthrough indicates a need for the regeneration of the dryers. In other cases, for example, it is necessary to ensure that the relative humidity does not exceed 30–35%. In many cases, humidity above 30–35% RH is of concern because the corrosion of various materials can occur at this humidity level. Studies have shown that this problem of qualitative and, sometimes, quantitative control over humidity levels can be solved by using cellulose paper impregnated with certain salt crystals that change color when wet. The use of this effect has been found to be the most economical method for detecting changes in humidity levels . ExperimAs the humidity increases further, the crystal structure changes again, this time rearranging to allow four more water molecules to surround each cobalt atom, forming the hexahydrate:Heating the hydrated forms of cobalt chloride reverses the above reactions, returning the cobalt chloride to its blue or anhydrous state. Water is “released” in these processes, which are known as dehydration reactions. Thus, this property makes it possible to create a color-changing indicator, which changes from blue to lavender and pink when the humidity increases, and becomes blue again when the humidity reduces. This characteristic has been found to allow these indicators to be reused if they are not exposed to humidity levels higher than 90% RH for 36 h or longer. With these treatments, cobalt chloride is washed out.Generally, cellulose and cellulose-acetate substrates form the basis of moisture indicators. However, other cobalt-impregnation materials can also be used . GeneralPaper-based moisture indicators are inexpensive, easy to store, and difficult to damage, and they can be used in many fields. The moisture indication can be performed by printing a comparative color guide next to the color-spot indication for ease of reading. Typically, a series of plots is labeled with values of relative humidity, typically in 10% increments from 10% RH to 80% RH, and various combinations of these humidity values.It has been established that irreversible moisture indicators can be also produced based on cobalt chloride. For these purposes, ammonia salts are added to the element. In the presence of ammonia vapor, the elements lose their color reversibility. These irreversible elements, frequently referred to as maximum-humidity-indicator cards, are necessary, for instance, to show when certain levels of humidity are exceeded or the recharging/replacement of a desiccant. These indicators can mark the highest humidity level encountered by cargo during a voyage regardless of current  humidity levels. Maximum moisture indicator cards provide a clear and unmistakable means of determining whether goods are exposed to harmful levels of humidity during their journey.As stated above, cobalt salts are water-soluble and, therefore, should not be in direct contact with water or steam, nor should they be exposed to extremely high humidity for extended periods of time. If these indicators are stored in a humid environment, they may become inaccurate. They are damaged by excessive moisture or condensation, which dilutes cobalt salt and results in nonuniform chemical distribution and inconsistent color response . Strong It should be noted that other salts can be used in humidity indicators, in addition to cobalt salts. Their development primarily followed a directive issued by the European Community (EC), in 1998, which classified products containing cobalt (II) chloride from 0.01 wt.% to 1 wt.% as toxic. Studies have shown that there is a fairly large range of materials that can also change color when exposed to a moisture ,166,167.Research has shown that humidity indicators can be developed on other principles. In particular, numerous studies have been carried out on cellulose nanocrystals (CNC), with the aim of studying reflection-color changes under the influence of air humidity. 3). This combination of polyelectrolytes, CHI and CMC, was chosen as the building material because CHI and CMC contain amino groups and carboxyl groups, respectively, which are charged in aqueous solutions and are easily chemically modified. These CHI/CMC-N3 films can swell/de-swell at various humidity levels. Due to the excellent hygroscopic swelling properties of cellulose, the thickness of the cellulose-derived hydrogel layer enhanced with increasing ambient humidity  PB humidity sensors usually have a fairly high sensitivity, with RH > 40% range. Sensors capable of operating in this sensitivity range are suitable for most applications. However, for special applications, it is desirable for sensors to operate effectively in the 10–40% RH range. Another limiting factor of most PB humidity sensors is their slow response and recovery. Consequently, PB humidity sensors cannot completely return to their original state within such certain time intervals (≈3–5 s) at room temperature, which reduces the accuracy of their measurements during cyclic changes in humidity . Therefo(2) Paper is sensitive to the state of the surrounding atmosphere, so PB devices require harsh storage conditions. Failure to comply with these conditions significantly reduces the period of storage and operation . This al(3) Various applications have specific paper requirements. Therefore, the ability to use paper with distinctive properties to meet the requirements of individual applications can be an effective approach to optimizing the performance of PB humidity sensors. For example, the use of hydrophobic paper makes it possible to reduce the influence of the paper substrate on the characteristics of humidity sensors see a when us(4) It is necessary to adapt the technology used to manufacture paper-based humidity sensors as much as possible to the requirements of mass production . In addi(5) The technologies used to manufacture flexible electrodes on paper substrates require further improvement. The electrodes used in PB humidity sensors should generally meet the following requirements: (i) good electrical conductivity; (ii) excellent mechanical flexibility; (iii) good adhesion to paper; (iv) a manufacturing process involving technologies compatible with mass-scale production; (v) low cost; and (vi) non-toxicity/low toxicity. Unfortunately, many of these requirements for electrodes in PB sensors are not met. The development of printable conductive inks and the use of advanced printing technologies  for the 2O molecules and the parameters of both humidity-sensitive materials and the electrodes formed on their surfaces. Furthermore, the stability of the parameters of bent sensors depends on the mechanical flexibility of the paper used. Stress–strain dependencies are crucial for studying the flexibility of materials, but they can only be determined for solid materials, while the structure of paper is characterized by heterogeneity and randomness [(6) Further challenges include the durability and stretchability of paper. The stretching properties of paper are generally poor. Therefore, great efforts should be undertake to conduct studies on this topic ,199,200,ndomness . In addindomness .(7) When developing PB humidity sensors, various humidity-sensitive materials can be used. However, they do not always determine the characteristics of humidity sensors, especially those of the resistive type. Very often, the resistance of humidity-sensitive materials is either excessively low resistance or excessively high. In the first case, the range of resistance changes is narrow, and in the second, the sensory response is controlled not by a sensitive material, but by a paper substrate. Neither case is optimal for achieving the best performance of sensors that use additional humidity-sensitive materials. Therefore, technologies for the synthesis and formation of humidity-sensitive layers with the required electrical parameters, which are optimal for use in humidity sensors, are urgently required. Paper-surface-passivation technologies are also necessary to reduce the influence of paper on the parameters of PB humidity sensors with additional humidity-sensitive materials.(8) New sensitive materials for PB humidity sensors need to be explored. At present, carbon and its derivatives  are the main sensing materials for PB humidity sensors. It is expected that the use of new materials will improve the performances of these sensors . Further(9) The impact of mechanical flexibility on PB humidity sensors must be reduced ,208. PapBased on the results obtained by Liu et al. , it was Regarding the AgNW-GO-ink electrodes, it is believed that their flexibility was mainly associated with two factors b. Firstl(10) Despite the great importance of multifunctional PB sensors, their development faces significant challenges ,215,216.Multifunctional PB sensors can be obtained by searching for materials specifically designed to detect gas, humidity, temperature, and strain ,220,221.According to the perceptual characteristics of different stimuli, cross-sensitivity effects can be reduced by means of various signal-conditioning modes, e.g., resistance, capacitance, frequency, and current.Multifunctional BP sensors can be implemented by combining a number of data-processing techniques, e.g., pattern recognition, principal component analysis, and machine learning.An example of a multifunctional paper-based testing system, developed for a healthcare monitoring , is prov2O molecules, as well as ionic diffusion in humidity-sensitive materials, are dependent on temperature. As the temperature rises, the conductivity of the materials used in the sensor increases in turn. Furthermore, the absolute humidity content, calculated as ordinary values in ppm (particles per million), increases as the temperature rises at a certain RH. This means that the characteristics of humidity sensors are also turn affected by the ambient temperature [(11) Notably, such processes as the adsorption and desorption of Hperature ,224, whiTwo solutions to this problem are possible. One (i) is to stabilize the temperature of the sensor, which is an energy-consuming method and increases the size of the measuring device. The other (ii) is to control the temperature, alongside the measurement of the humidity, and to automatically make appropriate corrections to the sensor readings ,226,227.(12) It is necessary to consider the development of integrated and automated systems based on paper platforms with multiple functions, such as sample preparation, separation, reaction, detection, processing, and the transmission of information . For rea13) The final issue is the ability to develop self-powered humidity sensors for wearable electronics ,245,246. The fina2 solution and maintained for 6 h, so that the cobalt-chloride solution could fully penetrate into its pores. A conductive tape was used as electrodes, equipped with a hole for penetrating water vapors. 2@FP sensor was placed in each humidity environment for 10 min to obtain the open-circuit voltage in the stationary state. At low humidity levels, of 11–33% RH, the voltage of the sensor approached 0 V (2@FP sensor for electric humidity sensors are long (143 s) and short (45 s), respectively. Kan et al. [Examples of the implementation of self-powered chemoelectric PB humidity sensors were presented in works by Kan et al.  and Li eched 0 V b. When tn et al.  stated tn et al.  found thn et al. , the PPyAnother solution is to drive a fast-response PB humidity sensor with another powered system. For example, microbial fuel cells (MFCs) , fluidicWhile paper offers significant potential as a development platform for humidity sensors, further improvements in fabrication and analysis methods are still needed to match the performance of the conventional humidity sensors currently on the market. The development of paper-based sensors is still in its infancy; therefore, currently, limitations remain regarding the sensitivity, accuracy, repeatability, and stability of paper-based sensors.After reviewing the parameters of paper-based humidity sensors, it is clear that extensive developments have taken place in recent years that have significantly improved the performances of these sensors, and there is no doubt that further advances will be made in this growing field in the future. This means that adequate sensor properties  will be achieved, and that these sensors will be used in the same areas as sensors on solid substrates and flexible polymer substrates. However, this requires further research related to the improvement of conventional methods and the search for new methods for the deposition of functional humidity-sensitive and electrode materials on paper surfaces, in order to overcome the concerns described in this study and to design better and more stable devices. Further in-depth and detailed research on paper-based sensors will also play a positive role in promoting their future maturity and commercialization. However, of course, when solving these problems, the simplicity and cost advantages of paper-based sensors must not be sacrificed .


Infant cry is an adaptive signal of distress that elicits timely and mostly appropriate caring behaviors. Caregivers are typically able to decode the meaning of the cry and respond appropriately, but maladaptive caregiver responses are common and, in the worst cases, can lead to harmful events. To tackle the importance of studying cry patterns and caregivers’ responses, this review aims to identify key documents and thematic trends in the literature as well as existing research gaps. To do so, we conducted a scientometric review of 723 documents downloaded from Scopus and performed a document co-citation analysis. The most impactful publication was authored by Barr in 1990, which describes typical developmental patterns of infant cry. Six major research thematic clusters emerged from the analysis of the literature. Clusters were renamed “Neonatal Pain Analyzer” (average year of publication = 2002), “Abusive Head Trauma” (average year of publication = 2007), “Oxytocin” (average year of publication = 2009), “Antecedents of Maternal Sensitivity” (average year of publication = 2010), “Neurobiology of Parental Responses” (average year of publication = 2011), and “Hormonal Changes & Cry Responsiveness” (average year of publication = 2016). Research clusters are discussed on the basis of a qualitative inspection of the manuscripts. Current trends in research focus on the neurobiology of caregiver responses and the identification of factors promoting maternal sensitivity. Recent studies have also developed evidence-based strategies for calming crying babies and preventing caregivers’ maladaptive responses. From the clusters, two topics conspicuously call for future research: fathers’ responsiveness to infant cry and the impact of caregiver relationship quality on cry responsiveness. In mammalian species, newborns use visual and acoustic signals to communicate different needs ,2. AcrosCry has also been studied in regard to atypical development. People with atypical development use both conventional and unconventional nonverbal behaviors to communicate. Esposito et al.  showed tFor caregivers, the capacity to understand and attribute the correct meaning to infant cry is fundamental to their establishment of a strong and secure relationship ,14,15. TGiven the developmental relevance and health significance of studying typical and atypical caregiver responses to infant crying, the current study aimed to summarize the literature in the field using a scientometric approach. Scientometrics lies at the intersection of scientific mapping  and bibliometric analysis  ,30,31. CThe current scientometric review follows the pipeline of previously published works in the field of social neuroscience, clinical psychology, and addiction neuroscience e.g., ,34)..34]).bibliometrix package for R [bibliometrix package.All publications used for this article were downloaded from Scopus, as it guarantees the widest coverage of indexed journals ,36. The ge for R . Scopus The scientometric analysis was conducted using CiteSpace software (Version 6.1.R6 Advanced) . RetrievDCA is a type of scientometric analysis that creates a network of co-cited documents. DCA reveals main research trends in a specific topic of interest. The premise underlying DCA is that publications that are frequently cited together  represent research clusters with a shared thematic interest ,41,42,43g-index, TOP N, and TOP N%. Node selection criteria set the strategy used to identify the main documents to include in the final network. The g-index is a derivative of the h-index, and it reflects the number of citations of an author’s top publications [N and TOP N% select and include in the final network the N and the N% most-cited items over a time interval, which, in this study, was fixed at 1 year. Node selection criteria are associated with scaling factors, which allow for controlling the size of the final DCA network. In the current study, the effects of multiple node selection criteria and scaling factors were compared to identify the optimal DCA network. As in previous publications , and mean silhouette score . Ultimately, the g-index with a scale factor k = 75 was selected to generate the DCA network. To create a balanced network of clusters, CiteSpace allows adjustment of node selection criteria and scaling factors. The most common node selection criteria are the ications ,44,45. Ts , we selResults are presented using structural and temporal metrics. Modularity Q, silhouette, and betweenness centrality are structural metrics. Modularity Q determines the level of divisibility of the network into separate modules or clusters. This metric varies between 0 and 1, with higher values indicating a more organized and divisible network . Silhoue network .The bibliometric analysis of the citing references revealed that the sample of 723 articles developed with an annual growth rate of 1.22% from 1962 to 2022. On average, each document received 31.24 citations.Infant Behavior and Development (number of documents = 24), Pediatrics (number of documents = 17), and Child Development (number of documents = 15).The main sources of documents were When considering authors’ affiliation country, the most frequent country that appeared in the sample was the United States of America  = 150, and multiple country publications, (MCP) = 12—followed by Italy , and United Kingdom .In the data pool, a total of 2656 distinct authors were found. Overall, an average of 0.272 documents per author were published, with each document having an average of 4.5 authors. The most productive authors in the sample were MH van IJzendoorn (number of documents = 16), BM Lester (number of documents = 14), K Michelsson (number of documents = 14), MJ Bakermans-Kranenburg (number of documents = 12), MH Bornstein (number of documents = 12), G Esposito (number of documents = 11), EM Leerkes (number of documents = 11), C Manfredi (number of documents = 11), CA Reyes-García (number of documents = 11), and LC Mayes (number of documents = 9).The most frequently cited documents were authored by Prechtl  , infant crying (number of documents = 35), infant (number of documents = 33), infant cry (number of documents = 29), pain (number of documents = 23), cry analysis (number of documents = 21), infants (number of documents = 21), maternal sensitivity (number of documents = 21), cry (number of documents = 18), and parenting  and 9300 links, with an average of 2.975 links for each node. The modularity Q value of the network is 0.978, and the weighted mean silhouette score is 0.961. These metrics indicate that the network of documents is divisible into distinct and homogeneous thematic clusters of research see .In the network, six major thematic clusters were selected by the software based on their metrics. Clusters are labeled with the CiteSpace’s Log-Likelihood Ratio (LLR) algorithm. LLR was chosen because it provides the most accurate labels across automated labeling methods in CiteSpace . The maiIn the final network, a total of 10 documents had a citation burst in their history when setting  by Barr , with a  by Barr   and their global citation score  are provided.The earliest major cluster to appear in the literature is cluster #44, which included documents whose average year of publication was 2002. The major citing document was authored by Sisto et al.  (coveragCluster #6 includes documents published on average in 2007. The major citing documents in the cluster were authored by Barr  (coveragThe third major cluster in terms of average year of publication is cluster #14, which includes papers published on average in 2009. The major citing documents in the cluster were authored by Riem et al.  (coveragCluster #28 consists of documents published on average in 2010. The major citing documents in the cluster were authored by Biro et al.  (coveragThe average year of publication of documents included in cluster #0 was 2011. The major citing documents in the cluster were authored by Bornstein  (coveragAt the neural level, some gender differences in detecting and responding to infant cry stimuli were documented. After the exposure to infant crying cues, De Pisapia et al.  found thIn cluster #36, the documents were published, on average, in 2016. The major citing documents in the cluster were authored by Rybicka et al.  (coveragScientometric analysis provides insight into the literature on infant crying and caregiver responsiveness. However, this approach to deconstructing the literature has some intrinsic limitations. Two factors that might alter the outcomes of scientometric analysis are the chosen search string and the platform for conducting the literature search. It is, therefore, possible that some relevant documents in the field were excluded because the terminology in their title, abstract, and keywords eluded the search string. Similarly, some documents might have been excluded because they were published in journals that are not indexed in Scopus. However, the current work was based on analysis conducted on a large sample of 723 documents, as well as the 26,498 references that they cite. Another limitation is that the scientometric approach relies heavily on quantitative relationships between documents, without providing insight into the qualitative relationships. Frequent co-citation does not necessarily indicate the value of documents. In fact, certain publications may receive many citations because they are innovative and impactful or because they receive considerable criticism from works in the same field. For this reason, in this analysis, we inspected clusters qualitatively to identify research trends .In the current study, the literature on infant crying and caregiver responsiveness was reviewed using a scientometric approach. In doing so, the scientometric approach proved useful in illuminating the main research themes and most impactful publications in the literature on infant cry and caregiver responsiveness. In particular, current trends prioritize exploration of the neurobiology of caregiver responses and the identification of antecedents of maternal sensitivity, such as hormonal levels and attachment quality. Likewise, the literature strongly focused on mothers and their responsiveness to infant cry. In this trend, the role of fathers was only marginally investigated. Research on paternal responsiveness to infant cry represents material for future lines of research that aim to provide insight into the neurobiology of fatherhood. Similarly, the ways in which the quality of the relationship between caregivers influences their responsiveness to infant cry are still poorly understood. In the literature, abusive head trauma has been found to correlate with a poor understanding of the typical development of infant crying but also with a lack of effective strategies to stem crying-related frustration and effective ways to console babies. In this context, recent studies have developed evidence-based strategies for soothing crying babies and preventing maladaptive responses in caregivers. Understanding the connections among infant crying and caregivers’ mental health, parenting practices, and cultural beliefs can help design evidence-based interventions to promote positive and responsive caregiving and reduce the risk of adverse outcomes for both infants and their caregivers.


Ps of 0.8785, 0.8587, 0.8762, and 0.8815) plus root mean square errors  showed that the EFO-MLPNN and TLBO-MLPNN perform slightly better than WCA-MLPNN in the testing phase. Besides, analyzing the complexity and the optimization time pointed out the EFO-MLPNN as the most efficient tool for predicting the DO. In the end, a comparison with relevant previous literature indicated that the suggested models of this study provide accuracy improvement in machine learning-based DO modeling.As an important hydrological parameter, dissolved oxygen (DO) concentration is a well-accepted indicator of water quality. This study deals with introducing and evaluating four novel integrative methods for the prediction of DO. To this end, teaching–learning-based optimization (TLBO), sine cosine algorithm, water cycle algorithm (WCA), and electromagnetic field optimization (EFO) are appointed to train a commonly-used predictive system, namely multi-layer perceptron neural network (MLPNN). The records of a USGS station called Klamath River  are used. First, the networks are fed by the data between October 01, 2014, and September 30, 2018. Later, their competency is assessed using the data belonging to the subsequent year . The reliability of all four models, as well as the superiority of the WCA-MLPNN, was revealed by mean absolute errors  in the training phase. The calculated Pearson correlation coefficients (R The concentration of dissolved oxygen (DO) is a well-known measure of water quality, reflecting the balance between the production and consumption of oxygen. Therefore, is an important criterion for water quality management 3. The variations in DO concentration are functions of several factors, however, major sources of DO are photosynthetic activities, aeration (at structures), and re-aeration (from the atmosphere)4.As is known, water quality is a primary indicator of ecosystem health in aquatic communities. For instance, in aquaculture, the quality and growth of aquatic products are highly affected by the quality of water6. Considering this dynamic nature, as well as the challenges in providing DO measurement equipment, developing DO predictive models is of great desire for monitoring water quality. Hence, non-linear methods have received increasing attention for exploring the relationship between the DO and environmental key factors. Water discharge (Q), water temperature (WT), pH, and specific conductance (SC) are among the most important parameters and different combinations of them have been considered in earlier research depending on data availability and environmental conditions.Measuring the DO is a difficult task due to the effect of various factors like salinity, temperature, oxygen source, etc.7. It is a research organization that provides high-quality and publicly available water data for different areas in the US. In general, the provided data are categorized as either (i) approved for publication or (ii) subject to revision. As the names imply, the first group of data has been reliably processed by the relevant staff, while the second group has not received this approval yet. In this work, the approved data of Klamath River Station (station number 11509370) is used. Many studies in the literature on water quality prediction have used USGS data9, especially for DO prediction of the Klamath River11.A very popular provider of these hydrological time series is the US Geological Survey (USGS)15. For instance, remote sensing facilities are among the most applicable tools for monitoring nature e.g., water bodies19. Hydrology is one of these fields that has been highly befitted by these developments23. In this sense, the involved subject may extend from precipitation analysis25 to water quality assessment26. Statistical and machine learning methods are two evident examples of suggested models for water quality analysis28.With recent advances in computational and measurement domains, the world of science has witnessed various developments aiming at facilitating complex problems regarding natural phenomena31. The advent of machine learning has shed new light on this domain as it provides fast, reliable, and inexpensive solutions to complex prediction problems. Recent sophisticated methods like artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have been highly regarded by engineers for DO prediction in different parts of the world33. Ay and Kisi34 investigated the ability of two popular notions of ANNs, namely radial based function (RBF) and multi-layer perceptron neural network (MLPNN) for analyzing the DO concentration. They compared these models with multilinear regression (MLR). It was found that the RBF performs better than the two other models. Liu et al.35 successfully employed an attention-based recurrent neural network (RNN) for long-term and short-term prediction of the DO. More research about the effectiveness of the ANNs can be found in38. Ji et al.39 proved the applicability of support vector machine (SVM) for predicting the DO concentration in hypoxic river systems. With reference to a larger than 86% correlation obtained for the testing phase, they used the SVM as a promising approach for this purpose. Huan et al.40 demonstrated the high efficiency of the SVM based on the least-squares theory (LSSVM). Shi et al.41 successfully applied a clustering-based softplus extreme learning machine (CSELM) for simulating the DO content in aquaculture. It was also shown that this model is more accurate than standard ELM. Kisi et al.42 proposed an intelligent model called Bayesian model averaging (BMA), for the DO estimation. They validated the performance of this model by five well-known models including ELM, classification and regression tree (CART), ANN, MLR, and ANFIS. Based on the obtained RMSEs , the superiority of the BMA was clearly derived. Najah et al.43 compared the ANFIS with ANN for the DO modeling and found that the results of the ANFIS were more accurate. Olyaie et al.44 conducted a comparison among four data-driven models including RBF, linear genetic programming (LGP), MLPNN, and SVM used for the same objective. Referring to the respective coefficient of determinations , the SVM surpassed other tested models. The feasibility of a so-called deep learning technique “gated recurrent unit” for the DO analysis in a fishery pond was shown by Li et al.45. This model also outperformed the RNN and long short-term memory. The applicability of other machine learning models such as evolving fuzzy neural network (EFuNN)11, radial basis function neural network (RBFNN) and general regression neural network (GRNN)46, long-short term memory (LSTM)47, support vector regression (SVR)48, dynamic evolving neural-fuzzy inference system (DENFIS)49 has been shown and compared in earlier studies. Further comparative studies can be found in earlier literature52.In a general sense, prediction/monitoring studies cover a wide range of scientific efforts for various environmental parameters55. Raheli et al.56 built an optimized version of the MLP neural network using firefly algorithm for forecasting the DO and biochemical oxygen demand. The performance of the hybrid model was found to be more reliable than the standard MLP. Furthermore, uncertainty analysis revealed an acceptable degree of uncertainty for the ANN. Yaseen et al.57 coupled an LSSVM with bat algorithm for approximating the DO. A comparison with conventional machine learning models like multivariate adaptive regression spline (MARS) and M5 tree pointed out a considerably higher accuracy  for the proposed hybrid. Three optimization techniques of particle swarm optimization (PSO), biogeography‐based optimization, and butterfly optimization algorithm were used by Fadaee et al.58 for optimizing the ANFIS applied to the seasonal analysis of the DO. The accuracy of the AFNIS experienced nearly 14, 16, 6, and 13% improvement in the spring, summer, fall, and winter, respectively. Liu et al.59 could enhance the accuracy of a least-squares SVR with an improved PSO. A similar application of the PSO was examined by Chen et al.60. Bayram et al.61 recommended the use of teaching–learning based optimization (TLBO) applied to quadratic regression for stream DO analysis. In a comparative effort by Azma et al.62, seven hybrids of MLP with biogeography-based optimization (BBO), sunflower optimization (SFO), atom search optimization (ASO), crow search algorithm (CSA), league championship algorithm (LCA), shuffled frog leaping algorithm (SFLA), and slime mould algorithm (SMA) were tested for DO prediction in the Rock Creek Station (USGS number 01648010) around Washington, USA. Their results showed the higher accuracy of the BBO-based model. Also, an importance assessment of the inputs reflected the largest and lowest importance for the TW and Q, respectively.Optimization of regular predictive models has been studied by many scholars in recent yearsConcerning the promising results obtained by hybrid algorithms, utilizing metaheuristic-empowered models is becoming a research hotspot in a wide range of engineering domains. In order to address the latest developments in this regard, this work employs the TLBO along with sine cosine algorithm (SCA), water cycle algorithm (WCA), and electromagnetic field optimization (EFO) as the training strategies of the MLPNN to predict daily DO using five-year records. The main contribution of these four metaheuristic algorithms to the problem of DO prediction lies in tuning the MLPNN computational variables that are responsible for establishing the relationship between the DO and its influential parameters. Hence, due to the optimization procedure of these algorithms, it can be said that the TLBO, SCA, WCA, and EFO will optimize the non-linear dependency of the DO on water conditions to achieve a reliable prediction for different conditions.The case study is Klamath River  whose initial part suffers from seasonal low water quality. This study also pursues comparing the efficiency of the used algorithms toward achieving a fast, inexpensive, and reliable DO evaluative model. The used models are optimized in terms of hyperparameters, and in the end, practical monolithic formulas are extracted to be used as DO-predictive equations; eliminating the need for running computer-aided programs and GUIs. Hence, the outcomes of this study may provide significant contributions to the early prediction of DO concentration within the Klamath River.63. This initial part of the river is characterized by seasonal low water quality preventing it from hosting aquatics64. This issue calls for proper water quality assessment in this area65.Figure https://waterdata.usgs.gov/nwis). Out of the available data for a five-year period , those between October 01, 2014, and September 30, 2018, are considered as training samples for deriving the relationship between the DO and WT, pH, and SC. The trained models are then tested using the data between October 01, 2018, and September 30, 2019, called testing data. Figure The time-series data consisting of WT, pH, SC, and DO records in Klamath River Station operated by USGS (station number 11509370) are downloaded from the USGS water data website  layers, namely the input layer, hidden layer(s), and output layer. Figure The MLPNNIn each neuron, the input is multiplied by a specific weight factor, and then, added to a bias. The neurons in the hidden layer and output layer can have a linear or non-linear activation function that releases the outcome of the neurons in the last step.69. In this work, this algorithm is replaced with TLBO, SCA, WCA, and EFO.The training mechanism of an MLPNN is described as iteratively adjusting the weights and biases toward a more accurate prediction  for the new network. A common algorithm that is responsible for this process is Levenberg–Marquardt70. It has been widely used for solving various problems71. In this algorithm, a class (with the students and their teacher) is simulated so that the teacher influences the learners to reach the most proper harmony. Improving the knowledge of the students takes place in two separate steps conducted by the teacher and students themselves . In this regard, the potential  of each individual is assessed by exams. In the teacher phase, after calculating the fitness values, the most potent individual is considered a teacher. In the next phase, the learners help together to improve each other's knowledge. Previous studies have detailed mathematical regulations of the TLBO73.The TLBO is a metaheuristic algorithm designed by Rao et al.74. After generating a random swarm, the algorithm conducts the optimization over two phases, namely exploration and exploitation. In the first phase, a suitable searching area is found by abruptly mixing the random solution with several others having a large rate of randomness. In the second phase, the random solutions experience changes gradually. Several random values are used in the SCA. Some are considered as the variables of the sine/cosine functions. A random number also plays the role of a criterion for determining the updating equation . The SCA has been mathematically described in studies like76.As a recently developed algorithm, the SCA mimics mathematical rules . This algorithm was proposed by Mirjalili77 developed the WCA by taking the main inspiration from the water cycle running in nature. Assuming that the algorithm commences by raining, the raindrops may finally take the form of a stream, river, and sea-based on their fitness value. In this designation, the sea is the most capable solution provided by the algorithm so far. The rivers also represent an improved version of the streams. These individuals iteratively replace each other to find the most powerful sea. More clearly, once a stream is more promising than a river, they exchange their position. The sea is likewise replaced with a more promising river. In the WCA, the mentioned process is repeated by repeating the rain process. It creates new raindrops and hereby prevents premature optimums. The WCA is detailed in earlier literature79.Eskandar et al.80 proposed the EFO in 2016. Similar to the initial classification executed in the WCA, each agent of the EFO algorithm, known as an electromagnetic particle (EMP), is first grouped in one of the positive, negative, and neutral fields. It is done with respect to the fitness of the proposed EMP. In each iteration, a new EMP is generated and if it brings a larger fitness, it replaces the worst existing EMP. Producing the new EMP begins with taking a member from each field. In the next step, the neutral EMP donates its position (and pole) to the new particle. Based on the fact that EMs with different poles attract each other (and vice versa), the new particle is then affected by positive and negative EMPs. Studies like82 contain the mathematical details of the explained process.As an electromagnetics-based search scheme, Abedinpourshotorban et al.P) is used to measure the correlation of the results. Equation P index. Another criterion called Nash–Sutcliffe efficiency (NSE) coefficient is also expressed by Eq. Q signifies the number of processed samples which equals 1430 and 352 for the training and testing data, respectively.For assessing the capability of these models, mean absolute error (MAE) and root mean square error (RMSE) indices are employed to report the prediction error. Equations Once again, this paper offers four novel models for DO prediction. The models are composed of an MLP neural network as the core and the TLBO, SCA, WCA, and EFO as the training algorithms. All models are developed and implemented in the MATLAB 2017 environment.Proper training of the MLP is dependent on the strategy employed by the algorithm appointed for this task . In this section, this characteristic is discussed in the format of the hybridization results of the MLP.x = y) and Tansig  to achieve the best model configuration. These tested NSWs were 10, 25, 50, 75, 100, 200, 300, 400, and 500 for the TLBO, SCA, WCA, while 25, 30, 50, 75, 100, 200, 300, 400, and 500 for the EFO84. Collecting the obtained objective functions  led to creating a convergence curve for each tested NSWs. Figure Furthermore, each algorithm was implemented with nine swarm sizes  was 1.3231, 1.4269, 1.3043, and 1.3210, respectively. These values plus the MAEs of 0.9800, 1.1113, 0.9624, and 0.9783, and the NSEs of 0.7730, 0.7359, 0.7794, and 0.7737 indicate that the MLP has been suitably trained by the proposed algorithms. In order to graphically assess the quality of the results, Fig. Ps  demonstrate a large degree of agreement for all used models. Moreover, the outcome of The RMSE of the recognized elite models , the WCA-MLPNN emerged as the most reliable model in the training phase. In other words, the WCA presented the highest quality training of the MLP followed by the EFO, TLBO, and SCA. However, the results of the testing data need more discussion. In this phase, while the EFO-MLPNN achieved the smallest RMSE (1.2903), the largest RP (0.8815), and the largest NSE (0.7695) at the same time, the smallest MAE (0.9728) was obtained for the TLBO-MLPNN. About the SCA-based ensemble, it was shown that this model yields the poorest predictions in both phases.In terms of all all accuracy criteria , shuffled complex evolution (SCE), and black hole algorithm (BHA) were combined with an MLPNN and the models were applied to the same case study (Klamath River Station). The best training performance was achieved by the MLP-MVO , while the best testing performance was achieved by the MLP-SCE . As per Table 85. In another study by Kisi et al.42, an ensemble model called BMA was suggested for the same case study, and it achieved training and testing RMSEs of 1.334 and 1.321, respectively (See Table 5 of the cited paper). These error values are higher than the RMSEs of the TLBO-MLPNN, WCA-MLPNN, and EFO-MLPNN in this study. Consequently, these model outperform benchmark conventional models that were tested by Kisi et al.42 . With the same logic, the superiority of the suggested hybrid models over some conventional models employed in the previous studies65 for different stations on the Klamath River can be inferred. Altogether, these comparisons indicate that this study has achieved considerable improvements in the field of DO prediction.In comparison with some previous literature, it can be said that our models have attained a higher accuracy of DO prediction. For instance, in the study by Yang et al.s,  s, and  s, while those of the EFO were bounded between 277.2 and 296.0 s. Another difference between the EFO and other proposed algorithms is related to two initial NSWs. Since NSW of 10 was not a viable value for implementing the EFO, two values of 25 and 30 are alternatively considered.Table NSWs of 500, 400, 400, and 50, respectively) shows that the WCA needs around 37% of the TLBO’s time to train the MLP. The EFO, however, provides the fastest training.Based on the above discussion, the TLBO, WCA, and EFO showed higher capability compared to the SCA. Examining the time of the selected configurations of the TLBO-MLPNN, SCA-MLPNN, WCA-MLPNN, and EFO-MLPNN , it can be said that the outstanding solution  has been excerpted among a large number of candidates .Apart from comparisons, the successful prediction carried out by all four hybrid models represents the compatibility of the MLPNN model with metaheuristic science for creating predictive ensembles. The used optimizer algorithms could nicely optimize the relationship between the DO and water conditions  in the Klamath River Station. The basic model was a 3 × 6 × 1 MLPNN containing 24 weights and 7 biases  was derived in the previous section. Hereupon, the MLPNNs constructed by the optimal responses of these two algorithms are mathematically presented in this section to give two formulas for predicting the DO. Referring to Fig. ith hidden neuron in the WCA-MLPNN and EFO-MLPNN, respectively. Given Tansig (x) = – 1 as the activation function of the hidden neurons, In the above relationships, More clearly, the integration of Eqs.  and 7 reP, and NSE) in the testing phase. Another advantage of this model was hiring a smaller number of search agents to find the optimal response. After that, the WCA-MLPNN emerged as the second-efficient model. Therefore, two DO predictive, based on the weights and biases tuned by the WCA and EFO were proposed in the last part of this research. Moreover, it was shown that the outstanding models of this study outperform several hybrid and conventional models from previous studies, indicating an improvement in practical DO predictions. It would also help in better solving the problem of poor water quality in the studied area.Four stochastic search strategies, namely teaching–learning-based optimization, sine cosine algorithm, water cycle algorithm, and electromagnetic field optimization were used to train an artificial neural network for predicting the dissolved oxygen of the Klamath River, Oregon, US. After designating the appropriate parameters for each algorithm, accuracy indices showed that all four methods can properly train the MLP to grasp a reliable understanding of the DO behavior. Due to the same reason, the models could reliably predict the DO for new environmental conditions. The hybrid models were compared in terms of accuracy, complexity, and computation time to detect the most efficient predictor. During the training process, it was deduced that although the EFO algorithm required 30 times more iterations, it accomplished this process far faster than three other algorithms. It also presented the most accurate results (in terms of the RMSE, R


On a regional scale, wetland vegetation in China and Russia had positive partial correlation with solar radiation and minimum air temperature, with precipitation showing a slight lag effect. In contrast, wetland vegetation in Mongolia had positive partial correlation with precipitation. These correlations were further investigated at different climate intervals. We found the precipitation is positively correlated with LAI in the warm regions while is negatively correlated with LAI in the wet regions, indicating an increase in precipitation is beneficial for the growth of wetland vegetation in heat sufficient areas, and when precipitation exceeds a certain threshold, it will hinder the growth of wetland vegetation. In the cold regions, we found solar radiation and minimum air temperature are positively correlated with LAI, suggesting SR and minimum air temperature instead of mean air temperature and maximum air temperature play more important roles in affecting the wetland vegetation growth in the heat limited areas. The LAI was found to be negatively correlated with maximum air temperature in the arid areas, indicating excessive temperature would inhibit the wetland vegetation growth when the water is limited. Our investigation can provide a scientific foundation for the trilateral region in wetland ecosystem protection and is beneficial for a more comprehensive understanding of the responses of wetlands in the middle and high latitudes to climate change.The Heilongjiang-Amur River Basin is one of the largest and most complex aquatic systems in Asia, comprising diverse wetland resources. The wetland vegetation in mid-high latitude areas has high natural value and is sensitive to climate changes. In this study, we investigated the wetland vegetation cover changes and associated responses to climate change in the Heilongjiang-Amur River Basin from 2000 to 2018 based on the growing season (May to September) climate and LAI data. Our results indicated that the wetland LAI increased at 0.014 m These vA wide range of ecological processes is being impacted by the ongoing and expected acceleration of climate change in mid-high latitudes, as a result of these environmental changes, the productivity, and composition of vegetation have been directly affected . CurrentThe Heilongjiang-Amur Basin (HARB), is a cross-border area including China, Mongolia, and Russia, with different natural conditions. Therefore, understanding the differences in wetland vegetation responses to climate change in the HARB could help improve our understanding of cross-border wetland vegetation in mid-high latitudes. There have been significant improvements in the wetland phenology, and a predominance of vegetation turning green . Becauseavg), maximum air temperature (Tmax), and minimum air temperature (Tmin) to the wetland LAI in the three countries under different natural and cultural conditions in HARB. In addition, we will discuss the annual climate change background and the unique relationship between LAI and climate drivers. Our findings are expected to provide valuable insights into the sustainable development of wetland ecosystems and enhance the understanding of climate factors’ effects on wetland vegetation. This research will also contribute to the understanding of the response of cold region ecosystems to global climate change.This study aims to investigate the spatiotemporal changes in the wetland leaf area index (LAI) during the growing season from 2000 to 2018 in the HARB. By using partial correlation analysis, we aim to quantify the correlation of precipitation, solar radiation (SR), mean air temperature , the Russian Far East Area (50%), and the Northeast of Mongolia (8%) comprised by the HARB, with the eleventh largest wetland in the world and covers 8% of the wetland area . Mixed f2.2avg), maximum (Tmax), and minimum (Tmin) air temperatures from 2000 to 2018. We calculate the monthly precipitation, Tavg, Tmax, and Tmin from daily data selected between May and September to obtain the growing season precipitation and air temperature. We used the ANUSPLIN software to interpolate all observation data, including precipitation and temperature, with the thin plate spline method, using DEM as a covariate (http://data.cma.cn), 6 stations in Mongolia were collected from the Mongolian Academy of Sciences; 32 Russia observation sites were from National Center for Environmental Information . The wetland map was derived from LULC data with a spatial resolution of 1km based on a human-computer interaction interpretation, with a total accuracy of 90.800%, which was verified by comparing the classification results with higher resolution Google earth images (The moderate Resolution Imaging Spectrometer (MODIS) LAI data were obtained for this research from 2000 to 2018 to examine vegetation growth and its response to climate change. We adopted LAI (MOD15A2H) data from 2000 to 2018, with a temporal resolution of 8-day and 500 m spatial resolution, and resampled to 1km by the resample tool in ArcGIS 10.2. In this paper, the solar radiation was extracted from the monthly value dataset TerraClimate, with a spatial resolution of a 0.5° and resampled to 1km . Both LAovariate . To match images . To anal33.1Wetlands are highly sensitive to climate change, and as a result, variations in climate can directly impact the structure and physiology of vegetation. Based on the LAI time series in the HARB in the growing season during 2000-2018, linear regression models due to ordinary least squares (OLS) were adopted to calculate the spatial and temporal patterns of wetland vegetation dynamics . The stuWhere 3.2In light of the high correlation in the climate factors, we studied the correlation coefficients to determine the relationship between LAI and precipitation, SR, mean, maximum, and minimum air temperature, or during the growing season at pixel scales in the HARB . The purWhere To assess the dominant factors from different climate variables, the partial correlation could measure the strength of a linear relationship between wetland vegetation and these climate factors. Meanwhile, partial correlation considers the influence of other variables .1y and 2y for fixing variable 3y; 1y; 2y and 3y; x and 2y. The significance test of the partial correlation coefficient is tested by student-t.Where n is the number of variables; m is the number of independent variables. The t distribution table can be used to determine the crucial value αt. If this is lower than αt, the partial correlation coefficient is significant, or it is nonsignificant.Where 44.12·m-2/yr, p = 0.012), with China having the highest growth  followed by Russia  and Mongolia . Generally, the LAI increasing mainly concentrated in 0 - 0.020 m2/m2 per year  and nonsignificant trends elsewhere. SR and Tmax had nonsignificant downward trends in all three countries. These findings suggest that the HARB region was experiencing significant changes in precipitation, with some variation among countries, while other climate variables exhibit nonsignificant trends.An analysis of climate data from 2000 to 2018 revealed significant upward trends in precipitation for HARB, while changes in Tnificant Table 1.4.2avg, and Tmax , while precipitation and Tavg had nonsignificant negative correlations with LAI (-0.194 and -0.299). In Russia, LAI showed negative correlations with precipitation and Tavg (-0.156 and -0.204), while the correlations with SR, Tmax, and Tmin were weak. Mongolia demonstrated strong positive correlations between LAI and both precipitation  and Tavg , with weak correlations observed for SR, Tmax, and Tmin. The highest ratio of significant positive partial correlation with LAI was found for SR in all three countries. However, Russia showed a significant negative partial correlation between LAI and Tmin , while China and Mongolia showed the highest ratios of significant negative partial correlation between LAI and SR .For the three countries, China exhibited positive correlations between LAI and SR  as well as T4.3avg zone during the growing season  correlated with air temperature in cold regions.When the other climate factors are removed, SR and Tavg and Tmax , whereas the cold and moist area (China and Russia) could exacerbate warming via decreasing albedo. The positive partial correlation value in high latitude regions also implies that SR, as well as air temperature, were what limit wetland vegetation greening  on wetland vegetation across HARB in the growing season from 2000-2018. Generally, the growing season LAI demonstrated an upward trend, which is 0.014 m2·m-2/yr. SR and the Tmin had a strong and significant positive influence on HARB wetland vegetation greening, which is typical in mid-high latitudes. On a regional scale, wetland vegetation in China and Russia were positively influenced by solar radiation and Tmin, while the wetland vegetation in Mongolia was positively influenced by precipitation. These correlations were further investigated at different climate intervals. We found the precipitation is positively correlated with LAI in the warm regions while is negatively correlated with LAI in the wet regions, indicating an increase in precipitation is beneficial for the growth of wetland vegetation in heat sufficient areas, and when precipitation exceeds a certain threshold, it will hinder the growth of wetland vegetation. In the cold regions, we found SR and Tmin are positively correlated with LAI, suggesting SR and Tmin instead of Tavg and Tmax play more important roles in affecting the wetland vegetation growth in the heat limited areas. The LAI was found to be negatively correlated with Tmax in the arid areas, indicating excessive temperature would inhibit the wetland vegetation growth when the water is limited.This study applied partial correlation analysis to draw the impacts of climatic factors (precipitation, SR, TTo improve on the results of this paper, the wetland vegetation type in HARB could be more specific, then could trigger different wetland vegetation types in different greening diversity under the similar climate background, for instance, wetland vegetation investigation should categorize natural and constructed wetland and discuss their impact of climate change respectively. Furthermore, climate factors are far more than that, the conclusions in this paper was discussed by using the 5 commenly used variables. In the follow-up studies, we could concentrate on these two aspects when conducting the study.https://www.ncei.noaa.gov/maps-and-geospatial-products, http://data.cma.cnUSGS, http://glovis.usgs.gov/https://www.nature.com/articles/sdata2017191.Publicly available datasets were analyzed in this study. This data can be found here: XC: methodology, software, writing - original draft, writing - review & editing, formal analysis, validation. LY: conceptualization, methodology, funding acquisition, formal analysis, software, writing - review & editing. GL: software, resources, investigation, writing - review & editing. XL and LB: data curation, software. All authors contributed to the article and approved the submitted version.