Recently, researchers have expressed their growing concern over the scrutiny of language test items in light of cognitive load theory (CLT). While cognitive load has been central to language learning research, it has not enjoyed due attention in high-stakes language tests. The current study set out to delve into the relationship between difficulty estimates and cognitive load of language test items. To measure cognitive load, examinees’ perceived level of difficulty and response time were considered. In this regard, empirical data were collected from 60 MA students and graduates through a quantitative correlational design. The current study further employed the Rasch model to estimate difficulties of the vocabulary and grammar items of the Iranian university entrance examination (IUEE) for MA in English majors held in 2018 and 2019. The study’s findings revealed statistically significant correlations between difficulty estimates and perceived level of difficulty for vocabulary items. As for grammar items, no statistically significant correlations were detected between the variables. Whereas the results indicated strong positive correlations between response time and difficulty estimates regarding vocabulary items, no statistically significant correlations were observed between the variables concerning grammar items. All in all, perceived level of difficulty, response time, and difficulty estimates appeared to be sound indicators of cognitive load with respect to vocabulary test items, but not with regard to grammar test items. The implications of the findings will be discussed. In recent years, the investigation of language test items, using cognitive load theory (CLT), has garnered researchers’ attention  are underlying CLT. Intrinsic load is concerned with the innate nature and main characteristics of test items and has a close connection with task difficulty  and objective behavioral measures  seem to pose some problems, including being intrusive and needing special expertise  for English majors.The current study first presents the general background of CLT. Cognitive load measures are then reviewed, and the pertinent empirical studies are expounded on. The explanation of the Rasch model’s theoretical underpinnings is also provided, and the relevant studies are explicated. The sections of method, results, discussion, limitations and future directions, and conclusion are subsequently presented.cognitive load and learning are at the heart of CLT. Many researchers have studied cognitive load in such a wide range of disciplines as cognitive psychology and instructional design  focuses on the effect of instruction. Stated more precisely, CLT is concerned with the “unobservable” phenomena  that learners experience whilst accomplishing various tasks. The constructs of Sweller, . As a frSweller, .Opening a line of inquiry to account for the comparability of instructional designs regarding difficulty, CLT is built on two memory stores of short- and long-term memory referring to working and storage memory, respectively Martin, . WorkingOn the other hand, task requirements or demands influence the amount of provoked load on the working memory , another behavioral measure to assess cognitive load is response time employed in some studies , in general, and the Rasch model, in particular, have recently entered on the scene to contribute to cognitive load measures , and e equals the constant value of 2.718. As an example, suppose the values of person ability and item difficulty are equal. In this case, a person has an equal chance either to endorse or to fail an item, which happens at the middle point of the item characteristics curve (ICC) where the rate of alterations changes  and the task or item difficulty have also intrigued many researchers compatibility between the measures , including 539 males and 1461 females, were randomly selected from a population who had sat the Iranian university entrance examination (IUEE) for an MA in English majors of Teaching English as a Foreign Language (TEFL), English Literature (EL), and Translation Studies (TS). Of the total population present at the exams in 2018 and 2019, 1000 examinees were screened from the examinees of each year such that out of the examinees sitting the examination in 2018, 751 test takers were female and 249 were male. Also, of the test takers who sat the exam in 2019, 710 examinees were female and 290 of them male.The participants attended the study in two phases. The first phase was necessary to compute item difficulty estimates because their calculation through the Rasch model required a large sample of at least 100 examinees, and it was not conceivable to have 100 participants due to the COVID-19 pandemic in Iran. Further, using the data collected under actual condition of examinations and taken from large samples of test takers would provide more dependable difficulty estimates. In the first phase, the participants (N = 5) attended the pilot study to set the fixed timing of the presentation of multiple-choice questions on the screen. To record mental processing demands, the presentation time of stimuli is suggested to be fixed. Sixty MA students and graduates  majoring in TEFL, EL, and TS, partook in the main study. They were aged between 21 and 39 , studying at the University of Tehran, Allameh Tabataba’i University, and Alzahra University in Iran.In addition to the data from these examinees, further data were obtained from two sets of participants who attended the pilot and main studies in the second phase. The selection of both sets of participants was based on convenience sampling Dornyei, . All theBy administering the grammar part (100 items) of the OPT, the homogeneity of the participants’ level of language proficiency was also assured. Scores ranged from 75 to 94.5 such that the participants’ language proficiency levels were in the C1–C2 bands based on the common European framework of reference (CEFR). That is, they were advanced and very advanced users of the English language according to their obtained scores from the grammar part of the Oxford placement test (OPT) Dave, .The current study consisted of a proficiency test, multiple-choice vocabulary and grammar items of IUEE for MA in English majors, and a subjective self-report rating scale. After the administration of the OPT test, the participants who obtained the required levels of language proficiency were invited to attend the main study in which they were required to answer language test items of MA IUEE. This is because test takers’ right level of English proficiency is assumed to be revealed by their performance in the general English section of the examination. This section includes four parts: grammar (10 items), vocabulary (20 items), cloze passage (10 items), and reading comprehension (20 items). The current study focused on grammar (20 items) and vocabulary (40 items) multiple-choice questions of the two tests of IUEE for MA in English majors in a selected-response format administered in 2018 and 2019. The Grammar section included 20 items altogether such that half of the items belonged to the test held in 2018, and the other half was taken from the test held in 2019. There were also 40 vocabulary items in a way that half of the items were related to the test of 2018 and the other half to the test of 2019. No modifications to the original design and structure of the items were made because the purpose of the present study was to explore functioning of the same items of the entrance examinations. The researchers also checked the validity of the tests, and their validity was confirmed.Level of Difficulty was initially developed and validated by Bratfisch et al. (very easy) to 7 (very difficult), validated by Prisacari and Danielson  was also displayed. The participants’ response time spent on each language item and their responses were recorded by the Psychopy software , and hence it was shown that this assumption was not violated. It should be noted that response times of only correct responses were taken into consideration because they seem to indicate cognitive load rather than those of incorrect answers. This approach to the analysis of time was also adopted and endorsed in several cognitive load studies .The first research question explored the relationship between the test takers’ perceived level of difficulty and difficulty estimates. As shown in Table r = .366, p < .001) with a medium effect size. There was also a significant positive correlation between difficulty estimates and perceived level of difficulty of the second set of vocabulary items . The results suggest that difficulty estimates can be assumed to indicate cognitive load in a similar way to perceived level of difficulty.Table The results of the correlational analyses of response time and difficulty estimates can be seen in Table r = .323, p < 0.05) with a medium effect size. On the other hand, the table illustrates a strong positive correlation between the variables concerning the second set of vocabulary items . Therefore, response time and difficulty estimates appear to measure cognitive load similarly.As evident from Table The current study attempted to ascertain whether the difficulty estimates obtained from the Rasch model match perceived level of difficulty and response time, and whether they can be regarded as indicators of cognitive load. The first research question aimed to examine the association between difficulty estimates and perceived level of difficulty. The results revealed no significant correlations between the aforementioned variables regarding the grammar items and significant correlations between the variables as for the vocabulary items.It was shown that item difficulty estimates seemed not to correspond to perceived level of difficulty, suggesting that item difficulty may not be a sound indicator of or sensitive to cognitive load of the grammar items. Stated another way, cognitive load of the grammar items could not be captured by difficulty estimates in much the same way as perceived level of difficulty: The grammar items that the test takers perceived to be more difficult were not in fact more difficult. These findings seem to be in disagreement with the results of previous studies that highlighted a positive relationship between task complexity and perceived level of difficulty Ayres, .There can be several explanations for the lack of relationships between perceived level of difficulty and difficulty estimates of the grammar items. A conceivable reason may be that the test takers might have under- or over-estimated the item difficulty, an inevitable side effect of using self-reports  models  to take discrimination and guessing parameters into account and hence minimize the effect of guessing, which is of concern in high-stakes exams  using difficulty estimates in the Iranian context. The study delved deeply into whether difficulty estimates behave in the same way as the subjective cognitive load measure in reflecting load of language test items, and whether response time and difficulty estimates, as two objective measures, can detect cognitive load of test items in a similar way.Our findings, overall, revealed that the predicted correlations were born out only for vocabulary items. One significant outcome was the congruence between perceived level of difficulty and difficulty estimates of vocabulary items. In other words, all the vocabulary items indicated to line up according to their difficulty as predicted; however, the grammar items turned out to behave with no specific pattern. Another major result was that difficulty estimates were deemed to capture cognitive load in line with response time; nevertheless, the response time recorded for the grammar items showed not to go together with their difficulty estimates. All in all, item difficulty estimate and response time revealed to be the sound indicators of cognitive load for vocabulary items.Also, the findings of the current study can provide significant theoretical implications in connection with CLT. Applying item difficulty estimates and cognitive load measures can enhance researchers’ understanding of the item evaluation and the influence of language items’ loads on test takers’ minds. Besides, due to some criticism leveled against the use of subjective measures such as being subject to under- or over-estimation of individuals, the implementation of response time can contribute to CLT through providing helpful information about item difficulty, cognitive processes, and item functioning de Jong, . Hence, Moreover, these outcomes have implications within the field of language testing. Cognitive load theory can play a key role in the exploration of item functioning in psychometrics through the concurrent use of cognitive load measures. In this way, test designers can have a thorough grasp of the load and the function of the items they develop. This awareness can help test designers to proceed with caution in the test design, considering the possible detrimental effects of item malfunctioning on the test takers’ minds, such as overloading human cognitive resources or influencing test scores detrimentally In other words, test developers can examine whether the test items they design correspond to the characteristics being experienced or perceived by the test takers regarding difficulty and speed of processing. This can also minimize the danger of over-reliance on difficulty estimates as the sole statistical index of difficulty in item analysis.


Free radicals are chemical molecules that are more reactive and have an unpaired electron. Free radicals formed inside the cell oxidize biomolecules, leading to cell death and tissue damage. Antioxidants are molecules that can stabilize or inactivate free radicals before they damage the cell. In this study; the availability of Malondialdehyde, Superoxide dismutase, Catalase and Reduced glutathione levels as markers for related diseases was evaluated by examining whether and in what range they may vary in some diseases. In study, nine groups consist of prostate cancer, cirrhosis, liver transplantation, chronic kidney damage, acute kidney injury, X-ray exposure, CT exposure, MR exposure and Osteonecrosis. Analysis of means is a method developed to compare group means with the overall mean and presents the results graphically in an easy-to-understand manner without the required for any post hoc test. In addition, related characteristics were categorized as “low and high” and Nonlinear Principal Component Analysis was conducted to visually present their relationship with related disease types in two-dimensional space. The upper and lower decision lines were found 3.123 and 2.794 μmol/L, respectively for Malondialdehyde. Those with cirrhosis, chronic kidney disease, acute kidney disease and tomography exposure were included in the upper and lower decision lines. Those with prostate cancer, osteonecrosis, and X-ray exposure were above the upper decision line and are found higher than the overall mean. Those with lung transplantation and MR exposure appear to be below the lower decision line and lower than the overall mean. The present study provides the first comprehensive assessment of the availability of Malondialdehyde, Superoxide dismutase, Catalase and Reduced glutathione levels as markers for some related diseases. This study has shown that Analysis of means can be used as an alternative graphical procedure for multiple group comparisons with an overall mean in the studies regarding as biochemical characteristics and relating diseases. In addition, Nonlinear Principal Component Analysis can be useful aid for decision marker in some biochemical characteristics and related diseases. Free radicals are chemical molecules that are more reactive and have an unpaired electron. It is constantly produced in cells as a by-product of metabolism. Free radicals formed inside the cell oxidize biomolecules, leading to cell death and tissue damage . Free raFree radicals (FR) are atoms containing an unshared electron pair. Free radicals are constantly produced in living cells. They contain both reactive oxygen species (ROS) and reactive nitrogen species (RNS). The importance of free radicals at physiological levels is that they play important roles by participating in protective reactions such as phagocytosis, detoxification and apoptosis . It is kIt is important to determine the variables that can affect the events or processes in nature or to solve their effect mechanisms, in terms of directing these events in the desired way. In this framework, one of the methods used to determine whether the relevant factor or factors are effective on the continuous variable of interest is an analysis of variance (ANOVA). Analysis of variance (ANOVA) is a widely used method when assumptions are met, and post-hoc tests are then applied to determine significant different groups. ANOVA generally compare the means with each other in pairs. In some cases, it may be desirable to compare means with the overall mean or with the population mean. In this case, analysis of means (ANOM) may be preferred. ANOM is a method that was first introduced by In this context, in this study; the availability of MDA, SOD, CAT and GSH levels as markers for related diseases was evaluated by examining whether and in what range they may vary in some diseases. In addition, related characteristics were categorized as “low and high” and NLPCA was conducted to visually present their relationship with related disease types in two-dimensional space.This retrospective single-center study includes data from nine different studies conducted in accordance with the 1989 Declaration of Helsinki and approved by the Ethics Committee. Necessary permission was obtained from Van Yuzuncu Yıl University, Faculty of Science, Biochemistry Laboratory for the study. The data of the diseases whose MDA, SOD, CAT and GSH levels were examined between September 2021 and December 2021 were collected from the biochemistry laboratory and included in the study. The research only included patients over the age of 18. In study, nine groups consist of prostate cancer, cirrhosis, liver transplantation, chronic kidney damage, acute kidney injury, X-ray exposure, CT exposure, MR exposure and Osteonecrosis.SOD activity by Popov et al., CAT activity by Aeibi, GSH level by Beutler et al. and MDA level was determined by Gutteridge .ANOM is a method developed to compare group means with the overall mean and presents the results graphically in an easy-to-understand manner without the required for any post hoc test. The method requires two assumptions: normal distribution and homogeneity of variances. There are three lines in the ANOM graph. The overall mean is in the middle of the chart. Above the overall mean is the upper decision line, and below one is the lower decision line. Group means are indicated by dots on the graph. The fact that any group mean is outside the lower and upper decision line indicates that this mean is significantly different from the overall mean . Decisioα is the level of significance; k is the number of groups; N is the total number of observations; nj is the sample size of the j. group; MSE shows the mean square error  and SPSS (v. 20) for Windows programs were used for statistical calculations.Descriptive statistics for MDA, SOD, CAT and GSH levels are given in On the other hand, the mean of each group was compared with the overall mean to determine statistically significant differences from the overall mean. As seen in The low and high categories of the considered features and the configuration of the groups in two-dimensional space are given in As seen in In this study, MDA activity was below the lower decision line and SOD, CAT and GSH activity were between decision lines in liver transplant patients according to the ANOM test. In cirrhosis patients, MDA and GSH activities were between the decision lines, and SOD and CAT activities were above the upper decision line. In a study conducted in patients with liver transplantation, MDA, SOD and CAT activities were found above the upper decision line . In anotIt has been observed that MDA and SOD levels are above the upper decision line, GSH level is below the lower decision line, and CAT level is between the decision lines in prostate cancer patients. In the study of ANOM visually presents whether the differences of the groups from the overall mean are statistically significant without any post hoc test. In ANOVA, multiple comparison testing is required to determine the difference between groups after the null hypothesis is rejected. However, ANOM can simultaneously identify these differences in a single step and present the results graphically. Ryan stated that ANOM can be used alone or after ANOVA . Nelson In this study, MDA, SOD, CAT and GSH activities were among the decision lines in patients with chronic and acute renal failure according to the ANOM test. In studies conducted with patients with chronic renal failure, MDA and SOD activity were found between decision lines . In anotIn this study, according to the ANOM test; MDA activity was located between the decision lines in those with computed tomography exposure, below the lower decision line in those with MR exposure, and above the upper decision line in those with X-ray exposure. SOD and CAT activity were between the decisions lines in those with computed tomography and MR exposure, and below the lower decision line in those with X-ray exposure. GSH activity was at the lower decision line in those with computed tomography exposure, above the upper decision line in those with MR exposure, and between the decision lines in those with X-ray exposure. In a study conducted on people working in radiation environments, the effects of ionizing and non-ionizing radiation were found above the upper decision line of MDA level, which is an indicator of oxidative stress . After hIn this study, according to the ANOM test; MDA activity was above the upper decision line, SOD and GSH activities were located on the decision lines, and CAT activity was below the lower decision line in Osteonecrosis patients. In a study in which oxidative stress levels were determined in osteonecrosis patients, MDA activity was found below the lower decision line and GSH activity above the upper decision line in both saliva and serum .In this study, MDA, SOD, CAT, and GSH levels in some disease groups were evaluated using the ANOM and NLPCA methods. The mean of each disease group was compared with the overall mean to determine statistically significant differences. In addition, the relationships between diseases and categories (low and high) of the biochemical characteristics were analyzed by NLPCA, and the result was presented in two-dimensional space. The present study provides the first comprehensive assessment of the availability of MDA, SOD, CAT and GSH levels as markers for some related diseases. This study has shown that ANOM can be used as an alternative graphical procedure for multiple group comparisons with an overall mean in the studies regarding as biochemical characteristics and relating diseases. In addition, NLPCA can be useful aid for decision marker in some biochemical characteristics and related diseases. However further studies required to determine clearly the relations between MDA, SOD, CAT and GSH levels and these diseases.


Basketball is one of the popular sports in colleges. Basketball injuries are a common thing, and the use of machine learning and other technologies can effectively reduce basketball injuries, which should start with prevention. Nonstandard basketball movements and lack of physical coordination will not only reduce sports efficiency for athletes but also increase the probability of injury. Therefore, effective reduction and targeted prevention of nonstandard actions are of great significance to college basketball. With the development of science and technology, artificial intelligence technology is closer to our lives. Based on the machine learning platform, this paper studies basketball injuries from the perspective of the integration of sports and medicine. Research on what aspects cause college students' basketball injuries is needed for the future. Effectively preventing college students from being injured in basketball is an urgent problem in the field of sports medicine. To find the most suitable machine learning platform for college basketball injury research, this article will introduce three different methods for comparative analysis. The techniques used in the experiment in this paper are traditional BP neural network technology, SCG neural network technology, and RBF neural network technology. Through experiments, it is known that, through experiments, RBF neural network technical prediction accuracy rate is as high as 95.4%, which is a relatively good neural network algorithm for studying the basketball loss of college students. Basketball injuries, as the name suggests, are injuries that occur while playing basketball and are widespread in the sports world. Basketball injuries are closely related to sports training, sports technology, sports items, sports training level, and sports environment. As colleges and universities pay increasingly attention to the athletic ability of college students, the competitive level of college basketball games is also getting higher and higher, and the intensity of confrontation is becoming increasingly intense. With the increasing training pressure, the probability of injury to college basketball players is also increasing. Due to the high intensity of basketball sports, injuries in sports affect the future development of college students to a large extent. The impact of basketball injury can be terrifying. An unfortunate injury to an athlete can have unimaginable consequences for the individual, their team, and their family. Sports injuries will not only make athletes unable to participate in high-intensity training and competitions, but also cause athletes to be disabled or even be life-threatening in severe cases. At the same time, it brings a certain psychological burden to athletes and hinders the development of their sports. Therefore, the field of basketball sports injuries is worthy of our in-depth study to avoid. Basketball injuries focus on prevention. There are many reasons for sports injuries although prevention is always emphasized, but sometimes athletes play too frequently, and basketball sports injuries are difficult to avoid. Therefore, it is essential to guide college students to prevent sports injuries in basketball. This is of great significance to promoting the development of basketball and protecting the health of basketball players.This paper focuses on college basketball injuries and is based on machine learning theory. Compared with the ordinary research on college basketball injury, this paper has the following innovations: (1) it proposes a machine learning method. The general way to study college basketball injuries is through expert interviews and mathematical statistics. It always stays at the level of interviewing and researching through people. Based on machine learning, this paper makes basketball injury research systematic and sustainable and greatly reduces the cost of personnel interviews. (2) This paper uses a variety of neural network techniques to study the injury of college basketball. Through systematic research of multimethods, the advantages and disadvantages of each method are compared, and a neural network method most suitable for college students' basketball injury is found. This makes the research results more reliable and authoritative. Therefore, this study is innovative.X into time series , [4 4.5], [3.5 4], [3 3.5], [0 3].In the experiment, the data of 300 samples were compared and analyzed. The valid sample set is taken from 275 student data. It divides these valid sample data into a test sample set and training sample set. The training samples of this experiment are taken from random data of 240 college students. The test sample set is taken from the remaining student data and used to test and train the resulting neural network model.In the college basketball injury system based on machine learning, there must be some indicators that affect the system results. And some indicators even have a negligible impact. Faced with negligible indicators during the experiment, they can be appropriately discarded during data input to achieve the purpose of dimensionality reduction. The purpose of correlation analysis is to make the main feature clear when the main eigenvalue is ambiguous. In this way, the main feature information is also easily recognized by the system, which can effectively improve the system recognition rate. In addition, due to the reduction of the dimension of the input vector, the number of nodes in the network structure will be correspondingly reduced, and the complexity of the network will be reduced. This is a good solution to the trouble caused by too many network dimensions.According to the relevant researches, the nearest neighbor algorithm can determine the relevant properties. Therefore, the nearest neighbor algorithm was used in this experiment to judge the relationship between injuries in college basketball sports in the event of pinching, heavy weight, intense confrontation, excessive use of lower limbs, body collision in the air, nonstop acceleration, emergency stop jump shot, number of blocked shots, loss of center of gravity, fast dribbling speed, excessive exercise time, insufficient rest, ill-fitting shoes, alcohol and smoking, and other indicators. The following data were obtained through systematic analysis, and the details are shown in From Due to the need to ensure the validity of the sample data used to build the model, this experiment uses cross-validation to select experimental sample data, especially when the total number of samples is not large enough.k-fold cross-validation: divide the data into k equal parts. Each time the experiment is run, one of these will be selected as the test set. The rest of the samples are all used as the training set, and the process is repeated k times, so that each test data is tested exactly once. The correct rate of the experiment is averaged over the sum of k validations.This experiment adopts the principle of Since the number of samples is not large, this experiment adopts 5-fold cross-validation, that is; the 275 groups of sample data are randomly divided into 5 equal parts. A total of 55 sets of data are used as the test set, and the remaining 220 sets of data are used as the training set. It is also used for 5-fold cross-validation for BP neural network and SCG neural network and RBF neural network, respectively. The result is as follows:The results based on the BP neural network model are shown in The data simulation is carried out on the basketball injury model of college students based on SCG neural network. It uploads the simulation sample data to the test system for experiment and obtains the following data. Details are shown in Through the experimental test, it can be concluded that the data accuracy rate of college basketball injury model based on SCG neural network is 91.4%, which has reached a high level.The data simulation of college basketball injury model based on RBF neural network is carried out. It uploads the simulation sample data to the test system for experiment and obtains the following data. The details are shown in From the experimental data in By comparing the accuracy of cross-checking and model testing based on BP neural network, SCG neural network, and RBF neural network, As colleges and universities pay increasingly attention to the development of sports, the risk of college students' injury in sports also increases. It uses artificial neural network knowledge and research on college basketball injuries to predict the risk of college basketball injuries. The experiments are carried out through modeling and analysis of several different neural network algorithms. It is tested by importing real data, and the advantages and disadvantages of several different algorithms are compared and analyzed. Thus, a neural network model most suitable for college basketball injury is obtained. In the era of intelligence, artificial neural network technology is used in all aspects of our lives. Using neural network technology as a medium, combining sports and medical fields can scientifically reduce sports injuries to a minimum.Through experimental research, the following conclusions are drawn: (1) using neural network technology and inputting the indicator data of college basketball players, the system can predict college basketball injuries after continuous learning. Through cross-checking, we know that the cross-validation rate of the neural network technology used in this paper is more than 70%, which can well predict college basketball injuries. (2) By comparing the BP neural network, the SCG neural network, and the RBF neural network, the most accurate prediction accuracy rate is the system based on the RBF neural network. The expected output is very close to the predicted output, and its accuracy is as high as 95.4%. However, this study still has shortcomings; that is, the prediction accuracy cannot be infinitely approached at 100%, which means that when the input data is too large, there will be a lot of inaccurate data prediction, which need to be improved. In addition, this experiment only uses three neural network methods for testing experiments. There are not enough methods, and there may be better neural network methods to achieve higher prediction accuracy. Therefore, improving the prediction accuracy of the system is the direction of future research.


Porous shape memory alloys (SMAs) have been widely used in the aerospace, military, medical, and health fields due to its unique mechanical properties such as superelasticity, biocompatibility, and shape memory effect. In this work, the pore shape was considered in the constitutive model of the porous SMAs by respectively introducing the parameter of aspect ratio and for different pore shapes including oblate, sphere, and prolate shapes, so the expression of Young’s modulus for the porous SMA can be derived. Then, the constitutive model for such a porous shape memory alloy was established. When the porosity was zero, the model can be degenerated to the dense case. The stress–strain curves for the porous SMA with a porosity of 13% with different aspect ratio are then given. Numerical results showed good agreement with the published experimental data that proved the validation of the model. Based on the proven constitutive model, the properties of porous SMA with different porosity and pore shapes are discussed. The results showed that the pore shapes and the porosities had a big effect on the stress–strain curves for the porous shape memory, while with the increasing porosities, the Young’s modulus and the hysteresis both decreased. With the same porosities, the Young’s modulus and hysteresis loop of SMA with round pores were the largest, while the Young’s modulus and hysteresis loop were the smallest when  Porous shape memory alloys have been widely used in various fields including the aerospace, military, medical, and health fields ,2,3,4 duThe mechanical properties of porous SMA have been given much attention . RegardiRegarding the theoretical work of the porous SMA, Zhao et al. established a macroscopic compression behavior model of porous SMA and predicted the effective elastic and superelastic behavior of porous shape memory alloys based on the assumption of the stress–strain curve by using the Eshelby heterogeneous inclusion method . In addiHowever, most of the above studies are spherical porous SMA. There is no detailed theory about the effect of pore shape on the mechanical behavior of porous SMA, especially in the case of different pore ratios. Although the above practical and theoretical research work on porous SMA have been carried out, at present, no scholars have considered the influence of pore shape on the properties of porous SMA, and the constitutive relationship considering pore shape has not been established. Qiu found that the influence of pore shape on metal materials is very important and demonstrated the great influence of pore shape on metal materials . TherefoFor porous SMA, it is regarded as a uniform porous material with uniform pore shape and uniform orientation, as shown in The porous SMA is assumed to be the composite of the SMA matrix and pore inclusions, which can be shown in When the inclusion with volume fraction For the average stress of pore inclusions, because the specific orientation of pores will produce a certain amount of stress–strain disturbance From Equations (2) and (3), The stress of thee matrix and pore inclusion shall be balanced with the external stress In addition, o et al. . Then, iWhich is substituted into Equation (1) to obtain the following relationSince the matrix and inclusion are isotropic, their elastic modulus can be expressed by the bulk modulus, shear modulus, and Kronecker delta Then, combine Equation (7) to obtainThe constants When inclusions are uniformly distributed in three-dimensional space, it is assumed that the composite, as a whole, is macroscopically isotropic. The stress or strain can be decomposed into hydrostatic and deviatoric parts, resulting in the effective bulk modulus and shear modulus, respectively.From Equation (7), the hydrostatic and deviatoric parts can be found byThenCombined with Equation (12), the average disturbance stress in the matrix is obtained byThen, the effective bulk modulus For the uniaxial loading case, the one-dimensional unified constitutive model degraded from three-dimensional model of Lagoudas is applied here ,(23)ε˙=For the uniaxial loading,The critical stress of porous SMA is affected by porosity, In order to analyze the influence of pores including the volume fraction and aspect ratio on the mechanical properties of SMA, the mechanical parameters of solid SMA in the references are quoted here , as showThe influence of pore aspect ratio on the Young’s modulus of porous SMA under different porosity is shown in Then, similar to The change in Young’s modulus of porous SMA under different loads is shown in In order to verify the correctness of the theoretical model in this paper, it is necessary to compare the theoretical results with the experimental results. However, it is difficult to prepare porous SMA with different pore shapes, and there have been few experimental reports on the mechanical behavior of porous SMA with different pore shapes. Due to the lack of experimental data, it is difficult to compare the theoretical curve of stress and strain of porous SMA with the different pore shapes in the experimental results. Therefore, in order to verify the correctness of this model, the constitutive model of porous SMA with spherical pores in this paper was compared with the experimental data of porous SMA with spherical pores , as showNext, the influence of various pore shapes on the mechanical properties of porous SMA is further discussed. The stress–strain curves can be obtained when the porosity is 13% and 25%, respectively, as shown in When the pore shape is determined, the stress–strain curves of SMA with different porosities are shown in The Young’s modulus of porous SMA is related to porosity and pore shape. With the increase in porosity The pore shape and porosity had a great influence on the stress–strain relationship of porous SMA, and the pore shape had an irregular influence on the stress–strain relationship. With the increase in porosity, the critical stresses decreased and the area of the hysteretic curve of the stress–strain relationship decreased.


With the power of Information and Communication Technologies (ICTs), the world has become a global village, where every digital object that prevails in the world is connected to each other with the Internet of Things (IoT). The fast proliferation of IoT-based technology has revolutionized practically every sector, including agriculture, shifting the industry from statistical to quantitative techniques. Such profound transformations are reshaping traditional agricultural practices and generating new possibilities in the face of various challenges. With the opportunities created, farmers are now able to monitor the condition of crops in real time. With the automated IoT solutions, farmers can automate tasks in the farmland, as these solutions are capable of making precise decisions based on underlying challenges and executing actions to overcome such difficulties, alerting farmers in real-time, eventually leading to increased productivity and higher harvest. In this context, we present a cloud-enabled low-cost sensorized IoT platform for real-time monitoring and automating tasks dealing with a tomato plantation in an indoor environment, highlighting the necessity of smart agriculture. We anticipate that the findings of this study will serve as vital guides in developing and promoting smart agriculture solutions aimed at improving productivity and quality while also enabling the transition to a sustainable environment.Agriculture is the primary and oldest industry in the world and has been transformed over the centuries from the prehistoric era to the technology-driven 21 At the beginning of the 21st century, we witnessed many technological revolutions, and all our lives have been changed to a greater extent with the absorption of these technologies in our daily tasks . Owing tAmong all these collations of fruitful technologies, the IoT is closer to our daily life by allowing ubiquitous connectivity of devices, connecting digital devices all around the world to the WWW through Machine to Machine (M2M) communication . In geneAs the world’s principal and primary industry, agriculture need to balance the food requirements of humankind as well as the production of essential raw materials for many industries . It is tSmart agriculture offers the potential to automate many agricultural fieldworks with minimal human intervention requiring a minimum amount of input resources such as less amount of fertilizer, pesticides, and water supply as the resources can be managed effectively as opposed to traditional agriculture . The bacAccording to the latest statistics , the gloDespite the growth of the market, however, the adoption of smart agricultural solutions are currently still at a low rate, owing to a variety of reasons, including reluctance by some farmers to move with technologies and trend, higher initial investment, tendency to stick with traditional farming methods and reachability issues to reach out farmers in remote areas . HoweverAs of now, many research studies in the field of smart agriculture have proposed different novel solutions and methodologies to address various research problems, including the prediction of daily climate for the next crop cycle and the amount of harvest in the next growth cycle . VariousTaking all of these concerns into account, we aim to develop a novel real-time cloud-enabled low-cost IoT crop management platform for monitoring and automating indoor tomato plantations. This is done to promote a low-cost but reliable and convenient smart agricultural solution for indoor plantations. We relied on low-cost IoT sensors and open-source technologies for the platform’s design. Additionally, the developed platform should be convenient for farmers residing in urban areas who want to grow their own produce despite space restrictions. Even though previous researchers have used related technologies, they have concentrated on particular applications, their implementations, or technical elements, as opposed to our study. In contrast, in our work, we focus on configuring and deploying our platform with a real plantation to check the feasibility and reliability of the proposed platform. In this regard, the main contribution of the study is outlined below.Propose a cloud-enabled real-time monitoring platform for monitoring the environmental and soil condition of indoor tomato plantation along with automating irrigation and lighting conditions.Design and implement the platform with low-cost IoT sensors and actuators with open-source technologies.Provide our perspectives on how the platform can be extended towards indoor plant condition monitoring and urban farming where the resources are highly limited.Provide a comparative analysis of similar research work to differentiate our work from theirs.The paper is organized in the following manner. Following the introduction, we highlight the necessity of smart agriculture along with enabling technologies. In the third section, the methodology of designing our platform is highlighted, followed by the results obtained after successful implementation with a brief discussion of the work. Section five compares similar research work related to smart agriculture to the proposed platform. The last section concludes the paper, highlighting future directions.The agricultural sector has seen many revolutions over the years, beginning with the domestication of plants and animals thousands of years ago, progressing to the systematic use of machinery and instruments a few hundred years ago, and the use of man-made pesticides and fertilizers a few decades ago . At presIn agriculture, IoT is not only providing answers to frequently time-consuming and tiresome activities, but it has also completely transformed the way people think about agriculture. In this regard, this section is entirely devoted to highlighting the necessity of smart agriculture and offers a brief overview of enabling technologies of smart agriculture.According to references , smart aObservation – IoT sensors deployed in the field record data from crops, soil, atmosphere, and livestock.Diagnostics –The sensor values collected from the observation phase are sent to remote data analytics servers or cloud-hosted platforms and compared with pre-determined business logic to ascertain the condition of the examined object and identify any deficiencies or needs.Decisions – In the event of anomaly detection, the IoT platform’s user and/or AI-driven components determine whether location-specific treatment is necessary.Action - After end-user evaluation and action, the cycle repeats itself.A smart agriculture solution provides the agricultural sector with new levels of control and automated decision-making, allowing for a coherent ecosystem. With the current pace of development of smart agriculture, it is now feasible to construct a farm-wide sensor network  that allUrbanization is inevitable as the world’s population continues to expand, leading to the loss of arable land and reduced water supplies. Limitations of these resources may lead to problems with food production in metropolitan areas . Thus, uIoT constitutes the core of a smart agriculture solution with collections of sensors, actuators, agriculture robots, and interconnected devices. Other enabling technologies that help strengthen and provide a stronger foundation for smart agriculture include big data from the collection of a vast amount of sensor data, AI for inferencing the meaning of the collected agricultural farming data, cloud computing for convenient and on-demand computing resources, mobile computing for allowing farmers to connect anytime with the smart agricultural solutions, and underlying communication technologies for data communication . Even thThus, in the next sub-section, we plan to go further on the role of IoT in smart agriculture since neither the other supporting technologies in smart agriculture nor smart agriculture would exist without IoT.The IoT is known to be the present and future of everything and will continue to affect the lives of everyone in the world by bringing intelligence to everything . Its priIn smart agriculture, IoT sensors deployed in the farming field and livestock first collect data from the environment in which they are deployed, and then the gathered data are sent to the cloud or data analytics servers through wireless and wired communication media , in accoIoT is made up of several physical devices in the smart agricultural domain and has a four-layer architecture , as showIn a typical smart farming setting, many sensors may be put in the agricultural field to measure different parameters, including humidity, soil pH, temperature, and light intensity . Each neAs farmers are unable to be physically present in the field 24 hours a day and due to a probable lack of skills in employing various technologies to assess the appropriate environmental conditions for their crops, smart agriculture offers them automated solutions that can run without human supervision and can assist the farmers in making proper decisions to cope with the various types of challenges they may encounter. Further smart agriculture can contact and alert the farmer even when the farmer is not in the field, allowing the farmer to manage more acreage and increase productivity. With the overwhelming benefits that the farmers can now gain with the adoption of smart agriculture, many seek to adapt the technology as a viable alternative to eliminate the massive burden associated with traditional farming practices. In recent years, there has been a clear growth of research activities in smart agriculture. After thoroughly investigating the most suitable plant to be grown in indoor lab conditions, the tomato plant has been chosen, as indoor lab environmental conditions best match with optimal conditions that tomato plants need . Tomato,Having provided an overview of why we have chosen tomatoes to grow inside our indoor lab, we discuss the system design further in the next section.Our platform’s main objectives are real-time monitoring of environmental and soil conditions and managing irrigation and lighting conditions for our indoor tomato plantations. The proposed platform consists of the following components.IoT sensing devices that collect relevant soil and environmental parameters.Arduino Uno and NodeMCU microcontrollers to gather data from the IoT sensing devices, process and showcase the results, and stream the data to the cloud for further processing and analytics.Thinger.io is an open-source cloud platform for further data visualization, analytics, and actuator control.Grow light kit for helping the tomato plants grow under indoor environmental conditions by providing light, and irrigation system comprising of three peristaltic water pumps which can be controlled through the cloud.The key component of our platform is the central NodeMCU microcontroller that streams data to the cloud through Wi-Fi and retrieves other sensing data from the Arduino Uno microcontroller through serial communication. NodeMCU is an open-source firmware and development kit that helps prototype IoT products within a few lines of code. It has 128 KB of RAM and 4 MB of flash memory for data storage and execution of programs . FurtherArduino UNO is an open-source microcontroller board based on the microchip AT-mega328P . The micIn terms of the IoT sensors, our platform accommodates eight sensors, a relay actuator, and three peristaltic pumps for the physical design of our platform. The sensors include a DHT-11 temperature and humidity sensor, H-101 analog pH sensor, capacitive soil moisture sensor, waterproof DS18B20 sensor, MQ135 CO2 sensor, and a light-dependent resistor (LDR), an ultrasonic sensor, and a DS1307 RTC  module. These sensors were chosen based on the parameters that needed to be measured whilst considering ease of use, economical value, and measurable parameters. Components in Having provided a brief technical overview of each of the physical components in the platform, the next steps involve designing the platform with correct wiring and connections. The pH sensor, CO2 sensor, LDR sensor, LED indicators, and Ultrasonic sensor were attached to the Arduino UNO microcontroller, whilst the capacitive soil moisture sensor, DHT-11 sensor, DS1820 module, and RTC module were attached to the NodeMCU. A serial communication gateway between UNO and NodeMCU was also opened to allow the UNO to send sensor data to the NodeMCU for dispatch the data to the cloud via its Wi-Fi connection.The two-channel direct current 5V relay module was used to control and automate the grow light kit and irrigation system, enabling control of them over the cloud. Three water pumps were parallelly connected, where one connection end was connected with the relay, whilst the other end of the relay was connected to the grow light kit. pH sensor, CO2 sensor, capacitive soil moisture sensor, and all remaining sensors were tested for their measuring accuracy. The ultrasonic sensor was attached to the top of a PVC pipe set up, as shown in Arduino Integrated Development Environment (IDE), written in Java programming language and C/C++ programming language, was used for programming the setup. Program sketches from the Arduino IDE were then compiled, debugged, and uploaded to both NodeMCU and UNO microcontrollers for real-time execution. As illustrated in To offer a brief overview of Thinger.io, it provides a free and open-source cloud solution that allows the simple and easy implementation of data fusion IoT applications in the cloud . MoreoveAfter the platform had been set up, tomato plants were grown inside the lab. The growing of tomato plants requires adequate ventilation and light. The lab was fully air-conditioned and did not have enough windows for proper ventilation. According to the stages of tomato plant growth; tomato plants grow in several stages: 1) the germination and early growth with initial leaves, which is between 25 and 35 days, 2) the vegetative period, which is between 20 to 25 days, 3) the flowering period, which is between 20 to 30 days, 4) an early fruiting period, which is between 20 to 30 days, and 5) mature fruiting stage which is between 15 to 20 days . HoweverThe seeds were seeded in the planting trays and kept for the initial germination phase of around 15 days. After completion of the germination stage, the healthy grown plants were moved to three planting pots, where the platform with sensors was deployed to capture data on the underlying soil and environmental conditions, as shown in For the data archiving, data bucket functionality in Thinger.io was used. The data bucket functionality allows virtual data storage of time series information, such as the sensor data gathered over time , as showAs can be seen from , such thThe soil was watered before planting the tomato plant in the pots, and consequently, the soil moisture percentage gave a reading of more than 75. Afterward, as seen in CO2 concentration inside the lab varied between 1470 to 2988 ppm during the observation period, as can be seen from In pH variation over the observation period is depicted in Based on the analysis of the collected sensor data and visual observation of the plants from the biological perspective (e.g.: height of the plants and number of secondary leaves), it is evident that our tomato plants are growing well and healthy in the indoor lab environment, with the plant height increasing almost 2 cm during the period that the proposed platform was deployed. However, more tests are required to further determine the data validity and operability of the current system before the system can be scaled up and deployed on a real plantation. It is noted that the designed prototype platform offers greater benefits for farmers with a simple IoT setup and open-source technologies. Additionally, the platform has been built with cost-effective IoT devices, which cost less than 70 USD. The platform has been designed as a low-cost smart agricultural solution, especially for indoor environments, with a special focus on urban farming by considering time saving, space, cost, and convenience, which are important considerations for urban agriculture. Overall, throughout the observation period, the platform has been demonstrated to be stable in terms of reliability and operability, with no malfunctioning of devices or loss of data. However, the prototype system does lack some capabilities and may require further upgrades and expansions, including the capability of visually monitoring the plants.To better compare our work with similar research, in Microcontroller – refers to the microcontrollers model that the research usesConnection- refers to the wireless or wired connection available.Platform – indicate whether the research uses a specific platform and its name.Realtime – shows whether the system works in real-time or not.Cloud-enabled – suggest that the research uses cloud computing services.Actuator control -indicates whether the devices in the system can be controlled depending on the situation.Data archival and download for further analytics – indicate whether the system facilitates to archival of the time series data and allows the users to refer to them at a later time.Allows crop management- indicate whether the system facilitates real-time monitoring and control.Development and implementation cost – describe whether the overall development and implementation cost is high, intermediate, or low.Based on the above, it is evident that most works have used Arduino as the main microcontroller and depended on Wi-Fi for their communication needs, with a cloud platform mostly utilized to perform real-time monitoring and actuator control. In terms of the sensing devices, a total of nine sensors have been utilized in our research for sensing the ambient environment as opposed to other research, which only used a limited number of sensors. Despite this, the total costs of the proposed platform is almost similar to some of the works found in the literature. Further, with the results we obtained, it is noted that the system is able to provide accurate results in real-time, providing us the opportunity to monitor the underlying crop conditions in real-time and control certain parameters. Apart from the Co2 concentration, we have obtained all other parameters in a manual way, and it is noted that all the values are almost accurate. On the other hand, during the time that the system is active, no functional anomalies were detected which proved the reliability of the system.The main intention of this section is to provide our readers with a brief understanding of how our proposed work can be extended toward indoor plant condition monitoring and urban farming. With the proposed work in the research, we have proved that our system can be built using low-cost IoT sensors and is capable of providing reliable service as long as the power and Internet connection are stable. Regarding indoor and urban farming, resources such as water and nutrient-rich soil are very limited, and the growers engaged in such farming have a limited time to spend. Such indoor and urban farming examples entail gardening, vertical farming, and soilless farming methods such as hydroponics, aeroponics, and aquaponics. According to the literature, it is evident that there is only less research work done pertaining to the use of smart agricultural solutions for indoor and urban soil-based farming . On the Due to various factors such as inflation, supply chain interruptions, natural disasters, the COVID-19 pandemic, and environmental pollution dealing with agriculture and food insecurity concerns, many people living in urban areas are now keen on producing their own food at home. This has become a common trend, and progressively within time, by the next two decades urban and indoor farming would be much more popular in urban areas. We have developed our platform, having all these things kept in our mind, and our platform can be deployed in such a way, as to monitor the condition of a particular chosen plant in the plantation and, based on that, provide insights pertaining to the underlying plantations as it is not feasible to deploy such IoT solutions throughout the plantation. With a simple maneuver, by replacing only the necessary sensors pertaining to the monitoring parameters, our proposed platform can be used to monitor the conditions for soil less-farming such as with hydroponics, aeroponics, and aquaponics.As per the next steps of our work, we have planned to integrate a real-time camera for real-time visualization of plants and integrate an intermediate Raspberry Pi device as an edge device for accumulating sensor data coming from NodeMCU to analyze our data in the edge of the network and provide real-time notifications/alerts based on the underlying conditions in near real-time for farmers. Further, with the introduction of this edge device and incorporating it with AI, we plan to provide total autonomy to our system as with the data analytics at the network edge, the system would be able to learn from the data and would be able to automate irrigation and lighting for plants with no sort of intervention from the cloud or farmers. Thus, this would be an ideal solution for indoor and urban farming for the optimum utilization of resources. In this work, we have used all low-cost sensors and open-source solutions, and we believe this would pave the way for the design of low-cost but effective and reliable smart agriculture solutions, which would become much more popular among farmers with low income.The main objective of this research is to design and implement a flexible cloud-enabled low-cost sensorized IoT platform for real-time monitoring and automating tasks dealing with indoor plantation. In this regard, we have chosen tomatoes to be planted in the lab environment and to deploy our platform to check the functionalities and reliability of our platform with the crop we have chosen. Through the platform, we have visualized real-time data and offer the facility to control the conditions dealing with our plants irrespective of where you are located, as everything happens over the cloud. Nonetheless, to provide a better overview of our data, we have analyzed our gathered data to learn insights and check whether the optimal indoor conditions match our plants’ needs. Compared to similar works, our study focused on building an affordable IoT platform for smart agriculture that can be specially used for the indoor environment; highly useable for urban farming to save cost and time considering our platform’s affordability and real-time management facilities. We have tested and applied our platform for remote monitoring of plants, soil, and environmental conditions, and based on results from simulations and analyses of previously stored data; our platform could be used to generate important analytics of real-time monitoring, enabling choices and actions such as managing the irrigation system or creating alters, for example. Throughout our experiment, only a few limitations we have noted, such as keeping a constant power supply and keeping the wireless connection stable, as there is no way to communicate with the cloud if one such factor is missing.Overall, our results indicated that our plants are growing well, and the system is reliable; however, more tests are required to further determine the validity of our system before deploying elsewhere, as site-specific calibration is needed for sensors before deploying. In addition, our prototype system can be further extended with more functionalities and can be upgraded and customized according to the context, such as monitoring and controlling conditions of soil-based and soil-less farming. Examples of this would be the requirement to provide adequate ventilation with automated ventilation fans that work based on the environmental conditions and monitor the visual feed of plants through our platform, which could be developed to a greater extent with real-time decision-making capabilities. For our experiment, we have used Arduino UNO and NodeMCU microcontrollers in conjunction, but our current system can be further upgraded and can connect with more sensors with low-cost microcontrollers like Arduino Mega, which has 16 analog signals as opposed to Arduino UNO. With the comparison of similar work, we elaborated that our work uses more sensors than any other work owing to the integration of two interconnected microcontrollers, which would be ideal for monitoring all sorts of conditions pertaining to the plantation. On the other hand, we also have the option of using the Raspberry Pi Model, which is compatible with the system that is already in place, as it delivers many features in terms of the speed of the CPU, memory, and networking, where it can also act as an edge gateway. Doing so would allow us to perform real-time analytics at the network edge with AI and subsequently execute actions on its own based on the analytics, such as by monitoring the real-time feed, identifying any pests or diseases, and alerting farmers, as we explained in the future work section. In summary, in our research, we have implemented an affordable crop management platform for managing crop conditions in indoor environments. We believe this would pave the way for the design of more smart agricultural solutions, especially for urban farming, to save precious time and cost.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.NT - Writing and experiment. Others – Supervision. All authors contributed to the article and approved the submitted version.


Malnutrition has a multifactorial origin and can be caused by cancer. This study determined the consensus of a panel of experts on the nutritional approach for cancer patients in Spain using a multidisciplinary approach. Using the Delphi methodology, a 74-question questionnaire was prepared and sent to 46 experts. The areas of knowledge addressed were the nutritional status of the cancer patient, nutritional screening, nutritional therapy, patient referral, and multidisciplinary care. A total of 91.7% of the experts agreed with the questions posed on nutritional status, 60.0% with those on nutritional screening, 76.7% with those on nutritional therapy, and the entire panel of experts agreed with the questions posed on patient referral and multidisciplinary care. The experts agreed upon a high prevalence of malnutrition among cancer patients in Spain. Unlike medical and radiation oncologists, medical nutrition specialists believe that body composition assessment should not be carried out in all types of cancer patients during nutritional screening and that interventions can be conducted outside the oncology clinic. In general, it is recommended that nursing staff routinely perform nutritional screening before starting cancer treatment. It is necessary to develop a multidisciplinary action protocol that includes nutritional and/or sarcopenia screening.  Malnutrition has a multifactorial origin and can be caused by inadequate dietary intake , reduced absorption of nutrients , or an increase in nutrient requirements  ,2. MalnuApproximately 72.5% of cancer patients experience eating problems during the course of their disease, and overall, 69.6% experience weight loss after diagnosis . In the On the other hand, sarcopenia consists of a decrease in muscle mass associated with a reduction in the functional capacity of the patient, which in the case of cancer can cause a significant dependence to carry out activities of daily living and can become a hardly reversible situation. This is due to the secretion of proinflammatory cytokines and tumor factors that cause metabolic alterations, systemic inflammation, and decreased appetite . In the Personalized nutritional interventions can be beneficial by offering cancer patients advice on oral intake accompanied and, if necessary, by oral nutritional supplements (ONSs). If oral intake is insufficient or unfeasible, enteral tube nutrition should be considered, or parenteral nutrition should ultimately be considered in patients with contraindications for the former ,16. TherHowever, even knowing the importance of nutritional status in the prognosis and quality of life of cancer patients and the benefits of nutritional treatment, screening for malnutrition and/or sarcopenia is not widespread in oncology units. This should be carried out from the moment of diagnosis and periodically during the clinical evolution of the oncological process .This study aimed to reach a consensus on the nutritional approach for cancer patients in Spain from a multidisciplinary approach.The Delphi methodology was used based on a group facilitation technique that allows the opinions to be transformed into a group consensus .The panel of experts was chosen based on the following criteria: five or more years of experience in their specialty, interest in nutrition, and at least one scientific publication in the area of nutrition. These experts were invited to participate in the consensus through questionnaires by email. The steering committee and the members of the panel did not know the identity of the respondents. The panel initially consisted of 46 experts: 15 medical nutrition specialists, 15 medical oncologists, and 16 radiation oncologists.A literature review was conducted to identify gaps in evidence supporting the content of the survey. The steering committee led the development of the surveys for each round of voting, reviewed the responses and created response summaries, validated the systematic literature search, and critically evaluated the evidence. A total of 74 initial questions were asked that were distributed among the following blocks of knowledge: (1) the nutritional status of the cancer patient, (2) nutritional screening, (3) nutritional therapy, and (4) patient referral and multidisciplinary care.Two rounds were conducted with the experts: the first between 18 May and 22 June, 2021, and the second between 14 July and 27 August, 2021. The questions were anonymous and were answered through online questionnaires.The study was based on a survey and did not involve the participation of human subjects or the management of patient data, nor was it intended to modify the current clinical practice of the participants. Consequently, this study did not require ethical approval.The questions with discrete quantitative answers for each item were evaluated using a Likert scale from 0 to 11 points . The consensus criterion used was as follows: for agreement, a median ≥ eight and an interquartile range (IQR) <0.4; for disagreement, a median ≤ two and an IQR <0.4. Consensus was obtained for the questions with nominal categorical answers when one of the answers reached at least 50% of the total mentions.The items were evaluated as a whole by medical specialists . Nonparametric tests were used to determine whether there were significant differences among the specialist groups. For the questions with a nominal categorical response, the chi-square test was performed. In the questions with a discrete quantitative response, the Levene test was performed to ensure the assumptions of the application of the Kruskal–Wallis test. If significant differences were found, the Mann–Whitney test with the Bonferroni correction was applied.p ≤ 0.05. Data were analyzed using Gandia Barbwin version 7.0.2110.5 and XLSTAT® version 21.04  of Microsoft Excel®.For all tests, the level of significance was The experts agreed that there is a high prevalence of malnutrition among cancer patients in Spain and that the assessment of their nutritional status should be a priority. This information should be included as part of the clinical data before starting any treatment. Experts agree that it must be taken into account that physical exercise and the patient’s functional capacity are related to nutritional status must be taken into account. Furthermore, there is a direct relationship between weight maintenance and the patient’s muscle mass.On the other hand, experts agree that improving the nutritional status of cancer patients who undergo surgery could optimize the results. The following should be taken into account: treatment with radium and/or chemotherapy produces nutritional alterations depending on the location of the tumor; there is a direct relationship between symptom control and the evolution of nutritional status; and nutritional alterations have an impact on the quality of life of patients that jeopardizes the results of the treatment in terms of cure. In the first round, the degree of agreement was 91.7%. In the second round, no final consensus was obtained .The experts agreed that nursing staff should routinely perform nutritional screening before initiating cancer treatment, as it enables more effective nutritional therapy. The screening tool must be simple, fast, and specific, and the Malnutrition Universal Screening Tool (MUST) may be a suitable option. It is also necessary to perform simple sarcopenia screening and functionality tests associated with nutrition.In case of a lack of time to carry out the screening, experts agree that the screening could be carried outu remotely by a health care professional. A total of 58.7% of the experts considered that the most appropriate outpatient nutritional screening characteristic to implement in the oncology unit is feasible, with simple variables and short duration, although it is less sensitive and specific.In the first round, the degree of agreement was 60.0%, and in the second round, the experts agreed only that body composition assessment is required only in cancer patients with positive nutritional or sarcopenia screening .The experts agreed that nutritional therapy and cancer treatment should be simultaneous, taking into account certain times to scale the nutritional intervention according to the disease evolution of the patient. Patients with positive nutritional screening should be given dietary recommendations and ONSs, with recommendations to perform physical exercise (if appropriate). Agreement among medical nutrition specialists was less than that among medical and radiotherapy oncologists that these interventions should be carried out in the oncology consultation.Prophylactic gastrostomy placement is recommended in patients with head and neck cancer with impaired swallowing, regardless of their nutritional status. In these patients and those treated for digestive tumors, experts recommended the perisurgical use of immunomodulatory formulas.The initiation of empiric use of pancreatic enzymes after partial or total pancreatectomy is recommended. However, 63.0% of experts considered that in patients with grade IV oral and/or esophageal mucositis that prevents proper swallowing, the most appropriate therapeutic option is transitory parenteral nutrition.Regarding the characteristics of the nutritional formulas, for patients with postsurgical feeding jejunostomies due to digestive tumors, these formulas must be of low viscosity and osmolarity, have high energy density and protein content and be in 1000 cc containers to avoid night replacement. For patients discharged with ileostomy, the nutritional formula characteristics that should be met are low osmolarity, easily digestible, and high energy density and protein content.Experts agree that in cancer patients, it is essential to assess whether the nutritional formula has a low glycemic index, a high energy density and protein content, and fiber, as well as the omega-3 fatty acid (in patients with cachexia) and slow-release carbohydrate content. For cancer patients with a loss of muscle mass, it is important to assess the high energy density and protein content and whether hydroxymethyl butyrate (HMB) and vitamin D are present. For cancer patients with digestive disorders, it is important to assess the high energy density and hydrolyzed protein content and whether medium-chain triglycerides (MCTs) and various flavors are present.In the first round, the degree of agreement was 76.7%. In the second round, the experts finally agreed on the placement of a prophylactic gastrostomy in patients with head and neck cancer with obvious malnutrition who are to undergo surgery and/or chemotherapy; the empiric use of pancreatic enzymes in all patients with advanced pancreatic cancer; and, for patients with postsurgical feeding jejunostomies due to digestive tumors, the importance of the use of nutritional formulas that do not contain fiber.In the first round, confusion was detected in the questions regarding the nutritional formulas, and these questions were redrafted for the second round. The experts agreed only on the perisurgical use of immunomodulatory formulas in patients with digestive or head and neck tumors .All experts agreed that nutritional and/or sarcopenia treatment should be started in the oncology clinic itself based on the screening results. In the case of poor nutritional evolution, the patient would be referred to the corresponding nutrition unit. It is necessary to develop an action protocol according to the screening results and to urgently initiate outpatient nutritional treatments. The degree of agreement was 100% .In the first round, medical nutrition specialists disagreed with radiation oncologists  that the assessment of the body composition of all cancer patients is feasible. In the second round, both specialties had a neutral opinion. The same happened in the aspect that assessing the body composition only of cancer patients with positive nutritional screening/sarcopenia is feasible. In the second round, both specialties agreed.In the second round, the opinions of the specialists differed for only one approach. Medical nutrition specialists agreed that screening patients with a remote tool was appropriate, whereas medical oncologists had a neutral opinion.This survey is one of the first carried out in Spain in which the nutritional management of cancer patients is addressed. The general results showed a high degree of agreement among the experts.In general, medical oncologists, radiation oncologists, and medical nutrition specialists specialized in nutrition agreed that there is a high prevalence of malnourished cancer patients, and although this incidence varies greatly depending on the type and location of tumor and cancer stage, this condition affects the results of treatments and negatively impacts patients’ clinical course, as well as their quality of life. Several studies link malnutrition with an increase in the morbidity and mortality of cancer patients, among them the recently published SeDREno study in Spain , where aAlthough detection systems based on artificial intelligence have been developed in recent years , expertsConcerning nutritional assessment tools, there are many screenings with different characteristics , but none is considered a reference or gold standard . The panParallel to the global nutritional status of cancer patients, body composition and the importance of muscle mass and its relationship with the functional capacity of these patients are becoming increasingly important, as they are key aspects both in the clinical evolution of a patient’s disease and in a patient’s physical and mental health . In receIn this consensus, medical nutrition specialists, medical oncology, and radiation oncology were selected. The specialty that showed the most differences in agreement from other specialties was medical nutrition specialists. The questions with less consensus were very oriented toward a particular nutritional therapy, where medical nutrition specialists have more knowledge and experience, while the questions with more consensus were more general. These findings influence the need to develop and implement training plans for specialists in cancer patients’ nutritional management.To date, health professionals have been made aware of the impact that nutritional status has on cancer patients, but is necessary to implement tools for simple and feasible malnutrition and/or sarcopenia screenings, protocols for action based on screening results and for specific training for healthcare personnel, and to cooperate through multidisciplinary work, referring the patient to specialized units in the case of poor nutritional evolution.The experts agreed upon a high prevalence of malnutrition among cancer patients in Spain. Unlike oncologist and radiation oncologists, medical nutrition specialists believe that body composition assessment should not be carried out in all types of cancer patients during nutritional screening and that interventions can be conducted outside the oncology clinic. In general, it is recommended that nursing staff routinely perform nutritional screening before starting cancer treatment. It is necessary to develop a multidisciplinary action protocol that includes nutritional and/or sarcopenia screening.