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In this research, a new method of treating wastewater is introduced using a highly recyclable and sustainable material derived from coconut oil. This material aims to address the issues commonly faced by conventional sorbents, such as limited performance and costly production. These challenges impede a sorbent material from unlocking its full utility in treating wastewater. An exceptional sorbent material was synthesized by incorporating coconut shell-based activated carbon (AC) into a coconut oil-based polyurethane matrix to produce an activated carbon-infused polyurethane (ACIP). The effective adsorption was elucidated by the synergistic interaction between the ACIP material and methylene blue (MB) through electrostatic attraction, π-π interactions, and hydrogen bonding. To provide an exhaustive analysis of the ACIP material, several analytical techniques were employed, including Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) analysis, X-ray diffraction (XRD) analysis, and thermogravimetric analysis (TGA). A detailed assessment using a fixed-bed column setup investigated its adsorption behavior by encompassing various factors such as inlet concentration, adsorbent bed height, feed flow rate, and solution pH. Results revealed that the ACIP composite exhibited a maximum adsorption capacity of 28.25 mg g−1. Empirical evidence with a high correlation coefficient (R2 > 0.93) obtained from the Thomas and Yoon–Nelson model suggests the suitability of the composite material to operate efficiently under these diverse circumstances. Notably, after five consecutive adsorption–desorption cycles, ACIP demonstrated its remarkable reusability by maintaining 86% of its regeneration efficiency. Given its outstanding performance and potential for scalability, this innovative ACIP composite presents a more sustainable approach to addressing wastewater issues within industrial environments. [ABSTRACT FROM AUTHOR]

Polyvinyl chloride (PVC) and polyethylene terephthalate (PET) contribute significantly to global plastic waste, with only 9% recycled in recent years. In this work, these plastic wastes were upcycled as functional fillers to improve the rigid polyurethane foam (RPUF) properties. To attain this target, we leveraged the intrinsic polarity of the C=O and C-Cl groups of PVC and PET to induce intermolecular attractions with the N-H groups of the polyurethane matrix, evidenced by the observed IR peak shifts. This enhanced the nucleating effect during foaming, increasing the foams' compressive strengths by 77% and 22% with the addition of 10% PVC and 5% PET filler, respectively. Furthermore, the addition of PVC and PET fillers increased the foam volume. Thus, the collective utilization of PPW and its corresponding impact on the CO-based RPUF's properties signifies a reduction in carbon dioxide emissions by 14.15% and 17.52% for PVC and PET, respectively. Moreover, thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) revealed improved thermal stability and degradation profiles of the produced RPUFs. Overall, this work highlights potential advancement in environmentally responsible upcycling strategies for common end-of-life plastic wastes, while enhancing rigid foam properties. [ABSTRACT FROM AUTHOR]

This study attempts to link the clay soil in the locality to the topic of heat transfer as a contextualization point. It attempted to slightly modify the seven-step science, technology, engineering, and mathematics (STEM) lesson by having the part of the prototyping be first tried by the teacher, thereby having a change-of-hat to anticipate the ‘what if’ questions of the students. The teacher-researcher experimentation provided critical information to scaffold the students in the prototyping part. The evaluation of experts shows that the modified STEM lesson can be an excellent tool and the vodcast has been found to be a very satisfactory component of the STEM lesson. It is described as a very useful material in teaching heat transfer and related thermodynamics concepts and is highly recommended for use in both distance learning and face-to-face modality. Further, the clay oven exploration has come up with a refined clay oven production process, wherein the clay oven prototype has the capacity for the contextualization of heat transfer. It is recommended that a formal implementation be conducted to refine and standardize the lesson delivery [ABSTRACT FROM AUTHOR]

The pursuit of sustainable polyurethane (PU) product development necessitates a profound understanding of precursor materials. Particularly, polyol plays a crucial role, since PU properties are heavily influenced by the type of polyol employed during production. While traditional PUs are solely derived from hydroxyl functionalized polyols, the emergence of amine-hydroxyl hybrid polyols has garnered significant attention due to their potential for enhancing PU product properties. These hybrid polyols are characterized by the presence of both amine and hydroxyl functional groups. However, characterizing these polyols remains a daunting challenge due to the lack of established experimental testing standards for properties, such as fractional hydroxyl and amine moieties and thermo-kinetic parameters for amine reactions with isocyanates. Additionally, characterization methods demand extensive time and resources and pose risks to health and the environment. To bridge these gaps, this study employed computational simulation via MATLAB to determine the moieties' fractions and thermo-kinetic parameters for hybrid polyols. The computational method integrated energy balance and reaction kinetics analysis for various polyols to elucidate the influence of functional moieties on the thermo-kinetic behavior of PU formations. Validation of the simulated results was conducted by comparing their experimental and simulated prepolymer and foam temperature profiles, highlighting the direct influence of fractional moieties on PU formations. The comparisons revealed an average relative error of less than 5%, indicating the accuracy and credibility of the simulation. Thus, this study represents a pivotal opportunity for advancing knowledge and driving sustainable developments in bio-based polyol characterization for PU production streamlining and formulation optimization. [ABSTRACT FROM AUTHOR]

In the present study, the dynamic behavior of rotating carbon fiber reinforced polymer (CFRP) composite tapered plates reinforced with graphene nanoparticles has been investigated using a finite element (FE) formulation. The mechanical properties of CFRP laminates containing 0–0.5 wt% graphene nanoparticles were assessed using ASTM standard tests. The experimental dynamic analysis was conducted on CFRP and graphene reinforced CFRP composites under cantilever end conditions. The first‐order shear deformation theory (FSDT)‐based FE model was developed using MATLAB software to obtain the natural frequencies of the graphene reinforced CFRP plates. The validity of the FE results is compared with literature, and it shows excellent agreement for composite structures. Furthermore, parametric analysis is performed to explore the influence of wt% of graphene, rotating speed, setting angle, and ply thickness on the dynamic responses of graphene reinforced CFRP tapered plates. Highlights: Effects of graphene reinforcement in CFRP composites are investigated.The mechanical characteristics were evaluated using ASTM standards.FSDT is employed to model the rotating tapered graphene‐CFRP plates.Vibration behavior of graphene‐CFRP composites is studied experimentally.Effects of various factors on the vibrations of the CFRP plates are studied. [ABSTRACT FROM AUTHOR]

This study presents a novel polyurethane-activated carbon composite (PACC) as an effective and sustainable adsorbent for treating lead-ion-contaminated waters. The PACC was characterized using Scanning Electron Microscopy with Energy Dispersive X-ray (SEM-EDX), Fourier Transform Infrared Spectroscopy (FTIR), Brunauer, Emmett, and Teller (BET) surface area analyzer, X-ray Diffraction (XRD), and Thermogravimetric Analyzer (TGA) to evaluate its physicochemical and thermal properties. Furthermore, the PACC was employed in an experimental column adsorption setup to investigate its adsorption performance and to develop a dynamic method suitable for industrial implementation. Parameters such as bed height (50, 100, 150 mm), flow rate (4, 6, 8 mL min−1), pH (2, 4, 6), and initial metal-ion concentrations (10, 50, 100 mg L−1) were examined. The experimental data exhibited strong agreement with the Thomas and Yoon–Nelson models (R2 ≥ 0.96), indicating efficient adsorption mechanisms. Remarkably, the used adsorbent has the potential for facile regeneration without substantial loss in capacity. The PACC demonstrated excellent adsorption performance for lead ions in aqueous solutions in a fixed-bed column system. Thus, the novel PACC material holds the potential for scalable application in industrial settings to address water pollution challenges, especially in regions with uncontrolled effluent discharge. [ABSTRACT FROM AUTHOR]

Conventional lead halides perovskites have attracted much attention to become a next‐generation solar technology. Besides their low cost and excellent efficiency, high toxic nature and device instability become the major issue to limit their wide applications. Ti‐based (Cs2TiI6) double halide perovskite solar cells (PSCs) have emerged as a potential candidate to become the best alternate. However, poor device efficiency is still a restriction. Therefore, proper optimization and deep device analysis are needed to explore the full potential of Cs2TiI6‐based PSCs. In the present research work, the open‐circuit voltage (Voc), current density (Jsc), fill factor (FF), and power conversion efficiency of Cs2TiI6‐based PSC are successfully optimized. The device optimization is performed using the different combinations of electron transport layers and hole transport layers along with absorber thickness, acceptor doping density, and interface defect density. The effect of Rseries and Rshunt is explored and identified the best device efficiency of 28.07% along with other photovoltaic (PV) parameters; Voc = 1.41 V, Jsc = 22.44 mA cm−2, and FF = 88.15%, respectively. The supportive analysis of C–V and Mott–Schottky characteristics helps to get deep insight into the device's performance. Furthermore, the resultant PV parameters are compared to the previous studies as well. [ABSTRACT FROM AUTHOR]

This study propounds a sustainable alternative to petroleum-based polyurethane (PU) foams, aiming to curtail this nonrenewable resource's continued and uncontrolled use. Coconut fatty acid distillate (CFAD) and crude glycerol (CG), both wastes generated from vegetable oil processes, were utilized for bio-based polyol production for rigid PU foam application. The raw materials were subjected to catalyzed glycerolysis with alkaline-alcohol neutralization and bleaching. The resulting polyol possessed properties suitable for rigid foam application, with an average OH number of 215 mg KOH/g, an acid number of 7.2983 mg KOH/g, and a Gardner color value of 18. The polyol was used to prepare rigid PU foam, and its properties were determined using Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis/derivative thermogravimetric (TGA/DTA), and universal testing machine (UTM). Additionally, the cell foam morphology was investigated by scanning electron microscope (SEM), in which most of its structure revealed an open-celled network and quantified at 92.71% open-cell content using pycnometric testing. The PU foam thermal and mechanical analyses results showed an average compressive strength of 210.43 kPa, a thermal conductivity of 32.10 mW·m−1K−1, and a density of 44.65 kg·m−3. These properties showed its applicability as a type I structural sandwich panel core material, thus demonstrating the potential use of CFAD and CG in commercial polyol and PU foam production. [ABSTRACT FROM AUTHOR]

Bio-derived polyol products have gained global interest as a green and sustainable substitute for fossil-based polyols in a diverse range of polyurethane (PU) applications. According to previous studies, PU properties are highly influenced by the reaction kinetics during their formation. One major factor affecting this is the reactivity of their polyol's functional hydroxyl moieties that are classified as primary, secondary, and hindered-secondary. However, experimental quantitative characterization of these polyol hydroxyl moieties remains a challenge in the field due to various factors affecting them, including extensive time requirements, the need for substantial and expensive resources, large potential errors, and the generation of wastes, as well as health and safety considerations. In this study, the molar fraction of primary, secondary, and hindered-secondary hydroxyl moieties of a petroleum-based polyol (V490) and a rice straw-based polyol were determined via an iterative computational method. The method employed a MATLAB script that can simultaneously solve multiple differential equations involving PU gelling reaction kinetics and thermodynamics. In this manner, numerical combinations of the fraction of each type of hydroxyl moiety are generated by looping together the respective numerical fractions for each moiety. The best-fit combinations of the fractions of the mixed polyol's hydroxyl moieties were successfully found via curve fitting of the simulated and experimental gelling temperature profile with an average numerical deviation of less than 1%. Thus, the method presented in this study offers a faster and more reliable characterization of the polymeric reaction kinetics than the experimental and conventional computational methods for product property enhancement and development in the field. [ABSTRACT FROM AUTHOR]

Selenium is a mineral that is essential to human health and is widely recognized for its responsibilities as a powerful anticancer vitamin and antibacterial vitamin. Selenium also plays a critical part in the production of vitamin D. The purpose of this research was to evaluate the particular effects that selenium nano-particles (SeNPs') had on the infectious agent Staphylococcus aureus as well as the breast cancer cell lines MCF-7 and MDA-MB-231. The proportion of MDA-MB-231 and MCF-7 cells that underwent late apoptosis was dramatically increased by selenium nanoparticles, whereas the number of cells that underwent cell expansion was significantly reduced. There was a wide range of variability in the effects of selenium nanoparticle treatment on cell growth apoptosis, apoptosis rates and patterns of cell cycle arrest. After 2, 4 and 6 hours, researchers found that the development of S. aureus was significantly reduced by selenium nanoparticles at doses of 8.0, 16.0 and 32g/mL. In addition to this, the presence of selenium nanoparticles resulted in a reduced percentage of bacteria that were still alive. According to the findings of the study, there is a need for more research into selenium nanoparticles with the intention of preventing and treating infections caused by S. aureus. [ABSTRACT FROM AUTHOR]

The use of titanium dioxide (also known as titania) in solar cells, biomaterials, and photocatalytic processes has generated an ever-increasing amount of interest. Titania is widely used in a variety of applications because it is nanocrystalline, chemically stable, has a high refractive index, is mechanically hard, and transmits visible light well. In this work, titania nanoparticles were formed via sol–gel synthesis, wherein the effect of varying pH values such as 3, 5, 7, 9, and 11 has been investigated on the synthesized nanoparticles by studying their structural characteristics and analyzing their optical transmittance. Structural analysis of the synthesized TiO2 shows formation of the brookite phase at all pH values under as-synthesized conditions. Moreover, structural parameters including crystallite size, dislocation density, and unit cell volume were also calculated. Cell volume was calculated at various pH levels and measured between 255.7 and 257.2 Å3. Optical analysis shows a transmission increase with a pH increase, and maximum transmission has been observed above 80% for a pH value of 11. [ABSTRACT FROM AUTHOR]

In this research, the first work was carried out to manufacture MgO-based metal matrix composite containing 3 wt%. Sintering parameters, such as temperature, pressure, and time were subjected to Taguchi analysis to identify the most significant effect on magnesium oxide physical and mechanical characteristics. The impact of each sintering parameter explores using the analysis of variance-structure and microstructure analysis using XRD and EDS-equipped FE-SEM. The mechanical properties of the composite are evaluated by testing its Rockwell hardness (HR) and Vickers hardness (HV). The results showed that sintering temperature was the most influential of the sintering factors on microhardness. Densification at its peak was 100%, while it peaked at 62.19 Rockwell hardness and 58.7 Vickers hardness. [ABSTRACT FROM AUTHOR]

Most of the generations in Bangladesh power system are based on fossil fuels which is 99% of its total generation. Following other countries, Bangladesh is also planning to adopt renewable energy system (RES). It has a plan to incorporate 55 MW wind turbine generator (WT) and 100 MW solar photovoltaic generator (PV) in Western grid of its power system. Though RES is green energy but from stability aspect it needs to be studied as RES has limitations in generating reactive power and maintaining inertia. So, voltage stability and rotor angle stability should be given more attention. However, if any stability problem arises, flexible alternating current transmission system (FACTS) devices can be adopted. With a tuned power oscillation damper, FACTS can reduce the expected rotor angle and voltage stability problem which are associated with RES. This paper examines the stability of the Western grid with the incorporation of hybrid energy of both wind and solar power and shows the positive effect of FACTS devices like thyristor controlled series compensator and static synchronous compensator with respective tuned controller. In present paper, the combination of WT and PV is also examined which shows only incorporation of WT reduces both the steady and dynamic stability more than the only adoption of PV. Inclusion of both WT and PV, reduce the both types of stability more than their individual inclusion. This paper shows that FACTS can improve the stability with individual or both kinds of RES penetration. [ABSTRACT FROM AUTHOR]

Carbon nanotubes (CNTs) and graphene, in particular, have been the subject of many recent studies since their discovery in the early 2000s. Because of their unusual properties, carbon nanotubes (CNTs) have piqued the interest of scientists across a wide range of disciplines. An Al matrix was reinforced with powder metallurgy-fabricated B4C and CNT composites. The nanocomposite aluminium matrix was examined for tribological behaviour, density, stiffness, and compressive strength before and after hot isostatic pressing (HIP). Scanning electron microscopy and TEM were used to analyze the carbon nanotubes and their hybrid counterparts (SEM). The density of nanocomposites was reduced by 38% without HIP but by 45% after it was added to the mixture. Hardness was also increased by 40%, but following HIP, the hardness rose to 67%. Before and after HIP, the compression strength increased by 39% and 60%, respectively. HIP improves the wear rate by 45%, and B4C and CNTs improve the coefficient of friction by 20% in all volume fractions but only by 48% in the case of nanocomposites. [ABSTRACT FROM AUTHOR]

For the safe and economical construction of embankment dams, the mechanical behaviour of the rockfill materials used in the dam's shell must be analyzed. The characterization of rockfill materials with specified shear strength is difficult and expensive due to the presence of particles greater than 500 mm in diameter. This work investigates the feasibility of using an extreme gradient boosting (XGBoost) computing paradigm to estimate the shear strength of rockfill materials. To train and validate the proposed XGBoost model, a total of 165 databases obtained from the literature are chosen. The XGBoost model was compared against support vector machine (SVM), adaptive boosting (AdaBoost), random forest (RF), and K-nearest neighbor (KNN) models described in the literature. XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination (R2), Nash–Sutcliffe coefficient (NSE), and error in the root mean square ratio (RMSE) to the standard deviation of the measured data (RSR). The results demonstrated that the XGBoost model has the highest prediction performance with (R2 = 0.9707, NSE = 0.9701, and RSR = 0.1729), followed by the SVM model with (R2 = 0.9655, NSE = 0.9639, and RSR = 0.1899), RF (R2 = 0.9545, NSE = 0.9542, and RSR = 0.2140), the AdaBoost model with (R2 = 0.9390, NSE = 0.9388, and RSR = 0.2474) and the KNN model with (R2 = 0.6233, NSE = 0.6180, and RSR = 0.6181). A sensitivity analysis has been conducted to ascertain the impact of each investigated input parameter. This study demonstrates that the established XGBoost model for estimating the shear strength of rockfill materials is reliable. [ABSTRACT FROM AUTHOR]

The composite was made using the stir cast manufacturing method. Many parameters, like stirring speed, stirring time, ZrO2% reinforcement, and cast temperature, are evaluated in a Taguchi experimental design to see how they affected the composite properties. In terms of composite properties, ZrO2% reinforcement and the stir speed have the most significant impact. There were 25.02% gains in ultimate tensile strength and hardness, as well as a decrease in composite wear loss, when the optimal stir casting parameters were used compared to the initial stir casting settings. To get insight into the process and the qualities of the composite, the hot-pressing parameters were studied. Pressure, followed by temperature, is the most critical factor in determining the properties of composites. When a hot-pressing setting was determined to reduce the wear loss by a significant 39.3%, it was deemed perfect by the superranking concept. Under ideal conditions, hot-pressing procedures reduced wear loss by 40.8% while boosting ultimate tensile strength and hardness by 19.83% and 9.6%, respectively. The resulting microstructures and worn surface morphologies from stir casting and hot pressing show vastly different properties. [ABSTRACT FROM AUTHOR]

The optoelectronic properties of the ternary Cd0.25Zn0.75Se alloy are reported under the influence of a high pressure ranging from 0 to 25 GPa, within a modified Becke–Jhonson potential using density functional theory. This alloy has a cubic symmetry, is mechanically stable, and its bulk modulus rises with pressure. It is observed to be a direct bandgap material with a bandgap energy that increases from 2.37 to 3.11 eV with rise in pressure. Pressure changes the optical and electronic properties, causing the absorption coefficient to rise and absorb visible green-to-violet light. The static dielectric constant, along with the static index of refraction, both increase under the influence of pressure. Optical constants, including dielectric constant, optical conductivity, refractive index, extinction coefficient, and reflection, are also investigated and discussed. This DFT forecast explores important research directions for the usage of the CdZnSe semiconductor alloys in the manufacturing of space photovoltaic and optoelectronic devices operating at different pressures. [ABSTRACT FROM AUTHOR]

The microstructure and mechanical properties of an MMC based on AA 7075 and strengthened through silicon carbide (SiC) as well as boron carbide (B4C) elements were studied. The (SiC + B4C) combination was used in various weight percentages of 4, 8, 12, and 16% to create the hybrid composites utilizing the traditional stir casting procedure. XRD and SEM measurements were used to investigate the dispersion of the reinforced particles. For example, microhardness, impact strength, and ultimate tensile strength were measured on hybrid composites at room temperature. The density and porosity of the materials were also studied. The researchers found that increasing the weight percentage of the (SiC + B4C) mixture resulted in a small drop in % elongation. However, hybrid composites comprising 16% (SiC + B4C) weight reduction showed some decrease in hardness and tensile strength. Equated to unreinforced alloys, the hardness and tensile strength of hybrid composites rise by 8% and 21%, respectively. Reinforcement also resulted in a decrease in impact strength and density, as well as an increase in porosity. [ABSTRACT FROM AUTHOR]

Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-world industrial scenarios because of the complex and dynamic relationship between devices. The scope of this research is that it can detect equipment defects and deploys them in a natural production environment. The proposed research is describing an online detection method for system failures based on long short-term memory neural networks. In recent years, deep learning technology has taken over as the primary method for detecting faults. A neural network with a long short-term memory is used to develop an online defect detection model. Feature extraction from sensor data is done using the curve alignment method. Based on long-term memory neural networks, the fault detection model is built (LSTM). In the end, sliding window technology is used to identify and fix the problem: the model's online detection and update. The method's efficacy is demonstrated by experiments based on real data from power plant sensors. [ABSTRACT FROM AUTHOR]

Rockburst phenomenon is the primary cause of many fatalities and accidents during deep underground projects constructions. As a result, its prediction at the early design stages plays a significant role in improving safety. The article describes a newly developed model to predict rockburst intensity grade using Adaptive Boosting (AdaBoost) classifier. A database including 165 rockburst case histories was collected from across the world to achieve a comprehensive representation, in which four key influencing factors such as maximum tangential stress of the excavation boundary, uniaxial compressive strength of rock, tensile rock strength, and elastic energy index were selected as the input variables, and the rockburst intensity grade was selected as the output. The output of the AdaBoost model is evaluated using statistical parameters including accuracy and Cohen's kappa index. The applications for the aforementioned approach for predicting the rockburst intensity grade are compared and discussed. Finally, two real-world applications are used to verify the proposed AdaBoost model. It is found that the prediction results are consistent with the actual conditions of the subsequent construction. [ABSTRACT FROM AUTHOR]

Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundation for studying how cells work. Genome-wide protein interaction (PPI) data is growing as high-throughput experiments become more common. The aim of this research is that it provides a dual-tree complex wavelet transform which is used to find out about the structure of proteins. It also identifies the secondary structure of protein network. Many computer-based methods for predicting protein complexes have also been developed in the field. Identifying the secondary structure of a protein is very important when you are studying protein characteristics and properties. This is how the protein sequence is added to the distance matrix. The scope of this research is that it can confidently predict certain protein complexes rapidly, which compensates for shortcomings in biological research. The three-dimensional coordinates of C atom are used to do this. According to the texture information in the distance matrix, the matrix is broken down into four levels by the double-tree complex wavelet transform because it has four levels. The subband energy and standard deviation in different directions are taken, and then, the two-dimensional feature vector is used to show the secondary structure features of the protein in a way that is easy to understand. Then, the KNN and SVM classifiers are used to classify the features that were found. Experiments show that a new feature called a dual-tree complex wavelet can improve the texture granularity and directionality of the traditional feature extraction method, which is called secondary structure. [ABSTRACT FROM AUTHOR]

In researching social network data and depression, it is often necessary to manually label depressed and non-depressed users, which is time-consuming and labor-intensive. The aim of this study is that it explores the relationship between social network data and depression. It can also contribute to detecting and identifying depression. Through collecting and analyzing college students' microblog social data, a preliminary screening algorithm for college students' suspected depression microblogs based on depression keywords, and semantic expansion is researched; a comprehensive lexical grammar was proposed. This research provided has a preliminary screening method based on depression keywords and semantic expansion for college students' suspected depression microblogs, with a screening accuracy. This method forms a depression keyword table by constructing the basic keyword table and the semantic expansion based on the word embedding learning model Word2Vec. Finally, the word table is used to calculate the semantic similarity of the tested microblogs and then identify whether it is a suspected depression microblog. The experimental results on the microblog dataset of college students show that the comprehensive lexical method is better than the SDS questionnaire segmentation method and the expert lexical method in terms of screening accuracy; the comprehensive lexical approach can quickly and automatically screen out a tiny proportion of suspected doubts from a large number of college students' microblogs. Depression Weibo can reduce the workload of experts' annotation, improve annotation efficiency, and provide a suitable data processing basis for the subsequent accurate identification (classification problem) of patients with depression. [ABSTRACT FROM AUTHOR]

Aiming at the inadequacy of the group decision-making method with the current attribute value as interval language information, an interval binary semantic decision-making method is proposed, which considers the decision maker's psychological behavior. The scope of this research is that this paper is based on localized amplification method. The localized amplification method used in this research may amplify physiological movement after removing unwanted noise, allowing the movement trend to be seen with the naked eye, improving the CNN network's mental identification accuracy. These two algorithms analyze the input picture from various perspectives, allowing the CNN network to extract more information and enhance identification accuracy. A new distance formula with interval binary semantics closer to decision-makers thinking habits is defined; time degree is introduced. An optimization model is established to solve the time series weights by considering the comprehensive consistency of expert evaluation. Based on prospect theory, a prospect deviation value is constructed and minimized weight optimization model, using the interactive multiple attribute decision community making (TODIM) method based on the new distance measure to calculate the total overall dominance of the schemes to rank the schemes. Taking the selection and evaluation of supply chain collaboration partners as an example, the effectiveness and rationality of the proposed method are verified. [ABSTRACT FROM AUTHOR]