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A secure Multi-Party Computation (MPC) is one of the distributed computational methods, where it computes a function over the inputs given by more than one party jointly and keeps those inputs private from the parties involved in the process. Randomization in secret sharing leading to MPC is a requirement for privacy enhancements; however, most of the available MPC models use the trust assumptions of sharing and combining values. Thus, randomization in secret sharing and MPC modules is neglected. As a result, the available MPC models are prone to information leakage problems, where the models can reveal the partial values of the sharing secrets. In this paper, we propose the first model of utilizing a random function generator as an MPC primitive. More specifically, we analyze our previous development of the Symmetric Random Function Generator (SRFG) for information-theoretic security, where the system is considered to have unconditional security if it is secure against adversaries with unlimited computing resources and time. Further, we apply SRFG to eradicate the problem of information leakage in the general MPC model. Through a set of experiments, we show that SRFG is a function generator that can generate the combined functions (combination of logic GATEs) with $n/2$n/2 -private to $n$n-private norms. As the main goal of MPC is privacy preservation of the inputs, we analyze the applicability of SRFG properties in secret sharing and MPC and observe that SRFG is eligible to be a cryptographic primitive in MPCdevelopments. We also measure the performance of our proposed SRFG-based MPC framework with the other randomness generation-based MPC frameworks and analyze the comparative attributes with the state-of-the-art models. We observe that our posed SRFG-based MPC is $\approx 30\%$≈30% better in terms of throughput and also shows 100% privacy attainment.

The study proposes assessing the seismic vulnerability of G + 15-storey reinforced concrete (RC) buildings using finite element analysis (FEA) software (ETABS) and artificial neural networks (ANNs). The study utilizes finite element models of G + 15 buildings subjected to recent earthquake data, analysing them for seismic vulnerability and incorporating various retrofitting techniques, such as fluid viscous dampers (FVD). The damper locations are varied in the structure for the entire earthquake data considered to study the seismic vulnerability in storey displacements, storey shear, and storey drift. Key structural characteristics were systematically modified, and their impact on seismic response was evaluated through modal dynamic and non-linear time history analyses. The FEA results are used to train an ANN algorithm, creating a function that can predict the seismic behaviour of similar RC structures. This approach offers a fast and potentially generalizable method for seismic vulnerability assessment.

Background: Earlier research on internet addiction (IA) in the adult population in India has concentrated on isolated prevalence surveys conducted nationwide. A systematic grasp of pooled prevalence necessitates a meta-level evaluation of all prior literature. This study aims to investigate the prevalence of IA and its correlates among adults in India.Methods: We searched EBSCOhost, PubMed, PsycInfo, PsycArticles, and ProQuest for the studies published from 1990 to 2022 and included studies using Young’s Internet Addiction Test criteria (age range 18–40 years). The authors obtained a total of 358 non-duplicate records after a thorough search, and they found 10 pertinent epidemiological studies involving 12,286 adults.Results: The pooled prevalence for mild, moderate, severe, and any level of IA was 36.33% (95% confidence interval [CI]: 30.72%–42.13%), 18.04% (95% CI: 13.49%–23.08%), 0.88% (95% CI:0.50%–1.36%), and 58.34% (95% CI:49.34%–67.07%), respectively. The review of correlates suggested psychosocial factors like male gender, single-parent families, co-morbidities like depression, stress, anxiety, interpersonal issues, andmediating factors like loneliness.Conclusions: Based on the synthesis of findings, this review underlines the common factors of IA and emphasizes the significance of psycho-social factors in IA; thus, to promote healthy use of technology and to manage IA, a multi-modal psychotherapeutic intervention module for mental health practitioners needs to be developed in line with this review.

This paper proposes a hybrid method for controlling bidirectional power sharing among grids and dc microgrids. The interlinking converter (IC) and current-fed dual active bridge (CFDAB) converters connect the grid and dc microgrid. The proposed approach is the joint implementation of golden eagle optimization and radial basis function neural network, hence, named the GEO-RBFNN approach. The bidirectional power distribution control signal analysis significantly diminishes power distribution deviation and circulating current. This power-sharing control system neglects the need for battery backup since the required amount of power is brought from or handled by an AC utility. The dc microgrid incorporates the dc to dc converter, from which boost converters use sources and buckconverters use supply loads. The control of bidirectional power sharing among the microgrids and the grid is attained with the aid of the GEO-RBFNN algorithm. It delivers certain aids, like higher predictability, highly reliable results, high speed, and less complexity. The outcome is investigated on the MATLAB platform. According to the comparison outcome, the proposed method delivers higher performance than the existing system. The proposed method gives higher comparison outcomes than the current methods, which can conquer the associated problems. The proposed method-based settling time is 3.356 ms, and the rise time is 1.555 ms. The efficiency based on the proposed method is 97.54%, which is higher than the existing methods.

In this work, modification methods are used to ascertain the nutritional composition and functional characteristics of the cassava (rhizome), bananas (fruits), and corn (grains) starches. The physical modification by autoclaving-cooling cycle method results in higher starch yield than chemical modification using acid hydrolysis approach. Native starches are recorded with higher proximate chemical compositions than modified starches, including moisture, acidity, ash, protein, fat, and amylopectin concentrations. However, the modified starches had higher levels of crude fiber, pH, and amylose content. Native cassava starch (87.49 ± 0.76) and native corn starch (74.98 ± 0.51) were found to have higher values (L*) in the colour measurement. It was shown that the native starch required a lower gelatinization temperature (ºC) and shorter gelatinization times (min) than the modified starches from cassava, bananas, and corn. In comparison to modified starches (4.00 ± 0.03, 2.90 ± 0.09, and 14.10 ± 0.38%), the solubility of cassava, banana, and corn native starches was found to be 14.60 ± 0.12, 11.60 ± 0.46, and 22.64 ± 0.22%. Similarly, native starches have a stronger swelling power than modified starches. The solubility index (%) and swelling power (g/g) on the native and modified starches of cassava, banana, and corn were calculated, and the effects of temperature (60–90ºC), dry heat (50–200ºC), and moist heat (25–100ºC) treatment methods were explored. The association between the functional characteristics of starches from cassava, bananas, and corn and their modification processes and temperature variations is revealed by the study. The outcomes will offer the information needed to use these modified starches as functional food ingredients.

Vernal keratoconjunctivitis (VKC) is a chronic severe ocular allergic inflammation mostly observed in children and young adults. The ocular manifestations are the expression of multifactorial immune mechanisms that generally have a good prognosis, however long-term inflammation may remarkably reduce the visual function due to complications and poor therapeutic responses.

The Economics Department at the University of North Carolina at Chapel Hill (UNC) has had a long-term commitment to offering extra-curricular opportunities for undergraduate students to enhance their learning beyond the classroom. These opportunities include research, problem-solving, leadership development, Undergraduate Learning Assistance (ULA), peer tutoring, and networking events, among others. Given the Economics program’s substantial size within UNC’s College of Arts and Sciences—with over 1400 declared or intended majors in recent years—it is important to structure the activities in a manner that accommodates students’ diverse interests as well as their time constraints. In this paper, we introduce these different activities and discuss their scope, skill objectives, and time commitment. The benefits to students include opportunities for community, networking, exposure, application, teamwork, presentation, analysis, and research. We also discuss several challenges a department faces to successfully offer these diverse opportunities.

Fourier-transform infrared (FTIR) spectroscopy characterization is a powerful and easy-to-use technique frequently employed for the characterization and fingerprinting of materials. Although MXenes are a large and fastest growing family of inorganic 2D materials, the lack of systematic FTIR spectroscopy studies hinders its application to MXenes and often leads to misinterpretation of the results. In this study, we report experimental and calculated FTIR spectra of 12 most typical carbide and carbonitride MXenes with different compositions (5 transition metals) and all four basic structures, including Ti2CTx, Nb2CTx, Mo2CTx, V2CTx, Ti3C2Tx, Ti3CNTx, Mo2TiC2Tx, Mo2Ti2C3Tx, Nb4C3Tx, V4C3Tx, Ta4C3Tx, and Mo4VC4Tx. The measurements were performed on delaminated MXene flakes incorporated in KBr pellets in the 4000–400 cm–1range. We provide detailed instructions for sample preparation, data collection, and interpretation of FTIR spectra of MXenes. Background correction and spectra smoothing are applied to obtain clear FTIR peaks corresponding to bond vibrations in MXenes. Density functional theory calculations were used for the precise assignment of all characteristic FTIR peaks and an in-depth analysis of the vibration modes. This work aims to provide the 2D material community with the FTIR spectroscopy technique as a reliable method for identifying and analyzing MXenes.

AbstractThis research article was investigates the Prussian blue dye removal from aqueous solution using immobilized nano ferrite (Zn-Co-Fe) in alginate biopolymer. The preparation of alginate beads was done by metal encapsulation method using CaSO4as crosslinking agent. The Prussian blue dye removal efficiency was ascertained with various isotherm equations such as Langmuir and Freundlich. Isotherm equations are not providing physical, chemical and feasibility aspects. So, these were investigated with pseudo-first order, pseudo-second order kinetic equations. The mode of diffusion evaluated with intraparticle diffusion and thermodynamic feasibility was tested with the equations of enthalpy, Gibbs free energy, and entropy. The experimentally driven results were implies the Prussian blue removal process was feasible in all aspects. Moreover, the predicted diffusion parameters revealed that the interaction between Prussian blue and nanocomposite beads was predominant. The surface morphology, examined using a Scanning Electron Microscope, showed that the Prussian blue strongly covered on outer surface of the beads. The FTIR proves the Prussian blue dye was strappingly loaded on the surface of nanocomposite beads. The TGA curve revealed the synthesized beads has an exceptional temperature resistance.

Understanding the long-term stability of biologics is crucial to ensure safe, effective, and cost-efficient life-saving therapeutics. Current industry and regulatory practices require arduous real-time data collection over three years; thus, reducing this bottleneck while still ensuring product quality would enhance the speed of medicine to patients. We developed a parallel-pathway kinetic model, combined with Monte Carlo simulations for prediction intervals, to predict the long-term (2+ years) stability of biotherapeutic critical quality attributes (aggregates, fragments, charge variants, purity, and potency) with short-term (3–6 months) data from intended, accelerated, and stressed temperatures. We rigorously validated the model with 18 biotherapeutic drug products, composed of IgG1 and IgG4 monoclonal antibodies, antibody–drug conjugates, dual protein coformulations, and a fusion protein, including high concentration (≥100 mg/mL) formulations, in liquid and lyophilized presentations. For each drug product, we accurately predicted the long-term trends of multiple quality attributes using just 6 months of data. Further, we demonstrated superior stability prediction via our methods compared with industry-standard linear regression methods. The robust and repeatable results of this work across an unprecedented suite of 18 biotherapeutic compounds suggest that kinetic models with Monte Carlo simulation can predict the long-term stability of biologics with short-term data.

Biochar-based composites have proven to be the best solution to enhance the mechanical and thermal properties of polymeric composites. This paper presents the biochar produced from Anacardium occidentale nut shell (AONS) treated with alkaline materials. Composites were developed using the solution casting method at different weight proportions of untreated and alkali-treated AONS biochar/vinyl ester resin (VER). Thermal properties like thermogravimetric analyzer (TGA), differential scanning calorimetry (DSC), dynamic mechanical analyzer (DMA), and mechanical properties such as tensile, flexural, and impact. Electrical properties were carried out for the composites. DSC results showed the crystallization temperature of untreated and treated VER/15wt%. AONS composites are 13% and 19% higher than pure vinyl ester. Mechanical studies showed that treated and untreated AONS biochar/VER composite samples were increased significantly compared to the pure vinyl ester sample. More importantly, 15wt% of alkali-treated biochar-added composites obtained higher properties than the other composites. These make the AONS an exciting approach to developing a sustainable energy system.

Induction motors are frequently utilized in numerous manufacturing due to their reality, are robust in nature, and have low cost. The bearing of an induction motor is proven to be affected in electric mobility systems that use speed drive systems. In speed drive systems, inverters are used to generate alternating current (AC) from direct current (DC). When the induction motor is grounded, it generates a common mode voltage (CMV). The current must flow through the common mode voltage for the bearing system to work effectively. As a result, the bearing system is prematurely destroyed, and many characteristics of the variable speed drive system are impacted. This is known as "inverter generated circulating carrying current." as a result, this research compares and analyses the circulating bearing current, shaft voltage, and common mode voltage. To reduce the common mode voltage and circulating bearing current for inverter-fed induction motors used for traction, a modified PID—Fuzzy controller is used in this work.

In today’s transportation networks, bridges play an essential role as conduits that allow efficient access to a variety of locations. These structures are still vulnerable to outside pressures, though, and doing so can result in serious harm, especially during seismic occurrences. In this research, we model and analyze reinforced concrete (RC) T-beam bridges with elastomeric bridge bearings in order to thoroughly assess the seismic behavior of bridge components. We build and examine several span bridge models with CSI Bridge Software, altering pier heights and bearing stiffnesses in a methodical manner. In this work, we evaluate an RC bridge’s seismic susceptibility by taking regionally variable ground motions into account. Fragility curves, which are crucial instruments for evaluating risk, are at the center of our research. The probability of failure is represented by these curves over the whole load spectrum. Typically, fragility curves plot estimated probabilities (such as deflection) against ground motion parameters, providing insights into the likelihood of exceeding specific deformation limits during seismic events. Our research aims to create accurate fragility curves, facilitating precise loss calculations for bridge structures. By employing artificial neural networks (ANNs) and long short-term memory (LSTM), this research addresses uncertainties associated with influencing factors. It has been discovered that the inputs and outputs of the ANN and LSTM models are, respectively, the influencing traits and fragility parameters of significant components.

Amidst a concerning surge in urban losses attributed to disasters, this research paper explores the intricate relationship between urban development, disaster mitigation, and resilience emphasizing the significance of addressing disaster vulnerability in urban settings, where a substantial portion of the population faces risks stemming from high population density, limited resilience, and inadequate coping capabilities. The study advocates for the integration of disaster resilience principles into the Smart Cities Mission of India, placing particular emphasis on the necessity of developing infrastructure, establishing early warning systems, and fostering community engagement to bolster urban resilience. Furthermore, the paper draws comparisons and parallels between the components of smart cities, mitigation strategies, and disaster resilience, illuminating their interconnectedness and potential synergies. In conclusion, the study recommends the incorporation of essential network elements to establish a Smart Cities Mission that is resilient to disasters, ultimately aiming to safeguard urban communities from the adverse impacts of future calamities.

Plant diseases, a leading cause of economic loss in the agricultural field, greatly impact crop productivity and quality. Detecting plant diseases at an early stage is very important for controlling such sicknesses effectively. It helps to protect crops in the best way and keep high levels of agriculture production. Taking action as soon as possible can reduce how fast diseases spread, lessen the need for chemical treatments and encourage farming methods that are sustainable—all this helps protect food supplies and keep financial stability within agriculture sector intact. The existing ways to diagnose plant leaf diseases greatly depend on people who are highly skilled examining samples manually. This method takes a lot of time, requires much work and can have errors. These factors make it difficult for quick and precise disease identification. Therefore, finding an automated system that is accurate and efficient in detecting and diagnosing plant leaf diseases early becomes crucial for enhancing both crop productivity as well as quality of harvests. For these difficulties, the solution is to create a new system for detecting the diseases in plant leaves. This method, known as Deep Attention Convoluted Network (DACN), uses computer vision and deep learning methods. Here, a CLAHE based preprocessing is applied to generate the contrast enhanced images with low noise artifacts. Then, a lightweight Attention Gate ResNet (AGRNet) architecture is implemented to segment the preprocessed plant leaf image with increased accuracy. Moreover, the Convoluted Deep Xception Net (CDXNet) based classification algorithm is used identify the infected plant leaf images with low training complexity and time consumption. During classification, the activation function of the CDXNet is optimally computed with the use of Q-Parameter updated Mountain Gazelle Optimization (QMoGO) algorithm. To verify performance, a range of parameter types are used to compare and validate the classifier’s prediction outcomes. The results from the tests were that the system made an outstanding precision of 99.2, 99.1, 99.15, and 99.2%. These results showcase the capability of DACN to identify the diseased leaves of the plants with almost 100% accuracy while using the inexpensive training and classification method. These excellent figures of accuracy are the confirmation of the reliability of the process and its possible application in a more widespread manner in detecting diseases in farm automation.

Sports develop both physical and mental growth of an individual. In order to enhance the physical and mental health effectively and effortlessly, swimming is considered to be one of the healthier activities, in developing the metabolism via flexibility, weight loss, reducing asthma and enhancing the fitness levels of the body. In most of the cases, existing studies provide the current status of the swimmer by delivering details like time taken by the swimmers to finish the lap or location of the swimmer, however they lack in identifying the calorie level reduction of the swimmer during the swimming, as, the detection of calories burnt by the swimmer helps in overcoming issues like loss of weight and other health risk rates. Therefore, proposed study intensifies in achieving the prediction of the fitness of a swimmer as the primitive activity in view of reducing the calories by using PSO algorithm using resilient based techniques for enhanced exploration ability and using modified resilient PSO in order to tune the hyper parameter of the random forest for better optimization outcome. Modified resilient PSO helps in preventing the over-fitting of the model and delivering enhanced global solutions. Besides, hyper parameter are tuned in the Random Forest (RF), Decision Tree (DT), AdaBoost and Support Vector Machine (SVM) regression model for an error free outcome and with satisfactory performance analytical values. Finally, the performance of the proposed model is assessed using performance metrics such as R-square, RMSE, MSE and MAE, unveiling the effectiveness of the projected approach MPSO-XG-Boost. The projected model has achieved effectual outcomes encompassing less error rate based outcomes in ranges of 0.037, 0.191, 0.059, 0.9. Further, the proposed is compared with the existing models in order to determine the efficiency of the proposed framework.

Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, and problem-solving abilities. As a result, stress detection and modeling have become important areas of study in both psychology and computer science. This study links the fields of psychology and machine learning to deal with the urgent requirement of accurate stress detection methodologies and highlights sleep patterns as a key indicator for stress detection, discussing a novel approach to understand and determine stress levels. Psychologists use affective states to measure stress, which refers to a sense of feeling an underlying emotional state. However, most stress classification work has been limited to user-dependent models, which new users cannot use without additional training. This can be a significant time burden for new users trying to predict their affective states. Therefore, it is critical to address basic mental health issues in children and adults to prevent them from developing more complex problems on account of undergoing stress. The medical field processes vast amounts of medical data; the machine learning algorithms sift through patterns that might escape the human eye. The machine learning algorithms act as detectives, able to spot correlations and bring out a sense of complex information. The machine learning algorithms reveal fine correlations and patterns, aiding in more precise and prompt diagnoses particularly to focus fundamental mental health issues in individuals of all ages. This research work deploys an enhanced Multilayer Perceptron (MLP), exhibiting an extensive feature analysis for processing medical datasets, resulting in improved effectiveness in predicting stress levels. This helps us to diagnose issues more accurately and swiftly which improves the patient outcomes. The proposed and enhanced MLP model undergoes stringent evaluation and its performance metrics are measured as Accuracy 99%, Precision 98.6%, Recall 99%, and F1-Score 99.5% compared against existing competent machine learning algorithms that include Adaboost, Random Forest, Gradient Boosting, and Decision Tree for different stress levels undertaken. The results show that MLP provides best results of accuracy compared with existing machine learning techniques in identifying stress detection via sleep patterns.

The addition of metal intercalants into the van der Waals gaps of transition metal dichalcogenides has shown great promise as a method for controlling their functional properties. For example, chiral helimagnetic states, current-induced magnetization switching, and a giant valley-Zeeman effect have all been demonstrated, generating significant renewed interest in this materials family. Here, we present a combined photoemission and density-functional theory study of three such compounds: V1/3NbS2, Cr1/3NbS2, and Fe1/3NbS2, to investigate chemical trends of the intercalant species on their bulk and surface electronic structure. Our resonant photoemission measurements indicate increased hybridization with the itinerant NbS2-derived conduction states with increasing atomic number of the intercalant, leading to pronounced mixing of the nominally localized intercalant states at the Fermi level. Using spatially and angle-resolved photoemission spectroscopy, we show how this impacts surface-termination-dependent charge transfers and leads to the formation of new dispersive states of mixed intercalant-Nb character at the Fermi level for the intercalant-terminated surfaces. This provides an explanation for the origin of anomalous states previously reported in this family of compounds and paves the way for tuning the nature of the magnetic interactions in these systems viacontrol of the hybridization of the magnetic ions with the itinerant states.

Activated carbon (AC) is a porous material that is widely employed in a variety of industrial applications. The synthesis of corn cob activated carbon (CCAC) as a potential material for the removal of brilliant green (BG) dye solution is described in this work. We performed the chemical activation of corn cob to prepare high surface area materials with CCAC/H3PO4impregnation ratios (1:3, and 1:6) at 700 ºC in an N2atmosphere, so the obtained materials were labeled as CCAC1 and CCAC2, respectively. The synthesized materials were analyzed by XRD, Raman, FT-IR, FE-SEM with EDS, and BET analysis. The prepared CCAC2 material exhibited better pore formation (0.483 cm3/g) and larger surface area (429.63 m2/g) and improved the adsorption capacity of the dye. Among the CCAC/H3PO4materials used in a 1:6 ratio for the adsorbent, the parameters of adsorption time, pH, dose, and dye concentration were evaluated. The results showed that at pH = 5, the adsorption efficiency of 0.100 g/L of CCAC2 removed 99.5–84 % of 50–250 mg/L of BG dye. The adsorption isotherm correlates well with the Langmuir model, and CCAC2 showed an elimination rate of 99.5 % for 50 mg/L BG with a maximum adsorption capacity of 238.09 mg/g. Accordingly, the synthesized CCAC sample can be utilized for the remediation of wastewater from organic pollutants.

Heart disease is one of the foremost reasons for death globally. Machine learning (ML) can be used to predict heart diseases early, which can help improve patient outcomes. This research proposes a novel machine learning method for predicting heart disease using a combination of Grey Wolf Optimization (GWO) and stacked ensemble techniques. GWO is a metaheuristic algorithm that can be used to optimize the parameters of machine-learning models. The stacked ensemble technique is a combination of multiple machine learning models to improve the overall accuracy of the prediction. The model proposed was evaluated using a dataset of heart patients. The results showed that the model achieved a 93% accuracy, which was significantly higher compared to traditional machine learning methods. The proposed method also had a higher precision of 91%, sensitivity of 95.3%, F1 score of 92.9%, and Matthew coefficient of 0.83, less in Log_Loss 2.87 than the traditional methods. The results of this research suggest that the proposed model is a promising new approach for predicting heart diseases. This method is more accurate and reliable than traditional methods and has the potential to improve patient outcomes.

In this paper, with the development of an intelligent power system idea, sustainable energy sources were increasingly deployed, including transmission and distribution systems networks. As a result, optimal use of cascaded H-bridge inverter topologies (MLIs) and power distribution operations is critical for long-term power generation. Traditionally, selective harmonics reduction models must be performed to achieve the optimal switching frequency of multilevel inverters. This research aims to determine the switching frequency for wind-incorporated multilevel inverters to reduce overall harmonic components used in grid applications. This research adds towards the best possible solution by employing multiple newly established adaptive optimization techniques: MNSGA-II and salp swarm. The well-known genetic algorithm and particle swarm optimization are used for the wind-tied multilevel inverters optimization issue. Seven-level, eleven-level, and fifteen-level MLIs were employed to reduce overall harmonic distortion. The reliability and convergence rate of simulated data with various modulation indices for seven-, eleven-, and fifteen-level MLIs are obtained and compared. Models are developed based on MATLAB Simulink and are used to validate quantitative measurements.

ABSTRACTThe metallic plate is used for bone fixation in biomedical applications, which causes health risks to the patient by leaching ion elements due to the formation of micro-motion between the plate and screw. Currently, the polylactic acid is favoured for replacing metal alloys in temporary implants due to the biodegradable property. However, polylactic acid has low mechanical properties compared to metal alloys and is not preferred as a fixation device. Therefore, the polylactic acid was printed using FDM technique under both horizontal and vertical orientations to improve the mechanical properties. After printing, the samples were post-heat treated at 60℃ for 24 h (B1) and 70℃ for 12 h (B2). Later, compression and fretting wear tests were performed for the printed and post-heat-treated samples to estimate the compression strength and fretting wear. The results indicated that the post-heat-treated horizontal sample B2 obtained a higher compression yield strength of 80.15 ± 3.53 MPa compared to printed and post-treated sample B1. This value lies within the range of bone fixation device. Moreover, a low coefficient of friction (1.34 ± 0.23) was obtained for sample B2 due to the improvement of yield strength. The worn morphology showed that the formation of cracks and spalling was more for the printed and B1 sample compared to the B2