The automotive industry is increasingly focused on developing lightweight, fuel-efficient, and structurally robust components to meet stringent performance and sustainability requirements. Additively manufactured lattice structures have emerged as a promising solution due to their high strength-to-weight ratio, energy absorption capability, and geometric flexibility; however, achieving an optimal balance among competing objectives such as mass reduction, mechanical stiffness, strength, and manufacturability remains a significant challenge. This study proposes a multi-objective topology optimization framework for the design of 3D-printed lattice-based automotive components. The framework integrates density-based topology optimization with lattice parameterization and a multiobjective evolutionary optimization algorithm to simultaneously minimize structural mass and maximize mechanical performance under realistic automotive loading conditions. Finite element analysis is employed to evaluate stress distribution, displacement, and compliance, while additive manufacturing constraints—including minimum feature size and printability—are explicitly embedded within the optimization process to ensure fabrication feasibility. The resulting lattice-optimized configurations are assessed through extensive numerical simulations and comparative performance analysis. Simulation results demonstrate that the proposed approach achieves an average weight reduction of approximately 35% while maintaining or improving structural stiffness and strength compared to conventional solid and uniform lattice designs. The generated Pareto-optimal solutions provide designers with flexibility to select optimal trade-offs tailored to specific automotive applications. Overall, the proposed framework significantly outperforms traditional single-objective optimization approaches in terms of material efficiency and mechanical performance. This research presents a scalable and manufacturable design methodology that bridges the gap between theoretical topology optimization and industrially viable lightweight automotive components, supporting the broader adoption of additive manufacturing in sustainable vehicle design.

Purpose- The main objective of the proposed paper is to create and implement a real-time wearable health monitoring system based on IoT, i.e., Oxy Sense-Wear, that will enable the constant control of the main physiological parameters, such as ECG, EMG, SpO2, body temperature, and physical activity. The system is aimed at long-term surveillance of the elderly, bedridden, and long-term chronic disease patients, and this allows the patient to identify abnormal health conditions in time and provide proper medical care. Design/methodology/approach-The given device is a soft wearable chest strap with built-in biomedical sensors and powered by an ESP32 microcontroller. Live information is collected, analyzed, and sent through Wi-Fi to a cloud-based server and Android smartphone application. Physiological alerts will activate the buzzer and instant mobile notification when the physiological thresholds are surpassed. The software was used to design and simulate the hardware that was being developed with Proteus and create firmware in the Arduino IDE and the mobile application on Android Studio. Findings- There is a reliable real-time performance as experimental assessment shows heart rate changes with a deviation of +/-2 BPM, SpO 2 values were always in the range of 96-98, and body temperature was monitored accurately between 36.0 o C and 38.8 o C. Fall events were identified with great success at acceleration levels more than 2.5 g, and low false positives. The system recorded a mean alert latency of less than 500 ms and could operate continuously (8 to 12 hours, depending on charge) and thus proved to be viable in the case of personal and clinical remote healthcare monitoring. Originality/value-The proposed Oxy Sense-Wear platform is the first to offer a single multi-parameter sensing, real-time alerting, cloud synchronization, mobile connectivity, and OTA-enhanced platform in a small and wearable size. The work done in the future will be on the implementation of more sophisticated machine-learning algorithms for predictive health analytics, improving the security of the collected data by using encrypted authentication to provide more connection options with the use of the BLE and 5G technology to support the large-scale implementation and integration with the hospital information system. In an effort to be more concise and clearer, this manuscript lays emphasis on system-level insights, comparative appraisal, and quantitative performance assessment rather than a description of the components at a much more detailed level.

The paper suggests a hybrid multimodal sentiment analysis (MSA) model that would enhance the accuracy of sentiment prediction through the combination of textual, auditory, and visual information. In most cases, the traditional sentiment analysis models have been challenged because of numerous overlapping features and poor fusion methods when using multimodal data. To overcome these problems, propose a supervised contrastive learning-based methodology that will improve data representation and exploit multimodal feature fusion. The technique includes pre-processing Twitter information by tokenization, stemming, and feature extraction, and classifying it with the help of a Particle Swarm Optimization-Deep Learning Modified Neural Network (PSO-DLBMNN). The experimental findings, assessed based on the measures of accuracy, precision, recall, and F1-score, demonstrate that the suggested model is superior to the traditional approaches to deep learning, such as Bi-LSTM and BiGRU. In particular, the PSO-DLBMNN model had an accuracy of 95.48, a precision of 96.57, a recall of 94.87, and an F1-score of 93.45, which is a substantial increase over the baseline models. These results indicate that the model is capable of completing multiple tasks of integrating multimodal data alongside solving the problem of redundancy and data noise. The suggested method gives a fresh outlook on improving sentiment analysis through enhancing multimodal feature fusion. To sum up, the model has the potential to be applied to real-time analysis in social media and human-computer interaction systems, and it provides information about how multimodal data can be used to enhance sentiment prediction and emotional perception.

Purpose: The study aims to establish the connection between green entrepreneurship variables, environmental management, green technologies, green commitment of individuals, and the environmentally sustainable workforce, and the performance of internationalization of the SME in China. It further examines the mediating position of eco-innovation and the moderating position of the government support program in this connection. Theoretical Framework: The research hypothesizes the performance of internationalization as the dependent variable, and the variables of green entrepreneurship as the independent variables. The mediator is eco-innovation, whereas the government support programs moderate the connection between eco-innovation and the performance of internationalization. The model is anchored on the Resource-based View (RBV). Methodology: The quantitative research design is applied, and the data is gathered by means of a self-administered questionnaire that will be distributed among manufacturing SMEs engaged in export business in Shanxi province, China. Findings: The research paper identifies the importance of green entrepreneurship factors in promoting the internationalization performance of SMEs. Strong contributors to internationalization are environmental management (coefficient = 0.325, p < 0.01) and green technologies (coefficient = 0.418, p < 0.01). The individual green commitment (coefficient = 0.267, p < 0.05) also has a positive influence on internationalization, and this interaction is mediated by eco-innovation (coefficient = 0.415, p < 0.001). The indirect effect of the environmentally sustainable workforce (coefficient = 0.193, p = 0.053) on the internationalization performance is also marginal. Research Implication: The results provide a significant implication to the SMEs and the policy makers, since it is clear that green entrepreneurship practices should be integrated to promote internationalization. Eco-innovation is a key factor in enhancing competitiveness, and such initiatives can be enhanced by the government. Originality/Value: The study adds to the discussion of how the factors of green entrepreneurship and eco-innovation can lead to the internationalization performance in SMEs, which present the opportunity to achieve sustainable growth and competitiveness on the global level.

Rapid and accurate assessment of civic infrastructure following a natural or artificial disaster is essential to planning emergency response and recovery. This paper introduces a control system based on deep reinforcement learning (DRL) to coordinate unmanned aerial vehicle (UAV) swarms and methodically approach the post-disaster infrastructure inspection. The multi-UAV coordination problem is formulated as a cooperative Markov decision process, enabling the learning of optimal policies for navigation, coverage, and collision avoidance under highly dynamic, uncertain conditions in disasters. The training-and-decentralized-execution paradigm is centralized to provide scalable swarm behavior while retaining real-time operational feasibility. The simulation experiments are conducted in real postdisaster urban settings marked by damaged structures, blocked streets, and limited communication. The average spatial coverage of the proposed DNR-controlled swarm is 91.6 decision steps, which is better than that of the rule-based and heuristic baselines (138.4 and 126.7 decision steps, respectively). The trained policy incurs a 34.2% lower cumulative navigation cost and maintains a stable inter-UAV separation, with a variance of less than 0.12 across multiple trials. Convergence of the policy is obtained in 2,150 training episodes, which is more than 3,900 training episodes in the case of baseline learning methods. The statistical analysis of 50 simulation runs indicates that dispersion in mission completion time was reduced by 27.5% and coverage uniformity improved by 22.8%. Moreover, the trained system shows robustness to partial failures of UAVs and adaptable obstacles, as training is not needed. These results verify that deep reinforcement learning offers a powerful and effective tool for autonomous swarm UAV deployment in post-disaster civil infrastructure inspection, aiding timely situational awareness and evidence-based decision-making within disaster management agencies.

One of the major issues in the agricultural industry is cocoa quality determination that may be based on subjective sensory assessment techniques that may cause inconsistency and losses after harvesting. The following research will solve the set of issues by taking the Mumford-Shah functional algorithm with the image segmentation, to evaluate the quality of cocoa at various stages of development: un-ripe, ripe, and diseased. The operation of the segmentation is performed on MATLAB simulation, and the images are processed to measure the primary metrics like how many regions they have, the average area, the average perimeter, and the average eccentricity. The outcomes of the segmentation indicate a clear difference in such measures in the three stages of cocoa growth. In the case of unripe cocoa there are 997 regions and the average region area is 10,509.80 pixels, the average perimeter is 18.40 pixels and the average eccentricity is 0.20. In case of ripe cocoa, there are 768 regions with the average area of 13,997.49 pixels, perimeter of 24.57 pixels, and the average eccentricity of 0.21. The diseased cocoa had 1,705 regions with a mean area of 5,936.61 pixels, mean perimeter of 11.68 pixels and with a mean eccentricity of 0.21. The discussion has shown that the Mumford-Shah algorithm offers an accurate means of grading cocoa quality and has been shown to offer major benefits over the conventional sensory assessment procedures, post-harvest losses and quality control in cocoa production.

The study is an empirical study that examines the factors of employee engagement in the organized retailing sector of Bangalore, India. Since the industry is experiencing a rapid digitalization and high employee turnover, it is essential to understand what organizational and technological factors lead to workforce commitment. An organized survey was carried out on a purposive sample of 250 retail professionals in different format, which includes supermarkets and departmental stores. In the study, descriptive statistics, reliability tests, correlation analysis, and multiple regression modelling were used to test five important constructs, which include leadership support, supervisory practices, work environment, reward systems, and digital readiness. The internal consistency of all the constructs was established as high through statistical analysis, where the α of Cronbach has a range between 0.86 and 0.92. The results of correlation showed that there is a strong positive correlation between Work Environment and Culture and engagement (r = 0.70), then Leadership Support (r = 0.68). The multiple regression was found to be statistically significant (F = 237.62, p = not significant) and the combination of the predictors was found to explain 45.3 % of the variance in staff engagement (R2 = 0.453). The strongest predictor was Work Environment (β= 0.412), Digital Readiness (β = 0.331) and Reward Systems (β = 0.289). The results indicate that long-term motivation in the retail industry cannot be achieved without a moderate combination of supportive human leadership and technological agility. The findings provide an action plan that HR professionals can follow to enhance output and retention rates by using specific workplace culture and digital training programs.

An environment that is characterised by a high rate of technological change, short product life cycle, and fluctuating market demand demands a high level of technology, and therefore, the conventional project management approaches may no longer be effective. This paper analyzes how agile project management strategies can be used to support cross-functional coordination and better market adaptation in high-tech supply chains. The study will determine the important agile practices that maximise the operational performance, decrease the lead time, and enhance the organisational responsiveness to the dynamic market environment. A mixed-method approach is followed, combining a survey of the project and supply chain professionals working in high-tech industries with a close case study of a few chosen companies. The quantitative data are compared by regression and structural equation modelling in order to measure the relationships between agile adoption, cross-functional integration, and supply chain performance, whereas the qualitative insights offer contextual knowledge about the challenges and enablers of the implementation. The results indicate that iterative planning, real-time collaboration, and a continuous feedback mechanism can be of great benefit in enhancing interdepartmental alignment, decision speed, and demand responsiveness. The practices of an agile firm have been shown to result in greater schedule compliance, less rework, and increased customer satisfaction than conventional ones. The paper draws the conclusion that agile project management is an important key competence for a robust and adaptive high-tech supply chain. In practice, managers are advised to support cross-functional agile teams, invest in digital collaboration tools, and integrate learning cycles into project workflows to maintain a competitive edge in turbulent markets.

Urban infrastructure, especially smart bridges, is growing rapidly, requiring effective solutions to ensure structural integrity. Conventional Structural Health Monitoring (SHM) systems have limitations in scalability, speed, and accuracy. This paper presents a new edge-enabled digital twin platform for realtime SHM of smart bridges, combining IoT, cloud computing, and edge computing. The architecture offers a high-performance, decentralized scheme of continuous monitoring to facilitate real-time detection and forecasting of structural failure by integrating the sensors on the bridges and the edge devices. The fundamental approach uses an Autoencoder-based anomaly detection, in which Autoencoders are trained to learn to recreate sensor information, and learn to behave normally by modeling the structural behavior of the bridge. In the case of real-time monitoring, the differences between the real sensor values and the reconstructed data are compared, and anomalies are noted, which are indicators of structural problems. This architecture minimizes latency by often processing data at the edge and by improving decision-making by initiating maintenance actions based on identified anomalies. The digital twin model captures the actual behavior of a bridge, providing extensive information on the current condition of the infrastructure. The suggested system is tested in terms of five major performance indicators, namely accuracy, processing time, energy consumption, scalability, and false alarm rate. Indications show that the system delivers better results than traditional SHM systems across a range of key features, including much higher anomaly detection accuracy, shorter processing time, and more efficient energy use. The system can be scaled, and additional bridges with a significantly reduced false alarm rate can be supported, therefore reducing unnecessary maintenance intervention. In general, the edge-enabled digital twin architecture can provide a promising solution to real-time SHM to enhance the safety and efficiency of smart bridges. The next research will involve integrating AI-based predictive analytics into the digital twin system to increase further the capacity of the system to indicate structural failures prior to it happening.

Precision agriculture using smart technologies has transformed sustainable crop management to solve the problem of rising food security, resource optimization, and environmental sustainability. In precision agriculture, modern hardware, including Internet of Things (IoT) sensors and objects, drones, and artificial intelligence (AI), is used to boost crop production with minimal water, fertilizer, and pesticide use. The paper discusses how such smart technologies have affected sustainable crop management systems, and in particular, how have been able to maximize the use of resources and enhance crop yields. The paper employs quantitative and qualitative research methodologies, and it evaluates the research data gathered at different agricultural locations that have implemented smart technologies. The main statistical observations are that the agricultural lands that utilized the precision method of farming increased crop production by 15 % and reduced the use of resources, especially water and fertilizers, by 20 %. The study also emphasizes the role of smart technologies in assisting farmers to make evidence-based decisions that enhance the effectiveness of agricultural activities, minimize expenses, and ensure environmental sustainability. In addition, the paper talks about the obstacles and difficulties in the implementation of smart technologies in the agricultural sector, including a high initial cost, the need to train those skills, data privacy issues, etc. The results imply that the introduction of smart technologies is associated with great benefits in terms of sustainability, but needs to be planned, invested in, and supported in order to realize the successful implementation. Finally, the paper reinforces the importance of smart technologies in promoting sustainable agriculture and provides suggestions for further research on the elimination of barriers to adoption.