Abstract: Military target detection faces multiple challenges, such as the use of camouflage, diverse target sizes, and harsh environmental conditions. Moreover, the need for solutions suitable for edge computing environments, which have limited computational resources, adds complexity to the task. To meet these challenges, we propose MSCDNet (Multi-Scale Context Detail Network), an innovative and lightweight architecture designed specifically for efficient target detection in such environments. MSCDNet integrates three key components: the Multi-Scale Fusion Module, which improves the representation of features at various target scales; the Context Merge Module, which enables adaptive feature integration across scales to handle a wide range of target conditions; and the Detail Enhance Module, which emphasizes preserving crucial edge and texture details for detecting camouflaged targets. Extensive evaluations highlight the effectiveness of MSCDNet, which achieves 40.1% mAP50-95, 86.1% precision, and 68.1% recall, while maintaining a low computational load with only 2.22M parameters and 6.0G FLOPs. When compared to other models, MSCDNet outperforms YOLO-family variants by 1.9% in mAP50-95 and uses 14% fewer parameters. Additional generalization tests on VisDrone2019 and BDD100K further validate its robustness, with improvements of 1.1% in mAP50 on VisDrone and 1.2% in mAP50-95 on BDD100K over baseline models. These results affirm that MSCDNet is well-suited for tactical deployment in scenarios with limited computational resources, where reliable target detection is paramount.

Abstract: Air assistance and electrical charge transfer to droplets can optimize pesticide applications and reduce losses in sweet pepper cultivation. The objective of this study was to evaluate the effects of spray rate and pneumatic spraying with and without an electrostatic charge on spray deposition, spray coverage, and ground losses in sweet pepper crops. Four application techniques were employed: standard farmer hydraulics (SFH), reduced volume hydraulics (RVH), pneumatic with air and electrostatic assistance (PAEA), and pneumatic with air assistance (PAA). The effects of the application techniques on spray deposition varied as a function of plant height, canopy depth, and leaf surface. The SFH resulted in the greatest amounts of spray deposition on adaxial leaf surface. In contrast, PAEA resulted in the greatest amounts of deposition on the abaxial leaves. The PAEA treatment improved spray coverage on abaxial leaves of the external canopy but did not improve spray coverage on the internal canopy. Compared to the SFH treatment, the 50% reduction in the spray rate of the RVH treatment decreased deposition and spray coverage. The pneumatic treatments, regardless of electrostatic charges, resulted in a lower spray lost to the ground.

Abstract: Tannery is the place where higher putrescible outer cover rings of animals are converted into non putrescible leathers with improved physical, chemical and biological properties. From 2017, around 155 tanneries were moved to the Savar Tannery Industrial Estate. Approximately 40 small tanneries were not able to such relocation and unfortunately, stopped their operations [Akhi Akter-2018]. There are around nine lakhs’ peoples who are involved directly or indirectly with the leather sector [The Daily Jugantor]. The workers are the heart of the industries. They play a vital role in the development of the leather industry. But most of the workers are uneducated and unconscious about the health safety. Moreover, they have little knowledge about the chemicals they use. Some chemicals are toxic, some are hazardous, some are flammable, corrosive etc. This survey shows the problems they face during the use of various chemicals in leather processing and their preventive measures. If the workers feel physical illness or weakness, they don’t have the time to recover from it. As they don’t have proper knowledge about the chemicals, they are not aware of the risk and safety precautions. This study tries to know about the risks involved in the leather industry, chemicals used in manufacturing process in the tanneries, about the hazardous chemicals, their effects on human body and their preventive measures in leather industries.

Abstract: The underrepresentation of women in Science, Technology, Engineering, and Mathematics (STEM) remains a persistent challenge, influenced by socio-cultural norms, educational barriers, workplace inequalities, and limited participation at various levels. The review examines the challenges that contribute to gender disparities in STEM and explores interventions aimed at fostering inclusion in the South African context. The study identifies key factors affecting gender representation using a structured screening and data extraction process. The findings highlight the importance of multifaceted interventions to bridge the gender gap, emphasising the promotion of role models, mentorship programs, STEM education improvements at the high school level, professional development for educators, hands-on experiential learning, and parental involvement. This review provides insights into effective strategies that policymakers, educators, and industry stakeholders can implement to create a more inclusive and equitable STEM landscape.

Abstract: This work presents a study on the calculation of transmittance in an Air Handling Unit (AHU) through three methods: theoretical estimation, experimental approach and numerical simulations. First, a theoretical estimation based on simplified models of heat and mass transfer in the AHU was employed. In addition, experimental tests were carried out in a real AHU under controlled conditions, obtaining temperature measurements inside and outside the unit. These data were used to calculate the transmittance using predefined formulas. Finally, numerical simulations were performed on specific sections of the AHU and on a global model, with and without radiation considering its influence. The simulations provided detailed results on the flow behaviour and temperature distribution. The results obtained were compared and analysed to assess the accuracy and applicability of the three methods. This research contributes to the knowledge and understanding of transmittance in AHUs, providing valuable information for the design and optimisation of Heating, Ventilation, and Air Conditioning (HVAC) systems.

Abstract: The article addresses the issue of localization of a perceived sound source for various binau-ral renderers, in the context of spatial audio format processing for headphone listening. The study analyzed the accuracy of sound source localization for ambisonic technology and two dif-ferent versions of the renderer from the Dolby Atmos format. Audio samples were played to participants in a virtual reality (VR) environment, allowing for natural pointing toward sound directions. Results indicate that the Dolby Atmos renderer provides the highest accuracy in re-producing the position of sound sources, especially for samples originating from behind the listener's head. The study also showed that listeners could better recognize sound position in the horizontal plane (left-right) than in the vertical plane (up-down). The use of VR in psychoacous-tic research proved not only effective but also engaging for participants, underscoring the poten-tial of this technology for further spatial audio research.

Abstract: Fiber optic sensors (FOSs) have developed as a transformative technology in healthcare, often offering unparalleled accuracy and sensitivity in monitoring various physiological and biochemical parameters. Their applications range from tracking vital signs to guiding minimally invasive surgeries, enabling advancements in medical diagnostics and treatment. However, the integration of FOSs into biomedical applications faces numerous challenges. This article describes some of the challenges for adopting FOSs for biomedical purposes, exploring technical and practical obstacles, and examining innovative solutions. Major challenges include biocompatibility, miniaturization and addressing signal processing complexities as well as meeting regulatory standards. Through outlining solutions to the stated challenges, it is intended that this article will therefore provide a better understanding of FOSs technology in biomedical settings and their implementation. A wider appreciation of the technology provided in this article will ultimately lead to enhancing patient care and improved medical outcomes.

Abstract: Multi-key homomorphic encryption is widely applied into outsourced computing and privacy-preserving applications in multi-user scenarios. However, the existence of CRS weakens the ability of users to independently generate public keys, and it is difficult to implement in decentralized systems or scenarios with low trust requirements. In order to reduce excessive reliance on public parameters, a multi-key homomorphic encryption scheme without pre-setting CRS is proposed based on a distributed key generation protocol. The proposed scheme does not require the pre-generation and distribution of CRS, which enhances the security and decentralization of the scheme. Furthermore, in order to further protect the plaintext privacy from each user, by embedding the specified target user into the ciphertext, this paper proposes an enhanced multi-key homomorphic encryption scheme that only allows only the target user to decrypt. Finally, this paper applies the proposed lattice-based multi-key homomorphic encryption scheme into the data submission stage of the perceived users, and thereby proposes a crowd-sensing scheme with privacy preservation.

Abstract: The engineering profession in South Africa plays a significant role in driving innovation, infrastructure development, and economic growth. However, there is high attrition rate within the engineering skills pipeline which impacts the development of a sustainable engineering workforce in the country. This study aimed to address high attrition rates using a multifaceted approach with the academic institutions and the Engineering Council of South Africa. A mixed methods research design was used and data was be collected through qualitative surveys and questionnaires. The sample included n=10 academic staff and n=263 registered engineering candidates. The qualitative data was analysed using thematic analysis, while the quantitative data was analysed using SPSS. The study identified foundational gaps in basic education, institutional barriers and language proficiency as critical contributors to attrition. The Kruskal-Wallis test revealed no statistically significant differences for financial-related items. A recurring theme in the narratives was the call for ECSA to evolve beyond its current compliance-driven role and become a proactive enabler of transformation and socio-academic support. Based on the findings, the study recommends the development of strategies for enhancing the collaboration between ECSA and higher education institutions to foster a more sustainable engineering pipeline.

Abstract: This article explores the possibilities offered by the convergence of virtual reality, the metaverse, digital twins, and the creation of virtual events, highlighting their impact on digital transformation and social interaction. The role of virtual reality as an immersive technology that enables multisensory experiences and its integration into persistent virtual environments, such as the metaverse, is analyzed. Likewise, the concept of digital twins is examined as precise virtual representations of real-world objects, processes, or systems, and their application in simulation and optimization. In the context of virtual events, it addresses how these technologies facilitate the creation of interactive, personalized, and globally accessible spaces. A digital twin of the Alcoy campus has been created, and the design for the 50th anniversary event of the merger of the Alcoy Industrial School with the Polytechnic University of Valencia has been integrated. The virtual space was tested with students and faculty staff from the campus to evaluate the effect of its applications and assess the need for improvements. The article concludes by highlighting the opportunities and challenges presented by the implementation of these innovations, as well as their potential to redefine the way people work, learn, and connect in digital environments. This particular case allows us to offer a comprehensive view of emerging technologies and their role in shaping the digital future.

Abstract: For Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) systems, code-switching—the habit of alternately speaking in several languages within a single conversation—offers special difficulties and possibilities. ASR systems have to efficiently manage language transitions as multilingual communication gets more common if we want real-time speech recognition. This work investigates innovative approaches for processing code-switched audio, solves the dearth of multilingual datasets, and assesses several technologies applied to identify and analyze mixed-language speech. Emphasizing Hindi-Marathi code-switching, we present a dynamic language-switching architecture leveraging reinforcement learning methods including Q-Learning and Deep Q-Networks (DQN) to improve language transition identification. Moreover, we present a dataset especially meant for multilingual voice recognition and evaluate ASR performance with Character Error Rate (CER) and Word Error Rate (WER). Our study reveals current constraints and provides future directions to improve ASR adaptation, therefore guaranteeing more accurate and strong recognition in many multilingual settings.

Abstract: The increasing demands in high-performance sports have led to the integration of technological solutions for training optimization. This study aimed to develop and validate an algorithm-based system for analyzing three critical phases in canoe training: initial acceleration, steady-state cruising, and final sprint. Using inertial measurement units (WIMU PRO™) sampling at 10 Hz, we collected performance data from twelve young canoeists at the Mar Menor High-Performance Sports Center. The custom-developed algorithm processed velocity-time data through polynomial fitting and phase detection methods. Results showed distinctive patterns in the acceleration phase, with initial rapid acceleration (5 seconds to stabilization) deteriorating in subsequent trials (9-10 seconds). Athletes maintained consistent stabilized speeds (14.62-14.98 km/h) but required increasing space for stabilization (13.49 to 31.70 meters), with slope values decreasing from 2.58% to 0.74% across trials. Performance deterioration was evident through decreasing maximum speeds (18.58 to 17.30 km/h) and minimum speeds (11.17 to 10.17 km/h) across series. The algorithm successfully identified phase transitions and provided real-time feedback on key performance indicators. This technological approach enables automated detection of training phases and provides quantitative metrics for technique assessment, offering coaches and athletes an objective tool for performance optimization in canoeing.

Abstract: Introduction: Academic environments encounter significant challenges in managing front-desk operations, especially in specialized fields like medicine, where budget constraints and multilingual requirements are prevalent. This paper presents Badr, an AI-enabled robot receptionist created by repurposing an existing medical simulation mannequin for use at the Institute of Learning within an academic health system. Methods: We implemented a distributed hardware architecture that utilizes dual Raspberry Pi computing units, embedded AI components such as facial recognition and natural language processing, and integrated departmental-specific knowledge management systems. The implementation process included structural adaptation of the mannequin, development of multilingual capabilities supporting English and Arabic (including the Emirati dialect), and customization of interaction protocols for the Institute's four departments: Health Professions Education, Healthcare Simulation, Organizational Learning, and Research Center. Results: Preliminary evaluations during the pilot phase indicated promising performance, with the system showcasing its capabilities in face recognition, motion detection, and speech recognition, even under varying environmental conditions. Initial tests suggest the potential for performance comparable to commercial alternatives at a significantly lower implementation cost, though a thorough long-term evaluation is still necessary. Discussion: The initial implementation showcases the potential viability of repurposing existing simulation equipment for administrative functions, creating a framework for cost-effective AI reception systems in academic settings. Although still in the pilot phase, this approach expands the utility of institutional assets beyond their original training purposes and provides a model for similar implementations at other institutions with underutilized simulation resources.

Abstract: This work presents an alternative method for defining feasible joint-space boundaries and their corresponding geometric workspace in a planar robotic system. Instead of relying on traditional numerical approaches that require extensive sampling and collision detection, the proposed method constructs a continuous boundary by identifying key intersection points of boundary functions. The feasibility region is further refined through centroid-based scaling, addressing singularity issues and ensuring a structured trajectory. Comparative analyses demonstrate that the final robot pose and reachability depend on the selected traversal path, highlighting the nonlinear nature of the workspace. Additionally, an evaluation of traditional numerical methods reveals their limitations in generating continuous boundary trajectories. The proposed approach provides a structured method for defining feasible workspaces, improving trajectory planning in robotic systems.

Abstract: The increasing global interest in clean energy sources and the decreasing costs of solar panels position solar power as an advantageous option for wider adoption. However, the rapid uptake of intermittent renewable energy presents challenges, potentially causing power instability due to f luctuations between power generation and demand. Therefore, the accuracy of solar Photovoltaic (PV) power prediction becomes crucial to ensure stable system operations and optimize the integration of renewable sources. The current methods for forecasting solar PV power play a vital role in upholding system reliability and maximizing renewable energy integration. This scholarly paper offers a comprehensive and comparative evaluation of different Machine Learning (ML) techniques employed for PV power prediction, specifically focusing on short-term forecasts. The study provides insights into the factors influencing solar PV power prediction and presents an overview of existing prediction methods in the literature, with an emphasis on models based on Machine Learning approaches like Mutliple linear Regression, Ridge Regression, Lasso Regression, Decision Tree Regression and ensemble laerning methods like Random forest Regression ,Gradient boosting Regressor,ADA boost Regressor. To facilitate a more insightful comparison and a deeper understanding of advancements in this domain, the research conducts simulations to assess the performance of various ML methods used in predicting solar PV power. The article concludes a best machine learning model with a thorough discussion of the study's findings and their implications.

Abstract: Plastic injection molding is a fundamental manufacturing process utilized in diverse areas, producing approximately 30% of global plastic products. This review examines the optimization methodologies in injection molding, emphasizing the integration of advanced modeling, surrogate models, and multi-objective optimization techniques to enhance efficiency, quality, and sustainability. Key phases such as plasticizing, filling, packing, cooling, and ejection are analyzed, each presenting unique optimization challenges. The review highlights the significance of cooling, which constitutes 50-80% of the cycle time, and explores innovative strategies like conformal cooling channels (CCC) to improve uniformity and reduce defects. Various computational tools, including Moldex3D and Autodesk Moldflow, are discussed due to their role in process simulation and optimization. Additionally, optimization algorithms such as evolutionary algorithms, simulated annealing, and multi-objective optimization methods are explored. Integration of surrogate models like Kriging, response surface methodology, and artificial neural networks has shown promise in addressing computational cost challenges. Future directions emphasize the need for adaptive machine learning and artificial intelligence techniques to optimize molds in real time, offering more innovative and sustainable manufacturing solutions. This review is a comprehensive guide for researchers and practitioners, bridging theoretical advancements with practical implementation in injection molding optimization.

Abstract: The escalating global imperative for sustainable waste management has prompted innovative research into valorizing organic waste streams in construction materials. This study investigates coffee bagasse (BC) as a potential fine aggregate replacement in concrete, addressing both environmental challenges and material performance optimization. Through systematic analysis, the effect of coffee bagasse incorporation as fine aggregate in the compressive resistance of concrete was studied. Coffee bagasse was processed in raw and pyrolyzed states and incorporated into concrete mixtures at 5%, 10%, and 20% volume replacement levels. Differential scanning calorimetry (DSC) and comprehensive compressive strength assessments were carried out to analyze the performance and behavior of the material. The findings revealed critical insights: raw coffee bagasse lixiviation significantly impaired cement hydration, negatively affecting concrete mechanical properties, whereas pyrolysis at 349.5°C transformed BC into coffee biocarbon, yielding a remarkable 22.39% increase in concrete compressive strength. The thermal treatment emerged as a pivotal intervention for effective waste material integration, demonstrating a promising pathway for converting agricultural by-products into high-value construction materials. By bridging waste management strategies with material science innovation, this research contributes to circular economy principles, offering a sustainable approach to reducing waste and enhancing infrastructure material performance through advanced thermal processing techniques.

Abstract: Discrete Event System Specification (DEVS) is a formalism widely used for modeling and simulating complex systems. The main features of DEVS are defining models in a strict mathematical form and representing systems through hierarchical structures. However, when DEVS models have incorrect connection structures and inappropriate behaviors contrary to design intentions, simulation results can be distorted. This can cause serious problems that may lead to inaccurate decision-making. In this paper, we propose an automated verification framework to improve the accuracy and efficiency of coupled models in the DEVS-Python environment. This framework defines test scripts for coupled models, performs automatic verification before simulation execution, and provides the results to users. Experimental results showed that the proposed framework improved execution time by approximately 30-100 times compared to traditional unit testing methods, although memory and CPU usage increased slightly. Despite this increase in resource usage, the proposed framework provides high efficiency and consistent performance in verifying complex DEVS coupled models.

Abstract: In this study, a Möbius sensor is introduced as an instrument for detecting variations in the gravitational field by exploiting its structural characteristics. The gravitational field is modeled as a hypercone in six-dimensional spacetime, comprising stratified layers with distinct geometric density variations. Despite extensive experimental efforts, a direct interaction between electromagnetic forces and gravity has not been observed. Here, Möbius coils serve as detectors that capture distortions arising from fluctuations in gravitational density and convert these into measurable electromagnetic pulses. The results indicate potential for developing gravity radars, warp drive systems, and earthquake prediction devices.

Abstract: Smart textiles are also referred to as electronic textiles or e-textiles, which have emerged as any enabling technology dealing with active electronic elements integrated into conventional textile materials in several industries. This review article describes recent development about smart textiles toward their materials, applications, and challenges with respect to their development. Further synthesis of the findings from different research papers has established that smart textiles have huge potentials with regard to use cases in healthcare, sports, fashion, and environmental monitoring. It concludes by pointing out the likely future directionofresearchand development in this ever-changing field.