In this paper, novel triple-mode wideband filters based on a compact sixteenth-mode substrate integrated waveguide (HMSIW) resonator are presented. The proposed design achieves a remarkable size reduction of a conventional substrate integrated waveguide (SIW) circular cavity—while maintaining the same resonant frequency. The proposed filters demonstrate excellent potential for improving the performance of advanced telecommunication systems.In this paper, novel triple-mode wideband filters based on a compact sixteenth-mode substrate integrated waveguide (HMSIW) resonator are presented. The proposed design achieves a remarkable size reduction of a conventional substrate integrated waveguide (SIW) circular cavity—while maintaining the same resonant frequency. The proposed filters demonstrate excellent potential for improving the performance of advanced telecommunication systems.
<strong><em>With the growing application of Geographic Information Systems (GIS) in urban management, emergency response, and public services, traditional layer-based thematic map information services</em></strong><strong><em>&mdash;</em></strong><strong><em>relying on keyword retrieval and layer superposition</em></strong><strong><em>&mdash;</em></strong><strong><em>can no longer meet users</em></strong><strong><em>&rsquo;</em></strong><strong><em>needs for semantic understanding and intelligent recommendation in cartography. To address this gap, this study proposes a knowledge graph (KG)-driven geographic information recommendation system framework. First, a geographic KG for thematic maps is constructed, focusing on geographic entities and their semantic relationships. High-quality entity relation extraction is achieved using the BERT+BiLSTM+CRF model, while graph embedding representation is implemented via random walk and word2vec to enable high-dimensional matching between user interest vectors and geographic information node vectors. On this basis, a hybrid recommendation model integrating KG semantic reasoning and graph embedding algorithms is designed, and a system prototype with recommendation, visualization, and feedback optimization capabilities is developed. Experimental results demonstrate that the system outperforms traditional methods in both recommendation accuracy(Precision@10: 0.78 vs. 0.58</em></strong><strong><em>&ndash;</em></strong><strong><em>0.67 for traditional methods) and processing speed(average response time: 250 ms vs. 370</em></strong><strong><em>&ndash;</em></strong><strong><em>410 ms for traditional methods), with strong practicality and scalability. This research achieves innovative breakthroughs in knowledge extraction, hybrid recommendation strategies, and system performance optimization, providing effective support for semantic scenario-oriented geographic information services.</em></strong><br />&nbsp;
The rapid advancements in next-generation automotive engines demand engine pistons that can withstand extreme thermal and structural loads while ensuring durability, efficiency, and reduced emissions. Existing piston designs often face challenges such as thermal fatigue, stress concentration, material degradation, and reduced lifespan under high-pressure combustion environments. To address these issues, this study proposes an advanced thermal-structural optimization framework for engine pistons by integrating Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), and metaheuristic optimization techniques. The methodology involves coupling thermo-mechanical simulations with multi-objective optimization to minimize thermal stresses enhance heat dissipation and reduce mechanical deformation, while also improving fuel efficiency and emission control. The primary objective is to achieve a piston design that provides improved durability, lightweight characteristics and superior resistance to both thermal cracking and mechanical wear. Results from the optimized model indicate a significant reduction in peak thermal stress (by 18%), lower temperature gradients across the piston crown and enhanced fatigue life compared to conventional designs. These findings highlight the potential of advanced optimization-driven piston engineering to enable more durable, sustainable, and high-performance next-generation automotive engines.
The need for precise and simple diagnostic techniques is highlighted by the fact that cardiovascular diseases (CVDs) remain one of the major risks to world health. This study proposes a hybrid deep learning-based architecture for heart disease prediction using the publically accessible Heart Disease dataset, which includes 920 patient records and 13 significant clinical characteristics. The suggested model, known as Power Boost Ensemble, uses a stacking technique to merge four distinct base learners: Random Forest, Extra Trees, Gradient Boosting, and Logistic Regression. A Ridge Classifier serves as the meta learner in this configuration, gathering predictions from each base learner. With a test accuracy of 85% using 10-fold cross validation, the stacked ensemble exhibits good generalization and consistent performance across all significant assessment criteria. Shapley Additive Explanations (SHAP) are used to understand how the meta model develops its predictions in order to improve interpretability and clarity. The SHAP results show that the model&rsquo;s conclusions are significantly influenced by important clinical parameters including ca (number of main vessels), cp (kind of chest pain), thal (thalassemia), and oldpeak (ST depression). All things considered, the Power Boost Ensemble offers a dependable, comprehensible, and reproducible approach to cardiac sickness prediction, making it appropriate for upcoming clinical applications based on artificial intelligence.
<strong><strong>The expansion of digital resources together with growing university enrollment requires efficient library information systems to manage information effectively. The current tools which operate manually and separately fail to handle the extensive work required for university cataloging and circulation and reporting operations. The research describes the development process of a web-based library information system which meets the needs of universities and higher education institutions. The system operates through a three-tier framework which utilizes LEMP stack components (Linux, Nginx, MySQL, PHP) to deliver access permissions for students and librarians and administrators. The paper explains the essential operational and performance specifications before explaining the system architecture and database structure and detailing its core features and security features and testing approach. The virtual private server implementation of the solution proves its ability to manage university operations while maintaining data security and scalability.</strong></strong><strong>&nbsp;</strong><br />&nbsp;
<strong><strong>The research describes the development process of the Electronic NAC which functions as a digital platform for scientific and scientific-pedagogical personnel attestation across the Kyrgyz Republic. The system unites a public web interface with virtual workspaces for dissertation councils and applicants and standardized databases for dissertation tracking and artifacts and BigBlueButton remote meeting functionality. The systems-engineering approach led us to identify necessary system functions and performance characteristics by studying current operational procedures. The system uses a centralized design with permission-based access management and protected audit records to store all data. The system operates through a paperless dossier pipeline which runs on NAC infrastructure. The system operates through three main components which include the information model and access-control matrix and end-to-end document processing from submission to order publication. The system demonstrates enhanced visibility and faster document processing times during standard academic operations according to our pilot test results. The paper presents operational findings from the system while describing plans to link it with e-government platforms and data analytics systems.</strong></strong><br />&nbsp;
<p><em>Abstract</em>&mdash;State-Of-The-Art Neural Networks are accurate but are hungry for compute and memory. For MCU (Microcontrollers), it usually has much less resources: 32KB RAM, 256KB Flash, no GPU. In turn, it requires Neural Network model must meet stringent requirements for energy efficiency, low latency, and robust inferencing. To address this challenge in the paper, I propose EmberNet, a micro-friendly Neural Network based on an Augmented Depthwise Separable Convolution Network[5] for compute-efficiency and much smaller parameters. I illustrate the application of the model with the public dataset[8] using denial-of-service(DoS), Fuzzy, Gear-spoofing, and spoofing-RPM attack types. With EmberNet&rsquo;s tiny 514-parameter and model size 6.4KB, I am able to achieve 99.46% accuracy and 0.0085 false-negative rate across four attack types. In comparison, EmbernetNet is 1100+ times smaller than a 7MB Inception-ResNet baseline[1], 45 times smaller than specialized RGB-CNN[2]. To make these benchmark results production-viable and reproducible, a build pipeline using TVM (Tensor Virtual Machines), Zephyr Project, and QEMU (Quick EMUlator) has been established to enforce the reliability of the model.</p>
<div style="text-align:justify"><strong>As global demand for sustainable energy continues to rise, dye-sensitized solar cells (DSSCs) have gained attention as an attractive alternative to conventional silicon photovoltaics, offering advantages such as low cost, mechanical flexibility, and reliable performance under low-light conditions. This research investigates the application of two natural photosensitisers&mdash;anthocyanin extracted from red cabbage and betalain derived from beetroot&mdash;in both individual and dyad configurations, produced using a combined mathematical and chemical model for imprinting Anthocyanin&ndash;Betalain Dyad Photovoltaic Structures. Spectroscopic characterization techniques such as UV&ndash;Vis and ATR&ndash;FTIR have proved the dyes&rsquo; absorption properties and the presence of functional groups capable of anchoring to the TiO₂ photoanode layer. While single-dye DSSCs exhibited modest power conversion efficiencies (&eta;) of about 0.02%, the dyad-based devices showed a substantial enhancement, achieving &eta; = 0.3%, representing a 15-fold improvement. This increase is linked to the complementary absorption profiles of the dyes, expanded light-harvesting capability, and improved sensitizer coverage on the photoanode surface. The results align with global sustainability priorities, particularly SDG 7 (Affordable and Clean Energy), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). Nevertheless, the overall efficiencies remain below those of synthetic sensitizers, emphasizing the need for further optimization in dye extraction, deposition methods, and co-sensitization techniques to advance device performance.</strong></div>
Device-to-device (D2D) communications face challenges of spectrum scarcity and limited power, hence necessitating energy-efficient system design that also meets target data rate requirements. To address these issues, a reinforcement learning (RL)-based Q-learning scheme is proposed within a cognitive radio (CR) framework for primary user (PU) spectrum prediction (SP). This approach enables opportunistic data transmission and radio frequency (RF) energy harvesting (EH) for sustainable transmission of devices. The RL algorithm aims to maximize energy efficiency (EE) while satisfying constraints on target data transmission rate, energy harvesting requirements, and interference thresholds permissible at the PU receiver to protect it in the event of wrong prediction. A comprehensive set of simulations is conducted to evaluate the proposed method, reporting improvements in spectrum prediction accuracy, normalized energy efficiency, and residual energy. The results demonstrate a gain of 35% in EE, a 25% reduction in data collisions, and a 35% improvement in residual energy over the reported works at reduced trained parameters.
The emergence of AI (Artificial Intelligence) in the digital era has led to a digital transformation in helping students in their learning process. AI tools Adaptive learning technology, Personalized feedback, and interactive AI tools are some of the AIs most often used by students to better understand the learning that has been done. This study aims to determine the impact of using AI as a tool in the learning process. This study will target a minimum of 100 students studying in Indonesia with campuses that have implemented AI. This study can help determine the impact on students in implementing Adaptive learning technology, Personalized feedback, and interactive AI tools that can influence the learning process of students in using AI tools. Digital literacy will help as a moderator to determine whether it is more effective or less effective if students use AI tools in the learning process. This study would use quantitative data
This study examines a deep learning model for spatial localization and identification of objects within the collaborative robot workspace through the integration of computer vision, such as in public health and crowed workplaces. A method utilizing the YOLOv8 paradigm is proposed, incorporating depth assessment for each identified object. The approach facilitates the representation of the spatial co-ordinates of objects in the format (X, Y, Z). The simulation outcomes illustrate the efficacy of neuronal identification and dynamic localization under various experimental environmental situations, and show the following results: average depth reconstruction error (MSE = 0.018&ndash;0.026 m&sup2;) and average frame processing time (&asymp; 18&ndash;22 ms), confirming real-time operation. The generated graphs evaluate the algorithm&#39;s stability and its suitability for implementation in adaptive control systems by showing the variation in object quantity over time and their spatial distribution. The proposed implementation utilizes Python within the PyCharm environment, ensuring the flexibility and scalability of the analyzed systems.
<strong>The deployment of robotics in risky, remote, and hostile environments is revolutionizing how industries approach tasks that pose significant challenges to human safety and efficiency. This paper explores the advancements in robotic systems designed to operate in extreme conditions, such as disaster zones, deep-sea exploration, mining operations, and space exploration. Emphasizing their ability to adapt to unpredictable and dynamic environments, the study highlights key innovations, including autonomous navigation, robust materials, and advanced sensor integration.&nbsp;Additionally, it examines the ethical and logistical challenges of deploying robotics in scenarios where human lives and ecosystems are at stake. The paper presents case studies on robotics deployed in hazardous scenarios, such as autonomous underwater vehicles (AUVs) for deep-sea pipeline inspection reducing maintenance costs by 35%, legged robots for disaster-struck urban environments capable of traversing 90% of collapsed structures, and robotic arms for space station maintenance increasing operational efficiency by 75%. By synthesizing insights from these case studies, we propose a roadmap for the future of robotics in hostile conditions, identifying key areas for interdisciplinary research and development. The implications of ethical, legal, and societal challenges associated with autonomous robotic deployment in sensitive zones are also examined, providing a holistic perspective on the technological trajectory of the field.</strong>
