Zhen-Yu Chen¹, Feng-Chi Liu², Xin Wang³, Cheng-Hsiung Lee¹, Ching-Sheng Lin^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4287-4300, 2025, DOI:10.32604/cmc.2025.061661 - 06 March 2025

Abstract In the domain of knowledge graph embedding, conventional approaches typically transform entities and relations into continuous vector spaces. However, parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations. In particular, resource-intensive embeddings often lead to increased computational costs, and may limit scalability and adaptability in practical environments, such as in low-resource settings or real-world applications. This paper explores an approach to knowledge graph representation learning that leverages small, reserved entities and relation sets for parameter-efficient embedding. We introduce a hierarchical attention network designed to refine More >

Giovanni Miracco^1,*, Francesco Nicoletti¹, Vittorio Ferraro², Dimitrios Kaliakatsos¹

Energy Engineering, Vol.122, No.3, pp. 861-883, 2025, DOI:10.32604/ee.2025.060750 - 07 March 2025

Abstract Indoor air quality (IAQ) is often overlooked, yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational settings. This study presents an innovative control system for mechanical ventilation specifically designed for university classrooms, with the dual goal of enhancing IAQ and increasing energy efficiency. Two classrooms with distinct construction characteristics were analyzed: one with exterior walls and windows, and the other completely underground. For each classroom, a model was developed using DesignBuilder software, which was calibrated with experimental data regarding CO₂ concentration, temperature, and relative… More >

Shengjie Yang^1,2, Zijian Zhu^1,*, Xu Ouyang³

Energy Engineering, Vol.122, No.3, pp. 1073-1092, 2025, DOI:10.32604/ee.2025.060293 - 07 March 2025

Abstract In the context of advancing towards dual carbon goals, numerous factories are actively engaging in energy efficiency upgrades and transformations. To accurately pinpoint energy efficiency bottlenecks within factories and prioritize renovation sequences, it is crucial to conduct comprehensive evaluations of the energy performance across various workshops. Therefore, this paper proposes an evaluation model for workshop energy efficiency based on the drive-state-response (DSR) framework combined with the fuzzy BORDA method. Firstly, an in-depth analysis of the relationships between different energy efficiency indicators was conducted. Based on the DSR model, evaluation criteria were selected from three dimensions—drive… More >

Yuze Xue¹, Li Kou^2,3, Guosheng Jia^4,*, Liwen Jin⁴, Zhibin Zhang⁴, Jianke Hao⁴, Lip Huat Saw^5,*

Energy Engineering, Vol.122, No.3, pp. 1005-1024, 2025, DOI:10.32604/ee.2025.059198 - 07 March 2025

Abstract Geothermal energy, a form of renewable energy, has been extensively utilized for building heating. However, there is a lack of detailed comparative studies on the use of shallow and medium-deep geothermal energy in building energy systems, which are essential for decision-making. Therefore, this paper presents a comparative study of the performance and economic analysis of shallow and medium-deep borehole heat exchanger heating systems. Based on the geological parameters of Xi’an, China and commonly used borehole heat exchanger structures, numerical simulation methods are employed to analyze performance and economic efficiency. The results indicate that increasing the… More > Graphic Abstract

See Wei Jing, Md Tanjil Sarker^*, Gobbi Ramasamy^*, Siva Priya Thiagarajah, Fazlul Aman

Energy Engineering, Vol.122, No.3, pp. 905-927, 2025, DOI:10.32604/ee.2025.058916 - 07 March 2025

Abstract Electrical energy can be harvested from the rotational kinetic energy of moving bodies, consisting of both mechanical and kinetic energy as a potential power source through electromagnetic induction, similar to wind energy applications. In industries, rotational bodies are commonly present in operations, yet this kinetic energy remains untapped. This research explores the energy generation characteristics of two rotational body types, disk-shaped and cylinder-shaped under specific experimental setups. The hardware setup included a direct current (DC) motor driver, power supply, DC generator, mechanical support, and load resistance, while the software setup involved automation testing tools and… More >

Xinli Zhao^1,*, Qianhua Xiao², Xuewei Liu³, Yu Shi⁴, Xiangji Dou¹, Guoqiang Xing¹

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.2, pp. 427-444, 2025, DOI:10.32604/fdmp.2024.052646 - 06 March 2025

Abstract Tight oil reservoirs face significant challenges, including rapid production decline, low recovery rates, and a lack of effective energy replenishment methods. In this study, a novel development model is proposed, based on inter-fracture injection following volumetric fracturing and relying on a high-temperature and high-pressure large-scale physical simulation system. Additionally, the CMG (Computer Modelling Group Ltd., Calgary City, Canada) software is also used to elucidate the impact of various single factors on the production of horizontal wells while filtering out the interference of others. The effects of fracture spacing, fracture half-length, and the injection-production ratio are… More >

Abdullah M. Alqahtani^1,*, Kamran Ahmad Awan², Abdulaziz Almaleh³, Osama Aletri⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3155-3179, 2025, DOI:10.32604/cmes.2025.061472 - 03 March 2025

Abstract Efficient resource management within Internet of Things (IoT) environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities. This study introduces a neural network-based model that uses Long-Short-Term Memory (LSTM) to optimize resource allocation under dynamically changing conditions. Designed to monitor the workload on individual IoT nodes, the model incorporates long-term data dependencies, enabling adaptive resource distribution in real time. The training process utilizes Min-Max normalization and grid search for hyperparameter tuning, ensuring high resource utilization and consistent performance. The simulation results demonstrate the effectiveness of the proposed method, More >

Sonia Khan¹, Naqash Younas², Musaed Alhussein³, Wahib Jamal Khan², Muhammad Shahid Anwar^4,*, Khursheed Aurangzeb³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2641-2660, 2025, DOI:10.32604/cmes.2025.060973 - 03 March 2025

Abstract Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks. However, existing methods often fail in dynamic and high-demand environments, leading to resource bottlenecks and increased energy consumption. This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management (QIARM) model, which introduces novel algorithms inspired by quantum principles for enhanced resource allocation. QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically. In addition, an energy-aware scheduling module minimizes power More >

Rajanand Patnaik Narasipuram^1,*, Md M. Pasha², Saleha Tabassum³, Amit Singh Tandon⁴

Energy Engineering, Vol.122, No.2, pp. 431-469, 2025, DOI:10.32604/ee.2025.055134 - 31 January 2025

Abstract The global adoption of Electric Vehicles (EVs) is on the rise due to their advanced features, with projections indicating they will soon dominate the private vehicle market. However, improper management of EV charging can lead to significant issues. This paper reviews the development of high-power, reliable charging solutions by examining the converter topologies used in rectifiers and converters that transfer electricity from the grid to EV batteries. It covers technical details, ongoing developments, and challenges related to these topologies and control strategies. The integration of rapid charging stations has introduced various Power Quality (PQ) issues,… More >

Miloš Sedak^*, Maja Rosić, Božidar Rosić

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 2111-2145, 2025, DOI:10.32604/cmes.2025.059319 - 27 January 2025

Abstract This paper introduces a hybrid multi-objective optimization algorithm, designated HMODESFO, which amalgamates the exploratory prowess of Differential Evolution (DE) with the rapid convergence attributes of the Sailfish Optimization (SFO) algorithm. The primary objective is to address multi-objective optimization challenges within mechanical engineering, with a specific emphasis on planetary gearbox optimization. The algorithm is equipped with the ability to dynamically select the optimal mutation operator, contingent upon an adaptive normalized population spacing parameter. The efficacy of HMODESFO has been substantiated through rigorous validation against established industry benchmarks, including a suite of Zitzler-Deb-Thiele (ZDT) and Zeb-Thiele-Laumanns-Zitzler (DTLZ) More >