bluuebunny/crossref_metadata_2025 huggingface dataset:
Femoral Head Pathology in Subcapital Hip Fractures: Clinical Value and Cost-Effectiveness in a 230-Patient Case Series
Authors: Ohana Nissim , Marom Omer , Segal David , Behrbalk Refael , Ben-Sira Yuval , Tavdi Alex , Palmanovich Ezequiel , Yaacobi Eyal
Published: January 2025

Background/Objectives: Osteoporotic fractures, particularly subcapital hip fractures (SCF), pose a significant healthcare and economic burden. The routine pathological examination of resected femoral heads in such cases is common practice, aimed at identifying malignancies that may have contributed to bone fragility. This study evaluated the cost-effectiveness and clinical utility of routine femoral head pathology in patients undergoing surgical treatment for SCF. Methods: A retrospective cohort study was conducted at a university-affiliated, tertiary care hospital. Patients undergoing surgical treatment for SCF between 2015 and 2018, with available femoral head pathology reports, were included. Data on demographics, prior or active malignancies, surgical procedures, and pathology results were analyzed. Results: The study included 230 patients with a mean age of 82.4 ± 14.1 years, of whom 57% were female. A total of 72 (31%) patients had a history of malignancy at the time of surgery. Pathological examination identified malignancies in eight patients (3.4%), all of whom had active malignancies at the time of admission. The most common malignancies detected were breast cancer and multiple myeloma (three cases each). None of the findings led to changes in patient management. Conclusions: The routine pathological examination of femoral heads following SCF provided a limited diagnostic yield and did not alter clinical management in this cohort. These findings suggest that routine pathology may not be cost-effective and support the adoption of selective screening approaches based on clinical risk factors such as a history of malignancy or atypical fracture presentations.
Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions
Authors: Olawade David B. , Clement David-Olawade Aanuoluwapo , Adereni Temitope , Egbon Eghosasere , Teke Jennifer , Boussios Stergios
Published: January 2025

Background: Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy and radiation often result in significant side effects and varied patient outcomes. Immunotherapy has emerged as a promising alternative, harnessing the immune system to target cancer cells. However, the complexity of immune responses and tumor heterogeneity challenges its effectiveness. Objective: This mini-narrative review explores the role of artificial intelligence [AI] in enhancing the efficacy of cancer immunotherapy, predicting patient responses, and discovering novel therapeutic targets. Methods: A comprehensive review of the literature was conducted, focusing on studies published between 2010 and 2024 that examined the application of AI in cancer immunotherapy. Databases such as PubMed, Google Scholar, and Web of Science were utilized, and articles were selected based on relevance to the topic. Results: AI has significantly contributed to identifying biomarkers that predict immunotherapy efficacy by analyzing genomic, transcriptomic, and proteomic data. It also optimizes combination therapies by predicting the most effective treatment protocols. AI-driven predictive models help assess patient response to immunotherapy, guiding clinical decision-making and minimizing side effects. Additionally, AI facilitates the discovery of novel therapeutic targets, such as neoantigens, enabling the development of personalized immunotherapies. Conclusions: AI holds immense potential in transforming cancer immunotherapy. However, challenges related to data privacy, algorithm transparency, and clinical integration must be addressed. Overcoming these hurdles will likely make AI a central component of future cancer immunotherapy, offering more personalized and effective treatments.
Multi-UAV Task Assignment in Dynamic Environments: Current Trends and Future Directions
Authors: Alqefari Shahad , Menai Mohamed El Bachir
Published: January 2025

The rapid advancement of unmanned aerial vehicles (UAVs) has transformed a wide range of applications, including military operations, disaster response, agricultural monitoring, and infrastructure inspection. Deploying multiple UAVs to work collaboratively offers significant advantages in terms of enhanced coverage, redundancy, and operational efficiency. However, as UAV missions become more complex and operate in dynamic environments, the task assignment problem becomes increasingly challenging. Multi-UAV dynamic task assignment is critical for optimizing mission success. It involves allocating tasks to UAVs in real-time while adapting to unpredictable changes, such as sudden task appearances, UAV failures, and varying mission requirements. A key contribution of this article is that it provides a comprehensive study of state-of-the-art solutions for dynamic task assignment in multi-UAV systems from 2013 to 2024. It also introduces a comparative framework to evaluate algorithms based on metrics such as responsiveness, robustness, and scalability in handling real-world dynamic conditions. Our analysis reveals distinct strengths and limitations across three major approaches: market-based, intelligent optimization, and clustering-based solutions. Market-based solutions excel in distributed coordination and real-time adaptability, but face challenges with communication overhead. Intelligent optimization solutions, including evolutionary and swarm intelligence, provide high flexibility and performance in complex scenarios but require significant computational resources. Clustering-based solutions efficiently group and allocate tasks geographically, reducing overlap and improving efficiency, although they struggle with adaptability in dynamic environments. By identifying these strengths, limitations, and emerging trends, this article not only offers a detailed comparative analysis but also highlights critical research gaps. Specifically, it underscores the need for scalable algorithms that can efficiently handle larger UAV fleets, robust methods to adapt to sudden task changes and UAV failures, and multi-objective optimization frameworks to balance competing goals such as energy efficiency and task completion. These insights serve as a guide for future research and a valuable resource for developing resilient and efficient strategies for multi-UAV dynamic task assignment in complex environments.
A Crop Growth Information Collection System Based on a Solar Insecticidal Lamp
Authors: Jin Naiyun , Hu Tingting , Shu Lei , Zang Hecang , Li Kailiang , Han Ru , Yang Xing
Published: January 2025

To overcome the challenges during the crop growth process, e.g., pest infestation, inadequate environmental monitoring, and poor intelligence, this study proposes a crop growth information collection system based on a solar insecticidal lamp. The system comprises two primary modules: (1) an environmental information collection module, and (2) a multi-view image collection module. The environmental information collection module acquires crucial parameters, e.g., temperature, relative humidity, light intensity, soil conductivity, nitrogen, phosphorus, potassium content, and pH, by means of various sensors. Simultaneously, the multi-view image collection module employs three industrial cameras to capture images of the crop from the top, left, and right perspectives. The system is developed on the ESP32-S3 platform. WiFi-Mesh wireless communication technology is adopted to achieve high-frequency, real-time data transmission. Additionally, visualization software has been developed for real-time data display, data storage, and dynamic curve plotting. Field verification indicates that the proposed system effectively meets the requirements of pest control and crop growth information collection, which provides substantial support for the advancement of smart agriculture.
Study on Robust Path-Tracking Control for an Unmanned Articulated Road Roller Under Low-Adhesion Conditions
Authors: Qiang Wei , Yu Wei , Xu Quanzhi , Xie Hui
Published: January 2025

To enhance the path-tracking accuracy of unmanned articulated road roller (UARR) operating on low-adhesion, slippery surfaces, this paper proposes a hierarchical cascaded control (HCC) architecture integrated with real-time ground adhesion coefficient estimation. Addressing the complex nonlinear dynamics between the two rigid bodies of the vehicle and its interaction with the ground, an upper-layer nonlinear model predictive controller (NMPC) is designed. This layer, based on a 4-degree-of-freedom (4-DOF) dynamic model, calculates the required steering torque using position and heading errors. The lower layer employs a second-order sliding mode controller (SOSMC) to precisely track the steering torque and output the corresponding steering wheel angle. To accommodate the anisotropic and time-varying nature of slippery surfaces, a strong-tracking unscented Kalman filter (ST-UKF) observer is introduced for ground adhesion coefficient estimation. By dynamically adjusting the covariance matrix, the observer reduces reliance on historical data while increasing the weight of new data, significantly improving real-time estimation accuracy. The estimated adhesion coefficient is fed back to the upper-layer NMPC, enhancing the control system’s adaptability and robustness under slippery conditions. The HCC is validated through simulation and real-vehicle experiments and compared with LQR and PID controllers. The results demonstrate that HCC achieves the fastest response time and smallest steady-state error on both dry and slippery gravel soil surfaces. Under slippery conditions, while control performance decreases compared to dry surfaces, incorporating ground adhesion coefficient observation reduces steady-state error by 20.62%.
Optimized Design and Experimental Study of an Axis-Encircling Beam with Gently Varying Cusp Magnetic Field
Authors: Lei Chaojun , Wang E’Feng , Zhao Qixiang , Shi Shaoliang , Gao Dongshuo , Li Shufeng , Zhang Yichi , Feng Jinjun
Published: January 2025

Mode competition is a significant barrier to advancing gyrotrons towards high frequency, high power, and high efficiency. An axis-encircling beam enables gyrotrons to achieve high interaction efficiency while maintaining stable operation at higher-order harmonics. However, generating a high-quality axis-encircling beam requires an ideal cusp magnetic field, which is challenging to achieve experimentally. This paper discusses the optimization design of an axis-encircling beam with a gently varying cusp magnetic field. A non-ideal cusp magnetic field is designed using the existing magnetic field and power supply in the laboratory. Under this magnetic field, a large-orbit electronic optical system with 20 kV, 0.5 A, an axis-encircling radius of 3.3 mm at a guiding magnetic field of 0.122 T, and a velocity spread (both transverse and longitudinal) of less than 1.2% was obtained and tested.
Evaluation Method and Modeling Analysis of the Common Mode Noise Suppression Capability of Full-Bridge Transformers
Authors: Kong Yipeng , Chen Wei
Published: January 2025

The effective capacitance of the common mode port serves as a critical metric for assessing the common mode noise suppression capability of transformers in power converters. Conventionally, the evaluation of transformers in single-ended topologies, such as flyback converters, using a network analyzer necessitates a reference static point and a dynamic point at the transformer port. However, a full-bridge transformer without a center tap lacks a reference static point in both the primary and secondary stages. Consequently, this paper proposes an innovative measurement technique to evaluate the common mode noise suppression capability of full-bridge transformers. This method accounts for the intrinsic parameters of the transformer and refines the high-frequency equivalent circuit model for accurate measurement. Ultimately, the validity of the proposed model is confirmed through experiments conducted on a CLLC converter prototype, offering the industry a straightforward and efficient approach to assessing and testing the common mode noise suppression performance of transformers without a center tap.
Internet of Things Ontologies for Well-Being, Aging and Health: A Scoping Literature Review
Authors: Belani Hrvoje , Šolić Petar , Zdravevski Eftim , Trajkovik Vladimir
Published: January 2025

Internet of Things aims to simplify and automate complicated tasks by using sensors and other inputs for collecting huge amounts of data, processing them in the cloud and on the edge networks, and allowing decision making toward further interactions via actuators and other outputs. As connected IoT devices rank in billions, semantic interoperability remains one of the permanent challenges, where ontologies can provide a great contribution. The main goal of this paper is to analyze the state of research on semantic interoperability in well-being, aging, and health IoT services by using ontologies. This was achieved by analyzing the following research questions: “Which IoT ontologies have been used to implement well-being, aging and health services?” and “What is the dominant approach to achieve semantic interoperability of IoT solutions for well-being, aging and health?’ We conducted a scoping literature review of research papers from 2013 to 2024 by applying the PRISMA-ScR meta-analysis methodology with a custom-built software tool for an exhaustive search through the following digital libraries: IEEE Xplore, PubMed, MDPI, Elsevier ScienceDirect, and Springer Nature Link. By thoroughly analyzing 30 studies from an initial pool of more than 80,000 studies, we conclude that IoT ontologies for well-being, aging, and health services increasingly adopt Semantic Web of Things standards to achieve semantic interoperability by integrating heterogeneous data through unified semantic models. Emerging approaches, like semantic communication, Large Language Models Edge Intelligence, and sustainability-driven IoT analytics, can further enhance service efficiency and promote a holistic “One Well-Being, Aging, and Health” framework.
EOS: Edge-Based Operation Skip Scheme for Real-Time Object Detection Using Viola-Jones Classifier
Authors: Choi Cheol-Ho , Han Joonhwan , Oh Hyun Woo , Cha Jeongwoo , Shin Jungho
Published: January 2025

Machine learning-based object detection systems are preferred due to their cost-effectiveness compared to deep learning approaches. Among machine learning methods, the Viola-Jones classifier stands out for its reasonable accuracy and efficient resource utilization. However, as the number of classification iterations increases or the resolution of the input image increases, the detection processing speed may decrease. To address the detection speed issue related to input image resolution, an improved edge component calibration method is applied. Additionally, an edge-based operation skip scheme is proposed to overcome the detection processing speed problem caused by the number of classification iterations. Our experiments using the FDDB public dataset show that our method reduces classification iterations by 24.6157% to 84.1288% compared to conventional methods, except for our previous study. Importantly, our method maintains detection accuracy while reducing classification iterations. This result implies that our method can realize almost real-time object detection when implemented on field-programmable gate arrays.
An Evolutionary Deep Learning Framework for Accurate Remaining Capacity Prediction in Lithium-Ion Batteries
Authors: Liu Yang , Han Liangyu , Wang Yuzhu , Zhu Jinqi , Zhang Bo , Guo Jia
Published: January 2025

Accurate remaining capacity prediction (RCP) of lithium-ion batteries (LIBs) is crucial for ensuring their safety, reliability, and performance, particularly amidst the growing energy crisis and environmental concerns. However, the complex aging processes of LIBs significantly hinder accurate RCP, as traditional prediction methods struggle to effectively capture nonlinear degradation patterns and long-term dependencies. To tackle these challenges, we introduce an innovative framework that combines evolutionary learning with deep learning for RCP. This framework integrates Temporal Convolutional Networks (TCNs), Bidirectional Gated Recurrent Units (BiGRUs), and an attention mechanism to extract comprehensive time-series features and improve prediction accuracy. Additionally, we introduce a hybrid optimization algorithm that combines the Sparrow Search Algorithm (SSA) with Bayesian Optimization (BO) to enhance the performance of the model. The experimental results validate the superiority of our framework, demonstrating its capability to achieve significantly improved prediction accuracy compared to existing methods. This study provides researchers in battery management systems, electric vehicles, and renewable energy storage with a reliable tool for optimizing lithium-ion battery performance, enhancing system reliability, and addressing the challenges of the new energy industry.
Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities
Authors: Rojek Izabela , Mikołajewski Dariusz , Galas Krzysztof , Piszcz Adrianna
Published: January 2025

Advanced deep learning algorithms play a key role in optimizing energy usage in smart cities, leveraging massive datasets to increase efficiency and sustainability. These algorithms analyze real-time data from sensors and IoT devices to predict energy demand, enabling dynamic load balancing and reducing waste. Reinforcement learning models optimize power distribution by learning from historical patterns and adapting to changes in energy usage in real time. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) facilitate detailed analysis of spatial and temporal data to better predict energy usage. Generative adversarial networks (GANs) are used to simulate energy usage scenarios, supporting strategic planning and anomaly detection. Federated learning ensures privacy-preserving data sharing in distributed energy systems, promoting collaboration without compromising security. These technologies are driving the transformation towards sustainable and energy-efficient urban environments, meeting the growing demands of modern smart cities. However, there is a view that if the pace of development is maintained with large amounts of data, the computational/energy costs may exceed the benefits. The article aims to conduct a comparative analysis and assess the development potential of this group of technologies, taking into account energy efficiency.
Artificial Intelligence in Energy Economics Research: A Bibliometric Review
Authors: Jiao Zhilun , Zhang Chenrui , Li Wenwen
Published: January 2025

Artificial intelligence (AI) is gaining attention in energy economics due to its ability to process large-scale data as well as to make non-linear predictions and is providing new development opportunities and research subjects for energy economics research. The aim of this paper is to explore the trends in the application of AI in energy economics over the decade spanning 2014–2024 through a systematic literature review, bibliometrics, and network analysis. The analysis of the literature shows that the prominent research themes are energy price forecasting, AI innovations in energy systems, socio-economic impacts, energy transition, and climate change. Potential future research directions include energy supply-chain resilience and security, social acceptance and public participation, economic inequality and the technology gap, automated methods for energy policy assessment, the circular economy, and the digital economy. This innovative study contributes to a systematic understanding of AI and energy economics research from the perspective of bibliometrics and inspires researchers to think comprehensively about the research challenges and hotspots.
A Review on Performance Calculation and Design Methodologies for Horizontal-Axis Wind Turbine Blades
Authors: Zha Rongyu , Wu Siyuan , Cai Chang , Liu Xiaohui , Wang Dian , Peng Chaoyi , Feng Xuebin , Chen Qiuhua , Zhong Xiaohui , Li Qing’an
Published: January 2025

The efficient, low-cost, and large-scale development and utilization of offshore wind energy resources is an inevitable trend for future growth. With the continuous increase in the scale of wind turbines and their expansion into deep-sea locations, there is an urgent need to develop ultra-long, flexible blades suitable for future high-capacity turbines. Existing reviews in the field of blade design lack a simultaneous focus on the two core elements of blade performance calculation and design methods, as well as a detailed evaluation of specific methods. Therefore, this paper reviews the performance calculation and design methodologies of horizontal-axis wind turbine blades from three aspects: aerodynamic design, structural design, and coupled aero-structural design. A critical introduction to various methods is provided, along with a key viewpoint centered around design philosophy: there is no global optimal solution; instead, the most suitable solution is chosen from the Pareto set according to the design philosophy. This review not only provides a concise and clear overview for researchers new to the field of blade design to quickly acquire key background knowledge but also offers valuable insights for experienced researchers through critical evaluations of various methods and the presentation of core viewpoints. The paper also includes a refined review of extended areas such as aerodynamic add-ons and fatigue characteristics, which broadens the scope of the review to touch on multiple research areas and inspire further research. In future research, it is crucial to identify new key issues and challenges associated with increased blade length and flexibility, address the challenges faced in integrated aero-structural design, and develop platforms and tools that support multi-objective optimization design of blades, ensuring the safe, stable, and orderly development of wind turbines.
Research on the Formation Characteristics of Fog and Frost on Optical Windows of Unsealed Equipment Compartments in Aircrafts
Authors: Shen Chun , Liang Yuanyuan , Wei Bo , Zhang Chengchun , Zhao Tian
Published: January 2025

In this study, a numerical method for the formation and dissipation of fog and frost is established using the Eulerian multiphase flow liquid film model. In this approach, the formation and dissipation of fogging and frosting layers is directly determined by the saturation of the water vapor surface, and it does not depend on any empirical coefficients. Additionally, Buck’s formula is used to determine the saturation vapor partial pressure, which is applicable for a relatively wide temperature range (−50 °C to 10 °C). This numerical method was validated by the existing experimental data about fogging and frosting, and afterwards the fogging and frosting processes on the optical observation window in the aircraft are further analyzed for three typical working conditions, namely the ground, the fixed-altitude, and the high-altitude descent. The calculation results show that, under the ground working condition, the maximum thickness of the fog layer on the outer surface of the optical window can completely reach the millimeter level within one hour, and the average thickness of the frost layer can reach the sub-millimeter level, which is one order of magnitude smaller compared to under the ground working condition. Under the high-altitude descent working condition, by setting the fixed wall temperature boundary condition on the outer surface of the glass, it is found that in extreme cases, the maximum thickness of the frost layer on the inner wall of the glass can reach the sub-millimeter level within one hour. The research conclusions provide effective basic data support for the subsequent design of anti-fogging and defrosting devices under flight conditions.
Impact Assessment of Grid-Connected Solar Photovoltaic Systems on Power Distribution Grid: A Case Study on a Highly Loaded Feeder in Ulaanbaatar Ger District
Authors: Bat-Orgil Turmandakh , Bayarkhuu Battuvshin , Dugarjav Bayasgalan , Paek Insu
Published: January 2025

Adopting and widely implementing solar photovoltaic (PV) systems are regarded as a promising solution to address energy crises by providing a sustainable and independent electricity supply while significantly reducing greenhouse gas emissions to combat climate change. This encourages households, organizations, and enterprises to install solar PV systems. However, there are many solar PV systems that have been connected to the power distribution grid without following the required procedures. Power distribution grid operators cannot detect the locations of these solar PV systems. Thus, it is necessary to assess the impact of solar PV systems on the power distribution grid in detail, even though there are multiple economic and environmental advantages associated with installing solar PV systems. This study analyzes the changes in an overloaded power distribution grid’s power losses and voltage deviations with solar PV systems. There are two main factors considered for assessing the impact of the solar PV system on the power distribution grid: the total installed capacity of the solar PV systems and the location of the connection. Based on a comparison between the measurement results of three feeders with higher loads in the Ulaanbaatar area, the Dambadarjaa feeder, which has the highest load, was selected. The impact of the solar PV systems on the selected feeder was analyzed by connecting eight solar PV systems at four different locations. Their total installed capacities vary between 25 and 80 percent of the highest daily load of the selected feeder. The results show that the power loss of the feeder can be greatly reduced when the total installed capacity of the solar PV systems is selected optimally, and the location of the connection is at the end of the power distribution grid.
A Master–Slave Game-Based Strategy for Trading and Allocation of Virtual Power Plants in the Electricity Spot Market
Authors: Yang Na , Zhu Liuzhu , Wang Bao , Fu Rong , Qi Ling , Jiang Xin , Sun Chengyang
Published: January 2025

With the transformation of the energy structure, the integration of numerous small-scale, widely distributed renewable energy sources into the power grid has introduced operational safety challenges. To enhance the operational competitiveness, the virtual power plant (VPP) has emerged to aggregate and manage these distributed energy resources (DERs). However, current research on the VPP’s frequency modulation performance and bidding strategy remains insufficient in the joint market of electrical energy and frequency modulation (FM) ancillary services, with inadequate coordination of internally distributed resources. To fully leverage the flexibility of VPPs and incentivize their participation in electricity market operations, this paper investigates game-based bidding strategies and internal distributed resources allocation methods for VPPs in the joint market for electrical energy and frequency ancillary services. Firstly, the regulatory performance indicators of VPPs participating in the joint market and develops the corresponding market-clearing model. Secondly, to address the competition among distributed resources within VPPs, a master-slave game approach is innovatively employed to optimize the VPP’s trading strategies. This method ensures the rational allocation of electricity consumption among distributed energy resources within the VPP and derives the optimized bidding prices and quantities for both the VPP and its internal members. Finally, the case study shows that the proposed trading strategy provides effective bidding strategies for distributed energy resources participating in the joint market for energy and frequency regulation ancillary services. It enhances the regulatory performance of VPPs in the energy-frequency regulation market, ensures the profitability of distributed energy resources, and contributes to the economically stable operation of the market.
Coal Gangue Utilization: Applications, Challenges, and Sustainable Development Strategies
Authors: Sun Yinghui , Bai Bohao , Yang Xu , Zhu Shujun , Tian Jilin , Wang Zhuozhi , Xu Li , Xu Lianfei , Shen Boxiong
Published: January 2025

Coal gangue is a kind of typical by-product emitted during the coal mining and washing process. With the increase in coal resource utilization, a large amount of coal gangue was not reasonably utilized, causing environmental pollution and resource waste. The main purpose of this article is to introduce the surface structural features and compositional characteristics of coal gangue and to summarize the utilization of coal gangue in the fields of building materials, energy production, agricultural utilization, and high-value-added areas such as catalysts and adsorbents. Secondly, this review discussed the environmental challenges and technical difficulties derived from the process of coal gangue utilization and how to solve these problems through innovative methods and technological improvements. Finally, the article proposed the development direction and strategies for the future resource utilization of coal gangue, emphasizing the importance of coal gangue as a sustainable resource and its significant role in achieving a circular economy for reducing environmental pollution. By analyzing the potentiality of coal gangue for resource utilization systematically, this article aims to provide valuable references and insights for researchers and decision-makers in related fields.
Empirically Validated Method to Simulate Electric Minibus Taxi Efficiency Using Tracking Data
Authors: Abraham  Chris Joseph , Lacock  Stephan , du Plessis Armand André , Booysen Marthinus Johannes
Published: January 2025

Simulation is a cornerstone of planning and facilitating the transition towards electric mobility in sub-Saharan Africa’s informal public transport. The primary objective of this study is to validate and refine the electro-kinetic model used to simulate electric versions of the sector’s minibuses. A systematic simulation methodology is also developed to correct the simulation parameters and improve the high-frequency GPS data used with the model. A retrofitted electric minibus was used to capture high-frequency GPS mobility data and power draw from the battery. The method incorporates key refinements such as corrections for gross vehicle mass, elevation and speed smoothing, radial drag, hill-climb forces, and the calibration of propulsion and regenerative braking parameters. The refined simulation demonstrates improved alignment with measured power draw and trip energy usage, reducing error margins and enhancing model reliability. Factors such as trip characteristics and environmental conditions, including wind resistance, are identified as potential contributors to observed discrepancies. These findings highlight the importance of precise data handling and model calibration for accurate energy simulation and decision making in the transition to electric public transport. This work provides a robust framework for future studies and practical implementations, offering insights into the technical and operational challenges of electrifying informal public transport systems in resource-constrained regions.
Intelligent Control of the Air Compressor (AC) and Back Pressure Valve (BPV) to Improve PEMFC System Dynamic Response and Efficiency in High Altitude Regions
Authors: Gao Lei , Wang Xuechao
Published: January 2025

Proton exchange membrane fuel cells (PEMFCs), as a clean energy technology, show remarkable potential for a wide range of applications. However, high altitude regions pose significant challenges for PEMFC system operation due to thin air and low oxygen partial pressure. Existing logic judgement-based controls exhibit defects such as poor robustness and poor adaptability, which seriously restrict PEMFC system operation. In order to address this issue, this paper puts forth an intelligent control of a PEMFC system air compressor (AC) and back pressure valve (BPV) using an asynchronous advantage actor-critic (A3C) algorithm and systematically compares it with the logic judgement-based control. The application of an A3C-based control under three distinct high altitude test conditions demonstrated a notable enhancement in dynamic responsiveness, with an improvement of up to 40% compared to the results for the logic judgement-based control. Additionally, an improvement of 5.8% in electrical efficiency was observed. The results demonstrate that the A3C-based control displays significant robustness and control precision in response to altitude alterations.
Tree-Based Algorithms and Incremental Feature Optimization for Fault Detection and Diagnosis in Photovoltaic Systems
Authors: Chahine Khaled
Published: January 2025

Despite their significant environmental benefits, solar photovoltaic (PV) systems are susceptible to malfunctions and performance degradation. This paper addresses detecting and diagnosing faults from a dataset representing a 250 kW PV power plant with three types of faults. A comprehensive dataset analysis is conducted to improve the dataset quality and uncover intricate relationships between features and the target variable. By introducing novel feature importance averaging techniques, a two-phase fault detection and diagnosis framework employing tree-based models is proposed to identify faults from normal cases and diagnose the fault type. An ensemble of six tree-based classifiers, including decision trees, random forest, Stochastic Gradient Boosting, LightGBM, CatBoost, and Extra Trees, is trained in both phases. The results show 100% accuracy in the first phase, particularly with the Extra Trees classifier. In the second phase, Extra Trees, XGBoost, LightGBM, and CatBoost achieve similar accuracy, with Extra Trees demonstrating superior training and convergence speed. This study then incorporates Explainable Artificial Intelligence (XAI), utilizing LIME and SHAP analyzers to validate the research findings. The results highlight the superiority of the proposed approach over others, solidifying its position as an innovative and effective solution for fault detection and diagnosis in PV systems.
Environmental and Economic Life Cycle Impacts of Using Spent Mushroom Substrate as a Soil Improver
Authors: Iglesias Hasler , Ortiz Ana Paredes , Soriano Disla José M. , Lara-Guillén Andrés J.
Published: January 2025

The cultivation of white button mushrooms (Agaricus bisporus) generates significant quantities of spent mushroom substrate (SMS), a byproduct traditionally treated as waste despite its nutrient- and organic-carbon-rich composition. The EU-funded project FER-PLAY identified SMS as one of the most promising circular fertilizers (i.e., those produced from waste streams, transforming them into value-added products). Within the project, a life cycle assessment (LCA) and life cycle costing (LCC) analysis of SMS were conducted with a cradle-to-gate-to-grave scope across three European regions, comparing it to a non-renewable mix with equivalent N, P, K, and C inputs. The LCA results reveal substantial environmental benefits of SMS over the non-renewable baseline, particularly in land use, fossil resource depletion, freshwater ecotoxicity and climate change, which together account for 98% of total impacts. Although SMS exhibits higher water consumption, it represents only 2% of total impacts. LCC highlights the critical effects of fresh mushroom substrate composition on yield, economies of scale, and revenue generation. Overall, this study highlights the significant environmental and economic potential of repurposing SMS as a soil improver, offering a compelling case for its integration into agricultural systems as part of a sustainable, circular economy.
Oxygen Consumption and Carbon Budget in Groundwater-Obligate and Surface-Dwelling Diacyclops Species (Crustacea Copepoda Cyclopoida) Under Temperature Variability
Authors: Di Lorenzo Tiziana , Tabilio Di Camillo Agostina , Iepure Sanda , Galassi Diana M. P. , Mori Nataša , Simčič Tatjana
Published: January 2025

This study explores the metabolic response and carbon budget of two cyclopoid copepod species, Diacyclops belgicus Kiefer, 1936 (a stygobitic, groundwater-adapted species) and Diacyclops crassicaudis crassicaudis (Sars G.O., 1863) (a stygophilic, predominantly surface-associated species). We measured oxygen consumption rates (OCRs), carbon requirements (CRs), ingestion (I) rates, and egestion (E) rates at 14 °C and 17 °C, representing current and predicted future conditions in the collection habitats of the two species. Diacyclops belgicus displayed OCRs (28.15 and 18.32 µL O2/mg DW × h at 14 and 17 °C, respectively) and carbon budget (CR: 0.14 and 0.10 µg C/mg × d at 14 and 17 °C) lower than those of D. crassicaudis crassicaudis (OCR: 55.67 and 47.93 µL O2/mg DW × h at 14 and 17 °C; CR: 0.3 and 0.27 µg C/mg × d at 14 and 17 °C). However, D. belgicus exhibited metabolic rates and carbon requirements comparable to those of other epigean species, challenging the assumption that low metabolic rates are universal among stygobitic species. Temperature variations did not significantly affect the metabolic responses and carbon requirements of the two species, suggesting that they may cope with moderate temperature increases.
Radial Variation in Colorimetric Parameters, Chemical Composition, and Biological Resistance of Teak Wood Extracted from 13- and 22-Year-Old Teak Trees
Authors: Campos Camila Mendes , Hoffmann Waldelaine Rodrigues , Correia Francyele dos Santos , Lengowski Elaine Cristina , Silva Márcio José da , Natalino Ricardo , Oliveira Aylson Costa , Pereira Bárbara Luísa Corradi
Published: January 2025

This study aimed to evaluate the influence of age on the colorimetric parameters, chemical composition, and biological resistance of teak heartwood, transition zone, and sapwood. Samples of 13- and 22-year-old trees were collected from fast-growing commercial plantations in Mato Grosso, Brazil. From the heartwood, transition zone, and sapwood sections, we determined the CIEL*a*b* system colorimetric parameters and extractive contents and performed Py-CG/MS analysis and an accelerated degradation assay with the xylophagous fungus Trametes versicolor (L.) Lloyd. The 22-year-old wood presented greater redness and lower yellowness, and the heartwood was darker, with greater redness and lower yellowness than the other radial positions. The average content of total extractives varied between ages: 7.83% (13years) and 8.23% (22years). A total of 119 compounds were identified in teak wood, of which 51 presented areas greater than 1%. Quinones were identified in the heartwood and transition zone, with similar values between ages and approximately 7% in the heartwood. Although the durability increased significantly with age, the magnitude was slight. Wood from 22-year-old trees exhibited a lower average mass loss (10.30%) compared to wood from 13-year-old trees (12.68%). In contrast, differences between regions were more pronounced. Sapwood showed a mass loss of 22.5%, transition zone wood of 10.14%, and heartwood of 1.86%. We concluded that age influenced the colorimetric parameters, chemical composition, and biological resistance of teak wood. Teak heartwood from fast-growing plantations, both from final harvesting (22-years-old) and from thinning (13-years-old), is indicated for uses that require high biological resistance.
Assessment of Forest Route Planning Capabilities Using Various Spatial Data Sources: A Case Study of the Mazovia Region, Poland
Authors: Dawid Wojciech , Pokonieczny Krzysztof
Published: January 2025

This study examines the effectiveness of various spatial data sources and pathfinding algorithms for route determination in forested environments, focusing on the Mazovia region of Poland. Accurate and efficient forest route planning is critical for both military operations and crisis management, highlighting the need for reliable data and robust algorithms. The analysis centers on three primary spatial data sources that can support forest routing: the civilian Topographic Objects Database (TOD) and OpenStreetMap (OSM), along with the military-specific Vector Map Level 2 (VML2). Two commonly used pathfinding algorithms, Dijkstra and A* (the latter with six heuristic variations), were tested to assess their suitability and performance in these contexts. This study was conducted across ten of the largest forested areas in Mazovia, with route determinations performed between selected pairs of start and end points within each forest area. The findings indicate that the TOD database yielded the most stable and consistent routes, while the A* algorithm with Euclidean distance heuristics proved to be the fastest among the tested variants. In contrast, OSM data presented challenges due to inconsistencies, resulting in some routes being undeterminable, where connections between start and end points were lacking. These results underscore the importance of data quality and algorithm selection in effective forest route planning.
Growth, Productivity, and Nutrient Return of a Mixed Plantation of Fast-Growing Eucalyptus Hybrid and Acacia auriculiformis Trees in Thailand
Authors: Wongprom Jetsada , Jumwong Narinthorn , Sangvisitpirom Pattama , Diloksumpun Sapit , Thaopimai La-ongdao
Published: January 2025

Mixed-species plantations involving Eucalyptus and Acacia trees are an effective alternative for managing sustainable plantations. In this study, we evaluated the growth, productivity, nutrient return, and soil properties of a mixed Eucalyptus hybrid (Eucalyptus camaldulensis Dehnh. × E. urophylla S.T. Blake; E) and Acacia auriculiformis A. Cunn. ex Benth. plantation (A) and Eucalyptus hybrid and A. auriculiformis plantations. The mixed Eucalyptus hybrid and A. auriculiformis plantation included three ratios at E33:A67, E50:A50, and E67:A33, while the Eucalyptus (E100) and A. auriculiformis (A100) plantations were established on degraded lands in the Had Wanakorn Forestry Research and Student Training Station, Prachuap Khiri Khan province, Thailand. Three replications within a plot size of 20 × 20 m2 were designed to plant Eucalyptus hybrid and A. auriculiformis seedlings at a spacing of 2 × 3 m2. The diameters at breast height (DBH) and height (H) of the Eucalyptus hybrid and A. auriculiformis were measured and monitored after planting for five years. The aboveground biomass of the five-year-old mixed and monoculture plantations was then estimated. Litterfall production and nutrient return from the mixed and monoculture plantations were measured for three years. In addition, soil samples at depths of 0–5, 5–10, and 10–20 cm were collected to analyze the soil’s chemical properties. Differences in growth, aboveground biomass, litterfall production, nutrient return, and soil properties were analyzed and tested using Tukey’s HSD. The results indicated that both the DBH and H of the Eucalyptus hybrid in the mixed and monoculture plantations were not significantly different (p > 0.05). Similarly, the DBH and H of A. auriculiformis in each treatment were also not significantly different (p > 0.05). However, the DBH and H of the Eucalyptus hybrid were higher than those of A. auriculiformis. The aboveground biomass for the mixed plantation ratios E50:A50, E100, E67:A33, and E33:A67 was not significantly different, while the stem biomass was the highest in E100. Litterfall production was influenced by the proportion of the Eucalyptus hybrid relative to A. auriculiformis, but the monoculture A100 plantation had the highest litter production. The nitrogen return estimated for the mixed plantation was between A100 and E100. Similarly, the total nitrogen in the topsoil (0–5 cm) of the mixed plantation was higher than that in the monoculture E100 plantation. These results indicate that mixing A. auriculiformis with Eucalyptus can improve soil nutrients and nutrient cycling and increase nutrient returns, suggesting that mixed plantations are an effective option for sustainable plantation management and can mitigate the negative environmental impacts of Eucalyptus monocultures.
The Short-Term Impact of Logging Intensity on the Stand State of Middle-Aged Masson Pine (Pinus massoniana Lamb.) Plantations
Authors: Tu Jing , Zhao Zhongwen , Chai Zongzheng
Published: January 2025

By assessing the short-term impact that various logging intensities have on stand state in middle-aged P. massoniana plantations, this investigation aimed to establish a theoretical foundation to support the judicious management of Pinus massoniana plantations. Five distinct logging intensity categories were delineated (0%, 10%, 20%, 30%, 40%). To construct a robust stand-state evaluation framework, nine representative indicators across the three dimensions of structure, vitality, and diversity were selected. We scrutinized the short-term impacts of logging intensity by employing the unit circle method. The findings revealed that (1) four indicators—stand density, tree health, species composition, and species diversity—exhibited pronounced sensitivity to logging intensity. These four exhibited significant improvements in the short-term post-logging (p < 0.05). Conversely, the indicators of species evenness, diameter distribution, height distribution, tree dominance, and stand growth exhibited a more subdued response to logging intensity. These five necessitated an extended period to begin to improve. (2) The comprehensive evaluation values measuring the stand state of middle-aged P. massoniana plantations initially ascended but then subsequently descended as logging intensity escalated. The stand-state zenith was pinpointed at an approximate 30% logging intensity. (3) A highly significant linear correlation emerged between the unit circle method results and the principal component analysis results in evaluating stand state (R2 = 0.909, p < 0.001), and the unit circle method proved to be more intuitive and responsive. In summation, logging intensity exerted a substantial influence on stand state in middle-aged P. massoniana plantations, with moderate logging (circa 30% logging intensity) enhancing stand state the most. The unit circle method proficiently and effectively illuminated the short-term effects of logging intensity on the stand dynamics of middle-aged P. massoniana plantations, so it thereby may provide invaluable guidance for the formulation of specific forest management strategies.
Attitudes Toward Paying for Recreation in Urban Forests: A Comparison Between Warsaw and Lisbon’s Young Populations
Authors: Mandziuk Agnieszka , Fornal-Pieniak Beata , Stangierska-Mazurkiewicz Dagmara , Widera Katarzyna , Bihunova Maria , Arsenio Pedro Miguel Ramos , Janeczko Emila , Żarska Barbara , Parzych Stanisław
Published: January 2025

Urban forests are essential recreational spaces that enhance cultural heritage, foster social integration, and provide economic benefits through local tourism. Their significance for recreation has grown due to increased leisure time, better access to information, and heightened concerns about mental and physical well-being. This study seeks to evaluate social preferences for funding recreational activities in urban forests in Warsaw and Lisbon, focusing on their appearance and societal significance. Utilizing the Contingent Valuation Method (CVM), the research assessed individuals’ willingness to pay (WTP) for recreational access. Advanced techniques such as logistic regression and discriminant analysis were employed to identify the factors influencing WTP in Poland and Portugal. In Poland, WTP is primarily affected by preferences related to the appearance of forests, particularly the distinction between natural forests and those equipped with more infrastructure. In contrast, in Portugal, WTP tends to decrease with a greater appreciation for natural forests and a belief in the necessity of investing in urban forestry. The findings indicate that various social factors, such as the appearance of forests and perceived maintenance requirements, influence people’s willingness to fund urban forests. However, conducting research with only younger respondents limits the ability to generalize these findings to older populations.