bluuebunny/crossref_metadata_2025 huggingface dataset:
Fibrous Histiocytoma in the Far Lateral Frontal Sinus—A Rare Observation
Authors: Wilhelmer Alexander , Kiss Peter , Habenbacher Michael , Brčić Luka , Andrianakis Alexandros
Published: December 2024

This report describes a rare occurrence of benign fibrous histiocytoma in the frontal sinus of a 38-year-old male. The patient presented with acute symptoms, including sudden-onset headache, nausea, and general discomfort, although neurological, otorhinolaryngological and laboratory examinations showed no abnormalities. A cranial CT scan revealed a cystic, osteodestructive lesion measuring 2.5 cm in the far lateral right frontal sinus, initially suspected to be a mucocele due to radiological characteristics and the patient’s history of recurring frontal headaches and retrobulbar pressure. Elective surgical excision was performed via an external supraorbital approach due to the lesion’s lateral location. Histopathological examination of the excised tissue revealed characteristic features of benign fibrous histiocytoma, including spindle cell proliferation and the presence of histiocytes and siderophages. Immunohistochemistry further supported the diagnosis, showing EMA, S100, and creatinine negativity with SMA positivity. This case is unique, as it represents the first reported benign fibrous histiocytoma in the frontal sinus. During regular follow-up, the patient remained symptom-free and showed no recurrence. This report underscores the importance of considering rare diagnoses for cystic skull lesions and supports tailored surgical approaches based on lesion location.
PostCOVID-19 Impact on Perinatal Outcomes
Authors: Kurmanova Gaukhar , Ayazbekov Ardak , Kurmanova Almagul , Rakhimbayeva Madina , Trimova Gulzhan , Kulembayeva Anarkul
Published: December 2024

Background. Severe Acute Respiratory Syndrome-Related Coronavirus 2 (SARS-CoV-2) infection during pregnancy was associated with a number of adverse pregnancy outcomes, including miscarriage, preeclampsia, preterm birth, and stillbirth. The virus persistence can last for a long time, and the consequences of a previous coronavirus infection are currently under study. Objectives. This study aimed to establish the clinical features of the course of pregnancy and childbirth in women with a history of asymptomatic coronavirus disease 2019 (COVID-19). Methods. This study was conducted in the Regional Perinatal Center N3 of Turkestan city, Kazakhstan. A total of 229 participants were enrolled comprising individuals with (n = 133, exposed group) from August to September 2021 with a history of COVID-19 and without one in the same period in 2019 (n = 96, unexposed group). Results. There is a statistically significant strong association between a history of COVID-19 and the development of oligohydramnios (φ = 0.743, p < 0.001), and medium strength between a history of COVID-19 and the presence of anemia (φ = 0.254, p < 0.001), abnormal development of the placenta (φ = 0.254, p < 0.011), cord entanglement (φ = 0.343, p = 0.000), low birth weight (φ = 0.356, p < 0.001) and stillbirth (φ = 0.293, p < 0.001). Conclusions. The past COVID-19 infection in pregnant women has long-term consequences in the form of placenta abnormal development and oligohydramnios; and, as a result, the development of adverse perinatal outcomes
CDC20 and CCNB1 Overexpression as Prognostic Markers in Bladder Cancer
Authors: Sevim Nalkiran Hatice , Biri Ilknur , Nalkiran Ihsan , Uzun Hakki , Durur Sumeyye , Bedir Recep
Published: December 2024

Background: Bladder cancer (BC) is one of the ten most common cancers worldwide, with a high recurrence rate and significant variation in clinical outcomes based on tumor grade and stage. This study aimed to investigate the gene expression profiles at different cancer stages to assess their potential prognostic value. Methods: RNA was extracted from paraffin-embedded BC tissues and the gene expression levels of CDC20 and CCNB1 were analyzed using qRT-PCR. A total of 54 BC patient samples were included in the analysis and categorized into low-grade (LG) (n = 23) and high-grade (HG) (n = 31) tumors, as well as stages pTa, pT1, and pT2. Results: CDC20 gene expression was significantly higher in the HG group (mean fold-change: 16.1) compared to the LG group (mean fold-change: 10.54), indicating a significant association with tumor grade (p = 0.039). However, no significant differences were observed in CDC20 expression across the cancer stages. For CCNB1, while gene expression was significantly elevated in higher-stage tumors (pT2 vs. pTa; p = 0.038), no significant association was found between CCNB1 expression and tumor grade. Survival analysis revealed that increased CCNB1 expression and advanced cancer stage were associated with poorer overall survival, whereas no significant impact of CDC20 expression or tumor grade on survival was observed. Correlation analysis indicated a positive relationship between CDC20 expression and tumor grade (r = 0.284, p = 0.038) and between CCNB1 expression and tumor stage (r = 0.301, p = 0.027). Conclusions: Our findings suggest that CDC20 overexpression is linked to higher tumor grades, while CCNB1 overexpression is associated with more advanced cancer stages in BC. These results underscore the potential utility of CDC20 and CCNB1 as biomarkers for tumor prognosis and as therapeutic targets. Further studies with larger cohorts are needed to validate these findings and better understand the molecular mechanisms driving BC progression.
Persistent Post-COVID-19 Olfactory Dysfunction and Its Association with Autonomic Nervous System Function: A Case–Control Study
Authors: Ayoub Lojine , Almarzouki Abeer F. , Al-Raddadi Rajaa , Bendary Mohamed A.
Published: December 2024

Background: Following the Coronavirus Disease 2019 (COVID-19) pandemic, many patients have reported ongoing smell and taste issues. This study aims to investigate the prevalence of olfactory and gustatory dysfunction among patients with a history of COVID-19 and its association with autonomic dysfunction and disability. Patient and Methods: This case–control study included a COVID-19 group (n = 82) and a control group (n = 82). Olfactory dysfunction, including parosmia and taste problems, was explored using self-reports and the Quick Smell Identification Test (QSIT). The association between post-COVID-19 disability severity and taste and smell alterations was also analyzed. Moreover, autonomic function was evaluated using the Composite Autonomic Symptom Scale-31 (COMPASS-31) to assess the association between autonomic and olfactory dysfunction. Results: Significantly higher rates of ongoing smell (26.8%) and taste (14.6%) dysfunction were reported for the post-COVID-19 group compared to the control group. Post-COVID-19 patients reported 36.6 times more smell issues and 8.22 times more taste issues than controls. Parosmia scores were significantly worse in the post-COVID-19 group, while QSIT scores showed no significant difference between the groups. However, those with worse QSIT scores exhibited significantly more ongoing smell issues. No significant association was observed between disability and altered smell or taste. Higher secretomotor dysfunction scores were significantly associated with abnormal QSIT scores and worse parosmia scores; the other domains of the COMPASS-31 scale showed no significant associations. Conclusions: The findings indicated a potential link between autonomic and olfactory dysfunction. Further studies are needed to elucidate the mechanisms underlying persistent olfactory and autonomic dysfunction in post-COVID-19 patients.
Task Offloading Optimization Using PSO in Fog Computing for the Internet of Drones
Authors: Zaidi Sofiane , Attalah Mohamed Amine , Khamer Lazhar , Calafate Carlos T.
Published: December 2024

Recently, task offloading in the Internet of Drones (IoD) is considered one of the most important challenges because of the high transmission delay due to the high mobility and limited capacity of drones. This particularity makes it difficult to apply the conventional task offloading technologies, such as cloud computing and edge computing, in IoD environments. To address these limits, and to ensure a low task offloading delay, in this paper we propose PSO BS-Fog, a task offloading optimization that combines a particle swarm optimization (PSO) heuristic with fog computing technology for the IoD. The proposed solution applies the PSO for task offloading from unmanned aerial vehicles (UAVs) to fog base stations (FBSs) in order to optimize the offloading delay (transmission delay and fog computing delay) and to guarantee higher storage and processing capacity. The performance of PSO BS-Fog was evaluated through simulations conducted in the MATLAB environment and compared against PSO UAV-Fog and PSO UAV-Edge IoD technologies. Experimental results demonstrate that PSO BS-Fog reduces task offloading delay by up to 88% compared to PSO UAV-Fog and by up to 97% compared to PSO UAV-Edge.
Lattice Boltzmann Modeling of Additive Manufacturing of Functionally Graded Materials
Authors: Svyetlichnyy Dmytro
Published: December 2024

Functionally graded materials (FGMs) show continuous variations in properties and offer unique multifunctional capabilities. This study presents a simulation of the powder bed fusion (PBF) process for FGM fabrication using a combination of Unity-based deposition and lattice Boltzmann method (LBM)-based process models. The study introduces a diffusion model that allows for the simulation of material mixtures, in particular AISI 316L austenitic steel and 18Ni maraging 300 martensitic steel. The Unity-based model simulates particle deposition with controlled distribution, incorporating variations in particle size, friction coefficient, and chamber wall rotation angles. The LBM model that simulated free-surface fluid flow, heat flow, melting, and solidification during the PBF process was extended with diffusion models for mixture fraction and concentration-dependent properties. Comparison of the results obtained in simulation with the experimental data shows that they are consistent. Future research may be connected with further verification and validation of the model, by modeling different materials. The presented model can be used for the simulation, study, modeling, and optimization of the production of functionally graded materials in PBF processes.
Density Classification with Non-Unitary Quantum Cellular Automata
Authors: Wagner Elisabeth , Dell’Anna Federico , Nigmatullin Ramil , K. Brennen Gavin
Published: December 2024

The density classification (DC) task, a computation which maps global density information to local density, is studied using one-dimensional non-unitary quantum cellular automata (QCAs). Two approaches are considered: one that preserves the number density and one that performs majority voting. For number-preserving DC, two QCAs are introduced that reach the fixed-point solution in a time scaling quadratically with the system size. One of the QCAs is based on a known classical probabilistic cellular automaton which has been studied in the context of DC. The second is a new quantum model that is designed to demonstrate additional quantum features and is restricted to only two-body interactions. Both can be generated by continuous-time Lindblad dynamics. A third QCA is a hybrid rule defined by both discrete-time and continuous-time three-body interactions that is shown to solve the majority voting problem within a time that scales linearly with the system size.
Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach
Authors: Latif Saima , Aslam Faheem , Ferreira Paulo , Iqbal Sohail
Published: December 2024

Forecasting stock markets is challenging due to the influence of various internal and external factors compounded by the effects of globalization. This study introduces a data-driven approach to forecast S&P 500 returns by incorporating macroeconomic indicators including gold and oil prices, the volatility index, economic policy uncertainty, the financial stress index, geopolitical risk, and shadow short rate, with ten technical indicators. We propose three hybrid deep learning models that sequentially combine convolutional and recurrent neural networks for improved feature extraction and predictive accuracy. These models include the deep belief network with gated recurrent units, the LeNet architecture with gated recurrent units, and the LeNet architecture combined with highway networks. The results demonstrate that the proposed hybrid models achieve higher forecasting accuracy than the single deep learning models. This outcome is attributed to the complementary strengths of convolutional networks in feature extraction and recurrent networks in pattern recognition. Additionally, an analysis using the Shapley method identifies the volatility index, financial stress index, and economic policy uncertainty as the most significant predictors, underscoring the effectiveness of our data-driven approach. These findings highlight the substantial impact of contemporary uncertainty factors on stock markets, emphasizing their importance in studies analyzing market behaviour.
When Your Judgment (Mis)Matches Mine: How One’s Self and Others’ Metacognitive Judgments Impact Our Perception of Others
Authors: Jang Yoonhee , Lee Heungchul , Son Lisa K.
Published: December 2024

The current study explored whether other people’s judgments about one’s own performance affect one’s perception of them. In two experiments, participants were provided with scenarios of high (e.g., 90%) or low (e.g., 70%) estimations of exam performance from “others” and “themselves”, as compared to a fixed, benchmark performance score (e.g., 80%). Accordingly, there were four conditions varying by other- and self-estimations: high–high, high–low, low–high, and low–low. Participants were asked to choose the one, given a series of all possible pairs of two peers, who they would get along with better; and who they thought noticed/observed them better. Results showed that participants judged that they would get along with the other who overestimated their performance, suggesting that individuals exhibit a preference for others who demonstrate overconfidence in their abilities. However, they ranked the other with matching (to their own) estimations—either overconfident or underconfident—as having noticed/observed them better. These patterns were consistently found in Likert-scale responses. The results indicate that metacognitive judgments need not necessarily be matching when assessing the performance of another to form relationships, and that both the context and the relation between one’s self and other’s judgments play a large role in social perception.
Welcoming Historically Under-Represented Groups in Higher Education Through Awareness of Standard English Ideology
Authors: Hellermann John , Santelmann Lynn , Mittelstaedt Jennifer , Cowal Janet , Thorne Steven L.
Published: December 2024

In the context of changing demographics at regional universities (including our own), we highlight an ongoing project at our university that addresses the last area of acceptable bias in English-medium higher education: bias against speakers of other languages and non-standard dialects of English. We discuss the hegemonic aspect of the Standard Academic English used by default at most US institutions of higher education and its role in potential discrimination against users of languages other than English and dialects other than the Standard. Data from over 2000 surveys, 55 follow up interviews, and three focus groups from faculty, staff and students in the university community are being analyzed. Preliminary findings show pervasive ignorance of the nature of language variation and how that plays a role in continuing discrimination against those who use other languages and diverse varieties of English even in our very multilingual setting. We conclude by outlining next steps, including the development of onboarding materials for new faculty, staff, and students.
Impact of Simulation-Based and Flipped Classroom Learning on Self-Perceived Clinical Skills Compared to Traditional Training
Authors: Agostino Samuel , Cherasco Gian Maria , Papotti Grazia , Milan Alberto , Abate Daga Federico , Abate Daga Massimiliano , Veglio Franco
Published: December 2024

Introduction: Traditional medical education often emphasises theory, but clinical competence relies greatly on practical, hands-on skills. This quasi-experimental study examines how a combined approach—flipped classroom learning and simulation-based internships—affects medical students’ self-assessed clinical abilities. This model seeks to build students’ understanding, practical skills, and confidence in clinical settings through online preparation and realistic simulation exercises. Methods: This study examined changes in medical students’ self-perceived clinical skills after a flipped classroom and simulation-based internship. A total of 391 third- and fourth-year students completed a nine-hour program with morning practice sessions and afternoon high-fidelity scenarios. Surveys before and after the program assessed self-perceived thoracic and abdominal/general skills. ANCOVA controlled for baseline scores, and paired Wilcoxon signed-rank tests examined overall and subgroup improvements. Results: After the program, significant improvements were observed in self-perceived clinical skills across all domains. Thoracic skills increased from a median of 2.19 to 7.36, and abdominal skills from 5.11 to 9.46. Medical history, vital signs, and blood pressure scores also improved significantly. Third-year students and those attending the Clinical Methodology course showed the greatest gains. All post-intervention improvements were statistically significant (p < 0.001). Conclusions: The combination of flipped classroom learning and intensive simulation training markedly improved students’ perceived clinical competence. These findings suggest that such methods boost students’ practical skills and confidence. Further research is recommended to explore the long-term impact of this approach on skill retention and professional practice.
Efficient Real-Time Anomaly Detection in IoT Networks Using One-Class Autoencoder and Deep Neural Network
Authors: Ayad Aya G. , El-Gayar Mostafa M. , Hikal Noha A. , Sakr Nehal A.
Published: December 2024

In the face of growing Internet of Things (IoT) security challenges, traditional Intrusion Detection Systems (IDSs) fall short due to IoT devices’ unique characteristics and constraints. This paper presents an effective, lightweight detection model that strengthens IoT security by addressing the high dimensionality of IoT data. This model merges an asymmetric stacked autoencoder with a Deep Neural Network (DNN), applying one-class learning. It achieves a high detection rate with minimal false positives in a short time. Compared with state-of-the-art approaches based on the BoT-IoT dataset, it shows a higher detection rate of up to 96.27% in 0.27 s. Also, the model achieves an accuracy of 99.99%, precision of 99.21%, and f1 score of 97.69%. These results demonstrate the effectiveness and significance of the proposed model, confirming its potential for reliable deployment in real IoT security problems.
Cross-Domain Tibetan Named Entity Recognition via Large Language Models
Authors: Zhang Jin , Gao Fan , Yeshi Lobsang , Tashi Dorje , Wang Xiangshi , Tashi Nyima , Luosang Gadeng
Published: December 2024

With the development of large language models (LLMs), they have demonstrated powerful capabilities across many downstream tasks. Existing Tibetan named entity recognition (NER) methods often suffer from a high degree of coupling between data and models, limiting them to identifying entities only within specific domain datasets and making cross-domain recognition difficult. Additionally, each dataset requires training a dedicated model, and when faced with new domains, retraining and redeployment are necessary. In practical applications, the ability to perform cross-domain NER is crucial to meeting real-world needs. To address this issue and decouple data from models, enabling cross-domain NER, this paper proposes a cross-domain joint learning approach based on large language models, which enhances model robustness by learning the shared underlying semantics across different domains. To reduce the significant computational costs incurred by LLMs during inference, we adopt an adaptive structured pruning method based on domain-dependent prompt, which effectively reduces the model’s memory requirements and improves the inference speed while minimizing the impact on performance. The experimental results show that our method significantly outperformed the baseline model across cross-domain Tibetan datasets. In the Tibetan medicine domain, our method achieved an F1 score improvement of up to 27.26% compared with the baseline model at its best. Our method achieved an average F1 score of 95.17% across domains, outperforming the baseline Llama2 + Prompt model by 5.12%. Furthermore, our method demonstrates strong generalization capabilities in NER tasks for other low-resource languages.
Research on Indoor Multi-Scene Base Station Deployment Method Based on HDOP
Authors: Li Shuaichen , Wu Jianfeng , Liu Xiaoyan
Published: December 2024

In indoor positioning scenarios, the deployment of base stations plays a crucial role in the accuracy of positioning information. In recent years, how to reasonably deploy base stations to reduce HDOP (horizontal dilution of precision) has become a hot topic in the field of indoor positioning research. Currently, most research focuses only on HDOP in a specific indoor scenario and is often limited to simulation experiments, leaving room for further investigation. This paper conducts research on HDOP in multiple scenarios based on measured data. Firstly, the theoretical minimum value of HDOP is calculated for different numbers of base stations. Then, the relationship among the number of base stations, deployment methods, and HDOP is studied. Finally, an experimental platform is set up to analyze the relationship between HDOP and positioning errors based on measured data. The experimental results are as follows: (1) in simulated experiments in circular scenarios, the optimal deployment method entails evenly distributing the base stations around the circumference, with the deployment of an additional base station possibly reducing the average HDOP by about 10%, (2) in rectangular scenarios, the optimal deployment method entails evenly deploying the base stations along the two long sides. Measured data show that positioning errors are roughly proportional to the HDOP, but there are exceptions, and the reasons are analyzed. This research provides reference and support for the deployment of base stations in multi-scene indoor positioning systems.
Multi-Stage Hybrid Planning Method for Charging Stations Based on Graph Auto-Encoder
Authors: Wu Andrew Y. , Wu Juai , Lau Yui-yip
Published: December 2024

To improve the operational efficiency of electric vehicle (EV) charging infrastructure, this paper proposes a multi-stage hybrid planning method for charging stations (CSs) based on graph auto-encoder (GAE). First, the network topology and dynamic interaction process of the coupled “Vehicle-Station-Network” system are characterized as a graph-structured model. Second, in the first stage, a GAE-based deep neural network is used to learn the graph-structured model and identify and classify different charging station (CS) types for the network nodes of the coupled system topology. The candidate CS set is screened out, including fast-charging stations (FCSs), fast-medium-charging stations, medium-charging stations, and slow-charging stations. Then, in the second stage, the candidate CS set is re-optimized using a traditional swarm intelligence algorithm, considering the interests of multiple parties in CS construction. The optimal CS locations and charging pile configurations are determined. Finally, case studies are conducted within a practical traffic zone in Hong Kong, China. The existing CS planning methods rely on simulation topology, which makes it difficult to realize efficient collaboration of charging networks. However, the proposed scheme is based on the realistic geographical space and large-scale traffic topology. The scheme determines the station and pile configuration through multi-stage planning. With the help of an artificial intelligence (AI) algorithm, the user behavior characteristics are captured adaptively, and the distribution rule of established CSs is extracted to provide support for the planning of new CSs. The research results will help the power and transportation departments to reasonably plan charging facilities and promote the coordinated development of EV industry, energy, and transportation systems.
Applications of the FusionScratchNet Algorithm Based on Convolutional Neural Networks and Transformer Models in the Detection of Cell Phone Screen Scratches
Authors: Cao Zhihong , Liang Kun , Tang Sheng , Zhang Cheng
Published: December 2024

Screen defect detection has become a crucial research domain, propelled by the growing necessity of precise and effective quality control in mobile device production. This study presents the FusionScratchNet (FS-Net), a novel algorithm developed to overcome the challenges of noise interference and to characterize indistinct defects and subtle scratches on mobile phone screens. By integrating the transformer and convolutional neural network (CNN) architectures, FS-Net effectively captures both global and local features, thereby enhancing feature representation. The global–local feature integrator (GLFI) module effectively fuses global and local information through unique channel splitting, feature dependency characterization, and attention mechanisms, thereby enhancing target features and suppressing noise. The bridge attention (BA) module calculates an attention feature map based on the multi-layer fused features, precisely focusing on scratch characteristics and recovering details lost during downsampling. Evaluations using the PKU-Market-Phone dataset demonstrated an overall accuracy of 98.04%, an extended intersection over union (EIoU) of 88.03%, and an F1-score of 65.13%. In comparison to established methods like you only look once (YOLO) and retina network (RetinaNet), FS-Net demonstrated enhanced detection accuracy, computational efficiency, and resilience against noise. The experimental results demonstrated that the proposed method effectively enhances the accuracy of scratch segmentation.
Remaining Life Prediction Modeling Method for Rotating Components of Complex Intelligent Equipment
Authors: Deng Yaohua , Zhang Zilin , Huang Hao , Liu Xiali
Published: December 2024

This paper aims to address the challenges of significant data distribution differences and extreme data imbalances in the remaining useful life prediction modeling of rotating components of complex intelligent equipment under various working conditions. Grounded in deep learning modeling, it considers the multi-dimensional extraction method for degraded data features in the data feature extraction stage, proposes a network structure with multiple attention data extraction channels, and explores the extraction method for valuable data segments in the channel and time series dimensions. This paper also proposes a domain feature fusion network based on feature migration and examines methods that leverage abundant labeled data from the source domain to assist in target domain learning. Finally, in combination with a long short-term memory neural network (LSTM), this paper constructs an intelligent model to estimate the remaining lifespan of rotating components. Experiments demonstrate that, when integrating the foundational deep convolution network with the domain feature fusion network, the comprehensive loss error for life prediction on the target domain test set can be reduced by up to 6.63%. Furthermore, when adding the dual attention feature extraction network, the maximum reduction in the comprehensive loss error is 3.22%. This model can effectively enhance the precision of life prediction in various operating conditions; thus, it provides a certain theoretical basis and technical support for the operation and maintenance management of complex intelligent equipment. It has certain practical value and application prospects in the remaining life prediction of rotating components under multiple working conditions.
Effect of Injector Needle Tip Shape on Jet Flow for Pelton Turbine
Authors: Zhang Chun , Wang Chao , Tian Qianhe , Liang Quanwei , Zhu Yilin , Xiao Yexiang , Bai Yong , Wang Zhaoning , Zhou Hengte , Luo Xianwu
Published: December 2024

The Pelton turbine has been widely applied for the advantages of its simple structure, flexible mass flow rate, wide range of applicable heads and high efficiency. The nozzle and needle are a core part of the Pelton turbine injector. In this paper, the VOF (Volume of Fluid) model was used to simulate the jet flow behaviors and hydraulic performance for a Pelton injector with a needle tip with different breakage losses. Three types of needle tip breakage loss combined with normal needle tip were selected for numerical calculation and analysis, focusing on the influence of needle tip on the high-speed jet flow characteristics. An injector with normal needle tip hydraulic performance is compared with the model test. Finally, the injector hydraulic performance and the jet flow behavior changes with the needle tip shape were comprehensively analyzed. Results show that the needle tip shape almost does not affect the flow rate; when the tip breakage loss is larger than 0.1 of nozzle diameter, the jet efficiency will decrease rapidly and the jet will diffuse rapidly after outflow from the injector. The investigation provides a basis for the operation, maintenance and stability of the Pelton turbine.
Renewable Energy Expansion in West Pomerania: Integrating Local Potential with Global Sustainability Goals
Authors: Jaworski Jarosław , Dowejko Jakub
Published: December 2024

The expansion of renewable energy sources (RES) is essential to achieving regional sustainability in alignment with global climate goals. This study investigates the dynamics and projected growth of RES in West Pomerania, Poland, a region with significant potential due to its geographical characteristics and supportive policy frameworks. Historical data from 2010 to 2023 were used to perform a time series analysis that evaluated the annual growth rate (AGR) of various RES technologies, including wind, solar, biomass, and biogas. The analysis revealed a consistent upward trend in RES capacity, particularly in wind and solar energy, demonstrating effective resource mobilisation in the region. Subsequently, a forecasting model was employed to project the growth of the RES capacity through 2033 based on historical trends and technological advancements. The results indicate significant anticipated increases in RES capacity, highlighting West Pomerania’s potential to reduce its reliance on fossil fuels. This growth supports increased energy security and environmental sustainability. This study addresses a notable gap in the literature by linking regional renewable energy development with broader policy frameworks, such as the European Green Deal, and exploring the specific challenges of grid integration and economic disparities in the context of local energy transitions. These findings highlight the importance of sustained investment and policy support to scale renewable infrastructure while aligning regional initiatives with international sustainability goals. By bridging this gap, this study concludes that the West Pomerania strategy can serve as a model for other regions aiming to enhance their renewable energy portfolios and effectively meet the climate goals of the EU.
CFD and Statistical Analysis of the Impact of Surface Physical Parameters on the Thermal Resistance of Layered Partitions in ETICS Systems
Authors: Urzędowski Arkadiusz , Sachajdak Andrzej , Syta Arkadiusz , Zaburko Jacek
Published: December 2024

In the article, the authors attempted to analyze the impact of such materials factors as surface emissivity, surface roughness, air gap thickness, and type of concrete on heat transport in the microstructure of vertical multilayer building walls. The surface analysis conducted using three-dimensional modeling tools provided information about the formation of its microstructure before and after the application of a reflection-smoothing coating, which has a direct impact on the emissivity of the surface and was reduced from 0.93 to 0.29. Thermal analyses demonstrated that after applying the reflective coating, thermal resistance increased significantly in the air gap, by approximately 86%, which resulted in a 28% improvement of the evaluated walls samples. The studies have shown that increasing the gap thickness between concrete and thermal insulation results in a thermal resistance increase. It is feasible to enhance the thermal insulation of walls while simultaneously reducing their thickness, a development that holds significant potential for application in the production of prefabricated sandwich panels. The statistical analyzes performed showed significant differences between the analyzed configurations.
Selecting Energy-Efficient LED Retrofits with Optimal Color Qualities for New and Refurbished Buildings
Authors: Fryc Irena , Listowski Maciej , Supronowicz Robert , Martinsons Christophe
Published: December 2024

This study carried out a literature search and selection in accordance with the PRISMA 2020 protocol to conduct a complete review of the literature describing the electrical, photometrical, and colorimetric parameters of LEDs. Based on it, a significant research gap was found in the field of LED-based lighting retrofits. We prove that there is a lack of publications indicating the relationship between the energy consumption of LED retrofits and user preferences concerning the quality of lighting. This article presents an effective new method for selecting LEDs to retrofit typical 3000, 4000, and 6500 K fluorescent lighting installations in buildings. It is shown that when selecting an LED replacement for a fluorescent source, consideration of the classically used parameters in the field of lighting technology to describe the quality of white light, specifically correlated color temperature and the color rendering index, is insufficient. We demonstrate that energy efficiency and the color preference criteria for retrofitted LEDs must be considered and balanced with the potential impact on circadian rhythms as assessed by the melanopic daylight efficacy ratio. This research proves that LED replacements with the best color preference properties (P1 class in the CPC classification of ANSI/IES TM30-20 method) correspond to the least energy-efficient light sources.
Modeling Energetic Effectiveness and Breakage Intensity in Mining and Processing Circuits on Limestone Crushing in HPGR
Authors: Saramak Agnieszka , Saramak Daniel
Published: December 2024

This article refers to the aspects of energy consumption and comminution effectiveness in the mineral processing sector through the evaluation of limestone crushing in a high-pressure grinding roll. The investigative program included a series of crushing tests on limestone samples in a laboratory High Pressure Grinding Rolls (HPGR) press device. The tests were carried out in the scheme of factorial experiment with three levels of pressure (Fsp) and three levels of material moisture (M). The major finding was to determine energetic models referring to consumption of energy and reduction in Bond work index Wi, designed as a function of operational pressure in HPGR and material moisture. The other investigative results encompassed models on fineness effectiveness and throughput. The models appeared statistically significant and showed relationships both with pressure and moisture. The results of the investigations showed that Bond work index Wi decreases when the Fsp increases, but Wi increases as the moisture content decreases. The calculated models also showed an increase in unit energy consumption in the press together with increasing of Fsp and moisture. The models for throughput and finest particle content in HPGR product showed in turn that increasing of Fsp and M results in decreasing of the productivity.
Design and Implementation of Hybrid GA-PSO-Based Harmonic Mitigation Technique for Modified Packed U-Cell Inverters
Authors: Iqbal Hasan , Sarwat Arif
Published: December 2024

Multilevel inverters have gained importance in modern power systems during the last few years because of their high power quality with lower THD. Various topologies developed include the packed U-cell inverter and its different modified versions that have emerged as a compact and efficient solution to distributed energy systems. Most of the available harmonic mitigation techniques, that is, passive filtering and individual optimization techniques, which include GA and PSO, are susceptible to a variety of shortcomings regarding their inherent complexity and inefficiency; hence, finding an appropriate convergence may be quite hard. This paper proposes a hybrid version of the GA-PSO algorithm that exploits the exploratory strengths of GA and the convergence efficiencies of PSO in determining the optimized switching angles for SHM techniques applied to modified five-level and seven-level PUC inverters. By utilizing the multi-objective optimization method, the approach minimizes THD while keeping voltage and efficiency constraints. Simulated in MATLAB/Simulink, the results were experimentally verified using hardware-in-the-loop testing on OP5700. A large THD reduction in both MPUC7 (11.68%) and MPUC5 (17.61%) was obtained. The proposed hybrid algorithm outperformed the standalone approaches of GA and PSO with respect to robustness and with precise harmonic suppression. Other appealing features are reduced computational complexity and improved waveform quality; hence, the method is highly suitable for both grid-tied and standalone renewable energy applications. This work lays a basis for efficient inverter designs that can adapt well under dynamic load conditions.
Insight into the Microscopic Interactions Among Steam, Non-Condensable Gases, and Heavy Oil in Steam and Gas Push Processes: A Molecular Dynamics Simulation Study
Authors: Zhou Jiuning , Wang Xiyan , Sun Xiaofei , Fan Zifei
Published: December 2024

The SAGP (steam and gas push) process is an effective enhanced oil recovery (EOR) method for heavy oil reservoirs. Understanding the microscopic interactions among steam, non-condensable gasses (NCGs), and heavy oil under reservoir conditions in SAGP processes is important for their EOR applications. In this study, molecular simulations were performed to investigate the microscopic interactions among steam, NCG, and heavy oil under reservoir conditions in SAGP processes. In addition, the microscopic EOR mechanisms during SAGP processes and the effects of operational parameters (NCG type, NCG–steam mole ratio, temperature, and pressure) were discussed. The results show that the diffusion and dissolution of CH4 molecules and the extraction of steam molecules cause the molecules of saturates with light molecular weights in the oil globules to stretch and gradually detach from one another, resulting in the swelling of heavy oil. Compared with N2, CH4 has a stronger ability to diffuse and dissolve in heavy oil, swell the heavy oil, and reduce the density and viscosity of heavy oil. For this reason, compared with cases where N2 is used, SAGP processes perform better when CH4 is used, indicating that CH4 can be used as the injected NCG in the SAGP process to improve heavy oil recovery. As the NCG–steam mole ratio and injection pressure increase, the diffusion and solubility abilities of CH4 in heavy oil increase, enabling CH4 to perform better in swelling the heavy oil and reducing the density and viscosity of heavy oil. Hence, increasing the NCG–steam mole ratio and injection pressure is helpful in improving the performance of SAGP processes in heavy oil reservoirs. However, the NCG–steam mole ratio and injection pressure should be reasonably determined based on actual field conditions because excessively high NCG–steam mole ratios and injection pressures lead to higher operation costs. Increasing the temperature is favorable for increasing the diffusion coefficient of CH4 in heavy oil, swelling heavy oil, and reducing the oil density and viscosity. However, high temperatures can result in intensified thermal motion of CH4 molecules, reduce the interaction energy between CH4 molecules and heavy oil molecules, and increase the difference in the Hildebrand solubility parameter between heavy oil and CH4–steam mixtures, which is unfavorable for the dissolution of CH4 in heavy oil. This study can help readers deeply understand the microscopic interactions among steam, NCG, and heavy oil under reservoir conditions in SAGP processes and its results can provide valuable information for the actual application of SAGP processes in enhancing heavy oil recovery.
Review of Pre-Ignition Research in Methanol Engines
Authors: Li Zhijie , Zhai Changhui , Zeng Xiaoxiao , Shi Kui , Wu Xinbo , Ma Tianwei , Qi Yunliang
Published: December 2024

Methanol can be synthesized using green electricity and carbon dioxide, making it a green, carbon-neutral fuel with significant potential for widespread application in engines. However, due to its low ignition energy and high laminar flame speed, methanol is susceptible to hotspot-induced pre-ignition and even knocking under high-temperature, high-load engine conditions, posing challenges to engine performance and reliability. This paper systematically reviews the manifestations and mechanisms of pre-ignition and knocking in methanol engines. Pre-ignition can be sustained or sporadic. Sustained pre-ignition is caused by overheating of structural components, while sporadic pre-ignition is often linked to oil droplets entering the combustion chamber from the piston crevice. Residual exhaust gas trapped within the spark plug can also initiate pre-ignition. Knocking, characterized by pressure oscillations, arises from the auto-ignition of hotspots in the end-gas or, potentially, from deflagration-to-detonation transition, although the latter requires further experimental validation. Factors influencing pre-ignition and knocking, including engine oil, in-cylinder deposits, structural hotspots, and the reactivity of the air–fuel mixture, are also analyzed. Based on these factors, the paper concludes that the primary approach to suppressing pre-ignition and knocking in methanol engines is controlling the formation of pre-ignition sources and reducing the reactivity of the air–fuel mixture. Furthermore, it addresses existing issues and limitations in current research, such as combustion testing techniques, numerical simulation accuracy, and the mechanisms of methanol–oil interaction, and offers related recommendations.
Impact of Russia’s National ‘Ecology’ Project on Forest Loss Reversal
Authors: Pyzhev Anton I.
Published: December 2024

Restoring forest resources is becoming a challenge for many countries. Although Russia has the largest forest area in the world, it suffers from high forest losses due to fires and logging. When formulating the National Development Goals in 2018, the Russian government chose forest restoration as one of the key priorities. In this article, I examine the extent to which the National ‘Ecology’ Project has been able to reverse the trend of forest loss in Russia. Calculations, statistical comparisons, and panel data econometric modeling based on information from government statistics and satellite data from the Global Forest Watch project show that the Federal ‘Forest Restoration Project’ contributed to a more than 1.5-fold increase in annual reforestation rates in the country but did little to prevent forest loss due to natural disturbances and logging. The meager budget of the national project, which amounted to less than 10% of the federal government’s annual expenditure on forestry, was mainly sufficient to renew the fleet of specialized forestry equipment. The national forest restoration program should be continued, and its objectives should be expanded and adequately funded.
Unlocking the Value of Nature: A Deep Dive into China’s Ecological Product Realization and Its Driving Mechanisms
Authors: Sun Yong , Zhao Jianfeng , Qiao Qin , Lin Zhenyu , Zhang Wenting
Published: December 2024

Amidst severe ecological challenges and resource constraints, realizing the value of ecological products—such as timber, non-timber forest products, and ecosystem services like carbon sequestration and biodiversity conservation—has emerged as an innovative approach within the realm of paying for ecosystem services. This study examines 35 cases of ecological product value realization in China, using a comprehensive analytical framework encompassing policy support, market participation, and resource endowment utilization. Utilizing a qualitative comparative analysis method (cs-QCA), the study explores the driving mechanisms behind ecological product value realization. These findings indicate that policy guidance and planning are indispensable for realizing ecological product value. Additional factors, such as fiscal and financial support, leadership from pioneering enterprises, product innovation and promotion, and the utilization of both natural and human resources, play varying roles across different grouping paths to varying degrees. The study uncovers the following three typical driving mechanisms: government-led; government–market hybrid; and integrated type. The study reveals that each region should choose an appropriate path based on its unique conditions. This research significantly enhances the theoretical understanding of ecological product value realization and provides a scientific foundation for policy formulation and practical applications.
Study on the Response Mechanism of Soil Nitrogen Mineralization and Enzyme Activity of Typical Vegetation Types in Xinglong Mountain, Gansu Province, China
Authors: Tang Weichun , Liu Xiaoe , Su Shiping , Li Qing
Published: December 2024

Climate change is causing the soil environment to change (soil temperature and soil moisture), which affects the soil nitrogen cycling of vegetation. To examine this, the soil of three typical communities (grassland, shrub forests, and Picea wilsonii Mast. forests) was selected and cultured under three different moisture levels (20, 40, and 60% of the field water-holding capacity, referred to as W20, W40, and W60, respectively) and three different temperatures (15, 25, and 35 °C, referred to as T15, T25, and T35, respectively) to evaluate the effects of different vegetation types on soil nitrogen mineralization in the Xinglong Mountains, Gansu Province, China. The results showed that under the three moisture levels, the cumulative mineralization of soil nitrogen in different vegetation types was the largest under the T35 treatment. Under three temperature levels, the cumulative mineralization of soil nitrogen in different vegetation types was the largest under the W60 treatment. Under the three vegetation types, the cumulative mineralization of soil nitrogen in the P. wilsonii Mast. forest was the largest, which was 2.23% and 24.79% higher than those of grasslands and shrub forests, respectively. We concluded that soil nitrogen mineralization in the P. wilsonii Mast. forest was the best option under T35 and W60 in this study.