bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Impact of Urban Morphology on Building Energy Consumption and Outdoor Comfort: A Comparative Study in Hot-Humid Climates
Authors: Zhu Shuyan , Ma Chenlong , Wu Zhongping , Huang Yuqing , Liu Xiao
Published: May 2024

Research simultaneously examining building energy consumption and outdoor thermal comfort within urban environments remains limited. Few studies have delved into the sensitivity of design parameters based on building energy consumption and outdoor thermal comfort. The purpose of this study is to investigate the correlations between urban morphological design parameters and performance indicators, focusing on building energy consumption and outdoor thermal comfort (UTCI), across different urban block layouts in hot-humid regions, like Guangzhou. By establishing six fundamental morphological models—three individual unit layouts and three group layouts—the research explores both control and descriptive parameters through extensive simulation studies. Scatter plot visualizations provide insights into the impacts of various design parameters on energy consumption and UTCI, facilitating a comprehensive analysis of trends and quantitative relationships. Additionally, the study conducts sensitivity analyses on design parameters under different layout conditions to highlight their influences on target performance indicators. The findings reveal common trends, such as the significant impacts of plan dimensions and the Floor Area Ratio (FAR) on energy efficiency and outdoor comfort, as well as differential patterns, such as the varying sensitivities of the Shape Factor (S/V) and the Sky View Factor (SVF), across individual and collective layouts. Ultimately, this study offers a nuanced understanding of urban block morphology’s role in creating sustainable, comfortable, and energy-efficient urban environments, providing valuable guidelines for urban form design in hot-humid climates.
A Non-Intrusive Method for Lonely Death Prevention Using Occupancy Detection and an Anomaly Detection Model
Authors: Noh Seol-Hyun , Moon Hyeun Jun
Published: May 2024

In countries like Japan, Australia, France, Denmark, and South Korea, the numbers of single-person households and older adults living alone have been steadily increasing each year, leading to the social issue of lonely deaths among older adults. Against this backdrop, this study proposes a method to develop a system for preventing lonely deaths based on information technology, including the Internet of Things (IoT). IoT sensor data, which include nine environmental variables such as indoor temperature, relative humidity, CO2 concentration, fine dust particle levels, illuminance, total volatile organic compound levels, and occupancy data collected from passive infrared sensors, provide empirical evidence so that anomalies can be detected in the behavior patterns of older adults when they remain in one place for an unusually long time. Detecting such risky situations for older adults living alone involves anomaly detection through occupancy monitoring. The data from occupancy monitoring were analyzed using four classification models, namely Logistic Regression, k-Nearest Neighbor, Decision Tree, and Random Forest, with the performance of occupancy detection being compared across these models. Furthermore, the method proposed in this study includes data processing for environmental variables to improve the performance of occupancy detection.
Relapse Prevention in Acute Myeloid Leukemia: The Role of Immunotherapy with Histamine Dihydrochloride and Low-Dose Interleukin-2
Authors: Montesinos Pau , Buccisano Francesco , Cluzeau Thomas , Vennström Lovisa , Heuser Michael
Published: May 2024

The treatment and management of acute myeloid leukemia (AML) has improved in recent decennia by targeted therapy for subgroups of patients, expanded indications for allogeneic stem cell transplantation (allo-SCT) and surveillance of residual or arising leukemia. However, hematological relapse among patients who have attained complete remission (CR) after the initial courses of chemotherapy remains a significant cause of morbidity and mortality. Here, we review an immunotherapeutic option using histamine dihydrochloride and low-dose interleukin-2 (HDC/LD-IL-2) for remission maintenance in AML. The treatment is approved in Europe in the post-consolidation phase to avoid relapse among patients in CR who are not candidates for upfront allo-SCT. We present aspects of the purported anti-leukemic mechanism of this regimen, including translation of preclinical results into the clinical setting, along with relapse prevention in subgroups of patients. We consider that HDC/LD-IL-2 is a conceivable option for younger adults, in particular patients with AML of normal karyotype and those with favorable responses to the initial chemotherapy. HDC/LD-IL-2 may form an emerging landscape of remission maintenance in AML.
Advances in Hodgkin Lymphoma Treatment: From Molecular Biology to Clinical Practice
Authors: Benevolo Savelli Corrado , Bisio Matteo , Legato Luca , Fasano Filippo , Santambrogio Elisa , Nicolosi Maura , Morra Deborah , Boccomini Carola , Freilone Roberto , Botto Barbara , Novo Mattia
Published: May 2024

Classical Hodgkin Lymphoma (cHL) is a highly curable disease, but around 20% of patients experience progression or relapse after standard frontline chemotherapy regimens. Salvage regimens followed by autologous stem cell transplants represent the historical treatment approach for these cases. In the last decade, with the increasing understanding of cHL biology and tumor microenvironment role in disease course, novel molecules have been introduced in clinical practice, improving outcomes in the relapsed/refractory setting. The anti-CD30 antibody-drug conjugated brentuximab vedotin and PD-1/PD-L1 checkpoint inhibitors represent nowadays curative options for chemorefractory patients, and randomized trials recently demonstrated their efficacy in frontline immune-chemo-combined modalities. Several drugs able to modulate the patients’ T-lymphocytes and NK cell activity are under development, as well as many anti-CD30 chimeric antigen receptor T-cell products. Multiple tumor aberrant epigenetic mechanisms are being investigated as targets for antineoplastic compounds such as histone deacetylase inhibitors and hypomethylating agents. Moreover, JAK2 inhibition combined with anti-PD1 blockade revealed a potential complementary therapeutic pathway in cHL. In this review, we will summarize recent findings on cHL biology and novel treatment options clinically available, as well as promising future perspectives in the field.
The Use of Cyclin-Dependent Kinase 4/6 Inhibitors in Elderly Breast Cancer Patients: What Do We Know?
Authors: Giraudo Alexandre , Sabatier Renaud , Rousseau Frederique , De Nonneville Alexandre , Gonçalves Anthony , Cecile Maud , Braticevic Cecile , Viret Frederic , Seguin Lorene , Kfoury Maria , Naudet Dorothée , Hamon Marie , Tassy Louis
Published: May 2024

Background: Breast cancer (BC) incidence increases with age, particularly in HR-positive/HER2-negative subtypes. Cyclin-dependent kinase 4 and 6 inhibitors (CDK 4/6is) alongside endocrine therapy (ET) have emerged as promising treatments for HR-positive/HER2-negative advanced and early BC. However, their efficacy, safety, and impact on quality of life (QoL) in older and frail patients remain underexplored. Methods: This position paper assesses the existing literature from 2015 to 2024, focusing on CDK4/6is use in patients aged 65 years and older with HR-positive/HER2-negative BC. Results: Our analysis methodically addresses critical questions regarding the utilization of CDK4/6is in the elderly BC patient population, organizing findings from the metastatic and adjuvant settings. In the metastatic setting, CDK4/6is significantly improve progression-free survival (PFS), paralleling benefits observed in younger patients, and suggest potential overall survival (OS) benefits, warranting further investigation. Despite an increased incidence of grade ≥ 3 adverse events (AEs), such as neutropenia and asthenia, CDK4/6is present a markedly lower toxicity profile compared to traditional chemotherapy, with manageable side effects. QoL analysis indicates that integrating CDK4/6is into treatment regimens does not significantly impact elderly BC patients’ daily life and symptom management. Special attention is given to frail subgroups, and personalized approaches are recommended to balance efficacy and adverse effects, such as starting with ET alone and introducing CDK4/6is upon progression in patients with a low disease burden. Transitioning to the adjuvant setting, early results, particularly with abemaciclib, indicate positive effects on disease-free survival (DFS), emphasizing the need for continued analysis to validate these findings and assess long-term implications. However, data on older patients are insufficient to conclude whether they truly benefit from this treatment. Conclusion: Overall, CDK4/6is present a favorable benefit-risk profile in older BC patients, at least in advanced BC; however, further research is warranted to optimize treatment strategies and improve outcomes in this population
Longitudinal Evaluation of DCE-MRI as an Early Indicator of Progression after Standard Therapy in Glioblastoma
Authors: Arevalo-Perez Julio , Trang Andy , Yllera-Contreras Elena , Yildirim Onur , Saha Atin , Young Robert , Lyo John , Peck Kyung K. , Holodny Andrei I.
Published: May 2024

Background and Purpose: Distinguishing treatment-induced imaging changes from progressive disease has important implications for avoiding inappropriate discontinuation of a treatment. Our goal in this study is to evaluate the utility of dynamic contrast-enhanced (DCE) perfusion MRI as a biomarker for the early detection of progression. We hypothesize that DCE-MRI may have the potential as an early predictor for the progression of disease in GBM patients when compared to the current standard of conventional MRI. Methods: We identified 26 patients from 2011 to 2023 with newly diagnosed primary glioblastoma by histopathology and gross or subtotal resection of the tumor. Then, we classified them into two groups: patients with progression of disease (POD) confirmed by pathology or change in chemotherapy and patients with stable disease without evidence of progression or need for therapy change. Finally, at least three DCE-MRI scans were performed prior to POD for the progression cohort, and three consecutive DCE-MRI scans were performed for those with stable disease. The volume of interest (VOI) was delineated by a neuroradiologist to measure the maximum values for Ktrans and plasma volume (Vp). A Friedman test was conducted to evaluate the statistical significance of the parameter changes between scans. Results: The mean interval between subsequent scans was 57.94 days, with POD-1 representing the first scan prior to POD and POD-3 representing the third scan. The normalized maximum Vp values for POD-3, POD-2, and POD-1 are 1.40, 1.86, and 3.24, respectively (FS = 18.00, p = 0.0001). It demonstrates that Vp max values are progressively increasing in the three scans prior to POD when measured by routine MRI scans. The normalized maximum Ktrans values for POD-1, POD-2, and POD-3 are 0.51, 0.09, and 0.51, respectively (FS = 1.13, p < 0.57). Conclusions: Our analysis of the longitudinal scans leading up to POD significantly correlated with increasing plasma volume (Vp). A longitudinal study for tumor perfusion change demonstrated that DCE perfusion could be utilized as an early predictor of tumor progression.
Gestational Diabetes Mellitus and Its Correlation in the Development of Pancreatic Cancer: A 10-Year Systematic Review
Authors: Tsokkou Sophia , Konstantinidis Ioannis , Georgaki Maria-Nefeli , Kavvadas Dimitrios , Papadopoulou Kyriaki , Keramas Antonios , Sioga Antonia , Papamitsou Theodora , Karachrysafi Sofia
Published: May 2024

Purpose: Pancreatic cancer (PC) is a fatal malignancy with an aggressive course derived from the cells of pancreatic tissue. Gestational diabetes mellitus (GDM) is a state of spontaneous hyperglycemia occurring during gestation and has been suggested as a risk factor PC. Women with a history of GDM revealed a risk rate of 7.1% for the development of PC. The current systematic review aims to investigate the correlation between GDM and the degree to the prevalence of PC. Methodology: For this systematic review, the PICO model was prepared to construct and outline the exact questions of the study, a PRISMA flow diagram was prepared and quality assessment was conducted using the Newcastle Ottawa Scale (NOS) for Cohort Studies, the NIH Quality Assessment Tool-Criteria for Case Reports and the Cochrane quality assessment tool for Systematic Reviews and Meta-analysis studies. Result: A total of eight articles were retrieved from the main databases, and a table was created to summarize the information found. Even though the data found were limited, the quality assessment performed revealed that the articles were of high validity. Conclusions: It can be concluded that GDM has an association with the development of PC and can be considered as a risk factor.
Genome-Wide Methylation Analysis in Two Wild-Type Non-Small Cell Lung Cancer Subgroups with Negative and High PD-L1 Expression
Authors: Hutarew Georg , Alinger-Scharinger Beate , Sotlar Karl , Kraus Theo F. J.
Published: May 2024

We conducted a pilot study to analyze the differential methylation status of 20 primary acinar adenocarcinomas of the lungs. These adenocarcinomas had to be wild type in mutation analysis and had either high (TPS > 50%; n = 10) or negative (TPS < 1%; n = 10) PD-L1 status to be integrated into our study. To examine the methylation of 866,895 specific sites, we utilized the Illumina Infinium EPIC bead chip array. Both hypermethylation and hypomethylation play significant roles in tumor development, progression, and metastasis. They also impact the formation of the tumor microenvironment, which plays a decisive role in tumor differentiation, epigenetics, dissemination, and immune evasion. The gained methylation patterns were correlated with PD-L1 expression. Our analysis has identified distinct methylation patterns in lung adenocarcinomas with high and negative PD-L1 expression. After analyzing the correlation between the methylation results of genes and promoters with their pathobiology, we found that tumors with high expression of PD-L1 tend to exhibit oncogenic effects through hypermethylation. On the other hand, tumors with negative PD-L1 expression show loss of their suppressor functions through hypomethylation. The suppressor functions of hypermethylated genes and promoters are ineffective compared to simultaneously activated dominant oncogenic mechanisms. The tumor microenvironment supports tumor growth in both groups.
Current Preoperative Management of Vulvar Squamous Cell Carcinoma: An Overview
Authors: Della Corte Luigi , Cafasso Valeria , Guarino Maria Chiara , Gullo Giuseppe , Cucinella Gaspare , Lopez Alessandra , Zaami Simona , Riemma Gaetano , Giampaolino Pierluigi , Bifulco Giuseppe
Published: May 2024

Vulvar carcinoma is a rare cancer affecting the genital tract, constituting 4% of gynecological tumors. Vulvar squamous cell carcinoma (VSCC) is the most common type. Diagnosis relies on biopsy during vulvoscopy, plus imaging such as ultrasonography (USG), magnetic resonance imaging (MRI) and positron emission tomography (PET). This review aims to lay out a thorough overview as to the current preoperative management of VSCC, both in case of vulvar and lymph node involvement. The data research was conducted using the following databases: MEDLINE, EMBASE, Web of Sciences, Scopus, ClinicalTrial.gov, OVID and Cochrane Library from 2010 to 2024. The selection criteria included only original articles. Seventeen studies were assessed for eligibility. A concordance rate of 62.3% for vHSIL and 65.2% for carcinoma at vulvoscopy, with a sensitivity of 98%, specificity of 40%, PPV (Positive Predictive Value) of 37% and NPV (Negative Predictive Value) of 98% in identifying malignant lesions was found. Regarding the reliability of PET for staging and assessing lymph node involvement, a mean SUV (Standardized Uptake Value) for malignant vulvar lesions of 8.4 (range 2.5–14.7) was reported. In the case of MRI, useful for the evaluation of loco-regional infiltration and lymph node involvement, the ratio of the short-to-long-axis diameter and the reader’s diagnostic confidence for the presence of lymph node metastasis yielded accuracy of 84.8% and 86.9%, sensitivity of 86.7% and 87.5%, specificity of 81.3% and 86.2%, PPV of 89.7% and 87.5% and NPV of 76.5% and 86.2%, respectively. A long lymph node axis >10 mm and a short diameter >5.8 mm were found to be predictors of malignancy. At USG, instead, the two main characteristics of potentially malignant lymph nodes are cortical thickness and short axis length; the combination of these ultrasound parameters yielded the highest accuracy in distinguishing between negative and positive lymph nodes. Despite the heterogeneity of the included studies and the lack of randomized clinical trials, this review provides a broad overview of the three imaging tools used for the presurgical management of VSCC. Nowadays, although MRI and PET represent the gold standard, ultrasound evaluation is taking on a growing role, as long as it is carried out by expert sonographer. The management of this rare disease should be always performed by a multidisciplinary team in order to precisely stage the tumor and determine the most suitable treatment approach.
Targeting Anterior Commissure Involvement with Hyperfractionated Radiotherapy for T1–T2 Squamous Cell Carcinoma of the Glottic Larynx
Authors: Seno Satoshi , Iwashita Kazuma , Kajiwara Akifumi , Sasaki Rie , Furukawa Tatsuya , Teshima Masanori , Shinomiya Hirotaka , Kiyota Naomi , Lynch Rod , Yoshida Kenji , Ishihara Takeaki , Miyawaki Daisuke , Nibu Ken-ichi , Sasaki Ryohei
Published: May 2024

Anterior commissure is involved in about 20% of early-stage glottic squamous cell carcinomas (EGSCCs). Treatment outcomes and prognostic factors for EGSCC with anterior commissure involvement (ACI) were evaluated by focusing on hyperfractionated radiotherapy (74.4 Gy in 62 fractions). One-hundred and fifty-three patients with T1–T2 EGSCC were included in this study. The median total doses for T1a, T1b, and T2 were 66, 74.4, and 74.4 Gy, respectively. Overall, 49 (32%) patients had T1a, 38 (25%) had T1b, and 66 (43%) had T2 disease. The median treatment duration was 46 days. The median follow-up duration was 5.1 years. The 10-year overall and cause-specific survival rates were 72% and 97%, respectively. The 10-year local control rates were 94% for T1a, 88% for T1b, and 81% for T2 disease. Local control rates in patients with ACI were slightly better than those in patients without ACI with T1a and T1b diseases; however, the difference was not significant. The 10-year laryngeal preservation rate was 96%. Six patients experienced grade 3 mucositis, and four patients had grade 3 dermatitis. Hyperfractionated radiotherapy was effective for T1 disease with ACI, but insufficient for T2 disease with ACI. Our treatment strategy resulted in excellent laryngeal preservation.
What Is a Sarcoma ‘Specialist Center’? Multidisciplinary Research Finds an Answer
Authors: Wilson Roger , Reinke Denise , van Oortmerssen Gerard , Gonzato Ornella , Ott Gabriele , Raut Chandrajit P. , Guadagnolo B. Ashleigh , Haas Rick L. M. , Trent Jonathan , Jones Robin , Pretorius Lauren , Felser Brandi , Basson Mandy , Schuster Kathrin , Kasper Bernd
Published: May 2024

The management of sarcomas in specialist centers delivers significant benefits. In much of the world, specialists are not available, and the development of expertise is identified as a major need. However, the terms ‘specialist’ or ‘expert’ center are rarely defined. Our objective is to offer a definition for patient advocates and a tool for healthcare providers to underpin improving the care of people with sarcoma. SPAGN developed a discussion paper for a workshop at the SPAGN 2023 Conference, attended by 75 delegates. A presentation to the Connective Tissue Oncology Society (CTOS) and further discussion led to this paper. Core Principles were identified that underlie specialist sarcoma care. The primary Principle is the multi-disciplinary team discussing every patient, at first diagnosis and during treatment. Principles for optimal sarcoma management include accurate diagnosis followed by safe, high-quality treatment, with curative intent. These Principles are supplemented by Features describing areas of healthcare, professional involvement, and service provision and identifying further research and development needs. These allow for variations because of national or local policies and budgets. We propose the term ‘Sarcoma Intelligent Specialist Network’ to recognize expertise wherever it is found in the world. This provides a base for further discussion and local refinement.
Oncofertility as an Essential Part of Comprehensive Cancer Treatment in Patients of Reproductive Age, Adolescents and Children
Authors: Łubik-Lejawka Dominika , Gabriel Iwona , Marzec Adrianna , Olejek Anita
Published: May 2024

The number of children, adolescents and young adults diagnosed with cancer has been rising recently. Various oncological treatments have a detrimental effect on female fertility, and childbearing becomes a major issue during surveillance after recovery. This review discusses the impact of oncological treatments on the ovarian reserve with a thorough explanation of oncologic treatments’ effects and modes of oncofertility procedures. The aim of this review is to help clinicians in making an informed decision about post-treatment fertility in their patients. Ultimately, it may lead to improved overall long-term outcomes among young populations suffering from cancer.
Can China’s Regional Industrial Chain Innovation and Reform Policy Make the Impossible Triangle of Energy Attainable? A Causal Inference Study on the Effect of Improving Industrial Chain Resilience
Authors: Lu Tianyu , Li Hongyu
Published: May 2024

This study used a double machine learning model (based on the random forest algorithm) and spatial Durbin DIDs model to conduct quasi-natural experiments. The results are as follows: (1) innovation and reform policy regarding regional industrial chains as well as their resilience can significantly and positively address the development of China’s impossible triangle coupling of energy; (2) implementing the innovation and reform policy for regional industrial chains in other regions can have a significant positive spatial transmission effect on the impossible triangle coupling coordinated development of energy in the region; (3) regional industrial chain resilience can produce a significant positive mediating effect between the innovation and reform policy of regional industrial chains and the safety, reliability, and economic feasibility of green and clean energy systems; (4) under the counterfactual framework, the mechanism path “innovation and reform policy of the regional industry chain→regional industry chain resilience→coordination degree of impossible triangle coupling of energy” has significantly positive direct and indirect effects in both the treatment group and the control group. However, “innovation and reform policy of the regional industrial chain→regional industrial chain resilience→the energy sector’s impossible triangle coupling coordination degree” and “innovation and reform policy of the regional industrial chain→leading power of the regional industrial chain→the energy sector’s impossible triangle coupling coordination degree” have significantly positive direct and indirect effects in the treatment group, but only the direct effect is significant in the control group.
A Numerical Simulation and Experimental Study of Fluidization Characteristics of a Bubbling Fluidized Bed in Biomass Gasification
Authors: Gao Na , Zhu Kang , Fang Shiwen , Deng Lisheng , Lin Yan , Huang Zhen , Li Jun , Huang Hongyu
Published: May 2024

Traditional fossil energy sources still dominate the world energy structure. And fully utilizing biomass is a viable approach for energy transition. A bubbling fluidized bed has better heat and mass transfer, while particle agglomeration limits the development of its industrial application. In this paper, two-phase flow characteristics of a bubbling fluidized bed are investigated by combining numerical simulations and fluidized bed gasification experiments. Numerical simulations found that the bed fluidization height reached twice the initial fluidization height at the 0.054 m initial fluidization height with uniform particle distribution. Fluidized bed gasification experiments found that syngas yield increased with increasing temperature. The carbon conversion efficiency reached 79.3% and the effective gas production was 0.64 m3/kg at 850 °C. In addition, when the water vapor concentration reached 15%, the carbon conversion efficiency and effective gas production reached the maximum values of 86.01% and 0.81 m3/kg, respectively.
Optimizing Nanofluid Hybrid Solar Collectors through Artificial Intelligence Models
Authors: Margoum Safae , Hajji Bekkay , Aneli Stefano , Tina Giuseppe Marco , Gagliano Antonio
Published: May 2024

This study systematically explores and compares the performance of various artificial-intelligence (AI)-based models to predict the electrical and thermal efficiency of photovoltaic–thermal systems (PVTs) cooled by nanofluids. Employing extreme gradient boosting (XGB), extra tree regression (ETR), and k-nearest-neighbor (KNN) regression models, their accuracy is quantitatively evaluated, and their effectiveness measured. The results demonstrate that both XGB and ETR models consistently outperform KNN in accurately predicting both electrical and thermal efficiency. Specifically, the XGB model achieves remarkable correlation coefficient (R2) values of approximately 0.99999, signifying its superior predictive capabilities. Notably, the XGB model exhibits a slightly superior performance compared to ETR in estimating electrical efficiency. Furthermore, when predicting thermal efficiency, both XGB and ETR models demonstrate excellence, with the XGB model showing a slight edge based on R2 values. Validation against new data points reveals outstanding predictive performance, with the XGB model attaining R2 values of 0.99997 for electrical efficiency and 0.99995 for thermal efficiency. These quantitative findings underscore the accuracy and reliability of the XGB and ETR models in predicting the electrical and thermal efficiency of PVT systems when cooled by nanofluids. The study’s implications are significant for PVT system designers and industry professionals, as the incorporation of AI-based models offers improved accuracy, faster prediction times, and the ability to handle large datasets. The models presented in this study contribute to system optimization, performance evaluation, and decision-making in the field. Additionally, robust validation against new data enhances the credibility of these models, advancing the overall understanding and applicability of AI in PVT systems.
An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems
Authors: Tang Chao , Zhang Yufeng , Wu Fan , Tang Zhuo
Published: May 2024

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications.
The Impact of Replacing Synchronous Generators with Renewable-Energy Technologies on the Transient Stability of the Mangystau Power System: An Introduction to Flexible Automatic Dosage of Exposures
Authors: Aisayev Yerzhan , Tergemes Kazhybek , Zhauyt Algazy , Sheryazov Saken , Bakenov Kairat
Published: May 2024

Since the creation of the first parallel electrical power systems around the world, the rotor angle stability of synchronously operating generators has been one of the most crucial and challenging problems. In modern electricity networks, including in Kazakhstan, where renewable energy technologies are rapidly emerging, the issue of stability takes on even greater importance due to the technical shortcomings of inverter-based generation. In this framework, an analysis of rotor angle transient stability was carried out when replacing existing synchronous generators with doubly fed induction generators under a certain pre-emergency mode. A critical proportion of active power generation by DFIG units was identified at which transient stability can still be maintained due to the sufficient stored kinetic energy of the synchronous machines remaining in operation. In addition, two simple solutions were investigated to improve transient stability, such as an increased time of the automatic reclosure operation and the use of special load-shedding automation. Finally, this paper proposes a novel type of flexible smart-grid automation that is capable of monitoring the main operating parameters and issuing control actions depending on inertia, the availability of wind resources, and the load of the system as a whole. For this analysis, a real power system from the Mangystau region in Kazakhstan was considered, and the PowerWorld software 23 was used.
Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization
Authors: Li Zeyu , Xiong Junhua
Published: May 2024

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.
A Practical Hybrid Hysteresis Model for Calculating Iron Core Losses in Soft Magnetic Materials
Authors: Fu Xiaotong , Yan Shuai , Chen Zhifu , Xu Xiaoyu , Ren Zhuoxiang
Published: May 2024

Accurately calculating the losses of ferromagnetic materials is crucial for optimizing the design and ensuring the safe operation of electrical equipment such as motors and power transformers. Commonly used loss calculation models include the Bertotti empirical formula and hysteresis models. In this paper, a new hybrid hysteresis model method is proposed to calculate losses—namely, the combination of the Jiles–Atherton hysteresis model (J–A) and the Fourier hysteresis model. The traditional Jiles–Atherton hysteresis model is mainly suitable for fitting the saturation hysteresis loop, but the fitting error is relatively large for internal minor hysteresis loops. In contrast, the Fourier hysteresis model is suitable for fitting the minor hysteresis loops because the corresponding magnetic induction strength or magnetic field is lower and the waveform distortion is small. Moreover, Fourier series expansion can be expressed with fewer terms, which is convenient for parameter fitting. Through examples, the results show that the hybrid hysteresis model can take advantage of the strengths of each model, not only reducing computational complexity, but also ensuring high fitting accuracy and loss calculation accuracy.
Using Transfer Learning and XGBoost for Early Detection of Fires in Offshore Wind Turbine Units
Authors: Wan Anping , Du Chenyu , Gong Wenbin , Wei Chao , AL-Bukhaiti Khalil , Ji Yunsong , Ma Shidong , Yao Fareng , Ao Lizheng
Published: May 2024

To improve the power generation efficiency of offshore wind turbines and address the problem of high fire monitoring and warning costs, we propose a data-driven fire warning method based on transfer learning for wind turbines in this paper. This paper processes wind turbine operation data in a SCADA system. It uses an extreme gradient-boosting tree (XGBoost) algorithm to build an offshore wind turbine unit fire warning model with a multiparameter prediction function. This paper selects some parameters from the dataset as input variables for the model, with average cabin temperature, average outdoor temperature, average cabin humidity, and average atmospheric humidity as output variables. This paper analyzes the distribution information of input and output variables and their correlation, analyzes the predicted difference, and then provides an early warning for wind turbine fires. This paper uses this fire warning model to transfer learning to different models of offshore wind turbines in the same wind farm to achieve fire warning. The experimental results show that the prediction performance of the multiparameter is accurate, with an average MAPE of 0.016 and an average RMSE of 0.795. It is better than the average MAPE (0.051) and the average RMSE (2.020) of the prediction performance of a backpropagation (BP) neural network, as well as the average MAPE (0.030) and the average RMSE (1.301) of the prediction performance of random forest. The transfer learning model has good prediction performance, with an average MAPE of 0.022 and an average RMSE of 1.469.
Extracting Accurate Parameters from a Proton Exchange Membrane Fuel Cell Model Using the Differential Evolution Ameliorated Meta-Heuristics Algorithm
Authors: Kanouni Badreddine , Laib Abdelbaset
Published: May 2024

The electrochemical proton exchange membrane fuel cell (PEMFC) is an electrical generator that utilizes a chemical reaction mechanism to produce electricity, serving as a sustainable and environmentally friendly energy source. To thoroughly analyze and develop the features and performance of a PEMFC, it is essential to use a precise model that incorporates exact parameters to effectively suit the polarization curve. In addition, parameter extraction plays a crucial role in the simulation analysis, evaluation, optimum control, and fault detection of the proton exchange membrane fuel cell (PEMFC) system. Despite the development of many algorithms for parameter extraction in PEMFC, obtaining accurate and trustworthy results rapidly remains a challenge. This study presents a hybridized algorithm, namely differential evolution ameliorated (DEA) for reliably estimating PEMFC model parameters. To evaluate the proposed DEA-based parameter identification, a comparison analysis with previously published methods is conducted using MATLAB/SimulinkTM (R2016b, MathWorks, Natick, MA, USA) in terms of system correctness and convergence process. The proposed DEA algorithm is tested to extract the parameters of two PEMFC models: SR-12 500 W and 250 W. The sum of the squared errors (SSE) between the experimental and the obtained voltage data is defined as an objective function. The simulation results prove that the suggested DEA algorithm is capable of identifying the optimal PEMFC parameters rapidly and accurately in comparison with other optimization algorithms.
Design and Optimization of Cross-Corrugated Triangular Ducts with Trapezoidal Baffles Based on Response Surface Methodology and CFD
Authors: Liang Caihang , Zhang Rui , Mao Chaojian , Dong Yanfang , Yao Xiong , Hu Weipeng , Li Zhenxing
Published: May 2024

Plate heat exchangers are widely used in the Heating, Ventilation, and Air Conditioning (HVAC) field. Cross-corrugated triangular ducts are commonly employed in plate heat exchangers. Inserting baffles into the cross-corrugated triangular ducts can improve the heat transfer performance of the plate heat exchangers. This study focuses on intricate interdependencies among the flow channel apex angle, the trapezoidal baffle inclination angle, baffle position, and Reynolds number (Re) on heat transfer and pressure drop using response surface methodology (RSM) and computational fluid dynamic (CFD). To identify the factors that maximize the Nusselt number (Nu) and minimize friction factor (f), the RSM is used to design factors, conduct numerical studies, and establish regression equations. The results show that the apex angle, baffle angle, X-direction position, and Re have significantly affected Nu and f. Compared to a non-baffled channel with the same apex angle and Re conditions, the optimized channel enhances heat transfer by 1.54 times and has an almost identical pressure drop. The inclined baffle significantly enhances comprehensive performance at low Re. The synergistic effect of the heat transfer and pressure drop is most optimal when the apex angle of the flow channel is 90°, the trapezoidal baffle inclination angle is 52.5°, and the Re is 1000, with the baffle position at 0.625H in the X-direction.
Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market
Authors: Menéndez Medina Alberto , Heredia Álvaro José Antonio
Published: May 2024

The electricity market in Spain holds significant importance in the nation’s economy and sustainability efforts due to its diverse energy mix that encompasses renewables, fossil fuels, and nuclear power. Accurate energy price prediction is crucial in Spain, influencing the country’s ability to meet its climate goals and ensure energy security and affecting economic stakeholders. We have explored how leveraging advanced GPT tools like OpenAI’s ChatGPT to analyze energy news and expert reports can extract valuable insights and generate additional variables for electricity price trend prediction in the Spanish market. Our research proposes two different training and modelling approaches of generative pre-trained transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. We aim to shed light on the capabilities of GPT solutions and demonstrate how they can augment prediction models by introducing additional variables. Our findings suggest that insights derived from GPT analysis of electricity news and specialized reports align closely with price fluctuations post-publication, indicating their potential to improve predictions and offer deeper insights into market dynamics. This endeavor can support informed decision-making for stakeholders in the Spanish electricity market and companies reliant on electricity costs and price volatility for their margins.
Boil-Off Gas Generation in Vacuum-Jacketed Valve Used in Liquid Hydrogen Storage Tank
Authors: Hwang Hae-Seong , Woo Seong-Un , Han Seung-Ho
Published: May 2024

The boiling point of liquid hydrogen is very low, at −253 °C under atmospheric pressure, which causes boil-off gas (BOG) to occur during storage and transport due to heat penetration. Because the BOG must be removed through processes such as re-liquefaction, venting to the atmosphere, or incineration, related studies are required to estimate the heat transfer of storage and transport devices and to improve insulation to reduce BOG generation. In this study, a vaporization analysis was performed on a vacuum-jacketed valve used in liquid hydrogen storage and transport devices to calculate the amount of BOG generation considering the flow characteristics at the vena contracta and the saturation temperature. At a pressure of 1 bar in the liquid hydrogen storage tank, the maximum fluid flow velocity and minimum static pressure occurred at the vena contracta, with values of 62.9 m/s and −0.4 bar, respectively, and the BOG generation rate was estimated as 0.132 m3/h, where the saturation temperature was minimized at 19.3 K. Furthermore, through case studies, when the pressure in the liquid hydrogen storage tank increased to 1.5 and 2 bar, the static pressure and saturation temperature decreased, and the BOG generation rate increased to 0.221 and 0.283 m3/h, respectively.
Can Environmental Information Disclosure Improve Energy Efficiency in Manufacturing? Evidence from Chinese Enterprises
Authors: Tan Linfang , Gao Da , Liu Xiaowei
Published: May 2024

Improving the energy efficiency of enterprises is one of the key means to solve the problem of energy shortage. It is of great significance to investigate how environmental information disclosure (EDI) promotes the green total factor energy efficiency (GTFEE) of enterprises. Based on this, this study calculates the GTFEE of enterprises by combining the database of Chinese manufacturing and the pollutant emission of industrial enterprises and investigates the impact of EDI on the GTFEE of manufacturing industries by using a difference-in-difference model. The following is found: (1) EDI can significantly promote the manufacturing enterprises’ GTFEE, and the results are still valid after a series of robustness tests; (2) Mechanism analysis shows that EDI can improve the GTFEE of manufacturing enterprises by promoting technological innovation and optimizing energy structure; (3) The heterogeneity analysis shows that EID is more positive on firms’ GTFEE in the eastern than western regions. The positive impact is greater for non-state-owned, low-energy consumption, export, and polluting enterprises. The findings of this paper provide a theoretical basis and practical enlightenment for the government to promote the green development transformation of enterprises.
Second-Life Electric Vehicle Batteries for Home Photovoltaic Systems: Transforming Energy Storage and Sustainability
Authors: Sarker Md. Tanjil , Haram Mohammed Hussein Saleh Mohammed , Shern Siow Jat , Ramasamy Gobbi , Al Farid Fahmid
Published: May 2024

Solar-based home PV systems are the most amazing eco-friendly energy innovations in the world, which are not only climate-friendly but also cost-effective solutions. The tropical environment of Malaysia makes it difficult to adopt photovoltaic (PV) systems because of the protracted rainy monsoon season, which makes PV systems useless without backup batteries. Large quantities of lithium-ion battery (LIB) trash are being produced by the electric vehicle (EV) sector. A total of 75% of the highest capacity levels have been discarded. By 2035, it is predicted that the wasted LIBs held as a result of expensive recycling and difficult material separation would carry up to 1200 GWh. An economical and sustainable option is offered by our study, which prototypes a replicated LIB pack that is incorporated into a PV home system. This study investigates the transformational power of second-life electric vehicle batteries (SLEVBs) when incorporated into home photovoltaic (PV) systems. The concept entails reusing existing electric vehicle batteries for stationary applications, offering a unique approach to extending the life of these batteries while meeting the growing need for sustainable domestic energy storage. The study looks at the technological feasibility, economic viability, and environmental effect of introducing SLEVBs into household PV systems, giving vital insight into their role in revolutionizing energy storage techniques and promoting sustainability. In comparison to the Lead–Acid Battery (LAB) system, the SLEVB system has a cheaper total cost of ownership, with savings of 12.62% compared with new LABs. A CO2 emission reduction of at least 20% is achieved by using the SLEVB system compared with LABs. Electricity can be provided in houses in rural areas where there is no electricity. As a result, the security and superiority of the life of rural residents will improve. It is anticipated that the suggested strategy will lower EV pricing, enabling EV adoption for M40 and B40 groups. Consequently, the Malaysian and worldwide EV business will remain viable.
Blockchain-Based Zero-Trust Supply Chain Security Integrated with Deep Reinforcement Learning for Inventory Optimization
Authors: Ma Zhe , Chen Xuhesheng , Sun Tiejiang , Wang Xukang , Wu Ying Cheng , Zhou Mengjie
Published: May 2024

Modern supply chain systems face significant challenges, including lack of transparency, inefficient inventory management, and vulnerability to disruptions and security threats. Traditional optimization methods often struggle to adapt to the complex and dynamic nature of these systems. This paper presents a novel blockchain-based zero-trust supply chain security framework integrated with deep reinforcement learning (SAC-rainbow) to address these challenges. The SAC-rainbow framework leverages the Soft Actor–Critic (SAC) algorithm with prioritized experience replay for inventory optimization and a blockchain-based zero-trust mechanism for secure supply chain management. The SAC-rainbow algorithm learns adaptive policies under demand uncertainty, while the blockchain architecture ensures secure, transparent, and traceable record-keeping and automated execution of supply chain transactions. An experiment using real-world supply chain data demonstrated the superior performance of the proposed framework in terms of reward maximization, inventory stability, and security metrics. The SAC-rainbow framework offers a promising solution for addressing the challenges of modern supply chains by leveraging blockchain, deep reinforcement learning, and zero-trust security principles. This research paves the way for developing secure, transparent, and efficient supply chain management systems in the face of growing complexity and security risks.