bluuebunny/crossref_metadata_2025 huggingface dataset:
Conversational LLM Chatbot ChatGPT-4 for Colonoscopy Boston Bowel Preparation Scoring: An Artificial Intelligence-to-Head Concordance Analysis
Authors: Pellegrino Raffaele , Federico Alessandro , Gravina Antonietta Gerarda
Published: November 2024

Background/objectives:To date, no studies have evaluated Chat Generative Pre-Trained Transformer (ChatGPT) as a large language model chatbot in optical applications for digestive endoscopy images. This study aimed to weigh the performance of ChatGPT-4 in assessing bowel preparation (BP) quality for colonoscopy. Methods: ChatGPT-4 analysed 663 anonymised endoscopic images, scoring each according to the Boston BP scale (BBPS). Expert physicians scored the same images subsequently. Results: ChatGPT-4 deemed 369 frames (62.9%) to be adequately prepared (i.e., BBPS > 1) compared to 524 frames (89.3%) assessed by human assessors. The agreement was slight (κ: 0.099, p = 0.0001). The raw human BBPS score was higher at 3 (2–3) than that of ChatGPT-4 at 2 (1–3), demonstrating moderate concordance (W: 0.554, p = 0.036). Conclusions: ChatGPT-4 demonstrates some potential in assessing BP on colonoscopy images, but further refinement is still needed.
Effects of Medication Period and Gastrin Levels on Endoscopic Gastric Mucosal Changes in Long-Term Proton Pump Inhibitor Users
Authors: Suda Hiroko , Sakurai Koichi , Eto Sachi , Fujie Satomi , Okuda Ayako , Takeichi Takayuki , Urata Masayuki , Murao Tetsuya , Hasuda Kiwamu , Hirano Masahiro , Kato Yo , Haruma Ken
Published: November 2024

Background/Objectives: Proton pump inhibitor (PPI) use has increased worldwide, including in continuous and longer-term users. Recent reports highlight PPI-related endoscopic gastric mucosal changes, including fundic gland polyps, hyperplastic polyps, multiple white and flat elevated lesions, cracked and cobblestone-like mucosa (CCLM), and black spots. PPI use elevates gastrin levels because of acid inhibition, and hypergastrinemia might be relevant to these findings. In this cross-sectional study, we retrospectively examined gastric mucosal changes in long-term PPI users, focusing on medication period and gastrin levels. Methods: We enrolled 57 patients who received a PPI (>1 year) at two clinics between January 2021 and March 2022. Participants were classified according to medication period: 1 < 5, 5–10, and ≥10 years. Gastrin levels were categorized as low, middle, and high (<250, 250–500, and ≥500 pg/mL, respectively). Odds ratios (OR) were estimated to assess the risk of endoscopic findings. Results: Of the 57 patients, 6 (10.5%), 25 (43.9%), and 26 (45.6%) were PPI users of 1 < 5, 5–10, and ≥10 years, respectively. There were no significant differences in the incidence of endoscopic findings among the medication periods. Low, middle, and high gastrin groups included 21 (36.8%), 21 (36.8%), and 15 (26.3%) patients, respectively. CCLM incidence was significantly elevated in higher gastrin level groups: middle (OR, 6.60; 95% confidence interval [CI], 1.46–29.75; p = 0.014) and high (OR, 9.00; 95% CI, 1.79–45.23; p = 0.0008) (p-trend = 0.0171). No significant differences were observed for other findings. Conclusions: No elevated risk of PPI-related gastric epithelial changes in long-term PPI users was observed time-dependently. Notably, higher gastrin levels were positively associated with CCLM development, irrespective of the medication period.
Detection of Brain-Derived Cell-Free DNA in Plasma
Authors: Pellegrini Camilla , Ravaioli Francesco , De Fanti Sara , Pirazzini Chiara , D’Silva Chiara , Garagnani Paolo , Franceschi Claudio , Bonifazi Francesca , Zinzani Pier Luigi , Bonafè Massimiliano , Guarino Maria , Lodi Raffaele , Cortelli Pietro , Tonon Caterina , Mitolo Micaela , Sambati Luisa , Morandi Luca , Bacalini Maria Giulia
Published: November 2024

Background: Neuronal loss is a major pathological feature of neurodegenerative diseases. The analysis of plasma cell-free DNA (cfDNA) is an emerging approach to track cell death events in a minimally invasive way and from inaccessible areas of the body, such as the brain. Previous studies showed that DNA methylation (DNAm) profiles can be used to map the tissue of origin of cfDNA and to identify molecules released from the brain upon cell death. The aim of the present study is to contribute to this research field, presenting the development and validation of an assay for the detection of brain-derived cfDNA (bcfDNA). Methods: To identify CpG sites with brain-specific DNAm, we compared brain and non-brain tissues for their chromatin state profiles and genome-wide DNAm data, available in public datasets. The selected target genomic regions were experimentally validated by bisulfite sequencing on DNA extracted from 44 different autoptic tissues, including multiple brain regions. Sequencing data were analysed to identify brain-specific epihaplotypes. The developed assay was tested in plasma cfDNA from patients with immune effector cell-associated neurotoxicity syndrome (ICANS) following chimeric antigen receptor T (CAR-T) therapy. Results: We validated five genomic regions with brain-specific DNAm (four hypomethylated and one hypermethylated in the brain). DNAm analysis of the selected genomic regions in plasma samples from CAR-T patients revealed higher levels of bcfDNA in participants with ongoing neurotoxicity syndrome. Conclusions: We developed an assay for the analysis of bcfDNA in plasma. The assay is a promising tool for the early detection of neuronal loss in neurodegenerative diseases.
False-Positive Diagnosis of Congenital Heart Defects at First-Trimester Ultrasound: An Italian Multicentric Study
Authors: Andrietti Silvia , D’Agostino Serena , Panarelli Marina , Sarno Laura , Pisaturo Maria Laura , Fantasia Ilaria
Published: November 2024

Objective. Our objective was to assess the proportion of false-positive CHD cases at the first-trimester evaluation of the fetal heart, performed by experienced operators. Methods. This multicenter retrospective study included of pregnant women with suspicion of CHDs during first-trimester screening for aneuploidies. In all cases, the fetal heart assessments were performed by obstetricians with extensive experience in first-trimester scanning, following an extended protocol proposed by SIEOG national guidelines, which included an axial view of the fetal abdomen and chest to assess visceral situs and evaluation of the four-chamber view (4CV) and three-vessel trachea view (3VTV) with color Doppler. In all suspected cases, fetal echocardiography was offered within 16 and/or at 19–22 weeks’ gestation. Results. From a population of 4300 fetuses, 46 CHDs were suspected. Twenty-four cases were excluded from this analysis because the parents opted for early termination of the pregnancies due to associated structural and/or genetic anomalies. For the remaining 22, echocardiography was performed by 16 weeks in 14 cases (64%) and after 16 weeks in 8 cases. In 19 cases (86.4%), a fetal cardiologist confirmed the presence of a CHD. In three cases (13%), the cardiac anatomy was found to be normal at the fetal echocardiography and postnatally. Conclusions. This study shows that the proportion of false-positive cases at the first-trimester ultrasound examination of the fetal heart, performed by experienced operators, may carry a higher risk of false-positive diagnosis than expected. Therefore, this issue must be discussed in instances where a CHD is suspected at the first-trimester screening.
Best Practices for the Use of High-Frequency Ultrasound to Guide Aesthetic Filler Injections—Part 2: Middle Third of the Face, Nose, and Tear Troughs
Authors: Vasconcelos-Berg Roberta , Desyatnikova Stella , Bonavia Paula , Chammas Maria Cristina , Navarini Alexander , Sigrist Rosa
Published: November 2024

The midface is a key area in facial aesthetics, highly susceptible to age-related changes such as fat pad absorption, bone resorption, and loss of skin elasticity. These changes lead to the formation of prominent folds, such as the nasolabial fold. In addition, critical vascular structures and non-vascular components, such as the facial artery, angular artery, and parotid gland, make this region prone to complications during filler injections. High-frequency ultrasound (HFUS) offers real-time, radiation-free visualization of facial anatomy, enabling injectors to accurately target the desired treatment planes and avoid critical structures. This article is the second in a series of articles on ultrasound-guided facial injections and focuses on the midface. It provides a detailed overview of the sonographic anatomy of key areas, including the nose, tear trough, nasolabial fold, zygomatic, and preauricular regions. Step-by-step techniques for ultrasound-guided filler injections are described, emphasizing the importance of scanning both before and during injections to ensure safe filler placement. By using ultrasound in this area, injectors can possibly minimize risks such as vascular occlusion and other complications, such as the Tyndall effect and intra-parotid injection. With ongoing advancements, ultrasound-guided injections are expected to become more refined, enhancing both aesthetic outcomes and patient safety.
Audiometric Outcomes in Chronic Otitis Media with Mastoid Involvement: A Five-Year Clinical Overview
Authors: Popescu Cristina , Văruț Renata Maria , Popescu Mihaela , Popescu Alin Iulian Silviu , Singer Cristina Elena
Published: November 2024

Background/Objectives. Otomastoiditis, an inflammatory condition affecting the middle ear and mastoid cells, poses significant risks for hearing impairment. This study aimed to analyze the clinical presentations, anatomical variations, and audiometric outcomes associated with acute and chronic otomastoiditis over a five-year period at the ENT Clinic of the Clinical County Emergency Hospital of Craiova. Methods. A retrospective clinical–statistical analysis was conducted on 145 patients aged 2 to 78 years, who were treated for otomastoiditis. The study involved a comprehensive review of clinical and audiometric data, with a focus on the type of hearing loss (conductive or mixed), audiometric thresholds, and the relationship between the anatomical form of the disease and the severity of hearing loss. Results. The majority of cases (93.83%) were chronic otomastoiditis, with 66.89% of patients presenting with mixed hearing loss and 33.10% with conductive hearing loss. Audiometric assessments revealed significant air conduction deficits, particularly at low and mid-range frequencies, with losses averaging 50–55 dB in cases of conductive hearing loss. Chronic cases demonstrated notable bone conduction impairments, indicating progressive cochlear damage. Statistical analysis identified a moderate correlation between the anatomical form of the disease and the severity of hearing loss, particularly in patients with cholesteatomatous-suppurative forms. Conclusions. This study underlines the critical need for the early and precise diagnosis of otomastoiditis, supported by audiometric evaluations. Our findings emphasize the substantial risk of progressive cochlear damage in chronic cases, underscoring the necessity for timely intervention to mitigate long-term hearing loss. These results offer valuable insights for clinicians, potentially guiding improved therapeutic approaches and contributing to enhanced patient outcomes in managing chronic otomastoiditis.
AI Technology Adoption in Corporate IT Network Operations Based on the TOE Model
Authors: Min Seoungkwon , Kim Boyoung
Published: November 2024

As the digital environment evolves, the need to integrate artificial intelligence (AI) technology into corporate IT network operations increases. In this study, the aim was to define the factors that influence AI adoption in the network operations and analyze their impact on productivity and service stability. The technology–organization–environment (TOE) framework was employed for this investigation, focusing on technological, organizational, and environmental factors. In addition, in this study, structural equation modeling was employed to analyze the relationships between these influencing factors and the intention to adopt AI. The mediation effect was examined through the network operation productivity and network service stability. A survey was conducted targeting network operations and AI professionals to collect data. The analysis results revealed that technological and environmental factors positively influenced the network operation productivity, while only environmental factors positively influenced the network service stability. Furthermore, the findings of this study highlight that environmental factors are the most significant factors that influence network operation productivity and network service stability. Moreover, the direct positive impact of network operation productivity and IT network service stability on the intention to adopt AI underscores their crucial role. In conclusion, when evaluating AI adoption in terms of network operation productivity and network service stability, prioritizing technological and environmental factors over organizational factors is necessary.
Towards a Satisfactory Future—Multiprofessional Rehabilitation for Young Persons with ADHD or ASD
Authors: Heinijoki Hennariikka , Karhula Maarit , Vuoskoski Pirjo , Munukka Matti , Nikander Riku , Seppanen-Jarvela Riitta
Published: November 2024

There is a lack of knowledge about what kind of support should be offered in adulthood for persons with attention-deficit/hyperactivity disorder (ADHD) or autism spectrum disorder (ASD) to promote inclusive participation in meaningful life domains. The core components of multiprofessional rehabilitation intervention for young adults are explored from the perspective of multiprofessional teams. This qualitative study adopts a pragmatic perspective on the core components of rehabilitation. Twenty-six professionals participated in vignette-based focus group interviews (n = 5), and thematic reflexive analysis was used to abductively analyze the data. A total of ten core components were identified. Nine of them were aggregated into three categories: (1) rehabilitation readiness (two core components), (2) adaptive progress in personal goals (four core components), and (3) rehabilitation continuum (three core components). The overarching tenth core component is focusing on the personal goals of daily life, work, studies, and social interaction. A collaborative relationship between the client and professional is essential to ensure adaptive progress and to foster clients’ self-determination. The focus should be on strengths and solutions instead of deficits and challenges. To establish rehabilitation continuum, it is essential to collaborate within the clients’ networks and to promote awareness and inclusive opportunities for working and studying for people with ADHD and ASD.
Two-Dimensional Radiographic Diagnosis of Maxillary Canine Impactions
Authors: Iacob Alin M. , Escobedo Martínez Matías Ferrán , Olay García Sonsoles , Junquera Olay Sonsoles , Junquera Gutiérrez Luis Manuel
Published: November 2024

Background: The primary aim of this study was to evaluate, using conventional radiographic imaging, the height, angulation, and mesiodistal position of impacted maxillary canines to determine if these variables are significantly associated with the palatal or buccal positioning of the tooth. Methods: A retrospective analysis was conducted on all patients diagnosed and treated for at least one impacted maxillary canine over a 4-year period in the Principality of Asturias. The final sample included 159 impacted canines. The variables analyzed were age, gender, associated pathology, location, angulation, height, mesiodistal position, buccopalatal position, and treatment method. Follow-up periods ranged from 12 to 50 months. Results: The mean age of the patients was 21 years (range 10–41 years), with most cases occurring in patients aged 14 to 30 years. Females accounted for 66.1% of the sample, with a female-to-male ratio of 1.95:1. The type of treatment (extraction/traction, surgical–orthodontic approach) was significantly associated with patient age. Additionally, the proximity of the impacted canine crown to the palatal or buccal cortices was significantly related to the treatment choice. However, no significant relationship was observed between the palatal/buccal position of the canine crown and its height or angulation. Conclusions: In the present work, the upper canines included by the palatal did not have a greater height or angulation than the canines included by the vestibule.
The Flexural Strength and the Effect of the Autoclave Sterilization of Polypropylene/Natural Rubber Blended Materials
Authors: Rangsantham Paphavarin , Nonthiphalang Thitaporn , Wongwitthayakool Panjaporn , Sirisinha Chakrit , Krajangta Nantawan , Phumpatrakom Panupat
Published: November 2024

Background: Rubber dam clamps are used extensively in dentistry, especially during root canal treatment. However, existing rubber dam clamps have several drawbacks, including discomfort and potential damage to vital tissue in the oral cavity. To address these existing issues, a new rubber dam clamp should be developed. The aim of this study was to identify the optimum ratios of polypropylene and natural rubber (PP/NR) for a customized rubber dam clamp in dentistry. This study was focused on the flexural strength of PP/NR in various ratios. Moreover, the impact of autoclave sterilization was also considered. Methods: Six proportions of PP/NR blends (100/0, 90/10, 80/20, 70/30, 60/40 and 50/50) were prepared and assessed for flexural strength using a three-point bending test. After this test, the PP/NR blends with 100/0, 90/10 and 80/20 ratios were selected and underwent autoclave sterilization for 1, 5 and 10 cycles. Eventually, the flexural strength testing was repeated and investigated. An analysis of variance and Tukey’s test were used to evaluate the flexural strength of various PP/NR blends before autoclave sterilization at p < 0.05. An analysis of variance and Dunnett’s T3 test were used to evaluate the flexural strength of selected PP/NR blends before and after autoclave sterilization at p < 0.05. Results: The results revealed that the flexural strength of PP/NR blended materials showed a statistically significant difference in every group of this study. The autoclave sterilization test revealed that the flexural strength of the PP/NR 90/10 and 80/20 ratios was significantly increased after sterilization for 1, 5 and 10 cycles. In addition, the PP/NR 90/10 ratio was also comparable to the 100/0 ratio. The lower NR content in PP/NR blends resulted in significantly higher flexural strength, and autoclave sterilization had an effect on this property. Conclusions: This study suggested that the PP/NR blend with a 90/10 ratio might be considered as an alternative material for developing rubber dam clamps.
Mamba-UAV-SegNet: A Multi-Scale Adaptive Feature Fusion Network for Real-Time Semantic Segmentation of UAV Aerial Imagery
Authors: Huang Longyang , Tan Jintao , Chen Zhonghui
Published: November 2024

Accurate semantic segmentation of high-resolution images captured by unmanned aerial vehicles (UAVs) is crucial for applications in environmental monitoring, urban planning, and precision agriculture. However, challenges such as class imbalance, small-object detection, and intricate boundary details complicate the analysis of UAV imagery. To address these issues, we propose Mamba-UAV-SegNet, a novel real-time semantic segmentation network specifically designed for UAV images. The network integrates a Multi-Head Mamba Block (MH-Mamba Block) for enhanced multi-scale feature representation, an Adaptive Boundary Enhancement Fusion Module (ABEFM) for improved boundary-aware feature fusion, and an edge-detail auxiliary training branch to capture fine-grained details. The practical utility of our method is demonstrated through its application to farmland segmentation. Extensive experiments on the UAV-City, VDD, and UAVid datasets show that our model outperforms state-of-the-art methods, achieving mean Intersection over Union (mIoU) scores of 71.2%, 77.5%, and 69.3%, respectively. Ablation studies confirm the effectiveness of each component and their combined contributions to overall performance. The proposed method balances segmentation accuracy and computational efficiency, maintaining real-time inference speeds suitable for practical UAV applications.
Anxiety and Associated Factors Among Chinese Preschool Teachers
Authors: Wang Xiaohan , Wongpakaran Tinakon , Pojanapotha Pichaya , Chupradit Priyanut , Leung Kelvin C. Y.
Published: November 2024

In China, preschool teachers significantly influence the development of young children aged 3–6 years. Understanding their mental health and its associated factors is imperative to informing policies, optimizing their mental well-being, and supporting their job performance. This study aimed to investigate the factors contributing to anxiety symptoms among preschool teachers. A sample of 393 Chinese preschool teachers (279 women, 114 men), aged 21–56, completed online questionnaires collecting their sociodemographic and work-related information and assessing anxiety symptoms, interpersonal difficulties, personality traits, perceived stress, resilience, and inner strength. Pearson correlation and multiple linear regression analyses identified significant predictors of anxiety symptoms. The prevalence of anxiety symptoms among the studied cohort was found to be 12.2%. Statistically significant factors that positively correlated with anxiety symptoms included objective work-related stress (B = 0.149, p < 0.001), interpersonal difficulties (B = 0.921, p < 0.001), perceived stress (B = 0.108, p = 0.001), and neuroticism (B = 0.071, p = 0.002). These findings highlight the urgent need for measures to reduce work-related stress and anxiety. However, when negative mental health factors were included, the effect of positive psychological factors became nullified. Positive mental health might have a moderating role in a negative mental health outcome such as anxiety. Therefore, further research is required to understand the impact of positive mental health factors more clearly.
Preparing the Future Public Health Workforce: Fostering Global Citizenship Through the Relational Employability Framework—Insights from Two Case Studies
Authors: Cook Elizabeth J. , Doherty Sally-Anne , Wallace Ruth
Published: November 2024

A well-prepared public health workforce is essential for reducing disease burdens and improving population health, necessitating an education that addresses global and complex challenges. This paper explores the integration of the Relational Employability Framework in public health education as a tool to foster critical reflection and cultivate global citizenship among students and graduates. Global citizenship encompasses the social, political, environmental and economic actions of globally conscious individuals and communities, recognising that individuals operate within diverse local and global networks. Relational employability expands traditional notions of graduate employability, incorporating not only foundational career development and human interactions but also connections with more-than-human elements, including ecologies, technologies and materials. Our research, conducted at Edith Cowan University in Perth, Western Australia, involved two case studies using qualitative interviews. Case Study 1, drawn from the Cook’s doctoral research, explored student experiences with the Relational Employability Framework within an undergraduate capstone unit. Case Study 2, a school-funded project, gathered graduate perspectives to inform ongoing curriculum development. The findings indicate that the Relational Employability Framework can help support the development of global citizenship through critical reflective practice, as students reported improved ability to engage with diverse perspectives and societal challenges, and graduates highlighted the framework’s role in supporting ethical, evidence-based practice in professional contexts. Additionally, the framework supported the development of a reflective mindset, which graduates said helped them make informed, value-based career decisions, thus advancing their professional growth. This study suggests that adopting a relational employability approach can prepare globally competent and reflective public health professionals and recommends its implementation across health and higher education.
Deep Learning Assisted Diagnosis of Chronic Obstructive Pulmonary Disease Based on a Local-to-Global Framework
Authors: Cai Nian , Xie Yiying , Cai Zijie , Liang Yuchen , Zhou Yinghong , Wang Ping
Published: November 2024

To aid the diagnosis of chronic obstructive pulmonary disease (COPD), a local-to-global deep framework with group attentions and slice-aware loss is designed in this paper, which utilizes the chest CT sequences of the patients as the network input. To fully mine the medical hints submerged in the CT slices, two types of group attentions are designed to extract local–global features of the grouped slices. Specifically, in each group, a group local attention block (GLAB) and a group global attention block (GGAB) are designed to extract local features in the CT slices and long-range dependencies among the grouped slices. To alleviate the influence of different numbers of CT slices in the chest CT sequences for different patients, a slice-aware loss is proposed by incorporating a normalized coefficient into the cross-entropy loss. Experimental results indicate that the designed deep model performs a good COPD identification on a real COPD dataset with 96.08% accuracy, 94.12% sensitivity, 97.06% specificity, and 95.32% AUC, which is superior to some existing deep learning methods.
WKNN-Based Wi-Fi Fingerprinting with Deep Distance Metric Learning via Siamese Triplet Network for Indoor Positioning
Authors: Park Jae-Hyeon , Kim Dongdeok , Suh Young-Joo
Published: November 2024

Weighted k-nearest neighbor (WKNN)-based Wi-Fi fingerprinting is popular in indoor location-based services due to its ease of implementation and low computational cost. KNN-based methods rely on distance metrics to select the nearest neighbors. However, traditional metrics often fail to capture the complexity of indoor environments and have limitations in identifying non-linear relationships. To address these issues, we propose a novel WKNN-based Wi-Fi fingerprinting method that incorporates distance metric learning. In the offline phase, our method utilizes a Siamese network with a triplet loss function to learn a meaningful distance metric from training fingerprints (FPs). This process employs a unique triplet mining strategy to handle the inherent noise in FPs. Subsequently, in the online phase, the learned metric is used to calculate the embedding distance, followed by a signal-space distance filtering step to optimally select neighbors and estimate the user’s location. The filtering step mitigates issues from an overfitted distance metric influenced by hard triplets, which could lead to incorrect neighbor selection. We evaluate the proposed method on three benchmark datasets, UJIIndoorLoc, Tampere, and UTSIndoorLoc, and compare it with four WKNN models. The results show a mean positioning error reduction of 3.55% on UJIIndoorLoc, 16.21% on Tampere, and 16.49% on UTSIndoorLoc, demonstrating enhanced positioning accuracy.
Literacy Deep Reinforcement Learning-Based Federated Digital Twin Scheduling for the Software-Defined Factory
Authors: Ahn Jangsu , Yun Seongjin , Kwon Jin-Woo , Kim Won-Tae
Published: November 2024

As user requirements become increasingly complex, the demand for product personalization is growing, but traditional hardware-centric production relies on fixed procedures that lack the flexibility to support diverse requirements. Although bespoke manufacturing has been introduced, it provides users with only a few standardized options, limiting its ability to meet a wide range of needs. To address this issue, a new manufacturing concept called the software-defined factory has emerged. It is an autonomous manufacturing system that provides reconfigurable manufacturing services to produce tailored products. Reinforcement learning has been suggested for flexible scheduling to satisfy user requirements. However, fixed rule-based methods struggle to accommodate conflicting needs. This study proposes a novel federated digital twin scheduling that combines large language models and deep reinforcement learning algorithms to meet diverse user requirements in the software-defined factory. The large language model-based literacy module analyzes requirements in natural language and assigns weights to digital twin attributes to achieve highly relevant KPIs, which are used to guide scheduling decisions. The deep reinforcement learning-based scheduling module optimizes scheduling by selecting the job and machine with the maximum reward. Different types of user requirements, such as reducing manufacturing costs and improving productivity, are input and evaluated by comparing the flow-shop scheduling with job-shop scheduling based on reinforcement learning. Experimental results indicate that in requirement case 1 (the manufacturing cost), the proposed method outperforms flow-shop scheduling by up to 14.9% and job-shop scheduling by 5.6%. For requirement case 2 (productivity), it exceeds the flow-shop method by up to 13.4% and the job-shop baseline by 7.2%. The results confirm that the literacy DRL scheduling proposed in this paper can handle the individual characteristics of requirements.
Incentive Determination for Demand Response Considering Internal Rate of Return
Authors: Bae Gyuhyeon , Yoon Ahyun , Kim Sungsoo
Published: November 2024

The rapid expansion of renewable energy sources has led to increased instability in the power grid of Jeju Island, leading to the implementation of the plus demand response (DR) system, which aims to boost electricity consumption during curtailment periods. However, the frequency of curtailment owing to the increased utilization of renewable energy is outpacing the implementation of plus DR, highlighting the need for additional resources, such as energy storage systems (ESS). High initial investment costs have been the primary hindrance to the adoption of ESS by DR-participating companies but have not been fully considered in earlier studies on DR incentive determination. Therefore, this study proposes an algorithm for calculating appropriate incentives for plus DR participation considering the investment costs required for ESS. Based on actual load data, incentives are determined using an iterative mixed-integer programming (MIP) optimization method that progressively adjusts the incentive level to address the overall nonlinearity arising from both the multiplication of variables and the nonlinear characteristics of the internal rate of return (IRR), ensuring that the target IRR is achieved. A case study on the impact of factors such as IRR, ESS costs, and fluctuations in electricity rates on incentive calculations demonstrated that plus DR incentives required to achieve IRR targets of 5%, 10%, and 15% have increased linearly from 142.2 KRW/kWh to 363.0 KRW/kWh, confirming that the appropriate incentive level can be effectively determined based on ESS investment costs and target IRR. This result could help promote ESS adoption among DR companies and plus DR participation, thereby enhancing power grid stability.
Energy Consumption and Fume Analysis: A Comparative Analysis of the Blasting Technique and Mechanical Excavation in a Polish Gypsum Open-Pit Mine
Authors: Biessikirski Andrzej , Bodziony Przemysław , Dworzak Michał
Published: November 2024

This article presents a comparative assessment of energy consumption and fume emissions such as NOx, CO2, and CO associated with the excavation of a specified gypsum volume using two mining methods (blasting and mechanical extraction). The analysis was carried out based on a case study gypsum open-pit mine in Poland where both extraction methods are applied. The findings indicate that, for the same output volume, blasting operations require significantly less energy (ranging from 1298.12 MJ to 1462.22 MJ) compared to mechanical excavation (86,654.15 MJ). Furthermore, a substantial portion of the energy in blasting operations is attributed to explosive loading and drilling (970.95 MJ). Conversely, mechanical mining results in higher fume emissions compared to blasting. However, during mechanical extraction, the fumes are dispersed over a prolonged period of 275 h, whereas blasting achieves the same gypsum volume extraction in approximately 7.5 h. The prediction model suggests that, based on the obtained data, overall gypsum extraction will decline unless new operational levels are developed or the mine is expanded. This reduction in gypsum extraction will be accompanied by a corresponding decrease in energy consumption and emission of fumes.
Innovative Strategies for Thermal Energy Optimization and Renewable Energy Integration in Net-Zero-Energy Buildings: A Comprehensive Review
Authors: Hamdane Samia , Pires Luís C. , Gaspar Pedro D. , Silva Pedro D.
Published: November 2024

The thermal performance and energy efficiency of buildings are critical factors in achieving sustainable energy systems as energy needs for heating and cooling are expected to represent more than 50% of global final energy consumption. This study analyzes conventional renewable energy systems for heating and cooling in buildings, focusing on strategies for developing net-zero-energy buildings. This review covers the integration of renewable energy, the use of intelligent energy management systems, and the optimization of thermal processes. It also compares various systems based on their advantages and limitations and analyzes emerging trends in the thermal management of buildings in different climate zones. The synthesis of recent literature highlights practical recommendations for achieving high thermal performance in buildings, including the importance of selecting appropriate energy systems based on local climatic conditions, optimizing system efficiency, and taking advantage of new materials and advanced technologies. This review aims to contribute to promoting sustainable construction practices with the integration of renewable energy sources and improving the energy efficiency of buildings.
Evaluating Deep Learning Networks Versus Hybrid Network for Smart Monitoring of Hydropower Plants
Authors: Hajimohammadali Fatemeh , Crisostomi Emanuele , Tucci Mauro , Fontana Nunzia
Published: November 2024

One of the main goals of the International Energy Agency (IEA) is to manage and utilize clean energy to achieve net zero emissions by 2050. Hydropower plants can significantly contribute to this goal as they are vital components of the global energy infrastructure, providing a clean, safe, and sustainable power source. Accordingly, there is great interest in developing methods to prevent errors and anomalies and ensure full operational availability. With modern hydropower plants equipped with sensors that capture extensive data, machine learning algorithms utilizing these data to detect and predict anomalies have gained research attention. This paper demonstrates that deep learning algorithms are particularly powerful in predicting time series. Three well-known deep learning networks are examined and compared to previous approaches, followed by the introduction of a new, innovative hybrid network. Using real-world data from two hydropower plants, the hybrid model outperforms individual deep learning models by achieving more accurate fault detection, reducing false positives, offering early fault prediction, and identifying faults several weeks before occurrence. These results showcase the hybrid network’s potential to enhance maintenance planning, reduce downtime, and improve operational efficiency in energy systems.
Changes in Farm Supply Voltage Caused by Switching Operations at a Wind Turbine
Authors: Filipkowski Jacek , Skibko Zbigniew , Borusiewicz Andrzej , Romaniuk Wacław , Pisarek Łukasz , Milewska Anna
Published: November 2024

Renewable electricity sources are now widely used worldwide. Currently, the most common sources are those that use energy contained in biomass, water, sun, and wind. When connected to a medium-voltage grid, individual wind power plants must meet specific conditions to maintain electricity quality. This article presents field study results on the impact of switching operations (turning the power plant on and off) at a 2 MW Vestas V90 wind turbine on the voltage parameters at the connection point of a farm located 450 m from the source. The analysis showed that the wind turbine under study significantly affects customers’ voltage near the source, causing it to increase by approximately 2.5%. Sudden cessation of generation during the afternoon peak causes a 3% voltage fluctuation, potentially affecting equipment sensitive to rapid voltage changes.
An Open-Source Julia Package for RMS Time-Domain Simulations of Power Systems
Authors: Philpott Thomas , Agalgaonkar Ashish P. , Brinsmead Thomas , Muttaqi Kashem M.
Published: November 2024

This paper presents RMSPowerSims.jl, an open-source Julia package for the time-domain simulation of power systems. The package is designed to be used in conjunction with PowerModels.jl, a widely used Julia package for power system optimization. RMSPowerSims.jl provides a framework for the simulation of power systems in the time domain, allowing for the study of transient stability, frequency stability, and other dynamic phenomena. The package is designed to be intuitive and flexible, allowing users to easily define custom models for network components and disturbances, while also providing a range of pre-constructed models for common power system components. RMSPowerSims.jl simplifies the process of performing RMS simulations on power system models developed using the PowerModels.jl ecosystem, and provides an easy-to-use modeling that reduces the barrier to entry for new users wishing to perform RMS simulations. The accuracy of the package is verified against DIgSILENT PowerFactory for short-circuit and load-increase disturbances, using the New England 39-bus system. The active power generation delivered by several generators in the network, and the voltage magnitudes of selected busbars are analyzed and noted to be in close agreement with those obtained using PowerFactory. The computational performance of the package is compared to that of PowerFactory and is found to be comparable for load-step simulations; however, PowerFactory is found to be considerably faster for short-circuit simulations. As computational performance is not a priority at this stage of development, this is expected, and speed optimization is planned for future work. RMSPowerSims.jl is available under an open-source license and can be downloaded from GitHub.
Addressing Uncertainty in Renewable Energy Integration for Western Australia’s Mining Sector: A Robust Optimization Approach
Authors: Ghahramani Mehrdad , Habibi Daryoush , Ghamari Seyyedmorteza , Aziz Asma
Published: November 2024

The mining industry is a key contributor to Western Australia’s economy, with over 130 mining operations that produce critical minerals such as iron ore, gold, and lithium. Ensuring a reliable and continuous energy supply is vital for these operations. This paper addresses the challenges and opportunities of integrating renewable energy sources into isolated power systems, particularly under uncertainties associated with renewable energy generation and demand. A robust optimization approach is developed to model a multi-source hybrid energy system that considers risk-averse, risk-neutral, and risk-seeking strategies. These strategies address power demand and renewable energy supply uncertainties, ensuring system reliability under various risk scenarios. The optimization framework, formulated as a mixed integer linear programming problem and implemented in Python using the Gurobi Optimizer, integrates renewable energy sources such as wind turbines, photovoltaic arrays, and demand response programs alongside traditional diesel generators, boilers, combined heat and power units, and water desalination. The model ensures reliable access to electricity, heat, and water while minimizing operational costs and reducing reliance on fossil fuels. A comprehensive sensitivity analysis further examines the impact of uncertainty margins and the value of a lost load on the total system cost, providing insights into how different risk strategies affect system performance and cost-efficiency. The results are validated through three case studies demonstrating the effectiveness of the proposed approach in enhancing the resilience and sustainability of isolated power systems in the mining sector. Significant improvements in reliability, scalability, and economic performance are observed, with the sensitivity analysis highlighting the critical trade-offs between cost and reliability under varying uncertainty conditions.
Parameter Analysis of Anion Exchange Membrane Water Electrolysis System by Numerical Simulation
Authors: Chang Shing-Cheng , Gu Ru-En , Chan Yen-Hsin
Published: November 2024

Anion exchange membrane electrolysis, which combines the advantages of both alkaline electrolysis and proton-exchange membrane electrolysis, is a promising technology to reduce the cost of hydrogen production. The present work focused on the study of the electrochemical phenomena of AEM electrolysis and the investigation of the key factors of the AEM hydrogen production system. The numerical model is established according to electrochemical reactions, polarization phenomena, and the power consumption of the balance of plant components of the system. The effects of operation parameters, including the temperature and hydrogen pressure of the electrolyzer, electrolyte concentration, and hydrogen supply pressure on the energy efficiency are studied. The basic electrochemical phenomena of AEM water electrolysis cells are analyzed by simulations of reversible potential and activation, and ohmic and concentration polarizations. The results reveal that increasing the operating temperature and hydrogen production pressure of the AEM electrolyzer has positive effects on the system’s efficiency. By conducting an optimization analysis of the electrolyzer temperature—which uses the heat energy generated by the electrochemical reaction of the electrolyzer to minimize the power consumption of the electrolyte pump and heater—the AEM system with an electrolyzer operating at 328 K and 30 bar can deliver hydrogen of pressure up to 200 bar under an energy efficiency of 56.4%.
Advancing Water Security and Agricultural Productivity: A Case Study of Transboundary Cooperation Opportunities in the Kabul River Basin
Authors: Taraky Yar M. , McBean Ed , Binns Andrew , Gharabaghi Bahram
Published: November 2024

The Kabul River Basin (KRB) is witnessing frequent flood and drought events that influence food production and distribution. The KRB is one of the world’s poorest regions regarding food security. Food security issues in the KRB include shifts in short-term climate cycles with significant river flow variations that result in inadequate water distribution. Due to the lack of hydro-infrastructure, low irrigation efficiency, and continuing wars, the Afghanistan portion of the KRB has experienced low agricultural land expansion opportunities for food production. This research assesses the relationship between flood mitigation, flow balances, and food production and, cumulatively, assesses the social and economic well-being of the population of the KRB. SWAT modeling and climate change (CCSM4) implications are utilized to assess how these relationships impact the social and economic well-being of the population in the KRB. The intricacies of transboundary exchange and cooperation indicate that the conservation of ~38% of the water volume would nearly double the low flows in the dry season and result in the retention of ~2B m3/y of water for agricultural developmental use. Results show that the peak flood flow routing in reservoirs on the Afghanistan side of the KRB would have a substantial positive impact on agricultural products and, therefore, food security. Water volume conservation has the potential to provide ~44% more arable land with water, allowing a ~51% increase in crop yield, provided that improved irrigation efficiency techniques are utilized.
Analysis of the Diversity of Corticolous Lichens Associated with Tree Trunks in the Understories of Four Tropical Dry Forests of the Atlántico Department in Colombia
Authors: España-Puccini Pierine , Gómez Juan P. , Muñoz-Acevedo Amner , Posada-Echeverría Daniel , Martínez-Habibe María C.
Published: November 2024

Tropical dry forests (TDFs) are unique ecosystems with high biodiversity, including a rich variety of lichen species. Lichens are sensitive to environmental changes and can serve as bioindicators of ecosystem health. This study examined the diversity of lichen communities at four TDF sites in the Atlántico Department of Colombia. More than 700 tree lichen specimens were collected and identified at the four sites. A total of 135 species of lichens were identified, of which 19 are possibly undescribed. The most diverse sites were Usiacurí and Repelón, both protected areas with relatively well-preserved forests. The findings of this study demonstrate that the Atlántico TDFs host a large diversity of lichens, with a significant number of records of new species. The observed differences in species composition between sites highlight the importance of habitat heterogeneity and anthropogenic pressures on lichen communities. The results emphasize the need for conservation strategies to protect these ecologically valuable lichen communities within the Atlántico TDFs.
Karst Ecosystem: Moso Bamboo Intercropping Enhances Soil Fertility and Microbial Diversity in the Rhizosphere of Giant Lily (Cardiocrinum giganteum)
Authors: Zhang Jie , Wu Haoyu , Gao Guibin , Peng Yuwen , Ning Yilin , Huang Zhiyuan , Chen Zedong , Xu Xiangyang , Wu Zhizhuang
Published: November 2024

Intercropping affects soil microbial community structure significantly; however, the effects on understory medicinal plants in karst areas remain unclear. We investigated the effects of four intercropping systems (Moso bamboo, Chinese fir, bamboo-fir mixed forest, and forest gap) on the rhizosphere microbial communities of giant lily (Cardiocrinum giganteum), an economically important medicinal plant in China. We assessed the intercropping impact on rhizosphere microbial diversity, composition, and co-occurrence networks and identified key soil properties driving the changes. Bacterial and fungal diversity were assessed by 16S rRNA and ITS gene sequencing, respectively; soil physicochemical properties and enzyme activities were measured. Moso bamboo system had the highest fungal diversity, with relatively high bacterial diversity. It promoted a distinct microbial community structure with significant Actinobacteria and saprotrophic fungi enrichment. Soil organic carbon, total nitrogen, and available potassium were the most influential drivers of microbial community structure. Co-occurrence network analysis revealed that the microbial network in the Moso bamboo system was the most complex and highly interconnected, with a higher proportion of positive interactions and a greater number of keystone taxa. Thus, integrating Moso bamboo into intercropping systems can enhance soil fertility, microbial diversity, and ecological interactions in the giant lily rhizosphere in karst forests.