bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatiotemporal Distribution of nirS-Type Denitrifiers in Cascade Reservoir Sediments of the Qinghai Plateau
Authors: Wu Yi , Mao Xufeng , Yu Hongyan , Li Hongyan , Xiao Feng , Mo Yuhua , Ji Haichuan , Ma Yuanjie
Published: October 2024

Compared to single damming, the impact of cascade damming on nitrogen-related microorganisms in river ecosystems exhibits greater complexity. However, there is still a lack of research on the response of denitrifiers to the construction of cascade reservoirs. A study was conducted on 10 cascade reservoirs in the upper reaches of the Yellow River to investigate the impact of cascade reservoir construction on nirS-type denitrifying bacteria in sediments. Sediment samples were collected in May (dry season) and August (wet season) of 2023. The spatiotemporal characteristics of the nirS-type denitrifying bacterial community and gene abundance were analyzed using Illumina high-throughput sequencing and real-time fluorescence quantification PCR (qPCR). Redundancy analysis (RDA) and variation partitioning (VP) were utilized to assess the impact of environmental factors on these communities. The results showed the following: (1) Proteobacteria was the predominant phylum of nirS-type denitrifying bacteria in cascade reservoir sediments. At the genus level, unclassified Proteobacteria (69.51–95.64%) showed the highest relative abundance, followed by Paracoccus, Rhodanobacter, and Pseudomonas, indicating that unclassified Proteobacteria may dominate denitrification in these reservoir sediments. (2) The α and β diversity indices of nirS-type denitrifying bacteria were higher in the dry season than in the wet season, and also higher in young reservoirs compared to old reservoirs (p < 0.05). (3) Temporally, the abundance of the nirS gene was significantly higher in the wet season (12.71 × 107 copies/g dry sediment) compared to the dry season (66.35 × 105 copies/g dry sediment). Spatially, the abundance of the nirS gene was higher in the central region, while relatively lower at both ends. (4) RDA and VP analysis indicated that the community structure and abundance of nirS-type denitrifying bacteria were significantly influenced by the total nitrogen in sediments (19.31%) and water temperature (14.13%). Spearman correlation analysis showed that organic carbon significantly affected the diversity of nirS-type denitrifying bacteria (p < 0.05). The results contribute to a better understanding of the nitrogen-related microbial community in cascade reservoir sediments of the Yellow River, providing a scientific basis for reservoir management.
Bacterial Removal Separation with a Reduced Throughput Adapted for Artisan Cheese Dairies—Effect on Clostridial Spore Counts and Milk Quality
Authors: Wente Nicole , Zscherpe Christian , Leimbach Stefanie , Mallast Paula , Tellen Anne , Zhang Yanchao , Schwermann Saskia , Krömker Volker
Published: October 2024

Late blowing is a prevalent and costly cheese defect caused by clostridia. In organic cheese production, the use of additives that inhibit the growth of clostridia is prohibited. Furthermore, mechanical methods for the removal of clostridia are impractical in organic dairies due to the small batch sizes involved and separation process temperatures (~55 °C) that are incompatible with the standards required for raw milk cheese production. The aim of this study was to investigate whether sufficient spore reduction can be achieved at lower temperatures (10, 35 °C) with a downsized separator (CSC18-01-077, GEA Westfalia) by varying the process parameters to describe the influence on the suitability of the treated milk for cheese production. In addition to spore reduction, total mesophilic bacteria count, the effects of separation on fat and casein losses, and damage to milk fat globules were assessed, as they can affect the yield and cheese quality. A significant reduction (p < 0.01) in spore concentration and total bacteria count in milk was achieved, regardless of the process parameters employed. Casein losses are reduced at 35 °C compared to 55 °C. The extent of fat loss in the sludge at 35 °C was minimal. The reduction in milk fat globule size was significant. Nonetheless, the results of this study demonstrate that a downsized centrifuge can be employed to augment the quality of small-batch raw milk cheese, particularly at a temperature of 35 °C.
Sustainable Transportation Characteristics Diary—Example of Older (50+) Cyclists
Authors: Jevremović Sreten , Kachadoorian Carol , Arnaut Filip , Kolarski Aleksandra , Srećković Vladimir A.
Published: October 2024

Cycling is a sustainable and healthy form of transportation that is gradually becoming the primary means of transportation over shorter distances in many countries. This paper describes the dataset used to determine the cycling characteristics of seniors in the USA and Canada. For these purposes, a specially created questionnaire was used in a survey conducted from August 2021 to July 2022. The questionnaire contained sections related to the general socio-demographic characteristics of the respondents, general characteristics of cycling (type of bicycle, cycle time, mileage, etc.), and specific characteristics of cycling (riding in night conditions, termination of cycling, motivating and demotivating factors for cycling, etc.). The total sample consisted of 5096 respondents (50+ years old). This database is particularly significant because it represents the first set of publicly available data related to the cycling characteristics of older adults. The database can be used by various researchers dealing with this topic, but also by the decision-makers who want to design a sustainable and accessible cycling infrastructure, respecting the requirements of this category of users. Finally, this dataset can serve as an adequate basis in the process of determining the specificities and understanding the needs of older cyclists in traffic.
Collision of Basal Cell Carcinoma with Apocrine–Sebaceous–Follicular Unit Neoplasms
Authors: Piqué-Duran Enric
Published: October 2024

Background: Tumor collision is a rare event, with an estimated incidence of 0.0017%. Seborrheic keratosis, melanocytic nevi, and basal cell carcinoma (BCC) are by far the most common entities involved in collisions. Most authors consider collision to be an incidental event. I planned a retrospective study comparing BCC/apocrine–sebaceous–follicular unit (ASFu) neoplasm collisions with squamous cell carcinoma (SCC)/ASFu neoplasm collisions. Materials and methods: Files from 2005 to 2017 from Dr. José Molina Orosa Hospital were assessed; in the review, cases of collisions between BCCs or SSCs and ASFu tumors, including cysts, were identified. Results: Out of 3247 BCC cases, 12 biopsies were retrieved. Of 825 biopsies, none belonged to the SCC group. The ASFu tumors that collided with a BCC were as follows: four hidrocystomas, three infundibular cysts, two steatocystomas, two trichilemmomas, one spiradenoma, and one clear-cell hidradenoma (one patient had two cysts associated with a BCC). These cases correspond to seven female patients and five male patients aged between 26 and 91 years old. A quarter of these patients were immunosuppressed. Most ASFu neoplasms were found to be located beneath the BCC (8/12). Discussion: To the best of my knowledge, this report describes three new collisions of BCCs with ASFu neoplasms (infundibular cysts, steatocystomas, and a spiradenoma). My results also suggest that immunosuppression could be a factor that predisposes a patient to these collisions. I review current hypotheses in an effort to explain these collisions and contribute some new theories.
Abbreviated Multiparametric MR Solution (the “Liver Triple Screen”), the Future of Non-Invasive MR Quantification of Liver Fat, Iron, and Fibrosis
Authors: Low Gavin , Chee Ryan K. W. , Wong Yu Jun , Tandon Puneeta , Manolea Florin , Locas Stephanie , Ferguson Craig , Tu Wendy , Wilson Mitchell P.
Published: October 2024

Background/Objectives: To review the findings of a multiparametric MRI (the “liver triple screen”) solution for the non-invasive assessment of liver fat, iron, and fibrosis in patients with chronic liver disease (CLD). Methods: A retrospective evaluation of all consecutive triple screen MRI cases was performed at our institution over the last 32 months. Relevant clinical, laboratory, and radiologic data were analyzed using descriptive statistics. Results: There were 268 patients, including 162 (60.4%) males and 106 (39.6%) females. The mean age was 54 ± 15.2 years (range 16 to 71 years). The most common cause of CLD was metabolic dysfunction-associated steatotic liver disease (MASLD) at 45.5%. The most common referring physician group was Gastroenterology at 62.7%. In 23.9% of cases, the reason for ordering the MRI was a pre-existing failed or unreliable US elastography. There were 17 cases (6.3%) of MRI technical failure. Our analysis revealed liver fibrosis in 66% of patients, steatosis in 68.3%, and iron overload in 22.1%. Combined fibrosis and steatosis were seen in 28.7%, steatosis and iron overload in 16.8%, fibrosis and iron overload in 6%, and combined fibrosis, steatosis, and iron overload in 4.1%. A positive MEFIB index, a predictor of liver-related outcomes, was found in 57 (27.5%) of 207 patients. Incidental findings were found in 14.9% of all MRIs. Conclusions: The liver triple screen MRI is an effective tool for evaluating liver fat, iron, and fibrosis in patients with CLD. It provides essential clinical information and can help identify MASLD patients at risk for liver-related outcomes.
The Impact of Pneumoperitoneum on Mean Expiratory Flow Rate: Observational Insights from Patients with Healthy Lungs
Authors: Bogár Lajos , Domokos Kamilla , Csontos Csaba , Sütő Balázs
Published: October 2024

Background/Objectives: Surgical pneumoperitoneum (PP) significantly impacts volume-controlled ventilation, characterized by reduced respiratory compliance, elevated peak inspiratory pressure, and an accelerated expiratory phase due to an earlier onset of the airway pressure gradient. We hypothesized that this would shorten expiratory time, potentially increasing expiratory flow rate compared to pneumoperitoneum conditions. Calculations were performed to establish correlations between respiratory parameters and the mean increase in expiratory flow rate relative to baseline. Methods: Mechanical ventilation parameters were recorded for 67 patients both pre- and post-PP. Ventilator settings were standardized with a tidal volume of 6 mL/kg, a respiratory rate of 12 breaths per minute, a PEEP of 3 cmH2O, an inspiratory time of 2 s, and an inspiratory-to-expiratory ratio of 1:1.5 (I:E). Results: The application of PP increased both peak inspiratory pressure and mean expiratory flow rate by 28% compared to baseline levels. The elevated intra-abdominal pressure of 20 cmH2O resulted in a 34% reduction in dynamic chest compliance, a 50% increase in elastance, and a 20% increase in airway resistance. The mean expiratory flow rate increments relative to baseline showed a significant negative correlation with elastance (p = 0.0119) and a positive correlation with dynamic compliance (p = 0.0028) and resistance (p = 0.0240). Conclusions: A PP of 20 cmH2O resulted in an increase in the mean expiratory flow rate in the conventional I:E ratio in the volume-ventilated mode. PP reduces lung and chest wall compliance by elevating the diaphragm, compressing the thoracic cavity, and increasing airway pressures. Consequently, the lungs and chest wall stiffen, requiring greater ventilatory effort and accelerating expiratory flow due to increased airway resistance and altered pulmonary mechanics. Prolonging the inspiratory phase through I:E ratio adjustment helps maintain peak inspiratory pressures closer to baseline levels, and this method enhances the safety and efficacy of mechanical ventilation in maintaining optimal respiratory function during laparoscopic surgery.
Identification and Functional Investigation of SOX4 as a Novel Gene Underpinning Familial Atrial Fibrillation
Authors: Jiang Wei-Feng , Sun Yu-Min , Qiu Xing-Biao , Wu Shao-Hui , Ding Yuan-Yuan , Li Ning , Yang Chen-Xi , Xu Ying-Jia , Jiang Ting-Bo , Yang Yi-Qing
Published: October 2024

Background: Atrial fibrillation (AF) signifies the most prevalent supraventricular arrhythmia in humans and may lead to cerebral stroke, cardiac failure, and even premature demise. Aggregating strong evidence points to genetic components as a cornerstone in the etiopathogenesis of familial AF. However, the genetic determinants for AF in most patients remain elusive. Methods: A 4-generation pedigree with idiopathic AF and another cohort of 196 unrelated patients with idiopathic AF as well as 278 unrelated healthy volunteers were recruited from the Chinese population of Han ethnicity. A family-based whole-exome sequencing examination followed by a Sanger sequencing assay in all research subjects was implemented. The functional impacts of the identified SOX4 mutations were explored via a dual-reporter assay. Results: Two new heterozygous SOX4 mutations, NM_003107.3: c.211C>T; p.(Gln71*) and NM_003107.3: c.290G>A; p.(Trp97*), were observed in the family and 1 of 196 patients with idiopathic AF, respectively. The two mutations were absent in the 278 control individuals. The biochemical measurements revealed that both Gln71*- and Trp97*-mutant SOX4 failed to transactivate GJA1 (Cx43). Moreover, the two mutations nullified the synergistic activation of SCN5A by SOX4 and TBX5. Conclusions: The findings first indicate SOX4 as a gene predisposing to AF, providing a novel target for antenatal genetic screening, individualized prophylaxis, and precision treatment of AF.
Diagnosis and Management of Malignant Epithelial Nail Unit Tumors
Authors: Iorizzo Matilde , Vollono Laura , Richert Bertrand
Published: October 2024

Background: Malignant epithelial nail unit tumors pose significant diagnostic and therapeutic challenges due to their clinical presentation often mimicking benign conditions and due to the need to preserve as much nail unit function as possible during surgery. Early detection is crucial, even if none of these tumors represent a life-threatening disease. Objectives: This review focus on squamous cell carcinoma, verrucous carcinoma, eccrine porocarcinoma, onychocytic carcinoma, basal cell carcinoma, malignant onychopapilloma, malignant onycholemmal cyst and onycholemmal carcinoma. Methods: Existing literature on the aforementioned tumors has been revised and synthesized. Results: Clinical presentation, pathology, diagnostic procedures, risk factors and the challenges associated with surgical management have been described in detail. Conclusions: Malignant epithelial tumors of the nail unit require careful evaluation and management due to their complex presentation. Early detection and an informed surgical approach are essential to improve patient outcomes and minimize complications.
Accuracy Assessment of EM3D App-Based 3D Facial Scanning Compared to Cone Beam Computed Tomography
Authors: Barone Selene , Antonelli Alessandro , Salviati Marianna , Greco Vincenzo , Bennardo Francesco , Becker Kathrin , Giudice Amerigo , Simeone Michele
Published: October 2024

Background: The use of 3D facial scans is becoming essential for dental practice. However, traditional scanners require labor-intensive procedures and are expensive, making them less accessible in routine clinical practice. In this context, high-performance smartphones and dedicated apps offer a more accessible alternative. This study aims to validate the accuracy of the EM3D app, which utilizes the iPhone’s TrueDepth camera technology, by comparing it to Cone Beam Computed Tomography (CBCT). Methods: Thirty patients requiring CBCT scans were recruited for the study. Facial scans obtained with the TrueDepth camera of the iPhone 13 Pro in conjunction with EM3D app were automatically superimposed onto the 3D models derived from the CBCTs through the implementation of a deep learning methodology. The approach enabled the automatic identification of fifteen landmarks to perform linear and angular measurements for quantitative assessment. A color map was created to highlight discrepancies between the overlaid meshes, and the overall surface differences between the models were automatically quantified. Results: The overall surface difference between the CBCT and EM3D scans was highly accurate, with a mean discrepancy of 0.387 ± 0.361 mm. The mean discrepancies of most measurements were lower than 1 mm (five out of six; 83.33%) between the groups, with no significant differences (p > 0.05). Conclusions: The combination of the iPhone’s TrueDepth camera and the EM3D app exhibited high accuracy for 3D facial modeling. This makes it a cost-effective alternative to professional scanning systems.
Bifurcations in a Model of Criminal Organizations and a Corrupt Judiciary
Authors: Harari G. S. , Monteiro L. H. A.
Published: October 2024

Let a population be composed of members of a criminal organization and judges of the judicial system, in which the judges can be co-opted by this organization. In this article, a model written as a set of four nonlinear differential equations is proposed to investigate this population dynamics. The impact of the rate constants related to judges’ co-optation and ex-convicts’ recidivism on the population composition is explicitly examined. This analysis reveals that the proposed model can experience backward and transcritical bifurcations. Also, if all ex-convicts relapse, organized crime cannot be eradicated even in the absence of corrupt judges. The results analytically derived here are illustrated by numerical simulations and discussed from a crime-control perspective.
Exploring the Dynamic Link Between Trade Openness, External Debt, and Economic Growth in Sub-Saharan Africa: Challenges and Considerations
Authors: Osuma Godswill , Nzimande Ntokozo Patrick
Published: October 2024

This study investigates the dynamic relationship between trade openness, external debt, and economic growth in Sub-Saharan Africa, focusing on the period from 1990 to 2023. The research examines how trade openness and external debt impact regional economic performance by employing Panel Autoregressive distributed lag (ARDL) techniques utilizing the pool mean group and mean group estimator’s approach. The analysis reveals that while trade openness does not stimulate economic growth, external debt similarly has significant challenges, often hindering long-term development prospects within Sub-Saharan African countries. The findings underscore the importance of managing debt sustainably and aligning trade policies with growth-enhancing strategies. Additionally, human capital and institutional quality are essential endogenous growth factors that significantly influence economic growth in Sub-Saharan Africa. The study recommends that while trade openness alone may not directly drive economic growth, its benefits can be amplified by complementary strategies such as investing in human capital, technological adoption, and industrial policy. The study concludes with policy recommendations to enhance economic resilience and foster sustainable growth, such as attracting foreign direct investment, combined with infrastructure development and sound fiscal management.
Demographic Change and Regional Specialisation: The Case of Greek NUTS II Regions
Authors: Christofakis Manolis , Gkouzos Andreas , Gaki Eleni
Published: October 2024

This paper investigates the impact of demographic changes on regional specialisation across Greek NUTS2 regions from 2001 to 2021. It explores the relationship between age-specific variations in the economically active population and gross value added (GVA) across different economic sectors. Using demographic and GVA data alongside spatial analysis techniques such as spatial regression models, median Local Moran’s I, and location quotient, the study identifies spatial autocorrelation patterns. Findings reveal strong correlations between the economically active population and GVA, highlighting demographic factors’ crucial role in regional economic performance. Over the period 2001 to 2021, the 15–24 and 25–34 age groups generally experienced declining growth rates in total, male, and female economically active populations, while the 45–54 and 55–64 age groups saw increases. Regions adjacent to those with higher economically active populations showed high–high clustering. Especially, Attiki and Kentriki Makedonia, the two metropolitan regions of Greece, influence neighbouring regions through economic spillover effects, fostering economic sector clustering and emphasising regional specialisation. These findings highlight the complex interplay between demographic shifts and regional economic outcomes, noting spatial disparities and the need for targeted policy interventions. The study provides empirical evidence demonstrating how demographic changes influence regional economic development across different regions.
Tax Evasion and Company Survival: A Brazilian Case Study
Authors: Tonetto Jorge Luis , Pique Josep Miquel , Fochezatto Adelar , Rapetti Carina
Published: October 2024

Enterprises face significant growth and survival challenges in highly competitive markets. Many companies fail to meet their tax obligations, which deprives society of essential resources and often results in tax penalties. This article examines whether companies that receive tax fines for evasion have a longer or shorter life expectancy compared to those that consistently comply with tax regulations. To analyze survival rates, the Kaplan–Meier estimator and Cox regression model were applied, considering factors such company size, sector, location, and tax evasion fines. The study included data from 11,297 firms established in 2017, in Rio Grande do Sul, Brazil. The findings indicate that companies fined for tax evasion had a higher survival rate (69%) compared to those without fines (38%) by 2023. This suggests that fines might serve as a corrective measure, helping companies realign and improve their chances of survival. Additionally, the study shows that medium-sized enterprises face significant challenges, possibly due to exceeding the limits of a simplified tax regime. This study highlights the importance of continued research across different regions and countries to validate these findings and enhance tax administration strategies.
Why Can’t My Child Play Too? Current Challenges of Public Playgrounds for Children with Disabilities.
Authors: Tait Kathleen , Liang Peng , Silveira Sue
Published: October 2024

Current Australian legislation promotes playground inclusion for all children, and playgrounds serve as local, safe, and vital spaces for children of all ages to play. The World Health Organisation International Classification of Functioning, Disability and Health, Children and Youth Version states that play is a key activity to engage children with disability in various areas of their lives, and there is no doubt that playgrounds can promote community and social engagement for all children. Consequently, accessible playgrounds are important because they can offer motor, psychological, and social skill development in a fun-filled environment. Nonetheless, some children encounter challenges in playground settings. Surprisingly, very little research has been conducted in Australia on the experiences of children with disabilities in public playgrounds. According to the National Autism Strategy announced by the Australian Government in 2022, there is a growing number of Australians on the autism spectrum. For many individuals with an autistic spectrum disorder (ASD), life outcomes in education, health, and family functioning are worse than they should be. Consequently, while this paper addresses a general perspective of inclusive playground experience for children with disabilities, there is also a focus on the experiences of children with ASD in playgrounds. As a result, this literature review begins with an introduction to the prevalence of autistic spectrum disorder and its associated characteristics. It presents existing research on play, examining various playground factors that impact the experiences of children with disabilities, including ASD. In addition, the role of parents/carers in influencing the outdoor experiences of children with disability is also explored. In conclusion, this paper summarises key findings and proposes relevant research questions to address these gaps.
Effectiveness and Long-Term Effects of SER+ FELIZ(mente): A School-Based Mindfulness Program for Portuguese Elementary Students
Authors: Magalhães Sofia , Carvalheiras Carolina , Ribeiro Catarina , Francisco Cátia , Mourisco Cristina , Leal Teresa , Limpo Teresa
Published: October 2024

School-based mindfulness programs (SBMPs) have gained global popularity. Yet, there is a need for more rigorous procedures to develop and assess them. This study aimed to address these limitations by examining the effectiveness of a Portuguese SBMP, called SER + FELIZ(mente). The final sample included 190 third and fourth-graders: 99 in the SBMP group and 91 in a wait-list group. Effects on attentional control, emotional regulation and wellbeing were examined in the short term (T2) and 6 months later (T3). We also tested the moderating role of age, gender, and baseline scores. Using a multilevel modeling approach, results showed a clear benefit of SBMP on emotional wellbeing at T2 but not T3. Despite SBMP students surpassing wait-list students in attentional control, cognitive reappraisal, and social wellbeing at T2, these effects were due to a levelling off among SBMP students, coupled with a decline among wait-list students from T1 to T2. These effects were moderated by age at T3. While universal improvements were limited, our SBMP seemed to have acted as a shield against school stressors (i.e., likely, test anxiety at T2). This opens a new avenue for research concerning the role of SBMPs in preventing (rather than repairing) mental-health issues in elementary students.
Status of 21st-Century Liberal Arts Education: Curriculum Reform in Japanese Universities and Evaluation by Society
Authors: Yamada Reiko
Published: October 2024

Ever since bachelor’s competencies were introduced as a reference standard in Japan, many Japanese higher education institutions have been exploring and practicing liberal arts education for the 21st century. This study posits that incorporating new elements into university education has been a significant trend in liberal arts education for the 21st century. Building on the results of a comprehensive survey, primarily involving universities and industry, this study provides an in-depth analysis of how 21st-century liberal arts education is evaluated and expected by companies and others. A questionnaire survey was administered to universities regarding their provision of 21st-century liberal arts education and the status of reforms toward this education, and to companies and other members of society regarding their expectations and evaluation of 21st-century liberal arts education provided by universities, as well as the content they evaluate as learning outcomes for university students and the qualities and skills they consider when employing them. The findings from the survey of universities and companies indicate that the average scores for curriculum reform and establishment of new courses at universities and host companies are relatively low, as indicated by the relatively low average scores for keywords that companies place importance on. Undeniably, universities’ efforts toward reform and the reality of such efforts should be conveyed to the public. Through dialogue with the industrial sector, we can understand the necessity of sharing and realizing openness to developing human resources through collaboration and innovation.
Enacting Fairly or Fearfully? Unpacking the Enactment of Critical Thinking Policies in Chinese Senior High Schools
Authors: Xie Yan , Davies Maree , Smith Joanna
Published: October 2024

This study explores the enactment of critical thinking policies in Chinese senior high schools through the lens of Ball et al.’s policy enactment theory and within the broader context of Chinese education reform aimed at enhancing students’ thinking abilities. Employing a case study methodology with diverse data types, the research assessed current school-level practices and the effectiveness of Ball et al.’s framework in capturing interactions among objective contexts, policy actors, and cultural artefacts. Findings indicate that the framework captures these complexities when the policy is actively enacted. In one school, a systematic enactment mechanism facilitated diverse policy roles and external connections, thereby promoting schoolwide critical thinking development; however, another school exhibited fragmented practices due to the lack of key policy roles, despite students’ interest in deeper engagement. Challenges were also identified, including deficiencies in the mid-level education bureau and conflicts between a collective-oriented educational paradigm and the promotion of independent thinking. The study unravelled the nuances of the enactment of critical thinking policies in Chinese senior high schools. Future research could test the framework’s applicability for guiding the construction of policy mechanisms across different settings.
Asynchronous Sliding Mode Control of Networked Markov Jump Systems via an Asynchronous Observer Approach Based on a Dynamic Event Trigger
Authors: Deng Jianping , Lou Haocheng , Jiang Baoping
Published: October 2024

This paper explores the utilization of sliding mode control, which relies on an asynchronous observer, for Markov jump systems subject to external disturbances. Firstly, given that the system’s mode is not directly measurable and could potentially differ from the observer’s and controller’s mode, the paper constructs an asynchronous observer employing a hidden Markov model. Secondly, a sliding surface is designed to correspond with the asynchronous observer. Moreover, a multi-parameter event-triggered mechanism is incorporated into the observer design to alleviate bandwidth strain. Thirdly, by applying the integrated sliding mode control law, we ensure that the system state trajectories will reach the sliding surface within a finite time. Fourthly, the achievement of H∞ stability is realized by making use of the Lyapunov function. Lastly, a practical-oriented example is presented to illustrate the efficiency of the established method.
Beam Orbital Parameter Prediction Based on the Deployment of Cascaded Neural Networks at Edge Intelligence Acceleration Nodes
Authors: Hou Mingyang , Guo Yuhui , Yang Guijin , Yang Xuhui , Cao Zigeng , Chen Youxin , He Yuan
Published: October 2024

During the beam current calibration process, accurate guidance of the beam current to the metal target is a challenging issue for proton accelerators. To address this challenge, we propose the use of beam orbital parameters combined with reinforcement learning algorithms to achieve automatic beam calibration. This study introduces a system architecture that employs edge intelligent acceleration nodes based on deep learning acceleration techniques. We designed a system to predict BPM parameters using a cascaded backpropagation neural network (CBPNN) that is informed by the physical structure. This system serves as an environmental map for reinforcement learning, aiding beam current correction. The CBPNN was implemented on the acceleration node to hasten the forward inference process, leveraging sparsification, quantization algorithms, and pipelining techniques. Our experimental results demonstrated that the simulated inference speed reached 28 μs with FPGA hardware as the edge acceleration node, achieving forward inference speeds 35.66 and 12.66 times faster than those of the CPU and GPU. The energy efficiency ratio was 10.582 MOPS/W, which was 989 and 410 times that of the CPU and GPU, respectively. This confirms the designed architecture’s energy efficiency and low latency attributes.
Categorical Multi-Query Subgraph Matching on Labeled Graph
Authors: Sun Yunhao , Chen Xiaoao , Chen Heng , Qi Ruihua , Ning Bo
Published: October 2024

Subgraph matching stands as a fundamental issue within the research realm of graph analysis. In this paper, we investigate a novel combinatorial problem that encompasses both multigraph matching and subgraph matching. The objective of this investigation is to identify all data graphs within a larger graph that are isomorphic to the given query graphs. Firstly, multiple query graphs are collaborated through the design of a categorical graph, which aggregates similar query graphs into a single cluster. Following this, these similarity-clustered query graphs are integrated into a unified categorical graph. Secondly, a minimal isomorphic data graph is derived from a larger data graph, guided by the categorical graph. Additionally, an analysis of the inclusive and equivalence relationships among query nodes is conducted, with the aim of minimizing redundant matching computations. Simultaneously, all subgraph isomorphic mappings of the categorical graph onto the data graph are performed. Extensive empirical evaluations, conducted on both real and synthetic datasets, demonstrate that the proposed methods surpass the state-of-the-art algorithms in performance.
SOAMC: A Semi-Supervised Open-Set Recognition Algorithm for Automatic Modulation Classification
Authors: Di Chengliang , Ji Jinwei , Sun Chao , Liang Linlin
Published: October 2024

Traditional automatic modulation classification methods operate under the closed-set assumption, which proves to be impractical in real-world scenarios due to the diverse nature of wireless technologies and the dynamic characteristics of wireless propagation environments. Open-set environments introduce substantial technical challenges, particularly in terms of detection effectiveness and computational complexity. To address the limitations of modulation classification and recognition in open-set scenarios, this paper proposes a semi-supervised open-set recognition approach, termed SOAMC (Semi-Supervised Open-Set Automatic Modulation Classification). The primary objective of SOAMC is to accurately classify unknown modulation types, even when only a limited subset of samples is manually labeled. The proposed method consists of three key stages: (1) A signal recognition pre-training model is constructed using data augmentation and adaptive techniques to enhance robustness. (2) Feature extraction and embedding are performed via a specialized extraction network. (3) Label propagation is executed using a graph convolutional neural network (GCN) to efficiently annotate the unlabeled signal samples. Experimental results demonstrate that SOAMC significantly improves classification accuracy, particularly in challenging scenarios with limited amounts of labeled data and high signal similarity. These findings are critical for the practical identification of complex and diverse modulation signals in real-world wireless communication systems.
Scenario Generation Based on Ant Colony Optimization for Modelling Stochastic Variables in Power Systems
Authors: Fernández Valderrama Daniel , Guerrero Alonso Juan Ignacio , León de Mora Carlos , Robba Michela
Published: October 2024

Uncertainty is an important subject in optimization problems due to the unpredictable nature of real variables in the power system area, which can condition the solution’s accuracy. The effective modelling of stochastic variables can contribute to the reduction in losses in the system under evaluation and facilitate the implementation of an effective response in advance. To model uncertainty variables, the most extended technique is the scenario generation (SG) method. This method evaluates possible combinations of complete curves. Classical scenario generation methods are founded on probability distributions or robust stochastic optimization. This paper proposes a novel approach for constructing scenarios using the Ant Colony Optimization (ACO) algorithm, referred to as ACO-SG. This methodology does not require a previous statistical study of uncertainty data to generate new scenarios. A historical dataset and the desired number of scenarios are the inputs inserted into the algorithm. In the case study, the algorithm used historical data from the Savona Campus Smart Polygeneration Microgrid of the University of Genoa. The approach was applied to generate scenarios of photovoltaic generation and building consumption. The low values of the Euclidean distance were used in order to check the validity of the scenarios. Moreover, the error deviation of the scenarios generated with the goal of daily power were 1.77% and 0.144% for the cases of PV generation and building consumption, respectively. The different results for both cases are explained by the characteristics of the specific cases. Despite these different results, both were significantly low, which indicates the capability of the algorithm to generate any kind of feature within curves and its adaptability to any case of SG.
Emergency Capability Evaluation of Port-Adjacent Oil Storage and Transportation Bases: An Improved Analytic Hierarchy Process Approach
Authors: Xie Baojing , Shi Yongguo , Zhang Jinfeng , Ye Mengdi , Huang Xiaolan , Yang Xinxiang , Pan Lidong , Xu Xin , Yang Dingding
Published: October 2024

The large-scale storage and stable supply of oil products are essential for national energy security and economic development. As the economy expands and energy demands rise, centralized storage and supply systems become increasingly vital for ensuring the efficiency and reliability of oil product distribution. However, large oil storage depots present substantial safety risks. In the event of fires, explosions, or other accidents, emergency response efforts face stringent demands and challenges. To enhance the emergency response capabilities of oil storage and transportation bases (OSTBs), this paper proposes an innovative approach that integrates the improved analytic hierarchy process (IAHP) with the Entropy Weight Method (EMW) to determine the combined weights of various indices. This approach reduces the subjective bias associated with the traditional analytic hierarchy process (AHP). The emergency response capabilities of OSTBs are subsequently evaluated through fuzzy comprehensive analysis. An empirical study conducted on an OSTB in the Zhoushan archipelago quantitatively assesses its emergency preparedness. The results show that the base excels in pre-incident prevention, demonstrates robust preparedness and response capabilities, and exhibits moderate recovery abilities after incidents. These findings provide a theoretical foundation for reducing the likelihood of accidents, enhancing emergency response efficiency, and mitigating the severity of consequences. Practical recommendations are also offered based on the results.
Enhanced Short-Term Load Forecasting: Error-Weighted and Hybrid Model Approach
Authors: Yu Huiqun , Sun Haoyi , Li Yueze , Xu Chunmei , Du Chenkun
Published: October 2024

To tackle the challenges of high variability and low accuracy in short-term electricity load forecasting, this study introduces an enhanced prediction model that addresses overfitting issues by integrating an error-optimal weighting approach with an improved ensemble forecasting framework. The model employs a hybrid algorithm combining grey relational analysis and radial kernel principal component analysis to preprocess the multi-dimensional input data. It then leverages an ensemble of an optimized deep bidirectional gated recurrent unit (BiGRU), an enhanced long short-term memory (LSTM) network, and an advanced temporal convolutional neural network (TCN) to generate predictions. These predictions are refined using an error-optimal weighting scheme to yield the final forecasts. Furthermore, a Bayesian-optimized Bagging and Extreme Gradient Boosting (XGBoost) ensemble model is applied to minimize prediction errors. Comparative analysis with existing forecasting models demonstrates superior performance, with an average absolute percentage error (MAPE) of 1.05% and a coefficient of determination (R2) of 0.9878. These results not only validate the efficacy of our proposed strategy, but also highlight its potential to enhance the precision of short-term load forecasting, thereby contributing to the stability of power systems and supporting societal production needs.
Optimisation of Integrated Heat Pump and Thermal Energy Storage Systems in Active Buildings for Community Heat Decarbonisation
Authors: Al-Atari Zaid , Shipman Rob , Gillott Mark
Published: October 2024

The electrification of residential heating systems, crucial for achieving net-zero emissions, poses significant challenges for low-voltage distribution networks. This study develops a simulation model to explore the integration of heat pumps within active building systems for community heating decarbonisation. The model optimises heat pump operations in conjunction with thermal energy storage units to reduce peak demand on low-voltage networks by using real-time measured electricity demand data and modelled heat demand data for 76 houses. The study employs an algorithm that adjusts thermal storage charging and discharging cycles to align with off-peak periods. Three scenarios were simulated: a baseline with unoptimised heat pumps, a fixed threshold model, and an active building model with daily optimised thresholds. The results demonstrate that the active building model achieves a 21% reduction in peak demand on the low-voltage substation compared to the baseline scenario; it also reduces the total electrical energy consumption by 12% and carbon emissions by 17%. The fixed threshold scenario shows a 16% improvement in peak demand reduction, but it also shows an increase in energy consumption and emissions. These findings highlight the potential of active buildings to enhance the efficiency and sustainability of residential energy systems, marking a significant step toward decarbonising residential heating while maintaining grid stability.
Scalability and Replicability Analysis in Smart Grid Demonstration Projects: Lessons Learned and Future Needs
Authors: Losa Ilaria , Cossent Rafael
Published: October 2024

This paper compares various approaches to the scalability and replicability analysis (SRA) of smart grid pilot projects, highlighting the need for a comprehensive SRA methodology as called for by the European Commission and International Energy Agency. This study addresses the need for a standardized SRA methodology and explores how three EU-funded projects—Platone, EUniversal, and IElectrix—adapted the general guidelines developed by the BRIDGE initiative. These guidelines provide recommendations for developing a comprehensive large-scale deployment analysis. The results show that while the guidelines are usable and flexible, project-specific conditions and data availability limitations—particularly in regulatory and technical analysis—can pose challenges. Some key recommendations to overcome these and facilitate future applications are identified. These include defining SRA methodologies and securing data-sharing agreements early. The lack of standardized approaches for presenting SRA results hampers cross-project comparison. Thus, creating an open-use case repository and updating the BRIDGE guidelines with more detailed examples, benchmarks, and reference networks is recommended. Additionally, linking SRA with cost–benefit analysis (CBA) is suggested in order to evaluate the commercial viability of smart grid solutions. The paper concludes that while the BRIDGE guidelines have proven to be fit for purpose, further developments are needed to facilitate their practical application in real-world projects.
Definition of Critical Metrics for Performance Evaluation and Multiphase Flow Modeling in an Alkaline Electrolyzer Using CFD
Authors: Dreoni Marco , Balduzzi Francesco , Hossain Syed Sahil , Neben Matthias , Ferro Francesco Maria , Ferrara Giovanni , Bianchini Alessandro
Published: October 2024

Gas evolution and flow patterns inside an alkaline electrolyzer cell strongly affect efficiency, although such effects have not been explored in detail to date. The present study aims to critically analyze the dependence of cell performance on the multiphase flow phenomena, defining some key metrics for its assessment using CFD. Six performance indicators, involving gas accumulation, bubble coverage, and flow uniformity, are applied to a 3D CFD model of an alkaline cathodic cell, and possible optimizations of the cell geometry are evaluated. The results demonstrate the complexity of defining the optimal indicator, which strictly depends on the case study and on the analysis at hand. For the cell analyzed herein, the parameters linked to the electrode volume fraction were indicated as the most influential on the cell efficiency, allowing us to define the best geometry case during the optimization. Furthermore, a sensitivity analysis was conducted, which showed that higher mass flow rates are generally preferable as they are linked to higher bubble removal. Higher current densities, allowing enhanced gas production, are instead associated with slightly lower efficiencies and stronger nonuniformity of the electrolyte flow inside the cell.
Identification and Mapping of Eucalyptus Plantations in Remote Sensing Data Using CCDC Algorithm and Random Forest
Authors: Zhou Miaohang , Han Xujun , Wang Jinghan , Ji Xiangyu , Zhou Yuefei , Liu Meng
Published: October 2024

Eucalyptus plantations are one of the primary artificial forests in southern China, experiencing rapid expansion in recent years due to their significant socio-economic benefits. This expansion has raised concerns about the ecological environment, necessitating accurate mapping of eucalyptus plantations. In this study, the phenological characteristics of eucalyptus plantations were utilized as the primary classification basis. Long-term time series Landsat and Sentinel-2 data from 2000 to 2022 were rigorously preprocessed pixel by pixel using the Google Earth Engine (GEE) platform to obtain high-quality observation data. The Continuous Change Detection and Classification (CCDC) algorithm was employed to fit the multi-year observation data with harmonic curves, utilizing parameters such as normalized intercept, slope, phase, and amplitude of the fitted curves to characterize the phenological features of vegetation. A total of 127 phenological indices were generated using the Normalized Burn Ratio (NBR), Normalized Difference Fractional Index (NDFI), and six spectral bands, with the top 20 contributing indices selected as input variables for the random forest algorithm to obtain preliminary classification results. Subsequently, eucalyptus plantation rotation features and the Simple Non-Iterative Clustering (SNIC) superpixel segmentation algorithm were employed to filter the results, enhancing the accuracy of the identification results. The producer’s accuracy, user’s accuracy, and overall accuracy of the eucalyptus plantation map for the year 2020 were found to be 96.67%, 89.23%, and 95.83%, respectively, with a total area accuracy of 94.39%. Accurate mapping of eucalyptus plantations provides essential information and evidence for ecological environment protection and the formulation of carbon-neutral strategies.