bluuebunny/crossref_metadata_2025 huggingface dataset:
“Mimics” of Injuries from Child Abuse: Case Series and Review of the Literature
Authors: Focardi Martina , Gori Valentina , Romanelli Marta , Santori Francesco , Bianchi Ilenia , Rensi Regina , Defraia Beatrice , Grifoni Rossella , Gualco Barbara , Nanni Laura , Losi Stefania
Published: September 2024

The phenomenon of child abuse/maltreatment is underestimated and often represents a difficult challenge for healthcare professionals and forensic pathologists who must proceed with the differential diagnosis with accidental or self-induced events, or with lesions due to pathologies that overlap with that of mistreatment, defined as “Mimics”. This study presents a case series with the aim of discussing lesions that may mimic signs of physical abuse in children but are due to a different etiology to raise awareness and train healthcare professionals and forensic pathologists on possible confounding factors in order to avoid diagnostic errors. Six cases of “Mimics” out of 418 cases of suspected mistreatment (1.43% of cases) were identified, presenting skin lesions initially classified as injuries of abuse due to their location and type and, in particular, sexual abuse for three cases. Then, the lesions and the subjects, in particular the anamnestic history, were examined by a multidisciplinary team and the diagnosis of genital lichen sclerosus et atrophicus in three cases, and the results of popular healing techniques (i.e., “cupping”) in the other three cases were ascertained. These situations require specific skills and a forensic background from healthcare professionals to conduct a correct differential diagnosis and the intervention of a multidisciplinary team to investigate every possible pathology or alternative therapeutic practice that could simulate child abuse. In particular, when “mimics” are due to alternative medicine, it should not strictly be considered child abuse, but professionals must be aware of the hypothesis of mistreatment in case of non-medical indication or potential personal injuries from other crimes, such as illegal practice of the medicine. This awareness is also crucial to direct the child toward appropriate medical care, and it is essential to recognize that these conditions can coexist within the same clinical presentation.
Parents in Neonatal Pain Management—An International Survey of Parent-Delivered Interventions and Parental Pain Assessment
Authors: Ullsten Alexandra , Beken Serdar , Campbell-Yeo Marsha , Cavallaro Giacomo , Decembrino Nunzia , Durrmeyer Xavier , Garrido Felipe , Kristjánsdóttir Guðrún , Amponsah Abigail , Lago Paola , Haslund-Thomsen Helle , Ojha Shalini , Pölkki Tarja , Gomez Monica , Roue Jean-Michel , Simons Sinno , Slater Rebeccah , Stenkjaer Rikke-Louise , Ünal Sezin , Bosch Gerbrich , Wielenga Joke , Eriksson Mats ,   on behalf of the ESPR Special Interest Group for Neonatal Pain,   PEARL Research Group
Published: September 2024

Background: While parent-delivered pain management has been demonstrated to effectively reduce neonatal procedural pain responses, little is known about to what extent it is utilized. Our aim was to explore the utilization of parents in neonatal pain management and investigate whether local guidelines promote parent-delivered interventions. Methods: A web-based survey was distributed to neonatal units worldwide. Results: The majority of the 303 responding neonatal intensive care units (NICUs) from 44 countries were situated in high-income countries from Europe and Central Asia. Of the responding units, 67% had local guidelines about neonatal pain management, and of these, 40% answered that parental involvement was recommended, 27% answered that the role of parents in pain management was mentioned as optional, and 32% responded that it was not mentioned in the guidelines. According to the free-text responses, parent-delivered interventions of skin-to-skin contact, breastfeeding, and parental live singing were the most frequently performed in the NICUs. Of the responding units, 65% answered that parents performed some form of pain management regularly or always. Conclusions: There appears to be some practice uptake of parent-delivered pain management to reduce neonatal pain in high-income countries. Additional incorporation of these interventions into NICU pain guidelines is needed, as well as a better understanding of the use of parent-delivered pain management in low- and middle-income countries.
Systematic Mapping of Climate Change Impacts on Human Security in Bangladesh
Authors: Sultana Ferdous , Petzold Jan , John Sonali , Muehlberger Verena , Scheffran Jürgen
Published: September 2024

There is an increasing consensus that climate change undermines human security by exacerbating existing challenges, acting as a “threat multiplier”. Bangladesh is a climate hot spot due to its geographical location, dense population and vulnerable socio-economic infrastructure. Although there is an increasing number of studies on the impacts of climate change in Bangladesh, aggregated research that combines this evidence and provides a comprehensive overview is lacking. The aim of this research is to thoroughly investigate the characteristics of the academic literature on the complex pathways through which climate variability affects different components of human security in Bangladesh, allowing for determination of the trends and research gaps, as well as whether they lead to conflict or cooperation. We used a systematic mapping methodology to search and screen 1839 publications in Web of Science, including 320 publications for the final synthesis. We found a predominant research focus on rural areas, especially in the southwestern region, with declining crop yield, economic loss, migration, water shortage, food scarcity and health hazards being the highlighted impacts of climate change for Bangladesh. The impacts on food, economic, environmental, health and water security have been well studied, but we found significant research gaps in some human security components, especially energy security. Women and the economically disadvantaged are disproportionately affected, and the causal pathways between conflict or cooperation and the ever-changing climate lack research efforts, implying a dire need to focus on under-researched areas before they become more complex and difficult to address. Policies and interventions should prioritise climate-resilient agricultural practices and infrastructure in high-risk areas, building local capacities and integrating climate risk assessments into urban planning, considering the high influx of environmental migrants. This systematic map provides a foundation for future longitudinal studies, establishes a baseline for this era for future comparisons and serves as a reliable database for relevant stakeholders and policy makers.
Thermal Corrosion Properties of Composite Ceramic Coating Prepared by Multi-Arc Ion Plating
Authors: Ding Feng , Wei Xiaoxin , Cao Jiangdong , Ma Yujie , Su Hongbin , Zhao Ting , You Jiahan , Lv Yazhong
Published: September 2024

In this study, a NiCr/YSZ coating was applied to a γ-TiAl surface using multi-arc ion plating technology to enhance its high-temperature performance and explore the mechanisms of high-temperature oxidation and thermal corrosion. The thermal corrosion properties of the γ-TiAl matrix and NiCr/YSZ coating were investigated at 850 °C and 950 °C using a constant-temperature corrosion test in a 75% Na2SO4 + 25% NaCl mixture. The results indicate that after 100 h, the thermal corrosion weight gain of the coating samples was 70.1 mg/cm2 at 850 °C and 118.2 mg/cm2 at 950 °C. At these temperatures, sulfide formation on the surface increases, leading to a loose and porous surface. After 100 h of high-temperature corrosion at 850 °C, the primary oxidation product on the surface of the coating was tetragonal-ZrO2. At 950 °C, Y2O3, which mainly acts as a stabilizer in YSZ, reacted with Na2SO4, resulting in the continuous consumption of Y2O3. This reaction caused a substantial amount of tetragonal-ZrO2 to transform into monoclinic-ZrO2, altering the volume of the ceramic layer, which induced internal stress, crack propagation, and minor spallation. A continuous and dense internal thermally grown oxide (TGO) layer effectively impeded the diffusion of molten salt substances and oxygen, thereby significantly improving the thermal corrosion resistance of the thermal barrier coating.
The Effects of Different Ultrasonic Composite Surface Modifications on the Properties of H13 Steel for Shield Tunnel Machine Cutter Ring
Authors: Chen Xiuyu , Li Yalong , Li Longhui , Xu Huanbin , Li Yi , Xu Zhilong , Jiang Qingshan , Mu Shixu , Li Yin , Liu Heng
Published: September 2024

Tunnel boring machines (TBMs) are exposed to the impact of the ground shattering force and the friction of sandstone during excavation work, and are prone to wear and breakage, and other failures. Traditional heat treatment processes cannot simultaneously achieve the required high-energy composite structure of hard external and tough internal properties for cutter rings, leading to inadequate wear resistance and impact toughness under working conditions. This study utilizes H13 steel as the base material, and based on a study of carburizing, nitriding, and ultrasonic impact processes for H13 steel analyzing the effects of different high-energy composite modification processes on the hardness distribution, microstructure, and residual stress of H13 steel, the mechanisms by which high-energy composite modification processes affect the wear resistance and impact resistance of H13 steel are revealed. The results indicate that the wear amount and impact toughness of the sample subjected to carburizing and ultrasonic surface rolling composite strengthening were 1.9 mg and 27.34 J/cm2, demonstrating the best wear and impact resistance. This combination of properties allows the H13 steel cutter ring to achieve the optimal overall performance in terms of wear resistance and impact resistance.
An Unsupervised Approach for Treatment Effectiveness Monitoring Using Curvature Learning
Authors: Sagreiya Hersh , Durot Isabelle , Akhbardeh Alireza
Published: September 2024

Contrast-enhanced ultrasound could assess whether cancer chemotherapeutic agents work in days, rather than waiting 2–3 months, as is typical using the Response Evaluation Criteria in Solid Tumors (RECIST), therefore avoiding toxic side effects and expensive, ineffective therapy. A total of 40 mice were implanted with human colon cancer cells: treatment-sensitive mice in control (n = 10, receiving saline) and treated (n = 10, receiving bevacizumab) groups and treatment-resistant mice in control (n = 10) and treated (n = 10) groups. Each mouse was imaged using 3D dynamic contrast-enhanced ultrasound with Definity microbubbles. Curvature learning, an unsupervised learning approach, quantized pixels into three classes—blue, yellow, and red—representing normal, intermediate, and high cancer probability, both at baseline and after treatment. Next, a curvature learning score was calculated for each mouse using statistical measures representing variations in these three color classes across each frame from cine ultrasound images obtained during contrast administration on a given day (intra-day variability) and between pre- and post-treatment days (inter-day variability). A Wilcoxon rank-sum test compared score distributions between treated, treatment-sensitive mice and all others. There was a statistically significant difference in tumor score between the treated, treatment-sensitive group (n = 10) and all others (n = 30) (p = 0.0051). Curvature learning successfully identified treatment response, detecting changes in tumor perfusion before changes in tumor size. A similar technique could be developed for humans.
First Record of the Complete Mitochondrial Genome for the Genus Borbo (Lepidoptera, Hesperiidae): Characterization and Comparative Genomic Analysis
Authors: Xue Chao , Zhang Dan , Liu Dongkai , Jiao Laizheng , Li Ran , Yi Xianfeng
Published: September 2024

Butterflies of the genus Borbo are mainly distributed in the Oriental and Australian regions and are considered pests of important crops. However, no mitochondrial genomes have been reported for this genus until now, leaving the evolutionary characteristics and differentiation patterns of their mitogenomes unclear. In this study, we present the first complete mitochondrial genome sequence of the rice swift, Borbo cinnara. The circular double-stranded mitogenome was 15,508 bp in length, comprising 13 protein-coding genes (PCGs), 2 ribosomal RNA genes (rRNAs), 22 transfer RNA genes (tRNAs), and 1 non-coding control region (CR). Among the mitogenomes of Hesperiinae, the ND3 gene was found to be the most variable PCG, while COX1 was the most conserved. Selection pressure analysis revealed that ND3 was under relaxed purifying selection, whereas COX1 was subjected to strong purifying selection. Phylogenetic trees reconstructed using both the Bayesian inference (BI) and maximum likelihood (ML) methods yielded robust and identical topologies, confirming the sister relationship between B. cinnara and Pelopidas mathias at the mitogenome level. Methodologically, this research enriches novel molecular markers for the species identification of butterflies and enhances our understanding of mitogenomic evolution in Lepidoptera.
Identifying Cross-Regional Ecological Compensation Based on Ecosystem Service Supply, Demand, and Flow for Landscape Management
Authors: Wei Hejie , Wu Jiahui , Ma Yu , Li Ling , Yang Yi , Liu Mengxue
Published: September 2024

Clarifying the issues related to the supply, demand, and flow of ecosystem services is crucial for regional landscape management. This study employs the equivalence factor method and demand index quantification to analyze the supply and demand of ecosystem services in the Zheng-Bian-Luo region in 2000 and 2020. We used hotspot analysis tools and the minimum cumulative resistance model to establish the ecological corridors, identifying the spatial flow paths of ecosystem services in our site. By calculating the flow volume of the key corridor value through the breakpoint formula and field strength theory and combining this with the ratio of the regulating service value, we computed the ecological compensation amount, thereby realizing the value of the ecosystem service. The results indicate that the area of balance between ecosystem service supply and demand gradually decreased and the deficit area in the Zheng-Bian-Luo region increased 43.62% from 2000 to 2020 along with rapid urbanization. The total value flow of ecosystem services by the important ecological corridors in 2000 and 2020 was USD 242.40 million and USD 365.92 million, respectively. In 2020, it was predicted that Luanchuan County would receive ecological compensation totals of USD 237.76 million from each ecological demand area, and mainly from Jinshui District. Our findings support enhancing the quality of the ecological environment and optimizing the landscape management of the Yellow River’s Henan section.
Human Papillomavirus Genotypes Distribution in High-Grade Cervical Lesions and Invasive Cervical Carcinoma in Women Living in Mauritania: Implications for Cervical Cancer Prevention and HPV Prophylactic Vaccination
Authors: Abdoudaim Mariem Salma , Mohamed Abdellahi Mohamed Val , Mohamed Baba Nacer Dine , Mboumba Bouassa Ralph-Sydney , Ahmed Mohamed Lemine Cheikh Brahim , Bélec Laurent
Published: September 2024

Cervical cancer related to high-risk human papillomavirus (HR-HPV) is the second female cancer in Mauritania (Northwest Sahelian Africa). We assessed the distribution of HPV genotypes in Mauritanian women with high-grade cervical intraepithelial neoplasia (CIN2/3) or invasive cervical cancer (ICC). A prospective study was conducted in the Centre Hospitalier National, Nouakchott, Mauritania, to collect cervical biopsies among women suspected of CIN2/3 or cancer. HPV DNA detection and genotyping were carried out from formalin-fixed, paraffin-embedded biopsies using multiplex PCR (Human Papillomavirus Genotyping Real-Time PCR Kit, Bioperfectus Technologies Co., Taizhou, China). Fifty biopsies were included from women (mean age: 56.7 years) suffering from CIN2/3 (28.0%) and ICC (72.0%) which corresponded to 32 (64.0%) squamous cell carcinomas (SCC) and 4 (8.0%) adenocarcinomas (ADC). HPV DNA detection was successful in 47 (94.0%) samples. The most prevalent HR-HPV genotypes were HPV-45 (40.4%), HPV-16 (38.3%), HPV-39 and HPV-52 (23.4%), HPV-33 (17.0%), HPV-18 (14.9%), HPV-35 (4.2%), and HPV-56 (2.1%). The majority (93.6%) of HPV-positive biopsies contained at least one HPV type covered by the 9-valent Gardasil-9® vaccine, and 40.9% were infected by multiple vaccine HPV genotypes. To eradicate cervical cancer in Mauritania, prophylactic HPV vaccination must be combined with primary molecular screening of cervical HR-HPV infection.
Differences in Health Status between People with and without Disabilities in Ecuadorian Prisons
Authors: Vargas-Fernández Rodrigo , Hernández-Vásquez Akram
Published: September 2024

People with disabilities experience significant health inequalities and inequities compared to people without disabilities. However, there is little evidence on the frequency of health conditions and their association with disability in the prison context. The aim of this study was to compare the proportions and associations between various health conditions and different disability indicators. A secondary analysis of existing data from the Ecuador Prison Census 2022 was conducted. The variable of interest was based on three disability indicators from the six-item Washington Group Short Set on Functioning. A cross-tabulation was carried out to estimate the proportion of prisoners with different disability indicators by gender. These proportions were also calculated for different health conditions. Moreover, we analyzed the association between each of the three disability indicators and the different health conditions using multivariable logistic regression. A total of 30,612 incarcerated persons were included in the analysis, revealing that 8% had disabilities. According to gender, a higher proportion of women had disabilities (10.7% vs. 8.5%), some difficulty functioning (33.8% vs. 28.0%), and multiple disabilities (2.4% vs. 2.0%) compared to men. Furthermore, people with disabilities had a higher proportion of health conditions compared to those without a disability. Strong and very strong evidence of associations were found between different disability indicators and several health conditions, while a lower odds of drug use was observed in people with disabilities and multiple disabilities compared to their counterparts, and no evidence of association was found with Human Immunodeficiency Virus (HIV) infection. Our results reflect the health inequalities and inequities experienced by prisoners with disabilities. This highlights the need to implement targeted policies within prisons and to include prisoners with disabilities in the country’s agenda.
Optimizing Prognostic Predictions in Liver Cancer with Machine Learning and Survival Analysis
Authors: Cai Kaida , Fu Wenzhi , Wang Zhengyan , Yang Xiaofang , Liu Hanwen , Ji Ziyang
Published: September 2024

This study harnesses RNA sequencing data from the Cancer Genome Atlas to unearth pivotal genetic markers linked to the progression of liver hepatocellular carcinoma (LIHC), a major contributor to cancer-related deaths worldwide, characterized by a dire prognosis and limited treatment avenues. We employ advanced feature selection techniques, including sure independence screening (SIS) combined with the least absolute shrinkage and selection operator (Lasso), smoothly clipped absolute deviation (SCAD), information gain (IG), and permutation variable importance (VIMP) methods, to effectively navigate the challenges posed by ultra-high-dimensional data. Through these methods, we identify critical genes like MED8 as significant markers for LIHC. These markers are further analyzed using advanced survival analysis models, including the Cox proportional hazards model, survival tree, and random survival forests. Our findings reveal that SIS-Lasso demonstrates strong predictive accuracy, particularly in combination with the Cox proportional hazards model. However, when coupled with the random survival forests method, the SIS-VIMP approach achieves the highest overall performance. This comprehensive approach not only enhances the prediction of LIHC outcomes but also provides valuable insights into the genetic mechanisms underlying the disease, thereby paving the way for personalized treatment strategies and advancing the field of cancer genomics.
Bifurcation Diagrams of Nonlinear Oscillatory Dynamical Systems: A Brief Review in 1D, 2D and 3D
Authors: Marszalek Wieslaw , Walczak Maciej
Published: September 2024

We discuss 1D, 2D and 3D bifurcation diagrams of two nonlinear dynamical systems: an electric arc system having both chaotic and periodic steady-state responses and a cytosolic calcium system with both periodic/chaotic and constant steady-state outputs. The diagrams are mostly obtained by using the 0–1 test for chaos, but other types of diagrams are also mentioned; for example, typical 1D diagrams with local maxiumum values of oscillatory responses (periodic and chaotic), the entropy method and the largest Lyapunov exponent approach. Important features and properties of each of the three classes of diagrams with one, two and three varying parameters in the 1D, 2D and 3D cases, respectively, are presented and illustrated via certain diagrams of the K values, −1≤K≤1, from the 0–1 test and the sample entropy values SaEn>0. The K values close to 0 indicate periodic and quasi-periodic responses, while those close to 1 are for chaotic ones. The sample entropy 3D diagrams for an electric arc system are also provided to illustrate the variety of possible bifurcation diagrams available. We also provide a comparative study of the diagrams obtained using different methods with the goal of obtaining diagrams that appear similar (or close to each other) for the same dynamical system. Three examples of such comparisons are provided, each in the 1D, 2D and 3D cases. Additionally, this paper serves as a brief review of the many possible types of diagrams one can employ to identify and classify time-series obtained either as numerical solutions of models of nonlinear dynamical systems or recorded in a laboratory environment when a mathematical model is unknown. In the concluding section, we present a brief overview of the advantages and disadvantages of using the 1D, 2D and 3D diagrams. Several illustrative examples are included.
Corruption’s Crossroads: Exploring Firm Performance and Auditors’ Role in Emerging Markets
Authors: Sundarasen Sheela , Ibrahim Izani , Alsmady Ahnaf Ali , Krishna Tanaraj
Published: September 2024

This study examines the relationship between country-level corruption (proxied by the Corruption Perception Index, CPI) and firm performance (measured by Return on Assets, ROA) across 18,286 firms in the East Asia, South Asia, and Southeast Asia regions. Additionally, the moderating effects of audit quality (proxied by auditors’ reputation) on the relationship are examined. The findings of the study indicate a positive association between corruption and ROA in high-income nations, thus providing evidence in favor of the “greasing the wheel” theory. On the other hand, a negative association is documented in the upper middle- and low-income nations, which is consistent with the “sanding the wheel” notion. Notably, audit quality has a positive moderating influence on the relationship between corruption and ROA, especially in nations with low corruption levels, reaffirming the pivotal role of reputable auditors in enhancing firm performance within these economic contexts. The results of this study have important ramifications for forming policy suggestions and enhancing governance. The findings highlight the opportunity to improve governance practices and regulations to reduce corruption and increase transparency. Policymakers can develop ways to strengthen institutional frameworks by recognizing the complex link between corruption, corporate profitability, and the function of respected auditors.
Beyond the Classroom: Integrating the ORID Model for In-Depth Reflection and Assessment in Service-Learning
Authors: Kayan Fadlelmula Fatma , Qadhi Saba Mansoor
Published: September 2024

Service-learning is a community-based learning approach that bridges academic knowledge with practical application through purposeful exploration, action, and reflection. In addition to enhancing academic learning in various disciplines, service-learning cultivates students’ self-awareness, personal values, and social responsibility, preparing them with essential skills for life beyond the classroom. However, due to its experiential nature, service-learning presents challenges for effective assessment. This study provides a concrete example of student reflections structured by the Objective, Reflective, Interpretive, and Decisional (ORID) model in practice. Content analysis was implemented by examining undergraduate students’ end-of-semester reflection papers while volunteering during the 2022 FIFA World Cup in Qatar. The results showed that the model provided a guided and structured format for students to reflect on their service-learning, going beyond reporting on factual details to engaging in profound reflections on the emotional, cognitive, and prospective aspects. Moreover, with the solution aspect added to the model, students could express their creativity, articulating on innovative solutions they proposed to overcome challenges and how they turned the challenges into favorable outcomes. Clearly, the model stands as a powerful tool for educators, offering deeper insights into students’ authentic experiences, fostering comprehensive, critical, and insightful reflection, and thereby facilitating the effective assessment of experiential learning.
MFAD-RTDETR: A Multi-Frequency Aggregate Diffusion Feature Flow Composite Model for Printed Circuit Board Defect Detection
Authors: Xie Zhihua , Zou Xiaowei
Published: September 2024

To address the challenges of excessive model parameters and low detection accuracy in printed circuit board (PCB) defect detection, this paper proposes a novel PCB defect detection model based on the improved RTDETR (Real-Time Detection, Embedding and Tracking) method, named MFAD-RTDETR. Specifically, the proposed model introduces the designed Detail Feature Retainer (DFR) into the original RTDETR backbone to capture and retain local details. Subsequently, based on the Mamba architecture, the Visual State Space (VSS) module is integrated to enhance global attention while reducing the original quadratic complexity to a linear level. Furthermore, by exploiting the deformable attention mechanism, which dynamically adjusts reference points, the model achieves precise localization of target defects and improves the accuracy of the transformer in complex visual tasks. Meanwhile, a receptive field synthesis mechanism is incorporated to enrich multi-scale semantic information and reduce parameter complexity. In addition, the scheme proposes a novel Multi-frequency Aggregation and Diffusion feature composite paradigm (MFAD-feature composite paradigm), which consists of the Aggregation Diffusion Fusion (ADF) module and the Refiner Feature Composition (RFC) module. It aims to strengthen features with fine-grained awareness while preserving a certain level of global attention. Finally, the Wise IoU (WIoU) dynamic nonmonotonic focusing mechanism is used to reduce competition among high-quality anchor boxes and mitigate the effects of the harmful gradients from low-quality examples, thereby concentrating on anchor boxes of average quality to promote the overall performance of the detector. Extensive experiments are conducted on the PCB defect dataset released by Peking University to validate the effectiveness of the proposed model. The experimental results show that our approach achieves the 97.0% and 51.0% performance in mean Average Precision (mAP)@0.5 and mAP@0.5:0.95, respectively, which significantly outperforms the original RTDETR. Moreover, the model reduces the number of parameters by approximately 18.2% compared to the original RTDETR.
Multi-Dimensional Resource Allocation for Covert Communications in Multi-Beam Low-Earth-Orbit Satellite Systems
Authors: Wang Renge , Chen Minghao , Xu Luyan , Wen Zhong , Wei Yiyang , Li Shice
Published: September 2024

Satellite communication systems, especially multi-beam low-Earth-orbit (LEO) satellites, could cater to the needs of different industrial applications through flexible resource allocation. Unfortunately, due to the wide coverage of LEO satellites, the data exchange within the LEO satellite networks suffers from the risk of eavesdropping and malicious jamming, which could severely degrade the performance of the industrial production process. To address such challenges, this paper introduces a multi-dimensional resource allocation strategy to facilitate covert communication within the multi-beam LEO satellite network. Our approach ensures the rate requirements of different user equipments while preventing the detection of communication signals by an eavesdropping geostationary orbit (GEO) satellite. Specifically, we formulate an optimization problem that jointly optimizes satellite beam-hopping scheduling, frequency band allocation, and the transmit power of different user equipments, under the covertness constraint. By introducing auxiliary binary variables, we transform this optimization problem into a Mixed-Integer Linear Programming (MILP) problem, which allows us to utilize machine learning-based techniques for efficient solution finding. The simulation results demonstrate the effectiveness of our proposed scheme.
Advancements in TinyML: Applications, Limitations, and Impact on IoT Devices
Authors: Elhanashi Abdussalam , Dini Pierpaolo , Saponara Sergio , Zheng Qinghe
Published: September 2024

Artificial Intelligence (AI) and Machine Learning (ML) have experienced rapid growth in both industry and academia. However, the current ML and AI models demand significant computing and processing power to achieve desired accuracy and results, often restricting their use to high-capability devices. With advancements in embedded system technology and the substantial development in the Internet of Things (IoT) industry, there is a growing desire to integrate ML techniques into resource-constrained embedded systems for ubiquitous intelligence. This aspiration has led to the emergence of TinyML, a specialized approach that enables the deployment of ML models on resource-constrained, power-efficient, and low-cost devices. Despite its potential, the implementation of ML on such devices presents challenges, including optimization, processing capacity, reliability, and maintenance. This article delves into the TinyML model, exploring its background, the tools that support it, and its applications in advanced technologies. By understanding these aspects, we can better appreciate how TinyML is transforming the landscape of AI and ML in embedded and IoT systems.
Blockchain Forensics: A Systematic Literature Review of Techniques, Applications, Challenges, and Future Directions
Authors: Atlam Hany F. , Ekuri Ndifon , Azad Muhammad Ajmal , Lallie Harjinder Singh
Published: September 2024

Blockchain technology has gained significant attention in recent years for its potential to revolutionize various sectors, including finance, supply chain management, and digital forensics. While blockchain’s decentralization enhances security, it complicates the identification and tracking of illegal activities, making it challenging to link blockchain addresses to real-world identities. Also, although immutability protects against tampering, it introduces challenges for forensic investigations as it prevents the modification or deletion of evidence, even if it is fraudulent. Hence, this paper provides a systematic literature review and examination of state-of-the-art studies in blockchain forensics to offer a comprehensive understanding of the topic. This paper provides a comprehensive investigation of the fundamental principles of blockchain forensics, exploring various techniques and applications for conducting digital forensic investigations in blockchain. Based on the selected search strategy, 46 articles (out of 672) were chosen for closer examination. The contributions of these articles were discussed and summarized, highlighting their strengths and limitations. This paper examines the selected papers to identify diverse digital forensic frameworks and methodologies used in blockchain forensics, as well as how blockchain-based forensic solutions have enhanced forensic investigations. In addition, this paper discusses the common applications of blockchain-based forensic frameworks and examines the associated legal and regulatory challenges encountered in conducting a forensic investigation within blockchain systems. Open issues and future research directions of blockchain forensics were also discussed. This paper provides significant value for researchers, digital forensic practitioners, and investigators by providing a comprehensive and up-to-date review of existing research and identifying key challenges and opportunities related to blockchain forensics.
Research on Space Operation Control of Air Float Satellite Simulator Based on Constraints Aware Particle Filtering-Nonlinear Model Predictive Control
Authors: Xu Lingfeng , Chen Danhe , Wang Chuangge , Liao Wenhe
Published: September 2024

This paper addresses the challenges of close proximity operations, such as rendezvous, docking, and fly-around maneuvers for micro/nano satellites, which require high control precision under the low power and limited computational capabilities of spacecraft. Firstly, a three-degree-of-freedom air float simulator platform is designed for ground-based experiments. Subsequently, model predictive controllers based on constraints aware of particle filtering (CAPF-NMPC) are developed for executing operations such as approach, fly-around, and docking maneuvers. The results validate the effectiveness of the experimental system, demonstrating position control accuracy less than 0.03 m and attitude control accuracy less than 3°, maintaining lower computational resource consumption. This study offers a practical solution for the onboard deployment of optimized control algorithms, highlighting significant value for further engineering applications.
Early Breast Cancer Detection Using Artificial Intelligence Techniques Based on Advanced Image Processing Tools
Authors: Zhu Zede , Sun Yiran , Honarvar Shakibaei Asli Barmak
Published: September 2024

The early detection of breast cancer is essential for improving treatment outcomes, and recent advancements in artificial intelligence (AI), combined with image processing techniques, have shown great potential in enhancing diagnostic accuracy. This study explores the effects of various image processing methods and AI models on the performance of early breast cancer diagnostic systems. By focusing on techniques such as Wiener filtering and total variation filtering, we aim to improve image quality and diagnostic precision. The novelty of this study lies in the comprehensive evaluation of these techniques across multiple medical imaging datasets, including a DCE-MRI dataset for breast-tumor image segmentation and classification (BreastDM) and the Breast Ultrasound Image (BUSI), Mammographic Image Analysis Society (MIAS), Breast Cancer Histopathological Image (BreakHis), and Digital Database for Screening Mammography (DDSM) datasets. The integration of advanced AI models, such as the vision transformer (ViT) and the U-KAN model—a U-Net structure combined with Kolmogorov–Arnold Networks (KANs)—is another key aspect, offering new insights into the efficacy of these approaches in different imaging contexts. Experiments revealed that Wiener filtering significantly improved image quality, achieving a peak signal-to-noise ratio (PSNR) of 23.06 dB and a structural similarity index measure (SSIM) of 0.79 using the BreastDM dataset and a PSNR of 20.09 dB with an SSIM of 0.35 using the BUSI dataset. When combined filtering techniques were applied, the results varied, with the MIAS dataset showing a decrease in SSIM and an increase in the mean squared error (MSE), while the BUSI dataset exhibited enhanced perceptual quality and structural preservation. The vision transformer (ViT) framework excelled in processing complex image data, particularly with the BreastDM and BUSI datasets. Notably, the Wiener filter using the BreastDM dataset resulted in an accuracy of 96.9% and a recall of 96.7%, while the combined filtering approach further enhanced these metrics to 99.3% accuracy and 98.3% recall. In the BUSI dataset, the Wiener filter achieved an accuracy of 98.0% and a specificity of 98.5%. Additionally, the U-KAN model demonstrated superior performance in breast cancer lesion segmentation, outperforming traditional models like U-Net and U-Net++ across datasets, with an accuracy of 93.3% and a sensitivity of 97.4% in the BUSI dataset. These findings highlight the importance of dataset-specific preprocessing techniques and the potential of advanced AI models like ViT and U-KAN to significantly improve the accuracy of early breast cancer diagnostics.
Interval Constrained Multi-Objective Optimization Scheduling Method for Island-Integrated Energy Systems Based on Meta-Learning and Enhanced Proximal Policy Optimization
Authors: Jia Dongbao , Cao Ming , Sun Jing , Wang Feimeng , Xu Wei , Wang Yichen
Published: September 2024

Multiple uncertainties from source–load and energy conversion significantly impact the real-time dispatch of an island integrated energy system (IIES). This paper addresses the day-ahead scheduling problems of IIES under these conditions, aiming to minimize daily economic costs and maximize the output of renewable energies. We introduce an innovative algorithm for Interval Constrained Multi-objective Optimization Problems (ICMOPs), which incorporates meta-learning and an improved Proximal Policy Optimization with Clipped Objective (PPO-CLIP) approach. This algorithm fills a notable gap in the application of DRL to complex ICMOPs within the field. Initially, the multi-objective problem is decomposed into several single-objective problems using a uniform weight decomposition method. A meta-model trained via meta-learning enables fine-tuning to adapt solutions for subsidiary problems once the initial training is complete. Additionally, we enhance the PPO-CLIP framework with a novel strategy that integrates probability shifts and Generalized Advantage Estimation (GAE). In the final stage of scheduling plan selection, a technique for identifying interval turning points is employed to choose the optimal plan from the Pareto solution set. The results demonstrate that the method not only secures excellent scheduling solutions in complex environments through its robust generalization capabilities but also shows significant improvements over interval-constrained multi-objective evolutionary algorithms, such as IP-MOEA, ICMOABC, and IMOMA-II, across multiple multi-objective evaluation metrics including hypervolume (HV), runtime, and uncertainty.
An Improved Collaborative Control Scheme to Resist Grid Voltage Unbalance for BDFG-Based Wind Turbine
Authors: Cai Defu , Chen Rusi , Hu Sheng , Sun Guanqun , Wang Erxi , Tang Jinrui
Published: September 2024

This article presents an improved collaborative control to resist grid voltage unbalance for brushless doubly fed generator (BDFG)-based wind turbine (BDFGWT). The mathematical model of grid-connected BDFG including machine side converter (MSC) and grid side converter (GSC) in the αβ reference frame during unbalanced grid voltage condition is established. On this base, the improved collaborative control between MSC and GSC is presented. Under the control, the control objective of the whole BDFGWT system, including canceling the pulsations of electromagnetic torque and the unbalance of BDFGWT’s total currents, pulsations of BDFGWT’s total powers are capable of being realized; therefore, the control capability of BDFGWT to resist unbalanced grid voltage is greatly improved. Moreover, improved single-loop current controllers adopting PR regulators are proposed for both MSC and GSC where the sequence extractions for both MSC and GSC currents are not needed any more, and hence the proposed control is much simpler. In addition, the transient characteristics are also improved. Moreover, in order to achieve the decoupling control of current and average power, current controller also adopts the feedforward control approach. Case studies for a two MW BDFGWT system are implemented, and the results verify that the presented control is capable of effectively improving the control capability for BDFGWT to resist grid voltage unbalance and exhibit good stable and dynamic control performances.
Technology for Green Hydrogen Production: Desk Analysis
Authors: Łosiewicz Bożena
Published: September 2024

The use of green hydrogen as a high-energy fuel of the future may be an opportunity to balance the unstable energy system, which still relies on renewable energy sources. This work is a comprehensive review of recent advancements in green hydrogen production. This review outlines the current energy consumption trends. It presents the tasks and challenges of the hydrogen economy towards green hydrogen, including production, purification, transportation, storage, and conversion into electricity. This work presents the main types of water electrolyzers: alkaline electrolyzers, proton exchange membrane electrolyzers, solid oxide electrolyzers, and anion exchange membrane electrolyzers. Despite the higher production costs of green hydrogen compared to grey hydrogen, this review suggests that as renewable energy technologies become cheaper and more efficient, the cost of green hydrogen is expected to decrease. The review highlights the need for cost-effective and efficient electrode materials for large-scale applications. It concludes by comparing the operating parameters and cost considerations of the different electrolyzer technologies. It sets targets for 2050 to improve the efficiency, durability, and scalability of electrolyzers. The review underscores the importance of ongoing research and development to address the limitations of current electrolyzer technology and to make green hydrogen production more competitive with fossil fuels.
Position Servo Control of Electromotive Valve Driven by Centralized Winding LATM Using a Kalman Filter Based Load Observer
Authors: Yang Yi , Cheng Xin , Zhou Rougang
Published: September 2024

The exhaust gas recirculation (EGR) valve plays an important role in improving engine fuel economy and reducing emissions. In order to improve the positioning accuracy and robustness of the EGR valve under uncertain dynamics and external disturbances, this paper proposes a positioning servo system design for an electromotive (EM) EGR valve based on the Kalman filter. Taking a novel valve driven by a central winding limited angle torque motor (LATM) as the object, we have fully considered the influence of the motor rotor position and load current, as well as the magnetic field saturation and cogging effect, improved the existing LTAM model, and derived accurate torque expression. The parameter uncertainty of the above internal model and the external stochastic disturbance were unified as “total disturbance”, and a Kalman filter-based observer was designed for disturbance estimations and real-time feed-forward compensation. Furthermore, using non-contact magnetic angle measurements to obtain accurate valve position information, a position control model with real-time response and high accuracy was established. Numerous simulated and experimental data show that in the presence of ± 25% plant model parameter fluctuations and random shock-type disturbances, the servo system scheme proposed in this paper achieves a maximum position deviation of 0.3 mm, a repeatability of positioning accuracy after disturbances of 0.01 mm, and a disturbance recovery time of not more than 250 ms. In addition, the above performance is insensitive to the duration of the disturbance, which demonstrates the strong robustness, high accuracy, and excellent dynamic response capability of the proposed design.
The Impact of Fractures on Shale Oil and Gas Enrichment and Mobility: A Case Study of the Qingshankou Formation in the Gulong Depression of the Songliao Basin, NE China
Authors: Bai Xuefeng , Li Junhui , Liu Wei , Li Jijun , Fu Xiuli , Su Yangxin , Zheng Qiang , Lu Shuangfang , Zeng Xu , You Hang , Xu Yingchao
Published: September 2024

To study the impact of faults on the enrichment and mobility of shale oil in the Gulong area, representative rock samples were selected in this paper. Based on geochemical data and chemical kinetics methods, coupled with shale oil enrichment and mobility analysis techniques, the shale oil generation quantity and in situ oil content were evaluated from the perspectives of shale oil generation and micro migration, and the mobility of shale oil was revealed. At the same time, the hydrocarbon expulsion efficiency (HEE) of shale was qualitatively and quantitatively characterized, combined with the development of faults. The research results indicate that the study area mainly develops organic-rich felsic (ORF)/organic-containing felsic (OCF) shale, their proportion in both wells exceeds 65%, and the resource amount is the largest in this type of lithofacies. The development of a fault controls the enrichment of shale oil, and the in situ oil content and oil saturation index (OSI) of the shale in well Y58, which is close to the fault, are significantly worse than those in well S2. Well Y58 has 9.52 mg/g and 424.83 mg/g TOC respectively, while well S2 has 11.34 mg/g and 488.73 mg/g TOC respectively. The fault enhanced the migration of shale oil, increasing the efficiency of oil expulsion. As a result, the components with weak polarity or small molecules, such as saturated hydrocarbons and low carbon number n-alkanes, are prone to migration, reducing the mobility of shale oil.
A Study Regarding the Relationship between Carbon Emissions, Energy Consumption, and Economic Development in the Context of the Energy Growth Nexus
Authors: Mihai Laurențiu-Stelian , Vasilescu Laura , Sitnikov Cătălina , Băndoi Anca , Mănescu Leonardo-Geo , Mandache Lucian
Published: September 2024

As the EU strives to achieve its climate goals, it is becoming increasingly crucial to understand the complex relationships between economic activity, energy consumption, and carbon emissions. In this context, our paper aims to investigate the correlation between carbon emissions, energy consumption, and economic development. To fulfill our aim, we have used Eurostat and OECD data for the EU-27 member states for a period of 13 years (2010–2022), using a linear regression as the main analysis method. Our results have shown that there is a strong correlation between demand-based and production-based CO2 emissions as well as between production-based CO2 emissions and final energy consumption, while at the same time, our findings have shown that there is no direct correlation between energy consumption and economic development, aligning our study with the neutrality hypothesis of the energy growth nexus. This paper expands the ongoing discussion on sustainable development and climate change mitigation by conducting a thorough analysis of the EU-27 countries over a span of thirteen years. The results emphasize the need for integrated strategies that address both production and consumption emissions, emphasize the vital role of energy efficiency, and raise questions about the effectiveness of increasing energy consumption to enhance economic productivity or CO2 efficiency.