bluuebunny/crossref_metadata_2025 huggingface dataset:
Advances in Ultrasonic-Assisted Directed Energy Deposition (DED) for Metal Additive Manufacturing
Authors: Zhang Wenjun , Xu Chunguang , Li Cencheng , Wu Sha
Published: January 2024

Directed Energy Deposition (DED), a branch of AM processes, has emerged as a significant technique for fabricating large metal components in sectors such as aerospace, automotive, and healthcare. DED is characterized by its high deposition rate and scalability, which stand out among other AM processes. However, it encounters critical issues such as residual stresses, distortion, porosity, and rough surfaces resulting from rapid melting and solidification. As a novel advancement, Ultrasonic-Assisted Directed Energy Deposition (UA-DED) integrates ultrasonic oscillations into DED aimed at addressing these challenges. Herein, the latest research related to the UA-DED process and the current major challenges of the DED process, residual stresses, porosity, and crack defects are critically reviewed. Subsequently, the paper also details the working principle and system components of UA-DED technology and reviews the material improvement by introducing UA into the DED process, grain, porosity, tensile properties, and deposition defects. The most critical optimization methods of process parameter variables for UA and the different material interaction mechanisms between UA and DED processes are identified and discussed in detail. Finally, the perspectives on the research gap and potential future developments in UA-DED are also discussed.
Sleep Health
Authors: Vorster Albrecht P. A. , van Someren Eus J. W. , Pack Allan I. , Huber Reto , Schmidt Markus H. , Bassetti Claudio L. A.
Published: January 2024

Together with (physical and mental) exercise, diet, and social activities, sleep is a key health behavior that occupies one third of our lives, yet remains neglected. In the first part of this review, we present the current knowledge on how sleep promotes body, brain, mental, occupational, and social health as well as creativity, productivity, and well-being. In the second part, we discuss how good sleep and screening for sleep–wake disorders may improve health and reduce the burden of brain, mental, cardiovascular, metabolic disorders and cancer. We also review the literature on measurements of sleep health and present the Bernese Sleep Health Questionnaire, a new and simple tool to assess sleep health and screen for sleep–wake circadian disorders in clinical practice.
Cerebrospinal Fluid Analysis in Rheumatological Diseases with Neuropsychiatric Complications and Manifestations: A Narrative Review
Authors: Castellazzi Massimiliano , Candeloro Raffaella , Pugliatti Maura , Govoni Marcello , Silvagni Ettore , Bortoluzzi Alessandra
Published: January 2024

The analysis of cerebrospinal fluid (CSF) remains a valuable diagnostic tool in the evaluation of inflammatory and infectious conditions involving the brain, spinal cord, and meninges. Since many rheumatic inflammatory diseases can involve the central and peripheral nervous system, the aims of this narrative review were to summarize the latest evidence on the use of CSF analysis in the field of neuropsychiatric manifestations of rheumatic diseases. Routine CSF parameters were taken into consideration for this review: appearance; total protein and cellular content (pleocytosis); lactate and/or glucose; CSF/serum albumin quotient; intrathecal synthesis of IgG. Data regarding the role of CSF analysis in the clinical management of neuropsychiatric systemic lupus erythematosus, primary Sjogren’s syndrome, rheumatoid arthritis, and Behçet’s syndrome are presented. Although no disease-specific picture has been identified, CSF analysis remains a useful diagnostic tool to confirm the presence of a neuro-inflammatory state or, conversely, to exclude the concomitant presence of other inflammatory/infectious diseases affecting the CNS in the context of systemic rheumatologic conditions.
Immature Sacrococcygeal Teratoma: A Case Report and Extensive Review of the Literature
Authors: Varlas Valentin Nicolae , Cloțea Eliza Maria , Varlas Roxana Georgiana , Pop Anca , Peneș Ovidiu , Crețoiu Dragoș , Dima Vlad , Bălănescu Laura
Published: January 2024

Immature sacrococcygeal teratoma represents a histological form with rapid tumor growth, a risk of premature birth, an enhanced rate of complications, an increased risk of recurrence, and a higher mortality rate than the mature type. Thus, prenatal diagnosis of immature forms would significantly improve the prognosis of these cases. To this end, we performed an extensive literature review on the diagnosis, therapeutic management, and follow-up of immature teratomas. Regarding this medical conduct, we also presented our case. In conclusion, the early identification of immature sacrococcygeal teratomas with or without other associated structural abnormalities and their correct therapeutic approach are basic principles for a favorable evolution of these cases.
A Comprehensive Review of COVID-19-Infection- and Steroid-Treatment-Associated Bone Avascular Necrosis: A Multi-Study Analysis
Authors: Sakellariou Evangelos , Argyropoulou Evangelia , Karampinas Panagiotis , Galanis Athanasios , Varsamos Iordanis , Giannatos Vasileios , Vasiliadis Elias , Kaspiris Angelos , Vlamis John , Pneumaticos Spiros
Published: January 2024

Background: The COVID-19 pandemic has presented numerous challenges to the global healthcare system and emerging evidence suggests a potential link between COVID-19 treatment, specifically steroid therapy, and the development of avascular necrosis (AVN) of the hip. This review aims to provide a comprehensive overview of recent studies and case reports investigating the relationship between COVID-19, corticosteroid therapy, and the development of AVN. Understanding the nuances of AVN in the context of COVID-19 is crucial for healthcare professionals to navigate treatment decisions and mitigate potential complications. Materials and Methods: The Preferred Reporting Items for Systematic Reviews and a Meta-Analysis (PRISMA) were used in the systematic review until 1 September 2023. The full texts of the remaining twenty-three (n = 23) articles were independently reviewed by the authors of this review. Conclusions: The association between steroid therapy for COVID-19 and the development of hip AVN is a noteworthy concern even though no relationship is evident between the duration of treatment, cumulative dosage of medication, maximum daily dosage received, and presence of AVN. Further research with larger cohorts and long-term follow up is needed to better understand the causative relationship and optimal management strategies for hip AVN in the context of COVID-19 and steroid therapy.
Pre-Analytical Factors Affecting Extracellular DNA in Saliva
Authors: Janovičová Ľubica , Holániová Dominika , Vlková Barbora , Celec Peter
Published: January 2024

Salivary DNA is widely used for genetic analyses because of its easy collection. However, its extracellular fraction in particular, similar to the extracellular DNA (ecDNA) in plasma, could be a promising biomarker for oral or systemic diseases. In contrast to genetics, the quantity of salivary ecDNA is of importance and can be affected by the pre-analytical processing of samples, but the details are not known. The aim of our study was to analyze the effects of centrifugation and freezing of saliva on the concentration of ecDNA in saliva. Fifteen healthy volunteers, free of any known systemic or oral diseases, were asked to collect unstimulated saliva samples. Aliquots were centrifuged at 1600× g and frozen or directly processed. The fresh or thawed cell-free saliva samples underwent subsequent centrifugation at 16,000× g. The supernatants were used for DNA isolation and quantification using fluorometry and real-time PCR. While freezing had minimal effects on the salivary ecDNA concentration, another centrifugation step decreased ecDNA considerably in both fresh and frozen samples (by 97.8% and 98.4%, respectively). This was mirrored in the quantitative PCR targeting a nuclear (decrease by 93.5%) and mitochondrial (decrease by 97.7%) ecDNA sequence. In conclusion, in this first study focusing on the technical aspects of salivary ecDNA quantitation, we show that, regardless of its subcellular origin, the concentration of ecDNA in saliva is mainly affected by additional centrifugation and not by the freezing of centrifuged cell-free saliva samples. This suggests that most salivary ecDNA likely is associated with cell debris and apoptotic bodies. Which fraction is affected by a particular disease should be the focus of further targeted studies.
Use of Edibles as Effective Tools in Myofunctional Therapy: A Pilot Study
Authors: Saccomanno Sabina , Quinzi Vincenzo , Santori Federica , Pisaneschi Alessia , Salvati Simone Ettore , Paskay Licia Coceani , Marci Maria Chiara , Marzo Giuseppe
Published: January 2024

Aim: The aim of this retrospective study is to explore the introduction of edible spread cream and small candies as tools to improve motivation and compliance in young children undergoing myofunctional therapy, with the purpose of optimizing oral functions, including swallowing. Methods: Six patients, one female and five males, between the ages of 7 and 14 years, presenting with atypical swallowing, were evaluated and treated at the clinic of the University of L’Aquila. The patients included in the study were randomly divided into two groups and were treated with two different treatment protocols: Group A: traditional myofunctional therapy and traditional tools; Group B: same exercises as group A, but with edible tools (spreadable cream and small candies). Results: As expected, the two patients who used edible tools demonstrated increased motivation and collaboration during myofunctional therapy. Conclusions: Patient compliance, especially in very young patients, limits the effectiveness of myofunctional therapy; therefore, creative solutions are needed to achieve greater cooperation, and edible tools can play a significant part in retraining correct swallowing. Although the sample of this pilot study is small, the results suggest that using actual edible tools in myofunctional therapy could increase compliance and provide better results in myofunctional therapy.
Non-Invasive High-Resolution Imaging of In Vivo Human Myelinated Axons
Authors: Lombardo Marco , Cesareo Massimo , Falsini Benedetto , Cusumano Andrea
Published: January 2024

This work aims to reveal the microscopic (2–3 micrometer resolution) appearance of human myelinated nerve fibers in vivo for the first time. We analyzed the myelinated retinal nerve fibers of a male patient without other neurological disorders in a non-invasive way using the transscleral optical phase imaging method with adaptive optics. We also analyzed the fellow eye with non-myelinated nerve fibers and compared the results with traditional ocular imaging methods such as optical coherence tomography. We documented the microscopic appearance of human myelin and myelinated axons in vivo. This method allowed us to obtain better details than through traditional ocular imaging methods. We hope these findings will be useful to the scientific community to evaluate neuro-retinal structures through new imaging techniques and more accurately document nerve anatomy and the pathophysiology of this disease.
The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV
Authors: Herich Dušan , Vaščák Ján
Published: January 2024

The Internet of Vehicles (IoV) and the Internet of Flying Vehicles (IoFV) are integral components of intelligent transportation systems with the potential to revolutionize the way we move people and goods. Although both the IoV and IoFV share a common goal of improving transportation efficiency, safety, and sustainability, they possess distinct characteristics and face unique challenges. To date, the existing literature has predominantly focused on specific aspects of either the IoV or IoFV, but a comprehensive review comparing and contrasting the two domains is still lacking. This review paper aims to address this gap by providing an in-depth analysis of the key differences between the IoV and IoFV systems. The review will examine the technological components, network infrastructure, communication protocols, data management, objectives, applications, challenges, and future trends associated with both domains. Additionally, this paper will explore the potential impact of technologies such as artificial intelligence, machine learning, and blockchain. Ultimately, the paper aims to contribute to a deeper understanding of the implications and potential of these technologies, both in the context of transportation systems and beyond.
The Impact of the COVID-19 Pandemic on the Economy of the Slovak Republic
Authors: Tomková Anna , Gonos Jaroslav , Čulková Katarína , Rovňák Martin
Published: January 2024

The main goal of this contribution is to assess the development of the economic condition of the Slovak Republic in the context of the impacts of the COVID-19 pandemic. The situation regarding the development of and changes in the economic condition of Slovakia is compared with that in selected EU countries, considering the effects of previous global crises, with a focus on the impacts on small and medium-sized enterprises. The economies of European countries are mentioned to illustrate the ideas of the presented paper, with an emphasis on the economic dimension of the COVID-19 pandemic and its subsequent impact on the Slovak Republic. This research is conducted through basic analytical tools and an analysis of the development of macroeconomic indicators, and by addressing the issue through data from a globally available database. The results in this paper serve as proposals and recommendations for the mitigation of negative economic impacts.
From Policy to Impact: Advancing Economic Development and Tackling Social Inequities in Central and Eastern Europe
Authors: Davidescu Adriana AnaMaria , Nae Tamara Maria , Florescu Margareta-Stela
Published: January 2024

This study challenges the traditional reliance on GDP as the sole indicator of the success of the EU’s cohesion policy, aligning with the evolving academic discourse that calls for a broader spectrum of metrics incorporating social factors. The research aims to assess the impact of cohesion on economic performance and social progress at the regional level in Central and Eastern European countries, using regression analysis on panel data. Inspired by the call to move beyond GDP-focused assessments, this research re-evaluates cohesion policy within an expanded framework that prioritizes economic and social dimensions. Specifically, it addresses the escalating concerns of income disparity and poverty in Central and Eastern European nations. Utilizing panel data regression models, this study scrutinizes data from 2007 to 2018, covering two recent programming periods, to offer a comprehensive, multifaceted analysis of the impact of cohesion policy. It underscores the policy’s dual role in spurring economic growth and fostering social progress, particularly in mitigating income inequality and reducing poverty. The findings reveal that cohesion policies positively affect both economic performance and social progress, with notable impacts on narrowing the income gap and alleviating poverty in these regions. However, the economic benefits for poverty reduction materialize over a prolonged period, reflecting the gradual nature of policy impact and the time needed for investments to materialize. The study emphasizes the need for a long-term strategic vision in implementing cohesion policies. This includes enhanced data collection, a deeper focus on the social ramifications of policies, streamlined policy processes, capacity building, institutional strengthening, and prioritizing equitable opportunities to bridge income gaps effectively. This comprehensive approach aims to maximize the dual benefits of cohesion policies, promoting balanced economic and social progress across Central and Eastern Europe.
Predicting Transfer of Generic Information Literacy Competencies by Non-Traditional Students to Their Study and Work Contexts: A Longitudinal Perspective
Authors: Testers Laurent , Alijagic Aldin , Brand-Gruwel Saskia , Gegenfurtner Andreas
Published: January 2024

Rapid developments in contemporary societies not only ask for lifelong learning but increasingly also for training in generic competencies suitable for multiple contexts and life stages. An indicator of training success is the transfer or application of new learning, a longitudinal process influenced by various theory- and evidence-based factors. The present study combined a multi-contextual and longitudinal approach by investigating non-traditional distance education students’ intention to transfer newly acquired generic information literacy competencies to their study and work contexts before, directly after, and three months after training. Three surveys, using a combination of Ajzen’s theory of planned behavior and Holton et al.’s Learning Transfer System Inventory model, measured the influence of performance outcomes expectations, organizational openness to change, and performance self-efficacy on intention to transfer and transfer behaviour. The participants were 82 adult educational professionals enrolled in an online information literacy course at the Open University. Partial least squares-based structural equation modelling (PLS-SEM) confirmed the value of employing a multi-contextual and longitudinal approach within this specific setting. Furthermore, notably, self-efficacy appeared to predict pre-training intention in both study and work contexts and transfer behaviour in the work context. Educational implications, limitations, and directions for future research are discussed.
Synchronous Distance Learning: Effects of Interest and Achievement Goals on Police Students’ Learning Engagement and Outcomes
Authors: Hefter Markus H. , Nitsch Holger
Published: January 2024

Online learning has boomed, especially in synchronous settings. Questions remain open regarding the influence of interruptions and learner factors such as interest and achievement goals on learning engagement and outcomes. To address these questions, the present field study relied on a synchronous online course and evaluated it with a sample of 136 police students (Mage = 29.58 years). Not only did the online course foster students’ self-efficacy, it was also given similarly high evaluations to previous offline iterations of the course. Furthermore, the students’ self-perceived learning gains correlated highly with actual test performance. Their interest was a positive predictor of these learning gains, whereas work avoidance goals were a negative predictor of learning gains. Learning engagement mediated these effects. Finally, learning outcomes and interruptions were negatively associated. Our results imply that instructors should consider interest and achievement goals as important learning predictors, as well as the detrimental effects of interruptions on learning outcomes.
Academic Stress and Anxiety among Portuguese Students: The Role of Perceived Social Support and Self-Management
Authors: Pontes Ana , Coelho Vera , Peixoto Carla , Meira Liliana , Azevedo Helena
Published: January 2024

Adolescents are experiencing increasing levels of anxiety and academic stress, with schools being privileged contexts to foster mental health literacy and hinder stress and anxiety. Social support and self-management skills have been described as protective factors for these phenomena. Therefore, this cross-sectional study examines the relations between social support, self-management skills, academic stress, and anxiety among adolescents, analyzing the mediational role of self-management in the relation between social support, academic stress, and anxiety. Participants included 137 adolescents, with an average age of 12.56 years (SD = 0.68), who completed the Perception of Academic Stress Scale, Generalized Anxiety Disorder Scale, Self-Control and Self-Management Scale, Child and Adolescent Social Support Scale, and a sociodemographic questionnaire. Results showed that students generally perceive moderate levels of social support from parents, teachers, and peers; moderate levels of academic stress and mild anxiety; and have self-management skills. Findings showed that self-management is related to social support, academic stress, and anxiety, with gender influencing academic stress and anxiety. Self-management was found to mediate the link between social support, academic stress, and anxiety. The results underscore the importance of fostering social support and self-management skills to mitigate academic stress and anxiety.
Artificial Intelligence Supporting Independent Student Learning: An Evaluative Case Study of ChatGPT and Learning to Code
Authors: Hartley Kendall , Hayak Merav , Ko Un Hyeok
Published: January 2024

Artificial intelligence (AI) tools like ChatGPT demonstrate the potential to support personalized and adaptive learning experiences. This study explores how ChatGPT can facilitate self-regulated learning processes and learning computer programming. An evaluative case study design guided the investigation of ChatGPT’s capabilities to aid independent learning. Prompts mapped to self-regulated learning processes elicited ChatGPT’s support across learning tools: instructional materials, content tools, assessments, and planning. Overall, ChatGPT provided comprehensive, tailored guidance on programming concepts and practices. It consolidated multimodal information sources into integrated explanations with examples. ChatGPT also effectively assisted planning by generating detailed schedules. However, its interactivity and assessment functionality demonstrated shortcomings. ChatGPT’s effectiveness relies on learners’ metacognitive skills to seek help and assess its limitations. The implications include ChatGPT’s potential to provide Bloom’s two-sigma tutoring benefit at scale.
Ontological Modeling and Clustering Techniques for Service Allocation on the Edge: A Comprehensive Framework
Authors: Karanik Marcelo , Bernabé-Sánchez Iván , Fernández Alberto
Published: January 2024

Nowadays, we are in a world of large amounts of heterogeneous devices with varying computational resources, ranging from small devices to large supercomputers, located on the cloud, edge or other abstraction layers in between. At the same time, software tasks need to be performed. They have specific computational or other types of requirements and must also be executed at a particular physical location. Moreover, both services and devices may change dynamically. In this context, methods are needed to effectively schedule efficient allocations of services to computational resources. In this article, we present a framework to address this problem. Our proposal first uses knowledge graphs for describing software requirements and the availability of resources for services and computing nodes, respectively. To this end, we proposed an ontology that extends our previous work. Then, we proposed a hierarchical filtering approach to decide the best allocation of services to computational nodes. We carried out simulations to evaluate four different clustering strategies. The results showed different performances in terms of the number of allocated services and node overload.
Innovative Approach to Android Malware Detection: Prioritizing Critical Features Using Rough Set Theory
Authors: Gupta Rahul , Sharma Kapil , Garg Ramesh Kumar
Published: January 2024

The widespread integration of smartphones into modern society has profoundly impacted various aspects of our lives, revolutionizing communication, work, entertainment, and access to information. Among the diverse range of smartphones available, those operating on the Android platform dominate the market as the most widely adopted type. With a commanding 70% share in the global mobile operating systems market, the Android OS has played a pivotal role in the surge of malware attacks targeting the Android ecosystem in recent years. This underscores the pressing need for innovative methods to detect Android malware. In this context, our study pioneers the application of rough set theory in Android malware detection. Adopting rough set theory offers distinct advantages, including its ability to effectively select attributes and handle qualitative and quantitative features. We utilize permissions, API calls, system commands, and opcodes in conjunction with rough set theory concepts to facilitate the identification of Android malware. By leveraging a Discernibility Matrix, we assign ranks to these diverse features and subsequently calculate their reducts–streamlined subsets of attributes that enhance overall detection effectiveness while minimizing complexity. Our approach encompasses deploying various Machine Learning (ML) algorithms, such as Support Vector Machines (SVM), K-Nearest Neighbor, Random Forest, and Logistic Regression, for malware detection. The results of our experiments demonstrate an impressive overall accuracy of 97%, surpassing numerous state-of-the-art detection techniques proposed in existing literature.
A Full-Duplex 60 GHz Transceiver with Digital Self-Interference Cancellation
Authors: Wang Yisheng , Thangarasu Bharatha Kumar , Mahalingam Nagarajan , Ma Kaixue , Meng Fanyi , Huang Yibo , Yeo Kiat Seng
Published: January 2024

This paper presents the design and measurement of an IEEE 802.11ad standard compatible RF transceiver for 60 GHz wireless communication systems. In addition to the traditional half-duplex (HD) mode, this work supports full-duplex (FD) operations to deliver better channel utilization and faster response times for the system. The isolation between the transmitter and receiver from the architecture design to system integration for FD operations has been fully considered. A digital self-interference cancellation (DSIC) is implemented in MATLAB to verify the FD performance. The super-heterodyne architecture with an intermediate frequency (IF) of 12 GHz is designed to suppress the image frequencies without using extra filters. A flexible phase-locked loop (PLL) synthesizer provides a local oscillator (LO) frequency with a 2 kHz resolution. Other than the time division duplex (TDD) mode used in the conventional 60 GHz system, a wide-bandwidth baseband digital variable-gain amplifier (DVGA) with a 3 dB bandwidth of more than 4 GHz also supports frequency division duplex (FDD) operations. The transceiver chip is fabricated using the Tower Jazz 0.18 µm SiGe BiCMOS process. With an on-board antenna, the transceiver covers all four channels in the 802.11ad standard, with MCS-12 (7.04 Gbps under 1.76 GSym/s and 16-QAM) under 1.5 m. In the proposed system design, the RF frontend-based self-interference (SI) suppression from the local transmitter to receiver LNA is around 54 dB. To achieve a practical FD application, the SI is further suppressed with the help of a digital SI compensation. The measured power consumption for the transmitter and receiver configurations are 194 mW and 231 mW, respectively, in HD mode and 398 mW for the FDD or FD operation mode.
A Job Recommendation Model Based on a Two-Layer Attention Mechanism
Authors: Mao Yu , Lin Shaojie , Cheng Yuxuan
Published: January 2024

In the field of job recruitment, traditional recommendation methods only rely on users’ rating data of positions for information matching. This simple strategy has problems such as low utilization of multi-source heterogeneous data and difficulty in mining relevant information between recruiters and applicants. Therefore, this paper proposes a recurrent neural network model based on a two-layer attention mechanism. The model first improves the entity representation of recruiters and applicants through user behavior, company-related knowledge and other information. The entities and their combinations are then mapped to the vector space using one-hot and TransR methods, and a recurrent neural network with a two-layer attention mechanism is used to obtain their potential interests from the click sequence, and then a recommendation list is generated. The experimental results show that this model achieves better results than previous models.
Parallel Operation Strategy of Inverters Based on an Improved Adaptive Droop Control and Equivalent Input Disturbance Approach
Authors: Ding Min , Tao Zili , Hu Bo , Tan Shengji , Yokoyama Ryuichi
Published: January 2024

The operation of parallel inverters in microgrids is an important way to expand system capacity, but there are problems of circulating current fluctuations and power sharing errors in parallel inverters’ operation. In this paper, a parallel operation strategy for inverters based on improved adaptive droop control and Equivalent Input Disturbance (EID) is proposed. Firstly, the model and control topology of parallel inverters are presented. Secondly, in order to solve the power sharing error caused by impedance difference and the voltage drop caused by virtual impedance, a reference voltage generator based on improved adaptive droop control is proposed. Then, considering the circulating current fluctuation as a disturbance, a voltage controller based on an equivalent input disturbance approach is proposed to reduce the disturbance. Finally, simulation in MATLAB/Simulink is carried out to verify the effectiveness of the parallel operation strategy.
Multi-Dimensional Moving Target Defense Method Based on Adaptive Simulated Annealing Genetic Algorithm
Authors: Xu Hanyi , Cheng Guozhen , Yang Xiaohan , Liu Wenyan , Zhou Dacheng , Guo Wei
Published: January 2024

Due to the fine-grained splitting of microservices and frequent communication between microservices, the exposed attack surface of microservices has exploded, facilitating the lateral movement of attackers between microservices. To solve this problem, a multi-dimensional moving target defense method based on an adaptive simulated annealing genetic algorithm (MD2RS) is proposed. Firstly, according to the characteristics of microservices in the cloud, a microservice attack graph is proposed to quantify the attack scenario of microservices in the cloud so as to conveniently and intuitively observe the vulnerability of microservices in the cloud and the dependency relationship between microservices. Secondly, the security gain and resource cost are quantified for the key nodes selected by measuring the degree of dependence of each node according to the degree centrality. Finally, the Adaptive Simulated Annealing Genetic Algorithm (ASAGA) is used to solve the optimal security configuration information of the moving target defense, that is, the combination of the number of copies of the multi-copy deployment and the rotation cycle of the dynamic rotation of microservices, in order to quickly evaluate the security risks of microservices and optimize the security policy. Experiments show that the defense return rate of MD2RS is 85.95% higher than that of the mainstream methods, and the experimental results are conducive to applying this method to the dynamic defense of microservices in the cloud.
Smart Contract Vulnerability Detection Based on Multi-Scale Encoders
Authors: Guo Junjun , Lu Long , Li Jingkui
Published: January 2024

Vulnerabilities in smart contracts may trigger serious security events, and the detection of smart contract vulnerabilities has become a significant problem. In this paper, to solve the limitations of current deep learning-based vulnerability detection methods in extracting various code critical features, using the multi-scale cascade encoder architecture as the backbone, we propose a novel Multi-Scale Encoder Vulnerability Detection (MEVD) approach to hit well-known high-risk vulnerabilities in smart contracts. Firstly, we use the gating mechanism to design a unique Surface Feature Encoder (SFE) to enrich the semantic information of code features. Then, by combining a Base Transformer Encoder (BTE) and a Detail CNN Encoder (DCE), we introduce a dual-branch encoder to capture the global structure and local detail features of the smart contract code, respectively. Finally, to focus the model’s attention on vulnerability-related characteristics, we employ the Deep Residual Shrinkage Network (DRSN). Experimental results on three types of high-risk vulnerability datasets demonstrate performance compared to state-of-the-art methods, and our method achieves an average detection accuracy of 90%.
An Embedded Machine Learning Fault Detection System for Electric Fan Drive
Authors: Aung Khin Htet Htet , Kok Chiang Liang , Koh Yit Yan , Teo Tee Hui
Published: January 2024

Industrial fans are critical components in industrial production, where unexpected damage of important fans can cause serious disruptions and economic costs. One trending market segment in this area is where companies are trying to add value to their products to detect faults and prevent breakdowns, hence saving repair costs before the main product is damaged. This research developed a methodology for early fault detection in a fan system utilizing machine learning techniques to monitor the operational states of the fan. The proposed system monitors the vibration of the fan using an accelerometer and utilizes a machine learning model to assess anomalies. Several of the most widely used algorithms for fault detection were evaluated and their results benchmarked for the vibration monitoring data. It was found that a simple Convolutional Neural Network (CNN) model demonstrated notable accuracy without the need for feature extraction, unlike conventional machine learning (ML)-based models. Additionally, the CNN model achieved optimal accuracy within 30 epochs, demonstrating its efficiency. Evaluating the CNN model performance on a validation dataset, the hyperparameters were updated until the optimal result was achieved. The trained model was then deployed on an embedded system to make real-time predictions. The deployed model demonstrated accuracy rates of 99.8%, 99.9% and 100.0% for Fan-Fault state, Fan-Off state, and Fan-On state, respectively, on the validation data set. Real-time testing further confirmed high accuracy scores ranging from 90% to 100% across all operational states. Challenges addressed in this research include algorithm selection, real-time deployment onto an embedded system, hyperparameter tuning, sensor integration, energy efficiency implementation and practical application considerations. The presented methodology showcases a promising approach for efficient and accurate fan fault detection with implications for broader applications in industrial and smart sensing applications.
Multi-Agent Vision System for Supporting Autonomous Orchard Spraying
Authors: Góral Piotr , Pawłowski Paweł , Piniarski Karol , Dąbrowski Adam
Published: January 2024

In this article, the authors propose a multi-agent vision system supporting the autonomous spraying of orchards and analyze the condition of trees and occurrence of pests and diseases. The vision system consists of several agents: first, for the detection of pests and diseases of fruit crops; second, for the estimation of the height of trees to be covered with spraying; third, for the classification of the developmental status of trees; and fourth, for the classification of tree infections by orchard diseases. For the classification, modified deep convolutional neural networks were used: Xception and NasNetLarge. They were trained using transfer learning and several additional techniques to avoid overfitting. Efficiency tests performed on the datasets with real orchard photos, showing accuracies ranging from 96.88% to 100%. The presented solutions will be used as part of an intelligent autonomous vehicle for orchard works, in order to minimize harm to the environment and reduce the consumption of water and plant protection products.
A Temporal Filter to Extract Doped Conducting Polymer Information Features from an Electronic Nose
Authors: Haj Ammar Wiem , Boujnah Aicha , Baron Antoine , Boubaker Aimen , Kalboussi Adel , Lmimouni Kamal , Pecqueur Sébastien
Published: January 2024

Identifying relevant machine learning features for multi-sensing platforms is both an applicative limitation to recognize environments and a necessity to interpret the physical relevance of transducers’ complementarity in their information processing. Particularly for long acquisitions, feature extraction must be fully automatized without human intervention and resilient to perturbations without significantly increasing the computational cost of a classifier. In this study, we investigate the relative resistance and current modulation of a 24-dimensional conductimetric electronic nose, which uses the exponential moving average as a floating reference in a low-cost information descriptor for environment recognition. In particular, we identified that depending on the structure of a linear classifier, the ‘modema’ descriptor is optimized for different material sensing elements’ contributions to classify information patterns. The low-pass filtering optimization leads to opposite behaviors between unsupervised and supervised learning: the latter favors longer integration of the reference, allowing the recognition of five different classes over 90%, while the first one prefers using the latest events as its reference to cluster patterns by environment nature. Its electronic implementation shall greatly diminish the computational requirements of conductimetric electronic noses for on-board environment recognition without human supervision.
Experimental Insights into the Fermentation of Pyro-Syngas to Ethanol in a Semi-Batch and Continuous Stirred Bioreactor with Mathematical Modelling and Optimization
Authors: Manna Dinabandhu , Chowdhury Ranjana , Calay Rajnish K. , Mustafa Mohamad Y.
Published: January 2024

Syngas fermentation can play an important role in implementing the concept of biorefinery as it can serve as a platform to convert high-lignin biomass to biofuels. For the utilization of this process in commercial scale, the generation of an experimental database supported by a deterministic mathematical model and optimization is necessary. In this study, a locally isolated clostridial consortium, UACJUChE1, was used to convert pyro-syngas to ethanol and acetic acid. Mathematical models were developed and validated for a 3 L stirred and gas-sparged bioreactor operated in both semi-batch and continuous modes. The volumetric productivity of ethanol was correlated with the dilution rate and the gas residence time. The performance of the bioreactor, run in both semi-batch and continuous modes, was optimized using response surface methodology. For the semi-batch operation, a maximum ethanol concentration of 13.122 g/L after 30 h operation was achieved at optimum values of pyrolysis temperature, ratio of gas to liquid volume (VG/VL), and volumetric gas flow rate of 648 °C, 0.46, and 6.7 L/h respectively. For continuous operation, a maximum ethanol concentration of 29.450 g/L after 300 h is obtained at optimum values of VG/VL and ratio of gas to liquid volumetric flow rate of 0.28 and 335.148, respectively.
Exploring Biomass Linkages in the Food and Energy Market—A Systematic Review
Authors: Igbeghe Christian Barika , Nagy Adrián , Gabnai Zoltán , Bai Attila
Published: January 2024

This study delves into the intricate landscape of biomass utilization within the food and energy markets. It entails a systematic review of the existing literature with the aim of unraveling the complexities of the food and energy discourse, especially in the context of significant market factors influencing biomass use for food and energy. We leveraged the Scopus database to examine 73 pertinent scientific articles carefully selected following the PRISMA framework. The articles were analyzed using the advanced qualitative data analytics tool NVivo 12 Plus. Furthermore, we employed the Biblioshiny R-package tool to extract valuable insights from the metadata, unveiling pivotal trends and providing descriptive statistical details. The findings offer comprehensive insights into the debate on biomass utilization from 2010 to 2023, tracing the influence of the COVID-19 pandemic. We identify regions that have made notable contributions and highlight those that require increased attention. The analysis underscores the collaborative nature of this field, with 281 authors contributing to 39 different sources. Surprisingly, the observed annual growth rate of −10.93% indicates a potential decline in research output in this field. Nevertheless, the sources identified in our research provide a valuable roadmap for further research exploration of the biomass–food–energy nexus.
How to Decarbonize Greece by Comparing Wind and PV Energy: A Land Eligibility Analysis
Authors: Wang Qilin , Gontikaki Evangelia , Stenzel Peter , Louca Vasilis , Küpper Frithjof C. , Spiller Martin
Published: January 2024

To achieve sustainable development, the energy transition from lignite burning to renewable energy resources for electric power generation is essential for Greece. Wind and solar energy have emerged as significant sources in this transition. Surprisingly, numerous studies have examined the potential for onshore wind based on land eligibility, while few studies on open-field photovoltaic (PV) installations have been conducted. Therefore, based on the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD-RES), along with insights from previous relevant studies, this work conducts a land eligibility analysis of onshore wind and open-field PV installations in Greece using the software Geospatial Land Availability for Energy Systems (GLAES 1.2.1) and ArcGIS 10.2. Additionally, through an in-depth exploration of wind and solar PV energy potential in decommissioned lignite mines integrated with wind power density (WPD) and global horizontal irradiation (GHI) maps, this study compares the suitability of wind versus solar as energy sources for the decarbonization of Greece. Overall, despite the greater spatial eligibility for onshore wind turbines compared to open-field PV power plants, the relatively lower wind energy potential and operational limitations of wind turbines lead to the study’s conclusion that solar energy (PV) is more suitable for the decarbonization of Greece.
Advancements in Fresnel Lens Technology across Diverse Solar Energy Applications: A Comprehensive Review
Authors: Rashid Farhan Lafta , Al-Obaidi Mudhar A. , Mahdi Ali Jafer , Ameen Arman
Published: January 2024

Concentration of solar energy may be obtained by reflection, refraction, or a combination of the two. The collectors of a reflection system are designed to concentrate the sun’s rays onto a photovoltaic cell or steam tube. Refractive lenses concentrate light by having it travel through the lens. The sun’s rays are partially reflected and then refracted via a hybrid technique. Hybrid focus techniques have the potential to maximize power output. Fresnel lenses are an efficient tool for concentrating solar energy, which may then be used in a variety of applications. Development of both imaging and non-imaging devices is occurring at this time. Larger acceptance angles, better concentration ratios with less volume and shorter focal length, greater optical efficiency, etc., are only some of the advantages of non-imaging systems over imaging ones. This study encompasses numerical, experimental, and numerical and experimental studies on the use of Fresnel lenses in various solar energy systems to present a comprehensive picture of current scientific achievements in this field. The framework, design criteria, progress, and difficulties are all dissected in detail. Accordingly, some recommendations for further studies are suggested.
Techno-Economic Assessment of a Hybrid Renewable Energy System for a County in the State of Bahia
Authors: Borba Ana Tereza Andrade , Simões Leonardo Jaime Machado , de Melo Thamiles Rodrigues , Santos Alex Álisson Bandeira
Published: January 2024

Installation of hybrid systems with storage is a way to maximize the amount of energy generated through exploring the complementarity of different sources. Understanding hybrid power plant (HPP) operation is crucial for optimizing new systems and reconfiguring existing plants, to their enhance efficiency. Alongside technical aspects, economic feasibility is also a fundamental feature. This study simulated an off-grid HPP to consider the energy consumption of Casa Nova in Bahia, Brazil. The methodology consisted of the selection of energy sources, choosing a reference location, acquisition of generation and operational data, modeling and simulating the system in different scenarios, and a financial analysis. HOMER Pro software Version 3.16.2 was used to optimize the plant configuration, and the outputs were evaluated using the perspectives of levelized cost of energy (LCOE), simple payback, and power load fulfillment. As a result, scenario 3 was the most competitive, emphasizing that the use of different energy sources increased the system generation capacity. However, the addition of battery energy system storage (BESS) resulted in a high LCOE when compared to individual sources, which demonstrated that the cost of battery integration is not yet nationally competitive. Moreover, the results highlighted the importance of research investments, energy governance, and regulation in promoting hybrid system adoption.
Fermented Beverages Revisited: From Terroir to Customized Functional Products
Authors: Paramithiotis Spiros , Patra Jayanta Kumar , Kotseridis Yorgos , Dimopoulou Maria
Published: January 2024

The production of fermented beverages dates back to antiquity [...]
The Influence of Natural and Anthropogenic Environmental Pressures on European Eel Abundances in French Estuaries
Authors: Denis Jérémy , Lepage Mario , Gruselle Marie-Christine , Amara Rachid
Published: January 2024

The aim of this study was to investigate the influence of environmental characteristics and anthropogenic pressures on the abundance of estuarine European eels (Anguilla anguilla L.) during their continental growth phase. European eels were collected with fyke nets from spring to autumn in twenty-nine estuaries along the French English Channel and the Atlantic coast. Eel abundance (catch per unit effort, CPUE) was assessed for all eels and by size class for small (total length < 300 mm), intermediate (≥300 to <450 mm), and large (≥450 mm) eels. The environmental characteristics of the French estuaries were described by twelve descriptor variables, mainly related to hydro-morphological and sedimentary factors. Based on principal component analysis and hierarchical clustering analysis, estuary size was identified as the main explanatory variable and used to compare eel abundance. Eel abundance differed significantly according to estuary size, with higher abundances observed in small estuaries (7.22 to 13.00 ind. fyke nets 24 h−1) compared to large estuaries (0.13 to 0.71 ind. fyke nets 24 h−1). Spatial variation in eel abundance was correlated with differences in estuary size for all eel size classes. The influence of anthropogenic pressures on eel abundance was assessed by nine anthropogenic estuarine pressure indicators. The results indicate that high values of the anthropogenic pressure indicators were correlated with low eel abundance. This study highlights that large French estuaries subject to stronger anthropogenic pressures were less favourable habitats than small estuaries with less anthropogenic pressure.