bluuebunny/crossref_metadata_2025 huggingface dataset:
Oxidative Stress and Age-Related Tumors
Authors: Di Carlo Emma , Sorrentino Carlo
Published: September 2024

Oxidative stress is the result of the imbalance between reactive oxygen and nitrogen species (RONS), which are produced by several endogenous and exogenous processes, and antioxidant defenses consisting of exogenous and endogenous molecules that protect biological systems from free radical toxicity. Oxidative stress is a major factor in the aging process, contributing to the accumulation of cellular damage over time. Oxidative damage to cellular biomolecules, leads to DNA alterations, lipid peroxidation, protein oxidation, and mitochondrial dysfunction resulting in cellular senescence, immune system and tissue dysfunctions, and increased susceptibility to age-related pathologies, such as inflammatory disorders, cardiovascular and neurodegenerative diseases, diabetes, and cancer. Oxidative stress-driven DNA damage and mutations, or methylation and histone modification, which alter gene expression, are key determinants of tumor initiation, angiogenesis, metastasis, and therapy resistance. Accumulation of genetic and epigenetic damage, to which oxidative stress contributes, eventually leads to unrestrained cell proliferation, the inhibition of cell differentiation, and the evasion of cell death, providing favorable conditions for tumorigenesis. Colorectal, breast, lung, prostate, and skin cancers are the most frequent aging-associated malignancies, and oxidative stress is implicated in their pathogenesis and biological behavior. Our aim is to shed light on the molecular and cellular mechanisms that link oxidative stress, aging, and cancers, highlighting the impact of both RONS and antioxidants, provided by diet and exercise, on cellular senescence, immunity, and development of an antitumor response. The dual role of ROS as physiological regulators of cell signaling responsible for cell damage and diseases, as well as its use for anti-tumor therapeutic purposes, will also be discussed. Managing oxidative stress is crucial for promoting healthy aging and reducing the risk of age-related tumors.
Effects and Mechanisms of Lutein on Aging and Age-Related Diseases
Authors: Ye Jialu , Cheng Jin , Xiong Ruogu , Chen Haoqi , Huang Siyu , Li Huabin , Pang Jinzhu , Zhang Xuguang , Zhu Huilian
Published: September 2024

Aging and age-related diseases are serious public health issues that are receiving growing attention from researchers. Lutein has a critical function in the prevention and management of these issues. Possible mechanisms mainly include suppressing inflammation and oxidative stress, regulating cell activity, and modulating the levels of toxic substances. In this narrative review paper, we sum up the most current developments in the study of the effects of lutein on aging and five age-related diseases (age-related macular degeneration, cataracts, Alzheimer’s disease, Parkinson’s disease, and osteoporosis), and fundamental mechanisms are reviewed. The bioavailability of lutein and the strategies to improve its bioavailability are discussed. This piece of work can bring a clearer comprehension of the protective effects of lutein against aging and age-related diseases and can be also helpful for developing lutein as functional food and dietary supplements for these age-related diseases.
Extraction Optimization of Quercus cerris L. Wood Chips: A Comparative Study between Full Factorial Design (FFD) and Artificial Neural Network (ANN)
Authors: Ponticelli Maria , Carlucci Vittorio , Mecca Marisabel , Todaro Luigi , Milella Luigi , Russo Daniela
Published: September 2024

From a circular bio-economy perspective, biomass valorization requires the implementation of increasingly efficient extraction techniques to ensure the environmental and economic sustainability of biorefining processes. This research focuses on optimizing the specialized metabolite extraction of Turkey oak chips from Quercus cerris L. by applying a 3 levels Full Factorial Design (FFD). The goal is to obtain an extract with the highest antioxidant activity [evaluated by 1,1-diphenyl-2-picryl hydrazyl (DPPH) scavenging activity and ferric reducing antioxidant power (FRAP) assays] and specialized metabolites content [measured as total phenolic content (TPC), total flavonoid content (TFC), condensed tannin content (CTC), and hydrolysable tannins content (THC)]. With this objective, three different variables were investigated and compared: temperature (20 °C, 50 °C, 80 °C), solvents EtOH/H2O (0%, 20%, 40%), and time (3 h, 6 h, 24 h), resulting in 27 different extracts. Following the FFD analysis, the optimal extractive conditions were determined to be 80 °C, 40% EtOH/H2O, and 19.8 h. Finally, the prediction ability of FFD was compared with that of artificial neural network (ANN) for DPPH scavenging activity, FRAP, and TPC data based on the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that ANN predictions were more precise than FFD ones; however, both methods were useful in optimizing the extraction process as they returned comparable optimized extraction parameters.
Fermented Foods and Food Microorganisms: Antioxidant Benefits and Biotechnological Advancements
Authors: Seo Myung-Ji
Published: September 2024

Fermented foods have been a part of human civilization since ancient times, offering enhanced flavors, extended shelf-life, and improved nutritional value through the action of microorganisms [...]
Carbon Monoxide: A Pleiotropic Redox Regulator of Life and Death
Authors: Abramov Andrey Y. , Myers Isabella , Angelova Plamena R.
Published: September 2024

Despite recent technological progress, carbon monoxide poisoning is still one of the leading causes of domestic and industrial morbidity and mortality. The brain is particularly vulnerable to CO toxicity, and thus the majority of survivors develop delayed movement and cognitive complications. CO binds to haemoglobin in erythrocytes, preventing oxygen delivery to tissues, and additionally inhibits mitochondrial respiration. This renders the effect of CO to be closely related to hypoxia reperfusion injury. Oxygen deprivation, as well as CO poisoning and re-oxygenation, are shown to be able to activate the production of reactive oxygen species and to induce oxidative stress. Here, we review the role of reactive oxygen species production and oxidative stress in the mechanism of neuronal cell death induced by carbon monoxide and re-oxygenation. We discuss possible protective mechanisms used by brain cells with a specific focus on the inhibition of CO-induced ROS production and oxidative stress.
Simplified Deep Learning for Accessible Fruit Quality Assessment in Small Agricultural Operations
Authors: Zárate Víctor , Hernández Danilo Cáceres
Published: September 2024

Fruit quality assessment is vital for ensuring consumer satisfaction and marketability in agriculture. This study explores deep learning techniques for assessing fruit quality, focusing on practical deployment in resource-constrained environments. Two approaches were compared: training a convolutional neural network (CNN) from scratch and fine-tuning a pre-trained MobileNetV2 model through transfer learning. The performance of these models was evaluated using a subset of the Fruits-360 dataset chosen to simulate real-world conditions for small-scale producers. MobileNetV2 was selected for its compact size and efficiency, suitable for devices with limited computational resources. Both approaches achieved high accuracy, with the transfer learning model demonstrating faster convergence and slightly better performance. Feature map visualizations provided insight into the model’s decision-making, highlighting damaged areas of fruits which enhances transparency and trust for end users. This study underscores the potential of deep learning models to modernize fruit quality assessment, offering practical, efficient, and interpretable tools for small-scale farmers.
Person Re-Identification Network Based on Edge-Enhanced Feature Extraction and Inter-Part Relationship Modeling
Authors: Zhu Chuan , Zhou Wenjun , Ma Jianmin
Published: September 2024

Person re-identification (Re-ID) is a technique for identifying target pedestrians in images or videos. In recent years, owing to the advancements in deep learning, research on person re-identification has made significant progress. However, current methods mostly focus on salient regions within the entire image, overlooking certain hidden features specific to pedestrians themselves. Motivated by this consideration, we propose a novel person re-identification network. Our approach integrates pedestrian edge features into the representation and utilizes edge information to guide global context feature extraction. Additionally, by modeling the internal relationships between different parts of pedestrians, we enhance the network’s ability to capture and understand the interdependencies within pedestrians, thereby improving the semantic coherence of pedestrian features. Ultimately, by fusing these multifaceted features, we generate comprehensive and highly discriminative representations of pedestrians, significantly enhancing person Re-ID performance. Experimental results demonstrate that our method outperforms most state-of-the-art approaches in person re-identification.
(R-ISSUES) Rural Interoperable System of Systems for Unified Environmental Stewardship
Authors: Pastor Raúl , Lecuona Antonio , Cortés Juan Pedro , Caballero David , Fraga Anabel
Published: September 2024

Spain has one of Europe’s most extraordinary biodiverse environments and a significant risk of fires in its forests. At the same time, rural areas are affected by several challenges, such as desertification, population decrease, and loss of income. Fortunately, some green sparks arise on the horizon. Among them, we use critical enabling technologies for fire prevention and extinction, renewable energy, and resilience solutions by adopting a system of systems approach given by the systems engineering frameworks. We analyse recent Research and Development (R&D) projects focused on fire prevention to detect (1) the key enabling technologies used and (2) engineering practices. A motivational case is presented, which evaluates the potential interest of the mineral water industry sector in applied R&D with key enabling technologies, including the replicability potential and the replicability potential for using the R&D results. After that, the authors initialize an innovative R-ISSUES model to promote early interoperability between energy and environment systems engineering towards the shared mission of designing digital and rural jobs to preserve our biosphere. The model is used to answer specific research questions and detect gaps or potential improvements for the model itself by using the recent scientific literature.
Autonomous Vehicle Ecosystem Security: Utilizing Autonomous Vehicle Security-Level Checks through Analytic Hierarchy Process
Authors: Lim Dong-Sung , Lee Sang-Joon
Published: September 2024

This study aimed to strengthen the security of autonomous vehicles by analyzing the current status of autonomous vehicle security, such as autonomous vehicle features, security threats, and compliance, and deriving security-level check items. Based on this, the relative importance could be obtained by applying it to the AHP (Analytic Hierarchy Process) model. The results of the empirical analysis showed that the order of priority was the establishment/implementation of a cybersecurity management system, encryption, and risk assessment. The significance of this study is that by deriving security-level check items related to autonomous vehicles and verifying the research model, we can reduce cyber security accidents that can cause loss of life and improve the level of autonomous vehicle security management of related companies. Additionally, by applying AHP evaluated by security experts to the autonomous vehicle field for the first time, it will contribute to the market expansion of the autonomous vehicle industry, which is concerned with security. Furthermore, major automobile companies have to manage the security levels of numerous tier companies due to the nature of the industry. Therefore, if they perform a Quick Check (QC) considering the relative importance of the autonomous vehicle security-level check items presented in this paper, they will be able to effectively identify the security levels of tier companies early.
Virtual Reality Associated with Functional Electrical Stimulation for Upper Extremity in Post-Stroke Rehabilitation: A Systematic Review
Authors: Minzatanu Diana , Roman Nadinne Alexandra , Manaila Adina Ionelia , Baseanu Ionut Cristian Cozmin , Tuchel Vlad Ionut , Basalic Elena Bianca , Miclaus Roxana Steliana
Published: September 2024

Background: This systematic literature review aims to explore the impact of rehabilitation in post-stroke patients, particularly highlighting the roles of virtual reality (VR) technology and functional electrical stimulation (FES). Methods: To ensure all relevant studies were included, a thorough search was conducted in PubMed and Web of Science databases using keywords such as ‘post-stroke’, ‘FES’, ‘functional electrical stimulation’, ‘virtual reality’, and ‘VR’. Studies on rehabilitating upper limb function through VR and FES in post-stroke patients were included, regardless of publication year. Studies had to compare this combination therapy with conventional methods and report outcomes related to upper limb coordination, functional mobility, and daily activities. Studies not meeting these criteria were excluded. The selection process involved screening titles, abstracts, and full texts by four independent reviewers. The quality and risk of bias of the included studies were assessed using the PEDro scale and Robvis tool. Results: The review included four studies involving 135 post-stroke patients. Two articles examined the effectiveness of an approach involving virtual reality, robotic therapy, and functional electrical stimulation in rehabilitating upper limbs in post-stroke patients, showing significant improvements in motor function and quality of life. The other two studies explored the effects of rehabilitation therapy using virtual reality combined with functional electrical stimulation on upper limb function in stroke patients, finding that combined therapy (FES with VR) was superior to functional electrical stimulation or robotic therapy. Discussion: The review was limited by the small number of studies and participants, which may affect the generalizability of the results. Variations in intervention protocols and outcome measures across studies posed challenges in synthesis. Integrating these technologies brings benefits and increases the potential for personalizing and optimizing the rehabilitation process, enhancing patient engagement and satisfaction, and promoting a holistic approach to post-stroke management. Future research should focus on larger, more standardized trials to confirm these findings and optimize intervention protocols.
DOUNet: Dynamic Optimization and Update Network for Oriented Object Detection
Authors: Deng Liwei , Zhao Dexu , Lan Qi , Chen Fei
Published: September 2024

Object detection can accurately identify and locate targets in images, serving basic industries such as agricultural monitoring and urban planning. However, targets in remote sensing images have random rotation angles, which hinders the accuracy of remote sensing image object detection algorithms. In addition, due to the long-tailed distribution of detected objects in remote sensing images, the network finds it difficult to adapt to imbalanced datasets. In this article, we designed and proposed the Dynamic Optimization and Update network (DOUNet). By introducing adaptive rotation convolution to replace 2D convolution in the Region Proposal Network (RPN), the features of rotating targets are effectively extracted. To address the issues caused by imbalanced data, we have designed a long-tail data detection module to collect features of tail categories and guide the network to output more balanced detection results. Various experiments have shown that after two stages of feature learning and classifier learning, our designed network can achieve optimal performance and perform better in detecting imbalanced data.
Bacterial Aerosol in Ambient Air—A Review Study
Authors: Brągoszewska Ewa , Mainka Anna
Published: September 2024

Bioaerosols, including airborne bacteria, are significant pollutants affecting both indoor and outdoor air quality, with implications for human health. Despite extensive research on indoor air quality, there is a notable lack of comprehensive data on ambient bacterial concentrations and their interactions with pollutants and meteorological factors. This review focuses on bacterial aerosols in the atmosphere, measured using the culture-based method, considered the “gold standard” for microorganism detection and identification. Studies reveal significant variability in bacterial concentrations across different environments and seasons, influenced by factors such as temperature, humidity, wind speed, solar radiation, and precipitation, underscoring the need for further research and monitoring to enhance health risk assessments and mitigation strategies. The presence of air pollutants such as particulate matter (PM) and ozone (O3) further complicates these dynamics. The authors emphasize the need for more extensive research on outdoor bacterial aerosols and recommend that future studies prioritize detailed bioaerosol characterization to establish comprehensive exposure standards in ambient air, thereby improving public health protection and environmental management practices.
Tin–Phosphorus Alloy: The Impact of Temperature on Alloy Formation and the Influence of the Dross Amount on the Solder Bath Surface
Authors: Körmendy Jana , Vavro Ján , Vavro Ján
Published: September 2024

The restriction of lead content in alloys for the production of solder, based on the Directive of the European Parliament and of the Council of the European Union of 8 June 2011, which is also known as RoHS (Restriction of the use of certain Hazardous Substances in electrical and electronic equipment), had a very positive impact on research into lead-free solder alloys, as well as on the economic impact of the production of solders. It opened the door to issues relating to the mechanical properties of lead-free solders and the microhardness of formed joints, with the aim of increasing their quality and efforts to reduce production costs. In addition to the production efficiency increase, without the need for the manual removal of so-called slagging, the moderation of oxide formation on the melt surface, standing for an increase in the yield of the total amount of solder, represents one of the many factors influencing the production of lead-free alloys for tin-based soldering. This work deals with the issues of material selection for the production of lead-free solders. Temperature affects the formation of different phases when there is a change in the concentration of the elements involved because it can be a negative aspect for soldering. Therefore, it is necessary to have detailed knowledge on the entire process that takes place during temperature changes.
Examining the Relationship between Psychological and Functional Status after a Sports Musculoskeletal Injury
Authors: Parlakidis Konstantinos , Krokos Dimitrios , Sagredaki Maria-Louiza , Kontopoulos Lazaros Alexandros , Christakou Anna
Published: September 2024

The purpose of the present study was to investigate the relationship between re-injury worry, confidence, and attention and athletes’ functional status upon returning to sport after an injury. The sample consisted of 28 amateur-level male football players, aged 18 to 35 years, with a previous lower-limb injury. The athletes followed a physiotherapy rehabilitation program and completed three valid questionnaires examining re-injury worry, sport confidence, and attention returning to sport. The sample also performed three functional tests: (a) single-leg hop for distance, (b) side hop, and (c) the vertical jump. The results showed high correlations between the psychological factors between the functional tests. Physiotherapy duration was positively highly correlated with the time of absence from sport and severity of injury. The severity of the injury was also positively highly correlated with the time of absence from sport. The factors “Functional Attention” and “Distraction Attention” showed a positive and negative correlation with the single-leg hop for distance and the vertical jump, respectively. Athletes with a grade II severity injury showed greater attention compared to grade III severity injury. The increased level of re-injury worry was positively related to “Distraction Attention” and negatively related to the functional ability of the injured limb. The psychological readiness was partially related to the athletes’ functional status. The present study reports the importance of psychological readiness and its relationship with athletes’ functional status of returning to sport following a musculoskeletal sport injury.
Enhancing E-Government Services through State-of-the-Art, Modular, and Reproducible Architecture over Large Language Models
Authors: Papageorgiou George , Sarlis Vangelis , Maragoudakis Manolis , Tjortjis Christos
Published: September 2024

Integrating Large Language Models (LLMs) into e-government applications has the potential to improve public service delivery through advanced data processing and automation. This paper explores critical aspects of a modular and reproducible architecture based on Retrieval-Augmented Generation (RAG) for deploying LLM-based assistants within e-government systems. By examining current practices and challenges, we propose a framework ensuring that Artificial Intelligence (AI) systems are modular and reproducible, essential for maintaining scalability, transparency, and ethical standards. Our approach utilizing Haystack demonstrates a complete multi-agent Generative AI (GAI) virtual assistant that facilitates scalability and reproducibility by allowing individual components to be independently scaled. This research focuses on a comprehensive review of the existing literature and presents case study examples to demonstrate how such an architecture can enhance public service operations. This framework provides a valuable case study for researchers, policymakers, and practitioners interested in exploring the integration of advanced computational linguistics and LLMs into e-government services, although it could benefit from further empirical validation.
Contamination in Bone Substitute Materials: A Systematic Review
Authors: Struzik Natalia , Kensy Julia , Piszko Paweł J. , Kiryk Jan , Wiśniewska Kamila , Kiryk Sylwia , Korjat Łuksza , Horodniczy Tomasz , Sobierajska Paulina , Matys Jacek , Wiglusz Rafal J. , Dobrzyński Maciej
Published: September 2024

Objective: Bone augmentation has become a significant practice in various areas of bone regeneration dentistry. This systematic review analyzes the research focused on evaluating bone substitute materials for the presence of contaminants. Methods: In June 2024, an extensive electronic search was conducted using renowned databases such as PubMed, Web of Science, and Scopus. Specific keywords employed in the search included ((bone AND (substitute) AND (remnants OR (purity)) OR ((graft AND tooth) AND (remnants OR purity)) OR ((graft AND dentin) AND (remnants OR purity)). The search adhered to the PRISMA protocol and the PICO framework. The review concentrated on the origin of bone substitute materials, the processing methods used for these materials, techniques for assessing purity, and types of contamination identified. A total of 594 articles were identified of which 22 met the criteria and were incorporated into the review. Results: Investigations into allogeneic and xenogeneic bone substitute materials have revealed that, despite manufacturers’ assurances of purity, some materials still contain contaminants. Sample analyses demonstrated the presence of donor cellular remains, cellular debris, intertrabecular fat, connective tissue, and collagen. Similarly, synthetically produced bone substitute materials (alloplastic materials) contained various impurities, such as polyvinyl alcohol (PVA), CaO phases, calcium-deficient HAp phases, oily substances containing carbon and silicone, cellulose derivatives, alpha-tricalcium phosphate (α-TCP), and heavy metals. Conclusions: Bone-derived and bone-like graft materials can contain various organic and inorganic impurities.
Multiphysics Optimization of a High-Speed Permanent Magnet Motor Based on Subspace and Sequential Strategy
Authors: Yan Honglin , Du Guanghui , Gao Wentao , Chen Yanhong , Cui Cunlong , Xu Kai
Published: September 2024

In the optimization of high-speed permanent magnet motors (HSPMMs), electromagnetic characteristics, rotor stress, rotor dynamics, and temperature characteristics must all be considered simultaneously, and there are numerous optimization parameters for both the stator and rotor. These factors pose significant challenges to the multiphysics optimization of HSPMMs. Therefore, this paper presents a multiphysics optimization process for the HSPMM of 60 kW 30,000 rpm by combining subspace strategy and sequential strategy to mitigate the issues of high training volume and mutual coupling. Ten optimization parameters of stator and rotor are determined firstly. Then, using finite element analysis of rotor stress and rotor dynamics, the range of values for critical parameters of the rotor is established. Next, in the electromagnetic optimization, the process is divided into rotor parameter subspace and stator parameter subspace according to the subspace optimization strategy. The temperature field is also checked based on the optimization results. Finally, a prototype is manufactured and the comprehensive performance is tested to validate the multiphysics optimization process.
Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer
Authors: Ding Can , Ma Pengcheng , Jiang Changhua , Wang Fei
Published: September 2024

When a single-phase grounding fault occurs in resonant ground distribution network, the fault characteristics are weak and it is difficult to detect the fault line. Therefore, a fast fault line selection method based on MCECA-CloFormer is proposed in this paper. Firstly, zero-sequence current signals were converted into images using the moving average filter method and motif difference field to construct fault data set. Then, the ECA module was modified to MCECA (MultiCNN-ECA) so that it can accept data input from multiple measurement points. Secondly, the lightweight model CloFormer was used in the back end of MCECA module to further perceive the feature map and complete the establishment of the line selection model. Finally, the line selection model was trained, and the information such as model weight was saved. The simulation results demonstrated that the pre-trained MCECA-CloFormer achieved a line selection accuracy of over 98% under 10 dB noise, with a remarkably low single fault processing time of approximately 0.04 s. Moreover, it exhibited suitability for arc high-resistance grounding faults, data-missing cases, neutral-point ungrounded systems, and active distribution networks. In addition, the method was still valid when tested with actual field recording data.
Co-Precipitation Synthesized Ag-Doped Ceria Redox Material (ACRM) for the Thermochemical Conversion of CO2 into Solar Fuels
Authors: Takalkar Gorakshnath , Akhter Sayma , Bhosale Rahul R.
Published: September 2024

In this investigation, an effort was made to introduce Ag into the CeO2 fluorite crystal lattice to form Ce0.99Ag0.01O2-δ (ACRM) using an ammonium hydroxide-assisted co-precipitation method. The resulting powder obtained after the co-precipitation reaction, filtration, and drying was annealed at 800 °C in a muffle furnace to obtain crystalline ACRM. The phase composition and microstructure of the synthesized ACRM were analyzed using a powder X-ray diffractometer (PXRD) and a scanning electron microscope (SEM). The characterized ACRM powder was then subjected to multiple thermochemical thermal reduction (TR) and CO2 splitting (CDS) cycles using a high-temperature thermogravimetric analyzer (TGA). The TR step was conducted using Ar gas as an inert atmosphere, maintaining the temperature at 1400 °C for 60 min. Subsequently, the same powder was subjected to the CDS step by treating it with a gaseous mixture of 50% CO2 and Ar gas at 1000 °C for 30 min. ACRM displayed stable redox reactivity towards thermochemical CDS cycles by generating an average of 50.9 μmol of O2/g·cycle and 101.6 μmol of CO/g·cycle, respectively, over 10 thermochemical cycles.
SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
Authors: Fiori Michele , Fois Giuliano , Gerardi Marco Secondo , Maggio Fabio , Milesi Carlo , Pinna Andrea
Published: September 2024

The prediction and monitoring of plant diseases and pests are key activities in agriculture. These activities enable growers to take preventive measures to reduce the spread of diseases and harmful insects. Consequently, they reduce crop loss, make pesticide and resource use more efficient, and preserve plant health, contributing to environmental sustainability. We illustrate the SMARTerra decision support system, which processes daily measured and predicted weather data, spatially interpolating them at high resolution across the entire Sardinia region. From these data, SMARTerra generates risk predictions for plant pests and diseases. Currently, models for predicting the risk of rice blast disease and the hatching of locust eggs are implemented in the infrastructure. The web interface of the SMARTerra platform allows users to visualize detailed risk maps and promptly take preventive measures. A simple notification system is also implemented to directly alert emergency responders. Model outputs by the SMARTerra infrastructure are comparable with results from in-field observations produced by the LAORE Regional Agency. The infrastructure provides a database for storing the time series and risk maps generated, which can be used by agencies and researchers to conduct further analysis.
Carbon Emission Analysis of Low-Carbon Technology Coupled with a Regional Integrated Energy System Considering Carbon-Peaking Targets
Authors: Zeng Yipu , Dai Yiru , Shu Yiming , Yin Ting
Published: September 2024

Analyzing the carbon emission behavior of a regional integrated energy system (RIES) is crucial for aligning with carbon-peaking development strategies and ensuring compliance with carbon-peaking implementation pathways. This study focuses on a building cluster area in Shanghai, China, aiming to provide a comprehensive analysis from both macro and micro perspectives. From a macro viewpoint, an extended STIRPAT model, incorporating the environmental Kuznets curve, is proposed to predict the carbon-peaking trajectory in Shanghai. This approach yields carbon-peaking implementation pathways for three scenarios: rapid development, stable development, and green development, spanning the period of 2020–2040. At a micro scale, three distinct RIES system configurations—fossil, hybrid, and clean—are formulated based on the renewable energy penetration level. Utilizing a multi-objective optimization model, this study explores the carbon emission behavior of a RIES while adhering to carbon-peaking constraints. Four scenarios of carbon emission reduction policies are implemented, leveraging green certificates and carbon-trading mechanisms. Performance indicators, including carbon emissions, carbon intensity, and marginal emission reduction cost, are employed to scrutinize the carbon emission behavior of the cross-regional integrated energy system within the confines of carbon peaking.
Hybrid Artificial Protozoa-Based JADE for Attack Detection
Authors: Al Hwaitat Ahmad k. , Fakhouri Hussam N.
Published: September 2024

This paper presents a novel hybrid optimization algorithm that combines JADE Adaptive Differential Evolution with Artificial Protozoa Optimizer (APO) to solve complex optimization problems and detect attacks. The proposed Hybrid APO-JADE Algorithm leverages JADE’s adaptive exploration capabilities and APO’s intensive exploitation strategies, ensuring a robust search process that balances global and local optimization. Initially, the algorithm employs JADE’s mutation and crossover operations, guided by adaptive control parameters, to explore the search space and prevent premature convergence. As the optimization progresses, a dynamic transition to the APO mechanism is implemented, where Levy flights and adaptive change factors are utilized to refine the best solutions identified during the exploration phase. This integration of exploration and exploitation phases enhances the algorithm’s ability to converge to high-quality solutions efficiently. The performance of the APO-JADE was verified via experimental simulations and compared with state-of-the-art algorithms using the 2022 IEEE Congress on Evolutionary Computation benchmark (CEC) 2022 and 2021. Results indicate that APO-JADE achieved outperforming results compared with the other algorithms. Considering practicality, the proposed APO-JADE was used to solve a real-world application in attack detection and tested on DS2OS, UNSW-NB15, and ToNIoT datasets, demonstrating its robust performance.
Curvature Sensing and Control of Soft Continuum Robots Using e-Textile Sensors
Authors: Galeta Eric Vincent , Nada Ayman A. , Hameed Ibrahim , El-Hussieny Haitham
Published: September 2024

Soft continuum robots, with their flexible and deformable structures, excel in tasks requiring delicate manipulation and navigation through complex environments. Accurate shape sensing is vital to enhance their performance, safety, and adaptability. Unlike rigid sensors, soft sensors conform to the robot’s flexible surfaces, ensuring consistent measurement of shape and motion. This paper introduces a new approach using soft e-textile resistive sensors, which integrate seamlessly with the robot’s structure. These sensors adjust their resistance in response to movements, capturing multidimensional force data. A deep Convolutional Neural Network (CNN) decodes the sensor signals, enabling precise shape estimation and control. Our findings indicate that soft e-textile sensors may surpass traditional rigid sensors in shape sensing and control, significantly improving the functionality of soft continuum robots in challenging applications.
A Review on the Arctic–Midlatitudes Connection: Interactive Impacts, Physical Mechanisms, and Nonstationary
Authors: Ding Shuoyi , Chen Xiaodan , Zhang Xuanwen , Zhang Xiang , Xu Peiqiang
Published: September 2024

In light of the rapid Arctic warming and continuous reduction in Arctic Sea ice, the complex two-way Arctic–midlatitudes connection has become a focal point in recent climate research. In this paper, we review the current understanding of the interactive influence between midlatitude atmospheric variability and Arctic Sea ice or thermal conditions on interannual timescales. As sea ice diminishes, in contrast to the Arctic warming (cooling) in boreal winter (summer), Eurasia and North America have experienced anomalously cold (warm) conditions and record snowfall (rainfall), forming an opposite oscillation between the Arctic and midlatitudes. Both statistical analyses and modeling studies have demonstrated the significant impacts of autumn–winter Arctic variations on winter midlatitude cooling, cold surges, and snowfall, as well as the potential contributions of spring–summer Arctic variations to midlatitude warming, heatwaves and rainfall, particularly focusing on the role of distinct regional sea ice. The possible physical processes can be categorized into tropospheric and stratospheric pathways, with the former encompassing the swirling jet stream, horizontally propagated Rossby waves, and transient eddy–mean flow interaction, and the latter manifested as anomalous vertical propagation of quasi-stationary planetary waves and associated downward control of stratospheric anomalies. In turn, atmospheric prevailing patterns in the midlatitudes also contribute to Arctic Sea ice or thermal condition anomalies by meridional energy transport. The Arctic–midlatitudes connection fluctuates over time and is influenced by multiple factors (e.g., continuous melting of climatological sea ice, different locations and magnitudes of sea ice anomalies, internal variability, and other external forcings), undoubtedly increasing the difficulty of mechanism studies and the uncertainty surrounding predictions of midlatitude weather and climate. In conclusion, we provide a succinct summary and offer suggestions for future research.
On the Existence, Uniqueness and a Numerical Approach to the Solution of Fractional Cauchy–Euler Equation
Authors: Mahmudov Nazim I. , Cival Buranay Suzan , Chin Mtema James
Published: September 2024

In this research paper, we consider a model of the fractional Cauchy–Euler-type equation, where the fractional derivative operator is the Caputo with order 0<α<2. The problem also constitutes a class of examples of the Cauchy problem of the Bagley–Torvik equation with variable coefficients. For proving the existence and uniqueness of the solution of the given problem, the contraction mapping principle is utilized. Furthermore, a numerical method and an algorithm are developed for obtaining the approximate solution. Also, convergence analyses are studied, and simulations on some test problems are given. It is shown that the proposed method and the algorithm are easy to implement on a computer and efficient in computational time and storage.
Whole-Head Noninvasive Brain Signal Measurement System with High Temporal and Spatial Resolution Using Static Magnetic Field Bias to the Brain
Authors: Hiwaki Osamu
Published: September 2024

Noninvasive brain signal measurement techniques are crucial for understanding human brain function and brain–machine interface applications. Conventionally, noninvasive brain signal measurement techniques, such as electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and near-infrared spectroscopy, have been developed. However, currently, there is no practical noninvasive technique to measure brain function with high temporal and spatial resolution using one instrument. We developed a novel noninvasive brain signal measurement technique with high temporal and spatial resolution by biasing a static magnetic field emitted from a coil on the head to the brain. In this study, we applied this technique to develop a groundbreaking system for noninvasive whole-head brain function measurement with high spatiotemporal resolution across the entire head. We validated this system by measuring movement-related brain signals evoked by a right index finger extension movement and demonstrated that the proposed system can measure the dynamic activity of brain regions involved in finger movement with high spatiotemporal accuracy over the whole brain.
Impact of Multi-Factor Features on Protein Secondary Structure Prediction
Authors: Dong Benzhi , Liu Zheng , Xu Dali , Hou Chang , Niu Na , Wang Guohua
Published: September 2024

Protein secondary structure prediction (PSSP) plays a crucial role in resolving protein functions and properties. Significant progress has been made in this field in recent years, and the use of a variety of protein-related features, including amino acid sequences, position-specific score matrices (PSSM), amino acid properties, and secondary structure trend factors, to improve prediction accuracy is an important technical route for it. However, a comprehensive evaluation of the impact of these factor features in secondary structure prediction is lacking in the current work. This study quantitatively analyzes the impact of several major factors on secondary structure prediction models using a more explanatory four-class machine learning approach. The applicability of each factor in the different types of methods, the extent to which the different methods work on each factor, and the evaluation of the effect of multi-factor combinations are explored in detail. Through experiments and analyses, it was found that PSSM performs best in methods with strong high-dimensional features and complex feature extraction capabilities, while amino acid sequences, although performing poorly overall, perform relatively well in methods with strong linear processing capabilities. Also, the combination of amino acid properties and trend factors significantly improved the prediction performance. This study provides empirical evidence for future researchers to optimize multi-factor feature combinations and apply them to protein secondary structure prediction models, which is beneficial in further optimizing the use of these factors to enhance the performance of protein secondary structure prediction models.
Fundus Image Deep Learning Study to Explore the Association of Retinal Morphology with Age-Related Macular Degeneration Polygenic Risk Score
Authors: Sendecki Adam , Ledwoń Daniel , Tuszy Aleksandra , Nycz Julia , Wąsowska Anna , Boguszewska-Chachulska Anna , Mitas Andrzej W. , Wylęgała Edward , Teper Sławomir
Published: September 2024

Background: Age-related macular degeneration (AMD) is a complex eye disorder with an environmental and genetic origin, affecting millions worldwide. The study aims to explore the association between retinal morphology and the polygenic risk score (PRS) for AMD using fundus images and deep learning techniques. Methods: The study used and pre-processed 23,654 fundus images from 332 subjects (235 patients with AMD and 97 controls), ultimately selecting 558 high-quality images for analysis. The fine-tuned DenseNet121 deep learning model was employed to estimate PRS from single fundus images. After training, deep features were extracted, fused, and used in machine learning regression models to estimate PRS for each subject. The Grad-CAM technique was applied to examine the relationship between areas of increased model activity and the retina’s morphological features specific to AMD. Results: Using the hybrid approach improved the results obtained by DenseNet121 in 5-fold cross-validation. The final evaluation metrics for all predictions from the best model from each fold are MAE = 0.74, MSE = 0.85, RMSE = 0.92, R2 = 0.18, MAPE = 2.41. Grad-CAM heatmap evaluation showed that the model decisions rely on lesion area, focusing mostly on the presence of drusen. The proposed approach was also shown to be sensitive to artifacts present in the image. Conclusions: The findings indicate an association between fundus images and AMD PRS, suggesting that deep learning models may effectively estimate genetic risk for AMD from retinal images, potentially aiding in early detection and personalized treatment strategies.
Skin Lesions Caused by HPV—A Comprehensive Review
Authors: Maghiar Laura , Sandor Mircea , Sachelarie Liliana , Bodog Ruxandra , Huniadi Anca
Published: September 2024

This narrative review provides a comprehensive analysis of skin lesions caused by human papillomavirus (HPV). Human papillomavirus is an infection involving a virus that is omnipresent and can range from benign wart lesions to malignant skin growths. This review includes an analysis of the skin manifestations caused by HPV, and the need for continued successful diagnostic techniques and treatment methods, given the increasing rates of infection among people worldwide. We reviewed all 135 studies related to pathophysiology involving skin, risk factors, and early detection methods like biopsy and molecular testing, from 2000 to 2023. The current treatments, including cryotherapy and laser therapy, are discussed, while the review emphasizes the role of HPV vaccination in preventing infection. Recommendations for the future would involve the improvement of public education and increased vaccine coverage, together with innovative therapies toward better management or control of skin diseases associated with the human papillomavirus (HPV). By advancing these recommendations, we will be in a better position to prevent and treat HPV skin conditions, thus improving the health condition of the general public across the world.
In Silico Investigation of the Interlaminar and Mechanical Fracture of Arteries with Atheromatic Plaque during Angioplasty Treatment
Authors: Psarras Spyridon , Skafidas Anargyros-Nektarios , Kostopoulos Vassilis
Published: September 2024

The reduction in the inner diameter of the artery due to the creation of atheromatic plaque on the artery lumen, known as artery stenosis, disrupts the blood flow, leading to medical complications, which can be fatal. The angioplasty procedure aims to reopen the artery and uses a stent to keep it open. In this study, an effort is made to determine the point of the stent, the plaque and the artery during the expansion phase of the angioplasty using the in silico Finite Element Analysis method. A literature-based design was chosen for the stent geometry, whereas simplified shapes of the balloon and the two artery layers were used. Additionally, two plaque designs were the benchmark for the eight distinct artery stenosis models within the Abaqus environment. In the context of stent angioplasty simulations, failure patterns were investigated. An inverse relationship was observed between artery stenosis and pressure at the artery failure point, while an increased danger of interlaminar failure was detected in models with larger artery stenosis. This study verifies the necessity for the inclusion of interlaminar failure in future angioplasty research.
User Sentiment Analysis Based on Securities Application Elements
Authors: Kim Minji , Kim Subeen , Park Yoonha , Bahn Sangwoo , Ahn Sung Hee , NambiNarayanan Bhavadharani
Published: September 2024

Designing securities applications for mobile devices is challenging due to their inherent complexity, necessitating improvement through the analysis of online reviews. However, research applying deep learning techniques to the sentiment analysis of Korean text remains limited. This study explores the use of Aspect-Based Sentiment Analysis (ABSA) as an effective alternative to traditional user research methods for securities application design. By analyzing large volumes of text-based user review data of Korean securities applications, the study identifies critical elements like “update”, “screen”, “chart”, “login”, “access”, “authentication”, “account”, and “transaction”, revealing nuanced user sentiments through techniques such as PMI, SVD, and Word2Vec. ABSA offers deeper insights compared to overall ratings, uncovering hidden areas of dissatisfaction despite positive biases in reviews. This research demonstrates the scalability and cost-effectiveness of ABSA in mobile-application design research.