bluuebunny/crossref_metadata_2025 huggingface dataset:
Trends and Opportunities in Sustainable Manufacturing: A Systematic Review of Key Dimensions from 2019 to 2023
Authors: Setyadi Antonius , Soekotjo Sundari , Dwi Lestari Setyani , Pawirosumarto Suharno , Damaris Alana
Published: December 2024

Purpose: This systematic literature review aimed to analyze trends, key findings, and research opportunities in manufacturing sustainability from 2019 to 2023. Methodology: A systematic review of 134 publications was conducted, focusing on technological advancements, research gaps, and the impact of global events on manufacturing sustainability. Findings: The review revealed: (1) A shift towards advanced technologies like AI and blockchain, driving sustainability improvements; (2) Significant research gaps in social, policy, and regulatory dimensions; (3) The COVID-19 pandemic's role in accelerating digital transformation in manufacturing. Practical implications: Strategic recommendations are provided for industry, policymakers, and academics to address identified gaps and leverage emerging technologies for sustainable manufacturing. Originality: This review offers a comprehensive analysis of current trends and critical areas for future research in manufacturing sustainability, particularly in the context of rapid technological advancements and global disruptions.
Exploring the Global Perspectives on Forest Wildfire Protection and Biodiversity Conservation Strategies
Authors: Adamopoulos Ioannis , Syrou Niki , Khan Ali Junaid , Dávid Lóránt Dénes
Published: December 2024

One of the primary focal points is the critical evaluation of the role that prescribed burning plays in temperate and boreal forests. The significance of understanding the intricate interplay between human intervention and natural processes in these ecosystems. While non-intervention strategies are often heralded as the optimal approach for preserving biodiversity, it is crucial to recognize the pivotal role that wildfires, both naturally occurring and prescribed, play in shaping and maintaining the robustness of forest ecosystems. This study a scoping review aims to explore in-depth the intricate and multifaceted relationship between forest wildfire protection and biodiversity conservation strategies, delving into the complexities and nuances that exist within this crucial field. This comprehensive examination draws insights into recent scholarly contributions, encompassing a diverse range of perspectives and methodologies to provide a holistic understanding. Relationship between forest wildfire management and biodiversity conservation, highlighting the necessity for adaptive strategies that incorporate both scientific insights and traditional ecological knowledge. In conclusion, the literature collectively underscores the critical need to re-evaluate current fire management practices to enhance biodiversity outcomes during the climate crisis. The integration of scientific insights with Indigenous fire stewardship presents a promising pathway for fostering resilient forest ecosystems and promoting biodiversity conservation.
Geographic Proximity of Forever Chemical
Authors: Gourishankar Anand
Published: December 2024

Background: Geographic Information Systems, or GIS, play a pivotal role in the study of Per- and Polyfluoroalkyl Substances, or PFAS. By focusing on their environmental distribution and pathways, GIS significantly contributes to understanding the sources of PFAS exposure. This method is instrumental in environmental risk assessment and developing effective management strategies.Objective: To geolocate PFAS sites in a map and perform proximity analysis around these sites. Methods: This was a cross-sectional study. Each PFAS site address was geocoded. We measured proximity using the Kriging interpolation method with each census tract centroid. Additionally, each site had a 1-mile buffer (exposure risk). Results: We geocoded nineteen industrial sites, twenty-nine military sites, and one wastewater management site. By empirical Bayesian kriging, a smoothed map showing proximity ranges from 200 to 2000 meters around the PFAS sites. A one-mile proximity buffer map covered the entire study area. Conclusion: This study’s findings underscore the urgent need for action. Using GIS in decision-making, we can allocate resources and prioritize interventions based on risk and vulnerability. The study highlights potential contamination in residential areas, public water supplies, public parks, and high-density population areas.
Collision Avoidance of Autonomous Vehicles Using the Control Lyapunov Function - Control Barrier Function - Quadratic Programming Approach with Deep Reinforcement Learning Decision Making
Authors: Chen Haochong , Zhang Fengrui , Aksun-Guvenc Bilin
Published: December 2024

Collision avoidance and path planning are critical topics in autonomous vehicle development. This paper presents the progressive development of an optimization-based controller for autonomous vehicles using the Control Lyapunov Function - Control Barrier Function - Quadratic Programming (CLF – CBF - QP) approach. This framework enables the vehicle to navigate to its destination while avoiding obstacles. A unicycle model is utilized to incorporate vehicle dynamics. A series of simulations were conducted, starting with basic model-in-the-loop (MIL) non-real-time simulations, followed by real-time simulations. Multiple scenarios with different controller configurations and obstacle setups were tested, demonstrating the effectiveness of the proposed controllers in avoiding collisions. Real-time simulations in Simulink were used to demonstrate that the proposed controller could compute control actions for each state within a very short timestep, highlighting its computational efficiency. This efficiency underscores the potential for deploying the controller in real-world vehicle autonomous driving systems. Furthermore, we explored the feasibility of a hierarchical control framework comprising a Deep Reinforcement Learning (DRL) specifically Deep Q-Network (DQN) based high-level controller and a CLF-CBF-QP-based low-level controller. Simulation results show that the vehicle can effectively respond to obstacles and generate a successful trajectory towards its goal.
Sustained Nitric Oxide Release Using Hybrid Magnetic Nanoparticles for Targeted Therapy: An Investigation via Electron Paramagnetic Resonance (EPR)
Authors: Hadra Rawan , Lavi Ronit , Harel Yifat , Levy Esthy , Lellouche Jean Paul , Gelperina Svetlana , Persky Rachel
Published: December 2024

This research describes the development and thorough characterization of a novel, versatile, and biocompatible hybrid nanocarrier of the NO-releasing agent NOC-18, with a specific focus on optimizing the purification process. In this study, we focused on the sustained release of NO using the biocompatible and diagnostic hybrid magnetic nanoparticles (hMNPs) containing cerium-doped maghemite (CM) NPs, embedded within human serum albumin (HSA) protein. A comprehensive study was conducted using Electron Paramagnetic Resonance (EPR) alongside the Griess assay to evaluate the NO release from the chosen NO donor NOC-18 and to assess the limitations of the molecule under various reaction conditions, identifying the optimal conditions for binding NOC-18 with minimal NO loss. Two types of particles were designed: In-hMNPs, where NOC-18 is encapsulated within the particles, and Out-hMNPs, where NOC-18 is attached onto the surface. Our results demonstrated that In-hMNPs provided a sustained and prolonged release of NO (half-life 50 h) compared to rapid release for the Out-hMNPs, likely due to the strong bonds formed with cerium, which helped stabilize the NO molecules. These results represent a promising approach to the design of the dual-function agent that combines the contrasting properties for tumor MRI with the possibility of increasing the permeability of tumor vasculature. Employment of such a dual-function agent in combination with nanotherapeutics may augment their efficacy by facilitating their access to the tumor.
Why Uncertainty is Essential for Consciousness: Local Prospect Theory vs. Predictive Processing
Authors: Heylighen Francis , Beigi Shima
Published: December 2024

We present and develop Local Prospect Theory (LPT) as a novel framework for understanding consciousness, and in particular subjective experience and free will. While Predictive Processing (PP) theories model the brain as minimizing uncertainty, LPT sees uncertainty as an essential feature of conscious decision making. This is achieved by creating a "local prospect"—a range of potential developments that allows an agent to consider and choose based on probabilities and subjective values rather than precise predictions. Drawing on Global Workspace Theory, LPT conceptualizes consciousness as a self-maintaining process of circulating neural activation, creating a temporary working memory where thoughts and feelings coming from different brain modules enter into an asynchronous, non-linear interaction. This contrasts with unconscious, feedforward processes, which operate automatically and deterministically. LPT proposes entropy-based measures, including determination of actions by conditions and breadth of prospect, to quantify the range of potential developments considered. This framework allows us to understand Buddhist practices like mindfulness and meditation, which broaden consciousness and deautomatize reactions by reducing the influence of conditioning. The proposed prospect measure may be operationalized by indicators such as variety of action, breadth of perception and behavioral unpredictability, thus allowing empirical testing of the theory.
Towards a Comprehensive Framework for Made-to-Measure Alginate Scaffolds for Tissue Engineering Using Numerical Simulation
Authors: Bäumchen Alexander , Balsters Johnn-Majd , Nenninger Beate-Sophie , Diebels Stefan , Zimmermann Heiko , Roland Michael , Gepp Michael M
Published: December 2024

Alginate hydrogels are integral of many cell-based models in tissue engineering and regenerative medicine. As a natural biomaterial, the properties of alginates can vary and be widely adjusted through the gelation process, making them versatile additives or bulk materials for scaffolds, microcarriers or encapsulation matrices in tissue engineering and regenerative medicine. The requirements for alginates used in biomedical applications differ significantly from those for technical applications. Particularly, the generation of novel niches for stem cells requires reliable and predictable properties of the resulting hydrogel. Ultra-high viscosity (UHV) alginates possess alginates with special physicochemical properties, and thus far, numerical simulations for the gelation process are currently lacking but highly relevant for future designs of stem cell niches and cell-based models. In this article, the gelation of UHV alginates is studied using a microscopic approach for disc- and sphere-shaped hydrogels. Based on the collected data, a multiphase continuum model was implemented to describe the cross-linking process of UHV alginate polysaccharides. The model utilizes four coupled kinetic equations based on mixture theory, which are solved using finite element software. A good agreement between simulation results and experimental data was found, establishing a foundation for future refinements in the development of an interactive tool for cell biologists and material scientists.
Clinical Whole-Body Gait Characterization Using a Single RGB-D Sensor
Authors: Boborzi Lukas , Bertram Johannes , Schniepp Roman , Decker Julian , Wuehr Max
Published: December 2024

Instrumented gait analysis is widely used in clinical settings for early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy. Here, we present vGait, a comprehensive 3D gait assessment method using a single RGB-D sensor and state-of-the-art pose tracking algorithms. vGait was validated in healthy participants during frontal and sagittal perspective walking. Performance was comparable across perspectives, with vGait achieving high accuracy in detecting initial and final foot contacts (F1 scores > 95%) and reliably quantifying spatiotemporal gait parameters (e.g., stride time, stride length) and whole-body coordination metrics (e.g., arm swing and knee angle ROM) at different levels of granularity (mean, step-to-step variability, side asymmetry). The flexibility, accuracy, and minimal resource requirements of vGait make it a valuable tool for clinical and non-clinical applications, including outpatient clinics, medical practices, nursing homes, or community settings. By enabling efficient and scalable gait assessment, vGait has the potential to enhance diagnostic and therapeutic workflows and improve access to clinical mobility monitoring.
Enhancing Automotive Intrusion Detection Systems with CHERI-Based Memory Protection
Authors: Kalutharage Chathuranga Sampath , Mohan Saket , Liu Xiaodong , Chrysoulas Christos
Published: December 2024

The automotive sector is changing fast with more integration of advanced communication technologies and further connectivity. The modern vehicle is already a collection of diverse Electronic Control Units (ECU) communicating over interconnected networks that decide critical functionalities such as engine control, braking, and entertainment. However, this increasing complexity also introduces major cybersecurity risks, including network vulnerabilities like IP spoofing, message replay, and denial-of-service(DoS) attacks, besides software vulnerabilities due to coding errors in unsafe languages like C/C++. These are serious threats to vehicle operational reliability, passenger safety, and data integrity, making robust automotive security a critical concern. This paper explores the application of CHERI(Capability Hardware Enhanced RISC Instructions) in enhancing the security of Intrusion Detection Systems(IDS) in automotive networks. CHERI introduces fine-grained memory protection mechanisms that mitigate software vulnerabilities by enforcing spatial memory safety and preventing unauthorized access to critical data. Moreover, CHERI secures IDS rule configurations from network-based threats, such as manipulation of rules and spoofing attacks, by utilizing strict memory bounds and capability-based access controls. This work experimentally demonstrates that CHERI-enhanced IDSs are highly effective in identifying and mitigating spoofing and IDS rule manipulation attacks, ensuring the integrity of rules even against attackers using forged traffic with legitimate-looking source IP addresses. The results highlight CHERI’s hardware-enforced security model as a robust solution for preventing network and software-level exploits without compromising performance while maintaining compatibility with automotive-friendly programming languages like C/C++. This study underscores the critical importance of integrating CHERI and other hardware-based security frameworks into connected and autonomous vehicles to address emerging cybersecurity challenges and build a safer automotive ecosystem.
A Two Dimensional Scale for Oral Discomfort
Authors: Buunk-Werkhoven Yvonne A.B. , Barelds Dick P.H. , Dijkstra Arie , Buunk Abraham Pieter
Published: December 2024

Subjective suffering due to oral diseases and disorders has been conceptualized as oral health-related quality of life and is often assessed with a multidimensional version of the Oral Health Impact Profile (OHIP). In the current study, a secondary analysis of a Dutch-language translated version of the original OHIP-14 was performed in different samples of approximately 1000 participants from diverse contexts (i.e., the Netherlands, the Caribbean, and Nepal). The dimensional structure and reliability of the scales resulting from these analyses were also examined. Based on a number of Confirmatory Factor Analyses (CFA) and Simultaneous Components Analysis (SCA) of the OHIP-14 scale, testing various models with different numbers of factors, several models were acceptable, but a two-factor solution, comprising psychological discomfort and physical discomfort was the most satisfactory in all three samples although a one-factor solution, oral discomfort was also acceptable.
Instead of using a large number of dimensions with few items each, as often is done, it is most adequate and feasible to use no more than two scales, i.e., psychological discomfort and physical discomfort, comprising 11 items in total. These subscales of 6 and respectively, 5 items are not only statistically, but also theoretically the most adequate. Additionally, all items together, i.e., oral discomfort as a one-dimensional scale are useful and easy to apply for practical use.
The Assessment of Sentinel Lymph Node Mapping Methods in Endometrial Cancer
Authors: Szatkowski Wiktor , Pniewska Karolina , Janeczek Maja , Ryś Janusz , Banaś Tomasz , Muzykiewicz Konrad , Iwańska Ewa , Jakubowicz Jerzy , Karolewski Kazimierz , Szadurska Agnieszka , Blecharz Paweł
Published: December 2024

Background/Objectives: Methods: This retrospective cohort study included 119 patients with early-stage EC treated at the Maria Skłodowska-Curie National Research Institute of Oncology between 2016 and 2021. Patients underwent SLNB using technetium-99m (Tc99m), indocyanine green (ICG), Patent Blue, or combinations of these tracers. Detection rates for unilateral and bilateral SLNs and the accuracy of metastasis identification were analyzed. Results: The overall SLN detection rate was 97.5%. Detection rates for individual tracers were 100% for ICG, 100% for Patent Blue, and 96% for Tc99m. Combining tracers achieved detection rates of 96.9% (Tc99m + ICG) and 97.3% (Tc99m + Patent Blue). Bilateral detection was highest with Tc99m + ICG (90.6%) and Patent Blue alone (91%). Metastases were identified in 12% of cases, with combined methods improving metastatic detection. No "empty nodes" were observed with Tc99m, compared to 1.7% with Patent Blue and 0.8% with ICG. Conclusions: While combining Tc99m with dyes did not significantly improve overall detection rates, it enhanced metastasis identification and reduced false-negative results. The findings suggest that combined tracer methods can optimize SLNB accuracy in endometrial cancer. Prospective studies are warranted to validate these results.
Numerical Simulation of Flame Propagation in a 1kN GCH4/GO2 Pintle Rocket Engine
Authors: Mereu Alexandru , Isvoranu Dragos
Published: December 2024

In the last years the appeal for using methane as green fuel for rocket engines has been on an in-creasing trend due to the more facile storage capability, reduced handling complexity and cost-effectiveness when compared to hydrogen. The present paper presents an attempt to simulate the ignition and propagation of the flame for a 1 kN gaseous methane-oxygen rocket engine using a pintle-type injector. By using advanced numerical simulations, the Eddy Dissipation Concept (EDC) combined with the Partially Stirred Reactor (PaSR) model and the Shielded Detached Eddy Simulation (SDES) were utilized in the complex transient ignition process. The results provide important information regarding the flame propagation and stability, pollutants formation and temperature distribution during the engine start-up, highlighting the uneven mixing regions and thermal load on the injector. This information could further be used for the pintle injector’s geometry optimization by addressing critical design challenges, without employing the need for iterative prototyping during early stages of development.
Automated Age Estimation from OPG images and Patient Records using Deep Feature Extraction and Modified Genetic-Random Forest
Authors: Ozlu Ucan Gulfem , Gwassi Omar Abboosh Hussein , Apaydin Burak Kerem , Ucan Bahadir
Published: December 2024

Background/Objectives: To overcome the disadvantages of the methods in the literature, such as the reliance on manual measurements requiring a lot of time and effort and the difficulty of routine clinical application due to large sample sizes, we aimed to automatically estimate tooth age from panoramic radiographs using artificial intelligence (AI) algorithms. Methods: Two Dimensional Deep Convolutional Neural Network (2D-DCNN) and One Dimensional Deep Convolutional Neural Network (1D-DCNN) techniques were used to extract features from panoramic radiographs and patient records. To perform age estimation using feature information, Genetic algorithm (GA) and Random Forest algorithm (RF) are modified and combined and defined as Modified Genetic-Random Forest Algorithm (MG-RF). The performance of the system used in our study was analyzed based on the MSE, MAE, RMSE and R2 value calculated during the implementation of the code. Results: As a result of the applied algorithms, the MSE value was 0.00027, MAE value was 0.0079, RMSE was 0.0888 and R2 score was 0.999. Conclusions: The findings of our study indicate that the AI-based system employed therein is an effective tool for age detection. Consequently, we propose that this technology could be utilized in forensic sciences in the future.
Experimental Study on Mechanical Properties of Graded EPS Steel Fiber Reinforced Concrete
Authors: Wang Yangsheng , Zhuang Xinshan
Published: December 2024

This study designs the aggregate gradation and calculates the benchmark mix proportion of concrete based on two sets of regression coefficients. Copper-plated micro-steel fibers and graded EPS (expanded polystyrene) particles, with varying volume ratios of cementing materials, were incorporated into concrete specimens of C30, C40, and C50 grades made from fine aggregates and gravel. The specimens underwent compressive strength, flexural strength, and slump tests to investigate and analyze the effects of steel fibers and graded EPS on the mechanical properties of concrete. A modified equation for the similar bending-to-compression ratio coefficient was established, and a method for calculating the extreme value of the similar bending-to-compression ratio under the combined action of graded EPS and steel fibers was proposed, in order to predict the optimal dosage of steel fibers and graded EPS.The results show that for concrete of all grades (fine aggregate and gravel groups), the compressive and bending failure modes exhibited high consistency. After incorporating EPS, the specimens displayed some ductility during compressive failure, and compressive strength was negatively correlated with the volume fraction of EPS. As the volume fraction of EPS increased, the workability of the concrete decreased. At 7 days of curing, the uniaxial compressive strength of the concrete specimens was 60%–75% of the 28-day strength. The tensile failure of graded EPS-steel fiber concrete was divided into four stages. For every 0.25% increase in steel fiber volume fraction, the increase in flexural strength was most likely between 14.35% and 17.61%, with the maximum growth rate being approximately 27%, and the distribution of growth rates was relatively dispersed.When applying the current bending-to-compression ratio model to calculate the flexural strength of concrete under the combined action of graded EPS and steel fibers, a significant deviation from experimental values was observed. By introducing the modified similar bending-to-compression ratio coefficient equation, a conversion relationship between compressive strength and flexural strength was established. The adjusted R-value was 0.989, and a comparison of the predicted values with the experimental results showed high accuracy, offering considerable reference value.
Formation of Mitochondrial Dysfunction of Nerve Cells in Cerebral Ischemia and Possibilities of Pharmacological Correction
Authors: Belenichev Igor , Popazova Olena , Bukhtiyarova Nina , Ryzhenko Victor , Pavlov Sergii V. , Suprun Elina , Oksenych Valentyn , Kamyshnyi Oleksandr
Published: December 2024

The study of mitochondrial dysfunction has become increasingly pivotal in elucidating the pathophysiology of various cerebral pathologies, particularly neurodegenerative disorders. Mitochondria are essential for cellular energy metabolism, regulation of reactive oxygen species (ROS), calcium homeostasis, and the execution of apoptotic processes. Disruptions in mitochondrial function, driven by factors such as oxidative stress, excitotoxicity, and altered ion balance, lead to neuronal death and contribute to cognitive impairments in several brain diseases. Mitochondrial dysfunction can arise from genetic mutations, ischemic events, hypoxia, and other environmental factors. This article highlights the critical role of mitochondrial dysfunction in the progression of neurodegenerative diseases and discusses the need for targeted therapeutic strategies to attenuate cellular damage, restore mitochondrial function, and enhance neuroprotection.
Antimicrobial Resistance in Poultry in Nigeria: Systematic Review of Reports Within 2005–2020
Authors: Adetunji Victoria Olusola , Fagbamila Idowu , Faroyin Omolara , Card Rod
Published: December 2024

Antimicrobial resistance (AMR) remains a persistent world-wide health concern. Poultry production in Nigeria has become a large-scale venture and is largely characterised by intensive management system. This intensive management system disposes to more antibiotic use and thus a greater risk of AMR. Previous reviews on AMR data in Nigeria have not considered the accuracy of methods and interpretation criteria. Therefore, in contribution to the “One Health” AMR Surveillance system, which is one of the five focus areas described by the Nigeria action plan on AMR control, this comprehensive review collates, curate, and analyse AMR published data in the Nigeria poultry sector and assessed the AST methods and interpretation criteria. A search of AMR reports from poultry in Nigeria prior to 2020 after screening for relevance yielded 69 studies from 2005-2020 with 91 AMR reports (some studies reported more than one organism). From these studies we reviewed the organisms reported, antibiotics tested, resistance to tested antibiotics, distribution of reports within the country’s geopolitical zones, multidrug resistance pattern, the inclusion or absence of control strains in antimicrobial testing, the antimicrobial testing method and interpretative criteria used.There was no AMR study in poultry earlier than 2005 in Nigeria, highest number of study was in 2019 (n=12). South-Western region has the highest number (n = 27) of study and the North-Western region has the lowest (n = 5). Organisms mostly reported were Salmonella, 31%, Escherichia coli, 26% and Staphylococcus aureus, 15%). Other organisms (≤5%) were Campylobacter, Klebsiella, Bacillus, Listeria, Enterobacter, Proteus, Norcardia, Streptococcus, Alkaligenes, Shigella, Micrococcus, Enterococcus, Ochrobacturm, and Yersinia. The antimicrobial susceptibility testing (AST) method mostly used was disc diffusion (80%), also majority (95.7%) of the studies used the CLSI standards for interpretation and 79.7% of the studies did not mention the use of control strains for AST. The 69 studies reviewed tested 69 antimicrobials belonging to 17 classes of Antibiotics; the most tested classes of antimicrobial were β-lactams (33%), quinolones (21%) and aminoglycosides (15.4%). All of the organisms showed varying multi-drug resistance; greater proportion (50-100%) has MAR Index greater than 0.2. The review of the various studies showed very high multi-drug resistance among the various organisms of poultry origin and also a gap in AST carried out in Nigeria. We therefore recommend that AST research methods must be of global standard and additional efforts should be made towards education of the general public on best practices of antibiotics usage and the dangers of AMR.
The Impact of Fires on Mosquito Populations in Eastern Siberian Forests
Authors: Shvetsov Alexey V.
Published: December 2024

Forest fires have a significant impact on forest fauna. Not only mammals and birds die in forest fires, but also less noticeable representatives of forest fauna - insects. There are now enough studies on the impact of forest fires on the population of mammals and birds, but at present there is still insufficient data available and the impact of fires on the insect population in the forest. This study is intended to partially fill this gap. It was studied how fires affect the mosquito population. Mosquitoes are an important starting point of the existing forest food chain and are part of the biomass that feeds various representatives of forest fauna, including fish, birds and amphibians. Changes in the mosquito population can lead to disruption of the food balance in the forest ecosystem, all this determines the relevance of the work. The study was conducted in the forests of Eastern Siberia. Experimental data were obtained from ten forest sites. Comparison of data from forest sites exposed to and not exposed to fires allowed us to establish the degree of impact of forest fire on the mosquito population.
Circulating microRNAs as Biomarkers of Various Forms of Epilepsy
Authors: Timechko Elena E. , Lysova Kristina D. , Yakimov Alexey M. , Paramonova Anastasia I. , Vasilieva Anastasia A. , Kantimirova Elena A. , Usoltseva Anna A. , Yakunina Albina V. , Areshkina I.G. , Dmitrenko Diana V.
Published: December 2024

Epilepsy is a group of disorders characterized by a cluster of clinical and EEG signs leading to the formation of abnormal synchronous excitation of neurons in the brain. Epilepsy is one of the most common neurological disorders worldwide. Epilepsy is characterized by aberrant expression patterns, both at the level of matrix transcripts and at the level of regulatory RNA sequences. Aberrant expression of a number of microRNAs can mark a particular epileptic syndrome, which will improve the quality of differential diagnosis. In this work, the expression profile of six microRNAs was analyzed: hsa-miR-106b-5p, hsa-miR-134-5p, hsa-miR-122-5p, hsa-miR-132-3p, hsa-miR-155-5p, hsa-miR-206-5p in the blood plasma of patients suffering from temporal lobe epilepsy TLE (n=52), juvenile myoclonic epilepsy JME (n=42) in comparison with healthy volunteers. The expression analysis was carried out using RT-PCR, mathematical processing of the data was carried out according to the Livak method. A statistically significant change in the expression of hsa-miR-106b-5p, hsa-miR-134-5p, hsa-miR-122-5p, hsa-miR-132-3p was found. An increase in the expression of hsa-miR-134-5p and hsa-miR-122-5p was registered in the group of patients with TLE compared to the control, as well as an increase in the expression of hsa-miR-132-3p and hsa-miR-106b-5p in the JME group compared to the control. hsa-miR-122-5p, 106b-5p, 132-3p are also able to discriminate TLE from JME. Additionally, a number of microRNAs are able to discriminate patients with drug-resistant and drug-sensitive forms of epilepsy from the control, as well as patients with hippocampal sclerosis and patients without hippocampal sclerosis from the control. Our data allow us to propose these microRNAs as plasma biomarkers of various epileptic syndromes.
Prediction of Mine Waste Rock Drainage Quantity Using a Machine Learning Model with Physical Constraints
Authors: Zhang Can , Ma Liang , Liu Wenying
Published: December 2024

Mining activities generate substantial amounts of waste rock, which are often disposed of in waste rock piles. Drainage from these piles can pose serious environmental risks. It is crucial to reliably predict drainage properties in order to effectively manage them. In previous work, we developed a machine learning model to predict waste rock drainage quantity using weather monitoring data as input and drainage flow rate as output. However, this model lacked physical constraints, limiting its interpretability, reliability, and applicability. In this study, we introduced a new machine learning model designed with physical constraints to improve predictions of drainage quantity. This new model incorporates a weather refining sub-model and integrates physical constraints to enhance the overall reliability of the model predictions. The weather refining sub-model transforms primary weather features (total precipitation and temperature) into secondary features (rainfall, snowmelt, and evaporation) through established mathematical relationships. These secondary features were then used as inputs for the machine learning model to predict drainage quantity. To embed physical principles within the machine learning model, we integrated a water balance equation into the neural network architecture and modified the loss function accordingly. In addition, we included an adjustable bias term to optimize the balance between model performance and interpretability. Compared with our previous model, the incorporation of physical constraints into the machine learning model improved the accuracy of drainage quantity predictions. More importantly, this approach ensures that the model outputs adhere to physical laws, thereby enhancing its interpretability, reliability, and applicability.
Effect of Heat Treatment and Anisotropy on Microstructure and Wear Resistance of 316L Stainless Steel Prepared via Selective Laser Melting
Authors: Sun Menghui , Zhang Qianqian , Wu Jinxiu , Wang Hao , Wang Xu , Zhang Hao , An Yinong , Liu Yujie , Ma Long
Published: December 2024

316L stainless steel was prepared via selective laser melting (SLM), and the effect of heat treatment on microstructure, density, microhardness and wear resistance were studied. The results show that specimens exhibit anisotropy in microstructure, density, microhardness and wear resistance, the microhardness and wear resistance of XOY plane is higher and better than XOZ plane. X-Ray diffraction (XRD) analysis indicated that specimens exhibit FCC austenite phase, no new phase is found after heat treated, indicating that it has good thermal stability. Microstructure of SLM 316L exhibits obvious melt pools composed of columnar crystals and cellular crystals, some nano silicon oxides adhere to the surface of the cellular crystals. SLM 316L presents as intersecting melt pools on the XOY plane and fish-scale-like melt pools on the XOZ plane. After heat treated, the melt boundaries annihilate and recrystallization of the specimens lead to the coarsening of the microstructure. Microhardness results show the microhardness decrease after heat treated and the specimen with the lowest decrease in microhardness after solution+aging treatment. The friction and wear tests results reveal the specimen after solution+aging treatment has the lowest friction coefficient and smallest wear loss, exhibiting the best wear resistance.
Vulnerability to HIV Among Transsexual Women and Transvestites
Authors: Carvalho Matheus Sousa Marques , Junior Eugênio Barbosa de Melo , Reis Renata Karina , Oviedo Aracely Diaz , Galiza Francisca Tereza de , Nogueira Lidya Tolstenko , Silva Grazielle Roberta Freitas da , Macêdo Danielle Nedson Rodrigues de , Jorge Herla Maria Furtado , Araujo Telma Maria Evangelista de , Gir Elucir , Magalhães Rosilane de Lima Brito
Published: December 2024

The aim of this study was to identify vulnerability to HIV among transsexual women and transvestites. Cross-sectional, quantitative-qualitative study carried out in northeastern Brazil. A virtual questionnaire and a semi-structured interview script were used for data collection among transsexual women and transvestites aged 18 years or older, with access to a messaging application and/or e-mail. The participants’ were recruited using the snowball technique. Descriptive analysis was carried out to characterize the participants, and qualitative data were processed using Content Analysis. Of the total of 72 participants, 04 participants reported being HIV-positive and 12 granted an interview. The following vulnerability markers were identified: inconsistent condom use; difficulty in negotiating condom use with sexual partners; difficulty in accessing health services, especially in situations of symbolic violence, such as not using the social name or pronoun errors; HIV stigma; and poor training of health professionals to deal with this population. The study population is characterized by young people with low level of schooling, showing vulnerabilities in the individual, social and programmatic dimensions. It is necessary to: improve access to health services; implement professional training programs since the undergraduate level in various areas of health; and develop more effective multidisciplinary approaches for this population.
The Temporal Protection and Declining Health of the COVID-19 Vaccinated in England: A 26-Month Comparison of the Mortality Involving and Not Involving COVID-19 Among Vaccinated vs. Unvaccinated
Authors: Aarstad Jarle
Published: December 2024

In England, from Apr 21 to May 23, all-cause mortality among COVID-19 unvaccinated ten years and older was higher than among vaccinated. But as the pattern was similar concerning mortality not involving COVID-19, the discrepancy is attributed mainly to unvaccinated having inferior health at the outset. There was nonetheless significant protection for vaccinated between July 21 and Jan 22. Absent of control variables as a means to compare non-randomized groups, I reached that conclusion by differentiating all-cause mortality from mortality not involving COVID-19. However, while mortality not involving COVID-19 decreased among unvaccinated compared to the first observation month, it was high among vaccinated, i.e., a relative increase in mortality among vaccinated. An interpretation is that vaccination, despite temporary protection, increased mortality. Strengthening the interpretation was relatively high mortality among vaccinated not involving COVID-19 counterintuitively following periods of excess mortality. Further strengthening the interpretation was relatively high mortality not involving COVID-19 among vaccinated corresponding with the excess mortality during the same period.
Structural Features of Nerve Guidance Conduits in Preventing Axonal Misdirection: Challenges and Further Perspectives
Authors: Mićić Aleksa , Aksić Milan , Savić Andrija , Poleksić Joko , Grujić Jovan , Lepić Milan , Aleksić Dubravka , Vujić Lazar , Rasulić Lukas
Published: December 2024

Most studies on nerve guidance conduits (NGCs) focuses on advancing biomaterials and enhancing their functionality to outperform autologous nerve grafts (ANGs). However, even when these strategies exceed the ANG effects, they may not guarantee satisfactory recovery in every case, considering their inability to impact the direction of axonal growth. Axonal misdirection occurs due to dispersive nature of axonal growth and variable intraneural branching topography, presenting one of the main obstacles in achieving consistent success in peripheral nerve repair. On the other side, implementation of various NGC structural features may offer a potential solution. This paper reviews the NGC structural designing strategies, and assesses their potential to influence axonal direction, exploring the key challenges and future perspectives. The literature was reviewed using PubMed, ScienceDirect, OpenMD, Cochrane Library, and Google Scholar databases. The simplest single-hollow NGC design may prevent axonal dispersion, however only in small nerve gaps. The multichannel and nanofiber-filled NGC designs provide a neural-like microenvironment and straightforward axonal guidance, supporting successful regeneration in larger nerve gaps. Even though these structural strategies may also surpass the efficacy of ANGs, such straightforward guidance does not exclude the axonal misdirection, due to variable distribution of fascicular trajectories. In the future, this issue could be addressed by intraoperative 3D scanning of the stumps, using the machine learning algorithms to predict the fascicular distribution, and on-site 3D printing of personalized NGCs.
Quantifying the Effects of Climate Change on Aircraft Take-Off Performance at European Airports
Authors: Williams Jonny , Williams Paul D. , Guerrini Federica , Venturini Marco
Published: December 2024

This work uses state-of-the-art climate model data at 30 European airport locations to examine how climate change may affect summer take-off distance required – TODR – and maximum take-off mass – MTOM – for mid-century (2035–2064) compared to a historical baseline (1985–2014). The data presented here are for the Airbus A320, however the methodology is generic and few changes are required in order to apply this methodology to a wide range of different fixed-wing aircraft. The climate models used are taken from the 6th Coupled Model Intercomparison Project (CMPI6) and span a range of climate sensitivity values, that is, the amount of warming they exhibit for a given amount of increase in atmospheric greenhouse gas concentrations. Using a Newtonian force-balance model, we show that 30-year average values of TODR may increase by around 50–100 m, albeit with significant day-to-day variability. The changing probability distributions are quantified using kernel density estimation and an illustration is given showing how changes to future daily maximum temperature extremes may affect distributions of TODR going forward. Furthermore, it is projected that the 99th percentile of the historical distributions of TODR may by exceeded up to half the time in the summer months for some airports. Some of the sites studied have runways that are shorter than the distance required for a fully laden take-off, which means they must reduce their payloads as temperatures and air pressures change. We find that, relative to historical mean weight-restriction values, passenger numbers may need to be reduced by approximately 10 passengers per flight, again with significant increases (as high as approximately 60%) in the probability of exceeding historical extreme values.
Temperature Monitoring in PIC Surface
Authors: Pandey Sushma , Teixeira Antonio , Lima Mario
Published: December 2024

In this work we present the extended work of the previously proposed scheme for in-PIC temperature monitoring. This can be used in any photonic integrated circuitry platform allowing simplified temperature monitoring and improved independence of the interrogating laser wavelength. Theoretically power sensitivity was observed to be 0.77dB/ ℃ and 0.98 dB/ ℃ for common mode and differential mode, respectively. The experimentally noted sensitivity of common mode and differential mode are 1.45 dB/ ℃ and 0.8 dB/ ℃ respectively at 1524 nm. The scheme allows the monitoring of the average temperature in the surface of the chip, which results from the global effect that affects both gratings (common mode).
Efficient Grey Wolf Optimization: A High-Performance Optimizer with Reduced Memory Usage and Accelerated Convergence
Authors: Tan Lianghao , Liu Dong , Liu Xiaoyi , Wu Weixin , Jiang Huangqi
Published: December 2024

This paper presents an efficient Grey Wolf Optimizer (EGWO) designed to address the limitations of the standard Grey Wolf Optimizer (GWO), focusing on reducing memory usage and accelerating convergence. The proposed method integrates Sinusoidal Mapping for enhanced population diversity and a Transverse- Longitudinal Crossover strategy to balance global explo- ration and local exploitation. These innovations improve search efficiency and optimization precision while main- taining a lightweight computational footprint. Experi- mental evaluations on 10 benchmark functions demon- strate EGWO’s superior performance in convergence speed, solution accuracy, and robustness. Its application to hyperparameter tuning of a Random Forest model for a housing price dataset confirms its practical utility, fur- ther supported by SHAP-based interpretability analysis.
Left Atrial Strain Value Versus Tissue Doppler Echocardiography and the Left Atrium Volume Index in The Evaluation of Left Ventricular Diastolic Function in Patients with Chronic Kidney Disease
Authors: Elsheikh Eman , Amjad Zainab , Abohamr Samah I , Al Sahlawi Muthana , Khairat Ibtsam
Published: December 2024

Background: The left atrium's (LA) filling and discharge is quantified by LA strain. LA strain has been established as a metric for diastolic dysfunction in heart failure with preserved and reduced ejection fraction (HFpEF and HFrEF, respectively). We sought to address the role of LA strain value versus tissue Doppler echocardiography and the left atrial volume index (LAVI) in the left ventricular (LV) diastolic function evaluation in chronic kidney disease (CKD) patients. Method: A prospective cross section study was conducted on 220 outpatients of CKD that fulfilled the inclusion criteria and referred to cardiology clinic at Tanta University for routine echocardiographic assessment during a period of six months (April to September 2024). Patients were divided into subgroups based on their estimated Glomerular Filtration Rate (eGFR) into 5 groups; Group 1: GFR 90-120 mL/min/1.73 m2, Group2: GFR 60-90 mL/min/1.73 m2, group 3: GFR 30-60 mL/min/1.73 m2, group 4: GFR 15-29 mL/min/1.73 m2, group 5: GFR <15 mL/min/1.73 m2. Echocardiographic parameters including systolic and diastolic volumes of left ventricle, LA strain, LAVI, ejection fraction (EF), and E/e ratio were measured in all participants. Results: LA strain conduit and reservoir strain can significantly diagnose LV Diastolic Function in CKD Patients (P <0.001 and AUC=0.819 and 0.869 respectively) at cutoff ≤36 and >-23, with 86% and 95.5% sensitivity, 65% and 60% specificity, 96.1% and 96% PPV and 31.7% and 57.1% NPV respectively. AUC=0.926 was observed with LAVI among grade 1 vs grade 0 (0.9 is considered excellent in diagnosing patients with and without the disease). While other markers among the grades of the diastolic function had AUC of 0.5-0.6, which suggests no discrimination in diagnosing the disease. Conclusions: LA conduit strain and reservoir strain are independent markers that is superior and more sensitive approach than LAVI and tissue Doppler echocardiography for evaluating LV diastolic dysfunction in patients with CKD even in the early stages.
Multiband Image Analysis Using Ensemble Neural Networks
Authors: Nanni Loris , Brahnam Sheryl
Published: December 2024

The classification of multiband images captured by advanced sensors, such as satellite-mounted imaging systems, is a critical task in remote sensing and environmental monitoring. These sensors provide high-dimensional data that encapsulates a wealth of spectral and spatial information, enabling detailed analyses of the Earth’s surface features. However, the complexity of this data poses significant challenges for accurate and efficient classification. Our study describes and highlights methods for creating ensembles of neural networks for handling multiband images. Two applications are illustrated in this work: 1) satellite image classification trained on the EuroSAT dataset and 2) a species-level identification of planktic foraminifera. Multichannel images are fed into an ensemble of Convolutional Neural Networks (CNNs), where each network is trained using three channels, one each for the multichannel images. The ensemble learning framework harnesses these variations to improve classification accuracy, surpassing other advanced methods. The proposed system, implemented in MATLAB, is shown to achieve higher classification accuracy than those of human experts for species-level identification of planktic foraminifera and state-of-the-art performance on both the tested planktic foraminifera and the EuroSAT datasets.