bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Microplastic Pollutants on Nematodes and Their Biodiversity in Irish River Sediments
Authors: Murphy Loriane , Germaine Kieran , Cleary John , Kakouli-Duarte Thomais
Published: October 2024

Microplastics (MP), defined as plastic particles less than 5 mm, pose a significant global environmental threat, particularly in aquatic ecosystems, due to their persistence and potential harmful effects on wildlife and human health. They can absorb persistent organic pollutants (POP) like polycyclic aromatic hydrocarbons (PAH) and polychlorinated biphenyls (PCB), raising concerns about their impact on biota. To elucidate this, the present study employed attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) to analyse the characteristics of MP sourced from commercial cosmetics. It also utilised nematodes, which are widely accepted indicators of environmental change. Indeed, despite the known risks of MP in aquatic habitats, knowledge of their effects on free-living and beneficial nematodes is limited. This study investigated the toxicity of MP on Caenorhabditis elegans and two entomopathogenic nematode species, Steinernema feltiae (the enviroCORE strain SB12[1]) and Steinernema carpocapsae (a commercial strain from e-NEMA) in laboratory bioassays. Nematodes were exposed to various concentrations of MP and other pollutants in controlled settings over 72 to 96 hours. Additionally, high-throughput 18S rDNA sequencing was used to analyse nematode biodiversity in sediments from the River Barrow (RB) in SE Ireland. Results revealed that MP adversely affected nematode survival and community structure, highlighting the ecological risks posed by MP pollution in river sediments and emphasizing the need for further research into the health of benthic ecosystems in Ireland.
Thermal Analysis of Radiation Heat Transfer of an Improved Fractal Solar Collectors
Authors: Kibishov Adylkhan Talgatovich , Kılıç Gülenay Alevay , Rustamov Nassim Tulegenovich , Genc Naci
Published: October 2024

This study proposes parabolic dish-based toroidal structured fractal solar collectors. The potential of fractal geometry to increase heat transfer and the ability of the parabolic dish to concentrate solar rays form the basis of the proposed design for increasing the efficiency. In the study, the thermal and hydrodynamic behaviors of the proposed 3-row, 4-row and 5-row parabolic collectors were investigated comprehensively. Using theoretical modeling and experimental results, the performances of the proposed parabolic dish-based toroidal fractal solar collectors were evaluated and compared via numerical simulation methods. After the experimental studies of the 3-row toroidal fractal collector, the analysis studies were completed using the ANSYS-Fluent program. Then, simulations were carried out for other toroidal solar collectors using the results of these experimental studies. As a result of the converging numerical analyzes, the radiative, hydrodynamic and thermal analysis results of the toroidal absorbers in 3-row, 4-row and 5-row structures integrated with the parabolic dish were compared. In the temperature distribution analysis, it was observed that the parabolic dish effectively focuses on the sun rays and provides a gradual temperature increase of approximately 21 K for the fractal collector. It is observed that 96.84% convergence was achieved between the experimental and numerical results.
The Urban Building Energy Retrofitting Tool: An Open-Source Framework to Help Foster Building Retrofitting Using a Life Cycle Costing Perspective. First Results for Montréal
Authors: Gavaldà Torrellas Oriol , Monsalvete Pilar , Eicker Ursula
Published: October 2024

Building decarbonization is a major challenge for cities. Deciding what, when, and how to retrofit a building is difficult, given the complex interaction between energy costs and investment requirements. Several tools have been developed in the last years to help public and private stakeholders with these decisions, but none cover aspects the authors think are fundamental. For this reason, an urban building retrofit tool was developed and is presented in this article. This new tool is based on a bottom-up approach, with all buildings simulated individually, considering aspects such as shading or adjacencies. As a second step, three scenarios with different levels of ambition have been implemented in the tool, and the energy demand and emissions resulting from these scenarios have been calculated. As a third step, the retrofitting scenarios' initial investment and operational costs have been implemented via a detailed Life Cycle Costs (LCC) approach. The authors describe the robust and scalable structure that has been developed and the application of this structure to calculate the LCCs of different retrofitting scenarios in Montréal.
AI Learning through SUSY Field Theory
Authors: Garayev Guman , Alili Azar
Published: October 2024

We propose a SUSY-inspired loss framework to address the generalizationrobustness trade-off in AI. Combining bosonic and fermionic components, the loss ensures smooth learning and robustness against adversarial attacks. Parallel transport stabilizes weight updates across non-Euclidean loss landscapes. Validation on CIFAR-10 demonstrates stable convergence and enhanced performance under FGSM and PGD attacks, confirming the effectiveness of the proposed approach. Keywords: Supersymmetry, Generalization, Robustness, Adversarial Attacks, Parallel Transport, Loss Optimization, CIFAR-10.
Design and Analysis of a Proportional Electromagnet with Experimental Validation for Static and Dynamic Behavior
Authors: Düzgün Ercan , Şefkat Gürsel
Published: October 2024

This study presents the design and analysis of a proportional electromagnet used in electro-pneumatic brake systems for heavy vehicles. The solenoid was designed using a traditional method and its static and dynamic characteristics were investigated both theoretically and experimentally. ANSYS Maxwell was employed for theoretical static analysis, focusing on the effects of geometric dimension parameters in the fixed and moving pole contact region on the Force-Displacement characteristics. Optimal dimensions for proportionality were determined under suitable operating conditions. The static analysis results provided magnetization curve data, which were used to create look-up tables for a dynamic model in MATLAB-Simulink. Following static analysis, a prototype electromagnet was manufactured and tested. The solenoid achieved a constant magnetic force of 45 ± 3N with a current of 1.3A over a working range of 1-3 mm. The dynamic model, incorporating data from ANSYS, yielded results that closely matched experimental findings.
Random Walk on T-Fractal with Stochastic Resetting
Authors: Sun Xiaohan , Li Anlin , Zhu Shaoxiang , Zhu Feng
Published: October 2024

In this study, we explore the impact of stochastic resetting on the dynamics of random walks on a T-fractal network. By employing the generating function technique, we establish a recursive relation between the generating function of the first passage time (FPT) and derive a relationship between the mean first passage time (MFPT) with resetting and the generating function of the FPT without resetting. Our analysis covers various scenarios for a random walker reaching a target site from the starting position, and for each case, we determine the optimal resetting probability γ* that minimizes the MFPT. We compare the results with the MFPT without resetting and find that the inclusion of resetting significantly enhances search efficiency, particularly as the size of the network increases. Our findings highlight the potential of stochastic resetting as an effective strategy for optimizing search processes in complex networks, offering valuable insights for applications in various fields where efficient search strategies are crucial.
Longitudinal Analysis of General Surgery Residency Match from 2020 to 2024
Authors: Abdelhamid Sultan , Dallal Zachary , Dallal Ramsey
Published: October 2024

Objective: The general surgery match process has changed with the transition to virtual interviews, the use of signaling, and the loss of the USMLE1 scoring. We undertook this study to describe the current state of the surgery match.Design: Describe changes in applicant behavior and characteristics between 2020-2024 and perform a multivariable analysis to measure applicant characteristics that predict obtaining an interview invite.Setting: Applicant-moderated, public database with voluntarily completed survey.Participants: 1117 applicants to general surgery residencyResults: Over the time period, applications submitted per student increased by 32%, from 78.0±3.8 in 2020 to 103±3.2 for all and 26% excluding international medical students (IMGs). The average number of applications submitted by student type - US allopathic (USMD), osteopathic (DO), and IMG - were 75±1.5, 105±4.3, and 159±6.3, respectively. The number of interview invitations (IIs) for US graduates decreased by 31%, from an average of 17.4±0.83 to 13.3±0.60 (p<0.0001). 33% of US applicants received more than 18 IIs. Controlling for all other factors, IIs per applicant decreased (-3.5; p<0.0001), and predictors of the number of IIs included USMLE1 (0.15±0.4), USMLE2 (0.17±0.4) scores, student type and higher class quartile rank (p=0.004), lack of gap years (4.7±2.2, p=0.03), AOA membership (3.1±1.0 p=0.003) and graduating from a top-tiered medical school (2.1, p=0.05). In 2024, USMLE2 had the same predictive weight as the combination of scores compared to the prior years (p<0.0001). Signaled program’s II yield was 68±2%.Conclusions: Over the last five years, substantially more applications per student were submitted, while fewer IIs were received, potentially resulting in poor optimization of candidates matching to programs. Loss of USMLE1 scores made no direct difference in the number of IIs.
Spatio-Temporal Distribution of Stored Product Insects in a Feed Mill in Greece
Authors: Agrafioti Paraskevi , Lampiri Evagelia , Kaloudis Efstathios , Ioannidis Philippos M. , Athanassiou Christos G.
Published: October 2024

Floor traps were placed in a feed mill in Greece, for a period of approx. 13 months to illustrate the relative abundance and the distribution of the stored product insects found. More than 20 taxa were found, with most of them belonging in Coleoptera. The most abundant species found were the rice weevil, Sitophilus oryzae (L.) and the granary weevil, Sitophilus granarius (L.) which are common primary colonizers of grains, and the confused flour beetle, Tribolium confusum Jacquelin du Val and the red flour beetle, Tribolium castaneum (Herbst), which are secondary colonizers that usually occur in processed amylaceous commodities. Interestingly, the highest population densities of all four species were recorded during the same period, with the secondary colonizers slightly preceding those of the primary ones. Although competition among these species has been recorded in previous studies, we found that these four species could coexist during the entire trapping period in the same sampling units, that indicates possible spatial segregation and different colonization patters in space and time. Our results demonstrate that trapping in storage and processing facilities is an essential component in making decisions for stored product pest management strategies in localized applications, and can drastically reduce the need for treating the entire facility.
Sustainable Processes Reusing Biomass Potassium-Rich Ash as a Green Catalyst for Biodiesel Production: A Mini-Review
Authors: Tsai Chi Hung , Tsai Wen Tien
Published: October 2024

To mitigate the emissions of greenhouse gases from fossil fuels, biodiesel production has be-come more attention over the past decade, especially for the use of waste cooking oils and non-edible oils as starting feedstock. For the biodiesel production process, the suitability of a catalyst is a core function implemented in the transesterification reaction. heterogeneous cata-lysts were superior to homogeneous catalysts due to some advantages such as no saponification products formed, recyclability, and less equipment corrosion. Recent studies also revealed that heterogeneous solid base catalysts are widely used for the production of biodiesel. Further-more, the biomass-based ash derived from herbaceous & agricultural biomass is increasing rap-idly because of its environmental sustainability, high biodiesel yield and low catalyst cost. To search for the depth of green catalysts, this mini-review paper thus focused on the summary of various heterogeneous biomass-based potassium-rich catalysts for the sustainable production of biodiesel. Due to cocoa pod husk that’s rich in potassium-containing minerals, it was found that its derived ash may be the most commonly used solid base catalyst in the biodiesel production.
Access to Justice and the Instrumentality of the Legal Process
Authors: Ferreira Ana
Published: October 2024

This article study was developed based on the relationship between access to justice and the instrumentality of the legal process, aiming to assess the effectiveness of the constitutional principle enshrined in Article 5, item XXXV: “the law shall not exclude any injury or threat to a right from the consideration of the Judiciary” (BRAZIL, 2008, p.27). To achieve this goal, the concept of justice was examined, as well as the definition and evolution of access to justice within legal science. In light of these concepts, it became possible to establish a connection between access to justice and the instrumentality of the legal process. This analysis reveals that the State has not yet achieved sufficient efficiency in providing support to its less favored citizens in facilitating access to justice.
Evaluation of Patients Monitored Due to SARS-CoV-2 Infection in Terms of Long-COVID-19 Syndrome
Authors: Hoşgel Sevdimbaş Sezin , İnal Ayşe Seza , Kuşcu Ferit , Kurtaran Behice , Candevir Aslıhan , Taşova Yeşim , Kömür Süheyla
Published: October 2024

Objectives: Persistent symptoms following acute COVID-19 infection, collectively termed Long COVID syndrome, have been increasingly reported. However, data on the prevalence and characteristics of Long COVID remain limited. This study aimed to evaluate the occurrence and presentation of Long COVID syndrome in individuals in-fected with COVID-19 and monitored in outpatient, ward, or intensive care settings. Methods: Patients diagnosed with COVID-19 and followed up at the Department of In-fectious Diseases and Clinical Microbiology, Çukurova University Faculty of Medicine, for a minimum of four weeks post-infection were included in this study. Demographic data, including age, gender, and comorbidities, were recorded. Patients were followed up for 12 months, with symptom assessment at baseline and 1, 3, 6, and 12 months post-infection. Functional status was evaluated using the Post-COVID-19 Functional Status (PCFS) scale, and quality of life was assessed using the SF-36 Health Survey question-naire. Results: 134 patients (71 males, 54.2%) participated in the study, with a mean age of 43.7±15.3 years (median: 45, range: 18-83). The distribution of patients across care settings was as follows: 51.1% outpatient, 35.9% ward, and 13% intensive care unit (ICU). Fatigue during exertion was the most frequently reported Long COVID symptom across all follow-up timepoints. Patients treated in the ICU experienced a higher burden of Long COVID symptoms compared to those in outpatient or ward settings. Furthermore, the PCFS scale revealed significantly poorer quality of life and functional status in patients treated in the ICU and wards than those receiving outpatient care. Conclusions: Long COVID syndrome poses a significant ongoing health concern, particularly for patients requiring intensive care during acute COVID-19 infection. Our findings underscore the importance of vigilant monitoring for long-term sequelae, particularly concerning quality of life and functional status, in all individuals recovering from SARS-CoV-2 infection.
Ulp1 Regulates Cell Proliferation Through INO1 in Pichia pastoris
Authors: Yang Junjie , Zhong Bo , Yang Lan , Luo Zhan , Jia Lei , Zheng Kaixi , Tang Wenjie , Shang Wenna , Jiang Xiaofeng , Lyu Zhengbing , Gai Qijing , Chen Jianqing , Chen Guodong
Published: October 2024

Pichia pastoris is extensively employed as a host for expressing foreign proteins, and its secretion system is especially advantageous for efficient protein purification. Ulp1 is essential for the cell cycle. In Saccharomyces cerevisiae, the absence of Ulp1 leads to G2/M cell cycle arrest. Similarly, in Pichia pastoris, we also observed G2 phase stalling in Ulp1 knockout cells. RNA-seq data revealed significant changes in seven genes, including INO1. Interestingly, overexpressing INO1 in Ulp1 knockout cells restored cell proliferation, suggesting that Ulp1 regulates cell proliferation through INO1. However, adding extra inositol to Ulp1 knockout cells was not able to restore cell prolifera-tion, indicating that the enzyme activity of INO1 is not essential for cell proliferation. Our data provide new insights into Ulp1 functions in the cell cycle and raise new questions about yeast cell cycle regulation.
Synthetic Jet Actuators for Active Flow Control: A Review
Authors: Ho Howard Haonan , Shirinzad Ali , Essel Ebenezer Ekow , Sullivan Pierre E.
Published: October 2024

A synthetic jet actuator (SJA) is a fluidic device often consisting of a vibrating diaphragm that alters the volume of a cavity to produce a synthesized jet through an orifice. The cyclic ingestion and expulsion of the working fluid leads to a zero-net mass-flux and the transfer of linear momentum to the working fluid over an actuation cycle, leaving a train of vortex structures propagating away from the orifice. SJAs are a promising technology for flow control applications due to their unique features, such as no external fluid supply or ducting requirements, short response time, low weight, and compactness. Hence, they have been the focus of many research studies over the past few decades. Despite these advantages, implementing an effective control scheme using SJAs is quite challenging due to the large parameter space involving several geometrical and operational variables. This article aims to explain the working mechanism of SJAs and provide a comprehensive review of the effects of SJA design parameters in quiescent conditions and crossflow.
An Enhanced Adaptive Ensemble Kalman Filter for AUV Integrated Navigation
Authors: Liang Zeming , Fan Shuangshuang , Wang Hui , Liao Danyu , Xu JiangJiagn
Published: October 2024

Autonomous Underwater Vehicles (AUVs) are highly efficient tools for underwater exploration, capable of carrying various payloads to perform specific missions as needed. To ensure the success of these missions and the spatiotemporal accuracy of sampled data, integrated navigation systems and appropriate filtering algorithms are essential. These systems process and merge motion data to provide accurate positioning information. The most commonly used algorithms in integrated navigation systems are Kalman filter and its nonlinear derivatives. Of these, the ensemble Kalman filter (EnKF) is particularly notable for its ability to handle nonlinear systems by incorporating Monte Carlo methods. This study proposes an enhanced adaptive EnKF algorithm to improve the smoothness and accuracy of the filtering process. Instead of the conventional Gaussian distribution, this algorithm employs a Laplace distribution to construct the system state vector and observation vector ensembles, enhancing stability against non-Gaussian noise. Additionally, the algorithm dynamically adjusts the number of vector members in the ensemble using adaptive mechanism by specifying thresholds during filtering, to adapt the requirements of real-world observational settings. Using measured data from field trials, including DVL, GPS, and electronic compass data, we examine the effects of various parameter settings on the algorithm's performance. Optimal parameter settings are identified to fine-tune and verify the filtering algorithm as well as its adaptive capability. The results demonstrate that the enhanced adaptive EnKF algorithm exhibits superior filtering performance in terms of both accuracy and smoothness compared to the conventional EnKF and EKF algorithms. This indicates the significant advantages of our proposed algorithm for AUV navigation.
An Applied Analysis of Securing 5G/6G Core Networks with Post-Quantum Key Encapsulation Methods
Authors: Scalise Paul , Garcia Robert , Boeding Matthew , Hempel Michael , Sharif Hamid
Published: October 2024

5th Generation (5G) cellular networks have been adopted around the world since the rollout began around 2019. It brought with it many innovations and new services, such as Enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). 5G also introduced a more scalable approach to network operations using fully software-based Virtualized Network Functions (VNF) in Core Networks (CN) rather than the prior hardware-based approach. However, while this shift towards a fully software-based system design provides numerous significant benefits, such as increased interoperability, scalability, and cost-effectiveness, it also brought with it an increased cybersecurity risk. Security is crucial to maintaining trust between vendors, operators, and consumers. Cyberattacks are rapidly increasing in number and sophistication, and we are seeing a shift towards zero-trust approaches. This means that even communications between VNFs inside a 5G core have to be scrutinized and hardened against attacks, especially with the advent of quantum computers. The National Institute of Standards and Technology (NIST), over the past 10 years, has led efforts to standardize Post Quantum Cryptography (PQC) to protect against quantum attacks. This paper covers a custom implementation of the open-source free5GC CN, to expand its HTTPS capabilities for VNFs by introducing PQC Key Encapsulation Methods (KEM) for TLS v1.3. This paper provides the details of this integration with a focus on the latency of different PQC KEMs in initial handshakes between VNFs, on packet size, and the implications on a 5G environment. This work also conducts a security comparison between the PQC-equipped free5GC and other open-source 5G CNs. The presented results indicate a negligible increase in UE connection setup duration and a small increase in connection setup data requirements, strongly indicating that PQC KEM’s benefits far outweigh any downsides when integrated into 5G and 6G core services.
Increasing the Robustness of Image Quality Assessment Models Through Adversarial Training
Authors: Chistyakova Anna , Antsiferova Anastasia , Khrebtov Maksim , Lavrushkin Sergey , Arkhipenko Konstantin , Vatolin Dmitriy , Turdakov Denis
Published: October 2024

The adversarial robustness of image quality assessment (IQA) models to adversarial attacks is emerging as a critical issue. Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-depth research has examined adversarial training as a way to improve IQA model robustness. This study introduces an enhanced adversarial training approach tailored to IQA models; it adjusts the perceptual quality scores of adversarial images during training to enhance the correlation between an IQA model’s quality and the subjective quality scores. We also propose a new method for comparing IQA model robustness by measuring the Integral Robustness Score; this method evaluates the IQA model resistance to a set of adversarial perturbations with different magnitudes. We used our adversarial training approach to increase the robustness of five IQA models. Additionally, we tested the robustness of adversarially trained IQA models to 16 adversarial attacks and conducted an empirical probabilistic estimation of this feature. The code is available at https://github.com/wianluna/metrics_at.
I. An Exceptional Multiplanetary Resonant Chain in TOI-270 and an Exact Laplace-like Resonance in HD 110067
Authors: Christodoulou Dimitris M. , Sorabella Nicholas M. , Bhattacharya Sayantan , Laycock Silas G. T. , Kazanas Demosthenes
Published: October 2024

Super-Earth b and sub-Neptunes c and d are orbiting about the M-dwarf TOI-270 in that order from the star. Their global resonant chain (3:5, 1:1, 2:1) is extremely surprising because planet d appears to be the only known planet occupying the 2:1 resonant orbit without participating in a Laplace resonance (LR) or another planet intervening between the 1:1 and 2:1 orbits as in HD 110067. We do not believe that TOI-270 d is an exception to the empirical rule calling for 2:1 vacancy except in 1:2:4 LRs and Laplace-like 2:3:4 chains. Instead, a LR might exist in this system, and we searched (to no avail) the TESS light curves of TOI-270 for hints of an outer planet that would complete the LR chain. Alternative explanations would be an unknown planet more massive than planet c (Mc=6.20M⊕) establishing the actual 1:1 orbit, or planet b residing in the 1:2 Laplace orbit with a period shorter by 0.53 days. But these possibilities are ruled out by current data. This leaves only one other option to explore: the observed orbits could be in a stable 35:1:2 resonant chain. Preliminary calculations do not preclude this possibility that should be investigated further by numerical orbit integrations. To this end, we determine two potentially resonant angles, φ and φ^, related via the Laplace phase φL by φ^=φL+2φ. In contrast, HD 110067 is shown to have planets d-e-f in a Laplace-like 1:32:2 resonance with phase φ=2φL precisely.
Compartmental Description of the Cosmological Baryonic Matter Cycle. II. Inclusion of Triggered Star Formation
Authors: Kröger Martin , Schlickeiser Reinhard
Published: October 2024

Context: The earlier introduced compartmental description, well-known from the description of infection diseases and epidemics, was applied here to describe the nonlinear temporal evolution of the baryonic matter in interstellar gas and stars in the presence of triggered star formation. Aims: The competition of triggered star formation, spontaneous star formation, stellar feedback and stellar evolution was theoretically investigated to understand the baryonic matter cycle including luminous baryonic matter in main-sequence stars and weakly luminous matter in white dwarfs, neutron stars and black holes. Of particular interest was the understanding of the cosmic star formation history and the redshift dependence of the gas and stellar fractions using compartmental models. Methods: For stationary rates of spontaneous and triggered star formation, continuous stellar feedback and stellar evolution exact and approximate analytical solutions of the time evolution of the fractions of stellar and locked-in stellar matter were derived involving the time dependence of the gaseous fraction G(t). The high accuracy of the analytical solutions is proven by comparison with the exact numerical solutions of the GSL equations. Results: The inclusion of the triggered star formation process explains the observed cosmological star formation rate, the integrated stellar density at redshifts below z=8, and the present-day gas and stellar fractions very well. The generalized GSL-model provides excellent fits to the observed redshift dependencies of the star formation rate and the integrated stellar density. Moreover, it explains the observed present-day gas and stellar fractions, and it makes predictions on the future evolution of these fractions in the universe.
From Natural Hosts to Agricultural Threats: The Evolutionary Journey of Phytopathogenic Fungi
Authors: Madhushan Asanka , Weerasingha Dulan Bhanuka , Ilyukhin Evgeny , Taylor Paul W. J. , Ratnayake Amila Sandaruwan , Liu Jian-Kui , Maharachchikumbura Sajeewa S.N.
Published: October 2024

Since the domestication of plants, pathogenic fungi have consistently threatened crop production, evolving genetically to develop increased virulence under various selection pressures. Understanding their evolutionary trends is crucial for predicting and designing control measures against future disease outbreaks. This paper reviews the evolution of fungal pathogens from natural habitats to agricultural settings, focusing on eight significant phytopathogens: Pyricularia oryzae, Botrytis cinerea, Puccinia spp., Fusarium graminearum, F. oxysporum, Blumeria graminis, Zymoseptoria tritici, and Colletotrichum spp. Also, we explore the mechanism used to understand evolutionary trends in these fungi. The studied pathogens have evolved in agroecosystems through either (1) introduction from elsewhere or (2) local origins involving co-evolution with host plants, host shifts, or genetic variations within existing strains. Genetic variation, generated via sexual recombination and various asexual mechanisms, often drives pathogen evolution. While sexual recombination is rare and mainly occurs at the center of origin of the pathogen, asexual mechanisms such as mutations, parasexual recombination, horizontal gene or chromosome transfer, and chromosomal structural variations are predominant. Farming practices like mono-cropping resistant cultivars and prolonged use of fungicides with the same mode of action can drive the emergence of new pathotypes. Furthermore, host range does not necessarily impact pathogen adaptation and evolution. Although halting pathogen evolution is impractical, its pace can be slowed by managing selective pressures, optimizing farming practices, and enforcing quarantine regulations. The study of pathogen evolution has been transformed by advancements in molecular biology, genomics, and bioinformatics, utilizing methods like next-generation sequencing, comparative genomics, transcriptomics and population genomics. However, continuous research remains essential to monitor how pathogens evolve over time and to develop proactive strategies that mitigate their impact on agriculture.
Sensorless Control of SPM Motor for e-Bike Applications
Authors: Pasund Abdin , Sartori Pierfrancesco , Bianchi Nicola
Published: October 2024

The aim of this research is to present both a sensorless control and a torque derating algorithm in the overload region of a permanent magnet motor for e-bike. First the theoretical backgrounds and the field oriented control are presented. Then a sensorless control, based on the back-emf estimation with a second order generalized integral flux observer for permanent magnet motor, has been designed. The second order generalized integral flux observer is an adaptive filter which can eliminate the DC offset and strongly attenuate the harmonics of the estimated rotor flux. The algorithms have been simulated and then validated by means of tests on a permanent magnet motor for e-bike.
Thermodynamic Modelling and Research for Processing of Complex Concentrate Blends in Custom Copper Smelters at Maximum Revenue
Authors: Shishin Denis , Tripathi Nagendra , Sineva Svetlana , Jak Evgueni
Published: October 2024

Custom copper smelters, dependent on purchased concentrates, face increasing economic pressures amid falling treatment and refining fees. With declining availability of high-grade, low-impurity concentrates, copper demand is expected to surge to support the transition to renewable energy. This study, based on recent observations of Chinese custom smelters, examines their strategies to address the challenge of purchasing concentrates at record-low treatment and refining charges. By investing in slag flotation technology, smelters can enhance copper, gold and silver recoveries. Through blending of high-grade and low-grade concentrates they can capitalize on the gap between the recoverable and payable metals, which is often referred to as “free metals”, while also benefiting from by-products, mainly sulfuric acid. While this approach offers economic opportunities, it introduces operational complexities. To mitigate these, laboratory testing combined with advanced digital predictive tools based on thermodynamics is crucial. This study demonstrates the use of thermodynamic models supported by experimental work for analyzing furnace operations. FactSage® software and a custom database are employed to define the operating window of two common flowsheets: 1) Flash Smelting + Flash Converting and 2) Bottom-Blown Smelting + Bottom Blowing Converting.
Computation of the Digital Elevation Model and Ice Dynamics of Talos Dome and the Frontier Mountain Region (North Victoria Land/Antarctica) by Synthetic Aperture Radar (Sar) Interferometry
Authors: Sterzai Paolo , Creati Nicola , Zanutta Antonio
Published: October 2024

In Antarctica, SAR interferometry was largely used in coastal glacial areas, while in rare cases this method was used on the Antarctic plateau. In this paper, the authors present a digital elevation and ice flow map based on SAR interferometry for an area encompassing Talos Dome (TD) and the Frontier Mountain (FM) meteorite site in North Victoria Land/Antarctica. A digital elevation model (DEM) was calculated using a double SAR interferometry method. The DEM of the region was calculated by extracting approximately 100 control points from the Reference Elevation Model of Antarctica (REMA). The two DEMs differ slightly in some areas, probably due to the penetration of the SAR-C band signal into the cold firn. The largest differences are found in the western area of TD, where the radar penetration is more pronounced and fits well with the layer structures calculated by the georadar and the snow accumulation observations. By differentiating a 70-day interferogram with the calculated DEM, a displacement interferogram was calculated that represents the ice dynamics. The resulting ice flow pattern clearly shows the catchment areas of the Priestley and Rennick Glaciers as well as the ice flow from the west towards Wilkes Basin. The ice velocity field was analyzed in the area of FM. This area has become well known due to the search for meteorites. The velocity field in combination with the calculated DEM confirms the generally accepted theories about the accumulation of meteorites over the Antarctic Plateau.
Evaluation of Electrical Characteristics of Weft-Knitted Strain Sensors for Joint Motion Monitoring: Focus on Plating Stitch Structure
Authors: Oh Youkyung , Kim Youn-Hee
Published: October 2024

We developed a sensor optimized for joint motion monitoring by exploring the effects of stitch pattern, yarn thickness, and NP number on the performance of knitted strain sensors. We conducted stretching experiments with basic weft-knit patterns to select the optimal stitch pattern and analyze its sensitivity and reproducibility. The plain stitch with a conductive yarn located on the reverse side exhibited the highest gauge factor value (143.68) and achieved excellent performance, with a stable change in resistance even after repeated sensing. For in-depth analysis, we developed six sensors using the aforementioned pattern with different combinations of yarn thickness (1-ply, 2-ply) and NP number (12, 13, 14). Based on bending experiments, the GF across all sensors was 60.2–1092, indicating noticeable differences in sensitivity. However, no significant differences were observed in reproducibility, reliability, and responsiveness, confirming that all the sensors are capable of joint motion monitoring. Therefore, the plain-patterned plating stitch structure with conductive yarn on the reverse side is optimal for joint motion monitoring, and the yarn thickness and NP numbers can be adjusted to suit different purposes. This study provides basic data for developing knitted strain sensors and offers insights into how knitting methods impact sensor performance.
Attention-Based Deep Learning Models for Cryptocurrency Price Prediction: A Comparative Analysis with Technical Indicators
Authors: Lee Ming Che
Published: October 2024

This study presents a comparative analysis of two advanced attention-based deep learning models—Attention-LSTM and Attention-GRU—for predicting cryptocurrency price movements. The models utilize historical OHLCV data, combined with four technical indicators: SMA, EMA, TEMA, and MACD, to enhance the accuracy of classification into three categories: uptrend, downtrend, and neutral. Both models aim to capture market dynamics through sequential data while incorporating attention mechanisms to focus on relevant time steps. Experimental results demonstrate that the inclusion of technical indicators significantly improves model performance, with MACD yielding the highest accuracy. The Attention-GRU model shows computational advantages, while the Attention-LSTM model excels in capturing long-term dependencies.
Implementation of a Freeware Scada System for a Continuous/Alternate Voltage Microgrid
Authors: Cerezo Quina David Juan , Santos Tadeu Félix , Butron  Llamoca Juan Antonio , Echaiz Espinoza German Alberto , Salazar Andrés Ortiz , Villarreal Elmer Rolando Llanos
Published: October 2024

Microgrids represent small-scale energy generation units with low cost-effectiveness, making use of renewable resources, such as photovoltaic and wind energy. These systems can operate both in connection with and independent from the primary power grid. The widespread adoption and expansion of microgrids are increasingly vital in light of the global shift towards minimizing fossil fuel consumption and promoting cleaner energy sources. Similar to conventional electrical networks, microgrids require Supervisory Control and Data Acquisition (SCADA) systems to ensure sustained and reliable operation.
In support of the proliferation and growth of microgrid networks, this paper proposes the implementation of an open-source SCADA system. This system, being platform-independent, significantly reduces the costs associated with microgrid implementation, thus aligning with the principles of social responsibility. The proposed SCADA system will be deployed on a simulated microgrid, featuring both photovoltaic and wind energy sources, and will include a Phase-Locked Loop (PLL) synchronization system for grid connection. The simulation will be carried out using the Matlab-Simulink environment, leveraging the power systems library.
The interface between Matlab and SCADA is done using an OPC Server. Additionally, the SCADA system will interface with an SQL database to facilitate the archiving of historical data.
Prediction of Temperature Distribution on Aircraft Hot-air Anti-icing Surface by ROM and Neural Networks
Authors: Chu Ziying , Geng Ji , Yang Qian , Yi Xian , Dong Wei
Published: October 2024

This study aims to address the inefficiencies and time-consuming nature of traditional hot-air anti-icing system designs by introducing reduced order models (ROM) and machine learning techniques to predict anti-icing surface temperature distributions. This study compares several classic neural networks, ultimately proposing two models: POD-AlexNet and multi-CNNs with GRU (MCG). Design variables of the hot-air anti-icing cavity are used as inputs, and the corresponding surface temperature distribution data serve as outputs. The performance of these models is evaluated on the test set. The POD-AlexNet model achieves a mean prediction accuracy of over 95%, while the MCG model reaches 96.97%. Both models significantly outperform traditional numerical simulation methods, delivering faster predictions. The proposed models achieve fast prediction of anti-icing surface temperature distribution while ensuring acceptable prediction accuracy. These models greatly enhance the prediction efficiency over existing numerical simulation approaches, contributing to the design of aircraft hot-air anti-icing systems based on optimization methods such as genetic algorithms.
Tribological and Hygroscopic Behavior of Polybutylene Terephthalate/Acrylonitrile Styrene Acrylate (PBT/ASA) Nanocomposites with Graphene-based Nanofiller
Authors: Lee Pyoung-Chan , Hong Seo-Hwa , Oh Ji Taek , Shin Donghyeok , Jung Jae-Uk , Ko Youn Ki , Ha Jin U. , Kim Myeong-Gi , Cho Beom-Gon
Published: October 2024

Fogging in automotive headlamps is a significant issue that affects both aesthetics and functionality. This study investigates the use of graphene-based nanocomposites to mitigate fogging by enhancing the hygroscopic properties of Polybutylene Terephthalate/Acrylonitrile Styrene Acrylate (PBT/ASA) composites commonly used in headlamps. The incorporation of functional-ized graphene improved the tensile and flexural strength of the nanocomposites, though it led to a reduction in elongation and melt flow. Additionally, the solid lubrication properties and increased surface hardness of the graphene contributed to enhanced wear resistance. The presence of gra-phene in the nanocomposites also reduced moisture diffusion, lowering the rates of both hygro-scopic and desorption when compared to commercial PBT/ASA composites. Furthermore, the nanocomposites exhibited a reduction in maximum moisture uptake. These improvements are expected to reduce the absolute humidity inside the headlamp, thereby effectively mitigating the fogging issue.
Design of Wearable Devices for “Wearability”: A Scoping Review
Authors: Seo Yeo Weon , La Marca Valentina , Tandon Animesh , Chiao Jung-Chih , Drummond Colin K.
Published: October 2024

Wearable smart devices have become ubiquitous in modern society, extensively researched for their health monitoring capabilities and convenience features. However, the "wearability" of these devices remains a relatively understudied area, particularly in terms of design informed by clinical trials. While wearable devices hold significant potential to enhance daily life, it is crucial to understand and validate design factors influencing their comfort, usability, and integration into everyday routines. This review aimed to evaluate the “wearability” of smart devices through a mixed-methods scoping review of the literature. By analyzing studies on comfort, usability, and daily integration, it sought to identify design improvements and research gaps to enhance user experience and system design. From 130 publications (1998-2024), 19 were included. The review revealed that most findings were qualitative, lacking the quantitative studies needed for validated design criteria. While qualitative data provides rich insights, the absence of quantitative research limits a comprehensive understanding of design issues in the field. This underscores the need for future studies to use quantitative methodologies to better assess and validate design guidance, improving wearability.
Road Car Image Target Detection and Recognition Based on YOLOv8 Deep Learning Algorithm
Authors: Wang Hao , Li Zhengyu , Li Jianwei
Published: October 2024

In this paper, target detection of car images in roads is performed based on the YOLOv8 model of YOLO family of models, which improves the accuracy and generalisation of the target detection task by combining multi-scale prediction, CSPNet structure and optimisation techniques such as BoF and BoS. The input images contain five types of vehicles such as Ambulance, Bus, Car, Motorcycle and Truck, which are analysed and learnt to have a classification accuracy of 75.4% on Ambulance, 53.5% on Bus, 55.1% on Car, 51.1% on Motorcycle and 42.5% on Truck. Despite the gap in specific classification accuracy, the YOLOv8 model can detect 100% of vehicles on the road, demonstrating good target detection capability. This research is of great significance for improving road traffic safety, intelligent traffic management, and the development of future autonomous driving technology. By optimising the deep learning model to achieve more accurate and efficient vehicle target detection, it can help to improve road safety and traffic efficiency, and promote the progress of intelligent transportation systems.
Enhancing SQL Injection Detection with Trustworthy Ensemble Learning and Boosting Models Using Local Explanation Techniques
Authors: Le Thi-Thu-Huong , Hwang Yeonjeong , Choi Changwoo , Wardhani Rini Wisnu , Putranto Dedy Septono Catur , Kim Howon
Published: October 2024

This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables attackers to exploit databases, gaining unauthorized access, and manipulating data. Traditional detection methods often struggle due to the constantly evolving nature of these attacks, the increasing complexity of modern web applications, and the lack of transparency in the decision-making processes of machine learning models. To address these challenges, we evaluated the performance of various models, including Decision Tree, Random Forest, XGBoost, AdaBoost, Gradient Boosting Decision Tree (GBDT), and Histogram Gradient Boosting Decision Tree (HGBDT), using a comprehensive SQL injection dataset. The primary motivation behind our approach is to leverage the strengths of ensemble learning and boosting techniques to enhance detection accuracy and robustness against SQL injection attacks. By systematically comparing these models, we aim to identify the most effective algorithms for SQL injection detection systems. Our experiments show that Decision Tree, Random Forest, and AdaBoost achieved the highest performance, with an accuracy of 99.50% and an F1 score of 99.33%. Additionally, we applied SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for local explainability, illustrating how each model classifies normal and attack cases. This transparency enhances the trustworthiness of our approach in detecting SQL injection attacks. These findings highlight the potential of ensemble methods to provide reliable and efficient solutions for detecting SQL injection attacks, thereby improving the security of web applications.
Metabolic Fingerprinting of Blood and Urine of Dairy Cows Affected by Bovine Leukemia Virus: A Mass Spectrometry Approach
Authors: Tobolski Dawid , Zwierzchowski Grzegorz , Wójcik Roman , Haxhiaj Klevis , Wishart David S. , Ametaj Burim N.
Published: October 2024

This study investigated metabolic changes associated with Bovine Leukemia Virus (BLV) infection in dairy cows, focusing on pre-parturition alterations. Of 145 cows examined, 42 (28.9%) were BLV-seropositive. 38% of infected cows showed high somatic cell counts indicative of subclinical mastitis, with 15 experiencing additional health issues such as ketosis, milk fever, and lameness. Despite these conditions, no significant differences in milk yield or composition were observed between infected and control groups. Metabolomic analysis conducted at -8 and -4 weeks prepartum revealed significant metabolic differences between BLV-infected and healthy cows. At -8 weeks, 30 serum metabolites were altered, including sphingomyelins, lysophosphatidylcholines, amino acids, and acylcarnitines, suggesting disruptions in membrane integrity, energy metabolism, and immune function indicative of early neoplastic transformations. By -4 weeks, the number of altered metabolites reduced to 17, continuing to reflect metabolic disruptions in cows with leukemia. Multivariate analysis highlighted distinct metabolic profiles between infected and control cows, identifying key discriminating metabolites such as choline, aspartic acid, phenylalanine, and arginine. Urine metabolomics revealed significant prepartum shifts in metabolites related to glucose, asymmetric dimethylarginine, and pyruvic acid, among others. The research confirmed metabolomics' efficacy in defining a BLV infection metabolic profile, elucidating leukosis-associated metabolic disruptions. This approach facilitates identification of BLV-infected cows and enhances understanding of infection pathophysiology, providing a foundation for advanced management and intervention strategies in dairy herds. The study underscores the profound impact of leukosis on metabolic processes and highlights urine metabolomics' utility in non-invasively detecting BLV infection, offering potential for improved herd health management.