Search

Fine-grained vehicle type recognition using on-road cameras is among interesting topics in machine vision. It has several challenges like inter-class similarity, different viewing angles, and different lighting and weather conditions. This paper presents a novel approach for vehicle classification based on a novel augmentation method and deep learning. In the proposed smart augmentation, the vehicle images of each class are registered on the reference vehicles of all other classes and then added to the training set of that class. In this way, we will have a lot of new images which are very similar to both reference and target classes. This helps the CNN model to handle inter-class similarities very well. In the test phase, the input image is registered on every reference image in parallel and applied to the model. Finally, the winner is determined by summing up the provided scores of all models. The targeted data augmentation along with the proposed classification strategy has high recognition power and is capable of providing high accuracy using small CNNs or any other classification method without the need for large datasets. The proposed method achieved a recognition rate of 99.8% with only 150K parameters. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Engineering Transactions B: Applications is the property of International Journal of Engineering (IJE) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Propagating micro-cracks in a structure decreases its load-bearing capacity and leads to the collapse of the entire structure. Addition of various additives in all kinds of concrete or concrete ingredients, as several studies have shown, could significantly make concrete reclaim from the specifications and attributes point of view. A possible manner to the common ruin and expensive preservation of concrete infrastructure, utilizing encapsulating healing factors is helpful for the self-healing of concrete. The selfhealing concrete with microencapsulated Preyssler, and calcium nitrate was studied in this paper. Microcapsules were synthesized by in-situ polymerization of urea-formaldehyde as a shell around the core materials inclusive of Preyssler, calcium nitrate. Physicochemical characterization of microcapsules was conducted by Fourier transformation infrared spectroscopy, field emission scanning electron microscopy, and Transmission Electron Microscope. The mechanical assessment of cementitious specimens with different dosages of microcapsules (0%,0.5%, 1%, 1.5%, and 2%) was performed by compressive tests. Also, by measurement before and after damage after 10 days, the self-healing potential was tested. After the concrete was damaged by exerting 30% of its final load, all samples were incubated by immersion in water. According to the results, the sample containing 0.5% UFN, the sample containing 1.5% UFP, and the sample containing 1.5% UFNP have higher repair rates than others. This scope of research because of its interdisciplinary nature would own several possibilities to be pioneering with making an opening gate to link sciences and engineering such as material, chemistry, science, nanotechnology, and the field of engineering to persuade a wide spectrum of contribution in engineering sciences and usages. [ABSTRACT FROM AUTHOR]

Constructing dual‐ion energy storage devices using anion‐intercalation graphite cathodes offers the unique opportunity to simultaneously achieve high energy density and output power density. However, a critical challenge remains in the lack of proper anodes that match with graphite cathodes, particularly in sustainable electrolyte systems using abundant potassium. Here, a surface grafting approach utilizing multifunctional azobenzene sulfonic acid is reported, which transforms V2C MXene into a high‐kinetics K+‐intercalation anode (denoted ASA‐V2C) for dual‐ion energy storage devices. Importantly, the grafted azobenzene sulfonic acid offers extra K+‐storage centers and fast K+‐hopping sites, while concurrently acting as a buffer between V2C layers to mitigate the structural distortion during K+ intercalation/de‐intercalation. These functionalities enable the V2C electrode with significantly enhanced specific capacity (173.9 mAh g−1 vs 121.5 mAh g−1 at 0.05 A g−1), rate capability (43.1% vs 12.0% at 20 A g−1), and cycling stability (80.3% vs 45.2% after 900 cycles at 0.05 A g−1). When coupled with an anion‐intercalation graphite cathode, the ASA‐V2C anode demonstrates its potential in a dual‐ion energy storage device. Notably, the device depicts a maximum energy density of 175 Wh kg−1 and a supercapacitor‐comparable power density of 6.5 kW kg−1, outperforming recently reported Li+‐, Na+‐, and K+‐based dual‐ion devices. [ABSTRACT FROM AUTHOR]

Introduction: The basic strategy to mitigate water crisis is to save agricultural water consumption by increasing productivity, which will result in more income for farmers and sustainable production. Due to the economic importance of barley production in the country, it is necessary to study the volume of irrigation water and water productivity to produce this strategic product. Based on extensive field research on irrigation water management and application of different irrigation methods in barley farms, the innovations of this research were: a) measuring water consumed and determining water use efficiency in barley production, b) the up-to-date of the measurements and research findings, c) findings applicability for application in agricultural planning at the national and regional levels, d) the ability to development the findings in barley farms at the national level to improve water use efficiency. The hypotheses of this research are: a) barley irrigation water is various in different regions, b) water applied in barley farms is more than the required one, c) the water use efficiency of barley is different in the main production areas, and d) The applied water of barley is not the same in different irrigation methods. Therefore, the main objective of this study is to determine the water consumed and water use efficiency in barley production; to measure the water applied to barley farms in the main production areas; to compare the water measured in the production areas with the net irrigation requirement; and finally to determine water use efficiency of the barley in the main production areas in the Iran. Materials and Methods For this purpose, the volume of irrigation water and barley yield in 296 selected farms in 12 provinces (about 75% of the area under cultivation and production of barley in Iran) including Khuzestan, East Azerbaijan, Ardabil, North Khorasan, Fars, Khorasan Razavi, Tehran, Semnan, Markazi, Isfahan, Hamedan and Qazvin were measured directly. Farms in the mentioned provinces were selected to cover various factors such as irrigation method, level of ownership, proper distribution and quality of irrigation water. By carefully monitoring the irrigation program of selected farms during the growing season, the amount of irrigation water for barley during one year was measured. At the end of the season and after determining the average yield of barley during the 2020-2021 year, the values of irrigation water productivity and total water productivity (irrigation+effective rainfall) were determined in selected barley farms in each region. The volume of water supplied was compared with the gross irrigation requirements estimated by the Penman-Monteith method using meteorological data from the last ten years, and compared with the values of the National Water Document. Analysis of variance was used to investigate the possible differences in yield, irrigation water and water productivity in barley production. Results and Discussion To assess the reliability of statistical analysis, we evaluated the sufficiency of the number of measurements needed for both the quantity of irrigation water and the ley yield on the farms. Subsequently, we computed statistical indices, such as the mean and standard deviation. The results showed that the number of measurements of irrigation water and barley yield was to be 296 and 283, respectively, which was more than the number of measurements required for irrigation water (41 dataset) and yield (50 dataset). Therefore, the sufficiency of the data for the statistical analysis was reliable. The results showed that the difference in yield, volume of irrigation water and water productivity indices were significant in the mentioned provinces. The volume of barley irrigation water in the studied areas varied from 1900 to 9300 cubic meters per hectare and its average weight was 4875 cubic meters per hectare. The average barley yield in selected farms varied from 1630 to 7050 kg ha-1 and the average was 3985 kg ha-1. Irrigation water productivity in selected provinces ranged from 0.22 to 1.53 and its weight average was 0.90 kg m-3. Average gross irrigation water requirement in the study areas by the Penman-Monteith method using meteorological data of the last ten years and the national water document were 4710 and 4950 cubic meters per hectare, respectively. Irrigation efficiency of barley fields in the country is estimated at 6265% without deficit irrigation. Conclusion In order to reduce water consumption and improve water productivity, it is suggested to manage water delivery to farms during the season and deliver water rights to them according to crops water requirements. To reduce water losses and enhance productivity in the barley farms, it is suggested the application of modern irrigation systems according to the farms conditions with the suitable operation; and modification and improvement of surface and traditional irrigation methods. Note that, water is only one of several necessary and effective inputs in the optimal and economic production of barley. On the other hand, attention should be paid to the optimal application of other inputs including: seeds, fertilizers, equipment and tools etc. [ABSTRACT FROM AUTHOR]

Background: This study aimed to evaluate the capability of a novel non-Lead composite shield with a lower density compared to commercial Lead shield to protect against 10-100 keV X-ray. Materials and Methods: In the present study, Monte Carlo calculations were performed to study the attenuation properties of a composite designed shield. For this reason, the attenuation rate of 8-material alloy including Tungsten, Bismuth, Barium, Gadolinium, Cesium, Cerium, Tin, and Antimony elements was compared to the 8-layers combination of these elements. Tungsten, Gadolinium, Cerium, and Bismuth were selected to simulate 4-layer and an alloy shields. The resulting X-ray fluence spectra attenuated by the designed shields were calculated. Results: Preliminary results showed that at the energy of 68 keV, the attenuation value by the 8-layer composition was 2.6 times higher than the value achievable by the alloy. It should be noted that the attenuation of 4-layer shield combined from Tungsten, Gadolinium, Cerium and Bismuth in the energy range of 72-100 keV, was 3.5 times higher than that of the alloy shield built from these elements. Conclusions: According to the results, a combination of Tungsten, Gadolinium, Cerium, and Bismuth materials can be effectively used to create non-Lead shields. The four-layer shield with higher density offers better attenuation compared to the alloy shield. [ABSTRACT FROM AUTHOR]

One of the major issues related to traffic monitoring systems is analysis of the behavior of vehicles and identification of their characteristics. In this paper, an automated algorithm is proposed for calibration of road cameras. This calibration is used to estimate the speed and dimensions of passing vehicles. By using this method, a motion plane is obtained in the initial frames according to the direction of moving vehicles. After modeling the background, removing shadows, and identifying the exact boundaries of passing vehicles, a number of common vehicles are recognized through the bag of words method. Given the actual metric dimensions of these vehicles and the equivalent dimensions on the motion plane, the metric coefficients are calculated and the calibration process is completed. Each passing vehicle is projected on the motion plane, and speed and dimensions are calculated by tracking it along the road. To test the accuracy of the proposed method, a ground-truth video dataset is constructed by simultaneously capturing road vehicles using RGB and speed cameras. Furthermore, to identify common vehicles, a dataset of vehicle images is collected. The proposed method is evaluated based on our dataset and the mean error of 1.15 km/h is achieved. Accordingly, it outperforms previous methods. [ABSTRACT FROM AUTHOR]

Let and be positive integers. We study , the class of all groups that for all subsets and of of sizes and , there exist and such that is subnormal in . In fact, if is a finite group, then we find some sharp bounds for and such that implies that is a nilpotent group. Also we find a bound depending only on and for the order of all nonnilpotent finite groups in . [ABSTRACT FROM AUTHOR]

Given the significance of groundwater resources in arid and semi-arid regions and the effect of climate change on these resources, the present research aims to assess climate change in the Minab plain and its effect on groundwater level variability in the future period. Climate change was explored by the climatic model CanESM2 using the SDSM4.2 software package, and the effect of climate change on groundwater resources was quantitatively modeled in the GMS10.0.5 software package under the emission scenarios of RCP2.6, RCP4.5, and RCP8.5. The results for the effect of climate change in the future period (2035) in the context of these scenarios showed that temperature will increase by 1.88°C, 2.60°C, and 4.28°C and precipitation will decrease by 34.19%, 42.08%, and 59.43% versus the reference year, respectively. The results for the application of the climatic scenarios using the groundwater model revealed that due to the decline of precipitation in this period, the mean groundwater level balance in the future periods leading to 2019, 2024, 2029, and 2035 versus the reference year (2003-2004) will be -13.8, -18.9, -22.1, and -24.7 m/yr in RCP2.6, -13.8, -18.9, -22.7, and -25.1 m/yr in RCP4.5, and -14.2, -19.2, -23.0, and -25.4 m/yr in RCP8.5, respectively. The mentioned changes are only based on predicting changes in precipitation and temperature parameters in the future period in Minab plain, and if exploitation continues, the deterioration of conditions in this plain will increase. So, it is recommended to planners and authorities adopt strategies for adaptation to new climatic conditions and water scarcity in the region and accommodate themselves with future conditions. [ABSTRACT FROM AUTHOR]

Summary: With the onset of the COVID‐19 pandemic, healthcare providers have made increasing use of inpatient teledermatology; however, few studies have analysed the impact of teledermatology on patient outcomes. In this study, we investigated the diagnostic concordance between the primary team and teledermatologist, and we analysed the impact of this technology on the diagnosis and management of erythroderma, a condition with high morbidity and mortality. Overall, out of 2987 inpatient teledermatology encounters reviewed, we found 33 cases of erythroderma, and, of these, 78.8% had a change in diagnosis after teledermatology consult, 81.8% were recommended biopsy and all patients had a change in topical/systemic therapy. We hope to promote further study of the efficacy of teledermatology as it may begin to address large gaps in dermatological access to care particularly in regional and community hospitals. [ABSTRACT FROM AUTHOR]

Herein, the reinforcing effects of CaCO3 nanoparticles on the mechanical properties of E-glass chopped strand mat (CSM)/epoxy composites have been investigated. The CaCO3 nanoparticles were functionalized with 3-aminopropyltrimethoxysilane (APTMS), and the characterization techniques illustrated that functionalized-CaCO3 (f-CaCO3) was successfully prepared. In the composite fabrication stage, various f-CaCO3 loadings (0.5, 1, 3, and 5 wt%) were incorporated into the epoxy resin, and then the multiscale composites were prepared using the hand lay-up technique. Among these composites, the sample containing 0.1 wt% f-CaCO3 demonstrated the highest interlaminar shear strength (ILSS) (33% increase in comparison with the neat sample). Moreover, the maximum tensile strength and modulus were observed for the 3 wt% and 5 wt% f-CaCO3 additions with 19% and 41% improvements, respectively as compared to the neat sample. The examination of the fracture surface showed that the nanocomposite matrix was better bonded to the fibers in comparison to the neat epoxy matrix. [ABSTRACT FROM AUTHOR]

The objective of this work was to enhance the mechanical properties of E-glass chopped strand mat (CSM)/epoxy composite via the incorporation of CaCO3 nanoparticles in the matrix. 3-aminopropyltrimethoxysilane as an organosilane compound was utilized to functionalize the surface of CaCO3 nanoparticles. The multiscale composites containing functionalized-CaCO3 (f-CaCO3) with various weight fractions (0.5, 1, 3, and 5 wt.%) were prepared to explore the effects of f-CaCO3 on the flexural and wear properties. The results of the three-point bending test demonstrated that 1 wt.% f-CaCO3 addition led to a maximum enhancement of 44% in the flexural strength of the CSM/epoxy composite. In addition, 42% enhancement in flexural modulus was achieved with the addition of 3 wt.% f-CaCO3. The results of the wear tests showed that the f-CaCO3/epoxy matrix could provide enhanced wear resistance and the maximum resistance was obtained via adding 3 wt.% f-CaCO3. Incorporation of 3 wt.% f-CaCO3 resulted in 56% and 43% reductions in wear rate and friction coefficient of the CSM/epoxy composite, respectively. It was also found that the silanization of CaCO3 had a significant effect on flexural and wear properties. Scanning electron microscopy analysis of the fracture and worn surfaces clearly revealed that the nano-CaCO3 addition resulted in improved fiber-matrix interfacial bonding. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the challenges faced and the strategies employed by Filipino educators in teaching English expository writing based on their lived experiences. Participants who were enrolled in the program Doctor of Philosophy in Education, major in English during the School Year 2022-2023 in a private university in the Philippines were selected through convenience sampling method. Labov's thematic analysis approach was utilized as the framework for narrative analysis. Based on the findings of this research, the participants normally experience the challenges of cultural diversity, writing proficiency, time constraints, resource limitations, diverse learning styles, and student engagement. In order to navigate such challenges, however, the participants employ various strategies. For cultural diversity, the participants resort to the enhancement of a culturally inclusive content, respectful feedback, and peer collaboration. In terms of writing proficiency, the participants utilize the strategies of differentiation, skill building, and individualized support. To address time constraints, the courses of action of educators are prioritizing key concepts, chunking and sequencing, and structured lesson plans. In coping with resource limitations, the teachers of English expository writing utilize adaptable materials, minimalist approaches, and collaborative learning in their teaching methods. As regards diversity in students' learning styles, the teachers adapt multimodal instruction, student choice, and universal design. Lastly, the lack of student engagement is being addressed through interactive activities, real-world relevance, and gamification. A researcher-designed comprehensive training and development plan was recommended to further improve the educators' skills in teaching expository writing. This training and development model is envisioned to provide opportunities for teachers to upskill their pedagogies and better improve their teaching effectiveness. To verify the validity of the results, it is strongly recommended that a larger-scale variant of this study be conducted. [ABSTRACT FROM AUTHOR]

Iron is an essential mineral that supports numerous biological functions. Studies have reported associations between iron dysregulation and certain cardiovascular and neurodegenerative diseases, but the direction of influence is not clear. Our goal was to use computational approaches to better understand the role of genetically predicted iron levels on disease risk. We meta-analyzed genome-wide association study summary statistics for serum iron levels from two cohorts and two previous meta-analyses. We then obtained summary statistics from 11 neurodegenerative, cerebrovascular, cardiovascular or lipid traits to assess global and regional genetic correlation between iron levels and these traits. We used two-sample Mendelian randomization (MR) to estimate causal effects. Sex-stratified analyses were also carried out to identify effects potentially differing by sex. Overall, we identified three significant global correlations between iron levels and (i) coronary heart disease, (ii) triglycerides, and (iii) high-density lipoprotein (HDL) cholesterol levels. A total of 194 genomic regions had significant (after correction for multiple testing) local correlations between iron levels and the 11 tested traits. MR analysis revealed two potential causal relationships, between genetically predicted iron levels and (i) total cholesterol or (ii) non-HDL cholesterol. Sex-stratified analyses suggested a potential protective effect of iron levels on Parkinson's disease risk in females, but not in males. Our results will contribute to a better understanding of the genetic basis underlying iron in cardiovascular and neurological health in aging, and to the eventual identification of new preventive interventions or therapeutic avenues for diseases which affect women and men worldwide. [ABSTRACT FROM AUTHOR]

Faculty perspectives on the use of artificial intelligence (AI) in higher education are crucial for AI's meaningful integration into teaching and learning, yet research is scarce. This paper presents a study designed to gain insight into faculty members' (N = 122) AI self-efficacy and distinct latent profiles, perceived benefits, challenges, use, and professional development needs related to AI. The respondents saw greater equity in education as AI's greatest benefit, while students and faculty members' lack of AI literacy was among the greatest challenges, with the majority interested in professional development. Latent class analysis revealed four distinct faculty member profiles: optimistic, critical, critically reflected, and neutral. The optimistic profile moderates the relationship between self-efficacy and usage. The development of adequate support services is suggested for successful and sustainable digital transformation. [ABSTRACT FROM AUTHOR]

Urtica species is an angiosperm plant in the Urticaceae family. It serves as a traditional food and medicinal herb, possessing high nutritional value and various bioactive compounds, including polysaccharides, flavonoids, and polyphenolic compounds. In the realm of animal feeds, Urtica spp. can replace traditional protein feed sources and high-quality forage, thereby reducing feed costs. Moreover, Urtica spp. extract exhibits antioxidant and anti-inflammatory properties and boosts immune regulation. Hence, Urtica spp. plays a beneficial role in enhancing animal performance and improving their immune function. Recently, with the development of sustainable farming techniques, the demand for feed additives that prioritize safety, the absence of drug residues, and environmental friendliness have grown. Consequently, Urtica spp. and its extracts have received widespread attention in animal production. This article summarizes the biological functions of Urtica spp. and its application in animal husbandry while also outlining future prospects for its application. It will provide a scientific basis and reference point for the application of Urtica spp. in animal health and breeding. [ABSTRACT FROM AUTHOR]

This study examines the impact of Artificial Intelligence (AI) on the field of education, with particular focus on its implications for academic integrity and the adoption of comprehensive assessment approaches. This research fits within the specific setting of university students and faculty members in the Kingdom of Bahrain. Methods: A cross-sectional survey was designed to examine the impact Artificial Intelligence (AI) in field of education, with particular focus on its implications for academic integrity and the adoption of comprehensive assessment approaches. A total of 218 participants were randomly selected from 250 employed in this survey study. Results: Out of 250 invited participants, 203 responded to the survey. This study evaluated the influence of Educational Impact (EI), Policy and Ethics (PE), and Pedagogical Implications (PI) on Academic Outcomes (AO). Results revealed a significant association between EI → AO with a beta of 0.490, t-value of 4.504, and p <0.001. PI also showed a significant relationship (β=0.454, t =2.330, p = 0.010) with more variability. PE's impact on AO was modest (β = 0.243, t = 1.977, p = 0.024). Overall, EI was the strongest AO predictor. The R² value was approximately 39%, indicating a good fit. Conclusion: The research reveals a strong link between the Educational Impact (EI) of AI and academic success in Bahrain's universities, with EI being the primary predictor. Both Policy and Ethics (PE) and Pedagogical Implications (PI) play crucial roles in this relationship. [ABSTRACT FROM AUTHOR]

This study deals with the development of interlayer hybrid composites with improved low-velocity impact response. In the composites produced using the hand lay-up technique, glass, carbon, and Kevlar woven fabrics were used as reinforcement materials and epoxy resin was used as the matrix material. Shear thickening fluid (STF) was impregnated into the fabric for enhancing their performance. The effect of hybridization with different stacking sequences and the impregnation of STF on the peak load, deflection at peak load, energy absorption, impact strength, and damage degree were investigated. All samples were subjected to 3.12 m/s and 4.42 m/s impact velocities using a drop-weight impact tester applying 200J and 400J impact energy levels, respectively. The results revealed that in samples containing neat fabrics, the performances of the pure Kevlar samples were much better in comparison to hybrid samples, especially under high impact energy. However, STF significantly improved the impact strength and energy absorption (up to 30 times) of all samples, including hybrid ones. On the other hand, as the impact energy increased, the use of a Kevlar reinforced plate on the impact surface was crucial, providing higher energy absorption, and no perforation was observed since most of the energy was required to initiate the damage. In the samples with Kevlar in the intermediate layer, however, the majority of the impact energy caused propagation and expansion of the damage. According to the findings, up to 50% cost savings were achieved in STF-impregnated hybrid samples containing Kevlar. [ABSTRACT FROM AUTHOR]

DFT calculations are used to disclose the mechanism of Brønsted base-mediated cyclization of (2-alkynylbenzyl)oxy nitriles for the synthesis of benzofuroazepines. In 2015, the synthesis of substituted benzofuroazepines was reported by Zeni et al. via a stepwise mechanism known as 7-endo-dig. However, DFT calculations revealed that the anionic aza 8π-electrocyclization is more favorable than the proposed 7-endo-dig mechanism. [ABSTRACT FROM AUTHOR]

Biowaste-derived carbon quantum dots (CQDs) offer a wide range of applications, including bioimaging, drug degradation and manufacturing of sensors, photodetectors and photosensitisers. However, inaccurate measurements of their quantum confinement and valence/conduction band potentials complicate their application predictability. Herein, we report carbon dots synthesized hydrothermally (600 °C; 6 h) using sugarcane bagasse powder (SC-CQDs). These SC-CQDs were characterized through UV-visible, fluorescence, PXRD, XPS, DLS, FTIR, Raman, SEM, EDAX and TEM analyses. The obtained SC-CQDs resemble amorphous N-doped multilayer-nanographene-type spherical particles with a size of 9.09 nm and has a band gap of 2.46 eV (λabs: 298 nm) with –COOH/–OH functionalities. The quantum confinement and valence/conduction band potentials were estimated using Brus-Kayanuma quantum mechanical (BKQM) and virtual molecular dynamics (VMD) equations. These CQDs are potential candidates for sunlight-mediated methylene blue degradation in water within 36 min for up to four catalytic cycles. [ABSTRACT FROM AUTHOR]

The carbon spheres synthesized by pyrolysis of resorcinol–formaldehyde (RF) semiconducting resins exhibit enhanced activity for electrocatalytic oxygen reduction. The spheres consist of narrow reticulated carbon layers, which are derived from the donor–acceptor π-stacking interaction of the resins, and show high electron conductivity. [ABSTRACT FROM AUTHOR]

In this article, a game theoretic non‐cooperative dynamic target tracking algorithm that empowers defensive unmanned aerial vehicles (UAVs), with directional sensing capabilities to track and collect information on intrusive UAVs, is proposed. Specifically, defenders aim to maximize the collection of identity information from intruders possessing anti‐tracking and evading capabilities, while simultaneously preventing their entry into protected areas. Game theory is employed to determine the optimal confrontation paths for defenders against the intruders. The probability perception model is utilized for evaluating the dynamic target tracking capability and designing a tracking merit function to assess tracking performance, taking into account both the target's position and the perception relative angle. Furthermore, considering the dynamic interactive behaviors between intruders and defenders, the iterative linear quadratic game (ILQG) algorithm is employed to solve the Nash equilibrium of the non‐cooperative target tracking game. Through simulation experiments, the effectiveness of the proposed algorithm in accomplishing multi‐agent dynamic target tracking tasks is demonstrated and the performance of the algorithm under varying parameters in the intruder's cost function is evaluated, which represent different intrusion intentions. [ABSTRACT FROM AUTHOR]

Cybersecurity threats, particularly those involving lateral movement within networks, pose significant risks to critical infrastructures such as Microsoft Active Directory. This study addresses the need for effective defense mechanisms that minimize network disruption while preventing attackers from reaching key assets. Modeling Active Directory networks as a graph in which the nodes represent the network components and the edges represent the logical interactions between them, we use centrality metrics to derive the impact of hardening nodes in terms of constraining the progression of attacks. We propose using Unsupervised Learning techniques, specifically density-based clustering algorithms, to identify those nodes given the information provided by their metrics. Our approach includes simulating attack paths using a snowball model, enabling us to analytically evaluate the impact of hardening on delaying Domain Administration compromise. We tested our methodology on both real and synthetic Active Directory graphs, demonstrating that it can significantly slow down the propagation of threats from reaching the Domain Administration across the studied scenarios. Additionally, we explore the potential of these techniques to enable flexible selection of the number of nodes to secure. Our findings suggest that the proposed methods significantly enhance the resilience of Active Directory environments against targeted cyber-attacks. [ABSTRACT FROM AUTHOR]

Umbilical cord mesenchymal stem cell-derived extracellular vesicles (UC-EVs) are valuable in nanomedicine as natural nanocarriers, carrying information molecules from their parent cells and fusing with targeted cells. miRNA-126, specific to endothelial cells and derived from these vesicles, supports vascular integrity and angiogenesis and has protective effects in kidney diseases. Objective: This study investigates the delivery of miRNA-126 and anti-miRNA-126 via UC-EVs as natural nanocarriers for treating nephrotoxic injury in vitro. Method: The umbilical cord-derived mesenchymal stem cell and UC-EVs were characterized according to specific guidelines. Rat kidney proximal tubular epithelial cells (tubular cells) were exposed to nephrotoxic injury through of gentamicin and simultaneously treated with UC-EVs carrying miRNA-126 or anti-miRNA-126. Specific molecules that manage cell cycle progression, proliferation cell assays, and newly synthesized DNA and DNA damage markers were evaluated. Results: We observed significant increases in the expression of cell cycle markers, including PCNA, p53, and p21, indicating a positive cell cycle regulation with newly synthesized DNA via BrDU. The treatments reduced the expression of DNA damage marker, such as H2Ax, suggesting a lower rate of cellular damage. Conclusions: The UC-EVs, acting as natural nanocarriers of miRNA-126 and anti-miRNA-126, offer nephroprotective effects in vitro. Additionally, other components in UC-EVs, such as proteins, lipids, and various RNAs, might also contribute to these effects. [ABSTRACT FROM AUTHOR]

Bias could be defined as the tendency to be in favor or against a person or a group, thus promoting unfairness. In computer science, bias is called algorithmic or artificial intelligence (i.e., AI) and can be described as the tendency to showcase recurrent errors in a computer system, which result in "unfair" outcomes. Bias in the "outside world" and algorithmic bias are interconnected since many types of algorithmic bias originate from external factors. The enormous variety of different types of AI biases that have been identified in diverse domains highlights the need for classifying the said types of AI bias and providing a detailed overview of ways to identify and mitigate them. The different types of algorithmic bias that exist could be divided into categories based on the origin of the bias, since bias can occur during the different stages of the Machine Learning (i.e., ML) lifecycle. This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches that have been proposed to identify and mitigate them. This study not only provides ready-to-use algorithms for identifying and mitigating bias, but also enhances the empirical knowledge of ML engineers to identify bias based on the similarity that their use cases have to other approaches that are presented in this manuscript. Based on the findings of this study, it is observed that some types of AI bias are better covered in the literature, both in terms of identification and mitigation, whilst others need to be studied more. The overall contribution of this research work is to provide a useful guideline for the identification and mitigation of bias that can be utilized by ML engineers and everyone who is interested in developing, evaluating and/or utilizing ML models. [ABSTRACT FROM AUTHOR]

Recognising the urgency of addressing climate change and the imperative to mitigate its effects, the European Union (EU) has embarked on a transformative journey to reshape its energy landscape, with a pronounced emphasis on harnessing renewable energy sources (RESs) and augmenting their contribution to electricity generation. To propel Europe towards sustainable development through its energy transition, EU member states are encouraged to foster collaborative efforts on a European scale, inviting neighbouring countries to participate in joint ventures aimed at leveraging RESs for electricity generation. Consequently, it becomes imperative to evaluate the potential depth of cooperation among these nations, assessing how such partnerships can align with Europe's overarching objectives while fostering mutually beneficial conditions. This paper seeks to undertake a thorough analysis and evaluation of the potential impacts of such cooperation, both in advancing RES objectives and in promoting broader cooperation goals concerning the countries involved. The appropriate methodological framework has been developed, utilising and implementing the fuzzy PROMETHEE multicriteria decision analysis method, to address the problem's multidimensional character, intending to implement an appropriate action plan and promote production from RESs. The methodology has been applied to assess alternative strategies in two case study countries, Morocco and Egypt, while important outcomes have emerged towards the successful implementation of cooperation mechanisms promoting RES. [ABSTRACT FROM AUTHOR]

Background: Sleep is essential for health, especially during adolescence. However, most adolescents do not obtain the recommended 8 to 10 h of sleep, and their health is significantly affected. While both physical activity (PA) and diet have been shown to help improve the sleep quality and duration, the combined association of these two factors with sleep has yet to be analysed. Objectives: Therefore, the main objective of this study was to assess the evidence on the combined association of PA and diet with the quality and duration of sleep in adolescents. Secondary objectives were to analyse the evidence on the single association of PA with the quality and duration of sleep in adolescents and to analyse the single association of diet with the quality and duration of sleep in adolescents. Methods: To this end, a scoping review was conducted with a structured search in four online databases (PubMed, Scopus, Web of Science and ERIC). Results: The findings suggest that the amount of PA (time/week) and healthy dietary patterns, characterised by meal regularity and high consumption of fruits and vegetables, favour a better quality and a longer duration of sleep. Conversely, less weekly PA and the intake of less healthy foods, such as ultra-processed foods, are associated with decreasing the sleep quality and duration. Conclusions: In conclusion, the results underscore the importance of considering PA and diet as an appropriate approach to investigating sleep quality and duration in adolescents. Studies analysing the interplay between PA, diet and sleep in adolescents are scarce. [ABSTRACT FROM AUTHOR]

In classical amyloidoses, amyloid fibres form through the nucleation and accretion of protein monomers, with protofibrils and fibrils exhibiting a cross-β motif of parallel or antiparallel β-sheets oriented perpendicular to the fibre direction. These protofibrils and fibrils can intertwine to form mature amyloid fibres. Similar phenomena can occur in blood from individuals with circulating inflammatory molecules (and also some originating from viruses and bacteria). Such pathological clotting can result in an anomalous amyloid form termed fibrinaloid microclots. Previous proteomic analyses of these microclots have shown the presence of non-fibrin(ogen) proteins, suggesting a more complex mechanism than simple entrapment. We thus provide evidence against such a simple entrapment model, noting that clot pores are too large and centrifugation would have removed weakly bound proteins. Instead, we explore whether co-aggregation into amyloid fibres may involve axial (multiple proteins within the same fibril), lateral (single-protein fibrils contributing to a fibre), or both types of integration. Our analysis of proteomic data from fibrinaloid microclots in different diseases shows no significant quantitative overlap with the normal plasma proteome and no correlation between plasma protein abundance and their presence in fibrinaloid microclots. Notably, abundant plasma proteins like α-2-macroglobulin, fibronectin, and transthyretin are absent from microclots, while less abundant proteins such as adiponectin, periostin, and von Willebrand factor are well represented. Using bioinformatic tools, including AmyloGram and AnuPP, we found that proteins entrapped in fibrinaloid microclots exhibit high amyloidogenic tendencies, suggesting their integration as cross-β elements into amyloid structures. This integration likely contributes to the microclots' resistance to proteolysis. Our findings underscore the role of cross-seeding in fibrinaloid microclot formation and highlight the need for further investigation into their structural properties and implications in thrombotic and amyloid diseases. These insights provide a foundation for developing novel diagnostic and therapeutic strategies targeting amyloidogenic cross-seeding in blood clotting disorders. [ABSTRACT FROM AUTHOR]

This research aims to overcome three major challenges in foreign object detection on power transmission lines: data scarcity, background noise, and high computational costs. In the improved YOLOv8 algorithm, the newly introduced lightweight GSCDown (Ghost Shuffle Channel Downsampling) module effectively captures subtle image features by combining 1 × 1 convolution and GSConv technology, thereby enhancing detection accuracy. CSPBlock (Cross-Stage Partial Block) fusion enhances the model's accuracy and stability by strengthening feature expression and spatial perception while maintaining the algorithm's lightweight nature and effectively mitigating the issue of vanishing gradients, making it suitable for efficient foreign object detection in complex power line environments. Additionally, PAM (pooling attention mechanism) effectively distinguishes between background and target without adding extra parameters, maintaining high accuracy even in the presence of background noise. Furthermore, AIGC (AI-generated content) technology is leveraged to produce high-quality images for training data augmentation, and lossless feature distillation ensures higher detection accuracy and reduces false positives. In conclusion, the improved architecture reduces the parameter count by 18% while improving the mAP@0.5 metric by a margin of 5.5 points when compared to YOLOv8n. Compared to state-of-the-art real-time object detection frameworks, our research demonstrates significant advantages in both model accuracy and parameter size. [ABSTRACT FROM AUTHOR]

Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vehicle-to-everything (V2X) communication is an emerging technology that facilitates communication among vehicles and numerous environmental entities. However, it encounters certain challenges throughout the handover process. This study analyses the challenges and complexity of managing handover, specifically in maintaining uninterrupted connection and meeting the service criteria outlined in the 3GPP 5G new radio (NR) standard. Various applications of V2X technology that require handover management are explored, such as vehicle safety and traffic management, enhanced driver assistance, and autonomous driving. Furthermore, this paper illuminates the most recent developments in V2X communication, highlighting the significance of efficient handover management, resolving technical issues, based on the full potential of the use of V2X apps that contribute to the establishment of a transportation ecosystem that is characterized by enhanced safety, increased intelligence, and improved connectivity. This paper can be used as a starting point for thinking about how to improve C-V2X communication. [ABSTRACT FROM AUTHOR]

I consider recent attempts to establish standards, principles, and goals for artificial intelligence (AI) through the lens of educational measurement. Distinctions are made between generative AI and AI-adjacent methods and applications of AI in formative versus summative assessment contexts. While expressing optimism about its possibilities, I caution that the examples of truly generative AI in educational testing have the potential to be overexaggerated, that efforts to establish standards for AI should complement the Standards for Educational and Psychological Testing and focus attention on the issues of fairness and social responsibility, and that scientific advance and transparency in the development and application of AI in educational assessment may be incompatible with the competitive marketplace that is funding this development. [ABSTRACT FROM AUTHOR]

In convolutional neural networks, pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models. These methods reduce the computational amount of convolutional neural networks, making the neural network more efficient. Maximum pooling, average pooling, and minimum pooling methods are generally used in convolutional neural networks. However, these pooling methods are not suitable for all datasets used in neural network applications. In this study, a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural networks. This method, which we call MAM (Maximum Average Minimum) pooling, is more interactive than other traditional maximum pooling, average pooling, and minimum pooling methods and reduces data loss by calculating the more appropriate pixel value. The proposed MAM pooling method increases the performance of the neural network by calculating the optimal value during the training of convolutional neural networks. To determine the success accuracy of the proposed MAM pooling method and compare it with other traditional pooling methods, training was carried out on the LeNet-5 model using CIFAR-10, CIFAR-100, and MNIST datasets. According to the results obtained, the proposed MAM pooling method performed better than the maximum pooling, average pooling, and minimum pooling methods in all pool sizes on three different datasets. [ABSTRACT FROM AUTHOR]

Background Despite extensive research, the effects of L-carnitine supplementation in treating obesity are still unclear and equivocal. L-carnitine transports fatty acids into mitochondria for oxidation and is marketed as a weight loss supplement. The purpose of the present research is to investigate the efficacy of L-carnitine during concurrent training on the functional capacities and body composition in obese men. Methods Thirty nonactive, obese males (age = 37.2 ± 1.5 years; body mass index = 33.8 ± 2.5 kg/m 2) participated in this research. The participants were randomly divided into three groups: experimental group 1 (EXP1)—concurrent training with L-carnitine supplementation; experimental group 2 (EXP2)—L-carnitine supplementation without training; and control group—without training or L-carnitine supplementation. Concurrent training was performed for 8 weeks, three sessions per week, with a training intensity ranging from 60 to 75% of the maximum heart rate reserve and one-repetition maximum. Both experimental groups were supplemented with 35 mg L-carnitine supplement per kilogram body weight. Various functional and body composition variables were collected at three time points (pre-test, mid-test, and post-test). Results A number of variables were significantly improved in EXP1 after 4 and 8 weeks (systolic blood pressure, maximal oxygen consumption, weight, body mass index, and one-repetition maximum) and only after 8-weeks (diastolic blood pressure, resting heart rate, percentage of body fat, and fat-free mass). No significant changes were observed for EXP2 and the control group. Conclusion L-carnitine supplementation, in conjunction with concurrent training, emerges as a highly effective approach for enhancing body composition and boosting functional capacities in obese adult men. Therefore, it is recommended that overweight male individuals integrate concurrent training into their regimen while taking L-carnitine. [ABSTRACT FROM AUTHOR]

Purpose: This research was developed to identify artificial intelligence (AI) opportunities and challenges in higher education. Design/methodology/approach: This qualitative research was developed using the six-step thematic analysis method (Braun and Clark, 2006). Participants in this study were AI PhD students from Tehran University in 2022–2023. Purposive sampling was used to select the participants; a total of 15 AI PhD students, who were experts in this field, were selected and interviews were conducted. Findings: The authors considered the opportunities that AI creates for higher education in eight secondary subthemes (for faculty members, for students, in the teaching and learning process, for assessment, the development of educational structures, the development of research structures, the development of management structures and the development of academic culture). Correspondingly, The authors identified and categorized the challenges that AI creates for higher education. Research limitations/implications: Concerning the intended research, several limitations are significant. First, the statistical population was limited, and only people with characteristics such as being PhD students, studying at Tehran University and being experts in AI could be considered the statistical population. Second, caution should be exercised when generalizing the results due to the limited statistical population (PhD students from Tehran University). Third, the problem of accessing some students due to their participation in research grants, academic immigration, etc. Originality/value: The innovation of the current research is that the authors identified the opportunities and challenges that AI creates for higher education at different levels. The findings of this study also contribute to the enrichment of existing knowledge in the field regarding the effects of AI on the future of higher education, as researchers need more understanding of AI developments in the future of higher education. [ABSTRACT FROM AUTHOR]

Carbonatites are the primary geological sources for rare earth elements (REEs) and niobium (Nb). This study applies machine learning techniques to generate national-scale prospectivity models and support mineral exploration targeting of Canadian carbonatite-hosted REE +/− Nb deposits. Extreme target feature label imbalance, diverse geological settings hosting these deposits throughout Canada, selecting negative labels, and issues regarding the interpretability of some machine learning models are major challenges impeding data-driven prospectivity modeling of carbonatite-hosted REE +/− Nb deposits. A multi-stage framework, exploiting global hierarchical tessellation model systems, data-space similarity measures, ensemble modeling, and Shapley additive explanations was coupled with convolutional neural networks (CNN) and random forest to meet the objectives of this work. A risk–return analysis was further implemented to assist with model interpretation and visualization. Multiple models were compared in terms of their predictive ability and their capability of reducing the search space for mineral exploration. The best-performing model, derived using a CNN that incorporates public geoscience datasets, exhibits an area under the curve for receiver operating characteristics plot of 0.96 for the testing labels, reducing the search area by 80%, while predicting all known carbonatite-hosted REE +/− Nb occurrences. The framework used in our study allows for an explicit definition of input vectors and provides a clear interpretation of outcomes generated by prospectivity models. [ABSTRACT FROM AUTHOR]

This study aims to capture the stance of YouTube video content creators regarding the use of generative AI for language learning. Opinion mining and sentiment analysis techniques were employed to analyse the content, comments, and transcriptions of 66 YouTube videos published from December 2022 to October 2023. The findings revealed that most videos focused on speaking (n = 40) and writing skills (n = 24), with fewer videos addressing listening (n = 3) and reading (n = 19) skills. Sentiment analysis showed that videos predominantly conveyed optimistic (n = 42) and analytical (n = 17) sentiments, indicating a generally positive stance towards generative AI for language learning. Clustering analysis identified four thematic clusters: "language development and practices" (n = 33), "basic expression skills" (n = 25), "intercultural communication skills" (n = 6), and "language structure and meaning" (n = 2), representing different approaches to language learning with generative AI. Cross-sectional analyses revealed fluctuations in video counts and sentiment scores over time, with higher expectations for generative AI in writing and speaking skills, and relatively lower interest in listening skills. The findings suggest that YouTube video creators generally view generative AI as a promising tool for language learning, with a focus on developing practical communication skills, fostering intercultural understanding, and facilitating language development. These insights can inform the design and implementation of AI-supported language learning materials and practices. [ABSTRACT FROM AUTHOR]

The association between vitamin D concentrations and the occurrence of diabetic foot ulcers (DFUs) remains a topic of ongoing debate. In order to provide a comprehensive and updated review, we conducted this meta‐analysis to further investigate the relationship between vitamin D concentrations and DFUs occurrence. The following databases, including Cochrane Library, EMBASE, Web of Science, PubMed, CBM, CNKI, WANFANG DATA and VIP Database, were systematically searched for studies published up to Dec. 20th, 2023. The combined estimation was calculated using both fixed-effects and random‐effects models. The overall effect size was reported as a weighted mean difference (WMD) with a corresponding 95% confidence interval (95%CI). Data analysis was performed utilizing Review Manager 5.4 and Stata 14. The Protocol has been registered in PROSPERO CRD42024503468. This updated meta-analysis, incorporating thirty-six studies encompassing 11,298 individuals with or without DFUs, demonstrated a significant association between vitamin D deficiency/insufficiency and an elevated risk of DFUs occurrence (< 25 nmol/L, OR 3.28, P < 0.00001; < 50 nmol/L, OR 2.25, P < 0.00001; < 75 nmol/L, OR 1.67, P = 0.0003). Vitamin D concentrations were significantly lower in individuals with DFUs compared to those without DFUs (P < 0.00001). Subgroup analyses consistently demonstrated this trend among the older population (> 50 years, P < 0.00001), individuals with long duration of diabetes (> 10 years, P < 0.00001), and those with poor glycemic control (mean HbA1c 8%-9% and > 9%, P < 0.00001). [ABSTRACT FROM AUTHOR]

Groundwater arsenic poisoning has posed serious health hazards in the exposed population. The objective of the study is to evaluate the arsenic ingestion from breastmilk among pediatric population in Bihar. In the present study, the total women selected were n = 513. Out of which n = 378 women after consent provided their breastmilk for the study, n = 58 subjects were non-lactating but had some type of disease in them and n = 77 subjects denied for the breastmilk sample. Hence, they were selected for the women health study. In addition, urine samples from n = 184 infants' urine were collected for human arsenic exposure study. The study reveals that the arsenic content in the exposed women (in 55%) was significantly high in the breast milk against the WHO permissible limit 0.64 µg/L followed by their urine and blood samples as biological marker. Moreover, the child's urine also had arsenic content greater than the permissible limit (< 50 µg/L) in 67% of the studied children from the arsenic exposed regions. Concerningly, the rate at which arsenic is eliminated from an infant's body via urine in real time was only 50%. This arsenic exposure to young infants has caused potential risks and future health implications. Moreover, the arsenic content was also very high in the analyzed staple food samples such as rice, wheat and potato which is the major cause for arsenic contamination in breastmilk. The study advocates for prompt action to address the issue and implement stringent legislative measures in order to mitigate and eradicate this pressing problem that has implications for future generations. [ABSTRACT FROM AUTHOR]

Effective drought management requires precise measurement, but this is challenging due to the variety of drought indices and indicators, each with unique methods and specific uses, and limited ground data availability. This study utilizes remote sensing data from 2001 to 2020 to compute drought indices categorized as meteorological, agricultural, and hydrological. A Gaussian kernel convolves these indices into a denoised, multi-band composite image. Further refinement with a Gaussian kernel enhances a single drought index from each category: Reconnaissance Drought Index (RDI), Soil Moisture Agricultural Drought Index (SMADI), and Streamflow Drought Index (SDI). The enhanced index, encompassing all bands, serves as a predictor for classification and regression tree (CART), support vector machine (SVM), and random forest (RF) machine learning models, further improving the three indices. CART demonstrated the highest accuracy and error minimization across all drought categories, with root mean square error (RMSE) and mean absolute error (MAE) values between 0 and 0.4. RF ranked second, while SVM, though less reliable, achieved values below 0.7. The results show persistent drought in the Sahel, North Africa, and southwestern Africa, with meteorological drought affecting 30% of Africa, agricultural drought affecting 22%, and hydrological drought affecting 21%. [ABSTRACT FROM AUTHOR]

This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students' performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Modern Medicine & Health is the property of Journal of Modern Medicine & Health and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Inflammation contributes to the pathogenesis of type 2 diabetes (T2DM). This study sought to document how the systemic biomarkers of inflammation varied based on food choices among patients with T2DM. This cross-sectional study enrolled ambulatory patients with T2DM. Demographic and clinical information was collected. Five drops of fingerstick blood were collected using an absorbent paper device (HemaSpot HFR). C-reactive protein (CRP), serum amyloid A protein (SAA), and fibrinogen were measured using a Luminex assay. Patient-generated 7-day food diaries were analyzed using a validated food processor software. Data were analyzed by Pearson's correlation tests, linear regression and logistic regression with the significance level set at 0.05. Among the 71 participants, 43 (60.6%) were females. The average age and duration of T2DM were 64.1 ± 10.3 and 15.8 ± 9.1 years, respectively. In a simple linear regression run with selected micronutrients, iron [F (1, 53) = 5.319, p < 0.05, adj. R2 = 0.074] significantly predicted plasma CRP. This significance was lost with multiple linear regressions including age, gender, BMI, T2DM duration, T2DM complications, glycohemoglobin A1c (HbA1c) and other micronutrients. The average intake of most micronutrients by the participants was below the recommended daily intake. A higher intake of iron-rich foods was associated with higher levels of systemic inflammation in a simple linear regression model, but the association was not present after adjusting for patient factors like age, gender, BMI and T2DM-related variables. This relationship needs to be explored further given the key role of inflammation in the pathogenesis of T2DM and its associated complications. [ABSTRACT FROM AUTHOR]