bluuebunny/crossref_metadata_2025 huggingface dataset:
Vagal nerve biofeedback intervention for improving health outcomes among Ukrainian forced migrants: A proof-of-concept study
Authors: Gidron Yori , Levy Einav , Ryder Chen Hanna , Shaul Sharon , Sirota Rita , Atias Drorit
Published: September 2024

Background - The ongoing conflict in Ukraine has forced numerous migrants into neighboring countries, many suffering from pre-existing or newly acquired physical and mental health conditions. Addressing these complex challenges in humanitarian settings requires innovative, evidence-based interventions that are cost-effective and easy to administer. Drawing upon research highlighting the vagus nerve's role in regulating well-being, we hypothesized that vagal nerve activation could offer a promising therapeutic approach. Method- We conducted a proof-of-concept study in which 21 Ukrainian forced migrants were trained in a biofeedback-guided paced breathing intervention designed to stimulate the vagus nerve and promote self-regulation of stress response systems. Changes in pain perception, perceived stress, blood pressure, and heart rate variability (an established marker of vagal tone) were assessed before and after the vagal breathing intervention using t-test. These multi-dimensional outcome measures were chosen to capture both subjective and objective physical and mental well-being indicators. Correlations were examined at baseline. Results- Statistically significant improvements were observed in all measures except systolic blood pressure, providing preliminary evidence for the efficacy of vagal nerve activation in alleviating stress-related health symptoms. Conclusions- This study demonstrates the feasibility and therapeutic potential of a vagal nerve-activating intervention in a humanitarian setting. These findings warrant replication in larger, controlled trials. If substantiated, this low-cost, scalable intervention could help mitigate health burdens among forced migrant populations worldwide. This intervention is relevant to south global countries which face an epidemiological shift from infectious to non-communicable diseases.
Genetic analysis and association detection of agronomic traits in maize genotypes
Authors: Hosseini Seyyed Mohammad Sadegh , Shiri Mohammadreza , Mostafavi Khodadad , Mohammadi Abdollah , Miri Seyyed Mehdi
Published: September 2024

In maize breeding, enhancing yield through genetic insights is crucial yet challenged by the complex interplay of agronomic traits. This study utilized a diallel mating design involving nine advanced early maize lines to dissect the genetic architecture underlying key agronomic traits and their impact on yield. Over two consecutive years (2018–2019 and 2019–2020), 36 hybrids derived from these lines were grown across two locations, Karaj, Alborz, Iran and Kermanshah (2019–2020), Iran, in a randomized complete block design with three replications. The study aimed to evaluate the general combining ability of the parental lines and the specific combining ability of their hybrids, alongside the mutual influences of critical traits on yield. The analysis of variance revealed significant differences at 1% and 5% probability levels among the hybrids for all traits studied, indicating substantial genetic variability. Diallel analysis suggested that both additive and non-additive genetic effects are crucial in controlling traits such as kernel yield, kernel rows, kernel in row, 1000 kernel weight, plant height, ear height, kernel moisture, and ear wood. Additive effects, as indicated by the Baker's ratio, predominated for these traits. Among the parental lines, KE 79017/3211 demonstrated the strongest general combining ability for kernel yield. Hybrids K 1264/5 − 1×KE 76009/311, KE 77005/2×KE 75016/321, KE 77008/1×KE 77004/1, and KE 77008/1×KE 79017/3211 exhibited significant and positive specific combining ability effects for kernel yield, highlighting their potential in yield-enhancing breeding programs. Correlation analysis showed no significant association between KY*KIN with the KY*KW. However, there were weak positive correlations between KY*KR with other traits such as KY*PH, KY*KR, and KY*EH. The biplot analyses identified genotypes 4, 12, and 31 as superior across various trait combinations. Genotype 12 emerged as notably high-yielding based on average tester coordinates. Using the multi-trait stability index and imposing a selection pressure of 25%, genotype 10 was ranked highest, followed by genotypes 9, 13, 11, 1, 2, and 16, which were considered the most stable and ideal across all evaluated traits. This comprehensive study underscores the importance of both general combining ability and specific combining ability in maize breeding and highlights specific genotypes and hybrid combinations with promising traits for yield enhancement.
Research on the Dynamic Characteristics and Disaster Mechanism of Anti-Slide Short Pile-Supported Slopes
Authors: Lai Jie , Liu Yun , Liu Yuan
Published: September 2024

The terrain in Southwest China is predominantly mountainous, with many slopes requiring reinforcement. In addition, this region experiences frequent rainfall and earthquakes that can easily trigger landslides. Therefore, studies on slope reinforcement are crucial. This paper presents a case study of a slope stabilisation project along the Yunyang–Fengjie Expressway in Chongqing, focusing on the seismic performance of anti-slide short pile-supported slopes. The failure evolution process of an anti-slide short pile-supported slope is obtained by inputting a Wenchuan earthquake wave and continuously increasing the amplitude of the seismic wave. The experimental results indicate that the failure mode of the slope consists of tensile and shear components with tensile cracks at the crest and shear cracks in the remaining fractured sections. The anti-slide short piles effectively alter the downward extension direction of the shear cracks. As the shear crack approaches the station of the anti-slide piles, its development path along the weak structural plane changes, extending towards the top of the anti-slide piles and ultimately leading to cap failure. The bending moment of the anti-slide piles under seismic action primarily follow a parabolic shape, with the maximum value located near the weak structural plane.
Multi-View Stereo of UAV Remote Sensing Images Based on Adaptive Propagation with Multi-Region Refinement
Authors: Fu Haohai , Nie Zixuan , Pan Xin
Published: September 2024

Based on computer vision and image processing techniques, 3D reconstruction of targets such as the ground surface or buildings from unmanned aerial vehicle (UAV) images can be realized, in which the dense reconstruction for weak texture regions is very important. In this paper, we propose a novel multi-view stereo method for UAV remote sensing images based on adaptive propagation and multi-region refinement, named APMRR-MVS. Firstly, in the propagation step, we propose an adaptive propagation strategy based on a checkerboard grid, which expands the distal sampling region by continuously selecting pixels with better hypotheses, that can explore the distal end more flexibly and improve the quality of sampling hypotheses for the pixels within the same view. Secondly, in the refinement step, we propose a multi-region refinement strategy, which can improve the efficiency of exploring the solution space by arranging several regions independently and reduce the possibility of the target pixel hypothesis being trapped in a local optimum. It is demonstrated on DTU and Blended MVS datasets that our method has better performance in the reconstruction of weakly textured regions, in addition to preserving specific texture details to a greater extent and reducing the erroneous estimation of spatial points.
Study of the Cognitive and behavior in the learning process to support learning in order to improve the quality of learning outcomes
Authors: Listiadi Agung
Published: September 2024

Based on the way the human brain and the cortex work, something that can be related to learning is that when the human brain receives information or lessons, it must be in a calm and relaxed state so that the cortex can take over the information and store it in cognitive memory. If you receive information in the form of MyOB computer accounting application material while under stress, the information will be taken over by the reptile's brain, resulting in the information not being processed into cognitive. The brain is the control center for accounting learning activities that take place in the human body. The results of research in learning using brain accounting computer applications have a function in regulating all mental activities, namely those related to intelligence, memory, awareness and consideration. Cells in the brain that will play a role in processing learning activities in an individual. The more often someone stimulates their brain to think, the more cognitive will develop. However, it is not uncommon for inconsistencies to occur in the learning process in the brain so that what is perceived in the brain is different from the attitude or behavior carried out.
A high sensitivity D-shaped microgroove photonic crystal fiber gas sensor based on surface plasmon resonance
Authors: Gan Xiaoyong , Xu Hongzhi , Jiang Shubo
Published: September 2024

A D-shaped open-ring microgroove photonic crystal fiber (PCF) sensor based on surface plasmon resonance (SPR) is proposed for the simultaneous measurement of methane gas concentration and temperature. The sensitive coatings are polysiloxane-doped cryptane E and polydimethylsiloxane (PDMS), and the designed microgroove structure with out-of-hole coating is expected to improve the sensitivity. The finite element method (FEM) is employed to analyze the data numerically, and the results demonstrated that the average sensitivities could reach − 96.57 nm/% for methane concentration in the range of 0.5 ~ 3.0% and − 6.86 nm/°C for methane temperature in the range of 0 ~ 50℃. Both measurement channels exhibited excellent linearity, making them highly valuable for environmental monitoring and other applications.
Adoption of twin transition technologies in developing countries: a bivariate analysis
Authors: Menéndez María de las Mercedes , Lavopa Alejandro
Published: September 2024

Industrial firms worldwide face two major technological challenges: digitalizing their processes and increasing the environmental sustainability of their production. Both challenges can be addressed separately or in an integrated way. This paper empirically examines the extent to which the adoption of digital and green technologies is done in a complementary way in developing countries’ industrial firms and identify certain characteristics that tend to be present when such complementarity takes place. The analysis is based on a firm-level survey conducted by UNIDO in Ghana, Thailand, and Vietnam. The results suggest that adopting green and digital technologies are interrelated and complementary. Larger firms, more innovative firms and firms participating in global value chains are more likely to jointly adopt green and digital technologies.
JEL: 014, 033, O25, Q55
Ammonium nutrition enhances rhizosphere mobilization and uptake of silicon in white lupin grown in low phosphorus soil
Authors: Kostic Ljiljana , Pavlovic Jelena , Bosnic Predrag , Kostic Igor , Trailovic Maja , Dubljanin Tijana , Nikolic Miroslav
Published: September 2024

Background and Aims
While nitrogen (N) supply can enhance plant silicon (Si) accumulation, the mechanisms by which different forms of N affect Si mobilization in the rhizosphere are not well understood. This study aims to elucidate how pH changes induced by ammonium (NH4+) and nitrate (NO3−) affect Si availability in the rhizosphere, especially under low phosphorus (P) conditions.
Methods
White lupin (Lupinus albus) plants were grown in non-fertilized low-P soil, supplied with a low dose of N, either as NH4+ or NO3−, with or without supply of monosilicic acid. We measured Si levels in various rhizosphere soil pools, along with different plant and rhizosphere soil parameters.
Results
The addition of NH4+ significantly lowered rhizosphere pH and decreased both Si adsorbed to pedogenic Fe/Mn oxides and amorphous phytogenic Si, resulting in higher concentrations of plant available Si in the white lupin rhizosphere. This led to greater Si uptake and improved plant growth compared to both the –N and + NO3− treatments. The supply of Si further enhanced these effects, with NH4+ showing a consistently different pattern of influence compared to NO3−. Additionally, –N white lupin plants accumulated more P than those treated with N, while Si supply significantly improved P acquisition across all treatments.
Conclusions
Our study demonstrates that rhizosphere acidification induced by NH4+ nutrition can significantly enhance Si mobilization from the rhizosphere soil in the absence of Si supply and reduce Si adsorption when Si is applied. These findings may have practical implications for improving both Si mobilization in the rhizosphere and the effectiveness of Si fertilizers.
Further Analysis of "Lattice-based key agreement protocol under ring-LWE problem for IoT-enabled smart devices"
Authors: Saeidi Mohammad Reza , Mala Hamid
Published: September 2024

The increasing interconnectedness of devices via the Internet of Things (IoT) highlights the growing importance of addressing IoT security issues. Authenticated Key Agreement (AKA) protocols ensure secure communication between IoT devices, users, and central servers. Preserving anonymity in AKA protocols is especially crucial given privacy issues and user preferences. The emergence of Shor's algorithm in 1994 and recent advancements in quantum computing have posed a threat to the security of classical AKA schemes relying on integer factorization, discrete logarithm, and other number theory problems. Consequently, researchers have turned their attention to developing quantum-resistant AKA schemes. In this context, Rana and Mishra introduced a lattice-based key agreement protocol under ring-LWE problem for IoT-enabled smart devices. However, a thorough security assessment uncovered vulnerabilities in their protocol, specifically susceptibility to replay and Key Compromise Impersonation (KCI) attacks. Furthermore, their approach reveals potential weaknesses in user impersonation if the user's identity is compromised.
Noise Mapping, Identification of Hot Spots, and Mitigation Plan for Control of Noise Pollution for Ahmedabad
Authors: Singh Bhupendra P. , Kalra Chahat , Akthar Nasim
Published: September 2024

Worldwide, urban centres are facing challenges due to road traffic, leading to increased noise pollution that impact residents' quality of life and the environment. This study presents a comprehensive analysis of noise pollution levels in Ahmedabad, India, across different urban zones (residential, commercial, and industrial) and times of day, utilizing QGIS software to generate noise heat maps. The noise level was measured at 133 locations in different areas of the city. The monitoring was performed using a digital sound level meter and a noise map was produced using the recorded equivalent energy noise level values. The mean noise levels were recorded consistently high, with daytime levels averaging 79.88 dB, nighttime levels at 74.76 dB, and late-night levels at 61.47 dB, respectively. Industrial areas recorded the highest noise levels, followed by commercial and residential zones. A comparative analysis with similar studies across India and globally indicates that Ahmedabad’s noise pollution exceeded the levels reported in other major urban centers. The correlation analysis calculated a strong correlation between noise levels in different zones, particularly between residential and industrial areas was observed, suggesting the widespread impact of noise pollution across the city. The findings underscore the urgent need for targeted noise management strategies, including stricter enforcement of noise regulations, urban planning that minimizes noise exposure in residential areas, and the implementation of noise barriers and soundproofing measures in industrial and commercial zones. The study highlights the critical role of spatial analysis tools like QGIS in visualizing noise distribution and informing public policy.
Variations and trends in the stand structure and structural complexity of selected mangrove stand types in the Philippines
Authors: Gianan Eunice Lois D. , Yñiguez Aletta T. , Salmo Severino G.
Published: September 2024

The declining mangrove forest cover in the Philippines led to increased attention towards their conservation and restoration, driven by growing awareness of their importance and the ecosystem services they provide. Yet, time-series information on forest structure, growth, and development of naturally occurring and planting efforts is still lacking. The conventional "complexity index" (Ic) rapidly characterizes forest structure using species richness and mean values (of only the top three tallest trees ≥ 10 cm in diameter for height, basal area, and density). However, it does not capture unique mangrove characteristics like canopy diameter and spatiotemporal growth dynamics. Mangroves often have spread-out canopies with varying tree diameters in response to environmental conditions. This study used a modified complexity index (Icm) that integrates foliage and all tree parameters' mean values to assess mangrove structural complexity. We assessed and compared Ic and Icm (at five to seven-month intervals) on selected mangrove stands in the Philippines composed of the natural and planted stands of Bani, Pangasinan; the natural stands of Calapan, Oriental Mindoro; and the natural stands of Las Piñas-Parañaque Critical Habitat and Ecotourism Area (LPPCHEA), Metro Manila. Results showed that LPPCHEA and Calapan have intact stand structural features, while Bani stands are either in their secondary forest growth or disturbed stage. The LPPCHEA had the highest structural complexity regardless of the method used, followed by Calapan, Bani-natural, and Bani-planted. There was high variability across stands and time, but the Icm was 4x higher than the Ic and is more sensitive to detecting growth variability between stand types and sampling periods. Overall, the selected Philippine mangrove stand types showed higher structural complexity than those from neighboring countries. This study suggests that mono-species stands are less structurally complex than multi-species stands. Our results imply the need for (1) better assessment of the stability of structural complexity in mature mangroves, (2) better tracking of the structural complexity (in a chronosequence) in restored mangroves, and (3) better detection of the impacts of disturbance (e.g., typhoons) and post-disturbance recovery of structural complexity.
An Incremental Entity Resolution Approach based on Deep Metric Learning
Authors: Xu Yaoli , Li Chenglin , Li Pu , Duan Xiaoyu , Wang Yanyan , Qi Wanhua , Guo Yanbu , Li Yuhua , Shi xiayang , Zhang Suzhi
Published: September 2024

Entity resolution (ER), a crucial component of data fusion, aims to identify and aggregate disparate entity descriptions describing the same real-world object. While existing approaches have achieved good performance in ER tasks, they fall short in scenarios with dynamic data sources. The inherent dynamism of these data sources requires ER models to progressively resolve entities with incoming data while maintaining high accuracy. To address such issue, we propose a novel dynamic data processing framework that leverages the matching model from the previous time step to parse incoming incremental data, and implement an Incremental Entity Resolution Approach based on Deep Metric Learning (IER-DML), developed to handle dynamic data and enhance matching capability. an uncertainty metric-based data augmentation mechanism is proposed to mitigate calibration issues caused by suboptimal training data, and to generate high-confidence labeled entity pairs. These labeled data are then used for the iterative optimization of IER-DML, enhancing ER capabilities of IER-DML. For modeling dynamic changes in the distributions of clustering structure among entity pairs, we propose a deep metric learning-based multi-center matcher. This matcher learns metric relationships between the deep semantic features of entity pairs, and models intrinsic structures using "soft centers". These centers capture multiple information clusters among entities, and flexibly represent clustering structures. By integrating deep metric learning with a multi-center strategy, it can dynamically adjust classification boundaries and metric standards in complex scenarios. Comprehensive experiments demonstrate that our approach outperforms state-of-the-art methods across all four dynamic benchmark datasets.
Improved photocatalytic activity of Ce-doped NiO nanoparticles against methylene blue and rhodamine B dyes
Authors: Kistan A. , Priya G. Hari Hara , Raj S. Jagan , Mayavan L.
Published: September 2024

The current study intended to investigate the photocatalytic efficiency of synthesised pure and Ce-doped NiO nanoparticles for the degradation of organic contaminants, particularly Methylene blue and Rhodamine B dyes. Initially, the co precipitation method was used to synthesize these nanoparticles.The size of the crystallites was determined using X-ray diffraction analysis,which also verified that the prepared nanoparticles included a single cubic phase. In addition, several characterisation techniques were used to assess the band gap energy, functional group, phase identification, shape, surface area, and oxidation states of the synthesised nanoparticles' elements. These techniques included UV-vis, FT-IR,FESEM, TEM, BET, and XPS. It was demonstrated that Ce-doped NiO nanoparticles had a 98% and 79% degradation efficiency for MB and RhB dyes respectively.
Modulation of noradrenergic signalling reverses stress-induced changes in the hippocampus: involvement of orexinergic systems
Authors: Sarfi Masoumeh , Salmani Mahmoud Elahdadi , Lashkarbolouki Taghi , Goudarzi Iran
Published: September 2024

Stress can be beneficial for adapting to dangerous situations in the short term, but can be damaging in the long term, especially in the hippocampus. The noradrenergic and orexinergic systems play important roles in the stress response. This study investigated the effect of noradrenergic activation on the changes induced by chronic stress in the hippocampus and the involvement of orexinergic modulation in this process. Twenty male Wistar rats were subjected to chronic stress, acute stress, administration of α2 receptor antagonist yohimbine, or their combinations. Plasma corticosterone (CORT) was measured using a fluorometric method. Expression of prepro-orexin (prepro-OX), orexin receptor-1 (OXr1), and glucocorticoid receptor (GR) was analyzed using RT-PCR. Neuronal populations were quantified using Nissl staining. Chronic and acute stress increased plasma CORT levels, gene expression of prepro-OX, OXr1, and GR, while decreasing neuronal number, with the chronic stress having a more pronounced effect. The stress- induced and Yohimbine treated groups demonstrated a higher level of plasma CORT. Chronic stress substantially increased prepro-OX expression, while yohimbine recovered the profile in chronically stressed animals. OXr1 expression was profoundly higher in the chronic stress group, while chronic stress combined with yohimbine decreased that profile. Similarly, chronic stress upregulated glucocorticoid receptor, while chronic stress combined with yohimbine reversed the effect. Conversely, the chronic stress reduced hippocampal neuronal populations and chronic stress combined with yohimbine partially compensated the neuronal numbers compared to chronic stress alone. The results suggest that noradrenergic signalling can reverse the chronic stress-induced impairments in prepro-OX, OXr1, GR, and neuronal population.
Escape of Kdm6a from X chromosome is detrimental to ischemic brains via IRF5 signaling
Authors: Ngwa Conelius , Misrani Afzal , Manyam Kanaka Valli , Xu Yan , Qi Shaohua , Sharmeen Romana , McCullough Louise , Liu Fudong
Published: September 2024

The role of chromatin biology and epigenetics in disease progression is gaining increasing recognition. Genes that escape X chromosome inactivation (XCI) can impact neuroinflammation through epigenetic mechanisms. Our prior research has suggested that the X escapee genes Kdm6a and Kdm5c are involved in microglial activation after stroke in aged mice. However, the underlying mechanisms remain unclear. We hypothesized that Kdm6a/5c demethylate H3K27Me3/H3K4Me3 in microglia respectively, and mediate the transcription of interferon regulatory factor 5 (IRF5) and IRF4, leading to microglial pro-inflammatory responses and exacerbated stroke injury. Aged (17–20 months) Kdm6a/5c microglial conditional knockout (CKO) female mice (one allele of the gene) were subjected to a 60-min middle cerebral artery occlusion (MCAO). Gene floxed females (two alleles) and males (one allele) were included as controls. Infarct volume and behavioral deficits were quantified 3 days after stroke. Immune responses including microglial activation and infiltration of peripheral leukocytes in the ischemic brain were assessed by flow cytometry. Epigenetic modification of IRF5/4 by Kdm6a/5c were analyzed by CUT&RUN assay. The demethylation of H3K27Me3 by kdm6a increased IRF5 transcription; meanwhile Kdm5c demethylated H3K4Me3 to repress IRF5. Both Kdm6afl/fl and Kdm5cfl/fl mice had worse stroke outcomes compared to fl/y and CKO mice. Gene floxed females showed more robust expression of CD68 in microglia, elevated brain and plasma levels of IL-1β or TNF-α, after stroke. We concluded that IRF5 signaling plays a critical role in mediating the deleterious effect of Kdm6a; whereas Kdm5c’s effect is independent of IRF5.
Air quality in the U.S. during the 2023 wildfire season
Authors: Dinavahi Siddhi , Archer Cristina
Published: September 2024

During the summer of 2023, Canada was devastated by a series of large and uncontrolled wildfires that set the unprecedented record of over 40 million acres burned and over 120,000 people evacuated. The smoke from the Canadian wildfires reached the U.S. and negatively affected the air quality in several states. The goal of this paper is to document the U.S. air quality in 2023 within the historical and regulatory contexts; more specifically, to compare the concentrations of fine particulate matter (PM2.5) and tropospheric ozone (O3), assessed using statistics as close as possible to those recommended for comparison against the National Ambient Air Quality Standards (NAAQS), in 2023 against those in the past 20 years. We report that the year 2023 was an anomaly, meaning that the well-established trend of decreasing concentrations and improving air quality was suddenly reversed in 2023, with national-median PM2.5 and O3 concentrations exceeding \SI{33}{\micro\gram\per\cubic\metre} and 75 parts per billion (ppb), respectively. These values had not been reached since 2007 and 2012 for PM2.5 and O3, respectively. Notably, the 75th percentile for PM2.5 was the highest and the 90th percentiles was the third highest in 2023 since 2000. Out of 50 states (plus the District of Columbia), 34 and 49 experienced summer-average concentrations in 2023 that were higher than those in 2013--2022 for PM2.5 and O3, respectively. For PM2.5, the states that experienced the highest median concentrations were: California, Alaska, Oregon, and Idaho with 95, 64, 52, and \SI{50}{\micro\gram\per\cubic\metre}; for O3, they were: California, Maryland, Texas, Connecticut, and Pennsylvania with 114, 88, 88, 86, and 85 ppb. In summary, in the year 2023 the air quality in the U.S. was the worst of the past 15 and 10 years in terms of PM2.5 and O3, respectively.
A Selection Method of Agricultural Product Price Forecasting Model based on Time Series Image Encoding
Authors: Jiang Wentao , Wang Quan , Li Hongbo
Published: September 2024

The forecasting and early warning of agricultural product price time series is an important task in the field of stream data event analysis and agricultural data mining. The existing forecasting methods of agricultural product price time series have the problems of low precision and low efficiency. To solve these problems, we propose a forecasting model selection method based on time-series image encoding technology. Specifically, we use Gramian Angle fields (GAFs), Markov transition fields (MTF), and Recurrence Plots (RP) to encode time series to images and retain all information about the event. Then, we propose a information fusion feature augmentation method (IFFA) to combine time series images. The time series combined images(TSCI) are input into the CNN forecasting model selection classifier. Finally, we introduce the idea of transfer learning to optimize the selection method of agricultural product price time series forecasting model. This idea can effectively reduce the overfitting phenomenon caused by insufficient data or unbalanced samples in real data set. Experimental results show that, compared with the existing methods, our IFFA-TSCI-CNN time series classification method has great advantages in efficiency and accuracy.
Burnout Prevalence Among Postgraduate Medical Trainees in the Eastern Health Cluster, Saudi Arabia: A Cross-Sectional Study
Authors: Alzain Nasser M. , Almsned Fahad , AlBunaian Nada A. , Alotaibi Fahad , AlBeshir Mohammad S. , Yosef Mostafa , Ali Moatazbellah I.
Published: September 2024

Background: Burnout among medical professionals is recognized globally, with significant implications for mental health and patient care. This study evaluates the prevalence of burnout among postgraduate medical trainees in the Eastern Health Cluster of Saudi Arabia, aiming to fill the research gap in this region, where specific cultural and training factors may contribute uniquely to burnout.
Methods: A cross-sectional study was conducted from March to June 2024 across various healthcare facilities within the Eastern Health Cluster. A total of 422 postgraduate medical trainees participated, providing data through a Google Form survey, which included the Maslach Burnout Inventory-Human Services Survey for Medical Personnel (MBI-HSS(MP)).
Results: The study found a high prevalence of burnout symptoms among trainees: 30.3% reported high levels of emotional exhaustion, 24.9% reported high levels of depersonalization, and a significant 86.3% felt low levels of personal accomplishment. Burnout rates varied significantly across specialties and training levels, suggesting targeted interventions were needed.
Conclusions: The high prevalence of burnout underscores the critical need for tailored preventive strategies and interventions within the training environments to effectively address and mitigate burnout among medical trainees. Future research should explore longitudinal interventions to reduce burnout and enhance trainee well-being.
Assessment of Apoptotic Effect of Carbamazepine on Tongue Squamous Cell Carcinoma Cell Line (in vitro Study)
Authors: Bahaa Rahaf Abdel Wahab , Abdellatif Mohsen Kazem , Elhalawany Mai , Saleh Heba Ahmed
Published: September 2024

Background
This study investigates the apoptotic and anti-cancerous effects of carbamazepine on the tongue squamous cell carcinoma (HNO-97) cell line.
Methods
The research compares carbamazepine-treated HNO-97 cells with untreated HNO-97 cells as a control. The Hematoxylin and Eosin staining method was used to observe cellular morphological changes. MTT assay was performed to assess cell viability. ELISA technique was done to measure reactive oxygen species and p53 upregulated modulator for investigating the drug apoptotic and potential anticancer effect.
Results
Thestudy findings indicate that carbamazepine reduces cell viability in a concentration-dependent manner via induction of apoptosis by increase production of reactive oxygen species and p53 upregulated modulator as they led finally to induction of apoptosis in oral cancer cells, which were confirmed by ELISA. Prominent cellular apoptotic signs were detected microscopically for the oral cancer cells in both early and late stages of apoptosis.
Conclusion
The study findings suggested the potent apoptotic and anti-cancerous effect of carbamazepine on oral squamous cell carcinoma cells through the activation of the caspase apoptotic pathway. This drug may serve as a promising adjuvant therapy for oral cancers.
The Diffusion Interaction Region of Dayside Magnetic Reconnection
Authors: Beedle Jason , Gershman Daniel , Shuster Jason , Uritsky Vadim , Phan Tai , Norgren Cecilia , Burch James , Genestreti Kevin , Heuer Steven , Giles Barbara , Torbert Roy
Published: September 2024

Magnetic reconnection is a universal process that allows for the transfer and release of energy previously stored in a magnetic field configuration. At Earth, this process can occur in the boundary layer between Earth's magnetic field and the solar wind, called the magnetopause. This allows the magnetopause to act as the primary ``entry gate" for the solar wind's energy into the Earth’s magnetosphere and upper atmosphere. Along the magnetopause, magnetic reconnection is triggered inside diffusion regions: an electron diffusion region (EDR) embedded inside a larger ion diffusion region (IDR). Inside these diffusion regions, plasma decouples from the magnetic field, allowing the release of magnetic energy into the plasma. We identify a hitherto undefined region in the diffusion regions, which results from the interaction of demagnetized electrons in the EDR's reconnection current sheet and remagnetizing electrons in the IDR. We call this region the Diffusion Interaction Region (DIR). Using in situ MMS plasma data and 2.5D particle-in-cell simulations, we isolate its defining features, which includes a significant parallel current structure and regions of parallel energy conversion. Defining this important and underexplored region of magnetic reconnection helps us determine how the local diffusion regions may couple with the large-scale current structures within which they are embedded.
Interplay of In-O bond activation and stabilization for a high-performance CO2 hydrogenation to methanol
Authors: Huang Yanqiang , Zhao Fengwang , Zeng Ting , Yang Xiaoli , Wang Qi , Zhao Yang , Zhou Jing , Yang Xiaofeng , Lin Chunfu , Ben Haoxi , Zhai Nianxue , Yuan Youzhu , Li Xingyun , Gu Xiang-Kui , Zhao Xiusong
Published: September 2024

Precise manipulation of catalyst structure at atomic level holds the key to regulating the catalyst performance. Herein, we show that single Cr atom doping in In2O3 can modulate the In-O bond with significantly improved performance in CO2 hydrogenation to methanol. A “distance effect” concept is proposed to interpret the function of Cr doping, wherein Cr stimulates the directly coordinated oxygen to form Cr and In confined oxygen vacancy (Ov) active sites for the enhancement of CO2 activation and lowering the energy barrier for the HCOO* to H2COO* transformation (the rate-determining step). In addition, Cr assists in maintaining the structure stability and inhibits the over-reduction of In2O3 through the tethering of nearby oxygen atoms. The Cr-In2O3 catalyst exhibits a CO2 conversion of 9.4% and CH3OH selectivity of 92.0% under the reaction conditions of 250°C, 5 MPa, and 3000 mL·gcat−1·h− 1, along with a remarkable stability over 500 h on-stream testing. This study demonstrates a new paradigm for the modulation of In-O bonds in In2O3-based catalysts to break the trade-off between the reactivity and stability of the CO2 hydrogenation reaction.
Bogoliubov phonons in a Bose-Einstein condensate from the one-loop perturbative renormalization group
Authors: Rasch Niklas , Mikheev Aleksandr , Gasenzer Thomas
Published: September 2024

Wilson’s renormalization-group approach to the weakly-interacting single-component Bose gas is discussed within the symmetry-broken, condensate phase. Extending upon the work by Bijlsma and Stoof, wave-function renormalization of the temporal derivative contributions to the effective action is included in order to capture sound-like quasiparticle excitations with wave lengths larger than the healing-length scale. By means of a suitable rescaling scheme we achieve convergence of the coupling flows, which serve as a means to determine the condensate depletion in accordance with Bogoliubov theory, as well as the interaction-induced shift of the critical temperature.
FAMILY CONFLICTS AND PSYCHOLOGICAL WELLBEING OF CHILDREN A CASE OF KAYONZA DISTRICT IN RWANDA
Authors:   Masters Degree Candidate, Departmentof Psychology, Mount Kenya University, P.O.Box:5826 Kigali-Rwanda., Iragena Joselyne , Safari Ernest ,   Lecturer, Department of Hospitality, Mount Kigali University, P.O.Box:5826 Kigali-Rwanda., Monchama Monica ,   Lecturer, Department of Public Health, Mount Kigali University, P.O.Box:5826 Kigali-Rwanda.
Published: September 2024

The present study was carried out to examine the impacts of family conflict on the psychological well-being of children in Kayonza district, Eastern Province of Rwanda. It was based on three specific objectives: firstly, it assesses the experience of family conflict among children, secondly analyzes the impact of family conflict on psychological well-being of children and thirdly, establishes the relationship between family conflict and psychological well-being of children in Kayonza district of Rwanda. The research sought to provide valuable knowledge that could guide policymaking and interventions geared toward enhancing the well-being of children experiencing familial conflicts. The study employed a mixed of quantitative and qualitative research design. The target population comprised 750 participants from 258 families in Kayonza district from Mukarange sector. A sample of families who participated in the study were purposively selected and the sample size was calculated from the target population using Slovins formula. Contrary, children were randomly sampled from two secondary schools in Mukarange sectorin Kayonza District. A five-point Likert scale questionnaire ranked from strongly agree (5) to strongly disagree (1) and the interview guide were used as the data collection instruments. The result findings indicated Pearson Correlation of .704** tested at a significant level of 0.01. This implies that Family conflicts affect psychological wellbeing of children. There is a need to develop and find programs that offer early intervention and support for families experiencing conflicts. These programs can provide counseling, mediation, and education on conflict resolution to help mitigate the negative effects on children.
A broadband variable fluid damper with frequency selective valves for spacecraft micro-vibration isolation
Authors: Wang Wenlin , Liu Yong , Chen Ruxing , Ding Yang , Li Haoyu
Published: September 2024

In order to improve the vibration isolation performance of current three-parameter damper using bellow and fluid damping, a concept of fluid damper with variable damping and secondary variable stiffness was proposed, a prototype has been designed with several frequency selective valves to adjust the damping characteristics at specific frequency bands, especially that in the low-frequency range. Followed mathematical modelling and simulation of the damper have been carried out, the results show that the designed damper has obtained excellent damping characteristics both in the high- and low-frequency ranges, the micro-vibration isolation efficiency of the damper under realistic excitation of a Control Moment Gyroscope used in a manned spacecraft reaches a significant 71.03 %.The proposed damper concept and designed prototype have laid the foundation for further testing and optimization of high-end isolators for modern spacecrafts.
Trends of Transfusion Transmissible Infections among Predominantly Male Blood Donors in Rawalpindi: A Five-Year Retrospective Study
Authors: Rathore Muhammad Ali , Abbasi Anum Javed , Khuskh Imdad Ali , Ghafoor Tahir , Sabir Nagis , Rathore Mommana Ali
Published: September 2024

Background: Blood transfusion carry an inherent risk of transfusion transmissible infections (TTIs). Monitoring trends and prevalence of TTIs may reveal crucial information regarding safety of blood supply and donor recruitment strategies.
Objective: To evaluate the critical indicators of blood safety including seroprevalence and temporal trends of hepatitis B virus, hepatitis C virus, human immunodeficiency virus and syphilis among blood donors in Rawalpindi.
Methods: The study was conducted in Armed Forces Institute of Transfusion. The data of 2,78,774 blood donors from 2016-2020 was retrospectively analyzed. Donors’ demographics, type of donation, donation frequency and serological results of TTIs were retrieved and analyzed.
Results: Of the 2,78,774 blood donations, 3.6% (n=10,091) blood donors were positive for one or more TTIs. Among these, the most prevalent was HCV (1.5%) and the least prevalent was HIV (0.08%). The seroprevalence of TTIs illustrated a declining trend from 4.2 to 3.4% over the period under study. Regarding trend of each TTI with respect to year, HBV, HCV and HIV depicted fluctuating trend while decreasing trend was observed for syphilis from 2016-2020.
Conclusion: The seroprevalence of four major TTIs was evaluated between the year 2016 and 2020. A decreasing trend in the seroprevalence from 4.2 % to 3.6 % was observed over a five-year period. The HCV remained highest throughout the period whereas a gradual increase in HIV and a decrease in syphilis was observed.
Advancing Pharmaceutical Science with Artificial Neural Networks: A Review on Optimizing Drug Delivery Systems Formulation
Authors: Salarpour Simin , Salarpour Soodeh , Dogaheh Mehdi Ansari
Published: September 2024

Drug Delivery Systems (DDS) have been developed to address the challenges associated with traditional
drug delivery methods. These DDS aim to improve drug administration, enhance patient compliance, reduce
side effects, and optimize target therapy. To achieve these goals, it is crucial to design DDS with optimal
performance characteristics. The final properties of a DDS are determined by several factors that go into formulating
a pharmaceutical preparation. Thus, optimizing these factors can lead to the ideal DDS formulation.
Artificial Neural Networks (ANN) are computational models that mimic the function of biological neurons
and neural networks and perform mathematical operations on inputs to generate outputs. ANN is widely used
in medical sciences for modeling disease diagnosis and treatment, dose adjustment in combination therapy,
medical education, and other fields. In the pharmaceutical sciences, ANN has gained significant attention for
designing and optimizing pharmaceutical formulations. This article reviews the use of ANN in the design and
optimization of pharmaceutical formulations, specifically DDS. Since DDS is highly diverse, different factors
are examined for each type of DDS. These factors are considered independent and dependent parameters for
each ANN model, and various examples are provided. By utilizing ANN, it is possible to establish the relationship
between the formulation factors and the resulting DDS characteristics, ultimately leading to the development
of optimized DDS.
Copper Dyshomeostasis and Diabetic Complications: Chelation Strategies for Management
Authors: Subramaniam Jahnavi , Aditi Aarya , Arumugam Kishore , Sri Sathya , Bharathidevi Subramaniam Rajesh , Ramkumar Kunka Mohanram
Published: September 2024

Cuproptosis, an emerging concept in the field of diabetes research, presents a novel and
promising perspective for the effective management of diabetes mellitus and its associated complications.
Diabetes, characterized by chronic hyperglycemia, poses a substantial global health burden,
with an increasing prevalence worldwide. Despite significant progress in our understanding of this
complex metabolic disorder, optimal therapeutic strategies still remain elusive. The advent of cuproptosis,
a term coined to describe copper-induced cellular cell death and its pivotal role in diabetes
pathogenesis, opens new avenues for innovative interventions. Copper, an indispensable trace
element, plays a pivotal role in a myriad of vital biological processes, encompassing energy production,
bolstering antioxidant defenses, and altered cellular signaling. However, in the context of diabetes,
this copper homeostasis is perturbed, driven by a combination of genetic predisposition, dietary
patterns, and environmental factors. Excessive copper levels act as catalysts for oxidative stress,
sparking intricate intracellular signaling cascades that further exacerbate metabolic dysfunction. In
this review, we aim to explore the interrelationship between copper and diabetes comprehensively,
shedding light on the intricate mechanisms underpinning cuproptosis. By unraveling the roles of
copper transporters, copper-dependent enzymes, and cuproptotic signaling pathways, we seek to
elucidate potential therapeutic strategies that harness the power of copper modulation in diabetes
management. This insight sets the stage for a targeted approach to challenge the complex hurdles
posed by diabetes, potentially transforming our therapeutic strategies in the ongoing fight against
this pervasive global health concern.
INVESTIGATION OF THE EFFECTS OF DENSITY ON PHYSICAL, MECHANICAL AND THERMAL PROPERTIES OF FOAM CONCRETE
Authors: Davraz Metin , Kılınçarslan Şemsettin , Koru Murat
Published: September 2024

Foam concrete; It is a type of lightweight concrete that can be produced by adding cement, water, surfactant, and if desired, sand or fine aggregate. It has high fluidity in fresh form and low density after hardening. It can provide a thermal conductivity of 90 mW/mK at a density of 400 kg/m3. In this study, the foam concrete samples were produced by adding 80 g/L density foam to the mortars prepared at 300 kg/m3 cement dosage and 0.30 water/solid ratio using CEM I 42.5 R type Portland cement, limestone powder, polypropylene fiber, and superplasticizer additive. The compressive strength, split tensile strength and thermal conductivity properties of the samples produced in 12 different dry densities in the range of 300-1400 kg/m3 were tested. As the dry density values of the samples increased, the thermal conductivity and compressive strength values increased. Equations were proposed to estimate the compressive strength and thermal conductivity depending on the dry density of the foam concrete with limestone powder aggregate from the findings.
DEEP LEARNING BASED NETWORK INTRUSION DETECTION
Authors: Harman Güneş , Cengiz Emine
Published: September 2024

As a direct consequence of the unrelenting march of technological innovation, the use of the Internet has become an unavoidable condition for the life of modern humans. The Internet has increased both the quantity and range of situations in which information products can be useful or non-useful. It’s no surprise that as the number of different systems and users has grown, so have the number of different ways to exploit those systems. A security issue has arisen with such diversity and growth. Its diversity and increase in quantity introduce new system weaknesses and thus new attack strategies. Methods for detecting both internal and external attacks are suggested as a solution to this issue. The purpose of this research, a Convolutional Neural Network was utilized to identify intrusions, also known as attacks for the imbalanced class distribution in the NF-BoT-IoT data set, Synthetic Minority Over Sampling Technique, Random Over Sampling and Random Under Sampling methods were used. K-Fold Cross Validation, one of the strategies for splitting the data set, was utilized to evaluate the performance of classification models and to train the developed model. The model’s performance was evaluated using the accuracy, precision, recall, and F1-score performance criteria.