bluuebunny/crossref_metadata_2025 huggingface dataset:
Designing a Fuzzy Sliding Mode Controller for 2-DOF Lower Limb Exoskeleton Rehabilitation Robot
Authors: Tkue Hafte , Berisha Kassahun , Teklay Fisseha
Published: June 2024

One promising approach to resolving mobility issues in people with lower limb disabilities is the incorporation of robotics into rehabilitation programs, especially lower limb exoskeletons. The complex mechanics resulting from the nonlinear and erratic dynamics of the lower limb provide difﬁculties for conventional controllers in providing stable and individualized support during rehabilitation exercises. A Fuzzy Sliding Mode Controller for a 2-DOF lower limb exoskeleton rehabilitation robot takes into account the complexity of the rehabilitation process for lower limbs by combining sliding mode control with the adaptable nature of fuzzy logic. By utilizing user-speciﬁc physiological feedback to help with real-time decision-making, the fuzzy logic component enables the exoskeleton to dynamically modify its control parameters. Analyses that compare the FSMC to a traditional sliding mode controller demonstrate how well it handles nonlinear dynamics and provides a more relevant and adaptive control approach.
The Rate of Switching from First-line to Second-line Antiretroviral Therapy Among People Living with HIV in Aden City, Yemen: A retrospective cohort study
Authors: Al-Haidary Naif Mohammed , Radman Enas Abobakr
Published: June 2024

Background
Effective management of antiretroviral therapy (ART) is crucial in combating the global HIV pandemic. This study, the first of its kind in Yemen, investigates the rate and determinants of switching from first-line to second-line ART among people living with HIV (PLWH) in Aden City, Yemen.
Methods
A retrospective cohort study was conducted using data from PLWH who started first-line ART at Al-Wahda Hospital from 2007 to May 2022. Patients in prevention of mother-to-child transmission (PMTCT) programs, those already on second-line ART at enrollment, and those with less than 3 months of follow-up were excluded. Cumulative incidence curves and multivariable proportional hazards models were used to identify factors associated with switching, considering death and loss to follow-up as competing risks. Analyses were carried out using IBM SPSS version 26.
Results
Out of 149 patients, 18 (12.1%) switched to second-line ART with a cumulative incidence rate of 1.8 per 100 person-years. Significant factors for switching included being older than 33 years (HR: 1.45, 95% CI: 1.12–1.89), having WHO stage 3 disease (HR: 1.58, 95% CI: 1.21–2.06), and being on a TDF-FTC-EFV-based first-line regimen (HR: 1.35, 95% CI: 1.03–1.77).
Conclusions
The study highlights key factors associated with switching to second-line ART in Yemen, emphasizing the need for targeted interventions and continuous patient monitoring to enhance treatment outcomes. These findings are consistent with regional data from other resource-limited settings.
Ameliorative Effect of Green synthesized Zinc Oxide Nanoparticles of Malvastrum coromandelianum Linn. on CCl 4 Induced Oxidative Stress in Sprague Dawley Rats Liver
Authors: Naseer Sana , Naseer Abeel , Fatima Mehreen , Iqbal Javed , Kanwal Sobia , Abbasi Banzeer Ahsan , Kazi Mohsin , Mahmoodi Mahboobeh , Mahmood Tariq
Published: June 2024

Background: Malvastrum coromandelianum is an important medicinal plant enriched with active secondary metabolites. Leaves of plant have anti-inflammatory, antibacterial and hypoglycemic properties.
Results: This study is based on hepato-protective properties of zinc-oxide nanoparticles synthesized using Malvastrum coromandelianum (MCZNPs) against inflammation and toxicity caused by CCl4 in Sprague-Dawley rats. Nanoparticle’s formation was confirmed using UV visible spectroscopy by showing peak at 363nm. Shape (hexagonal) and size (23nm) was estimated using XRD and further confirmed by SEM (32.06 nm). MCZNPs possess antioxidant abilities, DPPH (44.3±0.6 µg/mL), superoxide scavenging activity (27.6±0.9 µg/mL), TAC (52.3±0.9 AAE mg/g) and TRP (47.5±0.9 GAE mg/g) values were comparable with standards. Initial treatment of female rats with MCZNPs revealed that MCZNPs were non-toxic. Anti-inflammatory potential was evaluated on forty two (1:1 male: female) rats divided into seven groups; I-control group (saline water-0.9%), II-treated with CCl4 1ml/kg bw, III-positive control group; CCl4 (1ml/kg bw)+silymarin (200mg/kg bw), IV and V-injected with 1ml/kg bw CCl4 and 150ml/kg bw and 300mg/kg bw of MCZNP, VI and VII-administered with 150ml/kg bw and 300mg/kg bw of MCZNP alone. Serum and antioxidant enzyme markers were found in normal range on co-administration of MCZNPs+CCl4. MCZNPs significantly lowered CCl4 induced elevations in serum markers and restored antioxidant enzymes (SOD, POD, CAT, GSH) levels. However, gene expression analysis revealed that CCl4 elevated oxidative stress and upregulated inflammatory markers (P<0.05). Whereas, MCZNPs suppressed oxidative stress markers. Hepatocyte morphology was examined by histopathological investigation.
Conclusion: Nanoparticles synthesized using Malvastrum coromandelianum exhibited exceptional anti-inflammatory and antioxidant potential. Clinical trials of MCZNPs and investigation of underlying mechanisms would lead to the development of FDA approved drug.
Balancing energy efficiency and biomass yield through Computational Fluid Dynamics of a commercial scale bubble-column photobioreactor
Authors: Kubelka B. G. , Abreu P. C. , Pinto W. T.
Published: June 2024

Microalgae biomass derived fuel need to maximize production with minimal energy input to cultivation and harvesting operations. The mixing/circulation comprises an essential step in the expensive microalgae cultivation process. Additionally, prevents cell settlement and increases exposure to light and nutrients. Nevertheless, it is responsible for at least one-third of total cost to produce lipid from microalgae. Regulating air input is an effective way to save energy, but it can compromise production. Computational Fluid Dynamics was used to determine the percentage of dead-zones in 330 L photobioreactors (aspect ratio of 4.54) for production of the microalgae Nannochloropsis oceanica with different airflow. Three numerical cases were selected for growth experiments. The airflow rate of 0.0024 vvm allowed satisfactory production and low energy input. It is noteworthy that experiments with the highest Energy Return On Investment values were also observed in this airflow rate (0.0024 vvm). The complementary application of the tools (Energy Return On Investment and Computational Fluid Dynamics) have shown high potential to reach the economic viability of microalgae biofuel.
Dynamic Task Offloading Strategy for Multi-Agent Deep Reinforcement Learning Based on Lyapunov
Authors: ZheXing Yang , XiaoLan Xie , Qian Guo , ShuRu Tan
Published: June 2024

Aiming at the issue of communication overload and task backlog caused by the dynamic nature of the environment and the surge in the number of mobile devices in multi-user Mobile Edge Computing scenarios, a dynamic task offloading strategy based on Lyapunov-guided multi-agent deep reinforcement learning is proposed. This strategy aims to ensure long-term system stability while minimizing the average task processing latency and energy consumption. First, the dependency relationships among subtasks are modeled using a directed acyclic graph, and this is expressed as an optimization problem to minimize task offloading costs with long-term constraints. Then, using Lyapunov optimization theory, the long-term average problem is decoupled into deterministic problems for each time slot. Finally, the issue is further transformed into an optimal policy problem under a Markov Decision Process framework, and solved using the designed Lyapunov Multi-Agent Deep Deterministic Policy Gradient (Ly-MADDPG) algorithm. The simulation experiment results indicate that compared to the alternative algorithms, our proposed method reduces the task offloading cost while ensuring queue stability.
Content of Biogenic Elements in Sheep Wool by the Regions of Slovakia
Authors: Janíček Martin , Massányi Martin , Kováčik Anton , Halo Marko , Tirpák Filip , Blaszcyk-Altman Martyna , Albrycht Marzena , Stawarz Robert , Massányi Peter , Halo Marko
Published: June 2024

The quality of nutrition and environmental pollution are crucial chemical indicators influencing animal health, reflected in element concentrations in animal tissues and coats. This study investigates the concentrations of biogenic elements (calcium, potassium, magnesium, sodium, copper, and iron) in sheep wool from various regions of Slovakia to compare concentrations and identify possible correlations between individual elements. Samples were collected from six different regions, and concentrations of elements were measured using flame atomic absorption spectrometry. Statistical analysis revealed significant differences in element levels among the examined regions. Calcium concentrations ranged from 729.2 to 4065 ppm, with the highest concentrations in the Kysuce region and the lowest in the Šariš region. Potassium concentrations ranged from 2315 to 3282 ppm, with the lowest values in the Zemplín region and the highest in the Liptov region. Magnesium, sodium, copper, and iron also exhibited varying concentrations across the regions. Correlation analysis identified significant associations between several elements. The findings suggest that regional environmental differences influence element concentrations in sheep wool, highlighting the importance of monitoring biogenic elements for assessing environmental pollution and animal health. Further research is warranted to explore the underlying mechanisms driving element accumulation in wool and its implications for animal welfare and environmental management.
Short-term Changes in Brain Networks and Cognition in Children with Frontal Lobe Lesions Treated Solely with Neurosurgical Procedures
Authors: Guan Xueyi , Hu Bohan , Zheng Wenjian , Zhai Huina , Han Xu , Hu Cuiling , Yan Zihan , Chen Ning , Li Xiang , Lu Zheng , Gong Jian
Published: June 2024

Objective:
Intracranial lesions in children often have good prognoses, allowing long-term survival. Cognitive functions, crucial for life quality, need more attention. Previous research has focused on adults, with pediatric studies limited by varied lesions and complex treatments. This study aims to evaluate cognitive and brain network changes in children with frontal lobe lesions, which significantly impact cognitive function, using a before-and-after comparison.
Methods
The study enrolled 20 children with frontal lesions who underwent fMRI and cognitive tests before and after surgery, with only surgical treatment initially. Brain network changes were evaluated using functional metrics, and cognitive shifts were measured through test scores. Correlations were analyzed to explore brain mechanisms behind cognitive changes. Additionally, 20 healthy children underwent the same assessments for baseline data.
Results
Preliminary evidence of cognitive recovery, notably in social cognition, was observed about three months post-surgery, potentially linked to increased functional connectivity between the right lingual gyrus and right middle temporal gyrus.
Conclusion
Children with frontal lobe lesions show short-term cognitive recovery and related brain network changes post-surgery, suggesting a more aggressive surgical approach. This study offers unique insights into cognitive neuroscience and potential rehabilitation targets.
Adapting and pilot testing a tool to assess the accessibility of primary health facilities for people with disabilities in Luuka District, Uganda
Authors: Mactaggart Islay , Ssemata Andrew Sentoogo , Menya Abdmagidu , Smythe Tracey , Rotenberg Sara , Marks Sarah , Mbazzi Femke Bannink , Kuper Hannah
Published: June 2024

People with disabilities frequently experience barriers in seeking healthcare that lead to poorer health outcomes compared to people without disabilities. To overcome this, it is important to assess the accessibility of primary health facilities – broadly defined to include a disability-inclusive service provision – so as to document present status and identify areas for improvement. We aimed to identify, adapt and pilot test an appropriate tool to assess the accessibility of primary health facilities in Luuka District, Uganda.
Methods
We conducted a rapid literature review to identify appropriate tools, selecting the Disability Awareness Checklist (DAC) on account of its relative brevity and development as a sensitization and action tool. We undertook three rounds of adaptation, working together with youth researchers (aged 18 – 35) with disabilities who then underwent 2 days of training as DAC facilitators. The adapted tool comprised 71 indicators across four domains and 12 sub-domains. We also developed a structured feedback form for facilitators to complete with healthcare workers. We calculated mean accessibility scores overall, per domain and per sub-domain, and categorised feedback form suggestions by type and presumed investment level. We pilot-tested the adapted tool in 5 primary health facilities in one sub-district of Luuka, nested within a pilot healthcare worker training on disability.
Results
The average overall facility accessibility score was 19.7% (range 12.3 – 28.8). Facility scores were highest in the universal design and accessibility domain (29.7%, 22.6 – 41.9), followed by reasonable accommodation (21.3%, 6.7 – 33.3). Average scores for capacity of facility staff (9.3%, 6.7 – 20.0), and linkages to other services were lower (5.0%, 0 – 25.0). Within the feedback forms, there were on average 21 suggestions (range 14 – 26) per facility. Most commonly, these were categorised as minor structural changes (20% of suggestions), with a third categorised as no (2%) or low (33%) cost, and the majority (40%) medium cost.
Conclusions
Overall accessibility scores were low, with many opportunities for low-cost improvement at the facility level. We did not identify any issues with the implementation of the tool, suggesting few further adaptations are required for its future use in this setting.
Assessment of Seismic Susceptibility in Crude Oil Producing Ogoni Community in Rivers State of Nigeria
Authors: Kennedy Charles
Published: June 2024

The chances of earthquake occurrence in Ogoni, Rivers State of Nigeria was investigated via Resonant column and Bender element (RC/BE) tests on compacted soil obtained from various soil depths. From the tests, frequency, shear modulus, shear wave velocity, accelerometer output and damping ratio of the soil samples were determined at various confinement pressures. The test results revealed that confinement has effects on soil dynamic properties. Thus, the frequency (vibration) of soil increased with increasing confinement pressure, while the RC/BE tests at various confinements revealed high variations in shear modulus, shear wave velocity, accelerometer output and damping ratio. The shear modulus and shear wave-velocity generally increased as confinement pressure was increased, while damping ratio decreases as confinement pressure was increased. The variations in RC/BE test parameters showed that the Ogoni land of Rivers State, as an oil and gas bearing area, is susceptible to earthquake. Hence, continuous monitoring and assessment of oil exploration activities are recommended to ensure geological data are collected, especially when seismic activities occur in the areas, for prevention of factors that lead to earthquake occurrence.
Numerical Simulation of the Behaviour of RC Columns Strengthened Using Hybrid NSM Steel and GFRP Bars
Authors: Al-Thairy Haitham , Al-Shimarry Nibras Atiaa
Published: June 2024

RC columns in concrete buildings frequently require strengthening, either as a result of problems in the columns themselves, having to sustain more loads than those expected during the initial design or as a result of aging or accidental damage. In recent years, the use of fiber-reinforced polymers (FRP) in strengthening existing RC columns has become more common, and the results are very promising. In this paper, a numerical study has been conducted on the behavior of eccentrically loaded RC columns strengthened with hybrid near surface mounted (NSM) steel and glass fiber-reinforced polymer (GFRP) bars. A numerical model is first proposed to simulate the behavior of hybrid NSM bars strengthened columns using the finite element program ABAQUS/Standard. The proposed model was validated against the experimental test published by the authors. Validation results have shown good agreement between the numerical and experimental outcomes in terms of load-displacement curves and failure modes. Further, a parametric study has been conducted to understand the effect of important parameters on the response of the hybrid NSM bars strengthened RC columns. Parametric study results have shown that the ultimate load of hybrid NSM bars strengthened RC columns increased up to 70% compared with that of columns strengthened with NSM steel bars only. Numerical results have also revealed that the ultimate load of the strengthened RC columns increased with increasing the number of NSM bars but decreased with increasing the slenderness ratio.
Traditional agroforestry systems in Timor-Leste can store large amounts of carbon in both soil and biomass
Authors: Piponiot Camille , Cogné Marguerite , Freycon Vincent , Thoumazeau Alexis , Gusmão Marçal , Peltier Régis
Published: June 2024

Agroforestry has the potential to make agriculture more resilient while improving carbon sequestration by incorporating trees and other woody perennials into agricultural land and diversifying landscapes. Traditional agricultural systems in tropical areas often include trees, but their carbon sequestration potential is not always well described, hindering their inclusion in climate change mitigation strategies. In this study, we quantified carbon storage in both vegetation biomass and soil in five traditional agroforestry systems (AFS) in Timor-Leste, namely cropping systems with fallow, silvopastures, young agroforests, home gardens, and forest gardens. Our results show that these traditional AFS can store large amounts of carbon, with the average being 156 Mg C ha-1. The AFS with the highest carbon stocks (forest gardens) stored an average of 210 Mg C ha-1, close to the values of old-growth tropical forests. Biomass carbon was strongly dependent on the type of AFS (which differed in tree cover), while soil carbon was less variable between AFS but more dependent on site. We found no relationship between the amount of carbon stored in biomass and soil. Our results highlight the high diversity of traditional AFS in Timor-Leste and their high carbon sequestration capacity. These results could provide an important baseline for the inclusion of AFS in Timor-Leste’s climate change mitigation strategy, and could serve as a reference for future AFS studies in different agro-climates of Timor-Leste.
Characterization of a Native Microbial Consortium from Mining Tailings of the Arequipa Region and its potential application in Biolixiviation
Authors: Arequipa-Loayza Daniela V. , Valdes Jorge H. , Lazarte-Rivera Antonio M.
Published: June 2024

This study addresses the issue of mining environmental liabilities in the southeastern region of Arequipa, Peru, specifically in Yarabamba, pristine areas for molecular characterization studies through metagenomics, where the microbial consortia present in mining tailings were determined, exploring their diversity and biolyxative capacity. In the studied region, treatments with native and adapted microbial consortia were implemented, using seawater in the latter. Over 14 days, analyses of cell count, pH, and oxidation-reduction potential (ORP) were performed, showing an increase in microbial biomass and changes in the medium conditions, indicating a positive impact of the microorganisms on the sustainable recovery of copper. Atomic absorption analysis of the copper content revealed a significant increase at the end of the treatments, reaching 4.42% for the native microbial consortium (NMC) and 4.61% for the adapted microbial consortium (AMC), compared to the initial sample's 2.19%. The final results revealed significant species diversity, with Cutibacterium namnetense, Staphylococcus caprae, and Pelomonas aquatica predominating. This would indicate a positive impact of microbial consortia on sustainable copper recovery, and the microbial diversity suggests that the presence of the identified species could correspond to a signal of natural recovery of the environment, through a possible ecological succession over time. This would be an adaptation to address associated environmental challenges, and it would also highlight the importance of long-term strategies in the bioremediation of affected mining areas.
MRD-Net: Multi-scale Refinement Dehazing Network for Autonomous Driving Perception Images
Authors: Wang Juan , Wang Sheng , Wu Minghu , Yang Hao , Cao Ye , Hu Shuyao , Shao Jixiang , Zeng Chunyan
Published: June 2024

In hazy weather conditions, a significant accumulation of haze poses a severe challenge to the quality of image capture for autonomous driving systems, thereby heightening safety risks for autonomous vehicles. To tackle this issue, we propose the Multi-scale Refinement Dehazing Network (MRD-Net), an innovative architecture comprising a front-end module, a backbone module, and a tail-end module, specifically designed to eradicate haze with precision. To enhance the extraction of multi-scale features, the backbone module employs the Squeeze-Excitation Residual Dense Block (SRD). It not only learns the intricate multi-scale features of the image but also adaptively recalibrates the feature response of each feature map, ultimately bolstering the network's performance and resilience. The tail-end module, crafted with the Dilation Refinement Block (DRB), serves as a compensatory measure for any detail loss or pseudo-artifacts that might arise from the backbone module's operations. By incorporating this refinement block, the overall dehazing effect is further optimized. Empirical evaluations reveal that the proposed MRD-Net achieves impressive results, with a PSNR value of 28.12, an SSIM value of 0.964, and an LPIPS value of 0.032. These figures indicate that the network is adept at removing haze from images while preserving intricate details, ensuring the efficacy and reliability of autonomous driving systems in hazy environments.
Object/Scene Recognition based on Directional PixelVoting Descriptor from Edge-based Segmentation
Authors: Aguilar-González Abiel , Santiago Alejandro Medina , Osuna-Coutiño J. A. de Jesús
Published: June 2024

Detecting objects in images is crucial across a wide range of applications, including surveillance, autonomous navigation, augmented reality, and more. While AI-based approaches such as Convolutional Neural Networks (CNNs) have proven highly effective in object detection, in some industrial applications where the objects being recognized are confidential, it is difficult to train an AI for such tasks. On the other hand, feature-based approaches like SIFT, SURF, and ORB offer the capability to search any template but may struggle with complex visual variations. In this work, we introduce a novel edge-based object/scene recognition method. We propose that utilizing feature edges, instead of feature points, offers high performance under complex visual variations. Our primary contribution is a directional pixel voting descriptor based on image segments. Experimental results are promising; compared to previous approaches, ours demonstrates superior performance under complex visual variations, enabling real-time processing with embedded capabilities.
Time-series transcriptome analysis reveals the cascade mechanism of biological processes after perturbation of the MVA pathway in Salvia miltiorrhiza
Authors: Liu Fang , Li Nan , Yan Zhu-Yun , Chen Xin
Published: June 2024

Background
Various biological processes are interconnected in plants. Transcription factors (TFs) often act as regulatory hubs to regulate plant growth and responses to stress by integrating various biological pathways. Despite extensive studies on TFs functions in various plant species, our understanding of the details of TFs regulation remains limited. In this study, clonal seedlings of Salvia miltiorrhiza were exposed to specific inhibitors for 12 hours. Time-series transcriptome data, sampled hourly, were used to construct co-expression networks and gene regulatory networks (GRNs). Transcriptome dynamic analysis was utilized to capture the gene expression dynamics of various biological processes and decipher the potential molecular mechanisms that regulate these processes.
Results
The perturbation results showed the growth and development processes of S.miltiorrhiza were primarily affected at the early stage, whereas stress response-related biological processes were mainly influenced at the later stage. And there was a correlation between the series of key differentially expressed genes in terpenoid biosynthesis pathways and the topological distribution of these pathways. Furthermore, the GRNs based on TFs indicate that TFs play a crucial role in connecting various biological processes. In the cytoplasmic lysate gene regulatory module, SmWRKY48-SmTCP4-SmWRKY28 constituted a regulation hub regulating S.miltiorrhiza responses to perturbation of the MVA pathway. The regulation hub mediated various pathways, including pyruvate metabolism, glycolysis/gluconeogenesis, amino acid metabolism, and ubiquinone and other terpenoid-quinone biosynthesis.
Conclusion
Our findings suggest that perturbation of a key biological pathway in S.miltiorrhiza has time-dependent effects on other biological processes. And SmWRKY48-SmTCP4-SmWRKY28 constitutes the regulatory hub in S.miltiorrhiza responses to perturbation of MVA pathway.
Comparative Analysis of Outlier Detection Approaches in MetOcean Domain: Extreme Value Analysis, Functional Data Analysis, and Unsupervised Learning
Authors: Sazali Faris Haziq , Jawaddi Siti Nuraishah Agos , Ismail Azlan , Zain Jasni Mohamad , Jaafar Jafreezal , Hasan Mohd Hilmi , Aziz Izzatdin Abdul
Published: June 2024

Detecting outliers within MetOcean datasets is crucial for identifying extreme weather conditions to facilitate informed decision-making whilst various outlier detection approaches and methods are available to be selected. However, choosing the most appropriate one requires an in-depth understanding of their capabilities and this poses a great challenge to the novice within the domain. Therefore, this study is proposed to empirically compare these approaches and provide insights on their performance when dealing with large MetOcean data. The comparison is driven by three outlier detection approaches using the SEAFINE dataset: Extreme Value Analysis (EVA), Functional Data Analysis (FDA), and Unsupervised Learning (UL). A framework is designed to systematically conduct the comparative analysis, which involves five phases: data preprocessing, model training, outlier detection, parameter estimation, and evaluation. This analysis provides insights into the performance of each outlier detection method for each approach and the comparison between them. Overall, the results indicate that the EVA approach exhibits the fastest average execution time compared to the FDA and UL. Within the EVA approach, Peak-Over-Threshold is the most effective, closely aligning the distribution of extreme values with the estimated distribution. Functional Boxplot emerges as the optimal method for FDA, capable of detecting outliers far from the median. Cluster-Based Local Outlier Factor (CBLOF) is the most effective UL method, predicting outliers close to the expected total, with high Excess-Mass values and fast execution.
Improving Citrus Fruit Classification Accuracy in Xray Images Using Features Enhanced Vision Transformer Architecture
Authors: Raza Syed Mudassir , Raza Awais , Babeker Mohamed Ibrahim Abdullah , Haq Zia-Ul , Islam Muhammad Adnan , Li Shanjun
Published: June 2024

Quality assessment is a cornerstone of fruit production and distribution, particularly regarding storage conditions and duration. Citrus fruits, a staple in global consumption patterns, serve as aultimate example. This study employs a non-destructive analytical technique, X-ray Computed Tomography (CT) scanning to meticulously analyze a substantial sample of 300 citrus fruits, specifically satsuma, subjected to both ambient (20-22°C, 50-60% humidity) and refrigeration conditions (6-8°C, 65-75% humidity). Through a methodologically rigorous approach, we conduct Stratified Dataset Splitting, allocating 60% of the X-ray datasets for training, with 20% dedicated to validation and testing, respectively. Our research introduces a pioneering methodology termed Feature Enhancement Vision Transformers (FEViT), meticulously designed to augment precision in 1 citrus fruit classification via X-ray image analysis. By leveraging the transforma-tive potential of Vision Transformer architecture, FEViT integrates convolutional layers and expanded input dimensions to enhance classification performance substantially. Our empirical findings unequivocally demonstrate the superior efficacy of FEViT models vis-à-vis conventional ViT counterparts across diverse datasets. Particularly noteworthy are the marked performance gains exhibited by FEViT-Large variants, evidenced by notable increases in precision (5.08%), accuracy (5.47%), recall (4.55%), and F1 scores (5.28%). This underscores the distinguish-able enhanced discriminatory prowess of FEViT models. Extensive validation through rigorous experimentation ratifies FEViT’s supremacy over traditional deep learning architectures, affirming heightened accuracy, precision, recall, and F1 scores. Our study heralds the advent of FEViT architecture as a milepost in citrus fruit image classification, promising augmented accuracy and robustness vis-à-vis extant methodologies. This research holds profound implications for the agricultural sector, especially in domains such as citrus and broader fruit classification , where nuanced image analysis is indispensable for quality assessment and informed decision-making.
An efficient method for the isolation and transfection of Pea (Pisum sativum) mesophyll protoplasts
Authors: Jo Leonardo , Xu Qiuyu , Kajala Kaisa
Published: June 2024

Background
Pea (Pisum sativum L.) is an important crop with a wide range of uses and benefits, including food source, forage, and potential use as a biofuel crop. Developing methods for the transformation in this species can provide opportunities to improve this important crop. The transient transformation of protoplasts serves as a method for plant engineering, providing a quick and cheap way to study gene and protein function in plants. Developing a protocol for protoplast transfection is especially important for species like pea, where stable transformation methods are laborious and inefficient.
Results
In this report, we established and optimized an efficient protoplast isolation method for pea mesophyll cells. Using the tape-sandwich method we were able to isolate an average of 6.7x105 protoplasts per pea leaflet. Additionally, we determined the optimal conditions for the PEG4000 mediated transfection to achieve about 40% to 60% transfection efficiency in pea mesophyll protoplasts. Finally, we showed that with the protoplast transfection system, we were able to quickly assess protein subcellular localization in pea.
Conclusion
With the inclusion of this efficient and fast protoplast isolation and transfection system to the pea transformation toolbox, we hope to further aid many molecular and biochemical studies in this important legume crop.
Organic Nutrient Management Affecting Growth and Yield in Strawberry (Fragaria× ananasa Duch.) cv. Flavia under Punjab Conditions
Authors: Sharma Jyoti Bharti , Wani Ab. Waheed , Chauhan Nidhi , Jabroot Khan , Chaudhuri Madhurima
Published: June 2024

The demand for increased food production while minimizing environmental impacts poses a significant challenge for sustainable agricultural intensification. Organic farming is considered eco-friendly but less productive compared to conventional farming. To boost yields, a balanced application of organic manures, bio-fertilizers, and inorganic fertilizers is recommended. However, appropriate combinations of organic sources alone can also meet crop requirements. Therefore, an experiment was conducted at Lovely Professional University, Punjab, to investigate the effect of different organic sources of nutrients, alone or in combinations, on the growth and yield attributes of the strawberry cultivar Flavia. The study comprised ten treatments structured using a randomized block design (RBD). The results indicated that all treatments contributed to increased growth and yield of strawberry compared to the control, but treatment combination including 60% RDN – 30% through Vermicompost (90 g/m2) + 30% through neem cake (45 g/m2) and biochar (800 g/m2) + Panchgavya + VAM each @ 50 mL/plant (T9) resulted in significant increase in vegetative growth parameters viz. plant height (6.7 and 13.33 cm), plant spread EW (15.87 and 24.63 cm), plant spread NS (14.00 and 25.59 cm), and number of trifoliate leaves (5.97 and 17.40) at 45 and 90 days after planting (DAP) respectively. Similarly, this treatment combination also contributed to maximum chlorophyll index (57.90 SPAD), leaf area (48.76 cm2) and stem girth (3.41 cm) along with greater yield and yield attributing parameters such as number of fruits per plant (28.14), average berry weight (16.53 g), yield per plant (447.88 g) and yield efficiency (0.47 kg/cm2) compared to other treatment combinations and control.
Antiviral Activity of Probiotics in the Prophylaxis and Therapy of Respiratory Infections Associated with Coronavirus (COVID-19): Meta-analysis of Randomized Controlled Trials
Authors: Mokadem Slimane Chawki , Naimi Mostefa , Alami Omar
Published: June 2024

Introduction:
Probiotics, living microorganisms administered in sufficient quantities, exert beneficial effects on host health. Given the high prevalence of SARS-CoV-2, recent studies suggest potential positive impacts of probiotics on COVID-19 patients.
Methods
A predetermined search strategy encompassing seven databases: NCBI, PubMed, Science Direct, Springer Link, Embase, CNKI, and Cochrane Library Databases, was implemented. Human RCTs studies were scrutinized independently, involving data extraction, quality and risk of bias assessment, and statistical analysis. Pooled data, employing the random-effects model, were expressed as Standardized Mean Differences (SMD) with a 95% Confidence Interval (CI). Assessments of (p) value and heterogeneity (I²) were conducted and quantified.
Results
Five studies, comprising 282 out of 375 participants, were included. Meta-analysis revealed effects on various parameters: CRP (SMD = 0.26 MG/L, 95% CI [0.10, 0.43], p = 0.002, (I²=67%, p = 0.03)), BMI (SMD = 0.28 KG/m², 95% CI [0.07, 0.50], p = 0.01, (I²=67%, p = 0.40)), T-cells (SMD = 0.09 G/L, 95% CI [-0.07, 0.26], p = 0.26, (I²=0%, p = 0.73)), Albumin (SMD = 0.28 G/DL, 95% CI [0.04, 0.52], p = 0.02, (I²=7%, p = 0.34)), IL-6 (SMD = 0.67, 95% CI [0.45, 0.90], p = 0.00001, (I²=94%, p = 0.0001)), LDH (SMD = 0.12mmol/L, 95% CI [-0.05, 0.30], p = 0.17, (I²=55%, p = 0.13)), and Ferritin (SMD = 0.19mmol/L, 95% CI [-0.27, 0.66], p = 0.41, (I²=77%, p = 0.04)).
Conclusion
This meta-analysis suggests significant positive effects of probiotics on various measures for COVID-19 treatment.
Hardware Implementation of Multi-LFSR Pseudo Random Number Generator
Authors: Teo Tee Hui , Xiang Maoyang , Xie Jiacheng , Gerald Ek Hong Sheng , Augustine Lee Kang Yi , Yi Isabel Judith Koh Wee
Published: June 2024

This is an implementation of a hardware pseudo-random number generator (pRNG) using multiple LFSR (Multi-LFSR) for generation, and ambient electromagnetic noise for seed generation. The pRNG algorithm is evaluated in field-programmable-gate-array (FPGA) board, which is CMOS A7. 16-bit random numbers are generated at a frequency of 50 kHz. In the FPGA setup, a new seed is set by clicking the reset button. A dedicated high speed UART is designed with a baud rate of 1,152,000 to transfer the generated numbers from the FPGA to PC.
Cannabinoid Ethers: Identification, Binding interactions, and their ADME properties
Authors: Docampo-Palacios  , Ramirez Giovanni. A. , Tesfatsion Tesfay. T. , Cruces Westley
Published: June 2024

New cannabinoid analogs are constantly being synthesized and marketed to the public as ways to increase psychotropic activity or to remain competitive within the market. The issue with new analogs is the lack of proper testing and characterization—companies are misrepresenting what they sell, and customers are consuming unknown research compounds. Our group performed in-silico docking in CB1 and CB2 receptors, drug metabolism, and pharmacokinetics (DMPK) studies of methyl-, ethyl-, and pivaloy- ether cannabinoids analogs. A dataset of eight cannabinoid ethers was docked to the model to further analyze key interacting residues on the CB1 and CB2 receptors. In addition, the methyl ether derivatives of D8 tetrahydrocannabinol and hexahydrocannabinol were synthesized and the full NMR characterization of these compounds was achieved.
A Longchain Approach to Reduce Hallucinations in Large Language Models
Authors: Li Jinchao , Hong Quan
Published: June 2024

The increasing deployment of natural language processing models in critical domains necessitates addressing the issue of hallucinations, where generated outputs may be factually incorrect or nonsensical. The longchain approach, which involves an iterative refinement process, offers a novel and significant method to mitigate hallucinations by enhancing both the accuracy and coherence of model outputs. The methodology involved modifying the GPT-3 architecture to incorporate additional layers for intermediate evaluations and corrections, followed by rigorous training and evaluation using the MMLU dataset. Quantitative results demonstrated that the modified model significantly outperformed baseline models across various performance metrics, including precision, recall, F1-score, logical coherence, and hallucination rate. Qualitative analysis further supported these findings, showcasing the practical benefits of the longchain approach in producing accurate and contextually relevant outputs. The study emphasizes the theoretical foundations of iterative learning and continuous improvement, providing a robust framework for enhancing the reliability of natural language processing models. The implications of the findings are substantial for applications in healthcare, legal advice, and education, where the generation of accurate and reliable text is paramount. By reducing hallucinations and improving coherence, the longchain approach contributes to the development of more trustworthy and effective language models.
Utilization and predictors of postpartum modern family planning methods in Felegehiwot referral hospital, Northwest Ethiopia
Authors: Moges Fisseha
Published: June 2024

Achieving adequate spacing between pregnancies is a major pathway through which contraceptive practice can render benefits. Directing efforts to increase modern contraception among postpartum women could have a significant impact on increasing contraceptive prevalence rate and other health benefits. However, contraceptive use in the postpartum period is less emphasized by policymakers and program designers in Ethiopia. Therefore, information about the current utilization of modern contraceptives and its associated factors among postpartum women is needed to design strategies and improve service delivery practices in health institutions. The objective of the study is to assess the utilization and factors associated with modern contraceptives use among postpartum women in Felegehiwot referral hospital, Bahirdar city, Northwest Ethiopia. A facility-based cross-sectional study was employed from January 1-31, 2019 among postpartum women in Felegehiwot referral hospital. The data were collected through a face to face interview for 425 women who were attending basic maternal and child health services. Bivariable and multivariable logistic regression was employed to identify the association between the dependent variable, utilization of postpartum family planning, and the explanatory variables. The association was considered statistically significant when P-value was < 0.05. A total of 425 postpartum women participated in the study with a 100% response rate. Over half (53%) of women interviewed used modern contraceptives during the postpartum period. Women who are aware of PPFP during ANC and PNC visits (AOR, 16.99; 95% CI, 2.85 to 101.16); married (AOR 3.052; 95% CI, 1.644 to 5.667); and higher age (AOR, 5.825; 95%CI, 2.519 to 13.467) were more likely to use modern contraceptives in the postpartum period. However, educational status, parity, the spousal reaction for FP related discussions, and contraceptive education failed short of any statistically significant association with modern postpartum contraceptive use. The utilization of modern contraceptives in the postpartum period was found to be relatively lower. Proper counseling on available methods for postpartum women at different instances coupled with increasing contraceptive method mix, enhancing male engagement in family planning programs, and expanding contraceptive information during facility visits for antenatal and postnatal care using different media outlets are important measures to boost contraceptive use.
Corruption and Social Capital: Findings From Selected OECD Countries
Authors: Bayraktar Astt. Prof. Dr. Murat
Published: June 2024

Corruption, a pervasive global challenge, erodes trust in institutions, hinders development, and deepens inequalities. While numerous factors contribute to its persistence, this abstract explores the potential of social capital, the networks and norms of trust within a society, to act as a powerful antidote. However, the relationship between social capital and corruption is not always straightforward. Informal networks, while fostering trust, can also create opportunities for collusion and exclusion. Moreover, the effectiveness of social capital in combating corruption is contingent upon its equitable distribution within a society. Unequal access to social networks can exacerbate existing power imbalances and hinder collective action against corrupt elites.
Panel regression anaylsis reveals that a 1 unit increase in social capital increases Corruption Perceptions Index (CPI) by 0.138891 units (100 is very clean and 0 is highly corrupt). In conclusion, while social capital is not a panacea for corruption, its potential to foster trust, norms of fairness, and collective action makes it a valuable tool in the fight against this complex phenomenon. Further research is needed to understand the specific contexts and mechanisms through which social capital can be most effectively harnessed to promote transparency, accountability, and ultimately, good governance regarding to fight against corruption.
JEL Codes: D73, H26, O11
Unveiling the Impact of Ownership Structure on SMEs’ Cybersecurity Perceptions
Authors: Pekarcik Marek , Safar Leos , Rutecka Paulina , Morawiec Patryk
Published: June 2024

As we move towards a more digitalized and interconnected world, new cybersecurity challenges emerge. While most research focuses on large companies, this study aims to fill a gap in the existing literature by exploring cybersecurity issues in small and medium-sized enterprises (SMEs), particularly in relation to non-technical, soft-skill, and intellectual capital aspects. This study examines the interplay between cybersecurity awareness, perception, and ownership structure in SMEs in the Silesian Region of Poland. Unlike the majority of cybersecurity literature, our focus is on how ownership structure influences risk perception. We surveyed 200 SMEs and utilized hierarchical and simple linear regression analyses to assess the relationships between these factors and financial performance. Our results indicate that larger enterprises and those without a family-owned structure exhibit significantly higher levels of cybersecurity. Additionally, we found a positive correlation between cybersecurity and a firm's financial performance and overall health. These findings underscore the importance of cybersecurity awareness and practices for the growth and stability of SMEs.
Relationship between resilience, social support and job burnout among journalists in Greece
Authors: Katsiroumpa Aglaia , Bistaraki Angeliki , Moisoglou Ioannis , Giazitzi Dimitra-Ioanna , Kalogeropoulou Maria , Gallos Parisis , Galanis Petros
Published: June 2024

Background
Journalists experience high levels of job burnout. However, little is known about the factors that affect job burnout among journalists.
Aim
To investigate the relationship between resilience, social support and job burnout in a sample of journalists.
Methods
We conducted a cross-sectional study with a convenience sample. We performed our study in Greece during April 2023. We used the Multidimensional Scale of Perceived Social Support, the Brief Resilience Scale and the single item burnout measure to measure social support, resilience and job burnout, respectively. We measured the following demographic variables: gender, age, chronic disease, educational level and job experience.
Results
Among our sample, mean age was 50.4 years (standard deviation = 11.4). More than half of our journalists were females (57.3%). Our journalists experienced moderate to high levels of social support, and moderate levels of resilience. Mean job burnout score was 5.99 (standard deviation = 2.22). In our sample, 58.7% (n = 88) of journalists had a job burnout score ≥ 6 indicating high levels of burnout, while 41.3% (n = 62) had a job burnout score < 6 indicating low levels of burnout. Both multivariable linear and logistic regression models showed a negative relationship between resilience and job burnout (linear regression model: adjusted coefficient beta = -0.585, 95% CI = -1.133 to -0.038, p-value = 0.036), (logistic regression model: adjusted odds ratio = 0.575, 95% CI = 0.330 to 0.999, p-value = 0.049). There were no statistically significant relationships between social support and job burnout in both models.
Conclusions
Our sample experienced high levels of job burnout. We found that more resilient journalists experienced lower levels of job burnout. Social support did not affect levels of job burnout among journalists.