Miracle-dz/UniFAR-FTUs huggingface dataset:
Replication control strategy based on a simple game of life in opportunistic networks

Although flooding-based message dissemination in opportunistic mobile networks yields high delivery performance, the nodes' resources, such as energy and buffer, are rapidly depleted due to the enormous quantity of message replicas dispersed over the networks. This study aims to reduce the number of message replicas in the networks while maintaining an acceptable delivery rate. Inspired by Conway's Game of Life, which uses cellular automaton-based neighbor conditions to regulate the total population, we propose RiGoL, a replication control-based technique for determining how many neighbors hold the same messages. We utilize a counter to keep track of the number of neighbors and two thresholds, namely forward and drop thresholds. Our strategy works as follows: when a pair of nodes meet, the node checks to see if the peer has the same message. The counter is increased by one if the peer does not hold the same message. On the other hand, if the peer already has the message, the counter is decremented by one. Only when the counter exceeds the forward threshold, then the message forwarded to another node. In contrast, the message is deleted from the node's buffer if the counter value is less than the drop threshold. We conduct extensive simulations using ONE Simulator to evaluate our proposed strategy. The simulation results show that although RiGoL yields higher latency than Epidemic, RiGoL successfully reduces the message replication in the network and prolongs the hub node's lifetime.
Effect of Economic Policy Uncertainty on Exchange Rate Volatility in Pakistan

This study examines the effect of economic policy uncertainty on Pakistan’s exchange rate. Pakistan’s exchange rate always remains under pressure due to the widening inflow-outflow gap in foreign exchange. The volatile exchange rates make it harder for businesses to invest and make inflation more uncertain, which ultimately slows down productivity and GDP growth. Due to the close linkages of the exchange rate with the economic indicators, it is required to examine the effect of different economic variables on the exchange rate and its volatility by incorporating the role of economic policy uncertainty. The study uses the sample of Jan 2011 to Dec 2022 for the empirical results. The study employs the EGARCH model for empirical results. The aftermaths of the mean equation of the EGARCH model suggest that economic policy uncertainty has a positive effect on Pakistan’s exchange rate. Moreover, an increase in the economic policy uncertainty scale, increases the volatility in Pakistan’s exchange rate. The primary source of exchange rate volatility is the nominal variable rather than the real variables. On the basis of the study’s findings, certain relevant recommendations were given in order to stabilize the foreign exchange market in Pakistan.
Antimicrobial Activity of Two Different Types of Silver Nanoparticles against Wide Range of Pathogenic Bacteria

The emergence of antibiotic-resistant bacteria, particularly the most hazardous pathogens, namely Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. (ESKAPE)-pathogens pose a significant threat to global health. Current antimicrobial therapies, including those targeting biofilms, have shown limited effectiveness against these superbugs. Nanoparticles, specifically silver nanoparticles (AgNPs), have emerged as a promising alternative for combating bacterial infections. In this study, two types of AgNPs with different physic-chemical properties were evaluated for their antimicrobial and antibiofilm activities against clinical ESKAPE strains. Two types of silver nanoparticles were assessed: spherical silver nanoparticles (AgNPs-1) and cubic-shaped silver nanoparticles (AgNPs-2). AgNPs-2, characterized by a cubic shape and higher surface-area-to-volume ratio, exhibited superior antimicrobial activity compared to spherical AgNPs-1. Both types of AgNPs demonstrated the ability to inhibit biofilm formation and disrupt established biofilms, leading to membrane damage and reduced viability of the bacteria. These findings highlight the potential of AgNPs as effective antibacterial agents against ESKAPE pathogens, emphasizing the importance of nanoparticle characteristics in determining their antimicrobial properties. Further research is warranted to explore the underlying mechanisms and optimize nanoparticle-based therapies for the management of infections caused by antibiotic-resistant bacteria.
Soil quality and microbial communities in subtropical slope lands under different agricultural management practices

Land degradation is a major threat to ecosystem. Long-term conventional farming practices can lead to severe soil degradation and a decline in crop productivity, which are challenging for both local and global communities. This study was conducted to clarify the responses on soil physicochemical properties and microbial communities to changes in farming practices. Slope land orchards under three agricultural management practices—conventional farming (CF), organic farming (OF), and ecofriendly farming (EFF)—were included in this study. We found that soil carbon stock increased by 3.6 and 5.1 times in surface soils (0–30 cm) under EFF and OF treatments, respectively. EFF and OF significantly increased the contents of total nitrogen by 0.33–0.46 g/kg, ammonia-N by 3.0–7.3 g/kg, and microbial biomass carbon by 0.56–1.04 g/kg but reduced those of pH by 0.6 units at least, and available phosphorous by 104–114 mg/kg. The application of phosphorous-containing herbicides and chemical fertilizers might increase the contents of phosphorous and nitrate in CF soil. High abundances of Acidobacteria and Actinobacteria were observed in EFF and OF soils, likely because of phosphorous deficiency in these soils. The abundance of fungi in OF soil indicated that plants’ demand for available soil phosphorous induced the fungus-mediated mineralization of organic phosphorous. High abundances of Gammaproteobacteria, Planctomycetes, Firmicutes, and Nitrospirae were observed in CF soil, possibly because of the regular use of herbicides containing phosphorous and chemical fertilizers containing high total nitrogen contents.
Explaining graph convolutional network predictions for clinicians—An explainable AI approach to Alzheimer's disease classification

Introduction Graph-based representations are becoming more common in the medical domain, where each node defines a patient, and the edges signify associations between patients, relating individuals with disease and symptoms in a node classification task. In this study, a Graph Convolutional Networks (GCN) model was utilized to capture differences in neurocognitive, genetic, and brain atrophy patterns that can predict cognitive status, ranging from Normal Cognition (NC) to Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Elucidating model predictions is vital in medical applications to promote clinical adoption and establish physician trust. Therefore, we introduce a decomposition-based explanation method for individual patient classification. Methods Our method involves analyzing the output variations resulting from decomposing input values, which allows us to determine the degree of impact on the prediction. Through this process, we gain insight into how each feature from various modalities, both at the individual and group levels, contributes to the diagnostic result. Given that graph data contains critical information in edges, we studied relational data by silencing all the edges of a particular class, thereby obtaining explanations at the neighborhood level. Results Our functional evaluation showed that the explanations remain stable with minor changes in input values, specifically for edge weights exceeding 0.80. Additionally, our comparative analysis against SHAP values yielded comparable results with significantly reduced computational time. To further validate the model's explanations, we conducted a survey study with 11 domain experts. The majority (71%) of the responses confirmed the correctness of the explanations, with a rating of above six on a 10-point scale for the understandability of the explanations. Discussion Strategies to overcome perceived limitations, such as the GCN's overreliance on demographic information, were discussed to facilitate future adoption into clinical practice and gain clinicians' trust as a diagnostic decision support system.
MANAGEMENT CAPABILITIES AND PERSONAL SAFETY OF EMPLOYEES IN FEDERAL ROAD SAFETY CORPS (FRSC), LAGOS STATE

This research investigates management capabilities and the personal safety of employees. This study's objective is to look into the managerial role in employee safety and also explore the influence of employee commitment on personal safety using the Federal Road Safety Corps (FRSC) as a case theatre. The study adopted a descriptive survey research design because it’s suitable for both qualitative and quantitative methods with fewer challenges. The population of this study comprised all staff of the Federal Road Safety Corps (FRSC), Lagos State with the use of a primary source of data. Results of this study have revealed that in the FRSC, there was an established significant and positive effect of the managerial role on employee safety (β = .314, Sig. = .001, P<0.05). Also, it further revealed that there was a significant positive effect of employee commitment on personal safety (β = .770, Sig. = .003, P<0.05). The study concluded that management capabilities are a fundamental aspect that any organization that seeks to achieve the best from its employees should aim towards. The study therefore recommends that the management should be more inclusive in allowing younger staff members of the Federal Road Safety Corp to contribute their ideas toward employee personal safety
Characteristics of temporal-spatial variation and evaluated pollution level of heavy metals in surface seawater of Honghai Bay

The surface seawater samples from 35 monitoring stations around Shanwei Port in Honghai Bay of Guangdong Province had been collected continuously in June 2007 and January 2008.Using atomic fluorescence photometer(XGY-1011A) and multifunctional polarograph(HY-1F),the contents and distribution of heavy metals(Hg,Cu,Pb,Zn and Cd) in surface seawater were studied.The pollution level was evaluated with water quality index(WQI) model.The result showed that the contents of Hg,Cu,Pb,Zn and Cd in surface seawater ranged 0.009~0.050,0.9~4.3,0.4~1.4,1.6~19.0,0.1~0.7 μg/dm3,and averaged 0.025,2.4,0.8,9.4,0.2 μg/dm3,respectively.The contents diminished from Pinqinghu Bay towards Shanwei Port.Both the single pollution index of heavy metals(Cif) and the comprehensive pollution index(WQI) at all stations were less than 1.The pollution level of heavy metal in surface seawater was low and under the second-class seawater quality standard.The WQI value(averaged 0.17) was slightly higher in January 2008 than June 2007(averaged 0.14),showing a trend of increment.Multivariate linear regression and multiple correlation analysis show that the correlation between heavy metal elements and environmental factors(pH,S,CCOD) was lined in orders of CdZnCuHgPb.
A Lightweight Hybrid Model with Location-Preserving ViT for Efficient Food Recognition

Food-image recognition plays a pivotal role in intelligent nutrition management, and lightweight recognition methods based on deep learning are crucial for enabling mobile deployment. This capability empowers individuals to effectively manage their daily diet and nutrition using devices such as smartphones. In this study, we propose an Efficient Hybrid Food Recognition Net (EHFR–Net), a novel neural network that integrates Convolutional Neural Networks (CNN) and Vision Transformer (ViT). We find that in the context of food-image recognition tasks, while ViT demonstrates superiority in extracting global information, its approach of disregarding the initial spatial information hampers its efficacy. Therefore, we designed a ViT method termed Location-Preserving Vision Transformer (LP–ViT), which retains positional information during the global information extraction process. To ensure the lightweight nature of the model, we employ an inverted residual block on the CNN side to extract local features. Global and local features are seamlessly integrated by directly summing and concatenating the outputs from the convolutional and ViT structures, resulting in the creation of a unified Hybrid Block (HBlock) in a coherent manner. Moreover, we optimize the hierarchical layout of EHFR–Net to accommodate the unique characteristics of HBlock, effectively reducing the model size. Our extensive experiments on three well-known food image-recognition datasets demonstrate the superiority of our approach. For instance, on the ETHZ Food–101 dataset, our method achieves an outstanding recognition accuracy of 90.7%, which is 3.5% higher than the state-of-the-art ViT-based lightweight network MobileViTv2 (87.2%), which has an equivalent number of parameters and calculations.
The critical role of neutrophil-endothelial cell interactions in sepsis: new synergistic approaches employing organ-on-chip, omics, immune cell phenotyping and in silico modeling to identify new therapeutics

Sepsis is a global health concern accounting for more than 1 in 5 deaths worldwide. Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis can develop from bacterial (gram negative or gram positive), fungal or viral (such as COVID) infections. However, therapeutics developed in animal models and traditional in vitro sepsis models have had little success in clinical trials, as these models have failed to fully replicate the underlying pathophysiology and heterogeneity of the disease. The current understanding is that the host response to sepsis is highly diverse among patients, and this heterogeneity impacts immune function and response to infection. Phenotyping immune function and classifying sepsis patients into specific endotypes is needed to develop a personalized treatment approach. Neutrophil-endothelium interactions play a critical role in sepsis progression, and increased neutrophil influx and endothelial barrier disruption have important roles in the early course of organ damage. Understanding the mechanism of neutrophil-endothelium interactions and how immune function impacts this interaction can help us better manage the disease and lead to the discovery of new diagnostic and prognosis tools for effective treatments. In this review, we will discuss the latest research exploring how in silico modeling of a synergistic combination of new organ-on-chip models incorporating human cells/tissue, omics analysis and clinical data from sepsis patients will allow us to identify relevant signaling pathways and characterize specific immune phenotypes in patients. Emerging technologies such as machine learning can then be leveraged to identify druggable therapeutic targets and relate them to immune phenotypes and underlying infectious agents. This synergistic approach can lead to the development of new therapeutics and the identification of FDA approved drugs that can be repurposed for the treatment of sepsis.
A Full 4-Way Tensor Network for Crop Classification with Time-Series Fully Polarization SAR Image

Time-Series Polarimetric Synthetic Aperture Radar (PolSAR) image is extensively applied in real-world crop classification tasks due to its ample characterization of crop growth stages and electromagnetic properties. However, this abundance information often leads to “dimension disaster” problem. Traditional methods attempt to mitigate the impact of high dimensional features by employing dimensionality reduction techniques in advance, which typically result in diminished classification accuracy due to the hard coupling between the dimensionality reduction model and the classifier. In this paper, a full 4-way tensor network is proposed, which is designed to efficiently extract the high-level time-series information and structural features directly from the original data, enabling classification without the need for dimensionality reduction procedure beforehand. Moreover, the proposed model has a simple structure and holds less parameters than the state-of-art methods. The performance of the proposed method is evaluated on simulated Sentinel-1 data provided by European Space Agency (ESA). The results validate the merits on agricultural applications of the proposed method.
Extracellular Vesicles and Exosomes: Novel Insights and Perspectives on Lung Cancer from Early Detection to Targeted Treatment

Lung cancer demands innovative approaches for early detection and targeted treatment. In addressing this urgent need, exosomes play a pivotal role in revolutionizing both the early detection and targeted treatment of lung cancer. Their remarkable capacity to encapsulate a diverse range of biomolecules, traverse biological barriers, and be engineered with specific targeting molecules makes them highly promising for both diagnostic markers and precise drug delivery to cancer cells. Furthermore, an in-depth analysis of exosomal content and biogenesis offers crucial insights into the molecular profile of lung tumors. This knowledge holds significant potential for the development of targeted therapies and innovative diagnostic strategies for cancer. Despite notable progress in this field, challenges in standardization and cargo loading persist. Collaborative research efforts are imperative to maximize the potential of exosomes and advance the field of precision medicine for the benefit of lung cancer patients.
Feeding Practices, Parent Perceptions, and Diet Diversity in a Sample of Children Aged 0–5 Years from Western Sydney, Australia: A Mixed Methods Study

(1) Background: Sub-optimal feeding practices and dietary intakes in childhood are associated with poor health outcomes in adulthood. This mixed methods study aims to profile feeding practices, parent perceptions, and dietary diversity in a sample of children aged 0–5 years (n = 29) from Western Sydney, Australia. (2) Methods: In 2019, semi-structured interviews were conducted with parents, exploring their child’s feeding practices. An online quantitative survey was used to assess children’s feeding history. Nutrient intakes and dietary diversity scores were assessed using an online 24-h dietary recall. Quantitative data were analysed using descriptive statistics and one-way ANOVA, while qualitative data were analysed using inductive thematic analysis. (3) Results: The analysis showed that 27.6% of children were exclusively breastfed until 6 months of age and that 62.1% of children were introduced to solids before 6 months. Over 60% of children achieved minimum dietary diversity. The thematic analysis identified four themes, including mothers’ feeding choices, mothers’ perceptions of their children’s diet, influences on feeding choices, and mothers’ personal experiences. (4) Conclusions: The feeding history of over half the children in this sample indicated non-compliance with Australian and WHO infant feeding guidelines. The thematic analysis revealed various possible influences on parent feeding choices that can be used to inform targeted support.
CRISPR technology towards genome editing of the perennial and semi-perennial crops citrus, coffee and sugarcane

Gene editing technologies have opened up the possibility of manipulating the genome of any organism in a predicted way. CRISPR technology is the most used genome editing tool and, in agriculture, it has allowed the expansion of possibilities in plant biotechnology, such as gene knockout or knock-in, transcriptional regulation, epigenetic modification, base editing, RNA editing, prime editing, and nucleic acid probing or detection. This technology mostly depends on in vitro tissue culture and genetic transformation/transfection protocols, which sometimes become the major challenges for its application in different crops. Agrobacterium-mediated transformation, biolistics, plasmid or RNP (ribonucleoprotein) transfection of protoplasts are some of the commonly used CRISPR delivery methods, but they depend on the genotype and target gene for efficient editing. The choice of the CRISPR system (Cas9, Cas12), CRISPR mechanism (plasmid or RNP) and transfection technique (Agrobacterium spp., PEG solution, lipofection) directly impacts the transformation efficiency and/or editing rate. Besides, CRISPR/Cas technology has made countries rethink regulatory frameworks concerning genetically modified organisms and flexibilize regulatory obstacles for edited plants. Here we present an overview of the state-of-the-art of CRISPR technology applied to three important crops worldwide (citrus, coffee and sugarcane), considering the biological, methodological, and regulatory aspects of its application. In addition, we provide perspectives on recently developed CRISPR tools and promising applications for each of these crops, thus highlighting the usefulness of gene editing to develop novel cultivars.
Potential contributions of the intrinsic retinal oscillations recording using non-invasive electroretinogram to bioelectronics

Targeted electric signal use for disease diagnostics and treatment is emerging as a healthcare game-changer. Besides arrhythmias, treatment-resistant epilepsy and chronic pain, blindness, and perhaps soon vision loss, could be among the pathologies that benefit from bioelectronic medicine. The electroretinogram (ERG) technique has long demonstrated its role in diagnosing eye diseases and early stages of neurodegenerative diseases. Conspicuously, ERG applications are all based on light-induced responses. However, spontaneous, intrinsic activity also originates in retinal cells. It is a hallmark of degenerated retinas and its alterations accompany obesity and diabetes. To the extent that variables extracted from the resting activity of the retina measured by ERG allow the predictive diagnosis of risk factors for type 2 diabetes. Here, we provided a comparison of the baseline characteristics of intrinsic oscillatory activity recorded by ERGs in mice, rats, and humans, as well as in several rat strains, and explore whether zebrafish exhibit comparable activity. Their pattern was altered in neurodegenerative models including the cuprizone-induced demyelination model in mice as well as in the Royal College of Surgeons (RCS–/–) rats. We also discuss how the study of their properties may pave the way for future research directions and treatment approaches for retinopathies, among others.