Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Transmission of Data by a Sequence of Clusters for a Dissruption Tolerant Network

Presently wireless networks are deployed in various extreme environments where they suffer from different levels of link disruptions depending on the severity of the operating conditions. Disruption/Delay tolerant networks (DTN), are designed with node density, node mobility, and less global network information. The major research work in DTNs is focused on data forwarding, but there are many challenges in providing an efficient access the data to a mobile user. In this paper, we put forward a new method to enhance the caching in DTNs, which is needed for sharing the cached data for multiple nodes and decreasing the delaying access. Here the cached data in group of nodes, a center node can be access the data from all neighbor nodes in the network. We propose an efficient method which ensures that proper selection of center node based on a probabilistic selection metric. The simulations prove that our approach extensively improves data access performance compared to existing schemes.
Post-prandial serum concentrations of Trimethylamine-N-oxide in overweight and obese adults with low and high systemic inflammation

Purpose: Elevated trimethylamine-N-oxide (TMAO) levels have been implicated in the development of atherosclerosis. The impact of a high-fat meal on the post-prandial TMAO response in healthy overweight and obese adults was explored, accounting for additional variables that may affect the rate of TMAO conversion. Methods: Forty overweight and obese men and women participated in a high-fat meal challenge containing 50 g fat. Blood samples were collected at fasting and hourly in the 4 hours after meal ingestion and tested for concentrations of TMAO, liver enzymes, lipids, creatinine, insulin, and inflammatory markers. An initial mixed-effects model was constructed to analyze TMAO changes in participants who classified as having low or high levels of systemic inflammation accounting for factors that may influence TMAO concentrations. Backward refinement of the initial model was performed based off pvalues. Results: Model refinement found that the log TMAO response was best explained by inclusion of estimated glomerular filtration rate (eGFR), insulin over time, a linear interaction between time and inflammation phenotype, and random effects on subject. Log TMAO after the meal was not different between inflammation phenotypes (p = 0.33). Estimated GFR was the most important determinant in the log TMAO response (p = 0.03), with lower log TMAO observed with higher eGFR values. In conclusion, serum TMAO concentrations during a high fat-meal challenge were strongly determined by eGFR, but not by fasting inflammation status.
8. Cultural Continuity, Identity, and Archaeological Practice in the Indian Context

This essay aimed to examine cultural continuity and archaeological practice in Indian archaeology. Contemporary practice in Indian archaeology is marked by presumed cultural continuity. A recent call for the archaeological study of caste has presented an opportunity to discuss keys issues in Indian archaeology. An issue is raised on the characterization of South Asian archaeology. The chronological and spatial characterisation indicates the scale at which archaeological study is examined. There is disproportionate emphasis on research efforts in Independent India. It is suggested that archaeological study be examined within the social setting in which it is practised.
Deep Reinforcement Learning Based Control of a Grid Connected Inverter With LCL-Filter for Renewable Solar Applications

This research paper presents a novel approach to current control in Grid-Connected Inverters (GCI) using Deep Reinforcement Learning (DRL) based Twin Delayed Deep Deterministic Policy Gradient (TD3) method. The study focuses on addressing the limitations of traditional control techniques and state of the art techniques, particularly Proportional-Integral (PI) control and Model Predictive Control (MPC), by leveraging the adaptive and autonomous learning capabilities of DRL. The proposed novel modified TD3-based DRL method learns an optimal control policy directly from raw data, enabling the controller to adapt and improve its performance in real-time. The research includes a comprehensive analysis of the implementation and validation of the modified TD3-based DRL control in a grid-connected three phase three level Neutral Point Clamped (NPC) inverter system with Inductor-Capacitor-Inductor (LCL) filter. Real-time validation experiments are conducted to evaluate the control performance, power transfer capability in grid compliance. Furthermore, a detailed comparison is presented with experimentation, highlighting the advantages of the TD3-based DRL control over PI and MPC control techniques. Robustness checking is performed under various operating conditions, including parameter variations and dynamic conditions in the grid. The results analysis demonstrates that the TD3-based DRL control outperforms traditional PI control techniques in terms of static, dynamic response, and robustness. Additionally, The DRL based grid connected inverter current control method is validated in Renewable Energy Source (RES) solar PV grid integration application.
Web Feature Deprecation: A Case Study for Chrome

Deprecation – signaling that a function is becoming obsolete in the near future – is necessary for the web ecosystem. However, an unsuccessful deprecation can lead to pain for both developers and end-users. In this paper, we analyze web feature deprecations with the goal of improving Chrome's deprecation process by balancing the need for deprecation and minimizing user pain. We produce a taxonomy of reasons why developers want to deprecate web features and a deprecation methodology for deciding when it is safe to deprecate a web feature. We also discuss the challenges faced during the deprecation process.
Identification of hub genes significantly linked to temporal lobe epilepsy and apoptosis via bioinformatics analysis

Background Epilepsy stands as an intricate disorder of the central nervous system, subject to the influence of diverse risk factors and a significant genetic predisposition. Within the pathogenesis of temporal lobe epilepsy (TLE), the apoptosis of neurons and glial cells in the brain assumes pivotal importance. The identification of differentially expressed apoptosis-related genes (DEARGs) emerges as a critical imperative, providing essential guidance for informed treatment decisions. Methods We obtained datasets related to epilepsy, specifically GSE168375 and GSE186334. Utilizing differential expression analysis, we identified a set of 249 genes exhibiting significant variations. Subsequently, through an intersection with apoptosis-related genes, we pinpointed 16 genes designated as differentially expressed apoptosis-related genes (DEARGs). These DEARGs underwent a comprehensive array of analyses, including enrichment analyses, biomarker selection, disease classification modeling, immune infiltration analysis, prediction of miRNA and transcription factors, and molecular docking analysis. Results In the epilepsy datasets examined, we successfully identified 16 differentially expressed apoptosis-related genes (DEARGs). Subsequent validation in the external dataset GSE140393 revealed the diagnostic potential of five biomarkers (CD38, FAIM2, IL1B, PAWR, S100A8) with remarkable accuracy, exhibiting an impressive area under curve (AUC) (The overall AUC of the model constructed by the five key genes was 0.916, and the validation set was 0.722). Furthermore, a statistically significant variance (p < 0.05) was observed in T cell CD4 naive and eosinophil cells across different diagnostic groups. Exploring interaction networks uncovered intricate connections, including gene-miRNA interactions (164 interactions involving 148 miRNAs), gene-transcription factor (TF) interactions (22 interactions with 20 TFs), and gene-drug small molecule interactions (15 interactions involving 15 drugs). Notably, IL1B and S100A8 demonstrated interactions with specific drugs. Conclusion In the realm of TLE, we have successfully pinpointed noteworthy differentially expressed apoptosis-related genes (DEARGs), including CD38, FAIM2, IL1B, PAWR, and S100A8. A comprehensive understanding of the implications associated with these identified genes not only opens avenues for advancing our comprehension of the underlying pathophysiology but also bears considerable potential in guiding the development of innovative diagnostic methodologies and therapeutic interventions for the effective management of epilepsy in the future.
Estimation of Canopy Height From a Multi-SINC Model in Mediterranean Forest With Single-Baseline TanDEM-X InSAR Data

TanDEM-X interferometric synthetic aperture radar (InSAR) data have demonstrated promising advantages and potential in recent years for the inversion of forest height. InSAR coherence becomes the primary input feature when a precise digital terrain model is unavailable, but the relationship between InSAR coherence and forest height remains uncertain because of the complexity of forest scenes. In this article, a method for retrieving canopy height in Mediterranean forests, characterized by short and sparse trees, using a single-pass bistatic TanDEM-X InSAR dataset is proposed. To improve the accuracy of forest height inversion from the uncertain correlation between InSAR coherence and canopy height, we begin by using the established SINC model with two semiempirical parameters and then expand the single curve into a collection of three curves, forming the multi-SINC model. To determine the optimal relationship (curve) between TanDEM-X InSAR coherence and canopy height, the problem is shifted from parameter inversion to classification. To solve the problem, we used optical remote sensing data, a small amount of light detection and ranging (LiDAR) data, and TanDEM-X InSAR data in combination with machine learning for classification. As a proof-of-concept, we conducted forest height retrieval at two study sites in Spain with complex terrain and diverse forest types. The results were verified by comparing them with LiDAR product forest height, which demonstrated improved performance (RMSE = 2.49 m and 1.7 m) compared with the semiempirical SINC model (RMSE = 3.28 m and 2.36 m).
Establishment and Practice of Physical Education Evaluation Using Grey Cluster Analysis Under the Data Background

This article uses scientific methods and means to evaluate the value, elements, and processes of physical education, consistent with preset evaluation indicators through sample calculation, and then derives the characteristics of decision-making, the objectivity of indicators, the order, and other characteristics of the process. The authors have analyzed the main problems in current physical-education evaluation and its future reform and development trends under the requirements of quality education.
Periodic Leg Movements during Sleep Associated with REM Sleep Behavior Disorder: A Machine Learning Study

Most patients with idiopathic REM sleep behavior disorder (iRBD) present peculiar repetitive leg jerks during sleep in their clinical spectrum, called periodic leg movements (PLMS). The clinical differentiation of iRBD patients with and without PLMS is challenging, without polysomnographic confirmation. The aim of this study is to develop a new Machine Learning (ML) approach to distinguish between iRBD phenotypes. Heart rate variability (HRV) data were acquired from forty-two consecutive iRBD patients (23 with PLMS and 19 without PLMS). All participants underwent video-polysomnography to confirm the clinical diagnosis. ML models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were trained on HRV data, and classification performances were assessed using Leave-One-Out cross-validation. No significant clinical differences emerged between the two groups. The RF model showed the best performance in differentiating between iRBD phenotypes with excellent accuracy (86%), sensitivity (96%), and specificity (74%); SVM and XGBoost had good accuracy (81% and 78%, respectively), sensitivity (83% for both), and specificity (79% and 72%, respectively). In contrast, LR had low performances (accuracy 71%). Our results demonstrate that ML algorithms accurately differentiate iRBD patients from those without PLMS, encouraging the use of Artificial Intelligence to support the diagnosis of clinically indistinguishable iRBD phenotypes.
The quantitative reconstruction of temperature and precipitation in the Guanzhong Basin of the southern Loess Plateau between 6200 BP and 5600 BP

Understanding the features of the paleoclimate and paleoenvrionment from key areas is crucial for predicting the climate variation of the future. In this study, fossil charcoal coupled with high-accuracy accelerator mass spectrometry (AMS) 14C dating from the Guanzhong Basin is analyzed to reconstruct the paleoclimate. Here, the coexistence approach (CA) was applied, and the result showed that the mean annual temperature (MAT) was about 14.8°C, and the annual precipitation (AP) was about 831.1 mm in the Guanzhong Basin during 6200–5600 cal. a BP. Comparing the climate between the mid-Holocene and present in the Xi’an area, the MAT was about 1.1°C higher than today and the AP was about 278 mm higher than today, similar to the modern climate of the Hanzhong area in the southern Qinling Mountains.
Gendered patterns in first-year engineering students’ career aspirations and expectations

Her thesis is exploring how the quality of work-integrated learning experiences shape women engineers’ career intentions, with a focus on intersectionality and diverse engineering fields including emerging and non-traditional areas of practice. MacKenzie has an undergraduate engineering degree in Biomedical Systems Engineering, where her research focused on high-intensity focused ultrasound.
Owner expectations and surprises of dog ownership experiences in the United Kingdom

Introduction Although many owners are satisfied by dog ownership, large numbers of dogs are relinquished annually, with an estimated 130,000 dogs cared for each year by rescue organisations in the UK. Unrealistic ownership expectations are a potential factor in the decision to relinquish and therefore understanding what surprises owners about the realities of ownership and how this meets their expectations is vital. Methods Using a retrospective cross-sectional cohort study design, as part of Dogs Trust’s National Dog Survey 2021, owners were asked ‘what has surprised you most about owning a dog? ’ and to classify how their experiences had compared with their expectations on a list of aspects of ownership as either more than, less than or as expected. Free text responses (n= 2,000) were analysed using reflexive thematic analysis in NVivo Pro (v.12 QSR) and a quantitative summary of classified expectations (n=354,224) was conducted in R. Results Many aspects of ownership were reported to be as expected, however a discrepancy between expectation and reality regarding some aspects was revealed. The cost of vet visits was greater than expected for the majority of respondents (52%), whilst other factors that often exceeded expectations included buying/rehoming cost (33%) and amount of patience needed (25%). Damage to furniture was less than expected for many (50%) as was damage to garden (33%). From the thematic analysis, four themes were generated that reflected what surprised owners most about ownership: emotional connectedness of human–dog relationships; dog’s impact on human health/wellbeing; understanding what dogs are like; and meeting the demands of ownership. Conclusion Overall these results aid our understanding of dog-human interactions, highlighting the complexity of the dog-owner relationship which may come with unanticipated costs. Whilst this study’s results are reassuring given many aspects of ownership were as expected, and surprises were often positive, some areas had greater impacts than expected, raising opportunities for intervention, resources or support. The aim would be to manage owners’ expectations prior to acquisition or ensure these are more realistically met, reducing the likelihood of negative welfare implications for both dog and owner.
Joint Q-Learning Based Resource Allocation and Multi-Numerology B5G Network Slicing Exploiting LWA Technology

The emergence of the sixth generation (6G) era has highlighted the importance of Network Slicing (NS) technology as a promising solution for catering the diverse service requests of users. With the presence of a large number of devices with different service requests and since each service has different goals and requirements; efficiently allocating Resource Blocks (RBs) to each network slice is a challenging task to meet the desired Quality of Service (QoS) standards. However, it is worth noting that the majority of research efforts have primarily concentrated on cellular technologies, leaving behind the potential benefits of utilizing unlicensed bands to alleviate traffic congestion and enhance the capacity of existing LTE networks. In this paper we propose a novel idea by exploiting LTE-WLAN Aggregation technology (LWA) in Multi-Radio Access Technology (RAT) Heterogeneous Networks (HetNet), aiming to solve radio resource allocation problem based on the Radio Access Network (RAN) slicing and 5G New Radio (NR) scalable numerology technique. A joint optimization problem is proposed by jointly finding an efficient resource allocation ratio for each slice in each Base Station (BS) and by finding the optimum value of scalable numerology with the objective of maximizing users’ satisfaction. In order to solve this problem, a novel three-stage framework is proposed which is based on channel state information as a pre-association stage, Reinforcement Learning (RL) algorithm as finding the optimum value of slice resource ratio and scalable numerology, and finally Regret Learning Algorithm (RLA) as users’ re-association phase. Furthermore, a comprehensive performance evaluation is conducted against different baseline approaches. The simulation results show that our proposed model balances and achieves improvement in users’ satisfaction by deploying the proposed Multi-RAT Het-Net architecture that leverages LWA technology.
Plug-and-play DPC-based quantitative phase microscope

Point-of-care testing (POCT) plays an increasingly important role in biomedical research and health care. Quantitative phase microscopes (QPMs) with good contrast, no invasion, no labeling, high speed and automation could be effectively applied for POCT. However, most QPMs are fixed on the optical platform with bulky size, lack of timeliness, which remained challenging in POCT solutions. In this paper, we proposed a plug-and-play QPM with multimode imaging based on the quantitative differential phase contrast (qDPC) method. The system employs a programmable LED array as the light source and uses the GPU to accelerate the calculation, which can realize multi-contrast imaging with six modes. Accurate phase measurement and real-time phase imaging are implemented by the proposed qDPC algorithms for quantitative phase targets and biomedical samples. A 3D electric control platform is designed for mechanical control of field of view and focusing without manual operations. The experimental results verify the robustness and high performance of the setup. Even a rookie could finish the POCT scheme for biomedical applications at the scene using the QPM with a compact size of 140 × 165 × 250 mm3.
Computer-Supported Collaborative Learning in Higher Education: An Experimental Analysis of Small-Group Collaboration in Web-Conferencing

: Computer-supported collaborative learning (CSCL) is a widely known and used teaching and learning approach in higher education online and hybrid scenarios. When planning an online or hybrid session from a didactic point of view, in most cases only the maximum capabilities of the planed CSCL-tool are considered. However, participants differ in their interindividual tool usage, e.g., webcam usage, due to personal or technical reasons. In result, a CSCL-session planned on a web-conferencing platform can unintentionally turn into a session on a spectrum from videoconferencing (all participants use their webcam and microphone) over audioconferencing (participants refusing webcam usage) to synchronous text-chat (webcam-and microphone-refusal). In worst case this can cause misleading conclusions about the didactic match between tool and task with negative effect on teaching and learning. To consider the users’ interindividual tool usage, we conducted an online experiment with 45 undergraduate students building 15 three-student groups performing a murder mystery based on hidden-profile-paradigm. The murder mystery task simulated a typical CSCL-session task in higher education, where an ad-hoc small-group of three students collaborate by pooling shared and unshared knowledge into a common solution. This data-driven approach targeted to evaluate potential differences in (a) task performance, (b) interaction-process quality, and (c) mental workload between CSCL-sessions intentionally planned to be performed on a web-conferencing platform by the lecturer for didactic reasons but participants refuse to use webcam or microphone. The gained insights should serve to define CSCL related policies and practices, which are conducive to learning. Despite from theory and prior empirical findings deduced expected differences, no evidence of superiority of one of the three experimental conditions (videoconferencing, audioconferencing, and synchronous text-chat) could be observed in this contribution. Possible reasons for this result, limitations of this study, and practical implications are discussed.
Fair Congestion Control Protocol for Data Center Bridging

Data center networking brought a new era of data-intensive applications such as remote direct memory access, high-performance computing, and cloud computing, which raise new challenges for network researchers. Such applications require minimum network latency, no packet loss, and fairness between flows. Therefore, IEEE Data Center Bridging Task Group presents several enhancements for Ethernet networks to fulfill these requirements. In this context, we investigate the possibility of achieving dropless Ethernet. We extend our previously proposed congestion control protocol, named Heterogeneous Flow (HetFlow), to achieve minimum queue length and consequently minimum network latency. In addition, we present a mathematical model, stability analysis, and scalability study of the proposed protocol. Further, extensive simulation experiments are conducted to verify our mathematical analysis. Moreover, it is illustrated by simulations that HetFlow improves fairness between flows of different packet sizes and different round trip times.
Hydrological modelling in Amazonia- : use of the MGB-IPH model and alternative databases

Different hydrological data sources were tested in this work. These data sources should be used in mathematical models developed for large basins. Precipitation data from the hydrometeorological network of the Brazilian National Water Agency (ANA) and from NCEP/NCAR reanalysis corrected by the Center for Ocean Land Atmosphere (COLA) were used. Climatological data from International Satellite Land Surface Climatology Project (ISLSCP) and from NCEP/NCAR reanalysis were used to calculate evapotranspiration. The Hydrological Model for Large Basins (MGB-IPH) was used to test the alternative databases from COLA/NCEP/NCAR and ISLSCP. The model was applied in the Brazilian portion of the Madeira River basin. The results showed that the daily COLA precipitation series has values very close to the ANA precipitation series for the period 1979-1990. The model simulations showed that the results are quite similar using either ANA or COLA precipitation over the period 1979-1990.