Miracle-dz/UniFAR-FTUs huggingface dataset:
QAVA: QoE-Aware Adaptive Video Bitrate Aggregation for HTTP Live Streaming Based on Smart Edge Computing

Currently video streaming in heterogeneous network environments is affected by limited network bandwidth availability and consequent low and variable user Quality of Experience (QoE) levels. In particular, for the case of live video streaming, a very high number of end-clients request content at the same time, generating huge concurrent traffic, and putting pressure on the existing network infrastructure. An approach which helps address this issue is deployment of emerging edge computing technologies to smooth the live streaming traffic and improve QoE by adapting client bitrates and caching content at the edge server. In this context, this paper proposes a novel QoE-aware Adaptive Video bitrate Aggregation scheme for HTTP live streaming based on smart edge computing (QAVA). As an intelligent proxy server, a “smart edge” which deploys QAVA aggregates all the traffic requested by clients for the same live streaming service and adapts their bitrates based on network conditions, client states and video characteristics. The adaptation is performed based on a Deep Reinforcement Learning (DRL)-based algorithm, which is also proposed. The QAVA DRL algorithm is trained and modeled based on a real client experience dataset. The experimental evaluation results presented in this paper show how QAVA outperforms other state-of-the-art adaptive bitrate algorithms in terms of average QoE and QoE fairness.
Research on classification and recognition of fruit and vegetable based on lightweight ResNet-50

With the rise of smart cities, classification and identification of fruits and vegetables in unmanned supermarkets has become a hot spot in recent years. In order to improve the efficiency of fruit and vegetable classification and recognition in practical applications, this paper proposes a lightweight ResNet-50 fruit and vegetable recognition method, which integrates a deep separable convolution module into the residual module of the ResNet-50 network, which makes the classification model more lightweight, improves the recognition efficiency, and is better applied to mobile devices or in a microcomputer. At the same time, the network model is fine-tuned and optimized to ensure that the classification model still maintains a high accuracy rate after being lightweight. In the end, under the premise of ensuring high recognition precision, the recognition speed of this method is increased from 6.4s to 5.2s. Compared with the original network, the recognition speed is increased by 19.19%, and the model weight file size is reduced by 57.8%.
Biophysical Controls of Ecosystem‐Scale Methane Fluxes From a Subtropical Estuarine Mangrove: Multiscale, Nonlinearity, Asynchrony and Causality

Mangroves are complex ecosystems subject to periodic and irregular changes in meteorology, tides, water quality and biological factors. Methane dynamics in mangroves and their dependence on biogeochemical controls across scales are poorly characterized using traditional parametric statistics. We measured the ecosystem‐scale methane flux (FCH4) using an eddy covariance system in a subtropical estuarine mangrove for two years. A combination of nonparametric statistical approaches, including singular spectrum analysis and information theory, was used to isolate multiscale FCH4 variations and explore their variability and dominant controls at different time scales. FCH4 exhibited the largest variability at the diel scale (57%), followed by seasonal (25%) and multiday (18%) scales. Mutual information metrics suggested that variation in FCH4 was dominantly coupled to plant activities through synchronous processes at the diel scale. Moreover, fluctuations in atmospheric turbulence and soil temperature dominated multiday variation in FCH4 through asynchronous processes. Seasonally, soil temperature and water salinity were dominant variables leading to changes in FCH4 with a time lag of 12 and 17 days, respectively. Weak links between FCH4 and tidal heights were found across all the studied time scales. Transfer entropy metrics indicated that a super typhoon event substantially weakened causal links between biophysical variables and FCH4 by causing severe defoliation and disturbing the soil microbial community. The typhoon also temporarily decoupled the causal link between FCH4 and temperature. These findings call for the inclusion of the properties of scale emergence, nonlinearity, asynchrony and causality for comprehensively understanding and accurately predicting the interactions between mangrove FCH4 and its biophysical controls.
Drift-diffusion cognitive models: description, applications and perspectives (Modelos cognitivos de deriva-difusión: descripción, aplicaciones y perspectivas)

ABSTRACT Drift-diffusion models (DDM) have been proposed as a way to study unobservable cognitive processes underlying decision-making. DDM have been widely used and developed in psychology and cognitive neuroscience, but there are no general references in Spanish on the subject. The purpose of this review is to develop reference materials for Spanish-speaking researchers, professionals and scholars in the area of cognitive sciences who are interested in the experimental study of decision-making in the psychology and neuroscience fields. It will also explore the standard description of DDM, its parameters, mathematical expression and applications in cognitive tasks. Recent publications in the fields of cognitive psychology and neuroscience that use DDM are reviewed, and their prospects for new and future applications are discussed, in light of current discussions of cognitive processes.
Design of a GaN Totem-Pole PFC Converter Using DC-Link Voltage Control Strategy for Data Center Applications

In today’s power industry, a compact and highly efficient power converter is the main component for server power applications. The objective of this paper is to propose a totem-pole power factor correction (PFC) converter with DC-link voltage control strategies for data center applications. Gallium Nitride High Electron Mobility Transistors (GaN HEMTs) are also used to increase power density and conversion efficiency without the reverse recovery loss. With the derived equations of the converter loss, the dc-bus voltage control strategy is proposed to improve the conversion efficiency. Furthermore, this paper proposes the hold-up time extension method to increase the DC bus voltage under heavy load conditions. The reduction of the DC link capacitor capacitance is achievable, and the power factor can be improved in a wide range. A 2.9-kW GaN totem-pole PFC prototype is developed and fabricated to verify this paper’s theoretical analysis. As demonstrated in the prototype’s experimental results, the tested peak efficiency is 98.5% and the tested total harmonic distortion (THD) is less than 3%.
Federated learning-based Explainable Anomaly Detection for Industrial Control Systems

We are now witnessing the rapid growth of advanced technologies and their application, leading to Smart Manufacturing (SM). The Internet of Things (IoT) is one of the main technologies used to enable smart factories, which is connecting all industrial assets, including machines and control systems, with the information systems and the business processes. Industrial Control Systems of smart IoT-based factories are one of the top industries attacked by numerous threats, especially unknown and novel attacks. As a result, with the distributed structure of plenty of IoT front-end sensing devices in SM, an effectively distributed anomaly detection (AD) architecture for IoT-based ICSs should: achieve high detection performance, train and learn new data patterns in a fast time scale, and have lightweight to be deployed on resource-constrained edge devices. To date, most solutions for anomaly detection have not fulfilled all of these requirements. In addition, the interpretability of why an instance is predicted to be abnormal is hardly concerned. In this paper, we propose the so-called FedeX architecture to address those challenges. The experiments show that FedeX outperforms 14 other existing anomaly detection solutions on all detection metrics with the liquid storage data set. And with Recall of 1 and F1-score of 0.9857, it also outperforms those solutions on the SWAT data set. FedeX is also proven to be fast in terms of training time of about 7.5 minutes and lightweight in terms of hardware requirement with memory consumption of 14%, allowing us to deploy anomaly detection tasks on top of edge computing infrastructure and in real-time. Besides, FedeX is considered as one of the frameworks at the forefront of interpreting the predicted anomalies by using XAI, which enables experts to make quick decisions and trust the model more.
ROBUST TECHNIQUES FOR DESIGNING POWER SYSTEM STABILIZER

mail: - E ABSTRACT This paper introduces an analysis of the phenomena of stability of synchronous machines under small perturbations by examining the case of a single machine connected to a large system through external impedance, and uses robust control H∞ techniques to design stabilizer for electric power system. H ∞ techniques that are used are H ∞ -optimal controller synthesis, H ∞ - mixed sensitivity controller synthesis and H ∞ - loop shaping controller synthesis.
GENETIC AND PHENOTYPIC TRENDS FOR TEST DAY MILK, FAT AND PROTIN YIELDS APPLYING RANDOM REGRESSION MODEL IN EGYPTIAN BUFFALOES

SUMMARY The aim of this study was to detect genetic and phenotypic trends for test day milk, fat and protein yields in Egyptian buffalo applying the random regression model (RRM) and determining the genetic and phenotypic trend. Data of 4971 test days (TD) milk yield traits were recorded for 691 Egyptian buffalo cows, daughters of 120 sires and 532 dams from four herds belonging to the Animal Production Research Institute, Egypt. Ten-month classes of lactation days were considered for the test-day yields. The model included the random effects of direct additive genetic, permanent environment and error, while the fixed effects were herd-test day, year and season of calving and parity as well as days in milk as a covariable, which was modeled by orthogonal Legendre polynomials. The additive genetic variance estimates at first test day for milk, fat and protein yields were respectively 0.035kg, 2.26g, 0.80g, increased until the fourth (0.807kg, 30.52g, 12.52g), decreased thereafter, reaching the lowest value at the ninth test day for milk and protein yields (0.238kg, 0.97g) and at the tenth test day for fat yield (7.28g). Heritability estimated at first test day was 0.05, increased until the fourth test day (0.30), and decreased thereafter and reached the lowest value at the tenth test day (0.06). The highest heritabilities were found to be 0.29 and 0.31 for fourth test day in fat and protein yields, respectively. The range in phenotypic values change decreased from 7.99 to 5.66kg, 53.37 to 35.07g and 30.86 to 21.54g, while the respective genetic values change increased from -0.22 to 0.17kg, -1.41 to 1.36g and 0.82 to 0.70g for milk, fat and protein yields, respectively. The genetic trends were slightly positive for all traits indicating that the selection program performs correctly. For all traits, the phenotypic trends showing deteriorating trends indicating the presence of some environmental inadequacies especially for nutritional level.
Performance Evaluation of Mobility Robustness Optimisation (MRO) in 5G Network with Various Mobility Speed Scenarios

The massive deployment of small-sized cells for the Fifth Generation (5G) mobile network will increase the Handover Probability (HOP), potentially causing higher Handover Ping-Pong Probability (HPPP) and/or Radio Link Failure (RLF). Inappropriate usage of Handover Control Parameter (HCP) settings may further exasperate this issue. Therefore, Mobility Robustness Optimisation (MRO) has been introduced and further developed as a significant Self-Optimisation Network (SON) function in the 5G network and beyond. The main aim of MRO is to address Mobility Management (MM) issues during user mobility between cells to ensure a smooth connection. Although various algorithms were suggested in the literature, they mostly cater to 4G networks which may not be effective for the 5G network due to different network characterisations. This paper analyses the performance of various MRO algorithms with various system settings and scenarios for the 5G network. The investigated algorithms from the literature include the Distance (Dis), Cost Function (CF), Fuzzy Logic Controller (FLC) and Handover Performance Indicator (HPI). Validation has been accomplished for different mobility conditions in the 5G network. A simulation based on the MATLAB software has been conducted using various system tools. The evaluation analysis is in terms of Signal to-Interference-plus-Noise-Ratio (SINR), HPPP and RLF effects since these are major indicators in assessing system performance and selecting the handover decision during user mobility. The simulation outcomes show that the HPI algorithm performance is more reactive to mobile speed scenarios over time, significantly reducing the HPPP compared to the other algorithms which do not provide large reactions in the same conditions. Simultaneously, the HPI algorithm exhibits the highest RLF and SINR from among the other algorithms. The distance algorithm is the best in terms of RLF and SINR, achieving an acceptable level in terms of HPPP. These results point to that the MRO algorithm that operate based on distance is the most robust compared to the other investigated algorithms, confirming the potential of the Dis approach for the 5G network.
Institutional capacity for the care of people with chronic diseases in primary health care

Objective: to investigate the institutional capacity for the care of people with chronic non-communicable diseases in primary health care. Method: cross-sectional, quantitative and exploratory study. Data collection used the questionnaire, translated and adapted for Brazil, Assessment of Chronic Illness Care. Data collection took place between December 2017 and June 2018. 159 professionals working in 49 primary health care units responded to the instrument. Results: the ability to care for people with chronic diseases was classified as basic. The components with the best and worst scores were the design of the service delivery system and clinical decision support, respectively. Conclusion: the results of this study showed that it is necessary to invest primarily in expert feedback on counter-reference, partnerships with the community, especially in units that work in the traditional model, and training of professionals to support self-care.
Study on the nano and physicochemical properties of soot particles produced by kerosene combustion

Abstract In this article, soot particles generated and discharged from the flame during the kerosene combustion were studied. The soot particles collected in the flame were characterized by transmission electron microscopy, while the soot particles collected above the flame were characterized by x-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, Raman, and thermogravimetric techniques. The results show that with the increase of the sampling height within the flame, the morphology of the soot particles gradually changed from viscous, connected dense state to well-defined particles which were accumulated to form chain structure, and eventually formed particle superposition. Meanwhile, the soot particles mainly contain four elements of C, O, N, and H. The soot particles showed good oxidation activity in the thermogravimetric analysis in an oxygen environment. On the other hand, by heating in a nitrogen environment, after 567 °C, the soot particles were subject to thermal degradation by self-oxidation with O-containing functional groups, involving bond rupture and restructuring to generate volatile substances.
Development and evaluation of dental film of doxycycline for the treatment of periodontitis

--- The study has been designed to formulate intrapocket dental film loaded with doxycycline to be used for insertion into the periodontal pockets and evaluate its clinical efficacy. Doxycycline dental films were prepared by the solvent casting technique using polymers such as gelatin was used as film former. Glycerin as plasticizer and glutaraldehyde as cross linking agent were used. The prepared films (F1-F9) were evaluated for various physicochemical parameters employing established pharmaceutical procedures such as surface pH, folding endurance, tensile strength, swelling index, drug content, in-vitro drug release, antibacterial efficacy, stability studies. Experimental parameters of the obtained film exhibited results within the desired limit. Fourier Transform Infrared Spectroscopy and Differential Scanning Calorimetry studies revealed the formulated film to be stable during drug stability and compatible between drugs and excipients. In-vitro dissolution studies showed an initial burst release to achieve an immediate therapeutic level of drug in the periodontal pocket followed by a progressive fall and extended-release of the drug for 12 days. The antimicrobial activity was retained for 168 hours against Staphylococcus aureus and E. coli . 5% dental film showed sustained drug release by kill kinetics as there is decrease in the colony count and it is compared with pure drug and control both strain of bacteria. The formulations were subjected to stability studies, the results thus obtained showed no significant changes in the formulation. The experimental results suggest that doxycycline dental film can be experimentally identified as a potential drug delivery system. Hence, doxycycline dental film can be used for topical treatment of periodontal diseases and to achieve optimum therapeutic efficacy. excipients. All of the films had a burst discharge that was then regulated and extended for the duration of the study. The work demonstrates first-order drug release via a diffusion process. The antibacterial activity of the produced formulations was tested using staphylococcus aureus and E. coli strains, and the antimicrobial activity was found to last for 168 hours. Stability tests were performed on the formulations, and the findings revealed no major changes in the formulation.
The force resulting from the action of mono- and biarticular muscles in a limb

Human and animal limbs can be modelled as a chain of segments connected at joints. For a static limb, the force exerted at the endpoint due to the force of a single muscle has been calculated. It turns out that there are marked diﬀerences in the action of mono-vs. biarticular muscles. Monoarticular muscles produce an endpoint force that is directed in the lengthwise direction of the limb, i.e. in the direction of one of the segments. The force from biarticular muscles can have a marked transverse component. The ‘principal direction’ of this endpoint force is also the movement direction of the endpoint which is the most favourable for the muscle to do work. The reasoning presented can explain e.g. the diﬀerences in the activity of mono- and biarticular muscles in cycling. # 2001 Elsevier Science Ltd. All rights reserved.
How Real Estate Shocks Affect Manufacturing Value Chain Upgrading: Evidence from China

Contrary to the conventional viewpoint that “high housing price promotes industrial upgrading”, this study finds that increasingly high housing prices are a pivotal factor that obstructs industrial value chain upgrading. Based on city-level data, micro-level data for Chinese industrial enterprises, and data for listed Chinese enterprises, this study examines the impacts of urban housing prices on value chain upgrading. We find that soaring housing prices in China since 2004 stunted industrial value chain upgrading, as indicated by the value-added rate. When housing prices increase by 100%, the enterprise value-added rate decreases by 12.4%. Intermediary mechanism analysis shows that housing price increases lead to innovation input suppression effects and resource misallocation effects, which in turn obstruct industrial value chain upgrading. Further analysis demonstrates that the impacts of housing price increases on industrial value chain upgrading, presenting an inverted-U shape, are varied in terms of time and region. Enterprises’ value chain upgrading also showcases different degrees of sensitivity to housing price increases, due to their respective features. The conclusions of this study carry significant policy implications for the sound development of the real estate market and industrial upgrading in China, as well as in developing countries at large.
ColorNephroNet: Kidney tumor malignancy prediction using medical image colorization

Renal tumor malignancy classification is one of the cru- cial tasks in urology, being a primary factor included in the decision of whether to perform kidney removal surgery (nephrectomy) or not. Currently, tumor malignancy prediction is determined by the radiological di- agnosis based on computed tomography (CT) images. However, it is estimated that up to 16% of nephrec- tomies could have been avoided because the tumor that had been diagnosed as malignant, was found to be be- nign in the postoperative histopathological examination. The excess of false-positive diagnoses results in unnec- essarily performed nephrectomies that carry the risk of periprocedural complications. In this paper, we present a machine-aided diagnosis system that predicts the tumor malignancy based on a CT image. The prediction is performed after radiological diagnosis and is used to capture false-positive diagnoses. Our solution is able to achieve a 0.84 F1-score in this task. We also propose a novel approach to knowledge transfer in the medical do- main in terms of colorization based pre-processing that is able to increase the F1-score by up to 1.8pp.
Expression in the Embryonic Thymic Stroma Organogenesis by Eliciting RANK Lymphotoxin Signal Promotes Thymic

It has recently become clear that signals mediated by members of the TNFR superfamily, including lymphotoxin- b receptor (LT b R), receptor activator for NF- k B (RANK), and CD40, play essential roles in organizing the integrity of medullary thymic epithelial cells (mTECs) required for the establishment of self-tolerance. However, details of the mechanism responsible for the unique and cooperative action of individual and multiple TNFR superfamily members during mTEC differentiation still remain enigmatic. In this study, we show that the LT b R signal upregulates expression of RANK in the thymic stroma, thereby promoting accessibility to the RANK ligand necessary for mTEC differentiation. Cooperation between the LT b R and RANK signals for optimal mTEC differentiation was underscored by the exaggerated defect of thymic organogenesis observed in mice doubly deﬁcient for these signals. In contrast, we observed little cooperation between the LT b R and CD40 signals. Thus, the LT b R signal exhibits a novel and unique function in promoting RANK activity for mTEC organization, indicating a link between thymic organogenesis mediated by multiple cytokine signals and the control of autoimmunity. The Journal of Immunology , 2011, 186: 5047–5057.
SENSE AMPLIFIER BASED FLIP-FLOP

A flip-flop including a first Stage and a Second Stage. The first Int. Cl. " . H03K 3/356 Stage receives a pair of differential Signals to generate a Set 327/217; and reset Signal. The complement of the Set and reset Signal 327/217, generates output signals Q and Q. These signals have equal 327/210, 211, 212, 214, 51-57 rising and falling transitions with the same delays for the Q signal and the Q signal. The second stage