Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Intelligent Data Mining Method for Traffic Forecasting

Potential knowledge useful for traffic management optimization is hidden in a huge amount of data. Previous works use the prior data pattern labels to train the artificial neural network to attain the intelligent data mining models. The performance of the models suffers from the experts’ experience. To relieve the impact of the human factor, a new hybrid intelligent data mining model is proposed in this work based on self-organizing map (SOM) and support vector machine (SVM). The SOM was firstly used to capture the clustering information of the database through an unsupervised manner. Then the identified samples were treated as input to train the SVM. To optimize the SVM model, the particle swarm optimization (PSO) algorithm was employed to tune the SVM parameters and hence the satisfactory SVM data mining model was obtained. 2000 practical data sets from the Intelligent Transportation Systems (ITS) were applied to the validation of the proposed mining model. The analysis results show that the proposed method can extract the underlying rules of the testing data and can predict the future traffic state with the accuracy beyond 97%. Hence, the new SOM-PSO-SVM data mining model can provide practical application for the ITS.
Applications of RFID technology for improving production efficiency in an integrated-circuit packaging house

Radio frequency identification (RFID) is an emerging technology that can expand the possibilities for data capture. The use of RFID technology for process control and material handling applications can produce benefits of flexibility and interactivity. An integrated-circuit (IC) packaging house must be able to provide clients with instant and accurate information on its products and services. For doing that, an IC packaging house must keep track of all its wafers, regardless of whether those wafers are in packaging, testing, or shipping. In this study, RFID technology is combined with an enterprise resource planning (ERP) system for resolving various wafer receiving and inventory transaction problems. An electronic control system is developed using Oracle application implementation methodology (AIM) to integrate RFID technology and the ERP system. This electronic control system has been implemented at a local IC packaging company. Results from this study suggest that RFID contributes significant improvements to the wafer receiving process and the inventory transaction process that reduce labour costs and man-made errors. Besides that, our control system greatly reduces operators’ workloads and operation costs.
Prediction and Control of Cutting Tool Vibration in Cnc Lathe with Anova and Ann

A R T I C L E I N F O Cutting tool vibration, Passive damping pad, Data acquisition, ANOVA, ANN. Received 20 February 2011 Accepted 23 February 2011 Available online 24 February 2011 Machining is a complex process in which many variables can deleterious the desired results. Among them, cutting tool vibration is the most critical phenomenon which influences dimensional precision of the components machined, functional behavior of the machine tools and life of the cutting tool. In a machining operation, the cutting tool vibrations are mainly influenced by cutting parameters like cutting speed, depth of cut and tool feed rate. In this work, the cutting tool vibrations are controlled using a damping pad made of Neoprene. Experiments were conducted in a CNC lathe where the tool holder is supported with and without damping pad. The cutting tool vibration signals were collected through a data acquisition system supported by LabVIEW software. To increase the buoyancy and reliability of the experiments, a full factorial experimental design was used. Experimental data collected were tested with analysis of variance (ANOVA) to understand the influences of the cutting parameters. Empirical models have been developed using analysis of variance (ANOVA). Experimental studies and data analysis have been performed to validate the proposed damping system. Multilayer perceptron neural network model has been constructed with feed forward back-propagation algorithm using the acquired data. On the completion of the experimental test ANN is used to validate the results obtained and also to predict the behavior of the system under any cutting condition within the operating range. The onsite tests show that the proposed system reduces the vibration of cutting tool to a greater extend. ________________________________ * Corresponding Author
Impact of dietary nutrients (functional foods/nutraceuticals) and micronutrients on COVID-19: A review

The world health organization (WHO) announced that coronavirus 2019 (COVID-19) is a pandemic. Considering the pandemic spread of Covid-19, many researchers have shown immense interest in various functional foods/nutraceuticals and dietary supplements to improve immune function and overall health status and thus lower the risk of COVID-19 infection. Studies have indicated that a balanced diet rich in various nutrients especially micronutrients plays a huge role in recovering and preventing COVID-19 related health issues. Improvement of obesity could reduce the risk of COVID-19 infection owing to the less angiotensin-conversion enzyme 2 receptors and increase the effect of vaccination. This review signifies the importance of various food bioactives along with dietary supplements (balanced diet) rich in various micronutrients against COVID-19 and its related anomalies. Moreover, this contribution helps non-specialists to understand the importance of various functional foods/nutraceuticals based on the dietary/supplementation recommendations indicated by various popular nutritional or dietary or health organizations. To sum up this contribution, healthy life style and balanced nutrition play important role on immune system. Reducing obesity becomes a critical point on the COVID-19 infection. The most important is to find the potential and effective food bioactives, which might act as supportive or complementary therapy (prophylactic) for mitigating the risk and comorbidity associated with COVID-19.
Reinforcement Learning Based Self-Constructing Fuzzy Neural Network Controller for AC Motor Drives

A self-constructing fuzzy neural network (SCFNN) based on reinforcement learning is proposed in this study. In the SCFNN, structure and parameter learning are implemented simultaneously. Structure learning is based on uniform division of the input space and distribution of membership function. The structure and membership parameters are organized as real value chromosomes, and the chromosomes are trained by the reinforcement learning based on genetic algorithm. This paper uses Matlab/Simulink to establish simulation platform and several simulations are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem with the implementation of AC motor speed drive. The simulation results show that the AC drive system with SCFNN has good anti-disturbance performance while the load change randomly.
Afforestation of Mongolian steppe: patterns of biomass partitioning in Populus sibirica and Ulmus pumila trees in response to management supporting measures

Abstract Climate challenges impose the need for successful afforestation strategies which will increase the amount of carbon sequestered from the atmosphere. In practice, this means evaluation of suitable plant species and management practices for the long-term effects to prove beneficial. In the present study we employed a clasmometric approach to look into biomass partitioning in two tree species, Populus sibirica and Ulmus pumila, which have been included in the formation of the Green Belt project in Asia. Comparing the total biomass comprised of the above ground (AG) and below ground (BG) biomass of trees grown in different irrigation and fertilization regimes, we aim to better understand where the two species invest more biomass as a tool to deal with the environmental challenges. The results suggest that these two tree species prioritize different aspects of development when faced with various challenges. U. pumilia tends to be more resistant to drought making it favorable for the semi-arid and arid regions. P. sibirica is more sensitive to the lack of water but shows greater potential in terms of biomass production (especially AG biomass) and, therefore, overall higher C-sequestration. The fertilization treatments made no significant impact on tree development on Mongolian steppe soil.
Shrinkage quantile regression for panel data with multiple structural breaks

We consider a shrinkage quantile regression model for high‐dimensional panel data with multiple structural breaks. The structural breaks are assumed to be common across all individuals, but may vary across different quantile levels while sharing an identical location shift effect. We impose an L1 penalty on the individual effects and an L1‐type fusion penalty to estimate both the slope coefficients and the structural breaks by combining information at multiple quantile levels. The proposed method can detect “partial” changes of the regression coefficients and consistently estimate both the number and dates of the breaks with probability tending to 1. We establish the asymptotic properties of the proposed regression coefficient estimators as well as their post‐selection counterparts, where the dimensionality of the covariates is allowed to diverge. Simulation results demonstrate that the proposed method works well in finite‐sample cases. Using the proposed method, we obtain many interesting results by analyzing a dataset concerning environmental Kuznets curves.
Quantification of Ambient p-Cresol Concentrations Downwind of Beef Cattle Feedyards

Livestock operations have become more concentrated in the Texas Panhandle, amplifying the need to manage odor and other nuisances while maintaining operational capabilities. As the cattle feeding industry sustains many small communities in the Texas Panhandle economically, it is imperative we develop greater understanding of odors and odor transport from beef cattle feedyards. Ambient concentrations of p-cresol, a significant odor component, was measured downwind of two beef cattle feedyards using thermal desorption tubes and gas chromatography-mass spectrometry (GC-MS). Downwind chemical concentrations of p-cresol were highest at the property line, but were drastically lower at the 0.8 km (0.5 mi) sampling locations and below most published odor thresholds at 2.4 km (1.5 miles). A sample obtained during stable atmospheric conditions 13 km (8 miles) downwind of a feedyard following a rain event had a noticeable odor and an elevated p-cresol concentration.
The impact of ownership and size heterogeneity on hotel efficiency in the Canary Islands (Spain)

This study was conducted to analyse the influence of technological differences on hotel efficiency in the Canary Islands (Spain), with particular regard to the heterogeneity observed in hotel ownership and size. A metafrontier approach, based on non-parametric deterministic efficiency methods (data envelopment analysis and free-disposal hull) and robust non-parametric estimators (order-α), is used. This empirical analysis considered a panel data sample selection model of Canary Islands hotels for the period 2002–2015. The results obtained show that the frontiers against which the hotels are compared (metafrontier or group) and the consideration or otherwise of outliers are factors of crucial importance. We find that efficiency depends on hotel size (large hotels are more efficient than small ones), but not on the type of ownership. The results also show that the impact of the global financial crisis on the average technical efficiency of these hotels was slight or non-existent. Finally, the technological gap narrowed over time, especially in large hotels and those with no majority shareholder.
Analyzing tweets to enable sustainable, multi-modal and personalized urban mobility: approaches and results from the Italian project TAM-TAM

This paper presents the approaches proposed in the Italian project TAM-TAM to support a smarter, personalized and sustainable urban mobility, by taking into the loop the users of the transportation services, in particular citizens, tourists and commuters in Milan. A computational module has been defined and developed in order to collect and analyze relevant tweets posted by users as well transport operator. The main goals are two: identifying events (e.g. accidents, sudden traffic jams, service interruptions, etc.) and evaluating the overall sentiment about the service as well as mobility options. Detected events are used by other computational modules of TAM-TAM in order to support a more effective travel planning; on the other hand, the perceived quality of service is provided both to users, enabling more personalized choices, and to transport company, supporting them in the management of mobility supply.
Objects Shape Activation during Spoken Word Recognition in Preschoolers with Typical and Atypical Language Development: An Eye-tracking Study

ABSTRACT Visually situated spoken words activate phonological, visual, and semantic representations guiding overt attention during visual exploration. We compared the activation of these representations in children with and without developmental language disorder (DLD) across four eye-tracking experiments, with a particular focus on visual (shape) representations. Two types of trials were presented in each experiment. In Experiment 1, participants heard a word while seeing (1) an object visually associated with the spoken word (i.e., shape competitor) together with a phonologically related object (i.e., cohort competitor), or (2) a shape competitor with an unrelated object. In Experiment 2 and 3, participants heard a word while seeing (1) a shape competitor with an object semantically related to the spoken word (i.e., semantic competitor), or (2) a shape competitor with an unrelated object. In Experiment 4, children heard a word while seeing a semantic competitor with (1) the visual referent of the spoken or (2) with an unrelated object. The visual context was previewed for three seconds before the spoken word, except for Experiment 2, where it appeared at the onset of the spoken word (i.e., no preview). The results showed that when a preview was provided both groups were equally attracted by cohort and semantic competitors and preferred the shape competitors over the unrelated objects. However, shape preference disappeared in the DLD group when no preview was provided and when the shape competitor was presented with a semantic competitor. Our results indicate that children with DLD have a less efficient retrieval of shape representation during word recognition compared to typically developing children.
Experimental analysis of HEMT behavior under low-temperature conditions

An experimental analysis of high-electron-mobility transistor (HEMT) behavior under low-temperature conditions is presented. Specific measurements have been performed to investigate the deep-level trapping effects on basic device characteristics such as carrier concentration, electron mobility in the structure, and access resistances. The influence of the collapse phenomenon on the microwave device parameters completes the knowledge of these parasitic effects. Explanation of mechanisms responsible for the anomalous phenomena and means to suppress them are reported. Microwave parameters measurements demonstrate that HEMTs showing no parasitic collapse effects exhibit improved performance at 77 K. Large improvements of current gain cutoff frequency and noise figure are presented. >
A smart method for multi-zonal virtual power plant scheduling with presence of electric vehicles

Microgrids are practical views of integration of distributed generations (DGs) into distribution systems. In this regard, utilizing appropriate technologies and accurate recognition of energy generation and storage systems, as well as optimal scheduling for these resources are of the paramount importance in microgrids. Therefore, connection of DG resources and storages to the grid in the form of virtual power plant in order to increase efficiency and owners’ interest has attracted significant attention of researchers and distribution network operators. This research presents a model for optimal day-ahead scheduling of heat-power generation units in a multi-zonal virtual power plant (VPP). This VPP includes a number of combined heat-power generations, distribution network loads, and electrical vehicles with smart charging as well as energy storages. In order to approach the reality of distribution systems, uncertainty related to behavior of electrical vehicles was modeled with Monte-Carlo simulation while uncertainties of generation and electrical/thermal loads were modeled using a probabilistic method. Matlab software and swarm robotics search & rescue (SRSR) has been used as an optimization tool in this paper. The results confirmed the effectiveness of the proposed method.
Avaliando Modelos de Graph Neural Networks para Detecção de Usuários Fraudulentos em e-Commerce

Modelos baseados em grafos, Graph Neural Networks ou (GNN), vêm sendo empregados na detecção de fraudes no comércio eletrônico com resultados promissores. Neste trabalho foram avaliados modelos que utilizam classificação sobre os nós, baseados em informações da vizinhança, e o reconhecimento de comunidade, utilizando datasets reais. Os resultados demonstram que, embora promissores (acurácia variando de 50% a 86%), ainda é preciso estudar e investigá-los melhor para que possam, no futuro, atuarem em soluções anti-fraude.
Pond Sediments as Indicator of Traffic Related Lead Pollution in Rural Area: A Case Study from Sixian County, Northern Anhui Province, China

Lead concentration of pond sediments near the road, as well as pond sediments far away from roads in a rural area in Sixian County, northern Anhui Province, China, have been analyzed, and the spatial distribution of lead and the index of geo-accumulation have been brought to light. The results indicate that the lead concentrations of pond sediments near the road range from 20 to 34 mg/kg with average= 26.7 mg/kg, whereas pond sediments far away from the road have lower lead concentrations from 20 to 21 mg/kg with average= 20.8 mg/kg. The lead concentration from high to low is Xinji, Gonggou, Dazhuang, Huangwei, Yangzhuang and area far away from the road. A similarity between spatial distributions of lead concentration, index of geo-accumulation (Igeo) and towns (including village and the roads between them) indicates that motor vehicles play important role in controlling the distribution of lead in rural areas.
MedShare: A Privacy-Preserving Medical Data Sharing System by Using Blockchain

Electronic Health Record (EHR) and its privacy have attracted widespread attention with the development of the healthcare industry in recent years. As locking medical data in a single healthcare center causes information isolation, healthcare centers are motivated to build medical data sharing systems. However, existing systems highly rely on the trusted centralized servers, which are vulnerable to distributed denial of service (DDoS) attacks and the single point of failure. Moreover, it is a non-trivial matter to authorize multiple users to search and access EHR in a privacy-preserving manner. In this article, we propose MedShare, a decentralized framework for secure EHR sharing. Our design utilizes the smart contract technique of blockchain to establish a trusted platform for healthcare centers to share their encrypted EHR. Considering that fine-grained access control is essential in practical EHR sharing service, we devise a constant-size attribute-based encryption (ABE) scheme, where the access policy is embedded in search result on the blockchain. Besides, we propose an efficient scheme that enables authorized MedShare users to perform multi-keyword boolean search operations over encrypted EHR. We formally analyze the security strengths and implement the system prototype on Ethereum. Evaluation results demonstrate that MedShare is efficient for EHR sharing.
The application of water-absorbing geocomposites to support plant growth on slopes

The study presents the results of experiments with a new type of water-absorbing geocomposite and its influence on the strength of biotechnological protection. The geocomposite influences the increase of water retention in soil and the improvement of the conditions of plant vegetation. The increase in the shearing strength of roots reached 66.7% for shrubs and 20.1% for grass. Increased growth of the root system and the above-ground parts of both shrubs and grass was noted.
Upotreba kariprazina u lečenju unipolarne depresije: prikaz slučaja

Celoviti tretman afektivnih poremećaja, i to posebno različitih formi depresije još uvek predstavlja značajan izazov u kliničkom radu. Aktuelni prikaz slučaja ima za cilj upravo razmatranje izazova u svakodnevnoj kliničkoj praksi u slučajevima kompleksne dijagnostike, diferencijalne dijagnostike i tretmana rekurentne, teške depresivne epizode, kao i razmatranje uloge dodatne terapije. U aktuelnom radu prikazana je pacijentkinja srednjih godina, lečena na Klinici za psihijatriju Univerzitetskog kliničkog centra Srbije, u okviru rekurentne, teške depresivne epizode, sa prethodnim višestrukim pokušajima suicida. Na prethodno primenjeni antidepresiv iz grupe inhibitora ponovnog preuzimanja serotonina i noradrenalina i stabilizator raspoloženja litijum karbonat, pacijentkinji je u terapiju uveden atipični antipsihotik kariprazin što je dovelo do značajnog kliničkog poboljšanja i subjektivnog zadovoljstva pacijentkinje. Upotreba atipičnih antipsihotika, među njima i kariprazina, kao psihofarmaka sa multidimenzionalnim delovanjem u lečenju teških i izazovnih oblika afektivnih poremećaja, od posebne je važnosti sa aspekta svakodnevne kliničke prakse. Samo detaljno, integrativno i individualno prilagođeno sagledavanje terapijskih mogućnosti, bazirano na naučnim saznanjima i praktičnim iskustvima, može dovesti do njihove adekvatne, pravovremene i utemeljene kliničke primene, čiji je cilj pre svega bolje funkcionisanje pacijenata sa afektivnim poremećajima kako po objektivnim kriterijumima, tako i prema subjektivnom/ličnom doživljaju funkcionisanja.
A Review of Modern Computational and Experimental Tools Relevant to the Field of Drying

The aim of this review paper is to encourage the drying community to discover and use modern tools derived from the fields of applied mathematics and physics. Among the computational methods, multiscale and multiphysics approaches are proposed as operational tools to address the practical problems of optimization, design, and innovation encountered by the industry. On a more fundamental level, some methods from the large family of meshless techniques offer vast potential for investigations at the pore level. On the experimental side, many amazing tools have emerged recently from the field of physics. Several time-resolved imaging possibilities are presented here. Among such techniques, X-ray micro-tomography is perfect for an accurate 3-D description of pore morphology, whereas NMR imaging is well adapted to obtaining time-resolved 3-D images of the moisture content field or other variable fields.