Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Measurement and Segmentation Toward Automated Sewing Operations

In this study, we developed a system that acquires the operation skills of a human operator in a sewing task and a data-processing method to utilize the skills in automating sewing tasks. We propose a force sensor that measures the force at the fingertip of the operator based on changes in the width of the finger. We employed the proposed sensor in measuring a system without losing the sensation of the finger pad during a sewing task. Further, we established a method to automatically divide sewing tasks into a combination of simple motions. We also established a method to classify the motions of the hands using the force data acquired from the proposed force sensor. To verify the effectiveness of the proposed system and methods, we experimented with a simulated sewing task and applied the proposed methods to the data acquired by the system.
A Comparative Study of the Structural and Electronic Properties of Orthorhombic and Cubic CsPbI3 and Trigonal CsGeI3 using First-Principles Calculations

Lead-free halide perovskites are of great importance as prospective materials for efficient solar cells. Germanium is a very promising non-toxic alternative to lead. In this study, the crystal configuration, projected density of states and band structure of the trigonal CsGeI3, the yellow and the quenched black orthorhombic CsPbI3, and the cubic CsPbI3 were investigated using the density functional theory with Perdew-Burke-Ernzerhof functional. Our calculations showed that for the CsGeI3, the valence band maximum is mainly contributed by the I 5p and Ge 4s orbitals whereas the conduction band minimum is mainly contributed by the Ge 4p orbitals. The replacement of Pb with Ge results in a narrower bandgap.
Identifying Hierarchical Individual Functional Network under Naturalistic Paradigm via Two-stage DBN with Neural Architecture Search

Functional magnetic resonance imaging under naturalistic paradigm (NfMRI) is gaining increasing attraction, as it offers an ecologically-valid condition to understand brain function in real life. Characterizing the hierarchical organization of brain function while taking the nature of fMRI activities under naturalistic condition into account has been a critical issue in identifying naturalistic functional networks. Recent studies have made efforts on characterizing the brain's hierarchical organizations from fMRI data via a variety of deep learning models. However, most of those models have ignored the properties of group-wise consistency and inter-subject difference in brain function under naturalistic paradigm. Another critical issue is how to determine the optimal neural architecture of deep learning models, as manual design of neural architecture is time-consuming and less reliable. To tackle these problems, we proposed a two-stage deep belief network (DBN) with neural architecture search (NAS) combined framework (two-stage NAS-DBN framework) to model both the group-consistent and individual-specific naturalistic functional brain networks. Our results demonstrated that the optimized DBN-based framework can characterize meaningful group-wise and individual-level naturalistic functional networks, which reflected the hierarchical organization of brain function and the properties of brain functional activities under naturalistic paradigm.
A Distributed Digital Twin Architecture for Shop Floor Monitoring Based on Edge-Cloud Collaboration

Throughout recent years, many “Smart Factory” concepts have been emerged to escort the current technological progress. Among these concepts, we found the Digital Twin which is a burgeoning technology that attracts great interests from academics and industry. Akin to other technologies, the power of Digital Twin can be strengthened by leveraging other technologies' benefits such as those of cloud computing and edge computing. First, to support the implementation of the Digital Twin for the shop floor monitoring, a conceptual architecture of the Digital Twin based on edge-cloud collaboration is proposed. This architecture is believed to enhance the real-time capabilities of the Digital Twin while dealing with the abundant amounts of manufacturing data. Moreover, the paper discusses how the microservices architecture can benefit the proposed Digital Twin architecture. On the other hand, in order to provide an underpinning to researchers on the Digital Twin topic, the paper sums up the description and an up-to-date output of the Digital Twin, highlighting four manufacturing application scenarios.
Potential Induced Degradation Mechanism in Rear-Emitter Bifacial Silicon Heterojunction Solar Cells Encapsulated in Different Module Structures

Recent studies showed that silicon heterojunction (SHJ) solar cells can be prone to potential induced degradation (PID) when encapsulated with ethylene vinyl acetate (EVA). Here, to gain understanding in the role of EVA, we perform PID test in humid conditions (85°C/85% RH) under a negative bias (-1000V). We study the effect of moisture ingress and cover materials by using different module structures. We focus on studying both sides of the cell for modules packaged in a glass/glass scheme after 500 hours of test (corresponding to ~5 times the duration foreseen by the corresponding IEC standard). The front-side degradation is dominated by a reduction in short-circuit current (JSC), whereas the rear-side degradation is driven by a loss in fill factor (FF). EQE measurements show that increased front-surface recombination is largely responsible for the observed degradation of the front-side. From TEM and EDX measurements, it seems that the degradation at the cell level is predominantly caused by diffusion of sodium into the cell, which is triggered by humidity and low encapsulant resistivity. Assuming that the Na is also accumulated at the rear-side of the cell, this would create defects at the p-n junction, leading to the loss of FF observed.
A Control Strategy for High Quality of Hybrid DC Power Supply with Auto-correcting Ultracapacitor Voltage

In this paper, a new control strategy is designed and evaluated for a Hybrid DC power supply that provides regulated DC bus voltage, and the fast transient response against load variation and source variations. The proposed DC bus is powered by a Solar photovoltaic source and supported with a battery and ultracapacitor storage. The averaged model is developed for the system under study and established its control strategy. The same model is used to determine compensator parameters used in various loops of the control strategy. After serving each transient, the perturb value of ultracapacitor voltage is brought back to nominal value using the proposed control strategy to prepare it for the future transient. Bi-directional converter associated with ultracapacitor is regulating DC bus voltage against load and source variations. The battery is providing a prolonged backup for the entire system. Analytically derived compensators for feedback and feedforward loops are verified by simulation results.
Cryogenic HfOₓ-Based Resistive Memory With a Thermal Enhancement Capping Layer

Memory devices for low-temperature electronics have great application potential in emerging fields like quantum computing, deep-space exploration etc. However, the practical non-volatile memory device for cryogenic use still remains as a challenge today. We propose a delicately designed resistive memory device with bi-layer structure. The resistive switching layer is covered by a thermal enhancement capping layer which confines the Joule heat for better switching performance and also serves as an oxygen vacancy reservoir. This structure gives the device the multilevel resistive switching capability within a large temperature range from 8 K to 300 K. The endurance more than $10^{{6}}$ cycles at 8 K is demonstrated. Combined with kinetic Monte-Carlo simulation, the role of the thermal enhancement capping layer is analyzed.
A Novel State of Charge Estimation Method of Lithium-ion Battery Based on NARX Neural Network Model

Accurate state of charge (SOC) estimation is crucial for the lithium-ion batteries management system in electric vehicles. This paper proposed a novel method to improve the estimation performance of SOC from the three aspects. First, to overcome the dependence of model on the internal parameters of battery, this paper uses the NARX neural network to build the experimental model. Compared to the feedforward neural network, the NARX model with timing characteristics shows a better performance in state of charge estimation. Second, analyzing the characteristics of the experimental data and combining with the moving window method, the real-time segmentation and output of the data are carried out, the overall model is simplified and the modeling accuracy and speed are improved. Third, the superiority of the newly trained model is verified under several driving cycles by comparing with other existed model.
Seasonal Evolution of Sea Surface Salinity in the Northwestern Indian Ocean: Argo Data Study

This research explores the seasonal evolution of sea surface salinity (SSS) in the Northwestern Indian Ocean (NWIO) over the Sea of Oman (SOO) and the Arabian Sea. For this study we used the datasets provided by the Argo floats within the timeframe from 2004 to 2019. For SSS, values of salinity are used nearest to the sea surface (depth ≤ 10 m), as it would be comparable to satellite SSS measurements. From the seasonal evolution of SSS, we investigated the co-existence of two water masses in the Arabian Sea region: 1) the Arabian Sea High Saline Water (ASHSW) that is in excess of salinity (> 36.9) and occupying the north and central basins of the Arabian Sea and 2) the Bay of Bengal Water (BBW) that is inferior in salinity (˂ 35) and occupying the south basin of the Arabian Sea.
Impurity gettering by silicon nitride films: kinetics, mechanisms and simulation

The mechanisms behind the gettering effect of silicon nitride films for removing iron impurities in silicon are investigated in this study. The silicon nitride films are from plasma-enhanced chemical vapor deposition (PECVD). By monitoring the iron reduction kinetics in the silicon wafer bulk during cumulative anneals, it is found that silicon nitride gettering takes place mainly via a segregation mechanism with an activation energy of 0.9±0.1 eV for the investigated PECVD silicon nitride film.
Analysis of short time dynamics of time resolved photoluminescence applied on perovskite absorbers

Short time carrier dynamics of time resolved photoluminescence (TR-PL) decays and time resolved fluorescence imaging (TR-FLIM) acquisitions contain valuable information on the optoelectronic properties of photovoltaic materials. We performed a theoretical analysis on drift diffusion models to provide a scaling law for the time derivative of the transient photoluminescence signal as a function of both laser excitation power and wavelength. This innovative approach allowed us to design a new interpretation method for transient photoluminescence data. With this method, we extract the external radiative recombination rate without fitting complex drift diffusion models. We tested this scaling on state-of-the-art triple cation mixed halide perovskite absorbers. We discussed the validity of this method and its applicability on imaging data. Moreover, by coupling this analysis with longer decay fitted with classical drift-diffusion model we obtained a coherent model for the triple cation lead halide perovskite under study.
Predicting pragmatic discourse features in the language of adults with autism spectrum disorder

Individuals with autism spectrum disorder (ASD) experience difficulties in social aspects of communication, but the linguistic characteristics associated with deficits in discourse and pragmatic expression are often difficult to precisely identify and quantify. We are currently collecting a corpus of transcribed natural conversations produced in an experimental setting in which participants with and without ASD complete a number of collaborative tasks with their neurotypical peers. Using this dyadic conversational data, we investigate three pragmatic features – politeness, uncertainty, and informativeness – and present a dataset of utterances annotated for each of these features on a three-point scale. We then introduce ongoing work in developing and training neural models to automatically predict these features, with the goal of identifying the same between-groups differences that are observed using manual annotations. We find the best performing model for all three features is a feed-forward neural network trained with BERT embeddings. Our models yield higher accuracy than ones used in previous approaches for deriving these features, with F1 exceeding 0.82 for all three pragmatic features.
The bioeconomy as a tool for sustainable economic growth

Technological development has made it possible to diversify the products obtained in the framework of the bioeconomy, from food, to forest products, textiles, energy, extracts, active compounds and a wide range of bioproducts. The new socio-economic context, in which it is essential to decarbonize and promote the circular economy, has furthered the development of the circular bioeconomy, especially within the new framework of the European Union Green Deal. The use of biomass generated in Spain, especially residual biomass from the agri-food, forestry and urban spheres, both from MSW and sewage sludge, constitute an opportunity for economic development. The technologies that are being developed make it possible to obtain basic chemical compounds, fuels, biopolymers, and compounds apt for different types of activities and functions, potential raw materials for new economic activity. The challenges and opportunities involved in the development of the bioeconomy in Spain are analyzed, as well as the need to carry out an analysis of the life cycle of the products that are going to be placed on the market, both those for end consumers and those supplied to industrial sectors. Traditional industrial sectors, such as energy, waste management, textiles, construction, packaging, motor vehicles, machinery and equipment, can represent opportunities for business for activities linked to the bioeconomy through the generation of bioproducts.
Modeling Text using the Continuous Space Topic Model with Pre-Trained Word Embeddings

In this study, we propose a model that extends the continuous space topic model (CSTM), which flexibly controls word probability in a document, using pre-trained word embeddings. To develop the proposed model, we pre-train word embeddings, which capture the semantics of words and plug them into the CSTM. Intrinsic experimental results show that the proposed model exhibits a superior performance over the CSTM in terms of perplexity and convergence speed. Furthermore, extrinsic experimental results show that the proposed model is useful for a document classification task when compared with the baseline model. We qualitatively show that the latent coordinates obtained by training the proposed model are better than those of the baseline model.
Is PFA-100® ADP/Epinephrine Platelet Aggregometry a Helpful Tool in Neurosurgical Patients on Acetyl Salicylic Acid Medication?

Objectives: Due to an aging patient population, neurosurgeons are increasingly confronted with patients taking Acetylsalicylic Acid (ASA) as an antiplatelet agent. Since a normal platelet function is frequently deemed necessary for neurosurgical interventions, the usefulness of the PFA-100® test to evaluate the platelet function is assessed. Methods: We retrospectively evaluated data on all neurosurgical patients subjected to a PFA-100® Platelet Aggregometry (PA) test between 6/12 and 2/17 and noted the reasons the test was done, the time required for platelet function to normalize, and how well the test results correlated with intraand postoperative findings. Results: PA had been performed on 171 patients, the reason being ASA treatment in 154 and coagulation analysis in 17. The PAs of the operated patients admitted for emergency treatment normalized between 0-19 days after ASA withdrawal (53%: 0-3, 34%: 4-6, 6%: 7, 7%: >7 days). With a mean of 3.7 days, the average time was found to be considerably shorter than the expected 7 days. 144 ASA patients underwent surgery. None of the 124 patients with normal and 3/20 with abnormal PAs had to be re-operated on. Discussion: Since normal PA test results correlated well with uncomplicated surgery, the study suggests that the test might be a reliable tool to time an operation in neurosurgical ASA patients. As 87% of the patients regained a normal platelet function in less than 7 days, the interval between last ASA administration and a neurosurgical procedure can often be reduced. Claudia Janz1*, Jürgen Heinrich2 and Ralf Buhl1 1Department of Neurosurgery, Municipal Hospital Solingen, Germany 2Former Head of the Central Laboratory, Municipal Hospital Solingen, Germany
Research on Adjustable K and C Values of Analogous Active Suspension System with Automotive Chip Electronic Control Technology

This research integrates the electronic control unit of the analogous active suspension system, which includes the adjustment of adjustable air springs, adjustable hydraulic dampers, and a variety of sensors, and retains the CAN Bus node, which can communicate with other electronic control units . This system can adjust the elastic coefficient K value of the spring and the damping coefficient C value of the hydraulic damper simultaneously through electronic control according to the different road conditions or user preferences. It also integrates the height, pressure value and six-axis inertia. The measuring sensor can monitor the dynamic characteristics of the car body. The suspension system is regulated by the electronic control unit to improve the comfort or handling during riding.
Structural Characteristics of Particulate Matter Time Series Observed in an Urban Environment

In this study, we used the fractal and multifractal analysis to explore the structural characteristics of РМ10 time series, among which self-similarity and invariance can be considered particularly important. The eXtreme Gradient Boosting method was used to fill in the missing data for multiscale multifractal analysis. The analysis has revealed self-similarity in РМ10 time series with a positively correlated structure which was stable over a study period. Small fluctuations of РМ10 levels were observed as a result of variations in local emissions and meteorological conditions. The uncoordinated and uncorrelated intervals in concentration time series were observed as a consequence of occasional pollution events in the areas dominated by industrial activities or as a consequence of the remote emission source activity when wind direction and speed were favorable.
3D depth map estimation using ToF-Stereo sensor fusion

High quality depth maps are required in various applications including robotics, computer vision, and autonomous driving. In order to obtain precise depth information, there has been an increasing interest in the combination of different depth sensors. Depth information can be acquired real-time by Time-of-Flight (ToF) cameras and stereo cameras. There are various Depth-Stereo fusing methods to overcome the limitations of a single depth sensor, including Conditional Cost Volume Normalization algorithm. The CCVNorm framework is generic and closely integrated with the cost volume component that is commonly utilized in stereo matching neural networks. In this paper, we give an overview over methods for the fusion of depth and passive stereo data. We experimentally evaluate the performance and robustness of various Depth-Stereo fusion methods with the KITTI Stereo and Depth Completion datasets
Creating Smart Health Services Using Nlp Techniques

: The rapid growth of the urban population and the development of information and communication technologies have led to the creation of a new concept - a smart city. The smart city uses information and communication technologies to improve the quality of life of its citizens. One of its most important components is smart health. Also, heaps of textual data are created every day, in medical information systems, then there are the government documents, and citizens' comments, reviews, etc. This paper examines the possibility of using text mining techniques and natural language processing to create smart health services. Three services intended for these purposes have been proposed, designed, and implemented: a service for answering patients' questions, an information board for visualizing data about an epidemic, and automatic processing of questionnaires and psychological tests.
BRAIN TUMOUR DETECTION USING MACHINE LEARNING

— In the medical field, medical image fusion plays an important role in diagnosis of brain tumors that may be classified as malignant or benign. To cut back uncertainty and minimize redundancy whereas extracting all the helpful info from the supply pictures, it's the method of grouping multiple pictures of the identical scene into one united image. SVM is employed to fuse 2 totally different visions brain tomography pictures. The image united is going to be a lot more informative than the pictures from the supply. The image united allows us to extract the characteristics of texture and wavelet. Based on supported trained and tested options, the SVM Classifier classifies brain tumors. The planned technique achieved sensitivity of 80.48 percent, accuracy of 99.69 percent and specificity of 99.9 percent. Experimental results obtained from the fusion method show that the utilization of the planned approach to image fusion shows higher performance compared to standard fusion methodologies.
VARIATION IN AVIAN DIVERSITY IN RELATION TO PLANT SPECIES IN URBAN PARKS OF AYDIN, TURKEY

Among wildlife species, birds are important indicators of biodiversity and habitat quality in urban ecosystems. Parks, which are among the important components of urban ecosystems, are home to many bird species with their diversity of plant species. Due to this aspect, parks make significant contributions to increase bird diversity. The bird species were observed with the naked eye and using an Olympus 10x50 DPS I brand binocular. Bird observations were performed on sunny days without rain and excessive wind in the mornings (07.00-09.00 a.m./2 h after sunrise) and evenings. Sixteen bird species observed in urban parks of Aydin comprised 11 native, 11 resident and 9 insectivorous species. The Eurasian Collared-Dove (Streptopelia decaocto Frivaldszky), Eurasian Jackdaw (Corvus monedula L.), Hooded Crow (Corvus cornix L.), Great Tit (Parus major L.), and House Sparrow (Passer domesticus L.) were the most frequently observed bird species in all parks. The Australian Pine (Casuarina equisetifolia L.) attracted the highest number of bird species with 12 species, followed by the Turkish Pine (Pinus brutia Ten.) with 11 species. A significant variance was found between plant species and avian diversity.