Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel EMD-Based Technological Procedure for Speech Signal Processing

The article presents a novel technological procedure for speech signal processing based on the empirical mode decomposition, being an adaptive time-frequency analysis method. The proposed procedure is based on the uniform splitting of the original speech signal into fragments, the decomposition of fragments into empirical modes, and the formation of new mode speech signals. The goal of the technological procedure elaboration is to expand the space for informatively significant amplitude, time, frequency, and energy characteristics of the original speech signal. A brief description of various types of empirical mode decomposition has been presented, and their advantages and disadvantages have been revealed. The functionality of the proposed technological procedure has been detailed, and the research outcomes have been reported. An analysis of the research results has evidenced that the minimum time for the formation of a set of modal speech signals is afforded when analyzing 300–1000 ms fragments; the minimum error in the formation of a set of mode speech signals is obtained when the fragments are decomposed into 8–10 empirical modes, and the difference between the original and reconstructed signals being less than 0.001 V (0.1 %). It has been concluded that the proposed technological procedure actually provides an expansion of the space for informatively significant amplitude, time, frequency, and energy characteristics due to the formation of a set of new mode speech signals. Thus, it can be efficiently used in the formation of an optimal set of speech parameters relevant to naturally expressed human emotions.
An Improved Method to Quantify Short-Chain Fatty Acids in Biological Samples Using Gas Chromatography–Mass Spectrometry

Gut microbial metabolites, short-chain fatty acids (SCFAs), are found at multiple locations in the host body and are identified as important metabolites in gut microbiome-associated diseases. Quantifying SCFAs in diverse biological samples is important to understand their roles in host health. This study developed an accurate SCFA quantification method by performing gas chromatography–mass spectrometry (GC/MS) in human plasma, serum, feces, and mouse cecum tissue. The samples were acidified with hydrochloric acid, and the SCFAs were extracted using methyl tert-butyl ether. In this method, distilled water was selected as a surrogate matrix for the quantification of SCFAs in target biological samples. The method was validated in terms of linearity, parallelism, precision, recovery, and matrix effect. The developed method was further applied in target biological samples. In conclusion, this optimized method can be used as a simultaneous SCFA quantification method in diverse biological samples.
Demographic composition, post-release and natal dispersal, and breeding success of the reintroduced Oriental Stork Ciconia boyciana

Summary Species restoration through reintroduction and reinforcement is widely considered as an effective method for the conservation of threatened animals. Although these approaches have been increasingly implemented over the last half-century, many attempts have failed. Dispersal behaviour is one of the key factors determining reintroduction success. We reported the demographic composition and dispersal pattern (i.e., post-release and natal dispersal) of a reintroduced Oriental Stork Ciconia boyciana population in Japan and identified the determinants of breeding success in 2005–2019. We hypothesized that one of the key determinants of breeding success is the density-dependent effect through post-release and natal dispersal. The F1 generation occurrence corresponded to two years, F2 to seven, F3 to 11, and F4 to 14 years after initial reintroduction. Wild-born birds accounted for 74% of the reintroduced population at F4 occurrence. Post-release dispersal distances were shorter than natal dispersal. Inter-nest distance was selected as the key factor determining breeding success. These results may have been due to feeding habitat restoration and conspecific interactions. In conclusion, steady recruitment to the next generation is thought to be due to a moderate natal dispersal and the subsequent low breeding density of wild-born birds.
Tiling surfaces with straight strips

We present an algorithm computing geodesic curves partitioning an open mesh into segments which can be approximated using long, trimmed strips of material possessing a prescribed width. We call this straight strip tiling of a curved surface, with applications such as the surfacing of curved roofs. Our strips are straight since they conform to being rectangular, in contrast to possibly highly curved strip segments studied for developable surface decomposition. Starting from a geodesic curve defined by a user-specified starting point and direction we compute recursively neighbouring geodesics which respect the constraints and lead to optimal material usage. Our algorithm is exact with respect to the polyhedral geometry of the mesh and runs on a variety of surfaces with modest time complexity of O(n1.5), where n is the mesh size. We extend the algorithm by relaxing the constraint that geodesics span the mesh allowing application to meshes with greater undulation.
Oral Administration of East Asian Herbal Medicine for Inflammatory Skin Lesions in Plaque Psoriasis: A Systematic Review, Meta-Analysis, and Exploration of Core Herbal Materials

Psoriasis is an inflammatory autoimmune skin disease with various clinical manifestations. The aim of this review was to systematically evaluate the efficacy and safety of oral administration of East Asian herbal medicine (EAHM) for inflammatory skin lesions in psoriasis and to explore core herbal materials for drug discovery. A comprehensive search was conducted in 10 electronic databases for randomized controlled trials from their inception until 29 July 2021. Statistical analysis was performed in R version 4.1. 2 and R studio. When heterogeneity in studies was detected, the cause was identified through sensitivity analysis, meta-regression, and subgroup analysis. Methodological quality was independently assessed using the revised tool for risk of bias in randomized trials. A total of 56 trials with 4966 psoriasis patients met the selection criteria. Meta-analysis favored EAHM monotherapy on Psoriasis Area Severity Index (PASI) 70 (RR: 1.2845; 95% CI: 1.906 to 1.3858, p < 0.0001), PASI 60 (RR: 1.1923; 95% CI: 1.1134 to 1.2769, p < 0.0001), continuous PASI score (MD: −2.3386, 95% CI: −3.3068 to −1.3704, p < 0.0001), IL-17, IL-23, TNF-α, and Dermatology Life Quality Index. Patients treated with EAHM monotherapy had significantly reduced adverse events incidence rate. In addition, based on additional examination of the herb data included in this meta-analysis, 16 core materials were identified. They are utilized in close proximity to one another, and all have anti-inflammatory properties. The findings in this study support that oral EAHM monotherapy may be beneficial for inflammatory skin lesions in psoriasis. Meanwhile, the identified core materials are expected to be utilized as useful drug candidate hypotheses through follow-up studies on individual pharmacological activities and synergistic effects.
A Compact Non-Contact Heart Sound Sensor Based on Millimeter-Wave Radar

A novel radar sensing system for non-contact and continuous detection of heart sound is proposed in this paper, which is a custom designed 120 GHz interferometric radar. The moving trajectory of the heart pulses can be accurately demodulated based on the radar signals, and thus the heart sounds can be reconstructed. Experiments have been carried out with electrocardiogram (ECG) and phonocardiogram (PCG) as reference. The results show the heart pulse signatures to an accuracy of micrometer level can be detected by the designed radar sensor, and the reconstructed heart sound can be visually and acoustically recognized. There is also a certain degree of similarity between the PCG signals and the heart sound signals reconstructed by the proposed radar sensor. The experimental results reveal the possibility for the proposed radar system as a heart sound sensor to be employed inside and outside the clinic, such as applications in the vast internet-of-things (IoT) and in-home health monitoring.
High-Resolution SAR Typical Targets Extraction and Heterogeneous Image Registration

ABSTRACT To overcome current difficulties in target extraction from high resolution Synthetic Aperture Radar (SAR) and in heterogeneous image registration, new extraction and registration methods are proposed in this paper according to two typical targets: airport and road targets. For airport targets, Otsu segmentation method is firstly used to obtain potential Region of Interest (ROI). Secondly, Fuzzy C-means Clustering Method (FCM) and HU Invariant Moments are jointed to describe regional features of ROI. Finally, Euclidean distance similarity and nearest neighbor distance ratio (NNDR) are jointly used to complete image registration (between optical and SAR image); for road targets, self-adaptive threshold segmentation method is firstly used to obtain ROI. Secondly, the ratio of average (ROA) and Hough transformation are jointed to get the directions of ROI. Thirdly, opening operators using linear operators based on these directions are used to obtain better ROI. Finally, a new method called Triangle Mesh Angle Method (TMAM) is proposed to complete image registration. Experimental results demonstrate the proposed methods have good matching accuracy and robust registration results, and own research value and promising applications.
Reachability Map for Diverse and Energy Efficient Stepping of Humanoids

In legged locomotion, the relationship between different gait behaviors and energy consumption must consider the full-body dynamics and the robot control as a whole, which cannot be captured by simple models. This work studies the totality of robot dynamics and whole-body optimal control as a coupled system to investigate energy consumption during balance recovery. We developed a two-phase nonlinear optimization pipeline for dynamic stepping, which generates reachability maps showing complex energy-stepping relations. We optimize gait parameters to search all reachable locations and quantify the energy cost during dynamic transitions, which allows studying the relationship between energy consumption and stepping locations given different initial conditions. We found that to achieve efficient actuation, the stepping location and timing can have simple approximations close to the underlying optimality, resulting in optimal step positions with a 10.9% lower energy cost than those generated by linear inverted pendulum model. Despite the complexity of this nonlinear process, we found that near-minimal effort stepping locations are within a region of attractions, rather than a narrow solution space suggested by a simple model. This provides new insights into the nonuniqueness of near-optimal solutions in robot motion planning and control, and the diversity of stepping behavior in humans.
Work-Related Driving of Heavy Goods Vehicles: Factors That Influence Road Safety and the Development of a Framework for Safety Training

Road traffic accidents are a major health concern all over the world. Each year, 1.3 million people die in fatal road traffic accidents. Fatal and serious heavy goods vehicle (HGV) crashes are over-represented in many countries. This paper is a contribution to the road safety literature and has two aims. First, the study seeks to identify important factors in managing road safety for work-related driving of HGVs. Second, the study proposes an overall framework for how safety training could be executed and its overall content. Methods used were a literature review and a case study. The results show that important factors for management of road safety could be arranged at different levels: governmental level, third-party level, organizational level and driver level. Most important is that a systematic approach to road traffic safety for HGVs is essential. Every party is jointly responsible for road traffic safety, and parties must communicate and work together to increase road traffic safety for work-related driving of HGVs. By developing a safety training program for all parties in the system, the study proposes a method for increased communication, collaboration and cooperation between parties.
Dynamic Evaluation of the Quality Parameters of Raw Buffalo Milk Using a High-Performance Equipment

The aim of this study was to evaluate the quality parameters of raw buffalo milk, by determining the physicochemical parameters with major impact in terms of milk quality, number of somatic cells and total number of viable counts, at the level of a commercial farm. Over a period of 12 months, samples of raw buffalo milk were collected and analyzed from a farm authorized for the production of buffalo milk, resulting in a dynamic evaluation of quality parameters. The raw milk samples were analyzed using the - CombiFossTM7 equipment. which analyzes all the parameters necessary for official controls of milk production and quality. The averages for the results of the physicochemical parameters fat, protein, lactose, total solids and urea were 8.821 g/100 g, 4.400 g/100 g, 4.291 g/100 g, 17.93 g/100g and 27.08 mg/dL, respectively. Even if the gross composition does not exceed the allowed limits, the TVC exceeded the European standards in the case of 58.3% of the analyzed samples, the annual average being 2059.65x1000 cfu/ml. The value of the SCC average obtained was 304.85x1000 cells/ml, in accordance with the European standard. The values of SCC and TVC are increasing during the sheltering period, which means poor hygiene conditions.
Self-Triggered DMPC Design for Cooperative Multiagent Systems

This paper considers the cooperation control problem for a team of dynamically decoupled agents with resource constraints. A codesign of self-triggered mechanism and distributed model predictive control (DMPC) is proposed to achieve the cooperative objectives while efficiently exploiting communication network. The proposed self-triggered DMPC (ST-DMPC) possesses three important features. First, the communication cost is explicitly incorporated in the cost function. In this way, the triggering instant and control inputs are simultaneously optimized, and a desired tradeoff between control performance and communication cost is achieved. Second, at triggering instants, the first element of the optimal control input sequence along with the current state instead of the whole trajectory is broadcast to neighbors for cooperation, which further reduces communication load. Third, sufficient conditions on design parameters related to predictive states of neighbor agents are constructed to ensure stability of the overall system. The application of the proposed ST-DMPC to four robot manipulators validates the effectiveness of this method.
Optimal Economic Dispatch in Microgrids with Renewable Energy Sources

Due to the opening of the energy market and agreements for the reduction of pollution emissions, the use of microgrids attracts more attention in the scientific community, but the management of the distribution of electricity has new challenges. This paper considers different distributed generation systems as a main part to design a microgrid and the resources management is defined in a period through proposed dynamic economic dispatch approach. The inputs are obtained by the model predictive control algorithm considering variations of both pattern of consumption and generation systems capacity, including conventional and renewable energy sources. Furthermore, the proposed approach considers a benefits program to customers involving a demand restriction and the costs of regeneration of the pollutants produced by conventional generation systems. The dispatch strategy through a mathematical programming approach seeks to reduce to the minimum the fuel cost of conventional generators, the energy transactions, the regeneration of polluted emissions and, finally, includes the benefit in electricity demand reduction satisfying all restrictions through mathematical programming strategy. The model is implemented in LINGO 17.0 software (Lindo Systems, 1415 North Dayton Street, Chicago, IL, USA). The results exhibit the proposed approach effectiveness through a study case under different considerations.
Towards Tomography-Based Real-Time Control of Multiphase Flows: A Proof of Concept in Inline Fluid Separation

The performance of multiphase flow processes is often determined by the distribution of phases inside the equipment. However, controllers in the field are typically implemented based on flow variables, which are simpler to measure, but indirectly connected to performance (e.g., pressure). Tomography has been used in the study of the distribution of phases of multiphase flows for decades, but only recently, the temporal resolution of the technique was sufficient for real-time reconstructions of the flow. Due to the strong connection between the performance and distribution of phases, it is expected that the introduction of tomography to the real-time control of multiphase flows will lead to substantial improvements in the system performance in relation to the current controllers in the field. This paper uses a gas–liquid inline swirl separator to analyze the possibilities and limitations of tomography-based real-time control of multiphase flow processes. Experiments were performed in the separator using a wire-mesh sensor (WMS) and a high-speed camera to show that multiphase flows have two components in their dynamics: one intrinsic to its nonlinear physics, occurring independent of external process disturbances, and one due to process disturbances (e.g., changes in the flow rates of the installation). Moreover, it is shown that the intrinsic dynamics propagate from upstream to inside the separator and can be used in predictive and feedforward control strategies. In addition to the WMS experiments, a proportional–integral feedback controller based on electrical resistance tomography (ERT) was implemented in the separator, with successful results in relation to the control of the distribution of phases and impact on the performance of the process: the capture of gas was increased from 76% to 93% of the total gas with the tomography-based controller. The results obtained with the inline swirl separator are extended in the perspective of the tomography-based control of quasi-1D multiphase flows.
Activity Recognition Using Temporal Optical Flow Convolutional Features and Multilayer LSTM

Nowadays digital surveillance systems are universally installed for continuously collecting enormous amounts of data, thereby requiring human monitoring for the identification of different activities and events. Smarter surveillance is the need of this era through which normal and abnormal activities can be automatically identified using artificial intelligence and computer vision technology. In this paper, we propose a framework for activity recognition in surveillance videos captured over industrial systems. The continuous surveillance video stream is first divided into important shots, where shots are selected using the proposed convolutional neural network (CNN) based human saliency features. Next, temporal features of an activity in the sequence of frames are extracted by utilizing the convolutional layers of a FlowNet2 CNN model. Finally, a multilayer long short-term memory is presented for learning long-term sequences in the temporal optical flow features for activity recognition. Experiments11https://github.com/Aminullah6264/Activity_Rec_ML-LSTM. are conducted using different benchmark action and activity recognition datasets, and the results reveal the effectiveness of the proposed method for activity recognition in industrial settings compared with state-of-the-art methods.
Analysis of Omics Data Reveals Nucleotide Excision Repair-Related Genes Signature in Highly-Grade Serous Ovarian Cancer to Predict Prognosis

Most of the high-grade serous ovarian cancers (HGSOC) are accompanied by P53 mutations, which are related to the nucleotide excision repair (NER) pathway. This study aims to construct a risk signature based on NER-related genes that could effectively predict the prognosis for advanced patients with HGSOC. In our study, we found that two clusters of HGSOC with significantly different overall survival (OS) were identified by consensus clustering and principal component analysis (PCA). Then, a 7-gene risk signature (DDB2, POLR2D, CCNH, XPC, ERCC2, ERCC4, and RPA2) for OS prediction was developed subsequently based on TCGA cohort, and the risk score-based signature was identified as an independent prognostic indicator for HGSOC. According to the risk score, HGSOC patients were divided into high-risk group and low-risk group, in which the distinct OS and the predictive power were also successfully verified in the GEO validation sets. Then we constructed a nomogram, including the risk signature and clinical-related risk factors (age and treatment response) that predicted an individual’s risk of OS, which can be validated by assessing calibration curves. Furthermore, GSEA showed that the genes in the high-risk group were significantly enriched in cancer-related pathways, such as “MAPK signaling pathway”, “mTOR signaling pathway”, “VEGF signaling pathway” and so on. In conclusion, our study has developed a robust NER-related genes-based molecular signature for prognosis prediction, and the nomogram could be used as a convenient tool for OS evaluation and guidance of therapeutic strategies in advanced patients with HGSOC.
AC-Driven Ultraviolet-B Electroluminescence From Gd3+-Doped ZnGa2O4 Film on SiOx/Silicon Substrate

Ultraviolet (UV) light sources with wavelengths ranging from 100 to 400 nm are of fundamental interest in a wide range of applications, such as water purification, UV communication, disinfection, and sterilization. This heartens the development to provide UV light sources derive from different approaches. Here, we present UV-B electroluminescence (EL) from Gd3+-ZnGa2O4 (ZGO:Gd3+) that formed in silicon substrate through a one-step annealing method. Structurally, it consists of two oxide layers; amorphous SiOx as an interfacial layer and ZGO:Gd3+ as a UV-B emitting layer, a so-called metal-oxide-semiconductor (MOS) structure. Under applied sinusoidal waveform, the device emits a sharp UV-B spectrum and increases gradually under voltage variations without any spectral change. We show that our device has asymmetrical optical transient behavior due to the asymmetric two oxides layer across the MOS-EL device.
Predicting the Football Players’ Market Value Using Neural Network Model: A Data-Driven Approach

The domain of this research is about football business in which the market value of football players was predicted. The researcher designed a neural network model to predict the price of a football player for football clubs utilizing data from FIFA 19. The researcher thoroughly conducted an in-depth research on literature review to understand the inner workings of the neural networks as well as found related works on sports analytics and prediction. On the other hand, this research involved the hyper parameter optimization of the neural network with different activation functions, number neuron and layers, learning rate and its decay and L1 regularization. K-Fold cross validation is carried out to ensure the quality of the neural network. Simultaneous exploration is done to various aspects of neural network training is performed and their evaluation metrics such as mean squared error, R-squared and Root Mean Square are investigated. Lastly, the best performing model is chosen for the final model. The final model achieved a 95 percent accuracy and a 0.037 mean square error.