Miracle-dz/UniFAR-FTUs huggingface dataset:
The somatosensory system in anorexia nervosa: A scoping review

Anorexia nervosa (AN) is a severe psychiatric disorder characterized by a number of symptoms including food restriction and body perception distortions. In the present scoping review, we outline the current literature on sensory submodalities related to the somatosensory system in AN including affective touch, haptic perception, interoception, nociception, proprioception, and tactile perception as well as multisensory integration. The evidence suggests that individuals with AN exhibit abnormalities in multisensory integration, discrimination (but not detection) of complex haptic and tactile stimuli, and reduced sensitivity to nociceptive stimuli. This review provides an outline of the current literature, identifies gaps within the literature, and suggests novel directions for future research.
A Hybrid Model for Documents Representation

Text representation is a critical issue for exploring the insights behind the text. Many models have been developed to represent the text in defined forms such as numeric vectors where it would be easy to calculate the similarity between the documents using the well-known distance measures. In this paper, we aim to build a model to represent text semantically either in one document or multiple documents using a combination of hierarchical Latent Dirichlet Allocation (hLDA), Word2vec, and Isolation Forest models. The proposed model aims to learn a vector for each document using the relationship between its words’ vectors and the hierarchy of topics generated using the hierarchical Latent Dirichlet Allocation model. Then, the isolation forest model is used to represent multiple documents in one representation as one profile to facilitate finding similar documents to the profile. The proposed text representation model outperforms the traditional text representation models when applied to represent scientific papers before performing content-based scientific papers recommendation for researchers.
Piece-wise linear regression: A new approach to predict COVID-19 spreading

The coronavirus disease 2019 (COVID-19) pandemic is the most rapidly evolving global emergency since March 2020 and one of the most exercised topics in all aspects of the world. So far there are numerous articles that have been published related to COVID-19 in various disciplines of science and social context. Since from the very beginning, researchers have been trying to address some fundamental questions like how long it will sustain when it will reach the peak point of spreading, what will be the population of infections, cure, or death in the future. To address such issues researchers have been used several mathematical models from the very beginning around the world. The goal of such predictions is to take strategic control of the disease. In most of the cases, the predictions have deviated from the real data. In this paper, a mathematical model has been used which is not explored earlier in the COVID-19 predictions. The contribution of the work is to present a variant of the linear regression model is the piece-wise linear regression, which performs relatively better compared to the other existing models. In our study, the COVID-19 data set of several states of India has been used.
Message Passing and Hierarchical Models for Simultaneous Tracking and Registration

Sensor registration is an important problem that must be considered when attempting to perform any kind of data fusion in multimodal, multisensor target tracking. In this multiple target tracking (MTT) application, any inaccuracies in the registration can lead to false tracks being created, and tracks of true targets being stopped prematurely. This article introduces a method for simultaneously tracking multiple targets in a surveillance region and estimating appropriate sensor registration parameters so that sensor fusion can be performed accurately. The proposed method is based around particle belief propagation (BP), a recent but highly efficient framework for tracking multiple targets. The proposed method also uses a hierarchical model which allows for multiple processes to be linked and interact with one another. We present a comprehensive set of simulations and results using differing, asynchronous sensor setups, and compare with a random finite set (RFS) approach, namely the sequential Monte Carlo (SMC)-probability hypothesis density (PHD) filter. The results show the proposed method is 17% more accurate than the RFS approach on average.
Production of polyclonal anti-β-tubulin antibodies and immunodetection of Vairimorpha (Nosema) ceranae (Opisthosporidia: Microsporidia) proliferative stages in the midguts of Apis mellifera and in the Sf9 cell culture

Summary A number of methods to detect microsporidia infections are based on recovery of spores due to their thick refractive spore walls and presence of chitin that easily binds to fluorogenic and chromogenic stains. Visualization of intracellular stages in infected cells and organs has been always a challenge. In this report a new immunodetection method based on the use of β -tubulin antibodies, has been developed to mark the intracellular life cycle stages of Vairimorpha ( Nosema ) ceranae , an emergent pathogen of Apis mellifera . For this purpose, we carried out the heterologous expression of the V. ceranae β -tubulin gene in E. coli cells and obtained polyclonal antibodies to the recombinant protein. The antibodies were successfully applied for immunolabelling of microsporidian cells in insect midguts in vivo as well as in Sf9 cell culture in vitro .
Improvement of the Ohmic Loss Process Model of the Proton Exchange Membrane Fuel Cell

The impedance characteristics of the ohmic overpotential of the proton exchange membrane (PEM) fuel cells are studied analytically using the process modeling approach. The water transport in the membrane, the cathode catalyst layer, and gas diffusion layer are analyzed. The analytical relation of the impedance of the ohmic loss is determined and is converted to an equivalent circuit. Then, the impedance of a PEM fuel cell is measured experimentally in different current densities, operating temperatures and the anode and cathode relative humidities. The measured impedances are compared with the predicted ones from the analytical model. It is shown that the predicted impedance characteristics are in great agreement with the measured ones in all different operating conditions.
Machine learning and Deep Learning Techniques: A Review

: Today, intelligent frameworks that offer computerized reasoning abilities regularly depend on Machine Learning. AI portrays the limit of frameworks to gain from issue explicit preparing information to computerize the cycle of insightful model structure and settle related assignments. Profound learning is an AI idea dependent on fake neural organizations. For some applications, profound learning models outflank shallow AI models and conventional information examination draws near. In this article, we sum up the essentials of AI and profound figuring out how to create a more extensive comprehension of the systems supporting current shrewd frameworks. Specifically, we give a reasonable qualification between significant terms and ideas, clarify the interaction of computerized insightful model structure through AI and profound learning, and talk about the difficulties that emerge when carrying out such insightful frameworks in the field of electronic business sectors and arranged business. These normally go past mechanical perspectives and feature issues in human-machine cooperation and computerized reasoning servitization.
Comparative Study of Data Sources, Features, and Approaches for Automatic Personality Classification from Text

Personality is a concern with individual differences in characteristic patterns of thinking, feeling, and behavior. Computational recognition of user personality is likely to be useful in many computational applications and technologies such as career counseling, relationship, and health counseling, human resource management, forensics, and mental health diagnosis. It involves understanding, prediction, and analysis of human behavior. The different methods have been proposed to automatically infer the user's personality from their user generated content. The paper discusses state-of-the-art personality recognition on various data sources, features, and their impact on different application areas
Proposal of Prediction Accuracy Improvement in Non-invasive Blood Glucose Measurement using MHC Method

According to the World Health Organization, 422 million adults worldwide have diabetes. Diabetic patients must regularly measure and manage their blood glucose levels. However, existing blood glucose meters require needles to draw blood, causing pain and infection problems. The measuring instrument to solve this problem would be a non-invasive blood glucose measuring instrument, but none is practically used at present. The authors have been studying non-invasive blood glucose measurement methods to reduce the burden on diabetic patients. As a result of previous research, we developed a simple non-invasive blood glucose meter and a blood glucose control system. Currently, our aim is to improve accuracy of blood glucose level prediction. In this paper we report on the blood glucose level prediction technique realized by using biological information. As a result, heart rate and MHC method are used as input data, and prediction using machine learning can be performed with high accuracy.
Feasibility Study of a High‐Resolution Shallow Surface Penetration Radar for Space Application

We modified and justified the radar equation for ground penetration by including the backscattering effect. We propose a strawman system on an orbiter 50 km above the surface based on lunar conditions. The Tightly coupled dipole arrays antenna substantially reduces the size and mass and consists of an array with 8 × 8 cells on a light weight plate that is 1.62‐m2 wide with cells 0.25‐m high providing increased directionality and beam‐steering capability. The system includes 8 transmitters and 16 receivers that are able to measure wave polarization so that it is sensitive to subsurface water/ice. The system is controlled by central computers with transceivers all programmable providing more functionalities and flexibilities enabling systems of an even larger number of transceivers. It uses the synthetic aperture radar method to enhance the spatial resolution along the orbit track and has phase steering capability to increase the resolution in the cross‐track direction. The total transmitted power is 160 W. The radar is able to operate in three modes: a fine‐resolution mode that provides the full Multiple‐Input Multiple‐Output ultrawideband (MIMO UWB) capability with vertical range resolution up to 0.2 m, a deep penetration mode that is able to penetrate 100 m for high attenuation targets or over few kilometers for low attenuation targets, and a survey mode that provides overall characterization of the planet economically. This system can provide internal properties and structures of a target and detailed information about most useful resources such as ice, water, minerals, and shelter caves, for human space exploration on the Moon, Mars, and asteroids.
Endorectal digital prostate tomosynthesis (endoDPT): a proof-of-concept study

In this study we present endorectal digital prostate tomosynthesis (endoDPT), a proposed method of high resolution prostate imaging using an endorectal x-ray sensor and an external x-ray source. endoDPT may be useful for visualizing the fine detail of small structures such as low dose rate brachytherapy (LDRBT) seeds that are difficult to visualize with current methods. The resolution of endoDPT was characterized through measurement of the modulation transfer function (MTF) and artifact spread function (ASF) in computational and physical phantoms. The qualitative resolution of endoDPT was assessed relative to computed tomography (CT) through imaging of LDRBT seeds implanted in ex vivo canine prostates. The x-ray sensor MTF reached 10% at 11.50 mm−1, the reconstruction algorithm MTF reached a maximum at 7.08 mm−1, and the ASF was 2.5 mm (full-width at half-maximum). Fine structures in LDRBT seeds like the 0.05 mm thick shell were visible with endoDPT but not CT. All endoDPT images exhibited an overshoot artifact. The measured MTFs were consistent with other studies using similar x-ray sensors and demonstrated improved resolution compared to digital breast tomosynthesis; this result was due to the smaller endoDPT x-ray sensor detection element size and quantitatively demonstrates the high resolution of endoDPT. The ASF demonstrated worse depth resolution compared to in-plane resolution, due to partial angular sampling; partial angular sampling also caused the observed overshoot artifact in the endoDPT images. However, endoDPT still was able to visualize fine structures such as the LDRBT seed shell to a much higher degree than CT. This high-resolution visualization may be useful for improvements in patient specific LDRBT dosimetry. Overall, these results indicate endoDPT is capable of high in-plane spatial resolution and is thus well poised for optimization and studies assessing clinical utility.
Local bi‐directional funnel network for salient object detection

Existing deep-learning–based saliency detection methods mainly de-sign a sophisticated architecture to integrate multi-level convolutional features, for example, recurrent network or bi-directional message passing model, and achieve gratifying performance. However, the direct transmission of information with large span, for example, the information from the deepest layer and the shallowest layer, may lead to antagonistic problems. To address this problem, we propose a local bi-directional funnel network (LBDFN) to effectively integrate multi-level features for salient object detection. In the proposed local bi-directional funnel network, a local bi-directional feature integration (LBDFI) module is designed to fuse the features with similar properties from adjacent convolution layers, which can solve the problem of mismatch between fusion features well. The extensive experiments on ﬁve saliency detection datasets clearly demonstrate that the proposed method outperforms the state-of-the-art approaches.
Vitik Detection of Aerobic Spore-Forming Bacteria Isolated from Raw Milk, Skim Milk Powder and UHT Milk

Background Aerobic spore-forming bacteria can be found in a wide range of environmental niches suchas food production. Among them, the aerobic spore-forming bacteria like Bacillus ceruse, pose a risk ofcausing dairy product poisoning by the production of toxins.Materials and Method: Out of 40 samples, 10 from each of Raw Milk, Skim Milk Powder, UHT Milk andWight chees were collected to isolate aerobic spore-forming bacteria by morphological, physiological andbiochemical tests.Conclusion: A total of 25 isolates of heat resistant bacteria, 10(40%), 8(32%), 3(12%), 4(16%) from “RM,white chees, SMP and UHT milk respectively,” were purified and characterized. Various spore formingbacteria belonging to Bacilli spp. which include pathogenic were biochemically identified using vitek 2systems, the result showed the highest rate of Bacillus subtilis (28%) followed by Bacillus ceruse andBacillus thuringiensis (24%).
Evaluation of Collaborative Filtering for Recommender Systems

Recently, due to the increasing amount of data on the Internet along with the increase in products’ purchasing via e-commerce websites, Recommender Systems (RS) play an important role in guiding customers to buy products they may prefer. Furthermore, these systems help the companies to advertise their products to the most potential customers, and therefore raise their revenues. Collaborative Filtering (CF) is the most popular RS approach. It is classified into memory-based and model-based filtering. Memory-based filtering is in turn classified into user-based and item-based. Several algorithms have been proposed for CF. In this paper, a comparison has been performed between different CF algorithms to assess their performance. Specifically, we evaluated K-Nearest Neighbor (KNN), Slope One, co-clustering and Non-negative Matrix Factorization (NMF) algorithms. KNN algorithm is representative of the memory-based CF approach (both user-based and item-based). The other three algorithms, on the other hand, are under the model-based CF approach. In our experiments, we used a popular MovieLens dataset based on six evaluation metrics. Our results reveal that the KNN algorithm for item-based CF outperformed all other algorithms examined in this paper.
Graphite felt modified with electroless Co-Ni-P alloy as an electrode material for electrochemical oxidation and reduction of polysulfide species

Electroless deposition of a Co−Ni−P alloy on the surface of graphite felt filaments was performed in a low-temperature pyrophosphate solution under flow-through conditions. The loading, composition, morphology, and structure of electroless the Co−Ni−P alloy deposit on the filaments of the modified graphite felt were investigated by gravimetric analysis, energy-dispersive X-ray spectroscopy, scanning electron microscopy and X-ray diffraction, respectively. Electrochemical characterization of a graphite felt electrode modified with electroless Co−Ni−P alloy was performed by cyclic voltammetry, chrono-techniques, and the electrochemical impedance spectroscopy test in an aqueous solution of polysulfide composed of the mixture of 1 M Na2S, 1 M NaOH and 1 M S. It was found that the electroless Co−Ni−P alloy deposit on graphite felt has good cycling stability and high electrocatalytic activity toward reversible electrochemical redox reactions of polysulfide species. In comparison with the bare graphite felt electrode, the electrode modified with the electroless Co−Ni−P alloy showed five to seven times lower values of anodic and cathodic overpotentials in the aqueous solution of polysulfide. It is very likely that the good electrochemical performance of the modified graphite felt electrode is related to the high surface area of the electroless Co−Ni−P alloy deposit.
A Stochastic Optimal Planning Model for Fully Green Stand-Alone PEV Charging Stations

Electric vehicle users need access to recharging services within the all-electrical driving range of their vehicles. However, public charging stations to be located on remote highways are in many cases far from the power grid, where a connection to the power grid may be impossible, costly, or impractical. This article formulates a stochastic mixed integer linear programming (MILP) planning model for energy storage and renewable energy systems to cover the energy demand of stand-alone charging stations for plug-in electric vehicles (PEVs) entirely using green energy generated by renewable energy sources (RESs). The proposed model accounts for the stochastic behavior of PEVs and RESs as well as the technical, economic, and operational characteristics of a variety of energy storage technologies. A customer satisfaction criterion is introduced to allow for a tradeoff between performance and investment costs. The proposed model can also be used for designing fully green PEV charging stations in urban and rural settings to capitalize on PEV environmental benefits by avoiding the use of fossil-fuel-generated electric power in their charging or to reduce the main grid load. A set of numerical studies have been performed in General Algebraic Modeling System (GAMS) environment to demonstrate the feasibility and effectiveness of the proposed model.
Impact of Market Segmentation Strategies on Customer Loyalty: The Mediating Role of Positioning Effectiveness of Interior Design Industries within the Kumasi Metropolis

The buying behaviour of potential customers in terms of interior designs in Ghana differs from those in the Western context. There is a relatively low patronage of interior design products or services compared to the Western context (Ogunduyile et al, 2008). Existing literature have often looked at the direct causal relationship between market segmentation strategies and customer loyalty without emphasizing the effectiveness of positioning which could drive customer loyalty (Yim et al., 1999). The paper focuses on assessing the impact of market segmentation strategies on customer loyalty of Interior Design Industries, with the mediating role of positioning effectiveness. A total of 220 customers of Interior Design Industries within the Kumasi Metropolis of which 200 responses were obtained representing 90% response rate. Convenience sampling techniques was used in selecting customer’s whiles Structural Equation Model was used to analyze the data. The study revealed that, Behavioural, psychographic and demographic segmentations impacted positively on customer loyalty. Geographic segmentation variable however, impacted negatively on customer loyalty. Psychographic segmentation impacted positively on positioning effectiveness and customer loyalty. Positioning Effectiveness partially mediates between psychographic segmentation strategies and customer loyalty. Positioning Effectiveness has no mediation between geographic segmentation, behavioural segmentation, demographic segmentation and customer loyalty.
The negative corporate governance impact of distracted shareholders: evidence from value of cash holdings

ABSTRACT This paper examines whether distracted shareholders reduce firms’ value of cash holdings. Following prior literature, we construct a firm-year level shareholder ‘distraction’ measure, by exploiting exogenous shocks to institutional shareholders’ portfolios. We find that firms with distracted shareholders experience a decrease in the marginal value of cash holdings, which indicates that firms with distracted shareholders are more likely to misallocate company resources due to lack of monitoring. We further find that this effect is more pronounced for firms with low information asymmetry, few product market competitive threats, and high analyst coverage. The results of this paper suggest that institutional investor attention distraction will reduce firms’ monitoring intensity, which exacerbates firms’ opportunistic behaviors.