Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Pre-Treatment miRNAs Levels in Untreated Major Depressive Disorder Patients Predict Early Response to Antidepressants and Interact with Key Pathways

Major depressive disorder (MDD) is a life-impairing disorder, and early successful treatment is important for a favorable prognosis. However, early response to antidepressants differs widely among individuals, and is difficult to predict pre-treatment. As miRNAs have been reported to play important roles in depression, identification of miRNAs associated with antidepressant treatment responses and their interacting genes and pathways will be beneficial in understanding the predictors and molecular mechanisms of depression treatment. This randomized control trial examined miRNAs correlated with the early therapeutic effect of selective serotonin reuptake inhibitors (SSRIs; paroxetine or sertraline) and mirtazapine monotherapy. Before medication, we comprehensively analyzed the miRNA expression of 92 depressed participants and identified genes and pathways interacting with miRNAs. A total of 228 miRNAs were significantly correlated with depressive symptoms improvements after 2 weeks of SSRIs treatment, with miR-483.5p showing the most robust correlation. These miRNAs are involved in 21 pathways, including TGF-β, glutamatergic synapse, long-term depression, and the mitogen-activated protein kinase (MAPK) signaling pathways. Using these miRNAs enabled us to predict SSRI response at week 2 with a 57% difference. This study shows that pre-treatment levels of miRNAs could be used to predict early responses to antidepressant administration, a knowledge of genes, and an identification of genes and pathways associated with the antidepressant response.
Analisis Sentimen Data Provider Layanan Internet Pada Twitter Menggunakan Support Vector Machine Dengan Penambahan Algoritma Levenshtein Distance

Komentar pada data twitter mengandung banyak opini terkait suatu objek atau topik. Dari kumpulan komentar, dapat dilakukan analisis sentimen menggunakan Support Vector Machine untuk memperoleh hasil klasifikasi positif dan negatif. Data yang digunakan berkaitan dengan provider atau penyedia jaringan internet yang ada di Indonesia. Penambahan algoritma Levenshtein Distance pada tahap text preprocessing bertujuan untuk meningkatkan hasil klasifikasi. Tahapan Proses klasifikasi meliputi, pengumpulan data menggunakan API twitter, penghapusan duplicate data, pemberian label data, tahap text preprocessing (convert emoticon, cleansing, case folding, stemming, stopword removal, and tokenizing, penerapan algoritma Levenshtein Distance, stopword removal lanjutan, convert negation), feature extraction (TF-IDF), serta proses klasifikasi menggunakan Support Vector Machine.Hasil pengujian dengan menggunakan confusion matrix, menunjukan peningkatan hasil klasifikasi yang lebih baik setelah menggunakan algoritma Levenshtein Distance pada tahap text preprocessing. Nilai accuracy mengalami peningkatan sebesar 2%, recall positif 3%, recall negatif 1%, precision positif 1%, dan precision negatif 2%. Tetapi kecepatan waktu proses pada tahap text preprocessing dengan penambahan algoritma Levenshtein Distance lebih lambat sebesar 295,606 detik, jika dibandingkan tanpa adanya penambahan algoritma Levenshtein Distance.
Examination of a Split Transformer Arrangement for the Inductive Link of Contactless Power Transfer Systems

In this work, a split transformer topology is proposed for use in a wireless electric vehicle charger. The topology presents desirable characteristics concerning minimization of the air gap, easiness of alignment, weight and cost. Comparison is performed with spiral topologies presented in relevant literature. The coupling coefficient of the system for various air gaps and misalignment conditions is extracted by 3D finite element simulations and experimentally validated by a 3 kW prototype inductive power transfer system. The magnetic field at nominal power of operation is also simulated for evaluation of the compliance to the human exposure limits.
Recent Advances and Remaining Challenges in Manufacturing of Amorphous and Nanocrystalline Alloys

A review of advanced Fe-based amorphous and nanocrystalline soft magnetic alloys that are currently being used in the market and their primary industrial applications is discussed as well as emerging trends. The high-efficiency distribution transformer market continues to be the largest segment by volume for Fe-based amorphous foil. The industry has been shifting toward higher induction levels (1.63 T) with increased stacking factor (>90%) to minimize the transformer size and reduce costs. New magnetic domain refined grades of amorphous foil are now commercially available which show a reduction of ~25% in core losses compared to the standard amorphous material. Nanocrystalline soft magnetic alloys have become the standard material for many applications including low-loss electrical components, magnetic shielding, electronic filtering, medium frequency transformer cores, and a variety of others. The nanocrystalline alloys are commonly cast fully amorphous and devitrified through controlled heat treatments. New nanocrystalline alloy compositions and processing methods have enhanced both the mechanical and magnetic performance of the final properties. Amorphous and nanocrystalline alloys show promise for use in electric motors but fabrication challenges for limited their commercial adoption.
An economic approach to marriage

ABSTRACT The purpose of this paper is to outline an economic approach to marriage. The first part contains an introduction to the economics of a family. The second part analyses the marriage market. The third part discusses the division of household chores in a household. The fourth part examines marriage as a cooperative or non-cooperative game. The fifth part deals with the dilemma between monogamy and polygamy on marriage markets. The sixth part confronts the economics of marriage with the findings of sociobiology.
Continuous Hidden Markov Model Based Spectrum Sensing with Estimated SNR for Cognitive UAV Networks

In this paper, to enhance the spectrum utilization in cognitive unmanned aerial vehicle networks (CUAVNs), we propose a cooperative spectrum sensing scheme based on a continuous hidden Markov model (CHMM) with a novel signal-to-noise ratio (SNR) estimation method. First, to exploit the Markov property in the spectrum state, we model the spectrum states and the corresponding fusion values as a hidden Markov model. A spectrum prediction is obtained by combining the parameters of CHMM and a preliminary sensing result (obtained from a clustered heterogeneous two-stage-fusion scheme), and this prediction can further guide the sensing detection procedure. Then, we analyze the detection performance of the proposed scheme by deriving its closed-formed expressions. Furthermore, considering imperfect SNR estimation in practical applications, we design a novel SNR estimation scheme which is inspired by the reconstruction of the signal on graphs to enhance the proposed CHMM-based sensing scheme with practical SNR estimation. Simulation results demonstrate the proposed CHMM-based cooperative spectrum sensing scheme outperforms the ones without CHMM, and the CHMM-based sensing scheme with the proposed SNR estimator can outperform the existing algorithm considerably.
Salt Consumption and Blood Pressure in Rural Hypertensive Participants: A Community Filed Trial

Purpose Hypertension is a major cause of morbidity and mortality in the world. This study aimed to evaluate an intervention based on the Health Belief Model regarding the whole family's salt consumption and blood pressure among hypertensive patients in rural areas in Iran. Methods This clinical multicenter trial (clinical and community) with a control and an intervention group was conducted on the residents of 14 villages covered by 14 health houses. Totally, 200 hypertensive patients (n = 100 in each group) were selected via multistage random sampling. The intervention included a two-day workshop on blood pressure and reducing salt consumption based on HBM structures for health personnel and an eight-session workshop on how to reduce salt intake and blood pressure for mothers who were responsible for the families' diets. Participants completed the questionnaires before and immediately after the intervention. Results Compared to the control group, in the intervention group, a significant reduction was observed in salt consumption by the families (urine sodium and creatinine reduced by 35 mEq/l and 7.5 mg/dL, respectively). The results also revealed a significant decrease in blood pressure in the intervention group. Conclusion The results showed that the mothers' model-based education could effectively improve the diet of the whole family members and, as a result, reduce the associated diseases. The main advantage of this study was the involvement of the rural health personnel, which helped run longer and larger-scale health-promotion programs in the communities.
Nonlinear behavior mechanism of change in electrical resistance on 3D printed carbon fiber / PA6 composites during cyclic tests

The aim of this paper is to investigate the cause of nonlinear behavior mechanism of change in electrical resistance of 3D printed carbon fiber reinforced plastics (CFRP) due to tensile loading. The electrical resistance change method is a nondestructive testing method that helps detect strain and damage. In previous research for electrical resistance change method applied to 3D printed CFRP, the change in electrical resistance is different compared with the one of conventional CFRP. The behavior makes health monitoring difficult, and the phenomenon needs to be clarified. In this research, we focused on the electrode fabrication method and viscoelasticity effect during cyclic tests. Polishing and laser processing are used as surface treatments for electrode fabrication. Consequently, the treatments are found to affect the reverse piezo-resistivity. In addition, the decrease in electrical resistance depends on the test speeds of cyclic test. The viscoelasticity is found to affect the nonlinear behavior.
Research on Urban Road Traffic Flow Prediction Based on Wavelet Denoising and Multi-layer Perceptron

Aims: Develop a novel traffic flow prediction model to improve the accuracy of traffic flow prediction, better assist intelligent traffic management, improve traffic efficiency, reduce traffic congestion, and thus better improve sanitation and quality of life. Study Design: Develop an urban road traffic flow prediction model with strong predictive power and excellent stability. Place and Duration of Study: Southwest University of Science and Technology, between July 2021 and March 2022. Methodology: Adopting wavelet threshold to denoise, first decompose the original data, then perform noise filtering on the subsequences obtained after decomposing, and finally reconstruct the denoised data. Use denoised data to train a multilayer perceptron and make predictions on future data. At the same time, several representative models are selected to compare with the proposed model to verify the competitiveness of the proposed model. Results: The proposed model has the smallest prediction error in the two training sets with different temporal granularity. In addition, we are using the data after wavelet denoising for training and prediction results in a smaller prediction error than using the data without denoising. Conclusion: The proposed prediction model has strong prediction ability and generalization performance in the field of traffic flow prediction. The wavelet denoising method can effectively improve the prediction accuracy of traffic flow prediction.
Recognizing Semi-Natural and Spontaneous Speech Emotions Using Deep Neural Networks

We needed to find deep emotional features to identify emotions from audio signals. Identifying emotions in spontaneous speech is a novel and challenging subject of research. Several convolutional neural network (CNN) models were used to learn deep segment-level auditory representations of augmented Mel spectrograms. The proposed study introduces a novel technique for recognizing semi-natural and spontaneous speech emotions based on 1D (Model A) and 2D (Model B) deep convolutional neural networks (DCNNs) with two layers of long-short-term memory (LSTM). Both models used raw speech data and augmented (mid, left, right, and side) segment level Mel spectrograms to learn local and global features. The architecture of both models consists of five local feature learning blocks (LFLBs), two LSTM layers, and a fully connected layer (FCL). In addition to learning local correlations and extracting hierarchical correlations, LFLB comprises two convolutional layers and a max-pooling layer. The LSTM layer learns long-term correlations from local features. The experiments illustrated that the proposed systems perform better than conventional methods. Model A achieved an average identification accuracy of 94.78% for speaker-dependent (SD) with a raw SAVEE dataset. With the IEMOCAP database, Model A achieved an average accuracy of an SD experiment with raw audio of 73.15%. In addition, Model A obtained identification accuracies of 97.19%, 94.09%, and 53.98% on SAVEE, IEMOCAP, and BAUM-1s, the databases for speaker-dependent (SD) experiments with an augmented Mel spectrogram, respectively. In contrast, Model B achieved identification accuracy of 96.85%, 88.80%, and 48.67% on SAVEE, IEMOCAP, and the BAUM-1s database for SI experiments with augmented reality Mel spectrogram, respectively.
The effect of test environment on usability testing

Improvements in networking and communication technologies enable the use of synchronous and asynchronous methods to support usability evaluation techniques when applied in different spatial and temporal situations. Usability testing has been described as the most frequent and appropriate technique used to evaluate website usability. One of the fundamental elements in the usability testing process is the test environment. When testing a website with remote users in their normal environments, other factors affect that environment (distraction, type of computer system and networking technologies) and may affect usability testing performance. A brief technical review of experimental studies (exploratory and comparative studies) based on the use of remote synchronous and asynchronous methods, is presented. A Review of literature leads to a discussion of some of the factors in remote usability evaluation environment and the reviewed literature reveals that there is a lack of studies investigating the effect of a user environment factors on usability testing, and a consequent failure to address them during usability evaluation.
An analysis of foreign residents’ perceptions and behaviors regarding digital government portal services in the Republic of South Korea

Governments have made significant investments to deliver information and services to governmental institutions, citizens, and businesses through e-services. The re-adoption of such governmental services has been focused on in various studies. However, this study focuses on the post-adoption of digital government services and information system quality. Therefore, evaluating the digital government portal services from the user's perspective is important. This study proposes an expanded conceptual framework that includes confirmation of expectation and perceived functional benefits as mediating variables based on continuance literature and information systems’ performance. Online and offline surveys were conducted to get the responses of emigrants in the Republic of Korea to assess the proposed relationship among the variables. Overall, 371 samples were collected and analyzed. The regression and mediation analyses were performed using the Macro PROCESS model. The results indicate that confirmation of expectation and perceived functional benefits significantly predict the foreigners’ continuance intention as Korean digital government websites users. The study also confirms the direct and indirect impacts of digital government services quality factors on continuance intention through perceived functional benefits. However, it does not find evidence for the influence of confirmation of expectation as a mediator. The research concludes with positive implications for academicians, governmental policymakers, and the overall body of knowledge. Points for practitioners The results of this study indicate that government officials should concentrate on technical features and psychological characteristics to understand people's intention for e-service continuance. The relation of digital government facilities with perceived functional benefits enables lawmakers to establish responsive and quick service portal systems. Besides, the government can help users by providing 24-hour customer support, electronic assistance desks, and users’ manuals to increase the consistent use and efficiency of government systems.
Effect of different floor space allowances on the performance and behaviorof Beetal kids under stall-fed conditions

Present study was conducted to investigate the effect of different floor space allowances on performance and behavior of Beetal kids from 3 to 6 months of age. Twenty four spring born Beetal kids (3 month old) were randomly divided in to four groups (n=6 each group) on the basis of space allowances i.e. 0.6 m2, 0.7 m2, 0.8 m2 and 0.9 m2 covered area and 1.2 m2, 1.4 m2, 1.6 m2 and 1.8 m2 open area per kid. The results indicated that most of the performance parameters i.e. body weight (BW), average daily gain (ADG) and feed conversion ratio (FCR) were marginally higher with higher space allowance, but these did not differ statistically. However behavioural activities like moving, exploring pen, grooming and resting were significantly higher (p < 0.05) in large space allowance groups whereas, eating, standing and negative social interactions were significantly better (p < 0.05) in lower floor space allowance groups. It is concluded that the increase in floor space allowance had favorable effect on the performance and behavior of Beetal kids.
The Cerebellar Involvement in Autism Spectrum Disorders: From the Social Brain to Mouse Models

Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders that include a variety of forms and clinical phenotypes. This heterogeneity complicates the clinical and experimental approaches to ASD etiology and pathophysiology. To date, a unifying theory of these diseases is still missing. Nevertheless, the intense work of researchers and clinicians in the last decades has identified some ASD hallmarks and the primary brain areas involved. Not surprisingly, the areas that are part of the so-called “social brain”, and those strictly connected to them, were found to be crucial, such as the prefrontal cortex, amygdala, hippocampus, limbic system, and dopaminergic pathways. With the recent acknowledgment of the cerebellar contribution to cognitive functions and the social brain, its involvement in ASD has become unmistakable, though its extent is still to be elucidated. In most cases, significant advances were made possible by recent technological developments in structural/functional assessment of the human brain and by using mouse models of ASD. Mouse models are an invaluable tool to get insights into the molecular and cellular counterparts of the disease, acting on the specific genetic background generating ASD-like phenotype. Given the multifaceted nature of ASD and related studies, it is often difficult to navigate the literature and limit the huge content to specific questions. This review fulfills the need for an organized, clear, and state-of-the-art perspective on cerebellar involvement in ASD, from its connections to the social brain areas (which are the primary sites of ASD impairments) to the use of monogenic mouse models.
Weakened amplitude and delayed phase of the future temperature seasonal cycle over China during the twenty‐first century

The surface air temperature seasonal cycle in China has clearly changed in response to global warming, but its future projection has yet to be studied. Here, we investigate the performance and projection of the temperature seasonal cycle in China in 32 global climate models participating in the Coupled Model Intercomparison Project 6 (CMIP6). The characteristics of the temperature seasonal cycle, as represented by the amplitude and phase, are measured by the Fourier transform. The results show that the CMIP6 models can reasonably capture the observed typical temperature seasonal cycle feature with clear north–south gradients both for amplitude and phase in China. In the twenty‐first century, the models robustly project a delay in the phase across the whole country and a reduction in the amplitude in most of China relative to the reference period. Those changes are similar in the two shared socioeconomic pathway (SSP) scenarios, with overall larger magnitudes in the SSP5‐8.5 scenario than in the SSP2‐4.5 scenario. Projected amplitude changes are mainly linked to surface energy flux changes, especially latent heat flux changes. In response to future global warming, larger increases in the latent heat flux and thus stronger surface evaporative cooling lead to weaker surface warming in summer than in winter, contributing to a decrease in the amplitude of the temperature seasonal cycle in most regions of China.
The Hallmarks of Glioblastoma: Heterogeneity, Intercellular Crosstalk and Molecular Signature of Invasiveness and Progression

In 2021 the World Health Organization published the fifth and latest version of the Central Nervous System tumors classification, which incorporates and summarizes a long list of updates from the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy work. Among the adult-type diffuse gliomas, glioblastoma represents most primary brain tumors in the neuro-oncology practice of adults. Despite massive efforts in the field of neuro-oncology diagnostics to ensure a proper taxonomy, the identification of glioblastoma-tumor subtypes is not accompanied by personalized therapies, and no improvements in terms of overall survival have been achieved so far, confirming the existence of open and unresolved issues. The aim of this review is to illustrate and elucidate the state of art regarding the foremost biological and molecular mechanisms that guide the beginning and the progression of this cancer, showing the salient features of tumor hallmarks in glioblastoma. Pathophysiology processes are discussed on molecular and cellular levels, highlighting the critical overlaps that are involved into the creation of a complex tumor microenvironment. The description of glioblastoma hallmarks shows how tumoral processes can be linked together, finding their involvement within distinct areas that are engaged for cancer-malignancy establishment and maintenance. The evidence presented provides the promising view that glioblastoma represents interconnected hallmarks that may led to a better understanding of tumor pathophysiology, therefore driving the development of new therapeutic strategies and approaches.
Digital management system of ship power station

In this paper on the background of main conception of automatic saving the state of ship there is presented the solution of control and measuring system of power ship which is based on programmable controller. Presented system of getting and recording date is saving the parameters of working power station which are getting from on board ship's control and measuring system. This system is elastic and has the open structure which allow to increase the number of input channels. In this conception of automatic saving the state of ship we can see that it is working on local network and network of higher level.