Miracle-dz/UniFAR-FTUs huggingface dataset:
The stat3/socs3a Pathway Is a Key Regulator of Hair Cell Regeneration in Zebrafish stat3/socs3a Pathway: Regulator of Hair Cell Regeneration

All nonmammalian vertebrates studied can regenerate inner ear mechanosensory receptors (i.e., hair cells) (Corwin and Cotanche, 1988; Lombarte et al., 1993; Baird et al., 1996), but mammals possess only a very limited capacity for regeneration after birth (Roberson and Rubel, 1994). As a result, mammals experience permanent deficiencies in hearing and balance once their inner ear hair cells are lost. The mechanisms of hair cell regeneration are poorly understood. Because the inner ear sensory epithelium is highly conserved in all vertebrates (Fritzsch et al., 2007), we chose to study hair cell regeneration mechanism in adult zebrafish, hoping the results would be transferrable to inducing hair cell regeneration in mammals. We defined the comprehensive network of genes involved in hair cell regeneration in the inner ear of adult zebrafish with the powerful transcriptional profiling technique digital gene expression, which leverages the power of next-generation sequencing (‘t Hoen et al., 2008). We also identified a key pathway, stat3/socs3, and demonstrated its role in promoting hair cell regeneration through stem cell activation, cell division, and differentiation. In addition, transient pharmacological inhibition of stat3 signaling accelerated hair cell regeneration without overproducing cells. Taking other published datasets into account (Sano et al., 1999; Schebesta et al., 2006; Dierssen et al., 2008; Riehle et al., 2008; Zhu et al., 2008; Qin et al., 2009), we propose that the stat3/socs3 pathway is a key response in all tissue regeneration and thus an important therapeutic target for a broad application in tissue repair and injury healing.
Factors Influencing Resistance to Information Technology Related Change in The Telecommunication Industry: A Case Study of Dialog Telecom

iii ACKNOWLEDGEMENT . iv TABLE OF CONTENTS. v LIST OF TABLES . ix LIST OF FIGURES . xi LIST OF ACRONYMS . xii CHAPTER 01: INTRODUCTION . 1 1.
Actualizing the affordance of mobile technology for classroom Actualizing the affordance of mobile technology for classroom orchestration: A main path analysis of mobile learning orchestration: A main path analysis of mobile learning

Ubiquitous and increasingly accessible, mobile technology enhanced learning in the learning process, referred to as "classroom orchestration," is inspiring an increasing number of studies that examines mobile learning from various perspectives. Nonetheless, educators find themselves confronted by the ever-evolving features of mobile technology and challenges in implementation context. This study, therefore, surveys the research literature on mobile learning using main path analysis, and cites "affordance actualization" by Strong (Strong et al. 2014) as a theoretical lens to identify the research themes from results found in main paths, to develop a “mobile learning actualization” framework. This particular framework integrates several research themes, ranging from system features, educator and learner, the goal of mobile technology adoption, contextual implementation, to the outcome of mobile learning. These insights have proven constructive for educators to adapt mobile technology to a learning environment, thus successfully achieving classroom orchestration.
Is Natural More Beautiful? Research on Natural Product Preference Based on IAT

This study aims at testing consumers’ preference for natural products behind buying decisions and exploring moderating factors as rural life experience and gender for their effects on natural preference. With the help of implicit association theory, we used IAT (Implicit Association Test) to measure the remoteness between positive words and natural products to identify whether there is an implicit preference. And we examined the moderating effects through online research in China. Three hundred one participants have joined this experiment. The result shows that Chinese consumers do have an implicit preference for natural products. When consumers had more than five years of rural life experience, their preference for natural products was significantly higher than those without. However, gender did not make any significant difference, except that women have more preference for nature in the field of food. Our conclusion not only enriches the understanding of consumer behavior but could also help product designing and development, particularly in the food area.
Automated Operational Modal Analysis of a Cable-Stayed Bridge

11 Automated techniques for analyzing the dynamic behavior of full-scale civil structures are becoming 12 increasingly important for continuous structural health monitoring applications. This paper aims to extract the 13 structural modal parameters of a full-scale cable-stayed bridge from the collected ‘output-only’ vibration data 14 without the need of any user interactions. The work focuses on the development of an automated and robust 15 operational modal analysis (OMA) algorithm, utilizing a multi-stage clustering approach. The main 16 contribution of the work is to define a novel way of automatically defining the hierarchical clustering threshold 17 to enable the accurate identification of a complete set of modal parameters. The proposed algorithm is 18 demonstrated to work with any parametric system identification algorithm that uses the system order ‘n’ as the 19 sole parameter. In particular the results from Covariance-driven Stochastic Subspace Identification (SSI-Cov) 20 methods are presented. 21
Analysis of microbial diversity and succession during Xiaoqu Baijiu fermentation using high‐throughput sequencing technology

In this study, high‐throughput sequencing (HTS) was used to compare and analyze the microbial diversity and succession during the brewing process of xiaoqu Baijiu. A total of 34 phyla and 378 genera of bacteria, as well as four phyla, 32 genera of fungi were detected. At the phylum level, Firmicutes, Proteobacteria, Ascomycota, and Mucoromycota were the dominant groups. During the brewing process of xiaoqu Baijiu, the dominant bacteria were Weissella and unidentified Rickettsiales within the first 2 days of brewing, followed by Lactobacillus at 3 days until to the end of brewing. The dominant fungi were Rhizopus, Saccharomyces, and Issatchenkia. The relative abundance of Rhizopus decreased with the extension of brewing time, while the relative abundance of Saccharomyces increased, and Saccharomyces became the dominant species at the second day of brewing. This study revealed the diversity and changes of the microbial community during the brewing process of xiaoqu Baijiu, providing theoretical support and laying a foundation for future study on the contribution of microbial metabolism during brewing of xiaoqu Baijiu, thereby promoting the development of xiaoqu Baijiu industry.
Opportunistic sensing platforms to interpret human behaviour

Understanding human behaviour in an automatic but also non-intrusive manner, constitutes an important and emerging area for various fields. This requires collaboration of information technology with humanitarian sciences in order to transfer existing knowl- edge of human behaviour into self-acting tools to eliminate the human error. This work strives to shed some light in the area of Mobile Social Signal Processing by trying to understand if today’s mobile devices, given their advanced sensing and computational capabilities, are able to extract various aspects of human behaviour. Although one of the core aspects of human behaviour are social interactions, current tools do not pro- vide an accurate, reliable and real-time solution for social interaction detection, which constitutes a significant barrier in automatic human behaviour understanding. Towards filling the aforementioned gap in order to enable human behaviour under- standing through mobile devices, particular contributions were made. Firstly, an interpersonal distance estimation technique is developed based upon a non-intrusive opportunistic mechanism that solely relies on sensors and communication capabilities of off-the-shelf smartphones. Secondly, based on user’s interpersonal distance and relative orientation, a pervasive and opportunistic approach based on off-the-shelf smartphones for social interaction detection system is presented. Leveraging information provided by psychology, analytical and error models are proposed to estimate the probability of people having social interactions. Then, to showcase the ability of mobile devices to infer human behaviour, a trust relationship quantification mechanism is developed based on users’ behavioural traits and psychological models. Finally, a prediction and compensation mechanism for the device displacement error that leverages human loco- motion patterns to refine the device orientation is introduced. The above contributions were evaluated through experimentation and hard data collected from real-world environments to prove their accuracy and reliability as well as showing the applicability of the proposed approaches in daily situations. This work showed that mobile devices are able to accurately detect social interactions and further social and trust relationships among people, despite the noise induced in real-world situations. Close collaboration between informatics and social sciences is imperative, to overcome the significant barrier in the development of human behaviour understanding. This work could constitute a fundamental building block, as the computational power and battery autonomy of mobile devices increases, for the development of novel techniques towards understanding human behaviour, by including multiple behavioural traits and enabling the creation of socially-aware information systems.
Improved model‐free adaptive predictive control based on recursive least‐squares estimation algorithm

Data‐driven control has received increasing attention by researchers in recent years because no system modeling procedure is required. By combination of data‐driven and model predictive control, this paper discusses an improved model‐free adaptive predictive control approach with application to vibration reduction of an elastic crane. For system linearization, instead of using traditional compact‐form dynamic linearization, this contribution considers the partial‐form dynamic linearization (PFDL) technique in case of multivariable systems. A linearized output predictive model of the unknown system is constructed locally. To estimate and predict the time‐varying parameter matrices, namely, pseudo‐Jacobian matrix (PJM), recursive least‐squares algorithms are utilized for online estimation improvement. As a result, an improved PFDL‐based model‐free controller is designed and applied firstly to the ship‐mounted boom crane representing a typical flexible system. Simulation results indicate that significant reduction of the elastic boom and payload oscillations are achieved, and better control performance can be observed in comparison with other traditional methods.
Low Serum Dehydroepiandrosterone Is Associated With Diabetic Kidney Disease in Men With Type 2 Diabetes Mellitus

Background The associations of dehydroepiandrosterone (DHEA) and dehydroepiandrosterone sulfate (DHEAS) with diabetic kidney disease (DKD) remained unclear. Thus, this cross-sectional study aimed to explore the associations of DHEA and DHEAS with the risk of DKD in patients with T2DM. Methods The information of 1251 patients with T2DM were included in this study. Serum DHEA and DHEAS were quantified using liquid chromatography-tandem mass spectrometry assays. Multivariate logistic regression analyses were used to assess the associations of DHEA and DHEAS with DKD as well as high urine albumin to creatinine ratio (ACR). Results In men with T2DM, the risk of DKD decreased with an increasing DHEA concentration after adjustment for traditional risk factors; the fully adjusted OR (95% CI) for tertile3 vs tertile1 was 0.37 (0.19-0.70; P = 0.010 for trend). Similarly, when taking high ACR as the outcome, low DHEA levels were still significantly associated with increased odds of high ACR (OR, 0.37; 95% CI, 0.19–0.72 for tertile3 vs tertile1; P = 0.012 for trend). The restricted cubic spline showed that the risk of DKD gradually decreased with the increment of serum DHEA levels (P-overall = 0.007; P-nonlinear = 0.161). DHEAS was not independently associated with the risk of DKD in men. In contrast, no significant relationships were found between DHEA and DHEAS and the risk of DKD in women (all P > 0.05). Conclusions In men with T2DM, low serum DHEA levels were independently related to the risk of DKD after adjustment for traditional risk factors. Our finding highlights the potential role of DHEA in the development of DKD in men with T2DM.
Application of thermohydraulic dispatcher in low temperature district heating systems for decreasing heat carrier transportation energy cost and increasing reliability of heat supply

Low pressure district heating systems have low breakdown rate and allow decreasing heat carrier transportation energy cost by means of avoiding throttling of available water head. One of the basic elements of such systems is thermohydraulic dispatcher (THD) which separates primary circuit and secondary circuit (or circuits) that allows avoiding mutual hydraulic influence of circuits on each other and reducing water heads of network pumps. Analysis of perspective ways of using thermohydraulic dispatcher (THD) in low temperature district heating systems is made in this paper. Principal scheme and mathematical model of low pressure and temperature district heating system based on CHP generation with THD are considered. The main advantages of such systems are pointed out.
Heuristic Modeling for Solving Inventory Routing Problem with Simultaneous Delivery and Pickup

In this paper, we propose a new model of the Inventory Routing Problem (IRP) encountered in the context of reverse logistics. The vehicles here are required not only to deliver forward goods to retailers but also to collect simultaneously returned goods at their locations. The new model will be referred to as the Inventory Routing Problem with Simultaneous Delivery and Pickup (IRPSDP). The IRPSDP is based on the existing Periodic Vehicle Routing Problem (PVRP) model. The objective of the IRPSDP is to minimize the average inventory holding and travelling costs over a given m-day period. The IRPSDP treats the visit frequency as a decision variable instead of a given parameter, to seek the best trade-off between both costs. We present a mathematical formulation and give heuristic procedures to solve the problem. Finally, we test the performance of our approach by conducting some numerical experiments.
LegalNLI: natural language inference for legal compliance inspection

Legal compliance inspection (LCI) plays an essential role in the judicial applications of laws and rules but has been neglected in the development of legal artificial intelligence (LegalAI). Currently, few methods in LegalAI can be utilized to solve this task. In this work, we propose to use natural language inference (NLI) framework to solve the LCI problems based on a fundamental fact that the legal judgment should be subject to judicial syllogisms. Specifically, we present LegalNLI – a specially constructed dataset reformatted from the Chinese legal datasets for other problems. The proposed LegalNLI is a document-level NLI dataset in the legal domain whose premises and hypotheses vary from hundreds to thousands of words in length. In addition, there are few artifacts in LegalNLI that are some clues so as to possibly identify the label by looking only at the hypothesis without observing the premise. Therefore, it is more effective to solve the LCI task by adopting the NLI framework instead of direct text classification methods. Finally, we provide some experiments for evaluating some existing state-of-the-art systems of sentence-level NLI task on the LegalNLI dataset and find it is challenging.
Role of vacancies in tuning the electronic and magnetic properties of BiCoO3

Effect of vacancies on structural, electronic and magnetic properties of BiCoO3 (BCO) have been studied using first principles calculations. Formation energy calculation suggest that VBi is stabilized in −2 charge state with no net change in magnetization and is likely to be formed spontaneously. VCo is stabilized in −1 state under O-poor conditions and exhibits various transition levels under O-rich conditions leads to a net magnetization. VO are formed readily under O-poor conditions with 0 and +2 charge state. Under O-rich conditions, VO(1) is stabilized in 0 and +1 states with a change in magnetization and leads to a net magnetization of 2 μ B (0.25 μB per formula unit), and VO(2) is stabilized as 0 and +2 charge states with no change in net magnetization. DOS shows that VBi leads to the formation of acceptor levels which may be responsible for p-type conduction, on the other hand, VO(2) leads to the formation of deep traps in BCO.
Vaccine apartheid and settler colonial sovereign violence: from Palestine to the colonial global economy

ABSTRACT This article examines the distribution of the COVID-19 vaccine both in Palestine and globally through a decolonial lens. In dominant Euro-American discourse, the invention, production, and distribution of the vaccine is largely judged as an indicator of sophisticated and advanced health care systems and economies. The underlying premise being that the advanced, wealthy, and capable nation-states have endogenously earned the position of power and prosperity. The world’s poor nation-states are posited as the recipients of charity from these rich states only after the latter have sufficiently inoculated themselves. The entire discourse turns the question of vaccines into a series of technical questions about capabilities, facilities, infrastructure, economic purchasing power, and so on. Concealed in this discourse is a settler colonial foundation – an aspiration towards omnipresent and absolute power – which not only creates the contrast between Palestinians and Israelis, rich and poor, colonizer and colonized, but also seals a forcefully imposed settler colonial contract in which colonizing populations ensure their ability to inoculate themselves by debilitating the colonized.
Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods

Forecasting road flow has strong importance for both allowing authorities to guarantee safety conditions and traffic efficiency, as well as for road users to be able to plan their trips according to space and road occupation. In a summer resort, such as beaches near cities, traffic depends directly on weather conditions, variables that should be of great impact on the quality of forecasts. Will the use of a dataset with information on transit flows enhanced with meteorological information allow the construction of a precise traffic flow forecasting model, allowing predictions to be made in advance of the traffic flow in suitable time? The present work evaluates different machine learning methods, namely long short-term memory, autoregressive LSTM, and a convolutional neural network, and data attributes to predict traffic flows based on radar and meteorological sensor information. The models trained to predict the traffic flow have shown that weather conditions were essential for this forecast, and thus, these variables were employed in the evaluated deep-learning models. The results pointed out that it is possible to forecast the traffic flow at a reasonable error level for one-hour periods, and the CNN model presented the lowest prediction error values and consumed the least time to build its predictions.
A Compressed Unsupervised Deep Domain Adaptation Model for Efficient Cross-Domain Fault Diagnosis

As one of the most important artificial intelligence-enabled industrial applications, fault diagnosis is vital in the safe, stable, and reliable operation of the equipment. Many existing deep learning-based fault diagnosis methods assume that the distribution of training data is the same as that of testing data, which is almost impossible in practical industrial applications. In addition, most of these fault diagnosis methods are generally memory-intensive and computationally expensive. A compressed unsupervised deep domain adaption model-based fault diagnosis method is proposed to overcome the abovementioned two issues. First, a standard unsupervised domain adaption model is designed to extract the features of training data and testing data, respectively. Then, the maximum mean discrepancy term is introduced to minimize the discrepancy between the extracted features of them. Next, the standard model is compressed through iteratively pruning the redundant convolutional channels. Finally, the obtained compressed model is applied to diagnose faults. The performance of the proposed method is verified on the Case Western Reserve University bearing dataset. Experimental results show that the compressed model can significantly reduce the memory occupation, computational cost, and inference time compared with the standard model, but still achieve comparable or even better accuracy on ten transfer diagnostic tasks.
Ensemble Method for Indonesian Twitter Hate Speech Detection

Due to the massive increase of user-generated web content, in particular on social media networks where anyone can give a statement freely without any limitations, the amount of hateful activities is also increasing. Social media and microblogging web services, such as Twitter, allowing to read and analyze user tweets in near real time. Twitter is a logical source of data for hate speech analysis since users of twitter are more likely to express their emotions of an event by posting some tweet. This analysis can help for early identification of hate speech so it can be prevented to be spread widely. The manual way of classifying out hateful contents in twitter is costly and not scalable. Therefore, the automatic way of hate speech detection is needed to be developed for tweets in Indonesian language. In this study, we used ensemble method for hate speech detection in Indonesian language. We employed five stand-alone classification algorithms, including Naïve Bayes, K-Nearest Neighbours, Maximum Entropy, Random Forest, and Support Vector Machines, and two ensemble methods, hard voting and soft voting, on Twitter hate speech dataset. The experiment results showed that using ensemble method can improve the classification performance. The best result is achieved when using soft voting with F1 measure 79.8% on unbalance dataset and 84.7% on balanced dataset. Although the improvement is not truly remarkable, using ensemble method can reduce the jeopardy of choosing a poor classifier to be used for detecting new tweets as hate speech or not.
Sustainable Environmental Economic Dispatch Optimization with Hybrid Metaheuristic Modification

Today’s Economic Dispatch (ED) solutions are featured with environmental obligations. Hence, the significant objective functions contribute to cost minimization, lower emission and less total system losses. As an alternative, New Meta Heuristic Evolutionary Programming (NMEP) technique was proposed to optimize the individual ED problem categorized as Single Objective Environmental Economic Dispatch (SOEELD), developed from an integration of original Meta Heuristic Evolutionary Programming (Meta-EP) with Artificial Immune System (AIS) with new arrangement in the mutation and cloning processes. The comparative analysis was conducted between the original Meta-EP and classical method of Hadi Saadat to verify the performance of NMEP method. Each particular objective function identified the best possible outcomes through the NMEP method. The simulations were conducted using MATLAB programming which tested both standard IEEE 26 and 57 bus systems.