Reviewerly/exHarmony huggingface dataset:
Entrepreneurship, Financial Development and Economic Growth

This study adds to the current body of research on economic growth by demonstrating how the interplay between financial development and entrepreneurship fosters economic advancement. By utilizing the two-step Generalized Moment Method (GMM), we have identified three primary outcomes. Firstly, entrepreneurship has a beneficial influence on economic growth. Secondly, financial development has a conditional effect that stimulates economic growth. Lastly, the combined effect of entrepreneurship and financial development on economic growth is largely positive, suggesting that financial development can amplify the already positive but weak entrepreneurship’s influence on economic growth. The research investigation also delves into the empirical significance and policy implications of these findings
Challenges for Internet of Things (IoT) Applications in Vocational Education in Bahrain

Internet of Things (IoT) has entered all the product and service sectors around the world, even the educational services sector. And to cope with the rapid development of the technology, it has become necessary to integrate the IoT even in the vocational education domain in the public high schools, especially in those days and after the COVID-19 pandemic that changed the world totally. This paper aims mainly to analyze the effects of IoT on vocational education in the Bahraini public high schools, to determine the factors that hinder the application of IoT in vocational education in the Bahraini public high schools, especially during the COVID-19 pandemic, and to suggest solutions to enhance the application of IoT in vocational education in the Bahraini public high schools. For conducting this research, a comparison between literature reviews and SWOT analysis as well as qualitative approaches have been followed. The results show that the IoT has a positive and significant effect on improving the quality of the services that are provided in the vocational education in the Bahraini public high schools, and to optimize its effects, more supportive regulations and rules are needed. This research paper provides a roadmap for the decision-makers in Bahrain for improving the application of the IoT in vocational education by showing the main obstacles and difficulties that exist. Also, this research can serve as a guideline and reference for future studies aiming to develop the practice and application of the IoT in education in any country and in the vocational education track.
Uncalibrated Visual Servoing Using Dynamic Broyden and Least-Squares Methods

Visual servo systems usually require a calibration operation before performing their tasks. This involves extra time cost, and the calibration results may fail as the internal and external environment changes. To address this problem, this paper proposes a calibration-free visual servo implementation way based on the least-squares optimized dynamic Broyden method. The dynamic Broyden method estimates the image Jacobian matrix online with unknown depth information of the target, and uses least-squares to optimize the dynamic Broyden method by minimization. The simulation results show that the effectiveness of the method can be achieved under the condition that the camera parameters and hand-eye relationship are unknown.
ACMA-GAN: Adaptive Cross-Modal Attention for Text-to-Image Generation

Automatically generating realistic and natural high resolution images from text descriptions is a complicated problem in the cross-modal research field. Recently, multi-stage conditional generative adversarial networks based on word attention are the mainstream of Text-to-Image generation. A close examination of these methods reveals two fundamental issues. Firstly, the granularity difference between the words and local image features makes the words cannot accurately express the local image features. Second, the discriminators cannot extract enough image information, which will result in poor discrimination effect. In this paper, we address these issues by proposing an adaptive cross-modal attention generative adversarial network (ACMA-GAN). Specifically, we design (1) an adaptive word attention module, which can reform the granularity of words and mine the context information of words; (2) a feature alignment module, which uses the pre-trained CNN model to improve the feature extraction ability of discriminator. Extensive experiments on CUB-200 and MS-COCO datasets demonstrate that our method is superior to the existing methods.
Ore-forming fluid evolution of the Wunugetushan porphyry Cu-Mo deposit, Inner Mongolia: Evidence from the short-wave infrared spectrum, mica composition and fluid inclusion of quartz

内蒙古乌努格吐山斑岩铜钼矿床是中国东北地区最大的斑岩矿床之一, 发育显著的石英-绢云母化蚀变, 对早期的钾硅化蚀变有强烈的叠加改造, 模糊了各蚀变的分带特征和不同矿化之间的关系, 复杂的流体改造阻碍了精细热液演化过程的重建。本文以白云母为研究对象, 在细致的显微镜、扫描电镜分析确定云母的形态特征以及矿物共生组合等基础上, 利用电子探针(EPMA)、激光剥蚀电感耦合等离子体质谱(LA-ICP-MS)以及短波红外光谱(SWIR)分析手段, 详细揭示不同阶段、不同产状白云母的成分差异, 探讨了其对矿床热液演化以及成矿机制的指示意义。结果显示, 乌山斑岩矿床的白云母由白云母端员及少量钠云母、绿鳞石、伊利石端员组成。与辉钼矿有关的云母更富集Al、K、Sr、Ba, 且Al-OH特征波长在2197~2203nm之间。与黄铜矿有关的云母具有更高的Si、Ti、Fe、Mn、Mg、Cu、Zn、Sn、W等元素含量, Al-OH特征波长在2200~2206nm之间。氧化环境下形成的云母更富集Sc、V、W。乌山斑岩矿床的白云母在2200nm处吸收峰位置的波长偏移与距离成矿中心的远近及矿化类型有关, 特征波长 &lt; 2203nm则指示钼矿化, 并且靠近成矿中心, 因此可以利用SWIR特征圈定热液活动区域, 确定矿化类型, 指导找矿勘查工作。石英流体包裹体测温证据、白云母成分等信息, 暗示铜沉淀阶段存在其他流体补充注入。综上所述, 白云母作为常见的蚀变、热液矿物, 对研究成矿流体演化及矿床勘查评价具有重要的科学意义。
Multi-level Semantic Extraction Using Graph Pooling Network for Text Representation

Graph Neural Networks (GNNs) have achieved remarkable results in several areas of pattern recognition, since GNNs handle complex structures well. Recently, GNNs have been used to learn text representation. However, the existing GNNs-based methods ignore the structure of graphs when generating graph representations from node representations, which limits their ability to learn hierarchical representations of graphs and capture hierarchical semantics. In this paper, we propose Multi-Level Semantic Graph Pooling Network (MLSGPool), a novel hierarchical graph pooling for text representation. MLSGPool consists of two parts: the local pooling layer and the semantic interaction layer. The local pooling layer utilizes GNNs to get a score for each node and then selects nodes with higher scores to form a smaller subgraph. We treat the representation of each subgraph as a semantic level. By stacking multiple local pooling layers, we can learn the hierarchical graph structure and extract multi-level semantics. In addition, we design a multi-head attention-based semantic interaction layer to capture the interaction between selected nodes and the nodes before the local pooling layer, which addresses the difficulty of interaction between a word and its distant neighbours. We apply our proposed model to text classification tasks, and experimental results on several benchmark datasets showed that the proposed method outperformed the baseline methods.
The Level of Social Media Communication Skills of the Faculty of Law Students at Prince Mohammad Bin Fahd University in view of Certain Variable

The current study aimed to identify the level of social communication skills among students of the Faculty of Law at Prince Mohammad Bin Fahd University, according to some variables. The study used the descriptive approach to achieve its objectives, and the scale (Al-Qarni: 2015) was applied to the study sample in a random manner consisting of (300) students, of whom (50) males and (250) females, and after processing the data, the study reached the following results: The degree of the level of social communication skills is medium if the arithmetic mean is (2.33), and that the most used skills are the symbolic communication skill, and the least is the skill of listening . The results also revealed that there were no statistically significant differences at the level of statistical significance (0.05) or less, according to the gender variable.&#x0D; The study reveals the presence of statistically significant differences at the level of statistical significance (0.05) or less in favor of the study sample who are in the first and second academic levels. There are statistically significant differences at the level of statistical significance (0.05) or less in favor of the study sample who are at the seventh and eighth levels of study.&#x0D; There are statistically significant differences at the level of statistical significance (0.01) or less in favor of the study sample who are in the first and second academic levels. The researcher concluded the study with a number of recommendations, the most important of which is urging those in charge of the matter in the College of Law to activate the psychological, social and academic counseling unit to pay attention to increasing the level of social communication skills for students of the College of Law due to the importance of their role in the future and the need for them to possess these skills through study curricula, university activities and training programs.&#x0D;
A Neural Network Model with the Ringed Residual Block and Attention Mechanism for Image Forgery Localization

In recent years, significant progress has been made in image forgery localization technology, but there are still shortcomings in accuracy and robustness, especially for different types of forgeries. Currently, convolutional neural networks are the mainstream forgery localization technology, but the local receptive field of convolutional operations limits the accuracy of the model. In this paper, an image forgery localization neural network model is proposed, which uses the ringed residual structure to extract features better and multiple attention mechanisms to focus on important features better. The method can effectively handle various common forgeries types, such as copy-paste, region replacement, and local modification, and can also overcome the problem of the local receptive field of convolutional operations to achieve long-distance feature dependencies. The authors conducted experiments on multiple datasets to verify and compare the proposed method with existing methods. The experimental results show that the method achieved excellent performance on different datasets, demonstrating its effectiveness and robustness.
The state of and prospects for the application of blockchain technology in e-commerce

Whilst e-commerce continues to grow, it has met privacy, trust, and security challenges. Blockchain technology is among the solutions that e-commerce can use to address these challenges. Blockchain technology poses benefits but comes with its challenges. The purpose of this study was to systematically review literature that focused on the state and prospects of blockchain technology in e-commerce. Specifically, the research question is what is the state of the application of blockchain technology in e-commerce? The study conducted searches using four databases (EbscoHost, Science Direct, IEEE Explore, and Jstor) as well as Google Scholar. Exclusion and inclusion criteria were applied in the searches to answer the research question. In total, 25 articles were used for this research and were thematically analysed. The results showed that while there are many benefits in implementing blockchain technologies in e-commerce, there are also challenges. The benefits include an increase in privacy, enhanced security, and minimized costs of transactions. The challenges are tax evasion, black market transactions, and lack of regulation. Results also showed blockchains are used to track product movement and originality. The study's novel contribution is to theory by developing a model for the application, benefits, and challenges of the blockchain technology ecosystem in e-commerce. The study contributes to policy by focusing on sustainable development goals 7 and 9. The research also contributes to the body of knowledge on the application of blockchain. Businesses, scholars, government officials, and other stakeholders can use this study to understand the application, challenges, and benefits of blockchain in e-commerce.
MONITORING OF PHYSICAL EDUCATION CLASSES IN HYPERTHERMIC CONDITIONS

The article presents the results of a pedagogical experiment with schoolchildren in conditions of hyperthermia. The high efficiency of the inclusion in the training process of a complex of gymnastic exercises taken from the yoga system and the use of supercooling pauses is shown. It can be concluded that the inclusion in the training program of specially selected physical exercises of sufficiently high intensity and volume leads to an increase in energy costs (volleyball, basketball and national outdoor games) and leads to stabilization of body weight by equalizing the energy consumption of the body with energy consumption. The combination of static physical exercises aimed at developing physical abilities, such as flexibility, combined with the inclusion of sports and national outdoor games in the training process, has the greatest impact compared to traditional physical education classes.
The Impact of Blockchain in Tracking the Pharmaceuticals

This paper introduces an architecture to improve the pharmaceutical supply chain's security by using the Internet of Things, semantic web, and blockchain. This architecture increases transparency and visibility of drug flows and improves the representation of the increasing amounts of data that have been generated from pharmaceutical supply chain transactions. The pharmaceutical companies have problems dealing with the complexity of their supply chains, which represent the full life cycle of drugs from extracting raw materials, production, distribution, tracking of drugs, and quality assurance to use by patients. They need to create an efficient, effective, transparent, immutable, and secured supply chains to achieve a competitive advantage in a fast-changing market. In recent years, blockchain technology has emerged and gained considerable attention due to its reliability and secure data sharing capabilities in various domains such as supply chain management, finance, the internet of things, agriculture, food, healthcare, and pharmaceutical industries. The track of pharmaceutical supply chains has become more critical in the health society in any country or region to ensure the proper trackability and safety of medicines. With time, it has become more complex and costly to track thousands of various medical products. Without a proper supply chain system, there is a possibility of the availability of counterfeit drugs. Building a traceability system over a blockchain platform would provide a trusted and secured method to track and trace the medical supply chain from the manufacturer to the patient. This work proposes a traceability system for pharmaceutical products in Bahrain using blockchain technology to maintain the integrity of the data of such sensitive valuable products. In addition to this, different pharmaceutical workflows have been designed and implemented using the Ethereum blockchain platform.
Determining the Dimensions of Electronic Customers’ Relationship Management in Jordanian Insurance Companies

The study aims to determine the dimensions of electronic customers’ relationship management E-CRM in Jordanian insurance companies. Three major determinants were detected, customer-related, e-service and website-focused, those three determinants were refined out of 128 determinants which again filtered to 28 determinants. The sample size of 60 customers was selected for a pilot test to ensure that the initial scale is reliable. The second questionnaires, based on 28 determinants, were distributed for 300 respondents, these customers are randomly selected; 283 questionnaires were returned with a response rate of 94%. SPSSAMOS® and G*power software’s were conducted to measure the generation, refinement and validation of determinant. The results indicated that E-CRM could be determined using three key constructs related to customers, electronic service, and website focused.
Evaluating the Performance of ChatGPT in the Automation of Maintenance Recommendations for Prognostics and Health Management

Until now, automation of maintenance recommendations for Prognostics and Health Management (PHM) has been a domain-specific technical language processing (TLP) task applied to historical case data. ChatGPT, Bard, GPT-4 and Sydney are a few examples of generative large language models (LLMs) that have received significant media attention for their proficiency in natural language tasks across a variety of domains. Preliminary exploration of ChatGPT as a tool for generating maintenance recommendations has shown promise in its ability to generate and explain engineering concepts and procedures, but the precise scope of its capabilities and limitations remains uncertain. Currently we know of no performance criteria related to formally measuring how well ChatGPT performs as a tool for industrial use cases. In this paper, we propose a methodology for the evaluation of the performance of LLMs such as ChatGPT for the task of automation of maintenance recommendations. Our methodology identifies various performance criteria relevant for PHM such as engineering criteria, risk elements, human factors, cost considerations and corrections. We examine how well ChatGPT performs when tasked with generating recommendations from PHM model alerts and report our findings. We discuss the various strengths and limitations to consider in the adoption of LLM’s as a computational support tool for prescriptive PHM as well as the different risks and business case considerations.
Text Simplification using Hybrid Semantic Compression and Support Vector Machine for Troll Threat Sentences

Text Simplification (TS) is an emerging field in Natural Language Processing (NLP) that aims to make complex text more accessible. However, there is limited research on TS in the Malay language, known as Bahasa Malaysia, which is widely spoken in Southeast Asia. The challenges in this domain revolve around data availability, feature engineering, and the suitability of methods for text simplification. Previous studies predominantly employed single methods such as semantic compression, or machine learning with the Support Vector Machine (SVM) classifier consistently achieving an accuracy of approximately 70% in identifying troll sentences—statements containing threats from online trolls notorious for their disruptive online behavior. This study combines semantic compression and machine learning methods across lexical, syntactic, and semantic levels, utilizing frequency dictionaries as semantic features. Support Vector Machine and Decision Tree classifiers are applied and tested on 6,836 datasets, divided into training and testing sets. When comparing SVM and Decision Tree with and without semantic features, SVM with semantics achieves an average accuracy of 92.37%, while Decision Tree with semantics reaches 91.21%. The proposed TS method is evaluated on troll sentences, which are often associated with cyberbullying. Furthermore, it is worth noting that cyberbullying has been reported to be a significant issue, with Malaysia ranking as the second worst out of the 28 countries surveyed in Asia. Therefore, the outcomes of the study could potentially offer means, such as machine translation and relation extraction, to help prevent cyberbullying in Malaysia.
Implementation of the REST API Model using QR Codes on Mobile Devices to Order Parking Tickets

Many parking lots are still operate manually, and delays are commonly caused during the parking process when unforeseen events occur, such as when the parking ticket paper runs out or the ticket machines jam. New services are added to the parking system online with the aim of decreasing the amount of time that people spend waiting in line to park. This is done by conducting a parking booking system to obtain a parking ticket in the form of a QR Code as well as parking information, payment transactions, and other things that interfere with the parking process. In this study, the Forward Chaining Algorithm will be combined with the survey research method as the research methodology. The Rapid Application Development model is used for analysis and design (RAD). Representational State Transfer Application Programming Interface (REST API) is one of the solutions offered to overcome this problem. With the advent of online parking services, it is envisioned that customers who intend to park their vehicles in public spaces will be able to reserve a parking space in advance, greatly simplifying the process and eliminating the problem of the drawn out queue process.
Effectiveness of written asthma action plan in improving asthma control in children

<b>Introduction:</b> Pediatric asthma guidelines recommend giving asthma action plans to parents. However, its impact on reducing asthma complications is still controversial. Our study aimed to assess the impact of providing a written asthma action plan on disease control in asthmatic children. <b>Methods:</b> We conducted a cross-sectional study including asthmatic children followed in our department between January 2018 and June 2022. A written asthma action plan (WAAP) was explained and given to all children’s parents at diagnosis. Parents were contacted in January 2023 and asked about the WAAP and asthma control. <b>Results:</b> We collected 101 children. The mean age was 7.2 years ± 2.9 [3-14 years] with a male predominance (a gender ratio of 1.3). Most parents confirmed receiving a WAAP when asthma was diagnosed (74%). Among them, only 52 still have it (64%), and 35% reported knowing the WAAP instructions by heart. Forty-five parents said that WAAP helped manage asthma symptoms (60%). Thirty-one parents used the WAAP during respiratory infections (41%). Twenty parents checked the WAAP when giving asthma controller treatment to their children(27%). Lower literacy was associated with WAAP loss in our study (p=0.003). Using the WAAP was associated with better asthma control test (p&lt;0.001), better treatment adherence (p=0.01), fewer school missing days (p=0.006) and less severe exacerbation rate (p=0.04). <b>Conclusion:</b> A written asthma action plan is a simplified, valuable tool that helps to improve parental self-efficacy in managing pediatric asthma, leading to fewer symptoms and exacerbations. Lower literacy can negatively impact the effectiveness of this tool.
Generating Conversation Opportunity for Elderly People Integrating Agent and Conferencing Service

The increase in single-person households and the onset of “COVID-19 frailty” due to COVID-19 have become problems. Our research group focuses on technology support and develops virtual agents using speech recognition technology. In this service, we examined how to promote self-care by having the agent listen to the older person and increase conversation opportunities. However, this service has the problem that it cannot increase opportunities for dialogue with family and friends, which leads to mutual support. In this study, we aim to propose and implement a service called “Easy Video Chat Service” that increases the opportunities for conversation with others through a virtual agent listening service for the elderly person at home.