Reviewerly/exHarmony huggingface dataset:
Determination of alteration zones applying fractal modeling and Spectral Feature Fitting (SFF) method in Saryazd porphyry copper system, central Iran

The target of this research is recognition of the alteration zones utilizing concentration-area fractal methodology according to the reflection of the main minerals of each alteration zone that enhanced by Spectral Feature Fitting (SFF) method due to Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) satellite images in Saryazd porphyry system, central Iran. The alterations and mineralization developed in Eocene volcanics. Remote sensing results achieved by the SFF method and Concentration -Area (C - A) fractal modeling represent different parts of propylitic, argillic and phyllic alteration zones due to their intensity and pixel values. In addition, the results reveal that there is a ring-shaped structure in the alteration zones, which are correlated with results, derived from X-Ray Diffraction (XRD) analyses and field observations.
The Role of Emotional Responses in the VR Exhibition Continued Usage Intention: A Moderated Mediation Model

During COVID-19, many renowned galleries and art fairs used Virtual Reality (VR) exhibitions for art information dissemination and online displays. To avoid the risks of offline viewing of exhibitions, users can access a web-based VR exhibition platform for remote appreciation of artworks, gaining a rich art experience and thus contributing to physical and mental health. The reasons affecting users' continued usage intentions are not clear enough in the existing studies of VR exhibitions. Therefore, further studies are needed. This paper explores the relationship between users' escapist experience, aesthetic experience, presence, emotional responses, and continued usage intention through a survey of VR exhibition users. The survey data were collected from 543 users who had experienced the VR exhibition through an online survey website. The study results show that users' continued usage intentions are influenced by escapist experience and aesthetic experience. Presence plays a mediating role in the influence of escapist experiences and aesthetic experiences on continued usage intention. Emotional responses play a moderating role in the impact of user experience on continued usage intention. This paper provides a theoretical reference for the study of the impact mechanism of continued usage intention of VR exhibitions from the perspective of mental health. In addition, this study enables VR exhibition platforms to better understand the emotional state of users during art experiences to create and share healthy aesthetic information that can contribute to the management and enhancement of mental health. At the same time, it provides valuable and innovative guidance solutions for the future development of VR exhibitions.
Performance Investigation of Software Agents in Artificial Intelligence and Document Object Model Domain

Assessing the performance impact of Artificial Intelligence on questionnaires of pharmacological unit is necessary from the perspective of medical practitioners as well as patient’s perspectives. The proposed study ascertained that software agents based on Artificial Intelligence and Document Object Model domain can deliver better service in medical units in contrast to its other deployment methodologies. So, the proposed work, a prototype is developed by using dialog control class file which accesses the system resources through the kernel objects. We call the software agent as MBot (prototype bot for medical unit). The prototype is deployed for pharmacological unit where the clinical instruction against the disease can be suggested. The experimental arrangement, deployment architecture of MBot, performance metrics and the statistical analysis for the observed data sample are discussed here. The novelty of the proposed work highlights the performance aspects of MBot against its counterpart. It reveals that better response time and validates that the dialog controller class of MBot can process questionnaires through its intelligence.
Does Foreign Exchange Rate Moderate the Relationship Between Foreign Capital Outflows and Economic Growth? A Panel Data Investigation of Kenya’s Economy (1986-2021)

Purpose – This study aims to evaluate the nexus between foreign exchange rate, foreign capital outflow and economic growth in Kenya. This study focused on analyzing the moderating role of foreign exchange rate on the relationship between foreign capital outflow and economic growth in Kenya. The foreign exchange rate is an important measure in the economic development. Its fluctuations affect the levels of foreign capital outflow. The loss of foreign exchange reserve due to foreign capital outflow translates to great amount of savings lost in the country.&#x0D; Design/methodology/approach – This study employed a Panel Data approach and the World Bank’s residual model in the estimation of the magnitude of foreign capital outflow from Kenya during the period between 1986 to 2021.&#x0D; Findings – The panel data results revealed that the interaction variable MOD_FER had a coefficient of 0.05 and a significant probability value of 0.0001 which is significant at 5 percent level of significance. This means that foreign exchange rate moderated the relationship between foreign capital outflow and economic growth in Kenya during the period of this study. When the relationship with foreign capital outflow was moderated by 0.05 units then GDP grew by 1 unit. As such, foreign exchange rate is a significant factor in the relationship between foreign capital outflow and economic growth in Kenya.&#x0D; Originality/value – This study calculates the magnitude effect of foreign exchange rate in the relationship between foreign capital outflow and economic growth in Kenya for the first time. In addition, the study calculates the magnitude change of foreign capital outflow for MNE’s due to foreign exchange rate oscillations. This study suggests many policy proposals to deal with the challenge of foreign exchange rate, foreign capital outflow and economic growth in Kenya. The results of this study will benefit policy makers by providing them with data-based evidence that will guide them in making appropriate policies that discourage foreign capital outflow and institute proper management of foreign exchange rate to boost economic growth in Kenya.
Challenges and opportunities: application of clean and affordable energy in the development of the national capital of the archipelago

IKN is in a good and sufficient position in terms of energy security, especially in terms of renewable energy. To prevent harm to Kalimantan's ecology and natural beauty, it is vital to pay attention to and routinely checkfactors of energy efficiency and emission levels. This research aimed to look at the challenges and opportunities of implementing clean and affordable energy in the development of the National Capital of the Archipelago. This study used qualitative research methods that described the findings throughonline media, and the data source of this research is online news media. The data was obtained using the Ncapture feature on the Nvivo 12 Plus. The results of the study showed that the development of the Kayan River Hydroelectric Power Plant encourages economic development by as much as 37%, environmental development by as much as 35%, and social development by as much as 28%. Then the second challenge of hydropower development is environmental development, as much as 33.33%. The third challenge is economic development, which is 31.82%. Kayan hydropower construction will consist of five dams, the first phase will produce a 900-megawatt dam, the second dam of 1,200 megawatts, the third dam of 1,800 megawatts, the fourth dam of 1,800, and the fifth dam of 3,200 megawatts.
The Impact of COVID-19 on the Financial Performance of Firms in the Kingdom of Saudi Arabia

COVID-19 pandemic has been highly detrimental to both the overall economy and the financial performances of firms in all sectors. It has placed increased pressure on the manufacturing capacity and supply chains throughout the world, but it has also provided new opportunities for the logistics industry to make advancements in ecommerce. This study analyses the financial performance of 130 Saudi firms that listed on the Saudi Stock Exchange in order to explore those dynamics which belong to the following sectors (energy, material, industrials, consumer discretionary, communication services, consumer staples, healthcare, financial, information technology and utilities). To facilitate comparison between several ratios in 2019 and 2020, the Wilcoxon Signed Rank test was conducted. This study determined that there was no improvement to any of the 130 listed firms in the sample during this time, which is reassuring for businesses in Saudi Arabia, as over the same period, the Saudi Economy shrunk. These findings are useful for businesses to plan for Covid-19 like disruptions in the future.
Research Approach and Challenges in the Development of Ai-Based Doctor Bots

One of the fields in which artificial intelligence has been successfully implemented in healthcare, and there is currently a significant amount of research being conducted in this particular field. Additionally, we are a team of researchers who are conducting research in the area of disease diagnosis utilizing artificial intelligence. By asking patients about their symptoms, chatbots appear to play an important part in the process of disease diagnosis, as our research has shown. This research was carried out with the intention of determining how far along the path of development chat bolts have progressed in relation to the healthcare industry and disease diagnostics. We looked at more than one hundred papers published between 2015 and the present day, narrowed our focus to a few documents that were particularly pertinent, and then tried to determine the obstacles that remain in the way of future research in the area of diagnosis of diseases using chatbots. This paper will assist researchers in better understanding the methodologies utilized in the development of chatbots for the purpose of disease diagnosis. Additionally, it will assist them in determining where there are gaps in the research, which will enable them to conduct research to fill those gaps.
Training of future teachers of natural sciences for the use of information and communication technologies in their professional activities

Today, an important aspect of future teachers training, in particular, natural sciences, is the usage of innovative technologies and approaches in the educational process of higher education institutions. The features of training future teachers of natural sciences using information and communication technologies are considered. The general global trends in the development of informatization of education are highlighted: expansion of the scope of use new information technologies. The principles of using information and communication technologies in the educational process are highlighted: the principle of visibility; the principle of expediency; the principle of systematicity and consistency; the principle of strength of knowledge; the principle of accessibility; the principle of individualization; the principle of connecting theory with practice; the principle of multimedia; the principle of interactivity. Online tools are highlighted to enable the use of innovative technologies for teaching future teachers of natural sciences. It is noted that in the educational system there are applications and information and communication technologies that widely used in the education system: word supercomputers, databases, demonstration programs, catalogue organization schemes, planners, graphic packages, etc.
A Novel Underwater Image Enhancement Method Based on the Dual-Image Fusion

In underwater target recognition and tracking, it is necessary to utilize high-quality underwater image. However, there are a series of degraded problems with underwater image, such as low contrast, color distortion, hazy, blurry and so on. To solve the above problems, this paper proposes a fusion framework based on the dual-image. Firstly, the chroma component and illuminance component of the degraded image are improved respectively to obtain the chroma improved image and the illuminance improved image. Secondly, we use optimized adaptive gamma correction to find an intermediate image with good exposure between the exposure of the improved chroma image and the exposure of the improved illumination image. Thirdly, we obtain the hierarchical structures of the illumination improved image and the intermediate virtual image respectively by the Laplace pyramid decomposition. According to the normalized weights, the Gaussian pyramid mapping for each weight is formed. Finally, the images are reconstructed by the pyramid fusion method. Experimental results show that compared with other three underwater image enhancement algorithms, the proposed algorithm has certain advantages in underwater image enhancement.
Hierarchical Subgoal Generation from Language Instruction for Robot Task Planning

In the field of "Visual-Language Task Planning", how to flexibly process various language inputs determines the accuracy of action prediction. In this paper, we mainly study: what useful information can be obtained from language inputs and how to organize it; how to map instructions to the organized information. For these problems, (1) we organize the "Task-Subgoal-Action" logic structure, to help complete the complex tasks step-by-step, (2) we also propose the hierarchical subgoal generation model, which learns operation knowledge from training data, to generate executable subgoal sequences according to the given instructions. Our model is trained and evaluated with the datasets from “Action Learning From Realistic Environments and Directives ”(ALFRED), the subgoal sequences extracted from predicted texts successfully enable the robot to complete nearly 97% of ALFRED tasks" What’s more, our model performs better than the language processing module in FILM, and the robot system which integrates our model performs well. These results indicate that our model can make full use of language inputs efficiently and provides great help to robot task planning.
Design of a self-organizing modular neural network with inter-module connections

To further promote the generalization ability of modular neural networks (MNNs) and design a network that approximates the modular structure of the brain, a self-organizing MNN with inter-module connections (SOIMNN) is designed in this study. Firstly, a soft clustering method is employed to divide the feature dimensions of datasets to generate several feature subsets. Secondly, subnetworks with the same number of subsets are used to learn subtasks. Each sub network adopts a feedforward neural network with double hidden layers, and connections between different subnetworks are constructed. At the same time, a gradient-based growth mechanism and a threshold-based pruning strategy are employed to adjust the structure of the SOIMNN, and all parameters are learned by the improved Levenberg-Marquart algorithm. Finally, the output of SOIMNN is obtained from the weighted average of each subnetwork. Experimental results on several benchmark datasets demonstrate that SOIMNN has better generalization performance and more compact structure than other MNNs.
A Public Key Searchable Encryption Method Based on Multiple Keywords

In recent years, the secure search of encrypted cloud data has become a hot research topic and a challenging task.Several secure search schemes have been proposed to address this challenge.However, existing public-key searchable encryption schemes still face many problems.Among these schemes, most of them are based on single-key searchable encryption schemes, and although some schemes are designed for multi-key search, they still disclose the secret information of the encrypted index.Based on this, this paper proposes a multi-key public-key searchable encryption scheme without a secure channel, which uses a random element padding method to construct an encrypted index to ensure the security of the index information, and then improves the efficiency of the search by aggregating the query keyword information to generate query trapdoors.The simulation results of the algorithm show that the algorithm improves the query efficiency and query accuracy under the condition that the index and trapdoor are secure.
PELATIHAN TENTANG SUPLEMENTASI RASIONAL OLEH BIDAN PADA IBU HAMIL DAN EDUKASI MENGGUNAKAN MEDIA SOSIAL INSTAGRAM

Rational drugs use is a must know-knowledge in preventing disability and death due to unnecessary drug use. Giving supplementation during pregnancy aims to reduce infant mortality and disability. Refreshing the rational use of supplementation in pregnancy and sharing knowledge in how to use Instagram as an education and communication media between midwives and pregnant women aims in improving the midwives’ skills as educators and good communicators. The method of this activity was divided into three stages, preparation, implementation, and evaluation. In the implementation stage, midwives’ knowledge about supplementation rational and social media use were increasing. the evaluation stage showed that the midwives could use Instagram as education and communication media between midwives and pregnant women
Explaining the Performance of Collaborative Filtering Methods with Optimal Data Characteristics

The performance of a Collaborative Filtering (CF) method is based on the properties of a User-Item Rating Matrix (URM). And the properties or Rating Data Characteristics (RDC) of a URM are constantly changing. Recent studies significantly explained the variation in the performances of CF methods resulted due to the change in URM using six or more RDC. Here, we found that the significant proportion of variation in the performances of different CF techniques can be accounted to two RDC only. The two RDC are the number of ratings per user or Information per User (IpU) and the number of ratings per item or Information per Item (IpI). And the performances of CF algorithms are quadratic to IpU (or IpI) for a square URM. The findings of this study are based on seven well-established CF methods and three popular public recommender datasets: 1M MovieLens, 25M MovieLens, and Yahoo! Music Rating datasets
Features Matching using Natural Language Processing

The feature matching is a basic step in matching different datasets. This article proposes shows a new hybrid model of a pretrained Natural Language Processing (NLP) based model called BERT used in parallel with a statistical model based on Jaccard similarity to measure the similarity between list of features from two different datasets. This reduces the time required to search for correlations or manually match each feature from one dataset to another.
Sales Prediction on the Diamond Cell Counter Using Autoregresive Integrated Moving Average (ARIMA) Method

Diamond Cell is a specialized retailer that offers a diverse range of smartphone accessories, electronic credits, and internet vouchers from different providers, each with varying active periods. However, the uncertainty surrounding internet voucher sales transactions often leaves counter owners hesitant to increase their stock due to the short active period of the vouchers. This leads to frequent customer dissatisfaction as the internet vouchers run out, resulting in lost sales opportunities. To address this issue, this study aimed to predict voucher sales for the upcoming month to serve as a reference for calculating the stock of voucher supply. The Auto-regressive Integrated Moving Average (ARIMA) method was used based on voucher sales data from November 2022 to January 2023. Out of the three tentative models obtained, only one proved suitable for use. The best ARIMA model was the (2,1,0) model, with a MAD value of 29.65, an MSE value of 2409.95, and a MAPE value of 23.3%. Based on the February voucher sales, the stock level can remain the same as the previous period since the sales were stable. The findings of this study can help Diamond Cell counter owners make more informed decisions about stocking internet vouchers, resulting in better customer satisfaction and reduced likelihood of losses.
Information Cardiac Platform to Support Healthcare

The article presents a software information platform for storing, processing, researching, and protecting cardiology information obtained during the study of patients with various cardiovascular diseases and a healthy control group. The information platform contains non-confidential data about the research subjects, which is freely available; as well as a confidential part; cardiological data, containing information about the biomedical tests carried out; as well as the parametric and graphical results of the mathematical analyzes obtained based on the registered cardiac data. The created integrated information platform can be used by cardiology specialists to evaluate the results of cardiac examinations and assist cardiologists in making a correct diagnosis and prescribing effective treatment. The platform is a tool with a user-friendly interface and can be useful for cardiac data researchers as well.
Interplay between Non-Markovianity of Noise and Dynamics in Quantum Systems

The non-Markovianity of open quantum system dynamics is often associated with the bidirectional interchange of information between the system and its environment, and it is thought to be a resource for various quantum information tasks. We have investigated the non-Markovianity of the dynamics of a two-state system driven by continuous time random walk-type noise, which can be Markovian or non-Markovian depending on its residence time distribution parameters. Exact analytical expressions for the distinguishability as well as the trace distance and entropy-based non-Markovianity measures are obtained and used to investigate the interplay between the non-Markovianity of the noise and that of dynamics. Our results show that, in many cases, the dynamics are also non-Markovian when the noise is non-Markovian. However, it is possible for Markovian noise to cause non-Markovian dynamics and for non-Markovian noise to cause Markovian dynamics but only for certain parameter values.
Flexible acousto-optically driven single-shot imaging

Countless ultrafast imaging techniques have been developed and even though they brought significant insights, their application out of a laboratory environment is however often quite limited, either by the complexity of operation or by a heavy data processing. We demonstrate flexible single-shot imaging via the combination of sequentially timed all-optical mapping photography with acousto-optics programmable dispersive filtering and digital in-line holography. The frame rate and exposure time can be independently adjusted without complex shaping stages, making the system remarkably agile. In-line holography allows to achieve an even higher simplicity through its lensless operation and the reconstruction on a wide depth of field.
Sosialisasi Undang-Undang Informasi Transaksi Elektronik Kepada Siswa SMAN 1 Gunung Toar Kabupaten Kuantan Singingi

Law Number 11 of 2008 concerning Electronic Transaction Information is a regulation that is often in direct contact with various groups of people in this advanced technology era. In general, the ITE Law has not been well socialized among high school students, especially SMAN 1 Gunung Toar, due to various factors, one of which is the lack of student understanding of the existing laws and regulations. As for the material in the Community Service material at SMAN 1 Gunung Toar, among others: What is the ITE Law, Am I covering the actions regulated in the ITE Law, what actions and sanctions are given to the perpetrators ITE Law criminal