Reviewerly/exHarmony huggingface dataset:
Decentralizing Healthcare: The History and Opportunities of Web3 (Preprint)

This paper explores the relationship between the development of the internet and health care, highlighting their parallel growth and mutual influence. It delves into the transition from the early, static days of Web 1.0, akin to siloed physician expertise in health care, to the more interactive and patient-centric era of Web 2.0, which was accompanied by advancements in medical technologies and patient engagement. This paper then focuses on the emerging era of Web3-the decentralized web-which promises a transformative shift in health care, particularly in how patient data are managed, accessed, and used. This shift toward Web3 involves using blockchain technology for decentralized data storage to enhance patient data access, control, privacy, and value. This paper also examines current applications and pilot projects demonstrating Web3's practical use in health care and discusses key questions and considerations for its successful implementation.
Hole Extraction Beyond the Depletion Layer in Ge-Doped Hematite Photoanode

As extensively discussed in the literature, photoelectrochemistry water splitting has enormous potential for the generation of green hydrogen. However, to obtain an efficient and economically viable photoelectrochemistry cell, it is necessary to use efficient semiconductor materials from the photogenerated charge separation point of view. In n-type semiconductors, based on transition metal oxides, where the diffusion length (Lp) is small compared to the absorption depth (α-1), i.e. , αLp&lt;&lt; 1, the charge separation process will occur in the depletion layer region. Since this layer is typically tens of nanometers in size, only a fraction of the material exposed to light will contribute to the water photo-oxidation process, thus drastically restricting the efficiency of these semiconductor oxides. Hematite (α-Fe 2 O 3 ) is an n-type semiconductor oxide widely explored as a photoanode for water photo-oxidation and is known to be a material dominated by the charge separation process in the depletion layer, following the Gartner-Butler model. In this way, we will only have photocurrent in V appl &gt;V fb (V appl is the applied potential, and V fb is the flat band potential). Here we will explore the possibility of extracting holes at potentials smaller than V fb in Ge-doped hematite photoanodes through rigorous measurements of V fb and absorbed photon-to-current conversion efficiency (APCE) as a function of V appl . We determined V fb based on the Gartner–Butler analysis in the presence of a sacrificial reagent (H 2 O 2 ) under different wavelength excitation and observed a dependence of the V fb with the wavelength. The APCE measurements as a function of V appl -V fb clearly show the existence of photocurrent at potentials below V fb , strongly suggesting that we are extracting holes even before the formation of the depletion layer (before the band bending). We estimate that the holes arrive from a region approximately 3nm close to the electrolyte-semiconductor interface. This phenomenon was only observed for Ge-doped hematite and not for pristine hematite.
Novel and Effective Approach for Multiview Biometric Object Detection using Deep Learning based Cutting Edge Techniques

Currently, deep learning is experiencing rapid and significant growth in many fields. It offers solutions to a wide range of real-world problems. Additionally, its strong capacity for human authentication can help overcome challenges including human trafficking, border security, immigration control, etc. Many case studies failed to deal with crowdy places where human authentication is important. Addressing these challenges requires the development of a more robust and adaptable facial recognition model that can handle crowded and dynamic environments. So, in this paper, both RetinaFace and ArcFace (additive angular margin loss) are used to overcome the limitation. RetinaFace is employed for the detection of faces, while ArcFace is utilized to authenticate whether the detected faces are known or unknown. The main contribution of this work is to develop an application for personface recognition in crowdy places using RetinaFace and ArcFace. The model demonstrates strong performance, with an accuracy of 96% when tested in crowded places.
Prairie Dog Optimization Based Efficient Task Scheduling in the Cloud Computing

In Cloud Computing CC, scheduling the algorithms depicts the significant role of identifying the possible tasks scheduling. An efficient task scheduling is considerable to achieve the cost-effective execution as well as enhance the resource utilization. The task scheduling problem is to classified as the Nondeterministic Polynomial (NP)-hard problem. To solve this issue, this research proposed an efficient metaheuristic algorithm named Prairie Dog Optimization (PDO) to enhance the task scheduling behaviour in the cloud. The PDO is proposed to improve the task transmitting performance by the cloud network based on the workload of the cloud resources. The proposed method utilizes four prairie dog activities to attains the two basic optimization phases such as exploration and exploitation. The PDO utilizes the two strategies named burrow and foraging to attains the efficient and effective resource allocation. The PDO is modelled for scheduling and distributing the tasks are developed by utilizing the Virtual Machine (VM) factors, time as well as cost. The proposed PDO method attains better results and it achieves the makespan of 112.65, energy consumption of 90.47 and Degree of Imbalance (DoI) of 1.1 respectively when compared to the existing methods like Particle Swarm Optimization, Antlion Optimization (ALO) and Genetic Algorithm (GA).
Web Scraping and Job Recommender System

Web scraping is the process of autonomously obtaining data from webpages using computer software or tools. It has grown in popularity in recent years because of the volume of data available on the internet and the necessity for organizations and individuals to analyze and utilize this data to get the necessary and important information out of it for further analysis and application. Web scraping, together with automation tools, can create wonders. In this paper, we have focused on the latest web scraping techniques and their applications. One of the ideas is to assist those looking for employment on different firm job portals, which is discussed in this paper. Job tracking and scraping collect data from multiple platforms and are web-based tools that help job searchers locate the ideal job for them based on their categories and organize their job search more effectively. People are always on the lookout for a place where they can get their desired information so that they can easily apply for a job. After the collection of data based on user profile attributes, we recommend their success rate based on previous records and an appropriate machine learning model, and we also suggest what skills they can improve to land that job. Web scraping tools allow us to extract the necessary data from different websites in real time and present it to the user. We have discussed various ways to implement scraping and propose a job algorithm to process the extracted data and make it into meaningful information to help users land the best job.
Microservices based Ticket Booking Platform deployed on Cloud

The applications earlier used, have monolithic architecture, and show low scalability and cost management. The enhanced capabilities of cloud computing, viz. containerization, elastic scaling, managed services, optimized resource utilization and fault tolerance, help software developers to develop more robust, scalable, and reliable applications. The cost-effective infrastructure services provided by cloud service providers minimize the complexities associated with the operations of monolithic applications. This paper proposes a novel architecture and solution which could overcome the limitations associated with monolithic applications. The Proposed architecture is the fusion of microservices and cloud computing for the deployment and development of applications. Primarily this research reveals the merits of employing microservices architecture and the challenges that shall be addressed while the deployment of microservices on a cloud environment. These challenges encompass the need for meticulous service decomposition, efficient management of inter-service communication, and the heightened complexity of distributed systems. Furthermore, the deployment of microservices on the cloud necessitates careful consideration of security and data privacy to safeguard sensitive information.
Bridging Modalities with VarVit-GAN: A Generative Adversarial Network for Multi-Modal Brain MRI Translation

In the realm of medical imaging, capturing diverse aspects of brain structures is crucial for accurate diagnoses of brain tumors. Magnetic Resonance Imaging (MRI) has emerged as the most trusted imaging modality due to its superior soft tissue contrast and non-invasiveness. However, the complexity of brain pathologies necessitates different modalities like T1, T2, T1CE and FLAIR for a comprehensive understanding. However, this process poses challenges, such as the need for contrast agent injection and extended scanning times, which can entail potential risks, leading to patient discomfort and resource inefficiencies. To address these issues, we propose an approach named VarVit-GAN, harnessing the power of Generative Adversarial Networks (GANs). By leveraging the capabilities of both Autoencoders and Vision Transformer techniques, the proposed model enables the generation of high-quality T1CE images from the T1 modality. Through comprehensive experiments on a brain MRI dataset, VarVit-GAN demonstrates its effectiveness. Rigorous evaluations using metrics such as Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) validate its superior performance compared to traditional methods. In summary, VarVit-GAN offers a promising avenue for multi-modal brain image translation, benefiting medical practitioners and researchers in improved diagnostics and patient care.
Presentation of research-oriented curriculum model for preschool period

Background and purpose: The present study was conducted with the aim of presenting a research-oriented curriculum model for the preschool period. Research method: In terms of practical goals and in terms of data, this research is mixed exploratory (qualitative-quantitative) and in terms of nature, in the qualitative dimension, the data of the foundation is of its kind, and in the quantitative dimension, it is a cross-sectional survey. The statistical population of the qualitative part includes university professors and experts in the field of educational sciences and psychology, and in the quantitative part, it consists of all managers and teachers of preschool centers in Tehran with a master's degree and above in the field of educational sciences, in the number of 300 people. The sampling method in the qualitative section was purposeful, snowball type, and 15 experts were selected, and we reached theoretical saturation. In the quantitative section, 169 people were selected based on Morgan's volume determination formula. In the current research, after the open and axial coding of the measurement tool, it was sent in the form of a set form and sent to the experts for selective coding and validated, and based on this, a researcher-made questionnaire was designed and distributed among the randomly selected statistical sample. . Then the collected data was analyzed using descriptive and inferential statistics. Findings: From the results of data analysis, 3 dimensions of structure, education and skills in 26 components of 142 indicators were finalized for the research-oriented curriculum model of preschool period and the results of factor analysis All three dimensions of structure, education and skill were confirmed (P=0.05). Conclusion: After the analysis of the results and its factor approval, the final approval and prioritization by experts, the dimensions, components and indicators of the model were drawn and the said model was again validated by the experts.
Visualisation of the combinatorial effects within evolutionary algorithms: the compass plot

This chapters in this book highlight the breadth of smart water technologies and applications. From the use of classical machine learning and data transformation methods for process improvement, to data management and assimilation in models for better monitoring control, a selection of these chapters describes the acquisition and preparation of data as an initial step in its use as part of a digital framework for smart water applications. Importantly, several chapters examine the use of smart tools and contemporary AI technologies, such as neural networks and Internet of Things, that demonstrate value in non-conventional or remote environments. This collection demonstrates the innovation possible through the entire ‘pipeline’ of the process of applying smart water techniques, from data measurement and collection, through initial analysis to the application of machine learning and AI techniques and finally through to system deployment. Each of these steps plays an important role in the application of data science and AI techniques to water problems.Collectively, the book showcases the innovation required to leverage modern data science and AI approaches in the water sector and collectively point the way towards a future of new measurement techniques, innovative methodologies, and intuitive human interaction to truly ‘Make Water Smart’.In Focus–a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector.
Metode Doa dalam Pembelajaran

This study aims to determine the implementation of the prayer method in learning and its impact or influence on student learning achievement. The research used descriptive qualitative method which was carried out by collecting data obtained from documentation, observation and interviews, and supported by literature studies, both from books and journals. The results showed that the prayer method is very necessary in learning. However, in its implementation, the prayer method cannot stand alone. The prayer method in learning must be combined or integrated with other methods such as lecture methods, discussions, question and answer, storytelling, exercises (drills), sociodrama, and other methods. The prayer method is proven to make students peace of mind and are motivated to improve their learning achievements. Therefore, this method must be introduced and implemented in learning, both for educational institutions, especially Islamic educational institutions, as well as public schools. In addition, the teacher must plan every lesson, regardless of the method used in learning, both in class and outside the classroom, to guide students to pray, both before the lesson begins and before the lesson ends.&#x0D; Keywords: prayer method; motivation; peace of soul
Hubungan Hasil Evaluasi Pembelajaran dengan Motivasi Belajar Siswa pada Mata Pelajaran SKI di Madrasah Tsanawiyah Swasta An-Nikmah Pulau Rakyat Kabupaten Asahan

This study aims to determine the relationship between the results of learning evaluation and students' learning motivation in Class VIII SKI Subjects at Madrasah Tsanawiyah Private An-Nikmah Pulau Rakyat, Asahan District, Academic Year 2023/2024. This type of research is descriptive correlational. The population in this study were 160 students of the An-Nikmah Private Madrasah Tsanawiyah Madrasah Tsanawiyah class, Pulau Rakyat, Asahan Regency, as many as 160 people from all 4 classes. The sample taken was 32 students. The technique of collecting data is by giving a questionnaire containing statements of motivation to learn using a Likert scale and documents of student learning outcomes in the SKI subject, the object of class VIII students at Madrasah Tsanawiyah Private An-Nikmah Pulau Rakyat, Asahan Regency. Data analysis technique uses person product moment correlation analysis and simple linear regression test. The results obtained in the person product moment correlation test were 0.592 which showed that the correlation level was at a moderate level, while the results of the simple linear regression test obtained an R square of 0.351 which showed the effect of the results of the evaluation of learning on learning motivation of 35.1%. The research instrument was tested for validity and reliability. Data analysis used the Pearson product moment correlation test and a simple regression test with the help of SPSS for window 20. Thus the results of the study showed that there was a relationship between the results of learning evaluation and learning motivation.&#x0D; Keywords: Learning Evaluation Results, Learning Motivation
Reviewing the Optimized Mechanism for Deep Learning Based Bot Detection to Evaluate Genuine Crypto Assets

The scope of research in employing deep learning for bot detection in the identification of genuine crypto assets is expansive, encompassing a multitude of challenges and opportunities at the intersection of artificial intelligence, cyber security, and financial technology. Researchers can delve into the intricate dynamics of adversarial interactions between bots and detection systems. Adversarial attacks aimed at undermining the integrity of deep learning models pose a significant challenge, requiring innovative defenses and methods to enhance the resilience of the detection systems against manipulation. A promising avenue of exploration involves investigating the transferability of deep learning models across various crypto currencies. Analyzing the extent to which models trained on one cryptocurrency can be effectively applied to others may streamline the process of identifying patterns indicative of bot-driven activities, especially in cases where labeled data is scarce or non-existent for certain crypto currencies. Temporal dynamics play a crucial role in the evolving landscape of bot behaviors. Researchers can focus on understanding the temporal patterns of bot activities, studying their evolution over time, and developing deep learning models that can adapt to these changes. Real-time detection capabilities become paramount in this context, particularly when dealing with the rapid pace of activities on social media platforms and cryptocurrency exchanges. Scalability is also a key consideration, ensuring that models can efficiently handle the vast amounts of data generated in the crypto space.
The Effect of Firm-level Factors on the Practice of Cash Management in Small-scale Housing Companies

This paper examines the impact of firm-level factors on cash management among small-scale housing companies. Firstly, the study explores the relationship between housing developers and their suppliers and bankers in managing cash. Then, the effect of technology on the cash management is examined. The findings of this study provide mixed evidence of the effects of external factors on the practice of cash management. It is revealed that housing developers in Malaysia generally attempt to minimize external borrowings from banks in order to minimize financing costs. Companies that have strong relationship with their suppliers are associated with low effectiveness in cash management, while those that use a computerized system are found to be more effective than those using the manual system. The study suggests that a study which focuses on the issues and nature of bankers-suppliers relationships be undertaken to further understand the characteristics and uniqueness of this critical industry.
Aspects of creating the development environment of students capable of high performance in mathematics

The article presents the the authors' opinions regarding various aspects of the process of training students capable of high performance in mathematics, focused on three important areas: intellectual development; development of communication skills; personal development. The design of the fields of training for this category of students is developed taking into account the concept of the resulting development, the Pedagogical Model for the development of the skills of children gifted in mathematics and the Integrative Model of the process of training teachers for extracurricular activity in mathematics.
The experiment - method of teaching biology

In the current work, the results of the experiment carried out in the biology classes with the involvement of the 7th grade students, from the Dorotcaia Theoretical High School, Dubasari district, are presented. The experiment was one of research and description. Through this teaching method, the vegetative organs of soybean plants were identified: root, stem and leaf. As a result, the biology lessons were attractive, the accumulation of new knowledge was more effective, and the students were motivated and curious to discover the phenomena accompanying the growth and development processes of soybean plants.
Meningkatkan Kemampuan Membaca Permulaan Pada Anak Kelompok A Media Pop Up berbasis Flipchart di TK Hidayatus Shibyan

This research aims to find a solution to the problem of difficulties in improving language skills in terms of early reading in early childhood in group A1 in Hidayatus Shibyan Surabaya. The research method used in this research is the research and development method or often called Research and Development (R&amp;D). The subjects of this research are students in group A1 of the Hidayatus Shubiyan Kindergarten for the 2023-2024 academic year with a total of 15 students with an average age 4-5 years old, consisting of 9 female students and 6 male students. The results of the research presented data on the differences between the use of Flipchart-based Pop Up learning media and increasing initial reading ability in group A1 of Kindergarten Hidayatus Shibyan Surabaya. From the table above, the result is tcount = - with a significance level of 0.000. Because the significance level is smaller than 0.05, Ha is accepted, and Ho is rejected. Based on the results of this research, it can be concluded that "There is an influence of the use of Pop Up learning media on increasing the initial reading ability of children in group A1 of Kindergarten Hidayatus Shibyan Surabaya"
COHESIVE DEVICES IN DONALD TRUMP’S FIRST SPEECH AS THE AMERICAN PRESIDENT

The presence of cohesive devices in speech plays a crucial role in representing the functioning of language. Beyond enhancing the effectiveness of written communication, cohesive devices guide listeners into the realm of context, fostering a shared understanding of meaning. This research focuses on analyzing grammatical and lexical cohesions in Donald Trump's inauguration speech from the 2016 United States presidential election, which took place on January 20th, 2017. The objective of the study is to explore the representation of both grammatical and lexical cohesions. A qualitative method was employed, and data were extracted from pauses in Trump's speech, specifically noting cohesive devices. The findings reveal that grammatical cohesion encompasses references, substitutions, ellipsis, and conjunctions. Lexical cohesions identified include repetition, synonymy, antonymy, hyponymy, meronymy, and collocation. Repetition was observed in 29 pauses, substitutions in 6 pauses, ellipsis in 8 pauses, and conjunctions in 31 pauses. Regarding lexical cohesion, the study identified repetition in 32 pauses, synonymy in 10 pauses, antonymy in 5 pauses, hyponymy in 3 pauses, meronymy in 2 pauses, and collocation in 6 pauses. However, it was noted that incohesive instances occurred in the case of conjunctions, potentially influenced by the situational or contextual factors. In conclusion, this study sheds light on the diverse cohesive devices employed in Trump's inauguration speech, emphasizing the significance of context in understanding language use. Keywords: cohesive device, conjunctions, ellipsis, grammatical cohesion, lexical cohesion, substitutions