Reviewerly/exHarmony huggingface dataset:
$R$-dependence of jet observables with JEWEL+v-USPhydro

The $R$-dependence of jet observables provides a new tool in understanding the interplay between the jet energy-loss mechanism and medium response in heavy-ion collisions. This work applies the Monte Carlo events generator JEWEL and PYTHIA, coupled with $\rm T_{R}ENTo$ initial conditions and the state-of-the-art (2+1)D v-USPhydro, for the simulation of jet distributions and substructure observables for lead-lead collisions at LHC energy scales. We present the jet nuclear modification $R_{AA}$ and anisotropic flow coefficients $v_{n=2,3}$ varying the jet cone radius $R$, in the context of anti-$k_T$ jets, in addition to leading subjet fragmentation. The calculations indicate the impacts of the hydrodynamic evolution and weakly-coupled medium response, given by recoils, on the distributions. Results are compared to experimental data in a wide range of jet $p_T$ and collision centrality, and displayed along large jets ($R \ge 0.6$) predictions.
Exploring Effective Models of Leadership in the Age of AI within Private Higher Education Institution in England

Artificial intelligence (AI) has gained significant interest and application in various fields and the impacts are evident in teaching, learning, and assessment. On one hand, AI can bring the much-needed revolution in higher education by assisting leaders and management to offer personalized teaching approaches that meet specific student needs, it can also enhance efficient feedback and the automation of administrative functions including assessment and grading. These capabilities make AI an important instrument in promoting efficiency in higher education and creating room for institutions to redirect their focus on other equally important aspects such as curriculum development. However, despite these advantages, AI comes with significant setbacks that adversely impact the quality of education. Particularly, various platforms allow students to do their assignments automatically thus negating their responsibility to research and learn. As such, institutions must put in place effective models of leadership and approaches that will ensure the use of AI in an ethical manner without compromising the quality of education.
Effective Strategies and Methods to Enhance Music Practical Teaching in Higher Vocational Preschool Education

Abstract:This paper studies of effective strategies and methods to elevate music practical teaching in higher vocational preschool education. Acknowledging the pivotal role of higher vocational preschools in shaping the foundational years of a child's development, the study navigates through a comprehensive exploration of the significance of music education. Grounded in a robust literature review, the paper highlights the importance of curriculum design, instructional techniques, assessment methods, and the seamless integration of technology in shaping impactful music education experiences.Theoretical frameworks, notably the Suzuki Method and Vygotsky's Sociocultural Theory, offer insights into pedagogical and socio-cultural aspects, emphasizing the creation of positive and immersive musical environments and collaborative learning experiences. Subsequently, the paper presents effective strategies, including the incorporation of technology, active learning approaches, and the customization of lessons to individual needs. The multifaceted benefits of music practical teaching in cognitive, emotional, and social domains are explored, emphasizing its role in holistic preschooler development. The study advocates for continuous professional development for music educators, stressing the importance of staying abreast of evolving pedagogical trends, engaging in collaboration and networking, and remaining informed about current trends and research in music education. By embracing these strategies, educators can cultivate a vibrant and enriching musical environment that fosters a lifelong love for music and nurtures the holistic development of preschool learners.
Adaptive teaching competence in university teacher education: theoretical framework, results and implications of interventional research

Preparing future teachers for teaching heterogeneous learning groups can be seen as one of the fundamental challenges in teacher education. In this context, fostering adaptive teaching competence stands out as a promising approach for inclusive teaching and learning. To do so, the project ’Subject-Specific Didactic Qualification of Future Teachers for Inclusive Settings at the Humboldt-University Berlin’ (FDQI-HU) has developed digital teaching-learning modules for teacher education in a cyclical approach over several years. These modules can be adapted for individual subjects and used in a variety of ways. This article presents the theory and implementation of the modules for fostering adaptive teaching competence and – based on the empirical research on the interventions – presents central results as well as further impulses for innovative university teaching and teaching evaluation.
Natural Language Processing Advancements: Breaking Barriers in Human-Computer Interaction

Natural Language Processing (NLP) advancements have revolutionized human-computer interaction, breaking barriers and opening new frontiers in technology. NLP techniques enable machines to understand, interpret, and generate human language, facilitating seamless communication between humans and computers. This paper explores recent advancements in NLP technology, highlighting their impact on various domains and discussing challenges and future directions in the field
Creating an atlas of normal tissue for pruning WSI patching through anomaly detection

Patching whole slide images (WSIs) is an important task in computational pathology. While most of them are designed to classify or detect the presence of pathological lesions in a WSI, the confounding role and redundant nature of normal histology are generally overlooked. In this paper, we propose and validate the concept of an "atlas of normal tissue" solely using samples of WSIs obtained from normal biopsies. Such atlases can be employed to eliminate normal fragments of tissue samples and hence increase the representativeness of the remaining patches. We tested our proposed method by establishing a normal atlas using 107 normal skin WSIs and demonstrated how established search engines like Yottixel can be improved. We used 553 WSIs of cutaneous squamous cell carcinoma to demonstrate the advantage. We also validated our method applied to an external dataset of 451 breast WSIs. The number of selected WSI patches was reduced by 30% to 50% after utilizing the proposed normal atlas while maintaining the same indexing and search performance in leave-one-patient-out validation for both datasets. We show that the proposed concept of establishing and using a normal atlas shows promise for unsupervised selection of the most representative patches of the abnormal WSI patches.
Challenges in the Adoption of Mobile Money Services by Mobile Phone Users in Lusaka, Zambia.

Purpose: This study investigates mobile money adoption in Lusaka, Zambia, utilizing a mixed-methods approach.&#x0D; Methodology: The study employs a mixed-methods approach to explore mobile money adoption in Lusaka, Zambia.&#x0D; Findings: The study uncovers several key findings regarding mobile money adoption in Lusaka, Zambia. Convenience, ease of use, and perceived security emerge as primary motivators for adoption. Conversely, lack of understanding, perceived risk, and a preference for traditional banking methods are identified as major deterrents. Interestingly, cultural factors, such as religious beliefs, play a minimal role in adoption decisions. Demographic factors, including age, education level, and income, exhibit a complex interplay with adoption rates, with younger, more educated, and higher-income individuals generally more inclined to use mobile money.&#x0D; Unique Contribution to Theory, Practice, and Policy: The study's findings contribute to both theory and practice in the field of mobile money adoption. They highlight the predominance of practical considerations over cultural factors in technology adoption, suggesting the need for enhanced adoption models that go beyond traditional socio-economic determinants. Additionally, the study identifies technological discomfort as a significant deterrent to adoption, indicating a need for improved digital literacy and user-friendly interfaces. Furthermore, an awareness-confidence paradox is revealed, wherein increased awareness of cyber scams negatively impacts trust in mobile money security. This suggests the importance of balancing risk education with strong security reassurances. Fee perception also emerges as a hurdle, with over 60% of respondents perceiving fees as unfair, underscoring the importance of educating users on the value proposition of mobile money services. Overall, the study offers valuable insights for strategic product and service development, regulatory and policy formulation, and initiatives aimed at advancing financial inclusion and promoting economic growth in similar urban and semi-rural settings.
Peningkatan literasi numerasi melalui model pembelajaran dan hubungannya dengan kemampuan self-efficacy: Systematic literatur review

This study aims to describe the improvement of numeracy literacy through learning models and its correlation with self-efficacy capabilities.. The research method employed is a Systematic Literature Review (SLR) comprising three stages: planning, conducting, and reporting. Data collection involved identifying relevant articles based on predetermined criteria. The study examined 19 articles retrieved through a Google Scholar database search, facilitated by the Publish Or Perish software. Data synthesis was presented through tabulation and reinforced with a narrative format. The findings indicate that the improvement of numeracy literacy in elementary and secondary schools from 2020 to 2023 is predominantly pursued through the implementation of problem-based learning (PBL) models. Additionally, students' self-efficacy capabilities influence their success in learning. Furthermore, students' numeracy literacy correlates positively with self-efficacy, evidenced by higher self-efficacy correlating with higher numeracy literacy skills.
Interactive Tomi Tool To Improve Attitudes Towards Cyberbullying In Upper Basic Students

Education changes frenetically at the pace of technological advances, likewise there are a series of difficulties that must be faced to avoid falling into cyberbullying that is converted into a way of discriminating against children and adolescents of any social class, the abuse of ICT exposes schoolchildren to this type of violation, where parents and teachers often do not know when a student is being a victim. This article aims to determine the effectiveness of the interactive TOMi tool in improving the attitudes of cyberbullying in students. of an Educational Unit, La Concordia - Ecuador 2023. The approach was quantitative, with a quasi-experimental design, using an intentional non-probabilistic sample of 30 students for the experimental group and 30 students for the control group. The instrument applied was the "European Cyberbullying Intervention Project Questionnaire", validated under the judgment of experts; reaching a coefficient of 0.75 in Cronbach's alpha, the data were displayed in frequency distribution tables and analyzed inferentially. It was concluded that the interactive TOMi tool positively influenced the improvement of attitudes towards cyberbullying in the students where the intervention was carried out.
Chatbot Service Quality in Banking : Analyzing Indian Banking Customer Perceptions and Influence on Customer Satisfaction and Value

Purpose : The study has two objectives : first, to determine the quality of chatbot services provided by Indian banks; second, to assess the influence of chatbot service quality variables on customer satisfaction and customer value. Research Methodology : The study used a quantitative methodology, selecting individuals at random from a group of Indian banking clients. We used a questionnaire to collect data from the selected sample as part of a causal research investigation. We made use of SPSS and Python for this analysis. Customer satisfaction and value were taken into account as the dependent variables in our study. The seven elements of service quality—functionality, convenience, security, design, customization, enjoyment, and assurance—made up the independent variables. Findings : According to this study, client satisfaction and value were significantly shaped by the quality of the services provided. Customers’ value was significantly impacted by functionality and enjoyment, and their satisfaction was greatly influenced by assurance, design, and personalization. The unexpected negative impact assurance had on customer value is noteworthy and calls for more research. Practical Implications : In the highly competitive banking industry, this research has important ramifications for banks. It highlighted how important service quality is, which led banks to give priority to customer pleasure and think about making strategic changes. Banks could obtain a competitive advantage by improving the quality of their services, improving chatbot services, and implementing a customer-centric strategy by utilizing the research findings that have been presented. Our research helped banks evolve with the needs of their customers in mind, enabling them to gain credibility, repeat business, and long-term success in the ever-changing banking services market. Originality/Value : This study examined how consumers in Indian banks perceive the value and satisfaction of chatbot services and how they use them. The study provided useful recommendations and concepts to improve the general consumer experience.
Restructuring the Knowledge Platforms of ICID

Irrigation and DrainageVolume 73, Issue 1 p. 378-380 ICID Restructuring the Knowledge Platforms of ICID Ashwin B. Pandya, Ashwin B. PandyaSearch for more papers by this author Ashwin B. Pandya, Ashwin B. PandyaSearch for more papers by this author First published: 15 February 2024 https://doi.org/10.1002/ird.2935Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume73, Issue1February 2024Pages 378-380 RelatedInformation
Cracking Down on Artificial Intelligence Malpractice in the EU

Inside Precision MedicineVol. 11, No. 1 InterviewsCracking Down on Artificial Intelligence Malpractice in the EUHelen AlbertHelen AlbertHelen Albert is senior editor at Inside Precision Medicine and a freelance science journalist. Prior to going freelance, she was editor-in-chief at Labiotech, an English-language, digital publication based in Berlin focusing on the European biotech industry. Before moving to Germany, she worked at a range of different science and health-focused publications in London. She was editor of The Biochemist magazine and blog, but also worked as a senior reporter at Springer Nature's medwireNews for a number of years, as well as freelancing for various international publications. She has written for New Scientist, Chemistry World, Biodesigned, The BMJ, Forbes, Science Business, Cosmos magazine, and GEN. Helen has academic degrees in genetics and anthropology, and also spent some time early in her career working at the Sanger Institute in Cambridge before deciding to move into journalism.Search for more papers by this authorPublished Online:1 Feb 2024https://doi.org/10.1089/ipm.11.01.09AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 11Issue 1Feb 2024 InformationCopyright © GEN PublishingTo cite this article:Helen Albert.Cracking Down on Artificial Intelligence Malpractice in the EU.Inside Precision Medicine.Feb 2024.42-44.http://doi.org/10.1089/ipm.11.01.09Published in Volume: 11 Issue 1: February 1, 2024 TopicsArtificial intelligenceBiotechnology laws and regulatory affairs PDF download
Candidate Selection in Teams: Be the Best or Surround Yourself with the Best?

ABSTRACT This study investigates, via an experiment, how the decentralization of a firm’s selection process affects the caliber of the chosen candidate in a team-based environment. We predict and find that, when decision makers have comprehensive and unambiguous candidate-specific information regarding who is the best for the job, the quality of the selected candidates is lower under a decentralized versus centralized selection process. We also find that nonpecuniary status concerns drive the effect. Results of two boundary conditions reveal that, as the clarity of information regarding who is the best candidate for the job decreases (due to decision-makers having weaker or mixed signals about job candidates), the quality of selected candidates becomes no worse under a decentralized than under a centralized selection process. Overall, our results indicate that nonpecuniary status considerations and information environment can influence candidate selection decisions in organizations. Data Availability: Data are available from the authors upon request.
Impact of Green Transformational Leadership on Employee Green Behavior : A PLS-SEM Approach

Purpose : The purpose of this paper was to understand the impact of green transformational leadership (GTL) on employee green behavior (EGB) in the information technology (IT) sector. Methodology : For this research, the data were collected from IT employees of Pune, Maharashtra. The association between the latent variables, along with various characteristics, was observed. One hundred thirty employees from IT organizations were assessed through the survey method, and analysis was done using SPSS and the PLS-SEM approach. Findings : The empirical study showed a positive relationship between GTL and EGB. The results of the study also showcased that a green organizational identity (GOI) strongly mediated the relationship between GTL and EGB, and green training (GT) as a moderator strengthened the relationship. Practical Implications : Pro-environmental initiatives and policies cultivated an atmosphere of unity and collaboration in the workplace toward the environment. Originality/Value : This study added value to GTL. The findings would be helpful for policymakers.
Blockchain in the Lens of Populism : A Short Communication of Research Evidence

Purpose : Blockchains are a sophisticated and extensively utilized technology that facilitates bulk data management operations and increases productivity for many companies, including governments. However, because of the high prices and complexity, not all large firms could take advantage of this disruptive weapon, which leads to populist ideas. The purpose of this brief message was to examine the new, popular ideas in blockchain technology that improve the global productive efficiencies of businesses. Methodology : The two-stage method that produced the insights in this communication was followed by a sentiment analysis, network map, theme map, and latent Dirichlet allocation. Findings : Blockchain technology has been widely adopted by the majority of the major services businesses. The industries that are currently scaling up, however, lack the viability to use this technology. The results also showed that neither academic research nor the corporate framework had examined the new populism situation. Practical Implications : The productive efficiency of industries would be improved if governments and policymakers used populistic settings in blockchain technology. Additionally, eliminating the cost-based inequalities would inspire populist ideas throughout all industries, generating combined revenues. Originality/Value : This brief message implied negative opinions about blockchain technology and was taken from a significant study on the impact of this technology on business success. This message is meant to benefit not only corporations but also governments and policymakers in facilitating regular access to technology for expanding industries that could strengthen economies.
Distributed asynchronous measurement system fusion estimation based on inverse covariance intersection algorithm

For state estimation of multi-source asynchronous measurement systems with measurement missing phenomena, this paper proposes a distributed sequential inverse covariance intersection (DSICI) fusion algorithm based on conditional Kalman filtering method. It is mainly divided into synchronized state space module, local filtering module and fusion estimation module. The missing measurements occurring in the system are modelled and described by a set of random variables obeying a Bernoulli distribution. The synchronized state space module uses a state iteration method to synchronize the asynchronous measurement system at the moment of measurement update and it ensures the integrity of the measurement information. The local filtering module uses a conditional Kalman filtering algorithm for filter estimation. The reliability of the local filtering results is guaranteed because the local estimator designs a method to interact information with the domain sensors. The fusion estimation module designs a DSICI fusion algorithm with higher accuracy and satisfying consistency, which fuses the filtering results provided by each sensor when the relevant information between multiple sensors is unknown. Simulation examples demonstrate the excellent performance of the proposed algorithm, with a 33% improvement in accuracy over existing algorithms and an iteration time of less than 3 ms.
Efficient image restoration with style-guided context cluster and interaction

Abstract Recently, convolutional neural networks (CNNs) and vision transformers (ViTs) have emerged as powerful tools for image restoration (IR). Nonetheless, they encountered some limitations due to their characteristics, such as CNNs sacrificing global reception and ViTs requiring large memory and graphics resources. To address these limitations and explore an alternative approach for improved IR performance, we propose two clustering-based frameworks for general IR tasks, which are style-guided context cluster U-Net (SCoC-UNet) and style-guided clustered point interaction U-Net (SCPI-UNet). The SCoC-UNet adopts a U-shaped architecture, comprising position embedding, Encoder, Decoder, and reconstruction block. Specifically, the input low-quality image is viewed as a set of unorganized points, each of which is first given location information by the continuous relative position embedding method. These points are then fed into a symmetric Encoder and Decoder which utilize style-guided context cluster (SCoC) blocks to extract potential context features and high-frequency information. Although SCoC-UNet has obtained decent performance for image restoration, its SCoC block can only capture connectivity at points within the same cluster, which may ignore long-range dependencies in different clusters. To address this issue, we further propose a SCPI-UNet based on SCoC-UNet, which leverages a style-guided clustered point interaction (SCPI) block in place of the SCoC block. The SCPI block utilizes a cross-attention mechanism to establish the connections of feature points between different clusters. Extensive experimental results demonstrate that the proposed SCoC-UNet and SCPI-UNet can handle several typical IR tasks (i.e., JPEG compression artifact reduction, image denoising, and super-resolution) and achieve superior quantitative and qualitative performance over some state-of-the-art methods.