Reviewerly/exHarmony huggingface dataset:
The mediating effect of entrepreneurship on financial support and economic growth in African emerging economies

Purpose The aim of the research is to examine in depth the relationship between financial support, entrepreneurship and economic growth in emerging African economies. Design/methodology/approach The study adopts the system-generalized methods of moments (sys-GMM) technique for data analysis and hypothesis testing on a sample of 34 African emerging economies (340 observations) from 2010 to 2019. Findings The results show that there is significant positive correlation between financial support, entrepreneurship, and economic growth. Moreover, entrepreneurship served as a partial mediator between financial support and economic growth in African emerging economies. Practical implications This research suggests that African governments should focus on entrepreneurial systems, which are essentially networks driven by the pursuit of individual opportunities and the promotion of new business creation; and introduce other forms of financial assistance, such as loans, loan guarantees, interest subsidies, technical assistance, insurance, etc. Originality/value The main novelty of the paper is that the authors empirically investigate the mediating role of entrepreneurship in the association between financial support and economic growth in 34 African emerging economies.
Partial Reasons

Abstract Partial reasons are considerations in favor of something that, taken individually, are not sufficient to establish an obligation. I consider the extent to which partial reasons are reasons, and why they cannot be reduced to or identified with pro tanto reasons. I lay out two approaches to the content of reasons, the flat theory and the structured theory. I argue that parts of reasons are not partial reasons, by showing that natural ways to represent parts of reasons in the flat theory and the structured theory lead to overgeneration problems with regard to partial reasons. I then formulate two notions of partial reasons: one based on a notion of partial support, which is in turn captured by the notions of full support and partial content, and one based on the notion of in exact verification. I show under which conditions the two notions of partial reasons (based on partial content, and based on inexact verification) coincide.
Hate speech detection: A comprehensive review of recent works

Abstract There has been surge in the usage of Internet as well as social media platforms which has led to rise in online hate speech targeted on individual or group. In the recent years, hate speech has resulted in one of the challenging problems that can unfurl at a fast pace on digital platforms leading to various issues such as prejudice, violence and even genocide. Considering the acceptance of Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques in varied application domains, it would be intriguing to consider these techniques for automated hate speech detection. In literature, there have been efforts to recognize and categorize hate speech using varied Machine Learning (ML) and Deep Learning (DL) techniques. Hence, considering the need and provocations for hate speech detection we aim to present a comprehensive review that discusses fundamental taxonomy as well as recent advances in the field of online hate speech identification. There is a significant amount of literature related to the initial phases of hate speech detection. The background section provides a detailed explanation of the previous research. The subsequent section that follows is dedicated to examining the recent literature published from the year 2020 onwards. The paper presents some of the hate speech datasets considered for hate speech detection. Furthermore, the paper discusses different data modalities, namely, textual hate speech detection, multi‐modal hate speech detection and multilingual hate speech detection. Apart from systematic review on hate speech detection, the paper also implement several multi‐label models to compare the performance of hate speech detection by employing classic ML technique namely, Logistic Regression and DL technique namely, Long Short‐Term Memory (LSTM) and a multiclass multi‐label architecture. In the implemented architecture, we have derived two new elements to quantify the hatefulness and intensity of hatred to improve the results for hate speech detection using Indonesian tweet dataset. Empirical Analysis of the model reveals that the implemented approach outperforms and is able to achieve improved results for the underlying dataset.
Weighted structure tensor total variation for image denoising

For image denoising problems, the structure tensor total variation (STV)-based models show good performances when compared with other competing regularization approaches. However, the STV regularizer does not couple the local information of the image and may not maintain the image details. Therefore, we employ the anisotropic weighted matrix introduced in the anisotropic total variation (ATV) model to improve the STV model. By applying the weighted matrix to the discrete gradient of the patch-based Jacobian operator in STV, our proposed weighted STV (WSTV) model can effectively capture local information from images and maintain their details during the denoising process. The optimization problem in the model is solved by a fast first-order gradient projection algorithm with a complexity result of O(1/i2). For images with different Gaussian noise levels, the experimental results demonstrate that the WSTV model can effectively improve the quality of restored images compared to other TV and STV-based models.
Solar Radiation Forecast Utilizing an Artificial Neural Network Model with Three Distinct Learning Algorithms

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Solar energy, which has always been at the forefront, has discovered numerous uses in a variety of fields. One of the key targets of scientists and producers in the twenty-first decade is sustainable solar energy collecting. The maximization of solar energy is totally dependent on the radiation absorbed by the photovoltaic panels. Radiation is observed using numerous equipment and calculated using diverse methods. If the device is to be totally reliant on solar energy, it must be calculated far ahead. It is difficult to work because solar radiation is affected by various factors, including region as well as seasonality. In forecast scenarios, Artificial Neural Networks (ANN) is a popular approach among scientists. Therefore, this research provides a technique for estimating solar radiation that makes use of back-propagation algorithms. The data of 17 stations in Tamil Nadu, India, were acquired for analysis and split into three clusters: training, validation, and testing. This research is focused on nine input variables and one outcome variable. Solar radiation is estimated via feed-forward back-propagation in this case. The presented approach is ascertained for training techniques such as Levenberg Marquardt (LM), Bayesian-Regularization (BR), as well as Scaled Conjugate Gradient (SCG). In all three scenarios, the resulting statistical error values and regression values prove the adequacy of the presented approach. The Root Mean Square Error (RMSE) for the BR approach is found to be the lowest, with a value of 0.0013. Also, from all statistical error and regression values, the training approach BR produces the best value than LM and SCG training approaches.&lt;/div&gt;&lt;/div&gt;
Event Data Recorder Trigger Probability in the Crash Investigation Sampling System Database

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The objective of this study was to quantify the field performance of passenger vehicle event data recorders (EDRs) in recording data into non-volatile memory at the 8 km/h delta-v (Δ&lt;i&gt;v&lt;/i&gt;) trigger threshold specified by Title 49, Part 563 of the Code of Federal Regulations (Part 563). Part 563 applies to passenger vehicles manufactured on or after September 1, 2012. The trigger threshold is distinct from the threshold required to deploy an airbag. Events meeting the trigger threshold will cause data to be preserved on the EDR even if airbags are not deployed. This is the first study to quantify EDR trigger threshold performance. This data is valuable in the evaluation of sub-airbag deployment crashes. The study was accomplished via analysis of EDR and reconstructed Δ&lt;i&gt;v&lt;/i&gt; data from 3,960 cases in the Crash Investigation Sampling System (CISS) database maintained by the National Highway Traffic Safety Administration (NHTSA). The binary presence or non-presence of an event on the EDRs of vehicles exposed to a collision was compared to the CISS reconstructed Δ&lt;i&gt;v&lt;/i&gt; for each vehicle. Logistic regression models were developed to predict the probability of an event present on the EDR at the trigger threshold. We found that vehicles manufactured by Toyota had lower Δ&lt;i&gt;v&lt;/i&gt; thresholds compared to other manufacturers. For Toyota manufactured vehicles, the probability of an EDR event at 8 km/h ranged from 87% to 99%. EDR event probabilities for non-Toyota vehicles in vehicle-to-vehicle collisions ranged from 58% to 93% for Part 563 compliant vehicles and 33% to 83% for pre-Part 563 vehicles. The persistence of EDR events in memory was analyzed using the number of ignition cycles present between events and imaging of the EDR, finding average duration of 3,595 ignition cycles for pre-existing EDR events unrelated to the CISS case.&lt;/div&gt;&lt;/div&gt;
Computational Fluid Dynamics Analysis of a Coaxial Unmanned Aerial Vehicle

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The focus of this project is on the preliminary design of an unmanned aerial vehicle (UAV) utilizing a coaxial rotor setup, taking into account its flight dynamics. Additionally, a comprehensive aerodynamic analysis is conducted using computational fluid dynamics (CFD). The idealogy for our design came from the Rukma Vimana, a flying craft mentioned in the ancient Hindu Scriptures. The design is optimized with the coaxial copter setup, a different approach that has yet to be widely explored in the UAV aspect. Coaxial rotors are installed in pairs, with one rotor mounted above the other on concentric shafts. Both rotors have the same axis of rotation, but they rotate in opposite directions (contra-rotate). CFD simulations are conducted to see how the fluid medium flows over the unconventional design of the UAV. Three different in terms of three designs heights are considered, ie, a 30 cm height model, a 35 cm height model, and a 40 cm height model. The inclination of the analysis done is parallel to gravity; therefore, we have tested for vertical flow since it is a vertical take-off and landing (VTOL) configuration. Variations in the nose cones are also carried, such as the blunt nose cone and the round nose cone. Overall, with three different heights and two variations in the nose cone, we had a total of six simulations to analyze in CFD from which 30cm performed better out of which we chose the blunt nose cone with a height of 30cm is assumed to be the best configuration as it was feasible to fabricate.&lt;/div&gt;&lt;/div&gt;
Social VR design features and experiential outcomes: narrative review and relationship map for dyadic agent conversations

Abstract The application of virtual reality to the study of conversation and social interaction is a relatively new field of study. While the affordances of VR in the domain compared to traditional methods are promising, the current state of the field is plagued by a lack of methodological standards and shared understanding of how design features of the immersive experience impact participants. In order to address this, this paper develops a relationship map between design features and experiential outcomes, along with expectations for how those features interact with each other. Based on the results of a narrative review drawing from diverse fields, this relationship map focuses on dyadic conversations with agents. The experiential outcomes chosen include presence &amp; engagement, psychological discomfort, and simulator sickness. The relevant design features contained in the framework include scenario agency, visual fidelity, agent automation, environmental context, and audio features. We conclude by discussing the findings of the review and framework, such as the multimodal nature of social VR being highlighted, and the importance of environmental context, and lastly provide recommendations for future research in social VR.
Blockchain for data protection and cyber fraud reduction: systematic literature review and technology adoption dynamics among gen Y and Z

Purpose The purpose of this study is to investigate the role of blockchain technology in reducing online fraud among Gen Y and Gen Z. Design/methodology/approach This study uses a two-phase approach. Firstly, a systematic literature review is conducted to examine various aspects of blockchain technology and cyber security. Secondly, the Technology Adoption Model is used to study the adoption dynamics of blockchain-based platforms for online data exchange among 415 Gen Y and Z users. Findings The results indicate that efficiency and security, perceived ease of use, perceived usefulness, and trust have a positive effect on behavioral intention and attitude towards use. This is the first study to examine the role of blockchain technology to reduce fraud among Gen Y and Gen Z. Originality/value This study provides new insights into the adoption of blockchain-based platforms for online data exchange among Gen Y and Gen Z. The study highlights the critical areas of concern for the adoption of blockchain technology and provides managerial implications for fintech firms, banks, and e-commerce platforms. It is unique in its focus on the potential for blockchain technology to reduce fraud among Gen Y and Gen Z, which has not been previously explored in the literature.
The lonely traveller’s mind: exploring the influence of loneliness on revisit intention

Despite the fact that feelings of loneliness offer invaluable opportunities for marketing and management, studies on its impacts and consequences in the travel context are still in their infancy. Drawing on compensatory consumption behaviour theory, and using a mixed-methods approach, this research explored the influence of loneliness (i.e. trait loneliness and state loneliness) on tourists' revisit intention. In light of the findings derived from interviews and scenario-based experiments, the research validates the positive effects of each loneliness variable on revisit intention. Moreover, the mediating effects of nostalgia on the relationships between trait loneliness and revisit intention, and between state loneliness and revisit intention, have been revealed. This study enriches the literature on tourism and consumer behaviour and offers several tailor-made suggestions for relevant practitioners.
Joint first and second order total variation decomposition for remote sensing images destriping

Stripe noise remains a significant source of errors and image quality degradation in remote sensing systems. A prominent approach for tackling this problem is the first-order Total Variation (TV) regularization, which has a proven efficiency in dealing with stripe noise. Unfortunately, denoised images, in this case, may suffer from texture loss and staircase artefacts around smooth areas. In this paper, a novel image decomposition scheme is proposed to tackle these drawbacks. This decomposition is based on the use of directional first- and second-order TV regularizers that are employed to separate the clear image from the stripe component while considering the directionality and smoothness of the latter. The proposed model is solved using a Chambolle-based algorithm and its performance is compared to traditional destriping methods using different noise structures and intensities. The results have shown comparable performance to the existing state-of-the-art methods with some improvements in structure preservation and noise cancellation.
ASSESSING THE RELIABILITY AND VALIDITY OF KNOWLEDGE, ATTITUDE, AND PRACTICE (KAP) ASSESSMENTS ON COVID-19 TRANSMISSION KNOWLEDGE AND PREVENTIVE MEASURES AMONG ECOTOURISM OPERATORS

This cross-sectional study conducted in rural Pahang state, Malaysia, aimed to validate a questionnaire examining ecotourism operators’ Knowledge, Attitude, and Practice (KAP) regarding COVID-19 transmission and preventive measures. Data collection utilised the snowball technique. The questionnaire, comprising 34 items covering knowledge, attitude, and practice constructs, underwent rigorous validation and piloting before the actual fieldwork. All factor loading scores (&gt;0.65) and Cronbach’s alpha (α≥0.69) were greater than the reference value, relaying indicators of reliability and internal consistency of the measured latent variables. The findings revealed that the KAP model met the goodness-of-fit criteria (HTMT&lt;.90, SRMR&lt;0.08, NFI&gt;0.90) and convergent validity was achieved (AVF≤0.50). The study confirms the meticulous instrument validation, ensuring the survey tool’s effectiveness in gauging KAP among ecotourism operators. This study’s novelty lies in its focus on the KAP spectrum vis-à-vis COVID-19 among operators engaged in these ecotourism domains. By bridging this gap, the research aspires to inform tailored interventions, ultimately fortifying resilience against future health crises in ecotourism communities.
Brave New World: Navigating New Paradigms in the Educational Landscape for the Evolving Learner in a Rapidly Changing Era

This literature review explores the significant transformations in the educational landscape prompted by technological advancements and changing learner profiles in the digital era. The review examines the evolution of learner profiles, highlighting the shift towards multisensory learning, digital fluency, and a preference for interactive, personalized learning experiences. It delves into integrating innovative technologies such as virtual reality (VR), augmented reality (AR), and blockchain in education and discusses their implications for teaching and learning. The review also considers the impact of neuroscience on educational practices, emphasizing the importance of understanding brain functioning and neuroplasticity in developing effective teaching strategies. The unprecedented shift to online and blended learning models, accelerated by the COVID-19 pandemic, is analyzed for its long-term effects on educational delivery and pedagogy. Additionally, the review addresses the growing emphasis on interdisciplinary learning, sustainability, mental health, and well-being within educational curricula. The implications of these transformative trends for educators, policymakers, and learners are discussed, highlighting the need for adaptive and responsive approaches to education in the face of rapid technological and societal changes. The review concludes with reflections on the future trajectory of education, anticipating continued technical integration, evolving pedagogical theories, and an increased focus on preparing learners for global challenges and lifelong learning.
Feasibility Study of Autonomous Wave-Photovoltaic (PV) Buoy to Power Subsea Equipment

Abstract Autonomous wave-photovoltaic (PV) buoy is a cost-effective and efficient solution for powering subsea equipment in offshore oil and gas operations. The buoy can be deployed in remote locations where fixed structures may not be feasible. This study assesses the feasibility of the buoy to electrify a subsea Christmas tree in the Johan Sverdrup field in the North Sea. A Python mathematical model is developed to evaluate the power output of the buoy. The finding shows that wave energy harnessed by using a wave energy converter (WEC) at the selected site is sufficient to electrify a subsea Christmas tree. Further investigation is required to identify the suitable subsea equipment to be electrified by the buoy.
Unsupervised framework for evaluating and explaining structural node embeddings of graphs

Abstract An embedding is a mapping from a set of nodes of a network into a real vector space. Embeddings can have various aims like capturing the underlying graph topology and structure, node-to-node relationship, or other relevant information about the graph, its subgraphs or nodes themselves. A practical challenge with using embeddings is that there are many available variants to choose from. Selecting a small set of most promising embeddings from the long list of possible options for a given task is challenging and often requires domain expertise. Embeddings can be categorized into two main types: classical embeddings and structural embeddings. Classical embeddings focus on learning both local and global proximity of nodes, while structural embeddings learn information specifically about the local structure of nodes’ neighbourhood. For classical node embeddings, there exists a framework which helps data scientists to identify (in an unsupervised way) a few embeddings that are worth further investigation. Unfortunately, no such framework exists for structural embeddings. In this article, we propose a framework for unsupervised ranking of structural graph embeddings. The proposed framework, apart from assigning an aggregate quality score for a structural embedding, additionally gives a data scientist insights into properties of this embedding. It produces information which predefined node features the embedding learns, how well it learns them, and which dimensions in the embedded space represent the predefined node features. Using this information, the user gets a level of explainability to an otherwise complex black-box embedding algorithm.
Does corruption sand the wheels of sustainable development? Evidence through green innovation

Abstract This study investigates the impact of corruption on green innovation, as corruption may impede or foster green innovation in developing economies due to their weak governance systems. We develop a dataset of Chinese non‐financial firms listed between 2007 and 2020 and apply static and dynamic regression techniques. The results indicate a highly significant negative association between corruption and green innovation. This supports the notion that corruption culture reduces corporate legitimacy concerns ( institutional theory ), increases managerial rent‐seeking ( agency theory ), and hinders green innovation, thus impeding sustainable development and supporting the “ sand the wheels ” hypothesis. Our analysis also reveals that corruption's negative impact on green innovation is particularly significant for firms with lower media and analyst coverage, non‐state‐owned firms, and firms in heavy‐polluting industries. These results are robust to alternate proxies of green innovation and corruption as well as econometric specifications that account for endogeneity issues and industry, region, and time‐fixed effects.
A quantum secure multi-party summation protocol based on one-direction quantum walks on a circle

<p indent="0mm">This paper presents a novel quantum secure multi-party summation protocol based on one-direction quantum walks on a circle (ODQWC), which allows the summation computation of different integer sequences from "<italic>n</italic>" secret holders. Crucially, it ensures that this process does not infringe on the privacy of these secret holders by delivering quantum walk states in a circular pattern. Our proposed protocol employs two-particle product states rather than quantum entangled states as the initial quantum resource and eliminates the need for different secret holders to perform quantum entanglement swapping operations. Instead, it emphasizes imposing one-direction evolution operators on quantum walk states. Additionally, our protocol requires single-particle measurements. Security analysis validates that our proposed protocol can effectively counter external attacks and those initiated by participants. Under specific conditions, our proposed protocol can be adapted into a quantum secure multi-party bit summation protocol based on ODQWC.