Reviewerly/exHarmony huggingface dataset:
A record of Late Cretaceous to early Paleogene Insular terrane accretion within the northern Cordillera: Insights from monazite petrochronology across the Kluane Schist, southwest Yukon, Canada

The Kluane Schist is a metamorphosed package of siliciclastic and lesser calcareous rocks that lies between the inboard pericratonic Intermontane terranes and outboard Insular terranes of the North American Cordillera within Yukon, Canada. The metamorphic sequence of the Kluane Schist preserves a record of the tectono-thermal evolution and timing of Insular terrane accretion. Here we document the timing of metamorphism and deformation across the Kluane Schist using in situ laser ablation−inductively coupled plasma−mass spectrometry U-Th-Pb monazite petrochronology. Monazite-bearing samples collected across an inverted metamorphic sequence preserved in the northern regions of the Kluane Schist yield dates ranging from ca. 70 Ma to 55 Ma. Complementary phase equilibria modeling and thin section analysis indicate monazite grew between ∼450 °C and 3.0−3.5 kbar to ∼700−715 °C and 4.0−4.5 kbar, coeval with the development of the Kluane Schist’s inverted metamorphic sequence. Dating the four chemical zones preserved by monazite demonstrates its protracted growth during three distinct periods of garnet crystallization and breakdown, as well as coeval with melt generation. Our data illustrate that peak metamorphic conditions were reached at progressively younger ages with decreasing structural level within the Kluane Schist. Our results are consistent with Buchan-style metamorphism associated with the terminal accretion of the outboard Insular terranes and southwest-directed overriding of the inboard Yukon-Tanana terrane from ca. 70 Ma to 55 Ma. These findings are further congruous with a Late Cretaceous timing for the terminal accretion of the Insular terranes within southwest Yukon, facilitated by east-dipping subduction beneath a westward migrating North American continent.
Knowledge Collaboration Mechanism and Validity Assessment Based on Design Awards: A Case Study of Design Intelligence Award

The essence of design award lies in the curation of outstanding design cases. Through the organizational mechanisms established by award organizers, it can serve as an exceptional platform for knowledge exchange, dissemination, and innovation. This paper introduces the concept of Design Award Knowledge Network (DAKN), taking Design Intelligence Award (DIA) for example, elucidates the collaborative mechanisms of knowledge dissemination and innovation within the DAKN, notably facilitated through the D·will Lecture and DI Project Workshops. Additionally, surveys were conducted among faculty and students participating in the series of events initiated by DIA. The findings underscore the prominent advantage of a ‘stimulative policy environment’ within this network, while revealing the need for improvements in the ‘accessibility of knowledge resources.’ This paper proposes that enhancing the vitality of DAKN rely on the diversification of knowledge, interdisciplinary approaches, and organizational collaboration. This entails the aggregation of talent from various universities, institutions, and enterprises to establish collaborative innovation teams. Through collaborative governance, the integration of diverse foundational capabilities, data information, and tool skills from various stakeholders should be orchestrated to achieve effectiveness in knowledge exchange.
Requirement Analysis of Smart Kitchen Dietary and Social Service System Based on Older Adults Mental Models

Using kitchen life as an entry point and the theory of mental modelling, we carried out a study on the needs analysis of a smart kitchen diet and social service system, aiming to meet the new needs of the elderly in terms of health and social interaction. Through questionnaires and user interviews, we collected and analyzed the mental data of the elderly and created a mental model of “diet and social interaction” as well as user requirements. The Analytic Hierarchy Process was used to identify the importance of dietary and social needs, covering the four demand directions of diet and nutrition, health management, social interaction, and health awareness. This study highlights the potential of user-centred research methodology in the health and social care of older people and provides useful guidance and insights for the design of future smart kitchens and related products.
“Ask Aloud Protocol”: Toward a Participant-Driven Question-Asking Design Research Method

In the field of problem formulation for design research, the conventional Think Aloud Protocol (TAP) has faced challenges in addressing complex problems such as Wicked Problems (WPs) over its two-decade history. Instead, the utilization of questions as a verbal tool has emerged as a more effective approach, producing insightful outcomes for intricate design challenges. Moreover, the largely untapped potential of participant-generated questions in design research holds promise for reducing subjective biases in designer-driven problem formulations and strengthening the role of participants. This study presents the Participant-Driven Ask Aloud Protocol (AAP) and employs it in spatial planning experiments at both QILI South Urban Village and the China World Mall’s Transit-Oriented Development (TOD) in Beijing, China. The analysis highlights AAP’s effectiveness in broadening the scope of generative design possibilities and facilitating a more precise and efficient identification of WPs in the design process.
Security Challenges and Economic Development in Emerging Economics: The Nigeria Experience

The study focused on security challenges and economic development in emerging economic in Nigeria and solutions. The continuous rise in insecurity and deterioration in the economic development in Nigeria call for a concern among researchers and policy makers over the years. Nigeria has witnessed an unprecedented security challenge occasioned by the activities of militants in the South-South region, kidnappers in the south east, violent armed robbery in almost parts of the country, political assassination, ritual killings and more recently activities of Boko Haram in some parts of the northern region especially north east. These social menaces, when put together impinge on the security of lives and property of both Nigerian citizens and foreigners living or even trying to invest in the country. To ensure economic development in Nigeria therefore, the study recommends various measures of curbing security challenges including preventive community policing, human capital development, equitable distribution of resources as well as channeling of resources to frontline sectors of the economy among others. This paper recommend that Federal government should engage peace and conflict resolution experts in dealing with issues concerning Fulani herdsmen and Farmers crises and that ranching method of cattle rearing location across the country while the herdsmen should be given needed training for effective and efficient management of the ranching. Also, effective leadership and good governance as a panacea to solving problems of insecurity, unemployment, poverty, hunger, disease, among other negative indices.
Fast grid search: A grid search-inspired algorithm for optimizing hyperparameters of support vector regression

This study presents a fast hyperparameter optimization algorithm based on the benefits and shortcomings of the standard grid search (GS) algorithm for support vector regression (SVR). This presented GS-inspired algorithm, called fast grid search (FGS), was tested on benchmark datasets, and the impact of FGS on prediction accuracy was primarily compared with the GS algorithm on which it is based. To validate the efficacy of the proposed algorithm and conduct a comprehensive comparison, two additional hyperparameter optimization techniques, namely particle swarm optimization and Bayesian optimization, were also employed in the development of models on the given datasets. The evaluation of the models' predictive performance was conducted by assessing root mean square error, mean absolute error, and mean absolute percentage error. In addition to these metrics, the number of evaluated submodels and the time required for optimization were used as determinative performance measures of the presented models. Experimental results proved that the FGS-optimized SVR models yield precise performance, supporting the reliability, validity, and applicability of the FGS algorithm. As a result, the FGS algorithm can be offered as a faster alternative in optimizing the hyperparameters of SVR in terms of execution time.
Complexity of Shakespeare’s Social Networks

A recommendation of: Christian M. Thurn; Simone Sebben; Zoran Kovacevic Using Shakespeare to Answer Psychological Questions: Complexity and Mental Representability of Character Networks https://osf.io/3zsvn
Bitcoin_ML: An Efficient Framework for Bitcoin Price Prediction Using Machine Learning

Econometrics can be used to understand and forecast price movements, assess market efficiency, and explore the factors influencing Bitcoin's value and adaptation. Econometrics is related to bitcoin in seven categories: price analysis and prediction, market efficiency, determination of Bitcoin prices, risk analysis, adaptation and network effects, causality tests, and simulation and stress. Testing these analyses can be invaluable for policymakers, investors, and financial institutions interested in the economics of digital currencies. Bitcoin price prediction in machine learning has many challenges that have deep roots in 2 main properties: cryptocurrencies and complexities in the Machine Learning models. Many problems are associated with machine learning for bitcoin price prediction, such as overfitting, data quality and availability, latent variables, model interpretability, computational complexity, dynamic adaptation, market manipulation, anomalies, data snooping bias risk, and time horizon mismatch. In the paper, we proposed an efficient framework for the prediction of bitcoin using nine different machine learning algorithms (linear Regression, random forest, adaboost, tree, KNN, gradient boosting, constant, neural network, SVM) on five different datasets. The results revealed that linear Regression emerged as the optimal model for the first data set. In the second data set, the random forest model demonstrated superior performance. The third data set exhibited the highest efficacy when the Adaboost model was employed. The fourth data set yielded the best outcomes with the random forest model, while linear Regression was the most effective choice for the final data set.
Octonion-based transform moments for innovative stereo image classification with deep learning

Abstract Recent advances in imaging technologies have led to a significant increase in the adoption of stereoscopic images. However, despite this proliferation, in-depth research into the complex analysis of the visual content of these stereoscopic images is still relatively rare. The advent of stereoscopic imaging has brought a new dimension to visual content. These images offer a higher level of visual detail, making them increasingly common in a variety of fields, including medicine and industrial applications. However, exploiting the full potential of stereoscopic images requires a deeper understanding. By exploiting the capabilities of octonion moments and the power of artificial intelligence, we aim to break new ground by introducing a novel method for classifying stereoscopic images. The proposed method is divided into two key stages: The first stage involves data preprocessing, during which we strive to construct a balanced database divided into three distinct categories. In addition, we extract the stable Octonion Krawtchouk moments (SOKM) for each image, leading to a database of moment images with dimensions of 128 × 128 × 1. In the second step, we train a convolutional neural network (CNN) model using this database, with the aim of discriminating between different categories. Standard measures such as precision, accuracy, recall, F1 score, and ROC curves are used to assess the effectiveness of our method. These measures provide a quantitative assessment of the performance of our object classification approach for stereoscopic images.
Implementation Importance-Performance Analysis Method to Increase Customer Satisfaction of Honda Motorcycle Dealer using Text Mining

Transportation is an essential aspect of people’s lives that plays an essential role in supporting economic, social, and cultural activities. Private transportation has become a basic need for Indonesian people, increasing yearly, especially for motorcycles. One of the biggest motorcycle brands in Indonesia is Honda, and West Java is one of the most significant contributors to motorcycle usage. However, their market share is decreasing through the years. It is essential to review customer satisfaction in purchasing Honda products both in terms of product and service through their customer journey in purchasing Honda products. Therefore, this research will discover the aspects customers need from online reviews, especially Google reviews. This research conducted case studies on Dealer Daya Adicipta Motora Bandung, Dealer Bintang Niaga Jaya Bogor, and Dealer Murni Motor II Bogor. Using Importance Performance Analysis (IPA) will recommend aspects that must be prioritized to improve. The first step is to identify the customer’s needs from Google reviews using non-negative matrix factorization (NMF) that produces matrix H and matrix W as the keywords for every aspect. Then, this research also calculates the sentiment for each review using a dependency tree and lexicon SenticNet5. With the output from NMF and sentiment, we will calculate the performance and importance levels. Ultimately, the Performance Level and Importance Level will be plotted into the graph and divided into four quadrants, with Quandrant A as the top priority for improvement. Eight aspects identified in Dealer Daya Adicipta Motora Bandung, the waiting room facility become the priority of improvement. Dealer Bintang Niaga Jaya Bogor has 7 aspects identified, and the services and buying experience become the top priority of improvement; meanwhile, there are 4 aspects identified in Dealer Murni Motor II Bogor, with the waiting time and waiting room facility become the top priority for improvement.
Speech Recognition for Smart AI Devices

This article looks at the progress, challenges and future directions in the field of speech recognition for intelligent AI devices. Speech recognition technology has made significant progress over the years, enabling seamless interactions between humans and artificial intelligence. This article provides an in-depth analysis of modern speech recognition techniques used in smart AI devices and the potential applications and limitations of the technology. It also discusses the current challenges faced by developers and researchers and suggests possible solutions to overcome them. Keywords-Natural language processing Neat vs. scruff, Soft vs. hard computing, Narrow vs general AI, Rule-based approaches, Statistical and machine learning based methods, Deep Neural Networks, Deep learning models for speech recognition, Hybrid and ensemble approaches, Voice biometrics and authentication, Speech translation and language learning, Natural language processing improvement.
Associated Factors with Premenstrual Dysphoric Disorder among Undergraduate Medical Students of a Teaching Hospital

Introduction: Premenstrual Dysphoric Disorder (PMDD) is a severe form of premenstrual syndrome in women which adversely affects social, academic, or work performance. The mechanisms underlying PMDD are still insufficiently known, the findings indicate a multifactorial genesis. Prevalence data of PMDD and its potential associated risk factors is sparse in Nepal. This study aimed to determine the associated factors with PMDD among undergraduate medical students of a teaching hospital in Nepal. Methods: This was a cross sectional study conducted among unmarried female medical students at KIST medical college and teaching hospital from 1st June to 30th July, 2023. Students were assigned from each of the four MBBS years (First, Second, Third, and Final) through random sampling till the desired sample size was achieved. Questionnaires about socio-demographic, menstrual, lifestyle variables and questionnaire for diagnosis of PMDD were administered. PMDD was diagnosed from the Diagnostic and Statistical Manual – fifth edition (DSM-5) of the American Psychiatric Association (APA). Data Analysis was performed using SPSS version 16. Data calculated as frequencies, mean, standard deviation and chi square test, independent t test with p-value of &lt; 0.05 was considered as statistically significant. Results: The prevalence of PMDD according to DSM-V criteria was 41 (36.28%). PMDD was significantly associated with dysmenorrhea (P=0.030) and with intake of caffeinated beverages (P=0.034). Conclusion: PMDD is prevalent among medical students in Nepal. The potential associated risk factors are dysmenorrhea and intake of caffeinated beverages.
Economic Security and Gender Budgeting in Nigeria

Gender budgeting has emerged as a crucial tool in addressing gender disparities and invariably promoting economic security. This article examines the intersection of economic security and gender budgeting in the Nigerian context. The study highlights the significant impact of gender- responsive budgeting on narrowing gender gaps in economic security. The article explores the concept of economic security within the framework of gender equality and sustainable development, emphasizing the importance of addressing gender-based discrimination as barriers to women's economic empowerment. Furthermore, the research examines the potential benefits of gender budgeting in achieving broader national development goals, including poverty reduction, increased productivity, and social cohesion. It underscores the need for a holistic approach that integrates gender considerations into all aspects of budget formulation, implementation, and monitoring. Ultimately, the work underscores that gender budgeting is not merely a fiscal exercise but a transformative strategy for building a more equitable and prosperous society in Nigeria.
Deteksi Perubahan Berat Badan Akseptor KB Hormonal dengan Menggunakan Model Regresi Linear Berganda

Hormonal contraception, which uses estrogen and progesterone preparations, is a contemporary method of contraception that is popular among active birth control participants. Apart from stopping population growth, it also has negative effects on its users. The side effect of increasing body weight can occur due to the nature of the acceptor and the use of hormonal contraceptives for a long period of time. The aim of the study was to detect changes in body weight of hormonal birth control acceptors based on age, parity and length of use of contraception using a multiple linear regression model. Non-experimental quantitative analytical research method with a cross sectional design. The research was conducted at several PMBs in Palembang City in July-September 2023 with samples consisting of hormonal birth control acceptors taken using a purposive sampling technique. Data were collected using questionnaires and body weight measurements and analyzed by multiple linear regression. The results of the research show that the prediction model for weight change = 5.789 - 0.098 Age + 1.4 Parity - 0.008 Length of birth control. Weight gain will definitely affect the health of the recipient and can increase the risk of obesity if it continues. To maintain a stable weight, birth control acceptors must pay attention to their daily diet and exercise regularly. It is very important to obtain information about the effects caused by the use of hormonal birth control with a high number of parities and ages at risk.
How to implement and evaluate with scrum methodology in business subjects - an experience in an educational innovation Project

The following paper arises from the Educational Innovation Project PIE 15-132 "Implementation and evaluation of the scrum methodology (project based learning) in the subjects of Business Creation and Management", funded by the Vice-Rectorate for Teaching and Research Staff of the University of Malaga. The idea stems from the analysis and study of the agile scrum methodology, focused on iterations and continuous reviews. The proposal consists of transferring the application of this methodology, widely used in software development environments, to subjects linked to the business environment, given the flexibility that characterises it. Its application made it possible to obtain interesting results, both related to the objectives of the subject "Business Creation", as well as others of a transversal nature, which are in great demand in today's environments.
An Assessment of Capital Structure Decisions among Listed Deposit Money Banks in Nigeria

This study aimed to assess Capital Structure Decision among listed Deposit Money Banks (DMBs) in Nigeria by assessing the effects of total equity and debts on capital structures of DBMs in Nigeria. The study alsocorrelated asset tangibility to firm size and examined the effect on the capital structure of the Banks. The study used secondary data collected from the annual accounts and reports of the selected ten (10) banks purposively selected for twelve (12) years covering 2011- 2022. The study’s population is made up of twenty-four existing listed deposit money banks on the list on the Nigerian Exchange Group plc based on the availability of data. The study employed explanatory variables of total equity, total debt, asset tangibility, and firm size to capture financial statement components. Data was analyzed using descriptive analysis and panel regression models. Findings revealed that equity, debts, and assets tangibility have positive and significant effects on the capital structure decision of the selected banks, while the firm’ size does not. The study concluded that equity capital, debt capital, and long-term assets are relevant in capital structure decision of listed deposit money banks in Nigeria. It is recommended that management should carefully assess the appropriate mix of equity and debt that maximizes income potentials and continuity of the firm in the context of substantial tangibility of the assets.
The Impact of Financial Integration on Economic Growth in Nigeria

The study investigated the effect financial market integration on economic growth in Nigeria over the period of thirty-two years ranging from 1990 to 2021. The study designed and specified a multiple regression model to examine the individual and joint effects of the proxies of financial market integration (trade openness, credit to private sector and government expenditure) on economic growth in Nigeria (measured in Gross Domestic Product). The model was estimated by Ordinary Least Square technique using E-views 12 statistical package. The annual time series data used were collected from Central Bank of Nigeria (CBN) statistical bulletin and analyzed with the aim of achieving the stated objectives. The study found that trade openness, credit to private sector and government expenditures have individual and joint significant effect on economic growth in Nigeria. Based on the findings, the study therefore concluded that the financial market integration plays a significant positive role in the sustenance of growth of the Nigerian economy