Search

In this paper, we present the unit-power half-normal distribution, derived from the power half-normal distribution, for data analysis in the open unit interval. The statistical properties of the unit-power half-normal model are described in detail. Simulation studies are carried out to evaluate the performance of the parameter estimators. Additionally, we implement the quantile regression for this model, which is applied to two real healthcare data sets. Our findings suggest that the unit power half-normal distribution provides a robust and flexible alternative for existing models for proportion data. [ABSTRACT FROM AUTHOR]

In contrast to the field's predominant focus on teacher-led talk in reading contexts, this study considers child-led relaxed conversations about texts. It examines what is afforded by the occurrence of such relaxed book talk and how this is enabled with young children in school. Data are drawn from a co-participative study with four teachers in England that captured naturalistic video observations. The paper offers novel insights about the social and personal affordances of informal book talk, the role it can play in developing positive dispositions towards reading and the salience of familiar texts and the classroom reading culture. [ABSTRACT FROM AUTHOR]

In many studies on disease progression, biomarkers are restricted by detection limits, hence informatively missing. Current approaches ignore the problem by just filling in the value of the detection limit for the missing observations for the estimation of the mean and covariance function, which yield inaccurate estimation. Inspired by our recent work [Liu and Houwing-Duistermaat (2022), 'Fast Estimators for the Mean Function for Functional Data with Detection Limits', Stat, e467.] in which novel estimators for mean function for data subject to detection limit are proposed, in this paper, we will propose a novel estimator for the covariance function for sparse and dense data subject to a detection limit. We will derive the asymptotic properties of the estimator. We will compare our method to the standard method, which ignores the detection limit, via simulations. We will illustrate the new approach by analysing biomarker data subject to a detection limit. In contrast to the standard method, our method appeared to provide more accurate estimates of the covariance. Moreover its computation time is small. [ABSTRACT FROM AUTHOR]

Artificial Intelligence technologies have elicited a range of policy responses in India, particularly as the Government of India attempts to position and project the country as a global leader in the production of AI technologies. Policy responses have ranged from providing public infrastructure to enable market-led AI production, to nationalising datasets in an effort to enable Big Data analysis through AI. This paper examines the recent history of AI policy in India from a critical political economy perspective, and argues that AI policy and governance in India constructs and legitimises a globally-dominant paradigm of informational capitalism, based on the construction of data as a productive resource for an information-based economic production, and encouraging self-regulation of harmful impacts by firms, even as it attempts to secure a strong hand for the state to determine, both through law and infrastructure, how such a market is structured and to what ends. [ABSTRACT FROM AUTHOR]

Deep learning-based approaches for understanding and analyzing 3D flow field grids have been extensively studied in recent years due to their importance in exploring the physical mechanisms of flow fields. However, these methods have shown significant success, which fail to fully utilize the available information and robustly accomplish the 3D flow field grid data analysis. Specifically, the limitation of grid information hampers flow field-based methods from analyzing global velocity distance. Conversely, the absence of velocity information poses challenges for grid-based methods to understand local grid structure. To address the aforementioned issues and cater to most downstream tasks, this paper introduces a flow field-aware network (FFANet). The main innovations of FFANet include: (i) constructing a multi-scale feature learning module using the self-attention mechanism to independently learn features of different scales for velocity distribution and grid structure information. This module aims to generate a global feature with enhanced discriminative representation to improve overall performance; (ii) building a co-attention module to adaptively learn the co-matrix between the aforementioned two features, enhancing effective information utilization of the global feature; (iii) proposing a wall identification method based on the classification module of FFANet, which facilitates generating surface cloud maps and streamlining. Experimental results demonstrate the superior performance of FFANet compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Quantile regression has been widely used in many fields because of its robustness and comprehensiveness. However, it remains challenging to perform the quantile regression (QR) of streaming data by a conventional methods, as they are all based on the assumption that the memory can fit all the data. To address this issue, this paper proposes a Bayesian QR approach for streaming data, in which the posterior distribution was updated by utilizing the aggregated statistics of current and historical data. In addition, theoretical results are presented to confirm that the streaming posterior distribution is theoretically equivalent to the orcale posterior distribution calculated using the entire dataset together. Moreover, we provide an algorithmic procedure for the proposed method. The algorithm shows that our proposed method only needs to store the parameters of historical posterior distribution of streaming data. Thus, it is computationally simple and not storage-intensive. Both simulations and real data analysis are conducted to illustrate the good performance of the proposed method [ABSTRACT FROM AUTHOR]

A log data analysis plays an important role in the uranium mining process. Automating this analysis using machine learning methods improves the results and reduces the influence of the human factor. In particular, the identification of reservoir oxidation zones (ROZs) using machine learning allows a more accurate determination of ore reserves, and correct lithological classification allows the optimization of the mining process. However, training and tuning machine learning models requires labeled datasets, which are hardly available for uranium deposits. In addition, in problems of interpreting logging data using machine learning, data preprocessing is of great importance, in other words, a transformation of the original dataset that allows improving the classification or prediction result. This paper describes a uranium well log (UWL) dataset generated with the employment of floating data windows and designed to solve the problems of identifying ROZ and lithological classification (LC) on sandstone-type uranium deposits. Comparative results of the ways of solving these problems using classical machine learning methods and ensembles of machine learning algorithms are presented. It has been shown that an increase in the size of the floating data window can improve the quality of ROZ classification by 7–9% and LC by 6–12%. As a result, the best-quality indicators for solving these problems were obtained, f1_score_macro = 0.744 (ROZ) and accuracy = 0.694 (LC), using the light gradient boosting machine and extreme gradient boosting, respectively. [ABSTRACT FROM AUTHOR]

This study examined how 44 preservice teachers reflected on their teacher self-identities through exploring multicultural children's books, which are published in different time periods, with a focus of analyzing characterization of main characters. The researcher collected data, including a multicultural book project, pre- and post-surveys on multicultural children's literature and teacher identities, and a reflection paper. Data analysis consisted of looking for and coding emergent themes. The findings of the study showed that preservice teachers analyzed multicultural books from authentic perspectives, focused on how the authors used appropriate language, and examined trends in cultural details and illustrations published in different time periods. They analyzed characteristics of main characters by parsing the facts, by examining the context of the story, and by examining the personalities of main characters. They also refined their teacher identities and strengthened their commitment to become culturally responsive teachers by reflecting on the importance of equity, diversity, and inclusion and embedding high-quality multicultural children's books into the curriculum. Implications and recommendations are also shared. [ABSTRACT FROM AUTHOR]

Hydrothermal liquefaction is a new, sustainable pathway to generate biogenic liquids from organic resources. The technology is compatible with a wide variety of resources such as lignocellulosic resources, organic waste, algae, and sewage sludge. The chemistry is complex and predictions of yields are notoriously difficult. Understanding and modeling of hydrothermal liquefaction is currently mostly based on a simplified biochemical analysis and product yield data. This paper presents a large dataset of 2439 experiments in batch reactors that were extracted from 171 publications in the scientific literature. The data include biochemical composition data such as fiber content and composition, proteins, lipids, carbohydrates, and ash. The experimental conditions are recorded for each experiment as well as the reported yields. The objective of this paper is to make a large database available to the scientific community. This database is analyzed with machine‐learning tools. The results show that there is no consensus on the analysis techniques, experimental procedures, and reported data. There are many inconsistencies across the literature that should be improved by the scientific community. Machine‐learning tools with a large dataset allow the generation of reliable yield production tools with a large application field. Given the accuracy of the data, the overall precision of prediction in an extrapolation to new results can be expected to be around 10%. [ABSTRACT FROM AUTHOR]

Photonic neural networks (PNNs) are gaining significant interest in the research community due to their potential for high parallelization, low latency, and energy efficiency. PNNs compute using light, which leads to several differences in implementation when compared to electronics, such as the need to represent input features in the photonic domain before feeding them into the network. In this encoding process, it is common to combine multiple features into a single input to reduce the number of inputs and associated devices, leading to smaller and more energy-efficient PNNs. Although this alters the network's handling of input data, its impact on PNNs remains understudied. This paper addresses this open question, investigating the effect of commonly used encoding strategies that combine features on the performance and learning capabilities of PNNs. Here, using the concept of feature importance, we develop a mathematical methodology for analyzing feature combination. Through this methodology, we demonstrate that encoding multiple features together in a single input determines their relative importance, thus limiting the network's ability to learn from the data. However, given some prior knowledge of the data, this can also be leveraged for higher accuracy. By selecting an optimal encoding method, we achieve up to a 12.3% improvement in the accuracy of PNNs trained on the Iris dataset compared to other encoding techniques, surpassing the performance of networks where features are not combined. These findings highlight the importance of carefully choosing the encoding to the accuracy and decision-making strategies of PNNs, particularly in size or power constrained applications. [ABSTRACT FROM AUTHOR]

Background/Objectives: Women of childbearing age not only reside in agricultural communities but also form an integral part of the agricultural labor force. Limited research investigates the impact of prenatal fungicide exposure on infant health, specifically ethylenebisdithiocarbamates and their toxic by-product, ethylenethiourea (ETU), particularly in occupational settings. This paper describes the background, aims, protocol, and baseline sample characteristics for the SEMILLA study, which investigates prenatal ETU exposure, neonatal thyroid function, infant growth, and neurobehavioral development in an agricultural region of Ecuador. Methods: This cohort study follows pregnant women and their infants up to 18 months of age, incorporating urinary biomarkers and survey data on ETU exposure and infant growth and neurodevelopmental measures. Data collection includes detailed questionnaires, scales, and physical examinations on maternal and infant health and development, as well as environmental factors. Descriptive statistics on key characteristics of the study population at baseline are presented. Results: SEMILLA enrolled 409 participants (72% enrollment rate): 111 agricultural workers (mostly floricultural), 149 non-agricultural workers, and 149 non-workers. Baseline characteristics show comparability between work sector groups, with some economic differences. Conclusions: SEMILLA will provide key evidence on prenatal fungicide exposure and infant development and encompass comprehensive multistage data collection procedures in pregnancy and infancy, focusing on structural and social determinants of health as well as individual-level chemical exposures. The community-based approach has proven essential, even amid challenges like the COVID-19 pandemic. The medium-term objective is to inform sustainable interventions promoting maternal and child health, with a long-term goal to reduce community exposures and improve worker health policies, particularly for women and pregnant workers. [ABSTRACT FROM AUTHOR]

Objectives: This paper explores parent coaching experiences supporting parents of young children newly diagnosed with type 1 diabetes in a clinical trial. Methods: In a trial for 157 parents, those in the intervention arm (n = 116) were paired with a parent coach (n = 37; Mage = 37.9 years, SD = 3.9; 94.6% mothers, 81.1% White non-Hispanic). Parent coaches provided diabetes-specific social support. Parent coaches completed monthly surveys and satisfaction/feasibility surveys, with a subset (n = 7) undergoing qualitative interviews at the end of this study. Results: There were 2262 contacts between participants and their parent coaches, averaging 14.4 (SD = 9.3) per participant. Parent coaches reported that the most commonly used methods were text messages (67.9%) and emails (18.7%), with 33.6% having in-person visits. Coaches reported high satisfaction and belief in their usefulness to participants during the first 9 months after T1D diagnosis. Themes discussed by parent coaches about their experience in mentoring included relationship building, expertise sharing, personal growth, gratification, and intervention optimization suggestions. Conclusions: Parent coaching post T1D diagnosis involves regular, multi-method contacts. It is highly acceptable and valuable for parent coaches to mentor other parents of young children newly diagnosed with T1D. [ABSTRACT FROM AUTHOR]

With the widespread popularity of electric vehicles in the domestic market, large‐scale electric vehicle user data has been collected and stored. Highly accurate user‐level charging load prediction has a wide range of application scenarios and great business value. However, most existing EV load prediction methods are modelled from the charging station perspective, ignoring the user's travel habits and charging demand. Therefore, this paper proposes a temporal spatial neural network based on graph attention and Autoformer to predict electric vehicle charging load. Firstly, the urban map of Wuhan is rasterized. Then, driving and charging data from the user level are aggregated into the raster module according to the time sequence, and a spatio‐temporal graph data structure of user travel trajectory is constructed. Finally, the temporal spatial neural network is used to construct the EV charging load prediction model from the user's perspective. The experimental results show that, compared with other baseline prediction methods, the proposed method effectively improves the accuracy of the EV charging load prediction model by fully exploiting the distribution of EV user clusters in time and geographic space. [ABSTRACT FROM AUTHOR]

Why you should read this article: • UK care homes are an important example of a complex environment where research is essential but faces multiple challenges in terms of rigour and methodology • This paper offers a variety of important methodological strategies to identify and address challenges that could impact findings and the ability to carry out research • It highlights the importance of taking a critical stance with all elements of the research process and illustrates the need to identify and mitigate challenges when conducting research in complex environments. Background: The evidence base for hydration practice in care homes is underdeveloped. High-quality research is therefore needed to determine what practices support older people with dementia in drinking sufficient fluid. However, methodological developments are needed to be able to do this. Aim: To highlight the methodological issues researchers encountered during a feasibility cluster, randomised controlled trial of ThinkDrink, a hydration care guide for people with dementia living in UK care homes. Discussion: This is a challenging area because of the complexity of recruitment, participation and data collection in care homes. Researchers must pay extra attention to rigour and quality in the design of their studies. There may be multiple challenges, so various strategies may be required. Conclusion: It is important that researchers continue to reflect on rigorous approaches to develop evidence in a crucial area of care, despite these challenges. Implications for practice: Researchers working in complex environments face a variety of challenges to complete methodologically rigorous research. It is important for researchers to be critical of research processes and data, to mitigate and overcome these challenges. [ABSTRACT FROM AUTHOR]

For many years, spectral remote sensing has been essential for research on the Earth's surface. The data from a single satellite sensor is sometimes insufficient to fulfil the expanding needs of remote sensing applications. Spatial-temporal fusion techniques have become an effective way for merging spectral data from many sources and times, enabling improved data analysis and interpretation. The goal of this review paper is to offer a thorough examination of the historical growth of spatio-temporal fusion techniques for spectral remote sensing. The classification of all currently used fusion approaches, such as Unmixing, Weight-based, Bayesian-based, machine learning-based, and hybrid methods, is covered in detail. Additionally, it evaluates pixel-level, decision-level, and feature-level-based data fusion techniques and compares and contrasts their advantages and disadvantages. The report also discusses spatiotemporal fusion's difficulties and recommends future advances. For those working in remote sensing research and practice, it offers an invaluable resource. In conclusion, this review paper provides a comprehensive overview of spatio-temporal fusion systems for spectral remote sensing, including an analysis of their comparative benefits and drawbacks and a description of their historical development. It aims to stimulate further research and development of spatio-temporal fusion methods for spectral remote sensing. In summary, this review paper presents a comprehensive overview of spatio-temporal fusion methods for spectral remote sensing, including their historical development, categorization of existing techniques and applications, and a comparative analysis of their strengths and limitations. It also discusses the current challenges and future research directions, providing a valuable resource for the remote sensing community. [ABSTRACT FROM AUTHOR]

The current investigation took place in five districts of Haryana, India, namely, Ambala, Kaithal, Karnal, Kurukshetra, and Yamunanagar. The primary objective was to understand the challenges confronted by farmers in adopting water conservation management practices. The findings presented in this paper stem from a field survey conducted during the 2020-21 period involving 150 respondents. These participants were interviewed using a well-structured and pretested interview schedule. The analysis of the collected data was performed with the Statistical Package for the Social Sciences (SPSS) tool. The results indicate that a majority of respondents identified financial constraints (WMS 2.45) and infrastructure constraints (WMS 2.39) as highly serious, while constraints related to manpower (WMS 2.15) and awareness were considered comparatively less serious (WMS 2.03). The study's conclusion highlights that most respondents perceive these constraints as significant, underscoring the urgent need to motivate and encourage farmers to embrace effective management practices. Additionally, the study emphasizes the necessity for a water policy to ensure its proper utilization. [ABSTRACT FROM AUTHOR]

This paper introduces a bounded probability distribution which is derived from the Muth distribution. The main statistical properties are studied and analytical expressions are provided for the moments, incomplete moments, inverse of the cumulative distribution function, extropy, Lorentz and Bonferroni curves, among others. Moreover, it possesses both monotone and non-monotone hazard rate functions so the new distribution is rich enough to model real data. Different estimation methods are applied to estimate the parameters of the model and a Monte Carlo simulation study assesses their performances. The usefulness in practical applications is illustrated using two real data sets and the results show that the proposed distribution provides better fits than other competing distributions commonly used to model data with bounded support. [ABSTRACT FROM AUTHOR]

Public participation in local governance is crucial for effective climate action and for ensuring that policies are designed in a way that respects the rights of communities. Policy developments and choices are shaped by the groups that participate, by the ideas that they hold, and by the institutions that enable and constrain their participation. This paper seeks to understand local climate change governance in Ireland by identifying the environmental interests and the ideas of the groups that participate, and by examining how they engage with institutionalised local policymaking processes and with the organisations that represent the officially recognised views of the country's national environmental movement. An analysis of survey data collected from the groups that are members of one of Ireland's Public Participation Networks shows that a majority of groups are small, rural, voluntary, interested in a wide variety of environmental issues and have a pro-ecological worldview. Most groups follow a pro-institutional advocacy strategy at the local level, while only a minority interact with the national environmental movement, mostly limiting their engagement to the acquisition of information. This paper contributes to the literature that examines how interests, ideas, and institutions shape public participation in local climate politics. [ABSTRACT FROM AUTHOR]

In network data analysis, one of the important problems is determining if two collections of networks are drawn from the same distribution. This problem can be modeled in the framework of two-sample hypothesis testing. Several graph-based two-sample tests have been studied. However, the methods mainly focus on binary graphs, and many real-world networks are weighted. In this paper, we apply empirical likelihood to test the difference in two populations of weighted networks. We derive the limiting distribution of the test statistic under the null hypothesis. We use simulation experiments to evaluate the power of the proposed method. The results show that the proposed test has satisfactory performance. Then, we apply the proposed method to a biological dataset. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of the Verhulst logistic equation to model in retrospect the total COVID-19 cases in Senegal during the period from April 2022 to April 2023. Our predictions for April 2023 are compared with the real COVID-19 data for April 2023 to assess the accuracy of the model. The data analysis is conducted using Python programming language, which allows for efficient data processing and prediction generation. [ABSTRACT FROM AUTHOR]

iGA Istanbul Airport is the first and only airport established with a wireless Internet of Things (IoT). IoT systems offer an effective solution for receiving data from many points with advantages such as fast implementation, low installation costs and long lifetime. In addition to standard products, special wireless IoT sensors designed for specific needs are also used in iGA Istanbul Airport. This infrastructure provides data for the execution, improvement and development of airport processes. The big data and analysis application created with the data collected from IoT devices and system integrations manages efficiency and predictive maintenance activities. With the effective use of IoT systems, iGA Istanbul Airport has achieved significant gains in increasing customer satisfaction, managing predictive maintenance from a more proactive perspective, reducing environmental impacts and increasing operational efficiency. This paper discusses the use of the IoT infrastructure at iGA Istanbul Airport and the benefits it provides. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the present paper, the influence of liquid flow above the needle on a periodic or chaotic nature of the bubble departures process was numerically investigated. During the numerical simulations bubbles departing from the needle was considered. The perturbations of liquid flow were simulated based on the results of experimental investigations described in the paper [1]. The numerical model contains a bubble growth process and a liquid penetration into a needle process. In order to identify the influence of liquid flow above the needle on a periodic or chaotic nature of bubble departures process, the methods data analysis: wavelet decomposition and FFT were used. It can be inferred that the bubble departure process can be regulated by altering the hydrodynamic conditions above the needle, as variations in the liquid velocity in this area affect the gas supply system's conditions. Moreover, the results of numerical investigations were compared with the results of experimental investigation which are described in the paper [2]. It can be considered that, described in this paper, the numerical model can be used to study the interaction between the bubbles and the needle system for supplying gas during the bubble departures from two needles, because the interaction between the bubbles is related to disturbances in the liquid flow above the needle. [ABSTRACT FROM AUTHOR]

Neutrosophic topological spaces (NTS) offer a novel framework for uncertainty modeling by incorporating degrees of truth, indeterminacy, and falsity. This paper investigates the potential applications of NTS in computer science. We provide background on neutrosophic sets and their extension to topological spaces. We then explore how NTS could be used for uncertainty modeling in data analysis (e.g., handling noisy data in sensor networks), pattern recognition (e.g., improving image classification with imprecise features), and information retrieval (e.g., enhancing search results by considering relevance uncertainty). We discuss the challenges associated with applying NTS and highlight promising areas for future research, such as developing efficient algorithms for NTS operations. Overall, this paper aims to stimulate further exploration of how neutrosophic topological spaces can contribute to advancements in various computer science domains. [ABSTRACT FROM AUTHOR]

In this paper, the concept of hybrid predictive maintenance for a single industrial machine is presented. A review of the solutions in the area of machine maintenance (especially predictive maintenance) which have been described in the literature is provided. The assumptions of the hybrid predictive maintenance model for modules, machines, or systems are presented. The methods used within the developed methodology are described. This includes the use of diagnostic data, experience, and a mathematical model. A case study of an industrial machine on which a system for collecting diag-nostic data has been pilot-implemented, using, among others, vibration sensors and drive system pa-rameters for damage detection is presented. The registered data can be used to precisely determine the time of upcoming failure after detection of the characteristic symptoms resulting from component wear In addition, an analysis of the durations of correct operation and failure events was performed and indicators describing these values were determined. The values of the aforementioned indicators were determined based on empirical data and described using a gamma distribution. The objective of the research was to prepare, implement and draw conclusions on a hybrid predictive maintenance model. A real industrial machine was used in the research study. The hybrid predictive maintenance model presented in this paper enables the use of data of different types (diagnostic, historical and mathemat-ical model-based) in scheduling machine downtime for maintenance actions. On the basis of the re-search conducted, it was determined which machine operating parameters are characterised by varia-bility that enables the detection of upcoming failure. This allows for precise planning of maintenance activities and minimization of unplanned downtime. [ABSTRACT FROM AUTHOR]

Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. The statistical properties of parameter and forecast estimates strongly depend on the structure of such matrices. The least squares (LS) method is the most flexible and can estimate systems of large dimensions; however, it is biased in the presence of multilateral (sparse) matrices. Instead, the unilateral specification of SAR models provides triangular weight matrices that allow consistent LS estimates and sequential prediction functions. These two properties are strictly related and depend on the linear and recursive nature of the system. In this paper, we show the better performance in out-of-sample forecasting of unilateral SAR (estimated with LS), compared to multilateral SAR (estimated with maximum likelihood, ML). This conclusion is supported by numerical simulations and applications to real geological data, both on regular lattices and irregularly distributed points. [ABSTRACT FROM AUTHOR]

With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data. [ABSTRACT FROM AUTHOR]

Floating photovoltaic (FPV) systems are gaining popularity as a valuable means of harnessing solar energy on unused water surfaces. However, a significant gap persists in our comprehension of their thermal dynamics and the purported cooling benefits they provide. The lack of comprehensive monitoring data across different climatic regions and topographies aggravates this uncertainty. This paper reviews the applicability of established module temperature prediction models, originally developed for land-based PV systems, to FPVs. It then details the refinement of these models using FPV-specific data and their subsequent validation through large-scale, ongoing FPV projects. The result is a significant improvement in the accuracy of temperature predictions, as evidenced by the reduced Mean Absolute Error (MAE) and improved R-squared ( R 2 ) after parameter optimisation. This reduction means that the tailored models better reflect the distinct environmental influences and cooling processes characteristic of FPV systems. The results not only confirm the success of the proposed method in refining the accuracy of current models, but also indicate significant post-tuning changes in the parameters representing wind and convective effects. These adjustments highlight the increased responsiveness of FPVs to convective actions, especially when compared to ground-based systems, possibly due to the evaporative cooling effect of bodies of water. Through this research, we address a critical gap in our understanding of heat transfer in FPV systems and aim to enrich the knowledge surrounding the acknowledged cooling effect of FPVs. [ABSTRACT FROM AUTHOR]

This study outlines an approach that captures the messy landscape of the learning opportunities on which teaching focused academics draw. The Activity Centred Analysis and Design framework was employed to analyse data from a focused ethnographic study of seven TFAs in a research-intensive university. The Activity Centred Analysis and Design framework revealed processes of reconfiguring structures that allow for teaching focused academics to focus on their goals (influencing their peers and being recognized as legitimate academics). This paper offers an avenue for understanding authentic practices of teaching focused academics with broader implications for research into the professional learning of academics. [ABSTRACT FROM AUTHOR]

Introduction There is increasing interest in food biomarkers to address the shortcomings of self-reported dietary assessments. Berries are regarded as important fruits worldwide; however, there are no well-validated biomarkers of berry intake. Thus, the objective of this study is to identify urinary biomarkers of berry intake. Methods For the discovery study, participants consumed 192 g strawberries with 150 g blueberries, and urine samples were collected at 2, 4, 6, and 24 h post-consumption. A dose–response study was performed, whereby participants consumed three portions (78 g, 278 g, and 428 g) of mixed strawberries and blueberries. The urine samples were profiled by an untargeted LC-MS metabolomics approach in the positive and negative modes. Results Statistical analysis of the data revealed that 39 features in the negative mode and 15 in the positive mode significantly increased between fasting and 4 h following mixed berry intake. Following the analysis of the dose–response data, 21 biomarkers showed overall significance across the portions of berry intake. Identification of the biomarkers was performed using fragmentation matches in the METLIN, HMDB, and MoNA databases and in published papers, confirmed where possible with authentic standards. Conclusions The ability of the panel of biomarkers to assess intake was examined, and the predictability was good, laying the foundations for the development of biomarker panels. [ABSTRACT FROM AUTHOR]

In business process management, business process change analysis is the key link to ensure the flexibility and adaptability of the system. The existing methods mostly focus on the change analysis of a single business process from the perspective of control flow, ignoring the influence of data changes on collaborative processes with information interaction. In order to compensate for this deficiency, this paper proposes a rule-driven consistency analysis method for data changes in collaborative processes. Firstly, it analyzes the influence of data changes on other elements (such as activities, data, roles, and guards) in collaborative processes, and gives the definition of data influence. Secondly, the optimal alignment technology is used to explore how data changes interfere with the expected behavior of deviation activities, and decision rules are integrated into the Petri net model to accurately evaluate and screen out the effective expected behavior that conforms to business logic and established rules. Finally, the initial optimal alignment is repaired according to the screened effective expected behavior, and the consistency of business processes is recalculated. The experimental results show that the introduced rule constraint mechanism can effectively avoid the misjudgment of abnormal behavior. Compared with the traditional method, the average accuracy, recall rate, and F1-score of effective expected behavior are improved by 4%, 4.7%, and 4.3%, respectively. In addition, the repaired optimal alignment significantly enhances the system's ability to respond quickly and self-adjust to data changes, providing a strong support for the intelligent and automated transformation of business process management. [ABSTRACT FROM AUTHOR]

Spherical data arise widely in various settings. Spherical statistics is an analysis of data on a unit hyper-spherical domain. In this paper, we mainly consider the local kernel estimators for regression models with a binary response and the predictors including spherical variables. We apply the random forests kernel to nonparametric binary regression models with spherical predictors. Simulation experiments and real examples are used to validate the performance of the new models. Compared with the classical von Mises–Fisher kernel and the linear-spherical kernel, the random forests kernel has better fitting effect and faster computation speed. Compared with other classifiers, the models proposed in this paper have better classification performance in both low and high dimensional cases. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19) has had a significant impact on dermatology, but to date no bibliometric analysis of this field has been identified. Therefore, the aim of this study was to perform a bibliometric indicator analysis of the worldwide scientific production of COVID-19 in dermatology. Materials and Methods: An advanced bibliographic search was performed in the Scopus database to identify articles on COVID-19 and dermatology from 2020 to 2021. The collected information was analysed with SciVal software. Bibliometric data were described through figures and summary tables. Results: A total of 1448 documents were collected and analysed. Torello Lotti was the author with the greatest scientific production; however, Esther Freeman had the greatest impact. Harvard University was the institution with the highest number of published articles. Most papers were published in the first quartiles. The United States and Italy were the leading countries in terms of production. Articles with international collaboration had the highest impact. Conclusion: Articles related to dermatology and COVID-19 are mostly published with American and Italian affiliations. In addition, there has been an increase in the distribution of articles published in the first quartile, which would reflect a growing interest in the community. Publications with international collaboration reported the highest impact, so future authors should take this into account. [ABSTRACT FROM AUTHOR]

Copyright of Society & Leisure / Loisir & Société is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to explore the SCImago Institutional Ranking (SIR) 2022 to gain insight into the profile of Sri Lankan universities. Additionally, the study aims to investigate whether there was consistency between Scopus profiles and SIR. Furthermore, the research delves into various factors that impact the research ranking of an academic institution as defined by SCImago, which goes beyond just the number of publications. The data for the study were retrieved from SIR (2022) and Scopus database (2022) and were systematically analyzed. The author chose the following options to extract the data for overall ranking, Universities as a sector, Sri Lanka as the country and 2022 as the year based on all subject areas. The study found that the number of Sri Lankan universities eligible for the SCImago ranking has gradually increased from 2013 to 2022. According to SIR, the University of Colombo is the top-ranked academic institution in Sri Lanka, followed by Rajarata University of Sri Lanka and the University of Jaffna. Out of the fourteen universities in Sri Lanka, eight were ranked by SIR, with six being in the Q1 (first cluster) and two in the Q2 (second cluster). Interestingly, the Rajarata University of Sri Lanka was ranked 1st in SIR 2022 for its research performance among Sri Lankan universities with their 105 publications indexed in Scopus. Analysis of data shows that there is no relationship between the Scopus profile and the SIR. It is also suggesting that having a high number of articles in the Scopus database does not necessarily guarantee a high rank in SIR. The SIR not only depends on the number of publications but also on other factors related to the quality of the publications. Therefore, universities cannot assume their SIR position by considering only the quantity of Scopus indexed publications. SIR mainly considers the quality of the publications to measure the institutional research performance. The SCImago ranking evaluates the institutional whole performance through three of its indicators, and research performance is measured only through Scopus indexed publication. Sri Lankan publications in local and international journals which are not indexed in Scopus and conference papers were not counted for research performance. The findings of this study will facilitate the institutions to compare their position with other institutions, standardize their research practices, improve the international collaboration to uphold the academic benchmark, regulate their research publications and promote their visibility and finally support government bodies and policymakers regarding fund allocations and strategic planning. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To develop an automated algorithm to detect, quantify, and differentiate between tremor using pen-on-paper spirals. METHODS: Patients with essential tremor (n = 25), dystonic tremor (n = 25), Parkinson’s disease (n = 25), and healthy volunteers (HV, n = 25) drew free-hand spirals. The algorithm derived the mean deviation (MD) and tremor variability from scanned images. MD and tremor variability were compared with 1) the Bain and Findley scale, 2) the Fahn–Tolosa–Marin tremor rating scale (FTM–TRS), and 3) the peak power and total power of the accelerometer spectra. Inter and intra loop widths were computed to differentiate between the tremor. RESULTS: MD was higher in the tremor group (48.9±26.3) than in HV (26.4±5.3; p < 0.001). The cut-off value of 30.3 had 80.9% sensitivity and 76.0% specificity for the detection of the tremor [area under the curve: 0.83; 95% confidence index (CI): 0.75, 0.91, p < 0.001]. MD correlated with the Bain and Findley ratings (rho = 0.491, p = 0 < 0.001), FTM–TRS part B (rho = 0.260, p = 0.032) and accelerometric measures of postural tremor (total power, rho = 0.366, p < 0.001; peak power, rho = 0.402, p < 0.001). Minimum Detectable Change was 19.9%. Inter loop width distinguished Parkinson’s disease spirals from dystonic tremor (p < 0.001, 95% CI: 54.6, 211.1), essential tremor (p = 0.003, 95% CI: 28.5, 184.9), or HV (p = 0.036, 95% CI: -160.4, -3.9). CONCLUSION: The automated analysis of pen-on-paper spirals generated robust variables to quantify the tremor and putative variables to distinguish them from each other. SIGNIFICANCE: This technique maybe useful for epidemiological surveys and follow-up studies on tremor. [ABSTRACT FROM AUTHOR]

This study presents the outcomes of a semi‐systematic literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on previous systematic literature reviews in MMLA and an additional new search, 35 MMLA works were identified that use theory. The results show that MMLA studies do not always discuss their findings within an established theoretical framework. Most of the theory‐driven MMLA studies are positioned in the cognitive and affective domains, and the three most frequently used theories are embodied cognition, cognitive load theory and control–value theory of achievement emotions. Often, the theories are only used to inform the study design, but there is a relationship between the most frequently used theories and the data modalities used to operationalize those theories. Although studies such as these are rare, the findings indicate that MMLA affordances can, indeed, lead to theoretical contributions to learning sciences. In this work, we discuss methods of accelerating theory‐driven MMLA research and how this acceleration can extend or even create new theoretical knowledge. Practitioner notesWhat is already known about this topicMultimodal learning analytics (MMLA) is an emerging field of research with inherent connections to advanced computational analyses of social phenomena.MMLA can help us monitor learning activity at the micro‐level and model cognitive, affective and social factors associated with learning using data from both physical and digital spaces.MMLA provide new opportunities to support students' learning.What this paper addsSome MMLA works use theory, but, overall, the role of theory is currently limited.The three theories dominating MMLA research are embodied cognition, control–value theory of achievement emotions and cognitive load theory.Most of the theory‐driven MMLA papers use theory 'as is' and do not consider the analytical and synthetic role of theory or aim to contribute to it.Implications for practice and/or policyIf the ultimate goal of MMLA, and AI in Education in general, research is to understand and support human learning, these studies should be expected to align their findings (or not) with established relevant theories.MMLA research is mature enough to contribute to learning theory, and more research should aim to do so.MMLA researchers and practitioners, including technology designers, developers, educators and policy‐makers, can use this review as an overview of the current state of theory‐driven MMLA. [ABSTRACT FROM AUTHOR]

This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights. [ABSTRACT FROM AUTHOR]

This paper summarises the evolution of the Water Poverty Index (WPI) application at different scales since its emergence. The review captures the main milestones and remarkable developments around the world. It sets the foundation for identifying the most appropriate version of the WPI, building on learning from previous versions. In addition, the paper sheds light on the linkages between the WPI and sustainable development goals and applications to fragile contexts. Therefore, it provides a synthesis of knowledge researchers and practitioners' need in sustainable water resources management that helps boost human development in unstable/fragile arid and semi-arid contexts. The methodology included (i) WPI literature shortlisting and reviewing, (ii) review literature links WPI with sustainable human development and fragility, and (iii) data analysis, identification of gaps and future trends. Intensive research was found to address the limitations of the WPI. However, further research is needed to shortlist the multiple versions of the WPI and match them to their respective scale, purpose and context (including fragile contexts). In addition, a time-based WPI was rarely touched to forecast the impact of decisions on community welfare. [ABSTRACT FROM AUTHOR]

The rational science of the Qur'an is a branch of science that focuses on understanding the relationships, interconnectedness, and harmony between various verses in the Quran. In this paper, the author wants to explain the importance of Munasabah knowledge in studying or interpreting this Islamic holy book regardless of the pros and cons to the existence of this discipline. This study uses a qualitative-descriptive approach to collect data in the form of written or oral descriptions through media such as YouTube or video. Data analysis techniques are further described in this paper. In addition, case studies are also included to provide concrete examples of the application of the knowledge of the rational science in qualitative research, especially in analyzing the relationships and linkages between Quranic verses and their implications in classical interpretation. The results and conclusions in this research are that we are able to understand the meaning of rational science, find the relevance of rational knowledge to the study of the Qidri, and know the discourse around the opinions of scholars about rational science. [ABSTRACT FROM AUTHOR]

Aim: This study aims to adapt the "Nursing Practice Readiness Scale" to Turkish culture, and evaluate its psychometric properties. Background: New graduates' nursing practice readiness can impact their work adaptation and performance. Design: The research employed a methodological design. Methods: Data were collected between May and July 2022. The sample consisted of 436 newly graduated nurses. Content validity, construct validity and criterion validity were evaluated. Reliability was examined with adjusted item‐total correlation, Cronbach's a coefficient, composite‐reliability and split‐half reliability. Results: The Turkish version of Nursing Practice Readiness Scale was found to have good content and criterion validity. As a result of confirmatory factor analysis, the original five‐factor structure of the scale was also confirmed for the Turkish version. The scale's overall Cronbach's α coefficient was determined to be 0.96, with subscale coefficients ranging from 0.73 to 0.94. The composite reliability values of the subscales were found between 0.75 and 0.94. In split‐half reliability, the correlation coefficient between half was 0.952, with a Spearman–Brown Coefficient (Unequal Length) of 0.976. Conclusions: The Turkish version of Nursing Practice Readiness Scale is a valid and reliable measurement tool for evaluating the nursing practice readiness of newly graduated nurses. Summary statement: What is already known about this topic? The nursing practice readiness of newly graduated nurses may impact their work adaptation and performance.Work adaptation is an important predictor of intention to leave the profession and productivity. What this paper adds? This study adapted the Nursing Practice Readiness Scale to Turkish culture, and evaluated its psychometric properties.The Turkish version of Nursing Practice Readiness Scale was shown to be a valid and reliable tool that can be used to evaluate newly graduated nurses' nursing practice readiness. The implications of this paper Nurse educators will be able to assess the readiness of graduating students for nursing practice by using the scale.Nurse managers will be able to evaluate the readiness of new graduate nurses for nursing practice using the scale.Nurse managers and educators will be able to identify areas that need to be prioritized to increase the readiness of newly graduated nurses for nursing practice by using the scale. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study is to describe and evaluate how nurses caring for COVID and non‐COVID patients assess changes in their work and in nursing activities during the two waves of the COVID‐19 pandemic. Methods: Two cross‐sectional surveys were conducted for Estonian nurses working during the first and second waves of the COVID‐19 pandemic, using The impact of COVID‐19 emergency on nursing care questionnaire. Based on convenience sampling, the data were collected among the members of professional organizations, unions and associations. Responses from the first (n = 162) and second wave (n = 284) were analysed using descriptive statistics, Fisher's exact test and McNemar's test. Results: The COVID‐19 pandemic changed the working context during both waves for nurses caring for COVID and non‐COVID patients. Changes were considered to a greater extent during the second wave, when Estonia was severely affected, and by nurses caring for COVID patients. During the second wave, the number and complexity of patients increased, and nurses caring for COVID patients performed fundamental care, nursing techniques and symptom control significantly more frequently compared to nurses caring for non‐COVID patients. Conclusion: Taking care of COVID patients is demanding, requiring nurses to perform more direct patient care. However, the pandemic also increased the frequency of activities not related with direct patient care. Summary statement: What is already known about this topic? The COVID‐19 pandemic has influenced the context of care and all dimensions of nurses' work.Despite increasing research on the impact of the COVID‐19 pandemic on nursing care, little attention has been given to differences between caring for COVID and non‐COVID patients during the different waves of the COVID‐19 pandemic. What this paper adds? Nursing care for COVID patients requires from nurses more direct patient care through fundamental care activities, nursing techniques and symptom control compared to non‐COVID patients.The impact of the COVID‐19 pandemic on nurses' work reflects the severity and progress of different waves of the pandemic, which needs to be considered in preparing for future pandemics.Nursing care during a pandemic may also lead to an extensive workload due to tasks not related to direct patient care as nurses contribute to the management of the pandemic on all levels of health care. The implications of this paper: The COVID‐19 pandemic has had a significant impact on nursing, where the mitigation of long‐term effects of the pandemic is still ongoing, and thus, knowledge about the details of the resulting changes is required.Study findings enable us to mitigate the impact of the pandemic on nurses and to highlight aspects that need to be taken into account when preparing for future pandemics. [ABSTRACT FROM AUTHOR]

Aims: To investigate the relationship between the psychological resilience and burnout of nurses caring for COVID‐19 patients and to determine the factors that affect their psychological resilience and burnout. Background: In pandemic diseases such as COVID‐19, nurses experience burnout due to long working hours, decreased quality of life and anxiety/fear about their own/families' health. Psychological resilience helps to control burnout in nurses and prevent the development of a global nurse shortage. Design: This was a descriptive, correlational study. Methods: The sample of this study included 201 nurses in a Training and Research Hospital. The study used the Brief Resilience Scale and the Burnout Measure Short Version. Data were collected between 4 May and 1 June 2020. Statistical analysis was made with Pearson/Spearman, independent sample t test, one‐way analysis of variance (ANOVA) test. Results: Nurses reported moderate burnout and psychological resilience, with a negative and highly significant correlation between psychological resilience and burnout levels. Conclusions: In order to increase the quality of patient care/treatment, nurse managers need to reduce nurses' burnout and increase their psychological resilience. Nurses are recommended to adopt a healthy lifestyle, organize training programmes and implement psychological resilience interventions to prevent sleep disorders. Giving nurses the tools to understand what they need to manage within their locus of control will allow them to find a new sense of resilience, preventing potential burnout. Summary statement: What is already known about this topic? Epidemics and pandemics cause many negative effects on the individual/society in terms of physical, psychological, social and economic aspects.During epidemic and pandemic, nurses experience burnout caused by the accumulation of professional stress.Nurses with high levels of psychological resilience experience less psychological distress such as anxiety, fear, burnout, sensory and psychosocial problems. What this paper adds? Nurses experienced moderate burnout and resilience during the early stages of the COVID‐19 pandemic.A negative correlation was found between nurses' burnout and psychological resilience.The burnout levels of nurses who were not college graduates, who were dissatisfied with their jobs and who intended to leave their jobs were found to be high. The implications of this paper: Nurses were vulnerable to burnout due to increased workloads in the COVID‐19 crisis, increased shifts and the fear of infecting themselves and their families.It is necessary to increase the psychological resilience of nurses in order to provide the highest level of care to patients with COVID‐19.Nurse manager should focus on reducing burnout in their nursing teams by supporting the postgraduate education of nurses and reducing their workload. [ABSTRACT FROM AUTHOR]

The relative advantages and disadvantages of sum scores and estimated factor scores are issues of concern for substantive research in psychology. Recently, while championing estimated factor scores over sum scores, McNeish offered a trenchant rejoinder to an article by Widaman and Revelle, which had critiqued an earlier paper by McNeish and Wolf. In the recent contribution, McNeish misrepresented a number of claims by Widaman and Revelle, rendering moot his criticisms of Widaman and Revelle. Notably, McNeish chose to avoid confronting a key strength of sum scores stressed by Widaman and Revelle—the greater comparability of results across studies if sum scores are used. Instead, McNeish pivoted to present a host of simulation studies to identify relative strengths of estimated factor scores. Here, we review our prior claims and, in the process, deflect purported criticisms by McNeish. We discuss briefly issues related to simulated data and empirical data that provide evidence of strengths of each type of score. In doing so, we identified a second strength of sum scores: superior cross-validation of results across independent samples of empirical data, at least for samples of moderate size. We close with consideration of four general issues concerning sum scores and estimated factor scores that highlight the contrasts between positions offered by McNeish and by us, issues of importance when pursuing applied research in our field. [ABSTRACT FROM AUTHOR]

The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge‐based learning systems. Smart healthcare systems leverage knowledge‐based learning to become more context‐aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X‐rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI. Moreover, in knowledge‐driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high‐quality encryption, a large key space, key sensitivity, and resistance to statistical attacks. [ABSTRACT FROM AUTHOR]

Geospatial data describe objects and things with relation to geographic space often with location coordinate in a spatial referenced system. Rural roads are geospatial entities which can be captured and stored using geographic information system techniques. Therefore, a geographic information system is an essential tool to be placed on comprehending the information of spatial and non-spatial data over space and time. Data required for this paper include high resolution satellite imageries (QuickBird, SPOTS, IKONOS), Landsat (EOI Hyperion, DEM); local, state, and international boundaries; all Edges of transport routes connecting all settlements in the state, settlement data, stream network data, and terrain data. Roads associated attributes include location of potholes, bumps, drainages, drainage direction, and last date of road repaired, highest point, lowest point, mean elevation, maximum slope, average slope, road tears and wears which is expressed as roads condition. Road geometry data involve length of each road edge, width, and referential measurement. Data on nature of surfacing such as tar, asphalt, concrete, and laterite. Other data on roads are name, type, classification, and Geotagged pictures and video of all roads in Adamawa state. The field survey involves trailing the whole length of the roads from a referenced baseline at a vehicle speed using GPS Waypoint Navigators, handheld GPSs, and RoadLab application in iPad. These devices were used in collecting data on roads roughness index expressed as good, bad, excellent; visual assessment of road conditions and drainages were carried out during the field survey. When navigating the roads records taking of roads data, geotagged pictures, videos, and coordinates of event areas were captured. However, the use of RoadLab in assessing road conditions was only limited to Trunk a, b, and c roads across the state since they are the most tarred roads in regional road classification. Therefore, rigorous physical/visual surveys and assessment on all other rural roads were conducted. The result of the research indicate that trunk b, c and feeder roads are in bad shape and geospatial database of all road network in Adamawa State was developed. [ABSTRACT FROM AUTHOR]

Motivation Reconstructing evolutionary histories of biological entities, such as genes, cells, organisms, populations, and species, from phenotypic and molecular sequencing data is central to many biological, palaeontological, and biomedical disciplines. Typically, due to uncertainties and incompleteness in data, the true evolutionary history (phylogeny) is challenging to estimate. Statistical modelling approaches address this problem by introducing and studying probability distributions over all possible evolutionary histories, but can also introduce uncertainties due to misspecification. In practice, computational methods are deployed to learn those distributions typically by sampling them. This approach, however, is fundamentally challenging as it requires designing and implementing various statistical methods over a space of phylogenetic trees (or treespace). Although the problem of developing statistics over a treespace has received substantial attention in the literature and numerous breakthroughs have been made, it remains largely unsolved. The challenge of solving this problem is 2-fold: a treespace has nontrivial often counter-intuitive geometry implying that much of classical Euclidean statistics does not immediately apply; many parametrizations of treespace with promising statistical properties are computationally hard, so they cannot be used in data analyses. As a result, there is no single conventional method for estimating even the most fundamental statistics over any treespace, such as mean and variance, and various heuristics are used in practice. Despite the existence of numerous tree summary methods to approximate means of probability distributions over a treespace based on its geometry, and the theoretical promise of this idea, none of the attempts resulted in a practical method for summarizing tree samples. Results In this paper, we present a tree summary method along with useful properties of our chosen treespace while focusing on its impact on phylogenetic analyses of real datasets. We perform an extensive benchmark study and demonstrate that our method outperforms currently most popular methods with respect to a number of important 'quality' statistics. Further, we apply our method to three empirical datasets ranging from cancer evolution to linguistics and find novel insights into corresponding evolutionary problems in all of them. We hence conclude that this treespace is a promising candidate to serve as a foundation for developing statistics over phylogenetic trees analytically, as well as new computational tools for evolutionary data analyses. Availability and implementation An implementation is available at https://github.com/bioDS/Centroid-Code. [ABSTRACT FROM AUTHOR]

This paper explores the synergistic application of weak gravitational lensing effects and advanced deep-learning techniques in detecting and predicting dark matter. Dark matter, an elusive component of the universe's mass, remains undetectable through conventional electromagnetic observation methods. Its presence and properties have been primarily inferred through gravitational effects on visible celestial objects. One such effect, weak gravitational lensing, has been pivotal in dark matter research. It involves the subtle bending of light from distant galaxies as it passes near massive objects, predominantly dark matter. The intricate patterns of this lensing provide insights into the distribution and concentration of dark matter. Recent advancements in deep learning offer groundbreaking tools for analyzing these complex patterns. We integrate neural score matching and other deep learning algorithms to interpret weak gravitational lensing data, enhancing the accuracy of dark matter maps. Our methodology demonstrates improved predictive capabilities compared to traditional approaches, offering a more detailed understanding of dark matter's role in cosmic structures. This paper presents a comprehensive analysis of dark matter theories, reviews gravitational lensing as a detection method, and discusses the innovative application of deep learning in astrophysical phenomena. [ABSTRACT FROM AUTHOR]