Data Sources Research Articles

Authentic Leadership-A Concept Analysis.

To report an analysis of the concept of authentic leadership. Concept analysis. Data sources included core databases: CINAHL, PubMed, Embase, ProQuest, APA PsycINFO, ERIC, ABI/Inform and Tomlinson's Nursing Core Collection (2010). Walker and Avant's method was used to identify descriptions, antecedents, consequences and empirical referents of the concept. Model, related and contrary cases were developed. Defining attributes were identified as self-awareness, transparency, balanced processing, internalised moral perspective, caring, shared decision making and moral/ethical courage. Antecedents were authenticity, positive psychological capacities, life experiences, leadership development programme, supportive organisational climate and an ethical climate. Consequences are employee well-being and satisfaction, positive work environment and enhanced performance and safety. While authentic leadership is an emerging and popular leadership approach, there are gaps in relation to agreed definitions. Moreover, core textbooks within the subject area also fail to provide these definitions. This concept analysis represents the first comprehensive and robust analysis of the concept of authentic leadership that will serve to reliably inform research, education and practice. This analysis reveals that authentic leadership can have a positive effect on staff well-being and satisfaction, their work environment, performance and ultimately safety. This has potential for a positive impact on patient care and patient outcomes by fostering an ethical and supportive work environment. This study revealed a new comprehensive understanding of authentic leadership that provides conceptual clarity that will guide research and practice in the field, which refines the existing understanding, highlighting essential attributes and their relevance in nursing practice. No patient or public contribution.

Tire wear monitoring using feature fusion and CatBoost classifier

Addressing the critical issue of tire wear is essential for enhancing vehicle safety, performance, and maintenance. Worn-out tires often lead to accidents, underscoring the need for effective monitoring systems. This study is vital for several reasons: safety, as worn tires increase the risk of accidents due to reduced traction and longer braking distances; performance, as uneven tire wear affects vehicle handling and fuel efficiency; maintenance costs, as early detection can prevent more severe damage to suspension and alignment systems; and regulatory compliance, as ensuring tire integrity helps meet safety regulations imposed by transportation authorities. In response, this study systematically evaluates tire conditions at 25%, 50%, 75%, and 100% wear, with an intact tire as a reference, using vibration signals as the primary data source. The analysis employs statistical, histogram, and autoregressive–moving-average (ARMA) feature extraction techniques, followed by feature selection to identify key parameters influencing tire wear. CatBoost is used for feature classification, leveraging its adaptability and efficiency in distinguishing varying wear patterns. Additionally, the study incorporates feature fusion to combine different types of features for a more comprehensive analysis. The proposed methodology not only offers a robust framework for accurately classifying tire wear levels but also holds significant potential for real-time implementation, contributing to proactive maintenance practices, prolonged tire lifespan, and overall vehicular safety.

Partial hand and finger amputations in Sweden: an observational study of 6918 patients

BackgroundWe aimed to use open source data to understand the incidence, trends, and regional differences of finger and partial hand amputations on a national level in individuals aged 15 or older in Sweden.MethodsWe analyzed 6,918 patients aged 15 and older who had experienced finger and partial hand amputations. Incidence rates, trends, and regional disparities were assessed using negative binomial regression models and Student’s t-tests. Future trend prediction was performed using Poisson regression.ResultsFinger amputations declined most, followed by partial hand and thumb amputations. Regional variations existed, with Stockholm having the lowest and Gotland highest incidence respectively. Overall, the incidence of finger, thumb and partial hand amputations in Sweden decreased slightly. Future trend analysis indicated decreasing incidence.ConclusionAlthough, lacking in definition, publicly available data can be used for monitoring of finger, thumb, and partial hand amputation incidence on a national level. Sex, age, and regional differences were observed, suggesting the need for targeted interventions to address disparities and mitigate the burden of finger and partial hand amputations on affected individuals.

Fusing remote and social sensing data for flood impact mapping

AbstractThe absence of comprehensive situational awareness information poses a significant challenge for humanitarian organizations during their response efforts. We present Flood Insights, an end‐to‐end system, that ingests data from multiple nontraditional data sources such as remote sensing, social sensing, and geospatial data. We employ state‐of‐the‐art natural language processing and computer vision models to identify flood exposure, ground‐level damage and flood reports, and most importantly, urgent needs of affected people. We deploy and test the system during a recent real‐world catastrophe, the 2022 Pakistan floods, to surface critical situational and damage information at the district level. We validated the system's effectiveness through various statistical analyses using official ground‐truth data, showcasing its strong performance and explanatory power of integrating multiple data sources. Moreover, the system was commended by the United Nations Development Programme stationed in Pakistan, as well as local authorities, for pinpointing hard‐hit districts and enhancing disaster response.

An Improved Reptile Search Algorithm with Multiscale Adaptive Deep Learning Technique and Atrous Spatial Pyramid Pooling for IoT-based Smart Agriculture Management

Smart farming is an enhanced option for increasing food production, resource management and labour. Existing prediction methods need trained experts to analyse the data, which is a time-consuming process, and thus, there is a need for smart farming with the Internet of Things (IoT). Hence, a well-developed IoT-assisted smart agriculture model is proposed for managing agricultural needs to improve the economic value of farmers. The proposed framework constitutes four major aspects like Crop prediction, Crop yield prediction, Plant disease prediction and Smart irrigation. Initially, the crop images are taken as the input and fed to the Multi-scale Adaptive and Attention-based Convolution Neural Network with Atrous Spatial Pyramid Pooling (MACNN-ASPP), where the dissimilar crops are classified and obtained. In the second aspect, the crop image as well as crop-related data, are fetched from the data sources and given as input. Further, the deep features of the image are extracted that are added with soil and environment condition data. Then this feature is subjected to the Multi-scale Adaptive and Attention-based One-Dimensional Convolution Neural Network with Atrous Spatial Pyramid Pooling (MA1DCNN-ASPP) for crop yield prediction. While in the third aspect, the leaf images are assembled from the data file and fed as input to the MACNN-ASPP for detecting the variety of diseases affecting the plants. In the final aspect, smart irrigation is done by collecting field images with related data. Further, the deep features retrieved from the field images are applied to MA1DCNN-ASPP, where the different conditions of the field will be attained. In order to develop the model adaptively, the hyper-parameters in the network are optimised using the Improved Reptile Search Algorithm (IRSA). Finally, the investigation is done over the proposed methodology using multiple evaluation metrics. In contrast with other approaches, the proposed smart agriculture outperforms the performance of managing crops without any hazardous effects. Accuracy validation executed in the implemented IRSA-MACNN-ASSP-based crop yield prediction model accomplished better efficiency as 6.89%, 4.45%, 2.19% and 1.08% better than the classical techniques like CNN, LSTM, 1DCNN and MA1DCNN-ASPP.

The Impact of Inflation, Stocks, Sukuk, and Sharia Mutual Funds on Economic Growth in 2017-2023

This study aims to ascertain, at least in part: 1) The impact of inflation on the development of the national economy 2) The performance of sharia stocks and the expansion of the national economy 3) The impact of sukuk on the development of the national economy, as well as the impact of sharia mutual funds on the same, which are the third and fourth impacts 5) Correlation between the development of the national economy in 2017-2023 through inflation, sharia mutual funds, sukuk, and stocks. Bank Indonesia (BI), the Financial Services Authority (OJK), the Central Statistics Agency (BPS), and other reliable sources of information provided the population used in the study. To sort the sample based on criteria set as independent variables, the monthly data source is used once a year. Data sources include time series data published for 2017-2023, which is considered secondary data. The analysis of the data is double linear, this research strategy uses a quantitative-associative methodology. It can be concluded that inflation has a significant adverse impact on the development of the national economy (sig. 0.0001 < 0.05), indicating that increasing inflation causes economic decline, in line with Simanungkalit's (2020) research. Sharia stocks and sukuk show a positive and significant impact on the national economy, with a value of sig. 0.0021 and 0.0019 (< 0.05), respectively, which means that an increase in the instrument will increase economic growth, according to research by Radjak & Kartika (2020) and Ardi (2018).

Mapping Open Access Publishing Trends in Central Universities of North East India: A Scientometric Approach

ABSTRACT The study analyses open access publications across the 10 central universities of North East India, focusing on publication trends, disciplinary distribution, citation impact, collaboration patterns, and open access licensing patterns. It compiles scholarly outputs from the OpenAlex API, integrating various data sources such as Microsoft Academic Graph, ORCID, Crossref, and Unpaywall. A total of 28,832 publications from 2004–2023 were collected and analyzed using statistical methods. The findings reveal a positive trend in research output across the represented universities, with an increasing emphasis on OA publications. However, the proportion of OA publications remains relatively low compared to closed access. Citation impact varies among universities, reflecting differences in research quality and visibility. Open repositories such as SSRN, Research Square, and Zenodo are crucial in promoting OA publishing. The study also reveals common usage of licenses such as CC-BY and CC-BY-NC-ND, with variations across universities. The impact on libraries is also discussed, emphasizing the role of libraries in promoting OA, supporting researchers, and contributing to scholarly communication advancement. This study provides valuable insights for policymakers, academic administrators, and researchers seeking to promote a culture of openness, collaboration, and innovation in scholarly communication within the region, country, and at the global level.

Historical expansion of tea plantations over 150years (1876-2023) in North Bengal, India.

The Camellia sinensis (L.) Kuntze agroforestry system has been a dominant driver of land transformation for over a century. However, most previous studies have not captured the dynamics of tea plantations since their inception. To address this research gap, this study investigates 150years (1876 to 2023) of tea area dynamics and two decades (2001-2022) of tree loss in the Bengal-Dooars region (Jalpaiguri and Alipurduar districts). Various data sources were employed, including Sentinel-2A imagery (10m), early twentieth century topographic maps at a 1:50,000 scale, and historical records from the British colonial period. Results revealed that the tea area expanded from 331 to 95,800ha (a 70% increase) during the study period. The 1-km grid-wise spatial analysis indicated that approximately 70% of the increase was attributed to the 60-90% and > 90% grid categories, signifying the expansion of large tea estates. Most tea gardens(TG) are sustained with trees, which can significantly contribute to carbon stock. Tree loss within and outside TG for 2001-2022 was analyzed using annual tree loss data at 30m from the Global Forest Watch (GFW) platform. Between 2001 and 2022, about 52.7% of tree loss was attributed to TG, although tea covers only 15.3% of the geographic area of the study region. Our findings highlight the historical expansion of tea plantations and their impact on natural land cover. We suggest that future studies address the mean age of TG along with tree fractions for improved carbon estimates.

THE EFFECT OF MAKING ATOMIC MODELS ON THE INTEREST IN LEARNING CHEMISTRY OF CLASS X STUDENTS AT PRAWIRA MARTA HIGH SCHOOL

Chemistry subjects are often considered difficult and boring because of their abstract nature. There are various learning methods that can be applied by teachers to increase students' interest in learning Chemistry. One of them is an experiment to make Atomic Model props using rubber balls. This article is compiled based on the results of Classroom Action Research which was carried out in two stages, namely Cycle I and Cycle II with the aim of increasing interest in learning Chemistry in grade X students at Prawira Marta High School in the even semester of the 2023/2024 school year. In each cycle, there are several stages, namely planning, implementation, observation, and reflection. The instruments used are observation sheets and questionnaires where the data source used is the primary data source. The results of the study were processed from data collection in Cycle I and Cycle II and analyzed descriptively to determine the achievement of learning interest indicators. The percentage of comparison of Cycle I and Cycle II data shows an increase in interest in learning Chemistry on all set indicators. On the indicators of feeling happy, high enthusiasm, and special attention showed an increase of 10.5%. Meanwhile, the full contribution indicator increased by 5.3%. It can be concluded that making an Atomic Model can increase the interest in learning Chemistry of grade X students at Prawira Marta High School.

Pengaruh Perubahan Kurikulum Pendidikan Terhadap Motivasi Belajar Siswa

This research aims to investigate the impact of curriculum changes on students' learning motivation at the secondary education level. The curriculum is a key element in the education system that can influence the learning process and student achievement. The data collection technique uses qualitative with a literature study approach. The data source used is a literature study, the data analysis technique used is a comparative study of the literature and identifying the impacts of main curriculum changes on student learning motivation, both positive and negative impacts. The research results show that curriculum changes can have varying impacts on student learning motivation. Positive impacts can occur when curriculum changes include refreshing learning materials, a more interactive teaching approach, clear educational goals, fair evaluation, and challenges that are appropriate to students' ability levels.

Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

Feeding global aquaculture.

The growth of animal aquaculture requires ever more feed. Yet, fish and crustacean farming is argued to be sustainable because wild fish use is low and has improved over time. Here, accounting for trimmings and by-products from wild fish in aquaculture feed, and using four different sources of industry-reported feed composition data, we find ratios of fish inputs to farmed outputs of 0.36 to 1.15-27 to 307% higher than a previous estimate of 0.28. Furthermore, a metric that incorporates wild fish mortality during capture and excludes unfed systems raises the wild fish mortality-to-farmed fish output ratio to 0.57 to 1.78. We also evaluate terrestrial ingredients in aquaculture feeds. Widely cited estimates of declines in wild fish use from 1997 to 2017 entailed a trade-off of more than fivefold increase in feed crops over the same period. Our assessment challenges the sustainability of fed aquaculture and its role in food security.

Key Drivers of Consumption, Conceptual, Sensory, and Emotional Profiling of Cheeses Based on Origin and Consumer Familiarity: A Case Study of Local and Imported Cheeses in Greece

The origin of a product, consumer familiarity, and purchasing identity are factors that affect the perception of cheese consumption. The present study aims at identifying consumers’ conceptualizations and attitudes towards local Greek cheeses of the North-Aegean Sea islands, such as Ladotyri, Graviera, Kasseri, Kaskavali, Melichloro, and Kalathaki, some of which have a Protected Designation of Origin (PDO) status, as opposed to cheeses of non-Greek origin, such as Cheddar, Regatto, and Gouda. Sensory and emotional attributes of local, local PDO, and imported cheeses, as well as drivers associated with consumers’ choice and acceptance above and beyond their sensory attributes, were studied using three methods: (a) flash profile to gain insight into the sensory positioning of products and description of samples; (b) qualitative analysis of focus groups to pinpoint consumer knowledge, preference, and consumption criteria; and (c) a new methodology for natural language processing and sentiment analysis of social media posts to determine consumer conceptualizations. Social media posts have proven to be a valuable source of linguistic and cultural data for cheeses. Local cheeses, including PDO products, were found to be linked to village life and family gatherings, home, tradition, and childhood memories, with saltiness and hardness being their main sensory attributes. Imported cheeses were linked to fast food, pizza, and snacking, with elasticity and gumminess as prominent sensory qualities. The main criteria for purchase were intended usage and versatility, taste and texture, price, and familiarity. The findings provide key sensory attributes, information about consumer purchasing criteria, and relevant vocabulary for the promotion of cheeses as agri-food and gastronomic identity key products.

Pancasila as State Ideology and Pillar of Religious Harmony in Indonesian Society

Religious diversity in a country is natural, but even so, an arrangement within the country is needed to accommodate differences in religion and belief. Without a fair arrangement, this can make the state less stable and cause religious conflicts in a country.This article examines Pancasila as the state ideology in forming religious tolerance. This research is a qualitative research with a literature study approach, namely by using books, documents, journals and other related sources as sources of research data. This study discussed about Pancasila as state ideology and religion, Pancasila Philosophy in Islamic Views, and Pancasila as an ideology forming religious tolerance. While data analysis is carried out by grouping, sorting and then looking for themes that are in accordance with the focus of research for later interpretation. Results of studi show;1) Pancasila as the state ideology has been fully supported by religious normativity. 2) The values and norms contained in Pancasila have compatibility with the values that become the teachings of Islam. and 3) Pancasila plays an important role in creating religious tolerance, mutual respect and respect for adherents of other religions, and not forcing beliefs on other parties.

Dual-view jointly learning improves personalized drug synergy prediction.

Accurate and robust estimation of the synergistic drug combination is important for medicine precision. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples. We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features. JointSyn outperforms existing state-of-the-art methods in predictive accuracy and robustness across various benchmarks. Each view of JointSyn captures drug synergy-related characteristics and make complementary contributes to the final prediction of drug combination. Moreover, JointSyn with fine-tuning improves its generalization ability to predict a novel drug combination or cancer sample using a small number of experimental measurements. We also used JointSyn to generate an estimated atlas of drug synergy for pan-cancer and explored the differential pattern among cancers. These results demonstrate the potential of JointSyn to predict drug synergy, supporting the development of personalized combinatorial therapies. Source code and data are available at https://github.com/LiHongCSBLab/JointSyn. Supplementary data are available at Bioinformatics online.

Ancient Fishing Strategies for the Extinct Thicktail Chub (Gila crassicauda) in the Sacramento-San Joaquin Delta

ABSTRACT Now extinct, Thicktail chub (Gila crassicauda) was endemic to central California and was a key component of local fisheries before the 1800s. Little is known about their biology, behavior, ecology, or role in precontact Native American fisheries. Archaeological sites contain large numbers of thicktail chub bones and represent a key source of data to fill our considerable gap in knowledge. We developed regression equations to convert thicktail chub skeletal elements to estimates of standard length (SL). Bones from two contemporaneous precontact Late Period (ca 700 -200 cal BP) sites, CA-CCO-138, and CA-CCO-647, in the Sacramento-San Joaquin Delta, show that precontact thicktail chub achieved a maximum standard length (SL) of 306 mm. Bone size distributions suggest spearing was the most likely capture technique, likely during a spring-summer nearshore fishery. The results contribute vital information to thicktail chub historical ecology and their key role in precontact Indigenous fisheries.

Variables Influencing Participants’ Choice of Training Institutions for Training Program

This study compares variables influencing Participants’ choice of training institutions for participant program in Kathmandu Valley. Choice of training institutions for training program is taken as dependent variable. Costs, locations, academic career goal, training institutions affiliations and environment are independent variables. The primary sources of data are utilized to reach the opinions of respondents with respect to choice of training institution for training program in Kathmandu Valley. The survey is based on one hundred respondents. To obtain the purpose of the study, structured questionnaires are framed. The regression models are projected to trial the significance and impact of variables affecting choice of the training institutions for training program in Kathmandu Valley. The result shows that location, environment and Training Academic affiliation are positively connect to choice of training institutions for training program. It signifies that better the environment and Training Academics affiliation higher would be the choice of liked training institutions. The result also shows that more proper the location of the training institutions, greater is the chance of selecting the training institutions for training program. The regression result shows the cost, environment and training institutions affiliation have positive impact on choice of training institutions for training program. In the same manner, the beta coefficients for carrier goal and locations have significant positive impact of the selecting training institutions for training program, which is significant at 5 percent level of significance. Overall, the selecting of training institutions for training program in Kathmandu Valley is affected by the carrier goals followed by the location.

Efficacy and safety of sucroferric oxyhydroxide versus sevelamer carbonate: A systematic review and meta-analysis.

Phosphate binders are commonly used in patients receiving kidney replacement therapy (KRT), aiming to reduce and maintain serum phosphorus. Chronic kidney disease-mineral and bone disorder has been linked to reduced lifespan and worsened quality of life. This study aims to examine the efficacy and safety of sucroferric oxyhydroxide versus sevelamer carbonate in patients receiving KRT. The data sources examined were MEDLINE (PubMed), Scopus, and the Cochrane Central Register of Controlled Clinical Trials with a search deadline of October 2023. We examined randomized controlled trials that compared sucroferric oxyhydroxide versus sevelamer carbonate in the adult population receiving KRT. We performed a meta-analysis combining the data from trials, using R-studio. Inclusion criteria were met by five randomized trials. There was no statistically significant difference in the reduction of serum phosphorus between the two groups (MD: -0.07 mmol/L, 95% CI-random effects: -0.15 to 0.02). In the same line, a non-statistically significant difference was observed in serum i-PTH reduction between the two drugs (MD = -1.53 mg/dL, 95% CI = (-4.45, 1.4), p = 0.26, random effects model). No statistically significant difference was observed in all adverse events between the two groups (odds ratio: 1.11, 95% CI: 0.65-1.88, random effects model). Further analysis of gastrointestinal adverse events revealed that sevelamer carbonate increases gastrointestinal adverse events by up to 60% (odds ratio: 1.60, 95% CI: 1.31-1.97, common (fixed) effect model). This meta-analysis of randomized trials showed that both drugs, sucroferric oxyhydroxide and sevelamer equally and effectively controlled serum phosphorus levels, whereas sucroferric oxyhydroxide revealed a better profile in terms of gastrointestinal adverse events. Sucroferric oxyhydroxide is a valuable option for patients receiving KRT when sevelamer carbonate is more difficult to tolerate.

Analysis of Factors Affecting Participation of Smallholder Farmers in Wheat Cluster Farming: The Case of Gasera District, Bale Zone, Oromia Region, Ethiopia

Agriculture is the main means of livelihood in Africa. Wheat is one of the globally produced and marketed cereal crops which cover 15% of the total sowing areas of cereal crops in the world. This study focused on the analysis of factors affecting smallholder farmer’s participation in wheat cluster farming in Gasera District of Bale zone. A multi-stage sampling method was employed for this study. Simple random sampling technique was used to select 259 sample respondents in three selected Kebeles out of 24 total kebeles in the woreda. Primary data sources were generated using an interview schedule, five Focus Group Discussions, and eight key informant interviews. Besides, secondary data were obtained from literature and the Woreda Agricultural Office. Both descriptive statistics and econometric models were employed to analyze the collected data. The descriptive statistics results showed that from the total sampled households, 169 (65.3%) were participant and, 90 (34.7%) were non-participant smallholder farmers of wheat cluster farming technology. According to the probit regression model, education status, wheat land, livestock size, frequency of extension contact, credit access, availability of fair price, market information, agricultural input access, training access and membership in a cooperative had a positive influence on farmers’ decisions to participate in wheat cluster farming. Therefore, the local community, Woreda Agriculture Office and research institutes need to expand cluster farming technology to increase wheat production and policies aiming at increasing farmers’ awareness of producing wheat in cluster to increase yield of farmers.

Building a community-driven bioinformatics platform to facilitate Cannabis sativa multi-omics research.

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