Data Sources Research Articles

Maintenance OCS Were Used More Frequently Than Biologics in Patients with Uncontrolled GINA 4/5 Asthma in Germany in 2019.

Asthma is affecting 4-5% of all adults (10% of children) in Germany, ≥ half are inadequately controlled. In 2019 up to 54 thousand patients suffered from severe uncontrolled asthma, 52% were treated/co-treated by pneumonologists. 45% of them had continuous oral corticosteroid (OCS)- and short-acting β2-agonist (SABA) overuse for ≥2 years. The aim of the current study was to analyze the main treatments, escalation schemes and the adherence to the GINA recommendations. Retrospective analysis in 2021 based on data from January to December 2019 in Germany, using the IQVIA™ LRx prescription database and the IQVIA™ Disease Analyzer database containing anonymized electronic medical records as the main data sources. In 2019 25,200 patients with severe, uncontrolled asthma treated in a pneumonologist´s practice in Germany received GINA 3 (0,4%), GINA 4 (76%) or GINA 5 therapy (24%) during the study year compared to 59% GINA 5 therapy in the 5-10% (1,500-3,000) co-treated in a specialized outpatient department. In Pneumonologists` practices the most frequent choice in GINA 5 was OCS in 69% of patients (biologicals 37%, long-acting muscarinic antagonist (LAMA) 20%) compared to 66% biologicals, 55% OCS, and 25% LAMA in the outpatient department. 54,958 of 613,000 GINA 4/5 patients were treated with OCS, 9,725 even with doses above the so called "Cushing threshold" for prednisolone of 2700 mg/year. After introduction of a biological treatment, patients reduced their SABA prescriptions by 28%, OCS by 55%, and OCS overall exposure by 40%, one-third did not need OCS anymore. In 75% of patients with uncontrolled asthma for ≥2 years therapy was not escalated beyond GINA 4 or low dose OCS was used as the most frequent add-on treatment in GINA 5 contradictory to treatment recommendations. Use of biologics reduced on demand rescue medication and OCS use.

Developing Prototype Digital Twins for biodiversity conservation and management: achievements, challenges and perspectives

The BioDT project, funded by the European Union Horizon Europe Programme, seeks to investigate and push the boundaries of methodological, intellectual, data, technical and relevance aspects of the digital twin (DT) approach for understanding and predicting biodiversity patterns and processes. In this special issue, we present the reasoning and goals of the project and the overall advancements made towards the development of ten prototype Digital Twins (pDTs). Based on models of ecosystems, species and biological processes, digital twins integrate diverse data sources with advanced modelling and simulation techniques to provide a comprehensive, dynamic and data-driven understanding of biodiversity. Leveraging the EuroHPC LUMI infrastructure and data and expertise from GBIF, eLTER, DiSSCo and LifeWatch ERIC research infrastructures, the project aims to develop hybrid models combining the strengths of statistical and process-based models for improved predictive capabilities. While there are specific challenges and limitations to biodiversity DTs, the pDTs of BioDT lay the foundation for model-data fusions that will offer potential benefits to a wide range of end-users, from researchers and conservation organisations to policy-makers, land managers, educators and private sector companies. By fostering global interoperability in biodiversity data and research, BioDT paves the way for future collaborative efforts in biodiversity conservation and management.

Soil cover heterogeneity associated with biocrusts predicts patch-level plant diversity patterns

ContextSoil resource heterogeneity drives plant species diversity patterns at local and landscape scales. In drylands, biocrusts are patchily distributed and contribute to soil resource heterogeneity important for plant establishment and growth. Yet, we have a limited understanding of how such heterogeneity may relate to patterns of plant diversity and community structure.ObjectivesWe explored relationships between biocrust-associated soil cover heterogeneity and plant diversity patterns in a cool desert ecosystem. We asked: (1) does biocrust-associated soil cover heterogeneity predict plant diversity and community composition? and (2) can we use high-resolution remote sensing data to calculate soil cover heterogeneity metrics that could be used to extrapolate these patterns across landscapes?MethodsWe tested associations among field-based measures of plant diversity and soil cover heterogeneity. We then used a Support Vector Machine classification to map soil, plant and biocrust cover from sub-centimeter resolution Unoccupied Aerial System (UAS) imagery and compared the mapped results to field-based measures.ResultsField-based soil cover heterogeneity and biocrust cover were positively associated with plant diversity and predicted community composition. The accuracy of UAS-mapped soil cover classes varied across sites due to variation in timing and quality of image collections, but the overall results suggest that UAS are a promising data source for generating detailed, spatially explicit soil cover heterogeneity metrics.ConclusionsResults improve understanding of relationships between biocrust-associated soil cover heterogeneity and plant diversity and highlight the promise of high-resolution UAS data to extrapolate these patterns over larger landscapes which could improve conservation planning and predictions of dryland responses to soil degradation under global change.

Unveiling soil coherence patterns along Etihad Rail using Sentinel-1 and Sentinel-2 data and machine learning in arid region

This research applies machine learning to predict soil coherence for Etihad Rail, marking the first comprehensive study in the United Arab Emirates (UAE)'s arid regions. By integrating Sentinel-1 SAR and Sentinel-2 data with MODIS Aerosol Optical Depth (AOD) observations, the study develops detailed models that depict complex soil coherence patterns crucial for urban planning and risk assessment. Findings show variations in soil coherence between operational and under-construction phases, influenced by seasonal changes in aerosol dynamics and sand dust levels. Higher soil coherence is linked with lower annual sand dust deposition and AOD measurements, emphasizing the importance of this data for informed decision-making. The study employs a unique combination of data sources and machine learning algorithms to predict soil coherence, including Support Vector Machine (SVM), Extreme Gradient Boosting (XGBOOST), Gaussian Process Regression (GPR), Random Forest (RF), and 1D Convolutional Neural Network (CNN), with the Random Forest model achieving the lowest root mean squared error (RMSE) of 0.0826. These contributions enhance our understanding and provide a valuable framework for infrastructure development in similar environments.

Automatic monitoring of the bio[sbnd]colonisation of historical building's facades through convolutional neural networks (CNN)

Built cultural heritage is exposed to various deterioration problems caused by different types of actions. To reduce the need for major interventions, preventive conservation (PC) approaches were proposed, based on data collection, regular monitoring, inspections, and control of environmental factors. Monitoring actions able to depict the evolution of buildings’ deterioration state, have been proposed and implemented in real cases. Considering that digital images (DI) of historical facades are constantly collected by different subjects and for different purposes, they represent the widest existing data source to support PC approaches and develop predictive tools. DI of historical façades can be used to help in the early recognition of different types of deterioration processes, supporting the creation and application of predictive models based on machine learning (ML) methods. This work proposes a method for the automatic detection of biological colonisation of building facades. A convolutional neural network (CNN) has been trained and tested with images representing the microalgae and cyanobacteria growth process on historical bricks’ facades, collected during experimental activities in controlled conditions. The trained model is characterized by an accuracy of 87 % and can recognise bio-colonisation on different types of bricks. The trained model has been applied to a historical building used as a case study. The facades of the case study are constantly monitored by surveillance cameras, and DI of the facades are often collected due to the public function of the building. The study shows that by simply processing these images with the trained network it is possible to detect the first stage of bio-deterioration processes. This work is part of more extensive research for the early detection of different types of building façade damages and can be easily implemented where DI coming from surveillance cameras or other sources are available.

Makna Ider Bumi pada Bulan Sura di Kelurahan Penataban Kecamatan Giri Banyuwangi

Penataban Village is one of the neighborhoods located in the center of Banyuwangi. Although situated in the center of Banyuwangi, Penataban Village continues to uphold traditional customs and cultural practices, such as the Ider Bumi ceremony. According to Kridalaksana (2023:13), meaning refers to the speaker's intention, the influence of language units in understanding human perception or behavior, relationships in terms of similarities or differences between language and non-language contexts, or between speech and all that it denotes, or the way language symbols are used. This study employs a qualitative research method. The data source for this research is the Ider Bumi event held during the Suro month in Penataban Village, Banyuwangi. The data focuses on the aspects of the meaning of Ider Bumi during the Suro month in Penataban Village, Banyuwangi. Data collection methods include (1) documentation, (2) note-taking, and (3) interviews. The Ider Bumi ceremony takes place both at night and in the morning. The meaning of the Ider Bumi ceremony in Penataban Village differs from that in other villages. The Ider Bumi event in Penataban is specifically held on the first day of Sura. In addition to the nighttime village procession, there is a communal meal held in the morning. This tradition is an essential part of the cultural and social identity of Penataban Village. The harmonious relationship between humans and nature is demonstrated through the communal meal, where residents come together to share food made from local produce. Furthermore, the harmonious relationship between humans and spirituality is reflected in the procession, which includes recitations of istighfar (a form of seeking forgiveness), the call to prayer, and prayers at every corner of the village

Comparative analysis of matrix factorization and graph convolutional networks in student

Abstract. For the educational worker, predicting students grades and having a deep understanding of students levels is quite important to improve their teaching methods. Fortunately, there have been several research to predict students grades in applying different models, such as Matrix Factorization (MF) and Graph Convolutional Networks (GCNs). This essay is talking about comparing two different models, MF and GCNs, which are going to exhibit the difference between them. By comparing their performance in predictive accuracy, interpretability, and computational efficiency, people can identify their strengths and areas for improvement. In this essay, the advantages and disadvantages of the two models will be listed and their performance will be compared. Therefore, in this context, this essay will introduce two models first, then show their performance in different experiences from past research and compare their results. As a result, MF shows a better performance in handling large-scale sparse datasets and providing meaningful interpretations, whereas GCNs are good at capturing complex dependencies and integrating multiple data sources.

Analysing influential factors in e-learning technology acceptance for digital learning effectiveness enhancement

This study aims to discover the factors that influence the acceptance of electronic learning media and its effect on the effectiveness of digital instruction. The study involved a learning management system and online tutorials using management system (MS) teams from various faculties at the Universitas Terbuka. These include the Faculty of Economics and Business, the Faculty of Teacher Training and Education, the Faculty of Science and Technology, and the Faculty of Law, Social Sciences, and Political Sciences. also conducted in three regional offices in Ambon, Gorontalo and Makassar, which were selected through purposive sampling. Primary data sources include participant questionnaires and interviews. Analytical methods include correlation and multiple regression analysis, with descriptive statistics used to summarize the dataset. Variable selection involves reliability and validity tests and classical assumption tests like multicollinearity. The F-test demonstrates a statistically significant and beneficial influence of utilizing e-learning media and individual ability, training, and intention to use it for digital learning success. The results of ttests and regression models provide empirical evidence supporting the significant effect of e-learning media and intention to use on the effectiveness of digital learning. However, variables related to training and ability do not show any significant influence.

A two-stage deep learning architecture for detection global coastal and offshore submesoscale ocean eddy using SDGSAT-1 multispectral imagery

Submesoscale ocean eddies are essential oceanic phenomenon that control and influence the ocean energy cascade. Most existing eddy detection methods rely on low-resolution satellite altimeter data, which fail to capture submesoscale ocean features and oceanographic phenomena in shallow water. Introducing high-resolution multispectral data can alleviate these problems, yet it has been largely overlooked. A generalized and efficient deep learning architecture that combines developments in deep learning with Sustainable Development Goals Science Satellite 1 (SDGSAT-1) multispectral data from earth observations offers a potential pathway for more fine detection of ocean eddies. Considering that oceanic eddy exhibits spatially sparse characteristics on high-resolution remote sensing scenes, the oceanic eddy detection (OED) model suitable for global coastal and offshore regions is divided into two stages: eddy information judgment and eddy position determination. Correspondingly, SDGSAT-1 multispectral data from November 2021 to December 2022 were carried out to construct two submesoscale eddy datasets for training and testing each stage model. The union validation of multiple metrics demonstrates that the proposed OED model and its stage models achieve state-of-the-art (SOTA) performance, especially in optically complex coastal and offshore waters. We applied the model to real-world scenes captured by SDGSAT-1 in 2023, and found that the detected results were mainly located at the water depth below 200 m. The authenticity of the recognition results is validated using sea surface chlorophyll concentration, temperature, and topography data, indicating that the OED model has achieved remarkable effectiveness under various sea conditions. In addition, the temporal distributions and statistical characteristics of detected submesoscale eddies are analyzed over an extended period (November 2021 to November 2023). Finally, HISEA-2, Landsat-9, and Sentinel-2 served as testing grounds to validate the generalization of the proposed methodology, with experimental results demonstrating that the OED model possesses significant developmental potential for multi-source remote sensing data. This paper presents a comprehensive deep learning framework for the global-scale detection of submesoscale eddies and underscores the pivotal role of high-resolution multispectral imagery as an innovative data source for global coastal and offshore eddy identification.

A StarGAN and transformer-based hybrid classification-regression model for multi-institution VMAT patient-specific quality assurance.

The field of artificial intelligence (AI)-based patient-specific quality assurance (PSQA) for volumetric modulated arc therapy (VMAT) faces challenges in terms of developing general models across institutions due to the prevalence of multi-institution data collection and multivariate heterogeneity. Building a general model that is capable of handling diverse multi-institution data is critical for enabling large-scale integration and analysis. This study aims to develop a star generative adversarial network (StarGAN) and transformer-based hybrid classification-regression PSQA framework to address unification of heterogeneous data from different institutions. A StarGAN and transformer-based hybrid classification-regression model was developed as a general PSQA framework to predict gamma passing rates (GPRs) and classify quality assurance (QA) results as "Pass" or "Fail" at multiple institutions. A total of 1815 VMAT plans were collected from eight institutions to develop the general PSQA framework and perform clinical commissioning and implementation. Among them, 20 independent clinical plans from each of eight institutions, for a total of 160 plans, were used for the clinical commissioning, and 205 new clinical plans from eight institutions were used for clinical implementation. For the 3%/3, 3%/2, and 2%/2mm gamma criteria, the sensitivity of the proposed PSQA framework with pretraining was 90.13%, 92.03%, and 95.84%, respectively, while the specificity was 76.01%, 76.12%, and 85.34%, respectively. The mean absolute errors (MAEs) of the proposed PSQA framework with pretraining were 1.36%, 2.37%, and 3.96%, respectively, while the root-mean-square errors (RMSEs) were 2.31%, 3.89%, and 5.17%, respectively. The results demonstrated visible improvement at multiple institutions. For clinical commissioning, the deviations between the predicted and measured results were all within 3% for 3%/3 and 3%/2mm at eight institutions. For clinical implementation, all failure plans were correctly identified by the proposed PSQA framework. The general PSQA framework enables diverse clinical data sources to be handled to achieve enhanced model performance and generalizability, and provides a solution to the unification of heterogeneous data from different institutions to construct robust QA models. This approach can be clinically deployed for VMAT QA.

An automated computational data pipeline to rapidly acquire, score, and rank toxicological data for ecological hazard assessment.

Biological Evaluations support Endangered Species Act (ESA) consultation with the US Fish and Wildlife Service and National Marine Fisheries Service by federal action agencies, such as the USEPA, regarding impacts of federal activities on threatened or endangered species. However, they are often time-consuming and challenging to conduct. The identification of pollutant benchmarks or guidance to protect taxa for states and tribes when USEPA has not yet developed criteria recommendations is also of importance to ensure a streamlined approach to Clean Water Act program implementation. Due to substantial workloads, tight regulatory timelines, and the often-protracted length of ESA consultations, there is a need to streamline the development of biological evaluation toxicity assessments for determining the impact of chemical pollutants on ESA-listed species. Moreover, there is limited availability of species-specific toxicity data for many contaminants, further complicating the consultation process. New approach methodologies are being increasingly used in toxicology and chemical safety assessment to rapidly and cost-effectively provide data that can fill gaps in hazard and/or exposure characterization. Here, we present the development of an automated computational pipeline-RASRTox (Rapidly Acquire, Score, and Rank Toxicological data)-to rapidly extract and categorize ecological toxicity benchmark values from curated data sources (ECOTOX, ToxCast) and well-established quantitative structure-activity relationships (TEST, ECOSAR). As a proof of concept, points-of-departure (PODs) generated in RASRTox for 13 chemicals were compared against benchmark values derived using traditional methods-toxicity reference values (TRVs) and water quality criteria (WQC). The RASRTox PODs were generally within an order of magnitude of corresponding TRVs, though less concordant compared with WQC. The greatest utility of RASRTox, however, lies in its ability to quickly and systematically identify critical studies that may serve as a basis for screening value derivation by toxicologists as part of an ecological hazard assessment. As such, the strategy described in this case study can potentially be adapted for other risk assessment contexts and stakeholder needs. Integr Environ Assess Manag 2024;20:2203-2217. © 2024 Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Understanding the role of machine learning in predicting progression of osteoarthritis.

Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice.

Feasibility of coaxial deep borehole heat exchangers in southern California

This paper investigates the feasibility of coaxial deep borehole heat exchanger (CDBHE) applications to the University of California San Diego (UCSD) campus. By collecting different geophysical source data for various formations and well logs around the UCSD campus, a multilayered thermophysical model for the ground on the site is established. Water circulation within a closed coaxial loop system considers the geothermal energy extraction under uncertainty consideration of the unknown deeper layers heat flow gradient as coupled with the variation of pipe insulation properties, flow rates, outer pipe diameter, grout, and depths between 1 and 4 km. A finite-element framework models the Navier–Stokes fluid flow and heat transfer in the CDBHE system, validated with a field test on CDBHE from the literature. Results show that a 4-km CDBHE could produce a thermal power of 600 kW under the optimum geological conditions at the UCSD site: the water flow rate of 2.78 L/s and a ground thermal gradient of 60 ℃/km. Thermal power shares from different layers indicate that deeper formation layers contribute more to the thermal power than the shallower layers because increasing the CDBHE length from 1 to 4 km can lead to a maximum of 900% increase in thermal power and a 50% expansion in thermal plume for a CDBHE with an insulated inner pipe between the upper and lower bound heat flow bounds. An inner pipe with an insulated depth of 2 km produces only 1–6% less power than a fully insulated inner pipe for the 4-km CDBHE, and thus, a partially insulated vacuum-insulated tube (VIT)-plastic inner pipe is suggested as the best practice. Furthermore, the CDBHE thermal power increases by 5% when the grout thermal conductivity increases from 1 to 3.65 W/(K∙m), close to the formation thermal conductivity, and then maintains almost the same, and the 4-km CDBHE with flow rates of 2.78–6.94 L/s at the UCSD site can directly supply a low-temperature heating radiator system for room heating. This study suggests practical ranges for geothermal energy extraction for southern California. A CDBHE with a well-insulated inner pipe of 0.05 W/(m∙K), the thermal power of lower and upper-bound heat flow cases can vary by 60% from the mean. Finally, water as the working fluid is more efficient than CO2, doubling CDBHE's thermal power. The effects of the investigated factors provide guidelines for future geothermal resource exploitation in southern California.

Pricing of Halal Fashion Products in Facing Competition Reviewed From an Islamic Economic Perspective

This article discusses the pricing of halal fashion products in facing competition with an Islamic economic perspective. This study aims to analyze the pricing of halal fashion products in facing competition with an Islamic economic perspective. This study uses a literature review approach, namely reviewing related research to be used as a source of data in the study. The results of the study show that in determining the price of halal fashion products, namely by looking at production costs, halal certification, product quality, and distribution and marketing costs. In facing competition, a marketing strategy for Muslim fashion products is needed, preferably based on digital marketing in order to reach a wider market. In the competition perspective of Islamic economics, there are also several principles that need to be applied, namely healthy competition, honesty, good and sympathetic thinking, justice, and the prohibition of gharar and usury.

Intellectual Stimulation of Women's Transformational Leadership in Improving Institutional Brand Image

Education is one of the efforts to improve human quality. One of the elements that makes education more qualified, Various problems that are inseparable from life as an organization. These problems cover several aspects, such as defining goals, determining policies, developing programs, employing people, providing facilities, achieving results and coordinating separate activities. All of these activities require the involvement of people with different ability backgrounds, such as professional supervisors, and other personnel. This study aims to describe: Intellectual stimulation of women's transformational leadership in improving institutional brand image at the Bahrul Ulum Mambaiyyah Islamic Boarding School (BUM) in South Lampung Regency. This study uses a qualitative approach. This type of research can be used to find and understand what is hidden behind a phenomenon that is sometimes difficult to know or understand. Data collection in this study uses in-depth interview techniques, observation, and documentation where the data sources are from people, documents (paper) and places (place). While to obtain data obtained from informants in various forms such as written notes, interviews, observations and photos. Both from leaders, staff, stakeholders and also those involved in the management of the Bahrul Ulum Mambaiyyah Islamic Boarding School (BUM) in South Lampung Regency. The results of this study can be concluded that; In intellectual stimulation, Mbah Nyai is seen from her intelligence, tenacity, relationships, willingness to sacrifice and uniqueness in her preaching. As well as the charismatic and revolutionary leadership model approach and policies based on the mandate of the vision, mission and objectives of the institution and the mandate.

Deconstruction of Early Marriage: A Critical Study of Imam Shafi'i's Views and Implications on Community Welfare

Early marriage is still a crucial issue in various countries, including Indonesia. Imam Shafi'i's views on early marriage need to be examined critically in a modern context. This article aims to deconstruct Imam Shafi'i's views on early marriage and analyze its implications on the welfare of society. A qualitative approach is used with the method of literature study. Data were collected from books of jurisprudence and other secondary sources. Imam Shafi'i's view of early marriage has several dimensions, including the minimum age of marriage, the legal requirements of marriage, and the consequences of early marriage. This view has complex implications for people's well-being, both positive and negative. The purpose of this article is to critically discuss Imam Shafi'i's views on early marriage and identify its implications on the welfare of society. In doing this, we will conduct a literature study that involves an analysis of the books of jurisprudence and other secondary sources. Thus, this article will provide a deeper understanding of Imam Shafi'i's views and their impact on society as a whole. A qualitative approach was used in this study with the method of literature study. Through this method, data were collected from various books of jurisprudence relating to Imam Shafi'i's views on early marriage. In addition, other relevant secondary sources are also used to enrich the data collected. By combining these two data sources, this study has a solid basis in analyzing Imam Shafi'i's views and their implications on people's welfare. Imam Shafi'i has a complex view of early marriage. In the modern context, this view needs to be critically deconstructed to understand its implications for the well-being of society. One important factor to consider is age. Imam Shafi'i sets a minimum age of marriage that must be met before a person can marry. In addition, maturity must also be considered. One must have sufficient physical, mental, and emotional maturity before deciding to get married. In addition, socioeconomic conditions are also important to consider. Financial conditions and economic stability greatly affect the welfare of married couples. Therefore, in making marriage decisions, it is important for individuals and society to consider these various factors carefully.

Vision rehabilitation workforce in Italy: a country-level analysis

BackgroundResearch and monitoring of human resources available for vision rehabilitation services has been a neglected area of work in the past. This study aims to offer an overview of the vision rehabilitation workforce available in Italy, in order to profile the distribution and number of human resources for vision rehabilitation.MethodsData on the available vision rehabilitation professionals were collected from the yearly report on the state of implementation of policies relating to the prevention of blindness, education and vision rehabilitation, according to a law which was passed by the Italian Ministry of Health, Department of Health Prevention. The report presents a review of all professional workers dealing with low vision rehabilitation centers in Italy between January 2005 and December 2019. Data on the distribution and type of services of government-supported low vision centers across the country were also obtained and examined.ResultsOf the 289 low vision rehabilitation workers in 2019, 28% were ophthalmologists, 31% orthoptists, 19% psychologists, 17% nurses and 5% social workers.The health workforce densities across the Italian regions ranged from 1.62 to 0.12 per 100.000. The density of vision rehabilitation workers showed a no growing trend from 2006 to 2015. During the study period, it was found a weak but statistically significant association of workforce density with the number of government-supported low vision centers across the Italian territory (r2 = 0.3, p < 0.05). The vision rehabilitation workforce was not associated with the number of low vision patients who accessed to a vision rehabilitation center (r2 = 0.05, p < 0.0001).DiscussionA critical review has identified the following national situation: need-based shortages of workers in the vision rehabilitation service sector, as well as deficiencies in data sources. Based on our results, we would recommend increasing the development of human resources trained and dedicated to vision rehabilitation and improve data collection and analysis; provide structural enhancements, across all service levels. These considerations may contribute to the enhancement of policy decisions in order to guarantee an adequate vision rehabilitation workforce and meet national rehabilitation needs. Furthermore, this analysis should be used as a lesson learned by other countries, as low-income ones, in order to develop vision rehabilitation services.

Automatic Method for Detecting Deformation Cracks in Landslides Based on Multidimensional Information Fusion

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5–55.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901.

Educational Data Mining: A Foundational Overview

Educational data mining (EDM) is a novel scientific area that focuses on developing and applying methods to analyze datasets generated within educational settings. This paper outlines the evolution, significance, and applications of EDM. With the increasing popularity of e-learning in web-based educational systems, EDM has expanded to include a variety of analytical methods and data sources. Some key methodologies addressed include classification, regression analysis, clustering techniques, association rule mining, and Natural Language Processing, among others. Additionally, this paper looks at how EDM can facilitate data-driven decision-making among other areas such as curriculum development and customization of learners’ experiences. It also touches on issues related to the challenges of the scientific field. Finally, some projections about EDM’s future trends are made, especially concerning its integration into AI technologies and development trends like augmented reality or virtual reality, which imply greater possibilities for changes than any other series witnessed before within this sphere.

Optimizing Traveler Behavior Between MADINA and JEDDA Using UPPAAL Stratego: A Stochastic Priced Timed Games Approach

This study looks at how travelers move between MADINA and JEDDA, using the UPPAAL Stratego tool to tackle the complexities of urban mobility. As cities grow, effective transportation planning becomes more challenging. Travelers have three options: car, bus, and train. The choices for car and bus travel are impacted by traffic conditions, which can vary between heavy and light, affecting both travel time and cost. We propose a detailed mathematical model that captures all possible scenarios related to these travel options, incorporating the uncertainties of real life. This allows us to simulate different traffic situations. By using UPPAAL Stratego, we evaluate three strategies: the Safe Strategy, which minimizes risk; the Fast Strategy, which aims to reduce travel time; and the Fast and Safe Strategy, which seeks a balance between speed and safety. This paper starts with an introduction to the Stochastic Priced Timed Games approach, highlighting its relevance in modeling dynamic travel environments. We then provide an overview of UPPAAL Stratego, showcasing its abilities in generating, optimizing, and comparing strategies. Next, we outline our mathematical model, explaining the assumptions, parameters, and data sources we used. Our simulation results illustrate how each strategy performs under different conditions, shedding light on traveler preferences and behaviors. The findings underscore the significance of accounting for traffic variability in travel planning and offer important insights for urban transportation policies aimed at improving the traveler experience and optimizing resource use. Additionally, we emphasize the theoretical contributions of our model by demonstrating its applicability to real-world scenarios and its potential to inform future research in urban mobility optimization. Ultimately, this research adds to the growing knowledge of smart transportation systems, demonstrating how formal mathematical modeling can address complex real-world challenges and inform future urban mobility strategies.