Aim: To assess healthcare professionals’ awareness, attitudes, and utilization of community-based digital health platforms for preventive care in underserved districts of Khyber Pakhtunkhwa, Pakistan, and to identify key barriers associated with routine use. Methods: A cross-sectional survey was conducted between December 2024 and February 2025 among 400 healthcare professionals (doctors, nurses, and allied health practitioners) working in primary, secondary, and tertiary facilities in Swabi and Mardan. Participants were recruited using purposive, stratified (quota-based) sampling. The questionnaire captured knowledge/awareness, attitudes, self-reported utilization, and perceived barriers (infrastructure, training, and privacy). Descriptive statistics were produced, and multivariable regression was used to examine factors associated with utilization. Results: Among the 400 respondents, 332 (83.0%) reported awareness of digital health platforms and 312 (78.0%) reported positive attitudes toward their use. Overall, 297 (74.3%) reported using digital health platforms in practice. The most frequently reported barriers were lack of infrastructure (n = 309, 77.3%), limited training (n = 297, 74.3%), and data privacy concerns (n = 295, 73.8%). In the adjusted logistic regression model, greater knowledge of digital health platforms was associated with higher odds of routine use (aOR = 10.56, 95% CI: 2.36–47.35; p = 0.002), whereas attitude and infrastructure barriers were not significant (p > 0.05). Conclusions: Healthcare professionals in Swabi and Mardan reported high awareness and favorable attitudes toward community-based digital health platforms, but infrastructure gaps, limited training, and data privacy concerns were common barriers. Greater platform knowledge predicted routine use. Strengthening facility readiness, workflow-based training, and practical safeguards to address data privacy concerns may enable safer, more equitable scale-up; findings are context-specific due to non-probability sampling.
Christian Simeoni, Elisa Furlan, Angelica Bianconi
et al.
Human well-being and societal resilience are deeply tied to marine coastal ecosystems, yet they are threatened by multiple endogenic and exogenic pressures. This study implements a MAchine leaRnIng-based CumulaTive Impact AssessMEnt (MARITIME) model to understand better the risk of cumulative impacts on seagrass meadows in the Mediterranean Sea and their cascading effects on the capacity to provide ecosystem services for this vital blue carbon ecosystem under a reference state (2017) and future climate change scenarios, which encompass various levels of global warming, from moderate to more extreme. The model employs a Random Forest algorithm to support robust, data-driven analysis of complex environmental interactions. The results indicate a shrinkage of seagrass meadows, particularly at lower depths, and a corresponding reduction in ecosystem services capacity by the years 2050 and 2100. This trend is notably severe across the entire Mediterranean basin in the long-term scenario, with an estimated reduction of approximately 20 % in seagrass distribution under the most severe scenario considered in this study (RCP8.5 projected to the end of the century). Such shrinkage may trigger further impacts, starting with a decline in ecosystem service provision: model projections indicate a potential decrease of approximately 3.5 %–6 % in carbon sequestration and up to 20 % in denitrification potential by the century's end, contingent upon the scenario considered. Furthermore, the loss of seagrass could also lead to the release of buried carbon, potentially transforming Mediterranean coastal areas from carbon sinks into sources of carbon emissions - a phenomenon likened to a ‘carbon bomb.’
In the realm of healthcare, an imperative necessity for all, institutions are increasingly recognizing the advantages of adopting lean strategies to enhance performance. Lean implementation in healthcare can lead to significant improvements in efficiency, patient care, and overall institutional performance. This paper aims to assess the readiness levels for implementing lean practices in healthcare institutes in Bangladesh, employing a fuzzy logic approach. The construction of a conceptual model is grounded in literature review and expert opinions, incorporating critical enablers, criteria, and attributes identified from extensive research. Factors measured include leadership commitment, workforce capability, operational processes, technological infrastructure, and organizational culture, each pivotal in determining readiness for lean implementation. The fuzzy logic approach is particularly useful in this context due to its ability to handle uncertainty and imprecision, which are common in complex environments like healthcare. This methodology not only provides a clear picture of current capabilities but also highlights specific areas that need enhancement, paving the way for more targeted and effective lean interventions. Data sourced from consultations with experts in three prominent hospitals in Bangladesh forms the basis of the analysis, enabling a detailed examination of readiness levels. The model's application of fuzzy logic facilitates a comprehensive assessment, revealing 12 critical attributes across the hospitals that require attention. Interestingly, the evaluation identifies varying levels of readiness, with two hospitals demonstrating moderate readiness and one showing a lower level. This conceptual approach has significant potential to assist top management in healthcare companies by providing a structured framework to prioritize crucial areas for improvement. By accurately assessing readiness levels and pinpointing weaker aspects before implementing lean strategies, this study aims to transform the healthcare industry. Ultimately, its implementation has the potential to enhance organizational performance and elevate standards in patient care, contributing to improved healthcare delivery in Bangladesh and beyond.
In this study, we propose a population-based, data-driven intelligent controller that leverages neural-network-based digital twins for hypothesis testing. Initially, a diverse set of control laws is generated using genetic programming with the digital twin of the system, facilitating a robust response to unknown disturbances. During inference, the trained digital twin is utilized to virtually test alternative control actions for a multi-objective optimization task associated with each control action. Subsequently, the best policy is applied to the system. To evaluate the proposed model predictive control pipeline, experiments are conducted on a multi-mode heat transfer test rig. The objective is to achieve homogeneous cooling over the surface, minimizing the occurrence of hot spots and energy consumption. The measured variable vector comprises high dimensional infrared camera measurements arranged as a sequence (655,360 inputs), while the control variable includes power settings for fans responsible for convective cooling (3 outputs). Disturbances are induced by randomly altering the local heat loads. The findings reveal that by utilizing an evolutionary algorithm on measured data, a population of control laws can be effectively learned in the virtual space. This empowers the system to deliver robust performance. Significantly, the digital twin-assisted, population-based model predictive control (MPC) pipeline emerges as a superior approach compared to individual control models, especially when facing sudden and random changes in local heat loads. Leveraging the digital twin to virtually test alternative control policies leads to substantial improvements in the controller’s performance, even with limited training data.
Image anomaly detection problems aim to determine whether an image is abnormal, and to detect anomalous areas. These methods are actively used in various fields such as manufacturing, medical care, and intelligent information. Encoder-decoder structures have been widely used in the field of anomaly detection because they can easily learn normal patterns in an unsupervised learning environment and calculate a score to identify abnormalities through a reconstruction error indicating the difference between input and reconstructed images. Therefore, current image anomaly detection methods have commonly used convolutional encoder-decoders to extract normal information through the local features of images. However, they are limited in that only local features of the image can be utilized when constructing a normal representation owing to the characteristics of convolution operations using a filter of fixed size. Therefore, we propose a vision transformer-based encoder-decoder model, named AnoViT, designed to reflect normal information by additionally learning the global relationship between image patches, which is capable of both image anomaly detection and localization. While existing vision transformers perform image classification using only a class token, the proposed approach constructs a feature map that maintains the existing location information of individual patches by using the embeddings of all patches passed through multiple self-attention layers. Subsequently, the feature map, which has been transformed into three dimensions, is used to perform decoding. This design preserves the spatial information sufficiently by excluding the fully-connected layer, which extracts latent vectors in existing convolution-based encoder-decoders. The proposed AnoViT model performed better than the convolution-based model on three benchmark datasets. In MVTecAD, which is a representative benchmark dataset for anomaly localization, it showed improved results on 10 out of 15 classes compared with the baseline. Furthermore, the proposed method showed good performance regardless of the class and type of the anomalous area when localization results were evaluated qualitatively.
A major goal in pre-detonation nuclear forensics is to infer the processing conditions and/or facility type that produced radiological material. This review paper focuses on analyses of particle size, shape, texture (“morphology”) signatures that could provide information on the provenance of interdicted materials. For example, uranium ore concentrates (UOC or yellowcake) include ammonium diuranate (ADU), ammonium uranyl carbonate (AUC), sodium diuranate (SDU), magnesium diuranate (MDU), and others, each prepared using different salts to precipitate U from solution. Once precipitated, UOCs are often dried and calcined to remove adsorbed water. The products can be allowed to react further, forming uranium oxides UO3, U3O8, or UO2 powders, whose surface morphology can be indicative of precipitation and/or calcination conditions used in their production. This review paper describes statistical issues and approaches in using quantitative analyses of measurements such as particle size and shape to infer production conditions. Statistical topics include multivariate <i>t</i> tests (Hotelling’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>T</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>), design of experiments, and several machine learning (ML) options including decision trees, learning vector quantization neural networks, mixture discriminant analysis, and approximate Bayesian computation (ABC). ABC is emphasized as an attractive option to include the effects of model uncertainty in the selected and fitted forward model used for inferring processing conditions.
El presente artículo muestra el origen del desarrollo sostenible en forma breve, así como las proyecciones futuras de los problemas que pueden ocasionar el crecimiento demográfico y el impacto del avance industrial en los países. También se analiza sobre los conceptos y la caracterización que se da al desarrollo sostenible, sus alcances y las ventajas que pueden proporcionar las políticas establecidas en las regiones que implementan dichas normativas. Asimismo, las Tecnologías Limpias (TL), que aportan a la mejora de las emisiones y desechos que emiten las industrias al ambiente, son tratadas en la dimensión de la eficacia de las industrias. Se determina además la forma que pueden aportar ciertos indicadores en la evaluación del desarrollo y cómo estos contribuyen a la resolución de varios problemas relacionados al medio ambiente y la energía tales como: Sustentabilidad ecológica que se relaciona con el mantenimiento de los ecosistemas, Sustentabilidad social, que propone mejorar la calidad de vida de las personas y, Sustentabilidad económica.
Abstract The present study investigates some factors affecting college students’ tendency to use Facebook for different educational purposes. We reached 120 participants who were college students. Our sample comprised of 63 (52.5%) females and 57 (47.5%) males. We applied convenience sampling technique and an online questionnaire to collect data. Descriptive statistics, multiple regression analysis, and Structural Equation Modelling using IBM SPSS AMOS were utilized. The findings provide that GPA, Personal Use of Facebook for Studying and Socialization, Autonomy Psychological Need, and Academic Procrastination foresee college students’ willingness to use Facebook in their courses. GPA and Personal Use of Facebook for studying are the most influential factors while Autonomy Psychological Need is the least impactful. We also examined the impact of these factors on different educational use types of Facebook: communication, collaboration, resources and material sharing. The results are discussed, and further recommendations for future research and implications are presented in the current study.
Special aspects of education, Information technology
The cognitive, developing, illustrative potential of three-dimensional computer graphics actualizes the training of future teachers in its technologies. The future teacher of computer science should be able to develop three-dimensional models and animations of the studied objects (phenomena and processes) in computer science, physics, mathematics and other academic disciplines, create copyright electronic educational resources with three-dimensional illustrations, apply augmented reality and 3D printing in professional activities.The article describes the features of teaching three-dimensional computer modeling and visualization of future informatics teachers, identified on the basis of an analysis of the training system for students studying in specialty 1–02 05 02 Physics and Informatics at the Maxim Tank Belarusian State Pedagogical University.The main directions for improving the learning process of three-dimensional computer graphics of future informatics teachers are indicated. Questions on three-dimensional computer modeling and visualization will pass through the content line through the content of six academic disciplines («Computer graphics and multimedia», «Computational methods and computer modeling», «Programming Technologies and Algorithmization Methods», «Information Technologies in Education», «Methods teaching informatics» and «Architecture and software of computer systems»). This will ensure consistency in the introduction and study of concepts, the choice of forms and methods of teaching, the development of teaching and methodical support. For the implementation of interdisciplinarity, it is proposed to carry out practice-oriented interdisciplinary educational projects in the framework of the study of academic disciplines in three subject areas «Computer Science», «Physics» and «Mathematics». The development of a methodical system of interdisciplinary education will ensure the formation of readiness to teach three-dimensional graphics at the stage of general secondary education and to realize its didactic possibilities in organizing educational and research activities of students.
Selvaraj Geetha, Samayan Narayanamoorthy, Daekook Kang
et al.
Waste produced from medical facilities systems incorporates a blend of dangerous waste which can posture dangers to humans and ecological receptors. Lacking administration of healthcare waste can prompt hazard to medicinal service specialists, patients, public health, communities and the wider environment. Hence, proper management of healthcare waste is imperative to reduce the associated health and environment risk. In this paper, we extend the MULTIMOORA decision making method with intuitionistic hesitant fuzzy set to evaluate the healthcare waste treatment methods. Intuitionistic hesitant fuzzy set is a generalized form of a hesitant fuzzy set. Intuitionistic hesitant fuzzy set considers the uncertainty of data in a single framework and take more information into account. The MULTIMOORA method consists of three parts namely the ratio system, reference point approach and the full multiplicative form. In the optimal ranking methods, the IHF-MULTIMOORA method is uncomplicated it is able to be used practically with high dimension intuitionistic hesitant fuzzy sets. For pathological, pharmaceutical, sharp, solid and chemical wastes, the preferred waste disposal methods are deep burial, incineration, autoclave, deep burial, and chemical disinfection, respectively.
The problem regeneration of prefabricated housing in Poland is invariably the current issue. Due to a housing shortage, the demand for apartments in pre-fab housing is still high. Western Europe has begun to struggle with this problem much earlier and has a large experience in the subject of revitalization. However, the context of socio - economic development in Poland, different from the Western European, makes the transfer of complete solutions revitalization impossible . Presented examples show different strategies used in the past and now in Europe (housing revitalization Bijlmer in Amsterdam and building in Halle - Neustadt). They were based on the demolition to a lesser or greater extent. The difficult housing situation in Poland, related to the lack of sufficient amount of housing, poor infrastructure of social housing and complicated housing ownership structure of the buildings, mostly excludes demolition. However, the quality of prefabricated housing living could be significantly improved, without the need for demolition, as evidenced by the spectacular example of the revitalization of the Parisian skyscraper Tour Bois le Prêtre.
Muhammad Busyairi, La Ode Ahmad Safar Tosungku, Ayu Oktaviani
Human Resources (HR) is the most important asset in a company. Employees can be a good potential if managed properly and correctly, but employees can also be a burden if the company can not manage it properly. As the increase in the industry, particularly coal mining industry in East Kalimantan is always the problem that arises is the possibility of the occurrence of occupational accidents, occupational diseases, and environmental pollution. The occurrence of occupational accidents, occupational diseases, and environmental pollution due to poor human resource management can reduce employee productivity and company. Research objectives to be achieved is describing the safety and health of employees produkitivitas particularly in the production of PT. XYZ, Loa Kulu aquatic mammal, and to know the significant influence of each variable. Survey respondents were employees in the production of PT. XYZ, Loa Kulu aquatic mammal with a high of 165 employees. By using the Slovin formula can be determined that a sample of respondents is as many as 99 employees.Based on the description of each item answer the answer of respondents indicated that employees responded positively to the questions given. Multiple regression analysis showed that there were significant effects of occupational safety and health of employee productivity, it can be seen from the significance of each variable is significant <0.05. Significance X1 to Y by 0.000, and the significance of X2 to Y by 0.017. Can be inferred by looking at the results of studies showing that the implementation of occupational safety and health on the productivity of employees of PT. XZY Loa Kulu Kukar, generally not very good, is expected to be further enhanced supervision.
En el presente trabajo, consideramos un problema mixto para una ecuación de viga no lineal de tipo Kirchhoff con término disipativo en un dominio acotado. Probamos la existencia y unicidad de soluciones locales con argumentos del punto fijo de Banach. También obtenemos la propiedad de singularidad en tiempo finito de las soluciones y las estimativaspara el tiempo de explosión.
Abstract Good tolerance- and variation management is essential to achieve high value adding products with cost-effective processes. The link between Tolerance Engineering and popular manufacturing improvement philosophies such as Lean and Six Sigma is, however, not always that clear. The possibilities and limitations of these two approaches on Tolerance Engineering are discussed in this paper. The case describes cross-collaborative improvement work within industry on tolerance and variation management which is similar to a work model called “Closed Loop Tolerance Engineering” (CLTE). The case is focused on the process of revising existing drawings and tolerance specifications for the manufacturing of products with a long lifetime. Although both Lean and Six- Sigma have been important for the improvement work in the case company for several years, there is still a gap to be filled on tolerance and variation management. The novelty of this paper is found in the link between an industrial case on improvements and an academic model (CLTE) for cross-collaborative engineering on variation and tolerances.