One of the challenges of deploying Wireless Sensor Networks (WSN) at large scale is to operate for prolonged periods of time relying on the limited amounts of energy stored in its nodes. Generally, data communication is the main factor of draining energy. Consequently, reducing the amount of transmitted data by the nodes is of great interest. At the same time, reducing data to be transmitted should not decrease the quality of the deduced information. Solving this problematic is what we call in this article "Ensuring the Quality of Information Service (QoIS)". This document provides a state-of-the-art for the QoIS regarding WSNs. At first, we give a quick overview of the Internet of Things (IoT) paradigm and the WSN technologies in order to understand how data is gathered and used in these networks. Also, we introduce a novel concept: QoIS, in contrast with classic QoS. Then, it is expected a summary of criteria that asses the QoIS in WSNs plus some techniques to improve it. In this article, we will focus on data management techniques. These techniques can improve raw data, information and/or knowledge quality in energy-constrained WSNs. Finally, we give some conclusions and a glimpse about our future work.

Digital agricultural technology extension service and sustainable food production have attracted attention from academia. However, the association between digital agricultural technology extension service and sustainable food production has not yet been fully investigated. This research investigates the average and heterogeneous impacts of digital agricultural technology extension service use on eco-efficiency among 1302 maize producing farmers from Northeast China major maize producing area in 2022 to fill this knowledge gap. The slack-based measure model with undesirable outputs is applied to calculate the eco-efficiency of maize production. To obtain an unbiased estimation of the average effect, the self-selection problem generated by observable and unobservable factors is solved by the endogenous switching regression model. Quantile regression is utilized to analyze the heterogeneous effect. Notably, the mediated effects model is utilized to examine the potential mechanism between them. Our findings indicate that digital agricultural technology extension service use can increase maize production's eco-efficiency. Digital agricultural technology extension service users would reduce eco-efficiency by 0.148 (21.11%) if they had not used the digital agricultural technology extension service. Digital agricultural technology extension service nonusers would improve eco-efficiency by 0.214 (35.20%) if they had used it. The robustness check reconfirms the results. Moreover, digital agricultural technology extension service use is more helpful to maize farmers who have lower eco-efficiency than those who have higher eco-efficiency. Digital agricultural technology extension service use can improve eco-efficiency of maize production through the application of organic fertilizers, green pesticides and biodegradable agricultural films.

We confidently approach current issues in Green Finance (GF) from the perspective of an international organization, where the first author has previous experience as a support assistant. In fact, the economy of the EU (European Union) is deeply involved in the changing needs related to the dramatic consequences of climate change and environmental degradation, which has placed the needs of a more sustainable economy at the top of the international agenda. The environmental, social and governance (ESG) aspects are marginally addressed to arouse a major interest in questions raised by green financial products which require long-term profit expectations, and which above all contain a margin of risk that is difficult to calculate in the landscape of traditional financial instruments. Through a careful approach akin to artificial intelligence research methods, we attempt to “materialize” at least the most visible clues of this fashionable economic shift through what we call SMC (sustainable material culture) objects. The presented SMC objects serve as tangible representations of sustainable practices and can promote awareness and action incorporating into everyday life and business practices a powerful reminder and catalyst for a shift towards more sustainable investment and consumption patterns.

As most Korean OTT subscribers use mobile devices as their primary medium for watching OTT video content, mobile network traffic is soaring. However, the existing build-out of mobile communication technology – such as deploying more base stations or expanding the network bandwidth – is struggling to keep pace with the enormous increased demands of mobile network traffic. Accordingly, wireless device-to-device (D2D) caching networks are considered one of the most reliable alternatives for reducing the overload from mobile network traffic caused by streaming video content online. However, wireless D2D caching networks have pros and cons, like any other technology. Thus, this study aims to examine the practical decision-making process of mobile OTT service users in response to the application of wireless D2D caching networks on their mobile devices. An online survey of 94 respondents was conducted in South Korea during 19–23 May 2022; the data obtained were analyzed using the analytic hierarchy process (AHP) approach. The study finds that it was the risks, rather than the situational conditions and benefits, that mostly affected mobile OTT service users’ behavioral intentions and strategies. Streaming video content using mobile data or Wi-Fi was perceived as the most viable option. Mobile OTT service users were also willing to take the leap of shifts towards downloading video content in advance rather than adopting wireless D2D caching networks on a mobile device. However, mobile OTT service users’ decisions are heavily dependent on situational conditions related to use of mobile OTT services that make the quality of service consumption different. As one of the first to examine the responses of mobile OTT service users, this study provides meaningful implications for a timely and sensitive issue.

The article focuses on the integrated environmental accounting model called 'eValue', developed for protected areas and applied in the research programme coordinated by the Italian Ministry of the Environment and aimed at implementing an environmental accounting system for Italian Marine Protected Areas (MPAs). eValue adopts a cost-benefit analysis approach. Financial accounting based on costs and revenues is integrated with environmental accounting, which reflects environmental costs and environmental revenues, i.e. environmental benefits. The environ-mental costs assess the impacts related to human activities in the MPA expressed by calculating the carbon footprint and the environmental benefits of the marine ecosystem services calculated by applying monetary valuation techniques. The values thus estimated flow into the annual flow account, where the value produced (or consumed) by the MPA is estimated by difference. The eValue model was applied to the Porto Cesareo MPA (Italy). eValue showed that the annual benefit-cost ratio reaches a value of 3.4. Furthermore, the ratio of net benefit to public funding is 3.7, completely covering the amount of public transfers and thus summarizing the MPA overall value for money.

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

The Maastricht VI/Florence consensus recommends, as one of the measures to enhance the efficacy of Helicobacter pylori (H. pylori) infection eradication, a personalized treatment approach involving the selection of an antimicrobial agent based on the pre-determined resistance of H. pylori. To meet the need to develop test systems for personalized drug selection, we conducted a study of H. pylori molecular resistance in Moscow using a self-developed Sanger sequencing test platform. We validated our test system on 25 pure culture samples of H. pylori with known resistance. Sensitivity and specificity for detecting resistance to clarithromycin was 100%, and to levofloxacin - 93% and 92%, respectively. Then we tested our platform on 112 H. pylori -positive patients who had not previously received proton pump inhibitors (PPI) or antibacterial drugs. Mutations indicating resistance to clarithromycin were found in 27 (24%) samples, and to levofloxacin in 26 (23%) samples. Double resistance was observed in 16 (14%) samples. The most common mutations leading to clarithromycin resistance were 2143G and 2142G, to levofloxacin resistance – 261A and 271A in the gyrA gene, which account for 69% of all identified genetic determinants in levofloxacin-resistant bacteria. Thus, a personalized approach to the selection of H. pylori eradication therapy based on the detection of bacterial resistance before prescribing first-line therapy could help avoid the prescription of ineffective H. pylori eradication therapies and, overall, contribute to the control of antibiotic resistance of H. pylori.

Rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 seconds over intervals varying from 15 up to 41minutes in two different locations and in two different years using two different Micro-Rain Radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor ‘frozen’ turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that, MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations, but not the need of those radars for surveillance.

Spinal Cord Stimulation (SCS) is an effective and validated treatment to address chronic refractory neuropathic pain in Persistent Spinal Pain Syndrome-Type 2 (PSPS-T2) patients. Surgical SCS lead placement is traditionally performed under general anesthesia due to its invasiveness. In parallel, recent works have suggested that Awake Anesthesia (AA), consisting in Target Controlled Intra-Veinous Anesthesia (TCIVA), could be an interesting tool to optimize lead anatomical placement using patient intra-operative feedback. We hypothesized that combining AA with Minimal Invasive Surgery (MIS) could improve SCS outcomes. The goal of this study was to evaluate SCS lead performance (defined by the area of pain adequately covered by paraesthesia generated via SCS), using an intraoperative objective quantitative mapping tool, and secondarily to assess pain relief, functional improvement and change in quality of life with a composite score. We analyzed data from a prospective multicenter study (ESTIMET) to compare the outcomes of 115 patients implanted with MIS under AA (MISAA group) or General Anesthesia (MISGA group), or by Laminectomy under General Anesthesia (LGA group). All in all, MISAA appears to show significantly better performance in terms of patient pain coverage, as well as improved secondary outcomes. One step further, our results suggest that MISAA combined with intra-operative hypnosis could potentialize patient intraoperative cooperation and could be proposed as a personalized package offered to PSPS-T2 patients eligible for SCS implantation in highly dedicated neuromodulation centers.

The scale of China's floating population is very large, and it is a key group that needs attention in promoting the equalization of basic public services. This study explores the impact and mechanism of adjusting the urban household registration threshold on the acquisition of citizenship status by the "rural-urban" floating population, with the household registration threshold as the link. It was found that the threshold for household registration has an impact on the willingness of rural urban migrant population to settle down from two aspects. On the one hand, a high threshold for household registration implies the unaffordability of obtaining citizenship status for the "rural-urban" migrant population, which has a significant crowding out effect on their willingness to settle down. Moreover, the high skilled "rural-urban" migrant population without housing is more sensitive to the high threshold for household registration. On the other hand, from the perspective of urban scale, the increase in the threshold for household registration has had a crowding out effect on the floating population for Type I large cities, mega cities, and super large cities. The increase in the threshold for settling in mega cities has the strongest crowding out effect on the floating population, which means that as the urban population expands, the welfare benefits enjoyed by the floating population in these cities will decrease, leading them to be more inclined to leave these cities and seek development opportunities in other cities. This study effectively answers how the threshold for household registration affects the willingness of rural urban migrant population to settle, providing mathematical support for urban development.

The One Health approach often reaches classrooms through Environmental Education (EE), which aims to guide society's response to current crises (environmental, health, economic, values). However, traditional EE teaching has focused on isolated ecological concepts and standardized solutions, ignoring the multidimensional nature of EE and failing to integrate the interdependence between environmental, animal, and human health. Moreover, teacher training often neglects didactic content knowledge, preventing students from acquiring the systemic vision needed to address eco-social problems and create sustainable solutions aligned with the Sustainable Development Goals (SDGs). This limits teachers’ ability to influence students' concerns and behaviours. In this context, this study aims to reflect on the current state of the issue and propose strategies informed by Science Education research to improve EE teaching, enabling the integration of One Health dimensions through effective didactics to achieve Transformative Environmental Education (TEE). For this purpose, we begin by addressing the limitations identified in recent systematic reviews, shifting the paradigm towards a symbiosis of EE and Science Education through scientific practices. We then present practical examples showcasing successful EE initiatives that foster a deeper understanding of socio-environmental issues, encourage innovative solutions, and nurture engaged citizens from early education onwards. These proposals can support classroom practice and ongoing teacher self-development. Pedagogical strategies include tackling issues that require systemic and critical thinking by developing scientific and epistemic practices while raising awareness of environmental justice. Thus, this study advocates for a new vision of EE, integrating the One Health approach, which could be applied to develop new educational programs, including teacher training. This would lead to a new learning evaluation model and help identify key determinants that trigger pro-environmental behaviours.

Education and heritage are fundamental to achieving sustainable development goals. Integrating heritage into education is crucial for cultivating children's connection to heritage and promoting sustainable development. However, the lack of a scientific framework to guide educational goals across age groups results in unclear integration. This study aims to develop a framework for cultural identity enlightenment within heritage education by conducting literature research and validating it through field observations in kindergartens. It identifies cognitive, emotional and behavioral dimensions, outlining the developmental trajectory of these goals. This framework illustrates how children's understanding progresses from direct experiences to deeper appreciation and creative involvement. It provides teachers with a tool to set goals that empower children as agents, fostering the emergence of cultural awareness and identity. This approach not only enhances cultural identity but also supports sustainable development by advancing the transmission and innovation of cultural heritage.

This study explores innovation and sustainability within the Construction, Environment, and Territory (CAT) curriculum in Italian Upper Secondary Schools. It begins with an overview of Education for Sustainable Development (ESD) and its integration at various educational levels, focusing on technical/vocational curricula in Upper Secondary Education. The study examines the evolution of the technician's role, the CAT pathway in the Italian educational system, and comparisons with other European contexts. It analyzes the alignment between skills developed during the CAT course and the professional competencies required by the community and job market. A survey conducted among recent graduates and companies involved in the Pathways for Transversal Skills and Orientation (PCTO) at a CAT-focused school reveals significant training and professional needs related to Sustainable Development. To address these needs, the study proposes integrating ministerial programs in professional disciplines such as Topography, Building and Technological Systems Design, and Geopedology, Economics, and Appraisal, with new modules dedicated to Sustainable Development. These modules are to be integrated into the last three years of the course, utilizing the autonomy and flexibility in curricular planning per-mitted by current regulations. Additionally, the proposal includes actions to redistribute existing educational modules within ministerial programs without increasing teaching hours. An initial step in updating professional discipline programs involves implementing a cross-disciplinary educational path within Civic Education, focusing on Sustainable Mobility. The investigation, evaluation, and educational design process presented in this study aims to serve as a model for swiftly implementing structured curriculum reforms, emphasizing educational innovation and comprehensive student preparation to meet emerging social and professional needs.

Polycystic ovary syndrome (PCOS) is a common condition in reproductive-age women. Women with PCOS are at higher risk of pregnancy complications including gestational diabetes (GDM). The prevalence of PCOS and GDM varies according to the diagnostic criteria used. The mechanism for the occurrence of GDM in women with PCOS is still unclear. Materials & Methods- A systematic search of the electronic database was done using keywords like PCOS, and GDM to include studies between April 2008 and March 2023 where uniform diagnostic criteria were used. Studies were excluded with high-risk of bias after the assessment of bias, and meta-analysis was done with relative risks and confidence intervals. Results & Discussion-Out of 1896 search results, 95 were selected for full-text. The quality of the studies was scrutinized and a total of 28 studies were included as they fulfilled the inclusion criteria. The incidence of GDM in women with PCOS was found to be 10.55 % (2.9- 54.9%) from pooled data involving 1,280,245. The prevalence of PCOS in women diagnosed with GDM was found from pooled data of 36,351 women in retrospective studies to be 2.47% (1.5- 50.1%). Selection predominantly included cohort studies, most commonly from China. The meta-analysis reported the risk of GDM is higher in women with PCOS than in those without PCOS (HR: 1.59, 95% CI: 1.27–1.91, p < 0.001). Family history of diabetes, ethnicity, pre-pregnancy BMI, insulin resistance, gestational weight gain, use of assisted reproductive techniques, and multifetal gestation were the risk factors for GDM in women with PCOS. Conclusion- The prevalence of GDM in patients with PCOS is high, but the causality is complex. The newer predictive models are promising in clarifying the causative relationships, yet use various parameters at different cut-offs. There is a need for the development of universally acceptable parameters for the early prediction of GDM in women with PCOS.

This study considers the values of the credit payments that are made at random values for both payment size and number. The simultaneous existence of the two random variables increases the total value of the volatility of the payment process. The dependence for the simultaneous influence of the two random variables is derived by applying conditional probabilistic formulations. Formal relations apply to animal husbandry credit payments. The credit policy was analyzed for the cases of regular payments and with a stochastic number of payments. A recommendation for the upper level of credit payments is proposed based on a model predictive approach using historical payment data. A credit management algorithm is derived, in which decision-making for credit payments is formally derived and numerically discussed. The application of derived quantitative relations is empirically applied to real data on animal husbandry. The recommendations from the dependencies obtained allow a reduction in the mean values of credit payments, which correspond to a reduction in the credits used for business management, without a change in business policy.

When in operation, the 25 kV traction networks may encounter the contact wire short circuits to rails or ground. The short-circuit conditions cause high-intensity magnetic fields (EMFs), which, despite the short duration of exposure, can negatively affect electronic devices and lead to significant induced voltage on adjacent power lines. There can be two main types of emergency conditions in 25 kV traction networks: the contact wire short circuit to the rail, when the supports are grounded to the rail track; and the contact wire short circuit to the ground in sections with supports disconnected from the rails. A three-dimensional electromagnetic field near a metal support when a short-circuit current flows through it is characterized by a complex spatial structure, which significantly complicates the calculations of intensities. The paper discusses the results of computer modeling aimed at determining the EMF intensity in the described emergencies. The objects involved in modeling were represented by segments of thin wires to calculate the electric charge distribution and find out the EMF intensity. This approach is implemented in the Fazonord software. The modeling results have shown that EMF intensities near the support grow significantly; their levels decrease noticeably as one moves away from the support; and that the three-dimensional electromagnetic field under the considered emergency conditions has a complex spatial structure.

This article develops a duality principle and a related convex dual formulation suitable for a large class of models in physics and engineering. The results are based on standard tools of functional analysis, calculus of variations and duality theory. In particular, we develop applications to a model in non-linear elasticity.

This article develops duality principles and related convex dual formulations suitable for the local optimization of non-convex primal formulations for a large class of models in physics and engineering. The results are based on standard tools of functional analysis, calculus of variations and duality theory. In particular, we develop applications to a Ginzburg-Landau type equation.

Pandemic scenario has significantly changed several factors of life, including teaching, and learning at university. The development of suitable teaching materials to support university studies is mandatory to overcome distance learning difficulties and improve traditional teaching methodologies. This work explains a novel method for the preparation of teaching materials for medical sciences, but also plausible for other learning fields. An encephalon was extracted and prepared by using this methodology for teaching purposes. More than 200 students evaluated several factors of the material prepared this way, indicating a better understanding (up to 80%) of theoretical contents related to this human section, together with a high usability and good physical appearance

Since the 21st century, China's urbanization process has been rapid development, the concept and function of urban public space in the city has been gradually paid attention to. In order to guarantee life and water, most urban construction relies on rivers, and Bridges are the most important way to communicate between urban areas. The main functional part of the bridge is the span structure, that is, the bearing structure of the bridge, and the lower part of the "gray" space formed by the bridge structure. Considering the social level, with the economic growth and urbanization development, people have brought a better living environment and quality of life, and also improved the requirements for urban public environment. In the increasingly tense urban space, how to use and transform the space under the bridge is a problem that needs to be considered and solved. In view of this problem, in this study, we try to solve the micro-transformation of space under Bridges in cities through public art from the perspective of "regional culture" and "art for all". This paper analyzes the micro-transformation of space art under Bridges in two large cities of Shanghai and Foshan, namely, the space under Bridges under Songhong Road in Shanghai, the space under Bridges under Central Of Suzhou River and the space under Bridges under Pingsheng Bridge in Foshan. This paper discusses the cultural intervention of "regional culture" in the micro-transformation of the space under the bridge, and the influence and effect of "art for all" on the public art space under the bridge after the transformation to the community and the public.

Assisted reproductive technology has been developed significantly throughout the past few years, particularly diagnosing and treating male infertility. Many studies have been performed showing that Intracytoplasmic Sperm Injection (ICSI) is a successful method to attain clinical pregnancy and live birth through impaired spermatozoa characteristics or low sperm count, such as severe oligospermia. Severe oligospermia indicates low sperm count, which in some cases leads to azoospermia. Severe oligospermia can be caused by several factors such as genetics or medication. In search of efficient treatment for couples with Severe oligospermia, numerous retrospective and prospective researches have reported high pregnancy and live birth rates through testicular sperm for men with severe oligospermia and cryptozoospermia with or without high sperm DNA damage. The research showed that the use of testicular sperm in combination with ICSI yielded a high pregnancy rate and live births over another source of sperm, such as ejaculated sperms. Moreover, the use of ICSI in severe oligospermia has shown successful fertilization and pregnancy.

In this paper a new approximate procedure is developed for calculating the inclination angle of the end points of statically determinate beams. The method is based on the topology comparison of simple (hinge-roller combination) supported beam and a resemblant cantilever beam. Assuming that the support reactions of the beam are active forces, the virtual displacements at the points of the reaction forces are calculated. Based on these values the inclination angle is calculated. Several examples are considered and the suggested in this paper, while the procedure is applied for various types of structures and loadings. The results, obtained by the suggested numerical procedure, are compared with analytical ones, and they are in good agreement.

Multiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real- time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

Hearing impairment is an immensely diagnosed genetic cause, 5% of the total world population effects with different kind of congenital hearing loss (HL). In third-world countries or countries where consanguineous marriages are more common the frequency rate of genetic disorders are at its zenith. Approximately, the incidence of hearing afflictions is ostensibly 7-8:1000 individuals whereas it is estimated that about 466 million peoples suffer with significant HL, and of theses deaf cases 34 million are children’s up to March, 2020. Several genes and colossal numbers of pathogenic variants cause hearing impairment, which aided in next-generation with recessive, dominant or X-linked inheritance traits. This review highlights on syndromic and non-syndromic HL (SHL and NSHL), and categorized as conductive, sensorineural and mixed HL, which having autosomal dominant and recessive, and X-linked or mitochondrial mode of inheritance. Many hundred genes involved in HL are reported, and their mutation spectrum becomes very wide. Mapping of pathogenic genes in consanguinity family is facilitated to understand the disease history. Review presents the bases of HL and also focused on various genetic factors that cause deafness like the basics of genetic inheritance, and classic and well-characterized inherited factors of it. It also overviews the application of linkage analysis, SNPs genotyping and whole exome sequencing methods, in mapping and identification of new locus, causative genes and their variants in families inherited with HL. Conclusively, this review supports researchers in understanding the location of chromosome, the causative genes and specific locus which causing deafness in humans.

In this research we are reporting gas sensing properties of TiO_2-x/TiO₂-based hetero-structure, which was ‘self-heated’ by current that at constant potential passed through the structure. Amperometric measurements were applied for the evaluation of sensor response towards ethanol, methanol, n-propanol and acetone gases/vapors. The sensitivity towards these gases was based on electrical resistance changes, which were determined by amperometric measurements of current at fixed voltage applied between Pt-based contacts/electrodes deposited on TiO_2-x/TiO₂-based layer. XRD analysis revealed the formation of TiO_2-x/TiO₂-based hetero-structure, which is mainly based on Ti₃O₅/TiO₂ formed during hydro-thermal oxidation based sensing layer preparation process. Additionally, photoluminescence and time-resolved photoluminescence decay kinetics based signals of this sensing structure revealed the presence of TiO₂ mainly in the anatase phase in the TiO_2-x/TiO₂-based hetero-structure, which was formed at 400°C annealing temperature. The evaluation of TiO_2-x/TiO₂-based gas sensing layer was performed at several different temperatures (25°C, 72°C, 150°C, 180°C) and at these temperatures different sensitivity to aforementioned gaseous materials was determined.

Educating for Sustainability involves promoting sustainable competences in students. Not in vain, wider societal changes that ensure a balance between economic growth, respect for the environment and social justice must start with individual actions, implying knowledge, capacity and willingness to act. However, and although there is wide consensus that education should promote the development of competences for life, putting this theoretical tenet into may entail more problems. Competence is most often expressed in general terms without a specific definition of the intervening elements (knowledge, skills, values, attitudes), which may collide with the necessity of teachers – as learning planners - concrete entities on which to base their process of design. So that, in this work we propose a series of indicators that serve to characterize the four dimensions of scientific competence – contents of science, contents about science, value of science and utility of science-. Although they are primarily intended to be used to filter multimedia resources in an educational platform, this proposal of indicators can be extrapolated to the management and selection of a variety of resources and activities, and for sharing the objectives and evidences for the acquisition of competencies.

Although health policy resides mainly with the government, industry can play an important role in building a health ecosystem. From March to May, we administered questionnaires to 1,200 individuals from the general Korean population asking about their perception of health-friendly labels, and if they would purchase such labeled products (foods, pharmaceuticals, etc.) and services (purifying water, preventing air pollution, etc.) at extra cost. The participants placed a high value on the importance of mental, social, spiritual, and physical health factors in terms of company’s products and services with a score of about 8 out of 10 (range, 7.74-8.33). Most respondents (72.4%) said they were interested in adopting health-friendly labels. When a health-friendly label is introduced (such as one by the Business for Social Responsiveness), 65.1% of the respondents said they intended to purchase the product or service, while 6.8% said they did not, and 75.0% said they were willing to pay extra for the health-friendly product or service. Multivariate logistic regression models showed urban residence, high education level, and good social health to be significantly associated with positive attitudes toward health-friendly labels. People with high income, no religion, or normal weight were more likely to say they intend to purchase products and services with health-friendly labels. They also had a more positive attitude toward paying more for such products and services, as did people with good spiritual health. This study provides data that illustrate the importance of health-friendly products and services to the general population and companies.

This is a theoretical and empirical article. In the first part, an original definition of the cross-border market for cultural services has been developed, based on classic market definitions. The structure and conditions of functioning for this market have also been presented. The next part of the article discusses the supply side of the cross-border market of cultural services for the two twin towns: Cieszyn (Poland) and Český Těšín (Czech Republic). Next, based on the surveys conducted, the participation of residents of both towns in the most important cultural events organised by cultural institutions on both sides of the border was examined.

The effective dissemination and implementation of health service interventions into practice requires a range of strategic and systematic approaches. This paper applies a conceptual implementation framework to the evaluation of a hospital-wide clinical pharmacy initiative, a redesign of the discharge medication prescription pathway. The influencing factors and strategies used to overcome potential negative influences are described and assessed.

This qualitative research explores the profound implications of integrating real-time supply chain information on marketing decisions within service industries. Through semi-structured interviews with professionals from logistics, retail, hospitality, healthcare, and telecommunications sectors, the study examines how real-time data enhances demand forecasting, inventory management, personalized marketing approaches, agility in marketing responses, and supplier relationship management. Findings reveal that real-time supply chain information significantly improves accuracy in predicting customer demand and optimizing inventory levels, thereby reducing costs and enhancing service delivery efficiency. Moreover, real-time data enables more targeted marketing strategies through granular customer segmentation and dynamic pricing adjustments based on real-time market dynamics. The agility afforded by real-time information allows companies to swiftly adapt marketing strategies to emerging trends and consumer behaviors, maintaining competitiveness in dynamic service markets. Enhanced supplier collaboration and risk management further underscore the strategic value of real-time supply chain integration, fostering stronger partnerships and supply chain resilience. This study contributes to a deeper understanding of how real-time supply chain information transforms marketing practices in service industries, highlighting its role in improving operational efficiency, customer satisfaction, and overall competitiveness. Future research could explore implementation challenges and long-term impacts across diverse service sectors, providing insights into optimizing the strategic use of real-time data for sustained business success.

The adoption of digital health technologies accelerated during Covid-19, with concerns over the equity of access due to digital exclusion. Using data from a text-based online mental health service for children and young people we explore the impact of the pandemic on service access and presenting concerns and whether differences were observed by sociodemographic characteristics in terms of access (gender, ethnicity and deprivation). We used interrupted time-series models to assess whether there was a change in the level and rate of service use during the Covid-19 pandemic (April 2020-April 2021) compared to pre-pandemic trends (June 2019-March 2020). Routinely collected data from 61221 service users were extracted for observation, those represented half of the service population as only those with consent to share their data were used. The majority of users identified as female (74%) and White (80%), with an age range between 13 and 20 years of age,. There was evidence of a sudden increase (13%) in service access at the start of the pandemic (RR 1.13 95% CI 1.02, 1.25), followed by a reduced rate (from 25% to 21%) of engagement during the pandemic compared to pre-pandemic trends (RR 0.97 95% CI 0.95,0.98). There was a sudden increase in almost all presenting issues apart from physical complaints. There was evidence of a step increase in the number of contacts for Black/African/Caribbean/Black British (38% increase; 95% CI: 1%-90%) and White ethnic groups (14% increase; 95% CI: 2%-27%) ), the sudden increase in service use at the start of the pandemic for the most (58% increase; 95% CI: 1%-247%) and least (47% increase; 95% CI: 6%-204%) deprived areas. During the pandemic, contact rates decreased, and referral sources change at the start. Findings on access and service activity align with other studies observing reduced service utilisation. The lack of differences in deprivation levels and ethnicity at lockdown suggests exploring equity of access to the anonymous service. The study provides unique insights into changes in digital mental health use during Covid-19 in the UK.

This study selected the Yangtze River Delta urban agglomeration as the research area, combining it with the current situation of the transportation development of the Yangtze River Delta urban agglomeration to construct the urban agglomeration transportation integration index system and evaluate the development status of the Yangtze River Delta urban agglomeration transportation integration. The study examined the influence mechanism of transportation infrastructure on service industry agglomeration. The results are as follows: (1) From 2011–2020, the Yangtze River Delta urban agglomeration’s transportation integration index showed a clear upward trend. (2)The development of transport integration in urban agglomerations has heterogeneous effects on local service agglomeration. The development of the integration level of local transportation has a certain inhibitory effect on the agglomeration of local service industry. The transportation integration of the Yangtze River Delta urban agglomeration plays an important role in promoting the agglomeration of local wholesale and retail industry, transportation, storage and postal services. (3) The transportation integration of urban agglomeration can affect the agglomeration of service industry through the knowledge spillover brought by the free flow of various factors. The knowledge spillover effect caused by local transportation integration can promote the agglomeration of local service industry to a certain extent. The Yangtze River Delta urban agglomeration needs to accelerate the construction of trans-provincial and trans-municipal transportation infrastructure, and further improve the connectivity level of the urban agglomeration, so as to promote the integrated development of high-quality transportation in the Yangtze River Delta urban agglomeration.

This article shows the numerical results and the analysis of households' degree of knowledge in an intermediary city such as Huelva (Andalusia, Spain) about the sustainable use of urban water. It analyzes the needs and values regarding water and the attitudes that households maintain regarding the acceptance of reclaimed water and the use of new technologies to achieve more efficient and sustainable consumption. These results are part of the stages of needfinding and synthesis of Design Thinking methodology, adopted as a framework to improve the efficiency and sustainability of urban water among households in this city. Different statistical analysis techniques of surveys sent to households and the use of clustering are the mathematical tools used to draw conclusions and recommendations that allow the design of a web-based prototype grounded on Product Service Systems methodology, as a tool to improve the engagement of households concerning water and align citizens with the sustainability of their city. Strategies of customization and technological facilitators will be the means to improve the hydrosocial contract among households in Huelva in future later stages of the project.

This paper develops a novel energy universal service bus system (EUSBS) based on emerging energy Internet (E-net) technologies. This EUSBS is a unified identification and plug-and-play interface platform to which high penetration distributed energy and equipment (DEE), including photovoltaic (PV), fans, electric vehicle charging stations (EVCSs), energy storage equipment (ESE), and commercial and residential users (CRUs), can access in a coordinated control and optimized utilization mode. First, the functions design, overall framework and topology architecture design of the EUSBS are expounded, among which the EUSBS is mainly composed of a hardware system and a software platform. Moreover, several future application scenarios are presented. Then, the hardware part of EUSBS is designed and developed, including the framework design of this hardware subsystem, and development of the hardware equipment for PV access, fans access, EVCS access, ESE access, and CRU access. The hardware subsystem consists of smart socket, and household/floor/building concentrators. Based on this, the prototypes development of EUSBS hardware equipment is completely demonstrated. Third, the software part of the EUSBS is developed as a cloud service platform for electricity use data analysis of DEE. This software subsystem contains the power quality & energy efficiency analysis module, optimization control module, information and service module, and data monitoring and electricity behavior analysis module. Based on this design, the software interfaces are developed. Finally, an application study on energy management and optimization of a smart commercial building is conducted to evaluate the functions and practicality of this EUSBS. The EUSBS developed in this paper is able to overcome difficulties in big data collection and utilization on sides of distribution network and electricity utilization, and eventually implement a deep information-energy fusion and a friendly supply-demand interaction between the grid and users. This contribution presents a detailed and systematic development scheme of the EUSBS, and moreover, the laboratory prototypes of the hardware and software subsystems have been developed based on E-net technologies. This paper can provide some thoughts and suggestions for the research of active distribution network and comprehensive energy management and optimization in power systems, as well as references and guidance for researchers to carry out research regarding energy management, optimization and coordinated control of the smart buildings.

Assuming a two component, positive and negative mass, superfluid/supersolid for space (the Winterberg model), we model the Higgs field as a condensate made up of a positive and a negative mass, planckion pair. The connection is shown to be consistent (compatible) with the underlying field equations for each field, and the continuity equation is satisfied for both species of planckions, as well as for the Higgs field. An inherent length scale for space (the vacuum) emerges, which we estimate from previous work to be of the order of,

Introduction: Thyroid nodules are extremely common and require complex management to prevent unnecessary surgical intervention and ensure that no malignant disease is overlooked. Several diagnostic tools and scoring systems are available to evaluate the Risk of Malignancy (ROM). The goal is to assess variables that can aid and support the clinical recommendations suggested by the updated Bethesda System for Reporting Thyroid Cytopathology (TBSRTC), such as the ultrasonographic features of thyroid nodules, particularly for indeterminate categories III (atypia of undetermined significance) and IV (follicular neoplasm). Methods: We retrospectively analysed the correlation of the demographic and ultrasonographic characteristics of thyroid nodules with the cytopathological and histopathological diagnoses of TBSRTC categories III (atypia of undetermined significance), IV (follicular neoplasm), V (suspicious for malignancy), and VI (malignant) in patients who underwent surgery in a single Portuguese centre over a 10-year period. Results: In total, 360 nodules were evaluated in 341 patients and 57% were histopathologically malignant or borderline. The majority were included in the TBSRTC indeterminate categories III and IV with an ROM of 44% and 43%, respectively. The ultrasonographic characteristics associated with a higher TBSRTC category and a greater ROM value were hypoechogenicity, presence of microcalcifications, irregular margins, and the presence of cervical adenopathy. When correlating with a malignant histology, only adenopathy and the presence of microcalcifications were observed to be statistically significant. Discussion: The indeterminate categories of the TBSRTC have been the most challenging ones to manage. The new TBSRTC (2023) guidelines as well as the ultrasonographic characteristics of a patient can be helpful in assessing the ROM and deciding on an appropriate course of treatment. Other resources, such as molecular tests, are also gaining a more important role in the clinical decision process and may become crucial in the future. Conclusion: The clinical management of thyroid nodules requires a careful analysis of clinical history and an evaluation of demographic details, the symptoms of the patient, comorbidities, personal and family history, ultrasonographic features, and the results of cytopathology, thyroid function, and molecular/genetic tests to provide the best care possible. Patients with thyroid nodules of an indeterminate character can obtain a definitive diagnosis through surgery. However, by employing a complete analysis of all relevant data, some patients may be suitable for active surveillance instead of a surgical intervention.

Despite significant prevention efforts, the numbers of physically inactive individuals, chronic illnesses, exhaustion syndromes and sick leaves are increasing. A still unresolved problem with exercise promotion is the low participation of sedentary persons. This collective term covers heterogeneous subgroups. Their engagement with movement campaigns and resistance to change are influenced by numerous factors. Our aim was to analyse survey data on health, performance, lifestyle habits and the approachability to physical activity campaigns obtained from the Germany-wide ActIv survey. From 2,888 study participants aged 50-60 years, 668 persons were categorised into the subgroups "never-athletes", "sports-dropouts", "always-athletes" and "sports-beginners". Large and significant group differences were found for BMI, assessment of quality of life, health and fitness, risk factors and health problems. 42.5% of "never-athletes" and 32.5% of "sports-dropouts" did not state any barriers to sport. There are substantial disparities between the non-athlete groups in terms of their motivation to exercise. In contrast, there are comparatively minor differences in motivation between "sports-dropouts" and "sports-beginners", whose health and fitness are the primary motivators for sport. Our analyses suggest that (i) negative health and performance trends cannot be compensated by appeals for voluntary participation in exercise programmes and (ii) powerful incentive systems are required.

Transformation formula under the action of a general linear fractional transformation for generalized Dedekind eta-function has been a subject of intensive study, Rademacher, Dieter, Meyer et al. However, the (Hecke) modular relation structure has not been recognized until the work of Goldstein-de la Torre, and streamlining the classical proofs in the modular relation will reveal the meaning hidden in those works. Our main aim is to elucidate the works of these researchers in the context of modular relations.

Software stands as one of the most rapidly evolving technologies in the present era, characterized by its swift expansion in both scale and complexity which leads to challenges in quality assurance. Software Defect Prediction (SDP) emerges as a methodology crafted to anticipate undiscovered defects, leveraging known defect data from existing codes. This methodology serves to facilitate software quality management, thereby ensuring overall product quality. Machine Learning (ML) and Deep Learning (DL) methodologies exhibit superior accuracy and adaptability compared to traditional statistical approaches catalyzing active researches in this domain. However, it makes us hard to generalize not only because of the disparity between open-source project and commercial project but also the difference on each industrial sectors. Consequently, further research utilizing datasets sourced from diverse real-world sectors becomes imperative to bolster the applicability of these findings. In this study, we utilized embedded software for telecommunication systems of SAMSUNG ELECTRONICS, supplemented by the introduction of nine novel features to train the model, and subsequent analysis of results ensued. Experimental outcomes revealed that the F-measurement metric has been enhanced from 0.58 to 0.63 upon integration of the new features, thereby signifying a performance augmentation of 8.62%. This case study is anticipated to contribute to bolster the application of SDP methodologies within analogous industrial sectors.

The positional cloning of single nucleotide polymorphisms (SNPs) of the neutrophil cytosolic factor 1 (Ncf1) gene, advocating that a low oxidative burst drives autoimmune disease, demands an understanding of the underlying molecular causes. A cellular target could be T cells, which have been shown to be regulated by reactive oxygen species (ROS). However, the pathways by which ROS mediate T cell signaling remain unclear. The adaptor molecule linker for activation of T cells (LAT) is essential for coupling T cell receptor-mediated antigen recognition to downstream responses, and it contains several cysteine residues that have previously been suggested to be involved in redox regulation. To address the possibility that ROS regulate T cell-dependent inflammation through LAT, we established a mouse strain with cysteine-to-serine mutations at positions 120 and 172 (LATSS). We found that redox regulation of LAT through C120 and C172 mediate its localization and phosphorylation. LATSS mice had reduced numbers of double-positive thymocytes and naïve peripheral T cells. Importantly, redox insensitivity of LAT enhanced T cell-dependent autoimmune inflammation in collagen-induced arthritis (CIA), a mouse model of rheumatoid arthritis (RA). This effect was reversed on an NCF1-mutated (NCF1m1j), ROS-deficient, background. Overall, our data shows that LAT is redox-regulated, acts to repress T cell activation, and is targeted by ROS induced by NCF1 in antigen-presenting cells (APCs).

We performed urine cytology analysis of a pharmacologically induced diuresis for diagnostics of upper tract urothelial carcinoma. To evaluate the diagnostic value of cytology of pharmacologically forced diuresis, a first cohort of 77 consecutive patients with primary upper tract urothelial carcinoma treated with radical surgery was enrolled. To evaluate pharmacologically forced diuresis cytology as follow-up procedure, a second cohort of 1250 patients who underwent radical cystectomy for bladder cancer was selected. In the first cohort, sensitivity of cytology of pharmacologically forced diuresis in patients with invasive, high-grade, low-grade, and concomitant carcinoma in situ was 8%, 9%, 0% and 14%, respectively. In the second cohort, cytology of pharmacologically forced diuresis was positive in 30/689 (4.3%) of patients, in whom upper urinary tract recurrence was present in 21/30 (70%) and urethral recurrence in 8/30 (26%) of cases. As a follow-up tool, cytology of pharmacologically forced diuresis showed a sensitivity, specificity, and positive and negative predictive value of 60%, 99%, 70% and 98%, respectively. Summarized, as diagnostic tool, sensitivity of cytology of pharmacologically forced diuresis is slightly better in patients with invasive upper tract urothelial carcinoma and concomitant carcinoma in situ. As follow-up method, positive cytology of pharmacologically forced diuresis is strongly related to cancer recurrence and can reveal urethral recurrence. cytology of pharmacologically forced diuresis might be useful in patients with contraindications for imaging or with difficult endoscopic access to the upper urinary tract.

The understanding of structural alteration mechanisms due to relative humidity impact on organic supports of paintings is of crucial significance for the preventive conservation strategies in order to ensure the long-term preservation of important cultural identity artworks. The effort in this paper is to simulate Museum realistic relative humidity condition in a climate chamber while monitoring the structural responses through a quantitative interferometric method allowing the quantitative correlation between the physical values of deformation and mass alteration relevant to relative humidity fluctuations.

The use of sensors in monitoring of lateral accelerations in delivery van transport focuses on measuring lateral accelerations on routes with roundabouts and curves to increase road safety. Using microelectromechanical system (MEMS) sensors, it measures the lateral accelerations acting on the vehicle and the load being transported during the test drives to study vehicle dynamics for cargo securing. Using an accelerometer and position tracking, accelerations can be detected when traversing curves and roundabouts at selected locations on the vehicle. The level of accelerations on the roof of the vehicle was found to be like those occurring on a lashed load with limited movements, where regression analysis can be used to determine the relationship between lateral accelerations at different sensor positions. If we compare the mean values of the lateral accelerations of the individual events between the sensors, the sensor on the side of the vehicle body at the height of the sensor on the load had approximately 5% lower mean values than the sensor on the roof. The sensor on the load measured approximately 5% higher mean values than the sensor on the roof. Hence, the mean lateral accelerations of the individual events for the sensor on the load are 10% higher than for the sensor at the same height on the vehicle body. From the above testing, we can say that the values of the mean lateral accelerations of the van from the sensor on the roof of the vehicle, are closer to the values of the accelerations of the sensor on the load than to the values of the sensor on the body of the vehicle at the same height.

The number of Internet of Things (IoT) applications is increasing at a fast-paced and so is the interest in blockchain-enabled IoT ecosystem. IoT applications make our day-to-day life more efficient, while integrating blockchain into the IoT ecosystem (blockchain-IoT) brings security, transparency, trust, and privacy to IoT applications. Perhaps, smart logistics, smart health, and smart city are some of the potential blockchain-enabled IoT application domains. One of the reasons that is hindering the mass adoption of blockchain-IoT into mainstream applications is the lack of a dedicated governance. Without proper governance and regulations, and because of the obfuscated and concealed cryptographic nature of blockchain, blockchain can be used for various illicit activities such as ransomware, money laundering, fraud and so on. Furthermore, blockchain and IoT are relatively contemporary technologies and paucity of exclusive governance can ultimately lead to lack of confidence in these technologies. Therefore, in order to fully avail from blockchain and IoT integration (blockchain-IoT) and as well as further prevail this integration, governance can play an important role. Proper regulations and standarization are required to benefit from the novel characteristics of blockchain-IoT and avoid blockchain being used for malicious purposes. In this research, we address the role of blockchain in governance mechanisms, governance for blockchain, and finally proposed a vigorous governance framework for blockchain-enabled IoT ecosystem. We also demonstrate the applications of our proposed governance framework through a smart logistics case study. We anticipate that our proposed governance framework can facilitate and encourage blockchain and IoT integration in various application domains.

This paper presents a design and performance analysis of a 10-element 5G massive MIMO antenna array for sub-6GHz mobile handsets, specifically for LTE bands 42 (3400-3600 MHz), 43 (3600-3800 MHz), 48/49 (3550-3700 MHz) applications. The proposed antenna array consists of 10 closely spaced linearly polarized inverted-F antennas with a compact size of 20 × 9 mm2 of a single element. The proposed antenna array provides high gain, high efficiency, and low correlation between the antenna elements, which results in improved channel capacity, increased data rate, and enhanced signal quality. The performance of the antenna array is evaluated in terms of the radiation pattern, gain, efficiency, and correlation coefficient. The simulation and measured results show that the proposed antenna array achieves an approximate peak gain of 3.1 dBi at the resonance frequency, a total efficiency of 65 %, and a low correlation coefficient of 0.06 between the antenna elements. Therefore, the proposed 5G massive MIMO antenna array is a promising candidate for sub-6 GHz mobile handsets, particularly for LTE bands 42/43/48/49 applications.

In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in in-dustrial item sorting systems and proposes an object recognition and sorting system for auton-omous driving. In industrial sorting lines, goods sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot's real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consump-tion. These issues lead to the fact that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array) based item sorting system that achieves similar perfor-mance to current mainstream sorting systems at a lower cost and consumption than existing sorting systems. The recognition part employs a morphological recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degree-of-freedom robotic arm is introduced in the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs.

A cell filtration platform that affords accurate size separation and minimizes fouling was developed. The platform features an ultra-thin porous membrane (UPM) filter, a pumping head for backflushing (PHB), and cell size measurement (CSM) software. The UPM chip is an ultrathin free-standing membrane with a large window area of 0.68 mm2, a pore diameter of 5 to 9 μm, and a thickness of less than 0.9 μm. The PHB prevents filter fouling. The CSM software analyzes the size distributions of the supernatants and subnatants of isolated cells and presents the data visually. The D99 particle size of cells of the chronic myeloid leukemia (CML) line K562 decreased from 22.2 to 17.5 μm after passage through a 5-μm filter. K562 cells could be separated by careful selection of the pore size; the recovery rate attained 91.3%. The method was compared to conventional blocking models by evaluating the mean square errors (MSEs) between the measured and calculated filtering volumes. The filtering rate was fitted by a linear regression model with a significance that exceeded 0.99 based on the R2 value. The platform can be used to separate various soft biomaterials and affords excellent stability during filtration.

This paper proposes the optimization method for the minimum mass of permanent magnet (PM) excited homopolar inductor alternator (HIA) with air-gap winding used for high-speed driving system. The calculation results show that there is an optimal value for shell diameter and unit number of HIA to make the mass to be smallest. A 1 MW at 22000 rpm PM excited HIA is designed. The analytical results are verified by the FEM simulations. The losses, temperature rise, rotor stress and modal of the PM excited HIA are also analyzed. The calculation results show that the solid rotor eddy-current losses and the temperature rise of PM excited HIA are acceptable. Also the rotor strength is adequate, and the rotor system is a stiffness rotor, and it can safely operate. Finally, the comparison between PM excited HIA and permanent magnet rotor machine is discussed.

It is unclear how maternal glycemic status and maternal iodine status influence birth weight in mild-to-moderate iodine deficiency (ID). We studied the association between birth weight and both maternal glucose levels and iodine intake in pregnant women with mild-to-moderate ID. Glucose values were assessed using a glucose challenge test (GCT), non-fasting glucose before delivery; iodine status was assessed using an iodine food frequency questionnaire, serum thyroglobulin (Tg) and urinary iodine concentrations (UIC). Thyroid antibodies and free thyroxine (FT4) were measured. Obstetric and an-thropometric data were also collected. Large for gestational age (LGA) was predicted using a Cox proportional hazards model with multiple confounders. Tg>13g/L was in-dependently associated with LGA (adjusted hazard ratio = 3.4, 95% CI: 1.4–10.2, p=0.001). Estimated iodine intake correlated with FT4 among participants reporting io-dine-containing supplements (ICS) after adjusting for confounders (β = 0.4 95 %CI: 0.0002-0.0008, p=0.001). Newborn weight percentiles were inversely correlated with maternal FT4 values (β=-0.2 95 %CI:-0.08 - -56.49, p=0.049). We conclude that in mild-to-moderate ID regions, maternal insufficient iodine status may increase LGA risk. Iodine status and ICS intake may modify the effect maternal dysglycemia has on offspring weight.

Abstract: This paper summarises an early and successful piece of citizen science, performed within The University of Queensland Seismograph Stations (UQSS) observatory, in cooperation with colleagues at CSIRO. It was designed to en-courage young STEM students from Brisbane high schools to engage in “real” re-search, back in 1995. Having completed the project report, their (analog) results sat in a cupboard until the report was dusted off and the project was re-analysed in 2022 by an honors student, considering timely climate change applications for the study. This is a time when science is changing considerably from analog to digital medium and operational methods. The original project was called Earthquake generated T phases on BRS Seismograph (Brisbane, Q’ld)- a predictor for Tasman Sea Tsunamis? [1] Fortunately, seismology is a very collaborative field. The research question has since changed. There is a lot of data analysis involved in the science of recording earthquake signals, with auxiliary definitive catalogues, observers logbooks, housing of the recordings themselves (analog and digital) and the software mediums that change over time. In other words, a lot of headaches can be encountered in longitudinal data collection study such as this. The citizen science students used a pre-prepared decadal collection (1980-90) derived from the BRS observatory data catalogue. BRS is part of the global World-Wide Seismograph Station Network (WWSSN). Currently in Australia, university earth science observatories have diminished, and in their place, public seismic networks (PSN) have evolved, either in back-yard sheds or school science labs. The level of expertise required fits the role of advancing citizen science for a real science advantage. This is a topical citizen disaster preparedness action area for today’s climate emergency, which is threatening the globe and all lifeforms. Citizen engagement or mobilisation is an essential preparedness strategy. Keywords: citizen science 1; seismology 2; earth observation 3; climate change 4; education for sustainable development 5; disaster preparedness 6; disaster alerts 7

We sought to calculate the extra social costs resulting from child abuse in residential care facilities (RCFs) for children with disabilities (CWD) in Japan. We distributed a survey to 260 residential facilities for CWD in 2020 and obtained responses from 91 facilities. Among the children admitted to these facilities, 23–67% were affected by child abuse. We estimated the extra costs to be an average of USD 647.7 million. This study is meaningful in that there are no existing official statistics or research findings on the extra costs of residential care due to child abuse in Japan.

The recent global calamity of sea level rise and flooding is a major disaster. Some settlements on Java Island, Indonesia, have been reported to be sinking. Bedono Village is one village on the Central Java Coast that is vulnerable to sea level rise. Aside from the possibility of sea level rise, the calamity in this village is linked to prior mangrove degradation and fish pond expansion. This tragedy has displaced 78.63% of the population over a decade, and the remaining population is still surviving in the remaining lands. This study aims to investigate the effects of sea-level rise-related permanent inundation on the lives of the remaining residents in Bedono Village, with a focus on their adaptation and resilience methods. The results disclosed that remaining populations have taken active initiation by adopting multiple strategies that include structural prevention measures and nature based solution through mangrove reforestations. As a result based on mangrove assessment using Geographic Information Systems, mangrove cover increased from 1 ha in 2010 to 9.5 ha in 2012. This achievement has provided an alternative income through mangrove ecotourism. Women and multi stakeholder participation have also contributed to the implementation of adaptation strategy. Despite those significant achievements, the adaptation and resilience was challenged by lack of education and awareness towards sea level rise issues and their confounding disasters. The nature-based solution and local wisdom in Bedono Village is an example that is in line with GPDRR UNDRR 2022, the Sendai Framework, and The Bali Agenda for Resilience that ensures a whole-of-society approach to sea-level rise by ensuring that no one is left behind, creating an inclusive environment, and promoting gender.

Eating Disorders (ED) constitute a serious public health issue affecting predominantly women and appearing typically in adolescence or early adulthood. EDs are extremely difficult to treat as these disorders are ego-syntonic and many patients do not seek treatment. It is vital to focus on the development of successful early-intervention programs for individuals presenting at-risk and are on a trajectory towards developing EDs. This study is a randomized controlled trial evalu-ating an innovative digital gamified Acceptance and Commitment early-intervention program (AcceptME) for young females showing signs and symptoms of an ED and at high-risk for an ED. Participants (N=92; Mage=15.30 years, SD=2.15) received either AcceptME (N=62) or a waitlist control (N=30). Analyses indicated that the AcceptME program effectively reduced weight and shape concerns, with large effects when compared to waitlist controls. Most participants scored below the at-risk cut-off (WCS score<52) in the AcceptME at end-of-intervention (57.1%) com-pared to controls (7.1%) with odds of falling into the at-risk group being 14.5 times higher for participants in the control group. At follow-up, 72% of completers reported scores below the at-risk cut-off in the AcceptME group. The intervention also resulted in a decrease in ED symp-tomatology and increased body image flexibility. Overall, results suggest that the AcceptME program holds promise for early-intervention of young women at-risk for developing an ED.

The emotional dimension in education has become increasingly important in recent decades. Enhancing the emotional dimension of prospective teachers in science subjects is higher education stuff responsibility. The implementation of active methodologies could modify the traditional student-teacher roles that are encouraged by the educational policies implemented in the Bologna Process. The principal aim of this work is to describe a Project Based Learning methodology and to introduce it as potential resource for the emotional and cognitive improvement of 19 prospective primary teachers enrolled in a scientific subject. This is a qualitative study with a transversal sustainability approach in the context of a research line focused on Higher Education for Sustainable Development. A questionnaire was designed and filled by the students at two different times, before and after implementation of the activity. The initial feedback from students was surprisingly enthusiastic by the fact that they were working with rockets, despite of this is not a common emotion in the science field. The results show the emotional improvement of prospective teachers after the implementation. It is concluded that a correct science education is necessary during the training of teachers taking into account their emotional dimension and the social repercussion due to the future transmission.

Assuming a geometrically closed universe, we predict a value for the cosmic curvature, , a value within current observational bounds. We also propose a thermodynamic heat engine model for the universe, which bypasses the need for an inflaton field. Our model is based on a Carnot Cycle where we have isothermal expansion, followed by adiabatic expansion, followed by isothermal contraction, followed by adiabatic contraction, bringing us back to our original starting point. For the working substance, we focus specifically on the CMB radiation filling the collective voids in the universe. Using this construct, we identify cosmic inflation as the isothermal expansion phase, which lasts just under, . The collective CMB volume we see today only increases by a factor of 5.65 times during this process, and homogeneity and perturbations in the CMB are explained. The singularity problem is avoided and we have a clear mechanism for the work done by the cosmos in causing expansion, and later contraction. For scaling laws with respect to the density parameters in Friedmann’s equations, we will assume a susceptibility model for space, where, , the smeared cosmic susceptibility, decreases with increasing cosmic scale parameter, . Within this framework, we can predict a maximum Hubble volume with minimum CMB temperature for the voids before contraction begins, as well as a minimum volume with maximum CMB temperature when expansion starts. The thermodynamic heat cycle deviates from efficiency in converting heat energy into mechanical energy (expansion) by a minuscule amount, namely, . The significance of this number is not known.

Experimental evidence showed how various complex systems, characterized by a fluctuation scaling, satisfy the well-known Taylor's law. The present work aims to apply for the first time Taylor's law, given its general treatment, for a flow field at $Re$ around $10^4$, since activity of each fluid particle is highly fluctuating, especially in the context of vortex shedding. In addition, the further element of innovation is the use of an innovative thin-films based device consisting of an elastic piezoelectric flapping flag that is proposed as a measuring instrument of the flow field. The oscillations of the flapping flag, due to the vortexes release downstream to an obstacle of cylindrical shape, generate an alternating piezoelectric voltage whose time history is similar to the chaotic components of the fully developed turbulent speed. Preliminary experimental results about the use of thin-films based device in a flume channel are reported together with a second order analysis on the voltage difference and a scaling law of the exponent scale.

As traffic congestion gets worse year by year in metropolitan areas, cities search for solutions to improve their traffic performance and reduce their environmental impacts. This paper focuses on parking pricing and congestion pricing and their short-term effects not only on traffic congestion but also on the potential revenue for a city. We develop an easy to implement multimodal macroscopic traffic and parking search model for a central area based on aggregated data at the network level. Our methodology allows us to analyze how introducing parking pricing inside a network, or a congestion toll combined with a park and ride (P+R) scheme can affect the drivers’ decision between entering the network by car (private vehicle) or using P+R instead. This decision directly influences the number of drivers using P+R, and this impacts, in turn, the traffic performance. Based on such analysis, a city can get valuable insights to evaluate whether congestion pricing is a necessity or if the traffic improvements resulting from implementing parking pricing strategies are sufficient when combined with P+R facilities. A search algorithm is used to find the best trade-off between the parking fees and the congestion toll. Any additional revenue collected through these schemes can then be used to improve public transport or the P+R facilities themselves. With minor data collection efforts and little computational costs compared to most existing parking and congestion pricing models, we illustrate our proposed framework in a case study of an area with a high parking demand for public parking spaces within the city of Zurich, Switzerland. Results show that parking pricing combined with P+R is indeed a viable option compared to congestion pricing for improving traffic performance, even if parking pricing schemes do not target all the drivers.

In the context of the increasing migration into Germany that has taken place in recent years and German efforts to establish an inclusive school system, which enables learners from different religious, ethnic, language and social backgrounds with and without disabilities to participate, religious education has become a key topic for interdisciplinary discourse between theology, philosophy, and pedagogy in German schools. The following questions are of special interest: How can we manage diversity in inclusive classroom settings in general, and specifically: how can we do so with regard to worldview diversity? Does worldview diversity in schools exist, and if so, how can we recognize it in its plurality and complexity? How can we acknowledge different worldviews in the context of a changing inclusive school system? In this article, we would like to present the theoretical foundation, the research setting and the first findings of our ongoing pilot studies of worldview education at an inclusive German school. The experiments are part of a larger project context that is also described. The case study presented in this article, in which innovative language and machine learning technology was used for data analysis, illustrates the potential of inclusive methods and didactic concepts such as Universal Design for Learning [1], Learning in the Presence of the Other [2,3] and Reflexive Inclusion [4] for inclusive worldview education in the context of a religiously pluralized and secularized society.

Special preparation of SBA-15 materials has been carried out for creating adsorbents exhibiting an enhanced and selective adsorption toward CO2. This creation starts from an adequate conditioning of the silica surface, via a thermo-alkaline treatment to increase the population of silanol species on it. CO2 adsorption is only reasonably achieved when the SiO2 surface becomes aminated after put in contact with a solution of an amino alkoxide compound in the right solvent. Unfunctionalized and amine-functionalized substrates were characterized through X-ray diffraction, N2 sorption, Raman spectroscopy, electron microscopy, 29Si solid-state NMR, and NH3 thermal programmed desorption. These analyses proved that the thermo-alkaline procedure desilicates the substrate and eliminates the micropores (without affecting the SBA-15 capillaries), present in the original solid. NMR analysis confirms that the hydroxylated solid anchors more amino functionalizing molecules than the unhydroxylated material. The SBA-15 sample subjected to hydroxylation and amino- functionalization displays a high enthalpy of interaction, a reason why this solid is suitable for a strong deposition of CO2 but with the possibility of observing a low-pressure hysteresis phenomenon. Contrastingly, CH4 adsorption on amino-functionalized, hydroxylated SBA-15 substrates becomes almost 5 times lesser than the CO2 one, thus giving proof of their selectivity toward CO2.

Background: The research is conducted against the backdrop of the rising incidence of diabetes and the demand for novel approaches to improve patient outcomes. With the goal of revolutionizing conventional methods and addressing the changing healthcare landscape, the study concentrated on the application of AI technology in diabetes treatment. Objective: Examining the various ways AI can be used in diabetes care and assessing how it can enhance automated screenings, tailored treatment recommendations, predictive modeling, and patient self-management were the main goals of the study. Result: The findings demonstrated how well AI works to anticipate a patient's unique reaction to a therapy, assist medical personnel in making decisions, and give patients more control over their own care. Artificial intelligence (AI)-powered solutions showed promise in improving real-time monitoring and encouraging patient adherence, supporting a comprehensive strategy for managing diabetes. Conclusion: The research indicates that artificial intelligence (AI) has great potential to change the way diabetes is treated. However, it also highlights the need for more research, ethical issues, and validation of AI. The results advocate for joint efforts to ensure responsible and patient-centered technology innovation in diabetes care and encourage the integration of AI with new technologies.

In today's world, the significance of reducing energy consumption globally is increasing, making it imperative to prioritize energy efficiency in the 5th Generation (5G) networks. However, it is crucial to ensure that these energy-saving measures do not compromise the Key Performance Indicators (KPIs) such as user experience, quality of service (QoS), or other important aspects of the network. To address this challenge, advanced wireless technologies have been incorporated into the design of 5G networks across various network layers. Given the rapid advancements in Artificial Intelligence (AI) and emerging technology trends such as Machine Learning (ML), which is considered a subset of AI, the integration of such trends into 5G networks has become a significant area of research. The primary objective of this survey is to analyze AI integration into 5G networks for enhanced energy efficiency. By exploring this intersection between AI and 5G, we aim to identify potential strategies and techniques for optimizing energy consumption while maintaining the desired network performance and user experience.

The recent past has witnessed a growing interest in technologies for creating chatbots. Advances in Large Language Models for natural language processing are underpinning rapid progress in chatbot development, and experts predict revolutionary changes in the labour market as many manual tasks are replaced by virtual assistants in a range of business functions. As the new technology becomes more accessible and advanced, more companies are exploring the possibilities of implementing virtual assistants to automate routine tasks and improve service. This article reports on qualitative inductive research undertaken within a chatbot development team operating in a major international enterprise. The findings identify critical success factors for chatbot projects, and, based on the in-depth case study, a model is put forward to aid the manageability of chatbot implementation. The presented model can serve as an exemplar guide for researchers and practitioners working on the design, development and implementation of virtual assistants. It is flexible and applicable in a wide range of business contexts, linking strategic business goals with execution steps. It is particularly applicable for teams with no experience of chatbot implementation, reducing uncertainty and managing decisions and risks throughout the project lifecycle, thereby increasing the likelihood of project success.

Recently, smart mobility intelligent traffic services have become a critical task in Intelligent Transportation Systems (ITS). This involves not only the use of advanced sensors and controllers but also the ability to respond to real-time traffic situations at intersections, alleviate congestion, and generate policies to prevent traffic jams. DRL (Deep Reinforcement Learning) provides a natural framework for processing tasks. In DRL, each intersection can control itself and coordinate with neighbors to achieve optimal network-wide policies. However, comparing approaches remains a challenging task due to the existence of numerous possible configurations. This research performs a critical comparison of various traffic controllers found in the literature. It demonstrates that using a nonlinear approximator for coordination mechanisms and enhancing observability at each intersection are key performance drivers.

In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner. The proposed UCM consists of network-attached data drive that stores data from SHM logger, population count system and Geographic Information System (GIS) enhanced with a Cloud IoT data backup, display, and analysis server. The UCM using this data and data from building information systems applies a simple machine learning algorithm to generate real-time structure health and suggests re-planning of SHM units. The health of structure varies dynamically with disturbances created by higher occupancy and structure density per zone. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This was tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU simulated occupation and zone calculation models and then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.

(1) Predictive assessment of the performance characteristics of metal-polymer (MP) plain bearings at the design stage is necessary and important for engineering practice. They include bearing capacity, linear wear of the bushing, and bearing life. However, no method for their calculation has been developed. (2) The authors have created a generalized analytical method for the mathematical modeling of MP sliding bearings as a classical science-based method of contact mechanics of conformal cylindrical bodies with consideration of polymer bushing wear. (3) Contact problem of the theory of elasticity takes into account the significant difference in the elasticity characteristics of polymeric materials from those of steel: Young's modulus is 60...250 times smaller, Poisson's ratio is 1.27...1.37 times larger. The method has been used to study MP bearings with several common groups of tribopolymers used for bushings: PA, PTFE, PET, and PEEK. The maximum contact pressures and durability of dry friction bearings have been calculated taking into account the load, radial clearance, bushing thickness, elasticity characteristics of tribopolymers, their wear resistance, sliding friction coefficient, and type of tribopolymer materials. (4) The paper establishes quantitative and qualitative regularities of the influence of these factors on the maximum contact pressures and bearing durability.

The research identified challenges faced by teachers, suggested effective pedagogical strategies, and proposed practical recommendations to mitigate the negative impacts of ChatGPT in higher education. These strategies promote the ethical and responsible use of artificial intelligence, ensuring academic integrity. The article deeply analyzes pedagogical strategies to prevent plagiarism and promote academic integrity, offering a practical guide for educators on how to ethically integrate AI into their pedagogical practices. By filling a gap in the literature, the study contributes to the discussion on the ethical use of AI in education, offering evidence-based recommendations and fostering critical reflection on the impact of emerging technologies in the academic environment. The research highlights effective pedagogical strategies to mitigate the negative effects of ChatGPT, promoting an educational approach that prevents plagiarism and encourages the ethical use of AI. Through documentary analysis of scientific articles and data coding according to Bardin's methodology, patterns and trends were identified for the development of innovative pedagogical practices. The qualitative approach included a literature review and content analysis of articles published in the last five years. The results indicated that, although ChatGPT can personalize teaching and support content development, it also increases the risk of plagiarism and reduces students’ critical engagement. To combat these challenges, it is suggested to create personalized assessments, promote critical thinking, and use plagiarism detection tools. Continuous teacher training and the development of clear policies on the ethical use of AI are essential to ensure academic integrity and quality of learning.

With consumers increasingly aware of the effects of human activity on the environment, tourism products and destinations are increasingly marketed as sustainable and socially responsible. As most sustainable practices lead to additional costs, and tourists’ decisions tend to be price sensitive, achieving sustainability goals necessarily involves understanding how much tourists are willing to pay extra for sustainable practices, as well as the antecedents of such Willingness to Pay (WTP). The present study aims to advance knowledge on the antecedents of WTP for Sustainable Destinations (WTP-4-SD), for which it builds on previous studies employing the Theory of Planned Behavior (TPB) and the New Environmental Paradigm (NEP). In this context, a theoretical model involving Ecotourism Attitudes, Environmental Beliefs, Climate Change-Related Risk Perceptions (CC-RRP), Environmental Concern During trip (ECDT), and Sustainable Consumption Behavior (SCB) as antecedents of WTP-4-SD is proposed. The model was tested based on data collected through an online survey, form a sample of 1,545 Spanish and Portuguese tourists, and analyzed through Structural Equations Modelling (SEM). The findings suggest that a cohesive set of attitudes and beliefs regarding the man-nature relationship, the risks of climate change and the role of tourism – which have been labelled “No Planet-B Attitudes” – is the main driver of WTP-4-SD.

Indoor residual spraying (IRS) and use of insecticide treated bed nets for malaria vector control have contributed substantially to reduction in malaria disease burden. However, these control tools have important shortcomings including being donor-dependent, expensive, and often fail because of insufficient uptake. We assessed the safety and efficacy of a user-friendly, locally tailored malaria vector control approach dubbed ‟House Decoration for Malaria Control” (HD4MC) based on incorporation of a WHO-approved insecticide, pirimiphos-methyl CS (Actellic 300CS), into a customary hut decoration practice in rural Uganda where millions of the most vulnerable and malaria-prone populations live in mud-walled huts. Three hundred and sixty households were randomly assigned to either HD4MC (120 households), IRS (120 households) or control group without any wall treatment (120 households). Entomological indices were assessed using pyrethrum spray catch, CDC Light traps and human landing catches. Organophosphate (OP) toxicity on acetyl cholinesterase activity among applicators of HD4MC was evaluated using Test-mate (Model 400) erythrocyte acetylcholinesterase (AChE) test V.2 whereas toxicity in household occupants was monitored clinically. OP level in house dust was analyzed using reversed-phase high-performance liquid chromatography (RP-HPLC). Entomological indices were compared between the 3 study arms at 1.5, 3- and 6-months post intervention. HD4MC- and IRS-treated huts had significantly reduced malaria vector density and feeding rate compared to control huts. There was no significant reduction in acetylcholinesterase activity at 1.5- and 24-hours post exposure. Organophosphate exposure did not result in any serious adverse event among the household occupants. In conclusion, HD4MC was safe and had comparable efficacy to canonical IRS.

Today the Vocational Education and Training curriculum is state-regulated, combining school-based and work-based learning it is offered at upper-secondary and post-secondary levels. The main problem of nowadays period for Vocational Education and Training is about preparing a new agenda for technical education in Greece for the new circumstances. Our paper aims to enrich, redefine, and expand the current curricula and educational practices, while also proposing a complete plan for the overall reformation of Vocational Education and Training’s learning conditions from the point of view of teaching and learning analytics. Through the existing changing institutional framework, the updated bibliographic references, and the experiential data of the educational reality research, we can understand the expected needs of technological educators and decide how to address the priorities that must be determined. As a result of expectation and one of the main topics of this paper, we focus on the preparation of the trainers and the overall redefinition of the role of education in preparing graduates. This paper's main conclusions or interpretations for the cognitive orientation of the existing curricula, as well as the directions of the thinking and teaching skills of teachers, and to build confidence in the prospects of technical education in Greece.

This paper aims to demonstrate how the detection of communities for epidemiological networks can be used to reduce the dimensionality of optimal control problems. A range of planted partition stochastic blockmodels are generated, where the underlying communities in the model vary in detectability. A high-performance computing workflow is then used to simulate and estimate reduced model dynamics for the networks, which in turn is used to find optimal control solutions.

This paper addresses the conceptual exploration of various issues that form an integral part of the ethical dimensions surrounding the use of artificial intelligence (AI) in the field of marketing. We critically review some of the main ethical challenges AI poses to humanity: privacy, data security, bias, transparency, and accountability. Therefore, we will discuss the delicate balance between exploiting the transformation potential of AI for personally designed marketing strategies and ethical imperatives to safeguard the rights of consumers to retain trust. Then, the present regulatory landscape responding to these ethical challenges will be assessed by building on effective regulation, like the GDPR, existing proposed legislative frameworks, and industry guidelines. To underscore the role of an ongoing dialogue between marketers, technologists, ethicists, and regulators for developing a responsible AI ecosystem in marketing. Further, strategic recommendations for implementing company-based ethical AI marketing practices. The further strong guidelines provide enhancement transparency to consumers and investment in research to remove biases from algorithms. It also underscores principal scopes of further research and development, focusing on the need for new-age solutions to guide the ethical quagmires that AI-led marketing throws up. We suggest that integration should be done proactively and collaboratively. Thus, if attention is paid more to ethics and multidisciplinary dialogue, only such hyperbolic promises can be realized by AI-driven marketing. There are such promises through which AI-driven marketing will meet business goals and be huge at societies' values.

The regional recycling of plant waste from municipal green space maintenance, private households and private gardens is traditionally carried out in partially open composting plants. In addition to climate-relevant gases, dust, germs and odours are emitted. Compared to aerobic rotting the pyrolysis at higher temperatures and/or reforming offers industrial utilisation of plant based biowaste, with additional availability of energetic by-products. A high-quality plant charcoal derived from a pilot plant is used in a one-year field trial to establish a theoretical comparability with substrates from composting. The advantages and disadvantages of composting and high-quality pyrolysis is shown for comparison, to foster further investigation in advanced technologies for production of plant charcoal aiming the sequester of biogenic Carbondioxide.

The integration of AI in educational settings, particularly through Large Language Models (LLMs), is accelerating, reshaping pedagogical approaches due to students' interactions with new learning tools. This study assesses the impact of generative AI on the educational experiences of computer and data science students at the Center for Informatics, University of Paraíba (CI/UFPB), Brazil. Through Exploratory Factorial Analysis (EFA) of five psychometric scales, the research examines students' acceptance of LLMs, their associated burnout levels, technology anxiety, and the prevalence of metacognitive and dysfunctional learning strategies associated with LLMs. Results indicate significant adoption of AI technologies among students, accompanied by a low incidence of technology anxiety, manifesting as fears of losing jobs to AI. However, a significant correlation was observed between academic burnout and dysfunctional learning strategies, which could likely be attributed to the rigorous academic environment. Additionally, the employment of metacognitive strategies in conjunction with LLMs reflects an advanced learning approach, yet challenges with functional learning strategies persist. This study contributes to the discourse on AI in education, highlighting the need for educational frameworks that support effective AI adoption while addressing the psychological demands on students.

Resveratrol is currently considered an option for chemoprevention and treatment of oral cancer, but the instability and unfavorable pharmacokinetics hinder its use in clinical practice. The present study deals with the preparation of resveratrol liposomes by a method that preserves stability and provides payload high enough for chemoprevention and/or treatment of oral cancer. Different temperatures, RSV concentrations and molar fractions, as well as the presence or absence of vitamin C in the hydration media were assayed. Liposome hydrodynamic diameter, polydispersity and zeta potential were evaluated by dynamic light scattering. In vitro biocompatibility, antioxidant activity and permeability were evaluated using TR146 cells and PermeaPad® membranes. Liposomes were included in hydrogels and films, and their suitability for buccal application was evaluated. According to the results, a temperature of 37ºC was optima to efficiently produce the resveratrol liposomes. Vitamin C was found to influence the liposome size, while resveratrol concentration significantly influenced the entrapment efficiency. High cell biocompatibility and potent intracellular antioxidant activity were observed and the inclusion of vitamin C reinforced the antioxidant effects. High permeation across PermeaPad® membranes and efficient uptake by TR146 cells was proved for resveratrol loaded liposomes. Liposomal hydrogels and films appropriated for buccal application were obtained and the payload of the formulations was high enough to produce mucosa concentrations above resveratrol IC50 values reported for cancer cells.

In Japan, where the working population is declining, there is a growing expectation that the potential working population will play an active role, and in this context, administrative measures such as the promotion of women's activities have been implemented. However, there is no end to the number of women who leave the workforce after life events such as childbirth or child rearing.　For example, in Fukuoka Prefecture (population 5.1 million) alone, where we are based, it is estimated that there are 43,300 potential female workers. Many of these women have have been employed in the past, but have left the workforce due to childbirth, family care, or other reasons, and still wish to work. However, although this group has potential employment needs, they have not yet engaged in specific job-hunting activities, and thus are on the periphery of the labor market, without access to the current active labor market. There are many promising potential employees there, and support for their career redevelopment and participation in the labor market is required. On the other hand, few studies have been conducted in Japanese prefectures to systematically estimate social value, i.e., to determine the scope of impact on society and the stakeholders. This is an obstacle to the formulation of policies for women's advancement and the establishment of related social projects. This paper is aware of this problem and, based on the support for women seeking to re-enter society (career redevelopment) that the authors are implementing, selects seven prefectures in Kyushu, which is also a wide economic zone, as a support area and estimates the social value to companies and families in these areas by using SROI. As a result, it was derived that if 3,586 individuals were reemployed, the SROI ratio (1:X) X would be 76.1 in the first year of reintegration assistance activities and X would be 313.3 in the fourth year.

This paper uses a qualitative case study to investigate the experiences of Korean special education teachers who instruct children with disabilities from multicultural families. Through a purposive sampling method, study participants were chosen from a pool of individuals that the Korean multicultural support center recommended. These individuals are special teachers who are currently teaching children with disabilities from multicultural families. The qualitative case study approach used in this study seeks to elicit the participants’ experiences of teaching their students with special needs. The teachers’ experiences were analyzed and categorized into two main categories: 1) a mother's lack of Korean linguistic ability and 2) governmental and societal support for multicultural special education. Additionally, results also produced four sub-categories and seventeen minor categories. Such analyses lead to a greater understanding of the intricacies of educating children with special needs. Lastly, the implications of this study provide direction for future policy--including social welfare and educational support—that will meet the needs of multicultural families raising children with disabilities.

Financial capacity instruments are psychometric tools designed to evaluate individual decisional capacity based on financial decisions. As tests are complex and need special conditions for administration and evaluation, it is difficult to use them in daily geriatric clinics. Our scoping review objective was to evaluate existing financial capacity instruments from the perspective of simplicity and portability. We evaluated one English speaking knowledge database (Medline) using a dedicated MeSH terminology. The review yielded one independent instrument, The Semi-Structured Clinical Interview for Financial Capacity, that can be easy adapted for every-day clinical use. It is simple to understand and perform and do not need trained personnel for administration. It can be finalized in 15 minutes. Initially validated on 261 subjects (with different forms of cognitive impairment), it showed good accuracy and precision mainly in subjects with cognitive impairment. The test is less apt to detect early or fluctuating cognitive impairment. Simplicity, the main advantage of the test, allows gamification fact that increases portability. Familiar images (coins, money) that are used for performing simple tasks does not need complex translation and adaptation. In form of a game, the test is suitable for serial administration, increasing the chance for early capacity reduction detection. Results reflect a physician judgement related to the subjects’ capacity to understand and execute simple financial instructions and not financial proficiency scores. The main limitation of our review is that we investigated only one, English speaking, knowledge database. The scoping strategy generated a financial capacity instrument that can be used in geriatric clinics for early diagnostic of decisional capacity reduction. Further studies are needed to evaluate the reliability and validity of the test in conditions of serial administration and in populations having various financial experience.

In contemporary design practices, there is a disconnect between the design techniques used for early-stage design experimentation and performance analysis, and those used for the manufacture and construction. This study addresses the problems in developing an integrated digital design workflow and provides a research framework for integrating environmental performance requirements with robotic manufacturing processes on a construction site. The proposed method enables the user to import a design surface, identify design parameters, set several environmental performance goals, and thereafter simulate and select a robotic building strategy. Based on these inputs, design alternatives are developed and evaluated, considering their robotically simulated constructibility, in terms of their performance criteria. To validate the proposed method, the design is evaluated in an experiment wherein a double-skin facade perforation is generated using the proposed methodology. The results suggest a heuristic feature to improve the simulated robotic constructibility. Moreover, the functionality of the prototype is demonstrated.

In contemporary design practices, there is a disconnect between the design techniques used for early-stage design experimentation and performance analysis, and those used for the manufacture and construction. This study addresses the problems in developing an integrated digital design workflow and provides a research framework for integrating environmental performance requirements with robotic manufacturing processes on a construction site. The proposed method enables the user to import a design surface, identify design parameters, set several environmental performance goals, and thereafter simulate and select a robotic building strategy. Based on these inputs, design alternatives are developed and evaluated, considering their robotically simulated constructibility, in terms of their performance criteria. To validate the proposed method, the design is evaluated in an experiment wherein a double-skin facade perforation is generated using the proposed methodology. Initial results suggest a heuristic feature to improve the simulated robotic constructibility. Moreover, the functionality of the prototype is demonstrated.

In order to be able to face competition, thriving corporations have to be compelled to maintain an awfully smart relationship with their existing customers and additionally to be able to anticipate their future wants. Thus, corporations do not target customers as teams, however they’re attempting to focus on them as individuals. However, to be able to use this information, corporations have to be compelled to use promoting automation tools. Marketing Automation was an idea first introduced in 2001 by John D.C. Little in his presentation at the 5th Invitational Choice Symposium UC Berkeley 2001. This survey paper assesses prominent research on Marketing Automation and suggests how it can be modified to adapt to the current marketing scenario.

Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.

We address security and privacy problems for digital devices and biometrics from an information-theoretic optimality perspective, where a secret key is generated for authentication, identification, message encryption/decryption, or secure computations. A physical unclonable function (PUF) is a promising solution for local security in digital devices and this review gives the most relevant summary for information theorists, coding theorists, and signal processing community members who are interested in optimal PUF constructions. Low-complexity signal processing methods such as transform coding that are developed to make the information-theoretic analysis tractable are discussed. The optimal trade-offs between the secret-key, privacy-leakage, and storage rates for multiple PUF measurements are given. Proposed optimal code constructions that jointly design the vector quantizer and error-correction code parameters are listed. These constructions include modern and algebraic codes such as polar codes and convolutional codes, both of which can achieve small block-error probabilities at short block lengths, corresponding to a small number of PUF circuits. Open problems in the PUF literature from a signal processing, information theory, coding theory, and hardware complexity perspectives and their combinations are listed to stimulate further advancements in the research on local privacy and security.

For crystal periodic structure prediction, a new and concise approach based on the principles of statistical physics was employed to derive a new form of the equation to determine their period vectors (cell edge vectors), under general external stress. Then the new form is applicable to both classical physics and quantum physics. It also turned out to be the equation of state and the mechanical equilibrium condition for crystals under external stress and temperature. It should be helpful in piezoelectric and piezomagnetic studies, as the period vectors were changed by the external stress. For linear elastic crystals, it is actually also the microscopic but temperature-dependent form of the generalized Hooke's law, then can be used to calculate the corresponding elastic constants of the law, for given temperatures.

This study presents a platform for ex-vivo detection of cancer nodules, addressing automation of medical diagnoses in surgery and associated histological analyses. The proposed approach takes advantage of the property of cancer to alter the mechanical and acoustical properties of tissues, because of changes in stiffness and density. A force sensor and an ultrasound probe were combined to detect such alterations during force-regulated indentations. To explore the specimens, regardless of their orientation and shape, a scanned area of the test sample was defined using shape recognition applying optical background subtraction to the images captured by a camera. The motorized platform was validated using seven phantom tissues, simulating the mechanical and acoustical properties of ex-vivo diseased tissues, including stiffer nodules that can be encountered in pathological conditions during histological analyses. Results demonstrated the platform’s ability to automatically explore and identify the inclusions in the phantom. Overall, the system was able to correctly identify up to 90.3% of the inclusions by means of stiffness in combination with ultrasound measurements, paving pathway towards robotic palpation during intraoperative examinations.

Augment reality (AR) is crucial for immersive human-computer interaction (HCI) and vision of artificial intelligence (AI). Labeled data drove object recognition in AR. However, manual annotating data is expensive and labor-intensive, and furthermore, scanty labeled data limits the application of AR. Aiming at solving the problem of insufficient training data in AR object recognition, an automated vision data synthesis method called BAGAN is proposed in this paper based on the 3D modeling and GAN algorithm. Our approach has been validated to have better performance than other methods through image recognition task on natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand the application scope of AR, which is of great significance to immersive interactive systems.

Sustainability, sustainable development and education for sustainable development are increasingly central concepts, both in social practice and in the field of scientific knowledge. Sociology, and in particular Sociology of Education as a specialised Sociology, can provide relevant contributions in its promotion. This article aims to explore the importance of Sociology of Education in promoting sustainability literacy in higher education, using the Sustainable Development Goals and key competencies (United Nations and UNESCO) as the central reference in this field, and intends, thus, to become an added contribution for this discussion. The article seeks to demonstrate that the learning of sustainability literacy would benefit from the use of a sociological stance throughout this whole process that considers dimensions that are often not directly emphasised and articulated between each other, such as: interconnection of scale levels, sociological imagination, multi-paradigmatic nature, heuristic interdisciplinarity, reflexivity and use of Sociology for action.

Background: Access to rehabilitation is crucial for the realization of the right to health and a proper concern of global health. Yet, reliable information to guide rehabilitation service planning is unavailable in many countries in part due to the lack of appropriate indicators. To ensure universal health coverage and meet the central imperative of “leaving no one behind” countries must be able to assess key aspects of rehabilitation policy and provision and monitor how they have discharged their human rights responsibilities towards those most disadvantaged, including people with disability. This article describes the process of developing an expert guided indicator framework to assess governments’ efforts and progress in strengthening rehabilitation in line with the Convention on the Rights of Persons with Disabilities. Methods: A systems methodology - concept mapping - was used to capture, aggregate and confirm the knowledge of diverse stakeholders on measures thought to be useful for monitoring the implementation of the Convention with respect to health related rehabilitation. 56 individuals generated a list of 107 indicators through online brainstorming which were subsequently sorted by 37 experts from the original panel into non overlapping categories. 41 participants rated the indicators for importance and feasibility. Multivariate statistical techniques where used to explore patterns and themes in the data and create the indicators’ organizing framework which was verified and interpreted by participants. Results: A concept map of 11 clusters of indicators emerged from the analysis grouped into three broader themes: Governance and Leadership (3 clusters); Service Delivery, Financing and Oversight (6 clusters); and Human Resources (2 clusters). The RESYST was comprehensive and well aligned with the Convention. On average, there was a moderately positive correlation between importance and feasibility of the indicators (r=.58) with experts prioritizing the indicators contained in the clusters of the Governance and Leadership domain. Two of the most important indicators arose from the Service Delivery, Financing and Oversight domain and reflect the need to monitor unmet needs and barriers in access to rehabilitation. In total, 59 indicators achieved above average score for importance and comprised the two–tiered priority set of indicators. Conclusion: Concept mapping was successful in generating a shared model that enables a system’s view of the most critical legal, policy and programmatic factors that must be addressed when assessing country efforts to reform, upscale and improve rehabilitation services. The RESYST provides a data driven basis for the development of standardized data collection tools to facilitate comparative analysis of rehabilitation systems.

Automatic garments design class identification for recommending the fashion trends is important nowadays because of the rapid growth of online shopping. By learning the properties of images efficiently, a machine can give better accuracy of classification. Several methods, based on Hand-Engineered feature coding exist for identifying garments design classes. But, most of the time, those methods do not help to achieve better results. Recently, Deep Convolutional Neural Networks (CNNs) have shown better performances for different object recognition. Deep CNN uses multiple levels of representation and abstraction that helps a machine to understand the types of data (images, sound, and text) more accurately. In this paper, we have applied deep CNN for identifying garments design classes. To evaluate the performances, we used two well-known CNN models AlexNet and VGGNet on two different datasets. We also propose a new CNN model based on AlexNet and found better results than existing state-of-the-art by a significant margin.

The reliability of concrete cable-stayed bridge structure is time variant. This is due to strength deterioration and time-variant mechanical and environmental loadings. Demands for keeping concrete cable-stayed bridge structure in functional and reliable states require maintenance interventions. Limited financial resources require optimal maintenance strategies. These strategies have to consider the effects of uncertainties not only on the structural reliability deterioration but also on the maintenance interventions. By analyzing variation of concrete cable-stayed concrete cable-stayed bridge liabilities and benefit expectation through different maintenance programs, on the condition of satisfying the concrete cable-stayed bridge performance, with the maximum expected benefit as the target to discuss repair and reinforcement strategies for the concrete cable-stayed bridges in service. The method takes into account the present performance and future expected benefit, optimizing the performance of the concrete cable-stayed bridge and the expected benefit through determination policies of concrete cable-stayed concrete cable-stayed bridge maintenance program. The proposed method also includes an advanced cost model which takes into account the interaction between costs of maintenance interventions and their effect on reliability. Examples are presented demonstrating the application of the proposed method to a stock of existing deteriorating concrete cable-stayed bridges under various maintenance scenarios.

Burden of diseases measured as disability-adjusted life years per 100,000 people can be mined from public domain data, when they are made available by population health surveillance systems. This can be analysed to allow insightful comparisons with the national average, and to understand differences in trends between the sexes, age groups, time periods, geographic regions and sub-regions. In this illustrative case study, we have analysed the Scottish burden of disease database to understand what ailed the population of the Grampian region before the COVID-19 pandemic. We have identified selected cancers, ischaemic heart disease, Alzheimer’s disease and other dementias amongst the highest contributors to the burden; that drug use disorders and colorectal cancer are showing worsening trends and require health promotion and disease prevention measures from ages 15 and 25 respectively, especially in Aberdeen City; and that males are more vulnerable to atrial fibrillation and flutter, diabetes mellitus and oesophageal cancer, while females are to cerebrovascular disease. We demonstrate the usefulness of our analysis and methodology for the wider health system, allowing targeted medical research investments and coordinated response from public health and health service delivery. We also show the need for up-to-date surveillance data, forecasts and evidence on the impact of interventions to be made available widely.

The development of sustainable tourism in the territory requires the monitoring of all diverse aspects of the tourism sector and the creation of information technologies that provide the collection, analysis, processing and visualization of relevant information. The aim of this work is to describe the concept of the tourism monitoring service in Irkutsk Oblast as part of the Lake Baikal area, define methods and software of implementing this service, and present individual results of its prototyping. The creation of a service includes the development and software implementation of methods for collecting, processing and visualizing data, as well as ensuring the use of the functionality of existing cloud platforms for the main information processes. Data collection methods are based on Application Programming Interface and web scraping technologies, processing methods include statistical methods and artificial intelligence methods, visualization methods are based on Business Intelligent systems methods. In this paper described the concept of service, justified methods of data analysis and visualization, built an ontological model, defined the content of dashboards, implemented a prototype of a service, and demonstrated the results of data collection, processing and presentation obtained from the research. Currently, information has been collected and processed for the following tourist attractions: 685 accommodation facilities, 16 services, 809 catering establishments, 352 attractions, 94 tourist routes and excursions. The proposed technology (methods and software) of the territory tourism monitoring service has demonstrated its effectiveness.

The objective of this study was to identify the key elements used by prehospital emergency physicians (EP) to decide whether or not to attempt advanced life support (ALS) in asystolic out-of-hospital cardiac arrest (OHCA). From 01.01.2009 to 01.01.2017, all adult victims of asystolic OHCA in Geneva, Switzerland, were retrospectively included. Patients with signs of "obvious death" or with a Do-Not-Attempt-Resuscitation order were excluded. Patients were categorized as having received ALS if this was mentioned in the medical record, or, failing that, if at least one dose of adrenaline had been administered during cardio-pulmonary resuscitation (CPR). Prognostic factors known at the time of EP's decision were included in a multivariable logistic regression model. 784 patients were included. Factors favourably influencing the decision to provide ALS were witnessed OHCA (OR=2.14, 95%CI1.43–3.20) and bystander CPR (OR=4.10, 95%CI2.28–7.39). Traumatic aetiology (OR=0.04, 95%CI0.02–0.08), age >80 years (OR=0.14, 95%CI0.09–0.24) and a Charlson comorbidity index greater than 5 (OR=0.12, 95%CI0.06–0.27) were the factors most strongly associated with the decision not to attempt ALS. Factors influencing the EP’s decision to attempt ALS in asystolic OHCA are the relatively young age of the patients, few comorbidities, presumed medical aetiology, witnessed OHCA and bystander CPR.

Multi-criteria decision analysis (MCDA), one of the prevalent branches of operations research, aims to design mathematical and computational tools for selecting the best alternative among several choices with respect to specific criteria. In the cloud, MCDA based online brokers uses customer specified criteria to rank different service providers. However, subjected to limited domain knowledge, the customer may exclude relevant or include irrelevant criterion, which could result in suboptimal ranking of service providers. To deal with such misspecification, this research proposes a model, which uses notion of factor analysis from the domain of unsupervised machine learning. The model is evaluated using two quality-of-service (QoS) based datasets. The first dataset i.e., feedback from customers, was compiled using leading review websites such as Cloud Hosting Reviews, Best Cloud Computing Providers, and Cloud Storage Reviews and Ratings. The second dataset i.e., feedback from servers, was generated from cloud brokerage architecture that was emulated using high performance computing (HPC) cluster at University of Luxembourg (HPC @ Uni.lu). The simulation runs in a stable cloud environment i.e. when uncertainty is low, shows that online broker (equipped with the proposed model) produces optimized ranking of service providers as compared to other brokers. This is due the fact that proposed model assigns priorities to criteria objectively (using machine learning) rather than using priorities based on subjective judgments of the customer. This research will benefit potential cloud customers that view insufficient domain knowledge as a limiting factor for acquisition of web services in the cloud.

As cities evolve into smarter and more connected environments, there is a growing need for innovative solutions to improve urban mobility. This study examines the potential of integrating blockchain technology into passenger transportation systems within smart cities, with a particular emphasis on a blockchain-enabled Mobility as a Service (MaaS) solution. In contrast to traditional technologies, blockchain's decentralized structure improves data security and guarantees transaction transparency, thus reducing the risk of fraud and errors. The proposed MaaS framework enables seamless collaboration between key transportation stakeholders, promoting more efficient utilization of services like buses, trains, bike-sharing, and ride-hailing. By improving integrated payment and ticketing systems, the solution aims to create a smoother user experience while advancing urban goals of efficiency, environmental sustainability, and secure data handling. This research evaluates the feasibility of a Hyperledger Fabric-based solution, demonstrating its performance under various load conditions and proposing scalability adjustments based on pilot results. The conclusions indicate that blockchain-enabled MaaS systems have the potential to transform urban mobility. Further exploration into pilot projects and the expansion to freight transportation are needed for an integrated approach to city-wide transport solutions.

Developing sustainability competences is an important endeavor for education, as competence development can be complex and requires appropriate approaches. In the case of preparing future educators, this becomes even more important, because of their multiplier role. The current study presents how a project, based on living-lab methodology, helped preservice preschool teachers, attending a course on sustainability issues, develop sustainability-specific professional action competence. Students collaborated with an organization and a school and were trained to deliver to 6-year-old pupils, activities concerning the protection of sea turtles. The process included reflection, based on a given template. After the intervention, the preservice preschool teachers reflected upon their role and professional performance and discussed how they, benefited from this project. They further analyzed through a group interview, how the Living Lab methodology raised their professional confidence and self-efficacy, and helped them develop content knowledge, pedagogical content knowledge, and a sense of purpose and motivation for teaching about sustainability. Outcomes indicate that participants felt confident and efficacious to teach about sustainability, and developed their content knowledge, pedagogy content knowledge, motivation, and volition due to a strong sense of purpose and responsibility. The overall process facilitated the development of critical thinking, systemic thinking, reflection, and collaboration..

Open Education, Open Educational Practices (OEP), and Open Educational Resources (OER) have emerged as significant opportunities for enhancing global sustainability information sharing. However, the creation and sharing of OER, as well as the usage of OEP in Higher Education for Sustainable Development (HESD), remain limited. This study explores the implementation of OEP in HESD, aiming to empower students to co-produce OER on Sustainable Development (SD). This study, drawing on the theoretical approach of the principle of constructive alignment, proposes the development of students’ digital competence in OER production. A two-group pre-test post-test analysis of 409 students (Psychology, Economics, Education, Geography) reveals a significant increase in digital competence over time among students who produced OER on SD, compared to their peers enrolled in courses unrelated to OER content development. We delve into the practical implications for designing OEP in HESD and strategize to support students in their OER production processes.

The three-axis stabilization geostationary remote sensing satellite features high time resolution and plays a significant role in weather forecasting and environmental monitoring. With the future development of satellite technology, we propose a rapid geolocation algorithm for three-axis stabilization geostationary remote sensing satellites from the perspective of software algorithms. The method assesses conventional remote sensing data geolocation methods and the coordination group for meteorological satellites (CGMS, Coordination Group for Meteorological Satellites) nominal grid publishing format. First, the initial incident viewing vector of each detector on the sensor was constructed. Then, by reflecting the satellite platform's east-west and north-south mirrors, the outgoing vector of the payload coordinate system was obtained. This vector was converted to the satellite body coordinate system and, subsequently, to the satellite orbit coordinate system. The conversion of the earth-fixed coordinate system and the mirror rotation angle under ideal CGMS grid conditions were obtained, and the mirror rotation angle was converted to the corresponding CGMS nominal grid position to complete data positioning. The algorithm omits the complex process of calculating the intersection point between the viewing vector and the geodetic ellipsoid sphere from the earth-fixed coordinate system, resulting in an 83.3% improvement in computational efficiency without compromising positioning accuracy compared with conventional geolocation methods. This algorithm greatly improves the processing efficiency of geostationary meteorological satellite positioning, but its limitation is that the data processing results must be released in the CGMS nominal grid format.

Control of canine infections with Leishmania infantum (L. infantum), a major zoonotic disease in Brazil and southern Europe is becoming increasingly important due to their close proximity to humans, the increasing import of dogs from endemic regions and impact of climate change on vector spreading. Simple, rapid and reliable diagnostic tests are therefore needed to detect infected dogs. Here, we re-evaluated different serological methods for the diagnosis of canine leishmaniosis (CanL) in Croatia and Brazil. The diagnostic performance of the indirect fluorescent antibody test (IFAT) and the VetLine® Leishmania ELISA was compared with three rKLi8.3 based diagnostic test systems, the rKLi8.3 ELISA, the INgezim® Leishma CROM lateral flow test (LFT) and the VetBlot® Leishmania LineBlot. CanL symptomatic dogs were efficiently diagnosed by all tests, except the VetLine® Leishmania ELISA, which is based on whole Leishmania antigens. The advantage of rKLi8.3 was also observed in oligo-and asymptomatic dogs from Brazil and Croatia, although with reduced diagnostic efficiency compared to symptomatic dogs. Similar to IFAT and rKLi8.3 ELISA, the LFT did not cross-react with other common canine pathogens, showed a very high specificity for healthy dogs from endemic regions of both countries and did not react with healthy, vaccinated dogs in Brazil. In conclusion, sero-diagnostic tests based on the rKLi8.3 antigens are superior to whole parasite antigens and the LFT has the advantage of providing a laboratory independent, rapid and specific diagnosis of CanL.

Fit-for-purpose land administration (FFPLA) seeks to simplify cadastral mapping via lowering the costs and time associated with conventional surveying methods. The approach can be applied to both initial establishment and on-going maintenance of system. In Ethiopia, cadastral maintenance remains an on-going challenge, especially in rapidly urbanizing peri-urban areas, where farmers' land rights and tenure security are often jeopardized. Automatic Feature Extraction (AFE) is an emerging FFPLA approach, proposed as an alternative for mapping and updating cadastral boundaries. This study explores the role of the AFE approach for updating cadastral boundaries in the vibrant peri-urban areas of Addis Ababa. Open-source software solutions are utilized to assess the (semi-) automatic extraction of cadastral boundaries from orthophotos (segmentation), designation of 'boundary' and 'non-boundary' outlines (classification), and delimitation of cadastral boundaries (interactive delineation). Both qualitative and quantitative assessments of the achieved results (validation) are undertaken. A high-resolution orthophoto of the study area and a reference cadastral boundary shape file are used, respectively, for extracting the parcel boundaries and validating the interactive delineation results. Qualitative (visual) assessment verified the completed extraction of newly constructed cadastral boundaries in the study area, although non-boundary outlines such as footpaths and artefacts are also retrieved. For the buffer overlay analysis, the interactively delineated boundary lines and the reference cadastre were buffered within the spatial accuracy limits for urban and rural cadasters. As a result, the quantitative assessment delivered 52% correctness and 32% completeness for a buffer width of 0.4m and 0.6m, respectively, for the interactively delineated and reference boundaries. The study further demonstrated the potentially significant role AFE could assist in delivering fast, affordable, and reliable cadastral mapping. Further investigation, based on user input and expertise evaluation, could help to improve the approach and apply it to a real-world setting.

Introduction Headache is a common public health problem, but its burden could be avoided by raising headache awareness and the appropriate use of acute medication and prophylactic medication. Few reports on raising headache awareness in the general public have been reported, and there are no reports on headache awareness campaigns through social networking services (SNS), or social media, in Japan. We prospectively performed a headache awareness campaign from March 2022 through 2 SNS, targeting nurse and wind instrumental musicians, because they are with high headache prevalence. Methods Through the 2 SNS, the article and video were distributed, respectively. The article and video described the 6 important topics for the general public about headaches, which were described in the Clinical Practice Guideline for Headache Disorders 2021. Just after reading or watching them as e-learning, we performed online questionnaire sheets to investigate the awareness of the 6 topics through the 2 SNS. The awareness of the 6 topics before and after the campaign was evaluated. Results In the SNS nurse-senka, we obtained 1191 responses. Women comprised 94.4%, and the median (range) age was 45 (20 to 71) years old. Headache sufferers were 63.8%, but only 35.1% had consulted doctors. In the SNS Creatone, we got the response from 134 professional musicians, with 77.3% of women. The largest number of respondents were in their 20s (range 18-60 years old). Headache sufferers were 87.9%. Of them, 36.4% had consulted doctors, 24.2% were medication-overuse headache. The ratios of individuals who were aware of the 6 topics significantly increased from 15.2%-47.0% to 80.4-98.7% after the online questionnaire in both SNS (p < 0.001, all). Conclusions We conducted this headache awareness campaign through e-learning and an online survey via 2 SNS. The ratios of individuals who were aware of the 6 topics about headaches significantly increased 1 month after e-learning. Our results suggest that e-learning and online survey can improve headache awareness. The materials in this campaign can be installed into smartphone applications and further spread on SNS, leading to strong influence. With rapid digital transformations such as online telemedicine and artificial intelligence diagnosis, raising awareness will be more efficient and effective and should be important.