Search Results (91)

Disability Certification is an instrument that can contribute to the identification of persons with disabilities who are potential beneficiaries of social protection programmes. In Colombia, the disability certification process was changed in 2020 to include a human rights perspective. However, little information exists on how far the process aligns with the recommendations made by the Convention on the Rights of Persons with Disabilities or to what extent it provides useful information for the operation of the country’s social protection system. This study aims to analyse how the changes implemented in 2020 have contributed to the identification and determination of persons with disabilities in social protection programmes in Colombia. This research uses an analytical-descriptive design, in which legal documents related to the social protection programme and disability certification were analysed. In addition, we conducted semi-structured interviews in Colombia with people with disabilities, professionals conducting the certification, and with key stakeholders, and we analysed the information using thematic analysis. Colombia changed its certification process in 2020, moving from a capacity-to-work assessment to a process of assessing functioning capacity and participation limitations following the International Classification of Functional Disability and Health. However, the new certification process has not provided an instrument to contribute to determining persons with disabilities who should be prioritised or receive social benefits in Colombia. The certification does not establish the support needs of people with disabilities, does not recommend reasonable adjustments to support their participation in society, and has not been used systematically in social protection programmes. Finally, individuals with disabilities commented that the disability certification is a process that does not open up opportunities to receive social benefits and social transfers, and in most cases, it is an expensive and lengthy process. Disability certification in Colombia is not an instrument that determines the needs of people with disabilities, nor the reasonable accommodations that they require in order to participate in society. Full article

River embankments are critical flood defense structures, stretching for thousands of kilometers across alluvial plains. They often originated as natural levees resulting from overbank flows and were later enlarged using locally available soils yet rarely designed according to modern engineering standards. Substantially under-characterized, their performance to extreme events provides an invaluable opportunity to highlight their vulnerability and then to improve monitoring, management, and reinforcement strategies. In May 2023, two extreme meteorological events hit the Emilia-Romagna region in rapid succession, causing numerous breaches along river embankments and therefore widespread flooding of cities and territories. These were followed by two additional intense events in September and October 2024, marking an unprecedented frequency of extreme precipitation episodes in the history of the region. This study presents the methodology adopted to create a regional database of 66 major breaches and damages that occurred during May 2023 extensive floods. The database integrates multi-source information, including field surveys; remote sensing data; and eyewitness documentation collected before, during, and after the events. Preliminary interpretation enabled the identification of the most likely failure mechanisms—primarily external erosion, internal erosion, and slope instability—often acting in combination. The database, unprecedented in Italy and with few parallels worldwide, also supported a statistical analysis of breach widths in relation to failure mechanisms, crucial for improving flood hazard models, which often rely on generalized assumptions about breach development. By offering insights into the real-scale behavior of a regional river defense system, the dataset provides an important tool to support river embankments risk assessment and future resilience strategies. Full article

Food safety is a global issue that can be enhanced by collaboration with reliable suppliers. Given the complexities of international supply chains, identifying reliable suppliers is often challenging and resource-intensive. Integrating artificial intelligence (AI) offers a valuable opportunity to improve efficiency in this process. The aim of the present study was to develop a quantitative supplier assessment scheme for implementation in an AI-supported database. The framework developed incorporates different indicators, including the hazard risk, incident category level, vulnerability of a commodity, audit performance, logistic performance index, gross domestic product (GDP) growth, and GDP per capita. Each indicator is evaluated according to its own distinct assessment. Ultimately, the sub-assessments are integrated into the calculation of a supplier’s overall risk score. Hereby, it is possible to set individual weightings for each indicator. Manual testing using an exemplary selected supplier yielded promising results, indicating that the next steps involve implementation into an AI-supported database. It can be concluded that such an assessment framework can be an effective method for the identification of reliable suppliers. A future challenge will be to establish incentives to make audit data freely available, as these are often restricted and cannot be considered in the supplier risk assessment. Full article

Amid escalating ecological concerns and regulatory pressures, firms are adopting environmental sustainability commitments to enhance competitiveness and fulfill social responsibilities. However, the internal mechanisms linking these commitments to environmental performance remain insufficiently explored. This study investigates how corporate environmental sustainability commitments improve environmental performance by integrating the Planned Behavior Theory and Organizational Change Theory. Using structural equation modeling with 324 firm-level responses, we identify a chain mediation pathway. Results indicate that environmental sustainability commitment positively influences corporate environmental performance through the chain-mediating effects of green readiness and green opportunity identification and exploitation. By extending the Technology–Organization–Environment (TOE) framework, we delineate three dimensions of green readiness, showing that organization readiness exhibits the strongest mediating role. This study advances theoretical understanding by mapping the pathway from sustainability intentions to performance through internal capabilities and actions. Practically, it helps firms systematically align environmental and economic goals while avoiding greenwashing risks. Full article

The research concentrates on determining the degree of internationalization of born global SMEs, believing that some push factors determine internationalization, pull factors, and internal firm-specific factors. Three important factors were found in looking into the causes of internationalization in born global firms: push, pull, and internal firm-specific factors. The study used a survey instrument with a sample of 280 manufacturing-related SMEs chosen from manufacturing clusters in India. A metric called the “index of internationalization” is used to gauge how internationalization in SMEs takes shape. The results demonstrated that internal firm-specific factors influence the internationalization of firms relatively highly compared to push and pull factors. The results unequivocally demonstrate that developing economies have distinct factors that cause internationalization, opening up new avenues for further study. The research aids in the identification of the elements that will enhance early internationalization and tries to draw the attention of young entrepreneurs. This research also helps prioritize the factors responsible for early internationalization. These findings are pertinent for the practitioners and researchers working in this area. This research is helpful for start-ups looking for global opportunities; this research categorizes factors significant in the global journey of the born global firms. Full article

Systemic sclerosis (SSc) is an autoimmune disease of unknown aetiology characterised by vasculopathy with progressive fibrosis of the skin and internal organs. Tissue fibrosis is driven by activated fibroblasts (myofibroblasts) with exacerbated contractile and secretory properties. We previously reported that the long non-coding RNA HOTAIR is a key driver of SSc fibroblast activation. HOTAIR interacts with the chromatin modifiers, the polycomb repressor complex (PRC2) and coREST complex, promoting expression of pro-fibrotic genes. In this study, we show that acute activation of dermal fibroblasts from healthy subjects or SSc patients with transforming growth factor-β and other fibrotic stimuli requires the activity of the lysine-specific demethylase 1 (LSD1) subunit of the co-REST complex. Unexpectedly, LSD1 catalytic activity plays a minor role in fibrotic gene expression in HOTAIR-overexpressing fibroblasts and in maintenance of the stable myofibroblast phenotype of SSc fibroblasts. However, silencing of LSD1 in SSc fibroblasts has a profound effect on pro-fibrotic gene expression, supporting a non-canonical scaffolding function. Our study shows for the first time an essential non-canonical role for LSD1 in pro-fibrotic gene expression in SSc; however, given that this function is insensitive to LSD1 inhibitors, the therapeutic opportunities will depend on future identification of a targetable mediator. Full article

This paper presents a detailed bibliometric analysis of the interaction between financial development and climate change, with the main aim of elucidating the current state of research in this area, identifying existing gaps, and guiding future researchers interested in this rapidly expanding field. The study used VOSviewer software version 1.6.18 to analyze the bibliometric data, facilitating the mapping of co-author networks, institutional collaborations, and the identification of main research directions. Through this tool, 730 papers from the Web of Science database covering the period 2010–2024 were extracted and analyzed. The study highlights the authors and institutions that have made significant contributions to the investigation of the relationship between financial development and climate change. The identification of key contributors and international collaborative networks provides a solid foundation for future research and policy initiatives. In addition, the study identifies the most prolific journals and assesses the quality and impact of the research. This allows for a deeper understanding of current research directions and potential future developments. The study not only clarifies the current state of research but also opens up new opportunities for investigating innovative and sustainable solutions aimed at improving the quality of life and protecting the environment. Full article

The targeted therapies and immunotherapies in thoracic oncology, particularly for NS-NSCLC, are associated with an increase in the number of predictive biomarkers to be assessed in routine clinical practice. These treatments are administered thanks to marketing authorization for use in daily practice or are evaluated during clinical trials. Since the molecular targets to be identified are more and more complex and numerous, it is now mandatory to use NGS. NGS can be developed from both tissue and fluid (mainly blood). The blood tests in oncology, so-called “liquid biopsies” (LB), are performed with plasmatic circulating free DNA (cf-DNA) and are complementary to the molecular testing performed with a TB. LB use in lung cancer is associated with international guidelines, but additional algorithms could be set up. However, even if useful for better care of patients, notably with advanced and metastatic NS-NSCLC, until now LB are not often integrated into daily practice, at least in Europe and notably in France. The purpose of this review is to describe the different opportunities and algorithms leading to the identification of the molecular signature of NS-NSCLC, using both tissue and liquid biopsies, and to introduce the principle limitations but also some perspectives in this field. Full article

In modern society, the amount of e-waste is growing year by year. Waste electronic items are complex, highly heterogeneous systems, containing organic material as well as several exotic, valuable, toxic, mostly metallic elements. In this study, the potential of X-ray and neutron radiography to reveal the inner structure of various complex e-waste was investigated. The images obtained using the two techniques were evaluated together to investigate the possibility of a more efficient segmentation of the individual components. The advantages and limitations of the two methods were identified for the studied waste types. X-ray radiography was found to be preferable for the identification of small metallic parts and for revealing the internal structure of e-waste with thick plastic coatings. Neutron radiography allowed for the identification of several components that did not provide sufficient contrast with X-ray imaging due to their similar X-ray attenuation compared to their surroundings. The combination of the two methods opens up new opportunities and could provide much more effective segmentation than either method alone. Full article

Collaborative Opportunities for Professional Inquiry Leading to Organisational Transformation (COPILOT) represents an Erasmus+ initiative involving four transnational partners from three EU countries including IES Cristobal de Monroy secondary school [Spain], Laois Offaly Education Training Board (LOETB) [Ireland], University College Dublin (UCD) School of Education [Ireland], and Akadimos, a teacher continuing professional development provider [Greece]. The project encompassed three primary objectives, aligned with the Erasmus+ programmes’s emphasis on supporting teachers and school leaders to address digital transformation. These included the following: the identification of participatory approaches and digital methodologies employed by schools to facilitate digital transformation among educators; exploring and establishing dynamic professional environments in schools by identifying synergies with organisations operating in diverse fields or socio-economic contexts and enhancing the capacity and professionalism of partners to engage in EU/international collaboration. To investigate the outcomes of the project objectives, a small-scale qualitative study was conducted, which involved analysing participants’ reports on the project’s collaborative activities. Additionally, a focus group was held at the conclusion of the project to gain insights into the digital readiness, capacity, and resilience of teachers and school leaders within their respective organisations. Thematic analysis was employed to analyse the collected data. The findings of the study offer encouraging indications regarding the crucial symbiotic role played by school leaders and “early adopter” teachers in promoting digital transformation. Moreover, participating teachers displayed a general willingness to adapt their teaching methods to incorporate more technology, provided they receive both formal and informal support. Furthermore, there exists a willingness among transnational partners to digitise and share resources both at present and in the future. Full article

Dermatological conditions are primarily prevalent in humans and are primarily caused by environmental and climatic fluctuations, as well as various other reasons. Timely identification is the most effective remedy to avert minor ailments from escalating into severe conditions. Diagnosing skin illnesses is consistently challenging for health practitioners. Presently, they rely on conventional methods, such as examining the condition of the skin. State-of-the-art technologies can enhance the accuracy of skin disease diagnosis by utilizing data-driven approaches. This paper presents a Computer Assisted Diagnosis (CAD) framework that has been developed to detect skin illnesses at an early stage. We suggest a computationally efficient and lightweight deep learning model that utilizes a CNN architecture. We then do thorough experiments to compare the performance of shallow and deep learning models. The CNN model under consideration consists of seven convolutional layers and has obtained an accuracy of 87.64% when applied to three distinct disease categories. The studies were conducted using the International Skin Imaging Collaboration (ISIC) dataset, which exclusively consists of dermoscopic images. This study enhances the field of skin disease diagnostics by utilizing state-of-the-art technology, attaining exceptional levels of accuracy, and striving for efficiency improvements. The unique features and future considerations of this technology create opportunities for additional advancements in the automated diagnosis of skin diseases and tailored treatment. Full article

Gold mining in Egypt’s Central Eastern Desert (ECED) has a rich history dating back to the Old Kingdom period. In recent years, there has been renewed interest from international mining companies, and several potential areas for gold mining have been identified. Extensive studies have been conducted on the deposition and occurrence of gold in the region, identifying over 100 areas of deposition. Aeromagnetic and radioactive surveys have played a crucial role in locating gold occurrences in ECED by utilizing data from these techniques to identify potential gold deposits. This study utilized geophysical data, including total aeromagnetic intensity (TM) and radiometric data, to identify hydrothermal zones and locate potential areas of gold occurrence. The radiometric ternary map was used to refine the search for gold deposits. Magnetic data were analyzed using edge detection tools to determine the structural framework of the area, facilitating the identification of regions with a high potential for gold occurrence. By integrating these geophysical datasets, this study provided a comprehensive understanding of the geological features and potential for gold mineralization in the study area. The area is divided into four sections by three shear zones, with high magnetic anomalies observed in the southeastern part. The radiometric data revealed that gold occurrence is associated with hydrothermal alteration zones, identified using the K/Th ratio and radiometric ternary map. However, not all these zones contain gold, and the area’s structure and magnetic suitability must be considered when selecting suitable locations for gold extraction. The study area presents a promising opportunity for gold exploration, with the integration of edge detection and radiometric analysis being crucial in identifying suitable locations for exploration. Full article

The advent of the Metaverse, a convergence of virtual and physical realities, presents unprecedented opportunities for promoting cultural tourism along the historic Silk Roads within the Belt and Road Initiative (BRI) framework. However, this emerging technology’s potential remains largely unexplored in the context of cultural heritage preservation and its promotion. The aim of this study is to address this gap by empirically examining how the Metaverse can be harnessed to improve tourists’ experiences, support sustainable tourism development, and promote cultural exchange along the Silk Roads. In this study, we employed a mixed-methods approach grounded in attribution theory, using both surveys and semi-structured interviews with Silk Road tourists who had been exposed to Metaverse applications. To investigate the relationships between Metaverse features, tourists’ attributions, satisfaction, and behavioral intentions, we used structural equation modeling and thematic analysis. The findings revealed that perceived immersion, avatar identification, and cultural authenticity significantly influenced tourists’ internal, stable, and controllable attributions. Interviews further highlighted the Metaverse’s potential to promote accessibility, interpretation, and engagement with Silk Road heritage, while also raising sociocultural and ethical considerations. This paper also showcases the development of Metaverse digital twin prototypes for several iconic Silk Road cities to demonstrate the potential for participatory virtual environments to promote cultural heritage preservation, accessibility, and sustainable tourism. The study’s findings contribute to the theoretical understanding of Metaverse tourism and the application of attribution theory, offering practical insight for the responsible and effective harnessing of this transformative technology. This research provides actionable recommendations for policymakers, destination managers, and technology providers to optimize the Metaverse’s potential for cultural tourism along the Silk Roads. This has great potential in promoting the BRI’s connectivity and cooperation objectives. This paper lays the foundation for future research and responsible innovation at the intersection of emerging technologies, cultural heritage, and sustainable tourism development. Full article

As new trends are emerging worldwide, including innovation breakthroughs and the need for a sustainable approach to different aspects of economy and entrepreneurship, the need for orienting society towards sustainable entrepreneurial behavior is emerging. In this context, according to the literature, entrepreneurial education can have a positive impact on fostering entrepreneurial intention in university students. The main research conducted in this study includes the identification of different opportunities and barriers that universities in Albania are facing, by conducting interviews and questionnaires with key stakeholders. The main barriers identified include the legislation gap and lack of governmental funding for entrepreneurial and sustainable courses, while the main opportunities from which universities can benefit include digital innovation and human resources skills management by providing an added value to their internal environment. The model proposed in this study to overcome barriers and benefit from opportunities includes two key stakeholders, government and universities, as the case study analysis of three universities in Albania predicts the need for more steps to be taken by these two key stakeholders included in the model, while future further research on governmental funding would be of high importance to the cost–benefit analysis of this kind of governmental support. Full article

Electrocardiography (ECG) is a simple and safe tool for detecting heart conditions. Despite the diaspora of existing heartbeat classifiers, improvements such as real-time heartbeat identification and patient-independent classification persist. Reservoir computing (RC) based heartbeat classifiers are an emerging computational efficiency solution that is potentially recommended for real-time concerns. However, multiclass patient-independent heartbeat classification using RC-based classifiers has not been considered and constitutes a challenge. This study investigates patient-independent heartbeat classification by leveraging traditional RC and next-generation reservoir computing (NG-RC) solely or in a cascade. Three RCs were investigated for classification tasks: a linear RC featuring linear internal nodes, a nonlinear RC with a nonlinear internal node, and an NG-RC. Each of these has been evaluated independently using either linear ridge regression or multilayer perceptron (MLP) as readout models. Only three classes were considered for classification: the N, V, and S categories. Techniques to deal with the imbalanced nature of the data, such as the synthetic minority oversampling technique (SMOTE) and oversampling by replacement, were used. The MIT-BIH dataset was used to evaluate classification performance. The area under the curve (AUC) criterion was used as an evaluation metric. The NG-RC-based model improves classification performance and mitigates the overfitting issue. It has improved classification performance by 4.18% and 2.31% for the intra-patient and inter-patient paradigms, respectively. By cascading RC and NG-RC, the identification performance of the three heartbeat categories is further enhanced. AUCs of 97.80% and 92.09% were reported for intra- and inter-patient scenarios, respectively. These results suggest promising opportunities to leverage RC technology for multiclass, patient-independent heartbeat recognition. Full article