Search Results (2,805)

This paper presents a procedural method aimed at protecting maritime critical infrastructure, which is essential for the functioning of developed nations. A novel approach, developed by the authors, is introduced—focusing on the behavioral analysis of vessels to enable early identification of suspicious maritime activity and to prevent damage or destruction to key infrastructure elements. An integrated system is proposed, combining real-time electronic surveillance with continuous access to and analysis of data from both national and international databases. Drawing inspiration from medical sciences, a screening-based methodology has been developed. Data on vessels collected from various sources are processed according to the criteria adopted by the authors, using a multi-criteria decision analysis (MCDA) approach. MCDA is a decision-support method that considers multiple criteria simultaneously. It allows for the comparison and evaluation of different options, even when they are difficult to compare directly. This characteristic is used to select high-risk vessels for further monitoring. An initial classification of a vessel as suspicious does not constitute proof of criminal activity but rather serves as a trigger for further coordinated actions. Data on vessels is collected from the AIS (automatic identification system) and platforms that store vessel history. The AIS is a powerful tool that processes parameters such as a ship’s speed and course. This article presents sample results from surveillance and pre-selection analyses using the AIS, followed by a multi-criteria assessment of the behavior of vessels identified through this process. The results are presented both graphically and numerically. The authors conducted several scenarios, analyzing different groups of vessels. Based on this analysis, recommendations were developed for the interpretation of the findings. Full article

This study evaluates the effectiveness of the B Impact Assessment (BIA) as a catalyst for integrating sustainability into industrial firms through a qualitative case study of LC Paper, the first B Corp-certified tissue manufacturer globally and a pioneer in applying BIA in the pulp and paper sector. Based on semi-structured interviews, organizational documents, and direct observation, this study examines how BIA influences corporate governance, environmental practices, and stakeholder engagement. The findings show that BIA fosters structured goal setting and the implementation of measurable actions aligned with environmental stewardship, social responsibility, and economic resilience. Tangible outcomes include improved stakeholder trust, internal transparency, and employee development, while implementation challenges such as resource allocation and procedural complexity are also reported. Although the single-case design limits generalizability, this study identifies mechanisms transferable to other firms, particularly those in environmentally intensive sectors. The case studied also illustrates how leadership commitment, participatory governance, and data-driven tools facilitate the operationalization of sustainability. By integrating stakeholder and institutional theory, this study contributes conceptually to understanding certification frameworks as tools for embedding sustainability. This research offers both theoretical and practical insights into how firms can align strategy and impact, expanding the application of BIA beyond early adopters and into traditional industrial contexts. Full article

In scientific research and engineering practice, the design of deep spiking neural network (DSNN) architectures remains a complex task that heavily relies on the expertise and experience of professionals. These architectures often require repeated adjustments and modifications based on factors such as the DSNN’s performance, resulting in significant consumption of human and hardware resources. To address these challenges, this paper proposes an innovative evolutionary membrane algorithm for optimizing DSNN architectures. This algorithm automates the construction and design of promising network models, thereby reducing reliance on manual tuning. More specifically, the architecture of DSNN is transformed into the search space of the proposed evolutionary membrane algorithm. The proposed algorithm thoroughly explores the impact of hyperparameters, such as the candidate operation blocks of DSNN, to identify optimal configurations. Additionally, an early stopping strategy is adopted in the performance evaluation phase to mitigate the time loss caused by objective evaluations, further enhancing efficiency. The optimal models identified by the proposed algorithm were evaluated on the CIFAR-10 and CIFAR-100 datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, showing significant improvements in accuracy compared to the existing state-of-the-art methods. This work highlights the potential of evolutionary membrane algorithms to streamline the design and optimization of DSNN architectures, offering a novel and efficient approach to address the challenges in the applications of automated parameter optimization for DSNN. Full article

Background: Rapid urbanization has intensified the challenges of freight transport, particularly in last-mile (LM) delivery, leading to rising costs and environmental externalities. Autonomous vehicles (AVs) have emerged as a promising innovation to address these issues. While much of the existing literature emphasizes business and operational perspectives, this study focuses on the acceptance of AVs from the standpoint of e-consumers—individuals who make purchases via digital platforms—in an emerging market context. Methods: Grounded in an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), which is specifically suited to consumer-focused technology adoption research, this study incorporates five constructs tailored to AV adoption. Structural Equation Modeling (SEM) was applied to survey data collected from 304 e-consumers in Northeast Brazil. Results: The findings reveal that performance expectancy, hedonic motivation, and environmental awareness exert significant positive effects on acceptance and intention to use AVs for LM delivery. Social influence shows a weaker, yet still positive, impact. Importantly, price sensitivity exhibits a minimal effect, suggesting that while consumers are generally cost-conscious, perceived value may outweigh price concerns in early adoption stages. Conclusions: These results offer valuable insights for policymakers and logistics providers aiming to implement consumer-oriented, cost-effective AV solutions in LM delivery, particularly in emerging economies. The findings emphasize the need for strategies that highlight the practical, emotional, and environmental benefits of AVs to foster market acceptance. Full article

The transportation sector, as a major source of carbon emissions, plays a crucial role in the realization of dual carbon goals worldwide. In this study, an improved least absolute shrinkage and selection operator (LASSO) is used to identify six key factors affecting transportation carbon emissions (TCEs) in China. Aiming at the spatio-temporal characteristics of transportation carbon emissions, a CNN-BiLSTM neural network model is constructed for the first time for prediction, and an improved whale optimization algorithm (EWOA) is introduced for hyperparameter optimization, finding that the prediction model combining spatio-temporal characteristics has a more significant prediction accuracy, and scenario forecasting was carried out using the prediction model. Research indicates that over the past three decades, TCEs have demonstrated a rapid growth trend. Under the baseline, green, low-carbon, and high-carbon scenarios, peak carbon emissions are expected in 2035, 2031, 2030, and 2040. The adoption of a low-carbon scenario represents the most advantageous pathway for the sustainable progression of China’s transportation sector. Consequently, it is imperative for China to accelerate the formulation and implementation of low-carbon policies, promote the application of clean energy and facilitate the green transformation of the transportation sector. These efforts will contribute to the early realization of dual-carbon goals with a positive impact on global sustainable development. Full article

In comparative analyses, specific features of the Spanish welfare and housing systems have often been emphasized. The case of Barcelona illustrates the extent to which these features are the result of a long-standing historical trajectory and the decisive impact of the challenges and policy responses adopted during Franco’s lengthy, dark, and gloomy regime. This period marked a significant shift, not only due to the persistent shortage of social rental housing, but also because of the early consolidation of a homeownership culture and its dominance in working-class suburban areas—a legacy that is completely different from that of the welfare states of Western Europe. Through a review of the literature and the analysis of primary sources, ongoing research on Barcelona seeks to clarify the factors and processes that led to this transformation, as well as its evolution during the democratic period, within an international context of economic liberalization and the dismantling of the welfare state. Full article

Background/Objectives: Automatic classification of ECG signal arrhythmias plays a vital role in early cardiovascular diagnostics by enabling prompt detection of life-threatening conditions. Manual ECG interpretation is labor-intensive and susceptible to errors, highlighting the demand for automated, scalable approaches. Deep learning (DL) methods are effective in ECG analysis due to their ability to learn complex patterns from raw signals. Methods: This study introduces two models: a custom convolutional neural network (CNN) with a dual-branch architecture for processing ECG signals and demographic data (e.g., age, gender), and a modified VGG16 model adapted for multi-branch input. Using the PTB-XL dataset, a widely adopted large-scale ECG database with over 20,000 recordings, the models were evaluated on binary, multiclass, and subclass classification tasks across 2, 5, 10, and 15 disease categories. Advanced preprocessing techniques, combined with demographic features, significantly enhanced performance. Results: The CNN model achieved up to 97.78% accuracy in binary classification and 79.7% in multiclass tasks, outperforming the VGG16 model (97.38% and 76.53%, respectively) and state-of-the-art benchmarks like CNN-LSTM and CNN entropy features. This study also emphasizes interpretability, providing lead-specific insights into ECG contributions to promote clinical transparency. Conclusions: These results confirm the models’ potential for accurate, explainable arrhythmia detection and their applicability in real-world healthcare diagnostics. Full article

Optical Coherence Tomography (OCT) has evolved from a breakthrough ophthalmologic imaging tool into a cornerstone technology in interventional cardiology. After its initial applications in retinal imaging in the early 1990s, OCT was subsequently envisioned for cardiovascular use. In 1995, its ability to visualize atherosclerotic plaques was demonstrated in an in vitro study, and the following year marked the acquisition of the first in vivo OCT image of a human coronary artery. A major milestone followed in 2000, with the first intracoronary imaging in a living patient using time-domain OCT. However, the real inflection point came in 2006 with the advent of frequency-domain OCT, which dramatically improved acquisition speed and image quality, enabling safe and routine imaging in the catheterization lab. With the advent of high-resolution, second-generation frequency-domain systems, OCT has become clinically practical and widely adopted in catheterization laboratories. OCT progressively entered interventional cardiology, first proving its safety and feasibility, then demonstrating superiority over angiography alone in guiding percutaneous coronary interventions and improving outcomes. Today, it plays a central role not only in clinical practice but also in cardiovascular research, enabling precise assessment of plaque biology and response to therapy. With the advent of artificial intelligence and hybrid imaging systems, OCT is now evolving into a true precision-medicine tool—one that not only guides today’s therapies but also opens new frontiers for discovery, with vast potential still waiting to be explored. Tracing its historical evolution from ophthalmology to cardiology, this narrative review highlights the key technological milestones, clinical insights, and future perspectives that position OCT as an indispensable modality in contemporary interventional cardiology. As a guiding thread, the myth of Prometheus is used to symbolize the evolution of OCT—from its illuminating beginnings in ophthalmology to its transformative role in cardiology—as a metaphor for how light, innovation, and knowledge can reveal what was once hidden and redefine clinical practice. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

Nuclear energy can help stop climate change by generating large amounts of emission-free electricity. Nuclear reactor designs are continually being developed to be more fuel efficient, safer, easier to construct, and to produce less nuclear waste. The term advanced nuclear reactors refers either to Generation III+ and Generation IV or small modular reactors. Every reactor is associated with the nuclear fuel cycle that must be economically viable and competitive. An important matter is optimization of fissile materials used in reactor and/or reprocessing of spent fuel and reuse. Currently operating reactors use the open cycle or partially closed cycle. Generation IV reactors are intended to play a significant role in reaching a fully closed cycle. At the same time, we can observe the growing interest in development of small modular reactors worldwide. SMRs can adopt either fuel cycle; they can be flexible depending on their design and fuel type. Spent nuclear fuel management should be an integral part of the development of new reactors. The proper management methods of the radioactive waste and spent fuel should be considered at an early stage of construction. The aim of this paper is to highlight the challenges related to reprocessing of new forms of nuclear fuel. Full article

The COVID-19 pandemic catalyzed the rapid adoption of remote patient monitoring (RPM) technologies such as telemedicine and wearable devices (WDs), significantly transforming healthcare delivery. Telemedicine made virtual consultations possible, reducing in-person visits and infection risks, particularly for the management of chronic diseases. Wearable devices enabled the real-time continuous monitoring of health that assisted in condition prediction and management, such as for COVID-19. This narrative review addresses these transformations by uniquely synthesizing findings from 13 diverse studies (sourced from PubMed and Google Scholar, 2020–2024) to analyze the parallel evolution of telemedicine and WDs as interconnected RPM components. It highlights the pandemic’s dual impact, as follows: accelerating RPM innovation and adoption while simultaneously unmasking systemic challenges such as inequities in access and a need for robust integration approaches; while telemedicine usage soared during the pandemic, consumption post-pandemic, as indicated by the reviewed studies, suggests continued barriers to adoption among older adults. Likewise, wearable devices demonstrated significant potential in early disease detection and long-term health management, with promising applications extending beyond COVID-19, including long COVID conditions. Addressing the identified challenges is crucial for healthcare providers and systems to fully embrace these technologies and this would improve efficiency and patient outcomes. Full article

Tillage is a major factor influencing soil biological communities, particularly earthworms, which play a key role in soil structure and nutrient cycling. To address soil degradation, less-intensive tillage practices are increasingly being adopted globally and have shown positive effects on earthworm populations when applied consistently over extended periods. However, understanding of the earthworm population dynamics in the period following the implementation of changes in tillage practices remains limited. This three-year field study (2021–2023) investigates earthworm populations during the early transition phase (4–6 years) following the conversion from conventional ploughing to conservation (<8 cm depth, with residue retention) and no-tillage systems in a temperate arable system in central Slovenia. Earthworms were sampled annually in early October from three adjacent fields, each following the same three-year crop rotation (maize—winter cereal + cover crop—soybeans), using a combination of hand-sorting and allyl isothiocyanate (AITC) extraction. Results showed that reduced tillage practices significantly increased both earthworm biomass and abundance compared to conventional ploughing. However, a significant interaction between tillage and year was observed, with a sharp decline in earthworm abundance and mass in 2022, likely driven by a combination of 2022 summer tillage prior to cover crop sowing and extreme drought conditions. Juvenile earthworms were especially affected, with their proportion decreasing from 62% to 34% in ploughed plots and from 63% to 26% in conservation tillage plots. Despite interannual fluctuations, no-till showed the lowest variability in earthworm population. Long-term monitoring is essential to disentangle management and environmental effects and to inform resilient soil management strategies. Full article

Background: The identification and clinical implementation of robust biomarkers are essential for improving diagnosis, prognosis, and treatment across a wide range of diseases. Pancreatic stone protein (PSP) has recently emerged as a promising candidate biomarker. Objective: This narrative review aims to provide an updated and comprehensive overview of the clinical applications of PSP in infectious, oncological, metabolic, and surgical contexts. Methods: We conducted a structured literature search using PubMed^®, applying the SANRA framework for narrative reviews. Boolean operators were used to retrieve relevant studies on PSP in a wide range of clinical conditions, including sepsis, gastrointestinal cancers, diabetes, and ventilator-associated pneumonia. Results: PSP has shown strong diagnostic and prognostic potential in sepsis, where it may outperform traditional markers such as CRP and PCT. It has also demonstrated relevance in gastrointestinal cancers, type 1 and type 2 diabetes, and perioperative infections. PSP levels appear to rise earlier than other inflammatory markers and may be less affected by sterile inflammation. Conclusion: PSP represents a versatile and clinically valuable biomarker. Its integration into diagnostic protocols could enhance early detection and risk stratification in critical care and oncology settings. However, widespread adoption is currently limited by the availability of point-of-care assay platforms. Full article

Cancer disparities in low- and middle-income countries (LMICs) arise from multifaceted interactions between environmental exposures, infectious agents, and systemic inequities, such as limited access to care. The exposome, a framework encompassing the totality of non-genetic exposures throughout life, offers a powerful lens for understanding these disparities. In LMICs, populations are disproportionately affected by air and water pollution, occupational hazards, and oncogenic infections, including human papillomavirus (HPV), hepatitis B virus (HBV), Helicobacter pylori (H. pylori), human immunodeficiency virus (HIV), and neglected tropical diseases, such as schistosomiasis. These infectious agents contribute to increased cancer susceptibility and poor outcomes, particularly in immunocompromised individuals. Moreover, climate change, food insecurity, and barriers to healthcare access exacerbate these risks. This review adopts a population-level exposome approach to explore how environmental and infectious exposures intersect with genetic, epigenetic, and immune mechanisms to influence cancer incidence and progression in LMICs. We highlight the critical pathways linking chronic exposure and inflammation to tumor development and evaluate strategies such as HPV and HBV vaccination, antiretroviral therapy, and environmental regulation. Special attention is given to tools such as exposome-wide association studies (ExWASs), which offer promise for exposure surveillance, early detection, and public health policy. By integrating exposomic insights into national health systems, especially in regions such as sub-Saharan Africa (SSA) and South Asia, LMICs can advance equitable cancer prevention and control strategies. A holistic, exposome-informed strategy is essential for reducing global cancer disparities and improving outcomes in vulnerable populations. Full article

Background: Anti-synthetase Syndrome (ASyS) is an idiopathic inflammatory myopathy characterized by muscle weakness and inflammatory infiltrates in muscles. Sjogren’s disease (SD) is an autoimmune condition primarily affecting exocrine glands. Both these conditions may present lung involvement. We describe a female patient with anti-synthetase/SD overlap syndrome and review the literature to identify published cases describing this overlap, aiming to better define its clinical, radiological, and serological features. Methods: The case description was based on a retrospective collection of clinical, laboratory, and imaging data related to the patient’s diagnostic process and clinical course. Data were anonymized and handled in accordance with the competent territorial Ethics Committee. A literature review was performed using the MEDLINE and Scopus databases by combining the keywords “Anti-Synthetase syndrome”, “Sjögren disease”, “Sjögren syndrome”, “Myositis”, and “Interstitial lung disease” (ILD). Published cases were selected if they met the 2016 EULAR/ACR criteria for SD and at least one of the currently proposed classification criteria for ASyS. Results: The described case concerns a 68-year-old woman with rapidly progressive ILD. The diagnosis of anti-synthetase/SD overlap syndrome was based on clinical, serological (anti-Ro52 and anti-PL7 antibodies), histological, and radiological findings. Despite immunosuppressive and antifibrotic treatment, the clinical course worsened, leading to a poor outcome. In addition, six relevant cases were identified in the literature. Clinical presentations, autoantibody profiles, radiological findings, and outcomes were highly heterogeneous. Among the reported cases, no standardized treatment protocols were adopted, reflecting the lack of consensus in managing this rare condition. Conclusions: In anti-synthetase/SD overlap syndrome, ILD may follow a rapidly progressive course. Early recognition can be challenging, especially in the absence of muscular involvement. This case-based review highlights the need for more standardized approaches to the diagnosis and management of this rare and complex overlap syndrome. Full article