Search Results (9,407)

Underwater localization using airborne visible light beams offers a promising alternative to acoustic and radio-frequency methods, yet accurate modeling of light propagation through a dynamic air–water interface remains a major challenge. This paper introduces a physics-informed machine learning framework that combines geometric optics with neural network inference to localize submerged optical nodes under both flat and wavy surface conditions. The approach integrates ray-based light transmission modeling with a third-order Stokes wave formulation, enabling a realistic representation of nonlinear surface slopes and their effect on refraction. A multilayer perceptron (MLP) is trained on synthetic intensity–position datasets generated from this model, learning the complex mapping between received optical power (light intensity) and coordinates of the submerged receiver. The proposed method demonstrates high precision, stability, and adaptability across varying geometries and surface dynamics, offering a computationally efficient solution for optical localization in dynamic underwater environments. Full article

Maternal consumption of a high-fructose (HF) diet or exposure to microplastics (MPs) can each independently affect kidney development and increase the risk of hypertension in adult offspring, yet their combined impact remains poorly understood. Dysregulation of hydrogen sulfide (H₂S) signaling and alterations in gut microbiota are potential mediators of this programming. Pregnant rats received either standard chow or a 60% HF diet, with half of each group additionally exposed to sulfate-modified MPs (1 mg/L) with a 5 μm diameter throughout pregnancy and lactation. Male offspring were divided into four groups (n = 7–8 per group): control, HF, MP, and HF+MP. Maternal HF or MP exposure raised offspring blood pressure (BP), with additive effects when combined, and MP exposure caused renal injury. MP treatment also suppressed renal H₂S-generating enzymes and reduced H₂S production. Both HF and MP exposures altered gut microbial composition linked to BP regulation and induced metabolic changes in taurine/hypotaurine and sulfur pathways, suggesting impaired H₂S production. These results indicate that maternal HF and MP exposures interfere with H₂S signaling, gut microbiota, and metabolic programming, highlighting the H₂S signaling as a potential target to reduce long-term kidney and cardiometabolic risks. Full article

Immunocompromised patients, including those with advanced HIV infection, hematologic malignancies treated with anti-CD20 monoclonal antibodies, or combined immunodeficiencies, are at increased risk of persistent SARS-CoV-2 infection. While long-term viral shedding has been described in these patients, the extent and nature of intra-host viral evolution during long-term infection remain insufficiently documented. In this study, we report longitudinal genomic analyses of SARS-CoV-2 from three immunocompromised individuals with persistent COVID-19: (i) a female patient with follicular lymphoma receiving bendamustine-rituximab therapy with 9 months of persistence, (ii) a male patient with advanced HIV infection following prolonged antiretroviral therapy interruption with 10 months of persistence, and (iii) a female patient with Good’s Syndrome characterized by combined humoral and cellular immune deficiency with apparently four years of persistence. Replication-competent virus was detected over extended periods. Sequential whole-genome sequencing revealed the gradual accumulation of non-synonymous mutations across multiple viral genes, consistent with ongoing viral replication and intra-host diversification in the absence of effective immune control. Although based on a limited number of cases, these findings provide descriptive evidence that persistent SARS-CoV-2 infection in immunocompromised hosts can be associated with sustained viral evolution. This work highlights the importance of continued virological monitoring in selected patients with prolonged infection and contributes to the understanding of SARS-CoV-2 dynamics in settings of impaired immunity. Full article

Background/Objectives: Patients who undergo gastrectomy for gastric adenocarcinoma and subsequently develop chronic intestinal failure requiring long-term home parenteral nutrition (HPN) represent a clinically vulnerable cohort in whom survival is shaped by profound nutritional depletion and systemic inflammation. Immuno-nutritional biomarkers may support improved risk stratification in this setting. Methods: This retrospective study included adults who underwent gastrectomy for gastric cancer and developed malignant chronic intestinal failure requiring HPN. Immuno-nutritional status at HPN qualification was evaluated using the Controlling Nutritional Status (CONUT) score and the lymphocyte-to-monocyte ratio (LMR). Overall survival was analysed using Cox proportional hazards models. LMR discrimination was assessed using receiver operating characteristic (ROC) analysis with a Youden-derived cut-off, and differences in AUC were tested using DeLong’s method. Results: Ninety-seven patients met the inclusion criteria. Median overall survival was 176 days. In multivariable analysis, CONUT and LMR were the only independent predictors of survival. Each one-point increase in CONUT was associated with an approximately 70% increase in mortality risk. LMR demonstrated good discriminative ability (AUC 0.795), and a cut-off of 2.083 differentiated survival trajectories. The combined CONUT–LMR model improved prognostic classification, and DeLong’s test confirmed a significant AUC difference compared with single-marker models. Kaplan–Meier curves showed clear separation across CONUT and LMR strata (log-rank p < 0.001). Conclusions: Among patients requiring long-term HPN after gastrectomy for gastric cancer, CONUT and LMR provide complementary prognostic information. Their combined use enhances survival stratification and may support earlier identification of patients with high-risk trajectories. Full article

The shift toward a circular economy (CE) holds a central position in solving, globally, the long-standing environmental degradation and resource scarcity. Innovative sustainable processes and products lie at the core of such a shift, but they often face challenges associated with technological, organizational, financial, and regulatory paradigms. To date, two leading facilitators have been identified: sophisticated digital technologies, such as Artificial Intelligence, the Internet of Things, and Big Data, together with the collaborative creation of value among diverse stakeholders. Although the implications of each of these enablers on sustainability are known to some extent, little is understood about how their interrelatedness can counteract implementation barriers and drive innovation. The systematic thematic literature review examines how organizations utilize digital technologies and stakeholder engagement to facilitate green innovation in Circular Economy (CE) systems. Based on Stakeholder Theory, the Technology-Organization-Environment (TOE) framework, and the Resource-Based View (RBV), this review examines how organizations leverage digital technologies and stakeholder engagement to foster green innovation within circular economy systems. Following the PRISMA 2020 guidelines, a structured search was conducted in Scopus and Web of Science, covering peer-reviewed journal articles published in English between 2013 and 2024. Using predefined inclusion and exclusion criteria, 84 studies were retained for analysis from an initial pool of 850 records. The review integrates findings from five thematic areas: collaborative innovation among stakeholders, the use of digital technology to advance sustainability, challenges associated with adopting circular-economy values, linkages between technology and stakeholders, and the consequences of innovation. The findings suggest that collaboration between diverse stakeholders, combined with integration with digital technologies, provides a synergistic approach to maximizing innovation outcomes, overcoming implementation challenges, and diffusing circular practice. Skillfully crafted initiatives augment organizational capacities, foster collaborative actions, and advance sustainability initiatives. Despite providing a comprehensive synthesis of existing research, this review is limited by its reliance on secondary data. A qualitative quality appraisal was conducted to support the interpretation of findings. This review was not registered and received no external funding. Future research should conduct empirical analyses of these relationships and develop inclusive frameworks to guide initiatives emerging from collaborative and digital platforms across a wide range of sectors within the circular economy. Full article

Therapeutic inertia in lipid-lowering treatment remains a striking paradox of modern cardiovascular medicine: at a time when the causal role of LDL-cholesterol in atherosclerotic disease is unequivocal and potent therapies are widely available, a substantial proportion of high- and very-high-risk patients still fail to receive timely treatment intensification. Contemporary European and international data consistently show fewer than one in three patients in secondary prevention achieve guideline-recommended LDL-C targets, revealing a persistent and unacceptable gap between scientific evidence and clinical reality. This narrative review examines therapeutic inertia as a key explanatory framework for this gap, describing its epidemiology, mechanisms, and clinical consequences in secondary cardiovascular prevention. We summarize the main physician-, patient-, and system-level determinants and propose recurrent clinician “phenotypes” of inertia that may help explain why opportunities are missed even in the highest-risk patients. The consequences are profound: therapeutic inertia contributes to what we propose as the conceptual framework of an “avoidable atherosclerotic burden”, the cumulative vascular injury that accrues each period in which LDL-C remains above target, translating into higher rates of avoidable cardiovascular events, and increased healthcare costs. Emerging strategies such as upfront combination therapy, decision-support systems, structured lipid pathways, and the integration of artificial intelligence offer practical tools to shift lipid management from reactive to proactive care. Overcoming therapeutic inertia is therefore not merely a matter of improving process metrics, but a clinical and ethical imperative. Closing the gap between evidence and practice requires transforming optimal lipid management from an exception into a system-level default, ensuring that every patient receives the full benefit of therapies proven to save lives. This work proposes a novel characterization of clinician ‘phenotypes’ and the concept of ‘avoidable atherosclerotic burden’ as a framework to understand and address this gap. Full article

Accurate indoor localization is essential for navigation, monitoring, and industrial applications, especially in environments with Non-line of sight (NLOS) conditions. An indoor positioning system consists of fixed physical nodes, referred to as anchors, which serve as reference nodes with known locations, and entities that could be persons or objects that are also equipped with a node, referred to as targets, whose positions are estimated based on signal measurements exchanged with the surrounding anchors. Although RSSI is widely used due to hardware simplicity, its performance is often affected by signal degradation, multipath propagation, and environmental interference. To address this limitation, this work aims to develop an indoor positioning system, especially in wide areas with a minimal number of physical anchors, while maintaining high positioning accuracy and low latency. The proposed approach integrates VA, RSSI-based multilateration, and ML as a tool to refine and improve positioning accuracy, where ML models are used to predict the VA features and subsequently predict the corresponding distances. In addition, the system relies on energy-efficient WuRx nodes, which ensure a low power consumption and support on-demand communication. The study area covers two distinct floors with a total area of 366.9 m², covered using only four physical anchors. Two studies were performed, the offline and the online, in order to evaluate the proposed system under both the theoretical performance and real implementation conditions. In the offline phase, hexagonal and rectangular grid architectures were compared using multiple machine learning models under varying numbers of virtual anchors. By comparing different architectures and machine learning models, the rectangular grid with 10 virtual anchors combined with the XGBoost model achieved the best performance, resulting in an RMSE of $1.49\mathrm{m}$ with a processing time of approximately $0.15\mathrm{s}$. The online evaluation confirmed the performance of the proposed system, achieving an RMSE of $2.48\mathrm{m}$. Full article

Immunotherapy has become a cornerstone of cancer treatment in both the early and advanced setting in recent years, leading to the achievement of substantial and durable responses with an excellent safety profile across different tumor types. This demonstrates the high potential of engaging the immune system in the treatment of solid tumors. Consequently, there has been renewed interest in vaccines to enhance therapeutic effects, prevent tumor development, and eliminate or control minimal residual disease. Although therapeutic cancer vaccines have shown potential benefits in certain settings, their results in clinical trials remain highly variable and generally unsatisfactory, depending on tumor site, biology, and vaccine type. Currently, Sipuleucel-T for prostate cancer is the only cell-based vaccine that received FDA approval for the treatment of a solid tumor. Innovative techniques such as personalized neoantigen vaccines and mRNA-based vaccines have shown promising preclinical and early-phase clinical results, supporting their further development. Despite the current evidence of vaccine efficacy in treating solid tumors being derived from only a few clinical trials with relatively small sample sizes, ongoing trials are also exploring innovative approaches aimed at preventing cancer development or enhancing immune responses in combination with other immunotherapeutic agents. In this review, we provide an overview of the clinical results and the current state of vaccine development for cancer treatment, outlining future perspectives on their role in managing patients with cancer. Full article

Background/Objectives. Ebstein’s anomaly (EA), which accounts for fewer than 1% of congenital heart diseases, and atrioventricular canal defect (AVCD), present in approximately 4–5% of cases, exceptionally coexist, with this combination observed in fewer than 0.5% of patients with AVCD. We aim to report the oldest documented case of a 45-year-old female with the exceptionally rare combination of complete AVCD, EA, and right ventricular hypoplasia and to provide a concise review of these anomalies. Case presentation. Diagnosed in early childhood with a complete AVCD, pulmonary stenosis, and right ventricular (RV) hypoplasia, the patient underwent palliative surgical intervention with a modified Blalock–Taussig shunt at the age of 10 but did not receive subsequent regular follow-up. Over the ensuing 35 years, she remained largely untreated until presentation at 45 years of age with progressive exertional dyspnea, central cyanosis, and palpitations, corresponding to NYHA class III. Comprehensive multimodal imaging, including transthoracic echocardiography and cardiac magnetic resonance, revealed a complete AVCD with moderate-to-severe mitral regurgitation secondary to an anterior mitral leaflet cleft, severe tricuspid regurgitation, RV hypoplasia, and hallmark features of EA. Given the complex cardiac anatomy and the elevated surgical risk, the patient was considered inoperable, and a strategy of conservative management with multidisciplinary follow-up was implemented. Conclusions. This case highlights the exceptional longevity of a patient with the rare coexistence of complete AVCD, EA, and RV hypoplasia, surviving 45 years from diagnosis despite limited early intervention. It underscores the importance of lifelong follow-up in complex congenital heart disease and illustrates the role of multimodal imaging in assessing anatomy and guiding management when surgical options are high-risk or not feasible. Full article

The sewerage system is a vital part of the infrastructure in every settlement, and thus, of the state itself. It ensures the conveyance of sanitary wastewater from its sources (households, buildings and industrial facilities) and stormwater from urban surfaces through sewerage pipelines to wastewater treatment plants and ultimately to receiving bodies. In the Republic of Croatia, combined sewerage systems are predominant. This paper presents selected data on the characteristics of public sewerage systems in Croatia for the analyzed period. Additionally, a questionnaire was conducted to assess maintenance practices within the country. The analyzed characteristics include the total length of the sewerage network, the number of employees engaged in system maintenance, the number of connections, and the number of wastewater treatment plants. The study also evaluates whether a preventive maintenance strategy was in place during the observed period and whether respondents considered preventive or reactive maintenance to be more cost-effective. The collected data and responses are analyzed and discussed in the context of system efficiency and long-term sustainability. Full article

Background/Objectives: Children and adolescents with intellectual disabilities (ID) have an elevated burden of obesity and cardiometabolic risk, yet factors associated with high blood pressure (BP) in this group remain insufficiently described. This study assessed the prevalence of hypertension (HTN) and isolated systolic hypertension (ISH) at a single visit and examined anthropometric and body composition correlates of elevated BP in children with ID. Methods: A cross-sectional study was conducted among 461 children and adolescents with ID aged 7–18 y attending special education schools in southeastern Poland. Anthropometric indicators (BMI, waist circumference [WC], hip circumference [HC], and waist-to-height ratio [WHtR]) and body composition parameters (BF%, MM%, FFM%, TBW%) were measured using standardized procedures. BP was assessed three times during one visit, and the average of the second and third readings was used. Receiver operating characteristic (ROC) analyses were used for exploratory assessment of discriminatory performance of anthropometric and body composition parameters, and multivariable logistic regression examined associations with elevated BP (HTN + ISH). Results: Overall, 13.9% of participants had HTN and 10.4% had ISH (combined prevalence: 24.3%). Abdominal obesity was present in 39.5% of participants, and elevated HC in 28.2%, both more common in girls. Higher BP categories were associated with greater WC, HC, BMI, and BF%, and lower MM%, FFM%, and TBW% (p < 0.0001). HC showed the highest discriminatory accuracy for HTN + ISH (AUC = 0.844), followed by MM%, BF%, and FFM%, whereas WHtR demonstrated limited discriminatory performance in ROC analyses. In multivariable models, WHtR ≥ 0.5 was associated with increased odds of elevated BP (OR = 4.25), whereas higher TBW% (≥55.38%) was inversely associated with elevated BP (OR = 0.17) in the total sample; similar patterns were observed in sex- and age-stratified analyses. Conclusions: Children with ID show a high prevalence of elevated BP at a single visit, including HTN-range and ISH-range values. Anthropometric indicators, particularly HC and WHtR, and BIA-derived body composition parameters reflecting higher fat mass and lower lean tissue proportion were associated with elevated BP. These exploratory findings suggest that simple anthropometric and body composition measures may help identify individuals who warrant further BP assessment, although longitudinal studies with repeated measurements are required before clinical application. Full article

Recent advancements in Artificial Intelligence and eXtended Reality (XR) have laid solid foundations for the development of a new paradigm in industrial maintenance under the light of Industry 5.0 framework. This research presents the design, development, and implementation of an XR-enabled remote maintenance framework that integrates real-time video collaboration, AI-assisted guidance, and a persistent digital asset knowledge layer based on Asset Administration Shells for Maintenance and Repair Operations (MRO). By combining fine-tuned Large Language Models (LLMs) with immersive XR interfaces, the proposed framework enables technicians to interact with virtual representations of industrial assets, access contextual instructions, and receive expert support remotely in real-time. Through seamless integration of historical MRO data, digital twins, and real-time sensor streams, the system facilitates dynamic fault diagnostics and Remaining Useful Life (RUL) estimation. Therefore, the proposed approach is positioned as a Metaverse-aligned implementation, combining synchronous multi-user collaboration, digital–physical coupling through digital twins, and semantic interoperability. The framework is validated through two industrial case studies, demonstrating its feasibility and practical impact on maintenance efficiency and knowledge transfer. The findings position the Industrial Metaverse as a transformative enabler in the future of AI-driven machinery health monitoring. Full article

Pseudolite (PL) positioning technology is one of the effective methods to achieve high-precision indoor positioning. The Distributed Array Pseudolite System (DAPLS) is a ground-based augmentation architecture designed to provide high-precision positioning in GNSS-denied or indoor environments. However, maintaining the stability and integrity of pseudolite signals in distributed deployments remains a significant challenge. To address this, a Pseudolite Monitoring Station (PMS) was developed for real-time signal observation, performance evaluation, and anomaly detection. The proposed PMS integrates a multi-channel front-end, signal-processing engine, and monitoring algorithms capable of continuous assessment across three hierarchical levels: Signal Quality Monitoring (SQM), Receiver Processing Monitoring (RPM), and Measurement Quality Monitoring (MQM). To integrate multi-domain monitoring results, a Composite Quality Index (CQI) model is introduced, combining normalized sub-scores through weighted fusion to reflect overall system integrity. A comprehensive Signal Quality Assessment (SQA) framework is further introduced, including four dimensions of evaluation: constellation status, time reference, spatial coordinate reference, and signal anomaly detection. An indoor DAPLS experiment was conducted within a laboratory-level test field. The system comprised three pseudolite transmitter arrays (six transmitters each) and a central monitoring station. Experimental results showed stable synchronization within ±5 ns, coordinate accuracy within 0.2 m, and consistently high signal quality. The monitoring station effectively detected minor signal distortions and synchronization deviations, confirming its diagnostic precision and robustness. This study demonstrates a complete monitoring and evaluation framework for DAPLS, enabling both system-level quality assurance and signal integrity monitoring. The proposed PMS and SQA methods provide essential tools for future deployment of pseudolite-based indoor positioning and timing systems. Full article

This paper addresses the core conflict between long-range communication and ultra-low power requirements in sensing nodes for Wireless Sensor Networks (WSNs) by proposing a wake-up receiver (WuRx) design featuring nanowatt-level power consumption and high sensitivity. Conventional architectures are plagued by low energy efficiency, poor demodulation reliability, and insufficient clock synchronization accuracy, which hinders their practical application in real-world scenarios like WSNs. The proposed design employs an event-triggered mechanism, where a continuously operating, low-power WuRx monitors the channel and activates the main system only after validating a legitimate command, thereby significantly reducing standby power. At the system design level, a key innovation is direct conjugate matching between the antenna and a multi-stage rectifier, replacing the traditional 50 Ohm interface, which substantially improves energy transmission efficiency. Furthermore, a mean-detection demodulation circuit is introduced to dynamically generate an adaptive reference level, effectively overcoming the challenge of discriminating shallow modulation caused by signal saturation in the near-field region. At the baseband processing level, a configurable fault-tolerant correlator logic and a data-edge-triggered clock synchronization circuit are designed, combined with oversampling techniques to suppress clock drift and enhance the reliability of long data packet reception. Fabricated in a TSMC 0.18 µm CMOS process, the receiver features an ultra-low power consumption of 305 nW at 0.5 V and a high sensitivity of −47 dBm, enabling a communication range of up to 400 m in the 920–925 MHz band. Through synergistic innovation at both the circuit and system levels, this research provides a high-efficiency, high-reliability wake-up solution for long-range WSN nodes, effectively promoting the large-scale application of WSN technology in practical deployments. Full article

Accurately forecasting agricultural commodity prices is a complex and persistent problem for producers, traders, and policymakers. In this study we examine how artificial intelligence can be combined with large-scale global news data to refine daily corn price forecasts. A Long Short-Term Memory (LSTM) neural network was trained on Chicago corn futures between 2021 and 2024 to capture price dynamics, while agriculture-related news features were derived from the Global Database of Events, Language, and Tone (GDELT). Rather than sentiment polarity, the analysis shows that attention-based indicators, such as article volume, rolling intensity measures, and persistence of elevated coverage, carry stronger predictive information. These features are incorporated through a Ridge regression residual correction applied to the LSTM predictions, forming a lightweight two-stage hybrid model. While absolute forecast accuracy remains comparable to the price-only baseline (RMSE ≈ 9 ¢/bu; MAE ≈ 5.8 ¢/bu; R² ≈ 0.99), the hybrid framework improves directional accuracy by approximately 2.4 percentage points, with gains concentrated during periods of moderate news intensity. Feature attribution results indicate that media attention intensity and persistence dominate sentiment-tone variables, which receive zero weight under regularization. Overall, the proposed framework offers a transparent, computationally efficient, and reproducible approach for integrating open global news data into short-term agricultural price forecasting. Full article