Search Results (3,169)

We have developed a new method for remote sensing of the ionosphere and neutral atmosphere based on the creation of artificial periodic irregularities (APIs) in ionospheric plasma. This method is used, in particular, to study atmospheric turbulence, which can affect GNSS signal delays by causing disturbances in the radio wave propagation medium. We applied the API method to determine the turbopause height and turbulent velocity at altitudes of 60–130 km in the lower ionosphere. Turbulence is one of the most important phenomena in the Earth’s atmosphere. This article presents the results of turbulence parameter measurements based on the resonant scattering of APIs. These irregularities are created in the ionosphere by powerful high-frequency radio waves. The altitude profile of turbulent velocity is obtained from measurements of the relaxation time of the API scattered signal after the end of the powerful radio emission on the ionosphere. The turbopause level is defined as the altitude at which turbulent mixing of atmospheric gases gives way to diffusive separation. During the transition from diffusive separation to turbulent mixing (below the turbopause level), irregularities decay more rapidly than during ambipolar diffusion, and their relaxation time is determined predominantly by turbulent diffusion. This fact allows us to determine the velocity of turbulent motion as a function of the diffusion time and the measured relaxation time of the scattered signal. The turbopause level is determined from the altitude profile of the relaxation time. Measurements were carried out using the SURA heating facility (56.15° N, 46.11° E). The altitude resolution of the API method is 0.15 km–1 km with a time resolution of 15 s, which allows us to study both fast and slow processes. According to our data, the turbopause level varied in the altitude range from 85 km to 110 km. Significant variability in the turbopause level was observed during the day and from day to day. The minimum turbopause level was located at mesospheric altitudes. The average turbulent velocity over a 5 min interval varied from near zero at the turbopause altitude to 5–6 m/s below it. Such high velocities were typically observed during natural ionospheric disturbances. The average velocity of regular vertical plasma motion, measured using the API method, reached 10 m/s and varied with altitude and direction. Temporal variations in turbulent parameters were observed, with periods ranging from 15 min to several hours. The effect of atmospheric wave propagation on the characteristics of scattered signals and environmental parameters was confirmed. Full article

Artificial intelligence (AI) and machine learning (ML) are increasingly applied to road traffic congestion prediction, but heterogeneous outcomes, models, horizons, and evaluation practices limit comparability. The objective of this study was to synthesize methods, applications, validation, explainability, and reproducibility in AI/ML-based road traffic congestion prediction and forecasting. Following PRISMA 2020, Scopus, Web of Science Core Collection, and IEEE Xplore were searched through 5 July 2026 for English-language journal articles and full conference papers published from 2000 to 2026. Two external reviewers independently screened 734 unique records and assessed the retrieved full texts, while the author resolved disagreements against the predefined eligibility criteria. Study characteristics, prediction tasks, congestion indicators, model families, metrics, explainability, validation, and data/code availability were synthesized descriptively and narratively. Of 1131 records identified, 397 duplicates were removed and 734 records were screened. Full-text retrieval was sought for 339 reports; 195 could not be retrieved, 144 were assessed for eligibility, and 129 were included. Congestion level was the main prediction task, while traffic flow and speed were the most frequent indicators. Heterogeneity and the absence of verified numerical performance values precluded meta-analysis or model ranking. Explainability was limited, and external validation, transferability, and reproducibility were insufficiently documented. Progress requires standardized outcomes, transparent validation, reproducible workflows, explainable models, and independent testing across networks and cities. Full article

Water quality management is a growing global priority due to increasing pressures from climate change, population growth, urbanisation, industrialisation, agricultural intensification, and emerging contaminants. These challenges threaten freshwater ecosystems, public health, economic development, and progress towards Sustainable Development Goal 6 (SDG 6). This critical review employed a PRISMA-guided systematic literature review to evaluate future directions in water quality management from sustainability, resilience, and social equity perspectives. A total of 11,485 records were identified, with 3632 duplicates removed. Following the screening of 7853 records and assessment of 1441 full-text articles, 117 studies met the inclusion criteria for qualitative synthesis. Geographical analysis showed that 88 studies (75.2%) adopted a global perspective encompassing both developed and developing countries, while 24 studies (20.5%) focused on developing countries and 5 studies (4.3%) on developed countries. The review identified notable progress in water quality monitoring, wastewater treatment, pollution control, and integrated water resources management. Emerging technologies such as smart monitoring systems, artificial intelligence, advanced treatment processes, and resource recovery approaches were consistently highlighted as promising tools for improving water quality and enhancing adaptive capacity. Despite these advances, significant barriers remain, including fragmented governance systems, climate-related risks, emerging contaminants, and persistent inequalities in access to safe water resources. The findings underscore the importance of integrating sustainability, resilience, and social equity within water governance frameworks. Achieving long-term water security will require coordinated and transformative approaches that strengthen links between science, policy, and practice while promoting inclusive, resilient, and sustainable water management. Full article

Introduction: Active spitting competence is a developmental milestone potentially relevant to fluoride safety and preventive counselling in paediatric dentistry. It is defined as the voluntary ability to understand a simple instruction and consistently expel oral contents without swallowing. Its acquisition reflects neuromuscular and cognitive maturation, together with imitation, supervision, and repeated practice. This clinical perspective explores whether spitting competence may complement age-based fluoride counselling. Methods: Available developmental and clinical evidence concerning oral motor development, spitting acquisition, and fluoride dentifrice ingestion in early childhood was narratively examined and interpreted from a paediatric dental perspective. A conceptual framework was developed to consider the potential role of qualitative chairside observation of spitting ability. This article is not a systematic review and does not aim to establish normative cut-offs or age thresholds. Results: Evidence suggests that active spitting develops within a broad developmental window, commonly between approximately 2 and 5 years, with considerable interindividual variability. Young children may swallow a substantial proportion of toothpaste during brushing, and repeated ingestion of fluoride-containing dentifrice during tooth development may increase fluorosis risk. A brief qualitative observation of spitting competence could complement age-based fluoride guidance by identifying children requiring closer supervision and conservative toothpaste management. However, its predictive validity and clinical utility have not been established. Conclusions: Functional spitting assessment may represent a useful adjunct to individualized fluoride counselling in paediatric dental practice. However, current evidence does not support its use as a standalone criterion or the definition of specific age thresholds. Prospective validation studies are required to determine whether spitting competence can reliably predict dentifrice ingestion and inform future clinical recommendations. Full article

Background and Objectives: Psychogenic erectile dysfunction (ED) is a distinct clinical condition characterized by predominant psychological or relational factors. Despite its prevalence, diagnostic criteria remain heterogeneous and are often based on exclusion of organic causes. Current guidelines recommend cognitive behavioral therapy (CBT), eventually combined with medical therapy such as phosphodiesterase type 5 inhibitors (PDE5i), as first-line treatment. However, evidence regarding alternative conservative and surgical therapies remains limited. This narrative review aims to evaluate the available evidence on these treatments in psychogenic ED. Materials and Methods: A comprehensive literature search was conducted in PubMed, Scopus, and MEDLINE using MeSH terms and free-text keywords related to psychogenic ED and therapeutic interventions. Eligible studies included those involving human subjects, published in English, and reporting outcomes specifically for psychogenic or non-organic ED. Reviews, case reports, and non-full-text articles were excluded. Study selection and data extraction were performed independently by two authors. Due to heterogeneity, a narrative synthesis was conducted. Results: Evidence on alternative therapies is limited and heterogeneous, with many studies predating the 2000s. Vacuum erection devices (VEDs) may improve outcomes, particularly when combined with psychotherapy. Constriction rings showed promising preliminary results. Acupuncture demonstrated inconsistent efficacy, although recent randomized trials suggest potential benefits. Intracavernosal injections (ICI) showed favorable outcomes, particularly in patients refractory to first-line therapies, with some reports of restored spontaneous erections. Penile prosthesis (PP) implantation remains underexplored in contemporary literature on psychogenic ED; however, recent evidence reports good satisfaction rates. Conclusions: Beyond CBT and PDE5i, evidence for alternative treatments in psychogenic ED is scarce and methodologically limited. Selected patients may benefit from adjunctive therapies, including VED, ICI, and acupuncture, while PP can be considered a definitive treatment option. Further well-designed studies are needed to clarify their role within a structured, evidence-based management approach. Full article

Rural maternity care in the United States faces an unprecedented crisis. As of 2022, more than half of rural hospitals lacked obstetric services, leaving many rural communities without local maternity care. This descriptive case study documents the implementation of maternity services at a 25-bed rural critical access hospital in North Carolina. We reviewed and coded data derived from the following sources from 2020–2025: institutional planning documents, clinical protocols, standard workflows, operational procedures, administrative records, meeting minutes, presentations and summaries, and published commentaries, manuscripts and articles related to the new maternity unit. We identified six critical domains for sustainability: (1) hospital and community engagement, (2) stable multidisciplinary staffing models, (3) emergency preparedness, (4) anesthesia service delivery, (5) financial sustainability, and (6) risk-appropriate scope of care. Successful implementation required integrated approaches across organizational dimensions facilitated by committed institutional leadership; community advocacy; innovative staffing models with family physicians, certified nurse-midwives, and certified registered nurse anesthetists; competency-based emergency training; Medicaid-based financial sustainability; and explicit risk stratification with clear interfacility communication protocols. Workforce sustainability emerged as the most significant ongoing challenge. Rural maternity service sustainability requires multifaceted, evidence-based approaches integrating workforce development, organizational infrastructure, financial mechanisms, and clear scope definitions. This case study demonstrates a model of perinatal regionalized and risk-appropriate care with replicable strategies that can inform efforts to address maternal health equity and strengthen rural health care systems. Full article

This article investigates the numerical simulation of heat-and-aerodynamic cycles in the ventilated air gap of a multilayer composite wall façade system in a hot climate using ANSYS 19/2 Fluent. Standard normative techniques rely on averaged, stationary boundary conditions and account for neither the height-wise inequality of solar exposure nor the dependence of air density and viscosity on barometric pressure and temperature, resulting in significant errors in predicting the actual heating of such structures. The model was calibrated on the authors’ own full-scale, in situ measurements of temperature, air speed and solar exposure in the ventilated gap of a nine-storey building, from which linear height-dependent surface-temperature relations were derived and used as boundary conditions for 3D models of façades 25 and 60 m tall. Thirty-two finite-volume experiments were performed under free convection (Boussinesq approximation), varying gap width (5 and 10 cm), inlet width (20 and 40 cm), barometric pressure (690 and 770 mmHg) and external air temperature (20 and 40 °C). Façade height proved the dominant factor (air speed up to 1.8 times higher, temperature 3–12.1 °C higher), followed by gap width (speed lower by 1.7 times, temperature by 3–5 °C), whereas pressure and inlet width altered the results by no more than 6%. Discrepancies with the standard calculation reached 10 °C in temperature and a two-fold difference in flow speed, confirming the need for verified CFD simulation when designing ventilated composite wall façades in hot climates. Full article

Surface-Enhanced Raman Spectroscopy (SERS) has emerged as a powerful analytical platform for breast cancer (BC) detection, offering ultrasensitive, multiplexed, and label-free molecular recognition. However, despite the rapid expansion of the field, no prior study has combined quantitative bibliometric mapping with a cluster-validated technical and translational synthesis, limiting a comprehensive understanding of the field’s structure, evolution and clinical projection. In this review, a PRISMA-guided bibliometric analysis was conducted; 199 articles on SERS-based BC detection (2016–2025) were retrieved from SCOPUS, Web of Science and Google Scholar, mapping publication trends, keyword co-occurrence networks (VOSviewer), and Multiple Correspondence Analysis (MCA) with hierarchical clustering on principal components. The results reveal sustained growth in scientific output, led by Asia, North America, and Europe. The 20 most-cited articles (271 citations maximum) showed a shift from substrate optimization toward AI-assisted liquid biopsy platforms. MCA identified five clusters, corroborated by the co-occurrence network: (1) nanostructured platforms for diagnosis; (2) biofunctionalization strategies; (3) liquid biopsy approaches targeting exosomes, circulating tumor cells, and alternative biofluids; (4) diagnostic interpretation based on chemometrics, machine learning (ML) and artificial intelligence (AI); and (5) translational achievements in preclinical and clinical studies. Each cluster was anchored by a technical sub-analysis of its landmark studies, an integration largely absent from prior SERS reviews. This framework clarifies the field’s trajectory and positions SERS as a key technology for non-invasive, personalized diagnostics in precision oncology. Full article

Post-disaster underground mine rescue requires environmental perception and remote operation before personnel can enter hazardous areas. In this article, we describe the system-level integration of a tracked detection robot, a 1432–1442 MHz wireless Mesh link, three deployable relay beacons, a handheld terminal, and multi-parameter sensing. The radio modules use 0.5 W transmit power, a nominal 30 Mbps data rate, and vertically polarized 5 dBi omnidirectional antennas, while the analytical formulation links differential-drive commands, path-loss and link-quality estimates, and a consecutive-sample beacon deployment rule. Physical records from three mine rescue test facilities document manual motion, navigation interface operation, Mesh topology formation, environmental data display, and audio/video return. While these records establish prototype feasibility and functional-chain continuity, they do not provide retained quantitative RSSI, packet loss, latency, trajectory error, stopping distance, endurance, or repeated-trial statistics. Accordingly, the proposed models are presented as design and measurement frameworks rather than validated predictors or evidence of performance superiority. Full article

Background: Invasive pneumococcal disease (IPD) is predominantly caused by Streptococcus pneumoniae and is associated with high mortality. This vaccine-preventable disease has been a significant public health concern, particularly among children < 5 years and adults > 50 years in India. Pneumococcal vaccines help prevent pneumococcal infections caused by certain strains of S. pneumoniae, but diagnostic challenges in India hinder disease management. Methods: The objective of this expert panel discussion is to deliberate upon the pathogenesis and epidemiology of S. pneumoniae in India, techniques for overcoming the diagnostic challenges in the isolation and detection of S. pneumoniae, and to explore opportunities for partnerships between clinicians and microbiologists to bridge gaps. A non-systematic, targeted literature search was conducted to identify studies supporting key themes of expert panel discussion. Eight microbiologists, one infectious disease, one pediatrics and neonatology, and one respiratory medicine specialist from different parts of India participated in the discussion. Results: The panelists reiterated that while culture is the gold standard, multiplex real-time polymerase chain reaction and BioFire^® FilmArray^® technology have been the most common diagnostic methods for the detection of S. pneumoniae in India, especially in tier 1 cities. Conclusions: This article elucidates the challenges faced by microbiologists and clinicians in isolating and detecting the pathogen, and proposes solutions to determine the true burden of pneumococcal disease in India. This information could further inform public health policy and vaccination strategies. Full article

This article presents an evaluation of the effect of adding tire pyrolysis oil (TPO) to diesel fuel (DF) on its corrosive properties. Tests were conducted on DF/TPO blends with TPO fractions of 0, 5, 7, 10, 15, 20, and 100% m/m, by determining water content, sulfur content, and acid number, and by conducting a corrosion test on a copper strip. The corrosion test was performed for a standard test duration of three hours and for long-term observations of 10 and 18 days. It was observed that an increase in the TPO content affects the analyzed physicochemical properties of the fuel, which is reflected in changes to its corrosive properties. A relationship is determined between the TPO content in the fuel and the water content, sulfur content, acid number, and the results of the corrosion test on copper strips. The obtained results served as the basis for evaluating the potential use of TPO as a diesel fuel additive, taking into account the requirements regarding the corrosive properties of fuels for marine and automotive applications. Full article

This article introduces an experimental approach to evaluating the thermal resistance of industrial resistance temperature sensors and demonstrates their use for estimating the flow rate of the surrounding medium. A new method, the Self-Heating Effect Anemometry (SHEA) method, is proposed. Experimental studies were conducted for various flow rates (up to 6 m/s) and excitation current values (up to 5 mA) to evaluate the thermal properties of the sensors under dynamic operating conditions. The results obtained show that the proposed method can be effectively used to select an appropriate measurement current that minimises the self-heating effect and keeps the temperature measurement error within an acceptable range. At the same time, the method allows one to determine the flow rate of the surrounding medium from the measured thermal resistance of the sensor. The presented approach can be applied in both precise temperature measurements and flow monitoring systems using thermoresistive sensors. The proposed method simultaneously provides measurements of both the temperature and the flow rate of the surrounding medium. The research contributes to a better understanding of the self-heating effect in industrial measurement applications and provides practical guidance to improve measurement accuracy and sensor operating conditions. Full article

The use of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity, especially for creating Intrusion Detection Systems (IDSs), has become increasingly important. These systems are essential for detecting malicious behaviour, identifying network issues, and stopping cyberattacks in real time. Despite extensive research on various ML and Deep Learning (DL) models for IDS, the current literature remains incomplete. It has many different datasets, methods, and evaluation standards. As cyber threats become more advanced, it is crucial to conduct a thorough analysis of ML techniques for intrusion detection. The goal of this Systematic Literature Review (SLR) is to provide a full picture of the most recent academic articles on ML-based IDS. The study addresses important research questions about the most widely used algorithms, the types of attacks and network environments covered, the methodological problems that remain unsolved, and the new trends that should shape future research. Following the PRISMA framework, we conducted a systematic review of peer-reviewed articles published between January 2022 and May 2025. We searched IEEE Xplore, ACM Digital Library, and SpringerLink, yielding 22,558 initial records. After carefully applying strict inclusion criteria, 125 papers were selected for the final analysis. We created a standardised data extraction form (i.e., using MS Excel) to gather bibliographic details, research emphasis, methodological strategies, datasets, evaluation criteria, and recognised constraints. We employed thematic analysis to develop a clear taxonomy. We identified five main research themes in our analysis: (1) ensemble and hybrid learning pipelines focused on performance optimisation (30 papers), (2) context-specific IDS designs for Internet of Things (IoT), cloud, and Software-Defined Networking (SDN) environments (34 papers), (3) data-centric engineering that deals with class imbalance and feature selection (20 papers), (4) deep neural architectures for representation learning (31 papers), and (5) trustworthiness concerns like adversarial robustness, zero-day detection, and Explainable AI (XAI) (10 papers). Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Random Forests are the most commonly used algorithms, often combined. Nonetheless, significant deficiencies remain: about 2% of papers incorporate XAI, only 4% focus on adversarial robustness, and none validate their models in real-world production settings. Denial-of-Service (DoS) and Distributed DoS (DDoS) attacks are the most common types in the literature, whereas Web attacks, ransomware, and advanced persistent threats remain poorly studied. The number of publications grows at an average of 30.2% annually, but the field still relies on legacy benchmark datasets rather than operational validation. Full article

Background: Climate change is being recognized as a factor associated with increased suicidal behavior, especially in the older adult population. Extreme temperatures, thermal anomalies, and intense weather events can amplify pre-existing physical, psychological, and social vulnerabilities, increasing the risk of suicidal ideation and behavior in older adults. Objectives: This systematic review aims to map the literature on the relationship between climate change and suicidal behavior in older people, identifying risk and protective factors, as well as the prevention and intervention strategies aimed at this age group. Methods: A systematic review was conducted according to PRISMA guidelines. The literature search was performed in the EBSCO PsycINFO, Scopus and MEDLINE (PubMed) databases in the period 2007–2025, using combinations of the keywords “climate change”, “global warming”, “older adults”, “elderly”, “suicide”, “suicidal ideation” and related terms. Out of 35 studies, 16 articles were included in the final analysis. Results: Five thematic areas emerged from the analysis of the studies: (1) risk factors, (2) protective factors, (3) sociodemographic factors, (4) prevention and intervention strategies and (5) variables associated with suicidal behavior. Across the included studies, suicidal ideation, non-fatal attempts, and suicide mortality were examined with differing frequency and are reported separately in the thematic synthesis rather than pooled, reflecting the heterogeneity of outcome definitions and study designs. Significant limitations in the literature remain, including the paucity of longitudinal studies, the widespread use of aggregate data and the limited integration of environmental, social, and psychological factors. Conclusions: Climate change and extreme weather events may contribute to suicidal behavior in older adults. It is therefore necessary to develop multidimensional approaches that address structural vulnerabilities and promote the active role of older adults in climate resilience and social cohesion. Full article

Next-day stock direction forecasting combines market signals with news, but many trading days contain no firm-specific articles. We develop Gated News-Event Fusion (GNEF), an availability-aware model mapping price sequences, engineered market features, and news features to direction probabilities and a nonnegative return-magnitude estimate. GNEF retains price as a residual representation and uses a scalar sigmoid gate to interpolate between technical and news representations. We prove that the gated component is a norm-bounded convex combination whose gate-logit sensitivity is at most one quarter of the distance between these representations. We also establish conditional Lipschitz stability and forward complexity linear in sequence length. GNEF has 67,625 trainable parameters. Evaluation covers ten large-cap equities under prospective expanding walk-forward testing, paired moving-block bootstrap inference, and transaction-cost analysis. Across five random initializations, the residual design has higher mean directional accuracy than a gated mixture of price, technical, and news representations on 7 of 10 tickers. In a single-seed ablation, full GNEF exceeds no-gate and no-news variants in directional accuracy on 3 of 10 tickers. A broader validation-selected policy exceeds momentum in directional accuracy on 5 of 10 tickers. These findings provide selective support for residual fusion but do not establish a consistent advantage across equities. Full article