Search Results (738)

This paper presents an advanced signal processing framework for multi-channel forward-scatter radar (MC-FSR) systems based on the Multiple Signal Classification (MUSIC) algorithm. The proposed framework addresses the inherent limitations of FFT-based space-domain processing, such as limited angular resolution and the poor detectability of weak or closely spaced targets, which become particularly severe in low-cost FSR systems, which are typically operated with small antenna arrays. The MUSIC algorithm is adapted to operate on real-valued data obtained from the non-coherent, amplitude-based MC-FSR approach by reformulating the steering vectors and adjusting the degrees of freedom (DoFs). While compatible with arbitrary transmitting waveforms, particular emphasis is placed on Orthogonal Frequency Division Multiplexing (OFDM) signals, which are widely used in modern communication systems such as Wi-Fi and cellular networks. An analysis of the OFDM waveform’s autocorrelation properties is provided to assess their impact on target detection, including strategies to mitigate rapid target signature decay using a sub-band approach and to manage signal correlation through spatial smoothing. Simulation results, including multi-target scenarios under constrained array configurations, demonstrate that the proposed MUSIC-based approach significantly enhances angular resolution and enables reliable discrimination of closely spaced targets even with a limited number of receiving channels. Experimental validation using an S-band MC-FSR prototype implemented with software-defined radios (SDRs) and commercial Wi-Fi antennas, involving cooperative targets like people and drones, further confirms the effectiveness and practicality of the proposed method for real-world applications. Overall, the proposed MUSIC-based MC-FSR framework exhibits strong potential for implementation in low-cost, hardware-constrained environments and is particularly suited for emerging Integrated Sensing and Communication (ISAC) systems. Full article

Gas flaring from upstream oil and gas production remains a significant source of air pollution and toxic emissions, with major implications for human health and climate. However, the number of people living near flaring has not been quantified globally. This study presents the first worldwide, settlement-scale assessment of populations living within 1 km and 3 km of active upstream flare sites between 2016 and 2023, with projections to 2030. Using the VIIRS Nightfire satellite product, which provides global detections of high-temperature combustion sources, and the Global Human Settlement Layer (GHSL) population and settlement data, we developed a transparent and reproducible geospatial workflow to compute proximity-based exposure indicators by buffering flare locations and intersecting them with population rasters The analysis provides consistent estimates across five settlement categories: rural, peri-urban/suburban, semi-dense urban, dense urban, and urban centres. The VIIRS-based flaring time series combined with GHSL projections allows us to estimate how many people are likely to live near upstream flares under current flaring patterns by 2030. Results show that exposure is concentrated in a few oil-producing countries. Nigeria remains the most affected, with over 100,000 urban residents exposed in 2023. India and Pakistan dominate peri-urban and semi-urban exposures, while Indonesia and Iraq persist as multi-settlement hotspots. Although moderate declines are observed in China and Iran, little progress is evident in Nigeria, Mexico, and Indonesia. Projections for 2030 suggest exposure will increase substantially, driven by population growth and urban expansion, with about 2.7 million people living within 1 km and 14.8 million within 3 km of flaring sites. The findings establish the first globally consistent baseline for population exposure to gas flaring, supporting the monitoring and mitigation objectives of the Zero Routine Flaring by 2030 initiative. Full article

Background/Objectives: This study presents and explores the potential of Updated Bayesian Deduction (UBD) using colorectal cancer (CRC) detection and prioritisation as a case example. Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, and its prognosis strongly depends on early detection and timely treatment. In Chile, colonoscopy waiting lists for symptomatic patients in public hospitals can exceed one year, limiting access to early diagnosis and reducing survival rates. Traditional single-test screening strategies, such as a single faecal immunochemical test (FIT), often yield uncertain results, contributing to inefficiencies in resource allocation. Methods: We propose a deductive approach that integrates evidence from multiple sequential and independent FITs to dynamically update the posterior probability of CRC. A case study is analysed with this Updated Bayesian Deduction over a four-round FIT protocol to assess how this could improve risk stratification compared to standard symptoms-based screening. Results: Our mathematical model shows that over 85% of colonoscopies for symptomatic patients were not urgent. We then demonstrate that, if 4-FIT UBD were used to screen Chile’s Metropolitan Region population, only 96 out of 100,000 people would require an urgent colonoscopy to detect the 19.6 out of 100,000 individuals with CRC in this region. Many countries cannot afford a colonoscopy-based population screening, such as what is performed in Germany. Performing 4x FITs + a very small number of colonoscopies would be much more affordable and would get more countries to adopt general CRC screening. Conclusions: In countries with limited colonoscopy availability, such as Chile, where symptomatic patients can wait over a year for treatment in public hospitals, implementing a UBD-based strategy could drastically reduce costs and optimise the use of resources. This would improve access to colonoscopies for critical cases and ultimately enhance five-year survival rates. These findings highlight UBD as a promising approach for evidence-based precision medicine in CRC screening and prioritisation that is both explainable and adaptable. Full article

Polypharmacy and the use of potentially inappropriate medications (PIM) are prevalent issues among institutionalized older adults, contributing to adverse drug events and decreased quality of life. This study aimed to describe the sociodemographic and clinical characteristics associated with polypharmacy and the use of PIM in elderly people in nursing homes. A cross-sectional descriptive study was conducted among 151 residents aged ≥ 65 years. Data was extracted from institutional records. The mean age of participants was 86.48 ± 8.00 years; 71.5% were female. Excessive polypharmacy was observed in 49.7% of residents. The mean number of medications was 9.66 ± 4.18, with nervous system drugs being the most prescribed (3.73 ± 2.31). PDDIs were detected in 94% of the sample and PIMs were present in 82.8% of residents. The most common PIMs were proton pump inhibitors (ATC A) and anxiolytics (ATC N). Binary logistic regression identified two independent predictors for PIMs: the total number of medications (AOR = 1.259) and the use of ATC A (Alimentary tract and metabolism) medications (AOR = 2.315). Conversely, age and sex were not significant predictors. The study reveals a critical prevalence of excessive polypharmacy, PIM use, and PDDIs among institutionalized elderly in the Algarve. These findings underscore the urgent need for systematic, multidisciplinary medication reviews in Portuguese nursing homes to promote safer and more rational prescribing practices. Full article

Cannabis plants are susceptible to microbial contamination, including fungi capable of producing harmful mycotoxins. The presence of these toxins in cannabis products poses serious health risks, especially when used for medical purposes in immunocompromised people. This study evaluated the presence of fungi and mycotoxins in dried cannabis buds following gamma irradiation, using culture-based techniques, PCR/qPCR, and ELISA. Irradiation significantly reduced fungal and bacterial loads, eliminating culturable bacteria but did not achieve complete sterilization. Viable spores of toxigenic fungal genera, such as Aspergillus, Penicillium, and Fusarium, persisted. Sequencing of ITS amplicons revealed dominant mycotoxigenic fungi in non-irradiated (NR), irradiated (IR) and licensed producer (LP) samples, while next-generation sequencing (NGS) revealed additional non-culturable toxigenic species. PCR/qPCR detected biosynthetic genes for aflatoxins, trichothecenes, ochratoxins, and deoxynivalenol across all samples, with gene copy numbers remaining stable post-irradiation, suggesting DNA damage without full degradation. ELISA confirmed aflatoxin, ochratoxin, DON, and T2 toxins in both IR and LP samples at variable concentrations. While LP samples showed lower microbial counts and gene abundance, residual DNA and toxins were still detected. Our study shows that while irradiation decreases microbial loads, it does not completely remove toxigenic fungi or their metabolites. Ensuring the safety of cannabis products necessitates a multifaceted assessment that incorporates cultural, molecular, and immunological techniques, in parallel with more stringent microbial standards during production stage. Full article

This review provides an overview of existing accident prevention methods by monitoring the persons’ physiological state, observing movements, and physiological parameters. Firstly, different physiological parameters monitoring systems are introduced. Secondly, various systems dealing with position recognition on pressure sensing mats are presented. We conduct an in-depth literature search and quantitative analysis of papers published in this area and focus independently of the application (drivers, office and wheelchair users, etc.). Quantitative information about the number of subjects, investigated scenarios, sensor types, machine learning usage, and laboratory vs. real-world works is extracted. In posture recognition, most works recognize at least forward, backward, left and right movements on a seat. The remaining works use the pressure sensing mat for bedridden people. In physiological parameters measurement, most works detect the heart rate and often also add respiration rate recognition. Machine learning algorithms are used in most cases and are taking on an ever-greater importance for classification and regression problems. Although all solutions use different techniques, returning satisfactory results, none of them try to detect small movements, which can pose challenges in determining the optimal sensor topology and sampling frequency required to detect fine movements. For physiological measurements, there are lots of challenges to overcome in noisy environments, notably the detection of heart rate, blood pressure, and respiratory rate at very low signal-to-noise levels. Full article

In recent years, telecom fraud remains prevalent in many regions, severely impacting people’s daily lives and causing substantial economic losses. However, previous research has mainly relied on expert knowledge for feature engineering, which lags behind and struggles to adapt to the continuously evolving patterns of fraud effectively. In addition, the extreme imbalance in fraud amounts within real communication data hinders the development of deep learning methods. In response, we propose a feature transformation method to represent users’ communication behavior as comprehensively as possible, and develop a convolutional neural network (CNN) with a Focal Loss function to identify rare fraudulent activities in highly imbalanced data. Experimental results on a real-world dataset show that, under conditions of severe class imbalance, the proposed method significantly outperforms existing approaches in two key metrics: recall (0.7850) and AUC (0.8662). Our work provides a new approach for telecommunication fraud detection, enabling the effective identification of fraudulent numbers. Full article

Background: Recent hypotheses suggest that mutations associated with alpha1 antitrypsin (AAT) deficiency (AATD) may influence the clinical presentation and progression of cystic fibrosis (CF). This study employs a longitudinal design to determine the prevalence of AATD mutations and assess their impact on CF. Methods: The study Finding AAT Deficiency in Obstructive Lung Diseases: Cystic Fibrosis (FADO-CF) is a retrospective cohort study evaluating people with CF from November 2020 to February 2024. On the date of inclusion, serum levels of AAT were measured and a genotyping of 14 mutations associated with AATD was performed. Historical information, including data on exacerbations, microbiological sputum isolations, and lung function, was obtained from the medical records, aiming at a temporal lag of 10 years. Results: The sample consisted of 369 people with CF (40.9% pediatrics). Of these, 58 (15.7%) cases presented at least one AATD mutation. The AATD allelic combinations identified were PI*MS in 47 (12.7%) cases, PI*MZ in 5 (1.4%) cases, PI*SS in 3 (0.8%) cases, PI*SZ in 2 (0.5%) cases, and PI*M/P_lowell in 1 (0.3%) case. The optimal cutoff value for AAT levels to detect AATD-associated mutation carriers was 129 mg/dL in the overall cohort (sensitivity of 73.0%; specificity 69.2%) and 99.5 mg/dL when excluding PI*MS cases (sensitivity 98.0%; specificity 90.9%), highlighting the need for lower thresholds in clinically severe genotypes to improve case detection. The number of mild exacerbations during the follow-up appeared to be associated with AATD mutations. Conclusions: AATD mutations are prevalent in CF and may impact certain clinical outcomes. If systematic screening was to be planned, we recommend considering the proposed cut-off points to select the population for genetic studies. Full article

Background/Objectives: Neonatal screening programmes for thyroid function testing, based on thyroid-stimulating hormone (TSH) assessment, detect both Permanent Congenital Hypothyroidism (PCH) and Transient Congenital Hypothyroidism (TCH). Maternal iodine-deficient dietary intake may result in compensatory neonatal TSH elevation; screening for Congenital Hypothyroidism (CH) is used as an indicator of the degree of iodine deficiency and of its control. In Greece, newborn screening for CH, using TSH measurement in dried blood spots (Guthrie card), began in 1979 through the Institute of Child Health (ICH). Although the general Greek population is considered iodine-replete, most pregnant Greek people are mildly iodine deficient according to the stricter WHO criteria. The aim of this retrospective study was to record the cases of TCH and the main causative factors over a 10-year period (2010–2019) in Greece, when the country was deemed to be iodine-replete. Methods: The number of births in Greece between 2010 and 2019 was retrieved from the Hellenic Statistical Authority (ELSTAT) archives: 952,109 births were recorded. The total number of newborns assessed through the ICH was 951,342 (99%). During this period, 22,391 newborns were identified to have TSH > 7 mIU/L after the second check on the initial card. Among those, 17,992 underwent retesting with a serum sample. Out of the retested newborns, 1979 were screened positive for CH and immediately began treatment with levothyroxine. We followed up with families, paediatricians, and paediatric endocrinologists to determine whether L-thyroxine therapy had been successfully discontinued for at least two months after the child’s third birthday. Successful contact was achieved with 889 individuals. From this group, 329 children had successfully discontinued thyroxine, classified as TCH. Demographic data, including gender, gestational age, and birth weight, were collected from the archives of the ICH. Maternal data, including thyroid medication use and the presence of elevated thyroid autoantibodies during pregnancy and childbirth, were also recorded. Results: Logistic regression analysis revealed that, while controlling for all other predictor variables, the odds ratio of transient hypothyroidism was 2.078 (95% CI: 1.530 to 2.821) for prematurely born children compared to those born at term. The effects of other factors on TCH versus PCH were not significant. Conclusions: It seems that prematurity is the main factor contributing to Transient Congenital Hypothyroidism in Greece, a recently iodine-replete country. Full article

With the rapid development of modern industry, people’s living pressures are gradually increasing, and an increasing number of individuals are affected by sleep disorders such as insomnia, hypersomnia, and sleep apnea syndrome. Many cardiovascular and psychiatric diseases are also closely related to sleep. Therefore, the early detection, accurate diagnosis, and treatment of sleep disorders an urgent research priority. Traditional manual sleep staging methods have many problems, such as being time-consuming and cumbersome, relying on expert experience, or being subjective. To address these issues, researchers have proposed multiple algorithmic strategies for sleep staging automation based on deep learning in recent years. This paper studies MASleepNet, a sleep staging neural network model that integrates multimodal deep features. This model takes multi-channel Polysomnography (PSG) signals (including EEG (Fpz-Cz, Pz-Oz), EOG, and EMG) as input and employs a multi-scale convolutional module to extract features at different time scales in parallel. It then adaptively weights and fuses the features from each modality using a channel-wise attention mechanism. The integrated temporal features are integrated into a Bidirectional Long Short-Term Memory (BiLSTM) sequence encoder, where an attention mechanism is introduced to identify key temporal segments. The final classification result is produced by the fully connected layer. The proposed model was experimentally evaluated on the Sleep-EDF dataset (consisting of two subsets, Sleep-EDF-78 and Sleep-EDF-20), achieving classification accuracies of 82.56% and 84.53% on the two subsets, respectively. These results demonstrate that deep models that integrate multimodal signals and an attention mechanism offer the possibility to enhance the efficiency of automatic sleep staging compared to cutting-edge methods. Full article

Background/Objectives: The advent of combination antiretroviral therapy has led to significant reductions in HIV-related morbidity and mortality and, conversely, an increasing incidence of chronic diseases, such as cancer. This study aimed to assess the incidence of skin malignancies in a cohort of people living with HIV (PLWH) compared to HIV-uninfected individuals (HUPs). Methods: Between April 2023 and April 2025, PLWH attending the Infectious Disease Unit at Policlinico of Foggia, Italy, were invited for skin cancer screening (cases). During the same period, patients visiting the Dermatology Unit were asked to undergo skin cancer screening and a rapid HIV test. Those who tested negative were included as controls. Suspicious lesions were surgically excised at the Plastic Surgery University Unit and examined by a dermatopathologist. Results: We enrolled 91 cases and 91 controls. Precancerous and cancerous skin lesions were detected at similar rates in PLWH and HUPs (12% vs. 13.2% and 7.6% vs. 8.7%). The total number of cancerous and precancerous lesions was higher in the PLWH group. In both groups, basal cell carcinoma was the most common tumor. Squamous cell carcinoma, basosquamous carcinoma, and dermatofibrosarcoma protuberans were found only in PLWH. Conclusions: The higher risk of multiple and rare skin cancers in PLWH should be recognized by healthcare providers and patients. PLWH should have regular skin cancer screenings, especially if they have additional risk factors such as a history of extensive ultraviolet radiation exposure. Full article

Glaucoma is an eye disease caused by increased intraocular pressure (IOP) that affects the optic nerve head (ONH), leading to vision problems and irreversible blindness. Background/Objectives: Glaucoma is the second leading cause of blindness worldwide, and the number of people affected is increasing each year, with the number expected to reach 111.8 million by 2040. This escalating trend is alarming due to the lack of ophthalmology specialists relative to the population. This study proposes an explainable end-to-end pipeline for automated glaucoma diagnosis from fundus images. It also evaluates the performance of Vision Transformers (ViTs) relative to traditional CNN-based models. Methods: The proposed system uses three datasets: REFUGE, ORIGA, and G1020. It begins with YOLOv11 for object detection of the optic disc. Then, the optic disc (OD) and optic cup (OC) are segmented using U-Net with ResNet50, VGG16, and MobileNetV2 backbones, as well as MaskFormer with a Swin-Base backbone. Glaucoma is classified based on the vertical cup-to-disc ratio (vCDR). Results: MaskFormer outperforms all models in segmentation in all aspects, including IoU OD, IoU OC, DSC OD, and DSC OC, with scores of 88.29%, 91.09%, 93.83%, and 93.71%. For classification, it achieved accuracy and F1-scores of 84.03% and 84.56%. Conclusions: By relying on the interpretable features of the vCDR, the proposed framework enhances transparency and aligns well with the principles of explainable AI, thus offering a trustworthy solution for glaucoma screening. Our findings show that Vision Transformers offer a promising approach for achieving high segmentation performance with explainable, biomarker-driven diagnosis. Full article

The number of people and applications using the internet has increased substantially in recent years. The increased use of the internet has also resulted in various security issues. As the volume of data increases, cyber-attacks become increasingly sophisticated, exploiting vulnerabilities in network structures. The incorporation of modern technologies, particularly data mining, emerges as an essential method for analyzing huge amounts of data in real time, enabling the proactive detection of anomalies and potential security breaches. This research seeks to identify the most robust machine learning model for exploit detection. It applies five feature selection techniques and eight classification models to the UNSW-NB15 dataset. A comprehensive evaluation is conducted based on classification accuracy, computational efficiency, and execution time. The results demonstrate the efficiency of the Decision Tree model using Random Forest for feature selection in the real-time detection of exploit attacks, exhibiting an accuracy of 87.9%, along with a very short training (0.96 s) and testing time (0.29 ms/record). Full article

This study aims to develop a performance evaluation system specifically for the design phase of high-rise building projects within the architecture, engineering, and construction industry, where performance is often only measured during construction. The research process included three stages: identification of 21 key performance indicators through a literature review and expert validation; development of standardized indicator sheets detailing calculation protocols and data collection procedures; and creation of a functional dashboard-based evaluation system using Excel. The system was validated through expert review and tested with a simulated project generated using an AI-based language model. The evaluation system proved functional, accessible, and effective in detecting performance issues across five core categories: planning, cost, time, quality, and people. The results from the simulated application highlighted strengths in quality and stakeholder collaboration but also revealed significant gaps in cost and time performance. This study addresses a gap in the existing literature by focusing on performance evaluation during the design phase of construction projects, a stage often underrepresented in performance studies. The resulting system offers a structured, practical tool adaptable to real-world projects. The validation relied on a limited number of expert participants and a simulated project. Future research should recommend broader international validation and real-world application. Full article

Alzheimer’s disease (AD) is the most common form of dementia, affecting over 50 million people globally. Since 1906, efforts to understand this neurodegenerative disease and to develop effective treatments have continued to this day. Recognizing docosahexaenoic acid (DHA, 22:6n-3) as a safe, inexpensive and vital nutrient for brain health and cognitive protection due to its key role in brain development and function, this study explores novel, sustainable non-fish sources as potential dietary supplements to prevent or mitigate AD, within a blue biotechnology framework. Forty 5×FAD male mice, five weeks old, were allocated to five body weight-matched dietary groups (n = 8) and fed isocaloric diets based on AIN-93M standard chow for 6 months. Each diet, except the control feed (non-supplemented group), enclosed a modified lipid fraction supplemented with 2% of the following: (1) linseed oil (LSO, rich in alpha-linolenic acid (ALA,18:3n-3)); (2) cod liver oil (fish oil, FO, rich in both DHA and eicosapentaenoic acid (EPA, 20:5n-3)); (3) Schizochytrium sp. microalga oil (Schizo) with 40% of DHA; and (4) commercial DHASCO oil (DHASCO) with 70% of DHA. The different diets did not affect (p > 0.05) growth performance criteria (e.g., final body weight, daily feed intake, and body weight gain) suggesting no effect on the overall caloric balance or mice growth, but n-3 long-chain polyunsaturated-fatty acid (n-3 LCPUFA) supplementation significantly reduced total cholesterol (p < 0.001) and total lipids (p < 0.001). No systemic inflammation was detected in 5×FAD mice. In parallel, a beneficial modulation of lipid metabolism by DHA-enriched diets was observed, with polyunsaturated fatty acid incorporation, particularly DHA, across key metabolic tissues, such as the liver (p < 0.001) and the brain (p < 0.001). No behavioural variations were detected using an open-field test after 6 months of diet (p > 0.05). While mice fed a standard diet or LSO diet showed cognitive deficit, the incorporation of FO, Schizo or DHASCO oils into dietary routine showed promising protective effects on the working memory (p < 0.05) and the last two diets also on the recognition memory (p < 0.05) Increased neuronal count (p < 0.05), reflecting neuronal survival, was clearly observed with the fish oil diet. In turn, the number of TAU-positive cells (p < 0.05) was reduced in the Schizo diet, while β-amyloid deposition (p < 0.01) and the neuroinflammatory marker, IBA1 (p < 0.05), were decreased across all DHA-enriched diets. These promising findings open new avenues for further studies focused on the protective effects of DHA derived from sustainable and underexploited Schizochytrium sp. microalga in the prevention of AD. Full article