Search Results (679)

As the generation of Municipal Solid Waste (MSW) has exponentially increased, this poses a challenge for waste managers, such as municipalities, to effectively control waste streams. If waste streams are not managed correctly, they negatively contribute to climate change, marine plastic pollution and human health effects. Therefore, waste streams need to be identified, categorised and valorised to ensure that the most effective waste management strategy is employed. Research suggests that a more efficient process of identifying and categorising waste at the source can achieve this. Therefore, the aim of the paper is to identify the state of research of AI-powered drones in identifying and categorising waste. This paper will conduct a systematic review and meta-analysis on the application of drone technology integrated with image sensing technology and deep learning methods for waste management. Different systems are explored, and a quantitative meta-analysis of their performance metrics (such as the F1 score) is conducted to determine the best integration of technology. Therefore, the research proposes designing and developing a hybrid deep learning model with integrated architecture (YOLO-Transformer model) that can capture Multispectral imagery data from drones for waste stream identification, categorisation and potential valorisation for waste managers in small-scale environments. Full article

Marine biomass, particularly from waste streams, by-products, underutilized, invasive, or potential cultivable marine species, offers a sustainable source of high-value biopolymers such as collagen and chitin. These macromolecules have gained significant attention due to their biocompatibility, biodegradability, functional versatility, and broad applicability across health, food, wellness, and environmental fields. This review highlights recent advances in the uses of marine-derived collagen and chitin/chitosan. In alignment with the United Nations Sustainable Development Goals (SDGs), we analyze how these applications contribute to sustainability, particularly in SDGs related to responsible consumption and production, good health and well-being, and life below water. Furthermore, we contextualize the advancement of product development using marine collagen and chitin/chitosan within the European Union’s Blue bioeconomy strategies, highlighting trends in scientific research and technological innovation through bibliometric and patent data. Finally, the review addresses challenges facing the development of robust value chains for these marine biopolymers, including collaboration, regulatory hurdles, supply-chain constraints, policy and financial support, education and training, and the need for integrated marine resource management. The paper concludes with recommendations for fostering innovation and sustainability in the valorization of these marine resources. Full article

This study evaluates the potential health risks posed by geogenic arsenic in environments suitable for leisure activities, such as walking, bathing, and playing, for adults and children alike, as well as in neighbouring agricultural areas. The study includes an analysis of environmental characteristics and the main stream originating in the adjacent mining area, with water and sediment samples taken. The study area is representative of other areas in the vicinity of the Mar Menor Lagoon, which is one of the largest and most biodiverse coastal lagoons in the Mediterranean Sea. The general characteristics of the soil and water were determined for this study, as was the concentration of As in the soil and water samples. A granulometric separation was carried out into four different fractions (<2 mm, <250 µm, <100 µm, and <65 µm). The mineralogical composition, total As content, and bioaccessible As content are analysed in each of these fractions. This provides data with which to calculate the danger of arsenic (As) to human health by ingestion and to contribute to As bioaccessibility studies and the role played by the mineralogical composition and particle size of soil ingestion. The conclusions rule out residential use of this environment, although they allow for eventual tourist use and traditional agricultural use of the surrounding soils. Full article

This study introduces an algorithm for quantifying oceanic wind speed and identifying sound sources in the local underwater soundscape. Utilizing low-complexity metrics like one-minute spectral kurtosis and power spectral density levels, the algorithm categorizes different soundscapes and estimates wind speed. It detects rain, vessels, fin and blue whales, as well as clicks and whistles from dolphins. Positioned as a foundational tool for implementing the Ocean Sound Essential Ocean Variable (EOV), it contributes to understanding long-term trends in climate change for sustainable ocean health and predicting threats through forecasts. The proposed soundscape classification algorithm, validated using extensive acoustic recordings (≥32 kHz) collected at various depths and latitudes, demonstrates high performance, achieving an average precision of $89\%$ and an average recall of $86.59\%$ through optimized parameter tuning via a genetic algorithm. Here, wind speed is determined using a cubic function with power spectral density (PSD) at 6 kHz and the MASLUW method, exhibiting strong agreement with satellite data below 15 m/s. Designed for compatibility with low-power electronics, the algorithm can be applied to both archival datasets and real-time data streams. It provides a straightforward metric for ocean monitoring and sound source identification. Full article

Background/Objectives: Clostridioides difficile infection (CDI) is a major cause of infectious diarrhea in the inpatient and community setting. Real-world data outside the strict environment of randomized controlled trials (RCTs) are needed to improve the quality of evidence. The aim of this study was to compare different clinical outcomes of CDI patients treated with fidaxomicin with those treated with vancomycin using a representative patient population in the United States of America (USA). Methods: Comprehensive real-world data were analyzed for this retrospective observational study, provided by the TriNetX database, an international research network with electronic health records from multiple USA healthcare providers. This includes in- and outpatients treated with fidaxomicin (FDX) or vancomycin (VAN) for CDI between 01/2013 and 12/2023. The following cohorts were compared: (i) patients treated with fidaxomicin within 10 days following CDI diagnosis (FDX group) vs. (ii) patients treated with vancomycin within 10 days following CDI diagnosis (VAN group). Outcomes analysis between the two cohorts was performed after propensity score matching and included event risk and Kaplan–Meier survival analyses for the following concomitant diseases/events occurring during an observational period of 12 months following CDI diagnosis: death, sepsis, candidiasis, infections caused by vancomycin-resistant enterococci, inflammatory bowel disease, cardiovascular disease, psychological disease, central line-associated blood stream infection, surgical site infection, and ventilator-associated pneumonia. Results: Following propensity score matching, 2170 patients were included in the FDX group and VAN groups, respectively. The event risk analysis demonstrated improved outcomes of patients treated with FDX compared to VAN in 6 out of the 10 events that were analyzed. The highest risk ratio (RR) and odds ratio (OR) were found for sepsis (RR: 3.409; OR: 3.635), candidiasis (RR: 2.347; OR: 2.431), and death (RR: 1.710; OR: 1.811). The Kaplan–Meier survival analysis showed an overall survival rate until the end of the 12-month observational period of 87.06% in the FDX group and 78.49% in the VAN group (log-rank p < 0.001). Conclusions: Our comparative event risk analysis demonstrated improved outcomes for patients treated with FDX compared to VAN in most of the observed events and underlines the results of previously conducted RCTs, highlighting the beneficial role of FDX compared to VAN. Further big data analyses from other industrialized countries are needed for comparison with our observations. Full article

Accurate monitoring of animal behaviors enables improved management in precision livestock farming (PLF), supporting critical applications including health assessment, estrus detection, parturition monitoring, and feed intake estimation. Although both contact and non-contact sensing modalities are utilized, wearable devices with embedded sensors (e.g., accelerometers, pressure sensors) offer unique advantages through continuous data streams that enhance behavioral traceability. Focusing specifically on contact sensing techniques, this review examines sensor characteristics and data acquisition challenges, methodologies for processing behavioral data and implementing identification algorithms, industrial applications enabled by recognition outcomes, and prevailing challenges with emerging research opportunities. Current behavior classification relies predominantly on traditional machine learning or deep learning approaches with high-frequency data acquisition. The fundamental limitation restricting advancement in this field is the difficulty in maintaining high-fidelity recognition performance at reduced acquisition rates, particularly for integrated multi-behavior identification. Considering that the computational demands and limited adaptability to complex field environments remain significant constraints, Tiny Machine Learning (Tiny ML) could present opportunities to guide future research toward practical, scalable behavioral monitoring solutions. In addition, algorithm development for functional applications post behavior recognition may represent a critical future research direction. Full article

Reliable short-term forecasts of hepatitis B incidence are indispensable for sizing national vaccine and antiviral procurement. However, predictive modelling is complicated when surveillance streams experience reporting delays and episodic under-reporting, as has occurred in Ukraine since 2022. We address this challenge by training a deliberately compact two-layer long short-term memory (LSTM) network on 72 monthly observations (January 2018–December 2023) drawn from the Public Health Center electronic registry and evaluating performance on a strictly held-out 12-month horizon (January–December 2024). Grid-search optimisation selected a 12-month sliding input window, 64 hidden units per layer, 0.20 dropout, the Adam optimiser, and early stopping. Walk-forward validation showed that the network attained mean squared errors of 411 for acute infection and 76 for chronic infection on the monthly series. When forecasts were aggregated to the cumulative scale, the mean absolute percentage error remained below 1%. This study presents the first peer-reviewed hepatitis B forecasts calibrated on Ukraine’s registry during a period of pronounced reporting instability, demonstrating that robust accuracy is attainable without missing-value imputation. Full article

This study assesses the ecological status of alpine lotic ecosystems in Khunjerab National Park, Pakistan, situated at approximately 4000 m in the Karakoram Range along the Pakistan–China border. An integrative approach was employed, evaluating alpine stream ecosystems through benthic macroinvertebrate indices in conjunction with physicochemical habitat parameters. Samples were gathered using kick nets and hand-picking at seventeen randomly selected sites in early spring and summer. The study recorded 710 summer taxa from 41 families and seven orders, and 1250 early spring taxa from 30 families and six orders. The abundance of macroinvertebrates increased in early spring, while taxonomic diversity increased during the summer. Statistical tests revealed a strong relationship between water quality conditions and biological features. The biotic index reached its peak in early spring, while diversity indices peaked in summer when Ephemeroptera dominated. Due to the macroinvertebrate source in early spring, the majority of EPT taxa were abundant at all alpine stream sites during early spring, except for upstream sites in summer. The indices from the biotic metric evaluation revealed low levels of natural environmental disturbance caused by humans. This research is significant in terms of natural resource conservation and health assessment based on the benthic fauna community structure in alpine streams. Full article

Both the excess and alteration of bed sediments in river systems can cause socioeconomic and environmental damage; thus, the quantification of bedload transport is an important tool to assess the health of rivers and help in decision-making imposed by the agencies responsible for water resource management. This work aims to evaluate the efficiency of pressure-difference samplers (Helley–Smith) qualitatively and quantitatively when used in environments with sandy characteristics. The experiments were carried out in a stream with full transparency and two pressure-difference samplers with nozzle dimensions of 7.20 × 7.20 cm and 8.89 × 7.50 cm. The Particle Image Velocimetry technique was used to analyze the sampler efficiency simultaneously with an Acoustic Doppler Current Profiler. Qualitative results showed that the way the equipment is allocated at the bottom of the river can generate overestimated or underestimated sediment transport measurements. Additionally, evaluating it quantitatively, we see that the collection efficiency of the equipment varied between 15.45% and 534.78% when compared to the results obtained by the Particle Image Velocimetry technique. Full article

In today’s digital era, social media platforms like Instagram have become deeply embedded in daily life, generating billions of content items each day. This vast stream of publicly accessible data presents a unique opportunity for researchers to gain insights into human behaviour and personality. However, leveraging such unstructured and highly variable data for psychological analysis introduces significant challenges, including data sparsity, noise, and ethical considerations around privacy. This study addresses these challenges by exploring the potential of machine learning to infer personality traits from Instagram content. Motivated by the growing demand for scalable, non-intrusive methods of psychological assessment, we developed a personality prediction system combining convolutional neural networks (CNNs) and random forest (RF) algorithms. Our model is grounded in the Big Five Personality framework, which includes Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. Using data collected with informed consent from 941 participants, we extracted visual features from their Instagram images using two pretrained CNNs, which were then used to train five RF models, each targeting a specific trait. The proposed system achieved an average mean absolute error of 0.1867 across all traits. Compared to the PAN-2015 benchmark, our method demonstrated competitive performance. These results highlight that using social media data for personality prediction offers potential applications in personalized content delivery, mental health monitoring, and human–computer interactions. Full article

This study assessed the taxonomic and functional diversity of aquatic macroinvertebrate communities in Chacabuco stream, a 5500 ha pioneering open conservation ranch of Nahuel Huapi National Park in Argentina, focusing on the effects of seasonal hydrological regimes along a willow-invaded corridor. Sampling was conducted during the spring of 2018 and the summer and spring of 2019, covering high (spring) and low (summer) water levels. Results showed no significant differences in taxonomic diversity between hydrological periods (p = 0.6), and similar taxonomic diversity during the low- and high-water periods of 2019 due to an extreme drought event. Functional diversity varied significantly (p = 0.009) between hydrological periods, and a significant difference in taxonomic diversity between invasive and native plots (p = 0.0001, R = 0.53) was found, while functional diversity revealed less distinction (p = 0.02, R = 0.08). Functional diversity does not follow the same pattern, showing opportunistic taxa such as predators and collectors equally present during both periods, sign of resilience of these FFGs over the others. This pioneering study in the region highlighted the importance of exploring both taxonomic and functional diversity in riparian ecosystems to assess the impact of seasonal hydrological regimes along a willow-invaded corridor and develop a more comprehensive understanding of riparian ecosystem health. Full article

This paper describes the modelling of Key Health Indicators (KHI) of frail individuals through non-invasive sensors located in their environment and wearable devices. Primary care professionals defined four indicators for daily health monitoring: sleep patterns, excretion control, physical mobility, and caregiver social interaction. A minimally invasive and low-cost sensing architecture was implemented, combining indoor localisation and physical activity tracking through environmental sensors and wrist-worn wearables. The health outcomes are modelled using a knowledge-based framework that integrates knowledge graphs to represent control variables and their relationships with data streams, and fuzzy logic to linguistically define temporal patterns based on expert criteria. The proposed approach was validated in a real-world case study with an older adult living independently in Granada, Spain. Over several days of deployment, the system successfully generated interpretable daily summaries reflecting relevant behavioural patterns, including rest periods, bathroom usage, activity levels, and caregiver proximity. In addition, supervised machine learning models were trained on the indicators derived from the fuzzy logic system, achieving average accuracy and F1 scores of 93% and 92%, respectively. These results confirm the potential of combining expert-informed semantics with data-driven inference to support continuous, explainable health monitoring in ambient assisted living environments. Full article

This paper presents a novel small-scale system for drinking water treatment from surface waters, designed to rely on gravity as the only source of energy driving the treatment process. The pilot-scale setup, designed for a flow rate of 0.5 L/s, was tested at the Cornell University Water Filtration Plant (CWFP) for a total period of 5 months of operation. The experiments evaluated the influence of selected process parameters on system performance. The identified best operation practices were used to complete a comparative study against CWFP’s full-scale treatment process and to conduct a performance assessment in the context of various legislative landscapes. The objective of the work was to determine both the advantages and disadvantages of the proposed technology over established solutions. Over the study period, the average turbidity of the produced water was equal to 0.54 NTU. The pilot complied with the United States Environmental Protection Agency (US EPA) turbidity standard of <0.3 NTU 47.1% of the time and <1 NTU for 89.9% of the time, thus falling short of the standard of <0.3 NTU 95% of the time and <1 NTU 100% of the time. For 99.5% of the time, it complied with the World Health Organization turbidity guideline of <5 NTU for chlorination treatment. The benchmark conventional system outperformed the tested prototype, complying with the US EPA standards for the entire duration of the study. The tested process also generated a waste stream, which accounted on average for more than 10% of the total raw water volume. Full article

Land use and land cover changes driven by urbanization and agricultural expansion have increasingly degraded the ecological health of stream ecosystems across watersheds. In Republic of Korea, the Ministry of Environment has designated riparian zones to protect water quality and preserve aquatic ecosystems and continues to implement policies for their management. Given the long-term nature of riparian zone management, providing robust scientific evidence to justify and refine these policies is imperative. In this study, we quantitatively evaluated the role of riparian vegetation on water quality and aquatic ecosystems by using Bayesian Networks. Scenarios were designed to compare the individual effects of riparian vegetation and combined effects of urban and agricultural land use changes. The results indicated that riparian vegetation positively influenced water quality and the benthic macroinvertebrate index at the sub-watershed scale. When riparian vegetation and land use factors were jointly adjusted, scenarios with high riparian vegetation coverage showed improved probabilities of good BMI scores—24.3% under highly agricultural conditions and 27.4% under highly urbanized conditions—highlighting a substantial vegetation effect, particularly in urban areas. This study provides a scientific basis for guiding future riparian restoration and management efforts. Full article

Volatile Organic Compounds (VOCs) are essential for primary pharmaceutical manufacturing. Their permissible emission levels are strictly regulated due to their toxic effects both on human health and the environment. Activated carbon adsorption columns are used in industry to treat VOC gaseous waste streams from industrial plants, but their process efficiency suffers from quick and unpredictable saturation of the adsorbent material. This study presents the application of a validated, non-isothermal, multicomponent adsorption model using the Langmuir Isotherm and the Linear Driving Force model to examine multicomponent VOC mixture breakthrough. Specifically, three binary mixtures (hexane–acetone, hexane–dichloromethane, hexane–toluene) are simulated for four different bed lengths (0.25, 0.50, 0.75, 1 m) and six different superficial velocities (0.1, 0.2, 0.3, 0.5, 0.7, 0.9 m s⁻¹). Key breakthrough metrics reveal preferential adsorption of acetone and toluene over hexane, and hexane over dichloromethane, as well as breakthrough onset patterns. Temperature peaks are moderate while pressure drops increase for longer column lengths and higher flow rates. A new breakthrough onset metric is introduced, paving the way to improved operating regimes for more efficient industrial VOC capture bed utilisation via altering multicomponent mixture composition, feed flowrate, and column length. Full article