Search Results (46)

Mobile scaffolding is essential in construction but presents significant safety risks, particularly falls from height (FFH) due to improper use and insufficient monitoring. While prior research has identified hazards, it often lacks robust, actionable solutions, especially regarding the comprehensive analysis of worker behaviors and the spatial context. This study proposed a computer vision-based safety monitoring system that leverages depth cameras for accurate spatial assessments and incorporates temporal conditions to reduce false alarms. The proposed system extends object detection algorithms with mathematical logic derived from safety rules to classify four key unsafe conditions related to safety helmet use, guardrail and outrigger presence, and worker overcrowding on mobile scaffolds. A diverse dataset from multiple sources enhances the model’s applicability to real-world scenarios, while a status trigger module verifies worker behavior over a 3 s window, minimizing detection errors. The experimental results demonstrate high precision (0.95), recall (0.97), F1-score (0.96), and accuracy (0.95) for safe behaviors, with similarly strong metrics for unsafe behaviors. The qualitative analysis further confirms substantial improvements in worker position detection and safety compliance using 3D data over 2D approaches. These findings highlight the effectiveness of the proposed system in improving mobile scaffolding safety, addressing critical research gaps, and advancing construction industry safety standards. Full article

Non-expandable lung (NEL) occurs when the lung fails to fully re-expand after pleural fluid drainage, complicating management and limiting therapeutic options. Diagnosis, based on clinical symptoms, pleural manometry, and traditional imaging, is often delayed to the peri- or post-procedural stages, leading to improper management, complications, and higher healthcare costs. Therefore, early, pre-procedural diagnostic methods are needed. Thoracic ultrasound (TUS) has emerged as a non-invasive tool with the potential to enhance diagnostic accuracy and guide clinical decisions, yet, it remains inadequately studied within the context of NEL. We conducted a non-systematic narrative review using a structured methodology, including a comprehensive database search, predefined inclusion criteria, and QUADAS-2 quality assessment. This approach ensured a rigorous synthesis of evidence on TUS in NEL, with the aim of identifying knowledge gaps and guiding future studies. Non-invasive, real-time, bedside M-mode TUS has demonstrated efficacy in predicting NEL prior to thoracentesis by detecting an absent sinusoidal sign and reduced atelectatic lung movement. Emerging experimental techniques, including 2D shear wave elastography (SWE), speckle tracking imaging (STI) strain analysis, the lung/liver echogenicity (LLE) ratio, TUS assessment of dynamic air bronchograms, and pleural thickening evaluation, show additional potential to enhance pre-procedural NEL detection. However, all these methods have significant limitations that require further comprehensive investigation. Despite their significant promise, TUS modalities for early NEL detection still require rigorous validation and standardization before broad clinical use. A multimodal diagnostic approach, combining clinical manifestations, pleural manometry, radiologic and ultrasonographic findings, along with emerging techniques (once fully validated), may provide the most extensive framework for NEL. Regardless of advancements, patient-centered care and shared decision-making remain essential. Further research is needed to improve outcomes, reduce healthcare costs, and enhance long-term treatment strategies. Full article

The global challenge of food waste management poses severe environmental and public health risks. Traditional disposal methods, such as landfilling and incineration, exacerbate these issues. Decomposing food waste in landfills emits methane, a greenhouse gas 25 times more potent than CO₂, while landfill leachate contaminates soil and groundwater with hazardous pathogens and toxins. Additionally, improper waste disposal fosters microbial proliferation, posing severe health risks. Incineration, though commonly used, is inefficient due to the high moisture content of food waste, leading to incomplete combustion and further air pollution. Therefore, this review examines biodigesters as a sustainable alternative to traditional food waste disposal, assessing their effectiveness in mitigating environmental and health risks while promoting circular economy practices. It evaluates different biodigester designs, their operational scalability, and their economic feasibility across diverse global contexts. Through an analysis of case studies, this review highlights biodigesters’ potential to address localized waste management challenges by converting organic waste into biogas—a renewable energy source—and nutrient-rich digestate, a valuable natural fertilizer. The process reduces greenhouse gas emissions, improves soil health, and minimizes public health risks associated with microbial contamination. Various biodigester designs, including fixed-dome, floating-drum, and tubular systems, are compared for their efficiency and adaptability. Additionally, this review identifies key barriers to biodigester adoption, including feedstock variability, maintenance costs, and policy constraints, while also discussing strategies to enhance their efficiency and accessibility. This review is novel in its comprehensive approach, bridging the technological, environmental, and public health perspectives on biodigesters in food waste management. Unlike prior studies that focused on isolated aspects—such as specific case studies, policy analyses, or laboratory-scale evaluations—this review synthesizes the findings across diverse real-world implementations, offering a holistic understanding of biodigesters’ impact. By addressing knowledge gaps in terms of health risks, environmental benefits, and economic challenges, this study provides valuable insights for policymakers, researchers, and industry stakeholders seeking sustainable waste management solutions. Full article

Prior elicitation is an important issue in both subjective and objective Bayesian frameworks, where prior distributions impose certain information on parameters before data are observed. Caution is warranted when utilizing noninformative priors for hypothesis testing or model selection. Since noninformative priors are often improper, the Bayes factor, i.e., the ratio of two marginal distributions, is not properly determined due to unspecified constants contained in the Bayes factor. An adjusted Bayes factor using a data-splitting idea, which is called the intrinsic Bayes factor, can often be used as a default measure to circumvent this indeterminacy. On the other hand, if reasonable (possibly proper) called intrinsic priors are available, the intrinsic Bayes factor can be approximated by calculating the ordinary Bayes factor with intrinsic priors. Additionally, the concept of the integral prior, inspired by the generalized expected posterior prior, often serves to mitigate the uncertainty in traditional Bayes factors. Consequently, the Bayes factor derived from this approach can effectively approximate the conventional Bayes factor. In this article, we present default Bayesian procedures when testing the zero inflation parameter in a zero-inflated Poisson distribution. Approximation methods are used to derive intrinsic and integral priors for testing the zero inflation parameter. A Monte Carlo simulation study is carried out to demonstrate theoretical outcomes, and two real datasets are analyzed to support the results found in this paper. Full article

Water shortages, overexploitation, and sectoral conflicts have prompted the use of treated wastewater (TWW) in agriculture. While TWW provides essential nutrients, improper management can harm the soil and crops. To address this, case studies from Jordan and Palestine—where alfalfa and citrus crops are exclusively irrigated with TWW—were conducted to identify suitable irrigation schedules and assess adverse impacts on crops and soils. The Safe Irrigation Management (SIM) model was used to simulate irrigation in 2021, considering TWW quality, quantity, and initial soil conditions. Two scenarios were examined: FARMOD, based on farmers’ planning, and ON-DEMAND, suggested by SIM. The results showed significant differences in irrigation frequencies and volumes between the two scenarios. The ON-DEMAND scenario demonstrated improved nitrogen and phosphorus uptake, lower soil electrical conductivity (ECe 1.5 dS·m⁻¹), and reduced Escherichia coli (E. coli) levels (4 log₁₀ CFU·g⁻¹). A hypothetical scenario assuming initial soil conditions prior to TWW use yielded even lower ECe (0.8 dS·m⁻¹) and E. coli (3.3 log₁₀ CFU·g⁻¹). Sensitivity analysis identified ECe and nitrogen as crucial water quality indicators requiring continuous monitoring. Integrating field data and modeling practices is vital to maintaining soil quality, supporting long-term TWW reuse especially where it is a widely adopted irrigation solution. Full article

In modern aquaculture, accurate and efficient fish counting is crucial for the optimization of resource management and the enhancement of production profitability. Acoustic methods, known for their low energy consumption and extensive detection range, are widely utilized for underwater fish counting. However, traditional acoustic echo methods heavily rely on prior knowledge of fish schools and specific distribution models, leading to complexity and limited adaptability in practical applications. This paper introduces a fish-counting approach that integrates spatial sensing with temporal information. Initially, a spatial sensing matrix is constructed using ultrasonic Frequency-Modulated Continuous Wave (FMCW) technology, which facilitates the extraction of multidimensional features from fish echoes and reduces reliance on prior knowledge of fish schools. Subsequently, temporal information is extracted from echo signals using a Long Short-Term Memory (LSTM) network model, preventing missed detections caused by obstructions in single fish echoes during echo sessions. Finally, by fusing spatial and temporal feature information and employing a data-driven approach, we achieve fish counting while avoiding potential issues arising from improper selection of statistical distribution models. Tests on real fish datasets show that our proposed method consistently outperforms conventional statistical echo methods across all metrics, demonstrating its effectiveness in accurate fish counting. Full article

Slopes are formed as a result of mining operations. These slopes are classified as artificial slopes. Improper planning of slopes can lead to instability and potentially trigger landslides. PT. Allied Indo Coal Jaya employs the open-pit mining method in its coal mining operations. Slopes are naturally formed in open-pit mines. Additionally, PT. Allied Indo Coal Jaya utilizes blasting for rock demolition. Therefore, it is crucial to assess the impact of blasting activities on slope stability. This study investigates the influence of blasting on slope stability in coal mines using the limit equilibrium method (LEM). The study evaluates the effects of factors such as ground vibration, blast distance, and blast hole count on the factor of safety (FoS) of slopes. The limit equilibrium method (Fellenius, Bishop, Janbu, Spencer, and Morgenstern-Price) is employed to determine the factor of safety. The factor of safety is modeled using RocScience SLIDE version 6.0 in this study. The factor of safety (FoS) is defined as the ratio of the stabilizing force to the destabilizing force acting on the slope. This study also models the influence of ground vibration, distance, and total number of blast holes on the factor-of-safety (FoS) value. The results indicate that the slope remains stable both pre- and post-blasting, with an overall FoS value greater than 1 for the five slopes examined using various limit equilibrium method (LEM) techniques. However, the FoS value decreased prior to blasting due to the impact of ground vibration and blast distance. It is evident that the ground vibration (PPA) increases with the number of blast holes. The amount of ground vibration decreases as the number of blast holes increases. An increased number of blast holes leads to a decrease in the FoS value. The observed decline in slope FoS values and the increase in PPAs is attributable to the growing number of blast holes. The type of explosive, along with its power and rate of detonation, influences the amount of energy produced, which in turn affects the degree of ground vibration. The findings indicate that the slopes remain stable (FoS > 1) both before and after blasting, although blasting slightly reduces the FoS. The study reveals that as the number of blast holes increases, both ground vibration (PPA) and the reduction in FoS increase, underscoring the effects of explosive power and detonation rate on slope stability. Full article

Background: In the transition to a professional learning environment, healthcare professionals in their first year of specialized postgraduate clinical training (known as residents in Spain) are suddenly required to handle confidential information with little or no prior training in the safe and appropriate use of digital media with respect to confidentiality issues. The aims of this study were: (1) to explore the usefulness of an advanced clinical simulation program for educating residents from different healthcare disciplines about confidentiality and the dissemination of clinical data or patient images; (2) to explore the use of social networks in healthcare settings; and (3) to explore participants’ knowledge and attitudes on current regulations regarding confidentiality, image dissemination, and the use of social networks; Methods: This was a cross-sectional study. Data were collected from all 49 first-year residents of different health professions at a Spanish hospital between June and August 2022. High-fidelity clinical simulation sessions designed to address confidentiality and health information dissemination issues in hospital settings, including the use of social networks, were developed and implemented. Data were assessed using a 12-item ad hoc questionnaire on confidentiality and the use of social media in the healthcare setting. Descriptive of general data and chi-square test or Fisher’s exact test were performed using the SPSS 25.0 software; Results: All the participants reported using the messaging application WhatsApp regularly during their working day. A total of 20.4% of the participants stated that they had taken photos of clinical data (radiographs, analyses, etc.) without permission, with 40.8% claiming that they were unaware of the legal consequences of improper access to clinical records. After the course, the participants reported intending to modify their behavior when sharing patient data without their consent and with respect to how patients are informed; Conclusions: The use of advanced simulation in the training of interprofessional teams of residents is as an effective tool for initiating attitudinal change and increasing knowledge related to patient privacy and confidentiality. Further follow-up studies are needed to see how these attitudes are incorporated into clinical practice. Full article

Oily sludge (OS) has long been regarded as a hazardous waste, and improper disposal may lead to serious environmental concerns and human health risks. Despite various methods having been proposed and applied to the treatment of OS, the oil occurrence states and properties in sludge are rarely characterized, which may directly link to the selection and effectiveness of treatment methods. Here, confocal laser scanning microscopy (CLSM), X-ray diffraction (XRD), gas chromatography (GC), and four components (SARA) analysis were utilized to characterize the changes in the oil occurrence states and compositions in OS samples before and after high-speed stirring (HSS) treatment. Our results show a substantial reduction in the oil concentration of OS after HSS treatment (from 32.98% to 1.65%), while SARA analysis reveals a similar oil composition before and after treatment, suggesting the broad applicability of HSS in removing oil and its insignificant selectivity towards various hydrocarbon components. This is further supported by the total petroleum hydrocarbon (TPH) analysis results, which show that the separated oil phase has a hydrocarbon composition similar to that of the original OS sample. The CLSM and fluorescence analysis suggest a homogeneous distribution of oil in the sludge, with relatively light components more concentrated in the pore systems between coarse mineral particles, whereas relatively heavy components tend to coexist with clay minerals. After HSS cleaning, both light and heavy components are removed to varying degrees, but light components are preferentially removed while heavy components tend to be retained in the sludge due to adsorption by clay minerals. This is consistent with TPH analysis, where a significant decrease in n-alkanes with lower carbon numbers (n-C14 to n-C20) was observed in the residual sample. Our findings demonstrate the dynamic response of oil occurrence states and compositions to the OS treatment process and highlight the importance of characterizing these fundamental properties prior to the selection of OS treatment methods. Full article

In this investigation, a synthesis of Convolutional Neural Networks (CNNs) and Bayesian inference is presented, leading to a novel approach to the problem of Multiple Hypothesis Testing (MHT). Diverging from traditional paradigms, this study introduces a sequence-based uncalibrated Bayes factor approach to test many hypotheses using the same family of sampling parametric models. A two-step methodology is employed: initially, a learning phase is conducted utilizing simulated datasets encompassing a wide spectrum of null and alternative hypotheses, followed by a transfer phase applying this fitted model to real-world experimental sequences. The outcome is a CNN model capable of navigating the complex domain of MHT with improved precision over traditional methods, also demonstrating robustness under varying conditions, including the number of true nulls and dependencies between tests. Although indications of empirical evaluations are presented and show that the methodology will prove useful, more work is required to provide a full evaluation from a theoretical perspective. The potential of this innovative approach is further illustrated within the critical domain of genomics. Although formal proof of the consistency of the model remains elusive due to the inherent complexity of the algorithms, this paper also provides some theoretical insights and advocates for continued exploration of this methodology. Full article

Binaural beats are a low-frequency form of acoustic stimulation that may be heard between 200 and 900 Hz and can help reduce anxiety as well as alter other psychological situations and states by affecting mood and cognitive function. However, prior research has only looked at the impact of binaural beats on state and trait anxiety using the STA-I scale; the level of anxiety has not yet been evaluated, and for the removal of artifacts the improper selection of wavelet parameters reduced the original signal energy. Hence, in this research, the level of anxiety when hearing binaural beats has been analyzed using a novel optimized wavelet transform in which optimized wavelet parameters are extracted from the EEG signal using the flower pollination algorithm, whereby artifacts are removed effectively from the EEG signal. Thus, EEG signals have five types of brainwaves in the existing models, which have not been analyzed optimally for brainwaves other than delta waves nor has the level of anxiety yet been analyzed using binaural beats. To overcome this, deep convolutional neural network (CNN)-based signal processing has been proposed. In this, deep features are extracted from optimized EEG signal parameters, which are precisely selected and adjusted to their most efficient values using the flower pollination algorithm, ensuring minimal signal energy reduction and artifact removal to maintain the integrity of the original EEG signal during analysis. These features provide the accurate classification of various levels of anxiety, which provides more accurate results for the effects of binaural beats on anxiety from brainwaves. Finally, the proposed model is implemented in the Python platform, and the obtained results demonstrate its efficacy. The proposed optimized wavelet transform using deep CNN-based signal processing outperforms existing techniques such as KNN, SVM, LDA, and Narrow-ANN, with a high accuracy of 0.99%, precision of 0.99%, recall of 0.99%, F1-score of 0.99%, specificity of 0.999%, and error rate of 0.01%. Thus, the optimized wavelet transform with a deep CNN can perform an effective decomposition of EEG data and extract deep features related to anxiety to analyze the effect of binaural beats on anxiety levels. Full article

Contaminants of concern (CoCs) pose significant threats to Uganda’s ecosystems and public health, particularly in the face of rapid urbanization, industrial expansion, and intensified agriculture. This systematic review comprehensively analyzed Uganda’s CoC landscape, addressing imminent challenges that endanger the country’s ecosystems and public health. CoCs, originating from urban, industrial, and agricultural activities, encompass a wide range of substances, including pharmaceuticals, personal care products, pesticides, industrial chemicals, heavy metals, radionuclides, biotoxins, disinfection byproducts, hydrocarbons, and microplastics. This review identified the major drivers of CoC dispersion, particularly wastewater and improper waste disposal practices. From an initial pool of 887 articles collected from reputable databases such as PubMed, African Journal Online (AJOL), Web of Science, Science Direct, and Google Scholar, 177 pertinent studies were extracted. The literature review pointed to the presence of 57 pharmaceutical residues and personal care products, along with 38 pesticide residues and 12 heavy metals, across various environmental matrices, such as wastewater, groundwater, seawater, rainwater, surface water, drinking water, and pharmaceutical effluents. CoC concentrations displayed significant levels exceeding established regulations, varying based on the specific locations, compounds, and matrices. This review underscores potential ecological and health consequences associated with CoCs, including antibiotic resistance, endocrine disruption, and carcinogenicity. Inefficiencies in traditional wastewater treatment methods, coupled with inadequate sanitation practices in certain areas, exacerbate the contamination of Uganda’s aquatic environments, intensifying environmental and health concerns. To address these challenges, advanced oxidation processes (AOPs) emerge as promising and efficient alternatives for CoC degradation and the prevention of environmental pollution. Notably, no prior studies have explored the management and mitigation of these contaminants through AOP application within various aqueous matrices in Uganda. This review emphasizes the necessity of specific regulations, improved data collection, and public awareness campaigns, offering recommendations for advanced wastewater treatment implementation, the adoption of sustainable agricultural practices, and the enforcement of source control measures. Furthermore, it highlights the significance of further research to bridge knowledge gaps and devise effective policies and interventions. Ultimately, this comprehensive analysis equips readers, policymakers, and regulators with vital knowledge for informed decision-making, policy development, and the protection of public health and the environment. Full article

The ensemble Kalman filter is often used in parameter estimation, which plays an essential role in reducing model errors. However, filter divergence is often encountered in an estimation process, resulting in the convergence of parameters to the improper value and finally in parameter estimation failure. To alleviate this degeneration, various covariance inflation schemes have been proposed. In this study, I examined six currently used inflation schemes: fixed inflation, conditional covariance inflation, modified estimated parameter ensemble spread, relaxation-to-prior perturbations, relaxation-to-prior spread, and new conditional covariance inflation. The six schemes were thoroughly explored using the Zebiak–Cane model and the local ensemble transform Kalman filter in the observing system simulation experiment framework. Emphasis was placed on the comparison of these schemes when it came to estimating single and multiple parameters in terms of oceanic analyses and resultant El Niño–Southern Oscillation (ENSO) predictions. The results showed that the new conditional covariance inflation scheme had the best results in terms of the estimated parameters, resultant state analyses, and ENSO predictions. In addition, the results suggested that better parameter estimation yields better state simulations, resulting in improved predictions. Overall, this study provides viable information for selecting inflation schemes for parameter estimation, offering theoretical guidance for constructing operational assimilation systems. Full article

The wavelet threshold functions are widely used in oil chromatography denoising because high-quality signals are the basis for Dissolved Gas Analysis (DGA), which determines the accuracy of transformer fault monitoring. However, there are certain limitations of the wavelet threshold functions, such as the Pseudo-Gibbs phenomenon and improper threshold selection. To this purpose, a modified genetic particle swarm optimization-based improved threshold function denoising method (MGPSO-ITF) is proposed. Specifically, the method constructs a new parametric threshold function that possesses high-order derivability and a small constant deviation. To obtain optimal values for the tunable parameters, MGPSO is employed, which outperforms other methods in identifying the optimum and achieving fast convergence. The simulation results demonstrate that the enhanced thresholding function yields a higher Signal-to-Noise Ratio (SNR), higher Noise Suppression Ratio (NSR), and smaller Root Mean Square Error (RMSE) compared to prior methods. Specifically, for the originally relatively smooth signal, MGPSO-ITF does not over-correct it to cause distortion. Furthermore, experiments on measured signals illustrate that the MGPSO-ITF is highly effective at denoising and preserving the original signal properties. Particularly in cases where peak deformation is prominent, the algorithm outperforms both hard and soft thresholding methods, achieving a reduction of 2.934% and 1.029% in peak area error, respectively. Full article

Muslim scholars are not unanimous on the issue of the application of stunning in the halal slaughtering of animals. Appropriate stunning makes animals unconscious instantaneously, thus avoiding unnecessary pain and stress during the slaughtering of animals. The present review comprehensively summarizes the available scientific literature on stunning methods in view of their halal compliance during the slaughter of animals. The issue of maximum blood loss, reversibility of consciousness, and animals remaining alive during the halal cut are the key determinants of approval of stunning in the halal slaughter. Further, missed stuns due to poor maintenance of equipment, improper applications, and poor restraining necessitates additional stunning attempts, which further aggravates pain and stress in animals. Scientific findings suggest that halal-compliant stunning technologies are reversible, do not kill animals prior to the halal cut, and do not obstruct blood loss. There is a need to carry out further research on the refinement of available stunning technologies and their application, proper restraints, proper identification of the death status of animals, and assurance of animal welfare in commercial halal meat production. Full article