38139 research outputs found
Sort by
Cadmium toxicity, health risk and its remediation using low-cost biochar adsorbents
Get PDFAbstract: Cadmium induces toxicity to both flora and fauna,
even when it is present in trace amounts. Electroplating, pigments, smelting, mining, alloy production, plastic, cadmium–nickel batteries, fertilizers, pesticides, paint, synthesis of dye,textile operations, and refining sectors all release cadmium into the aquatic environment.“Solvent extraction, adsorption, ion exchange, and precipitation” are a few strategies for removing cadmium. Biochar is an inexpensive and sustainable adsorbent that has proven to be an efficacious adsorbent for the recovery of Cd(II) from water. This study discusses the toxicity of cadmium as well as some recent developments of pristine biochar and modified biochar for the elimination of cadmium (Cd) from aqueous solution
DMPat-based SOXFE: investigations of the violence detection using EEG signals
Get PDFAutomatic violence detection is one of the most important research areas at the intersection of machine learning and information security. Moreover, we aimed to investigate violence detection in the context of neuroscience. Therefore, we have collected a new electroencephalography (EEG) violence detection dataset and presented a self-organized explainable feature engineering (SOXFE) approach. In the first phase of this research, we collected a new EEG violence dataset. This dataset contains two classes: (i) resting, (ii) violence. To detect violence automatically, we proposed a new SOXFE approach, which contains five main phases: (1) feature extraction with the proposed distance matrix pattern (DMPat), which generates three feature vectors, (2) feature selection with iterative neighborhood component analysis (INCA), and three selected feature vectors were created, (3) explainable results generation using Directed Lobish (DLob) and statistical analysis of the generated DLob string, (4) classification deploying t algorithm-based k-nearest neighbors (tkNN), and (5) information fusion employing mode operator and selecting the best outcome via greedy algorithm. By deploying the proposed model, classification and explainable results were generated. To obtain the classification results, tenfold cross-validation (CV), leave-one-record-out (LORO) CV were utilized, and the presented model attained 100% classification accuracy with tenfold CV and reached 98.49% classification accuracy with LORO CV. Moreover, we demonstrated the cortical connectome map related to violence. These results and findings clearly indicated that the proposed model is a good violence detection model. Moreover, this model contributes to feature engineering, neuroscience and social security
Deep learning techniques for automated coronary artery segmentation and coronary artery disease detection: A systematic review of the last decade (2013-2024)
No full textBackground
Coronary artery disease (CAD) is the most common cardiovascular disease, exacting high morbidity and mortality worldwide. CAD is detected on coronary artery imaging; coronary artery segmentation (CAS) of the images is essential for coronary lesion characterization. Both CAD detection and CAS require expert input, are labor-intensive, and error-prone.
Objectives
Deep learning (DL) techniques have achieved significant success in CAS and CAD detection, with many studies published recently. This study is an up-to-date systematic review of research on automated DL models for CAS and CAD detection in the past decade (2013-2024).
Method
Using PRISMA methodology, an initial literature search of 1,589 publications was conducted, from which 97 high-impact Q1 studies were selected based on pre-defined eligibility criteria. These studies were analyzed in terms of DL techniques employed, datasets, modalities, and performance metrics.
Results
Of the 97 studies, most of which were published after 2016, 47 focused on CAS, 49 on CAD detection, and one on both tasks. CNN-based models were dominant in both domains. For CAS, CCTA was the most frequently used input modality, and U-Net was employed in 38 out of 48 studies, with recent works incorporating attention mechanisms and graph neural networks. ASOCA was the most widely used benchmark dataset. For CAD detection, ECG was the most common modality, with 45 out of 50 studies utilizing CNNs, and 20 of those relying purely on CNN architectures. Hybrid and multimodal models have become more prominent in recent years.
Conclusion
This review identified several challenges, including limited public datasets, variability in performance metrics, and model complexity. Future studies should focus on larger, diverse datasets and lightweight models integrating explainable artificial intelligence and uncertainty quantification to improve clinical applicability
Effects of flocculants on in–situ recycling potential of waste EPB shield muck with residual foams
Get PDFThe in–situ recycling technique offers a promising solution for the efficient management of the escalating volumes of waste muck or slurry generated during shield tunneling. While foam is extensively utilized for soil conditioning in earth pressure balance (EPB) tunneling, the effects of organic and inorganic flocculants on the in–situ recycling potential of waste EPB shield muck containing residual foams remain underexplored. To bridge this gap, laboratory experiments were conducted using four flocculants: cationic polyacrylamide (CPAM), nonionic polyacrylamide (NPAM), anionic polyacrylamide (APAM), and polyaluminum chloride (PACL), with an enhanced flocculation and press–filtration apparatus. The defoaming–flocculation–dewatering behavior of waste EPB shield muck was systematically investigated by evaluating key parameters, including residual foam height, defoaming ratio, antifoaming ratio, total suspended solids, turbidity, moisture content, and zeta potential, while accounting for both muck dry mass and fines content. Moreover, the microscopic structure of flocculates and filter cakes was characterized using nanoparticle size analysis and scanning electron microscopy. The experimental results reveal that CPAM exhibits constrained flocculation and dewatering efficiency, primarily attributed to diminished charge neutralization resulting from residual anionic surfactants. In contrast, PACL demonstrates superior dewatering performance compared to NPAM and APAM, but exhibits the lowest flocculation and foam–suppression efficiency. Additionally, a consistent linear negative correlation is identified between the flocculation and dewatering indices of EPB shield muck, independent of the flocculant type, whether organic or inorganic
Predicting the uplift capacity of circular anchors in frictional-cohesive soils using Kolmogorov-Arnold networks
Get PDFThis study investigates the uplift capacity of circular anchors embedded in frictional-cohesive soils under surcharge. The analysis focuses on three critical stability factors Fc, Fq, and Fγ using Terzaghi’s principle of superposition to evaluate ultimate bearing capacity. These factors are influenced by the soil’s internal friction angle, the geometric ratio of anchor depth to diameter, and the interface roughness between the anchor and soil. Three predictive models for these stability factors are developed using advanced computational methods, including finite element limit analysis (FELA) with adaptive meshing and Kolmogorov-Arnold Networks (KAN). This research is the first to apply KAN in anchor behavior studies, demonstrating its enhanced ability to model complex data relationships compared to artificial neural networks (ANN). Additionally, a closed-form solution for stability factors is derived through KAN, providing an efficient method for predicting bearing capacity. The optimized models exhibit high coefficient of determination (R²) values and low root mean square errors (RMSE) for training and testing datasets. Sensitivity analysis validates the robustness of the proposed models. These findings advance the understanding of circular anchors’ bearing capacity in frictional-cohesive soils, offering practical design insights for various soil conditions
Assessing misalignment effects on undrained HV capacity of caisson anchors in heterogeneous clays using a gradient Boosting–Differential evolution framework
No full textMisalignment is a critical factor influencing the horizontal-vertical (HV) load capacity of caisson foundation anchors. This study employs Finite Element Limit Analysis (FELA) integrated with a Gradient Boosting–Differential Evolution (GB-DE) framework to systematically investigate the impact of misalignment angles (β = 0°–90°) across varying embedment ratios (L/D) and soil heterogeneity levels (κ). Results reveal that the HV capacity increases with the load inclination angle β. This effect is amplified with greater embedment depth and influenced by the degree of soil heterogeneity, highlighting the complex interaction between geometric and geotechnical parameters. The findings demonstrate that misalignment can enhance soil-structure interaction, leading to increased resistance capacity. The GB-DE model achieves high predictive accuracy (R2 = 0.999), highlighting its effectiveness in optimizing caisson anchor performance under misaligned conditions. These insights emphasize the importance of incorporating misalignment considerations into anchor design to improve load-bearing efficiency and structural stability
Co-designed resources to improve sleep health in young shiftworkers: a qualitative study
Get PDFIntroduction
Shiftwork contributes to poor sleep and circadian disruption, leading to adverse health outcomes. With a high number of young adults (18–25 years) engaging in shiftwork, exposure to these health challenges can occur from early adulthood. Therefore, there is an urgent need for age- and career-stage-appropriate resources focused on sleep health to mitigate poor health outcomes as early as possible. Given the absence of such resources, this study aimed to develop tailored, evidence-based sleep health materials for young shiftworkers.
Methods
A participatory approach was employed, with co-designers (n = 48) attending 1–2 online workshops to develop sleep health resources for young shiftworkers. Co-designers included young, experienced, and previous shiftworkers, workplace health and safety experts, and science communications specialists, who worked alongside academic experts. Workshops explored which sleep health topics co-designers believe are important for young shiftworkers. Reflexive thematic analysis of workshop transcripts identified key themes, which were aligned with current scientific evidence, forming both the structure and content of the resulting sleep health resources for young shiftworkers.
Results
Analysis of workshop transcripts identified five themes: sleep science, impacts of poor sleep, habits impacting sleep, strategies to improve sleep, and recommendations for workplaces. Themes were populated with evidence-based information to develop a website, a pictorial infographic, an animated video, and a social media presence.
Discussion
Tailored, evidence-based sleep health resources for young shiftworkers were co-designed, with qualitative data elucidating individual and work-related sleep health themes. Future studies should evaluate the resources to determine their impact on knowledge and behaviour
Retention patterns of the public sector nursing and midwifery workforce in regional and rural settings of southern Queensland, Australia: a 12-year retrospective analysis
Get PDFBackground: The aims of this study were to investigate: (a) Specific time points of exit and time spent working in
location of the public sector nursing and midwifery workforce in regional, rural, and remote southern Queensland;
and (b) the influence of selected demographic, geographic, and employment variables on the risk of leaving a
location.
Methods: A retrospective cohort design was employed using the employment records of 3234 public sector nurses
and midwives between January 2010 and December 2021. Employment records were analysed using survival analysis
and Coxs proportional hazards regression, using the Andersen-Gill method to account for the inclusion of multiple
records for some employees.
Results: Study results revealed an overall median survival time of 1.83 years for public sector nursing and midwifery
professionals. Registered Nurses were the predominant group employed, yet they also exhibited high turnover rates.
Nurses and midwives in permanent full-time positions were more likely to leave location than those in part-time roles.
Conclusions Retention of nursing and midwifery professionals in rural Queensland is notably low, with high
turnover rates among younger nurses and midwives and those in full-time positions. This study underscores the
need for targeted retention strategies, such as flexible work arrangements, improved workplace conditions, and
comprehensive professional development programs. Results indicate the need to focus nursing and midwifery
workforce retention strategies within 12–18 months post recruitment to retain staff to avoid the current pattern of
staff turnover
The accuracy of simple, feasible alternatives to polysomnography for assessing sleep in intensive care: An observational study
No full textBackground
Sleep disturbance is common in intensive care patients. Understanding the accuracy of simple, feasible sleep measurement techniques is essential to informing their possible role in usual clinical care.
Objective
The aim of the study was to investigate whether sleep monitoring techniques such as actigraphy (ACTG), behavioural assessments, and patient surveys are comparable with polysomnography (PSG) in accurately reporting sleep quantity and quality among conscious, intensive care patients.
Methods
An observational study was conducted in 20 patients admitted to the intensive care unit (ICU) for a minimum duration of 24 h, who underwent concurrent sleep monitoring via PSG, ACTG, nursing-based observations, and self-reported assessment using the Richards–Campbell Sleep Questionnaire.
Results
The reported total sleep time (TST) for the 20 participants measured by PSG was 328.2 min (±106 min) compared with ACTG (362.4 min [±62.1 min]; mean difference = 34.22 min [±129 min]). Bland–Altman analysis indicated that PSG and ACTG demonstrated clinical agreement and did not perform differently across a number of sleep variables including TST, awakening, sleep-onset latency, and sleep efficiency. Nursing observations overestimated sleep duration compared to PSG TST (mean difference = 9.95 ± 136.3 min, p > 0.05), and patient-reported TST was underestimated compared to PSG TST (mean difference = −51.81 ± 144.1 7, p > 0.05).
Conclusions
Amongst conscious patients treated in the ICU, sleep characteristics measured by ACTG were similar to those measured by PSG. ACTG may provide a clinically feasible and acceptable proxy approach to sleep monitoring in conscious ICU patients