Hasil untuk "Hazardous substances and their disposal"

Yaoqi Huang, Julie Stephany Berrio, Mao Shan et al.

Maintaining situational awareness in complex driving scenarios is challenging. It requires continuously prioritizing attention among extensive scene entities and understanding how prominent hazards might affect the ego vehicle. While existing studies excel at detecting specific semantic categories and visually salient regions, they lack the ability to assess safety-relevance. Meanwhile, the generic spatial predicates either for foreground objects only or for all scene entities modeled by existing scene graphs are inadequate for driving scenarios. To bridge this gap, we introduce a novel task, Traffic Scene Graph Generation, which captures traffic-specific relations between prominent hazards and the ego vehicle. We propose a novel framework that explicitly uses traffic accident data and depth cues to supplement visual features and semantic information for reasoning. The output traffic scene graphs provide intuitive guidelines that stress prominent hazards by color-coding their severity and notating their effect mechanism and relative location to the ego vehicle. We create relational annotations on Cityscapes dataset and evaluate our model on 10 tasks from 5 perspectives. The results in comparative experiments and ablation studies demonstrate our capacity in ego-centric reasoning for hazard-aware traffic scene understanding.

Dimitar Nedanovski, Svetoslav Nenov, Dimitar Pilev

We study first-hitting times in Differential Evolution (DE) through a conditional hazard frame work. Instead of analyzing convergence via Markov-chain transition kernels or drift arguments, we ex press the survival probability of a measurable target set $A$ as a product of conditional first-hit probabilities (hazards) $p_t=\Prob(E_t\mid\mathcal F_{t-1})$. This yields distribution-free identities for survival and explicit tail bounds whenever deterministic lower bounds on the hazard hold on the survival event. For the L-SHADE algorithm with current-to-$p$best/1 mutation, we construct a checkable algorithmic witness event $\mathcal L_t$ under which the conditional hazard admits an explicit lower bound depending only on sampling rules, population size, and crossover statistics. This separates theoretical constants from empirical event frequencies and explains why worst-case constant-hazard bounds are typically conservative. We complement the theory with a Kaplan--Meier survival analysis on the CEC2017 benchmark suite . Across functions and budgets, we identify three distinct empirical regimes: (i) strongly clustered success, where hitting times concentrate in short bursts; (ii) approximately geometric tails, where a constant-hazard model is accurate; and (iii) intractable cases with no observed hits within the evaluation horizon. The results show that while constant-hazard bounds provide valid tail envelopes, the practical behavior of L-SHADE is governed by burst-like transitions rather than homogeneous per-generati on success probabilities.

Divina Navarro, Ben Hoffmann, Wenchao Lu et al.

Per- and polyfluoroalkyl substances (PFAS) are persistent contaminants that pose risks to ecological and human health. Soil stabilisation using sorbents such as granular activated carbon (GAC) can reduce PFAS mobility and bioavailability. Previous studies have focused on plants and earthworms, but bioavailability in species relevant to arid and semi-arid environments remains poorly understood. This study examined the effectiveness of GAC in reducing PFAS bioavailability to tropical fire ants (Solenopsis geminata). Two PFAS-contaminated soils were amended with 1 % or 5 % (w/w) GAC, incubated, then subjected to 5-day and 2-month ant exposure trials. Results showed that GAC reduced leachable ∑29PFAS by 73–100 %, with greater reductions at later post-treatment leaching assessments and at 5 % GAC. PFAS exposure in untreated soils impaired ant colony growth, whereas GAC addition mitigated these effects and reduced PFAS concentrations in ants by < 97 %, with the greatest reductions observed in the sandy soil, consistent with leaching results. Non-target PFAS detected in ants collected from untreated soils were not detected in ants from GAC-treated soils, indicating GAC's broad sorption performance. Risk quotients calculated suggest that GAC can substantially lower potential risk to mammals and birds that feed on ants. Overall, findings underscore the value of soil stabilisation strategies, especially in ecosystems where invertebrates influence contaminant exposure.

Гладких, Е.О., Пермякова, И.А., Кузнецова, Ю.В. et al.

Фосфаты магния представляют большой интерес как основной компонент остеокондуктивных материалов, подвергающихся в организме резорбции и обладающих свойством ангиогенеза, и при этом желаемым свойством таких материалов является высокая удельная площадь поверхности. Представлены результаты исследования процесса получения мезопористого фосфата магния из струвита бесшаблонным методом при различных условиях. Поверхностные свойства мезопористых продуктов исследованы методом БЭТ, определены размер и объемы пор. Показано влияние температуры, времени термической обработки и метода получения струвита на характеристики получаемого продукта. Состав и структура продукта с наилучшими свойствами исследована с применением рентгенофазового анализа и капиллярного электрофореза. Метод получения струвита как материала-предшественника оказывает значительное влияние на возможность получения продукта с развитой поверхностью.

Melanie Sartore-Baldwin, Bhibha Das

Background: The purpose of this work is to examine physical activity levels of volunteers who walk shelter dogs at an open-admission animal shelter in the southern United States.  In doing so, shelter dog walking is presented as an activity of relational community engagement that can enhance well-being at all levels.   Methods: For this quasi-experimental study, a purposive sample of volunteer dog walkers in rural North Carolina was asked to record daily activity for a twelve-week period, with a total of 336 days submitted. Descriptive statistics and a paired-samples t-test were analyze the data. Results:  Data from a total of 336 days was collected.  Paired-samples t-test was performed to compare outcomes assessed on days shelter dogs were not walked and days they were walked by volunteers.  Significant differences were demonstrated for the steps taken (t(154) = 9.5, p < .001), the distance walked (t(154) = 9.0, p < .001), and the calories expended (t(154) = 5.2, p < .001).  Conclusion:  The implications of these findings are multi-level and suggest walking shelter dogs can be a beneficial activity for all parties involved.  At the micro-level, volunteers walked further, burned more calories, and accumulated more steps on days they walked shelter dogs.  In turn, shelter staff and the local community benefited at the meso- and macro-levels, respectively.

Maximilian Stargardt, Justus Hugenberg, Christoph Winkler et al.

Due to climate change, natural hazards that affect energy infrastructure will become more frequent in the future. However, to incorporate natural hazard risk into infrastructure investment decisions, we develop an approach to translate this risk into discount rates. Thus, our newly developed discount rate approach incorporates both economic risk and natural hazard risk. To illustrate the impact of including the risk of natural hazards, we apply country-specific discount rates for hydrogen production costs. The country-specific relative difference in hydrogen generation cost ranges from a 96% surplus in the Philippines to a -63% cost reduction in Kyrgyzstan compared to a discount rate that only consists of economic risks. The inclusion of natural hazard risk changes the cost ranking of technologies as outcome of energy system models and thus policy recommendations. The derived discount rates for 254 countries worldwide are published in this publication for further use.

Jaya Sreevalsan-Nair, Aswathi Mundayatt

Data-driven susceptibility mapping of natural hazards has harnessed the advances in classification methods used on heterogeneous sources represented as raster images. Susceptibility mapping is an important step towards risk assessment for any natural hazard. Increasingly, multiple hazards co-occur spatially, temporally, or both, which calls for an in-depth study on multi-hazard susceptibility mapping. In recent years, single-hazard susceptibility mapping algorithms have become well-established and have been extended to multi-hazard susceptibility mapping. Deep learning is also emerging as a promising method for single-hazard susceptibility mapping. Here, we discuss the evolution of methods for a single hazard, their extensions to multi-hazard maps as a late fusion of decisions, and the use of deep learning methods in susceptibility mapping. We finally propose a vision for adapting data fusion strategies in multimodal deep learning to multi-hazard susceptibility mapping. From the background study of susceptibility methods, we demonstrate that deep learning models are promising, untapped methods for multi-hazard susceptibility mapping. Data fusion strategies provide a larger space of deep learning models applicable to multi-hazard susceptibility mapping.

Kishore Kumar Jagadeesan, Harry Elliss, Richard Standerwick et al.

In this proof-of-concept study, a new mass spectrometry-based framework was introduced for concurrent tracking of infectious disease prevalence and community responses. The study focused on the detection of SARS-CoV-2 as the test pathogen and C-reactive protein (CRP) as the representative acute phase response protein. Through mass spectrometry (MS), the research provided preliminary insights into the prevalence of the virus and community acute immune responses, suggesting its strong potential as an early warning system. The high specificity and sensitivity of MS, combined with wastewater-based epidemiology's ability to provide a population-level perspective on virus prevalence, make it a valuable tool for public health surveillance. The study's findings demonstrate the utility of targeted proteomics technology in detecting specific protein biomarkers associated with SARS-CoV-2 infection and inflammation in complex wastewater samples. This approach has advantages over traditional RNA-based methods, including the ability to simultaneously detect acute-phase response proteins such as CRP. The study lays the foundation for future research towards refining analytical techniques to extract more precise data from complex matrices. Synopsis: WBE proteomics holds a strong potential in wastewater surveillance for pathogens and disease outcomes

Banafsheh Abdi, Azam Alavi

Background: Nurses continuously face workaholism and work engagement that affect the professional performance of nurses. The aim of this study was to investigate the relationship between work engagement and workaholism with nurses' job well-being. Methods: This was a descriptive correlation study. Two hundred nurses from Lahijan Hospitals were selected by convenience sampling method in 1399. Data were collected using three standard questionnaires of work engagement, workaholism, and job well-being. Data analysis was used using SPSS21 software using descriptive statistics (mean, standard deviation, percentage) and inferential methods (Single regression test). Results: The results of the study showed that work engagement and workaholism can predict the job well-being of nurses in Lahijan hospitals (p<0.001). Among the dimensions of job well-being, the results showed that workaholism is negatively related to the job satisfaction of nurses(p=0.03), but has a positive relationship with the dimensions of perceived stress and nurses' sleep problems(p<0.001). Among the dimensions of job well-being, work engagement can predict positive the job satisfaction of nurses(p<0.001), but cannot predict the perceived stress and the nurses' sleep problems(p>0.05). Conclusion: According to the results of the study, work engagement and workaholism are good predictors for nurses' job well–being. Therefore, it is suggested that managers help to enhance nurses' job well-being by improving the motivated and vibrant work environment, creating work engagement, and reducing work addiction.

Mohammad Hossein Sorbi, Nahid Ardian, Saeedeh Qane-Mokhlesoon et al.

Background: In Iranian courts, counseling centers, and research institutes, the Family Emotional Atmosphere Questionnaire (FEAQ) by Navardgahfard in 1994 is widely used to assess the status of marital relationships. However, the FEAQ's contents are outdated, containing irrelevant questions, and lacking valid psychometric properties. Therefore, the aim of the present study is to revise and validate the psychometric properties of the Emotional Climate Scale for Couples (ECSC). Methods: This study was descriptive-correlational in nature and had practical implications. It was conducted from January 17th to July 30th, 2023. All divorce applicants referred by the courts to family counseling centers were included in the study population. Initially, the FEAQ was extracted from the website and modified to consist of 21 questions related to emotional climate between couples. Results: Subsequently, the face validity of ECSC was confirmed by experts, and exploratory factor analysis (N=307) demonstrated two subscales of lack of feelings and cooperation, and forced with limitations, explaining 63.26% of the variance in the 21 ECSC items. Confirmatory factor analysis (N=205) also indicated an acceptable fit for the ECSC model and showed good construct validity. The test-retest reliability (N=35, with a three-week interval) was 0.83 for the total scale, 0.82 and 0.81 for the two subscales, respectively, while Cronbach's alpha coefficients for the two factors were 0.93 to 0.94 and 0.96 for the total scale. Conclusion: Therefore, ECSC is a reliable and valid tool for assessing the emotional climate between couples and has wide applicability.

Agatha Natasya Putri, Harvan Akhmad Audi, Prasetya Fierza Rizky

Municipal waste management in Indonesia, particularly Jakarta, poses a significant environmental challenge. Jakarta has been relying on the Bantargebang landfill to address its waste disposal needs for many years. Due to the persistent accumulation of waste, the Bantargebang landfill nears its maximum capacity. In response, a Waste-to-Energy (WtE) power plant was introduced in Bantargebang, serving as a pioneering initiative for WtE technology implementation in Indonesia. The Bantargebang WtE Plant employs incineration technology to convert municipal waste into electricity. Despite its usefulness, there are general environmental concerns about WtE plants, specifically focusing on their emissions and the potential presence of hazardous substances This research assesses the Bantargebang WtE Plant’s performance based on 2022 operational data, specifically examining waste reduction efforts and comparing incineration byproducts, including FABA (Fly Ash, Bottom Ash), and flue gas emissions, against government standards. The study indicates the plant can reduce waste mass by 96.5%. Furthermore, the WtE plant’s byproducts align with government standards for flue gas emissions and FABA residue. These results emphasize the potential of large-scale WtE power plants to achieve sustainable waste management goals in Indonesia. Nonetheless, there are opportunities for maximising waste accumulation reduction performance and enhancing operational value of WtE plant.

Regan Mudhar, Dann M. Mitchell, Peter A. Stott et al.

The field of Detection and Attribution is rapidly moving beyond weather and climate, and towards incorporating hazards and their impacts on natural and human systems. Here, we review the comprehensive literature base relevant for the UK ahead of the next Climate Change Risk Assessment. The current literature highlights a detectable and non-trivial influence of climate change in many UK impact sectors already - notably health, agriculture, and infrastructure. We found that heatwaves were the most studied hazard overall, with a unanimous consensus on a strong attributable signal of human-induced climate change in their increased frequency and intensity over the last century. The most notable gap identified overall was in attributing climate-related impacts to human influence, with a few impact studies for only a handful of the hazards assessed. Furthermore, just under half of the 29 hazards were not found to have any UK-relevant attribution studies, with most of the remainder having three or fewer. This review highlights requirements for and opportunities to develop attribution scicnce to meet the needs of the UK. Diversifying hazards and impacts studied, in conjunction with the techniques and approaches used, will undoubtedly benefit the community.

Lukas Picek, Vojtěch Čermák, Marek Hanzl

This paper presents our submission to the COOOL competition, a novel benchmark for detecting and classifying out-of-label hazards in autonomous driving. Our approach integrates diverse methods across three core tasks: (i) driver reaction detection, (ii) hazard object identification, and (iii) hazard captioning. We propose kernel-based change point detection on bounding boxes and optical flow dynamics for driver reaction detection to analyze motion patterns. For hazard identification, we combined a naive proximity-based strategy with object classification using a pre-trained ViT model. At last, for hazard captioning, we used the MOLMO vision-language model with tailored prompts to generate precise and context-aware descriptions of rare and low-resolution hazards. The proposed pipeline outperformed the baseline methods by a large margin, reducing the relative error by 33%, and scored 2nd on the final leaderboard consisting of 32 teams.

Rei Aso, Sayaka Shiota, Hitoshi Kiya

We propose a novel method for securely training the vision transformer (ViT) with sensitive data shared from multiple clients similar to privacy-preserving federated learning. In the proposed method, training images are independently encrypted by each client where encryption keys can be prepared by each client, and ViT is trained by using these encrypted images for the first time. The method allows clients not only to dispose of the keys but to also reduce the communication costs between a central server and the clients. In image classification experiments, we verify the effectiveness of the proposed method on the CIFAR-10 dataset in terms of classification accuracy and the use of restricted random permutation matrices.

Lina Guo

In this paper, we consider finite mixture models with modified proportional hazard rates. Sufficient conditions for the usual stochastic order and the hazard order are established under chain majorization. We study stochastic comparisons under different settings of T-transform for various values of chain majorization. We establish usual stochastic order and hazard rate order between two mixture random variables when a matrix of model parameters and mixing proportions changes to another matrix in some mathematical sense. Sufficient conditions for the star order and Lorenz order are established under weakly supermajorization. The results of this paper are illustrated with numerical examples.

Nguyen Thach, Patrick Habecker, Bergen Johnston et al.

Substance use is a global issue that negatively impacts millions of persons who use drugs (PWUDs). In practice, identifying vulnerable PWUDs for efficient allocation of appropriate resources is challenging due to their complex use patterns (e.g., their tendency to change usage within months) and the high acquisition costs for collecting PWUD-focused substance use data. Thus, there has been a paucity of machine learning models for accurately predicting short-term substance use behaviors of PWUDs. In this paper, using longitudinal survey data of 258 PWUDs in the U.S. Great Plains collected by our team, we design a novel GAN that deals with high-dimensional low-sample-size tabular data and survey skip logic to augment existing data to improve classification models' prediction on (A) whether the PWUDs would increase usage and (B) at which ordinal frequency they would use a particular drug within the next 12 months. Our evaluation results show that, when trained on augmented data from our proposed GAN, the classification models improve their predictive performance (AUROC) by up to 13.4% in Problem (A) and 15.8% in Problem (B) for usage of marijuana, meth, amphetamines, and cocaine, which outperform state-of-the-art generative models.

Kaichao Yang, Ibrahim M. Abu-Reesh, Zhen He

Microbial electrochemical systems (MES) are studied to degrade organic contaminants with a lower energy demand, but degradation of recalcitrant compounds tends to be challenging. To enhance contaminant degradation in MES, advanced oxidation processes (AOPs) are synergistically linked to create cooperative processes such as bio-electro-Fenton (BEF) and enhanced bioanodes. BEF can achieve a high contaminant degradation efficiency with a low energy consumption due to the ability for energy recovery from the anodic organic wastes. Modifying a bioanode with catalytic oxidation materials, e.g., photocatalyst and MnO2, will achieve organic removal via the cooperation of catalysis and biodegradation. This paper has provided a concise review on the integration of AOPs with MES and identified and discussed the challenges such as deeper understanding of the electron transfer mechanisms, development of low-cost membrane, and the synergetic effects between functional materials and bacteria that are important to develop AOP-MES treatment systems.

Mohaddese Baghian, Zanire Salimi, Fatemeh Keshmiri et al.

Background: Medical assistants spend numerous hours of their day in the work environment, which may influence their performance. Empathy with the patients has a significant impact on the treatment process. This study was conducted among residents of different medical disciplines to determine self-efficacy in inter professional collaboration and empathy in dealing with the patients. Methods:  This was a descriptive-analytical cross-sectional study on all medical residents at Shahid Sadoughi University of Medical Sciences. Data was collected via the following questionnaires: demographic (including age, gender, marital status, year of study, and specialty), Jefferson’s scale of empathy, self-efficacy, and Hagemeier’s interring professional collaboration. Results: Of the 162 questionnaires distributed, 135 were received (response rate = 83.33%). The mean age and work experience were 31.42 ± 4.56 and 2.70 ± 4.18 years, respectively. Self-efficacy scores in inter professional cooperation and teamwork was at a good level and empathy scores were at a moderate level. There was a significant relationship between marital status and self-efficacy (p = 0.03). Empathy was related to medical residents’ level of interest in their field (p = 0.019). There was no gender difference in empathy (p = 0.77) and self-efficacy scores (p = 0.36). However, males had higher inter professional communication scores compared to females (p = 0.001). Psychiatric residents had the highest and orthopedic residents had the lowest scores in empathy with the patients, empathic patient care, and emotional separation.  Conclusion: This study showed that medical residents had an acceptable level of self-efficacy and empathy, which differed among medical fields. Similar studies should be conducted to therefore assemble an educational program for medical residents to increase empathic patient care and achieve inter professional cooperation goals.

Soumya Ranjan Mishra, Vishal Gadore, Md. Ahmaruzzaman

The possibility of combined adsorption-degradation processes in wastewater treatment using nanomaterials based on indium sulfide (In2S3) is examined in this review paper. Regarding the synergistic adsorption and degradation of pollutants, In2S3 performs exceptionally well, making it a suitable choice for wastewater remediation. Insights have been given to the pollutant removal mechanism through this integrated technique. The synergistic removal process is affected by several operational factors, including pH, catalyst dose, pollutant concentration, and contact duration. This analysis highlights the significance of optimizing these parameters for optimal contaminant removal efficiency. The influence of co-existing species, including cations, anions, and organic compounds, on the integrated elimination process is further highlighted by a discussion of their role. Future research directions are suggested, including a better comprehension of underlying processes, investigation of hybrid nanocomposites, and evaluation of long-term stability and recyclability to enhance the applicability of In2S3-based nanomaterials. This study aids in the creation of effective and long-lasting wastewater treatment methods by using the potential of In2S3-based nanomaterials.

Ronald Schnitzer, Andreas Hapfelmeier, Sven Gaube et al.

Recent advancements in the field of Artificial Intelligence (AI) establish the basis to address challenging tasks. However, with the integration of AI, new risks arise. Therefore, to benefit from its advantages, it is essential to adequately handle the risks associated with AI. Existing risk management processes in related fields, such as software systems, need to sufficiently consider the specifics of AI. A key challenge is to systematically and transparently identify and address AI risks' root causes - also called AI hazards. This paper introduces the AI Hazard Management (AIHM) framework, which provides a structured process to systematically identify, assess, and treat AI hazards. The proposed process is conducted in parallel with the development to ensure that any AI hazard is captured at the earliest possible stage of the AI system's life cycle. In addition, to ensure the AI system's auditability, the proposed framework systematically documents evidence that the potential impact of identified AI hazards could be reduced to a tolerable level. The framework builds upon an AI hazard list from a comprehensive state-of-the-art analysis. Also, we provide a taxonomy that supports the optimal treatment of the identified AI hazards. Additionally, we illustrate how the AIHM framework can increase the overall quality of a power grid AI use case by systematically reducing the impact of identified hazards to an acceptable level.