Probabilistic Techniques Research Articles

Optimized multiple-attribute group decision-making through employing probabilistic hesitant fuzzy TODIM and EDAS technique and application to teaching quality evaluation of international Chinese course in higher vocational colleges

With the construction and development of the “the Belt and Road”, the international community has an increasing demand for high-quality technical and skilled talents who both understand Chinese and have an international perspective. Exploring a Chinese curriculum system suitable for the development of international students is conducive to promoting the international development of vocational education, improving the quality of international student training, providing talent support for Chinese and foreign enterprises, and promoting the “going out” of Chinese enterprises and international production capacity cooperation. The training of international students in higher vocational colleges starts late, has little experience, lacks theoretical innovation, and lacks the development characteristics of vocational education. The teaching quality evaluation of international Chinese course in higher vocational colleges is multiple-attributes group decision-making (MAGDM). Recently, the TODIM and EDAS technique has been employed to manage MAGDM. The probabilistic hesitant fuzzy sets (PHFSs) are employed as a useful tool for depicting uncertain information during the teaching quality evaluation of international Chinese course in higher vocational colleges. In this paper, the probabilistic hesitant fuzzy TODIM-EDAS (PHF-TODIM-EDAS) technique is built to manage the MAGDM under PHFSs. Finally, the numerical example for teaching quality evaluation of international Chinese course in higher vocational colleges is employed to show the PHF-TODIM-EDAS technique. The main contribution of this paper is outlined: (1) the TODIM technique based on EDAS technique has been extended to PHFSs based on CRITIC technique; (2) the CRITIC technique is employed to derive weight numbers under PHFSs. (3) the PHF-TODIM-EDAS technique is founded to manage the MAGDM under PHFSs; (4) a numerical case study for teaching quality evaluation of international Chinese course in higher vocational colleges and some comparative analysis is supplied to validate the proposed PHF-TODIM-EDAS technique.

The Importance of Job Satisfaction in Dubai Police and Influencing Factors

Purpose: Government employees, like persons in the private sector, spend a lot of their time in public service. Consequently, employee satisfaction is essential for safeguarding long-term effectiveness and efficiency in both private and public sector organizations. Creating satisfactory working environments improves employees’ work-life balance, thus higher satisfaction levels. Every year, job satisfaction levels are measured throughout all the UAE public sector institutes. This is done through a survey sent by the Dubai Government Excellence Program (DGEP) to the employees every year. With the United Arab Emirates being heralded as one of the leading countries to create ideal work environments for employees, this study aims at determining the underlying factors that affect employee satisfaction in Dubai Police traffic department.
 Methodology: The study adopted descriptive research design. The research applied a purposive simple random sampling technique and probabilistic sampling technique. Data was collected on Dubai police traffic department employees. A quantitative and qualitative data analysis was conducted for the current research based on the collected data from the questionnaires circulated within the employees of police traffic department. Data were then analyzed using the Statistical Package for Social Sciences (SPSS-15) for statistical analysis. The results were presented in form of tables.
 Findings: While the socio-demographic factors could be considered factors explaining job satisfaction, age and marital status are the only factors that strongly explain the relationship due to their significant result. Additionally, for the nine environmental factors, using factor analysis to determine variables that strongly explain the dependent variables, the variable under “my work directly contributes towards organization’s goals” efficiently explains the status of job satisfaction. Furthermore, execution of mandates informed by the ability of the company to address public complaints and safe work environment influences job satisfaction levels.
 Unique Contribution to Theory, Practice and Policy: Although salary and work directly contribute towards an organization’s goals, companies can ensure that job levels are acquired in the right way. This way, the organization can efficiently address public complaints and promote a safe and healthy working environment. , these findings will inform further studies on behavioral management and workplace productivity in organizations

Decision-making for sustainable urban transportation: A statistical exploration of innovative mobility solutions and reduced emissions

This research aims to investigate sustainable urban transportation decision-making through innovative mobility solutions, aiming to curtail emissions. To this end, this research presents a smart stochastic architecture to optimize renewable energy-dependent systems, addressing challenges in networked microgrids. By integrating electric vehicle (EV) charging demands and harnessing the unpredictability of renewable energy resources into the urban networks, the research introduces a vehicle-to-grid layout and a novel machine learning-enabled probabilistic Technique. These advancements cover the way for stable, sustainable, and efficient urban transportation systems with reduced environmental impact. A smart stochastic architecture to optimize the operation and management of such systems that rely heavily on renewable energy is proposed in this study, taking into account the poor reliability and complicated energy management in networked microgrids (MGs). The stochastic and variable charging EVs and also the unpredictability of renewable energy resources (RERs) are incorporated in the suggested layout. A vehicle to grid layout, which has become suitable for the MG cost function, is being used for mitigating adverse impacts of vehicles on MGs. In order to support the use of RERs, this study proposes a new machine learning-enabled probabilistic method utilizing support vector machine and point estimation methods for keeping the system in a stable state taking into account their random behavior. Furthermore, this paper develops a novel optimization algorithm using modified particle swarm optimization to determine what is optimal. IEEE systems evaluate the suitability of the suggested smart layout.

Wind generation and power system interaction analysis using probabilistic techniques

. Energy power from renewable sources, especially wind turbine generators, are being considered as an important generation alternative in the electrical power systems around the world due to their non contaminant nature and low environmental effects. In particular, the power supplied by wind generators is widely random following the random nature of weather conditions; therefore a probabilistic approach during planning seems much more appropriate than the deterministic approach actually used in the electrical utilities. This paper presents a methodology that allows the application of probabilistic analysis, thru the use of programming tools, in the load flow planning studies. The methodology described provides the opportunity to obtain the steady state response in systems where wind power exists using a wind power generation model (WPGM) and an iterative routine to the load flow analysis considering each value of generation of the wind sources. A wind power generation model (WPGM) was developed using a random wind speed generator. The random wind speed generator describes the Weibull distribution of wind measurements, and then using Montecarlo simulation techniques, a program in Visual Basic executes the automatic evaluation of random samples of speed of wind considering the characteristic power curve of a wind turbine, obtaining a widely database of wind generation conditions. As a result of the investigation, a computational tool was developed in order to use it, in combination with the PSS/E load flow analysis software, for steady state power system analysis. The methodology was used as an assessment tool to identify the effects of wind turbines generation into the substation voltages and power flow profiles in the power network, for a wind farm project intended to be installed in Margarita Island. The analysis aimed to identify the transmission adjustments, reinforcements and expansions needed to guarantee the wind farm project integration successful into the power system.

Thalamic nuclei volume differences in schizophrenia patients and healthy controls using probabilistic mapping: A comparative analysis

AimWe aimed to investigate potential discrepancies in the volume of thalamic nuclei between individuals with schizophrenia and healthy controls. MethodsThe imaging data for this study were obtained from the MCICShare data repository within SchizConnect. We employed probabilistic mapping technique developed by Iglesias et al. (2018). The analytical component entailed volumetric segmentation of the thalamus using the FreeSurfer image analysis suite. Our analysis focused on evaluating the differences in the volumes of various thalamic nuclei groups within the thalami, specifically the anterior, intralaminar, medial, posterior, lateral, and ventral groups in both the right and left thalami, between schizophrenia patients and healthy controls. We employed MANCOVA to analyse these dependent variables (volumes of 12 distinct thalamic nuclei groups), with diagnosis (SCZ vs. HCs) as the main explanatory variable, while controlling for covariates such as eTIV and age. ResultsThe assumptions of MANCOVA, including the homogeneity of covariance matrices, were met. Specific univariate tests for the right thalamus revealed significant differences in the medial (F[1, 200] = 26.360, p < 0.001), and the ventral groups (F[1, 200] = 4.793, p = 0.030). For the left thalamus, the medial (F[1, 200] = 22.527, p < 0.001); posterior (F[1, 200] = 8.227, p = 0.005), lateral (F[1, 200] = 7.004, p = 0.009), and ventral groups (F[1, 200] = 9.309, p = 0.003) showed significant differences. ConclusionThese findings suggest that particular thalamic nuclei groups in both the right and left thalami may be most affected in schizophrenia, with more pronounced differences observed in the left thalamic nuclei. FundingsThe authors received no financial support for the research.

The Influence of Job Placement and Human Relations with Age as a Moderating Variable on Employee Performance of PT. KSA

The purpose of this research is to examine the impact of employment, human relations, and age on employee performance. The population for this study consists of employees at PT. KSA, and a probabilistic sampling technique (simple random sampling) was employed, resulting in a sample size of 55 individuals. The data analysis utilized multiple linear regression. The findings reveal the significance values of the influences of employment, human relations, and age on employee performance, which are 0.003, 0.006, 0.059, 0.277, and 0.862, respectively. The significance values for employment and human relations are less than 0.05, indicating a statistically significant impact on employee performance. In contrast, age, as a moderating variable, along with employment and human relations moderated by age, show significance values greater than 0.05. These results suggest that while recruitment and human relations significantly influence employee performance, age does not moderate the effects of employment and human relations. This is further evidenced by the age-adjusted significance values for employment and human relations, which are 0.277 and 0.862, respectively. Both values exceed 0.05, with regression coefficients of -0.048 and -0.010, respectively. Thus, we conclude that age does not have a significant effect and may even diminish the influence of employment and human relations on the performance of PT. KSA employees.

Enhancing subsurface contamination assessment via ensemble prediction of ground electrical property: A Colorado AMD-impacted wetland case study

Acid mine drainage (AMD) is recognized as a major environmental challenge in the Western United States, particularly in Colorado, leading to extreme subsurface contamination issue. Given Colorado's arid climate and dependence on groundwater, an accurate assessment of AMD-induced contamination is deemed crucial. While in past, machine learning (ML)-based inversion algorithms were used to reconstruct ground electrical properties (GEP) such as relative dielectric permittivity (RDP) from ground penetrating radar (GPR) data for contamination assessment, their inherent non-linear nature can introduce significant uncertainty and non-uniqueness into the reconstructed models. This is a challenge that traditional ML methods are not explicitly designed to address. In this study, a probabilistic hybrid technique has been introduced that combines the DeepLabv3+ architecture-based deep convolutional neural network (DCNN) with an ensemble prediction-based Monte Carlo (MC) dropout method. Different MC dropout rates (1%, 5%, and 10%) were initially evaluated using 1D and 2D synthetic GPR data for accurate and reliable RDP model prediction. The optimal rate was chosen based on minimal prediction uncertainty and the closest alignment of the mean or median model with the true RDP model. Notably, with the optimal MC dropout rate, prediction accuracy of over 95% for the 1D and 2D cases was achieved. Motivated by these results, the hybrid technique was applied to field GPR data collected over an AMD-impacted wetland near Silverton, Colorado. The field results underscored the hybrid technique's ability to predict an accurate subsurface RDP distribution for estimating the spatial extent of AMD-induced contamination. Notably, this technique not only provides a precise assessment of subsurface contamination but also ensures consistent interpretations of subsurface condition by different environmentalists examining the same GPR data. In conclusion, the hybrid technique presents a promising avenue for future environmental studies in regions affected by AMD or other contaminants that alter the natural distribution of GEP.

Research on personalized recommendation of teaching resources based on joint probability matrix decomposition model and CNN improvement algorithm

Abstract This study proposes a teaching resource recommendation method (TRRDLMF) based on deep learning and probabilistic matrix decomposition, aiming to improve teaching resource recommendation’s accuracy and personalization level. The study combines hybrid neural network feature extraction of teaching resources and user feature extraction based on extended noise-reducing self-encoder and extended probabilistic matrix decomposition-based teaching resource recommendation technique. The study validates the model’s effectiveness by conducting empirical analyses on four online learning platform datasets of different sizes. The recommendation system can effectively track learners’ knowledge mastery status and learning preferences, and provide corresponding teaching resources. Test question similarity and difficulty analysis results show that the model can accurately capture the correlation between test questions and provide complementary data in education. The personalized recommendation analysis reveals the learners’ knowledge preference states that change with learning, demonstrating the model’s ability to give explanations at the level of learners’ knowledge preferences. The analysis of the impact of data size on recommendation results shows that the recommender system can achieve up to 73.12% accuracy and 97.89% recall for different TOP-K recommendation lists, with the best F-value at TOP-6. The personalized recommendation system for teaching resources based on the joint probability matrix decomposition model and CNN improvement algorithm proposed by the research demonstrates significant effects in improving the accuracy and personalization level of teaching resources recommendation, which helps learners to acquire suitable learning resources more efficiently, and has high practical value and promotion potential.

Novel Self-Calibrated Threshold-Free Probabilistic Fibrosis Signature Technique for 3D Late Gadolinium Enhancement MRI.

Myocardial fibrosis, marked by excessive collagen buildup in the heart, is a crucial severity marker of heart muscle injury in several heart diseases, such as myocardial infarction, cardiomyopathies, and atrial fibrillation (AF). It is also vital for evaluating the efficacy of induced scarring (dense fibrosis) post-interventions, such as catheter ablation for AF. Cardiac MRI emerged as the gold standard for evaluating myocardial fibrosis and scarring for diagnosis and intervention planning. However, existing 3D cardiac MRI (CMR) fibrosis analysis methods are unreliable as they rely on variable thresholding and suffer from a lack of standardization and high sensitivity to typical MRI uncertainties. Importantly, these methods quantify severity based on fibrosis volume alone while ignoring the unique MRI characteristics of fibrosis distribution, which could better inform on disease severity. To address these limitations, we propose a novel thresholdfree and self-calibrating probabilistic method named "Fibrosis Signatures" for a comprehensive and reliable fibrosis analysis of 3D MRI cardiac images. Through a novel efficient (linear complexity) probabilistic encoding of 'multibillion' MRI intensity disparities into standardized probability density function, our method derives the patient's unique fibrosis signature profile and index (FSI). Our approach goes beyond mere measuring of fibrosis volume; it encodes both the extent and the unique MRI characteristics of fibrosis distribution beyond mere entropy for a more detailed evaluation of fibrosis burden/severity. Our self-calibrating design effectively adjusts for MRI uncertainties like noise, low spatial resolution, and segmentation errors to ensure robust and reproducible fibrosis evaluation pre- and post-intervention. Validated in numerical phantom and 143 in vivo MRI scans of AF patients and compared to five baseline methods, our method showed strong correlations with traditional volume measures of pre-intervention fibrosis and post-intervention scar and was up to 9- times more reliable and reproducible, highlighting its potential to enhance cardiac MRI's utility.

Application of RSM optimization model for slope stability analysis in geotechnical engineering

Abstract Slope stability is a critical area of research pivotal to ensuring public safety and advancing societal development. A thorough examination of slope stability holds substantial practical value in augmenting geological disaster prevention and shaping engineering economic strategies. This study develops an optimized Response Surface Methodology (RSM) model by incorporating the main effect analysis to enhance the foundational response surface method. Subsequently, to address the requirements of slope stability analysis, a numerical model calculating the stress and reliability of anti-slip piles is formulated. Building on this foundation, the Kriging model is refined using a particle swarm optimization algorithm, which is then integrated with the RSM optimization model to evaluate slope stability in geotechnical engineering applications. The efficacy of the proposed method is underscored by its performance across five-speed boundaries, where it achieves a mean reliability value of 90.40%, with a minimal average error of 0.69%. Moreover, empirical validation of the method through conditional probability calculations yields a value of 19.95%. This figure closely aligns with the results derived from the narrow-boundary method, which estimates slope system failure probabilities between 19.28% and 22.54%. Comparatively, the system failure probability determined by the Probabilistic Network Evaluation Technique (PNET) stands at 20.75%. These results affirm that the RSM-based optimization model described in this study is not only precise but also productive, establishing it as a robust method for conducting slope stability analysis in geotechnical engineering.

The Knowledge Of Mothers At The Beginning Of Breastfeeding Of Newborns In A Maternity Room Gleno Health Center In The Municipality Of Ermera Timor-Leste, 2024.

Introduction: Breastfeeding is an invincible way of providing optimal food for the healthy development of infants and has unique biological and psychological effects on maternal health and infants' health in general. Global Health Perspective (2023) generally, the prevalence of early-initiation breastfeeding is around 43%. While the prevalence in European countries is 44%, it ranges from 35.7% to 88.2% among African countries, 39.8 to 43% in Asia. According to the World Health Organization (WHO, 2021), in six developing countries with the risk of infant mortality between the ages of 6-12 months increased by 42% if the baby was not breastfed according to the criteria of healthy. Research Objectives: About the degree of knowledge of mothers at the beginning of breastfeeding it is clear that to the newborn in the maternity room at Gleno Inpatient Health Center of the Municipality of Ermera Timor-Leste. Research Methodology: This research uses the descriptive quantitative methodology, and with the Probabilistic Sampling technique with accidental type that defines 68 Sampling. We used the SPSS (Statistical Package for the Social Sciences) version 23 computer program to analyze the data with the questionnaire and analysis. Outcome of Discussion: Based on the result on the degree of knowledge of mothers about the initiation of breastfeeding to newborns, focus on the level of knowing with the sufficient category with percentage 58.4%, understanding with sufficient category percentage 5.8%, in the application in good layer 56.4%, thus, indicates in the research result. Conclusion: In the degree of knowledge of mothers about the beginning of breastfeeding, what to know in the group of sufficient (58.4%), understand with sufficient layer (5.7%) and application with good category 56.5%, according to the result of the research in the field of research cited by (Tilman CB., 2024).

The 2023 US 50-State National Seismic Hazard Model: Overview and implications

The US National Seismic Hazard Model (NSHM) was updated in 2023 for all 50 states using new science on seismicity, fault ruptures, ground motions, and probabilistic techniques to produce a standard of practice for public policy and other engineering applications (defined for return periods greater than ∼475 or less than ∼10,000 years). Changes in 2023 time-independent seismic hazard (both increases and decreases compared to previous NSHMs) are substantial because the new model considers more data and updated earthquake rupture forecasts and ground-motion components. In developing the 2023 model, we tried to apply best available or applicable science based on advice of co-authors, more than 50 reviewers, and hundreds of hazard scientists and end-users, who attended public workshops and provided technical inputs. The hazard assessment incorporates new catalogs, declustering algorithms, gridded seismicity models, magnitude-scaling equations, fault-based structural and deformation models, multi-fault earthquake rupture forecast models, semi-empirical and simulation-based ground-motion models, and site amplification models conditioned on shear-wave velocities of the upper 30 m of soil and deeper sedimentary basin structures. Seismic hazard calculations yield hazard curves at hundreds of thousands of sites, ground-motion maps, uniform-hazard response spectra, and disaggregations developed for pseudo-spectral accelerations at 21 oscillator periods and two peak parameters, Modified Mercalli Intensity, and 8 site classes required by building codes and other public policy applications. Tests show the new model is consistent with past ShakeMap intensity observations. Sensitivity and uncertainty assessments ensure resulting ground motions are compatible with known hazard information and highlight the range and causes of variability in ground motions. We produce several impact products including building seismic design criteria, intensity maps, planning scenarios, and engineering risk assessments showing the potential physical and social impacts. These applications provide a basis for assessing, planning, and mitigating the effects of future earthquakes.

Combining Karhunen–Loève expansion and stochastic modeling for probabilistic delineation of well capture zones in heterogeneous aquifers

The delineation of well capture zones (WCZs), particularly for water supply wells, is of utmost importance to ensure water quality. This task requires a comprehensive understanding of the aquifer’s hydrogeological parameters for precise delineation. However, the inherent uncertainty associated with these parameters poses a significant challenge. Traditional deterministic methods bear inherent risks, emphasizing the demand for more resilient and probabilistic techniques. This study introduces a novel approach that combines the Karhunen–Loève expansion (KLE) technique with stochastic modeling to probabilistically delineate well capture zones in heterogeneous aquifers. Through numerical examples involving moderate and strong heterogeneity, the effectiveness of KLE dimension reduction and the reliability of stochastic simulations are explored. The results show that increasing the number of KL-terms significantly improves the statistical attributes of the samples. When employing more KL-terms, the statistical properties of the hydraulic conductivity field outperform those of cases with fewer KL-terms. Notably, particularly in scenarios of strong heterogeneity, achieving a convergent probabilistic WCZs map requires a greater number of KL-terms and stochastic simulations compared to cases with moderate heterogeneity.

Developing a probability-based technique to improve the measurement of landslide vulnerability on regional roads

Considering the uncertainty in vulnerability curves, this study employs a probabilistic technique to improve the current landslide vulnerability on roads. The uncertainty in current vulnerability was first elaborated, and slope failure and the damage caused to roads were investigated. To minimize uncertainty, subsequently, the probabilistic landslide intensity was proposed, and hired to calibrate the empirical landslide intensity. Finally, an improved vulnerability curve was developed and compared with the empirical vulnerability, in terms of the fluctuation band and residual value. Results show that the uncertainty of the improved vulnerability is significantly reduced compared with the empirical vulnerability model. In detail, the uncertainty reduction in landslide vulnerability is reflected not only in the decrease of the residual range, from 0.6 to 0.3, but in the narrowing of the fluctuation band. It provides a route for enhancing the accuracy of landslide vulnerability from a probabilistic view.

Secret Key Management in Wireless Sensor Network Based on Probabilistic Technique

Data confidentiality implies that data access is limited to authorized entities only, thus measured data and transmitted data by wireless sensors require optimum security and privacy. Various techniques have been employed to implement effective security mechanisms for a wireless sensor network. However, the arsenal of these potential solutions is useless, considering the constrained nature of the Wireless Sensor Network (WSN) resources in terms of memory, processing and computational capacity. While considering an effective solution for this situation, care must be taken not to trade the desired solution for other factors. This paper considers the implementation of a probabilistic approach for key management in WSN. In the implementation, all kinds of communication within the wireless sensor nodes are presented by forwarding encrypted keys for mutual authentication. A successful authentication opens a communication channel for the communicating nodes. The encrypted keys are computed by generating a polynomial which constitutes the hashed ID concatenated with the master key and Message Authentication Code (MAC) address of the node. The results presented from the simulation of this model are benchmarked with the Dynamically Generated Polynomial (DGP). The proposed model was simulated using MATLAB tools and the comparison of the results obtained shows that the proposed model outperforms the DGP model by 87%, based on the key metrics which are energy consumption, storage and communication overhead.

A Probabilistic Approach to Turbine Uncertainty

Abstract Efficiency is an essential metric for assessing turbine performance. Modern turbines rely heavily on numerical computational fluid dynamics (CFD) tools for design improvement. With more compact turbines leading to lower aspect ratio airfoils, the influence of secondary flows is significant on performance. Secondary flows and detached flows, in general, remain a challenge for commercial CFD solvers; hence, there is a need for high-fidelity experimental data to tune these solvers used by turbine designers. Efficiency measurements in engine-representative test rigs are challenging for multiple reasons; an inherent problem to any experiment is to remove the effects specific to the turbine rig. This problem is compounded by the narrow uncertainty band required to detect the incremental improvements achieved by turbine designers. Efficiency measurements carried out in engine-representative turbine rigs have traditionally relied upon assumptions such as constant gas properties and neglecting heat loss. This research presents an uncertainty framework that combines inputs from experiments and computational tools. This methodology allows quantifying uncertainty for high-fidelity efficiency data in engine-representative turbine facilities. This paper presents probabilistic sampling techniques to allow for uncertainty propagation. The effect of rig-specific effects, such as heat transfer and gas properties, on efficiency is demonstrated. Sources of uncertainty are identified, and a framework is presented which divides the sources into bias and stochastic. The framework allows the combination of experimental and numerical uncertainty. Gaussian regression models are developed to obtain speed-lines for the turbine map using the uncertainty of the measured efficiency.

Financial Condition Indices in an Incomplete Data Environment

Abstract We construct a Financial Conditions Index (FCI) for the United States using a dataset that features many missing observations. The novel combination of probabilistic principal component techniques and a Bayesian factor-augmented VAR model resolves the challenges posed by data points being unavailable within a high-frequency dataset. Even with up to 62 % of the data missing, the new approach yields a less noisy FCI that tracks the movement of 22 underlying financial variables more accurately both in-sample and out-of-sample.

A Bayesian network-based tool for crisis classification in piracy or armed robbery incidents on passenger ships

Piracy and armed robbery continue to pose significant security threats to the shipping industry. This paper presents a real-time threat assessment and crisis classification tool for piracy or armed robbery incidents. The tool is part of a crisis classification module that addresses various categories of security threats. This module is currently being developed as part of the EU-funded research project ISOLA, which aims to introduce an intelligent security superintendence ecosystem. The ecosystem is designed to complement the existing ship security processes and measures applied onboard passenger ships. The tool operates by providing real-time threat classification and subsequent warnings by analysing data collected from the ship’s legacy systems and installed sensors with the utilisation of Bayesian probabilistic techniques, particularly Bayesian Networks. The BN model developed for this purpose is thoroughly examined, and its validation is presented through indicative case studies involving piracy and armed robbery. The main objective is to improve situational awareness, enhance vigilance and early threat detection, and support the decision-making process for the Master and crew, especially under time-sensitive circumstances and stressful conditions.

Resilience analysis in road traffic systems to rainfall events: Road environment perspective

The road traffic system is frequently disrupted by rainfall events, significantly impacting road system efficiency. It is challenging to capture the resilience of road traffic systems due to variations in road environments. We develop a method for measuring road segment and traffic system resilience using probabilistic modeling techniques. Hierarchical clustering is used to analyze the heterogeneity of resilience patterns from road environment perspectives. A decision support tool is developed to visualize resilience propagation among road segments and communities. Based on a case study in Harbin, China, we identify four typical traffic system resilience patterns considering the influence of land use. Results reveal that system resilience decreases by 6.7% for every 10mm increase in rainfall intensity when the rainfall intensity is below 50mm. Road segments over 1000 m exhibit an average resilience increase of 13.3% compared to those shorter than 1000 m. Additionally, 66% of low-resilience road segments are concentrated near commercial areas.

Bioaerosols in Wastewater Treatment Plants: Trends, Recent Advances, and the Influence of SARS-CoV-2 Outbreak

Bioaerosol emission at various WWTP treatment units has drawn attention due to their potential negative impacts on human health. This study conducted a bibliometric analysis of the global research on bioaerosol emissions from WWTPs from 1995 to 2022. The Scopus database was used to identify relevant articles and research trends, major contributors in the field, and recent developments. The study examined 122 articles in the field of bioaerosols in WWTPs. The analysis findings showed that publications and citations peaked in 2022, with values of 25 and 818, respectively. At the beginning of the study period, the USA, Poland, and Italy led the publications’ ranking, but with time, China emerged as the most influential country in the field. Recent advances in the field have revealed that spectral intensity bioaerosol sensors have contributed to the faster and more reliable identification and classification of bioaerosols. It was also observed that probabilistic techniques relying on mathematical models and assumptions to ascertain the risks associated with bioaerosols may result in false interpretations. Despite their high cost, epidemiological studies were best for assessing plant workers’ health risks. The outbreak has raised questions about accurately evaluating and modeling SARS-CoV-2 persistence, infectivity, and aerosolization over WWTP sites and environmental factors. Finally, the study highlighted the potential of three control treatment approaches: carbon absorption, UV irradiation, and ozone treatments, which proved efficient in reducing bioaerosol emissions.