Risk Assessment Model Research Articles

Identified a novel prognostic model of HCC basing on virus signature for guiding immunotherapy

Oncolytic viral immunotherapy is a cancer treatment that uses native or genetically modified viruses that selectively replicate and destroy tumor cells. In this study, we aimed to construct a virus-based prognostic model for risk assessment and prognosis prediction in patients with hepatocellular carcinoma (HCC) and determine the most appropriate virus as a candidate vector for oncolytic virus immunotherapy. Microbiome and RNA sequencing data and clinical information were obtained from The Cancer Genome Atlas, and viruses with prognostic value were identified (Deltabaculovirus, Sicinivirus, and Cytomegalovirus) to construct the prognostic model. Correlation analyses were performed to evaluate the predictive function of the viral signature. Bioinformatics analyses were conducted to explore the functional enrichment of viral expression in HCC. The risk score generated by this model could distinguish patients with different survival outcomes, have excellent reliability and accuracy, and could be used as an independent prognostic indicator. The high-risk score group showed significantly lower overall survival, and this trend was also observed in subgroups with different clinicopathological features. Furthermore, Deltabaculovirus positively correlated with amino acid metabolism, energy metabolism signaling pathways, peroxisomes, and complement coagulation cascades. In addition, Deltabaculovirus was significantly related to immune cell infiltration; therefore, patients with high Delta-baculovirus expression might respond better to HCC immunotherapy. Our study identified a promising predictive viral signature for assessing clinical prognosis and guiding immunotherapy in HCC. Deltabaculovirus might be a suitable viral vector for oncolytic virus immunotherapy.

A meso- to micro-scale coupled model under typhoon conditions considering vortex structure and coriolis effect for wind farms

Accurate simulations of typhoon-induced wind speeds on wind farms are crucial for the refined assessment of typhoon risks in wind turbines. Southeastern coastal area in China is rich in wind resources but is also severely threatened by typhoons. As an extremely destructive weather system with complex structures, the refined near-surface wind fields of typhoons are difficult to simulate by meso-scale models for risk assessment. Therefore, a coupled meso- and micro-scale model under typhoon conditions is proposed to simulate typhoon-induced winds on a wind farm. The Coriolis force is considered in the coupled model to reflect the rotating effect of typhoons caused by the vortex structure. A coupling algorithm based on objective analysis is proposed to construct boundary conditions at the coupling interface, to consider the variation of inflow variables in the vertical and cross-wind directions. Model constants of the two-equation turbulence model are calibrated based on typhoon field observations, to more reasonably simulate typhoons. A validation study is conducted, and the results show that the maximum wind speed on the wind farm simulated by the coupled model exhibits an increased accuracy compared with that of the meso-scale model, where the absolute error decreases by 34%.

A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing

The ability and rapid access to execution data and information in manufacturing workshops have been greatly improved with the wide spread of the Internet of Things and artificial intelligence technologies, enabling real-time unmanned integrated control of facilities and production. However, the widespread issue of data quality in the field raises concerns among users about the robustness of automatic decision-making models before their application. This paper addresses three main challenges relative to field data quality issues during automated real-time decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, and sensor fault-tolerant control. To address these problems, this paper proposes a risk assessment framework in the case of continuous production workshops. The framework aims to determine a method for systematically assessing data quality issues in specific scenarios. It specifies the preparation requirements, as well as assumptions such as the preparation of datasets on typical working conditions, and the risk assessment model. Within the framework, the data quality issues in real-time decision-making are transformed into data deviation problems. By employing the Monte Carlo simulation method to measure the impact of these issues on the decision risk, a direct link between sensor quality and risks is established. This framework defines specific steps to address the three challenges. A case study in the steel industry confirms the effectiveness of the framework. This proposed method offers a new approach to assessing safety and reducing the risk of real-time unmanned automatic decision-making in industrial settings.

A Risk Assessment Model of Gas Pipeline Leakage Based on a Fuzzy Hybrid Analytic Hierarchy Process

Given the rising urban demand for gas, it has emerged as a primary energy source for urban activities and daily life. However, China’s urban gas pipeline network has witnessed a surge in accidents, leading to significant losses and disasters. Therefore, it is particularly necessary to study the disaster risk assessment model caused by urban gas pipeline leakage. There are some problems in the previous evaluation methods, such as less consideration of the influence relationships between disaster factors. To redress this issue, a novel fuzzy hybrid analytic hierarchy process evaluation methodology is proposed. First, a hybrid hierarchical risk assessment model is developed by combining the analytic hierarchy process and the network analytic hierarchy process. Membership matrices and impact matrices are utilized to calculate comprehensive factor weights. This approach enhances the understanding of relationships between risk factors within the hierarchical structure model. Subsequently, employing a fuzzy evaluation method, the risk level matrix is derived by using multiplication and bounded operators to ascertain the risk level state. This solves the problem of the fuzzy boundaries when measuring the index factors of the gas pipeline network. Finally, experimental analysis is carried out on the gas pipeline network in the central area of a city and validates the model’s accuracy in practical applications.

The risk assessment of public-private partnership projects using artificial intelligence algorithms

Purpose: is to develop an innovative approach to risk management in public-private partnership (PPP) projects using advanced artificial intelligence technologies that allow creating the risk assessment model that takes into account non-linear relationships between various risk factors.Methods: in addition to traditional methods of scientific knowledge, interdisciplinary approaches of risk management and established practice of machine learning were used in the work. The methodological basis of the study was formed by works on the risk assessment and the application of AI algorithms in this area. The empirical basis of the study was the data of the official portal of ROSINFRA on public-private partnership projects.Results: the practice of applying AI algorithms to the task of assessing the risks of PPP-projects in Russia and abroad was studied. It is established that the most effective result is shown by the models based on Random-Forest-Classifier. However, the presented solutions do not take into account Russian economic realities. The authors have structured a database of implemented PPP-projects suitable for risk modeling. The model for assessing the risk of failure to achieve the objectives of Russian PPP-projects has been developed and its quality has been assessed. Recommendations on implementation of the model in the operational loop of PPP projects realization processes are offered.Conclusions and Relevance: the developed model allows, according to the general parameters of PPP project (region, authority, term of agreement, industry and scope of implementation, etc.) with the accuracy of 93% (according to the ROC\AUC metric), to assess the risk that the project will end incorrectly (due to a failed tender, refusal to launch, termination by court decision, cancellation/annulment of the tender). With the help of the model the executive authorities of the Russian Federation can build risk management for PPP projects management in the regions and thus contribute to their efficiency improvement. The article may also become useful for project management practitioners and appraisers.

Emergency evacuation risk assessment for toxic gas attacks in airport terminals: Model, algorithm, and application

Transportation infrastructure has often been the target of terrorist attacks, and mitigation of the risk of toxic gas attacks is a challenging task in the design of indoor emergency evacuation systems. Considering multiple emergency response modes, we propose an agent-based risk assessment model and its algorithm to integrate gas diffusion and pedestrian movement data for emergency response, quickly assessing average individual exposure risk. We assessed the exposure status of individuals with respect to their emergency response actions following a toxic gas attack in an airport terminal. The results indicate that in the event of a general gas attack on an airport terminal, ventilation must be immediately ceased along with early evacuation. In areas with a shelter-in-place environment, the ventilation mode and shelter-in-place time should be determined based on the concentration of indoor and outdoor gases. In areas with nerve gas exposure and high population density, a new exit must be established at evacuation bottlenecks, and pedestrians must be guided to evacuate while promptly closing ventilation. These results offer suggestions and strategies for emergency response and decision-making in airport terminals during such incidents.

(+) Anatoxin-a elicits differential survival, photolocomotor behavior, and gene expression in two alternative vertebrate models

Anatoxin-a is a globally occurring, yet understudied, chiral cyanobacterial toxin that threatens public health and the environment. It has led to numerous dog. livestock and bird poisonings and although it has been studied in rodent models, comparatively little research has occurred in aquatic species. To advance a comparative toxicology understanding of this toxin in alternative vertebrate models, developing zebrafish and fathead minnow were exposed to environmentally relevant and elevated levels (13–4400 μg/L) of (+) anatoxin-a to examine potential mortality and sublethal effects, including photolocomotor behavior and gene expression responses. We observed significantly higher mortality (p < 0.05) in fathead minnows exposed to ≥ 1400 μg/L (65 – 83 % survival versus 97 % in controls). Locomotor response profiles for zebrafish typically displayed hypoactivity after exposure to (+) anatoxin-a in both light and dark periods, while hyperactivity of fathead minnows was observed at the lowest treatment level, but only in light conditions. Gene expression in zebrafish was significantly (p < 0.05) downregulated for mbp, which is associated with myelin sheath formation, and elavl3, which is involved in neurogenesis, along with cyp3a65 and gst, two genes related to phase I and II metabolism. However, no significant (p > 0.05) transcriptional changes were observed in the fathead minnow model. These differential responses between commonly employed species employed as alternative vertebrate models in toxicology research and chemicals risk assessments highlight the need for more comparative studies to understand sensitivities and variations in organismal response. Furthermore, we identified higher mortality, refractory behavioral effects, and gene expression in (+) anatoxin-a exposed fish when compared to previously reported (±) anatoxin-a (racemic 50:50 enantiomer mixture) studies, which is frequently used as a surrogate chemical for experimental work. Our findings identify the importance of understanding species and enantiomer specific effects of natural toxins.

Content and Health Risks of Microplastics and Phthalate Esters in Bottled Water

To study the content and health risks of microplastics （MPs） and phthalate esters （PAEs） in bottled water, a quantitative analysis of MPs was conducted by using Rose Bengal staining and stereomicroscopy. Seven PAEs were quantified by using gas chromatography-triple quadrupole tandem mass spectrometry （GC-MS/MS）. The daily intake of MPs was estimated and the carcinogenic and non-carcinogenic risks of PAEs were evaluated through a health risk assessment model. The results showed that the abundance of MPs in 21 bottled waters ranged from 48 n·L-1 to 216 n·L-1 （with the median abundance of 88 n·L-1）. The majority （72.1%） of MPs were fibrous in shape, and fragments accounted for only 27.9%. The average proportion of small-sized （10-50 μm） MPs was 33.9%, and that of large-sized MPs （&gt;500 μm） was 4.3%. Most MPs were blue. The ∑(PAEs) in bottled water was 1.15-2.47 μg·L-1 （average 1.62 μg·L-1）. PAEs detected with high frequencies （100%） included dimethyl phthalate （DMP）, diethyl phthalate （DEP）, diisobutyl phthalate （DIBP）, di-n-butyl phthalate （DBP）, and di（2-ethylhexyl） phthalate （DEHP）, while the detection frequencies of butylbenzyl phthalate （BBP） and di-n-octyl phthalate （DNOP） were relatively low. The concentrations of DBP, DEHP, and DEP were all below the standard limits for drinking water in China. The ∑(PAEs) in the migration experiments was 0.61-2.04 μg·L-1 （average 1.33 μg·L-1）. The migration amounts of DBP and DEHP were also within the allowable range under the condition of 60℃ for 10 days. Seven PAEs were detected in both the bottles and caps, and the average content of DEHP in bottles was the highest, while DBP had the highest content in caps. The estimated intake of MPs （EDI） by drinking bottled water in different age groups of humans was 2.87 n·（kg·d）-1 for adults, 3.87 n·（kg·d）-1 for children, and 5.85 n·（kg·d）-1 for infants. The carcinogenic risks of DEHP in 21 bottled water samples and the migration test were less than the maximum acceptable risk level （1×10-6）, and the non-carcinogenic risk indices （HIs） of PAEs were all less than 1, indicating no non-carcinogenic risk to humans； however, the risk value of infants and children was higher than that of adults and should not be ignored.

Ecohydrological risk assessment model for the Pra River Basin using GIS and multi-criteria decision making

ABSTRACT The Pra River Basin in Ghana is a major source of ecological, cultural, and economic benefits. However, it has been experiencing degradation, and logistical constraints have limited the implementation of Integrated Water Resources Management. This study presents a GIS-based ecohydrological risk model that identifies ecohydrological units with the highest degradation risk. First, the PRB was divided into 25 eco-hydrological units. Second, seven drivers of degradation were identified and mapped. The drivers were reclassified into five categories with risk scores of 1–5, with 5 indicating higher risk scores. The variables were ranked using the Analytical Hierarchy Process, the degradation risk model was calculated using the weighted sum method, and the multi-criteria decision tool was used to identify the ecohydrological units with the highest degradation risk. Of the 25 ecohydrological units delineated, the Buama, Lower Offin, Ayensu, and Oda ecohydrological units were found to be at the highest risk of degradation due to high artisanal small-scale gold mining, high population density, low forest/vegetation cover, and high encroachment in waterways. The results can be used as a guide to prioritize management intervention, starting with the areas classified as high risk and moving down to those classified as moderate risk.

An FMEA decision support model for hotel risk assessment based on the risk tolerance of multi-type travelers and online reviews

As a reliability management tool, failure mode and effect analysis (FMEA) is widely used to solve the problem of risk assessment in all trades and professions. Considering the opinions interaction and the diversity of risk tolerance of experts, this paper constructs a decision-support model of FMEA based on dynamic interaction of opinions and risk tolerance. Firstly, word frequency statistics and Word2Vec analysis are applied on 14,194 online reviews collected from TripAdvisor.com to identify major risk factors. Secondly, the consensus opinion of risk is reached through the dynamic interaction of opinions. Furthermore, the weight problem of risk factors is solved by a linear programming model with the goal of minimum individual regret, and the priority of risk is determined by an improved TODIM method considering risk tolerance. Through simulation experiments, sensitivity analysis and comparative analysis, the effectiveness and superiority of the proposed method are verified, and the theoretical research of FMEA method is enriched. This study provides a direction for the future improvement of hotel services based on understanding the preferences and interactions of multi-type travelers.

Spatial variability, source identification, and partitioning of groundwater constituents in a typical lakeside plain on Yungui Plateau

The fragile ecological balance of lakeside plains on plateau makes groundwater hydrochemical composition highly susceptible to human disturbances. This research investigates a typical plain on Yunnan Plateau to quantitatively identify the potential sources of groundwater pollution in lakeside plains on plateau by combining receptor models with risk assessment models. Receptor models show there are three potential pollution sources for groundwater including water-rock interactions, agricultural pollution, and domestic sewage/manure discharge. Groundwater hydrochemical composition is predominantly shaped by natural water-rock interactions, accounting for an average of 50.12 % of the influence. This is followed by agricultural non-point source pollution, which contributes 10.79 %, and the discharge of domestic sewage and manure, which accounts for 10.61 %. Groundwater in the current plain could pose a minor non-carcinogenic risk for long-term drinking, with the total hazard index (THI) for adults varying between 0.015 and 1.089, and for children between 0.024 and 1.737. Notably, 4.35 % of groundwater samples have a THI exceeding the safe threshold of 1. NO3- (72 %) is the primary contributor to non-carcinogenic risks, accounting for 72 %, trailed by Mn (13 %), NH4+ (9 %), Fe (6 %) and NO2- (1 %). The non-carcinogenic risks are predominantly attributed to agricultural non-point source pollution (71.6 %) and the discharge of domestic sewage and manure (26.9 %). A conceptual model has been developed to encapsulate the dynamics of groundwater quality formation in lakeside plains on plateau. This research will aid residents in safeguarding against groundwater contamination and enhancing agricultural practices in present plain and similar lakeside plain worldwide.

RESEARCH METHODS AND GEOINFORMATION MODELS OF CHANGES IN LAND RESOURCES OF ALMATY REGION UNDER THE INFLUENCE OF HOUSEHOLD WASTE

This article deals with the solution of scientific problems, including the development of holistic conceptual models for presenting, processing and analyzing geographic information, and comprehensively assesses the extent of the impact of regional systems on many dangerous natural and artificial processes. This makes it possible to resolve the contradiction between the need to analyze the state of various territorial systems in order to prevent the occurrence of emergencies due to the influence of natural and man-made processes.On the one hand, providing information and support in decision-making, and on the other, ensuring the provision of cartographic models of transport systems risk assessment of dangerous natural and man-made processes and their mapping. Integrated environmental assessment and modeling of industrial systems. A method for mapping payment systems and assessing the health of the population. Since the city of Almaty is a large metropolis, the ecological situation is very poor. This directly affects the ecology of the City, Public Health. We manage, process data by processing GIS data. In solving upcoming environmental problems, in developing new solutions, a special data card will come to the rescue. This will lead to solving problems from this point of view. It is possible to create monitoring maps by combining space surveys and statistical data. One of the most effective aspects of GIS data is that it allows you to optimally consider problems and work in any area. From this point of view, the GIS industry is developing rapidly. In the country, this industry is developing rapidly and is gaining a lot of demand.

Impact of short-term total dissolved gas supersaturation on cognitive function and swimming performance in medaka (Oryzias latipes)

During the flood season, high dam discharge rates result in total dissolved gas (TDG) supersaturation. This condition causes gas bubble trauma and can lead to fish mortality, which poses a significant threat to downstream river ecosystems. Assessing the ecological risks of TDG supersaturation is a challenge in waterpower-intensive river basins worldwide. Few studies have explored the impact of TDG supersaturation on fish behaviours, such as aggression and memory, which are crucial for feeding, reproduction, and predator avoidance. In this study, behavioural tests were conducted in a T-maze to investigate the effects of acute TDG supersaturation on swimming behaviour, aggression, and memory in medaka (Oryzias latipes). The results demonstrated that medaka exposed to TDG levels of 115% and 130% for 2 h had significantly reduced swimming performance.At TDG levels of 100%, 115% and 130%, medaka activity rates in the mirror arm of the maze in the mirror test were 44.34 ± 12.88%, 40.27 ± 15.44% and 35.35 ± 16.07%, respectively. Similarly, the activity rates of medaka in the active stimulus arm of the maze in the memory test were 50.35 ± 14.75%, 40.76 ± 12.51% and 35.35 ± 18.47%, respectively. The behaviour of medaka changed with increasing TDG supersaturation. These findings contribute to the development of an ecological risk assessment model for TDG supersaturation based on memory and aggression in fish and provide data for developing management strategies to mitigate the adverse effects of TDG supersaturation.

Microplastic transport and ecological risk in coastal intruded aquifers based on a coupled seawater intrusion and microplastic risk assessment model

Seawater-groundwater interactions can enhance the migration process of microplastics to coastal aquifers, posing increased associated environmental risks. Here, we aim to analyze the relationship between seawater intrusion (SWI) and groundwater microplastic pollution in Laizhou Bay (LZB), which is a typical area of sea-land interactions. The results showed that modern seawater intrusion was the main process controlling the migration of microplastics. The detected microplastics in the study area showed a migration pattern from nearshore marine areas to groundwater aquifers along the SWI direction. In addition, the microplastics also reached the brine formed by palaeo-saltwater intrusion through hydraulic exchange between aquifers. By comparing the spatial distributions of different microplastic parameters, we found that nearshore fisheries, commercial, tourism, textile, and agricultural activities were the main sources of microplastics in groundwater in the study area. A risk assessment model of microplastics associated with SWI was further optimized in this study using a three-level classification system by assigning appropriate weights to different potential influencing factors. The results showed moderate comprehensive ecological risks associated with microplastics from seawater intrusion in the study area, with high microplastic enrichment risks. This study provides a scientific basis for future research on seawater-groundwater interactions and microplastic pollution in coastal regions.

Occurrence and spatial distribution of microplastics in water and sediments of Hatiya Island, Bangladesh and their risk assessment

This research has evaluated the MPs distribution, characteristics, and potential threats of MPs in surface water and sediments from Hatiya Island. The results showed that the abundance of MPs was 139 ± 44 items/m3 in surface water and 493 ± 80 items/kg dw in sediments, indicating higher levels of MPs contamination in sediment samples. Fibers were the predominant kind of microplastics, and microscopic sizes (0.3–1.5 mm) MPs were generally more frequent and largely present in both the surface water and sediments. Fourier-transform infrared spectroscopy (FTIR) confirmed that polyethylene terephthalate was the major polymer component of microplastics in surface water, whereas polyethylene was the most abundant polymer in sediments. MPs contamination risk was examined based on multiple risk assessment models. Nemerow pollution index (NPI) and pollutant load index (PLI) show minimal pollution levels of MPs. But potential hazard index (PHI), potential ecological risk factor (Er), and potential ecological risk index (RI), indicate severe MPs contamination due to the presence of polyurethane, polycarbonate, polyvinyl chloride, epoxy that were hazardous MPs and exhibited a critical concern for MPs risk. These statistics will help to understand the environmental difficulties generated by MPs and which hazard is waiting for mankind in the future.

Development and Validation of a Predictive Model for Growth of Salmonella Infantis in Ground Turkey

Most retail samples (25 g) of ground turkey contain no or low levels of Salmonella. However, temperature abuse after retail can lead to spread and growth of Salmonella in the package. In addition, it can lead to levels that pose a significant risk of salmonellosis. This is especially true when the serotype is a top human clinical isolate, like Infantis. Therefore, the current study was undertaken to develop and validate a predictive model for growth of Salmonella Infantis in ground turkey subjected to temperature abuse. The purpose was to fill a gap for serotype-specific data and models in risk assessments for this pathogen and food combination. Storage trials with a low initial inoculum (0.85 log10) of Salmonella Infantis in commercial ground turkey samples (0.2 g) with native microflora were conducted at 16 to 40°C for 0 to 28 h. Salmonella was enumerated in ground turkey samples using an automated, whole sample enrichment, miniature, most probable number (MPN) assay. The MPN data were fitted to a three-phase linear primary model. Secondary models for primary model parameters were developed and used in the primary model to create a tertiary model that predicted growth of Salmonella Infantis in ground turkey as a function of time and temperature. Data and tertiary model predictions were evaluated using the test data, model performance, and model validation criteria of the Acceptable Prediction Zones method in the Validation Software Tool. The tertiary model predictions were considered to have acceptable bias and accuracy when the proportion of residuals (observed – predicted) in the partly and fully acceptable prediction zones (pAPZ) was ≥ 0.7. The overall pAPZ of the tertiary model was 0.866 for dependent data (n = 406) and 0.853 for independent data for interpolation (n = 177). However, there were local prediction problems that limited the validated prediction range to a region from 0 to 8 h at 16 to 40°C. Nonetheless, this validation range was sufficient to simulate temperature abuse of ground turkey during meal preparation in the consumers’ home. Thus, the model fills an important data and modeling gap in risk assessments for Salmonella and ground turkey. Additional data is needed to repair and fully validate the model.

Updates to the wild bird abundance and movement models for the early warning system for avian influenza in the EU

Abstract Highly pathogenic avian influenza (HPAI) viruses pose a significant threat to both poultry and wild bird populations. Migratory wild birds play a key role in the intercontinental spread of avian influenza (AI), introducing the virus into poultry populations. In response to frequent AI outbreaks in Europe, the European Food Safety Authority (EFSA), at the request of the European Commission (EC), produces quarterly and annual epidemiological reports to monitor and analyse AI trends. A key component of this surveillance includes the integration of outbreak data from Member States and contributions from non‐governmental ornithological organisations like the European Bird Census Council (EBCC) and the European Union for Bird Ringing (EURING) together in a predictive spatio‐temporal risk assessment model. Previous data integration and modelling efforts led to the development of an early warning system for predicting HPAI outbreaks accessible through a publicly available online user interface: the Bird Flu Radar. This report presents an improvement of the system by expanding the species coverage and refining the existing base models behind the epidemiological model. Specifically, this report details the exploration to incorporate 12 additional wild bird species into the models, and the changes made to the base models predicting the distribution and movements of wild birds. We demonstrate the improvements respecting the existing base models while at the same time enhancing the effectiveness in predicting HPAI outbreaks and possibly mitigating negative effects in Europe by providing more accurate predictions to different stakeholders.

A risk assessment model of spontaneous combustion for sulfide ores using Bayesian network combined with grounded theory

Sulfide ores, collectively referred to as a class of minerals with extensive applications, are increasingly susceptible to spontaneous combustion incidents as the mining environment evolves. The spontaneous combustion of sulfide ores is a complex nonlinear process, and the mechanisms of which remain not fully understood. Consequently, it is challenging to delineate the accident trajectory through traditional accident causation models and subsequently construct a probabilistic model for spontaneous combustion risk. To address this issue, this paper proposed a risk assessment model based on grounded theory and Bayesian network. The advantage of this model is that it employs grounded theory to categorize and organize factors influencing the occurrence of accidents, thereby forming a clear accident trajectory and providing a framework and basis for constructing a probabilistic assessment model. Furthermore, the model presented in this paper introduces the D number theory and the Noisy-OR model to handle uncertainties in the risk assessment process and to establish more rational conditional probability tables. The findings suggest that environmental temperature, heat dissipation conditions, safety awareness, and safety skills are the most influential factors in spontaneous combustion incidents when considering a particular point in time. However, when considering the entire process of sulfide ore pile spontaneous combustion, the temperature of the ore pile is the most critical accident factor to monitor. Therefore, the key to suppressing the spontaneous combustion of sulfide ores lies in reducing environmental temperatures, enhancing heat dissipation, and elevating the safety awareness of relevant personnel.

Risk assessment model used to predict discharge care after total hip and total knee arthroplasty: A population-based study

BackgroundTransfer to a post-acute care facility or hospital readmission after total joint arthroplasty represent additional costs and increased surgical and health care resource utilization. Accurate prediction of post-acute care factors could help providers to plan the patient's discharge destination and have a positive impact on postoperative outcomes and readmission rates. ObjectiveTo develop a risk assessment model to predict discharge care after total hip arthroplasty (THA) and total knee arthroplasty (TKA). DesignA retrospective longitudinal observational study. Settingsand participants: This study included 209 patients who underwent primary unilateral THA or TKA at a major academic medical center in Switzerland from January 2018 to December 2019. MethodsA collection of computerized- and paper-recorded data identified the discharge destination, socio-demographic factors, comorbidities, and other factors related to the patient. Univariate and multivariate analyses were performed to describe the predictors of post-surgical discharge destinations. ResultsThe characteristics associated with post-acute care after primary unilateral THA or TKA were the absence of a caregiver, advanced age, female gender, presence of walking aids, high ASA score, and comorbidity severity. A prediction model demonstrated that these six characteristics were associated 52 % with discharge to a post-acute care destination. ConclusionsThis study allowed us to identify predictors of discharge to a post-surgical destination. Predictive models can be efficiently used to better predict which patients are predisposed to post-acute care after hospital discharge. Further studies are needed to determine the optimal criteria for different destinations.

Validity of simplified Cariogram models versus conventional Cariogram model in caries risk assessment in young adults: Cross-sectional observational study

Validity of simplified Cariogram models versus conventional Cariogram model in caries risk assessment in young adults: Cross-sectional observational study