Modified Risk Assessment Research Articles

Environmental and human health risk associated with heavy metals in sediment of a tropical river in India

ABSTRACT Heavy metals persist in the sediment and can induce toxicological effects on living organisms. For analysing the risk of heavy metals, geochemical fractionation is beneficial. These geochemical fractions or speciations help to determine the heavy metals availability, mobility, toxicity, enrichment, and various risks. Here, we show how heavy metal contamination of sediment poses risks to both the ecosystem and human beings. We found significant Zn, Co and Cd contamination by analysing the geoaccumulation index. We also observed higher geoaccumulation index values for the pre-monsoon season than the post-monsoon one. Furthermore, pollution load index indicated gradual contamination of heavy metals in the sediments. This study's risk assessment and modified risk assessment codes showed low or no risk, but contamination factors indicated moderate to high risk. Our analysis of the effective range median mean quotient revealed some sampling stations as medium to high-priority sites. Similarly, probable effect level mean quotient designated some sampling stations as medium-low priority sites. We anticipated a moderate to significant mortality effect on the organisms from toxicity unit index values. Potential ecological risk index indicated a very high ecological risk on the Mahananda River. Furthermore, we predicted non-carcinogenic health hazards and carcinogenic health risks in the river's sediment.

Vulnerability module in the multi-level ontology of risk assessment of Wireless Sensor Networks

This work presents the development of a vulnerability module within a multi-level ontology for risk assessment in wireless sensor networks (WSNs) used in industrial environments. WSNs consist of multiple sensor nodes with microcontrollers, sensors, communication devices, and power sources, playing a key role in monitoring, diagnostics, and managing industrial processes. Their main advantages include reduced costs due to the lack of cabling and ease of scaling by adding new nodes. However, WSNs face significant challenges, including limited power supply, vulnerability to electromagnetic interference, low bandwidth, and exposure to cyber-attacks. These issues make them less suitable for real-time systems where fast and reliable data transmission is critical. The vulnerability module developed in this study addresses these challenges by identifying weaknesses, analyzing potential threats, and assessing risks using logical rules. These rules assess various network components such as devices, communication protocols, and external factors like physical access and radio interference. The module continuously monitors the network, detects new vulnerabilities, and provides real-time feedback to the risk assessment module, suggesting measures to mitigate risks. This enhances the security and reliability of WSNs in industrial applications. In conclusion, the vulnerability module within the multi-level ontology provides a structured approach to identifying and mitigating risks in WSNs. Despite the inherent vulnerabilities of WSNs, advancements in security protocols and energy efficiency make them increasingly viable for industrial automation and optimization.

Comprehensive Risk Assessment in Patients With Pulmonary Arterial Hypertension Referred for Lung Transplantation.

Whether comprehensive risk assessment predicts post-referral outcome in patients with pulmonary arterial hypertension (PAH) referred for lung transplantation (LT) in Japan is unknown. We retrospectively analyzed 52 PAH patients referred for LT. Risk status at referral was assessed using 3- and 4-strata models from the 2022 European Society of Cardiology and European Respiratory Society guidelines. The 3-strata model intermediate-risk group was further divided into 2 groups based on the median proportion of low-risk variables (modified risk assessment [MRA]). The primary outcome was post-referral mortality. During follow-up, 9 patients died and 13 patients underwent LT. There was no survival difference among 3-strata model groups. The 4-strata model classified 33, 16, and 3 patients as low intermediate, high intermediate, and high risk, respectively. The 4-strata model identified high-risk patients with a 1-year survival rate of 33%, but did not discriminate survival between the intermediate-risk groups. The MRA classified 15, 28, 8, and 1 patients as low, low intermediate, high intermediate, and high risk, respectively. High intermediate- or high-risk patients had worse survival (P<0.001), with 1- and 3-year survival rates of 64% and 34%, respectively. MRA high intermediate- or high-risk classification was associated with mortality (hazard ratio 12.780; 95% confidence interval 2.583-63.221; P=0.002). Patients classified as high intermediate or high risk by the MRA after treatment should be referred for LT.

Assessing bioavailability risks of heavy metals in polymetallic mining regions: a comprehensive analysis of soils with varied land uses.

To accurately assess the bioavailability risk of heavy metals (HMs) in a representative polymetallic mining region, we undertook an exhaustive analysis of Cu, Pb, Ni, Co, Cd, Zn, Mn, and Cr in soils from diverse land-use types, encompassing agricultural, forest, residential, and mining areas. We employed speciation analysis methods and a modified risk assessment approach to ascertain potential ecological threats posed by the HMs. Our findings reveal that both the total potential ecological risk and the modified bioavailability risks are most pronounced in the soil of the mining area. The modified bioavailability threats are primarily caused by Pb, Ni, Cd, and Co. Although the total potential ecological risk of Cu is high in the local soil, the predominance of its stable forms reduces its mobility, thereby mitigating its detrimental impact on the ecosystem. Additionally, medium modified bioavailability risks were identified in the peripheries of agricultural and forest areas, potentially attributable to geological processes and agricultural activities. Within the urban district, medium risks were observed in residential and mining areas, likely resulting from mining, metallurgy, industrial operations, and traffic-related activities. This study provides critical insights that can assist governmental authorities in devising targeted policies to alleviate health hazards associated with soils in polymetallic mining regions.

A homogeneous multi-vehicle cooperative group decision-making method in complicated mixed traffic scenarios

Connected and Automated Vehicles (CAVs) are expected to reshape the transportation system, and cooperative group intelligence of CAVs has great potential for improving transportation efficiency and safety. One challenge for CAV group driving is the decision-making under scenarios mixed with CAV and human-driven vehicles (HDV). Current studies mainly use methods based on single physical rules such as platoon driving or formation switch control, failing to reach a balanced and homogeneous state of optimal efficiency and risk in mixed traffic environments. In addition, most studies focus only on one specific type of scene, lacking the scene adaptability to various surrounding conditions. This paper proposes a homogeneous multi-vehicle cooperative group decision-making method targeting mixed traffic scenarios. A bi-level framework composed of behavior-level and trajectory-level decision-making is established to achieve balanced optimal cooperation. A region-driven behavioral decision mechanism is designed to decompose vehicle actions into a unified form of sequential target regions. Solutions are derived based on Cooperative Driving Safety Field, a risk assessment module inspired by field energy theory. The trajectory-level decision module takes the target regions as input and generates the control quantities of the CAVs through target point selection, conflict reconciliation, and dynamic constraint consideration. Experimental results on 19 various scenarios and continuous traffic flow scenes indicate that the proposed method significantly increases passing efficiency, reduces driving risk, and improves scene adaptability. In addition, experiments on multiple kinds of scenarios including intersections, ramps, bottlenecks, etc. prove that our method can adapt to various road topology structures. Feasibility is also verified through scaled physical platform validations and real-vehicle road tests.

Experimental investigation of aging effects on thermal behavior of lithium-ion batteries during charging process

Thermal runaway generated by lithium-ion batteries is considered as one of the major hazards that exhibits great thermal risk during operation. The aging behavior, as an inevitable phenomenon after long-term cycling, largely contributes to the deterioration of thermal characteristics. In this paper, comparative experiments were conducted to investigate the aging cell thermal performance during the charging process. The results indicate that the aging cell with a high charging rate presents a higher thermal risk. With the decrease of SOH, the anode structure was destroyed and side reactions reduced the thermal stability. The thermal runaway onset temperature decreased from 274.8 ± 9.1 °C for fresh to 238 ± 1.2 °C for aging cells, and the corresponding critical time shortened by approximately 1 min. In addition, the maximum heat release rate increased by 37.5 %–45.5 % for aging cells under high charging conditions, manifesting that aging cells are more prone to trigger fire accidents. Finally, a modified risk assessment method was established. This study contributes to comprehending the coupling effects of aging behavior and charging rate thermal runaway mechanisms, and provides valuable guidance for thermal runaway detection during the whole life.

A task offloading approach based on risk assessment to mitigate edge DDoS attacks

Edge computing mitigates the high latency and other issues associated with cloud computing, but it also introduces new risks. One such issue is DDoS attacks on edge servers, which arise when edge tasks are offloaded. There is a dearth of research on countermeasures for these kinds of DDoS attacks. Consequently, we present EDM_TOS, a task offloading strategy. This technique makes sure that tasks are transferred to dependable edge nodes based on the edge nodes' risk assessment mechanism, thereby reducing edge DDoS attacks. On the other hand, EDM_TOS works better than current approaches in five domains of evaluating awareness and capacity limitations for resolving edge DDoS attacks and issues with edge computing offloading. The edge computing task offloading algorithm, weighted cluster analysis algorithm, and risk assessment algorithm, respectively, are used to implement the risk assessment module, cluster analysis module, and task offloading module of the EDM_TOS mechanism. The three modules were evaluated experimentally. The findings demonstrate that the task offloading module's transactions per second (TPS) and the risk assessment module's malicious detection rate (MDR) respectively outperform to those of other policies. The cluster analysis module's clustering effect is superior to that of the conventional K-means algorithm. EDM_TOS performs better than current methods for thwarting edge DDoS attacks, according to performance assessments on five datasets. EDM_TOS can successfully fend off edge DDoS attacks, as demonstrated by practice.

Development of a Korean offshore wind power HSE risk assessment module based on systems engineering approach

This study provides an in-depth comparison of various risk assessment models widely used in modern industry and proposes the most suitable models for offshore wind power risk assessment, focusing on the characteristics and requirements of the offshore wind power industry. The selection of the assessment models is based on various factors, such as the purpose of risk assessment, the stakeholder requirements, and the characteristics of offshore wind power. This study emphasizes the importance of using a combination of different risk assessment models in situations of high uncertainty. Using model-based systems engineering, risk assessment module is designed and implemented with traceability from requirements to detailed functionality. The risk assessment module supports organic conversion between risk assessment models to assess the risks of offshore wind power generation. The functionality and usability of the offshore wind risk assessment module were verified through the evaluation and the field test.

Development and outcomes of a pilot risk assessment process and expert panel for medicines procured in an NHS Acute Trust

Abstract Introduction The risk assessment process for new medicines procured into NHS Acute Trusts varies between organisations. National guidance identifying categories of medicines requiring risk assessment is lacking. A UK Medicines Information (UKMi) developed risk assessment tool1 was fully completed for all new medicines procured at the Trust without exception, consuming significant clinical pharmacist resource. Completed risk assessments received review and approval by the specialty Principal Pharmacist only. Wider input from the Medication Safety team was sought if identified as required by the pharmacist completing or reviewing the tool. Aim To pilot a rationalised risk assessment process for newly procured medicines and measure the effect of implementation on clinical pharmacist workload and satisfaction, identification of risks and mitigations. Methods The rationalised risk assessment process was informed by a working group encompassing formulary, governance, medication safety and clinical service leads. The UKMi risk assessment tool was adapted and National Patient Safety Agency (NPSA) parenteral medicine risk assessment2 incorporated. A flowsheet triaging medicines requiring risk assessment was agreed. This included; unlicensed medicines, off-label use, parenteral medicines with an NPSA score over 2, controlled drugs, changes to concentration/excipients, known safety concerns e.g. due to national alerts, and any other medicine deemed high risk such as anticoagulants, cytotoxics, insulins and intrathecals. A New Drug Panel (NDP) was established, comprising a broad range of senior pharmacy leaders including the chief pharmacist. The NDP convened 6 weekly to agree the process, review risk assessments and propose additional mitigations. The process was piloted for 6 months (November 2021-May 2022). During the pilot, the number of new medicines procured and number of completed risk assessments were measured, alongside outcomes of NDP review. At the end of the pilot, users were invited to provide feedback directly via email and this was collated. Ethical approval was not required for this service evaluation. Results During the pilot, 248 requests to add medicines to the pharmacy dispensing system were made; 124 (50%) were new medicines to the Trust. 55/124 (44%) were triaged requiring risk assessment. The NDP met 4 times; 45/55 completed risk assessments were reviewed, 4 of which were considered via email due to urgency. The remaining 10 were scheduled for review at the next meeting. A total of 14 additional mitigations were advised and actioned for 12/41 (29%) new drugs reviewed, the remaining risk assessments were approved without further mitigations. Four pharmacists provided feedback and 100% was positive. The modified risk assessment was preferred due to ease of completion and improved clarity. Discussion/Conclusion A modified risk assessment process was piloted successfully; the number of required risk assessments halved releasing clinical pharmacist time and improving satisfaction with the process. Risk of patient harm was reduced through establishment of an expert NDP who identified a number of additional risk mitigations. No issues were identified from the medicines which were triaged out of the risk assessment process, though these would only be identified retrospectively. Limitations included that user feedback could have been collated more robustly, e.g., to provide more specific responses.

Optimizing net present values of risk avoidance for mountain railway alignments with seismic performance evaluation

AbstractRailway alignment optimization in earthquake‐prone mountainous (EPM) regions should quantify and trade off construction investments and seismic risks. Unfortunately, slight attention has been previously devoted to this trade‐off. To this end, based on the FEMA‐P58 methodology, a net present value (NPV) model of risk avoidance is presented and solved. In the model, alignment alternatives are first segmented into structural groups with different probabilistic seismic fragility curves, which are then used to generate structural repair cost and repair time curves. Afterward, a probabilistic seismic hazard curve is introduced to estimate the expected annual repair cost and time for computing railway direct and indirect seismic losses. Hence, the railway total annual loss caused by seismic activity can be obtained. Next, a benefit–cost analysis is performed to combine construction cost and seismic loss as the risk‐cost NPV. To optimize this objective function, a particle swarm algorithm is used as the basic approach. For implementing the probabilistic seismic performance analysis, a Monte Carlo simulation (MCS) is employed as the risk assessment module. Furthermore, due to the computationally intensive nature of MCS, a CPU‐based parallelization is embedded into the algorithm to expedite the search. Finally, the proposed model and method are applied to a representative real‐world railway case in an EPM region. Their effectiveness is discussed and verified in five experiments, including algorithm convergence analysis, alignment solution comparison, seismic risk interpretation, computational efficiency test, and a specific sensitivity analysis.

Comprehensive risk assessment in patients with pulmonary arterial hypertension referred for lung transplantation: a single-center experience

Abstract Background Current guidelines recommend risk stratification to optimize the timing to refer patients with pulmonary arterial hypertension (PAH) for lung transplantation (LT). However, the optimal timing for referral may vary by country. It is unknown whether risk stratification could be helpful to predict the post-referral outcome in Japan where the waiting period is about 3 years. Purpose We aimed to investigate the predictive value of risk stratification at the LT referral time, and to provide a decision-making tool to refer patients for LT. Methods We performed a retrospective cohort study of consecutive PAH patients referred for LT from May 2014 to December 2021. Risk status was assessed by the ESC/ERS three-strata and four-strata model (Figure 1). For assessment, functional class, six-minute walk distance, brain natriuretic peptide, the ratio of TAPSE to PASP, mean right atrial pressure and cardiac index were used as variables. In the exploratory analysis, the intermediate risk group by the three-strata model was further divided into two groups based on the median proportion of low-risk variables (modified risk assessment): low-intermediate or high-intermediate risk. The primary outcome was all-cause mortality after referral. Results 52 patients were enrolled [median age 30.5 (22.0-40.0) years, 83% idiopathic/heritable PAH]. During a median follow-up period of 2.5 (1.8-3.4) years, 9 patients died, and 13 patients underwent LT. By the three-strata model, 15, 36, and 1 patients were classified as low-, intermediate- and high risk (Figure 2a). There was no significant difference in survival between low- and intermediate risk. By the four-strata model, 33, 16, and 3 patients were classified as low-intermediate-, high-intermediate and high risk (Figure 2b). The four-strata model identified high-risk patients with survival rates of 33% at 1 year, whereas did not discriminate survival between low-intermediate and high-intermediate risk. By the modified risk assessment, 15, 28, 8 and 1 patients were classified as low-, low-intermediate, high-intermediate and high risk (Figure 2c). Patients at high-intermediate risk or higher had significantly worse survival (P&amp;lt;0.001); survival rates at 3 years were 92%, 80% and 34% in the low-, low-intermediate, and high-intermediate or high-risk group, respectively. In Cox regression analysis, high-intermediate or high risk by the modified risk assessment was significantly associated with the primary outcome adjusted for age, sex and PAH etiologies (HR, 6.284; 95% CI, 1.743-22.662; p=0.005). Conclusion The prognosis of high-risk group by the four-strata model was too poor to survive the waiting period. Whereas, the modified risk assessment could discriminate patients who could continue medical therapy or should be referred for LT. Risk stratification could help clinicians to make decisions to refer patients for LT. Further work is needed to refine the assessment tool in the context of LT referral.

Stress and Emotion Detection System Using IoT and Machine Learning with a Fuzzy Inference-Based Mental Health Risk Assessment Module

This research paper describes the conceptualization, deployment, and application of a stress and emotion detection system (SEDS) that utilizes emerging technologies including machine learning, the Internet of Medical Things (IoMT), and fuzzy logic. Particle Photon microcontrollers were used to collect and analyze data from non-invasive biosensing devices, input switches for behavioral, emotional, and cognitive stress indicators, and input signals generated by the machine learning-based facial identification sub-module, which detects an individual's emotional states (happy, sad/upset, angry/irritable, or nervous/scared). The acquired parameters were stored and made readily accessible using IoT cloud services and dedicated mobile phone applications. The emotional condition of an individual was assessed by the utilization of the Personal Image Classifier tool within the MIT App Inventor, which involved the analysis of facial expressions. Furthermore, the SEDS was integrated with a module for mental health risk assessment which employs fuzzy logic to categorize the user's stress-related psychological health threat level as very low, low, moderate, high, or extremely high based on the data collected. The customized smartphone application provided users specific recommendations for effectively managing their mental health, based on stress level assessments. When the SEDS determined that the level of mental health risk posed by stress was high, it automatically generated a referral notification and transmitted it via text message to the mental health care professional, facilitating the provision of appropriate psychological counseling. Based on the collected data, the stress and emotion detection system produced results that were comparable to those from the DASS21 stress scale. The system demonstrated an improved accuracy of 90% in a test involving thirty individuals who volunteered to participate. The machine learning-based emotion detection system achieved a classification accuracy of 86.67% in correctly detecting happy, neutral, sad, angry, or nervous feelings through the analysis of facial expressions. This research is designed to provide mental health care professionals such as guidance counselors, psychologists, and psychiatrists with the resources essential to facilitate the evaluation and treatment of mental health issues. It also aims to raise people's understanding and detection of psychological conditions through enhanced awareness initiatives.

A framework for cyber-risk insurance against ransomware: A mixed-method approach

Hackers follow a standard cyber kill chain process and install ransomware payloads using phishing emails on firms belonging to critical industry, resulting in huge losses in revenue, reputation, and customer churn. This study uses a mixed-method explanatory approach to mitigate ransomware attacks. We present the quantitative Ransomware Risk Management Model (R2M2) based on protection motivation theory (PMT). The Ransomware Risk Assessment module based on the threat appraisal component of PMT and the National Institute of Standards and Technology (NIST) guidelines can help the chief information security officer (CISO) to assess the risk using predictive analytics techniques. The Ransomware Risk Quantification module uses collective risk modeling to compute the severity of a ransomware attack on an organization. The Ransomware Risk Mitigation module helps the CISO to minimize the probability and impact of a ransomware attack on their organization by (i) investing in perimeter security technologies and training to ensure prepare, deter, detect, restrain, recover, and review mitigation strategies, (ii) adopting the National Institue of Standards and Technology (NIST) Cybersecurity Framework to ensure business resilience, and (iii) mitigating the residual risk by investing in cyber-risk insurance. We validated the proposed method using a qualitative study by interviewing participants form firms affected by ransomware, managed security service providers, security consultants, and cyber-risk insurers.

MADM-Q, an efficient multi-attribute decision-making support system for offshore decommissioning

With decades of development, some offshore facilities are reaching their design lives and need to be decommissioned. However, due to the limitation of regulations, technologies, and other aspects, which decommissioning options should be chosen is a challenging task. This paper introduces a multi-attribute quantitative evaluation system called MADM-Q (Multi-Attribute Decision Making-Quantitative), which can show decision makers with decommissioning options’ cost, risk, and environmental impact assessment results. The system consists of an input, output, and three processing modules. The Engineering Cost Evaluation System (ECES) is the first bottom-up approach cost assessment model in offshore facility decommissioning research. The Hierarchical Analyst Domino Evaluation System (HADES) acts as the risk assessment module, considering Domino Effect Accidents (DEAs) and providing accurate and rapid quantitative risk assessment results. The third sub-model, Composite Impact Evaluation System (CIES) assesses the environmental and socioeconomic impacts of the project. The system can obtain the basic cost range and the Individual Risk Per Annum (IRPA) value of the project quickly by using some simple inputs. The results of ECES and HADES reflects excellent accuracy - 12% cost evaluation average deviation and 1.43E-04 IRPA evaluation bias. Although currently, the HADES is a quasi-dynamic risk assessment method, it would be easy to develop it into a real-time dynamic risk monitoring and assessment system by combining advanced technologies such as digital twin technology and sensor technology.

Validation of a guidelines-based brief patient-administered digital tool to assess the need for germline genetic testing.

10605 Background: Scalable and reliable approaches are needed to assist non-genetics clinicians in assessing whether a patient meets criteria for germline genetic testing. Insurance coverage of genetic testing often depends on such assessments. RISE Risk Assessment Module: Hereditary Cancer is a brief patient-administered digital tool designed for this purpose. The tool’s assessment is based on personal and family history and is informed by national guidelines. The aim of this study was to assess the clinical validity of this digital tool by evaluating its agreement with assessments made by genetic counselors. Methods: We performed a chart review of cases from a telehealth genetic counseling practice seen July to October 2020. Extracted history was entered into the tool by the research team to model how a patient would complete the digital tool. The tool’s assessment of whether the patient met criteria for genetic testing was compared to the assessment of the genetic counselor who saw the patient. To further validate the tool, a subset of cases were analyzed for agreement between the tool and research team on which specific history-based rules in the tool’s algorithm applied for a given case. Results: The majority (121/152; 80%) of cases were female and the mean age was 52.3 (SD 15.9). Just over half of cases (85/152; 56%) had a personal history of cancer, including breast (27/85; 32%), colon (15/85; 18%), prostate (10/85; 12%), endometrial (12/85; 14%), and ovarian (7/85; 8%). 100/152 (66%) of cases met criteria for genetic testing. In 146/152 (96%) cases there was agreement between the assessments made by the tool and the genetic counselor regarding whether the patient met criteria for germline genetic testing. Nearly all disagreement (4/6; 67%) resulted from history intentionally left out of the tool as patients can rarely report them (aspects of tumor pathology 3/4; risk of having a pathogenic variant based on predictive models 1/4). For a subset of cases (62/152) we compared the tool and research team’s assessments of which aspects of the patient’s personal and family history met criteria for genetic testing. This was done by looking at which rules in the tool’s algorithm were triggered by the patient’s history. There was complete agreement between the tool and research team on which rules should be triggered. Completion of the tool took a mean of 3.3 minutes. Conclusions: The high level of agreement between the digital tool and genetic counselors supports RISE Risk Assessment Module: Hereditary Cancer as a clinically valid method of identifying individuals in need of evaluation for hereditary cancer predisposition. Complete agreement on which history-based rules applied in these cases further supports the clinical validity of the tool. Our results also suggest that the tool’s assessment can be delivered in under 5 minutes, which is critical to feasibility and scalability of the digital tool.

Competitive advantages of HAB species under changing environmental conditions in the coastal waters of the Bohai Sea, Yellow Sea and East China Sea

Harmful algal blooms (HABs) have become a severe ecological problem in coastal waters in recent decades. Under the enhanced anthropogenic disturbance, the Eastern Chinese coastal seas (ECCS), including the coastal waters of the Bohai Sea (BS), Yellow Sea and East China Sea, have been suffering environmental changes dramatically during the past 40 years. However, it is not clear how changes in coastal nutrient pollution, hydrodynamics and climate promote the succession of dominant HAB species simultaneously. In this study we used physical criteria (light availability, temperature and salinity) and chemical criteria (strategy of nutrient utilization) to quantify the competitiveness of the primary HAB species in the ECCS, i.e. Skeletonema costatum, Noctiluca scintillans, Prorocentrum donghaiense, Karenia mikimotoi, and Aureococcus anophagefferens. A risk assessment module has been developed and applied to this study based on the physical criteria of the main HAB species. Model result indicated a strong reliability on locating the potential physical habitat of blooms dominated by specific species. Imbalanced nutrient stoichiometry plays a vital role in HAB species succession in the ECCS, especially potential limitation of dissolved inorganic phosphorus. Due to long water residence time, persistence of nutrient imbalance and organic nutrient accumulation induce frequent blooms of Aureococcus anophagefferens in the BS. Observations and experiments have limitations when describing combining processes that influence HAB development, and need to be complemented with advanced mechanistic models for describing the hydrodynamics and biogeochemistry to allow for hindcasting and forecasting HAB risk under changing environmental conditions.

The significance of applying different factors for the evaluation of sediment contamination by toxic elements and estimation of the ecological risk.

The optimized three-step sequential extraction procedure for the fractionation of micro- and macroelements, was conducted to determine fractional characteristics of PTEs (potentially toxic elements) in surface sediments of rivers in the Vlasina watershed. The sequential extraction results, which enable the evaluation of mobility of the studied elements, have indicated that Zn, Ni, Cu, Cr, and As can be considered slightly mobile, whereas Pb, Mn, Cd, and Co were regarded as possibly mobile elements. Lead was dominantly bounded (specifically adsorbed or co-precipitated) to iron and manganese oxides (up to 80%) and may be released by reduction. Since the content of the exchangeable fraction (F1) is an indicator for anthropogenic impact on the aquatic environment, a low percentage (0-8%) of studied toxic elements in this fraction indicated that these elements have lithogenic origin in most sampling locations in the area of study. Except for Pb, the substantial positive correlations between Al and other elements showed that studied elements came primarily from terrigenous sources. Although the values obtained for the risk assessment code (RAC) indicated a slightly increased mobility of some elements (up to 22.44%), the values of the modified risk assessment code (mRAC), which include toxic effects on the environment, showed there is no danger of pollution by studied elements (all values were < 1%). Our recommendation is to use mRAC instead of RAC in ecochemical studies and assessment of the degree of sediment and soil pollution, because mRAC includes toxic effects of elements. Based on ATI values, river sediments show no toxic to a low toxic degree. Even though obtained results indicate that there was no considerable risk for river water contamination, the ecological risk for Fe and Pb should be monitored in the future.

Penilaian Risiko Kegagalan Bendungan Dan Tindakan Pengurangan Risiko Pada Bendungan Nglangon

The dam failure that has been operating has a great potential to occur if the administrator does not carry out proper maintenance. Dams require proper and correct maintenance and operation which will greatly affect the safety of the dam itself. Especially for dams that have been operating for a long time, major maintenance activities including repairs and rehabilitation are urgently needed to avoid the worst conditions such as facing natural disasters. Because a dam can cause a large impact if it fails, it is necessary to have a risk assessment to analyze and evaluate the hazard of the dam. Risk research conducted at the Nglangon Dam uses the event tree method, traditional and modified ICOLD. Based on the results of individual and group extreme probability analysis, both traditional and event tree methods, the risk probability value is above the acceptable risk value threshold of 1.00E-05, which means that the Nglangon Dam requires further action. This is in line with the results of the ICOLD modified risk assessment method with classification III (high) which means that the Nglangon Dam is included in a dam that requires special attention and corrective actions to reduce the level of risk in the Nglangon Dam

An Emergency Driving Intervention System Designed for Driver Disability Scenarios Based on Emergency Risk Field.

Driver disability has become an increasing factor leading to traffic accidents, especially for commercial vehicle drivers who endure high mental and physical pressure because of long periods of work. Once driver disability occurs, e.g., heart disease or heat stroke, the loss of driving control may lead to serious traffic incidents and public damage. This paper proposes a novel driving intervention system for autonomous danger avoidance under driver disability conditions, including a quantitative risk assessment module named the Emergency Safety Field (ESF) and a motion-planning module. The ESF considers three factors affecting hedging behavior: road boundaries, obstacles, and target position. In the field-based framework, each factor is modeled as an individual risk source generating repulsive or attractive force fields. Individual risk distributions are regionally weighted and merged into one unified emergency safety field denoting the level of danger to the ego vehicle. With risk evaluation, a path-velocity-coupled motion planning module was designed to generate a safe and smooth trajectory to pull the vehicle over. The results of our experiments show that the proposed algorithms have obvious advantages in success rate, efficiency, stability, and safety compared with the traditional method. Validation on multiple simulation and real-world platforms proves the feasibility and adaptivity of the module in traffic scenarios.

Artificial intelligence can use physiological parameters to optimize treatment strategies and predict clinical deterioration of sepsis in ICU

Objective. Sepsis seriously threatens human life. Early identification of a patient’s risk status and appropriate treatment can reduce septic shock risk and mortality. Our purpose is to design and validate an adjunctive therapy system based on deep reinforcement learning (DRL), which can provide treatment recommendations with providence and assess the patient’s risk status and treatment options in the early stages. Approach. Data is from the Beth Israel Deaconess Medical Center. The raw data included 53 423 patients from MIMIC-III. Of these, 19 620 eligible samples were screened to form the final cohort. First, the patient’s physiological parameters were fed into the DRL therapy strategy recommendation module (TSRM), which provides a forward-looking recommendation for treatment strategy. The recommended strategies were then fed into the reinforcement learning risk assessment module (RAM), which predicts the patient’s risk status and treatment strategy from a long-term perspective. The DRL model designed in this paper assists in formulating treatment plans and evaluating treatment risks and patient status through continuous interaction with patient trajectory; this model therefore has the foresight that a supervising deep learning model does not. Main results. The experiment shows that, in the test set for the TSRM, mortality is the lowest when the treatment strategy that is actually implemented is the same as the AI-recommended strategy. Regarding the RAM, it can accurately grasp a patient’s deterioration trend, and can reasonably assess a patient’s risk status and treatment plans at an early stage. The assessment results of the model were matched with the actual clinical records. Significance. A DRL-based sepsis adjunctive therapy model is proposed. It can prospectively assist physicians in proposing treatment strategies, assess the patient’s risk status and treatment methods early on, and detect deterioration trends in advance.