Rapid Decision Research Articles

Clinical Trial Data-Driven Risk Assessment of Drug-Drug Interactions: A Rapid and Accurate Decision-Making Tool.

In clinical practice, the vast array of potential drug combinations necessitates swift and accurate assessments of pharmacokinetic drug-drug interactions (DDIs), along with recommendations for adjustments. Current methodologies for clinical DDI evaluations primarily rely on basic extrapolations from clinical trial data. However, these methods are limited in accuracy owing to their lack of a comprehensive consideration of various critical factors, including the inhibitory potency, dosage, and type of the inhibitor, as well as the metabolic fraction and intestinal availability of the substrate. This study aims to propose an efficient and accurate clinical pharmacokinetic-mediated DDI assessment tool, which comprehensively considers the effects of inhibitory potency and dosage of inhibitors, intestinal availability and fraction metabolized of substrates on DDI outcomes. This study focuses on DDIs caused by cytochrome P450 3A4 enzyme inhibition, utilizing extensive clinical trial data to establish a methodology to calculate the metabolic fraction and intestinal availability for substrates, as well as the concentration and inhibitory potency for inhibitors ( or ). These parameters were then used to predict the outcomes of DDIs involving 33 substrates and 20 inhibitors. We also defined the risk index for substrates and the potency index for inhibitors to establish a clinical DDI risk scale. The training set for parameter calculation consisted of 73 clinical trials. The validation set comprised 89 clinical DDI trials involving 53 drugs. which was used to evaluate the reliability of in vivo values of and , the accuracy of DDI predictions, and the false-negative rate of risk scale. First, the reliability of the in vivo and values calculated in this study was assessed using a basic static model. Compared with values obtained from other methods, this study values showed a lower geometric mean fold error and root mean square error. Additionally, incorporating these values into the physiologically based pharmacokinetic-DDI model facilitated a good fitting of the C-t curves when the substrate's metabolic enzymes are inhibited. Second, area under the curve ratio predictions of studied drugs were within a 1.5× margin of error in 81% of cases compared with clinical observations in the validation set. Last, the clinical DDI risk scale developed in this study predicted the actual risks in the validation set with only a 5.6% incidence of serious false negatives. This study offers a rapid and accurate approach for assessing the risk of pharmacokinetic-mediated DDIs in clinical practice, providing a foundation for rational combination drug use and dosage adjustments.

Construction of digital twin workshop integrated with edge computing and deep learning

Abstract This study addresses the challenges of real-time data synchronization and big data processing in the construction of digital twin workshops under the background of intelligent manufacturing. A solution that integrates edge computing with deep learning technology is proposed. Through a multi-layered, modular framework structure, this solution achieves real-time synchronous mapping and interaction between physical workshops and virtual workshops, effectively improving production efficiency, reducing maintenance costs, and ensuring product quality. The edge computing layer in the research is responsible for preprocessing heterogeneous data from multiple sources and making rapid response decisions. Simultaneously, deep learning algorithms are employed to conduct in-depth analysis, forecasting, and optimization of the production process. Case studies demonstrate that this solution can significantly improve equipment utilization and optimize workpiece scheduling, validating its application value in actual manufacturing environments.

Proposed Business Strategic Improvement to Accelerate Waste Management Business Unit at Building Materials Company (Case Study at Cement Inc)

The rising environmental concerns and regulatory measures in Indonesia have created new opportunities for the expansion of waste management services in the cement sector, specifically by utilizing thermal technology. The Waste Management Unit (WMU) of Cement Company presents substantial strategic opportunities for expansion and development. After undergoing a merger and acquisition, Cement Inc experienced a transformation and has now become a state-owned firm. This change offers WMU Cement Inc further opportunities for growth and expansion. The objective of this research is to identify the most effective strategy for the Waste Management Business Unit within the Cement Company using the Analytical Hierarchy Process. The study explores three fundamental research question: (1) What is the current situation for WMU Cement Inc in terms of market, money, customer, and organization? (2) What are the obstacles inside the organization and in the external environment that hinder the acceleration of waste management services in Indonesia, resulting in limited value creation for the company? (3) What are the suggested recommendations and strategic choices for the company to enhance its value absorption? The study used the SWOT (Strengths, Weaknesses, Opportunities, and Threats) and AHP (Analytic Hierarchy Process) framework to perform a comprehensive evaluation and evaluate three strategic options for the waste management division: retaining it within the cement firm, expanding it to the cement group as a whole, or establishing it as an independent entity. The results of this study indicate that the most favorable choice (40.6%) is to establish WMU as a separate entity, which offers greater advantages compared to fully integrating with the parent company (30.9%) and keeping the current business operations (28.4%). By creating WMU as an independent organization, it may effectively avoid market and pricing cannibalization amongst its subsidiaries and optimize operations. WMU Cement Inc possesses the capacity to assume a leading role in waste management services by offering waste solutions to all SIG subsidiary plants and other cement makers, including ITP. The benefits of this method include autonomous operations, targeted management, rapid decision-making, and varied income streams achieved via the cultivation of specialized skills and expertise in waste management. This decision presents a chance to attract targeted investments and collaborations that promote innovation and expansion in waste management.

Expand or not? Trade-offs of the remanufacturer on recycling channels

The integration of online trading has emerged as a crucial driver of operational efficiency in the reverse supply chain. This study expands the recycling model to include three distinct scenarios: basic-offline recycling (BR), single-platform recycling (SR), and dual-platform recycling (DR). The research delves into the implications of varying platform numbers, profit-sharing contracts, consumer recycling preferences, and recycling price adjustment speeds on supply chain stability within a multi-period decision-making framework. Findings reveal that the SR model is not always yield higher profits for remanufacturers through lower revenue allocation proportions to platforms, in stark contrast to the DR model. Furthermore, rapid decision adjustments do not consistently enhance the stability of the recycling system across multiple decision cycles. Remanufacturers' expansion strategies are significantly impacted by the costs associated with acquiring e-waste from individual recyclers. Importantly, an excess of platforms does not always result in favorable outcomes. Conversely, the SR model proves superior to the DR model in practical contexts that may arise. These insights offer valuable guidance to remanufacturers seeking to develop viable expansion strategies that foster mutually beneficial relationships among individual recyclers, remanufacturers, and platforms.

A real-time early warning classification method for natural gas leakage based on random forest

Serious natural gas leakage explosion accidents have brought seriously threatening to people's lives and properties. Efficient warning classifications is of great significance to make rapid response, thus reducing the losses caused by accidents. This paper describes a novel early warning classification method for natural gas leakage based on a multi-classification random forest (RF) model, which allows evaluating the level of early warning of gas accidents timely and accurately, assisting monitoring department and gas company in timely rapid decision and scientific disposal. Fully considering the laws of natural gas leakage and the change of comprehensive risks in underground spaces adjacent to natural gas pipeline, an early warning classification index system was established, and multiple warning factors features were extracted from recorded warning events of natural gas leakage. Then the early-warning level labels of the warning events was gained by K-mean clustering and experts scoring methods. The extracted warning features and the associated early-warning level labels were used to train and validate the proposed model. The effectiveness and feasibility of this model is further verified by comparing with other popular approaches. Furthermore, the verified model is loaded into real time module, which can achieve the real time warning classification. The research results demonstrated that the proposed method can timely and accurately classify the levels of the early warning events. The prediction accuracy of the natural gas leakage early warning classification model based on the RF algorithm is 88.02 %. For real time warning events, rapid decision can be made according to the characteristics of early-warning grades, and the emergency disposal can be guided more effectively based on the warning classification results.

Study on the regularities of flow‐sediment transport and sedimentation in the lower Yellow River

AbstractThe training effectiveness of the lower Yellow River (LYR) depends on the understanding of the regularity of flow‐sediment transport and riverbed sedimentation. The measured data of daily flow discharge and sediment transport rate at the five hydrological stations (Xiaolangdi [Xld], Huayuankou [Hyk], Gaocun [Gc], Aishan [As], and Lijin [Lj]) in the LYR during the period from 1960 to 2017 are used to investigate the regularity of flow‐sediment transport and sedimentation in the LYR. The Xld station is used as the inlet control station, and the LYR is divided into four segments using four other stations, and the whole year is divided into three periods, namely, the dry season, the flood period, and the nonflood period of the wet season. On this basis, the relationships between the sediment transport rates at the four stations (Hyk, Gc, As, and Lj) and the rates at their respective closest upstream stations are analyzed in each of the three periods. According to the incoming sediment coefficient of the Xld station, the flow and sediment processes in the three periods are classified, and the refined equations for the relationship between the sediment transport rates at the downstream station and its upstream station are established. The results show that the calculated amount and process of erosion and deposition in each period and each segment of the LYR using the equations are in good agreement with the measured values. The relationship equations established in this study can conveniently predict the amount of erosion and deposition in different periods and different segments of the LYR in the future, which is of great significance to the rapid decision of the impact of the construction and operation of hydraulic projects in the upper and middle reaches of the Yellow River on the sedimentation in the LYR.

Innovation through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics.

In this study, we propose the use of a machine learning system in emergency department triage during pandemics to detect patients at the highest risk of death and infection using the COVID-19 era as an example, where rapid decision making and comprehensive support have becoming increasingly crucial. All patients who consecutively presented to the emergency department were included, and more than 89 variables were automatically analyzed using the extreme gradient boosting (XGB) algorithm. The XGB system demonstrated the highest balanced accuracy at 91.61%. Additionally, it obtained results more quickly than traditional triage systems. The variables that most influenced mortality prediction were procalcitonin level, age, and oxygen saturation, followed by lactate dehydrogenase (LDH) level, C-reactive protein, the presence of interstitial infiltrates on chest X-ray, and D-dimer. Our system also identified the importance of oxygen therapy in these patients. These results highlight that XGB is a useful and novel tool in triage systems for guiding the care pathway in future pandemics, thus following the example set by the well-known COVID-19 pandemic.

The impact of hindsight bias on the diagnosis of perioperative events by anesthesia providers: A multicenter randomized crossover study

Study objectiveHindsight bias is the tendency to overestimate the predictability of an event after it has already occurred. We aimed to evaluate whether hindsight bias influences the retrospective interpretation of clinical scenarios in the field of anesthesiology, which relies on clinicians making rapid decisions in the setting of perioperative adverse events. DesignTwo clinical scenarios were developed (intraoperative hypotension and intraoperative hypoxia) with 3 potential diagnoses for each. Participants completed a crossover study reviewing one case without being informed of the supposed ultimate diagnosis (i.e., no ‘anchor’ diagnosis), referred to as their foresight case, and the other as a hindsight case wherein they were informed in the leading sentence of the scenario that 1 of the 3 conditions provided was the ultimate diagnosis (i.e., the diagnosis the participant might ‘anchor’ to if given this information at the start). Participants were randomly assigned to (1) which scenario (hypotension or hypoxia) was presented as the initial foresight case and (2) which of the 3 potential diagnoses for the second case (the hindsight case, which defaulted to whichever case the participant was not assigned for the first case) was presented as the ultimate diagnosis in the leading sentence in a 2 (scenario order) x 3 (hindsight case anchor) between-subjects factorial design (6 possible randomization assignments). SettingTwo academic medical centers. ParticipantsFaculty, fellow, and resident anesthesiologists and certified nurse anesthetists (CRNAs). InterventionsNone. MeasurementsAfter reading each clinical scenario, participants were asked to rate the probability (%) of each of three potential diagnoses to have caused the hypotension or hypoxia. Compositional data analysis (CoDA) was used to compare whether diagnosis probabilities differ between the hindsight and the foresight case. Main results113 participants completed the study. 59 participants (52%) were resident anesthesiologists. Participants randomized to the hypotension scenario as a hindsight case were 2.82 times more likely to assign higher probability to the pulmonary embolus diagnosis if provided as an anchor (95% CI, 1.35–5.90; P = 0.006) and twice as likely to assign higher probability to the myocardial infarction diagnosis if provided as an anchor (95% CI, 1.12–3.58; P = 0.020). Participants randomized to the hypoxia scenario as a hindsight case were 1.78 times more likely to assign higher probability to the mainstem bronchus intubation diagnosis if provided in the anchor statement (95% CI, 1.00–3.14; P = 0.048) and 3.72 times more likely to assign higher probability to the pulmonary edema diagnosis if provided as an anchor (95% CI, 1.88–7.35; P < 0.001). ConclusionsHindsight bias influences the clinical diagnosis probabilities assigned by anesthesia providers. Clinicians should be educated on hindsight bias in perioperative medicine and be cognizant of the effect of hindsight bias when interpreting clinical outcomes.

Emerging Microorganisms and Infectious Diseases: One Health Approach for Health Shared Vision.

Emerging infectious diseases (EIDs) are newly emerging and reemerging infectious diseases. The National Institute of Allergy and Infectious Diseases identifies the following as emerging infectious diseases: SARS, MERS, COVID-19, influenza, fungal diseases, plague, schistosomiasis, smallpox, tick-borne diseases, and West Nile fever. The factors that should be taken into consideration are the genetic adaptation of microbial agents and the characteristics of the human host or environment. The new approach to identifying new possible pathogens will have to go through the One Health approach and omics integration data, which are capable of identifying high-priority microorganisms in a short period of time. New bioinformatics technologies enable global integration and sharing of surveillance data for rapid public health decision-making to detect and prevent epidemics and pandemics, ensuring timely response and effective prevention measures. Machine learning tools are being more frequently utilized in the realm of infectious diseases to predict sepsis in patients, diagnose infectious diseases early, and forecast the effectiveness of treatment or the appropriate choice of antibiotic regimen based on clinical data. We will discuss emerging microorganisms, omics techniques applied to infectious diseases, new computational solutions to evaluate biomarkers, and innovative tools that are useful for integrating omics data and electronic medical records data for the clinical management of emerging infectious diseases.

A Qualitative Analysis of Barriers to Evidence-Based Care in the Prehospital Management of Patients with Suspected Acute Coronary Syndrome

Objectives Prehospital electrocardiogram (ECG) and administration of aspirin are evidence-based strategies for patients with acute coronary syndrome (ACS). However, emergency medical services (EMS) compliance in patients with suspected ACS varies widely. We sought to understand the barriers to prehospital ECG acquisition and aspirin administration for patients with suspected ACS. Methods In this qualitative study, we interviewed EMS clinicians at three geographically diverse United States (U.S.)-based EMS agencies. We interviewed practicing clinicians and quality and operations leaders at these agencies. Based on the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, interviews were recorded, transcribed, and analyzed using a grounded qualitative approach with open coding. The Systems Engineering Initiative for Patient Safety (SEIPS) framework and a constant comparison technique were used to identify and refine themes. Results Twenty-five paramedics and 20 additional agency personnel participated. Median age was 41 (IQR: 34–51) years and 13 (29%) were female. Themes were organized using SEIPS and longitudinally through four phases of an EMS call. During the pre-arrival phase, staffing challenges, training quality, and dispatch may anchor EMS clinicians on a diagnosis. During the diagnosis and treatment phase, safety and communication barriers may take priority over care delivery. Additionally, EMS clinicians must allocate assets (e.g. whether to send an advanced life support unit) and financial resources; veteran EMS clinicians identified their experience whereas newer clinicians cited their recent education when making these decisions. Also, diagnostic uncertainty due to increasing patient complexity and atypical presentations contributed to diagnostic errors. During the response and transport phase, the scope of practice limits the use and interpretation of the ECG, with clinicians reporting that liberal use of ECG led to more rapid decision-making. Finally, in the after phase, personnel reported the “psychologically taxing” nature of the job contributing to biases, bad habits, and burnout. Performance feedback was desired for personal development, though currently perceived as infrequent and punitive. Conclusions Multiple, interrelated themes underscored the complexities of delivering evidence-based care to prehospital patients with ACS. Education in ECG interpretation, resource allocation, bias, and enhancing feedback may serve as strategies to address the identified barriers.

Forward defense – concept, plan, and action for solving russian aggression at Nato's easternd border

Forward defense is not a new concept. It is rather traditional, coming from the Cold War and implying, originally, the nuclear posture and strategy. Following the issuance of the Madrid NATO Strategic Concept and Vilnius statement that not an inch of the Alliance’s territory will fall under the control of the opponents, a new approach to forward defense is needed to cope with the multiple shifts in the security environment: Russian war of aggression, the change of technological generation, dilemmas of resources and apabilities, limits and multiple challenges from the international environment with superposed simultaneous crises. The perspective of possible attacks on NATO territory – in the next 2-3-5-8 years – requires a review of the concept and, consequently, of the political decisions, strategic planning, enforcement of those decisions, and development of forces and capabilities on the ground. Combining nuclear flexible capabilities, a strategy of massive retaliation with conventional forces and deterrence by reinforcement, deterrence by denial, forward presence, rapid projection capabilities, resolution, effective decisionmaking, and forward posture, we could build a new, updated doctrine of forward defense. However, the debate has to consider what is theoretically developed, technically feasible, politically acceptable, financially sustainable, and strategically credible in the „new forward defense” for granting inviolability of allied territory. The basic limitation is to define and refine forward defense without a reconsideration beyond existing means.

Artificial Intelligence-Clinical Decision Support System in Infectious Disease Control: Combatting Multidrug-Resistant Klebsiella pneumoniae with Machine Learning.

The World Health Organization has identified Klebsiella pneumoniae (KP) as a significant threat to global public health. The rising threat of carbapenem-resistant Klebsiella pneumoniae (CRKP) leads to prolonged hospital stays and higher medical costs, necessitating faster diagnostic methods. Traditional antibiotic susceptibility testing (AST) methods demand at least 4 days, requiring 3 days on average for culturing and isolating the bacteria and identifying the species using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), plus an extra day for interpreting AST results. This lengthy process makes traditional methods too slow for urgent clinical situations requiring rapid decision-making, potentially hindering prompt treatment decisions, especially for fast-spreading infections such as those caused by CRKP. This research leverages a cutting-edge diagnostic method that utilizes an artificial intelligence-clinical decision support system (AI-CDSS). It incorporates machine learning algorithms for the swift and precise detection of carbapenem-resistant and colistin-resistant strains. We selected 4307 KP samples out of a total of 52,827 bacterial samples due to concerns about multi-drug resistance using MALDI-TOF MS and Vitek-2 systems for AST. It involved thorough data preprocessing, feature extraction, and machine learning model training fine-tuned with GridSearchCV and 5-fold cross-validation, resulting in high predictive accuracy, as demonstrated by the receiver operating characteristic and area under the curve (AUC) scores, laying the groundwork for our AI-CDSS. MALDI-TOF MS analysis revealed distinct intensity profiles differentiating CRKP and susceptible strains, as well as colistin-resistant Klebsiella pneumoniae (CoRKP) and susceptible strains. The Random Forest Classifier demonstrated superior discriminatory power, with an AUC of 0.96 for detecting CRKP and 0.98 for detecting CoRKP. Integrating MALDI-TOF MS with machine learning in an AI-CDSS has greatly expedited the detection of KP resistance by approximately 1 day. This system offers timely guidance, potentially enhancing clinical decision-making and improving treatment outcomes for KP infections.

Rapid genomic testing in critically ill pediatric patients: Genetic counseling lessons from a national program

Genetic counselors (GCs) face unique challenges in the acute care setting. Acute care environments—such as neonatal and pediatric intensive care units—are characterized by urgency, complexity, and rapid decision making. These settings require GCs to navigate a delicate balance between addressing the immediate clinical needs of patients and providing comprehensive genetic information to families, while demanding adaptation of existing skills for practice.Rapid genomic testing (rGT) is increasingly becoming standard of care in acute care. GCs are well placed to support families through the rGT process. Despite this, there is a lack of consistency in the provision of comprehensive acute care genetic counseling globally and a subsequent need for professional guidance in this area.The Acute Care Genomics study piloted a national approach to delivering rGT for infants and children admitted to intensive care units in Australia with suspected genetic conditions between 2018 and 2022. GCs from across Australia were involved in both pre- and post-test counseling for the families of these critically unwell children. Based on our collective experience of delivering this national rGT program, this article provides a discussion of common challenges for health professionals new to delivering rGT in intensive care. We share some practical solutions and make recommendations for supporting families in this area of practice.

Dynamics of Political Communication amid a Global Health Crisis: Government Response and Public Response

The global health crisis caused by the COVID-19 pandemic has changed the dynamics of political communication in various countries. Governments around the world are faced with unprecedented communications challenges, where rapid and effective decisions are urgently needed to address the spread of the virus. However, the effectiveness of political communication is often hampered by lack of information, misunderstanding and public distrust. This research aims to analyze how the government communicates with the public during a health crisis and how the public responds to this communication. The primary goal is to identify effective strategies and uncover communication gaps that can serve as valuable lessons for future crises. The method used in this research is content analysis from various news sources, government reports and social media. In addition, respondents were surveyed to measure public perceptions and responses to government communications. The research results show significant variation in the effectiveness of communication strategies between countries. Countries with open, transparent and consistent communication tend to experience higher levels of public compliance and more controlled spread of the virus. Meanwhile, countries with sporadic and ambiguous communications have had difficulty managing the pandemic and faced more excellent public resistance. This research concludes that success in managing a health crisis depends not only on the policies taken but also on how the government communicates with the public. Transparency, consistency and continuity in communications have proven critical in building public trust and supporting pandemic mitigation efforts. This research provides insights that can help policymakers improve communication strategies in the future.

UK horse owners and veterinary practitioners' experiences of decision-making for critical cases of colic.

The survival of horses diagnosed with critical colic (requiring referral or euthanasia) relies on rapid and effective decision-making by the owner and veterinary practitioner. To explore UK horse owners' and veterinary practitioners' experiences of decision-making for critical cases of equine colic. Qualitative study using a phenomenological approach. Individual, semi-structured telephone interviews were conducted with 14 horse owners and 13 veterinary practitioners (vets) who had experienced a critical decision (referral or euthanasia) for a horse with colic. A purposive, convenience sample of participants was recruited. Sessions explored participant's experience of colic, including recognition, help-seeking behaviour, and challenges. Thematic analysis was performed on collected data. Four over-arching themes were identified; 'head', 'heart', 'practicalities' and 'impact'. Owners acknowledged responsibility for their horse's welfare but had different perspectives than vets on the importance of finance ('head'). Both vets and owners described how the horse-human relationship ('heart') often led to conflict during decision-making. The vet-client relationship was influential on decision-making for both owners and vets; involving other people in decision-making was described both positively and negatively by participants ('heart'). 'Practicalities', such as lack of preparedness, transport issues and adverse weather conditions, were identified by both owners and vets as barriers. Owners described a 'rollercoaster' of emotions after a critical decision, with profound impacts on their mental wellbeing, feelings of guilt, and long-term changes in behaviour ('impact'), and a lack of support to manage these feelings. Small sample size. This study describes stakeholder decision-making during critical cases of equine colic. Factors that commonly influenced decisions included an owner's previous knowledge and beliefs, social pressures, logistics and the relationship between the owner and vet. The study highlighted long-term impacts on the owner, including their management and decisions for subsequent horses. These factors should be considered in shared decision-making.

Automatic lithology identification in meteorite impact craters using machine learning algorithms

Identifying lithologies in meteorite impact craters is an important task to unlock processes that have shaped the evolution of planetary bodies. Traditional methods for lithology identification rely on time-consuming manual analysis, which is costly and limits the efficiency of rapid decision-making. This paper utilizes different machine learning algorithms namely Random Forest, Decision Tree, K Nearest Neighbors, and Logistic Regression with Grid Search to classify rock lithologies using data from the Bosumtwi impact crater in Ghana. A repeated stratified k-fold cross-validation method is applied to Grid Search to select the best combination of hyperparameters. The findings demonstrate that the Random Forest algorithm achieves the most promising results in classifying lithologies in the meteorite impact crater with an accuracy score of 86.89%, a recall score of 84.88%, a precision score of 87.21%, and an F1 score of 85.48%. The findings also suggest that more high-quality data has the potential to further increase the accuracy scores of the machine learning algorithm. In conclusion, this study demonstrates the significant potential of machine learning techniques to revolutionize lithology identification in meteorite impact craters, thus paving the way for their influential role in future space exploration endeavors.

Assessment of household vulnerability to embankment breaching in the coastal area of the Indian Sundarban

The coastal and low-lying areas of the Indian Sundarban are highly vulnerable to multiple hazards. Cyclonic hazards and floods have become a regular phenomenon in this fragile region. The villages and their households of this region are very much vulnerable to embankment breaching. Embankments in this region have been built since 1770, but it was so much unsuccessful in coping with storm surges or flood hazards. The study focuses on assessing the vulnerability of households that are spatially affected by embankment breaching. The methodology is composed of four dimensions of vulnerability including social, economic, physical, and environmental, which cover thirty-four indicators. From dimension-wise vulnerability, the overall vulnerability of households has been estimated. Based on the proximity to the embankment breaching location and impact, 330 households from eleven villages were selected to conduct the household survey. The study also adopted the relative effect score method to delineate the influence of indicators on the vulnerability of each specific village. The result shows that households belonging to Patibunia, Lakshmipur Abad, and Ganeshnagar occupy a very high categorical position in overall household vulnerability. The higher degree of exposure due to embankment breaching, unequal infrastructure, unstable economic structure, and a low level of education is considered to be highly sensitive towards vulnerability. The method is flexible and can be replicated and modified in other spatial scales for rapid decision-making.

Self-Adaptive Server Anomaly Detection Using Ensemble Meta-Reinforcement Learning

As the user’s behavior changes at any time with cloud computing and network services, abnormal server resource utilization traffic will lead to severe service crashes and system downtime. The traditional single anomaly detection model cannot handle the rapid failure prediction ahead. Therefore, this study proposed ensemble learning combined with model-agnostic meta-reinforcement learning called ensemble meta-reinforcement learning (EMRL) to implement self-adaptive server anomaly detection rapidly and precisely, according to the time series of server resource utilization. The proposed ensemble approach combines hidden Markov model (HMM), variational autoencoder (VAE), temporal convolutional autoencoder (TCN-AE), and bidirectional long short-term memory (BLSTM). The EMRL algorithm trains this combination with several tasks to learn the implicit representation of various anomalous traffic, where each task executes trust region policy optimization (TRPO) to quickly adapt the time-varying data distribution and make rapid decisions precisely for an agent response. As a result, our proposed approach can improve the precision of anomaly prediction by 2.4 times and reduce the model deployment speed by 5.8 times on average because a meta-learner can immediately be applied to new tasks.

Plasmatic Inactive IL-18 Predicts a Worse Overall Survival for Advanced Non-Small-Cell Lung Cancer with Early Metabolic Progression after Immunotherapy Initiation.

This ancillary study includes 195 patients with metastatic non-small-cell lung cancer (NSCLC) treated with ICI in monotherapy, either pembrolizumab or nivolumab. Plasmatic levels of IL-18-related compounds, comprising the inhibitor IL-18 binding protein (IL-18BP), the inactive IL-18 (corresponding to IL-18/IL-18BP complex), and the active free IL-18, were assayed by ELISA. Objective tumoral response was analyzed by 18FDG PET-CT at baseline, 7 weeks, and 3 months post treatment induction, using PERCIST criteria. Plasmatic IL-18BP and total IL-18 levels are increased at baseline in NSCLC patients compared with healthy controls, whereas IL-18/IL-18BP complexes are decreased, and free IL-18 levels remain unchanged. Neither of the IL-18-related compounds allowed to discriminate ICI responding to nonresponding patients. However, inactive IL-18 levels allowed to discriminate patients with a first tumor progression, assessed after 7 weeks of treatment, with worse overall survival. In addition, we showed that neutrophil concentration is also a predictive indicator of patients' outcomes with OS (HR = 2.6, p = 0.0001) and PFS (HR = 2.2, p = 0.001). Plasmatic levels of inactive IL-18, combined with circulating neutrophil concentrations, can effectively distinguish ICI nonresponding patients with better overall survival (OS), potentially guiding rapid decisions for therapeutic intensification.

The use of evidence to guide decision-making during the COVID-19 pandemic: divergent perspectives from a qualitative case study in British Columbia, Canada

BackgroundThe challenges of evidence-informed decision-making in a public health emergency have never been so notable as during the COVID-19 pandemic. Questions about the decision-making process, including what forms of evidence were used, and how evidence informed—or did not inform—policy have been debated.MethodsWe examined decision-makers' observations on evidence-use in early COVID-19 policy-making in British Columbia (BC), Canada through a qualitative case study. From July 2021- January 2022, we conducted 18 semi-structured key informant interviews with BC elected officials, provincial and regional-level health officials, and civil society actors involved in the public health response. The questions focused on: (1) the use of evidence in policy-making; (2) the interface between researchers and policy-makers; and (3) key challenges perceived by respondents as barriers to applying evidence to COVID-19 policy decisions. Data were analyzed thematically, using a constant comparative method. Framework analysis was also employed to generate analytic insights across stakeholder perspectives.ResultsOverall, while many actors’ impressions were that BC's early COVID-19 policy response was evidence-informed, an overarching theme was a lack of clarity and uncertainty as to what evidence was used and how it flowed into decision-making processes. Perspectives diverged on the relationship between 'government' and public health expertise, and whether or not public health actors had an independent voice in articulating evidence to inform pandemic governance. Respondents perceived a lack of coordination and continuity across data sources, and a lack of explicit guidelines on evidence-use in the decision-making process, which resulted in a sense of fragmentation. The tension between the processes involved in research and the need for rapid decision-making was perceived as a barrier to using evidence to inform policy.ConclusionsAreas to be considered in planning for future emergencies include: information flow between policy-makers and researchers, coordination of data collection and use, and transparency as to how decisions are made—all of which reflect a need to improve communication. Based on our findings, clear mechanisms and processes for channeling varied forms of evidence into decision-making need to be identified, and doing so will strengthen preparedness for future public health crises.