Hasil untuk "Crisis management. Emergency management. Inflation"

Jaymin Harishkumar Sutarwala

Privacy incidents in healthcare customer relationship management (CRM) systems—including data breaches, unauthorized access events, and consent failures—constitute recurring organizational risks that can escalate into full-scale crises, eroding patient trust, damaging institutional reputation, and compromising care continuity. These events pose distinctive communication challenges: organizations must translate complex technical information for diverse stakeholders under time pressure, manage uncertainty about incident scope and impact, and coordinate messaging across legal, clinical, and administrative functions while satisfying regulatory notification requirements. Despite extensive scholarship on healthcare information privacy and on crisis and risk communication, limited research integrates these domains to explain how communication practices interact with privacy control environments to shape trust outcomes following incidents. This paper addresses that gap through an integrative literature review synthesizing health information technology privacy scholarship with crisis and risk communication theory, culminating in the development of the Privacy–Access–Communication (PAC) Framework. Grounded in Situational Crisis Communication Theory and Crisis and Emergency Risk Communication principles, the PAC Framework links technical privacy controls to governance structures and communication practices, providing a systematic approach to privacy risk communication in healthcare CRM contexts. This paper makes three contributions: (1) a theoretically grounded framework integrating technical, governance, and communication dimensions of privacy management; (2) identification of recurring communication patterns connecting access-control mechanisms to stakeholder messaging strategies; and (3) a readiness checklist enabling practitioners to assess communication preparedness for privacy incidents. Implications for crisis communication preparedness in healthcare organizations are discussed, along with directions for empirical validation.

Kyle Henry, Brittany Pelsue, Heather A. Hartman et al.

BACKGROUND Acute pulmonary edema is a life-threatening condition commonly occurring as a result of acute decompensated heart failure. The current pulmonary edema guidelines recommend initiating IV NTG at 5 μg/min and titrating by 5 μg every 3 to 5 min to reduce systolic blood pressure (SBP) by 25 % within the first hour of arrival to the emergency department (ED). Current studies do not determine if the dose of IV NTG has an impact on the time to oxygen weaning. The purpose of this study is to evaluate and analyze clinical outcomes associated with oxygenation status in patients treated with either low dose or high dose IV NTG for the management of SCAPE. METHODS This was a retrospective, single center, cohort study including adult patients who arrived to the ED with the diagnosis of pulmonary edema in the setting of a hypertensive crisis. Patients were categorized into two groups: low dose IV NTG 30 %), hypotension requiring vasopressors, improvement/worsening of respiratory status, rate of intensive care unit (ICU) admission, and ICU length of stay. RESULTS This study included 441 patients who received IV NTG. Patients in the high dose group (≥100 μg/min), had a median time from initiation of infusion to oxygen weaning of 2.7 h as compared to 3.3 h in the low dose group (<100 μg/min) (p = 0.01). Patients in the low dose group had a lower likelihood of obtaining an SBP reduction by 25 % (+/- 5 %) within 60 min when compared to the high dose group (RR 0.64, 95 % CI 0.52 to 0.79). Hypotension was more common in the low dose group when compared to the high dose group (RR 1.29, 95 % CI 1.02 to 1.62). There were no significant differences in incidence of hypotension requiring vasopressors, worsening respiratory status, and ICU length of stay between the two groups. CONCLUSIONS High dose IV NTG results in an earlier weaning of oxygen when compared to low dose IV NTG. High dose IV NTG was associated with a higher likelihood of obtaining initial SBP goals without concern for an increased risk of hypotension when compared to low dose IV NTG.

Yasaswini Matam, Harikrishna M.

PurposeThe study aims to create a realistic model for emergency logistics, specifically focusing on relief distribution during disasters, by incorporting the prioritization of nodes. It addresses the vehicle routing problem (VRP) and resource allocation problem (RAP) to optimize routes and resource delivery. By integrating these two problems, the study seeks to enhance the efficiency and effectiveness of emergency response operations, ensuring timely and equitable distribution of resources to affected areas. This model is particularly relevant for disaster management agencies looking to improve their logistical strategies and minimize the impact of disasters on communities.Design/methodology/approachThe study uses a two-phase multi-objective model. In the first phase, it minimizes routing costs associated with the VRP. In the second phase, it focuses on minimizing penalty costs for total unsatisfied demand, addressing the RAP. The model uses the CPLEX solver and introduces the modified decomposition and assignment heuristic (MDAH) and genetic algorithm (GA) for handling large-scale scenarios. This approach ensures both computational efficiency and solution quality, making it suitable for practical applications in emergency logistics.FindingsThe model effectively addresses both the VRP and RAP, optimizing routes and resource allocation. However among both the solution algorithms MDAH is found to solve the model faster for large-scale problems, whereas solution of the model from GA provides better solution quality. The practical application of the model to the 2019 Alappuzha flood data demonstrates its utility in real-world disaster scenarios, showcasing its potential to enhance emergency response operations and resource distribution during disasters.Research limitations/implicationsThe study is limited to single-depot scenarios and does not consider demand uncertainty. Future research could explore multiple depots, uncertain demands and integrate additional problems such as the location allocation problem and the casualty allocation problem. These extensions would enhance the model’s applicability and robustness, providing more comprehensive solutions for emergency logistics. Addressing these limitations would further improve the efficiency and effectiveness of disaster response operations, ensuring better preparedness and resource management.Practical implicationsThe model provides valuable insights for emergency management agencies, aiding in decision-making processes for relief distribution. By minimizing routing and penalty costs, the model ensures timely and efficient delivery of resources, improving overall resource utilization during disasters. This practical application can significantly enhance the effectiveness of emergency response operations, reducing the impact of disasters on affected communities and ensuring that resources are distributed equitably and efficiently.Social implicationsBy optimizing relief distribution, the model helps reduce fatalities and property damage during disasters. It ensures equitable resource distribution, addressing the needs of affected communities effectively. This contributes to the overall resilience of communities, helping them recover more quickly from disasters. The model’s focus on efficient and timely resource allocation can significantly improve the quality of life for disaster-affected populations, providing them with the necessary support to rebuild and recover.Originality/valueThe study presents a novel approach by integrating the VRP and RAP in a two-phase model for emergency logistics. This innovative approach offers practical solutions for large-scale disaster scenarios, enhancing the efficiency of relief operations. The model’s ability to address both routing and resource allocation challenges simultaneously sets it apart from existing models, providing a valuable tool for disaster management agencies looking to improve their logistical strategies and response capabilities.

Xuhui Zhai, Liming Wei, Supan Wang et al.

Effects of vessel weak link fracture and external impact-induced rupture on the onset of superheated boiling are distinct, and the scientific understanding of these effects remains limited. This study investigates the leakage processes in a small storage tank within a confined environment and compares the effects of natural rupture and needle puncture-induced leakage. Experimental results demonstrated that under the same leakage conditions, the depressurization rate of needle puncture was lower than that of natural rupture. Furthermore, in the needle puncture mode, tank pressure was maintained within the saturated vapor pressure range corresponding to the liquid temperature. In natural rupture mode, pressure exceeded this saturated vapor pressure threshold, initiating bubble formation. As release pressure increased, the bubble rise velocity initially increased, followed by a gradual decline, until the bubbles either ruptured or coalesced during their ascent. The confined space also restricted upward fluid motion, thereby slowing depressurization and preventing pressure rebound. The present study can offer a valuable preventive approach for mitigating tank leakage incidents within confined spaces.

Xueer Zhang, Xiaowen Shao, Jinghong Wang et al.

In high-rise emergencies, relying solely on stairs for evacuation may hinder timely escape. Therefore, elevator-assisted stair evacuation should be considered to improve efficiency. Limited research exists on coordinated stair-elevator evacuation, particularly for upward and downward movements involving refuge floors. A 10-story case study was constructed using MassMotion software, based on the Social Forces Model, to explore the effects of elevator-served floor, building population, and percentage of the population using the elevator on evacuation. Findings were applied to a real building. Results indicate that evacuation time increases with the number of elevator-served floors. Upward stair-elevator coordination outperformed downward evacuation, unaffected by building population. For upward evacuation, deploying elevators on floors 1 or 1–2 reduced evacuation times compared to stairs alone. Downward evacuation achieved time savings using elevators on floors 10 or 9–10. Allocating elevators to 100% of occupants on the lowest or highest floors, or 50% on floors 1–2 for upward, or 9–10 for downward evacuation, minimized evacuation duration. Elevator-assisted evacuation reduced evacuation times by 11.7% for upward evacuation, and 8.4% for downward evacuation, suggesting prioritization of elevators for upward evacuees. This research provides theoretical insights for optimizing the coordination of stairs and elevators in high-rise building evacuations.

Kewin Pączek, Damian Jelito, Marcin Pitera et al.

This paper explores the applications of the 20/60/20 rule-a heuristic method that segments data into top-performing, average-performing, and underperforming groups-in mathematical finance. We review the statistical foundations of this rule and demonstrate its usefulness in risk management and portfolio optimization. Our study highlights three key applications. First, we apply the rule to stock market data, showing that it enables effective population clustering. Second, we introduce a novel, easy-to-implement method for extracting heavy-tail characteristics in risk management. Third, we integrate spatial reasoning based on the 20/60/20 rule into portfolio optimization, enhancing robustness and improving performance. To support our findings, we develop a new measure for quantifying tail heaviness and employ conditional statistics to reconstruct the unconditional distribution from the core data segment. This reconstructed distribution is tested on real financial data to evaluate whether the 20/60/20 segmentation effectively balances capturing extreme risks with maintaining the stability of central returns. Our results offer insights into financial data behavior under heavy-tailed conditions and demonstrate the potential of the 20/60/20 rule as a complementary tool for decision-making in finance.

Arnon Archankul, Jacco J. J. Thijssen

We consider singular control in inventory management under Knightian uncertainty, where decision makers have a smooth ambiguity preference over Gaussian-generated priors. We demonstrate that continuous-time smooth ambiguity is the infinitesimal limit of Kalman-Bucy filtering with recursive robust utility. Additionally, we prove that the cost function can be determined by solving forward-backward stochastic differential equations with quadratic growth. With a sufficient condition and utilising variational inequalities in a viscosity sense, we derive the value function and optimal control policy. By the change-of-coordinate technique, we transform the problem into two-dimensional singular control, offering insights into model learning and aligning with classical singular control free boundary problems. We numerically implement our theory using a Markov chain approximation, where inventory is modeled as cash management following an arithmetic Brownian motion. Our numerical results indicate that the continuation region can be divided into three key areas: (i) the target region; (ii) the region where it is optimal to learn and do nothing; and (iii) the region where control becomes predominant and learning should inactive. We demonstrate that ambiguity drives the decision maker to act earlier, leading to a smaller continuation region. This effect becomes more pronounced at the target region as the decision maker gains confidence from a longer learning period. However, these dynamics do not extend to the third region, where learning is excluded.

Probowo Erawan Sastroredjo, Marcel Ausloos, Polina Khrennikova

Our research investigates the relationship between eco-innovation and earnings management among 567 firms listed on the FTSE All-Share Index from 2014 to 2022. By examining how sustainability-driven innovation influences financial reporting practices, we explore the strategic motivations behind income smoothing in firms engaged in environmental initiatives. The findings reveal a positive association between eco-innovation and earnings management, suggesting that firms may leverage ecoinnovation not only for environmental signalling but also to project financial stability and meet stakeholder expectations. The analysis further uncovers that the propensity for earnings management is amplified in firms facing financial constraints, proxied by low Whited-Wu (WW) scores and weak sales performance, and in those characterised by high financial opacity. We employ a robust multi-method approach to address potential endogeneity and selection bias, including entropy balancing, propensity score matching (PSM), and the Heckman Test correction. Our research contributes to the literature by providing empirical evidence on the dual strategic role of ecoinnovation -balancing sustainability signalling with earnings management, under varying financial conditions. The findings offer actionable insights for regulators, investors, and policymakers navigating the intersection of corporate transparency, financial health, and environmental responsibility.

Suyash Pal

This research paper explores RescueNet, a web-based application designed to enhance disaster management through real-time geospatial technology. RescueNet leverages geofencing to streamline emergency response, optimize resource allocation, and facilitate efficient coordination in both pre- and post-disaster scenarios. This study examines its key features, including its robust architecture, user-friendly interface, and ability to improve disaster preparedness and recovery efforts. By systematically reviewing related works and iterative development processes, this research highlights current challenges and opportunities in geofencing-based disaster management systems. RescueNet enables real-time alerts, situational awareness, and automated emergency task assignments, minimizing response time and maximizing efficiency in crisis situations. The system ensures accurate and timely notifications without requiring manual input, allowing responders and affected individuals to take prompt action. Additionally, this paper discusses system scalability, data privacy considerations, and future advancements for enhancing RescueNet’s impact in disaster resilience. Overall, RescueNet represents a significant step toward smarter, technology-driven disaster management, ensuring faster and more effective responses in an increasingly unpredictable world. Keywords: disaster response, emergency management, geofencing, real-time alerts, crisis coordination, GPS optimization, situational awareness, resource allocation, location-based notifications, disaster resilience.

Zhifei Xie

In the VUCA era, supply chain disruptions are increasingly frequent and severe, posing significant challenges to global manufacturing industries. This study investigates the application and challenges of emerging technologies, particularly digital twin (DT) technology, in supply chain risk management through a qualitative case study approach. Focusing on six manufacturing enterprises, three of which have deployed DT and three still rely on traditional models, this research aims to reveal the practical effectiveness and implementation obstacles of DT technology in risk identification, assessment, and response stages. Traditional risk management methods, often based on periodic assessments and static contingency plans, have proven inadequate in addressing sudden global crises, as exemplified by the 2023 Red Sea crisis's impact on the European automotive industry. This study employs document analysis of enterprise risk reports, audit records, and emergency response plans to demonstrate how DT technology can transform risk management into a proactive, predictive strategy. The findings show that DT technology enhances supply chain resilience by enabling real-time risk perception, dynamic simulation, and automated response. However, the adoption of DT technology also faces challenges such as organizational division, financial barriers, and human resistance to change. This research provides actionable guidelines for enterprises to navigate the complex path of digital transformation and offers a low-tech threshold risk management upgrade path, especially for small and medium-sized manufacturers.

M. Sartelli, Carlo Tascini, F. Coccolini et al.

Intra-abdominal infections (IAIs) are common surgical emergencies and are an important cause of morbidity and mortality in hospital settings, particularly if poorly managed. The cornerstones of effective IAIs management include early diagnosis, adequate source control, appropriate antimicrobial therapy, and early physiologic stabilization using intravenous fluids and vasopressor agents in critically ill patients. Adequate empiric antimicrobial therapy in patients with IAIs is of paramount importance because inappropriate antimicrobial therapy is associated with poor outcomes. Optimizing antimicrobial prescriptions improves treatment effectiveness, increases patients’ safety, and minimizes the risk of opportunistic infections (such as Clostridioides difficile ) and antimicrobial resistance selection. The growing emergence of multi-drug resistant organisms has caused an impending crisis with alarming implications, especially regarding Gram-negative bacteria. The Multidisciplinary and Intersociety Italian Council for the Optimization of Antimicrobial Use promoted a consensus conference on the antimicrobial management of IAIs, including emergency medicine specialists, radiologists, surgeons, intensivists, infectious disease specialists, clinical pharmacologists, hospital pharmacists, microbiologists and public health specialists. Relevant clinical questions were constructed by the Organizational Committee in order to investigate the topic. The expert panel produced recommendation statements based on the best scientific evidence from PubMed and EMBASE Library and experts’ opinions. The statements were planned and graded according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) hierarchy of evidence. On November 10, 2023, the experts met in Mestre (Italy) to debate the statements. After the approval of the statements, the expert panel met via email and virtual meetings to prepare and revise the definitive document. This document represents the executive summary of the consensus conference and comprises three sections. The first section focuses on the general principles of diagnosis and treatment of IAIs. The second section provides twenty-three evidence-based recommendations for the antimicrobial therapy of IAIs. The third section presents eight clinical diagnostic-therapeutic pathways for the most common IAIs. The document has been endorsed by the Italian Society of Surgery.

Robert T. Stenberg, Kristen Septaric, Erin L. Simon

Endometriosis is a debilitating chronic condition often accompanied by severe pelvic pain and infertility issues. When outpatient medical management is not adequate, controlling pain can be challenging for providers in the acute setting. We report the case of a 23-year-old female with a past medical history of endometriosis who presented to a freestanding emergency department with a chief complaint of 10/10 pelvic pain on a numeric rating scale. She had tried non-steroidal inflammatory medications and heat with no success. The patient had medication intolerances to opioid analgesics and was given ketorolac intramuscularly with no relief of her pain. The emergency physician discussed and offered to perform an erector spinae plane nerve block (ESPB) for pain relief. Ultrasonography was utilized for visualization of landmarks with a curvilinear transducer; a 20-gauge Pajunk® Sonoplex needle was used to inject a total of 100 mg bupivacaine 0.25% without epinephrine along with dexamethasone 10 mg under the bilateral erector spinae fascial planes at the T9 level. Post-procedure, the patient had significant improvement in pain and rated it a 2/10. Utilizing nerve blocks for endometriosis and other chronic pelvic pain in the acute care setting can serve as an effective alternative to opioids. In patients with multiple medication intolerances and for providers navigating pain control in the setting of a nationwide opioid crisis, ESPB blocks can help alleviate acute pain or exacerbations of chronic pain. This case demonstrates the first known use of an ESPB to relieve endometriosis pain in the emergency department.

Christopher Ehizemhen Igibah, Idowu Rudolph Ilaboya, Solomon Dibiamaka Iyeke et al.

Water quality, trace gas (SO2, CO2, and NO2), particulate matter (PM 10 as well as PM2.5), and effluent emissions were quantified near cement and oil factories and nearby suburban areas within Delta Nigeria. Results display that ambient air particulate matter PM2.5 varies between 2.1 to 7.9 μg/m3 and VOC (0.013−8.53 μg/m3), while CO and CO2 were 100% and 30% respectively not within regulatory limits, consequently leading to asthma, coughing, and difficulty breathing. Four out of nine sites investigated for noise effects were above WHO stipulated limits. While some parameters such as BOD and COD display critical levels for effluent scrutiny and conductivity, calcium, TDS, total hardness, DO (Dissolved oxygen) and total alkalinity were also above clean water specifications. Wastewater consists of spills and other water effects which produce pollutants such as soluble organic chemicals that deplete dissolved oxygen, anions, volatile materials, and other heavy metals. Based on age, the greatest impact (52%) was seen in ages varying from 0 to 16 while that of the age set 16 to 60 was 45%. Curbing of oil and cement particulate pollutants and requesting a buffer region between the cement and oil depots and neighborhoods, complemented with regulatory enforcement and persistent monitoring, should be a top precedence to the regulatory authority.

Hui Ying Wang, Némo Decamps

The molecular species composition of major toxic species, such as soot, CO and unburnt hydrocarbons from a boundary layer diffusion flame over heptane or dodecane surface at microgravity is numerically investigated. A two-step global reaction model for gas-phase chemistry and a simplified soot model consisting of laminar smoke point type for soot inception are used. Thermal radiation is calculated using the discrete-ordinates method coupled with a non-grey model for the radiative properties of CO, CO2, H2O and soot. The numerical results provide further insights into the intimate coupling between burning rate, flame length, thermal radiation, and toxic products at reduced gravity level. The importance of oxidizer flow speed to the flame structure, soot formation and thermal radiation at microgravity is demonstrated. The amount of soot from the microgravity heptane/dodecane flames augments with an increase of oxidizer flow velocity from 0.1 to 0.3 m/s due to an enhancement of burning rate. This finding is contrasted to the case with porous gas burners relative to toxic species production from microgravity flames. For the burning of various liquid fuels, the radiative loss fraction in general increase in a range of 0.5 to 0.7 due to enhanced soot formation with augmentation of the oxidizer flow velocity.

Алла Мечиславовна Брайкова, Константин Федорович Саевич, Татьяна Андреевна Базыльчук et al.

Цель. Определение химического состава образца осадка на корпусе и деталях скважинного электронасосного агрегата, эксплуатировавшегося в течение нескольких лет в условиях Курско-Белгородской аномалии; выявление возможных причин выхода из строя агрегата и разработка рекомендаций по эксплуатации оборудования. Методы. Разработана программа исследования образца осадка, включающая определение массовой доли органических веществ (органической составляющей осадка) весовым методом, массовой доли железа спектрофотоколориметрическим методом на спектрофотометре СФ-2000, массовой доли марганца титриметрическим методом, содержания цинка, кадмия, свинца и меди методом инверсионной вольтамперометрии на анализаторе вольтамперометрическом АВА-3. Результаты. Установлено содержание органических веществ в осадке (50,1 %), определены массовые доли железа (5,6 %), марганца (6,6 %), свинца (3,4 %), цинка (0,017 %), меди (0,005 %), кадмий не обнаружен. Сформулированы вероятные причины накопления осадка на поверхности скважинного электронасосного агрегата. На основании полученных результатов исследования предложены рекомендации по эксплуатации погружных электронасосных агрегатов, включающие в первую очередь необходимость забора и анализа проб воды из скважины перед монтажом оборудования. Область применения исследований. На основании полученных результатов исследования образца осадка на корпусе и деталях скважинного электронасосного агрегата сформулированы рекомендации по эксплуатации оборудования.

Liwei Deng, Tianfu Wang, Yan Zhao et al.

Portfolio management is an important yet challenging task in AI for FinTech, which aims to allocate investors' budgets among different assets to balance the risk and return of an investment. In this study, we propose a general Multi-objectIve framework with controLLable rIsk for pOrtfolio maNagement (MILLION), which consists of two main phases, i.e., return-related maximization and risk control. Specifically, in the return-related maximization phase, we introduce two auxiliary objectives, i.e., return rate prediction, and return rate ranking, combined with portfolio optimization to remit the overfitting problem and improve the generalization of the trained model to future markets. Subsequently, in the risk control phase, we propose two methods, i.e., portfolio interpolation and portfolio improvement, to achieve fine-grained risk control and fast risk adaption to a user-specified risk level. For the portfolio interpolation method, we theoretically prove that the risk can be perfectly controlled if the to-be-set risk level is in a proper interval. In addition, we also show that the return rate of the adjusted portfolio after portfolio interpolation is no less than that of the min-variance optimization, as long as the model in the reward maximization phase is effective. Furthermore, the portfolio improvement method can achieve greater return rates while keeping the same risk level compared to portfolio interpolation. Extensive experiments are conducted on three real-world datasets. The results demonstrate the effectiveness and efficiency of the proposed framework.

Alejandra de la Rica Escudero, Eduardo C. Garrido-Merchan, Maria Coronado-Vaca

Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set on assumptions that are not supported by data in high volatility markets. Hence, quantitative researchers are looking for alternative models to tackle this problem. Concretely, portfolio management is a problem that has been successfully addressed recently by Deep Reinforcement Learning (DRL) approaches. In particular, DRL algorithms train an agent by estimating the distribution of the expected reward of every action performed by an agent given any financial state in a simulator. However, these methods rely on Deep Neural Networks model to represent such a distribution, that although they are universal approximator models, they cannot explain its behaviour, given by a set of parameters that are not interpretable. Critically, financial investors policies require predictions to be interpretable, so DRL agents are not suited to follow a particular policy or explain their actions. In this work, we developed a novel Explainable Deep Reinforcement Learning (XDRL) approach for portfolio management, integrating the Proximal Policy Optimization (PPO) with the model agnostic explainable techniques of feature importance, SHAP and LIME to enhance transparency in prediction time. By executing our methodology, we can interpret in prediction time the actions of the agent to assess whether they follow the requisites of an investment policy or to assess the risk of following the agent suggestions. To the best of our knowledge, our proposed approach is the first explainable post hoc portfolio management financial policy of a DRL agent. We empirically illustrate our methodology by successfully identifying key features influencing investment decisions, which demonstrate the ability to explain the agent actions in prediction time.