Effective Response Strategies Research Articles

Know Thy Ransomware Response: A Detailed Framework for Devising Effective Ransomware Response Strategies

Ransomware has evolved into one of the most severe cyberthreats against private and public sectors alike. Organizations are inundated with a barrage of intrusion attempts that ultimately morph into full-scale ransomware attacks. Efforts to combat these threats tend to primarily focus on detection and prevention, and while thwarting an attack is always the best approach, we must additionally improve our response and recovery efforts with a post-breach mindset. Assume that the defenses have failed and the risk has materialized. Are we then prepared to best salvage the situation with efficient, ransomware-specific incident response procedures? In this work, we present a ransomware response framework that can be leveraged to create highly effective ransomware response strategies. We provide a level of detail in this framework that balances adaptability versus actionability that both technical and executive stakeholders will find of use.

Naegleria fowleri outbreak in Pakistan: unveiling the crisis and path to recovery.

The outbreak of Naegleria fowleri in Pakistan presents a significant public health concern due to its high fatality rate and limited treatment options. This review explores the impact of the outbreak on communities and the challenges faced in combating the disease. It evaluates available treatment options and highlights the need for early diagnosis and intervention. The study proposes recommendations to improve public health preparedness, including public awareness campaigns, enhanced healthcare infrastructure, and robust water surveillance systems. Collaboration between research institutions and public health organizations is emphasized to develop effective outbreak response strategies.

Global Preparedness and Response Strategies for Emerging Viral Hemorrhagic Fevers: Lessons from Past Outbreaks

Background: Emerging Viral Hemorrhagic Fevers (VHFs) pose significant threats to public health worldwide. These infections, characterized by high mortality rates and potential for rapid spread, demand effective preparedness and response strategies. This study explores the lessons garnered from previous VHF outbreaks, emphasizing the necessity of global collaboration and proactive measures in averting future pandemics.

The impact of intelligent customer service agents’ initial response on consumers’ continuous interaction intention

Intelligent customer service agents (ICSAs) have been widely used as automated service assistants in e-commerce. Although ICSAs are highly valued by enterprises, consumers have lower continuous interaction intention for them. The initial response content will largely affect consumers' continuous interaction intention, which directly determines the ICSAs' efficiency. Therefore, making an effective strategy for ICSAs' initial response has become a focus for enterprises. Previous studies mainly investigated ICSAs from the perspective of the complete interaction, and ignored the initial interaction. Based on the theory of computers as social actors and the stereotype content model, this study investigates the impact of ICSAs' initial response content (functional content vs. social content) on consumers' continuous interaction intention. Through two online scenario-based experiments, the results show that initial response content has a significant influence on consumers' continuous intention. The initial response content impacts consumers' trust and affect through perceived warmth and perceived competence, which further influence consumers’ continuous interaction intention. Furthermore, the functional response content has a greater impact on perceived competence, while the social response content has a stronger influence on perceived warmth. In addition, the effect of initial response content on perceived warmth and on perceived competence are moderated by interaction initiation modes. These findings enrich the literature on ICSAs and offer practical implications for e-retailers seeking to make effective initial response strategies.

An Analysis of the Evolution of Public Sentiment and Spatio-Temporal Dynamics Regarding Building Collapse Accidents Based on Sina Weibo Data

Monitoring, analyzing, and managing public sentiment surrounding urban emergencies hold significant importance for city governments in executing effective response strategies and maintaining social stability. In this study, we present a study which was conducted regarding the self-built house collapse incident in Changsha, China, that occurred on 29 April 2022, with a focus on leveraging Sina Weibo (a Twitter-like microblogging system in China) comment data. By employing the Latent Dirichlet Allocation (LDA) topic model, we identified key discussion themes within the comments and explored the emotional and spatio-temporal characteristics of the discourse. Furthermore, utilizing geographic detectors, we investigated the factors influencing the spatial variations in comment data. Our research findings indicate that the comments can be categorized into three main themes: “Rest in Peace for the Deceased”, “Wishing for Safety”, and “Thorough Investigation of Self-Built Houses”. Regarding emotional features, the overall sentiment expressed in the public discourse displayed positivity, albeit with significant fluctuations during different stages of the incident, including the initial occurrence, rescue efforts, and the establishment of accountability and investigative committees. These fluctuations were closely associated with the emotional polarity of the specific topics. In terms of temporal distribution, the peak in the number of comments occurred approximately one hour after the topic was published. Concerning spatial distribution, a positive sentiment prevailed across various provinces. The comment distribution exhibited a stair-like pattern, which correlated with interregional population migration and per capita GDP. Our study provides valuable insights for city governments and relevant departments in conducting sentiment analysis and guiding public opinion trends.

Analyzing the Connectivity of Combined Statistical Areas in Different Census Regions Using ArcGIS

The study aims to analyze the spatial relationship between critical infrastructure and socioeconomic regions in Texas cities, identify areas at risk, and develop effective emergency response strategies. It also focuses on analyzing the connectivity of Combined Statistical Areas (CSAs) in different census regions through various modes of transportation, identify areas that require infrastructure upgrades, and inform policy decisions aimed at improving transportation infrastructure and increasing connectivity. The goal is to provide a comprehensive understanding of the complex relationship between critical infrastructure, emergency response, and transportation infrastructure in Texas cities, and inform policy decisions aimed at addressing disparities and improving connectivity.

Beyond acquiescence and compromise: Organizational strategies in pluralizing institutional environments

AbstractWe set out to investigate how organizations respond to the variety of requirements as experienced in their pluralizing institutional environments. We found that, in addition to acquiescence and compromise, Dutch vocational education and training (VET) organizations predominantly respond with cooperation and coordination strategies. Extensive multistage qualitative data analysis of 26 semi‐structured in‐depth interviews with management team (MT) members showed that cooperation and coordination are viable and effective response strategies to face a divergent and highly differentiated set of sometimes‐conflicting institutional requirements. Our study advances understanding of how organizations deploy strategic choice to arrive at their strategic responses. It offers organizational leaders, legislators, policymakers, and other constituents' insights into complex reality of how contemporary organizations actually relate to and act in their pluralizing institutional environments.

Dynamic Dose-Based Emergency Evacuation Model for Enhancing Nuclear Power Plant Emergency Response Strategies

The safe evacuation of residents near a nuclear power plant during a nuclear accident is vital for emergency response planning. To tackle this challenge, considering the dynamic dispersion of radioactive materials in the atmosphere and its impact on evacuation routes under different meteorological conditions is crucial. This paper develops a dynamic dose-based emergency evacuation model (DDEEM), which is an efficient and optimized nuclear accident evacuation model based on dynamic radiological dose calculation, utilizing an improved A* algorithm to determine optimal evacuation routes. The DDEEM takes into account the influence of radiological plume dispersion and path selection on evacuation effectiveness. This study employs the DDEEM to assess radiological consequences and evacuation strategies for students residing 5 km from a Chinese nuclear power plant. Under various meteorological conditions, including the three typical meteorological conditions, random ordered and random unordered meteorological sequences, optimal routes obtained through the DDEEM effectively reduce radiological dose exposure and mitigate radiation hazards. The results indicate that all evacuation paths generated by the DDEEM have a maximum dose of less than 1 mSv. Through simulations, the model’s effectiveness and reliability in dynamic radiological environments in terms of radiological consequences and evacuation analysis is verified. The research provides valuable insights and a practical tool for nuclear power plant emergency decision-making, enhancing emergency management capabilities during nuclear accidents. The DDEEM offers crucial technical support and a solid foundation for developing effective emergency response strategies.

A data-driven analytic approach for investigation of electricity demand variability for energy conservation programs

Targeting and designing effective demand response and energy efficiency strategies is a challenging task owing to the variations in electricity usage patterns. To address this problem, we attempt to propose a clustering and Kullback-Leibler (KL) Divergence-based solution to understand the variability across the different consumption patterns. The proposed unsupervised approach utilized hierarchical clustering technique for segmenting the customers based on the similarity in their consumption behaviour, followed by the incorporation of the KL divergence to quantify the variability for different features of customer's load profiles. These features aid in understanding the diversity in load shapes. Depending on the different features, the results reveal that a few of the consumers having certain distinctive consumption can be easily considered as potential for demand flexibility, while others require careful consideration for program offerings. The result also shows significantly higher day-to-day variations in the clustered groups and can be managed by designing suitable behavioural modification strategies. The variability assessment of individual customer profiles helps better comprehend the diversity in usage behaviour. It leads to informed decision-making in planning demand response and energy efficiency initiatives, which was not taken into consideration in the previous studies.

Genetic diversity and evolutionary dynamics of the Omicron variant of SARS-CoV-2 in Morocco

ABSTRACT Among the numerous variants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that have been reported worldwide, the emergence of the Omicron variant has drastically changed the landscape of the coronavirus disease (COVID-19) pandemic. Here, we analyzed the genetic diversity of Moroccan SARS-CoV-2 genomes with a focus on Omicron variant after one year of its detection in Morocco in order to understand its genomic dynamics, features and its potential introduction sources. From 937 Omicron genomes, we identified a total of 999 non-unique mutations distributed across 92 Omicron lineages, of which 13 were specific to the country. Our findings suggest multiple introductory sources of the Omicron variant to Morocco. In addition, we found that four Omicron clades are more infectious in comparison to other Omicron clades. Remarkably, a clade of Omicron is particularly more transmissible and has become the dominant variant worldwide. Moreover, our assessment of Receptor-Binding Domain (RBD) mutations showed that the Spike K444T and N460K mutations enabled a clade higher ability of immune vaccine escape. In conclusion, our analysis highlights the unique genetic diversity of the Omicron variant in Moroccan SARS-CoV-2 genomes, with multiple introductory sources and the emergence of highly transmissible clades. The distinctiveness of the Moroccan strains compared to global ones underscores the importance of ongoing surveillance and understanding of local genomic dynamics for effective response strategies in the evolving COVID-19 pandemic.

Comparative Investigation Into the Current-Carrying Wear Properties of Two Kinds of Carbon Skateboard/Wire Contact Under Different Conditions

The poor matching performance of a carbon skateboard and contact wire leads to abnormal wear of a rigid pantograph–catenary system. However, an effective response strategy for reducing the frequency of abnormal wear of the arch network has not been achieved. In this article, the friction and wear properties of pure carbon and copper-impregnated carbon skateboards are used to investigate and compare the friction properties under different working conditions. The experimental results show that the contact resistance of the pure carbon skateboard/contact wire increases from 12 mΩ to 45 mΩ with increasing current and shows an overall low friction coefficient. The contact resistance of the copper-impregnated carbon skateboard/contact wire fluctuates in the range of 4 mΩ to 16 mΩ, with a relatively high friction coefficient. A high current density accelerates the electrical wear behavior and temperature increase of the interface of the pure carbon skateboard/contact wire. A matching strategy of carbon skateboard and contact wire is proposed by comparing the friction coefficient, contact resistance, and wear rate.

An Interpretable Time Series Data Prediction Framework for Severe Accidents in Nuclear Power Plants.

Accurately predicting severe accident data in nuclear power plants is of utmost importance for ensuring their safety and reliability. However, existing methods often lack interpretability, thereby limiting their utility in decision making. In this paper, we present an interpretable framework, called GRUS, for forecasting severe accident data in nuclear power plants. Our approach combines the GRU model with SHAP analysis, enabling accurate predictions and offering valuable insights into the underlying mechanisms. To begin, we preprocess the data and extract temporal features. Subsequently, we employ the GRU model to generate preliminary predictions. To enhance the interpretability of our framework, we leverage SHAP analysis to assess the contributions of different features and develop a deeper understanding of their impact on the predictions. Finally, we retrain the GRU model using the selected dataset. Through extensive experimentation utilizing breach data from MSLB accidents and LOCAs, we demonstrate the superior performance of our GRUS framework compared to the mainstream GRU, LSTM, and ARIMAX models. Our framework effectively forecasts trends in core parameters during severe accidents, thereby bolstering decision-making capabilities and enabling more effective emergency response strategies in nuclear power plants.

Incentive-based demand response strategies for natural gas considering carbon emissions and load volatility

With the continuous development of the global economy, the demand for natural gas is increasing year by year. At the same time, the widespread adoption of emission reduction measures around the world prompts many countries to switch to cleaner energy sources, which is driving the growth in demand for natural gas. In order to regulate the balance of natural gas supply and demand, maintain the stable operation of natural gas systems and reduce environmental pollution, this paper proposes an incentive-based demand respond (DR) model for natural gas, which takes carbon emissions and load volatility into account, and in the end, it concludes an effective response strategy. In order to get the multi-objective incentive-based DR, first of all, the reward function is constructed by adopting the TOPSIS method, and then it is solved by using the DQN algorithm, in the end, a multi-objective reinforcement learning method based on DQN is proposed. The DR model is evaluated by adopting a real load data from Xi'an city and DR strategies are proposed for different gas users in two periods: the heating and non-heating periods. The DR strategy proposed in this paper can effectively increase gas users motivation, reduce peak loads, maximize the revenue of gas suppliers and gas users, mitigate load volatility and reduce carbon emissions.

Antimicrobial prescription pattern in the Deido health district, Douala, Cameroon

Inappropriate antimicrobial prescriptions are among the highest contributing factors to Antimicrobial Resistance (AMR). Most low and middle-income (LMICS) countries with high AMR burdens like Cameroon, seldom document information on prescription patterns, whereas this information is crucial in addressing inappropriate antimicrobial prescriptions. This study was therefore designed to elucidate antimicrobial prescription patterns in order to tailor interventions to mitigate AMR in Cameroon. The study adopted a multicentre cross-sectional design. Information on antimicrobial prescriptions was collected from four hospitals within the Deido Health District, between October 2019 and March 2020. Of the 1398 participants that were enrolled in the study, the most presented age group were participants aged 15-45 years 913(65.3%) and prescriptions were higher amongst females (923,53.6%). The highest number of antimicrobial prescriptions was made in the outpatient department 592(42.3%) followed by the pediatric unit, 344(24.6%). Most of the prescriptions were for patients with respiratory tract infections 436 (31.2%), followed by those with digestive tract infections 248 (17.7%). The most frequently prescribed class of drugs were the Penicillins 690 (40.3%, 37.8 – 42.6), with Amoxicillin clavulanic acid accounting for 27.8% of the overall prescriptions followed by Cephalosporins 392 (22.7, 20.6 – 24.7), with Ceftriaxone being the most prescribed in the class (13.3%). The need for prescription was mainly determined by clinical judgement (61.1%), while only 9.5% of prescriptions were based on antimicrobial sensitivity test. In the struggle to mitigate AMR, there is a great need to exploit data on prescription patterns and develop stewardship programs in order to optimise antimicrobial use in Cameroon. We emphasize in this communication the potential benefits and outcomes of foresight thinking, such as improved resilience, better resource allocation, and effective response strategies.

How to anticipate and control possible future health crises through foresight approaches using the COVID-19 pandemic crisis?

Prospective studies have evolved towards a more integrated, complex, and diachronic way of thinking about the future. What is the future? It is a set of possibilities, delimited by what we think is impossible (limits of plausibility) and by the difficulty of estimating the degree of plausibility of an event in a V.U.C.A. period. The term V.U.C.A. refers to the four characteristics that reflect the world according to military thinking: Volatility; Uncertainty; Complexity; Ambiguity. The COVID-19 crisis is an example of V.U.C.A with an uncertain situation. Foresight is a discipline that allows us to understand the forces at work in the construction of the future. Resolutely oriented towards action, it aims to enlighten the choices of the present by exploring possible futures. Strongly linked to strategy, it allows the determination of a desirable future, a vision, and the implementation of the means to achieve it. Foresight is exploratory (of possible futures), normative (creating a chosen future), quantitative (using statistical data), and qualitative (using survey data). Foresight does not foresee, it anticipates. Prospective approaches could be used more in particular in the field of health to define more or less pessimistic scenarios in order to define strategies of anticipation of futures that we do not want to see happen. The foresight for pandemics and epidemics initiatives can create a space for dialogue and bring together diverse perspectives and lived experiences. We emphasize in this communication the potential benefits and outcomes of foresight thinking, such as improved resilience, better resource allocation, and effective response strategies.

Use and Experience of Tourism Green Spaces in Ishigaki City before and during the COVID-19 Pandemic Based on Web Review Data

Urban Tourism Green Spaces (UTGSs) in Okinawa are the most frequented areas by tourists and are an important part of the area’s unique tourism image and brand value. This study selected 40 UTGSs in Ishigaki City, Okinawa, for comparative analysis before the COVID-19 pandemic (January 2009–December 2019) and during the strict control period (January 2020–December 2021). Data on tourist reviews were collected in Python. Correlations were collated through Pearson correlation tests. Text-mining methods were used to examine tourist reviews to characterise changes in tourists’ use and experience of UTGSs amid the extreme hardship experienced by tourist cities during the pandemic. Visitors expressed positive emotions towards the green space, particularly in post-pandemic times, despite disruptions and uncertainties caused by travel restrictions. Rather than focusing on water activities as before, visitors preferred exploring the island’s hinterland areas with low crowd density and seeking spiritual comfort in open public spaces. This highlights the potential of these areas to be developed as a new tourism resource. This study’s findings will provide valuable insights for urban planners and tourism operators in developing effective crisis response strategies and guiding the development of urban tourism areas towards more appealing and sustainable destinations.

Mixed messages in a VUCA world: How the New Zealand government altered its SARS-CoV-2 crisis communication messages

The SARS-COV-2 pandemic typifies a volatile, uncertain, complex and ambiguous (VUCA) situation. For a time, Aotearoa New Zealand’s government was lauded for its quick and effective elimination response and clear communication strategy, which saw the citizens of the country enjoy months free from the trappings of SARS-COV-2. This changed with the arrival of Delta, a new variant of concern. It is against this context that we sought to analyse the two main speeches delivered by the prime minister of that time – Jacinda Ardern. The first, delivered on 17 August 2021, maintained the messaging of the previous years – let us eliminate the virus. However, the second speech, delivered on 4 October 2021, shifted to an emphasis on vaccination and living with the virus. Not unsurprisingly, the change in messaging prompted some factions of the public to express frustration and hostility towards Ardern and her government because of perceived inconsistencies and ambiguities in the messaging. Our findings highlight that while crisis communication in a VUCA environment needs to maintain consistency to garner trust, it also needs to be adaptive to respond to uncertainties. However, in being adaptive and engaging with the public to adjust their expectations, leaders can often be faced with resistance and even be perceived as failures. This adds further responsibility to the leader to address the adaptive challenges as effectively as possible. Our findings are a cautionary tale for scholarship and education in crisis communication to avoid emphasis on fixed solutions.

Photosynthetic response dynamics in the invasive species Tithonia diversifolia and two co-occurring native shrub species under fluctuating light conditions

To determine the invasiveness of invasive plants, many studies have compared photosynthetic traits or strategies between invasive and native species. However, few studies have compared the photosynthetic dynamics between invasive and native species during light fluctuations. We compared photosynthetic induction, relaxation dynamics and leaf traits between the invasive species, Tithonia diversifolia and two native species, Clerodendrum bungei and Blumea balsamifera, in full-sun and shady habitats. The photosynthetic dynamics and leaf traits differed among species. T. diversifolia showed a slower induction speed and stomatal opening response but had higher average intrinsic water-use efficiency than the two native species in full-sun habitats. Thus, the slow induction response may be attributed to the longer stomatal length in T. diversifolia. Habitat had a significant effect on photosynthetic dynamics in T. diversifolia and B. balsamifera but not in C. bungei. In shady habitat, T. diversifolia had a faster photosynthetic induction response than in full-sun habitat, leading to a higher average stomatal conductance during photosynthetic induction in T. diversifolia than in the two native species. In contrast, B. balsamifera had a larger stomatal length and slower photosynthetic induction and relaxation response in shady habitat than in full-sun habitat, resulting in higher carbon gain during photosynthetic relaxation. Nevertheless, in both habitats, T. diversifolia had an overall higher carbon gain during light fluctuations than the two native species. Our results indicated that T. diversifolia can adopt more effective response strategies under fluctuating light environments to maximize carbon gain, which may contribute to its successful invasion.

Integrated optimization of bus bridging service design and passenger assignment in response to urban rail transit disruptions

As the urban rail transit (URT) system plays an increasingly important role in supporting large cities’ mobility around the world, service disruptions have become more prevalent, potentially resulting in severe economic losses and passenger safety issues. It is imperative to investigate effective response strategies to mitigate the effects of such disruptions. In response to URT service disruptions, this paper systematically investigates the bus bridging service design (BBSD) problem, which concerns the integration of bus bridging route design, frequency determination, and passenger assignment in the integrated URT and bus network. The problem is formulated as a path-based integer linear programming (ILP) model with the goal of simultaneously minimizing operator-oriented and passenger-oriented costs. A column generation-based approach is proposed to solve this model efficiently, allowing nonintuitive bus routes to be freely generated on the network dynamically. Our method has been tested with two different case studies based on real data from the Hong Kong Mass Transit Railway (MTR). Experiments demonstrate that our proposed approach can assist public transit (PT) operators in developing efficient emergency response plans for various potential disruption situations in advance. Even in the face of unexpected disruptions that necessitate a quick response, our approach can generate high-quality solutions in a matter of minutes.

Resilience and risk-based restoration strategies for critical infrastructure under uncertain disaster scenarios

Improving the resilience of critical infrastructure (CI) systems to natural disasters is essential for society's long-term development. Considering the uncertainty in disaster scenarios, this study first proposes a two-stage risk-neutral stochastic model to develop restoration resource deployment and allocation strategies with the objective of maximizing the expected value of resilience (EVR) of a CI system. Secondly, a risk metric is applied to assess the risk on the value of resilience imposed by the uncertainty. And a multi-objective optimization model for risk management is proposed, in which minimizing the value of the risk metric is formulated as a secondary objective to the EVR. The trade-offs between the EVR and the risk objective are assessed via the ε-constraint method. Finally, the utility of proposed models is illustrated using the electric power system of the Greater Toronto Area (GTA). The results show that: (i) there is a significant advantage in using risk-neutral strategies to improve system resilience over strategies without considering the characteristics of disaster scenarios; (ii) the value of the risk metric can be significantly reduced at the expense of decreasing EVR; and (iii) if the use cost of the resources is considered, there are optimal amounts of available resources for risk-neutral and risk-averse CI system operators. The models can assist CI system operators in selecting effective emergency response strategies for disasters with uncertain scenarios.