Vulnerability Risk Research Articles

Threat Modeling for Cyber Warfare Against Less Cyber-Dependent Adversaries

Cyber warfare poses a substantial threat in today's interconnected world, where digital attacks can transcend physical boundaries and affect targets globally. Technologically, less advanced adversaries, such as smaller nations or organizations with limited resources, face unique challenges in defending against sophisticated cyber attacks from more advanced entities. This paper explores the threat landscape for these adversaries and proposes a tailored threat modeling framework to address their specific vulnerabilities and needs. By examining the evolution of cyber warfare, including historical incidents and the increasing sophistication of cyber attacks, the study highlights the limitations of existing threat modeling approaches like the Cyber Kill Chain, MITRE ATT&CK Framework, and SWOT analysis when applied to less advanced adversaries. A comprehensive literature review underscores the gaps in current research, particularly the necessity for frameworks tailored to asymmetric technological capabilities. Employing a mixed methods approach, the research combines qualitative and quantitative data from primary sources, such as interviews with cybersecurity experts, and secondary sources, including existing literature and case studies. The proposed framework focuses on asset identification and classification, vulnerability assessment, threat analysis, and risk assessment. Proactive measures, such as basic cyber hygiene practices, advanced threat detection systems, and collaboration with technologically advanced allies, are recommended alongside reactive measures like incident response planning and disaster recovery. The importance of international cooperation and information sharing is also emphasized. Case studies of cyber incidents involving less advanced adversaries, such as the attacks on Estonia, Georgia, and Ukraine, validate the framework and demonstrate its practical application. The findings indicate that the tailored threat modeling framework effectively addresses the unique challenges faced by less advanced adversaries, enhancing their ability to mitigate risks and improve their cybersecurity posture. This study provides valuable insights and offers a practical framework to bolster defenses against cyber warfare, with future research needed to explore emerging threats and technologies further.

Evaluasi Risiko Celah Keamanan Aplikasi E-Office menggunakan Metode OWASP

Based on data from Badan Siber dan Sandi Negara (BSSN) in 2022, it was reported that a total of 1,950 security vulnerabilities were found in 457 electronic systems across various applications widely used by the public. The purpose of this research is to evaluate the risk of existing security vulnerabilities in the E-Office application and determine the level and impact that these vulnerabilities can cause. This research focuses on information system security, specifically evaluating the risk of security vulnerabilities in the E-Office application of the Ogan Ilir Regency. The research was conducted using the Open Web Application Security Project (OWASP) method with a risk rating assessment. The research process began with a literature review to gather data and information sources, determine the scope and research objectives, test, identify security vulnerabilities, analyze security vulnerabilities, and the results of the analysis. The research subject is the E-Office application of Ogan Ilir Regency, with the object of the research being the security vulnerabilities in that application. OWASPZap was used as a tool to obtain data on security vulnerabilities, and using OWASPZap, 38 security vulnerabilities were found, with 18 of them meeting the criteria of the OWASP Top 10. Our findings indicate that the security vulnerabilities in the E-Office application of Ogan Ilir Regency include vulnerabilities in authentication levels, access control, configuration, and data validation processes.

Baseline vulnerable road user injury risk in multiple U.S. dense urban driving environments

Objective Understanding and modeling baseline driving safety risk in dense urban areas represents a crucial starting point for automated driving system (ADS) safety impact analysis. The purpose of this study was to leverage naturalistic vulnerable road user (VRU) collision data to quantify collision rates, crash severity, and injury risk distributions in the absence of objective injury outcome data. Methods From over 500 million vehicle miles traveled, a total of 335 collision events involving VRUs were video verified and reconstructed (126 pedestrians, 144 cyclists, and 65 motorcyclists). Data consisted of anonymized video and sensor data (Global Positioning System and accelerometer) from vehicles equipped with Nexar dash cameras. Each event was qualitatively evaluated to assess the collision geometries and other extrinsic collision factors (e.g., time of day, roadway location, presence of occlusions). Previously published injury risk models were utilized to provide estimates of Maximum Abbreviated Injury Scale MAIS2+ and MAIS3+ injury potential. Collision severity distributions and aggregated injury risk estimates were compared. Results Vehicle speeds at the time of impact ranged from 0 to 39 mph, and VRU speeds ranged from 0 to 27 mph. In general, vehicles were traveling at lower speeds at the time of impact when turning in comparison to straight travel. Nearly all events (95%) were associated with MAIS3+ injury risk estimates below 10%. Collisions involving a potential occlusion or the vehicle responding to surprising behavior by the VRU were associated with higher estimates of injury risk than those without occlusion or with the vehicle initiating the conflict. Based on accumulated risk for each event, it can be estimated that approximately 55 persons could be moderately injured (MAIS2+) and approximately 6 persons could be seriously injured (MAIS3+). Conclusion These data indicate that responding to surprising VRU behavior, having visibility be potentially occluded, and vehicle travel behavior were associated with differences in collision speed and injury risk estimation. The specific comparisons made in this study are not intended to be comprehensive but serve as a starting point for considering baseline driving risk associated with VRU collisions in dense urban areas.

Перспективні методи лікування передчасної недостатності яєчників

Premature ovarian failure (PОF) in women under 40 is a medical condition in which ovarian follicles are exhausted and cease to function normally as both reproductive and endocrine organs. This condition often leads to infertility because it is associated with hypoestrogenia, which causes menstrual irregularities and pregnancy failure. Decreased estrogen secretion also causes many menopausal symptoms, such as hot flashes, night sweats, and insomnia. In addition, the longterm consequences of POF increase the risk of lifelong skeletal vulnerability and cardiovascular as well as neurocognitive disorders. The purpose of the work is to collect, analyze and summarize data from recent years about promising methods of treatment of premature ovarian failure, namely about autologous ovarian tissue transplantation, creation of artificial ovaries and oocytes, treatment with iron nanoparticles, intraovarian injections of autolytic platelet-rich plasma. It should be noted that premature ovarian failure (POF) differs from menopause in that ovarian failure may not be permanent; in such patients with POF, pregnancy is spontaneous and occurs infrequently. Our analysis and summarization of data from recent years suggests that 1) because autologous ovarian tissue transplantation requires tissue collection, this method is generally applicable to a specific patient group, women who are expected to have ovarian failure after cancer therapy, and a group of patients who experience gamete depletion as a result of a prognosis or even a diagnosis of POF. Ovarian tissue transplantation, which has been known for two decades, still has significant risks and technical limitations and is not recognized as a reliable method in clinical practice. 2) the concept of "artificial ovary" remains at the stage of experimental development of cellular systems in animals. Whereas, by creating artificial oocytes from several different sources (cell types), live birth has already been achieved in animals. And expectations are aimed at a steady progression to their clinical use in humans. 3) the use of zero-valent iron nanoparticles under the conditions of experimental immune complex failure has a certain corrective effect on the disorder of ovarian function. There is reason to believe that the effect of the treatment with zero-valent iron nanoparticles is fundamentally different from the macroscopic one in the effects on cells and, apparently, in the mechanisms of action - which requires further study. 4) treatment with platelet-rich plasma (PRP) remains an invasive ultrasound-guided procedure with risks that are not yet fully understood. At this point, PRP should still be considered an experimental procedure for patients with POF (premature ovarian failure). It is relevant to continue research on the influence of PRP components on the ovary, to evaluate the effect of the PRP concentration, which could play a key role in the proliferation and differentiation of mesenchymal cells; and to establish the optimal time intervals between PRP procedures, the volume treatment, and the maximum and minimum number of such procedures. In general, the proposed promising methods (autologous ovarian tissue transplantation, creation of artificial ovaries and oocytes, use of nanomaterials (iron nanoparticles), intraovarian injections of autolytic platelet-rich plasma) are achievements in biomedical engineering aimed at overcoming infertility, associated with premature ovarian failure.

Diagnosis and Vulnerability Risk Assessment of Atherosclerotic Plaques Using an Amino Acid-Assembled Near-Infrared Ratiometric Nanoprobe.

To reduce the risk of atherosclerotic disease, it is necessary to not only diagnose the presence of atherosclerotic plaques but also assess the vulnerability risk of plaques. Accurate detection of the reactive oxygen species (ROS) level at plaque sites represents a reliable way to assess the plaque vulnerability. Herein, through a simple one-pot reaction, two near-infrared (NIR) fluorescent dyes, one is ROS responsive and the other is inert to ROS, are coassembled in an amphiphilic amino acid-assembled nanoparticle. In the prepared NIR fluorescent amino acid nanoparticle (named FANP), the fluorescent properties and ROS-responsive behaviors of the two fluorescent dyes are well maintained. Surface camouflage through red blood cell membrane (RBCM) encapsulation endows the finally obtained FANP@RBCM nanoprobe with not only further reduced cytotoxicity and improved biocompatibility but also increased immune escape capability, prolonged blood circulation time, and thus enhanced accumulation at atherosclerotic plaque sites. In vitro and in vivo experiments demonstrate that FANP@RBCM not only works well in probing the occurrence of atherosclerotic plaques but also enables plaque vulnerability assessment through the accurate detection of the ROS level at plaque sites in a reliable ratiometric mode, thereby holding great promise as a versatile tool for the diagnosis and risk assessment of atherosclerotic disease.

Challenges in access, representativeness, and bias in smart financial data relating to income volatility and economic insecurity.

Introduction & BackgroundFinancial transaction data are highly valuable sources of digital footprints data for behavioural and economic research, but to properly create impact we must closely consider their limitations. Financial institutions hold a wealth of consumer data with untapped potential for community intelligence. These datasets combine excellent coverage with extremely granular information on consumer finances, income and spending, yet these institutions face great challenges in leveraging this data for social good. Smart Data Foundry is a university-owned, non-profit organisation that facilitates safe access to these datasets for researchers and provides insights to enable government bodies to tackle today's major challenges including the cost-of-living crisis and climate change. Objectives & ApproachWe will explore the opportunities afforded by these datasets for social and economic research. For example, using pseudonymised individual consumer banking data from NatWest Group, we have developed metrics for understanding income volatility and economic insecurity in collaboration with the Joseph Rowntree Foundation. We can also use these data to study consumer spending patterns and responses to economic changes such as interest rate rises and the net zero transition. We will assess the limitations of the data including issues of representativeness, bias, and missing data, and describe methods and mitigations to account for these challenges. We also discuss the barriers to accessing this type of data, in both relationship development with data partners, and privacy and governance concerns. Relevance to Digital FootprintsIndividual level customer transaction data provides a rich and novel form of digital footprint for behavioural and economic analyses. Every point of income or expenditure is recorded in a uniquely valuable digital footprint by financial institutions. These can provide a variety of insights, such as responses to macroeconomic shocks across demographic sets, emerging areas of financial distress, and help us better understand the drivers and risks of financial vulnerability. In both its aggregated and individual form, the data can provide an additional layer of understanding for trends we may see in other data, such as health or administrative data. Conclusions & ImplicationsHaving addressed the challenges of data access and data quality, we demonstrate that consumer banking data is an incredibly valuable form of digital footprints data, capturing key information on consumer behaviour. We conclude with a call for further research to develop use cases of this data for social good.

Assessing risk of metro microenvironmental health vulnerability from the coupling perspective: A case of Nanjing, China

Metro, as a convenient transportation, has many potential environmental risks during operation that pose a great threat to users, resulting in metro microenvironmental health vulnerability (MMHV). While prior research has focused on metro construction and safety management, some investigations have also addressed isolated metro microenvironment factors, such as air quality and noise, ignoring the underlying causes of health risks. Therefore, this research aims to reveal driving factors contributing to MMHV and assess the risk of MMHV from the coupling perspective. 14 Driving factors related to personnel, facility, environment, and management were firstly identified, along with their coupling relationships. Then, a risk coupling simulation model of the identified driving factors has been devised, which integrates a cloud theory-based coupling degree model framework and system dynamics principles to represent the interconnected risks posed by these factors. Finally, the constructed model is applied practically to discern the developmental trajectory of coupled risks emanating from the driving factors influencing MMHV. And the key factors at different periods were determined by adjusting coupling coefficients, so as to propose targeted strategies. Findings in this research offer valuable guidance for the implementation of diversified and precisely tailored measures to address MMHV at various stages of metro operation. This, in turn, contributes to the mitigation of health risks and ensures the sustained and healthy operation and management of metro.

The Contribution of Genetic and Genomic Tools in Diversity Conservation: The Case of Endemic Plants of Greece

The conservation of endemic plant species has come into the global spotlight, not only because of their contribution to biodiversity but also their vulnerability and high extinction risk due to environmental and anthropogenic threats. Based on these developments, it is now essential to monitor and protect these species by applying integrated conservation strategies, especially in view of climate change, which is one of the most severe threats to plants. Genetic and genomic tools provide new potential in assessing and quantifying genetic diversity and thus can be utilized to devise conservation strategies and contribute to biodiversity conservation efforts. Greece comprises a plant biodiversity hotspot in the Mediterranean Basin with a wide variety of rare, threatened, and endemic plant taxa. In this review, we examine several cases where a broad spectrum of genetic tools has been utilized so far in the diversity assessment and conservation management of Greek Endemic Plants (GEPs). Following an extensive database search, we have identified and included in our final data collection 19 studies concerning 32 GEPs for which molecular markers have been used for the determination of population genetic structure and diversity assessment, while at the same time, the research outcomes have been taken into consideration for conservation management. The limited application of genetic and genomic tools in GEP management is demonstrated, while the significance of implementing a comprehensive conservation strategy that will integrate genetic analyses and the data derived therein is also highlighted.

Integrated measurement of public safety risks in international construction projects in the belt and road initiative

PurposeThe purpose of this paper is to develop a comprehensive understanding of the public safety risks of international construction projects (ICPs) from the perspective of threat and vulnerability. A novel and comprehensive risk assessment approach is developed from a systemic perspective and applied to the Belt and Road Initiative (BRI) to improve the public safety risk management strategy for ICPs in BRI.Design/methodology/approachFirst, a public safety risk indicator system was constructed from the two dimensions, namely threat and vulnerability. Next, an integrated measurement model was constructed by combining the Genetic Algorithm-Backpropagation (GA-BP) neural network, fuzzy comprehensive evaluation method and matter-element extension (MME) method. Data from 49 countries involved in the BRI, as well as five typical projects, were used to validate the model. Finally, targeted risk prevention measures were identified for use at the national, enterprise and project levels.FindingsThe findings indicate that while the vulnerability risks of typical projects in each region of the BRI were generally low, threat risks were high in West Asia and North Africa, Commonwealth of Independent States (CIS) countries and South Asia.Originality/valueFirst, the structure of the public safety risk system of ICPs was analyzed using vulnerability and system theories. The connotation of public safety risk was defined based on two dimensions, namely threat and vulnerability. The idea of measuring threat risk with public data and measuring vulnerability risk with project data was clarified, and the risk measurement was integrated into the measurement results to help researchers and managers understand and systematically consider the public safety risks of ICPs. Second, a public safety risk indicator system was constructed, including 18 threat risk indicators and 14 vulnerability risk indicators to address the gaps in the existing research. The MEE model was employed to overcome the problem of incompatible indicator systems and provide stable and credible integrated measurement results. Finally, the whole-process public safety risk management scheme designed in this study can help to both provide a reference point for the Chinese enterprises and oversea contractors in market selection as well as improve ICP public safety risk management.

Investigation of risk propagation and control in emergency response logistics networks: A cellular automata based approach

Emergency logistics is crucial for responding to natural disasters and public health emergencies. In emergency logistics systems, risk propagation must be effectively controlled and mitigated, and otherwise it may lead to serious consequences. This study, for the first time, investigates the risk propagation of emergency logistics networks and their protection strategies from a dynamic perspective using the method of cellular automata. The purpose is to reveal the risk propagation mechanism of emergency logistics networks and its influencing factors, which provides a basis for improving the risk resistance of emergency logistics networks. Specifically, this paper constructs an emergency logistics network based on the weighted scale-free algorithm and establishes a risk propagation model by using metric automata. Also, this paper carries out a comprehensive numerical investigation on risk propagation in the emergency logistics network by adjusting the model parameters and adopting different protection strategies. The results of this research indicate that (1) the speed and range of risk propagation are not sensitive to the network size, while the reduction of the network size leads to an increase in the volatility and a decrease in the stability of the propagation; (2) the state characteristics of the network nodes are more important than the structural characteristics of the network for controlling the risk of node vulnerability; (3) increasing the level of risk recovery is more effective than reducing risk infection in controlling the scope of risk propagation throughout the network; (4) finally, the targeted protection strategy is more effective than the random protection strategy. The findings are potentially impactful in that they reveal the nature of risk propagation in emergency logistics networks, further expand methodological research of risk propagation, and provide a solid foundation for effective risk management and control.

Institutional vulnerability to urban flood risk management: A case study of the 7·20 flooding disaster in Zhengzhou, China

AbstractThe ‘vulnerability’ approach highlights institutional factors as the primary drivers of flooding risk. However, the intricate connections between institutional vulnerability and urban flood disaster risk have not been extensively explored until now. This study aims to address this gap by examining the case of the 7·20 heavy rainfall disaster in Zhengzhou. Employing a hybrid analysis methodology that integrates text coding, fault tree analysis and analytic hierarchy process methods, the study seeks to identify and assess the distinct contributions of various institutional factors and their interplay, culminating in ineffective urban flood risk management. In contrast to previous research findings that emphasize the determinant role of policy‐making and organizational coordination, this study demonstrates that inadequate legislative compliance constitutes the root cause determining the ineffectiveness of urban flood risk management. It is further exacerbated by insufficient policy attention, resulting in inadequate allocation of resources that ensure the legislative quality, risk coping skills and knowledge and stakeholders' coordination and collaboration. By emphasizing the significance of legislative compliance and policy attention, the study offers a fresh conceptual perspective to understand the factors influencing the efficiency of urban flood risk management. It provides valuable insights to develop targeted countermeasures for mitigating similar urban flood risks in the future.

Environmental Determinants of Post-Discharge Acute Respiratory Illness among Preterm Infants with Bronchopulmonary Dysplasia.

To analyze the association of components of the Centers for Disease Control and Prevention (CDC) Environmental Justice Index (EJI) with respiratory health outcomes among infants with bronchopulmonary dysplasia (BPD) within one year after discharge from the neonatal intensive care unit. This was a retrospective cohort study of a cohort of preterm infants with BPD. Multivariable logistic regression models estimated associations of EJI and its components with medically attended acute respiratory illness, defined as an ED visit or inpatient readmission, within one year of discharge from the neonatal intensive care unit. A mediation analysis was conducted to evaluate how environmental injustice may contribute to racial disparities in acute respiratory illness. Greater EJI was associated with an increased risk of medically attended respiratory illness (per EJI standard deviation increment, aOR 1.38, 95% CI: 1.12-1.69). Of the index's components, the Environmental Burden Module's Air pollution domain had the greatest association (aOR 1.44, 95% CI: 1.44-2.61). With respect to individual indicators within the EJI, Diesel Particulate Matter (DSLPM) and Air Toxic Cancer Risk (ATCR) demonstrated the strongest relationship (aOR 2.06, 95% CI: 1.57-2.71 and aOR 2.10, 95% CI: 1.59-2.78, respectively). Among non-Hispanic Black infants, 63% experienced a medically attended acute respiratory illness as compared to 18% of non-Hispanic White infants. DSLPM mediated 39% of the Black-White disparity in medically attended acute respiratory illness (p = 0.004). Environmental exposures, particularly air pollution, are associated with post-discharge respiratory health outcomes among preterm infants with BPD after adjusting for clinical, demographic, and social vulnerability risk factors. Certain types of air pollutants, namely, DSLPM, are more greatly associated with acute respiratory illness. Environmental exposures may contribute to racial disparities in medically attended acute respiratory illness among infants with BPD.

Peculiarities of clabsi in oncologic hospitals

Currently, healthcare-associated infections (HAIs) are one of the most dangerous complications for hospitalized patients. The economic damage caused by HCAI in the Russian Federation annually is about 15 billion rubles; in addition, HCAI occupy the tenth place among the causes of mortality of the population. Catheter-associated bloodstream infection holds the leading place in the structure of ISMP morbidity. The concept of CVC-AIC (catheter-associated bloodstream infections or CLABSI), in turn, is included in CAIC. Purpose of the study. To analyze the current literature data of domestic and foreign authors for the years 2012-2022 concerning CAIC in patients of oncologic hospitals with CVCs, including subcutaneous central venous port catheters. Material and Methods. A review of 38 literature sources for the last 10 years was performed, including current information on catheter-associated bloodstream infections, measures to prevent them, and modern treatment approaches. Results. The studies have shown that the combination of drug resistance in microorganisms and immunity reduction in cancer patients, which occurs against the background of chemotherapy, makes them a risk group for the development of CAICs and episodes of their recurrence. Ensuring epidemiological safety of bloodstream catheterization in such patients is an important step in the prevention of CAIC. This is one of the priority tasks of oncoepidemiology today. Conclusion. The leading role in the occurrence of catheter-associated bleeding plays the term of catheterization and the condition of the patient, his age, stage of cancer development and concomitant chronic diseases. One of the most vulnerable risk groups are cancer patients from hemoblastosis chemotherapy and bone marrow and hematopoietic stem cell transplantation units. This may be due to the use of immunosuppressant drugs to suppress graft rejection, which significantly reduce patients' immunity. According to the results of studies, infection with drug-resistant Gram-negative microorganisms, including multidrug-resistant ones, is prevalent in cancer patients with CAIC. When using povidone iodine, there is a tendency to decrease the incidence of CAIC, but the use of chlorhexidine alcohol solution showed better results. A direct correlation was observed between the use of surgically implanted intravascular devices for long-term function and a lower incidence of CAIC, particularly in pediatric oncology.

Analyzing Coastal Vulnerability Using Analytic Hierarchy Process and Best–Worst Method: A Case Study of the Marmara Gulf Region

AbstractCoastal regions, integral to human economic and social frameworks, face increasing threats from disasters and climate change. This situation has made it necessary and a priority to study these areas at an international level. To be able to take precautions, protect, and manage coastal areas, it is essential to identify their coastal vulnerability. In this study, a coastal vulnerability analysis was conducted using best–worst method (BWM) in the Marmara Gulf Region, Türkiye. Moreover, comparison was made with the analytic hierarchy process (AHP) method in the subject of assessment of coastal vulnerability. According to the vulnerability map obtained with AHP, 17% of the region was determined to have a moderate vulnerability risk, 54% had a high risk, and 29% had a very high risk. On the other hand, the analysis results in BWM calculated the vulnerability percentages as follows: 18% moderate vulnerability, 57% high vulnerability, and 25% very high vulnerability. Visual and statistical comparisons revealed that the BWM method provides more consistent results and involves fewer pairwise comparisons than the AHP method. Thus, it offers ease of use and convenience to decision maker while maintaining relatively same level of weights for criteria. This study aims to lay the foundation for a dynamic system designed to assess coastal vulnerability, emphasizing usability for policymakers in decision-making and flexibility across various scenarios. Particularly, the adoption of the BWM provides notable benefits due to its direct approach and ability to yield more uniform and dependable evaluations, thereby efficiently addressing intricate decision-making challenges.

A machine-learning approach to model risk and protective factors of vulnerability to depression

There are multiple risk and protective factors for depression. The association between these factors with vulnerability to depression is unclear. Such knowledge is an important insight into assessing risk for developing depression for precision interventions. Based on the behavioral data of 496 participants (all unmarried and not cohabiting, with a college education level or above), we applied machine-learning approaches to model risk and protective factors in estimating depression and its symptoms. Then, we employed Random Forest to identify important factors which were then used to differentiate participants who had high risk of depression from those who had low risk. Results revealed that risk and protective factors could significantly estimate depression and depressive symptoms. Feature selection revealed four key factors including three risk factors (brooding, perceived loneliness, and perceived stress) and one protective factor (resilience). The classification model built by the four factors achieved an ROC-AUC score of 75.50% to classify the high- and low-risk groups, which was comparable to the classification performance based on all risk and protective factors (ROC-AUC = 77.83%). Based on the selected four factors, we generated a mood vulnerability index useful for identifying people's risk for depression. Our findings provide potential clinical insights for developing quick screening tools for mood disorders and potential targets for intervention programs designed to improve depressive symptoms.

A Study on the Dynamic Evolution Paths of Social Risks in PPP Projects of Water Environmental Governance—From the Vulnerability Perspective

The Chinese economy is transitioning from high-speed development to high-quality development, and water environmental governance is a key factor promoting economic transformation. Due to low returns and high investment in China’s water environmental governance, the PPP (public–private-partnership) model is often adopted. However, the PPP model has historically faced challenges adapting to local conditions in China, leading to social risks in the PPP projects of water environmental governance. To reduce these risks, this paper takes a vulnerability perspective, employing the system dynamics simulation method to explore the dynamic evolution process of social risks in the PPP projects of water environmental governance. The main results show the following: (1) the external manifestations of social risks during the construction and operation periods vary significantly, exhibiting notable fluctuations; (2) during the construction period, there is a surge in social risks followed by a returns to lower than normal levels, then a gradual upward trend; during the operation period, social risks initially decrease to a lower level before gradually increasing; and (3) city class, relevant legal systems, and resource reserves emerge as critical factors influencing vulnerability and social risks. The higher the city class, the lower the project vulnerability; the soundness of the legal system for PPP projects can effectively reduce vulnerability and social risks; sufficient resource reserves can reduce social risks. Based on the above findings, this paper proposes several suggestions aiming to reduce the vulnerability and social risks in PPP projects, optimize the process of water environmental governance, and further promote the sustainable development of water environmental governance and the high-quality economy of China.

Alterations in Circadian Rhythms, Sleep, and Physical Activity in COVID-19: Mechanisms, Interventions, and Lessons for the Future.

Although the world is acquitting from the throes of COVID-19 and returning to the regularity of life, its effects on physical and mental health are prominently evident in the post-pandemic era. The pandemic subjected us to inadequate sleep and physical activities, stress, irregular eating patterns, and work hours beyond the regular rest-activity cycle. Thus, perturbing the synchrony of the regular circadianclock functions led to chronic psychiatric and neurological disorders and poor immunological response in several COVID-19 survivors. Understanding the links between the host immune system and viral replication machinery from a clock-infection biology perspective promises novel avenues of intervention. Behavioral improvements in our daily lifestyle can reduce the severity and expedite the convalescent stage of COVID-19 by maintaining consistent eating, sleep, and physical activity schedules. Including dietary supplements and nutraceuticals with prophylactic value aids in combating COVID-19, as their deficiency can lead to a higher risk of infection, vulnerability, and severity of COVID-19. Thus, besides developing therapeutic measures, perpetual healthy practices could also contribute to combating the upcoming pandemics. This review highlights the impact of the COVID-19 pandemic on biological rhythms, sleep-wake cycles, physical activities, and eating patterns and how those disruptions possibly contribute to the response, severity, and outcome of SARS-CoV-2 infection.

Development of seismic fragility curves for hospital buildings using empirical damage observations

Fragility curves are widely recognized as an essential tool for assessing and projecting asset damage, vulnerability, and seismic risk. The technical literature provides different fragility curves for residential buildings that exhibit unique characteristics for different categories of buildings, however, comparatively fewer curves are available for hospital buildings. The specific design criteria for hospital buildings necessitate specialized fragility curves for them. In the current study, a set of empirical fragility curves for hospitals have been developed utilizing the data of 341 damaged hospital buildings in previous earthquakes. The peak ground acceleration of the occurred earthquake events in the location of the considered hospitals is utilized as earthquake intensity measure. Fragility curves are developed for the three categories of low-rise concrete moment frame, medium-rise concrete moment frame, and masonry hospital buildings. Due to the lack of appropriate available data, the fragility curves of masonry hospitals are developed by merging the collected data with the fragility curves acquired from the existing literature. The creation of fragility curves for non-structural components of hospitals is another contribution of this study. Finally, a comparison of the fragility curves derived in the current study with those documented in existing research papers is provided.

Drought Risk Assessment of Winter Wheat at Different Growth Stages in Huang-Huai-Hai Plain Based on Nonstationary Standardized Precipitation Evapotranspiration Index and Crop Coefficient

Soil moisture plays a crucial role in determining the yield of winter wheat. The Huang-Huai-Hai (HHH) Plain is the main growing area of winter wheat in China, and frequent occurrence of drought seriously restricts regional agricultural development. Hence, a daily-scale Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI), based on winter wheat crop coefficient (Kc), was developed in the present study to evaluate the impact of drought characteristics on winter wheat in different growth stages. Results showed that the water demand for winter wheat decreased with the increase in latitude, and the water shortage was affected by effective precipitation, showing a decreasing trend from the middle to both sides in the HHH Plain. Water demand and water shortage showed an increasing trend at the jointing stage and heading stage, while other growth stages showed a decreasing trend. The spatial distributions of drought duration and intensity were consistent, which were higher in the northern region than in the southern region. Moreover, the water shortage and drought intensity at the jointing stage and heading stage showed an increasing trend. The drought had the greatest impact on winter wheat yield at the tillering stage, jointing stage, and heading stage, and the proportions of drought risk vulnerability in these three stages accounted for 0.25, 0.21, and 0.19, respectively. The high-value areas of winter wheat loss due to drought were mainly distributed in the northeast and south-central regions.

코로나19에 대한 낙관적 편견에 영향을 미치는 요인

The purpose of this study is to figure out the factors that affect optimistic bias of COVID-19 risk. This study investigated the effects of media use, interpersonal communication, and self-efficacy, risk characteristics and social trust on two dimensions of the optimistic bias, vulnerability and general risk. The finding showed that optimistic bias, in the dimension of vulnerability, was affected by mass media use(+), interpersonal communication(-), self-efficacy(+), and uncertainty(+), and optimistic bias, in the dimension of general risk, was influenced by mass media use(+) and fatality(-). Furthermore it indicated that social trust was not the significant factor of optimistic bias in both dimensions. This study suggested that mass media use, interpersonal communication and self-efficacy as well as risk characteristics were the major factors affecting optimistic bias of COVID-19 risk. Therefore, it expands a theoretical ground for how optimistic bias is created and provides a meaningful insight on how to reduce optimistic bias and to promote preventive behavior for the public healthcare practitioners.