Defining Natural Landscape Qualities of the Southern Part of the Krka National Park in Croatia

The paper is based on a review of the established principles for evaluating the natural qualities of landscapes and developing procedures that can contribute to current methods, upgraded with indicators derived from the perception and attitudes of the public. They were implemented into an integrated model of natural landscape qualities. The method included modeling of the natural landscape qualities for the southern part of the Krka National Park in Croatia. The first evaluation model was based on a survey whose responses were processed and classified using the AHP method and GIS. The results showed that the respondents recognize the diversity of land cover and relief forms and the degree of their fragmentation as the highest natural landscape quality. The second step included overlapping the vulnerability model of natural landscape qualities and the model of perception of natural qualities. It was found that the implementation of perceived natural qualities in the evaluation process affects the model of vulnerability of landscape quality. It also indicates the possibility of implementing the perceived natural qualities of certain landscape types into the complete landscape evaluation process, which encompasses all values in the space, including human perception. Therefore, the whole procedure pointed out the importance of the implementation of perceived values into landscape evaluation, something which is also emphasized by the European Landscape Convention (ELC).

Evaluation method on seismic risk of substation in strong earthquake area

Frequent earthquakes in strong earthquake areas pose a great threat to the safety operation of electric power facilities. There exists a pressing research need to develop an assessment method for the seismic risk of substations, i.e., the hubs of power system networks. In this study, based on Incremental Dynamic Analysis (IDA), Probabilistic Seismic Demand Model (PSDM) and reliability theory, a vulnerability model for a substation is obtained, based on considering the relationships between Peak Ground Acceleration (PGA) and four seismic damage states (complete, extensive, moderate, and slight.) via a probabilistic approach. After an earthquake, the scope of influence and PGA distribution are evaluated using information recorded by the seismic observation stations, based on using interpolation or an empirical formula for the PGA attenuation. Therefore, the seismic risk can be evaluated by combining ground motion evaluation and the pre-built vulnerability model. The Wuqia- Kashgar area of Xinjiang was selected as the study area; it is an Earthquake-prone area, and one of the starting points for new energy transmission projects in China. Under a hypothetical earthquake (MS 7.9), the seismic risk of the substations was evaluated. The results show that: this method is able to give the probabilities of the four damage states of the substations, four substations close to the epicenter only have a probability of slight damage (45%-88%) and other substations are safer.

How Should Self-Esteem Be Considered in Cancer Patients?

Numerous studies showed that cancer significantly increases the risk of developing depressive and anxious symptoms. It has been shown that self-esteem is an important psychological resource and is associated with many health behaviors. Furthermore, the vulnerability model of low self-esteem, which has received strong empirical support, highlights that low self-esteem is a real risk factor in the development of depressive disorders. This article aims at providing an overview of the involvement of self-esteem in the psychological adjustment to cancer. After briefly reviewing the literature, we suggest that its implication in the development of depressive disorders and its association with coping strategies and social support in cancer patients justify the consideration of self-esteem in oncology psychological care, especially in young adult patients and those with significant physical impairment following treatment.

Feelings of not Mattering and Depressive Symptoms From a Temporal Perspective: A Comparison of the Cross-Lagged Panel Model and Random-Intercept Cross-Lagged Panel Model

Are feelings of not mattering an antecedent of depressive symptoms, a consequence, or both? Most investigations focus exclusively on feelings of not mattering as an antecedent of depressive symptoms. Our current study examines a vulnerability model, a complication model, and a reciprocal relations model according to a cross-lagged panel model (CLPM) and a random-intercept cross-lagged panel model (RI-CLPM). A sample of 197 community adults completed the General Mattering Scale (GMS), the Anti-Mattering Scale (AMS), and a depression measure at three time points (i.e., baseline, 3 weeks, and 6 weeks). GMS and AMS scores were associated robustly with depressive symptoms at each time point. Other results highlighted the need to distinguish levels of anti-mattering and mattering. CLPM analyses supported a reciprocal relations model of anti-mattering (assessed by the AMS) and depressive symptoms and a complication model linking mattering (assessed by the GMS) and depressive symptoms. The RI-CLPM analyses provided tentative support only for a complication model of anti-mattering and depressive symptoms. Our findings highlight the differences between measures of the mattering construct and the need to adopt a temporal perspective that considers key nuances and the interplay among feelings of mattering, feelings of not mattering, and depression.

An Enhanced Classification Model for Likelihood of Zero-Day Attack Detection and Estimation

The growing threat to sensitive information stored in computer systems and devices is becoming alarming. This is as a result of the proliferation of different malware created on a daily basis to cause zero-day attacks. Most of the malware whose signatures are known can easily be detected and blocked, however, the unknown malwares are the most dangerous. In this paper a zero-day vulnerability model based on deep-reinforcement learning is presented. The technique employs a Monte Carlo Based Pareto Rule (Deep-RL-MCB-PR) approach that exploits a reward learning and training feature with sparse feature generation and adaptive multi-layered recurrent prediction for the detection and subsequent mitigation of zero-day threats. The new model has been applied to the Kyoto benchmark datasets for intrusion detection systems, and compared to an existing system, that uses a multi-layer protection and a rule-based ranking (RBK) approach to detect a zero-day attack likelihood. Experiments were performed using the dataset, and simulation results show that the Deep-RL-MCB-PR technique when measured with the classification accuracy metrics, produced about 67.77%. The dataset was further magnified, and the result of classification accuracy showed about 75.84%. These results account for a better error response when compared to the RBK technique.

MULTI-HAZARD HURRICANE VULNERABILITY MODEL TO ENABLE RESILIENCE-INFORMED DECISION

Hurricanes or typhoons are multi-hazard events that usually result in strong winds, storm surge, waves, and debris flow. A community-level multi-hazard hurricane risk analysis approach is proposed herein to account for the combined impacts of hazards driven by hurricanes including surge, wave, and wind. A tightly coupled ADCIRC and SWAN model is used to account for the surge and wave hazard. Community-level exposure analysis is conducted using a portfolio of building archetypes associated with each hazard. A building-level hurricane vulnerability model is developed using fragility functions to account for content, building envelope, and structural damage. These fragility functions calculate the exceedance probability of predefined damage states associated with each hazard. Then, a building damage state is calculated based on the maximum probability of being in each damage state corresponding to each hazard. The proposed hurricane risk model is then applied to Waveland, Mississippi, a community that was severely impacted by Hurricane Katrina in 2005. The main contribution of this research is modeling the community-level hurricane vulnerability in terms of damage to the building envelope and interior contents driven by surge, wave, and wind using fragility functions to provide a comprehensive model for resilience-informed decision-making.

Migrants’ Vulnerabilities and Their Responses to the COVID-19 Pandemic: A Case Study of Mobile Shoe Shine Boys in the Cape Coast Metropolis

Covid-19 pandemic has impacted socio-economic activities in sub-Saharan Africa and Ghana for that matter. Occupations in the informal sector such as shoe-shine business have been affected by the disease outbreak. This paper focuses on migrant vulnerabilities and their responses to Covid-19 with a specific focus on shoe-shine boys in Cape Coast Metropolis. The study is guided by the IOM determinants of migrant vulnerability model, empirical review on the shoe-shine business and conceptual framework on shoe-shine business within the informal sector. Using an interview guide, ten shoe-shine boys were interviewed in the Cape Coast Metropolis. Their responses were transcribed and a content analysis was employed to analyze the data. The main challenges caused by Covid-19 were the reduction in income, decrease in customer base and changes in the nature of work. The study concluded that the irregular nature of the shoe-shine business exacerbated the impact of the covid-19 on the occupation but individual coping strategies were key in ensuring the sustainability of the occupation.