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Background: Previous research exploring the psychometric properties of the scores of measures of posttraumatic stress disorder (PTSD) suggests there is variation in their functioning depending on the target population. To date, there has been little study of these properties within UK veteran populations. Objective: This study aimed to determine optimally efficient cut-off values for the Impact of Event Scale-Revised (IES-R) and the PTSD Checklist for DSM-5 (PCL-5) that can be used to assess for differential diagnosis of presumptive PTSD. Methods: Data from a sample of 242 UK veterans assessed for mental health difficulties were analysed. The criterion-related validity of the PCL-5 and IES-R were evaluated against the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5). Kappa statistics were used to assess the level of agreement between the DSM-IV and DSM-5 classification systems. Results: The optimal cut-off scores observed within this sample were 34 or above on the PCL-5 and 46 or above on the IES-R. The PCL-5 cut-off is similar to the previously reported values, but the IES-R cut-off identified in this study is higher than has previously been recommended. Overall, a moderate level of agreement was found between participants screened positive using the DSM-IV and DSM-5 classification systems of PTSD. Conclusions: Our findings suggest that the PCL-5 and IES-R can be used as brief measures within veteran populations presenting at secondary care to assess for PTSD. The use of a higher cut-off for the IES-R may be helpful for differentiating between veterans who present with PTSD and those who may have some sy`mptoms of PTSD but are sub-threshold for meeting a diagnosis. Further, the use of more accurate optimal cut-offs may aid clinicians to better monitor changes in PTSD symptoms during and after treatment.

Our study aimed to investigate the immediate impact of the COVID-19 pandemic on mental health and quality of life among local Chinese residents aged ≥18 years in Liaoning Province, mainland China. An online survey was distributed through a social media platform between January and February 2020. Participants completed a modified validated questionnaire that assessed the Impact of Event Scale (IES), indicators of negative mental health impacts, social and family support, and mental health-related lifestyle changes. A total of 263 participants (106 males and 157 females) completed the study. The mean age of the participants was 37.7 ± 14.0 years, and 74.9% had a high level of education. The mean IES score in the participants was 13.6 ± 7.7, reflecting a mild stressful impact. Only 7.6% of participants had an IES score ≥26. The majority of participants (53.3%) did not feel helpless due to the pandemic. On the other hand, 52.1% of participants felt horrified and apprehensive due to the pandemic. Additionally, the majority of participants (57.8–77.9%) received increased support from friends and family members, increased shared feeling and caring with family members and others. In conclusion, the COVID-19 pandemic was associated with mild stressful impact in our sample, even though the COVID-19 pandemic is still ongoing. These findings would need to be verified in larger population studies.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging infection causing a widely spread pandemic of Coronavirus disease 2019 (COVID-19). The current COVID-2019 pandemic is prompting fear of falling sick, dying, helplessness and stigma, urgent and timely understanding of mental health status is needed to help the community. Our investigation designed to survey the general population in Saudi Arabia to assess the degree of psychological impact during the pandemic. During the early stage of the outbreak, we conducted an online-based survey using a snowballing sample technique. The surveys collected data about several aspects of participant sociodemographic, knowledge, concerns, psychological impact, and mental health status. We assessed the psychological impact and mental health status using the Impact of Event Scale-Revised (IES-R), and the Depression, Anxiety, and Stress Scale (DASS-21). Our survey recruited 1160 respondents of the general public of Saudi Arabia. Of them, 23.6% reported moderate or severe psychological impact of the outbreak, 28.3%,24%, and 22.3% reported moderate to severe depressive, anxiety, and stress symptoms, respectively. Females reported IES-R (B: 5.46, 95% CI: 3.61 to 7.31) and DASS subscales B coefficient ranged from 1.65 to 2.63, along with high-school students, working in the medical field, and poor self-reported health status was significantly associated with a high level of IES-R and DASS scales (p < .05). Experiencing breathing difficulty and dizziness showed a stronger association with higher IES-R and DASS subscales than other somatic symptoms (e.g., headache and fever);(p < .001). Respondents who practiced specific preventative measures (e.g., hand washing, social distancing) demonstrated a protective effect against stress, anxiety, and depression symptoms. Social distancing appeared to be protective on stress and anxiety subscales (B: -1.49, 95% CI: −2.79 to −0.19),(B: -1.53, 95% CI: −2.50 to −0.57),respectively; and hand hygiene on depression subscale (B: -2.43, 95% CI: −4.44 to −0.42). Throughout the early stage of the COVID-19 outbreak in Saudi Arabia, the results showed that nearly one-fourth of the sampled general population experienced moderate to severe psychological impact. Following specific precautionary measures appeared to have a protective effect on the individual's mental health. Our findings can be used to construct psychological interventions directed toward vulnerable populations and to implement public mental health strategies in the early stages of the outbreak. •There is Limited data about psychological impact during the COVID-19 pandemic in Saudi Arabia•Health care workers, females, were associated with higher levels of stress, anxiety, and depression symptoms.•Students and those with poor self-reported health status reported higher levels of stress, anxiety, and depression symptoms.•Participants with reported mental disorders (10.5%) showed high scores on all DASS and IER-S scales.•Preventative measures demonstrated a protective effect against, stress, anxiety, and depression symptoms

Since learning organizations (LOs) and performance are positively related, the aim is to identify whether a large, federal, public higher education institution in northeast Brazil is an LO. The interviewees’ perceptions were gathered through a non-probability sample survey using the multilevel Dimensions of Learning Organization Questionnaire – DLOQ-A. The qualitative research, carried out through documentary analysis, characterizes HEIs, the education sector in the world, and Brazil. We sent employees 7,638 questionnaires. Of the employees returned (403), 378 were valid. Confirmatory Factor Analysis (CFA) shows that the model fits the data well. We analyzed factor and item data globally and between teachers and technical-administrative staff. Correlations intra- and inter-factor were calculated. The results corroborate the hypotheses that the staff surveyed do not perceive the HEI as OA, that teachers’ perceptions of the HEI as OA are better than those of technical-administrative staff, and that HEI staff perceive stronger relationships between individual learning level and the group level, and between group learning level and the organizational level. These results add to other researches to further accumulate knowledge about HEIs as OA. They fill a gap in this field of study in Brazil. They can also support the formulation of Institutional Development Plans and HEI policies that focus on learning mechanisms and processes at the three levels of OA (individual, group, and organizational), positively impacting the performance of employees and HEIs.

Compared with the single energy structure in the past, the energy coupling system can realize the coordinated planning and mutual economic regulation among various energy systems, which plays an important role in improving the efficiency of energy utilization. At present there are many related studies but there is a lack of systematic summary. Combining the domestic actual situation, the article mainly lists several multi-energy coupling systems including EWN, IES, BPG, etc., to analyze the energy sources, structural composition and how these systems play a good role in improving energy efficiency, it also summarizes the present situation of comprehensive energy coupling system development research and the shortcomings of current research and prospects for future development are put forward.

This paper examines the properties of the Iterated Ensemble Smoother (IES) and the Multiple Data Assimilation Ensemble Smoother (ES–MDA) for solving the history matching problem. The iterative methods are compared with the standard Ensemble Smoother (ES) to improve the understanding of the similarities and differences between them. We derive the three smoothers from Bayes’ theorem for a scalar case which allows us to compare the equations solved by the three methods, and we can better understand which assumptions are applied and their consequences. When working with a scalar model, it is possible to use a vast ensemble size, and we can construct the sample distributions for both priors and posteriors, as well as intermediate iterates. For a linear model, all three methods give the same result. For a nonlinear model, the iterative methods improve on the ES result, but the two iterative methods converge to different solutions, and it is not clear which should be the preferred choice. It is clear that the ensemble of cost functions used to define the IES solution does not represent an exact sampling of the posterior-Bayes’ probability density function. Also, the use of an ensemble representation for the gradient in IES introduces an additional approximation compared to using an exact analytic gradient. For ES–MDA, the convergence, as a function of increasing number of uniform update steps, is studied for a huge ensemble size. We illustrate that ES–MDA converges to a solution that differs from the Bayesian posterior. The convergence is also examined using a realistic sample size to study the impact of the number of realizations relative to the number of update steps. We have run multiple ES–MDA experiments to examine the impact of using different schemes for choosing the lengths of the update steps, and we have tried to understand which properties of the inverse problem imply that a non-uniform update step length is beneficial. Finally, we have examined the smoother methods with a highly nonlinear model to examine their properties and limitations in more extreme situations.

For the purpose of environment protecting and energy saving, renewable energy has been distributed into the power grid in a considerable scale. However, the consuming capacity of the power grid for renewable energy is relatively limited. As an effective way to absorb the excessive renewable energy, the power to gas (P2G) technology is able to convert excessive renewable energy into hydrogen. Hydrogen-blending natural gas pipeline is an efficient approach for hydrogen transportation. However, hydrogen-blending natural gas complicates the whole integrated energy system (IES), making it more problematic to cope with the equipment failure, demand response and dynamic optimization. Nevertheless, dynamic simulation of distribution parameters of gas-electricity-hydrogen (GEH) energy system, especially for hydrogen concentration, still remains a challenge. The dynamics of hydrogen-blending IES is undiscovered. To tackle the issue, an iterative solving framework of the GEH-IES and a cell segment-based method for hydrogen mixing ratio distribution are proposed in this paper. Two typical numerical cases studying the conditions under which renewables fluctuate and generators fail are conducted on a real-word system. The results show that hydrogen blending timely and spatially influences the flow parameters, of which the hydrogen mixing ratio and gas pressure loss along the gas pipeline are negatively correlated and the response to hydrogen mixing ratio is time-delayed. Moreover, the hydrogen-blending amount and position also have a significant impact on the performance of the compressor.

The vast majority of the genetic variants (mainly SNPs) associated with various human traits and diseases map to a noncoding part of the genome and are enriched in its regulatory compartment, suggesting that many causal variants may affect gene expression. The leading mechanism of action of these SNPs consists in the alterations in the transcription factor binding via creation or disruption of transcription factor binding sites (TFBSs) or some change in the affinity of these regulatory proteins to their cognate sites. In this review, we first focus on the history of the discovery of regulatory SNPs (rSNPs) and systematized description of the existing methodical approaches to their study. Then, we brief the recent comprehensive examples of rSNPs studied from the discovery of the changes in the TFBS sequence as a result of a nucleotide substitution to identification of its effect on the target gene expression and, eventually, to phenotype. We also describe state-of-the-art genome-wide approaches to identification of regulatory variants, including both making molecular sense of genome-wide association studies (GWAS) and the alternative approaches the primary goal of which is to determine the functionality of genetic variants. Among these approaches, special attention is paid to expression quantitative trait loci (eQTLs) analysis and the search for allele-specific events in RNA-seq (ASE events) as well as in ChIP-seq, DNase-seq, and ATAC-seq (ASB events) data.

For nuclear power plants to remain competitive in energy markets increasingly penetrated by variable renewable energy sources, designs that allow flexible operation or incorporate additional revenue streams should be considered. This study models a nuclear reactor decoupled from a supercritical steam Rankine cycle through a two-tank thermal storage system using molten salt as the heat transfer fluid. The model allows steam extraction from the power cycle’s low-pressure turbine to provide thermal energy to a thermal desalination facility. The desalination facility likewise includes a two-tank thermal storage system. This study aims to determine the conditions under which thermal storage integrated with nuclear-desalination systems increases economic competitiveness compared to standalone nuclear power plants. We built a mixed-integer linear program that determines optimal dispatch schedules and subsystem sizing of the energy storage components given current price parameters in the literature. We then performed sensitivity analyses to turbine size, thermal storage system cost, and desalinated water price. We found that multi-effect distillation increased the revenue generation of the system beyond standalone conditions except when the price of desalinated water decreased beyond 30% of its nominal 2021 price. We also found that when the turbine is oversized, high-temperature and low-temperature thermal storage is dispatched in a complementary fashion that allows for load-following and continuous distillate production.

Energy generation and its utilization is bound to increase in the following years resulting in accelerating depletion of fossil fuels, and consequently, undeniable damages to our environment. Over the past decade, despite significant efforts in renewable energy realization and developments for electricity generation, carbon dioxide emissions have been increasing rapidly. This is due to the fact that there is a need to go beyond the power sector and target energy generation in an integrated manner. In this regard, energy systems integration is a concept that looks into how different energy systems, or forms, can connect together in order to provide value for consumers and producers. Cogeneration and trigeneration are the two most well established technologies that are capable of producing two or three different forms of energy simultaneously within a single system. Integrated energy systems make for a very strong proposition since it results in energy saving, fuel diversification, and supply of cleaner energy. Optimization of such systems can be carried out using several techniques with regards to different objective functions. In this study, a variety of optimization methods that provides the possibility of performance improvements, with or without presence of constraints, are demonstrated, pinpointing the characteristics of each method along with detailed statistical reports. In this context, optimization techniques are classified into two primary groups including unconstrained optimization and constrained optimization techniques. Further, the potential applications of evolutionary computing in optimization of Integrated Energy Systems (IESs), particularly Combined Heat and Power (CHP) and Combined Cooling, Heating, and Power (CCHP), utilizing renewable energy sources are grasped and reviewed thoroughly. It was illustrated that the employment of classical optimization methods is fading out, replacing with evolutionary computing techniques. Amongst modern heuristic algorithms, each method has contributed more to a certain application; while the Genetic Algorithm (GA) was favored for thermoeconomic optimization, Particle Swarm Optimization (PSO) was mostly applied for economic improvements. Given the mathematical nature and constraint satisfaction property of Mixed-Integer Linear Programming (MILP), this method is gaining prominence for scheduling applications in energy systems.