The purpose of this study was to explore the organizational climate in a women’s prison from the shared perceptions of correctional staff. This study was part of the Prison Research and Innovation Network (PRIN) project, which began in the fall of 2020. Forty-two correctional staff from a low-medium security women’s prison in the Midwest were interviewed in spring 2021. Thematic analysis was conducted to identify staff’s perceptions of organizational climate in the women’s correctional facility. The themes identified as most important to staff were mental health, correctional practices, job satisfaction, and workplace culture. An item pool was developed from the themes identified, and a newly developed organizational climate survey was administered to 80 correctional staff. Qualitative data point to the issues of staff shortages and mandated overtime work impacting the mental health of staff. In addition, organizational climate and its impact on mental health were examined through multiple logistic regressions. The results indicated that having job promotion opportunities, having daily communication between staff and administration, and staff’s knowledge and awareness of correctional policies were positively associated with mental health. This mixed-methods study contributes to understanding the unique challenges correctional staff face working in women’s correctional facilities.

The growing use of DGs presents challenges for system planners and operators, demanding strategic adaptations to accommodate diverse energy sources while ensuring grid stability and operational efficiency. HC analysis has recently been proposed as an essential tool capable of guiding investments into the areas of the network, most likely to offer optimal benefits. This paper presents a method for estimation of photovoltaic HC of the distribution network. For this purpose, the OpenDSS program, employing the Monte Carlo-based method, is utilized to quantify the HC of the electrical distribution network. The simulation is conducted using a real electrical MV network and then verified against the IEEE Test System for validation. This research reports higher HC in comparison with similar methods and models, investigates the influence of constant generation in daily simulation and proves that voltage constraint is violated before line loading. A considerable increase of the circuit losses is recorded if the optimal penetration of PV is exceeded. The contribution of this work is development, testing and implementation of HC estimation method in complex power systems using open-source tools and integrating them in innovative fashion. The results of this research contribute to collective endeavours of energy transition and sustainability.

This short review illustrates, using two recent studies, the potential and challenges of using machine learning methods to identify phenotypes of wheezing and asthma from childhood onwards.

When two parties need to securely communicate over an insecure channel,  Diffie-Hellman is often employed as the key exchange algorithm. This paper presents two novel approaches to generating Diffie-Hellman parameters for key exchange based on user biometrics, namely their fingerprint data. Fingerprint templates are extracted as bit strings via a fingerprint scanner and later used as inputs. In one approach, the whole fingerprint template is utilized as a user’s private key. In the second approach, fingerprint data is scrambled into smaller chunks and rearranged into two strings that serve as the user’s private key and the basis for prime p. Both approaches were implemented and tested experimentally. After analysis, the second approach that uses scrambled fingerprint data shows better execution times and improved security and usability considerations.

The actions required to achieve higher-quality and harmonised global surveillance of child and adolescent movement behaviours (physical activity, sedentary behaviour including screen time, sleep) are unclear. To identify how to improve surveillance of movement behaviours, from the perspective of experts. This Delphi Study involved 62 experts from the SUNRISE International Study of Movement Behaviours in the Early Years and Active Healthy Kids Global Alliance (AHKGA). Two survey rounds were used, with items categorised under: (1) funding, (2) capacity building, (3) methods, and (4) other issues (e.g., policymaker awareness of relevant WHO Guidelines and Strategies). Expert participants ranked 40 items on a five-point Likert scale from ‘extremely’ to ‘not at all’ important. Consensus was defined as > 70% rating of ‘extremely’ or ‘very’ important. We received 62 responses to round 1 of the survey and 59 to round 2. There was consensus for most items. The two highest rated round 2 items in each category were the following; for funding (1) it was greater funding for surveillance and public funding of surveillance; for capacity building (2) it was increased human capacity for surveillance (e.g. knowledge, skills) and regional or global partnerships to support national surveillance; for methods (3) it was standard protocols for surveillance measures and improved measurement method for screen time; and for other issues (4) it was greater awareness of physical activity guidelines and strategies from WHO and greater awareness of the importance of surveillance for NCD prevention. We generally found no significant differences in priorities between low-middle-income (n = 29) and high-income countries (n = 30) or between SUNRISE (n = 20), AHKGA (n = 26) or both (n = 13) initiatives. There was a lack of agreement on using private funding for surveillance or surveillance research. This study provides a prioritised and international consensus list of actions required to improve surveillance of movement behaviours in children and adolescents globally.

The study presents an in-depth analysis of the impact of noise from mining operations, focusing on the spatial distribution of noise levels and their compliance with regulatory limits. Utilizing advanced modeling and visualization techniques, it demonstrates effective noise management strategies that ensure compliance with national regulations. Findings highlight the importance of integrating environmental assessments and technological innovations in mitigating noise pollution, underscoring the mining industry’s commitment to sustainable practices and community well-being. This research contributes valuable insights into environmental management, offering a model for balancing industrial activities with ecological and health considerations. Key findings emphasize the significance of integrating environmental assessments and technological innovations to mitigate noise pollution, showcasing the mining industry’s commitment to sustainable practices and community well-being. The study underlines the importance of noise management strategies that align with national regulations to protect both the environment and public health. Using advanced modeling and visualization techniques, the research offers valuable insights into environmental management, presenting a model for balancing industrial activities with ecological and health considerations. It contributes significantly to the understanding of noise pollution in the mining sector, proposing effective solutions for its control. This work is grounded in a broad review of literature on environmental pollution and specific studies on noise pollution’s effects on health, highlighting the broader context of industrial and urban noise sources. It presents a detailed analysis of noise levels around a specific mining operation, including modeling and visualization of noise propagation and its impact on surrounding residential areas. The conclusion drawn from this study is that through strategic planning, technological interventions, and adherence to regulations, mining operations can effectively mitigate noise pollution. This ensures that noise levels remain within acceptable limits, thereby minimizing their impact on nearby communities and contributing to a safer and more sustainable mining environment.

The inadequacy of intensive care medicine in low-resource settings (LRS) has become significantly more visible after the COVID-19 pandemic. Recommendations for establishing medical critical care are scarce and rarely include expert clinicians from LRS. In December 2023, the National Association of Intensivists from Bosnia and Herzegovina organized a hybrid international conference on the topic of organizational structure of medical critical care in LRS. The conference proceedings and literature review informed expert statements across several domains. Following the conference, the statements were distributed via an online survey to conference participants and their wider professional network using a modified Delphi methodology. An agreement of ≥ 80% was required to reach a consensus on a statement. Out of the 48 invited clinicians, 43 agreed to participate. The study participants came from 20 countries and included clinician representatives from different base specialties and health authorities. After the two rounds, consensus was reached for 13 out of 16 statements across 3 domains: organizational structure, staffing, and education. The participants favored multispecialty medical intensive care units run by a medical team with formal intensive care training. Recognition and support by health care authorities was deemed critical and the panel underscored the important roles of professional organizations, clinician educators trained in high-income countries, and novel technologies such as tele-medicine and tele-education. Delphi process identified a set of consensus-based statements on how to create a sustainable patient-centered medical intensive care in LRS.

Objective This study aimed to assess the practicality and trustworthiness of explainable artificial intelligence (XAI) methods used for explaining clinical predictive models. Methods Two popular XAIs used for explaining clinical predictive models were evaluated based on their ability to generate domain-appropriate representations, impact clinical workflow, and consistency. Explanations were benchmarked against true clinical deterioration triggers recorded in the data system and agreement was quantified. The evaluation was conducted using two Electronic Medical Records datasets from major hospitals in Australia. Results were examined and commented on by a senior clinician. Results Findings demonstrate a violation of consistency criteria and moderate concordance (0.47-0.8) with true triggers, undermining reliability and actionability, criteria for clinicians’ trust in XAI. Conclusion Explanations are not trustworthy to guide clinical interventions, though they may offer useful insights and help model troubleshooting. Clinician-informed XAI development and presentation, clear disclaimers on limitations, and critical clinical judgment can promote informed decisions and prevent over-reliance.

Active malignant disease is associated with pulmonary embolism and the treatment of this condition is very challenging. The efficacy and safety of thrombolytic therapy for acute severe PE in patients with active malignant disease is unknown. This study aimed to investigate hospital mortality rate and the incidence of major bleeding at 7 days according to the International Society of Thrombosis and Hemostasis (ISTH) criteria in patients with active malignant disease who were treated with thrombolytic therapy due to severe acute PE. Patients with acute PE proven by computed tomography pulmonary angiography who were admitted to intensive care units have enrolled in the Regional PE Registry (REPER) since 2015, consisting of 10 hospitals from the 4 east Balkan countries. The decision to use thrombolytic therapy was at the discretion of the attending physicians, and it was used in high-risk, and intermediate-high-risk PE patients. Hospital mortality and the incidence of major bleeding at 7 days were compared between patients with active cancer and those without it who received thrombolytic therapy. Alteplase-based therapy was used. Among 2070 patients with acute PE enrolled in REPER, intermediate-high and high-risk PE had 795 patients without malignant disease and 135 had active malignant disease in the last 6 months. Patients with malignant disease had less chance to be treated with thrombolysis than patients without it (29.1% vs 44.7%, OR 0.508, 95%CI 0.341-0.756, p=0.001). For patients treated with thrombolysis, hospital mortality was non significantly higher in patients with the malignant disease compared to patients without it (25.6% vs 16,1%, OR 1.803, 95%CI 0.833-3.904, p=0.132), and the incidence of major bleeding at seven days was similar (15.4% vs 18.5%, OR 0.800, 95%CI 0.322-1.989, p=0.6131). There was no significant difference in age, sex, and PE risk distribution between patients with active malignant disease and those without it who were treated with thrombolysis. Thrombolytic therapy seems to be underutilized in patients with the active malignant disease compared to patients without it in severe acute PE. In the selected patients who were treated with thrombolysis for severe acute PE, the efficacy and safety are similar between patients with and without active malignant disease.

Acute pulmonary embolism (PE) management guidelines categorize normotensive patients with right ventricle dysfunction (RVD) and normal cardiac troponin (cTn) as intermediate low risk. This study explores the prevalence of cardiovascular comorbidities and their impact on risk stratification in this specific cohort. To investigate the characteristics of normotensive acute PE patients with RVD and normal cTn, emphasizing the role of pre-existing cardiovascular diseases in determining the intermediate-low risk status. A total of 1675 PE patients from a regional registry were screened, excluding high-risk and intermediate-high-risk cases. Among the remaining 400 normotensive patients with RVD, 353 with echocardiography and normal cTn were included. Patients were categorized into low or intermediate-low risk based on RVD presence. Cardiovascular comorbidities were assessed, and logistic regression analyzed their association with intermediate-low risk. Intermediate-low-risk patients (n=137) exhibited significantly higher rates of chronic heart failure, arterial hypertension, coronary artery disease, diabetes, and atrial fibrillation compared to low-risk patients (n=216). A substantial 77.4% of intermediate-low-risk patients had at least one cardiovascular comorbidity, significantly elevating the risk of RV dysfunction (adjusted OR 2.954, p<0.001). The all-cause hospital mortality was 5.1% in intermediate-low-risk and 1.4% in low-risk PE. Normotensive acute PE patients with RVD and normal cTn are predominantly burdened with chronic cardiovascular conditions. The majority of intermediate-low-risk patients have at least one cardiovascular comorbidity, indicating an increased risk of death during hospitalization compared to low-risk patients. This study underscores the necessity for nuanced risk stratification considering pre-existing cardiovascular diseases for tailored and effective management. These findings have important implications for optimizing treatment strategies and improving outcomes in this high-risk population.

The main aim of the study was to determine the difference in the effects between the two applied protocols (Unilateral and Bilateral), on the ability of planned agility and acceleration. For this research, the sample were active soccer players (N=30; 14 years in average). Two equal groups were formed randomly, unilateral group (EG=15) and bilateral group (CG=15). The study included an 8-week intervention of unilateral and bilateral plyometric training, applied as an integral part of soccer training, with three training sessions in one week. Both applied protocols were equalized according to the total load volume, the number of foot contacts with the ground and the character of the jump performance. Variables included tests of planned agility (side step test, and 505 test, arrowhead test), and acceleration tests (5- and 20 meters sprint). T-test for independent samples, and combined analysis of variance (2x2 / time x group) were calculated. The results showed no differences between the treatment groups, but absolute effects were achieved in both groups. The sidestep test, 505 planned agility test, arrowhead test, and 5 and 20-meter sprint test improved equally in both groups (p<0.05). In conclusion, unilateral and bilateral plyometric training lasting eight weeks led to significant improvements (pre/post= p<0.05) in sprint-type explosive power (acceleration ability) and preplanned agility, but without statistically significant differences in the magnitude of the effects between training groups.

This literature survey explores the domain of remote driving of road vehicles within autonomous vehicles, focusing on challenges and state-of-the-art solutions related to driving feedback, latency, support control, as well as remote driving platform and real applications. The advancement towards Level-5 autonomy faces challenges, including sensor reliability and diverse scenario feasibility. Currently, remote driving is identified as vital for commercialization, however, it comes with challenges like low situational awareness, latency, and a lack of comprehensive feedback mechanisms. Solutions proposed include enhancing visual feedback, developing haptic feedback, employing prediction techniques, and use control methods to support driver. This paper reviews the existing literature on remote driving in these fields, revealing research gaps and areas for future studies. Additionally, this paper reviews the industry applications of remote driving and shows the state-of-art use cases.