The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively.

BACKGROUND: Pancreatic cystic neoplasms (PCN) are considered premalignant conditions to pancreatic adenocarcinoma with varying degrees of cancerous potential. Management for individuals who do not require surgical treatment involves surveillance to assess for cancerous progression. Little is known about patients\' experience and the impact of living with surveillance for these lesions.
OBJECTIVE: To explore the experiences of patients living with surveillance for PCNs.
METHODS: Semi-structured qualitative interviews were conducted with patients under surveillance for pancreatic cystic neoplasms in the UK. Age, gender, time from surveillance and surveillance method were used to purposively sample the patient group. Data were analysed using reflexive thematic analysis.
RESULTS: A PCN diagnosis is incidental and unexpected and for some, the beginning of a disruptive experience. How patients make sense of their PCN diagnosis is influenced by their existing understanding of pancreatic cancer, explanations from clinicians and the presence of coexisting health concerns. A lack of understanding of the diagnosis and its meaning for their future led to an overarching theme of uncertainty for the PCN population. Surveillance for PCN could be seen as a reminder of fears of PCN and cancer, or as an opportunity for reassurance.
CONCLUSIONS: Currently, individuals living with surveillance for PCNs experience uncertainty with a lack of support in making sense of a prognostically uncertain diagnosis with no immediate treatment. More research is needed to identify the needs of this population to make improvements to patient care and reduce negative experiences.

BACKGROUND: Severe trauma accounts for a main factor inducing mortality for individuals aged < 45 years in China, which requires admission to intensive care unit (ICU) to receive comprehensive treatment. Family members of patients with unanticipated and life-threatening trauma during their ICU stays often experience psychosocial distress due to illness uncertainty. Previous research has shown that family function and psychological resilience are associated with illness uncertainty, respectively. However, little is known about the current situation and interacting mechanism between family function, psychological resilience, and illness uncertainty of family members for ICU trauma patients. Therefore, this study focused on exploring the current situation and relationships between these three factors in family members for ICU trauma patients.
METHODS: The convenience sampling approach was adopted in the present cross-sectional survey, which involved 230 family members for ICU trauma patients from 34 hospitals in Chongqing, China. Related data were extracted with self-reporting questionnaires, which included sociodemographic characteristic questionnaire, the Family Adaptability, Partnership, Growth, Affection and Resolve Scale (APGAR), the 10-item Connor-Davidson Resilience Scale (10-CD-RISC) and the Mishel\'s Illness Uncertainty Scale for Family Members (MUIS-FM). Pearson correlation analysis was conducted to examine the correlations between various variables. Additionally, a structural equation model was adopted to assess the mediating effect of psychological resilience on family function and illness uncertainty.
RESULTS: According to our results, family members for ICU trauma patients experienced high illness uncertainty with moderate family dysfunction and low psychological resilience. Family function directly affected illness uncertainty and indirectly affected illness uncertainty through psychological resilience in family members of ICU trauma patients.
CONCLUSIONS: Family function and psychological resilience are the protective factors for reducing illness uncertainty. Healthcare providers should take effective measures, including family-functioning improvement and resilience-focused interventions, for alleviating illness uncertainty in family members of ICU trauma patients.

Groundwater monitoring data can be prone to errors and biases due to various factors like borehole and equipment malfunctions, or human mistakes. These inaccuracies can jeopardize the groundwater system, leading to reduced efficiency and potentially causing partial or complete failures in the monitoring system. Traditional anomaly detection methods, which rely on statistical and time-variant techniques, struggle to handle the complex and dynamic nature of anomalies. With advancements in artificial intelligence and the growing need for effective anomaly detection and prevention across different sectors, artificial neural network methods are emerging as capable of identifying more intricate anomalies by considering both temporal and contextual aspects. Nonetheless, there is still a shortage of comprehensive studies on groundwater anomaly detection. The intricate patterns of sequential data from groundwater present numerous challenges, necessitating sophisticated modeling techniques that combine mathematics, statistics, and machine learning for viable solutions. This paper introduces a model designed for high accuracy and efficient computation in detecting anomalies in groundwater monitoring data through a probabilistic approach. We employed the Monte Carlo method and SEAWAT numerical simulation to ascertain the uncertainty in groundwater salinity. Subsequently, a Long Short-Term Memory (LSTM)-Autoencoder model was trained and evaluated, forming the basis of an anomaly detection framework. Each piece of training data was assessed by the LSTM-Autoencoder using the Negative Log Likelihood (NLL) score and a predefined threshold to determine the data\'s abnormality percentage. The accuracy evaluation of the proposed LSTM-Autoencoder algorithm revealed that this approach achieved commendable performance, with an accuracy of 98.47% in anomaly detection.

BACKGROUND: Reimbursement process of oncology drugs in Europe occurs within a complex decision-making process that varies between Member States. Distinctions between the States trigger societal debates since it is necessary to balance access to medicines and health systems sustainability.
OBJECTIVE: We aimed to review the evidence concerning factors associated with the reimbursement decision or Health Technology Agency recommendation of oncology drugs in Europe.
METHODS: A systematic literature search was performed in two databases from inception to august 2023. Screening and data extraction were performed by pairs.
RESULTS: Thirteen articles were included and encompassed data from 11 nations. Seven articles showed that cost-effective (C-E) drugs and lower Incremental Cost-Effectiveness Ratios (ICERs) had higher likelihood of reimbursement. Disease severity might influence the reimbursement decision with financial agreements. Improvement in clinical outcomes, substantial clinical benefit (p < 0.01) or overall survival gains (p < 0.05) were positively associated. Orphan drug designation impact varies between countries but positive decisions are usually achieved under specific conditions. Clinical and C-E uncertainty frequently led to reimbursement with financial agreements or outcomes-based conditions. Sociodemographic factors as: social health insurance system, higher Gross Domestic Product and larger elderly population were positively associated with reimbursement (p < 0.01).
CONCLUSIONS: There is a need for further research into key determinants of reimbursement decisions in Europe and the development of drug access models that can effectively address and overcome costs and effectiveness uncertainties.

UNASSIGNED: Though a worldwide period of uncertainty (COVID-19) has \'ended\', there exists a legacy of maladaptive experiences among people with significant appearance concerns (SAC) that requires care and attention.
UNASSIGNED: Using Giddens\' concept of ontological security, we explored how people experienced their SAC before, during and \"since\" COVID-19. Qualitative surveys allowed us to capture diverse perspectives from individuals transnationally, analysed with deductive reflexive thematic analysis using ontological security as our theoretical foundation.
UNASSIGNED: Themes named \"More Mirror(ed) Time\" and \"Locked Out, Shut Down, and Shut Out\" gave a contextual grounding for the embodied experiences of this group through times of social restrictions, and the theme \"Redefining Relevance\" explored the continued legacy of COVID-19 - and continued global uncertainties such as economic hardship and warfare - that impact the wellbeing of people with SAC.
UNASSIGNED: People with SAC are still \'locked out\' from essential healthcare support as those providing healthcare are overworked, under-resourced and rely on efficient interactive methods such as tele-health that may be triggers for people with SAC. Care providers may consider expanding appearance concerns verbiage, look to involve trusted others in the care-seeking process, and utilize modalities beyond digital health to support people with SAC.

OBJECTIVE: To analyze the uncertainties experienced by nursing professionals who contracted COVID-19.
METHODS: This qualitative research was conducted with 20 nursing professionals who fell ill from COVID-19. Data collection was carried out through semi-structured interviews; the data were then organized using thematic analysis and discussed in the context of Merle Mishel\'s Reconceptualized of Uncertainty in Illness Theory.
RESULTS: The antecedents of the disease had a strong influence on how nursing professionals who contracted COVID-19 perceived uncertainty. The media coverage of the increasing number of cases, the collapse of the healthcare system, and the high mortality rate contributed to associating the disease with fear and panic.
CONCLUSIONS: Viewing it from the perspective of the disease\'s antecedents, the illness of a nursing professional from COVID-19 underscores that before being professionals, they are human beings just like anyone else, undergoing adversities and facing the possibilities associated with being ill.

BACKGROUND: The European Regulation on Health Technology Assessment (EU HTA R), effective since January 2022, aims to harmonize and improve the efficiency of common HTA across Member States (MS), with a phased implementation from January 2025. At \"midterms\" of the preparation phase for the implementation of the Regulation our aim was to identify and prioritize tangible action points to move forward.
METHODS: During the 2023 Spring Convention of the European Access Academy (EAA), participants from different nationalities and stakeholder backgrounds discussed readiness and remaining challenges for the Regulation\'s implementation and identified and prioritized action points. For this purpose, participants were assigned to four working groups: (i) Health Policy Challenges, (ii) Stakeholder Readiness, (iii) Approach to Uncertainty and (iv) Challenges regarding Methodology. Top four action points for each working group were identified and subsequently ranked by all participants during the final plenary session.
RESULTS: Overall \"readiness\" for the Regulation was perceived as neutral. Prioritized action points included the following: Health Policy, i.e. assess adjustability of MS laws and health policy processes; Stakeholders, i.e. capacity building; Uncertainty, i.e. implement HTA guidelines as living documents; Methodology, i.e. clarify the Population, Intervention, Comparator(s), Outcomes (PICO) identification process.
CONCLUSIONS: At \"midterms\" of the preparation phase, the focus for the months to come is on executing the tangible action points identified at EAA\'s Spring Convention. All action points centre around three overarching themes: harmonization and standardization, capacity building and collaboration, uncertainty management and robust data. These themes will ultimately determine the success of the EU HTA R in the long run.

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.

The Precautionary Approach to Fisheries Management requires an assessment of the impact of uncertainty on the risk of achieving management objectives. However, the main quantities, such as spawning stock biomass (SSB) and fish mortality (F), used in management metrics cannot be directly observed. This requires the use of models to provide guidance, for which there are three paradigms: the best assessment, model ensemble, and Management Strategy Evaluation (MSE). It is important to validate the models used to provide advice. In this study, we demonstrate how stock assessment models can be validated using a diagnostic toolbox, with a specific focus on prediction skill. Prediction skill measures the precision of a predicted value, which is unknown to the model, in relation to its observed value. By evaluating the accuracy of model predictions against observed data, prediction skill establishes an objective framework for accepting or rejecting model hypotheses, as well as for assigning weights to models within an ensemble. Our analysis uncovers the limitations of traditional stock assessment methods. Through the quantification of uncertainties and the integration of multiple models, our objective is to improve the reliability of management advice considering the complex interplay of factors that influence the dynamics of fish stocks.