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The global demand for energy has increased exponentially over the years. To reduce the dominance of fossil fuels in energy production, there has been a shift towards energy production models based on renewable sources. In the design of hybrid energy systems, it is essential to keep investment costs low while ensuring the security of the energy supply by meeting the consumer’s energy demands without interruption. The success of a good energy production model can be directly associated with the results of load estimation. The primary objective of this research is to predict the electricity demand for the Göksun district until 2028, utilising a data set that encompasses electricity usage from 2019 through the first four months of 2024 for the Göksun district in Kahramanmaraş. This endeavour includes the application of various machine learning (ML) paradigms (long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN)-LSTM, support vector regression (SVR)) to produce load forecasting outcomes and to engineer an optimally performing hybrid system. On evaluation of the performance metrics derived from the experimental data, it has been established that the LSTM model outperforms other methodologies, yielding more favourable results. The simulation studies of the designed hybrid system were conducted using the hybrid optimisation model for electric renewables software (HOMER Pro), demonstrating improvements in both economic and environmental parameters. Our study is unique in that it is the first to utilise a data set specific to the Göksun region and to model predictions obtained from this data set using HOMER software.

With the growing adoption of light electric vehicles (LEVs), it is becoming increasingly important to optimize their propulsion systems. Brushless DC (BLDC) motors are widely utilized for their high efficiency, precise control, and strong performance. Nevertheless, their reliability faces challenges from various operational failures that can greatly affect the functionality of the vehicle. This study employs Finite Element Method (FEM) analysis to investigate and quantify the effects of specific faults in BLDC motors, with a focus on magnet and stator insulation issues. Two primary types of magnet faults—broken magnets and demagnetization—are explored. Our findings indicate that the severity of these faults correlates directly with adverse effects on motor performance, including changes in current levels, torque, and magnetic flux density. A simulated reduction in magnet coercivity by 30% showcases critical consequences such as increased current draw and failure to generate net torque, highlighting potential performance degradation under high-temperature conditions or other stressors. Additionally, the study examines the impacts of unbalanced single-phase short circuits, which increase harmonic content and torque oscillations, further degrading motor performance. By demonstrating the significant influence of these faults through detailed FEM analysis, this research underlines the necessity for robust motor design and proactive maintenance to enhance the reliability and efficiency of LEVs. This work contributes valuable insights into the fault dynamics of BLDC motors, providing a valuable reference for engineers and researchers in the field of electric vehicle propulsion systems.

Research on the diacylglycerol acyltransferase 1 (DGAT1) K232A marker in cattle shows inconsistent results across regions, largely due to small sample sizes, limited genetic variation, and data restricted to few lactations, which complicates establishing a reliable genotype–phenotype correlation. This research aimed to determine the effect of the K232A polymorphism of the bovine DGAT1 gene on milk production and quality traits in dairy cattle. We used 1104 cattle, including 828 Holstein Friesian and 276 Jersey cows. The analysis utilized extensive data from six lactations of cows raised on four commercial dairy farms. We genotyped the population using the polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) technique and Sanger sequencing for verification. We then evaluated the 305 d and test-day milk yields as well as fat and protein yields and percentages. The number of inseminations per conception and calving ease were also assessed as reproduction indices. Genotype–phenotype associations were quantified using linear mixed models. The AA genotype was absent in Jersey cows, and the heterozygous genotype was predominant in both breeds. The K232A marker was significantly associated with test-day milk yield, fat, and protein content in Jersey cows. Further, it substantially affected the fat percentage of milk in Holstein Friesian cows (p<0.001). We found that the KK genotype is highly desirable for milk quality and especially fat content. This comprehensive assessment demonstrated that the KK genotype of the DGAT1 K232A polymorphism significantly influenced fat and protein contents in dairy cattle.

The coronavirus pandemic resulted in universities rapidly changing how courses are delivered. Many traditionally on-campus courses have been adapted to be taught online, or in a hybrid manner with a mix of online and on-campus classes. This rapid shift in pedagogy and delivery mode has disrupted long-standing patterns of teaching and learning in higher education and has also presented a unique challenge for university staff with leadership roles. This project uses a UK university as a case study to understand how staff with educational leadership roles have handled the challenge of supporting their staff to adapt their teaching, making key decisions about how to implement aspects of hybrid teaching, and how lessons from their experiences of the pandemic period can guide future decision-making and support from senior management.

ABSTRACT This study examined the effects of age, housing environment, and strain (Lohmann Sandy (LS) and Lohmann LSL Classic (LW)) on egg quality traits. Deep litter (DL), free access to outdoor Mentha piperita (MP), Petroselinum crispum (PC), and Medicago sativa (MS) vegetated environments were examined. A total of 260 four-week-old birds were randomly distributed to DL and outdoor plant-associated groups, with four and three replicates, respectively, and 10 birds per replicate. Eggs were analyzed between 26 and 52 weeks of hen age, every 4 weeks. Overall, all egg quality parameters significantly differed as hen age increased (p0.05). Strain significantly affected shape index, shell thickness, albumen height, albumen index, Haugh unit, albumen pH, yolk index, and yolk color score. LS eggs had higher shape index, shell thickness, yolk index, and yolk color score (p0.05). This study showed that allowing hens access to MS, PC, or MP plant species improved shell quality traits. Moreover, it appears that there is a greater genetic variability in albumen and yolk quality traits.

Objective: To investigate the importance of immunization in preventing measles infection and to determine the most useful laboratory tests for confirmation of measles. Methods: This study included pediatric cases evaluated with a presumed diagnosis of measles between December 2022 and June 2023, at Marmara University Pendik Training and Research Hospital. The effects of vaccination status and underlying disease on the clinical course, treatments, and complications were evaluated. Results: In total, 117 patients were enrolled in the study with a median age of 80 months (IQR: 32.5-125.0). Twelve patients with contact history were asymptomatic and had an underlying disorder, and intravenous immunoglobulin was given to them for post-exposure prophylaxis. Fifty-one patients had confirmed measles diagnosis. Ribavirin treatment was given to three patients (a newborn, a girl with rhabdomyosarcoma, and a healthy boy) with respiratory distress. Seventy-eight percent of confirmed measles cases were unvaccinated, and all hospitalized cases were unvaccinated or under-vaccinated. Four full-vaccinated children had confirmed measles infection. Measles PCR from nasopharyngeal swabs was negative in all of them, and their diagnosis was established with anti-measles IgM positivity. Conclusions: The measles vaccine is the most effective way to protect from measles and measles-related complications. Although measles can also occur in fully vaccinated patients, the disease is milder than in unvaccinated patients. Using ELISA and RT-PCR tests together may be beneficial in patients with high clinical suspicion for early diagnosis.

The optimal power flow (OPF) problem remains a popular and challenging work in optimizing power systems. Although researchers have suggested many optimization algorithms to solve this problem in the literature, their comparison studies lack fairness and transparency. As these studies increase in number, they deviate from a standard test system, considering a common security and technical constraints., there is a growing trend away from a standard test system. Different studies used different search ranges for the same decision and constraint parameters, different than the standard ranges suggested by IEEE systems. This caused many unfair comparisons in literature. Furthermore, these studies are generally not transparent enough so that their results cannot be verified. This has resulted in numerous infeasible solutions in the literature, violating the limits of constraint parameters. The recent incorporating of renewable energy sources in OPF studies has made this situation more complicated. Sorting through the literature and identifying those OPF applications having exactly the same test conditions is a challenging process. The main contribution is this paper adapts the modified effective butterfly algorithm (MEBO) to solve OPF problem under the common parameter constraints and sufficient transparency. The focus is on a transparent comparison with works in the literature with the same constraint values. This paper compares the performance of the proposed algorithm with other state-of-the-art algorithms in the literature, focusing on the wind energy and without wind energy IEEE 30-bus and IEEE 57-bus systems and the most commonly used constraints. The results demonstrate the efficiency and superiority of the proposed algorithm. For instance, in the 30-bus test system, compared to the initial case, fuel cost has been reduced by 11.42 %, emission by 14.33 %, L-index by 45.10 %, active power losses by 51.60 %, and voltage deviation by 92.70 %.

IntroductionSocial activities are important for health and act as a driver of cognitive reserve during aging. In this perspective paper, we describe challenges and outline future (research) endeavors to establish better operationalization of social activities in multidomain interventions to prevent dementia.BodyWe first address the lack of conceptual clarity, which makes it difficult to measure engagement in social activities. Second, drawing from our experience with the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER), we discuss social activities in multidomain dementia prevention interventions. Using qualitative data from the Multimodal Preventive Trial for Alzheimer’s Disease (MIND-ADmini), we reflect on participant experiences with social activities. Third, we address the potential and challenges of digital solutions in promoting social activities in interventions for dementia prevention. Finally, we share insights from a workshop on digital technology, where we consulted with individuals with and without cognitive impairment who have been involved in three European projects (i.e., EU-FINGERS, Multi-MeMo, and LETHE).DiscussionBased on these insights, we advocate for research that strengthens and accelerates the integration of social activities into multidomain interventions for dementia prevention. We propose several ways to achieve this: (a) by conducting mixed methods research to formulate a broadly accepted definition and instructions to measure social activities; (b) by focusing on promoting engagement in social activities beyond the intervention setting; and (c) by exploring the needs and preferences of older adults towards digitally-supported interventions and co-design of new technologies that enrich in-person social activities.

Astragalus membranaceus Fisch. ex Bunge (syn. Astragalus mongholicus Bunge) is one of the notable medicinal and food plants. Therefore, the aim of this study was to calculate the phenolic composition and antioxidant, antimicrobial, as well as enzyme inhibitory [acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and tyrosinase (TYR)] activities with chemometric approaches of the hydromethanolic and water extracts of commercial A. membranaceus samples. Ten individual phenolic compounds were determined using high-performance liquid chromatography (HPLC), and only quercetin was found at a level of above 80 µg/g DW in both extracts. Moreover, the highest antioxidant activity in DPPH, FRAP, ABTS, and CUPRAC assays was found in the sample containing the roots in loose form from USA. A. membranaceus extracts displayed the inhibition zone diameters within the range from 10 to 22 mm antimicrobial activity against S. aureus, while there were no inhibition zones in any extracts in case of E. coli. The extracts of A. membranaceous showed an inhibition rate below 40% against TYR, and among tested extracts, only two samples were able to inhibit BChE with IC50 values of above 30 µg/mL. Correlation analysis showed a highly positive relationship between their phenolic composition and antioxidant activity. Concluding, the obtained results confirmed that A. membranaceus commercial samples could be an important dietary source of natural antioxidants.

The purpose of this study was to explore student's experiences when practicing information technology skills with an online simulation environment. After using the online simulation environment over a five-week period, 215 undergraduate students were surveyed regarding their usage-related experiences, satisfaction with the environment, and perceived learning. Both quantitative and qualitative data collection methods were employed. The quantitative results suggested that perceived ease of use had a direct effect on student's satisfaction with the online simulation environment, and that the satisfaction with the online simulation environment led to higher perceived learning. The qualitative findings revealed that technical problems, not being able to accomplish the objective of a step due to the precision required by the task, and the inflexibility of certain features of the online simulation environment were the commonly referenced issues, which might have impacted student's satisfaction and their perceived learning.

The assembly of the mammalian brain is orchestrated by temporally coordinated waves of gene expression. Post-transcriptional regulation by microRNAs (miRNAs) is a key aspect of this program. Indeed, deletion of neuron-enriched miRNAs induces strong developmental phenotypes, and miRNA levels are altered in patients with neurodevelopmental disorders. However, the mechanisms used by miRNAs to instruct brain development remain largely unexplored. Here, we identified miR-218 as a critical regulator of hippocampal assembly. MiR-218 is highly expressed in the hippocampus and enriched in both excitatory principal neurons (PNs) and GABAergic inhibitory interneurons (INs). Early life inhibition of miR-218 results in an adult brain with a predisposition to seizures. Changes in gene expression in the absence of miR-218 suggest that network assembly is impaired. Indeed, we find that miR-218 inhibition results in the disruption of early depolarizing GABAergic signaling, structural defects in dendritic spines, and altered intrinsic membrane excitability. Conditional knockout of Mir218-2 in INs, but not PNs, is sufficient to recapitulate long-term instability. Finally, de-repressing Kif21b and Syt13, two miR-218 targets, phenocopies the effects on early synchronous network activity induced by miR-218 inhibition. Taken together, the data suggest that miR-218 orchestrates formative events in PNs and INs to produce stable networks.