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Abstract This research offers a novel methodology for quantifying water needs by assessing weather variables, applying a combination of data preprocessing approaches, and an artificial neural network (ANN) that integrates using a genetic algorithm enabled particle swarm optimisation (PSOGA) algorithm. The PSOGA performance was compared with different hybrid-based metaheuristic algorithms’ behaviour, modified PSO, and PSO as benchmarking techniques. Based on the findings, it is possible to enhance the standard of initial data and select optimal predictions that drive urban water demand through effective data processing. Each model performed adequately in simulating the fundamental dynamics of monthly urban water demand as it relates to meteorological variables, proving that they were all successful. Statistical fitness measures showed that PSOGA-ANN outperformed competing algorithms.

Abstract Background To assess marginal bone loss and soft tissue health around two-implant mandibular overdenture retained with milled versus selective laser-melted cobalt chromium (Co-Cr) bars. Method This research was set to be a parallel, triple-blinded, randomised controlled trial. Twenty completely edentulous patients received new conventional complete dentures according to conventional techniques. Two implants were placed at mandibular canine areas bilaterally, and patients were randomly allocated into two equal groups: the milled Co-Cr bar group and the selective laser melted (SLM) Co-Cr bar group. Marginal bone loss (MBL), modified plaque index (mPI), modified gingival index (mGI), and probing depth (PD) were evaluated at 0-month (baseline), 6-month, and 12-month follow-up visits. Repeated measures ANOVA test and Bonferroni’s post-hoc test were used for parametric data as PD, while for non-parametric data as MBL, mGI, and mPI, Mann-Whitney U test and Friedman’s test were used. A P-value ≤ 0.05 was set as the statistical level of significance. The study protocol was approved by the Faculty Research Ethics Committee at Minia University (636 4/10/2022). Registration for the clinical trial was made retrospectively on clinicaltrials.gov with ID NCT06401200 at 04/30/2024. Results The follow-up period (one year) was completed without a dropout. Regarding MBL, no statistically significant difference was found between the two groups throughout the study. However, the milled group showed significantly increased MBL from 0- to 6-month follow up period. In both groups, mPI increased significantly from 0- to 6-months post-loading. On the other hand, no statistically significant difference between the two groups was found regarding mPI and mGI throughout the study follow-up periods. The PD was significantly lower in the milled compared to the SLM group at the 6- and 12-month follow up period. Conclusion Two-implant mandibular overdenture retained with milled or SLM Co-Cr bar can provide an acceptable treatment option for completely edentulous patients regarding marginal bone loss and soft tissue outcomes.

Abstract Background During embryogenesis, the spleen undergoes intricate developmental processes, originating from mesenchymal cells in the dorsal mesentery. An accessory spleen, a common anomaly found in autopsies and abdominal CT scans, can often be mistaken for different types of tumors. To the best of our knowledge, this is the first case in Syria documenting the occurrence of 3 accessory spleens in a patient who had previously undergone splenectomy. Case presentation A 33-year-old male presented with right hypochondrium pain, sharp and radiating to the right flank, exacerbated by movement and large meals. Past medical history included mild Irritable Bowel Syndrome (IBS) and splenectomy due to a traumatic accident in childhood. On admission, vital signs were stable, with abdominal tenderness in the right upper quadrant. Laboratory investigations showed normal values. Ultrasound revealed a lobulated mass at the right adrenal gland (4.5 × 5 cm) with an isoechoic to hypoechoic texture. Multi-slice computed tomography (MSCT) Scan showed multiple nodules in the right adrenal gland, regular in shape, exerting a compressive mass effect, and significant lymphadenopathy around the abdominal aorta. Elevated metanephrine levels raised suspicion of an extra-adrenal pheochromocytoma. Laparoscopic surgery was performed, revealing accessory spleens and normal adrenal tissue with no malignancy. Conclusion Healthcare providers should consider accessory spleens as a differential diagnosis for masses near the adrenal glands. Multiple accessory spleens in the adrenal region can complicate cases. Accessory spleens in uncommon locations like the adrenal glands can be challenging to diagnose and manage.

Background: Urban traffic demands efficient management solutions to reduce congestion and improve flow. Traditional traffic signal systems, mostly static, struggle to track urban activity. Objective: This article uses IoT technologies, Arduino microcontrollers, and LTE connection to create an adaptive traffic light system that constantly adjusts traffic signal lengths to maximize traffic flow. Methodology: We created a prototype adaptive traffic light system using Arduino microcontrollers with LTE modules and sensors. The sensors send Real-time traffic data over LTE to a cloud server. The technology uses machine learning algorithms to assess data and traffic conditions and remotely alter traffic signal timings via IoT. Results: The prototype improved traffic flow and reduced congestion during peak hours at chosen junctions. In quantitative terms, traffic throughput rose 25%, and intersection waiting times decreased by 35%. Idling time reduction was anticipated to lower vehicle emissions. Conclusion: Arduino and LTE connection in an IoT-based adaptive traffic signal system show promise for urban traffic management. Traffic flow, waiting times, and emissions improve, proving its scalability and enabling cities to a sustainable and effective traffic management plan as vehicle loads rise. Further study is needed to determine its efficacy in different metropolitan topologies and traffic patterns.

Background: Patients with myocardial infarction (MI) often experience sexual concerns, yet sexual counseling remains underutilized. This study explored barriers to sexual counseling for MI patients from the perspective of nurses working in southern Iraq. Methods: This cross-sectional study recruited 126 nurses (response rate 89%) from a specialized heart center in 2023. They were selected using convenience sampling. A valid adapted questionnaire assessed perceived barriers related to patients, nurses, and organizations. The obtained data were analyzed with descriptive statistics, analysis of variance, and independent t test using SPSS software, version 27. P≤0.05 was considered significant. Results: Religious issues (3.01±0.49), confidentiality concerns (2.92±0.55), and patient reluctance to discuss sex (2.87±0.54) were the most significant patient-related barriers. High workload (2.92±0.42), cultural beliefs (2.82±0.51), and nurse discomfort discussing sex (2.79±0.50) were the leading nurse-related barriers. Lack of administrative support (2.81±0.47), feedback mechanisms (2.77±0.51), and unsuitable consultation environment (2.76±0.53) were the primary organizational barriers. No significant relationships were found between the barriers and demographic data. Conclusion: Religious beliefs, confidentiality concerns, and patient reluctance, along with nurses’ high workload, cultural norms, and discomfort, pose significant barriers to sexual counseling for MI patients in this setting. Addressing these barriers through education, cultural sensitivity training, and organizational support could improve sexual counseling accessibility for MI patients.

Abstract Background Tanzania has been promoting community participation in health, either directly or through health facility governing committees (HFGCs), as part of its wider, ongoing health system reforms since the 1990s. Although some studies have assessed the functioning of the HFGCs, little is known about community knowledge and involvement in their activities. Methods A cross-sectional survey assessing community awareness and participation in HFGCs was conducted between July and October 2022, involving two rural districts of Tanzania, which were selected based on their performance: Handeni, showing low performance and Mbarali, high performance. A total of 1,184 household heads living in the catchment areas of diverse facilities were involved. Frequencies and percentages were calculated overall and by district. The Chi-squared test was applied to assess statistically significant differences in knowledge, awareness, and participation between the districts. Results The results revealed that 85.3% of the participants were unaware of the presence of the HFGCs and 91.7% did not know their roles. Additionally, 87% had neither heard nor seen any announcement about the selection of committee members. Only 14.5% considered that HFGCs were accountable to the community. While 96.7% of the respondents said they had never received any feedback from the HFGCs, only 8.1% reported that HFGCs were collecting views from the community. Regarding participation, 79.9% believed that the community had not been supporting their activities; however, 44.7% believed that the committees were important in improving health service delivery. Feedback and support were more common in Handeni. Conclusion The level of community awareness of and participation in HFGCs was very low in both districts. The Ministry of Health and the President’s Office of Regional Administration and Local Government should implement an action plan to raise community awareness of the role of the HFGCs and their significance in promoting social accountability within the Tanzanian health system.

Abstract This study explores the optimization of ironing parameters in Material Extrusion (MEX) technology, specifically targeting improvements in the mechanical properties of polylactic acid (PLA) printed objects, with a focus on enhancing Ultimate Tensile Strength (UTS) and minimizing printing time. To achieve this, we employed a systematic experimental approach that varied flow rate, path speed, and spacing between passes, followed by a comprehensive statistical analysis using Analysis of Variance (ANOVA). Our findings indicate that path speed and spacing between passes significantly influence both UTS and printing time, while flow rate has a minor effect within the tested range. Optimal parameters were determined to be a flow rate of 14.34%, path speed of 30 mm/s, and spacing between passes of 0.3 mm, achieving a composite desirability of 0.970537. Validation using the response optimizer feature in Minitab software confirmed a strong correlation between predicted and experimental results. This research provides practical insights for optimizing MEX parameters, contributing to enhanced mechanical properties and efficiency in additive manufacturing processes.

Abstract Single-cell RNA sequencing is a powerful tool to investigate the cellular makeup of tumor samples. However, due to the sparse data and the complex tumor microenvironment, it can be challenging to identify neoplastic cells that play important roles in tumor growth and disease progression. This is especially relevant for blood cancers, where neoplastic cells may be highly similar to normal cells. To address this challenge, we have developed partCNV and partCNVH, two methods for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. PartCNV uses an expectation-maximization (EM) algorithm with mixtures of Poisson distributions and incorporates cytogenetic information to guide the classification. PartCNVH further improves partCNV by integrating a hidden Markov model for feature selection. We have thoroughly evaluated the performance of partCNV and partCNVH through simulation studies and real data analysis using three scRNA-seq datasets from blood cancer patients. Our results show that partCNV and partCNVH have favorable accuracy and provide more interpretable results compared to existing methods. In the real data analysis, we have identified multiple biological processes involved in the oncogenesis of myelodysplastic syndromes and acute myeloid leukemia.

Abstract Introduction This study aimed to investigate medical students’ perceptions of telehealth in Pakistan and assess the relationship between students’ proficiency in computers and technology, gender, and age. Methods A cross-sectional study was conducted on 330 medical students from various universities in Karachi, Pakistan. Data on demographic characteristics, computer proficiency, and perceptions of telehealth were collected using a self-administered questionnaire. Descriptive statistics, chi-square tests, and Spearman’s rank correlation coefficient were utilized for data analysis. Results The study included 330 participants, predominantly from public universities (83.3%), with a mean age of 21.40 ± 4.24 years. Most participants were pursuing MBBS (67%). In terms of computer proficiency, 41.8% fell into the middle category. Gender correlated significantly with proficiency (p

Background: Traumatic reticulopericarditis (TRP) is a rarely reported in camels. This report provides traumatic reticulopericarditis in camel. Case Description: She-camel (3 years old) with a history of anorexia, and stinky smell-bloody diarrhea for more than three weeks associated with pain, ventral thoracoabdominal edema, positive for metal detection examination that raised the suspicion of traumatic reticulopericarditis. The result of the plasma cardiac troponin I (cTnI) level was 0.253 ng/mL. Electrocardiography on admission using a standard base-apex ECG revealed an irregular rhythm of heart rate and abnormal complex morphology in QRS duration and segments (ST and TP). Elevation of the S-T segment with a slight depression in the P-R segment matched with pericarditis. Conclusion: This report suggests a rare case of traumatic reticulopericarditis and demonstrated for the first time bTn1 and ECG patterns of camel with traumatic reticulopericarditis. [Open Vet J 2024; 14(9.000): 2493-2496]

Abstract This paper investigates the role of digitalization in enhancing wastewater treatment processes, emphasizing its potential to optimize resource utilization, reduce energy consumption, and improve water quality. By examining the implementation of digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), the study demonstrates how these tools enable real-time monitoring, predictive maintenance, and intelligent decision-making in wastewater treatment operations. The paper provides a comparative analysis based on key performance indicators (MAPE, RMSE, R 2 ) to evaluate the effectiveness of these digital solutions. Additionally, it discusses the benefits and challenges associated with integrating digital tools in wastewater treatment plants (WWTPs), including cost, complexity, and data security concerns. The study also addresses the impact of digitalization on carbon neutrality goals, highlighting how data-driven approaches can enhance resource allocation and management. By offering insights into current practices and future directions, this paper aims to contribute to the advancement of sustainable wastewater treatment and support the achievement of UN SDG#6, ensuring clean water and sanitation for all.

Abstract Phononic crystal (PnC) sensors are recognized for their capability to control acoustic wave propagation through periodic structures, presenting considerable potential across various applications. Despite advancements, the effects of fluid viscosity on PnC performance remain intricate and inadequately understood. This study theoretically investigates the influence of shear (dynamic) and bulk viscosity on acoustic wave damping in defective one-dimensional phononic crystal (1D PnC) sensors designed for detecting liquid analytes. Acetic acid with varying viscosities is considered to fill a cavity layer intermediated by a multilayer stack of lead and epoxy. The effects of dynamic and bulk viscosity on the resonance characteristics of the defective mode were analyzed. Numerical results reveal that increased dynamic viscosity leads to substantial broadening and decreased intensity of resonance peaks, accompanied by a shift to higher frequencies due to enhanced elastic wave attenuation and damping. At low dynamic viscosity (η = 0.2 ηd), numerous resonance peaks with varying intensities are observed. However, at higher viscosities (η = 2.0 ηd to η = 10.0 ηd), only one prominent peak appears in the spectrum. The intensity of this resonant peak starts at 98% for η = 2 ηd and decreases to 58.8% as the dynamic viscosity increases to η = 10 ηd. Additionally, the combined effect of dynamic and bulk viscosity introduces further damping, causing a strong shift of the resonance peak to higher frequencies, along with an increase in the full width at half maximum (FWHM) and a decrease in the quality factor (QF). These findings emphasize the necessity of incorporating both shear and bulk viscosity in the design of PnC sensors to enhance their sensitivity and accuracy in practical applications. This theoretical framework provides critical insights for optimizing sensor performance and bridging gaps between theoretical predictions and experimental observations, especially in 1D PnCs, offering potential solutions to challenges in real-world PnC sensor applications.

In recent years, biomass utilization has significantly increased, presenting challenges in its incorporation into various systems. Effective handling requires reliable data on biomass flow properties for designing warehouses and processing equipment. This study investigates the physical properties of ground barley grains, ground oak leaves, ground straw, and cut jute. Barley grains, oak leaves, and straw bales were milled, and jute was cut into 2–3 mm lengths and oven-dried. Particle size distribution, bulk density, Hausner ratio, Carr’s index, moisture content, static angle of repose, and flowability tests and SEM analysis were conducted. The study found that ground barley, having the smallest particle size and highest bulk density, showed superior flow properties due to its rounded particles and clusters, as reflected by a low Hausner ratio. In contrast, jute fibers had a low bulk density and poor flowability, while ground straw exhibited hindered flow due to its larger, more irregular particles. Additionally, the biomass sliding behavior varied with particle size and surface irregularities, with ground barley adhering well to plywood and ground oak leaves adhering well to aluminum. These findings underscore the pivotal roles of particle shape and interparticle forces in determining the biomass flow properties, pointing towards a future where precise environmental control and advanced analytical methods drive innovations in biomass utilization.

Abstract Ovarian insufficiency is one of the common reproductive disorders affecting women with limited therapeutic aids. Mesenchymal stem cells have been investigated in such disorders before yet, the exact mechanism of MSCs in ovarian regeneration regarding their epigenetic regulation remains elusive. The current study is to investigate the role of the bone marrow-derived mesenchymal stem cells (BM-MSCs) lncRNA (Neat-1 and Hotair1) and miRNA (mir-21-5p, mir-144-5p, and mir-664-5p) in mitigating ovarian granulosa cell apoptosis as well as searching BM-MSCs in altering the expression of ovarian and hypothalamic IGF-1 – kisspeptin system in connection to HPG axis in a cyclophosphamide-induced ovarian failure rat model. Sixty mature female Sprague Dawley rats were divided into 3 equal groups; control group, premature ovarian insufficiency (POI) group, and POI + BM-MSCs. POI female rat model was established with cyclophosphamide. The result revealed that BM-MSCs and their conditioned media displayed a significant expression level of Neat-1, Hotair-1, mir-21-5p, mir-144-5p, and mir-664-5p. Moreover, BM-MSCs transplantation in POI rats improves; the ovarian and hypothalamic IGF-1 – kisspeptin, HPG axis, ovarian granulosa cell apoptosis, steroidogenesis, angiogenesis, energy balance, and oxidative stress. BM-MSCs expressed higher levels of antiapoptotic lncRNAs and microRNAs that mitigate ovarian insufficiency.

Shaimaa G Ghazy,1 Mostafa A Abdel-Maksoud,2 Ibrahim A Saleh,3 Mohamed A El-Tayeb,2 Amr A Elsaid,4 Metwally A Kotb,5 Diana A Al-Sherif,6 Heba S Ramadan,5 Ahmed Elwahsh,7,8 Ahmed M Hussein,9,10 Ahmad S Kodous11,12 1Radiation Therapy Department, Armed Forces Medical Complex, Alexandria, Egypt; 2Botany and Microbiology Department- College of Science- King Saud University, Riyadh, Saudi Arabia; 3Faculty of Science, Zarqa University, Zarqa, 13110, Jordan; 4Oncology Department, Faculty of Medicine, Alexandria University, Alexandria, Egypt; 5Medical Biophysics Department, Medical Research Institute, Alexandria University, Alexandria, Egypt; 6Applied Medical Science Faculty, Sixth October University, Sixth October, Giza, Egypt; 7Central Radiology Institute, Kepler University Hospital GmbH, Linz, Austria; 8Department of Molecular and Translational Medicine, Division of Biology and Genetics, University of Brescia, Brescia, Italy; 9Department of Pharmaceutical Sciences, Division of Pharmacology and Toxicology, University of Vienna, Vienna, 1090, Austria; 10Zoology Department, Faculty of Science, Al Azhar University, Assiut, Egypt; 11Pharmacology Department, Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College & Hospitals, Chennai, TN, India; 12Radiation Biology Department, National Center for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority (EAEA), Cairo, EgyptCorrespondence: Ahmad S Kodous; Ahmed M Hussein, Email ahmadkmp11@gmail.com; ahmed.hussein@univie.ac.atPurpose: The local management approach for node-positive breast cancer has undergone substantial evolution. Consequently, there exists a pressing need to enhance our treatment strategies by placing greater emphasis on planning and dosimetric factors, given the availability of more conformal techniques and delineation criteria, achieving optimal goals of radiotherapy treatment. The primary aim of this article is to discuss how the extent of regional nodal coverage influences the choice between IMRT and 3D radiation therapy for patients.Patients and Methods: A total of 15 patients diagnosed with left breast cancer with disease involved lymph nodes were included in this study. Delivering the recommended dose required the use of a linear accelerator (LINAC) with photon beams energy of 6 mega voltage (6MV). Each patient had full breast radiation using two planning procedures: intensity-modulated radiotherapy (IMRT) and three-dimensional radiotherapy (3D conformal). Following the guidelines set forth by the Radiation Therapy Oncology Group (RTOG), the planned treatment coverage was carefully designed to fall between 95% and 107% of the recommended dose. Additionally, Dose Volume Histograms (DVHs) were generated the dose distribution within these anatomical contours.Results and Conclusion: The DVH parameters were subjected to a comparative analysis, focusing on the doses absorbed by both Organs at Risk (OARs) and the Planning Target Volume (PTV). The findings suggest that low doses in IMRT plan might raise the risk of adverse oncological outcomes or potentially result in an increased incidence of subsequent malignancies. Consequently, the adoption of inverse IMRT remains limited, and the decision to opt for this therapy should be reserved for situations where it is genuinely necessary to uphold a satisfactory quality of life. Additionally, this approach helps in reducing the likelihood of developing thyroid problems and mitigates the risk of injuries to the supraclavicular area and the proximal head of the humerus bone.Keywords: radiotherapy, LINAC, RTOG, breast cancer, photon exposure and secondary cancer risk

Tramadol is a widely prescribed analgesic for pain management in adults, but its safety during pregnancy remains a subject of concern. While previous research has highlighted potential risks associated with Tramadol use during gestation, there is a need for a comprehensive investigation into its effects on foetal brain development. This preclinical study sought to address this gap in knowledge by examining the impact of Tramadol exposure on the cerebral and cerebellar cortices in a murine model. Pregnant mice were subjected to controlled exposure to varying doses of Tramadol during the initial stages of gestation. The foetal brains were examined through a combination of histopathological analysis, assessment of apoptotic changes, and evaluation of amyloid precursor protein (APP) expression. These investigative methods allowed for a detailed examination of structural alterations, cell death patterns, and molecular changes within the foetal brain tissues. The study’s findings were notable for several significant observations. Firstly, there was clear evidence of global shrinkage within the cerebral and cerebellar cortices of foetuses exposed to Tramadol. This shrinkage was associated with disrupted cellular organization, marked apoptosis of neural cells, and a notable upregulation in the expression of APP, particularly within the cerebral cortex. Specifically, a 23% reduction in cerebral cortex size was observed in Tramadol-exposed foetuses compared to the control group. Furthermore, a dose-dependent relationship was established, indicating that higher Tramadol doses corresponded to more pronounced alterations in foetal brain structure and molecular profile. Prenatal tramadol treatment induces neurodegenerative changes in newborn cerebellum with agenesis in the Purkinje cells layer and up regulation of APP in response to the neurotoxic effect that could be considered a neuroprotective role for neurons.

AbstractPhotovoltaic (PV) modules’ efficiency decreases due to the presence of external electrical potentials due to the phenomenon known as potential induced degradation (PID). Powerlines or other external sources can generate this potential, or solar cells themselves can generate it through their electric field. An electric field changes the internal electrical properties of a PV module, which can result in a 30% loss of efficiency. PID can be prevented by designing PV modules to be PID-resistant, or by designing the system to minimize the electric field. Materials such as insulating materials, coatings on PV modules, and shielding materials can achieve this. A literature review is presented here, which analyzes the proposed causes of degradation effects as well as the methodological approaches employed in the test. This article presents and compares studies conducted at the cell, microunit, and module level. Several possible preventive measures are also discussed to prevent PV modules from degrading. In addition, modern mechanisms and techniques for mitigating PID effects were reviewed and discussed. This review discusses the main results and analyzes the simulations adopted by the studies, in addition to providing information about the experiments carried out. For the development of solar technology and to increase its spread in the world, it is crucial to understand how photovoltaic cells degrade and to identify other challenges.

Abstract Atopic dermatitis often begins in infancy and follows a chronic course of exacerbations and remissions. The etiology is complex and involves numerous factors that contribute to skin barrier defect and inflammation. In the Middle East, the burden of atopic dermatitis is understudied. Epidemiological data specific to the Gulf region are scarce but reveal a prevalence of up to about 40% in the United Arab Emirates. Region-specific factors, such as the climate and the frequency of consanguineous marriages, may affect atopic dermatitis incidence, prevalence, and evolution over time. A panel of experts predominantly from the United Arab Emirates analyzed the evidence from published guidelines, and considered expert guidance and local treatment practices to develop clear recommendations for the management of atopic dermatitis in the United Arab Emirates. They encourage a systematic approach for the diagnosis and treatment, using disease severity scores and quality-of-life measurement tools. Treatment recommendations take into consideration both established therapies and the approved systemic biologics dupilumab and tralokinumab, and the Janus kinase inhibitors baricitinib, upadacitinib, and abrocitinib.

Abstract This paper seeks to progress the field of topological photonic crystals (TPC) as a promising tool in face of construction flaws. In particular, the structure can be used as a novel temperature sensor. In this regard, the considered TPC structure comprising two different PC designs named PC1 and PC2. PC1 is designed from a stack of multilayers containing Silicon (Si) and Silicon dioxide (SiO2), while layers of SiO2 and composite layer named hyperbolic metamaterial (HMM) are considered in designing PC2. The HMM layer is engineered using subwavelength layers of Si and Bismuth Germinate, or BGO ( $${\text{Bi}}_{4}{\text{Ge}}_{3}{\text{O}}_{12}$$ Bi 4 Ge 3 O 12 ). The mainstay of our suggested temperature sensor is mainly based on the emergence of some resonant modes inside the transmittance spectrum that provide the stability in the presence of the geometrical changes. Meanwhile, our theoretical framework has been introduced in the vicinity of transfer matrix method (TMM), effective medium theory (EMT) and the thermo-optic characteristics of the considered materials. The numerical findings have extensively introduced the role of some topological parameters such as layers’ thicknesses, filling ratio through HMM layers and the periodicity of HMM on the stability or the topological features of the introduced sensor. Meanwhile, the numerical results reveal that the considered design provides some topological edge states (TESs) of a promising robustness and stability against certain disturbances or geometrical changes in the constituent materials. In addition, our sensing tool offers a relatively high sensitivity of 0.27 nm/°C.

Abstract The current investigation theoretically presents a one-dimensional phononic crystal (PnC) as a fluidic sensor. The sensor under consideration aims to distinguish the concentration of acetic acid. The primary configuration of the proposed sensor is constructed with lead, epoxy, and a defect layer in the middle of the structure, that is filled with acetic acid (vinegar). As a result of the rise in density and decline in the speed of sound at a 100% concentration of acetic acid in comparison to pure water, the peak frequency of the output has shifted towards lower frequencies. Given that the maximum permissible concentration of acetic acid in water for vinegar is above 30%, sensor simulations were conducted within the concentration range of 25–35% with a step size of 1%. Interestingly, the sensitivity of the sensor exhibits a polynomial change in response to the concentration of acetic acid. Consequently, the highest level of sensitivity, which corresponds to the lowest concentration of vinegar, is recorded as 48.44 × 106 (Hz). The proposed system exhibits a remarkable value of the quality factor of 2802.91. Furthermore, the optimal figure of merit (FOM) is achieved when the concentration is at its lowest, with a value of 94.00. Furthermore, the temperature effects are taken into account for a wide range between 10 $$\text{and}$$ and 60 °C. A pronouncing sensitivity is obtained for all temperatures changes and the highest one reached the value of 1.57 × 106 (Hz/°C) at a temperature of 25 °C. Considering the present circumstances, the suggested sensor configuration has the potential to cater to a diverse array of other fluids, specifically their concentration and temperature, thereby offering a broad scope of applications.

Abstract Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in forecasting due to the unpredictable nature of environmental factors influencing energy output. This study explores five distinct machine learning (ML) models which are built and compared to predict energy production based on four independent weather variables: wind speed, relative humidity, ambient temperature, and solar irradiation. The evaluated models include multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), support vector regression (SVR), and multi-layer perceptron (MLP). These models were hyperparameter tuned using chimp optimization algorithm (ChOA) for a performance appraisal. The models are subsequently validated on the data from a 264 kWp PV system, installed at the Applied Science University (ASU) in Amman, Jordan. Of all 5 models, MLP shows best root mean square error (RMSE), with the corresponding value of 0.503, followed by mean absolute error (MAE) of 0.397 and a coefficient of determination (R 2) value of 0.99 in predicting energy from the observed environmental parameters. Finally, the process highlights the fact that fine-tuning of ML models for improved prediction accuracy in energy production domain still involves the use of advanced optimization techniques like ChOA, compared with other widely used optimization algorithms from the literature.

Abstract Introduction Infertility increases women’s risk of intimate partner violence (IPV). Cognitive behavioral therapy (CBT) is commonly used to treat mental health problems among fertility treatment seeking patients. CBT has not been tested for its potential to reduce IPV in this population. We pilot test the use of CBT to prevent IPV and improve patients' mental health in a fertility clinic in Jordan. Methods Of 38 eligible fertility-treatment seeking couples, 16 consented and underwent up to 11 CBT sessions (average = 9) over 3 months. Interviews at baseline and 16 weeks post intervention (endline) assessed IPV, quality of life, social support, coping, and fear of spouse. Wilcoxon signed-rank and McNemar’s tests were used to assess change in outcomes. Results At baseline, women's rates of IPV, depression, and anxiety were 75%, 87.5%, and 75% respectively, whereas men's rates of depression and anxiety were each 80%. Average baseline post-traumatic stress disorder (PTSD) symptoms for men and women were 3.3 and 2.7 respectively out of 5. IPV decreased 25% after treatment, and women reported less spousal fear. For both men and women, depression, anxiety, and PTSD symptoms decreased and social support and fertility quality of life improved. Conclusion Psychosocial support should be standard of care for the treatment of infertility given the burden of mental health problems and IPV and the utility of CBT in this patient population. Co-design with couples is needed to identify strategies to bolster participation along with population-based interventions to combat the stigma of infertility and mental health service use and enhance women's status.

This paper suggests a new image encryption algorithm based on block compressive sensing (BCS) and chaotic scrambling techniques. With compressive sensing (CS), signals can be sampled much lower than the Nyquist-Shannon rate while preserving their information content. BCS can be used as a one-step process by using a 3D logistic chaotic map, the measurement matrix is used as a secret key for encryption. BCS algorithm used an individual image reconstruction algorithm that leverages l_1norm minimization to promote signal sparsity, and a smoothing operator to enhance image quality. An encrypted image is first decomposed into three sub-images based on the tricolor theory; then, a discrete wavelet transform is used to sparsely process the three decomposed images. A 2D Henon map is used to scramble the compressed image after compression. This process further enhances the security of the system by increasing the complexity of the encryption process. The results showed that the proposed system provides better security and has a lower computational complexity than other methods. Furthermore, the proposed system is resistant to known attacks such as brute force and statistical attacks.

Salinity is a classic problem in planning the quality of freshwater resources management. Recent studies related to hybrid machine learning models have shown it's capability to simulate salinity dynamics. However, previous studies of metaheuristic algorithms have not dealt with comparing single- and hybrid-based algorithms in much detail. The present study aimed to develop univariate salinity by applying an artificial neural network model (ANN) integrated with (hybrid-based) coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA). The methodology was developed and tested using electrical conductivity (EC) and total dissolved solids (TDS) data collected from the Euphrates River in Babylon Province, Iraq, from 2010 to 2019. The CPSOCGSA performance was evaluated by various single-based ones, including multi-verse optimiser (MVO), marine predator's optimisation algorithm (MPA), particle swarm optimiser (PSO), and the slim mould algorithm (SMA). The principal finding here confirms that hybrid-based outperformed four single-based algorithms based on different criteria. The outcomes for TDS were 0.004, 0.0248, and 0.98 for CPSOCGSA-ANN technique concern scatter index (SI), root-mean-squared error (RMSE), and correlation coefficient (R2), respectively. For EC, the results were 0.96 for R2, 0.0386 for RMSE, and 0.006 for SI. Due to its predictive accuracy, the proposed CPSOCGSA-ANN approach is suggested as a potential strategy for predicting monthly salinity data. Considering agriculture's vital role in Babylon Province's economy, this study may help inform future freshwater quality management decisions.

Abstract Satellite swarm networks have occupied a prominent position in many modern applications due to their low cost, simplicity of design, and flexibility. Reliability is an influential factor in the design of satellite networks with different structures. Usually, small satellites are based on COST components, which may reduce continues operability due to the lack of using backup system on board the sagecraft. Any failure in one subsystem means a complete loss of the function and data stored in this subsystem; hence the need for a reliable and applicable solution for this matter is a crucial topic. Using the redundancy strategy in satellite swarm networks increases reliability and availability. Blockchain is characterized by using a distributed ledger which enables the database to be replicated across nodes in the network and results in increasing transparency, security, and trust. This paper suggests adoption of blockchain technology in distributed multi-satellite mission swarm networks to provide a high level of reliability and availability of the entire system; the blockchain is usually used to secure system transactions in multilayer approach by storage of the key parameters in more than one node; here we suggest the adoption of this approach not only to secure satellite network transaction, but also to increase system reliability so that failure of one node can be recovered by other nodes. We compared this approach with similar traditional networks that do not use blockchain. The results show a higher reliability efficiency of 95.3% for applying blockchain technology compared to 64.3% without the use of blockchain, as well as a higher availability of 99% compared to 91%.

The spiking skill is one of the types of volleyball skills the mission and which leads The technical aspect plays an important role and this is what makes the player who studies the details of this skill work to confront incorrect performance by relying on additional visual sensory modeling using modern qualitative analysis techniques in a different way directly to identify some basic faulty biomechanical indicators for the stages of technical performance of the spiking skill and the extent of their need for change according to some exercises which contributes to increasing and supporting the motor path of the skill, which helps learners perform the skill successfully, therefore, the researchers used the experimental method using a one-group experimental design appropriate to the nature of the study, the research sample was chosen by the researchers intentionally. The juniors of the Iraqi national volleyball team, where (7) players were selected who are proficient in the skill of striking. Where the researchers were keen to implement specific evaluative exercises in terms of sensory modeling using different tools with a purpose development Some biomechanical indicators developing the muscular strength of a sample of individuals the study in a period of (8) weeks and included (24) training units, (3) units per week, the conclusions, The most important study was that sorting out the mechanical errors related to performance and witnessing them by each learner instilled in themselves the motivation to reach ideal performance according to the corrective exercises that aimed to develop the absolute strength of each of the muscular moments

Herpes simplex virus (HSV) encephalitis, predominantly caused by HSV-1, presents with significant morbidity and mortality challenges. This research investigates the particular role of adjunctive corticosteroid therapy in fulminant HSV encephalitis through in-depth analyses of two contrasting cases. Corticosteroids show potential benefits to improve an exaggerated immune response and limit viral dissemination within the brain. Daily assessments and frequent neuroimaging, particularly using magnetic resonance imaging, aid in the management of fulminant cases. Although existing evidence relies on limited case series and retrospective comparisons, the results of the present study emphasize the necessity for large-scale controlled trials to establish definitive guidelines. The discretion of the treating neurologist governs the decision to implement corticosteroids, emphasizing the imperative need for continued research and evidence-based strategies for this challenging neurological condition.

Sodium lauryl ether sulfate (SLES) is a surfactant commonly used in the formulation of detergents, which is typically disposed of in wastewater treatment plants. The current study describes the effectiveness of bacteria isolated from Iraqi wastewater to remove SLES. 16S rRNA genetic analysis revealed that this strain is Pseudomonas aeruginosa. Three temperatures (30, 35, and 40oC) and pH values (5,7, and 9) were chosen for this study, and three concentrations of SLES (25, 50, and 100 mg/L) were used. The SLES anionic surfactant showed that the best biodegradation by Pseudomonas aeruginosa was at a temperature of 30oC and both pH 7 and 9, while the removal percentages for them were 98.44% and 96.36%, respectively, at 25 mg/L of SLES. The outcomes of this study revealed the potential and significance of SLES removal in actual effluents by aerobic biodegradation. The ability of this bacterium to degrade SLES makes the bacterium an important tool for bioremediation.

Numerous research projects have discovered how trace elements affect people with end-stage renal disease (ESRD). Thus, understanding the mechanism of action and the potential treatment options requires research into the chemical compounds that have an impact on the kidneys. Trace elements are components of metalloproteins and act as cofactors for numerous essential enzymes. Among the most important trace elements that affect renal function are zinc and copper. The newly published papers on changes in the effects of both trace element products in ESRD patients were studied, and the justifications gathered from earlier studies were compiled. This review’s findings suggest that changes in trace element levels worsen ESRD patients’ health condition by deepening kidney dysfunction, which has a negative impact on their health. The mechanism by which these effects occur is reviewed, and schemes were plotted in order to show a comprehensive view of the role of dysregulation of copper and zinc in ESRD patients.

Imidacloprid is a systematic neonicotinoid widely used to combat piercing-sucking insects; however, neonicotinoids, despite having low effects on vertebrates, showed high adverse effects on honeybees (Apis mellifera L.). Glutathione-S-transferases (GST) are an important constituent of the defense system in detoxifying invading chemicals. This work examines the effects of imidacloprid on GST activity in vivo and in vitro conditions. Results revealed that there are two responses of GST activity toward IMI treatment; first, stimulation of enzyme activity to combat and detoxify the insecticide. Second, direct inhibition of GST, which is confirmed by the in vitro inhibition with IC50 887.42 ppm. At a short exposure time to IMI (2 hours) at an IMI concentration of 0.35 ppm, the enzyme was stimulated up to 113% while increasing exposure time or IMI concentration, the inhibition effect dominates.

This study includes the stratigraphic development of the Late Jurassic succession in eight boreholes namely, Si-1, Sal-1, Sa-1, Dn-1, Ki-1, R-172, WQ-15, and KSK -1, within the Mesopotamian Zone. The present study is concluded that the current area represents as part of the northern Arabian plate that was subjected to severe structural deformations prior to the beginning of the Late Jurassic sedimentation, which led to the formation of a vast depression (half graben) that formed a starved basin. This graben was created because of rift-related tectonism called extensional tectonic phase in the latest Middle Jurassic period, which led to the accumulation of high organic matter and formed the source rock (Naokelekan Formation) in the middle part of the graben during their highest and settings. Then this phase was followed by the occupation of the sedimentations (evaporates) in the center of the studied basin during the lowest conditions of the (Gotnia Formation) in the southern Iraq, Kuwait, and southwest Iran. During the highest and system tract after transgressive stage of the Sargelu Formation, the horst surrounding the graben, represents carbonate platforms, which consist of coral structures deposition that follow by Ooids deposits (non-skeletal) and then covered by evaporates sabkha type sediment (cap rock), this succession of sediments called the Najmah Formation. The lowstands stage conditions which are underlying by the sequence boundary type-I, which overlie the Najmah Formation and has been penetrated by Si-1, Sal-1, Sa-1 and Dn-1 wells in the southwestern of Iraq, the sequence boundary considered to be the time equivalent of the Gotnia Formation. The system of the north Arabian plate (Iraq, Kuwait) characterized by an extensional tectonic setting that formed a graben surrounded by shallow water carbonate (horst), while a relatively stable tectonic system predominant at the south-west of the Arabian plate due to its proximity to the Arabian shield, which led to the formation of a wide and flat carbonate platform (Najmah Formation) were covered by Sabkha evaporates.

The current study used 2D resistivity imaging to identify a shallow subsurface cavity in Haditha, western Iraq. The cavities and voids near the surface restrict land use and create a variety of geotechnical hazards, such as land subsidence, infrastructure damage, and structural cracks. As a result, the presence of an underground weak zone is a major concern for engineers before beginning any development, given the risk of future disasters. The four 2D resistivity traverses in this study were carried out using a dipole-dipole array with a - spacing of 3 m and an n-factor of 6. The data was inverted using RES2DINV software, which employs robust inversion methods. The 2D inverse model reveals a shallow cavity ranging from 5 to 12 meters and a width of around 40 m.

Non-alcoholic fatty liver disease (NAFLD), characterized by hepatic fat accumulation in individuals consuming little or no alcohol, has become highly prevalent globally. Oxidative stress plays a central role in instigating inflammation and cell death pathways driving NAFLD progression. This case–control study aimed to elucidate the association between circulating levels of the pivotal non-enzymatic antioxidants – coenzyme Q10 and vitamins E and C – and liver injury parameters among 60 Iraqi NAFLD patients versus 30 healthy controls. NAFLD diagnosis entailed over 5% hepatic steatosis on ultrasound excluding other etiologies. Patients spanned three age groups: 20–29, 30–39, and 40–49. Substantially diminished antioxidant levels concurrent with elevated alkaline phosphatase enzyme were unveiled in NAFLD patients relative to controls (all p < 0.001). Age-based analysis reinforced widespread antioxidant depletion and liver enzyme augmentation across NAFLD patients. Significant correlations also emerged between antioxidants and liver parameters. Our novel observations confirm an antioxidant inadequacy likely perpetuating pathogenic oxidative reactions in NAFLD. Restoring such deficits through lifestyle or therapeutic interventions may confer preventative and disease-modifying value.

High-speed impact performance has significantly expanded over the past few decades. The target response based on the impact conditions has been more difficult to visualise and evaluate. In this article, Ansys model analysis has been used to measure, visualise, and predict the projectile and target responses of Kevlar® (K) and Ramie® textile-reinforced unsaturated polyester resin (UP) matrix. The laminate thickness threshold was detected experimentally based on the highest stress intensity factor and energy release rates. Furthermore, tensile strength and bending of the laminate were found. The impact conditions have a significant impact on the target response; thus, an explicit dynamic analysis was used to visualise the impact response based on the number of target fixed supports (FSs). Two FS (2 FS) target absorbs 11% more energy than four FS (4 FS) target. Additionally, the target size has a major effect on the projectile and laminate responses, and a successful arrest of the projectile was detected in both cases. The smallest targets with 2 FS have the highest and wider response, where a successful change in the projectile trajectory was obtained.

Abstract Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios.

Mevlüt Karata&scedil;, Abdirahman Mohamed Hassan Dirie, Sedat Çolako&gbreve;lu, Abdirahman Osman Hussein, Amal Nor Ali Pulmonology Department, Somalia Mogadishu Türkiye Recep Tayyip Erdo&gbreve;an Training and Research Hospital, Mogadishu, Benadir, SomaliaCorrespondence: Mevlüt Karata&scedil;; Abdirahman Mohamed Hassan Dirie, Email fmkaratas@yahoo.com; dirie515@gmail.comBackground: In low-resource countries like Somalia, tuberculosis (TB) is still a serious global health concern. Understanding the treatment outcomes of TB patients in specific regions is crucial for developing effective strategies to combat the disease. This study aimed to assess the treatment outcomes of TB patients in Benadir, Somalia.Methods: A retrospective cohort study was conducted using data from TB treatment centers in Benadir, Somalia. The study included all TB patients who initiated treatment between July 1, 2019 and June 30, 2020. Treatment outcomes, including treatment success, treatment failure, lost, death, and transfer out, were analyzed. Factors associated with treatment outcomes were also examined using chi-square test.Results: The study comprised 3165 TB patients in total. The mean age of the observations was 29.9 years, with males making up the majority (64.58%). The overall success rate of TB treatment was 80.6%, with 5.3% lost, 4.6% died, 0.5% failed, 4.6% transferred out, and 4.4% not evaluated. Factors associated with unfavorable treatment outcomes included older age and HIV co-infection.Conclusion: The treatment success rate for TB patients in Benadir, Somalia, is below the global target of 90%. Enhancing access to quality TB diagnostic and treatment services, as well as addressing social and economic barriers to treatment adherence, are essential for improving TB control in Benadir, Somalia.Keywords: TB, treatment failure, treatment success, outcomes

Abstract Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the facial nerve, which can significantly impact patients’ quality of life. As facial nerve outcomes remain challenging to prognosticate, we endeavored to utilize machine learning to decipher predictive factors relevant to facial nerve outcomes following microsurgical resection of VS. A database of patient-, tumor- and surgery-specific features was constructed via retrospective chart review of 242 consecutive patients who underwent microsurgical resection of VS over a 7-year study period. This database was then used to train non-linear supervised machine learning classifiers to predict facial nerve preservation, defined as House-Brackmann (HB) I vs. facial nerve injury, defined as HB II–VI, as determined at 6-month outpatient follow-up. A random forest algorithm demonstrated 90.5% accuracy, 90% sensitivity and 90% specificity in facial nerve injury prognostication. A random variable (rv) was generated by randomly sampling a Gaussian distribution and used as a benchmark to compare the predictiveness of other features. This analysis revealed age, body mass index (BMI), case length and the tumor dimension representing tumor growth towards the brainstem as prognosticators of facial nerve injury. When validated via prospective assessment of facial nerve injury risk, this model demonstrated 84% accuracy. Here, we describe the development of a machine learning algorithm to predict the likelihood of facial nerve injury following microsurgical resection of VS. In addition to serving as a clinically applicable tool, this highlights the potential of machine learning to reveal non-linear relationships between variables which may have clinical value in prognostication of outcomes for high-risk surgical procedures.

Objective: Delayed cerebral ischemia (DCI) is one of the most feared complications in aneurysmal subarachnoid hemorrhage (SAH). Animal models are crucial to studying the disease mechanisms and potential treatments. DCI in rodents was thought to not exist; herein we examine literature and our experience with DCI in rodents. Methods: Daily behavioral performance was assessed every day from day 1 to up to 7 days post-SAH on mice from 5 different studies that used the endovascular perforation model. Performance was graded using an 8-test sensorimotor neuroscore previously described. The daily neuroscore was then used to identify the incidence and timing of delayed neurological deficits (DND), a clinical surrogate for DCI. A total number of 298 mice (134 males, 164 females) were subjected to SAH. Fifty-one mice had histological staining done to identify infarct volume. Results: The overall incidence of DND was 33.9%; 27.6% in males and 39.0% in females, but this difference was not statistically significant. The overall incidence of delayed death was 21.1%, and there was no significant difference for delayed mortality in females versus male mice. There is a non-statistically significant trend towards increased infarct volume in mice suffering DND. Conclusions: Mice with endovascular puncture induced SAH develop DND at rates comparable to human patients. Future work needs to correlate the DND seen with decreased regional cerebral blood flow, another hallmark of DCI, but in spite of this need, researchers may use the murine models to test therapies for DCI after SAH.

The GRACE and GRACE Follow-On (GFO) missions, led by American and German teams, along with the European mission Swarm, have revolutionized the study of Earth's dynamic gravity field through precise measurements. Our objective is to fill the data GRACE gaps and the gap between GRACE and GFO missions using Swarm data, focusing on Africa. We utilized data from GRACE processing centers (CSR, GFZ, and JPL), Swarm data from the Czech Academy of Sciences (ASU) and the International Combination Service for Time-variable Gravity (COST-G), QF, as well as IGG data. Both frequency and space domains were examined, evaluating Potential Degree Variances (PDV), harmonic coefficients, Terrestrial Water Storage (TWS), gravity anomaly, and potential/geoid using GRACE, GFO, and Swarm. Results indicated agreement among processing centers for potential degree variances, gravity anomaly, and geoid undulation. However, discrepancies were observed in harmonic coefficients and TWS. To address this, we employed parametric least square adjustment to estimate new Swarm-modified coefficients, selecting Swarm ASU and GRACE/GFO CSR data. Comparison of Singular Spectrum Analysis method (SSA), IGG, and Swarm-modified SHCs during the data gap period exhibited correlation coefficients exceeding 0.86. Overall, the new coefficients significantly improved agreement between original GRACE coefficients and modified coefficients in all aspects.

Introduction: Hypertension is the foremost contributor to cardiovascular disease(s) and premature death worldwide. Mental health disorders have a significant impact on global public health, affecting millions of individuals with disorders including anxiety, depression, bipolar disorder, and schizophrenia. Importantly, all of these conditions are distinctly associated with hypertension. Accordingly, this study aimed to investigate the prevalence of undiagnosed hypertension among individuals diagnosed with mental health disorders in Jordan and its associated risk factors. Methods: Data from individuals 18–80 years of age, who attended the psychiatry clinic at Jordan University Hospital (Amman, Jordan), were included. The study included participants without a previous diagnosis of hypertension, diabetes, or cardiovascular disease. Systolic and diastolic blood pressure, as well as heart rate, were assessed; in addition, demographic information from each participant was collected. Assessments were performed at the clinic facilities during 2 distinct appointments scheduled at a one-week interval. Results: In total, data from 484 patients (57.2 % female, 42.8 % male) were analyzed. The prevalence of undiagnosed hypertension among those diagnosed with psychiatric disorders was significantly high (30.8 %), the majority of whom were male. Mean age, body mass index, and a positive family history of cardiovascular disease(s) were significantly higher in patients with undiagnosed hypertension. Moreover, patients undergoing treatment with serotonin and norepinephrine reuptake inhibitors (SNRIs) exhibited a significantly higher prevalence of undiagnosed hypertension than their counterparts. Conclusion: Results of the present study revealed a significant prevalence of undiagnosed hypertension among patients with psychiatric disorders (30.8 %). Factors significantly associated with a higher prevalence of undiagnosed hypertension included male sex, older age, higher BMI, family history of cardiovascular disease(s), and use of SNRIs.

Abstract The mesoscale spectrum describes the distribution of kinetic energy in the Earth's atmosphere between length scales of 10 and 400 km. Since the first observations, the origins of this spectrum have been controversial. At synoptic scales, the spectrum follows a −3 spectral slope, consistent with two‐dimensional turbulence theory, but a shallower −5/3 slope was observed at the shorter mesoscales. The cause of the shallower slope remains obscure, illustrating our lack of understanding. Through a novel coarse‐graining methodology, we are able to present a spatio‐temporal climatology of the spectral slope. We find convection and orography have a shallowing effect and can quantify this using “conditioned spectra.” These are typical spectra for a meteorological condition, obtained by aggregating spectra where the condition holds. This allows the investigation of new relationships, such as that between energy flux and spectral slope. Potential future applications of our methodology include predictability research and model validation.

In this study, the structural, electronic and optical characteristics of Platinum (Pt)-doped cubic BaTiO 3 perovskite were inspected via density functional theory (DFT) calculations. Generalized gradient approximation (GGA) and Pedraw–Wang 91 (PW91) functional, as applied in CASTEP code, provide an atomic level understanding of the influence of substituting 0.125 Pt dopant at Ba and Ti positions. Results indicate that the optimized lattice parameters and band gap are in good agreement with the experimental and theoretical data. Furthermore, the total and projected density of states (TPDOSs) analysis demonstrates that Pt–dopant integration has an impact on diminishing the band gap and shifting the absorption spectra towards the visible light region. Thus, it is suggested that substituting Ti and Ba atoms with Pt would enhance the optoelectronic characteristics of the host system, due to existing Pt–5d electronic states. Moreover, the negative formation energy values indicate the thermodynamic stability of the modeled configurations. These detailed results provide valuable insights into the physical properties of Pt–BaTiO 3 and its behavior across a range of photon wavelengths. To our knowledge, this contribution evaluates for the first time the influence of introducing platinum (Pt) into a BaTiO 3 perovskite system. The overall findings demonstrate a valuable appraisal of support for experimental synthesis of Pt–BaTiO 3 to serve in various optoelectronic devices.

This study presents a detailed numerical modeling of the freezing within a cold storage unit enhanced by the incorporation of fins and CuO nanoparticles. The presence of fins meaningfully improves the thermal efficiency of the unit, while the addition of nano-powders further accelerates the solidification. Various concentrations (ϕ) and diameters (dp) were considered to evaluate their impact on the solidification rate. The mathematical model for this process was developed under two key assumptions: that the thermophysical properties could be accurately predicted using a homogeneous mixture model and that convective effects within the system were negligible. The FEM molding's accuracy was confirmed through rigorous verification tests. One of the novel aspects of this study is the adaptive grid configuration, which evolves over time to accurately track the advancing ice front during the freezing process. The results indicated that the introduction of CuO nano-powders led to a substantial decrement in the completion time about 41.59 %. In the optimal scenario, full freezing was achieved in just 131.56 s, compared to 225.27 s for the base case with water alone. Additionally, the study found that the performance of the cold storage unit was highly dependent on the size of the nanoparticles. An intermediate nanoparticle diameter provided the best performance, with a 19.93 % reduction in freezing time initially observed as dp increased.

Incorporating thermoelectric (TE) modules into photovoltaic/thermal (PVT) systems can markedly increase energy output by improving the overall efficiency of energy conversion. This research focused on the design and simulation of a PVT system integrated with a TE module, utilizing ANSYS Fluent. The study assessed four different tube cross-sectional shapes—circular, square, elliptical, and triangular—all with the same cross-sectional areas. Moreover, the investigation included the impact of Cu-alumina/H2O hybrid nanofluid at a 0.024 % volume concentration, fluid inlet velocity (ui), and solar radiation (G) on PV temperature (TPV) and the overall productivity. The outputs showed that the triangular configuration considerably reduced TPV compared to the other shapes. This configuration also generated the highest thermal power, reaching 130.84 W. Additionally, at ui = 0.19 m/s, the unit's thermal efficiency and overall electrical efficiency increased by 0.93 % and 0.22 %, respectively.

In current work, the productivity of a photovoltaic thermal (PVT) unit impacted by dust accumulation was improved using magnetic force. The magnetic force was implemented to a cooling duct with Y-shaped fins, while solar irradiation was included as heat sources in the equations. Dust effects were simulated by adjusting the optical properties. The addition of a thermoelectric generator (TEG) layer boosted the electrical output. The cooling fluid was a homogeneous water and iron oxide mixture. Dust accumulation led to a 9.3 % drop in thermal performance, but the use of magnetic force enhanced electrical efficiency. Higher concentrations of additives improved system performance, with a maximum gain of 15.88 % at the highest inlet velocity (Vinlet). Increasing Vinlet further improved thermal efficiency (ηth) by 10.96 %, photovoltaic efficiency (ηPV) by 1.16 %, and thermoelectric efficiency (ηTE) by 33.53 %. Moreover, the application of Lorentz force increased isothermal uniformity by approximately 5.91 %

The current article studies the improvement of the discharging rate in cold storage systems by modifying the tank configuration and incorporating additives. Specifically, the study inspects how varying the diameter (dp) and fraction (ϕ) of nano-powders affects the process duration. The governing equations, derived under the assumption of negligible slip velocity of nanoparticles and convection terms, were solved using the Galerkin method. The computational grid was modified owing to location of the ice front, and unsteady terms were discretized using an unconditionally stable approach. The results indicate that initially, increasing dp decreases the process duration by approximately 20.01 %, but further increases in dp lead to a 49.53 % rise in the duration. As the process time increases, the amount of ice produced also increases, with nanoparticle loading resulting in a significantly higher ice yield. Specifically, the incorporation of nanoparticles enhances the storage rate by approximately 41.37 %.

This study investigates the efficacy of applying a Lorentz force to improve the efficiency of a photovoltaic-thermal (PVT) system featuring a finned duct, while also addressing challenges associated with dust accumulation. The magnetic field helps to prevent nanoparticle aggregation, enhancing the cooling process. The use of a finned duct combined with a nanofluid as the cooling medium efficiently dissipates excess heat from the silicon layer. Dust accumulation on the glass layer reduces transmissivity, negatively impacting system performance. The magnetic field's interaction with the nanoparticles enhances convective cooling of the upper layer, leading to an overall improvement in performance. Increased pumping power results in higher cooling rates, with improvements of approximately 3.48 % in thermal efficiency (ηth), 75.01 % in thermoelectric generator efficiency (ηTEG), and 39.37 % in photovoltaic efficiency (ηPV). An increase in the Hartmann number (Ha) improves ηth by about 1.87 %, with corresponding enhancements in electrical performance components. A higher concentration of ferrofluid further boosts performance, with the effect being roughly 1.7 times more significant in the absence of MHD compared to when Ha = 97. Dust presence decreases ηth, ηTEG, and ηPV by approximately 9.39 %, 8.55 %, and 25.77 %, respectively. Furthermore, the presence of Ha diminishes the influence of Vin on ηth by around 1.33 %.

This research introduces a new concept called Accelerating Virtual Rotor Control (AVRC) to address the challenges of low inertia and damping in a multi-source microgrid with combined Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR). While existing controllers have shown effectiveness, they often suffer from complexity and impracticality in real-world applications, the AVRC offers simplicity and effectiveness; therefore, it has been applied to low- inertia microgrids (MGs) by incorporating Superconducting Magnetic Energy Storage (SMES) device for improved microgrid response. Nevertheless, the Phase-Locked Loop (PLL) usually suffers from the low bandwidth, which affects the system response, and stability in some cases. Consequently, a Proportional-Integral (PI) controller has been integrated into the VRC system, where the effects of the measurement delays in the PLL are mitigated. In terms of SMES response, PI controller can create a static error resulting an over charging/discharging issue. To overcome this effect, an integral feedback loop is added into the AVRC, resulting in a comprehensive control strategy known as PI-AVRC/I. Additionally, to achieve a better optimization performance for the parameters of the proposed control strategy, a modification has been introduced to the Zebra Optimization Algorithm (MZOA) using Levy Flight motion to enhance its global search capability and avoid local optima. A Hardware-In-the-Loop is demonstrated using a Real-Time Digital Simulator (RTDS) with the aid of RSCAD software in order to evaluate the efficacy of the proposed control strategy under different scenarios such as step load perturbations with/without high Renewable Energy Sources (RESs) integration, random domestic loads fluctuation, and Communication Time Delay (CTD). The results affirm the robustness of the proposed control strategy in maintaining frequency and voltage deviation withing favorable limits, especially with high RESs penetration.

An innovative approach to optimizing cold storage in elliptic containers has been pioneered by integrating tree-shaped fins and diverse nanoparticle shapes into the water. The use of a finite element method, deliberately excluding the velocity term, distinguishes this work and allows for a focused exploration of the efficacy of these elements on the unsteady freezing. Addressing a research gap in the understanding of such processes within elliptic containers, especially in conjunction with tree-shaped fins, a holistic approach is taken compared to prior publications. The increase in “m'' correlates with a significant 6.98 % decrease in the freezing period, reducing the process time from 335s to 245.23s with the inclusion of nano-powders. The outputs showed that incorporating powders decline the freezing time about 26.79 %.

Biodiesel has emerged as a compelling substitute for conventional diesel fuel, providing a sustainable and eco-friendly choice for fueling compression ignition engines. This comprehensive study investigates the influence of biodiesel, specifically derived from sunflower oil, through the esterification method, on crucial engine performance parameters and environmental effects. The study examines the impact of varying engine torque on the performance of a single-cylinder, four-stroke compression ignition engine, encompassing parameters such as brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC), as well as exhaust emissions, including unburned hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx). Four distinct biodiesel blends (B10, B20, B30, B40) with varying sunflower oil content are systematically compared to pure diesel (B0). The engine operates at a consistent speed of 1700 rpm, while the torque undergoes controlled adjustments from 0 to 10 Nm. Subsequently, this study explores the application of an alternating model tree (AMT) machine learning algorithm to establish relationships between independent factors, specifically torque and biodiesel volume (%vol), and dependent variables, including BTE, BSFC, CO, and NOx in a combustion engine. Additionally, the study employs a multi-objective ameliorative whale optimization algorithm (AWOA) to optimize the model's output. The objective is to identify optimal values for torque and%vol that maximize engine performance (BTE) while minimizing engine emissions (CO and NOx) and reducing fuel consumption (BSFC). The optimization process yields noteworthy results, with AWOA achieving peak BTE at 29.714 %, BSFC at 0.262 kg.kWh-1, and NOx emissions at 992 ppm at torque 7.3 N.m and 13% vol. In contrast, particle swarm optimization (PSO) secured the minimum CO level at 0.123 %, with torque set at 7.6 N.m and 26% vol. The AMT models demonstrate high prediction accuracy, with coefficient of determination (R2) values exceeding 0.98.