Search

Abstract Understanding the fate of subducted carbonates is a prerequisite for the elucidation of the Earth’s deep carbon cycle. Here we show that the concomitant presence of Ca[CO3] with CO2 in a subducting slab very likely results in the formation of an anhydrous mixed pyrocarbonate, $${{{{\rm{Ca}}}}}_{3}{\left[{{{{\rm{C}}}}}_{2}{{{{\rm{O}}}}}_{5}\right]}_{2}\left[{{{{\rm{CO}}}}}_{3}\right]$$ Ca 3 C 2 O 5 2 CO 3 , at moderate pressure ( ≈ 20 GPa) and temperature ( ≈ 1500 K) conditions. We show that at these conditions $${{{{\rm{Ca}}}}}_{3}{\left[{{{{\rm{C}}}}}_{2}{{{{\rm{O}}}}}_{5}\right]}_{2}\left[{{{{\rm{CO}}}}}_{3}\right]$$ Ca 3 C 2 O 5 2 CO 3 can be obtained by reacting Ca[CO3] with CO2 in a laser-heated diamond anvil cell. The crystal structure was obtained from synchrotron-based single crystal X-ray diffraction data. Density Functional Perturbation Theory calculations in combination with experimental Raman spectroscopy results unambiguously confirmed the structural model. The crystal structure of $${{{{\rm{Ca}}}}}_{3}{\left[{{{{\rm{C}}}}}_{2}{{{{\rm{O}}}}}_{5}\right]}_{2}\left[{{{{\rm{CO}}}}}_{3}\right]$$ Ca 3 C 2 O 5 2 CO 3 is characterized by the presence of $${\left[{{{{\rm{CO}}}}}_{3}\right]}^{2-}$$ CO 3 2 − - and $${\left[{{{{\rm{C}}}}}_{2}{{{{\rm{O}}}}}_{5}\right]}^{2-}$$ C 2 O 5 2 − -groups. The results presented here imply that the formation of $${{{{\rm{Ca}}}}}_{3}{\left[{{{{\rm{C}}}}}_{2}{{{{\rm{O}}}}}_{5}\right]}_{2}\left[{{{{\rm{CO}}}}}_{3}\right]$$ Ca 3 C 2 O 5 2 CO 3 needs to be taken into account when constructing models of the deep carbon cycle of the Earth.

Abstract Background Sepsis, which is described as a life-threatening organ malfunction brought on by an unbalanced host response to infection, continues to be a significant healthcare issue that affects millions of individuals each year. It is well-known that sepsis can affect anyone around the world, but the employed survey results showed that there are significant regional variations in sepsis incidence as well as mortality rates. Although there are no definite estimates for Egypt, the highest rates were in Low-Middle-Income Countries (LMICs). Procalcitonin (PCT) is a host response marker with high specificity for bacterial infections, unlike C-reactive protein (CRP) or white blood cell count (WBC), which represent the traditional methods of detecting inflammation and infection. Its dynamic profile and superior prognostic prediction make it invaluable for assessing response to antibiotic treatment and improving clinical care for sepsis patients. Our main purpose was to evaluate the cost-effectiveness of PCT guidance compared to no PCT guidance in the antibiotic management of adult sepsis patients according to the Egyptian context. Methods We developed a decision tree model to compare the PCT-guided antibiotic management duration endpoint versus the conventional laboratory culture-based antibiotic management in adult sepsis patients. We employed the“Delphi technique” to reach a satisfactory consensus regarding the resources attributed to each compared alternative. The primary measure of the study was the additional cost associated with each Quality-Adjusted Life Year (QALY) gained by sepsis survivors over a one-year time horizon. Base-case, deterministic and probabilistic sensitivity analyses were conducted using TreeAge, Software. Results Base-case analysis showed no dominance for either alternative and resulted in an Incremental Cost-Effectiveness Ratio (ICER) value of 297,783.57 Egyptian Pounds per Quality Adjusted Life Year (L.E/QALY) in favor of the PCT guidance alternative, Deterministic sensitivity analysis revealed that the highest impact magnitudes on ICER reside with seven input parameters, the top two parameters that had the most significant influence were the costs of ICU stay with and without PCT guidance. The CEAC showed a slightly higher probability in terms of acceptability in favor of the no PCT guidance choice along the WTP scale till reaching equal probabilities at the willingness-to-pay (WTP) value point of 390,000 (state currency) after which the - probability supports the PCT guidance choice. Conclusions In the Egyptian context, PCT guidance has no cost-effectiveness domination over no PCT guidance in Antibiotics management for adult sepsis patients. This may be attributed to the high cost of PCT investigation that shall be resolved by standardization of its cost when applying the approach of DRG cost packages.

Abstract Structural studies of pyrene have been limited to below 2 GPa. Here, we report on investigations of pyrene up to ~35 GPa using in situ single-crystal synchrotron X-ray diffraction in diamond anvil cells and ab initio calculations. They reveal the phase transitions from pyrene-I to pyrene-II (0.7 GPa), and to the previously unreported pyrene-IV (2.7 GPa), and pyrene-V (7.3 GPa). The structure and bonding analysis shows that gradual compression results in continuous compaction of molecular packing, eventually leading to curvature of molecules, which has never been observed before. Large organic molecules exhibit unexpectedly high conformational flexibility preserving pyrene-V up to 35 GPa. Ab initio calculations suggest that the phases we found are thermodynamically metastable compared to pyrene-III previously reported at 0.3 and 0.5 GPa. Our study contributes to the fundamental understanding of the polymorphism of polycyclic aromatic hydrocarbons and calls for further theoretical exploration of their structure–property relationships.

Abstract Background Evaluation of the influence of the age of the patients upon the outcomes of liver resection (LR) for hepatocellular carcinoma (HCC). Methods HCC patients who underwent LR between 2010 and 2020 were analyzed. They were divided into 3 groups depending on the patient’s age. Group I (patients less than 60 years), Group II (patients between 60 and 69 years), and Group III (patients equal to or more than 70 years). Results 364 patients were included. A significantly higher serum bilirubin and alpha feto-protein were noted in Group I and serum creatinine was noted in Group III. The study groups did not show any significant differences regarding HCC site, number, macrovascular invasion, the extent of LR, Pringle maneuver, and perioperative blood transfusions. Longer operation time was found in Groups II and III, while more blood loss was noted in Group (I) Group I patients had longer hospital stays. Higher postoperative morbidities were noted in both Group I and Group (II) Higher incidence of post-hepatectomy liver dysfunction was noted in Group I. More early mortalities were found in Group I, related to liver failure. We did not experience early mortality in Group (III) Late Mortalities occurred in 117 patients (32.1%). HCC recurrence occurred in 165 patients (45.3%). Regarding the overall- and tumor-free survival, we did not experience any significant differences among the 3 groups (Log Rank: p = 0.371 and 0.464 respectively). Conclusions Curative LR can be safely performed in selected elderly patients with HCC. An advanced patient’s age should not be considered as a contraindication for curative LR.

Abstract Background Holt–Oram syndrome is a rare genetic disorder caused by a mutation in the TBX5 gene, combining skeletal and cardiac malformations. Vital prognosis depends essentially on cardiac involvement, while skeletal malformations determine functional prognosis. Case presentation We describe the case of a young patient aged 49, with no particular history, who presented to the emergency department with de novo congestive heart failure. Clinical examination revealed not only signs of heart failure, but also malformations such as triphalangia of the left thumb, prono-supination defects of both forearms and dorsolumbar scoliosis. The electrocardiogram showed that an atypical atrial flutter and transthoracic echocardiography revealed an atrial septal defect. We also performed a spinal scan to assess the severity of the scoliosis. Genetic studies confirmed a TBX5 gene mutation in the patient, and family screening revealed no similar cases in the family. Management consisted mainly of pharmacological treatment of heart failure, in addition to scoliosis management. Conclusion Holt–Oram syndrome is a rare genetic disorder which should be suspected in the presence of any upper limb anomaly associated with cardiac malformation and confirmed by genetic study. A family investigation is necessary after diagnosis, because of autosomal dominant inheritance.

Abstract Background The characterization of pathologically enlarged mediastinal lymph nodes is clinically essential for effective disease management and accurate prognosis. Malignancy (metastases and lymphoma) and granulomatous conditions (sarcoidosis and tuberculosis) are the most common causes. Magnetic resonance imaging (MRI) is a good modality to characterize the mediastinal pathologically enlarged lymph nodes based on the excellent soft tissue contrast. It can save high-risk patients from radiation exposure and hazards of intervention such as general anesthesia and biopsy. Aim of the work To estimate the accuracy of different advanced MRI quantitative parameters in the differentiation between benign and malignant mediastinal lymphadenopathy. This would involve the lesion-to-cord signal intensity ratio (SIR) in the T2-WI and diffusion weighted image (DWI), the lesion-to-chest wall muscle SIR in T2-WI and DWI, and the mean apparent diffusion coefficient (ADC) values. These values would be correlated with the prospective pathological data and the results of the positron emission tomography (PET-CT). Results Prospectively, the study was conducted during the period between June 2022 and September 2023 on 45 patients with indeterminate or suspicious mediastinal lymphadenopathy identified by CT. MRI examination, PET-CT, and biopsy were applied for all patients. The intra-class correlation coefficient ranged between 0.89 and 0.95. (A) The lesion-to-cord SIR in T2-WI in the malignant group (1.49 ± 0.30) was higher than that in the benign group (0.83 ± 0.24) with P 1.2) estimated 90% sensitivity, 100% specificity, with AUC = 0.989. (B) The lesion-to-chest wall muscle SIR in T2-WI in malignant nodes (3.13 ± 0.84) was significantly higher than that in the benign nodes (1.90 ± 0.80) with P 2.4) estimated 86.67% sensitivity, 86.67% specificity, with AUC = 0.88. (C) The lesion-to-cord signal SIR in b500-DWI in the malignant node (1.80 ± 0.54) was higher than that in the benign group (0.75 ± 0.29) with P 1.2) estimated 100% sensitivity and specificity with AUC = 1.00. (D) The lesion-to-chest wall SIR in b500-DWI in the malignant node (6.43 ± 1.28) was higher than that in the benign node (2.63 ± 0.94) with P 4.1) estimated 96.67% sensitivity, 100% specificity, with AUC = 0.997. € The mean ADC values in the malignant nodes (0.83 ± 0.20 × 10–3 mm2/s) were significantly lower than that in the benign nodes (1.85 ± 0.19 × 10−3 mm2/s) with P

Abstract Background This is a triple-blinded, prospective split-mouth clinical trial. It is important to shed light on the effect of different apical preparation sizes regarding postoperative pain within the same patient with the same pulpal histological status. The aim is to compare and evaluate the severity of postoperative pain following apical enlargement with two different sizes after the IBF using the visual analogue scale. Methods Fifty “teeth” in 25 patients were assigned into two equal groups (25 per group) using E3 Azure rotary files; Group A was prepared two sizes greater than the Initial binding file (IBF) (the largest K file to bind at the actual working length) mesial canals, which were enlarged to 35#/0.04 and 40#/0.04 for the distal canals. Group B was prepared in three sizes larger than the IBF: 40#/0.04 for mesial canals and 45#/0.04 for the distal canals. On a modified VAS form, patients were questioned to indicate the degree of their pain and assisted in narrating their pain intensity during the following periods: 12, 24, and 72 h, and after a week. VAS data were non-parametric and analyzed using the signed-rank test for intergroup comparisons, Freidman’s test, and the Nemenyi post hoc test for intragroup comparisons. The significance level was set at p

Debonding at the cement-casing interface is recognized as a principal failure mechanism leading to CO2 leakage in wells. This detachment gives rise to a microannulus, which notably possesses greater permeability than undamaged cement, undermining its sealing efficacy. Conventionally, the permeability of the microannulus is regarded as a uniform value throughout the well. However, fundamentally, a microannulus is one type of fracture, and its gap or aperture size is affected by the effective stress. In this work, we developed a unique experimental apparatus. This equipment facilitates the curing of cement inside a steel casing, the formation of a microannulus between the casing and the cement, and the investigation of the fluid flow dynamics along the microannulus under laboratory-replicated in situ conditions. The microannulus was formed by injecting fluid from one end of the setup, and receiving similar amount of fluid on the other end signified the development of the leakage channel. Additionally, strain gauges affixed to the casing’s external surface yielded key information on the microannulus’s opening and closure. We observed a noticeable decline in microannulus hydraulic aperture (or permeability) in relation to effective stress and an exponential equation fits their relationship. Our findings also indicate a distinct behavior when comparing liquid CO2 with water. Specifically, it is easier for liquid CO2 to create the microannulus. However, the hydraulic aperture range for this microannulus (0.7–6 μm) is considerably smaller than that created by water flow (2–17 μm). Finally, we integrated the stress-dependent microannulus aperture size into the combined analysis of well mechanical integrity and well leakage. The outcomes consistently demonstrated that when factoring in the stress-dependent aperture sizes, the leakage rates are 3–5 times compared to a fixed aperture model. The traditional assumption of a constant aperture significantly underestimates fluid leakage risks.

This study presents a novel approach that employs a mixture of the tunable-Q wavelet transform (TQWT) and enhanced AdaBoost to address the issue of high impedance fault (HIF) recognition in power distribution networks. Traditional overcurrent protection relays frequently have lower fault current levels than normal current, making it exceedingly difficult to detect this HIF problem with the necessity to use a quick and effective approach to find HIF problems. Since the TQWT performs better with signals that exhibit oscillatory behavior, it has been utilized to extract special features for the training of the improved AdaBoost model. The procedure is accelerated by calculating the Kourtosis (K) value for each level and selecting the ideal level of decomposition to minimize computing work. Faulted zones are categorized using an enhanced AdaBoost approach. Under normal, noisy, and unbalanced conditions, the recommended approach is applied to an imbalanced 123-bus test system and an IEEE 33-bus test system. The efficiency of the recommended method is also being assessed for imbalanced distribution networks incorporating dispersed generation into real-time platforms. This procedure is quick compared to previous methods since it uses an upgraded AdaBoost classifier and optimal decomposition level.

Introduction Benign vocal fold lesions (BVFLs) disrupt the superficial lamina propria, impacting vocal fold pliability. Investigating the diagnostic competency of ear, nose, and throat (ENT) residents in identifying BVFLs, we hypothesize that factors such as residency year, subspecialty focus, and training center influence diagnostic accuracy.

AimInternet addiction is a mental health issue that can have detrimental effects on an individual’s life. This study aims to estimate the prevalence of Internet Addiction and identify the risk factors associated with this behavioral addiction.MethodsThis cross-sectional study was conducted in 6 universities in southern Morocco, involving 1,690 students who completed a self-administered questionnaire. The questionnaire collected socio-demographic data, and information on substance use, and included validated tools to assess Internet addiction (Young’s IADQ), depressive symptoms (Patient Health Questionnaire. PHQ-9), and social phobia (Mini Neuropsychiatric International Interview. M.I.N.I).ResultsThe findings of our study indicate that the prevalence of Internet addiction was 30.60% (95% CI), depressive symptoms were present in 44.10% of participants, and the prevalence of social Phobia was 30.20%. A significant correlation was identified between early initiation of tobacco and Internet addiction (p = 0.05). The multivariate regression model revealed a possible association between cocaine use and Internet Addiction (OR = 5.67, IC 95%: 0.45 to 10.80) (p = 0.03), as well as a significant association between social phobia and a higher internet addiction score (OR = 3.45, IC 95%: 1.70–5.02) (p

Objective Research over the past decade has extensively covered the benefits of electronic health records in developing countries. Yet, the specific impact of their limited access on doctors’ workload and clinical decision-making, particularly in Bangladesh, remains underexplored. This study investigates current patients’ medical history storage mechanisms and associated challenges. It explores how doctors in Bangladesh obtain and review patients’ past medical histories, identifying the challenges they face. Additionally, it examines whether limited access to digital health records is an obstacle in clinical decision-making and explores factors influencing doctors’ willingness to adopt electronic health record systems in such contexts. Method An online cross-sectional survey of 105 doctors with Bachelor of Medicine, Bachelor of Surgery/Bachelor of Dental Surgery (MBBS/BDS) degrees and at least 2 years of experience was conducted, covering (a) personal information, (b) workload, (c) patient history challenges, and (d) decision-making. Results Out of 105 participants, 51.4% of them use paper-based methods with 56% facing challenges, versus 20% using digital methods. Most (94.3%) interview patients directly, and 80.9% are interested in a web-based, comprehensive medical history system. An ordinal regression model identified that the physicians’ disciplines, workload, and efficiency level of the current workplace in facilitating patient history-taking variables significantly affected willingness to adopt the described electronic health record in the survey. Conclusion Doctors in Bangladesh encounter significant challenges related to workload and clinical decision-making, largely attributed to restricted access to patients’ past medical histories. Despite the prevalent use of paper-based records, there is a notable willingness among these medical professionals to embrace electronic health record systems, indicating a potential shift towards more efficient healthcare practices in the region.

The growing interest in the therapeutic potential of cannabidiol (CBD) has led to the need for effective and reliable delivery methods that overcome its low oral absorption. Zeolites, a class of porous nanoparticles, offer unique advantages as drug carriers due to their high surface area and adjustable pore size. In this study, a zeolite-based drug delivery system was developed for the encapsulation of CBD. The zeolite particles were characterized using various techniques such as Scanning Electron Microscopy (SEM), N2 adsorption analysis, Solid-state Fourier Transform Infrared (FTIR), Direct Light Scattering (DLS), X-ray diffraction (XRD) and thermogravimetric analysis (TGA) before and after the loading. The drug encapsulation efficiency, and the release profile of CBD from the zeolite matrix were evaluated in addition to in vitro dissolution experiments in the intestinal and gastric simulated fluids. The results showed that the loaded zeolite particles exhibited high encapsulation efficiency of 73.5 %. XRD analysis proved that the USY structure remained intact after loading with CBD. DLS and N2 adsorption analysis indicated that CBD was successfully loaded into the zeolite matrix. When compared to CBD containing particles in a commercialized capsule, the in-vitro dissolution rate of CBD loaded zeolite was significantly higher after 30 min in the simulated stomach (pH 1.8) and the intestinal (pH 6.8) fluids, 67.8 % versus 43.6 % and 62.6 % vs 38.4 % respectively. Our findings open new avenues for the use of zeolites as an efficient drug delivery system for drugs with low bioavailability like CBD.

Managing the vast quantities of waste constantly generated by mining activities is one of the major environmental and economic problems facing mankind today. Fluorapatite is separated from the associated gangue minerals by a series of crushing and screening, washing, and flotation processes. These processes produce a significant amount of phosphate sludge, which is stored on the mine site in drift rock and large surface ponds. One possible environmental option is to reuse it as an alternative raw material in ceramics and building materials. Consequently, two phosphate sludges from two different Moroccan towns, Youssoufia and Khouribga, were studied. Due to the complexity of these raw materials resulting from long geological processes, in-depth physical, chemical, mineralogical, and thermal characterization is required. Dry compressed powder pellets were sintered at 900 °C, 1000 °C, and 1100 °C for 2 h. The study focuses on the effect of sintering temperature on mineralogical transformations and ceramic properties such as apparent porosity, water absorption, and mechanical strength. At 1100 °C, a slight increase in density was observed for both phosphate sludges. Water absorption was reduced by 2.51 % in both sludges for pellets sintered at 1100 °C compared to those sintered at 900 °C. Mechanical strength improved significantly, with an increase of about 60 % for samples sintered at 1100 °C, recording 227 N for Youssoufia sludge and 247 N for Khouribga sludge. This work has provided new data on the physical, chemical, mineralogical, and thermal changes in ceramics as the sintering temperature increases. These data will be useful for the manufacture of high-value ceramics.

Purpose: To evaluate efficacy and safety of beta-3 adrenergic agonists in adults with neurogenic lower urinary tract dysfunction. Materials and Methods: According to a protocol (CRD42022350079), we searched multiple data sources for published and unpublished randomized controlled trials (RCTs) up to 2nd August 2022. Two review authors independently screened studies and abstracted data from the included studies. We performed statistical analyses by using a random-effects model and interpreted them according to the Cochrane Handbook for Systematic Reviews of Interventions. We used GRADE guidance to rate the certainty of evidence (CoE). Results: We found data to inform two comparisons: beta-3 adrenergic agonists versus placebo (4 RCTs) and anticholinergics (2 RCTs). Only mirabegron was used for intervention in all included studies. Compared to placebo, beta-3 adrenergic agonists may have a clinically unimportant effect on urinary symptoms score (mean difference [MD] -2.50, 95% confidence interval [CI] -4.78 to -0.22; I2=92%; 2 RCTs; 192 participants; low CoE) based on minimal clinically important difference of 3. We are very uncertain of the effects of beta-3 adrenergic agonists on quality of life (MD 10.86, 95% CI 1.21 to 20.50; I2=41%; 2 RCTs; 98 participants; very low CoE). Beta-3 adrenergic agonists may result in little to no difference in major adverse events (cardiovascular adverse events) (risk ratio 0.57, 95% CI 0.14 to 2.37; I2=0%; 4 RCTs; 310 participants; low CoE). Compared to anticholinergics, no study reported urinary symptom scores and quality of life. There were no major adverse events (cardiovascular adverse events) in either study group (1 study; 60 participants; very low CoE). Conclusions: Compared to placebo, beta-3 adrenergic agonists may have similar effects on urinary symptom scores and major adverse events. There were uncertainties about their effects on quality of life. Compared to anticholinergics, we are either very uncertain or have no evidence about urinary symptom scores, quality of life, and major adverse events.

Microorganism motility holds significant importance in the realm of biomedical research. It contributes to the investigation of bacterial infections, microbial pathogenesis, and the formulation of strategies for combating antimicrobial resistance. Motile microorganisms embody a multifaceted and dynamic facet of microbial existence, exerting influence on ecosystems, human health, and advancements in technology. The significance of the current investigations is to develop a steady 3D computational model for the magnetic field hydrodynamics micropolar nanofluid along with the Buongiorno model which incorporates Brownian motion and thermophoretic diffusion effects to describe the heat transfer enhancement of micropolar nanofluid. The influence of thermal radiation on convective boundary conditions is also considered here. Similarity transformations are employed to convert an equation of motion into an ordinary differential equation. The final shape of ODEs is solved numerically with the aid of MATLAB software. Graphical results against convergence parameters like the influence of magnetic parameter, velocity ratio parameter, Prandtl number, micropolar parameter, porosity parameter thermophoretic number, Brownian motion, bio-convective Schmidt number, Peclet number thermal radiation parameter is demonstrated on velocity, temperature, concentration, motile and angular profiles. The default values of physical parameter used our analysis are: 0.2≤M≤0.5, 0.2≤λ≤0.6, 0.2≤K≤0.5, 0.0≤K1≤2.0, 1.0≤Sb≤1.4, 0.1≤σ≤0.9 0.1≤Nr≤0.9, 0.3≤Nt≤1.8, 1.5≤A≤3.5, 0.0≤B≤2.0; 0.0≤Nb≤2.0, 0.5≤γ≤2.5, 0.0≤S≤0.8, 1.0≤Sc≤1.8, 0.0≤D≤2.0, 0.1≤Pe≤0.5.The novelty of the present model is validated with the previously published data and found an excellent agreement. Our obtained results signifies that a rise in the mass and temperature convective parameter causes the velocity profile to grow, but a rise in the porosity and magnetic parameter causes it to decrease. The temperature profile increases with a rise in, thermophoretic parameter, and magnetic parameter, whereas it decreases with a rise in temperature exponent and Prandtl number. This research brings esteemed insights the micropolar nanofluids make them attractive options for a variety of uses in a range of sectors, such as manufacturing, energy, electronics, and healthcare.

In the evolving field of solar energy, precise forecasting of Solar Irradiance (SI) stands as a pivotal challenge for the optimization of photovoltaic (PV) systems. Addressing the inadequacies in current forecasting techniques, we introduced advanced machine learning models, namely the Rectified Linear Unit Activation with Adaptive Moment Estimation Neural Network (RELAD-ANN) and the Linear Support Vector Machine with Individual Parameter Features (LSIPF). These models broke new ground by striking an unprecedented balance between computational efficiency and predictive accuracy, specifically engineered to overcome common pitfalls such as overfitting and data inconsistency. The RELAD-ANN model, with its multi-layer architecture, sets a new standard in detecting the nuanced dynamics between SI and meteorological variables. By integrating sophisticated regression methods like Support Vector Regression (SVR) and Lightweight Gradient Boosting Machines (Light GBM), our results illuminated the intricate relationship between SI and its influencing factors, marking a novel contribution to the domain of solar energy forecasting. With an R2 of 0.935, MAE of 8.20, and MAPE of 3.48%, the model outshone other models, signifying its potential for accurate and reliable SI forecasting, when compared with existing models like Multi-Layer Perceptron, Long Short-Term Memory (LSTM), Multilayer-LSTM, Gated Recurrent Unit, and 1-dimensional Convolutional Neural Network, while the LSIPF model showed limitations in its predictive ability. Light GBM emerged as a robust approach in evaluating environmental influences on SI, outperforming the SVR model. Our findings contributed significantly to the optimization of solar energy systems and could be applied globally, offering a promising direction for renewable energy management and real-time forecasting.

Abstract Background There are many percutaneous coronary approaches. The most commonly used one is the radial artery because of its lowest risk of adverse vascular events. However, it could not be an option in some situations as congenital radial artery hypoplasia and spasm. In these cases, the second most common access is the femoral artery. The current literature over the brachial artery access is controversial. Thus, the aim of this study was to verify the brachial artery approach's effectiveness and safety. Results We studied 300 patients who underwent elective coronary angiography and angioplasty in our institution with failed radial access between August 2022 and February 2023. They were classified into two groups; 150 patients with brachial access and 150 with femoral access. Access, procedural and fluoroscopy times were recorded. All patients were examined carefully immediately after the procedure and before discharge to assess any complications. Left brachial access was used more frequently than left femoral access (32.7% vs. 22.7%, P = 0.05), but no significant difference noted regarding right sided or bilateral access. Procedure time, fluoroscopy time, and contrast volume did not significantly differ (P = 0.19, 0.06 and 0.1 respectively). However, brachial group had shorter access time (2.6 ± 1.1 vs. 3.4 ± 0.7 min, P = 0.05) and hospital stay (3.5 ± 1.1 vs. 5.9 ± 1.3 days, P

Abstract Purpose To describe the successful use of a PET-CT-based resection guide for the treatment of auricular squamous cell carcinoma. Case presentation A 69-year-old male presented with a painless growing mass in his left ear helix that had been unsuccessfully treated conservatively by another surgeon. An incisional biopsy revealed moderately differentiated squamous cell carcinoma, and the patient was referred to the department for further management. A PET/CT was performed, which showed uptake of FDG in the ear with a SUV max value and T3N0M0 status. The treatment plan was based on a multidisciplinary team discussion and a thorough evaluation of the PET/CT imaging findings. A decision was made to proceed with excision of the mass with a wide safety margin, using a PET/CT-based resection guide for ear resection. Additionally, partial temporal bone resection, selective neck dissection, and superficial parotidectomy were planned. The patient opted for reconstruction with a scalp rotational flap. The patient’s treatment plan was successful, and at follow-up visits, he remained disease-free. This case report highlights the importance of using a PET-CT-based resection guide for the treatment of soft tissue tumors, specifically auricular squamous cell carcinoma. It also demonstrates the importance of a multidisciplinary team approach and thorough evaluation of imaging findings in determining an effective treatment plan for patients. Conclusions The use of PET-CT-based resection guide can be an effective tool in the treatment of auricular squamous cell carcinoma. It can provide more precise and accurate information about the extent of the tumor, leading to a more effective treatment plan. Multidisciplinary team approach and thorough evaluation of imaging findings are crucial in determining the best course of treatment for patients.

Introduction: From the beginning of suicide research by Durkheim (1897); the relationship between religiosity and suicidal risk has been seen as a crucial issue. During the last decade, studies on this question have given variable results. However, such studies became a gateway to multiple questions, not only on the relationship between religion and suicide risk but more specifically among patients with depressive disorders, in particular. This study’s main objective is to assess the influence of religiosity on suicidal ideation; suicidal intentionality and on the severity of depressive symptoms in patients with depressive disorders diagnoses or bipolar depression episodes. We carried out an observational, descriptive, and analytical study of 324 patients with a current diagnosis of a major depressive disorder or a bipolar depressive episode. Patients were all Muslims and were interviewed using the Beck Depression Inventory; Suicide Intent Scale and Mini International neuropsychiatric interview Suicide Risk. The religiosity was assessed using the BIAC (Belief Into Action) adapted to Muslims in Morocco. We found that higher religiosity scores were associated with older age (>50 years), marriage, no negative impact of depression on work, presence of bipolar depression, and regularity of medical follow-up. Similarly, higher scores of religiosity were associated with lower scores of depression, suicidal intentionality, or suicidal risk. It will be useful to compare our results with other published results to appraise the positive impact of religiosity on reducing suicidal ideation and behavior among different populations, regardless of their religious affiliations.

This paper presents a methodological approach to solving the fuzzy capacitated logistic distribution center problem, with a focus on the optimal selection of distribution centers to meet the demands of multiple plants. The distribution centers are characterized by fixed costs and capacities, while plant demands are modeled using fuzzy triangular membership functions. The problem is mathematically formulated by converting fuzzy demands into crisp values, providing a structured framework for addressing uncertainty in logistic planning. To support future research and facilitate comparative analysis, 20 benchmark problems were generated, filling a gap in the existing literature. Three distinct artificial bee colony algorithm variants were hybridized with a heuristic: one using the best solution per iteration, another incorporating chaotic mapping and adaptive procedures, and the third employing convergence and diversity archives. An experimental design based on Taguchi's orthogonal arrays was employed for optimizing the algorithm parameters, ensuring systematic exploration of the solution space. The developed methods offer a comprehensive toolkit for addressing complex, uncertain demands in logistic distribution, with code provided for reproducibility.Key contributions include: • Development of a fuzzy model for the selection of distribution centers with fixed costs and capacities under uncertain plant demands. • Generation of 20 benchmark problems to advance research in the fuzzy capacitated logistic distribution center problem domain. • Integration of a heuristic approach with three distinct ABC algorithm variants, each contributing unique methodological insights.

Polyethylene terephthalate (PET) is a widely used plastic that accounts for almost 18 % of global polymer production and is non-biodegradable, making it a major cause of environmental pollution. Innovative solutions are needed to address PET waste management and reduce its impact. This study aims to investigate the impact of adding PET, on the engineering characteristics of a Stone Mastic Asphalt (SMA-20) mixture. Laboratory tests were conducted to assess the volumetric and mechanical properties of asphalt mixes containing different proportions of PET as fine and filler (ranging from 0 % to 30 %) by volume of aggregates. The results show that the PET incorporation into the asphalt mixture has significantly improved the performance. The addition of PET (fine and filler) to asphalt mixtures significantly increases their resilient modulus (MR). The mixtures containing 10 % PET as fine and filler are 20 % and 22 % higher in MR than the control mixture. Moreover, the moisture assessment shows that the PET mixture has a higher tensile strength ratio (TSR) value of 85.9 % and 83.90 % for Fine-10 and Filler-20, thus meeting the AASHTO requirement of ≥80 %. The utilization of PET as aggregate replacement significantly improves the permanent deformation characteristics of asphalt mixtures by decreasing permanent strain. The Fine-20 and Filler-20 revealed promising results by reducing the strain to 0.92 % and 0.81 %. Also, the enhancement in the creep stiffness modulus with 216 and 244 MPa. Furthermore, the addition of PET up to a concentration of 20 % had a beneficial effect on the fatigue response of mixtures. For instance, the failure cycles of the Filler-20 and Fine-10 are 23,231 and 17,521 cycles at a load of 2250 N, whereas the control mixture only endures 8581 failure cycles. In conclusion, PET addition significantly improves SMA properties, promoting waste material utilization in the pavement industry.

Background Weight gain has been well-described with integrase strand transfer inhibitors (INSTIs) and tenofovir alafenamide (TAF). Doravirine (DOR) has been identified as a relatively “weight-neutral” drug; however, there is little data describing its effect on weight change in routine clinical practice. Methods We conducted a retrospective chart review of weight change among people with HIV changing from an INSTI- to a non-INSTI regimen with DOR. Results At the time of ART switch, among 49 people with HIV, the mean age was 47 years, 24% were female, and 75% had HIV-1 viral load

Tackling methane emissions is critical for mitigating climate change, emphasizing the need to identify, quantify, and mitigate emission sources. Methane emissions can be detected using airborne techniques equipped with suitable sensors; however, a method is needed to precisely estimate methane source flux rates from these measurements. We have developed a convolutional neural network (CNN) model using a dataset generated from Large-Eddy Simulation (LES) mimicking the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) measurements with different source flux rates (5–100 kg/hr) and varying wind speeds (1–10 m/s). Our proposed deep neural network architecture is simple, ensuring efficient training on synthetic data. Additionally, we have optimized model hyperparameters and architecture, improving accuracy and enabling precise quantification of source flux rates. We test the trained model on synthetic 2D images, demonstrating excellent overall predictability with mean absolute percentage errors of 2.15 % for the training dataset, 5.02 % for the validation dataset, and 4.84 % for the test dataset in predicting the source flux rates in kg/hr using the synthetic images. The error of predicting source flux rates using conventional flux inversion methods is around 25–50 %, whereas the machine learning method of predicting source flux rate achieves an accuracy of more than 17 %, which is the current state-of-the-art in this process. Validation against field data also showed excellent results, with a low error (∼5%) in estimating source flux rates in the Permian Basin oil and gas wells. Validation against field measurements indicates that this method can reduce the uncertainties associated with currently used conventional source flux inversions without relying on background information like wind speeds. Sometimes, the uncertainties of field measurements are as significant as the actual emission rates, especially for low emissions. This machine learning model can serve as a screening tool to prioritize top emitters for plugging orphaned and abandoned wells in large geographical areas, such as city boundaries covering more than 1,000 miles.

The transition from fossil fuels to carbon-neutral energy sources is necessary to reduce greenhouse gas (GHG) emissions and combat climate change. Hydrogen (H2) provides a promising path to harness fossil fuels to reduce emissions in sectors such as transportation. However, regional economic analyses of various H2 production techniques are still lacking. We selected a well-known fossil fuel-exporting region, the USA's Intermountain-West (I-WEST), to analyze the carbon intensity of H2 production and demonstrate regional tradeoffs. Currently, 78 % of global H2 production comes from natural gas and coal. Therefore, we considered steam methane reforming (SMR), surface coal gasification (SCG) and underground coal gasification (UCG) as H2 production methods in this work. We developed the cost estimation frameworks of SMR, SCG and UCG with and without carbon capture, utilization and sequestration (CCUS). In addition, we identified optimal sites for H2 hubs by considering the proximity to energy sources, energy markets, storage sites and CO2 sequestration sites. We included new production tax credits (PTCs) in the cost estimation to quantify the economic benefit of CCUS. Our results suggest that the UCG has the lowest levelized cost of H2 production due to the elimination of coal production cost. H2 production using the SMR process with 99 % carbon capture is profitable when the PTCs are considered. We also analyzed carbon utilization opportunities where CO2 conversion to formic acid is a promising profitable option. This work quantifies the potential of H2 production from fossil fuels in the I-WEST region, a key parameter for designing energy transition pathways.

Summary: Human genetic studies of critical COVID-19 pneumonia have revealed the essential role of type I interferon-dependent innate immunity to SARS-CoV-2 infection. Conversely, an association between the HLA-B∗15:01 allele and asymptomatic SARS-CoV-2 infection in unvaccinated individuals was recently reported, suggesting a contribution of pre-existing T cell-dependent adaptive immunity. We report a lack of association of classical HLA alleles, including HLA-B∗15:01, with pre-omicron asymptomatic SARS-CoV-2 infection in unvaccinated participants in a prospective population-based study in the United States (191 asymptomatic vs. 945 symptomatic COVID-19 cases). Moreover, we found no such association in the international COVID Human Genetic Effort cohort (206 asymptomatic vs. 574 mild or moderate COVID-19 cases and 1,625 severe or critical COVID-19 cases). Finally, in the Human Challenge Characterisation study, the three HLA-B∗15:01 individuals infected with SARS-CoV-2 developed symptoms. As with other acute primary infections studied, no classical HLA alleles favoring an asymptomatic course of SARS-CoV-2 infection were identified.

Abstract This study aimed to examine the validity and reproducibility of strain elastography (SE) for detecting prostate cancer (PCa) in patients with elevated prostate-specific antigen (PSA) levels. The study included 107 patients with elevated PSA levels. All eligible patients underwent transrectal ultrasound (TRUS) with real-time elastography (RTE) to detect suspicious lesions. Two readers independently evaluated the lesions and assigned a strain ratio and elastography score to each lesion. Histopathology was used as a reference standard to estimate the validity of RTE in predicting malignant lesions. An intraclass correlation (ICC) was performed to detect reliability of the strain ratios and elastography scores. TRUS-guided biopsy detected malignancies in 64 (59.8%) patients. TRUS with RTE revealed 122 lesions. The strain ratio index (SRI) cut-off values to diagnose malignancy were 4.05 and 4.35, with sensitivity, specificity, and accuracy of 94.7%, 91.3%, and 93.4%, respectively. An elastography score > 3 was the best cut-off value for detecting malignancy. According to readers, the sensitivity, specificity, and accuracy were 91.3–94.7%, 89.5–93.4%, and 91.3–90.9%, respectively. Excellent inter-reader agreement was recorded for SRI and elastography scores, with ICC of 0.937 and 0.800, respectively. SE proves to be an efficient tool for detecting PCa with high accuracy in patients with elevated PSA levels.

Background and Study Aim. This study compares the effects of high-intensity interval training (HIIT) on unstable and stable surfaces on explosive strength, balance, agility, and Tsukahara vault performance in gymnastics. Material and Methods. A nonrandomized trial study was conducted on twenty-seven well-trained male vault players who were assigned into 3 equal groups, a HIIT unstable group (UG), a HIIT stable group (SG), and the control group (CG). The training period for the subjects lasted eight weeks, with 3 sessions each week. All of the aforementioned variables were assessed both before and after the training period. Results. The main findings indicate that both UG and SG showed greater improvement than the CG on explosive strength, balance, agility, and Tsukahara vault performance in gymnastics (ES= 0.52 to 0.68, P 0.05). Conclusions. Accordingly, HIIT with unstable surfaces can be used as an alternative method to improve explosive strength and balance. Also, it can be used alongside stable surface exercises when developing agility.

This paper presents a new soft switching (SS) multiphase interleaved boost converter (MIBC) consisting of three boost converters (BCs) that operate rapidly and efficiently to charge a lithium-ion battery for photovoltaic (PV) power system applications. This suggested converter can operate both active power switches at zero current switching (ZCS), and when switched off, it works at zero voltage switching (ZVS), which reduces switching losses and increases switching efficiency (SF). An interleaving structure is employed to decrease the conduction losses and reduce the input current ripple (ICR) and output voltage ripple (OVR) to increase the power rating. The suggested converter regulates the input and output power by modulating the phase shift in the pulse width modulation (PWM) technique using the maximum power point tracking (MPPT) algorithm via two closed feedback loops, one for a slower external loop and the other for a faster PWM control internal loop, similar to current mode control. The suggested converter has high voltage (HV) gain due to the coupled inductors (CIs), which act as a transformer. This magnetic design structure in both the steady-state and transient states also decreases the size and enhances the converter’s performance. Finally, the suggested converter was experimentally verified on a 285W prototype, and the maximum efficiency was found to be 98%, based on modeling and experimental data.

Speech enhancement (SE) aims to improve the quality and intelligibility of speech signals, particularly in the presence of noise or other distortions, to ensure reliable communication and robust speech recognition. Deep neural networks (DNNs) have shown remarkable success in SE due to their ability to learn complex patterns and representations from large amounts of data. However, they face limitations in handling long-term temporal sequences. Spiking neural networks and transformers inherently manage temporal data and capture fine-grained temporal patterns in speech signals. This paper proposes a model that integrates self-attention with spiking neural networks for speech enhancement. The proposed model employs a convolutional encoder-decoder architecture with a spiking transformer acting as a bottleneck network. The spiking self-attention mechanism in this framework represents features using spike-based queries, keys, and values. This approach enhances features by effectively capturing temporal dependencies and contextual relationships in speech signals. The spiking transformer is divided into two branches to capture comprehensive global dependencies across the temporal and spectral dimensions. The encoder-decoder incorporates a multi-scale feature extractor, which extracts features at various scales, enabling the model to build a comprehensive hierarchical representation. This representation significantly enhances the model’s ability to learn and process noisy speech, leading to excellent SE performance. Experiments are conducted using two publicly available benchmark datasets: WSJO-SI84 and VCTK+DEMAND. The proposed model demonstrated improved SE performance, showing significant progress with notable improvements of 33.69% in ESTOI, 1.05 in PESQ, and 11.36 dB in SDR over the noisy mixtures.

This study proposes a novel isolated multiport bidirectional DC/DC converter (IMBC) that combines soft-switching (SS) and resistor-capacitor-diode (RCD) snubber circuits for battery charge and discharge applications in photovoltaic (PV) power systems. The proposed SS circuit operates at zero-current (ZC) switching (ZCS) when the switches are on to reduce the switching losses. The RCD snubber circuit also suppresses the overvoltage when turning off switches. Combining the two circuits also enables a more comprehensive operating range for SS, resulting in low losses and high efficiency. A thorough study of operating systems and design considerations is shown. PSIM illustrates the effectiveness of the suggested control method. A prototype of the suggested IMBC with an output voltage of 120 V and an output power of 150 W is constructed to confirm the viability and efficiency of the suggested topology. The maximum efficacy of the suggested topology in the step-up mode for the PV arrays is about 96.4% at an input voltage of 20.5 V and output power of 150 W. In comparison, the efficiency is about 98.5% in the step-down mode for charging and discharging the battery which is considered a highly satisfactory performance.

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality. Precise diagnosis of CRC plays a crucial role in increasing patient survival rates and formulating effective treatment strategies. Deep learning algorithms have demonstrated remarkable proficiency in the precise categorization of histopathology images. In this article, we introduce a novel deep learning model, termed DeepCon which incorporates the divide-and-conquer principle into the classification task. DeepCon has been methodically conceived to scrutinize the influence of acquired composition on the learning process, with a specific application to the classification of histology images related to CRC. Our model harnesses pre-trained networks to extract features from both the source and target domains, employing a two-stage transfer learning approach encompassing multiple loss functions. Our transfer learning strategy exploits a learned composition of decomposed images to enhance the transferability of extracted features. The efficacy of the proposed model was assessed using a clinically valid dataset of 5000 CRC images. The experimental results reveal that DeepCon when coupled with the Xception network as the backbone model and subjected to extensive fine-tuning, achieved a remarkable accuracy rate of 98.4% and an F1 score of 98.4%.

Abstract Background To evaluate the impact of tumor size on the perioperative and long-term outcomes of liver resection for hepatocellular carcinoma (HCC). Methods We reviewed the patients’ data who underwent liver resection for HCC between November 2009 and 2019. Patients were divided into 3 groups according to the tumor size. Group I: HCC

In deep learning, various optimizers use a starting value for the learning rate. In general, this value is the one defined by default in the optimizer or chosen by the user after searching for a value giving the best performances of the studied neural network. Finding this value in the case of complex models with large-scale data, either by fine-tuning it manually or by applying a grid search algorithm requires several training sessions, which is penalizing in terms of learning time. To solve this problem, we propose an efficient optimizer using an automatic setting of the learning rate value. Learning then requires a single training session. The proposed optimizer is based on the inexact linear search of Armijo’s rule that we adapt for neural networks. We have experimented with this optimizer for several types of loss functions such as mean squared error, mean absolute error, and cross-entropy. Different types of models such as regression and classification are studied. Also, the robustness of this optimizer against the noisy labels is verified. Empirical results demonstrate that the proposed optimizer works well in practice, it outperforms other state-of-the-art optimizers that use default learning rate in terms of accuracy in addition to the time saving when searching for the optimal value of the learning rate.

Environmental, safety, and availability constraints of mineral oils have led transformer manufacturers and researchers to turn to alternative liquid dielectrics to mineral oils. Thus, many works have focused on esters and more particularly on natural esters (NEs); commonly called vegetable oils; in recent decades. Besides their advantages (biodegradability, non-toxicity, recyclability, thermal stability, and safety) compared to mineral oils, natural esters have certain disadvantages including their high viscosity and their oxidation instability. Both of these properties can be improved by adding specific additives and nanoparticles. This paper is mainly focused on review of the breakdown voltage of natural ester-based nanofluids, one of the most significant parameters when selecting insulating fluids for high voltage devices and in particular for power transformers. Firstly, we take stock of the history of the introduction of fluids/oils in the field of electrical insulation and the return of vegetable oils in the transformer industry. Then, we present the main characteristics of vegetable oils and the different products based on vegetable oils currently on the market as well as those that are currently the subject of research and plants from which they come. The methods and techniques for the preparation/elaboration of these nanofluids and their stability are then exposed. Finally, we present the different results reported in the literature on the electrical breakdown of various types of natural esters when adding nanoparticles (NPs) issued from different materials (conducting, semiconducting, and insulating), under AC, DC and lightning impulse voltages, and discuss the involved mechanisms in the breakdown of nanofluids.

The successful integration of renewable energy resources into the power grid hinges on the development of energy storage technologies that are both cost-effective and reliable. These storage technologies, capable of storing energy for durations longer than 10 hours, play a crucial role in mitigating the variability inherent in wind and solar-dominant power systems. To shed light on this matter, a transparent, least-cost macro energy model with user-defined constraints has been utilized for a case study of California. The model addresses all included technologies, solving for both hourly dispatch and installed capacities. Real-world historical demand and hourly weather data have been utilized to do this analysis. A novel approach has been introduced to assess the significance of long-duration energy storage technologies (LDS) in terms of their energy and power capacity. This method explores the contributions of pumped hydropower storage (PHS), compressed air energy storage (CAES), and power-to-gas-to-power (PGP) storage toward minimizing the overall balance of system cost. Historical electricity demand, hourly weather data, and current technology costs are used to investigate high-level implications for California’s power system options. Increasing the storage capacity of each technology from 1 to 10 hours results in 29.6%, 14.4%, and 7.5% cost reduction for PHS, CAES, and PGP cases respectively. However, in studied simulations, maximum availability (maximum) of pumped hydropower storage reduces the balance of system costs by 72.3% followed by CAES (60.6%) and PGP (48.6%) and suggests that pumped hydropower storage in combination with CAES/PGP could play an important role in California’s electricity system, provided that suitable sites can be identified and constructed at reasonable costs.

The Single-Phase Induction Motor (SPIM) has gained widespread adoption in various power applications. This article introduces a new study and analysis of SPIM, focusing on its high-frequency (HF) modeling within the conducted electromagnetic interference (EMI) frequency range of interest (150 kHz– 30MHz). This research is crucial for ensuring electromagnetic compatibility (EMC), evaluating electromagnetic interference issues, optimizing machine design, and complying with EMC standards. To identify common-mode current propagation paths accurately, a precise impedance analyzer is required to determine the common-mode impedances models of the high-frequency motor. The article proposes an HF modeling method based on a genetic algorithm (GA), which offers the advantages of high accuracy, versatility, and fast simulation time. This approach effectively reproduces models, trends, and relationships observed in real data, indicating a high level of fidelity and reliability in its predictions. Case studies validate the efficiency of the proposed HF modeling method. These precise predictions of undesired HF phenomena provide valuable insights into identifying areas where parasitic currents may propagate in the motor and understanding the impact of each motor phase on the overall behavior of the motor’s common-mode impedance.

The development of data analysis techniques and intelligent systems has had a considerable impact on education, and has seen the emergence of the field of educational data mining (EDM). The Early Warning System (EWS) has been of great use in predicting at-risk students or analyzing learners’ performance. Our project concerns the development of an early warning system that takes into account a number of socio-cultural, structural and educational factors that have a direct impact on a student’s decision to drop out of school. We have worked on an original database dedicated to this issue, which reflects our approach of seeking exhaustiveness and precision in the choice of dropout indicators. The model we built performed very well, particularly with the K-Nearest Neighbor (KNN) algorithm, with an accuracy rate of over 99.5% for the training set and over 99.3% for the test set. The results are visualized using a Django application we developed for this purpose, and we show how this can be useful for educational planning.

Hydrogen provides the greatest performance in conditions of capacity and duration when related to other energy storage techniques. Renewable energy sources including solar, geothermal, wind, wave and tidal energy, and hydropower, are used to create green hydrogen. However, biogas is converted into renewable hydrogen via chemical procedures. A comparison of several water Electrolyzer types is provided by this research. All kinds of Electrolyzers have an impact on how well a green hydrogen production system works were discussed. This review gives a broad review of environmentally friendly hydrogen generation techniques based on renewable energy sources. These sources incorporate solar energy, hydropower, wind energy, tidal and wave energy, biogas systems, geothermal energy, and hybrid systems of these sources. The method used to obtain the hydrogen and its effects on the environment is the primary distinction between green hydrogen and other colors. Climate, control, management systems, and tracking systems. The innovations and difficulties that green hydrogen systems encounter are highlighted. In the final section, the costs associated with manufacturing hydrogen are explained through an economic study of the understudied production systems LCOE and LCOH. Furthermore, the advantages and disadvantages of all electrolyzers are discussed.

Thermal waters have a global distribution and find extensive therapeutic use. In Morocco, which boasts more than twenty high-quality therapeutic thermal springs, few studies have focused on the quality of these waters. In this context, the present study evaluated the water quality using physicochemical, bacteriological, and radioactive parameters for BENKACHOUR thermal spring waters in Morocco. The results indicate that all chemicals, bacteriological, and radiological parameters are below the standards, except for global beta activity and temperature, which makes it hyperthermal, with an annual average temperature of 50.54 °C. Based on the Piper diagram, the water type is chloride-sodium-potassium. Based on these results, we conclude that thermalism in the Eastern region of Morocco, specifically BENKACHOUR in Oujda, can contribute to sustainable tourist development at the national and international levels due to the quality of the parameters analyzed and can be an effective complementary approach in the treatment of low-grade pathologies. The data from this study can serve as a baseline for assessing future changes and defining standards for the quality of hydrothermal baths and balneotherapy worldwide.

Abstract Background Hypertensive patients are more susceptible to develop hypotension after the induction of general anesthesia (GA), most likely due to hypovolemia. An inferior vena cava collapsibility index (IVCCI) > 40–50% can predict hypotension after the induction of GA in the general population by variable accuracies. The current study aimed to investigate IVCCI% as a predictor of postinduction hypotension in hypertensive patients undergoing noncardiac surgery. Methods Ultrasound IVCCI % was assessed for all controlled hypertensive patients immediately before induction of GA. After induction of GA, patients were diagnosed with postinduction hypotension if their systolic arterial pressure (SAP) dropped by ≥ 30% of the baseline value and/or mean arterial pressure (MAP) dropped to 39% predicted the occurrence of postinduction hypotension with high accuracy (84%) (AUC 0.908, P 39% before anesthetic induction can be a simple noninvasive reliable predictor of hypotension after the induction of GA for hypertensive patients not treated with angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) and undergoing noncardiac surgery. Trial registration This clinical trial was approved by the Institutional Review Board (IRB) at Zagazig University (ZUIRB #9424 dated 03/04/2022), and patients’ informed consent for participation in the study was obtained during the period from May 2022 to May 2023. All study procedures were carried out in accordance with the ethical standards of the Helsinki Declaration of 2013.

Buried water pipelines are vulnerable to fail or break due to excessive loading or ground displacements. Accurate evaluation of pipe performance and serviceability relies on the proper understanding of pipe-soil interactions (PSI). Analytical methods are important approaches to studying PSI. However, a systematic and thorough literature review to analyze the existing research trends, technological achievements and future research opportunities is not available. This work investigates analytical methods that analyze the stress and deformation of pipes in terms of cross-sectional, transverse and longitudinal PSI problems. First, scientometric analysis is performed to acquire relevant research works from online databases and analyze the existing data of influential authors, productive research sources and frequent key word occurrence in the fields of interest. Second, a qualitative discussion is performed in the three categories of PSI: (1) cross-sectional, including ovalization and circumferential behaviours; (2) transverse, including seismic fault crossing, weak soil zones, ground settlement and pipe uplift; and (3) longitudinal. Third, six research opportunities are discussed, including the role of friction in cross-sectional deformation, combined effects of bending and compression, choice of soil reaction models and calibration of key parameters, effect of pipe flaws, soil spatial variability and behaviours of curved pipes. This study helps beginners familiarize themselves with PSI analytical methods and provides experienced researchers with ideas for future research directions.

The number of cybersecurity vulnerabilities keeps growing every year. Each vulnerability must be reported to the MITRE Corporation and assessed by a Counting Number Authority, which generates a metrics vector that determines its severity score. This process can take up to several weeks, with higher-severity vulnerabilities taking more time. Several authors have successfully used Deep Learning to automate the score generation process and used explainable AI to build trust with the users. However, the explanations that were shown were surface label input saliency on binary classification. This is a limitation, as several metrics are multi-class and there is much more we can achieve with XAI than just visualizing saliency. In this work, we look for actionable actions CNAs can take using XAI. We achieve state-of-the-art results using an interpretable XGBoost model, generate explanations for multi-class labels using SHAP, and use the raw Shapley values to calculate cumulative word importance and generate IF rules that allow a more transparent look at how the model classified vulnerabilities. Finally, we made the code and dataset open-source for reproducibility.

In a deregulated power system, managing congestion is crucial for effective operation and control. The goal of congestion management is to alleviate transmission line congestion while adhering to system constraints at minimal cost. This research proposes a hybrid Harris Hawk Optimization–Sine Cosine Algorithm (hHHO-SCA) for an efficient generation rescheduling approach to achieve the lowest possible congestion cost. The hybridization has been performed by introducing the features of SCA in the HHO to boost the exploration and exploitation steps of HHO, providing an efficient global solution and effectively optimizing rescheduled power output. The effectiveness of this methodology is evaluated using IEEE 30 and IEEE 118-bus test systems, taking into account system parameters. The potency of the proposed method is analyzed by comparing the results of the hHHO-SCA with those from other recent optimization techniques. The findings show that the hHHO-SCA outperforms other methods by avoiding local optima and demonstrating promising convergence characteristics.

Two new fusarochromanone derivatives, deacetylfusarochromene (1) and deacetamidofusarochrom-2′,3-diene (2), along with the previously reported metabolites fusarochromanone TDP-2 (3), fusarochromene (4), 2,2-dimethyl-5-amino-6-(2′E-ene-4′-hydroxylbutyryl)-4-chromone (5), fusarochromanone (6), (−)-chrysogine (7), and equisetin (8), were isolated from the marine fungus Fusarium equiseti UBOCC-A-117302. The structures of the compounds were determined by extensive spectrometric (HRMS) and spectroscopic (1D and 2D NMR) analyses, as well as specific rotation. Among them, 2 and 5 showed inhibition of three protein kinases with IC50 values ranging from 1.42 to 25.48 μM. Cytotoxicity and antimicrobial activity of all isolated compounds were also evaluated. Six fusarochromanone derivatives (1–6) exhibited diverse activities against three cell lines, RPE-1, HCT-116, and U2OS (IC50 values ranging from 0.058 to 84.380 μM). Equisetin (8) showed bactericidal activities against Bacillus cereus and Listeria monocytogenes (MBC values of 7.8 and 31.25 µM, respectively), and bacteriostatic activity against Enterococcus faecalis (MIC value of 31.25 µM). Compounds 2 and 4 showed bacteriostatic activities against Listeria monocytogenes (MIC of 125 µM).

Background: This study explores the use of tetra-hybrid nanoparticles consisting of gold, silver, alumina, and titania nanocomposites dispersed in a non-Newtonian Casson fluid modelled as blood. The motivation lies in harnessing the synergistic optical, electrical, thermal, and physicochemical properties of the multimodal nanoscale assembly to advance electrokinetic pumping processes. Objectives: The study aims to computationally investigate the complex transport assisted by tailored gold, silver, alumina, and titania dioxide nanoparticles in the tetra-hybrid nanofluid, with potential applications in targeted drug delivery, hyperthermia cancer treatment, Lab-on-Chip devices, and miniaturized biosensors. Methodology: The constructed streaming flow model examines the cumulative influence of viscous heating, Joule heating, conductive thermal, and external laser irradiation. Current simulations examine Casson flows with the integrated nanoparticles by modelling two- and three-dimensional conduits subject to transverse magnetohydrodynamics and localized laser irradiation. Key findings: The solutions provide mathematical predictions and physical insights into the fully coupled velocity, temperature, concentration, and pressure gradient contours. Additional plots examining dimensionless analytes against pertinent profiles, combined with streamlined visualization, further elucidate the multifaceted transport and complex trapping patterns stemming from non-Newtonian rheology. Applications/implications: This multiscale examination reveals performance improvements realizable by leveraging biocompatible tetra-hybrid nanocomposites in electrokinetic flows vital for next-generation biomedical technologies.

In various engineering and industrial utilizations, the heat-mass transportation concept has been rigorously witnessed, for illustration air conditioning of room, nuclear reactors cooling and abundant other realistic conditions. This scrutiny elaborates buoyancy-driven viscoelastic magnetized flow configured by stretched vertical surface. Heat-transfer effects are explored through Ohmic dissipations and radiation aspects. Chemical reaction characteristics are deliberated to address the mass-transfer effects. In addition, the stratification phenomenon together with Robin conditions is introduced. Mathematical systems are simplified under BLT (boundary-layer theory). Non-dimensional analysis is done by introducing similarity variables. Homotopic criteria is utilized to compute convergent solutions. Consequence of controlling variables emerging in non-dimensional problem is analyzed via pictorial and tabular illustrations. It is evident that temperature field together with heat transference rate are enlarged when radiation and thermal Biot parameters are augmented.

Accurate rock elastic property determination is vital for effective hydraulic fracturing, particularly Young's modulus due to its link to rock brittleness. This study integrates interdisciplinary data for better predictions of elastic modulus, combining data mining, experiments, and calibrated synthetics. We used the microstructural insights extracted from rock images for geomechanical facies analysis. Additionally, the petrophysical data and well logs were correlated with shear wave velocity (Vs) and Young's modulus. We developed a machine-learning workflow to predict Young's modulus and assess rock fracturability, considering mineral composition, geomechanics, and microstructure. Our findings indicate that artificial neural networks effectively predict Young's modulus, while K-Means clustering and hierarchical support vector machines excel in identifying rock and geomechanical facies. Utilizing Microscale thin section analysis in conjunction with fracture modeling enhances our understanding of fracture geometries and facilitates fracturability assessment. Notably, fracturability is controlled by specific geomechanical facies during initiation and propagation and influenced by continuity of geomechanical facies in small depth intervals. In conclusion, this study demonstrates data mining and machine learning potential for predicting rock properties and assessing fracturability, aiding hydraulic fracturing design optimization through diverse data and advanced methods.

This study focuses on addressing computational limits in smartphones by proposing an efficient authentication model that enables implicit authentication without requiring additional hardware and incurring less computational cost. The research explores various wrapper feature selection strategies and classifiers to enhance authentication accuracy while considering smartphone limitations such as hardware constraints, battery life, and memory size. However, the available dataset is small; thus, it cannot support a general conclusion. In this article, a novel implicit authentication model for smartphone users is proposed to address the one-against-all classification problem in smartphone authentication. This model depends on the integration of the conditional tabular generative adversarial network (CTGAN) to generate synthetic data to address the imbalanced dataset and a new proposed feature selection technique based on the Whale Optimization Algorithm (WOA). The model was evaluated using a public dataset (RHU touch mobile keystroke dataset), and the results showed that the WOA with the random forest (RF) classifier achieved the best reduction rate compared to the Harris Hawks Optimization (HHO) algorithm. Additionally, its classification accuracy was found to be the best in mobile user authentication from their touch behavior data. WOA-RF achieved an average accuracy of 99.62 ± 0.40% with a reduction rate averaging 87.85% across ten users, demonstrating its effectiveness in smartphone authentication.