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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.

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.

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.

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.

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.

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 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.

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

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.

Limonia acidissima Groff, commonly referred to as the Wood apple, is a tropical fruit belonging to Rutaceae family. Indigenous to Sri Lanka, India, and Myanmar, it is extensively cultivated throughout Southeast Asia. This fruit holds a profound historical significance in traditional medicine due to its exceptional nutritional and therapeutic attributes. Wood apple pulp is significantly abundant in β-carotene, a precursor to vitamin A, and contains a substantial amount of vitamin B, including riboflavin and thiamine, as well as trace amounts of ascorbic acid (vitamin C). Moreover health-benefitting properties associated with L. acidissima, such as, antioxidant, hepatoprotective, antimicrobial, neuroprotective, antidiabetic, anti-inflammatory, anti-spermatogenic, analgesic, antiulcer, and antihyperlipidemic properties, are attributed to a diverse range of phytochemicals. These encompass polyphenolic compounds, saponins, phytosterols, tannins, triterpenoids, coumarins, amino acids, tyramine derivatives, and vitamins. From the findings of the various studies, it was observed that wood apple fruit shows significant anticancer activity by inhibiting the proliferation of cancer. Furthermore, wood apple finds wide-ranging commercial applications in the formulation of ready-to-serve beverages, syrups, jellies, chutneys, and various other food products. In summary, this review highlights the nutritional and phytochemical constituents of wood apple, depicts its antioxidant, anti-inflammatory, and anti-diabetic capabilities, and explores its potential in value-added product development. Nevertheless, it is crucial to acknowledge that the molecular mechanisms supporting these properties remain an underexplored domain. To ensure the safe integration of wood apple fruit into the realms of the food, cosmetics, and pharmaceutical sectors, rigorous clinical trials, including toxicity assessments, are required. These endeavors hold the potential to promote innovation and contribute significantly to both research and industrial sectors.

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.

This investigation concentrates on refining the accuracy of earthquake parameters as reported by various Saudi seismic networks, addressing the significant challenges arising from data discrepancies in earthquake location, depth, and magnitude estimations. The application of sophisticated machine learning techniques, particularly the Isolation Forest algorithm, has markedly enhanced the precision in the estimation of seismicity parameters by effectively identifying and eliminating outliers and discrepancies. A newly developed and refined seismicity catalog was employed to accurately determine key seismic parameters such as the magnitude of completeness (Mc), a-value, and b-value, thereby underlining their indispensable role in regional seismic hazard assessment. The research underscores the substantial impact of data inconsistencies on the evaluation of seismic hazards, thereby advocating for the advancement of research methodologies within the field of seismotectonics. The insights derived from this study significantly contribute to a more profound understanding of the seismotectonic processes in the region. These insights are crucial for the development of comprehensive seismic hazard assessments and the formulation of targeted earthquake preparedness strategies, thereby enhancing resilience against seismic risks in the region.

Capturing patients’ medical histories significantly influences clinical decisions. Errors in this process lead to clinical errors, which increase costs and dissatisfaction among physicians and patients. Physicians in developing countries are overloaded with patients and cannot always follow the proper history-taking procedure. The challenges have been acknowledged; however, a comprehensive understanding of the status and the remedies has remained unexplored. This paper aims to investigate the workload, history-taking challenges, and the willingness of the physicians to accept digital solutions. A cross-sectional online survey was conducted on 104 physicians across Bangladesh, featuring 22 questions regarding their professional environment, workload, digitization status of health records, challenges in history taking, and attitudes toward adopting digital solutions for managing patient histories; 92.67% of the physicians face high workloads, 88.46% struggle in medical history taking, and only 4.81% use digital medical records. About 70% struggle to complete the necessary history-taking steps, emphasizing the urgent need for solutions. A novel visualization system, the Smart Health Gantt Chart (SHGC), has been introduced for their instant feedback. A total of 93.27% of physicians expressed their willingness to use such a system. The proposed SHGC has the potential to enhance healthcare efficiency in developing nations, benefit physicians, and improve patient-centered care.

Modern technology and analysis of emotions play a crucial role in enabling intelligent healthcare systems to provide diagnostics and self-assistance services based on observation. However, precise data predictions and computational models are critical for these systems to perform their jobs effectively. Traditionally, healthcare monitoring has been the primary emphasis. However, there were a couple of negatives, including the pattern feature generating the method’s scalability and reliability, which was tested with different data sources. This paper delves into the Discriminant Input Processing Scheme (DIPS), a crucial instrument for resolving challenges. Data-segmentation-based complex processing techniques allow DIPS to merge many emotion analysis streams. The DIPS recommendation engine uses segmented data characteristics to sift through inputs from the emotion stream for patterns. The recommendation is more accurate and flexible since DIPS uses transfer learning to identify similar data across different streams. With transfer learning, this study can be sure that the previous recommendations and data properties will be available in future data streams, making the most of them. Data utilization ratio, approximation, accuracy, and false rate are some of the metrics used to assess the effectiveness of the advised approach. Self-assisted intelligent healthcare systems that use emotion-based analysis and state-of-the-art technology are crucial when managing healthcare. This study improves healthcare management’s accuracy and efficiency using computational models like DIPS to guarantee accurate data forecasts and recommendations.

As part of the evaluation and valorization of plant genetic resources of fodder and pastoral interest in Algeria, seventeen (17) natural populations belonging to six (06) species of the genus Vicia (Vicia sativa, Vicia disperma, Vicia monardii, Vicia ohchroleuca, Vicia onobrychioides and Vicia lutea), originated from different ecological regions in the North-Eastern of the country, were considered. The populations have been the subject of mitotic and meiotic studies. Haploid and diploid numbers and chromosome measurements were determined. Original results were observed for the first time in some species. In fact, chromosome counts have revealed some new chromosome numbers. The first number (2n=14, n=7) was observed in the endemic subspecies of Algeria, V. ochroleuca subsp. atlantica and in the species Vicia onobrychioides. The second number (n=6) was observed in the species Vicia disperma. Within Vicia monardii, the three new chromosome numbers, previously observed only in mitosis by our research team, were confirmed for the first time through the present meiosis study (n=6, 7 and 8), indicating that they are A type chromosomes. The base number x=7 is the most frequently observed number in the six Vicia species. The observed chromosome numbers would be related to some ecological factors (altitude, rainfall) of the origin environment of the populations. Chromosome measurements and established karyotypes were determined for the first time in V. monardii, V. onobrychioides and V. ochroleuca subsp. atlantica. Chromosome size and karyotype formula are variable among the studied species and subspecies. Karyotypes vary from symmetrical to asymmetrical and the intrachromosomal asymmetry is higher than interchromosomal one. The new cytogenetic data would contribute to a better understanding of the evolution mechanism of the species in the genus Vicia L.

Internet of Things (IoT) security and reliability rely on the capacity to identify distributed denial-of-service (DDoS) assaults in IoT networks. This research presents a comprehensive study on DDoS attack detection using the NSL-KDD dataset. The dataset contains a diverse set of network traffic data. This paper proposes two approaches, one utilizing Principal Component Analysis (PCA) and another without PCA, to compare their performance. Robust scaling and encoding techniques are applied as preprocessing steps. The experiment outcomes demonstrate a noteworthy improvement in the accuracy of DDoS attack detection in IoT devices by integrating PCA and Robust Scaler. Notably, the Random Forest and KNN classifiers demonstrate exceptional performance with an accuracy of 99.87 % and 99.14 %, respectively, while Naïve Bayes shows a lower accuracy of 87.14 %. The findings from this experiment contribute valuable insights into enhancing the security of IoT devices against DDoS attacks. The proposed approach showcases the importance of appropriate preprocessing techniques in achieving robust intrusion detection systems for IoT environments.

[This corrects the article DOI: 10.1371/journal.pone.0300550.].

Egypt is among the world's largest producers of sugarcane. This crop is of great economic importance in the country, as it serves as a primary source of sugar, a vital strategic material. The pre-cutting planting mode is the most used technique for cultivating sugarcane in Egypt. However, this method is plagued by several issues that adversely affect the quality of the crop. A proposed solution to these problems is the implementation of a sugarcane-seed-cutting device, which incorporates automatic identification technology for optimal efficiency. The aim is to enhance the cutting quality and efficiency of the pre-cutting planting mode of sugarcane. The developed machine consists of a feeding system, a node scanning and detection system, a node cutting system, a sugarcane seed counting and monitoring system, and a control system. The current research aims to study the pulse widths (PW) of three-color channels (R, G, and B) of the RGB color sensors under laboratory conditions. The output PW of red, green, and blue channel values were recorded at three color types for hand-colored nodes [black, red, and blue], three speeds of the feeding system [7.5 m/min, 5 m/min, and 4.3 m/min], three installing heights of the RGB color sensors [2.0 cm, 3.0 cm, and 4.0 cm], and three widths of the colored line [10.0 mm, 7.0 mm, and 3.0 mm]. The laboratory test results s to identify hand-colored sugarcane nodes showed that the recognition rate ranged from 95% to 100% and the average scanning time ranged from 1.0 s to 1.75 s. The capacity of the developed machine ranged up to 1200 seeds per hour. The highest performance of the developed machine was 100% when using hand-colored sugarcane stalks with a 10 mm blue color line and installing the RGB color sensor at 2.0 cm in height, as well as increasing the speed of the feeding system to 7.5 m/min. The use of IoT and RGB color sensors has made it possible to get analytical indicators like those achieved with other automatic systems for cutting sugar cane seeds without requiring the use of computers or expensive, fast industrial cameras for image processing.

The current electric vehicles (EVs) market is experiencing significant expansion, underscoring the need to address challenges associated with the limited driving range of EVs. A primary focus in this context is the improvement of the wireless charging process. To contribute to this research area, this study introduces a circular spiral coil design that incorporates transceiver coils. First, an in-depth analysis is conducted using Ansys Maxwell software to assess the effectiveness of the proposed solution through the magnetic field distribution, inductance properties, and mutual inductance between receiver and transmitter coils. In the next step, a direct shielding technique is applied, integrating a ferrite core bar to reduce power leakage and enhance power transmission efficiency. The ferrite magnetic shielding guides magnetic field lines, resulting in a significant reduction in flux leakage and improved power transmission. Lastly, a magnetic resonance series (SS) compensation wireless system is developed to achieve high coupling efficiency and superior performance. The system's effectiveness is evaluated through co-simulation using Ansys Simplorer software. The results confirm the effectiveness of the proposed solution, showing its ability to transmit 3.6 kilowatts with a success rate approaching 99%. This contribution significantly advances the development of wireless charging systems for electric vehicles, addressing concerns and promoting global adoption.

Numerous controversies arose over the judgment of the racewalking events, and the rules were amended more than once. Before 1995, the rule required constant touch with the ground, and the need for a straight knee was only applied in the upright vertical posture. While, after 1995, the rule that applies today was published, which included two obvious changes. One related to maintaining a constantly straight knee through the first half of the stride, and the other to maintaining contact as seen by the human eye. This study aimed to investigate the impact of the last three modifications in racewalking rules on elite athletes' performance, athletes' eligibility, and nations' classification. Also, we investigated the regression between performance times of 20, and 50 km events and explanatory variables (BMI, and age). We collected data of 310 racewalkers from Olympic Games records (men) in 20km and 50km between 1956 and 2016. This period was divided into three stages according to the racewalking rules updates: Stage A (from 1996 to 2016), Stage B (from 1976 to 1992), and Stage C (from 1956 to 1972). There was a significant difference between all stages favoring stage A for Athlete’s Performance. Stage A has the highest speeds (4.06±.23m/sec) for 20km with large ES (η2=.54, p=.000), and (3.71±.06m/sec) for 50km with large ES (η2=.769, p=.000) compared to others. The participants had the efficiency and the ability to finish the race with the least percentage of withdrawals under the current rule compared to other rules. Some countries emerged on the scene, i.e. China, Poland and Ecuador, and others disappeared under the current rule; while the regression model's results revealed a substantial link between time and explanatory parameters where (BConstant=58.219, P.000) for 20km and (BConstant=164.744, P.000) for 50km. Results proved that the elite walkers, the youngest and lowest in BMI, are the fastest and most efficient walkers under the current rule.

Water erosion is a critical issue for Morocco, especially in its semi-arid regions, where climatic and edaphic conditions only allow erratic soil formation and vegetation growth. Therefore, water erosion endangers human activity both directly (loss of arable land, landslides, mudflows) and indirectly (siltation of dams, river pollution). This study is part of the Kingdom’s effort to assess the risk of water erosion in its territory. It is dedicated to the Bin El-Ouidane dam water catchment, one of the biggest water storage facilities in the country, located in the High Atlas Mountains. The poorly developed soils are very sensitive to erosion in this mountainous area that combines steep slopes and sparse vegetation cover. The calculation of soil losses is carried out with the RUSLE model and corrected by estimating areas of deposition based on the unit stream power theory. This method produces a mean erosion rate of around 6.3 t·ha -1·y -1, or an overall annual loss of 4.1 mln t, consistently with the siltation rate of the dam. Primary risk areas (erosion rates > 40 t·ha -1·y -1) account for 54% of the total losses, while they cover only 7% of the catchment. This distribution of the soil losses also shows that the erosion risk is mainly correlated to slope, directing the means of control toward mechanical interventions.

Abstract Floorplan energy assessments present a highly efficient method for evaluating the energy efficiency of residential properties without requiring physical presence. By employing computer modelling, an accurate determination of the building’s heat loss or gain can be achieved, enabling planners and homeowners to devise energy-efficient renovation or redevelopment plans. However, the creation of an AI model for floorplan element detection necessitates the manual annotation of a substantial collection of floorplans, which poses a daunting task. This paper introduces a novel active learning model designed to detect and annotate the primary elements within floorplan images, aiming to assist energy assessors in automating the analysis of such images–an inherently challenging problem due to the time-intensive nature of the annotation process. Our active learning approach initially trained on a set of 500 annotated images and progressively learned from a larger dataset comprising 4500 unlabelled images. This iterative process resulted in mean average precision score of 0.833, precision score of 0.972, and recall score of 0.950. We make our dataset publicly available under a Creative Commons license.

Abstract There is a lack of epidemiological data on fascioliasis in Egypt regarding disease characteristics and treatment outcomes across different governorates. We aimed to identify the demographic, epidemiologic, clinical, laboratory, and radiological characteristics and treatment outcomes of patients diagnosed with fascioliasis in Egypt. Data on human fascioliasis were collected retrospectively from patients’ medical records in the period between January 2018 and January 2020. The study included 261 patients. More than 40% of enrolled patients were in the age group of 21–40 years old. Geographically, 247 (94.6%) were from Assiut Governorate with 69.3% were from rural areas. The most frequent symptoms were right upper quadrant pain (96.9%), and fever (80.1%). Eosinophilia was found in 250 cases (95.8%). Hepatic focal lesions were detected in 131 (50.2%); out of them 64/131 (48.9%) had a single lesion. All patients received a single dose of 10 mg/kg of triclabendazole, 79.7% responded well to a single dose, while in 20.3% a second ± a third dose of treatment was requested. After therapy, there was a reduction in leucocytes, Fasciola antibodies titer, eosinophilic count, bilirubin, and liver enzymes with an increase in hemoglobin level. According to our findings, a high index of suspicion should be raised in cases with fever, right upper abdominal pain, and peripheral eosinophilia, and further imaging workup is mandated to detect hepatic focal lesions. Prompt treatment by triclabendazole can serve as a standard-of-care regimen even for suspected cases.