Search

The current experimental work uses die casting, a liquid processing method, to create functionally graded material (FGM). To reduce the production of undesired intermetallic compounds, the FGM samples were created both with and without interfacing foil during the procedure. Mechanical properties including impact strength and microhardness were examined throughout the manufactured sample's cross-section. In addition, the interfacial bonding of FGM samples with and without an interacting foil was determined by estimating the shear strength. Analysis of the samples using X-ray diffraction (XRD) and scanning electron microscopy (SEM) reveals the existence of compounds in the sample as well as the diffusion of Al and Zn particles from one side to the other. Compared to the FGM without foil, it is observed that the diffusion rate at the interface is regulated when the foil is present. In addition, it was found that, in contrast to pb foil, the inclusion of Ag foil limited the rate at which particles could move from one side to the other. Additionally, machining investigations are carried out at varying depths on both sides of the sample in the direction of the interface with the assistance of electric discharge machining.

Underwater cameras are crucial in marine ecology, but their data management needs automatic species identification. This study proposes a two-stage deep learning approach. First, the Unsharp Mask Filter (UMF) preprocesses images. Then, an enhanced region-based fully convolutional network (R–FCN) detects fish using two-order integrals for position-sensitive score maps and precise region of interest (PS-Pr-RoI) pooling for accuracy. The second stage integrates ShuffleNetV2 with the Squeeze and Excitation (SE) module, forming the Improved ShuffleNetV2 model, enhancing classification focus. Hyperparameters are optimized with the Enhanced Northern Goshawk Optimization Algorithm (ENGO). The improved R–FCN model achieves 99.94 % accuracy, 99.58 % precision and recall, and a 99.27 % F-measure on the Fish4knowledge dataset. Similarly, the ENGO-based ShuffleNetV2 model, evaluated on the same dataset, shows 99.93 % accuracy, 99.19 % precision, 98.29 % recall, and a 98.71 % F-measure, highlighting its superior classification accuracy.

Livestock farming is a significant source of ammonia emissions, which can have adverse effects on human health and the environment. Understanding the distribution and dispersion of ammonia within a livestock farm is crucial for developing effective mitigation strategies. In this study, computational fluid dynamics (CFD) simulations were employed to assess the spatial distribution of ammonia within a representative livestock farm. A detailed 3D model of the farm was created, incorporating the structural layout and ventilation systems. The CFD simulations were conducted using an approach validated by several other researcher groups, considering factors such as ammonia release rate, temperature, and ventilation rates. The results of the CFD simulations provided valuable insights into the distribution patterns of ammonia within the farm. The concentration contours revealed areas of high ammonia concentration, highlighting potential hotspots and vulnerable locations. To investigate the effect of air velocity and temperature on ammonia convection and to save electricity cost, the duty cycle was determined using transient analysis. We have studied the costs and benefits of agricultural ammonia emission abatement options for compliance with air quality regulations. The simulations allowed for the evaluation of different ventilation rates to mitigate ammonia concentration. The findings contribute to the understanding of ammonia emission sources and provide valuable guidance for the design and optimization of livestock farming systems, enabling the development of sustainable practices that mitigate the environmental impact of ammonia emissions.

Purpose: The study aimed to identify the risk factors of recurrence in chronic osteomyelitis (COM) and to document the microbiological patterns pre- and intra-operatively and at recurrence, if any. Methods: We performed retrospective review on COM patients treated with surgical debridement and a 6-week course of antibiotics. The patients with symptoms of osteomyelitis for at least 6 weeks, present or past episodes of discharging sinus, documentation of bone sequestration in operative notes or preoperative images were included in the study. Patients with symptoms of osteomyelitis < 6 weeks, lack of history of discharging sinus or lack of evidence of sequestration in preoperative images or intraoperative notes were excluded. Logistic regression models were used to assess the impact of risk factors of recurrence. Cohen-Kappa scores were derived to see the concordance between pre-operative and intra-operative isolates and at recurrence. Results: Totally, 147 COM patients (115 males and 32 females, mean age (33 ± 19) years) were included in this study. Recurrence was noted in 28 patients (19.0%). Polymicrobial growth and extended spectrum beta-lactamase producing Enterobacteriaceae increased the chance of recurrence. Cierny-Mader stage-1, hematogenous aetiology and negative intraoperative culture reduced the chance of recurrence. Concordance between pre-operative and intra-operative cultures was 59.85% (Kappa score 0.526, p

Photovoltaic power is most important cause of energy subsequently it is simultaneously clean and limitless. PV power conversion devices must be maintained at MPP there to maximize the energy production of Solar array. To get the most energy out of the Solar array, MPPT monitoring is necessary. In recent times, a slew of strategies for controlling the voltage was presented. Power electronic devices developments for the combination of wind and solar power generation are discussed. Based on the dependability and development of each technology, conversations concerning developments and trends in sustainable source systems are provided. The consumption of electricity generated is increasing exponentially, and as a result, the incorporation of photovoltaic panels into distribution companies is increasing rapidly as well, even though it has a substantial impact on the show’s voltage stability. The purpose of this report is to look at how solar PV integration affects distribution transmission energy reliability. The analysis is carried out using RSCAD software, with one of the radial distribution networks with the lowest voltage profile accessible during the maximum of linked loads. Furthermore, the influence of current and voltage on the transmission or distribution system is compared by analyzing distribution system configuration with various solar PV system penetration rates. The simulated findings show that as the penetrating capability of the PV system increases, large harmonic distortion levels are injected, indicating that the solar photo system should only integrate corresponding to the highest capability the connection can sustain. When a PV technology is connected further than this maximum penetration of renewable energy sources, it produces considerable harmonic components, which harms the system that improves. Total voltage output frequency and current consumption distortion are determined to be 4.98 percent and 14.99 percent, correspondingly, at the maximum diffusion way that enables the permissible harmonic distortion limitation. Thus, the research presents the uses of Grid Integration to estimate the performance and the quality of the solar PV model.

There is a rapid evolution in highway investigation due to the increased usage of vehicles. Recently, various investigators have huge attention to the Intelligent Transportation System (ITS). Predicting a particular image plays a crucial role in digital image processing. However, this process is more challenging due to its changing viewpoints, illumination, colour, shape, etc. This work concentrates on modelling a novel deep learning (DL) approach. Initially, a deep-Convolutional Neural Network (D–CNN)–based sample-level classifier is proposed iteratively to measure the sampling uncertainty. Here, the uncertainty evaluation method is integrated with the sample learning process for training the robust sample classifier. The proposed D-CNN model has the competency to identify the abnormal activities and feature representation simultaneously. Subsequently, Deep Recurrent Neural Network (D-RNN) is proposed to examine the feature representation from the bag of samples and generates the final classification output by considering the local and global sampling aggregately. The simulation is performed using MATLAB 2020a and the proposed D-CNN and D-RNN model provides better trade-off with the classification accuracy compared to other approaches. The model accuracy is 92.9%, sensitivity is 76.40%, and specificity is 100% which is substantially higher than other approaches.

Background and objectives: Genetic generalized epilepsy (GGE) is the most common form of generalized epilepsy. Although individual patients with GGE typically present without structural alterations, group differences have been demonstrated in GGE and some GGE subtypes like juvenile myoclonic epilepsy (GGE-JME). Previous studies usually involved only small cohorts from single centers and therefore could not assess imaging markers of multiple GGE subtypes. Methods: We performed a diffusion MRI mega-analysis in 192 participants consisting of 126 controls and 66 patients with GGE from four different cohorts and two different epilepsy centers. We applied whole-brain multi-site harmonization and analyzed fractional anisotropy (FA), as well as mean, radial and axial diffusivity (MD/RD/AD) to assess differences between controls, patients with GGE and the common GGE subtypes, i.e. GGE with generalized tonic-clonic seizures only (GGE-GTCS), GGE-JME and absence epilepsy (GGE-AE). We also analyzed relationships with patients' response to anti-seizure-medication (ASM). Results: Relative to controls, we identified decreased anisotropy and increased RD in patients with GGE. We found no significant effects of disease duration, age of onset or seizure frequency on diffusion metrics. Patients with JME had increased MD and RD when compared to controls, while patients with GGE-GTCS showed decreased MD/AD when compared to controls. Compared to patients with GGE-AE, patients with GGE-GTCS had lower AD/MD. Compared to patients with GGE-GTCS, patients with GGE-JME had higher MD/RD and AD. Moreover, we found lower FA in patients with refractory when compared to patients with non-refractory GGE in the right cortico-spinal tract, but no significant differences in patients with active versus controlled epilepsy. Discussion: We provide evidence that clinically defined GGE as a whole and GGE-subtypes harbor marked microstructural differences detectable with diffusion MRI. Moreover, we found an association between microstructural changes and treatment resistance. Our findings have important implications for future full-resolution multi-site studies when assessing GGE, its subtypes and ASM refractoriness.

Background: Non-prosthetic peri-implant fractures and under reported entities and their optimal management strategies are uncertain. Case details: We present two cases of peri-implant fractures of distal humerus with long dynamic compression plate in situ. We report for the first time a strategy of implant retention, open reduction with small incision, minimal internal fixation with K-wires and supplementary fixation with bilateral external fixator using Joshi's External Stabilizing System (JESS). Both fractures showed signs of union at three months from surgery. The fixators were removed at four months under sedation and patients were advised aggressive active mobilisation of the elbow. This resulted in sound union of the fracture with preservation of elbow motion without any complications. Conclusion: We recommend this method, as it is a simple, safe, effective and reproducible technique for management of peri-implant fractures of the humerus.

Summary: The post-synaptic density (PSD) is a phase-separated membraneless compartment of proteins including PSD-95 that undergoes morphological alteration in response to synaptic activity. Here, we investigated the interactome of a three-domain supramodule, PDZ3-SH3-GK (PSG) from PSD-95 using bioinformatics to identify potential binding partners, and biophysical methods to characterize the interaction with peptides from these proteins. PSG and the single PDZ3 domain bound similar peptides, but with different specificity. Furthermore, we found that the protein ADGRB1 formed liquid droplets with the PSG supramodule, extending the model for PSD formation. Moreover, certain mutations, introduced outside of the binding pocket in PDZ3, increased the affinity and specificity of the interaction and the size of liquid droplets. Other mutations within the ligand binding pocket lead to a new binding motif specificity. Our results show how the context in terms of supertertiary structure modulates affinity, specificity, and phase separation, and how these properties can evolve by point mutation.

Background and purpose: Radiotherapy treatment planning of radical doses for concurrent chemoradiation in Stage III non-small-cell lung cancer (NSCLC) presents many challenges. This dosimetric study aimed to analyse the impact of spatial location of tumour and nodal burden in limiting the achievement of normal organ constraints and the use of appropriate radiotherapy technique to address it. Materials and methods: Fifteen Stage III NSCLC patients underwent 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET)/computed tomography (CT) based treatment planning. VMAT (Volumetric Modulated Arc Radiotherapy) plans were made for all patients treated by 3D-CRT (3-Dimensional Conformal Radiotherapy). A binomial logistic regression was performed to ascertain the tumour and nodal characteristics that decreased the likelihood of being planned to 60 Gy. Results: Inability to achieve normal tissue constraints, particularly spinal cord dose to less than 50 Gy, during initial planning by the assigned treatment technique was the primary dose limiting factor in four patients (p = 0.02). Alternate VMAT plans could achieve the dose constraints where 3D-CRT was unsuccessful in patients with bulky central disease in two patients. This technique fell short when there was gross vertebral body erosion. Conclusions: For tumours with bulky central disease, VMAT should be preferred. With gross vertebral body erosion, even VMAT falls short if the planning target volume includes the spinal cord. In a subset of Stage III NSCLC upfront chemoradiation to radical doses may not be feasible. Keywords: Chemoradiotherapy, Locally advanced lung cancer, Radiotherapy technique, Dosimetry

Iliocaval arteriovenous fistulas most commonly occur after iatrogenic vascular injury and present as high-output heart failure. Treatment can be either via open surgical approach or endovascular repair, with the latter being favoured in the more recent but sparse literature. Here we present a case report of an iliocaval arteriovenous fistula manifesting 10 years after vascular injury and managed successfully with endovascular repair with unibody stent graft. This publication adds to the limited body of literature on this topic and offers technical considerations for endograft selection and operative approach.

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with (“lesional”) and without (“non-lesional”) radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68–75%) compared to models to lateralize the side of TLE (56–73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67–75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68–76%) than models that stratified non-lesional patients (53–62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.