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Bereaved older adults experiencing high grief in the first year after an attachment loss is at increased risk for prolonged grief disorder (PGD) via unknown mechanisms. Yearning, a core grief symptom, is linked to the ventral striatal (VS) brain function, but the role of this neuronal system in late-life grief is poorly understood. As a first step, we examined the VS subregional abnormalities associated with multidimensional symptoms in bereaved elders during the first year post-loss. Sixty-five bereaved elders completed clinical assessments within 13 months post-loss. Ventral caudate (VCau) and nucleus accumbens (NAcc) functional connectivity (FC) was assessed using seed-based resting-state functional MRI. VCau and NAcc FC differences between high (inventory of complicated grief [ICG] score≥30; n = 35) and low (ICG score30; n = 30) grief, and the relationships between ventral striatal subregional FC and clinical measures (yearning and depressive symptoms) were assessed after covariate adjustments (α 0.05; 3dClustSim corrected). Relative to low grief participants, those with high grief showed higher FC between VCau and the medial prefrontal, orbitofrontal, and subgenual cingulate cortices. VCau FC abnormalities positively correlated with yearning (r

The greater wings of the sphenoid bone (GWS) comprise the components of the sphenoid bone that make up most of the posterior orbital wall and form the anterior and medial parts of the floor of the middle cranial fossa. Many important skull base foramina, which transmit vital neurovascular structures, are present in these paired wings on either side of the central body of the sphenoid bone. A wide variety of diseases can affect the GWS, ranging from benign osseus lesions to malignant primary and secondary bone abnormalities. The complex three-dimensional curved (winged) shape of the GWS and the wide array of pathologic entities that affect this bone can make it challenging for the radiologist to report the imaging findings accurately, especially in relation to the important skull base foramina. The authors describe a systematic approach to understanding the three-dimensional anatomy of the GWS and review important diseases, with the aid of imaging examples. Useful imaging "pearls" that can help in making specific diagnoses are provided throughout the article.

In the normal heart cardiac rhythm is controlled by microscopic and macroscopic structures. Abnormality of pacemaker or electrical conduction aberrations causes arrhythmic disorders. Cardiac Arrhythmias may be fortunate, typical, threatening and eventually fatal. Cardio-vascular arrhythmia happens commonly in clinical practise affecting drastically to the patients on digitalis, anaesthesia and acute myocardial infarction. Both traditional and genetic animal models of arrhythmias, their characteristics, and their significance are summarized as: In Vitro Models: Isolated guinea pig papillary muscles, Action potential and refractory period in isolated guinea pig papillary muscle, Langendorff technique & Acetylcholine or potassium induced arrhythmia. In Vivo Models- Chemically induced arrhythmia: Aconitine antagonism in rats, Digoxin induced arrhythmia in rats, Strophanthin/ouabain induced arrhythmia, Adrenaline induced arrhythmia & Calcium induced arrhythmia. Electrically induced arrhythmia: Ventricular fibrillation electrical threshold, Programmed electrical stimulation induced arrhythmia & Sudden coronary death models in dogs. Exercise induced ventricular fibrillation. Mechanically induced arrhythmia: Reperfusion arrhythmia in rats, Reperfusion arrhythmia in dogs & Two stage coronary ligation in dogs. Genetic models of arrhythmias. Conclusion: Experimental models of cardiac arrhythmias, traditional and genetic gives quick summary of the most popular animal models, their characteristics, and their significance for new drug discovery of antiarrhythmic agents.

This article presents guidelines for initial imaging utilization in patients presenting with sinonasal disease, including acute rhinosinusitis without and with suspected orbital and intracranial complications, chronic rhinosinusitis, suspected invasive fungal sinusitis, suspected sinonasal mass, and suspected cerebrospinal fluid leak. CT and MRI are the primary imaging modalities used to evaluate patients with sinonasal disease. Given its detailed depiction of bony anatomy, CT can accurately demonstrate the presence of sinonasal disease, bony erosions, and anatomic variants, and is essential for surgical planning. Given its superior soft tissue contrast, MRI can accurately identify clinically suspected intracranial and intraorbital complications, delineate soft tissue extension of tumor and distinguish mass from obstructed secretions.The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Maxillofacial trauma patients comprise a significant subset of patients presenting to emergency departments. Before evaluating for facial trauma, an emergency or trauma physician must perform a primary survey to ensure patient stabilization. Following this primary survey, this document discusses the following clinical scenarios for facial trauma: tenderness to palpation or contusion or edema over frontal bone (suspected frontal bone injury); pain with upper jaw manipulation or pain overlying zygoma or zygomatic deformity or facial elongation or malocclusion or infraorbital nerve paresthesia (suspected midface injury); visible nasal deformity or palpable nasal deformity or tenderness to palpation of the nose or epistaxis (suspected nasal bone injury); and trismus or malocclusion or gingival hemorrhage or mucosal hemorrhage or loose teeth or fractured teeth or displaced teeth (suspected mandibular injury). The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Spondylolisthesis refers to the slippage of one vertebral body over the adjacent one. It is a chronic condition that requires early detection to prevent unpleasant surgery. The paper presents an optimized deep learning model for detecting spondylolisthesis in X-ray radiographs. The dataset contains a total of 299 X-ray radiographs from which 156 images are showing the spine with spondylolisthesis and 143 images are of the normal spine. Image augmentation technique is used to increase the data samples. In this study, VGG16 and InceptionV3 models were used for the image classification task. The developed model is optimized by utilizing the TFLite model optimization technique. The experimental result shows that the VGG16 model has achieved a 98% accuracy rate, which is higher than InceptionV3’s 96% accuracy rate. The size of the implemented model is reduced up to four times so it can be used on small devices. The compressed VGG16 and InceptionV3 models have achieved 100% and 96% accuracy rate, respectively. Our finding shows that the implemented models were outperformed in the diagnosis of lumbar spondylolisthesis as compared to the model suggested by Varcin et al. (which had a maximum of 93% accuracy rate). Also, the developed quantized model has achieved higher accuracy rate than Zebin and Rezvy’s (VGG16 + TFLite) model with 90% accuracy. Furthermore, by evaluating the model’s performance on other publicly available datasets, we have generalised our approach on the public platform.

Line-of-sight (LOS) is a critical requirement for mmWave wireless communications. In this work, we explore the use of access point (AP) infrastructure mobility to optimize indoor mmWave WiFi network performance based on the discovery of LOS connectivity to stations (STAs). We consider a ceiling-mounted mobile (CMM) AP as the infrastructure mobility framework. Within this framework, we propose two heuristic algorithms (basic and weighted) derived from Hamming distance computation and a machine learning (ML) solution fully exploiting available network state information to address the LOS discovery problem. Based on the ML solution, we then propose a systematic solution WiMove, which can decide if and where the AP should move to for optimizing network performance. Using both ns-3 based simulation and experimental prototype implementation, we show that the throughput and fairness performance of WiMove is up to 119% and 15% better compared with single static AP and brute force search.

Machine learning (ML) has shown to be an effective alternative to physical models for quality prediction and process optimization of metal additive manufacturing (AM). However, the inherent “black box” nature of ML techniques such as those represented by artificial neural networks has often presented a challenge to interpret ML outcomes in the framework of the complex thermodynamics that govern AM. While the practical benefits of ML provide an adequate justification, its utility as a reliable modeling tool is ultimately reliant on assured consistency with physical principles and model transparency. To facilitate the fundamental needs, physics-informed machine learning (PIML) has emerged as a hybrid machine learning paradigm that imbues ML models with physical domain knowledge such as thermomechanical laws and constraints. The distinguishing feature of PIML is the synergistic integration of data-driven methods that reflect system dynamics in real-time with the governing physics underlying AM. In this paper, the current state-of-the-art in metal AM is reviewed and opportunities for a paradigm shift to PIML are discussed, thereby identifying relevant future research directions.

Providing Reinforcement Learning (RL) agents with human feedback can dramatically improve various aspects of learning. However, previous methods require human observer to give inputs explicitly (e.g., press buttons, voice interface), burdening the human in the loop of RL agent’s learning process. Further, providing explicit human advise (feedback) continuously is not always possible or too restrictive, e.g., autonomous driving, disabled rehabilitation, etc. In this work, we investigate capturing human’s intrinsic reactions as implicit (and natural) feedback through EEG in the form of error-related potentials (ErrP), providing a natural and direct way for humans to improve the RL agent learning. As such, the human intelligence can be integrated via implicit feedback with RL algorithms to accelerate the learning of RL agent. We develop three reasonably complex 2D discrete navigational games to experimentally evaluate the overall performance of the proposed work. And the motivation of using ErrPs as feedbacks is also verified by subjective experiments. Major contributions of our work are as follows, (i) we propose and experimentally validate the zero-shot learning of ErrPs, where the ErrPs can be learned for one game, and transferred to other unseen games, (ii) we propose a novel RL framework for integrating implicit human feedbacks via ErrPs with RL agent, improving the label efficiency and robustness to human mistakes, and (iii) compared to prior works, we scale the application of ErrPs to reasonably complex environments, and demonstrate the significance of our approach for accelerated learning through real user experiments.

High electrical conductivity and superior redox properties of metal sulfide-based supercapacitors have attracted much attention in recent years. The simple and cost-effective method in the fabrication of high-performance supercapacitors is currently in high demand. In this paper, low-cost one-dimensional copper sulfide (Cu2S) electrodes are synthesized on glass as well as on flexible substrates such as polyethylene terephthalate (PET) and polypropylene (PP). The effect of the deposition quantity of Cu2S-1:1 on the glass substrate is also discussed. The synthesis of copper sulfide was done at room temperature by reducing copper sulphate pentahydrate using ascorbic acid as a reducing agent in sodium thiosulphate with 2 h of total reaction time. Scanning electron microscopy and x-ray diffraction characterizations are performed to validate the formation of Cu2S hollow rods. Electrochemical measurements such as cyclic voltammetry, galvanostatic charge-discharge, and electrochemical impedance spectroscopy are performed using a Metrohm Autolab workstation. Cyclic voltammetry is performed to measure the capacitance of Cu2S-based supercapacitors in which the ratio of copper sulphate and sodium thiosulphate was varied from 1:0.5 to 1:1.5 with a step size of 0.5, and the deposition quantity of Cu2S-1:1 film was also varied on glass substrate from 1 mg to 2 mg. The results show that the device with a 1:1 ratio shows the highest capacitance, i.e., 587 mF/cm2 as compared to the devices fabricated with a 1:0.5 ratio, 1:1.5 ratio, and 1:1 ratio with greater deposition. This is mainly because the 1:1 ratio has less resistance and has a hollow rod structure which allows the electrolyte ions to penetrate in Cu2S active material and thus, facilitates fast electron transport resulting in high-performance supercapacitors. Further, to understand the increased capacitive properties of a copper sulfide-based supercapacitor, processes involving charge transfer and mass transport are investigated by performing electrochemical impedance spectroscopy (EIS). The radius on the EIS plot of Cu2S-1:1 is smaller as compared to the other three samples on the glass substrate. Also, the resistance of Cu2S-1:1 with greater deposition is more than the Cu2S-1:1 sample because the increased amount of electrode material leads to increased paths for the electrolyte ions to interact with the electrode material. Further, this paper also discusses the successful fabrication of the supercapacitor devices on flexible PP substrate using 1-D Cu2S for the first time. The results show that the capacitance value on the flexible substrate is on par with that of glass substrates. Also, the synthesized copper sulfide 1:1 sample exhibits excellent stability with the capacitance retention of 85.7%, 91.1%, 86.18%, and 92.8%, respectively, on PP, glass, PET, and Cu2S-1:1 with more deposition on glass substrate after 3500 cycles.

Multi‐switchable supramolecular nano‐objects that respond to irradiation of different wavelengths with changes in size and shape have been built from two different water‐soluble molecular switches, joined by attachment to the same polyelectrolyte. Accordingly, two wavelength‐specific reactions, namely the excited‐state proton dissociation of a photoacid and the cis–trans isomerization of an azo dye, are combined in one supramolecular nano‐object that is stable in aqueous solution. The concept has potential in the fields of sensors, molecular motors, and transport.

Hemispherotomy surgery in adults is shrouded in doubts regarding the functional outcome. The age at surgery alone should not be the deciding factor for surgery. Language paradigms were used in functional magnetic resonance imaging (fMRI) to confirm the role played by the age at the onset of seizures to predict the postoperative functional outcome. The objective of the study was to formulate an optimal strategy for patient selection for the left-sided hemispherotomy in adults, based on functional outcome analysis.A retrospective analysis of 20 participants (age at surgery 1-26 years) who underwent left hemispherotomy (over a 5-year period) was conducted. The language and motor functional assessments of 18 participants (13 pediatric and five adult participants; attrition of participants- two) were recorded at presentation and during follow-up visits. After approval was obtained from the Institutional Ethics Committee, 13 cooperative participants (eight pediatric and five adult participants) underwent language fMRI. Motor fMRI with both active and passive paradigms was done in 16 participants.All 18 participants with a mean follow-up of 24 months had class I seizure-free outcome. Of these 18, five were adults (mean age = 21 years, range: 18-22 years) and 13 were in the pediatric age group (mean age = 8 years, range: 2-15 years). Postoperatively, four adults retained both verbal fluency and language comprehension at a mean follow-up period of 38 months (range: 24-48 months). Their pre- and post-op language fMRI showed word generation and regional activations for semantic comprehension in the right hemisphere. The motor area activations were seen in the right hemisphere in two and in the left hemisphere in two participants. Among the pediatric participants, four (group I [n = 4/13]) who had good language outcome showed activations in the right hemisphere. In two participants (group II [n = 2/13]) who deteriorated postoperatively, the activations were in the left hemisphere. Five participants (group III [n = 5/13]) who retained the telegraphic language postoperatively had bilateral activations of semantic comprehension areas in fMRI. All 13 pediatric participants had motor area activations seen in the left hemisphere, similar to controls.Left hemispherotomy can be advised to adults with comparably good postoperative language and motor outcome as in the pediatric age group, provided the weakness is acquired perinatally or below the age of 7 years. The fMRI is a valuable tool to aid in patient selection.

The two-dimensional titanium carbide MXene (Ti3C2Tx) acts as a promising pseudocapacitive material for supercapacitor electrodes. In this paper, the properties of vanadium-doped titanium carbide MXene (Ti3C2Tx) are tuned using a simple hydrothermal method to intercalate the alkali metal adsorbates (K+) into the electrode material. The synthesis of the supercapacitor device is carried on glass substrate as well as on a flexible graphite sheet. The X-ray diffraction and scanning electron microscopy are conducted to observe the change in structural properties of vanadium-doped MXene. The cyclic voltammetry and galvanostatic charge–discharge are carried out on Metrohm autolab workstation. The ratio of ammonium vanadate and MXene has been varied from 0.025:0.1 to 0.1:0.1 with a step size of 0.025 to obtain the capacitance results. The results depict that the ratio of 0.025:1 shows the highest capacitance of 258.07 mF/cm2 and 1107 mF/cm2 in 6 M KOH (20 mV/s) on glass and graphite substrate, respectively. This is mainly because the ratio of 0.025:1 provides the maximum exfoliation which allows electrolyte ions to penetrate in the active material and thus, facilitates fast electron transport resulting in high-performance supercapacitors. Further, this paper also discusses the successful fabrication of the supercapacitor devices on a flexible graphite sheet for the first time. The results show that the capacitance value on flexible substrate is at par with that of the glass substrate. To further understand the increased capacitive properties of vanadium-doped MXene, the processes involving charge transfer and mass transport are investigated by performing electrochemical impedance spectroscopy (EIS). The radius on the EIS plot of vanadium-doped MXene is smaller than that of the undoped DMSO MXene, which indicates that the vanadium doping made the charge transfer easier. Moreover, the capacitance retention of 92.7% and 82.2% is achieved on graphite as well as glass substrate after 3000 cycles.

The impression of gate dielectric and spacer dielectric on the performance of rectangular core shell double gate junctionless transistor (RCS-DGJLT) using extensive simulations is studied. The RCS-...

Aneurysmal bone cyst originating from the surface of the bone is an unusual subtype. Surface ABCs have a predilection for diaphysis of long tubular bones. Radiologically it may mimic telangiectatic osteosarcoma which can be differentiated on the basis of histology. We present here a case of 17 year old male with proximal humerus surface ABC with extensive cortical breach. Clinico-radiographic presentation of the patient was more aggressive than conventional ABC. Plain radiograph demonstrated a permeative type of destructive pattern and the cross-sectional imaging demonstrated extensive cortical breach and intramedullary spread. MR Imaging suggested that intramedullary spread was more extensive in dimension as compared to the subperiosteal component. Biopsy was suggestive of ABC. Preoperatively selective arterial embolisation was done. Management was done by wide en-bloc resection and treatment of resected segment with liquid nitrogen by free freezing method followed by reimplantation and internal xation with a proximal humerus plate along-with bular strut graft. Patient on follow up after 5 months post operatively was able to perform his activities of daily living. Most cases reported in literature were managed by curettage and bone grafting. Literature for aggressive management of surface ABC's is sparse with no literature available regarding the use of liquid nitrogen in its management

BackgroundThis study identified a clinically significant subset of glioma patients with tumor outside of contrast-enhancement present at autopsy, and subsequently developed a method for detecting non-enhancing tumor using radio-pathomic mapping. We tested the hypothesis that autopsy-based radio-pathomic tumor probability maps would be able to non-invasively identify areas of infiltrative tumor beyond traditional imaging signatures.MethodsA total of 159 tissue samples from 65 subjects were aligned to MRI acquired nearest to death for this study. Demographic and survival characteristics for patients with and without tumor beyond the contrast-enhancing margin were computed. An ensemble algorithm was used to predict pixelwise tumor presence from pathological annotations using segmented cellularity (Cell), extracellular fluid (ECF), and cytoplasm (Cyt) density as input (6 train/3 test subjects). A second level of ensemble algorithms were used to predict voxel-wise Cell, ECF, and Cyt on the full dataset (43 train/22 test subjects) using 5-by-5 voxel tiles from T1, T1+C, FLAIR, and ADC as input. The models were then combined to generate non-invasive whole brain maps of tumor probability.ResultsTumor outside of contrast was identified in 41.5 percent of patients, who showed worse survival outcomes (HR=3.90, p<0.001). Tumor probability maps reliably tracked non-enhancing tumor in the test set, external data collected pre-surgery, and longitudinal data to identify treatment-related changes and anticipate recurrence.ConclusionsThis study developed a multi-1 stage model for mapping gliomas using autopsy tissue samples as ground truth, which was able to identify regions of tumor beyond traditional imaging signatures.

The wireless sensor network (WSN) consist of battery-powered sensor nodes which are self-configured and are deployed for monitoring several physical or environmental conditions such as temperature, pressure, humidity, vibration, pollutants etc. The major constraint in most of the WSN applications is the replacement/recharging of the battery contained by the node once it gets exhausted. This limitation reduces the lifetime of WSN. The placement of energy harvesting device within the sensor node may be the best probable solution to recharge the exhausted battery. In this paper, the integration of low cost, light weight and foldable flexible solar cells with WSN has been focused. The aim of this paper is to fabricate the flexible solar cells and showing the potential use of them in WSN. Moreover, the use of flexible solar cell is the better selection for emerging wearable WSN. This paper also describes the various issues in the already developed energy harvesting models and suggested a self-powered model for energy management based on finite state machine (FSM). The proposed models completely avoid the overcharging and the frequent charging of the batteries. This optimal utilization of the battery maximizes the lifetime of WSN network. In the proposed model, the flexible p–i–n solar cells are used to convert solar energy into electrical energy that can charge the battery of the WSN node. Finally, it can be concluded that the node will continue to function actively till the battery lifetime i.e. approximately 25–30 years.

Wilson's disease (WD) is caused by copper accumulation in the brain and liver, and if not treated early, can lead to severe disability and death. WD has shown white matter hyperintensity (WMH) in the brain magnetic resonance scans (MRI) scans, but the diagnosis is challenging due to (i) subtle intensity changes and (ii) weak training MRI when using artificial intelligence (AI). Design and validate seven types of high-performing AI-based computer-aided design (CADx) systems consisting of 3D optimized classification, and characterization of WD against controls. We propose a "conventional deep convolution neural network" (cDCNN) and an "improved DCNN" (iDCNN) where rectified linear unit (ReLU) activation function was modified ensuring "differentiable at zero." Three-dimensional optimization was achieved by recording accuracy while changing the CNN layers and augmentation by several folds. WD was characterized using (i) CNN-based feature map strength and (ii) Bispectrum strengths of pixels having higher probabilities of WD. We further computed the (a) area under the curve (AUC), (b) diagnostic odds ratio (DOR), (c) reliability, and (d) stability and (e) benchmarking. Optimal results were achieved using 9 layers of CNN, with 4-fold augmentation. iDCNN yields superior performance compared to cDCNN with accuracy and AUC of 98.28 ± 1.55, 0.99 (p < 0.0001), and 97.19 ± 2.53%, 0.984 (p < 0.0001), respectively. DOR of iDCNN outperformed cDCNN fourfold. iDCNN also outperformed (a) transfer learning-based "Inception V3" paradigm by 11.92% and (b) four types of "conventional machine learning-based systems": k-NN, decision tree, support vector machine, and random forest by 55.13%, 28.36%, 15.35%, and 14.11%, respectively. The AI-based systems can potentially be useful in the early WD diagnosis. Graphical Abstract.

Real-time communication is a widely used technique that allows a system to communicate using peer-to-peer architecture in real-time without any transmission delays. The Internet, messaging, video chat and many more are examples of RTC. WebRTC is a standard feature for incorporating real-time multimedia communications into web browsers. WebRTC uses a range of APIs that are licensed by all successful modern browser distributors that can be found in all web and mobile browsers. Web RTC provides a secure connection as well as provides scalability with low latency. Considering the current scenario with COVID-19 there has been a rise in the usage of video conferencing in various industries like health, education for various purposes, it is also a means to enable work from home for a lot of companies.

The problem of scheduling of tasks in distributed, heterogeneous, and multiprocessing computing environment like grid and cloud computing is considered as one of the most important issue from research perspective. As the performance of such kind of systems is highly depends upon the way, how tasks are allocated among the multiple processing units for their efficient execution. The underlying objective of any task scheduling mechanism is to minimize the overall makespan for the execution of given set of jobs/tasks and computing machines. Scheduling of tasks in cloud computing falls in the class of NP-hard optimization problem. As a result, many meta-heuristic algorithms have been applied and tested to solve this problem but still lot of scope is there for the better strategies. The characteristic of the good algorithm is that it must be adaptable to the dynamic environment. Through this paper, we are proposing task scheduling mechanism based on particle swarm optimization (PSO) in which opposition-based learning technique is used to avoid premature convergence and to accelerate the convergence of standard PSO and compared same with the well-established task scheduling strategies based on PSO, mPSO (modified PSO), genetic algorithm GA, max–min, minimum completion time and minimum execution time. The results obtained for the various class of experiments clearly establish that the proposed opposition-based learning inspired particle swarm optimization based scheduling strategy performs better in comparison to its peers which are taken into the consideration.

Background: There are now clinically available automated MRI analysis software programs that compare brain volumes of patients to a normative sample and provide z-score data for various brain regions. These programs have yet to be validated in primary progressive aphasia (PPA). Objective: To address this gap in the literature, we examined Neuroreader™ z-scores in PPA, relative to visual MRI assessment. We predicted that Neuroreader™ 1) would be more sensitive for detecting left > right atrophy in the cortical lobar regions in logopenic variant PPA clinical phenotype (lvPPA), and 2) would distinguish lvPPA (n = 11) from amnestic mild cognitive impairment (aMCI; n = 12). Methods: lvPPA or aMCI patients who underwent MRI with Neuroreader™ were included in this study. Two neuroradiologists rated 10 regions. Neuroreader™ lobar z-scores for those 10 regions, as well as a hippocampal asymmetry metric, were included in analyses. Results: Cohen’s Kappa coefficients were significant in 10 of the 28 computations (k = 0.351 to 0.593, p≤0.029). Neuroradiologists agreed 0% of the time that left asymmetry was present across regions. No significant differences emerged between aMCI and lvPPA in Neuroreader™ z-scores across left or right frontal, temporal, or parietal regions (ps > 0.10). There were significantly lower z-scores in the left compared to right for the hippocampus, as well as parietal, occipital, and temporal cortices in lvPPA. Conclusion: Overall, our results indicated moderate to low interrater reliability, and raters never agreed that left asymmetry was present. While lower z-scores in the left hemisphere regions emerged in lvPPA, Neuroreader™ failed to differentiate lvPPA from aMCI.

Wilson's disease (WD) is caused by the excessive accumulation of copper in the brain and liver, leading to death if not diagnosed early. WD shows its prevalence as white matter hyperintensity (WMH) in MRI scans. It is challenging and tedious to classify WD against controls when comparing visually, primarily due to subtle differences in WMH. This Letter presents a computer-aided design-based automated classification strategy that uses optimised transfer learning (TL) utilising two novel paradigms known as (i) MobileNet and (ii) the Visual Geometric Group-19 (VGG-19). Further, the authors benchmark TL systems against a machine learning (ML) paradigm. Using four-fold augmentation, VGG-19 is superior to MobileNet demonstrating accuracy and area under the curve (AUC) pairs as 95.46 ± 7.70%, 0.932 (p < 0.0001) and 86.87 ± 2.23%, 0.871 (p < 0.0001), respectively. Further, MobileNet and VGG-19 showed an improvement of 3.4 and 13.5%, respectively, when benchmarked against the ML-based soft classifier – Random Forest.

Background & Objective: Cloud computing emerges out as a new way of computing which enables the users to fulfill their computation need using the underlying computing resources like software, memory, computing nodes or machines without owning them purely on the basis of pay-per-use that too round the clock and from anywhere. People defined this as the extension of the existing technologies like parallel computing, distributed computing or grid computing. Lots of research have been conducted in the field of cloud computing but the task scheduling is considered to be the most fundamental problem which is still in infancy and requires a lot of attention and a proper mechanism for the optimal utilization of the underlying computing resources. Task scheduling in cloud computing environment lies into the category of NP-hard problem and many heuristics and Meta heuristics strategies have been applied to solve the problem. Methods: In this work, Fuzzy Enabled Genetic Algorithm (FEGA) is proposed to solve the problem of task scheduling in cloud computing environment as classical roulette wheel selection method has certain limitations to solve complex optimization problem. Results & Discussion: In this work, an efficient fuzzy enabled genetic algorithm based task scheduling mechanism has been designed, implemented and investigated. The efficiency of the proposed FEGA algorithm is tested using various randomly generated data sets in different situations and compared with the other meta-heuristics. Conclusion: The authors suggest that the proposed Fuzzy Enabled Genetic Algorithm (FEGA) to solve the task scheduling problem helps in minimizing the total execution time or makespan and on comparing with other Meta-heuristic like genetic algorithm and greedy based strategy found that FEGA outperforms the both in different set of experiments.

MXene belongs to the family of 2D carbides and nitrides. The controlling of process parameters is key to obtain high-quality MXene films. The layered structure of MXene is obtained successfully by tuning the process parameters which is confirmed through the presence of XRD peak (110) at 2 theta value of 60 degrees. Moreover, the accordion-like structure of MXene is confirmed through SEM which is also highly dependent on the process parameters. It is also observed that if the etchant with sufficient concentration is used for an optimised time the (002) peak gets shifted to a lower angle and confirms the increase in spacing of MXene layers to 12.92 A. Further reduction in the etching time leads to a decrease in the d-spacing of MXene layers due to the presence of aluminium and the presence of defects corresponding to the unsuccessful removal of AlF3 by-products which is confirmed by (106), (108) and (109) XRD peaks. However, the occurrence of (106), (108) and (109) peaks which correspond to the growth of AlF3 is highly dependent on any variation in process parameters. The increase in the quantity also impacts the properties of MXene formed. It can be seen that increasing the quantity of hydrofluoric acid will lead to thicken the MXene layers. If etching is done for the greater quantity of HF, the toxicity is increased which leads to the greater number of fluorine groups which will lead to an increase in the number of defects in the MXene material. This paper also discusses the correlation of process parameters with that of the electrical properties of MXene layers. It is found that to get the best MXene layers in terms of structurally and electrically, the process parameters need to tune in such a way that the etching of aluminium can be done completely without increasing the fluorine content in the MXene. Herein, we report for the first time the fabrication of best optimised MXene film on the flexible polypropylene (PP) substrate using the best optimised parameters for supercapacitor applications and compared with the Polyethylene terephthalate (PET) and glass substrate results. The PP-supported MXene device exhibits lower contact resistance of 141 ohms and the areal capacitance of 82.6mF/cm2 at 5 mV/s and capacitive retention of 73.3%. The study opens up new possible designs for the high-performance devices employing different flake sizes, morphologies of MXene and their combinations.

The numerical simulation of lead-free all-perovskite tandem solar cell using solar capacitance one dimensional tool (SCAPS 1D) has been performed. The combination of methyl ammonium germanium halide (CH3NH3GeI3) as wide band-gap (1.9 eV) perovskite layer and FA0.75MA0.25Sn0.25 Ge0.5I3 (FAMASnGeI3) as a low band-gap (1.4 eV) perovskite layer is considered for the first time which shows the optimal device efficiency of 26.72%. The present work studies the effect of the different electron transport layer, perovskite absorber thickness and its defect density on photovoltaic performance. Further, the effect of front electrode work function on the device photovoltaic performance is also studied. It has been observed that the device open circuit voltage (VoC) is significantly affected by the built-in voltage (Vbi) across the perovskite layer. In addition, the effect of thickness of the perovskite absorber layers is also investigated. Moreover, the effect of defect density of the absorber layer is also explored, and it has been found that for better device performance the defect density needs to be as low as possible. Furthermore, it has been observed that the work function of the front contact plays a significant role and for the proposed device its value should not be more than 4.4 eV. The proposed all–perovskite tandem solar cell shows the remarkable photovoltaic parameters i.e., open circuit voltage (VOC) = 1.07 Volts, short circuit current density (JSC) = 28.36 mA/cm2, fill factor (FF) = 84.39% and efficiency (η) = 26.72%.

Multiple applications of dual energy computed tomography (DECT) have been described for the evaluation of disorders in the head and neck, especially in oncology. We review the body of evidence suggesting advantages of DECT for the evaluation of the neck compared with conventional single energy computed tomography scans, but the full potential of DECT is still to be realized. There is early evidence suggesting significant advantages of DECT for the extraction of quantitative biomarkers using radiomics and machine learning, representing a new horizon that may enable this technology to reach its full potential.

The traditional 'one-size-fits-all' approach to H&N cancer therapy is archaic. Advanced imaging can identify radioresistant areas by using biomarkers that detect tumor hypoxia, hypercellularity etc. Highly conformal radiotherapy can target resistant areas with precision. The critical information that can be gleaned about tumor biology from these advanced imaging modalities facilitates individualized radiotherapy. The tumor imaging world is pushing its boundaries. Molecular imaging can now detect protein expression and genotypic variations across tumors that can be exploited for tailoring treatment. The exploding field of radiomics and radiogenomics extracts quantitative, biologic and genetic information and further expands the scope of personalized therapy.

Tomato is the most popular crop in the world and in every kitchen, it is found in different forms irrespective of the cuisine. After potato and sweet potato, it is the crop which is cultivated worldwide. India ranked 2 in the production of tomato. However, the quality and quantity of tomato crop goes down due to the various kinds of diseases. So, to detect the disease a deep learning-based approach is discussed in the article. For the disease detection and classification, a Convolution Neural Network based approach is applied. In this model, there are 3 convolution and 3 max pooling layers followed by 2 fully connected layer. The experimental results shows the efficacy of the proposed model over pre-trained model i.e. VGG16, InceptionV3 and MobileNet. The classification accuracy varies from 76% to 100% with respect to classes and average accuracy of the proposed model is 91.2% for the 9 disease and 1 healthy class.