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In this study, the tensile behaviors of laser powder bed fused (L-PBF) and laser powder directed energy deposited (LP-DED) HAYNES® 214 were investigated at a wide range of test temperatures (−195, 25, 425, 650, 870 and 980 °C). Specimens fabricated in the vertical orientation were subjected to a three-step heat treatment including stress relief, hot isostatic pressing, and solution treatment. The non-heat treated microstructures of L-PBF and LP-DED specimens were dendritic and columnar, oriented in the build direction. Recrystallization occurred after solution annealing, with the nucleation and growth of equiaxed grains. Tensile results revealed higher strength for the L-PBF specimens compared to LP-DED up to 650 °C, attributed to their finer grain size. The highest strengths were recorded at cryogenic temperatures, where diminished dislocation mobility and deformation twinning provided increased resistance to plastic deformation. From room temperature to 425 °C, a gradual decrease in strength was ascribed to the increase in dislocation motion with temperature. Ductility was measured to be similar for both processes up to 650 °C, and fracture surface analysis showed transgranular failure at these temperatures. A ductility dip was observed between temperatures of 650–870 °C due to the activation of grain boundary sliding resulting in intergranular cracking. The heterogeneous grain size distribution in both L-PBF and LP-DED specimens led to the ductility being comparable for both processes. The highest test temperature of 980 °C introduced sufficient thermal energy to promote significant grain boundary sliding which resulted in intergranular fracture in the L-PBF and LP-DED specimens.

Abstract Introduction The incidence of oral tongue squamous cell carcinoma (OTSCC) is increasing. OTSCC is comparatively higher in our region owing to the consumption of tobacco, beetle nut, alcohol, and poor oral hygiene. There is paucity of survival data for OTSCC from our high-burden region. Background There is paucity of survival data for OTSCC from our high-burden region, we aimed to determine 5-year overall and disease-free survival of patients with OTSCC. Methods A retrospective chart review was conducted for all the patients treated for oral tongue squamous cell carcinoma from January 2000 and December 2013. The data on overall survival and disease-free survival was collected via telephonic interviews up till 2018. Kaplan Meier curves were plotted to graphically represent survival. Results A total of 131 patients were included. The mean age of the patients was 49.4 ± 12.98 years, with most patients being male (65.9%). The most common tumor stage was T2 (55.1%) and the most common nodal stage was N0 (57.7%). Five-year disease-free survival was 59.2%, and overall survival was 60.7%. Overall survival dropped steeply from 84.4 to 7.5% in patients who developed recurrence in our cohort. Conclusion The most significant factor influencing the survival of patients with OTSCC is tumor recurrence. 5-year recurrence-free survival was 84.4% which is greater than reported in literature. This could be due to the low incidence of perineural and perivascular invasion in our population. Further research on factors leading to recurrence should be done.

Recent world events and geopolitics have brought the vulnerability of critical infrastructure to cyberattacks to the forefront. While there has been considerable attention to attacks on Information Technology (IT) systems, such as data theft and ransomware, the vulnerabilities and dangers posed by industrial control systems (ICS) have received significantly less attention. What is very different is that industrial control systems can be made to do things that could destroy equipment or even harm people. For example, in 2021 the US encountered a cyberattack on a water treatment plant in Florida that could have resulted in serious injuries or even death. These risks are based on the unique physical characteristics of these industrial systems. In this paper, we present a holistic, integrated safety and security analysis, we call Cybersafety, based on the STAMP (System-Theoretic Accident Model and Processes) framework, for one such industrial system—an industrial chiller plant—as an example. In this analysis, we identify vulnerabilities emerging from interactions between technology, operator actions as well as organizational structure, and provide recommendations to mitigate resulting loss scenarios in a systematic manner.

Appendicitis is one of the frequent complaints for which patients visit the Emergency Department. Appendectomy is one of the most commonly performed procedures. Stump appendicitis is a rare but severe complication of appendectomy due to the inflammation of the remaining part of the appendix. Because it is rare and partly due to physicians’ lack of understanding of this entity, the diagnosis is highly delayed, leading to severe complications including perforation or peritonitis. This article aims to raise the importance and awareness and shed light on how possibly it could be avoided.

More adaptable and user-independent techniques are required for multi-sensors based daily locomotion detection (MS-DLD). This research study proposes a couple of locomotion detection methods using body-worn multi-sensors to successfully categorize several locomotion transitions. This research presents both standard and state-of-the-art methods for MS-DLD. Conventionally, to improve MS-DLD process, the proposed methodology consists of a wavelet transformed Quaternion-based filter for the inertial signals, patterns recognition in the form of kinematic-static energies, and state-of-the-art multi-features extraction. These features include entropy, spectral, and cepstral coefficients domains. Then, fuzzy logic-based optimization has been introduced in order to achieve the selective features by converting them into codewords. These nominated features with enhanced patterns are further detected by HMMs to improve the accuracy for classifying daily life locomotion actions. This paper also introduces another state-of-the-art way to model daily locomotion detection and derives body-specific modified HMMs. The model divides the sensor data into three active body-specific parts including head sensors, mid-body sensors, and lower body sensors. Body-specific modified HMMs have been provided with raw data for the three active body-specific sensors and gave better results with less computational complexities when compared to the conventional methods. The proposed systems have been experimentally assessed and trialed over three diverse publicly available datasets: the UP-Fall dataset consisting of falling and other daily life activities, the IM-WSHA dataset comprising everyday locomotion actions, and the ENABL3S gait and locomotion dataset consisting of multiple gait movements. Experimental outcomes indicate that the proposed conventional technique achieved improved results and outperformed existing systems based on detection accuracies of 90.0% and 87.5% over UP-Fall, 86.0% and 88.3% over IM-WSHA, and 86.7% and 90.0% over ENABL3S datasets for kinematic and static energy patterns, respectively. Further, the results show that the state-of-the-art body-specific modified HMMs method achieved 94.3% and 95.0% over UP-Fall, 92.0% and 93.3% over IM-WSHA, and 90.0% and 95.0% over ENABL3S datasets for kinematic and static patterned signals, respectively. The results of state-of-the-art efficient system show a significant increase in detection accuracy when compared to standard systems.

Built-up is one of the most significant type of land use-cover linked with urbanization. Computing, classifying, and mapping the built-up areas by Landsat image, is on priority demand for municipal and policymakers to investigate urban extension. Thus, increasing population and conversion of agricultural land into urban is a major topic understanding the bond between both types of land use. In the context of this, this study investigates the relationship between Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) in Khangarh taluka. Therefore, satellite images of Landsat 8 OLI/TIRS 2014, 2016, 2018, and 2021 were downloaded freely from the USGS-GloVIS Earth Explorer website. The images were processed in the ArcGIS 10.3 environment. NDVI was calculated using the Near-Infrared NIR (band 5), Red (band 4) and for NDBI, Middle Infrared Reflectance MIR (band 6) and NIR (band 5) was used following the equation of both indices. The calculated values were then exported in SPSS software for correlation determination and scatter plot development. The results from the case showed that there was a linear and negative correlation between vegetation index and built-up index in all years over the study area. Furthermore, in 2014 the coefficient of correlation explicated R2=0.96; in 2016 R2=0.23, in 2018 R2=0.34, and in 2021 R2=0.22 which indicated that NDBI could be used to illustrate the evaluation of urban construction land. The all-over study recommends that built-up index NDBI not only can be used as a significant indicator for built-up or urban areas estimation but also deliver a consistent source for urban development and planning.

Developing a gauge-discharge relationship in rivers, canals, and minor flow is vital for controlling floods, managing water resources, Spatio-temporal analysis, socio-economic development, and sustaining the ecosystem. Accurate and consistent data of irrigation networks are perilous to scheduling and managing for accurate application of irrigation water. Most of the hydrologic engineering activities like hydraulics structure, designs, flood monitoring, surplus water, reservoir, canal, and minor’s operation depend on flowing water derived from Rating Curve (RC). The effective management of irrigation water is necessary for crop water requirements and seepage losses estimation. In this context, the present study showed the actual field level work tested at two minors of the Ghotki feeder canal namely Pyaro minor and Dilwaro minor. The main object of the study is to develop gauge-discharge relation and development of RC and Rating Table (RT). The current meter was used for taking discharge measurements with the area velocity technique in both minors. Moreover, stage-discharge RC and RT were developed for different flows of water for both minors in Origin Software. From the calculated results, Power equations were developed for both minors for the actual requirement of crop water in the command area. The results of the study calculated in RT of Piyaro minor between 0.5-5ft stage gave discharge 0.053 cusecs to 90.616 cusecs. While the RT of Dilwaro minor showed the range between 0.5ft-4ft stage gave 26.575cusec to 168.888 cusecs. Hence, the present study suggested that for both minors, automatic gauging stations should be established for the actual demand of irrigation water in the command area and di-siltation should be done on both minors to make availability of water at the tail section.

Over the past few years, automatic recognition of human interactions has drawn significant attention from researchers working in the field of Artificial Intelligence (AI). And feature extraction is one of the most critical tasks in developing efficient Human Interaction Recognition (HIR) systems. Moreover, recent researches in computer vision suggest that robust features lead to higher recognition accuracies. Hence, an improved HIR system has been proposed in this paper that combines 2D and 3D features extracted using machine learning and deep learning techniques. These discriminative features result in accurate classification and help avoid misclassification of similar interactions. Ten keyframes have been extracted from each video to reduce computational complexity. Next, these frames have been preprocessed using image normalization and noise removal techniques. The Region Of Interest (ROI), which contains the two humans involved in the interaction, has been extracted using motion detection. Then, the human silhouettes have been segmented using the GrabCut algorithm. Next, the extracted silhouettes have been converted into 3D meshes and their heat kernel signatures (HKS) have been obtained to extract key body points. A Convolutional Neural Network (CNN) has been used to extract full-body features from 2D full-body silhouettes. Then, topological and geometric features have been extracted from the key body points. Finally, the combined feature vector has been fed into Long Short-Term Memory (LSTM) and each interaction has been recognized using a Softmax classifier. The proposed system has been validated via extensive experimentation on three challenging RGB+D datasets. The recognition accuracies of 91.63%, 90.54%, and 90.13% have been achieved with the SBU Kinect Interaction, NTU RGB+D, and ISR-UoL 3D social activity datasets respectively. The results of extensive experiments performed on the proposed system suggest that it can be used effectively for various applications, such as security, surveillance, health monitoring, and assisted living.

A nano-grid is an independent hybrid sustainable framework that utilizes non-renewable and renewable power resources for supplying continuous electrical energy to the load. Considering this scenario, in this research work, photovoltaic (PV) array, wind turbine, and fuel cell are taken as the three generation resources that have been used in the nano-grid. The active and reactive power of the all three generation resources is controlled using various controllers, i.e. integral, proportional-integral, proportional derivative, proportional integral derivative, fractional-order proportional-integral, fractional order proportional integral derivative (FOPID) and sliding mode controller (SMC). An advanced optimization technique based on a genetic algorithm (GA) and particle swarm optimization (PSO) algorithm has been utilized to optimize all of these controllers. The integral square error is taken as the objective function for both optimization algorithms. Finally, a graphical and tabular comparative analysis of all optimized controllers along with their control parameters and performance indexes is evaluated to find the best optimal solution. The performance of SMC has surpassed the performance of all other optimized controllers for power stability. In less than 0.267 seconds, the actual power yielded by using SMC is within 1% of the desired power. PSO algorithm has performed better than GA algorithm with all controllers. The worst performance is by FOPID controller with a steady state error of 6071.3W using GA algorithm and have a high magnitude of overshoot and undershoot. Moreover, a smart switching algorithm has been introduced for switching between the generation resources in accordance with the load demand and cost of the system in order to operate the nano-grid more economically. Finally, a case study has been performed in which the smart switching algorithm is utilized to switch to the best available generation resource in case of any fault at the generation side to provide uninterrupted power to the attached loads.

Behavior monitoring and classification is a mechanism used to automatically identify or verify individual based on their human detection, tracking and behavior recognition from video sequences captured by a depth camera. In this paper, we designed a system that precisely classifies the nature of 3D body postures obtained by Kinect using an advanced recognizer. We proposed novel features that are suitable for depth data. These features are robust to noise, invariant to translation and scaling, and capable of monitoring fast human bodyparts movements. Lastly, advanced hidden Markov model is used to recognize different activities. In the extensive experiments, we have seen that our system consistently outperforms over three depth-based behavior datasets, i.e., IM-DailyDepthActivity, MSRDailyActivity3D and MSRAction3D in both posture classification and behavior recognition. Moreover, our system handles subject's body parts rotation, self-occlusion and body parts missing which significantly track complex activities and improve recognition rate. Due to easy accessible, low-cost and friendly deployment process of depth camera, the proposed system can be applied over various consumer-applications including patient-monitoring system, automatic video surveillance, smart homes/offices and 3D games.

Muhammad Zaman,1 Sundus Qureshi,1 Kishwar Sultana,1 Muhammad Hanif,2 Asif Mahmood,1 Zaib Ali Shaheryar,1 Faisal Gulzar,1 Kashif Barkat,1 Mohamed M Abdel-Daim3 1Faculty of Pharmacy, University of Lahore, Lahore, Pakistan; 2Department of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan; 3Department of Pharmacology, Suez Canal University, Ismailia, Egypt Background: The present study was to develop a stable and sustained-release delivery system of tacrolimus (TCM). TCM is a macrolide antibiotic used as an immunosuppressant. It is formulated as a microsponge, which is a safe and effective delivery system with reduced side effects.Materials and methods: The method used to prepare ethyl cellulose (EC) and xanthan gum (XG)-facilitated EC-based microsponges employed emulsification and modified double emulsification techniques. TCM-containing microsponges were prepared using varying concentrations followed by evaluation of micromeritics, compatibility of drug and excipients, production yield, drug content and entrapment efficiency, zeta potential, size distribution and drug release.Results: The results showed excellent flow properties with adequate entrapment efficiency of the system and satisfactory release of active pharmaceutical ingredient. In vitro dissolution studies, which were conducted to determine the amount of drug released, illustrated a pronounced sustained effect up to 8 h. Zeta size and zeta potential analysis of microsponges confirmed the existence of micro-sized (1.99–3.09 µm) and stable particles (-15.33 to -3.38 mV), respectively.Conclusion: Conclusively, the applied technique and selected combination of ingredients were found suitable for the preparation of TCM-containing sustained-release microsponges. Keywords: ethylcellulose, xanthan gum, macrolides, tacrolimus, sustained release

Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR) realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs) to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition.

This work presents the grouping of dependent tasks into a cluster using the Bayesian analysis model to solve the affinity scheduling problem in heterogeneous multicore systems. The non-affinity scheduling of tasks has a negative impact as the overall execution time for the tasks increases. Furthermore, non-affinity-based scheduling also limits the potential for data reuse in the caches so it becomes necessary to bring the same data into the caches multiple times. In heterogeneous multicore systems, it is essential to address the load balancing problem as all cores are operating at varying frequencies. We propose two techniques to solve the load balancing issue, one being designated “chunk-based scheduler” (CBS) which is applied to the heterogeneous systems while the other system is “quantum-based intra-core task migration” (QBICTM) where each task is given a fair and equal chance to run on the fastest core. Results show 30–55% improvement in the average execution time of the tasks by applying our CBS or QBICTM scheduler compare to other traditional schedulers when compared using the same operating system.

PurposeTo document survival for patients treated with stereotactic radiosurgery (SRS) alone for brain metastases either at initial presentation or for salvage in conjunction with other known prognostic factors in a single institutional community setting with comparison to current literature.MethodsAll patients treated for brain metastases with SRS between October 2006 and October 2013 were reviewed. We identified 91 patients treated with SRS alone for first brain metastatic event (FBME) and 87 patients treated with SRS for second brain metastatic event (SBME). We excluded the 14 patients treated with SRS for both FBME and SBME to satisfy the independence assumption for comparison of groups. Patient demographics, including age, gender, primary cancer type, presence of extracranial metastases, number of brain metastases, initial site of metastases (brain vs. other), recursive partitioning analysis (RPA), and Karnofsky Performance status (KPS) were documented.ResultsThere were no significant differences in overall survival for patients treated with SRS for FBME compared with SBME (log-rank p = 0.9347). Univariate and multivariable Cox regression modeling revealed KPS (p = 0.0003) and RPA (p = 0.0143) were the only independent prognostic factors for survival. Specifically, patients with RPA 1 had a 61% decreased risk of death compared to those with RPA 3. Patients with RPA 2 had a 33% decreased risk of death compared to those with RPA 3. The 1-year survival rate was 36.5% for patients with RPA1, 33.3% for those with RPA 2, and 17.1% for those with RPA 3. Patients with KPS 90–100 had a 62% decreased risk of death compared to those with KPS

Nowadays, advancements in depth imaging technologies have made human activity recognition (HAR) reliable without attaching optical markers or any other motion sensors to human body parts. This study presents a depth imaging-based HAR system to monitor and recognize human activities. In this work, we proposed spatiotemporal features approach to detect, track, and recognize human silhouettes using a sequence of RGB-D images. Under our proposed HAR framework, the required procedure includes detection of human depth silhouettes from the raw depth image sequence, removing background noise, and tracking of human silhouettes using frame differentiation constraints of human motion information. These depth silhouettes extract the spatiotemporal features based on depth sequential history, motion identification, optical flow, and joints information. Then, these features are processed by principal component analysis for dimension reduction and better feature representation. Finally, these optimal features are trained and they recognized activity using hidden Markov model. During experimental results, we demonstrate our proposed approach on three challenging depth videos datasets including IM-DailyDepthActivity, MSRAction3D, and MSRDailyActivity3D. All experimental results show the superiority of the proposed approach over the state-of-the-art methods.

The maintenance of cellular function relies on the close regulation of adenosine triphosphate (ATP) synthesis and hydrolysis. ATP hydrolysis by mitochondrial ATP Synthase (CV) is induced by loss of proton motive force and inhibited by the mitochondrial protein ATPase inhibitor (ATPIF1). The extent of CV hydrolytic activity and its impact on cellular energetics remains unknown due to the lack of selective hydrolysis inhibitors of CV. We find that CV hydrolytic activity takes place in coupled intact mitochondria and is increased by respiratory chain defects. We identified (+)-Epicatechin as a selective inhibitor of ATP hydrolysis that binds CV while preventing the binding of ATPIF1. In cells with Complex-III deficiency, we show that inhibition of CV hydrolytic activity by (+)-Epichatechin is sufficient to restore ATP content without restoring respiratory function. Inhibition of CV-ATP hydrolysis in a mouse model of Duchenne Muscular Dystrophy is sufficient to improve muscle force without any increase in mitochondrial content. We conclude that the impact of compromised mitochondrial respiration can be lessened using hydrolysis-selective inhibitors of CV.