Mechanical and radiation shielding characterization of W-based alloys for advanced nuclear unit.

The paper aims to investigate the mechanical and radiation shielding properties of two tungsten-based alloys manufactured by powder technology; their features when compared with two standard stainless steel grades for advanced nuclear applications. Multiple measurements were performed to characterize the alloys' structural and mechanical properties. XRD analysis and average surface roughness measurements showed the crystalline and morphological structure of the alloys. Surface microhardness added to the abrasive wear analysis showed the final wear resistance of the selected alloys. In addition, the shielding features against gamma and fast neutrons radiation were calculated. The superior characteristics of W-based alloys manufactured by powder technology make them suitable to be used in advanced nuclear power units.

Improving the properties of commercial steel alloys in the industrial/nuclear field by RF plasma carbonitriding.

The study investigates the effect of RF plasma on the commercial SS201 and Steel alloys physical, structural and nuclear properties, using an atmosphere of nitrogen (85%) plus acetylene (15%) at 500 W working for 0.5 h. The results were compared with untreated alloys and AISI304L. The study indicated the ability of RF plasma to improve both the mechanical and tribological properties of treated alloys compared to the original commercial alloys and the AISI304L, and achieve good shielding properties for industrial/nuclear applications.

Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data.

Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model generalization. In addition to minimizing feature dimensionality, it also enhances classification accuracy and reduces model complexity, which are essential in several applications. Traditional methods for feature selection often fail in the optimal global solution due to the large search space. Many hybrid techniques have been proposed depending on merging several search strategies which have been used individually as a solution to the FS problem. This study proposes a modified hunger games search algorithm (mHGS), for solving optimization and FS problems. The main advantages of the proposed mHGS are to resolve the following drawbacks that have been raised in the original HGS; (1) avoiding the local search, (2) solving the problem of premature convergence, and (3) balancing between the exploitation and exploration phases. The mHGS has been evaluated by using the IEEE Congress on Evolutionary Computation 2020 (CEC'20) for optimization test and ten medical and chemical datasets. The data have dimensions up to 20000 features or more. The results of the proposed algorithm have been compared to a variety of well-known optimization methods, including improved multi-operator differential evolution algorithm (IMODE), gravitational search algorithm, grey wolf optimization, Harris Hawks optimization, whale optimization algorithm, slime mould algorithm and hunger search games search. The experimental results suggest that the proposed mHGS can generate effective search results without increasing the computational cost and improving the convergence speed. It has also improved the SVM classification performance.

The International Natural Product Sciences Taskforce (INPST) and the power of Twitter networking exemplified through #INPST hashtag analysis.

BACKGROUND: The development of digital technologies and the evolution of open innovation approaches have enabled the creation of diverse virtual organizations and enterprises coordinating their activities primarily online. The open innovation platform titled "International Natural Product Sciences Taskforce" (INPST) was established in 2018, to bring together in collaborative environment individuals and organizations interested in natural product scientific research, and to empower their interactions by using digital communication tools. METHODS: In this work, we present a general overview of INPST activities and showcase the specific use of Twitter as a powerful networking tool that was used to host a one-week "2021 INPST Twitter Networking Event" (spanning from 31st May 2021 to 6th June 2021) based on the application of the Twitter hashtag #INPST. RESULTS AND CONCLUSION: The use of this hashtag during the networking event period was analyzed with Symplur Signals (https://www.symplur.com/), revealing a total of 6,036 tweets, shared by 686 users, which generated a total of 65,004,773 impressions (views of the respective tweets). This networking event's achieved high visibility and participation rate showcases a convincing example of how this social media platform can be used as a highly effective tool to host virtual Twitter-based international biomedical research events.

An AI-Enabled Internet of Things Based Autism Care System for Improving Cognitive Ability of Children with Autism Spectrum Disorders.

Smart monitoring and assisted living systems for cognitive health assessment play a central role in assessment of individuals' health conditions. Autistic children suffer from some difficulties including social skills, repetitive behaviors, speech and nonverbal communication, and accommodating to the environment around them. Thus, dealing with autistic children is a serious public health problem as it is hard to determine what they feel with a lack of emotional cognitive ability. Currently, no medical treatments have been shown to cure autistic children, with most of the social assistive research to date focusing on Autism Spectrum Disorder (ASD) without suggesting a real treatment. In this paper, we focus on improving cognitive ability and daily living skills and maximizing the ability of the autistic child to function and participate positively in the community. Through utilizing intelligent systems based Artificial Intelligence (AI) and Internet of Things (IoT) technologies, we facilitate the process of adaptation to the world around the autistic children. To this end, we propose an AI-enabled IoT system embodied in a sensor for measuring the heart rate to predict the state of the child and then sending the state to the guardian with feeling and expected behavior of the child via a mobile application. Further, the system can provide a new virtual environment to help the child to be capable of improving eye contact with other people. This way is represented in pictures of these persons in 3D models that break this child's fear barrier. The system follows strategies that have focused on social communication skill development particularly at young ages to be more interactive with others.

Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics.

One of the major drawbacks of cheminformatics is a large amount of information present in the datasets. In the majority of cases, this information contains redundant instances that affect the analysis of similarity measurements with respect to drug design and discovery. Therefore, using classical methods such as the protein bank database and quantum mechanical calculations are insufficient owing to the dimensionality of search spaces. In this paper, we introduce a hybrid metaheuristic algorithm called CHHO-CS, which combines Harris hawks optimizer (HHO) with two operators: cuckoo search (CS) and chaotic maps. The role of CS is to control the main position vectors of the HHO algorithm to maintain the balance between exploitation and exploration phases, while the chaotic maps are used to update the control energy parameters to avoid falling into local optimum and premature convergence. Feature selection (FS) is a tool that permits to reduce the dimensionality of the dataset by removing redundant and non desired information, then FS is very helpful in cheminformatics. FS methods employ a classifier that permits to identify the best subset of features. The support vector machines (SVMs) are then used by the proposed CHHO-CS as an objective function for the classification process in FS. The CHHO-CS-SVM is tested in the selection of appropriate chemical descriptors and compound activities. Various datasets are used to validate the efficiency of the proposed CHHO-CS-SVM approach including ten from the UCI machine learning repository. Additionally, two chemical datasets (i.e., quantitative structure-activity relation biodegradation and monoamine oxidase) were utilized for selecting the most significant chemical descriptors and chemical compounds activities. The extensive experimental and statistical analyses exhibit that the suggested CHHO-CS method accomplished much-preferred trade-off solutions over the competitor algorithms including the HHO, CS, particle swarm optimization, moth-flame optimization, grey wolf optimizer, Salp swarm algorithm, and sine-cosine algorithm surfaced in the literature. The experimental results proved that the complexity associated with cheminformatics can be handled using chaotic maps and hybridizing the meta-heuristic methods.

Evaluation of efficacy and toxicity in two different hypofractionated 3D-conformal external beam radiotherapy schedules in localised muscle invasive bladder cancer.

PURPOSE: To evaluate the efficacy as well as acute and late toxicity of two different accelerated hypofractionated 3D-conformal radiotherapy (Hypo-3DCRT) schedules in patients with bladder cancer. METHODS: Between February 2006 and June 2011, 50 elderly patients with cT1-2N0 bladder carcinoma were treated with Hypo-3DCRT. Mean age was 75 years. All patients were medically inoperable, with poor performance status, who couldn't tolerate either cystectomy or radical external beam irradiation on a daily basis. A dose of 36 Gy in 6 weekly fractions (arm A, N=39) or 39.96 Gy of 3.33 Gy twice daily, once a week, for 6 weeks (arm B, N=11) were prescribed. The primary study endpoints were the evaluation of acute/late gastrointestinal (GI) toxicity according to the EORTC/RTOG scale together with the visual analogue bladder-related pain score (VAS). RESULTS: The GI acute toxicities were: grade 1: arm A 24/39 (61.5%), arm B 9/11 (81.8%); grade 2: arm A 14/39 (35.9%), arm B 1/11 (9.1%); grade 3: arm A 1/39 (9.1%) (x(2), p=0.29). Only grade 1 late GI toxicity was seen and was significantly higher in arm A: arm A 17/39 (43.6%) and arm B 1/11 (9.1%) (x(2), p=0.037). The reduction of VAS score was similar in both arms (p=0.065). The median relapse free survival (RFS) was 15 and 16 months for arm A and B, respectively (log rank, p=0.71). CONCLUSIONS: Beyond the non-randomized design of the trial, the Hypo-3DCRT schedules used appear to be an acceptable alternative to the traditional longer radiotherapy (RT) schedules for elderly patients unfit for daily irradiation.