Search

This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350 × 18 ). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946 % ), Mutual Information (i.e., 98.902 % ), Independent Component Analysis (i.e., 99.62 % ), and Principal Component Analysis (i.e., 98.884 % ) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89 % . The experiments' findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain–Computer Interfaces (BCI). [ABSTRACT FROM AUTHOR]

In the process of heat treatment of 8620 low alloy steel, the heating rate is a critical parameter that must be carefully controlled to achieve the desired combination of microstructural features, mechanical properties, and corrosion resistance while simultaneously ensuring process efficiency and cost-effectiveness. This study investigates, for the first time, the microstructural evolution and electrochemical properties of 8620 steel under identical quenching and tempering heat treatment routes with slow-rate (SR) and fast-rate (FR) heating rates. Microstructural analysis revealed martensitic phases for SR, while FR exhibited a dual-phase microstructure containing ferrite. Upon tempering, for both samples, the martensite transformed into tempered martensite, with tempered (Temp) FR exhibiting around 50% smaller ferrite grains. Mechanical testing indicated that SR had 17% higher hardness than FR, although hardness decreased after tempering by 22% (SR) and 17% (FR). All electrochemical tests indicated that the as-quenched SR exhibited significantly superior corrosion resistance than FR. For instance, the polarization resistance of SR was 440 Ω higher than that of the FR samples. Tempering resulted in a considerable decrease in corrosion resistance for Temp SR, whereas Temp FR improved. Electrochemical characterization revealed Temp FR displayed close-to-ideal capacitive behavior and low double-layer capacitance, indicating enhanced overall corrosion resistance. [ABSTRACT FROM AUTHOR]

Pakistan is spatially a diverse state in terms of location of its economic activities and regional disparities in various dimensions of development have been a vital concern in its history. The article aims to analyze the distribution of household welfare index across 97 districts of Pakistan through exploratory spatial data analysis for periods 2004-05 and 2014-15. For this purpose, an augmented Household Welfare index is constructed for measuring Household Welfare across districts. The index consists of consist of five indicators. Final index is obtained by aggregating these indicators through Principal Component Analysis (PCA). Findings of the study indicate positive global autocorrelation and thus indicating that a district with a high (low) is linked spatially with bordering districts which also have high (low) Household Welfare level. The results also display the HH quadrant in scatterplots of Household Welfare Index includes mostly districts of Punjab and KP, while LL illustrates a cluster of most districts from Interior Sindh and Baluchistan for both 2004-05 and 2014-15 periods. Overall, the findings demonstrate the twofold structure of Pakistan’s economic geography, as explained by most of the previous studies. Since geography of Household Welfare matters, it is recommended to reduce across districts inequalities by developing the social and economic institutions and infrastructure in the Baluchistan and interior Sindh. [ABSTRACT FROM AUTHOR]

In this study, mechanical and electrochemical performance of borided additively manufactured (AM) and wrought 8620 low alloy steel were investigated and compared to their bare counterparts. The microstructure of borided 8620 exhibited the presence of FeB and Fe2B phases with a saw tooth morphology. Both AM and wrought samples with boride layers showed a similar performance in hardness, wear, potentiodynamic polarization (PD), electrochemical impedance spectroscopy (EIS), and linear polarization resistance (LPR) experiments. However, borided steels exhibited about an 8-fold increase in Vickers hardness and about a 6-fold enhancement in wear resistance compared to bare ones. Electrochemical experiments of borided specimens (both AM and wrought) in 0.1 M Na2S2O3 + 1 M NH4Cl solution revealed a 3–6-fold lower corrosion current density, about a 6-fold higher charge transfer resistance, and about a 6-fold lower double-layer capacitance, demonstrating an improved corrosion resistance compared to their bare counterparts. Post-corrosion surface analysis revealed the presence of thick sulfide and oxide layers on the bare steels, whereas dispersed corrosion particles were observed on the borided samples. The enhanced wear and electrochemical performance of the borided steels were attributed to the hard FeB/Fe2B layers and the reduced amount of adsorbed sulfur on their surface. [ABSTRACT FROM AUTHOR]

Plant salt tolerance is controlled by various physiological processes such as water and ion homeostasis, photosynthesis, and cellular redox balance, which are in turn controlled by gene expression. In the present study, plants of six canola cultivars (DGL, Dunkled, Faisal Canola, Cyclone, Legend, and Oscar) were evaluated for salt tolerance by subjecting them to 0 or 200 mM NaCl stress. Based on growth, cultivars DGL, Dunkled, and Faisal Canola were ranked as salt tolerant, while cultivars Cyclone, Legend, and Oscar were ranked as salt-sensitive ones. Differential salt tolerance in these canola cultivars was found to be associated with a relatively lower accumulation of Na+ and greater accumulation of K+ in the leaves, lower oxidative damage (MDA), and better antioxidative defense system (Superoxide dismutase, SOD; peroxidase, POD, and catalase, CAT). Cultivar Oscar was the poorest to discriminate Na+ and K+ uptake and accumulation in leaves and had poor antioxidant potential to scavenge ROS. Salt stress did not affect the structural stability of photosystem-II (PSII) till three weeks, thereafter it caused a significant decrease. Salt stress increased the performance index (PIABS) by increasing the density of active reaction centers in Oscar. Salt stress decreased the antenna size thereby lowering the absorption and trapping energy flux, and maintaining the electron transport with an increase in heat dissipation. This may represent a potential mechanism to cope with salt stress. Transcriptome analysis of salt-sensitive cultivar Oscar further revealed that salt stress down-regulated DEGs related to hormonal signal transduction pathways, photosynthesis, and transcription factors, while DEGs related to the biosynthesis of amino acid and ion transport were up-regulated. In conclusion, salt tolerance in canola cultivars was associated with ion exclusion and maintenance of photosynthesis. Salt stress sensitivity in cultivar Oscar was mainly associated with poor control of ion homeostasis which caused oxidative stress and reduced photosynthetic efficiency. [ABSTRACT FROM AUTHOR]

Vitamin D is an important nutrient for bone health, and vitamin D deficiency increases the risk of various diseases. Gilgit Baltistan, the northern-most area of Pakistan, has a high prevalence of vitamin D deficiency, despite many nutritional and food safety programmes. The present study aimed to find how knowledge, attitudes and practices associated with vitamin D related to the prevalence of vitamin D deficiency among people residing in different areas of Gilgit Baltistan. The cross-sectional study was descriptive and used data from a survey carried out between February 2019 and December 2020 on individuals of both sexes aged 10 years or over in Gilgit Baltistan. Of the 575 survey participants, 306 (53.2%) had experienced signs and symptoms of vitamin D deficiency, i.e. tiredness, fatigue and bone weakness. Approximately 64.8% had some general knowledge of vitamin D and its relation to health. Participants aged 19-25 years had the highest scores on knowledge of vitamin D. Only 22.7% of interviewees had ever taken any supplements and only 25.6% often exposed themselves to sunlight. Females' mean knowledge score (28.7; SD 7.02) was higher than that of males (24; SD 9.01). A lack of consistency was observed between attitude towards daylight exposure and knowledge of vitamin D. There was a large correlation between knowledge and attitude (p = 0.001), while a non-significant association was demonstrated between knowledge and practices (p = 0.1). Better knowledge, attitude and practices by people living in cities or more-developed regions indicates that education can be an effective way to provide awareness regarding micronutrient deficiencies. More emphasis is needed on enhancing knowledge, awareness and practices associated with vitamin D deficiency in rural areas of Pakistan. It is strongly recommended that an awareness campaign on micronutrients is launched in both rural and urban areas of Pakistan, concentrating on poor socioeconomic settings. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) of metallic alloys offers a new avenue to print objects having complex geometries. This exclusive benefit of AM has made it an alternative route to conventional manufacturing. Importantly, additively manufactured (AMed) alloys often exhibit improved microstructures, which may provide better properties. The microstructure of an alloy can be tuned by controlling the processing parameters. This study includes an overview of the processing parameters that can influence the microstructural, mechanical, and corrosion properties of AMed alloys. Moreover, the effects of heat treatment on AMed alloys are also discussed. Among various processing parameters, it is observed that the laser power significantly influences the microstructure. The microstructures produced with high laser power are similar to heat-treated samples for 316L stainless steel (SS) and Ti6Al4V. Similarly, variation in scanning speed results in distinct morphology of grains in Ti6Al4V. Moreover, different AM processes, such as SLM and EBM, produce coarse and fine β grains, respectively, in Ti6Al4V. The fabrication of AlSi10Mg yields various sizes of melt pool due to different scanning strategies. Furthermore, mechanical properties such as microhardness is higher and the yield strength is lower for Ti6Al4V fabricated at lower laser power. The corrosion behavior of SLMed Ti6Al4V is different on the perpendicular and parallel planes to the build direction. Due to the increase in grain size after heat treatment, the corrosion resistance of AMed Ti6Al4V and AlSi10Mg is reduced. In contrast, heat treatment applied on 316L, Ti6Al4V, AlSi10Mg, and Inconel 718 is beneficial for mechanical properties. After the development of materials with optimized processing parameters, the research should be conducted on replacement of the wrought alloys with the AMed alloys. It is expected that new applications such as fuel cells and biomedical devices will utilize the AM technology to build parts in the recent future. [ABSTRACT FROM AUTHOR]

The performance of a typical solar energy-based system can be improved by accurately modeling the current versus voltage characteristics of the involved solar cells. However, estimating the exact value of parameters related to solar cells is quite challenging. The optimization function, considering the current–voltage characteristics of solar cells, requires the solution of sophisticated non-linear and multi-modal optimization methods. So far, various optimization approaches have been reported. This paper proposes the application of a new hybrid algorithm, i.e., Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), which is a combination of two algorithms, i.e., Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) method. The hybrid PSOGSA algorithm is superior to other algorithms in terms of higher accuracy in searching for optimal solutions and better explorative capability. Moreover, the developed hybrid algorithm is benchmarked using ten standard test functions to verify its efficiency. In this manuscript, monocrystalline and polycrystalline solar cells are considered. The parameter optimization results are obtained using PSOGSA and further compared with those obtained using other algorithms presented in the literature, such as PSO, GSA, MVO, HBO, PO and SCA. The complete error analysis is carried out for the modified single-diode model (MSDM), the modified double-diode model (MDDM), and the modified three-diode model (MTDM) of photovoltaic (PV) cells to prove the superiority of the PSOGSA. Moreover, statistical results are carried out based on Friedman's ranking and Wilcoxon's rank sum test. The comparison results show that the proposed PSOGSA is better than other algorithms in estimating the unknown PV model parameters. [ABSTRACT FROM AUTHOR]

Conventional Gastrointestinal (GI) cancer treatments are quite expensive and have major hazards. Nowadays, a different strategy places more emphasis on creating tiny biologically active peptides that do not cause severe poisoning. Anticancer peptides (ACPs) are found through experimental screening, which is time-dependent and frequently fraught with difficulties. Gastric ACPs are emerging as a promising GI cancer treatment in the current day. It is crucial to identify novel gastric ACPs to have an improved knowledge of their functioning processes and treatment of gastric cancer. As a result of the post-genomic era's massive production of peptide sequences, rapid and effective ACPs using a computational method are essential. Several adaptive statistical techniques for distinguishing ACPs and non-ACPs have recently been developed. A variety of adapted statistically significant methods have been developed to differentiate between ACPs and non-ACPs. Despite significant progress, there is no specific model for the prediction of gastric ACPs because the specific model will predict a particular type of peptide more accurately and quickly. To overcome this, an initiative is taken for the creation of a reliable framework for the accurate identification of gastric ACPs. The current technique in particular contains four possible features along with one hybrid feature encoding mechanisms which are the target-class motif previously indicated by Amino Acid Composition, Dipeptide Composition, Tripeptide Composition (TPC), Pseudo Amino Acid Composition (PAAC), and their Hybrid. Machine Learning algorithms make high-performance and accurate prediction tools. Moreover, highly variable and ideal deep feature selection is done using an ANOVA-based F score for feature pruning. Experiments on a range of algorithms are carried out to identify the optimal operating strategy due to the diverse nature of learning. Following analysis of the empirical results, Naïve Bayes with TPC and Hybrid feature space outperforms other methods with 0.99 accuracy score on the testing dataset. To find the model generalization an external validation is carried out. In external datasets, the Extra Trees with PAAC features outperforms with the accuracy of 0.94. The comparison study shows that our suggested model will predict gastric ACPs more accurately and will be useful in drug development and gastric cancer. The predictive model can be freely accessed at https://github.com/humeraazad10/G-ACP.git.Communicated by Ramaswamy H. Sarma.