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Coronavirus disease 2019, i.e., COVID-19, an emerging contagious disease with human-to-human transmission, first appeared at the end of year 2019. The sudden demand for disease diagnostic kits prompted researchers to shift their focus toward developing solutions that could assist in identifying COVID-19 using available resources. Therefore, it is imperative to develop a high-accuracy system that makes use of Artificial Intelligence and its tools considering its contribution to computer vision. The time consumed to diagnose test outcomes is to be taken care of as a crucial aspect of an efficient model. To address the global challenges faced by the COVID-19 pandemic, this study proposed two deep learning models developed for automatic COVID-19 detection and distinguish it from pneumonia, another common lung disease. The proposed designs implement layered convolutional neural networks and are trained on a data set of 1824 chest X-rays for binary classification (COVID-19 and normal) and 2736 chest X-rays for ternary classification (COVID-19, normal, and pneumonia). The input images and hyper-parameters in the convolution layers are fine-tuned during the model training phase. The observations show that the proposed models have achieved a better performance as compared to their earlier contemporaries' approaches, resulting in accuracy, precision, recall, and F-score of 98.91%, 98.5%, 98.5%, and 99% for binary-class and 95.99%, 96.3%, 96%, and 96.33% for ternary-class classifiers, respectively. The presented architectures have been built from scratch, thus with the implemented convolutional layered architecture, they were successful in providing more efficient and early diagnosis of the disease. [ABSTRACT FROM AUTHOR]

One of the defining properties of self-healing materials is their capacity to spontaneously and independently repair damage, i.e. without any intervention from an outside source. This is one of the ways in which these materials are distinguished from others. In comparison to polymers, designing and implementing a self-healing mechanism in metals is a challenging task due to the inherent high melting temperatures and low atomic diffusivities of metals. This makes it difficult to develop and execute a self-healing mechanism in metals. The materials that are frequently used in engineering, automobile, aviation, and deep ocean oil well applications are subjected to a significant amount of pressure during the manufacturing process. The forms of material damage that are typically encountered in these applications include plastic flow instability, compromise to the automobile structural integrity, fatigue, and shape deformation. As a result of this, the industry is on the hunt for composite materials that not only have a high strength but also excel in their mechanical and tribological attributes. This article gives a summary and analysis of the numerous research efforts that have been done to generate self-healing metals and the applications that can make use of them. In conclusion, we talk about a few potential directions that future studies into the self-healing characteristics of metals could take. [ABSTRACT FROM AUTHOR]

Automating tasks through human activity recognition in video data has become increasingly vital. Deep learning has yielded versatile activity recognition systems applicable in surveillance, healthcare analysis, sports, and human–computer interaction. Despite various proposed video-based activity recognition techniques over the years, the reliance over RGB frames, accompanied by other modalities like joint locations and depth maps, often proves less effective compared to multimodal methods. In response to this challenge, our paper introduces a competitive approach for identifying human activity in video frames. Leveraging a Convolutional Long Short-Term Memory (Conv-LSTM) network and a novel pre-processing step involving a Human Segmentation network, our method accentuates human subjects in each frame using segmentation maps. These highlighted frames undergo further processing through Convolutional Neural Networks (CNNs) to learn feature vectors, without including other modalities with RGB frames directly. The learned features are then subjected to Long Short-Term Memory (LSTM) units for comprehending sequential video data and drawing meaningful inferences. The proposed methodology undergoes rigorous testing on three publicly available datasets—KARD, MSR Daily Activity, and SBU-Interactions. Remarkably, our approach outperforms similar state-of-the-art methods, achieving benchmark accuracy scores exceeding 98% on MSR Daily Activity and 99% on KARD and SBU-Interactions datasets. In essence, our method not only provides a competitive solution for human activity recognition in video frames but also contributes to advancing the field by integrating Conv-LSTM networks and innovative pre-processing techniques. The comprehensive evaluation on multiple datasets underlines the robustness and superior performance of our proposed approach. [ABSTRACT FROM AUTHOR]

The effect of peak temperature for post-weld heat treatment (PWHT) on the dissolution of Nb-rich carbonitrides, Nb(C,N), in the weldline of an electric resistance welded (ERW) X70 grade pipeline steel was investigated. Selected-area electron diffraction patterns and corresponding electron energy loss spectroscopy mappings revealed both Nb-rich carbonitrides and Ti-rich nitrides, (Ti,Nb)N, in the sample heat-treated to 1080 °C peak temperature. However, only Ti-rich nitrides were revealed in the sample heat-treated to 1220 °C peak temperature. During continuous heating to higher than the Ac3 temperature, i.e., near 1100 °C temperature, the dissolution of Nb(C,N) was observed in dilation and derivative of dilation. This experimental result agreed with the predictions by a DICTRA model. Furthermore, a phenomenological model was developed to explain the volume shrinkage during the dissolution of Nb(C,N) precipitates. [ABSTRACT FROM AUTHOR]

This paper offers a thorough examination of cutting-edge membrane technologies used in water treatment, specifically highlighting the use of nanomaterials and nanoparticles in the production of membranes. The text examines advanced purification techniques, including nanofiltration, electro-dialysis, and photocatalysis. The study emphasizes the utilization of state-of-the-art nanomaterials such as carbon nanotubes, graphene oxide, titanium dioxide, and silver nanoparticles, as advanced substances that can adsorb and catalyze effectively. The article discusses the latest advancements in membrane technology, specifically focusing on improvements in processes like as reverse osmosis, ultrafiltration, and microfiltration. An assessment is conducted to determine the efficacy of these nanotechnologies in eliminating diverse organic and inorganic contaminants that provide considerable difficulties for conventional water treatment procedures. Furthermore, this study investigates the incorporation of biological processes such as bioremediation and phytoremediation with nanotechnology, highlighting both progress and existing constraints. This thorough examination highlights the advantages of various materials and compounds utilized in water filtration systems and pinpoints crucial areas for future investigation. In addition, the research examines high-quality materials mentioned in existing literature, emphasizing their substantial influence on the progress of membrane technologies. The offered insights seek to optimize the efficiency and efficacy of contemporary water treatment systems, thereby contributing to the worldwide provision of uncontaminated and secure water resources. [ABSTRACT FROM AUTHOR]

With innovative technologies developing at a rapid pace, machine learning has become essential to improve interdisciplinary applications such as wireless communications systems. In order to optimize, design and develop a compact (0.6 λ × 0.6 λ × 0.03 λ mm 3) four-elements MIMO antenna at 2.4 GHz resonating frequency, this study uses a machine learning technique. A single element antenna is selected among several steps (1–4) and thereafter, an antenna (A3) turns out to be the best option out of the antennas that were taken into consideration (A1–A3). The optimal antenna (A3) dimensions were carefully ascertained by means of a thorough investigation of 245,700 distinct iterations. The proposed antenna consists of a petal-shaped structure on the center of the radiating plane and rectangular ring with defected semicircles on the ground plane. The antenna exhibits 72.3% (simulated) and 70.2% (measured) impedance bandwidth with a high value of 20 dB of isolation (at 3 GHz). The suggested antenna achieves a maximum gain and radiation efficiency of 9.2 dB and 80% at 2.1 GHz, respectively. Moreover, MIMO characteristics such as Envelope Correlation Coefficient (ECC), Total Active Reflection Coefficient (TARC) and Channel Capacity Loss (CCL) of the intended antenna are studied and found to be well within acceptable limits. The suggested antenna is analysed by means of EM simulator high-frequency structure simulator (HFSS), thereafter it is fabricated and measured and findings show a fair degree of agreement. [ABSTRACT FROM AUTHOR]

The present study focuses on determining the post COVID 19 status of air pollution and temporal variability of pollutants over the Okhla industrial area, New Delhi. The 24 hourly concentration datasets of PM10, PM2.5, NO2 and SO2, for the period of November 2021 to November 2022 were collected from continuous ambient air quality monitoring station of study area. During the study period, the concentration of PM10 and PM2.5 was observed to be varying from 9.73 to 640.14 µg/m3 and 6.18 to 448.06 µg/m3, respectively. The average values of PM10 and PM2.5 were recorded as 236.57 and 107.33 µg/m3, respectively; which were much higher than the standard values of Central Pollution Control Board. In case of gaseous pollutants, the mean value of SO2 was recorded as 9.31 µg/m3 which was much lesser as compared to the set standards. Contrary, the values for NO2 varied from 14.75 to 219.36 µg/m3 and these values were on the higher side when compared with standards. The Bivariate plots were also drawn to examine the role of wind speed and wind direction in the study area. The results revealed that at low wind speed, high concentrations were prevailing and potential sources of pollutant are in south east direction. Overall, the results of this study highlighted that situation is alarming after COVID 19, and need immediate intervention for the abatement of air pollution in studied region. [ABSTRACT FROM AUTHOR]

In this study, we investigate a self-healing 60Pb40Sn reinforced Al6061 alloy's tribological behavior through experimentation, focusing on the influence of different reinforcement concentrations on tribological performance. Cylindrical pins with varying filler concentrations were tested using a pin-on-roller Tribometer to evaluate friction behavior, wear resistance, and fractured surface morphology. The results revealed intriguing findings, including a 5% higher coefficient of friction (COF) for the maximum solder concentration sample at lower loads and speeds. Under higher loads (100 N), the COF increased by approximately 16%. However, the three-hole pin samples consistently exhibited higher weight loss than the two-hole pins. Remarkably, at a 100 N load and extended sliding distances, the three-hole pins showed slightly lower friction values, with a reduced COF of 12%, 16%, and 12% at 53 rpm, 106 rpm, and 160 rpm, respectively. Morphological characterization using Scanning Electron Microscope (SEM), Energy Dispersive Spectrum (EDS), and Elemental Maps revealed the wear mechanism and oxidation on the pins' surfaces, contributing to enhanced wear resistance in this self-healing composite, thus presenting a novel insight into self-healing alloy tribology. [ABSTRACT FROM AUTHOR]

The most popular technique for polymer additive manufacturing is fused deposition modelling (FDM), which is growing in popularity in a variety of engineering applications since it makes complex parts quickly. The proper selection of the process parameters has a significant impact on the mechanical qualities of 3D printed items. The study delved into the mechanical properties of Wood–Polylactic acid (Wood-PLA) composites, specifically focusing on tensile strength, flexural strength, and durometer hardness, along with the impact of various processing factors. The principal effects of four FDM processing parameters—infill density, layer height, infill pattern, and printing speed—were examined using the Taguchi design of experiments. Wood-PLA 3D printer and a commercially accessible fused deposition modelling (FDM) machine was utilized for the experimentation purpose. In this study, The strength of the processing settings was shown by the mechanical properties of the manufactured FDM components. L9 orthogonal array was used for design of experiment. The fabricated samples were measured and tested in accordance with ASTM D638, ASTM D790, and ASTM2240, and the results were compared to the original 3D computer-aided design model. Design Expert's statistical analysis tools were used to construct an analysis of variance (ANOVA) to evaluate the significance of the variables and their interactions in terms of tensile strength, flexural strength, and durometer hardness. In order to check the validity of the results, scanning electron microscope (SEM) was performed. [ABSTRACT FROM AUTHOR]

The present study embraces two-fold sustainability benefits where in first-fold, minimum power consumption (Pc) and machining time (Mt) escort one pillar of sustainability named as environmental sustainability. Moreover, the highest material removal rate (MRR) reduces manufacturing costs and embraces the second pillar of sustainability named as economic sustainability. The study is experimentally conducted on globally emerging Inconel 825 super alloy with a copper electrode in EDM. The sustainable machining is evaluated under a cluster of 8 input process parameters, i.e., spark gap (Sg), gap voltage (Vg), pulse on time (Ton), pulse off time (Toff), peak current (Ip), servo feed (Sf), depth of cut (Dc), and difficulty index (Di) under multi objective optimization (MOO) domain. The study has originally devised two novel approaches based on Taguchi, i.e., simple additive weighting (SAWTAG) and grey relational analysis (GRATAG). Toff is found as the chief significant parameter having p values of 0.005 and 0.002 respectively. Dc is found as the second significant parameter with p values of 0.061 and 0.071 respectively. Significant improvements of 0.16262 and 0.48371 are recognized in conformity experiment under GRATAG and SAWTAG approaches; optimal parametric setting is found as Sg2Vg3Ton1Toff1Ip3Sf1Dc1Di1, which is experimentally validated as robust decision. Furthermore, the proportional contribution of process parameters is estimated where GRATAG reflected 41.11% and SAWTAG reflected 47.29% of the contribution of pulse off time respectively. The study investigated surface topography for highlighting the effects of process parameters on heat-affected layers, machining debris, and surface cracks. It is examined with an increase in Ip and Vg; MRR increases and machining time decreases. Moreover, it is also noticed that an increase in Vg prevents irregularities such as micro voids and re-solidified droplets in machining. [ABSTRACT FROM AUTHOR]

The accurate detection of R-peaks in Electro-Cardio-Gram (ECG) signals plays a crucial role in identifying cardiovascular issues and determining heart rate variability (HRV). However, existing peak detection methods often struggle with non-stationary ECG signals and are prone to false or missed detections. To address these challenges, this paper presents an innovative approach based on Intelligent Signal Processing (ISP) for R-peak detection. The proposed method comprises three main stages: Decomposition of the ECG signal using Fractional Wavelet Transform (FrWT) or Fractional Fourier Transform (FrFT). This stage allows for the effective analysis of non-stationary components in the ECG signal, enhancing the accuracy of subsequent processing. Extraction of the energy envelope using the calculation of Shannon energy envelope (SEE). By capturing the energy distribution of the signal, this step provides a robust representation of the ECG waveform, facilitating the identification of R-peaks. Identification of True R-peaks through advanced signal processing techniques. By analyzing the energy envelope and employing intelligent algorithms, true R-peaks are accurately distinguished from noise or artifacts, thereby minimizing false detections and missed peaks. Simulation results demonstrate the superior performance of the proposed R-peak detection method (i.e. FrWT) compared to existing techniques. The approach exhibits enhanced accuracy, sensitivity, and robustness, even in challenging scenarios characterized by non-stationary ECG signals. Overall, the presented ISP-based technique offers a promising solution for improving the reliability and effectiveness of R-peak detection in ECG analysis, thereby aiding in the early detection and diagnosis of cardiovascular abnormalities. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 from Wuhan, China in 2019 has spread across the world, with more than 687 million confirmed coronavirus cases still increasing. Vaccines and medicines are one aspect of fighting the pandemic outbreak, but modern Information Technologies, web/ mobile-based technologies, blockchain, and Internet of Things (IoT) devices are being used to control the pandemic outbreak at an earlier/later stage. The IoT devices with sensors, and mobile apps/ websites having self-assessment tests can help in tracking COVID-19 infections. These people's tracking information and self-assessment tests are generating huge amounts of data. This white paper introduces an IoT and cloud-based architectural framework in the COVID-19 pandemic to predict infection risk based on citizens' self-assessments and storage of data in the cloud and the intelligent technique of hesitant Intuitionistic Fuzzy Set (IFS) has been used on the self-assessment dataset to predict the risk. [ABSTRACT FROM AUTHOR]

Background: Musculoskeletal pain conditions are the largest contributors to disability and healthcare burden globally. Exercise interventions improve physical function and quality of life in individuals with musculoskeletal pain, yet optimal exercise prescription variables (e.g. duration, frequency, intensity) are unclear. Objective: We aimed to examine evidence gaps, methodological quality and exercise prescription recommendations in systematic reviews of exercise for musculoskeletal pain. Methods: In our prospectively registered umbrella review, PubMed, SPORTDiscus, Cochrane Database of Systematic Reviews, EMBASE, and CINAHL were searched from inception to 14 February 2023. Backward citation tracking was performed. We included peer-reviewed, English language, systematic reviews and meta-analyses of randomized controlled trials (RCTs) and controlled clinical trials (CCTs) that compared exercise with conservative treatment, placebo or other exercise interventions in adults with musculoskeletal pain. Data were extracted from the following groups of reviews based on their reporting of exercise prescription data and analysis of the relationship between prescription variables and outcomes: (1) those that did not report any exercise prescription data, (2) those that reported exercise prescription data but did not perform a quantitative analysis and (3) those that performed a quantitative analysis of the relationship between exercise prescription variables and outcomes. Outcome measures were physical function, pain, mental health, adverse effects and adherence to treatment. AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews) was used to assess methodological quality. Results: From 6757 records, 274 systematic reviews were included. 6.6% of reviews did not report any exercise prescription data, and only 10.9% quantitatively analyzed the relationship between prescription variables and the outcome(s). The overall methodological quality was critically low in 85% of reviews. Conclusion: High methodological quality evidence is lacking for optimal exercise training prescription variables in individuals with musculoskeletal pain. To better inform practice and evidence gaps, future systematic reviews should (1) identify optimum exercise prescription variables, for example, via dose–response (network) meta-analysis, (2) perform high-quality reviews per AMSTAR-2 criteria and (3) include outcomes of mental health, adverse events and exercise adherence. PROSPERO registration number: CRD42021287440 (https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021287440). [ABSTRACT FROM AUTHOR]

According to the report of Times of India, India is becoming the heart disease capital of the world. Consequently, its need of time to make more efforts to enhance the research regarding heart and heart disease to keep the country away from facing any unpleasant and disastrous situation in future. For correctly diagnosing the type of heart disease, Electrocardiogram (ECG) is an important tool which conveys the information in terms of three waves namely; P-wave, QRS-wave, and T-wave. For analyzing the patterns (characteristics) of these waves, Autoregressive (AR) coefficients are required. In this paper,various real recordings are done. For classification purpose, two techniques viz. K-Nearest Neighbor (KNN) and Principal Component analysis (PCA) are used individually. Autoregressive modelling is done on ECG signal with baseline wander (BLW) and ECG signal without BLW for comparing the performance. [ABSTRACT FROM AUTHOR]

Fabricating perovskite films using the antisolvent dripping method has gained significant attention recently due to its simplicity and scalability. Unfortunately, this approach often leads to uneven distribution of antisolvent across the entire surface of a large substrate, mainly for substrates area larger than 6.5 cm2. In this study, we compare the effect of using a single-channel pipette versus a multichannel (MC) pipette during the antisolvent dripping process for depositing large-area perovskite films (25 cm2). Our results demonstrate that implementing an MC pipette improves the uniformity and the crystallinity of the large-area perovskite film. The devices formed by the film made from the MC strategy showed more uniform efficiency distribution and higher thermal stability. Impressively, MC-based devices depicted an average power conversion efficiency (PCE) of 16.15% (Best 18.19%). Whereas SC-based devices show an average PCE of 14.3% (Best 17.16%). This work provides valuable insights into optimizing large-area perovskite film fabrication techniques for solar cell applications. [ABSTRACT FROM AUTHOR]

Continuous monitoring of physiological parameters is critical in order to minimize casualties, therefore automated healthcare computer-assisted diagnostic systems require efficient signal processing techniques. It is currently the primary concern upon which the majority of research is concentrated. It is becoming a way of life to reach long age by which accurate and error-free treatments are done. Electrocardiograms (ECGs) are crucial in the current era for minimizing the global mortality rate. An essential clinical feature of electrocardiograms (ECGs) is the determination of heart rate. It is measured in beats per minute. It differs based on age and a number of other associated variables, including body position, temperature, emotion, smoke, and hormonal imbalances, among others. Arrhythmia of the heart, characterised by an irregular heart rate, is currently the leading cause of increased mortality. The complete diagnosis of ECG is accomplished using the blocks such as pre-processing, feature extraction, and detection/classification. Unfortunately, the analysis of ECG becomes complex under the wide variety of the recording datasets which is influenced by variety of noises and artifacts. It motivates to choose a novel combination of above-mentioned blocks which can serve the purpose. The fractional wavelet transform (FrWT) is chosen in conjunction with independent principal component analysis (IPCA) in this article. FrWT, which combines the fractional Fourier transform and the wavelet transform, satisfies the requirements for feature extraction and pre-processing, respectively, whereas IPCA satisfies the requirement for detection and classification. It also helps to understand the cardiac and non-cardiac behaviour of the electrical recording of the specific critical cases. [ABSTRACT FROM AUTHOR]

The present study focuses on comparative assessment of impact strength of self-healing Al6061 composite reinforced with shape memory alloy (SMA) and low melting point alloy. The self-healing Al6061 composites have been prepared by filling (i) low melting point alloy 60Pb40Sn and (ii) NiTi SMA in the Al6061 alloy. Experimental investigations have been carried out to assess the impact strengths of both of these self-healing Al6061 composites using Charpy impact test. The results show that the low melting point alloy filled self-healing Al6061 composite has lower absorbed energy as compared to SMA wire self-healing Al6061 composite. Simulation of the all three-sample performed on software to ensure the experimental results. [ABSTRACT FROM AUTHOR]

This prospectively registered systematic review and meta-analysis examines whether exercise (EX) training has an additive effect to osteoanabolic and/or antiresorptive pharmacological therapy (PT) in people with osteoporosis on bone mineral density (BMD), bone turnover markers (BTMs), fracture healing, and fractures. Four databases (inception to 6 May 2022), 5 trial registries, and reference lists were searched. Included were randomized controlled trials comparing the effect of EX + PT vs. PT with regard to BMD, BTM, fracture healing, and fractures. Risk of bias was assessed using the Cochrane RoB2 and certainty of evidence by the GRADE approach. Random-effects meta-analysis with Hartung-Knapp-Sidik-Jonkman adjustment was used to estimate standardized mean differences and 95% confidence intervals. Out of 2593 records, five RCTs with 530 participants were included. Meta-analysis showed with very low certainty evidence and wide confidence intervals that EX + PT compared to PT had larger effect sizes for BMD at 12 months at the hip (SMD [95%CI]: 0.18 [− 1.71; 2.06], n = 3 studies), tibia (0.25 [− 4.85; 5.34], n = 2), lumbar spine (0.20 [− 1.15; 1.55], n = 4), and forearm (0.05 [− 0.35; 0.46], n = 3), but not femoral neck (− 0.03 [− 1.80; 1.75], n = 3). Furthermore, no improvement was revealed for BTM such as bone ALP (− 0.68 [− 5.88; 4.53], n = 3), PINP (− 0.74 [− 10.42; 8.93], n = 2), and CTX-I (− 0.69 [− 9.61; 8.23], n = 2), but with very wide confidence intervals. Three potentially relevant ongoing trials were identified via registries. No data were found for fracture healing or fracture outcomes. It remains unclear whether EX has an additive impact to PT in people with osteoporosis. High-quality, adequately powered, targetted RCTs are required. Protocol Registration: PROSPERO CRD42022336132. [ABSTRACT FROM AUTHOR]

Extracting road maps from high-resolution remotely sensed imagery has many practical applications: improving connectivity in remote areas, monitoring urban expansion, and providing aid to disaster-prone regions. Despite the abundance of satellite and aerial imagery, substantial obstacles such as opacity, shadows, inter-class similarity, and missing roadways persist, necessitating immediate attention from researchers. This paper proposes a memory-efficient end-to-end convolution neural network-based architecture called ConnectNet that exploits the powerful features of the Res2Net model. Multi-scale features within every residual block are captured by the hierarchical design, enhancing the proposed model's representation ability. A new block, Stacked Feature Fusion, is proposed having dilated convolution layers of different rates stacked with the squeeze and excitation blocks. Both long-range and narrow-range dependencies are captured by this block, minimizing the boundary and edge loss issue. A new loss function, collective loss, is introduced that combines dice coefficient, binary cross-entropy, and Lovasz sigmoid loss functions which further improves the convergence time and resolves the class imbalance issue. Extensive experiments have been conducted to demonstrate and compare the proposed model's results with other road extraction methods on two publicly available Massachusetts Road Dataset and SpaceNet 3 Road Network Detection Dataset. The quantitative and visual results show that the proposed model outperforms state-of-the-art methods by a significantly large margin. [ABSTRACT FROM AUTHOR]

In this article, the microwave absorption and dielectric properties of gallium(III) nitride (GaN) are reported. X-ray diffraction confirms the hexagonal wurtzite structure of GaN. Field emission scanning electron microscopy shows that this material has a stacked morphology with irregular thin sheets. Nanoparticles with a nearly spherical shape are observed in bright-field images of GaN. X-ray photoelectron spectroscopy demonstrates the oxidation states of Ga and N in GaN. Scattering parameters and corresponding permittivity and loss tangents of a GaN donut sample were measured in the frequency range of 2–18 GHz. Based on these parameters, the reflection and absorption characteristics of the proposed GaN-coated systems are simulated for various thicknesses. With an increase in system thickness, the reflection levels start to decrease, leading to increasing absorption. Observed minimum reflection loss value is − 47.27 dB at 13.77 GHz with a thickness of 2.41 mm. The dielectric constant value decreases rapidly with an increase in frequency up to 1 kHz. Beyond this, the change is nominal. The real part of the dielectric constant (ε′) at 55 Hz is around 12, whereas it reduces to 6 at 1 kHz and then gradually decreases. Observed dielectric loss values are in the range of 0.96–0.1 in the measured frequency range. [ABSTRACT FROM AUTHOR]

The pith is the internal part of the sugarcane plant with short and variable fiber length. The presence of pith creates process-related issues in papermaking, so it must be removed from the bagasse. Pith has low calorific value, and burning of pith in boilers also creates boiler operational issues as well as environmental pollution and health hazards. The imposition of stringent emission norms by the environmental regulatory bodies is compelling these industries to search for cleaner alternatives to fuels. Pith is generated in huge quantities so its disposal or management will become a major challenge if industries shift to cleaner fuel. Considering these pressing issues and some recent relevant research reported on pith valorization, this study was planned to explore the potential of pith valorization. Therefore, in the present review, research studies available on alternate routes for the valorization of pith into value-added products have been extensively covered. Since pith is also lignocellulosic biomass, therefore, its valorization after pretreatment and as such direct utilization (without pretreatment) has also been categorically discussed. Furthermore, the promising pathways and prospective research in pith valorization are discussed that will make the sugar and associated pulp and paper mill more economically and environmentally sustainable. [ABSTRACT FROM AUTHOR]

Coarse columnar grains are less desired feature of the components manufactured via wire and arc additive manufacturing (WAAM). This can be mitigated by introducing inoculants into melt pool which leads to microstructure grain refinement. In this work, titanium carbide (TiC) nanoparticles of 50 nm average size were added to the molten pool during WAAM of 316LSi stainless steel using cold metal transfer (CMT). The microstructural evolution was studied by optical microscope, scanning electron microscope and X-ray diffraction. The results showed more refined grains for TiC-inoculated specimen due to heterogeneous nucleation promoted by TiC-nanoparticles in the molten pool. Additionally, mechanical tensile testing and hardness profiles were also obtained for the TiC-inoculated and non-inoculated samples. An improvement in the tensile strength and hardness of the TiC-inoculated specimen along with reduction in anisotropy of tensile strength was observed, which could be attributed to the refined grain structure. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signal analysis has become an interesting and required area in the medical industry to analyze brain function for different diseases. But, the EEG signal's noise features might degrade the signal prediction's exactness score. So, the presented article aims to develop a novel EEG signal analysis system named a novel Firefly-based Deep Belief Signal Specification (FbDBSS). In addition, the disease signal considered in this research work is Schizophrenia (SZ) signal. Initially, the SZ signal with a normal EEG signal is trained to the system, and preprocessing function is performed. Then the filtered signal is entered into the classification layer for the feature extraction and signal analysis function. Furthermore, the proposed design is executed in the python environment, and the robustness score has been measured in terms of accuracy, sensitivity, and error rate. The chief parameter of the proposed FbDBSS design is compared with other models and has gained the finest 3% of improved signal analysis accuracy and sensitivity score. [ABSTRACT FROM AUTHOR]

In this article, a multi-period optimal scheduling framework considering load priorities is proposed to optimize the operation of an islanded smart distribution network. The main idea of this work is to provide sufficient power supply to high priority loads even during limited generation periods while also assuring network security and achieving optimal generation schedules for distributed energy resources and optimal charging/discharging schedules for battery bank (BB). The proposed scheduling framework is formulated as a multi-objective optimization problem in four different operating cases, over multiple time periods considering conventional generation sources, renewable energy sources, battery bank (BB), and various loads. Loads comprise hospitals, public consumer loads, industries, education centers, and domestic loads. The usual objectives of optimal scheduling problems in the literature are minimizing network power loss, total cost of generation, and maximizing customer benefit; however, less attention has been paid to distribution network security. Therefore, in this article, voltage stability index is considered as one of the objective functions along with network power loss, total cost of generation, and total load curtailment. The proposed scheduling problem is solved using Non-dominated Sorting Genetic Algorithm-II (NSGA-II), and its performance is evaluated on the modified IEEE 34 bus system. The accuracy of results is verified by comparing with the results obtained by solving the proposed scheduling problem using Python optimization modeling objects (Pyomo) software with interior point optimizer as the solver. [ABSTRACT FROM AUTHOR]

Exposure to wastewater treatment system (WWTS)-associated bioaerosols is not only linked with macro/field scale systems but also facilities which are operated in micro- and/or indoor environment such as university campus and research institutions. In this context, investigations on a laboratory-scale WWTS, adopting a sequential batch biofilm process and fed with synthetic municipal wastewater, were done in terms of its global treatment performance and characterization of emitted bioaerosols species. The microbial diversity of captured bioaerosols, collected through conventional particulate matter samplers, was identified on the basis of their metabolic properties using analytical profile index, biochemical tests, and other media specific growth patterns. Monitoring and analysis results of air quality in control and experimental period revealed that particulate emission from bioreactor increased the concentration of PM10 and PM2.5 up to 26.49 ± 4.18 µg/m3 and 12.84 ± 2.48 µg/m3 from an initial level of 17.26 ± 4.58 µg/m3 and 8.70 ± 1.84 µg/m3, respectively. Microscopic observations and staining characteristics revealed that cocci shape gram-negative and bacilli shape gram-positive bacteria dominated the bioaerosols with quantitative contribution as 70% and 9%, respectively. Based on the morphological and biochemical characterization, dominant isolated genera of opportunistic pathogenic bacteria in bioaerosols were identified as Escherichia coli, Bacillus cereus, Bacillus subtilis and Pseudomonas sp. with % dominance as 38.46, 13.46, 9.61 and 25, respectively. Overall, the findings of this study reiterate the concern of biological air pollution in research laboratories and represent an inevitable aspect for the validation of bioaerosol exposure in laboratory-scale WWTS workplaces. [ABSTRACT FROM AUTHOR]

Wire electrical discharge machining is used to cut thin sheets of insulated surface coated M-43 grade electrical steel sheets used in medium volume rotating systems. A thin sheet of insulation coated CRNGO steel (0.5 mm) is cut with different parameters and profiles of U, V and T shape to analyze the effect of wire cutting on different cross-sectional profiles. Digital single sheet testers are used to measure core losses, coefficient of hysteresis losses, and coefficient of eddy losses. Damaged coating areas near the cutting edge of sheet metal are analyzed with ImageJ software. In addition to the cut zone, macroscopical examination revealed the presence of different layers (A, B, and C). The work presents microstructural analysis to observe changes in surface roughness, the effect of cutting stress on insulating coatings, and crystal grain size near the cutting edge of sheet metal. The results demonstrate core loss ranges from 1.03 to 4.63 W/kg for cuts at 85 V with a flux density (B) of 1.0-1.8 T, 1.07-4.79 W/kg for cuts at 90 V, and 1.03 to 4.75 W/kg for cuts at 95 V. [ABSTRACT FROM AUTHOR]

Visual inspection of R-peaks in Electrocardiogram (ECG) signal is avoided because of limited resolution and in the variation of parameters of the underlying subject (patient). Therefore, Principal Component Analysis has been used for R-peak detection without any pre-processing in noisy and different morphologies of the ECG signal with fractional Fourier transform which is using as a feature extraction technique. For validating this research work MIT-BIH Arrhythmia database is considered. The performance of developed algorithm is judged based on sensitivity, positive predictive value and accuracy. It has been revealed that the developed algorithm is capable to analyze ECG signals in both situations either normal or abnormal. It opens their huge applications in the time varying signals which can capture important clinical attributes in critical pathological situations. [ABSTRACT FROM AUTHOR]

Water deficit is a significant impediment to enhancing rice yield. Genetic engineering tools have enabled agriculture researchers to develop drought-tolerant cultivars of rice. A common strategy to achieve this involves expressing drought-tolerant genes driven by constitutive promoters such as CaMV35S. However, the use of constitutive promoters is often limited by the adverse effects it has on the growth and development of the plant. Additionally, it has been observed that monocot-derived promoters are more successful in driving gene expression in monocots than in dicots. Substitution of constitutive promoters with stress-inducible promoters is the currently used strategy to overcome this limitation. In the present study, a 1514 bp AP2/ERF promoter that drives the expression of a transcription factor was cloned and characterized from drought-tolerant Indian rice genotype N22. The AP2/ERF promoter was fused to the GUS gene (uidA) and transformed in Arabidopsis and rice plants. Histochemical GUS staining of transgenic Arabidopsis plants showed AP2/ERF promoter activity in roots, stems, and leaves. Water deficit stress and ABA upregulate promoter activity in transformed Arabidopsis and rice. Quantitative PCR for uidA expression confirmed induced GUS activity in Arabidopsis and rice. This study showed that water deficit inducible Os-AP2/ERF-N22 promoter can be used to overcome the limitations of constitutive promoters. Transformants overexpressing Os-AP2/ERF-N22 showed higher relative water content, membrane stability index, total chlorophyll content, chlorophyll stability index, wax content, osmotic potential, stomatal conductance, transpiration rate, photosynthetic rate and radical scavenging activity. Drought tolerant (N22) showed higher expression of Os-AP2/ERF-N22 than the susceptible (MTU1010) cultivar. [ABSTRACT FROM AUTHOR]

Understanding the personality is beneficial for many purposes, e.g., it is natural to predict a user's personality before offering him or her any services. The personality is intrinsic in the behavior of a person in all aspects, such as text writing. Some work has been proposed in recent times for correctly classifying a person's personality from the text. However, it is still a significant challenge as the achieved accuracy is low; therefore, the proposed work addresses this issue. Effective feature selection techniques provide better classification accuracy in multi-label classification and personality traits identification as multi-label classification problem requires efficacy of feature selection methods. Therefore, to improve the accuracy using feature selection technique, this paper proposes a method for personality trait recognition from textual data called P ersonality T rait Classification based on L inguistic and F eature selection as M ulti-label classification (PTLFM). It combines analysis of variance's F-statistic, Chi-square, and Mutual information with the sequential feature selection wrapper method to rank features. These three criteria apprehend different aspects of the dataset. The experimental results demonstrate that the proposed PTLFM method achieves higher accuracy across all the personality traits than the prevailing state-of-the-art machine learning and deep learning models. PTLFM provides an impressive absolute improvement of 2.23% and 3.84% of comparative improvement over the existing prevalent method, with more than 90% of features discarded. Furthemore, the proposed PTLFM achieves a percentage gain compared to the competitive methods across different personality traits Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness in absolute terms 1.17, 1.94, 2.35, 1.64, and 0.35 respectively, and in comparative terms 2.01, 3.27, 4.14, 2.86, and 0.56 respectively. The results suggest that although deep learning is a popular paradigm, it does not always lead to a better predictive performance than machine learning models in all the problem domains. [ABSTRACT FROM AUTHOR]

The perovskites Nd1-xCexCo1-yCuyO3 (x = 0–0.05, y = 0–0.1) have been synthesized using PVP-assisted sol–gel method and applied for soot oxidation reactions. XRD technique reveals the formation of orthorhombic phase with crystal volume of around ~ 214 Å3 and crystal size of ~ 25–40 nm. The interconnected nanoparticles with hollow spherical network morphology of particles are observed for the samples NdCoO3 (NC1) and Nd0.98Ce0.02Co0.95Cu0.05O3 (NC2) with particle sizes of around 300–500 nm. The samples experienced a charge transfer from ligand (O2−) to cobalt cation in UV region (210–260 nm) and also observed broad absorption bands in the visible region (380–600 nm). In addition, the bandgap energy of NC2 showed the lowest value (4.21 eV); as well as surface morphological advantage promoted the transport of surface-chemisorbed oxygen species in the inner and outer surface of catalysts surface due to the reducibility of the catalyst with the soot O 2 x - O 2 x - + O 2 - = 0.90 . Furthermore, XPS results evidenced the higher content of Co2+ cation upon substitution of Ce/Cu into NC1, which successively formed more amount of Oβ-oxygen species. Remarkably, the perovskite NC2 showed the lowest soot oxidation temperature (T50% = 434 °C) among the investigated perovskites. Besides, the spherically networked morphology of NC2/NC1 samples also decided the soot oxidation process. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is an important diagnostic tool in medical engineering, presented in the form of electrical signal. Its complete analysis requires three stages viz. pre-processing, feature extraction, and classification/detection. The last stage provides the final outcome; hence, its careful selection is very important. Unfortunately, due to diverse and widely varied data management practices, no single technique is absolutely preferred over others. Therefore, an extensive literature survey of various classification/detection techniques is presented, and their effects are summarized. Also, different techniques related to ECG arrhythmia classification and detection are proposed and evaluated on-the-basis-of figure-of-merits (FoMs) for improved medical applications. This proposed technique is important to extract important clinical/pathological attributes of the ECG signals. It ensures the novelty in the biomedical signal processing (BSP). In this article different existing techniques are compared in the light of proposed techniques for various ECG arrhythmias classification and detection. [ABSTRACT FROM AUTHOR]

Worldwide, cervical cancer is the leading cause of death among women from cancer. The symptoms of this gynecological disease are difficult to recognize at early stage, especially in those countries that don't have facility of screening programs. In diagnosis of cervical cancer, machine learning methods can be used to detect the malignous cancer cells at initial stage. The foremost apprehension in disease diagnosis involves data imbalance issue and non-uniform scaling in dataset. In this article, a prevalent oversampling approach Synthetic Minority Oversampling Technique along with fivefold cross-validation is being used on unscaled and scaled data to handle these issues. A promising comparison is been made among the performance of most prevalent machine learning (ML) classifiers such as Naive Bayes, Logistic Regression, K-Nearest Neighbor, Support Vector Machine (SVM), Linear Discriminant analysis, Multi-Layer Perceptron, Decision Tree (DT) and Random Forest (RF) on unscaled data and scaled data obtained by Min–Max scaling, Standard scaling and Normalization. RF, SVM and DT are the top three ML algorithms obtained in cervical cancer diagnosis for which optimization possibilities are explored with feature selection methods as Univariate feature selection and Recursive feature elimination (RFE). Overall performance of Random Forest predictor with RFE (RF-RFE) is superior to all others being implemented. [ABSTRACT FROM AUTHOR]

NF-κB has been reported to both promote and inhibit bone formation. To explore its role in osteolineage cells, we conditionally deleted IKKα, an upstream kinase required for non-canonical NF-κB activation, using Osterix (Osx)-Cre. Surprisingly, we found no effect on either cancellous or cortical bone, even following mechanical loading. However, we noted that IKKα conditional knockout (cKO) mice began to lose body weight after 6 months of age with severe reductions in fat mass and lower adipocyte size in geriatric animals. qPCR analysis of adipogenic markers in fat pads of cKO mice indicated no difference in early differentiation, but instead markedly lower leptin with age. We challenged young mice with a high fat diet finding that cKO mice gained less weight and showed improved glucose metabolism. Low levels of recombination at the IKKα locus were detected in fat pads isolated from old cKO mice. To determine whether recombination occurs in adipocytes, we examined fat pads in Osx-Cre;TdT reporter mice; these showed increasing Osx-Cre-mediated expression in peripheral adipocytes from 6 weeks to 18 months. Since Osx-Cre drives recombination in peripheral adipocytes with age, we conclude that fat loss in cKO mice is most likely caused by progressive deficits of IKKα in adipocytes. [ABSTRACT FROM AUTHOR]

While many studies suggested the isolated effects of plyometric and whole-body vibration exercises on physical performance variables, only few studies have compared the acute effects of plyometric and whole-body vibration on the occurrence of post-activation potentiation and the resultant improvements in performance. Therefore, we aimed to compare the acute effects of plyometric exercises and whole-body vibration training on physical performance in collegiate basketball players. Twenty-four collegiate male basketball players (age 20.8 ± 2.02 years, height 1.79 ± 0.7 m, and weight 71.2 ± 7.6 kg) participated in this randomized crossover study. Subjects were received both plyometric and whole-body vibration exercises after a 48-h washed-out period. Countermovement Jump height, sprint, and agility time were measured at baseline, 4- and 12-min post-plyometric, and whole-body vibration exercises. The result suggests a positive effect of both the plyometric and whole-body vibration exercises on countermovement jump and agility time (p = 0.001). While the countermovement jump height and agility were higher in the plyometric group (mean difference 1.60 cm and 0.16 s, respectively), the sprint performance was higher in the whole-body vibration group. However, these differences were statistically non-significant between the two groups (p > 0.05). This study suggests that both plyometric and whole-body vibration exercises may improve post-activation potentiation, which leads to better physical performance. Trial registration CTRI/2019/05/019059. Registered with the Clinical trials registry, India on 10/05/2019. http://ctri.nic.in/Clinicaltrials/advsearch.php. [ABSTRACT FROM AUTHOR]

The rapid population growth has rendered the centralized sewerage systems a non-realistic option in sparsely populated areas, particularly in developing countries. In this context, on-site domestic wastewater treatment system (ODWTS) has proved to be a viable and prefered alternative for the management of domestic wastewater in such areas. Among various treatment systems available, the conventional septic tank is the oldest and the most widely utilized on-site treatment system. However, its sustainability is still questionable due to its inability to meet the strict discharge requirements. Such limitations have already given enough impetus to explore subsequent post-treatment or polishing technologies to achieve the desired effluent disposal or reuse standards. In order to improve the quality of the treated domestic wastewater, various ecologically safer and economically viable technological options have been implemented successfully. The present study summarizes various on-site domestic wastewater treatment technologies which have been successfully implemented under actual field conditions with special reference to the system's performance (individual and comparative) as well as techno-economic analysis. Further, various sustainability indicators of ODWTS, such as environmental, social, and economic viability for the selection of on-site treatment technologies, are also reviewed in this study. [ABSTRACT FROM AUTHOR]

For maintaining the healthy metabolic status, vitamin D is a beneficial metabolite stored majorly in its pre-activated form, 25-hydroxyvitamin D3 (25(OH)D3). Due to its important role in bone strengthening, the study was planned to quantify 25(OH)D3 levels in our blood. Quantification techniques for 25(OH)D3 are costly thus requiring a need for a low cost, and sensitive detection methods. In this work, an economic, and sensitive sensor for the detection of 25(OH)D3 was developed using aptamer and graphene oxide (GO). Aptamer is an oligonucleotide, sensitive towards its target, whereas, GO with 2D nanosheets provides excellent quenching surface. Aptamer labeled with fluorescein (5', 6-FAM) is adsorbed by π–π interaction on the GO sheets leading to quenching of the fluorescence due to Förster resonance energy transfer (FRET). However, in the presence of 25(OH)D3, a major portion of aptamer fluorescence remains unaltered, due to its association with 25(OH)D3. However, in the absence, aptamer fluorescence gets fully quenched. Fluorescence intensity quenching was monitored using fluorescence spectrophotometer and agarose gel based system. The limit of detection of 25(OH)D3 by this method was found to be 0.15 µg/mL whereas when GO-COOH was used, limit of detection was improved to 0.075 µg/mL. Therefore, this method could come up as a new sensing method in the field of vitamin D detection. [ABSTRACT FROM AUTHOR]

Monitoring of blood pressure (BP), heart rate (HR), respiratory (RSP), Photoplethysmography (PPG), and Electrocardiogram (ECG) are generally used for any particular surgery or operation. It becomes vital during anesthesia and other critical situation. A substantial research has been carried out to detect cardiovascular disease by analyzing the parameters such as ECG, BP, respiratory (RSP) cycle, Photoplethysmography (PPG) measurement, etc., which help in health monitoring of the patient. BP control is very significant in the lifespan of every individual. If it goes out of the boundary, then runs a risk of heart attack increases. The heart is responsible for purification and circulation of blood in the whole body by myocardium muscle. For analyzing the cardiac activity of the subject (patient), ECG and arterial blood pressure (ABP) signals are very important which are related to each other. Regular monitoring of the anaesthetic drug dosage during a surgical procedure is required to ward off the patient's inter-operative awareness but some time delay (TD) may happen and it is really serious for the patient life during anesthesia. Induced TD situation can't be analyzed using ordinary Z-transform (OZT) due to fractional time delay (FrTD) condition which requires the emerging techniques to handle the FrTD condition effectively. Therefore, modified Z-Transform (MZT) is used instead of OZT. Further, chaos analysis is used to find the optimal trajectory of the ECG dataset which helps in segregating the noise, distortion, noncardiac components, etc. Mean square error (MSE) and Root mean square error (RMSE) have been used for examining RSP, dZ/dt (rate of change of cardiac impedance), ECG, BP and PPG. [ABSTRACT FROM AUTHOR]

The bondline of electric-resistance-welded (ERW) linepipe steel, often etched white (i.e., ferrite) in optical microscopy, is generally believed to be carbon depleted. The mechanism for the carbon depletion, however, is not fully understood by researchers. To this end, atom probe tomography (APT) was used to measure elemental segregation of the as-welded and post-weld heat-treated bondline regions of X70 linepipe welds. The thin vertical features at the bondline in the as-welded condition were identified as carbon-rich martensite-austenite (M-A) constituents, and the majority ferrite phase in the bondline was identified as carbon-depleted ferrite. Following the post-weld normalization, all alloying elements, except Nb and Mo, are homogenized across the bondline and heat-affected zone. The carbon depletion in the ERW bondline was accurately measured. A new mechanism for carbon depletion has been proposed using Scheil calculations of elemental partitioning during weld formation. Segregation of elements in the heat-affected zone was shown to follow the negligible partitioning local equilibrium (NPLE) kinetics for bainite transformation. [ABSTRACT FROM AUTHOR]

COVID-19 has affected the global economy like no other crisis in the history of mankind. It forced worldwide lockdown and economic shutdown to the point from where the recovery process has been very difficult. It has affected demand, supply, production and consumption in such a way that the entire economic development cycle has gone to its lowest levels. COVID-19 has also affected the social and economic sustainability structure which has led from one crisis to another and the developing countries have been the worst hit. Economic crisis resulted in unemployment leading to labour migrations, inevitable casualties and rising poverty etc. However, at a certain level, a few industries and organizations have shown resilience with better anticipation and survivability which may lead them to a quicker recovery. The current study aims at presenting a holistic view of organizational resilience which leads to the overall sustainable development. The study considers three aspects of organizational resilience as crisis anticipation, organizational robustness and recoverability. It assesses the impact of the aspects of resilience on social sustainability and economic sustainability. The study uses empirical analysis of primary data which is analysed to verify the hypothesized relationships by using a structural equation modelling approach. The study finds out that predicting the crisis and disruptions, building robustness and recoverability have a positive effect on both the social and economic aspects of sustainability. Findings of the study have their practical implications for industry, researchers and society. [ABSTRACT FROM AUTHOR]