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Photoelectron ejection is important in technological applications. Recently, the photoelectric effect was explained based on the electromagnetic (EM) wave concept. According to the EM wave concept, an electron within the conduction band of a photocell moves circularly. In this paper, we explain few facts regarding the photoelectric emission process, such as the radius of circular motion, maximum ejection angle, and depth of the photoelectron, based on the EM wave concept. Further, the effects of the intensity and frequency on these parameters are discussed. The ejection angle depends on the frequency and intensity of the light and the depth of the photoelectrons. The explanations herein may increase the applications of light energy. [ABSTRACT FROM AUTHOR]

Abstract Albert Einstein has explained photoelectric effect by corpuscle concept of light. Few phenomena are ambiguous in the explanation. The authors have explained photoelectric effect based on recent reported electromagnetic wave theory. The theoretical explanations of this works are covered all phenomena of the photoelectric effect such as the cause of electron ejection, kinetic energy of the photoelectron, the value of number of the photoelectron in the photocurrent, clear concept of ejection direction, initial ejection time, ejection as flog, frequency and intensity dependence, cutoff frequency and work function. The frequency and intensity dependence have been observed experimentally. The experimental observations show similar results of the theoretical explanation i.e. the kinetic energy of the photoelectron is proportional to both the intensity and frequency of light. The works may help to explain other photonic natures by wave theory of light. [ABSTRACT FROM AUTHOR]

The electromagnetic (EM) wave energy is a significant part of the different purposes in the science, technical and engineering sectors. The phase difference between electric and magnetic fields holds important roles to give clear conception of (i) the EM wave energy and (ii) the action process of light-electron interactions. Most of the researchers think that the phase difference between electric and magnetic fields of the EM waves is 0°. In this work, we have discussed in detail about the phase difference between the fields. We have shown that the phase difference between the fields is 90° for all types of EM wave radiations in the both forms i.e. descriptive and mathematical. The electric field is the driver of the EM wave power. The source of the EM wave makes a sinusoidal electric field continuously then the changing electric field makes a magnetic field instantly in all paths of its propagation, so the magnetic field is sinusoidal also with a delay phase. The simulation of this research work shows that the radiated energy density is sinusoidal and continuous. The research work may help to increase the applications of the EM radiations. [ABSTRACT FROM AUTHOR]

Undoped thin films of cupric oxide have been prepared by a simple chemical spray technique. Obtained samples were mostly polycrystalline and p-type semiconductor. Thermopower of a set of as-deposited samples with thickness between 340 and 620 nm have been measured in the temperature range 300–450 K. The thermovoltages were found to change sign with thickness. The dominating carrier scattering process that can limit the electrical conductivity or the Hall mobility values at room temperature may be the lattice phonon scattering. The observed variations of the position of Fermi level with the film thickness have been qualitatively explained invoking the principle of modulation of the acceptor density profile by the density of donors during the time of growth of film. [Copyright &y& Elsevier]

Recently we have explained the photoelectric effect theoretically and (some part) experimentally by recent modified frequency dependent electromagnetic (EM) wave concept. Our experimental work has been done using the visible range light. Very recent S H Naqib has reported that the photoelectric effect should not be explained by EM wave concept. In the present works we have given the comparative analysis of his report with our report. To calculate the absorbed energy of surface electron Naqib has chosen the copper metal as photocell. However the work-function of the copper is larger than the absorbed energy of the surface electron from the visible light. Almost all parts of Naqib's report are not logical with the theories of basic physics and our new idea. Accordingly Naqib's claims have been refuted point to point. We hope that the new concept may help to explain all phenomena of light electron interactions. [ABSTRACT FROM AUTHOR]

Heart disease is among the leading causes of mortality globally. Predicting cardiovascular disease is a major difficulty in clinical data analysis. AI has been demonstrated to be powerful in deciding and anticipating an enormous measure of information created by the health domain. We provide a unique method for finding essential traits employing machine learning approaches in this paper, which enhances the effectiveness of identifying heart diseases. Decision tree (DT), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) are the classification techniques used to create the proposed system. Ensemble stacking integrates the four classification models to create a single best-fit predictive model using logistic regression. Many explorations have been directed at the identification of cardiac infection; however, the exactness of the outcomes is poor. Accordingly, to further enhance the efficiency, Moth-Flame Optimization (MFO) algorithm is proposed. The feature selection strategies are used to improve the classification accuracy while shortening the execution time of the classification system. Medical data are used to assess the probability of heart disease based on BP, age, gender, chest ache, cholesterol, blood sugar, and other variables. Results revealed that the proposed system excelled other existing models, obtaining 99% accuracy in the Cleveland dataset. [ABSTRACT FROM AUTHOR]

High dimensionality of the feature space is one of the problems in the field of text classification. Identification of optimal subset of features can optimize text classification process in terms of processing time and performance. In this paper, we propose a novel Relevant-Based Feature Ranking (RBFR) algorithm which identifies and selects smaller subsets of more relevant features in the feature space. We compared the performance of the RBFR against other existing feature selection methods such as balanced accuracy measure, information gain, Gini index, and odds ratio on 3 datasets, namely, 20 newsgroup, Reuters, and WAP datasets. We have used 5 machine learning models (SVM, NB, kNN, RF, and LR) to test and evaluate the proposed feature selection method. We found that the performance of the proposed feature selection method is 25.4305% times more effective than the existing feature selection methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

The impact of n-butanol, a next-generation biofuel, with watermelon methyl ester in a constant-speed diesel engine was analyzed. Methyl ester from watermelon seed oil is considered to be a promising alternative source to the standard diesel due to similar characterization. The n-butanol additive was added in small proportions as an oxygenated fuel for reducing emissions, improving thermal efficiency, and accelerating the combustion process. N-Butanol is blended with watermelon methyl ester in the form of emulsions in two different proportions (5% and 10% volume basis). Experiments were conducted with three different emulsions fuels, WME20, W20Bu5D75, and W20Bu10D70, and compared vis-à-vis standard diesel. Investigations revealed that the addition of n-butanol as an enhancer with WME20 improved characteristics owing to its inherent nature of oxygen content. The blending of WME with n-butanol improves brake thermal efficiency when compared to WME20 and slightly matches with standard diesel. The max BTE was recorded 32.79% for WME20Bu10D70 at the crest load. The peak BSFC was 0.26 kg/kWh for W20Bu10D70 at the crest load. The emissions such as CO, smoke opacity, and HC were significantly reduced, vis-à-vis diesel, and the oxides of nitrogen (NOX) and carbon dioxide (CO2) were decreased, relative to WME20. The maximum EGT was 354.98°C for W20Bu10D70 at the crest load. The peak CO emissions were 0.078% for W20Bu5D75 at the crest load. The blending of n-butanol with WME20 reduces the ignition delay while the combustion duration increases with an increase at full load conditions. The emulsion fuels tested in an unmodified engine did no negative impact on the engine stability. [ABSTRACT FROM AUTHOR]

In recent years, nanotechnology has received extraordinary attention as the demands of today's modern lifestyle are actively changing every day. The need for a sustainable environment and process is the prerequisite today for any advancement. Nanoparticles and its application have extended its wings into various fields. Researchers across the globe are constantly exploring the applications of NPs, especially those of metallic NPs. Among the metallic NPs, silver NPs has received special attention owing to their excellent physical and chemical properties. Nanobiotechnology has opened avenues for such a sustainable and environment-friendly progress. So, in this review, we have summarized the different methods employed in synthesising AgNPs, with special focus on the biological synthesis. This approach is of importance because of its advantages such as use of nontoxic compounds, inexpensive process, and high stability of products obtained in comparison to the other approaches. Several biomolecules of interest such as microbes and plant extracts that are used for synthesis are discussed in detail. The recent advancements in the environmental application of these biologically synthesised NPs include remediation of heavy metals and sensing, as catalysts in toxic dye degradation, remediation of waste water, and also, as a pesticidal agent for crop protection. Although AgNPs have been used in several sectors of research and industries, relevance to the environment is prominent owing to the impact it makes on the humans and all forms of life on earth. [ABSTRACT FROM AUTHOR]

The aim of the present work is to develop novel hybrid composites using areca, kenaf, and snake grass fibers as reinforcement and epoxy as the matrix. The areca, kenaf, and snake grass fibers were extracted from Catechu Linnaeus, Hibiscus cannabinus, and Sansevieria Ehrenbergii plants, respectively, and treated with 5% NaOH to improve the interfacial adhesion between the hydrophilic fiber and the hydrophobic matrix. Hybrid composites were developed by the compression molding technique and formulated based on the weight fraction of fibers. Tensile, flexural, and impact strength and hardness samples were prepared as per ASTM D 3039, ASTM D 790, ASTM D 256, and ASTM D 2240, respectively. The effects of alkaline treatment on developed hybrid composites were investigated. The developed hybrid composites with 20% wt. snake grass and 10% wt. areca fiber present interesting mechanical properties with a tensile strength of 58 MPa, flexural strength of 124 MPa, impact strength of 5.24 kJ/m2, and hardness of 88. The results indicate that maximum mechanical properties were obtained for alkaline-treated fiber composites with 20% wt. snake grass fiber compared to untreated fiber composites owing to better adhesion between the treated fiber and the matrix. The effect of alkaline treatment was analyzed by Fourier transform infrared. The fractured surfaces of tested samples were analyzed by scanning electron microscopy. [ABSTRACT FROM AUTHOR]

This paper proposes a blend of three techniques to select COVID-19 testing centers. The objective of the paper is to identify a suitable location to establish new COVID-19 testing centers. Establishment of the testing center in the needy locations will be beneficial to both public and government officials. Selection of the wrong location may lead to lose both health and wealth. In this paper, location selection is modelled as a decision-making problem. The paper uses fuzzy analytic hierarchy process (AHP) technique to generate the criteria weights, monkey search algorithm to optimize the weights, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the different locations. To illustrate the applicability of the proposed technique, a state named Tamil Nadu, located in India, is taken for a case study. The proposed structured algorithmic steps were applied for the input data obtained from the government of India website, and the results were analyzed and validated using the government of India website. The ranks assigned by the proposed technique to different locations are in aligning with the number of patients and death rate. [ABSTRACT FROM AUTHOR]

Climate change and the energy crisis substantially motivated the use and development of renewable energy resources. Solar power generation is being identified as the most promising and abundant source for bulk power generation. However, solar photovoltaic panel is heavily dependent on meteorological data of the installation site and weather fluctuations. To overcome these issues, collecting performance data at the remotely installed photovoltaic panel and predicting future power generation is important. The key objective of this paper is to develop a scaled-down prototype of an IoT-enabled datalogger for photovoltaic system that is installed in a remote location where human intervention is not possible due to harsh weather conditions or other circumstances. An Internet of Things platform is used to store and visualize the captured data from a standalone photovoltaic system. The collected data from the datalogger is used as a training set for machine learning algorithms. The estimation of power generation is done by a linear regression algorithm. The results are been compared with results obtained by another machine learning algorithm such as polynomial regression and case-based reasoning. Further, a website is developed wherein the user can key in the date and time. The output of that transaction is predicted temperature, humidity, and forecasted power generation of the specific standalone photovoltaic system. The presented results and obtained characteristics confirm the superiority of the proposed techniques in predicting power generation. [ABSTRACT FROM AUTHOR]

Image fusion can be performed on images either in spatial domain or frequency domain methods. Frequency domain methods will be most preferred because these methods can improve the quality of edges in an image. In image fusion, the resultant fused images will be more informative than individual input images, thus more suitable for classification problems. Artificial intelligence (AI) algorithms play a significant role in improving patient's treatment in the health care industry and thus improving personalized medicine. This research work analyses the role of image fusion in an improved brain tumour classification model, and this novel fusion-based cancer classification model can be used for personalized medicine more effectively. Image fusion can improve the quality of resultant images and thus improve the result of classifiers. Instead of using individual input images, the high-quality fused images will provide better classification results. Initially, the contrast limited adaptive histogram equalization technique preprocess input images such as MRI and SPECT images. Benign and malignant class brain tumor images are applied with discrete cosine transform-based fusion method to obtain fused images. AI algorithms such as support vector machine classifier, KNN classifier, and decision tree classifiers are tested with features obtained from fused images and compared with the result obtained from individual input images. Performances of classifiers are measured using the parameters accuracy, precision, recall, specificity, and F 1 score. SVM classifier provided the maximum accuracy of 96.8%, precision of 95%, recall of 94%, specificity of 93%, F 1 score of 91%, and performed better than KNN and decision tree classifiers when extracted features from fused images are used. The proposed method results are compared with existing methods and provide satisfactory results. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 is a novel virus, responsible for causing the COVID-19 pandemic that has emerged as a pandemic in recent years. Humans are becoming infected with the virus. In 2019, the city of Wuhan reported the first-ever incidence of COVID-19. COVID-19 infected people have symptoms that are related to pneumonia, and the virus affects the body's respiratory organs, making breathing difficult. A real-time reverse transcriptase-polymerase chain reaction (RT-PCR) kit is used to diagnose the disease. Due to a shortage of kits, suspected patients cannot be treated promptly, resulting in disease spread. To develop an alternative, radiologists looked at the changes in radiological imaging, like CT scans, that produce comprehensive pictures of the body of excellent quality. The suspected patient's computed tomography (CT) scan is used to distinguish between a healthy individual and a COVID-19 patient using deep learning algorithms. A lot of deep learning methods have been proposed for COVID-19. The proposed work utilizes CNN architectures like VGG16, DeseNet121, MobileNet, NASNet, Xception, and EfficientNet. The dataset contains 3873 total CT scan images with "COVID" and "Non-COVID." The dataset is divided into train, test, and validation. Accuracies obtained for VGG16 are 97.68%, DenseNet121 is 97.53%, MobileNet is 96.38%, NASNet is 89.51%, Xception is 92.47%, and EfficientNet is 80.19%, respectively. From the obtained analysis, the results show that the VGG16 architecture gives better accuracy compared to other architectures. [ABSTRACT FROM AUTHOR]

Biomass is a renewable and sustainable green energy material. It is made up of lignin, cellulose, and hemicellulose with considerable amount of water, extractives, and inorganic chemical compounds. The use of biomass materials and other biogenic wastes for energy recovery represents an eco-friendly way. Biomass material selection is one of the most significant aspects for any energy conversion process, and it is a common outsourcing problem that includes material preparation, reactor performance, economic assessment, and calorific value of the products. Fuzzy systems can be quite useful in high-performance computing during the selection of biomass materials. In each engineering process, material selection is a crucial step since each material is having its own set of characteristics. This study presents the application of type-1 fuzzy set for the selection of suitable biomass material for yielding maximum bio-oil. This study focuses on seven locally available materials such as rice straw (M-1), sunflower shell (M-2), hardwood (M-3), wheat straw (M-4), sugarcane bagasse (M-5), corn cop (M-6), and palm shell (M-7). The study evaluated seven important properties of the materials such as lignin (P-1), cellulose (P-2), hemicellulose (P-3), volatile matter (P-4), fixed carbon (P-5), moisture content (P-6), and ash content (P-7). The findings demonstrated that sugarcane bagasse (M-5) is the best option for maximum bio-oil yield. Furthermore, the potential of nanoscale catalysts in improving the yield of bio-oil through real-time experiments was studied. The findings of this work add to our understanding of the application of fuzzy-based systems for energy applications. [ABSTRACT FROM AUTHOR]

In recent decades, research in nanomaterial specific to biodegradation has gained attraction owing to its physicochemical characteristics. Biodegradation is the main strategy used in the management of wastes. The role of nanoparticles helps in controlling the biodegradation rate. The biodegradation might not be quite effective in certain cases like high chemical deposition, due to its toxic nature towards microorganisms. The conventional strategies demonstrated a limited use of nature source, and this could be overcome through microbes and enzyme-based biodegradation. Nanoparticles have improved the biodegradation rate through low-density polyethylene development, thereby corrupting microbes. Thus, a major problem is confronted. Though innovation in science had a great impact in everyone's life, it also has a negative impact through the increased use of toxic materials. Recent development and use of biodegradable nano-based compounds have led to many secured forms of nanomedicines. In this review, we would discuss the more recent findings of nanoparticles related to biodegradation applications and elaborate how their characteristics could influence in various biomedical applications. [ABSTRACT FROM AUTHOR]

Increased environmental concerns and global warming have diverted effort all over the world to focus on renewable and sustainable resources for the next generation of composite products due to their recyclability, renewability, cost effectiveness, and satisfactory mechanical performance. Bio/natural fibers which are environment friendly materials employed as reinforcement have led to developing a biocomposite for reduction in greenhouse emission and carbon footprints. However, biofibers are also having some limitations that need to be addressed including poor compatibility between the reinforcing fiber matrices, high moisture absorption, swelling, poor chemical and fire resistance, and high dispersion of mechanical properties. A lot of research has been performed on physical and mechanical properties of natural fiber composite. Properties of such novel composite mainly depend on adhesion between fiber and matrices. Consequently, poor adhesion, high moisture absorption, and swelling lead to formation of crack in both the matrix and fiber. Therefore, numerous techniques have been tried till date to modify both fiber surfaces to enhance their adhesion and reduce their water absorption. This review article provides comprehensive information about effect of various surface modification techniques that include alkaline, silane, acetylation, permanganate, peroxide, benzoylation, acrylonitrile grafting, maleic anhydride grafted, acrylation, and isocyanate. In addition, the effects of cellulose, hemicellulose, lignin, and pectin of biofibers are also reported. This review concluded that chemical treatment of biofibers with 5% NaOH concentration improves the physical, mechanical, and thermal properties of the resulting composites compared to untreated fiber composites. [ABSTRACT FROM AUTHOR]

Supercapacitors are a revolutionary type of energy storage device. They must be able to charge and discharge quickly while maintaining a high energy density. A storage material's cyclic stability is a desirable feature. The type of electrode materials employed for the specific study design affects supercapacitor performance. Manganese dioxide has long been regarded as one of the best and most abundant materials in nature, having a potentially high specific capacitance. They also offer a wider potential range, more electroactivity, and are more environmentally friendly. However, because of its decreased volume expansion and low conductivity, it is difficult to use as a capacitor material. As a result, carbon-based porous films and supports can be employed to produce critical composites to overcome the current shortcoming. These nanoparticle-based materials will have improved electrical conductivity and a large surface area. Graphene oxide (GO) has a high surface area, thermal stability, and porosity. As an electrode material, many types of MnO2/carbon-based materials have been widely used in supercapacitors. Their overall performance is influenced by their construction processes, metal ratios, electrolyte medium, and voltage factors. Microwave technology was chosen as a cost-efficient and effective alternative to expensive and laborious techniques for fabricating MnO2/GO composites. The production procedure of a supercapacitor has been explored in this study using MnO2-GO composite materials. Using the electrochemical deposition process, the nanocomposite materials of MnO2-GO are significantly deposited on the stainless steel (SS) substrate material. Galvanostatic charge-discharge techniques and cyclic voltammetry (CV) analytical methods were used to investigate the storage and cycle ability of supercapacitors. The composite MnO2-GO supercapacitor has a higher electrochemical capacitance based on these findings. [ABSTRACT FROM AUTHOR]

A new strategy for supercapacitor formation was carried out in the study using electrodes made of graphene oxide (GO) and manganese dioxide (MnO2) nanocomposites. To the present knowledge, only a few investigations have been carried out concerning the synthesis of GO-MnO2-based nanocomposites and their electrochemical properties, with varying mass ratios, as well as the change in electrochemical properties of their components as MnO2 and GO were tested individually for the enhanced stability and performances. A synthetic method was performed successfully to manufacture MnO2/GO nanocomposites. The findings of the present study show that the composites have a lot of potential as an effective conduction property. A composite of graphene oxide supported manganese dioxide nanocomposites fabricated with the simple soft chemical route. As-prepared nanocomposites can be improved in performance by the interactions between GO and MnO2. [ABSTRACT FROM AUTHOR]

The aim of the present work is to develop novel hybrid composites using areca, kenaf, and snake grass fibers as reinforcement and epoxy as the matrix. The areca, kenaf, and snake grass fibers were extracted from Catechu Linnaeus, Hibiscus cannabinus, and Sansevieria Ehrenbergii plants, respectively, and treated with 5% NaOH to improve the interfacial adhesion between the hydrophilic fiber and the hydrophobic matrix. Hybrid composites were developed by the compression molding technique and formulated based on the weight fraction of fibers. Tensile, flexural, and impact strength and hardness samples were prepared as per ASTM D 3039, ASTM D 790, ASTM D 256, and ASTM D 2240, respectively. The effects of alkaline treatment on developed hybrid composites were investigated. The developed hybrid composites with 20% wt. snake grass and 10% wt. areca fiber present interesting mechanical properties with a tensile strength of 58 MPa, flexural strength of 124 MPa, impact strength of 5.24 kJ/m2, and hardness of 88. The results indicate that maximum mechanical properties were obtained for alkaline-treated fiber composites with 20% wt. snake grass fiber compared to untreated fiber composites owing to better adhesion between the treated fiber and the matrix. The effect of alkaline treatment was analyzed by Fourier transform infrared. The fractured surfaces of tested samples were analyzed by scanning electron microscopy. [ABSTRACT FROM AUTHOR]

Integration of renewable energy sources to the grid-connected system has influenced scholarly research in recent times to evolve solutions for power electronic conversion. Particularly, solar photovoltaic (SPV), being a resource available throughout the year, demands needful research to meet the demand for industrial applications. To facilitate SPV, multilevel inverters (MLIs) and cascaded H-bridge inverters (CHBIs) are proposed in the literature to meet the power requirement. However, these circuits suffer from efficiency loss, economic aspects of DC sources usage, and switching losses. Hence, in this research, a new power converter topology is projected to improve the overall efficiency of SPV systems. Further, a three-level approach involving (i) SPV Panel-Temperature Reduction (SPV-PTR) Setup, (ii) Boost Multilevel Direct Current Link Converter (BMLDCLC), and (iii) use of effective snubber modules (SM) are effectively handled to promote the industry readiness of the proposed system. From a detailed system investigation, it is seen that the proposed arrangement has minimized the power loss to ensure better quality in output. Furthermore, the software-based results and hardware setup of the planned comprehensive converter have shown promising results in terms of (i) reduced voltage stress, (ii) reduced total harmonic distortion (THD) without filter component, and (iii) reduced power loss. It is observed that the experimental setup has reported a 12.9% of excess heat removal, 5% decrease in harmonics, and 33% switch reduction than the existing MLI schemes. In addition, the proposed setup is suggested to apply for industrial purposes indicate its efficacy to be a solution in real time. [ABSTRACT FROM AUTHOR]

Nowadays, emerging noise pollution by external factors causes harmful diseases in human beings. The development of a bio-based filler or panel will help to eliminate some unwanted noise in working places and living rooms. This work aimed to develop an ecowaste fiber (leftover after harvesting)-based sound absorber and analyze its capabilities for sound absorption. The ecowaste fibers are collected by the gleaning process, i.e., the process of collecting leftovers from fields. The sound absorption capabilities of three natural fibers extracted from Eleusine coracana (Finger millet) straw, Desmostachya bipinnata (Darbha), and Typha domingensis (Ripe bulrush) plants are investigated in this study, both individually and in hybrid combinations. The sound absorption property mainly depends on factors such as porosity, flow resistivity, thickness, density, and tortuosity. Fiber length and fiber type play a significant role when fibers are arranged individually or in hybrid combinations. The stacking effect on the sound absorption coefficient of hybridized fiber arrangement was experimentally analyzed. The sound absorption coefficient (α) was found to be lower in the range of 1000 Hz–2500 Hz for all the combinations. As a homogenous fiber arrangement, the darbha fiber exhibited the better NRC (noise reduction coefficient) of 0.86 for 50 mm thickness among three different fibers and as a hybrid composition, ripe bulrush and darbha fibers exhibited NRC of 0.90 which is more capable of absorbing sound in the critical frequency range of 500 to 2000 Hz. These types of natural fiber fillers are highly capable of better sound absorbing and used in the applications such as classrooms, sound recording rooms, and theatres. [ABSTRACT FROM AUTHOR]

Aluminum alloys with silicon, magnesium, and copper were extensively used alloying elements in various applications because of their excellent properties. In recent decades, aluminum matrix composites (AMCs) are an advanced engineering material widely utilized in diverse engineering applications, including aircraft, automobile, marine, and shipbuilding, owing to their low density, lightweight, good stiffness, superior strength, and good tribological properties. Aluminum is abundant and its use is as vast as the ocean. It is also the most used matrix material in the composite arena. Therefore, incorporating a ceramic particle into a relatively soft aluminum matrix improves hardness, strength, stiffness, creep, fatigue, and wear properties instead of the conventional materials. This article is an assay to review and spotlight some recent works on the mechanical behaviors of aluminum-based titanium diboride reinforced metal matrix composite. This review article concentrates on the mechanical properties and the fabrication processes of Al-TiB2 composites to provide a valuable reference to nurture future research precisely. [ABSTRACT FROM AUTHOR]

The drilling process is the most essential in the final assembly of the mechanical parts. Natural fiber based composites replace traditional materials due to their advantages, such as strength to weight ratio, availability, and environmental hazards. In this work, an attempt has been made to find the minimum thrust force produced by the drill tool at specified drilling process parameters. The drilling process parameters used for the investigation are rotational speed, tool feed, and resins. The spindle speed and feed rate are selected with three levels. Three resin materials were used, namely, epoxy, polyester, and vinyl ester. Taguchi's L27 orthogonal array was implemented. The result shows that the candlestick drill bit generated lesser thrust force at the specified drilling process parameters, followed by the twist and step cone drill bits. [ABSTRACT FROM AUTHOR]

Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection and timely clinical interventions are critical in improving glaucoma-related outcomes. As a typical and complicated ocular disease, glaucoma detection presents a unique challenge due to its insidious onset and high intra- and interpatient variabilities. Recent studies have demonstrated that robust glaucoma detection systems can be realized with deep learning approaches. The optic disc (OD) is the most commonly studied retinal structure for screening and diagnosing glaucoma. This paper proposes a novel context aware deep learning framework called GD-YNet, for OD segmentation and glaucoma detection. It leverages the potential of aggregated transformations and the simplicity of the YNet architecture in context aware OD segmentation and binary classification for glaucoma detection. Trained with the RIGA and RIMOne-V2 datasets, this model achieves glaucoma detection accuracies of 99.72%, 98.02%, 99.50%, and 99.41% with the ACRIMA, Drishti-gs, REFUGE, and RIMOne-V1 datasets. Further, the proposed model can be extended to a multiclass segmentation and classification model for glaucoma staging and severity assessment. [ABSTRACT FROM AUTHOR]

For comparison, the drilling behaviour of abaca fiber-reinforced polymer (AFRP) composites and Kevlar-reinforced epoxy polymer (KFRP) composites has been studied in the specified experimental condition. The different geometrical drilling tools have been used for the investigation, namely, candlestick (T1), core (T2), standard twist drill (T3), and step cone (T4). The tool feed of 30, 45, and 60 m/min and rotational speed of 1000, 1500, and 2000 rpm have been used for the investigation. The thrust force is chosen as a response parameter for this study. The results revealed that, at lesser rotational speed and tool feed, the thrust force has declined. The result obtained correlates with the abaca fiber-based systems. However, the thrust force of KFRP is higher compared to AFRP composite systems. The axial force generated by candlestick drill is minimal compared to the other drill bits. The following may be responsible for lower thrust force: (1) the axial force distributes circumferential of the cutting tool instead of focusing at the center and (2) the interfacial adhesiveness between the matrix and the fiber is higher. The optimization of drilling process parameters, namely, tool feed and rotational speed on thrust force, has been studied. The results reveal that the tool feed contributed more to axial force compared to rotational speed. [ABSTRACT FROM AUTHOR]

The main application of off-grid solar photovoltaic (SPV) systems is water extraction in rural areas where access to the grid is restricted. In this application, photovoltaic (PV) and pump system regulation are crucial to increase its overall efficiency. In this context, this work presents a simple and efficient off-grid SPV water pumping system (SPVWPS). The designed system is based on a DC-DC boost converter, a three-phase DC-AC inverter, and a three-phase induction motor (IM) coupled to the centrifugal pump. The proposed solution is operated using a control strategy that associates an improved fractional open-circuit voltage (FOCV) method for maximum power point tracking (MPPT) and closed-loop scalar control. This association avoids the use of a speed sensor/encoder and a current sensor for the IM. Finally, the effectiveness of the proposed off-grid SPVWPS and its control system for both steady-state and dynamic conditions of insolation change is verified using a 1KVA rated prototype. The relevance of the drive is also checked in various operating conditions and is found to be adequate for pumping water. Moreover, the proposed method guarantees a fast response, less oscillations around the MPP, a system efficiency of 99%, and a high flow rate due to the extraction of maximum power. [ABSTRACT FROM AUTHOR]