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The novelty of this paper revolves around monitoring the condition of nitrogen-filled tires through the fusion of features and utilization of weightless neural networks. A tire pressure monitoring system (TPMS) plays a crucial role in ensuring vehicle safety and comfort. Reckless driving, poor road conditions, continual operation, higher road friction and excessive load are certain factors that can degrade the longevity of tires. Such conditions can result in instantaneous fault attacks in tires raising a concern for safety and comfort. To apply instantaneous and accurate fault diagnosis, the present study leverages machine learning techniques through the integration of an adaptive and robust algorithm, namely, the Wilkes, Stonham and Aleksander Recognition Device (WiSARD) classifier. The experiment uses three types of features namely, statistical, histogram and autoregressive moving average (ARMA) features. The J48 decision tree algorithm was used to pinpoint the key attributes crucial for classification. Following this, the identified attributes were segregated into training and testing datasets, facilitating the evaluation of the WiSARD classifier. Hyperparameter tuning was carried out to achieve optimal value for maximizing classification accuracy and minimizing computational time. Among the features considered, ARMA features delivered the best test set accuracy of about 96.18%.

From the past decade, there is a demand for the development of highly sensitive and selective, operating at low temperature, and stable gas-sensing materials used to monitor hazardous gases. The discovery of graphene has led to the implication of the same as gas sensor as it possesses large value of surface to volume ratio and high value of electron mobility at room temperature. Few researchers have fabricated reduced graphene oxide–metal oxide composite gas sensors exhibiting good electrical and gas-sensing properties. But still, it is a very less explored area. This article provides an overview of electrical and gas-sensing properties of reduced graphene oxide–metal oxide nanocomposites with improved sensitivity, selectivity, stability, and other sensing performances. This review is mainly focuses on reduced graphene oxide–metal oxide nanocomposite-based gas sensors which are cost-effective and sensitive to the various gases/vapors.

Cucurbits are an essential summer-season vegetable crops, but they are highly vulnerable from a range of abiotic and biotic factors. One of the significant biotic factors posing a growing menace to the production of major cucurbits in India is the emergence of tomato-infecting begomoviruses. In this study, we utilized PCR-based species-specific primers, developed earlier in our laboratory for the detection of begomoviruses infecting tomato and chilli plants, to identify begomoviruses in cucurbits across various regions of India. Leaf samples from major cucurbits were collected from different regions of Haryana, Delhi, Uttar Pradesh, Chhattisgarh, Maharashtra, Telangana and Karnataka, during the year 2020–2021. Total nucleic acid (TNA) was extracted from the samples and subjected to PCR using a generic primer specific to begomoviruses. The samples that exhibited positive amplification were further tested using six different species-specific primers targeting specific begomovirus species, namely Tomato leaf curl New Delhi virus(ToLCNDV), Tomato leaf curl Palampur virus(ToLCPalV), Tomato leaf curl Bangalore virus(ToLCBV), Tomato leaf curl Joydebpur virus(ToLCJoV), Tomato leaf curl Gujarat virus(ToLCGuV), and Chilli leaf curl virus(ChiLCV). The PCR analysis revealed that among the 551 plant samples tested, a total of 124 samples exhibited positive amplification using the universal begomovirus PCR. Specifically, 47 samples tested positive for ToLCNDV, 73 samples were positive for ToLCPalV and only one sample showed positive amplification for ChiLCV. However, none of the samples tested positive for ToLCJoV, ToLCGuV and ToLCBV. These findings from our study indicate the prevalence of ToLCNDV and ToLCPalV in major cucurbits across India. Furthermore, the study highlights the varied distribution of begomoviruses in major cucurbits between northern and southern regions of India.

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The current study aimed to produce small-scale bioethanol from poultry excreta using traditional anerobic fermentation process. The predicted yield of the bioethanol was 1.47–2.16 gm/kg of commercial layer poultry excreta with a purity of 95.44%. According to gas chromatography (GC) study, the retention period for poultry excreta generated bioethanol was 3.738 min when compared to absolute ethanol. The signal peaks of the generated bioethanol were identified by 1HNMR analysis as ethyl alcohol consisting of CH3, OH, and CH2groups. As a result, this study shows the prospect of producing bioethanol-based fuels from poultry excreta.

In this paper, a sliding mode control-based cooperative guidance strategy for a team of two unmanned aerial vehicles (aircraft), which ensures their survival against pursuers, is proposed. The pursuing interceptors are assumed to employ augmented proportional navigation guidance. The cooperating aircraft maneuver in a way to lure their respective pursuers to an impact angle constrained collision. The problem of controlling impact angle is first transformed to that of tracking line-of-sight angle and its rate, and then the same is achieved by enforcing sliding mode on appropriate chosen switching surface. Furthermore, the allocation of aircraft’s lateral accelerations is performed in a way to minimize the instantaneous aerodynamic drag acting on both unmanned aerial vehicles. Numerical simulations are presented to validate the effectiveness of the proposed guidance strategy under different engagement geometries.

This article deals with the investigation of the effects of porosity distributions on nonlinear free vibration and transient analysis of porous functionally graded skew (PFGS) plates. The effective material properties of the PFGS plates are obtained from the modified power-law equations in which gradation varies through the thickness of the PFGS plate. A nonlinear finite element (FE) formulation for the overall PFGS plate is derived by adopting first-order shear deformation theory (FSDT) in conjunction with von Karman’s nonlinear strain displacement relations. The governing equations of the PFGS plate are derived using the principle of virtual work. The direct iterative method and Newmark’s integration technique are espoused to solve nonlinear mathematical relations. The influences of the porosity distributions and porosity parameter indices on the nonlinear frequency responses of the PFGS plate for different skew angles are studied in various parameters. The effects of volume fraction grading index and skew angle on the plate’s nonlinear dynamic responses for various porosity distributions are illustrated in detail.

Photovoltaic (PV) modules play a pivotal role in renewable energy systems, underscoring the critical need for their fault diagnosis to ensure sustained energy production. This study introduces a novel approach that combines the power of deep neural networks and machine learning for comprehensive PV module fault diagnosis. Specifically, a fusion methodology that incorporates autoencoders (a deep neural network architecture) and support vector machines (SVM) (a machine learning algorithm) is proposed in the present study. To generate high-quality image datasets for training, unmanned aerial vehicles (UAVs) equipped with RGB cameras were employed to capture detailed images of PV modules. Burn marks, snail trails, discoloration, delamination, glass breakage and good panel were the conditions considered in the study. The experimental results demonstrate remarkable accuracy of 98.57% in diagnosing faults, marking a significant advancement in enhancing the reliability and performance of PV modules. This research contributes to the sustainability and efficiency of renewable energy systems, underlining its importance in the quest for a cleaner, greener future.

Renewable energy is found to be an effective alternative in the field of power production owing to the recent energy crises. Among the available renewable energy sources, solar energy is considered the front runner due to its ability to deliver clean energy, free availability and reduced cost. Photovoltaic (PV) modules are placed over large geographical regions for efficient solar energy harvesting, making it difficult to carry out maintenance and restoration works. Thermal stresses inherited by photovoltaic modules (PVM) under varying environmental conditions can lead to failure of internal components. Such failures when left undetected impart a number of complications in the system that will lead to unsafe operation and seizure. To avoid the aforementioned uncertainties, frequent monitoring of PVM is found necessary. The fault identification in PVM using essential features taken from aerial images is presented in this study. The feature extraction procedure was carried out using convolutional neural networks (CNN), while the feature selection process was carried out by the J48 decision tree method. Six test conditions were considered such as delamination, glass breakage, discoloration, burn marks, snail trail, and good panel. Bayes Net (BN) and Naïve Bayes (NB) classifiers were utilized as primary classifiers for all the test conditions. Results obtained from the classifiers were compared and the best classifier for fault detection in PVM is suggested.

Monoblock centrifugal pumps are used daily and play an essential role in agriculture, wastewater treatment, food, paper, gas and oil industries. Extended and endless use of monoblock centrifugal pump can affect the desired pump characteristics, compromising the efficiency of the pump. The condition of a pump is judged based on the high vibration, unusual noise, leakage, etc. Maintaining pump health is essential to increase the productivity in industry. Countless experiments were carried out by researchers to maintain the efficiency of the pump. The main shortcomings of traditional fault diagnosis are the need for a high degree of human intellect and professional knowledge. Such drawbacks have made the researchers provide autonomous and intelligent diagnostic tools. The objective of this article is to investigate the possibility of use of deep learning technique to identify the type of fault present in the monoblock centrifugal pump. First, vibration signals from the monoblock centrifugal pump were acquired and plotted as images that serve as an input for deep learning (DL) algorithms. The deep learning system can analyse the status of the monoblock centrifugal pump and learn from the vibration signal plots. In this paper, pre-trained networks such as ResNet-50, AlexNet, GoogleNet and VGG-16 were used to diagnose the fault in the monoblock centrifugal pump. The outcome of the hyperparameters namely learning rate (LR), batch size (BS), split ratio (SR) and solver were considered and the best performing network was recommended for the monoblock centrifugal pump fault diagnostics. Findings: A higher classification accuracy of 100% with lower training time of 177 s was obtained while using ResNet-50.

It is quite challenging to find anomalies in camera or surveillance camera data because of the enormous environmental changes and human movement. These challenges are also exacerbated by the high dimensionality of video or picture data and the encoding of video features. Many machine learning algorithms produced incorrect results and, in many cases, required a lot of effort. So deep learning methods are the best for anomaly detection. The main design goal of our study is to present a way for monitoring and tracking human actions using a visual surveillance program. In this paper, anomaly detection method is developed, it is identified from surveillance camera in malls, college, or important crowded areas for anomaly detection. Human monitoring-based anomaly detection system is developed using a novel method for feature extraction using RPCA-MFTSL, where MFTSL (Multi-Feature Tensor Subspace Learning) for background subtraction and RPCA (Robust Principal Component Analysis) for feature and PSO (Particle Swarm Optimization)-based Convolutional Neural Network (CNN) for anomaly detection

While conducting field trial of 82 genotypes of bottle gourd at Delhi during 2020–2021, a particular genotype, IC-0262269 was found to be affected by chlorotic curly stunt disease (CCSD). The affected plants were severely stunted and bearing very small chlorotic and crinkle leaves. The disease incidence in the said genotype was as high as 80% among different replicated trial blocks. The application of PCR using a generic primers specific to begomoviruses, as well as species-specific PCR diagnostics to six tomato-infecting begomoviruses: tomato leaf curl New Delhi virus(ToLCNDV), tomato leaf curl Palampur virus(ToLCPalV), tomato leaf curl Joydebpur virus(ToLCJoV), tomato leaf curl Gujrat virus(ToLCGuV), tomato leaf curl Bangalore virus(ToLCBV), and chilli leaf curl virus(ChiLCV) showed that, only ToLCPalV could be detected in the genotype IC-0262269. Following, rolling circle amplification, cloning and sequencing of full-length DNA-A and DNA-B genome of an isolate BoG1-ND from the genotype IC-0262269 revealed association of ToLCPalV with the disease. The successful agro-infection of the cloned genome of BoG1-ND (DNA-A and DNA-B) in the plants of Nicotiana benthamianaand bottle gourd demonstrated that ToLCPalV is the causal begomovirus of CCSD. The study provides the first evidence of the natural occurrence of ToLCPalV in bottle gourd crop and also showed that the bottle gourd genotype IC-0262269 is super-susceptible to ToLCPalV.

Cucumber green mottle mosaic virus (CGMMV, genus Tobamovirus) is a widely occurring tobamovirus in cucurbits. The genome of CGMMV has been used previously for the expression of foreign genes in the plant. High throughput delivery and high viral titer are important requirements of foreign protein expression in plant through virus genome-based vector, in this study, Agrobacteriumcontaining infectious construct of CGMMV was infiltrated through syringe, vacuum and high-speed spray to N. benthamiana,cucumber and bottle gourd leaves. The success rate of systemic infection of CGMMV agro-construct through all three methods was higher (80–100%) in N. benthamianacompared to the cucurbits (40–73.3%). To determine the high-throughput delivery of CGMMV in the plant system, four delivery methods viz. rubbing, syringe infiltration, vacuum infiltration and high-speed spray using the progeny virus derived through CGMMV agro-construct were compared in the three different plant species. Based on the rate of systemic infection and time required to perform delivery by different methods, vacuum infiltration was found most efficient for the high-throughput delivery of CGMMV. The quantification of CGMMV through qPCR revealed that CGMMV load varied considerably in leaf and fruit tissues depending with the time of infection. Immediately after expression of symptoms, a high load of CGMMV (~ 1 µg/100 mg of tissues) was noticed in young leaves of N. benthamianaand cucumber. In bottle gourd leaves, the CGMMV load was far low compared to N. benthamianaand cucumber plants. In the fruit tissues of cucumber and bottle gourd higher virus load was observed in mature fruit but not in immature fruit. The findings of the present study will serve as an important base line information to produce foreign protein through CGMMV genome-vector.

Temporomandibular joint ankylosis is one of the most challenging airway disorders associated with varying anatomical abnormalities like adenotonsillar hypertrophy, craniofacial malformations, macroglossia, etc. This case highlights the intubation difficulties confronted during the airway management of a 10-year-old girl presenting lately with bilateral temporomandibular joint ankylosis, hypoplastic mandible, and adenoid hypertrophy. This patient was intubated successfully by using a suction catheter assembly to negotiate the endotracheal tube across the adenoid, and an unmatched-size flexible intubation fiberscope through a “separate insertion” technique and external laryngeal manipulation. This case emphasises the significance of a comprehensive preoperative evaluation in preparing the anaesthetic plan of an anticipated difficult airway in a paediatric population, having diverse anatomical hurdles presenting concurrently.

The article presents an interview with G. S. Naveen Kumar, special secretary to the chief minister in Uttar Pradesh, India, in which he talks about promotion and development of the electronic system design and manufacturing (ESDM) in the state, industry-friendly initiatives, and investment in the sector.

In this paper, cooperative guidance strategies for the survival of cooperating team of two unmanned aerial vehicles (UAVs) against two attackers are proposed. The guidance schemes are designed under the assumptions that each UAV is being pursued by one attacker without cooperation with other attacker. The guidance schemes are designed to combat attackers guided either by proportional navigation (PN) or augmented PN guidance methodologies. The attackers are lured onto a collision course with each other such that intra-attacker interception is achieved before the target UAVs are captured. Sliding mode control techniques are employed to satisfy the sufficiency conditions for the survival within a finite time. Due to the nonlinear framework used for guidance design, the proposed strategies remain effective even for the engagements with large heading angle errors. Numerical simulations are presented to demonstrate the effectiveness of the proposed guidance strategies under various engagement geometries.

•A mixed ligand copper(II) complex has been synthesized and characterized.•The copper centre in complex is square-pyramidal in geometry with an N2O3 donor set.•The intermolecular hydrogen bonding leads to the formation of dimers.•Hirshfeld surface analysis shows strong intermolecular interactions.