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Chemical-based interventions are mostly used to control insects that are harmful to human health and agriculture or that simply cause a nuisance. An overreliance on these insecticides however raises concerns for the environment, human health, and the development of resistance, not only in the target species. As such, there is a critical need for the development of novel nonchemical technologies to control insects. Electrocution traps using UV light as an attractant are one classical nonchemical approach to insect control but lack the specificity necessary to target only pest insects and to avoid harmless or beneficial species. Here we review the fundamental physics behind electric fields (EFs) and place them in context with electromagnetic fields more broadly. We then focus on how novel uses of strong EFs, some of which are being piloted in the field and laboratory, have the potential to repel, capture, or kill (electrocute) insects without the negative side effects of other classical approaches. As EF-insect science remains in its infancy, we provide recommendations for future areas of research in EF-insect science.

This study introduces a novel approach, combining substruct counting, $k$-mers, and Daylight-like fingerprints, to expand the representation of chemical structures in SMILES strings. The integrated method generates comprehensive molecular embeddings that enhance discriminative power and information content. Experimental evaluations demonstrate its superiority over traditional Morgan fingerprinting, MACCS, and Daylight fingerprint alone, improving chemoinformatics tasks such as drug classification. The proposed method offers a more informative representation of chemical structures, advancing molecular similarity analysis and facilitating applications in molecular design and drug discovery. It presents a promising avenue for molecular structure analysis and design, with significant potential for practical implementation., Comment: 12 Pages, 3 tables, Accepted at SimBig2023

In the field of biological research, it is essential to comprehend the characteristics and functions of molecular sequences. The classification of molecular sequences has seen widespread use of neural network-based techniques. Despite their astounding accuracy, these models often require a substantial number of parameters and more data collection. In this work, we present a novel approach based on the compression-based Model, motivated from \cite{jiang2023low}, which combines the simplicity of basic compression algorithms like Gzip and Bz2, with Normalized Compression Distance (NCD) algorithm to achieve better performance on classification tasks without relying on handcrafted features or pre-trained models. Firstly, we compress the molecular sequence using well-known compression algorithms, such as Gzip and Bz2. By leveraging the latent structure encoded in compressed files, we compute the Normalized Compression Distance between each pair of molecular sequences, which is derived from the Kolmogorov complexity. This gives us a distance matrix, which is the input for generating a kernel matrix using a Gaussian kernel. Next, we employ kernel Principal Component Analysis (PCA) to get the vector representations for the corresponding molecular sequence, capturing important structural and functional information. The resulting vector representations provide an efficient yet effective solution for molecular sequence analysis and can be used in ML-based downstream tasks. The proposed approach eliminates the need for computationally intensive Deep Neural Networks (DNNs), with their large parameter counts and data requirements. Instead, it leverages a lightweight and universally accessible compression-based model., Comment: Accepted at The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2024

Increased CO2 emissions on the earth causing global warming and climate change have provided a thrust to explore Li-CO2 battery chemistry, where CO2 is used as an energy carrier. In addition, the occurrence of CO2 as a major natural abundant gas in the Martian atmosphere opens the possibility of using Li-CO2 batteries for interplanetary Mars missions. In this work, we aim to investigate facile and inexpensive candle soot carbon nanoparticles as a cathode catalyst against commercially available multiwalled carbon nanotubes (MWCNTs) for stable and high-performance Li-CO2 batteries for Mars exploration. The unique interconnected morphology and higher surface area of candle soot nanoparticles facilitate better reversibility (more than 80 cycles) compared to MWCNTs even at a high current density of 200 mA g-1 with a cutoff capacity of 500 mAh g-1. The full discharge capacity for candle soot nanoparticles was measured to be 5318 mAh g-1 with a coulombic efficiency of 42% as compared to 16% for MWCNTs. The rate capability studies were performed to establish the ability to operate the system reversibly at different current densities in a simulated Martian atmosphere. The outcome of this study paves the way toward developing a candle soot cathode-based practicable Li-CO2 battery for utilization on Mars.

Increased CO2 emissions on the earth causing global warming and climate change have provided a thrust to explore Li-CO2 battery chemistry, where CO2 is used as an energy carrier. In addition, the occurrence of CO2 as a major natural abundant gas in the Martian atmosphere opens the possibility of using Li-CO2 batteries for interplanetary Mars missions. In this work, we aim to investigate facile and inexpensive candle soot carbon nanoparticles as a cathode catalyst against commercially available multiwalled carbon nanotubes (MWCNTs) for stable and high-performance Li-CO2 batteries for Mars exploration. The unique interconnected morphology and higher surface area of candle soot nanoparticles facilitate better reversibility (more than 80 cycles) compared to MWCNTs even at a high current density of 200 mA g-1 with a cutoff capacity of 500 mAh g-1. The full discharge capacity for candle soot nanoparticles was measured to be 5318 mAh g-1 with a coulombic efficiency of 42% as compared to 16% for MWCNTs. The rate capability studies were performed to establish the ability to operate the system reversibly at different current densities in a simulated Martian atmosphere. The outcome of this study paves the way toward developing a candle soot cathode-based practicable Li-CO2 battery for utilization on Mars.

Increased CO2 emissions on the earth causing global warming and climate change have provided a thrust to explore Li-CO2 battery chemistry, where CO2 is used as an energy carrier. In addition, the occurrence of CO2 as a major natural abundant gas in the Martian atmosphere opens the possibility of using Li-CO2 batteries for interplanetary Mars missions. In this work, we aim to investigate facile and inexpensive candle soot carbon nanoparticles as a cathode catalyst against commercially available multiwalled carbon nanotubes (MWCNTs) for stable and high-performance Li-CO2 batteries for Mars exploration. The unique interconnected morphology and higher surface area of candle soot nanoparticles facilitate better reversibility (more than 80 cycles) compared to MWCNTs even at a high current density of 200 mA g-1 with a cutoff capacity of 500 mAh g-1. The full discharge capacity for candle soot nanoparticles was measured to be 5318 mAh g-1 with a coulombic efficiency of 42% as compared to 16% for MWCNTs. The rate capability studies were performed to establish the ability to operate the system reversibly at different current densities in a simulated Martian atmosphere. The outcome of this study paves the way toward developing a candle soot cathode-based practicable Li-CO2 battery for utilization on Mars.

Purpose:The aim of the study is to look at the role relational climate might play in enhancing the organizational performance and understanding its influence on relationship between SHRM and organizational performance in the context of public sectors. Theoretical Framework:This research has been anchored on the ‘Relational Models Theory’ of Fiske (1992) to explain relational climate and ‘Social interdependence Theory’ of Johnson & Johnson (2005) to comprehend how relational climate affects organisational performance. Design/methodology/approach:Data were obtained from 327 Executives by means of a questionnaire survey from 10 public sector oil companies in India and analysed through SPSS and structural Equation Modelling (SEM). Findings:This study established that relational climate strongly affects organizational performance. By empirically examining the notion of relational climate and demonstrating a strong positive correlation between relational climate and organisational performance, this study adds to the body of knowledge on relational HRM. Research, Practical & Social implications: This study contributes by discussing practical implications that managers must build an effective relational climate which will facilitate in developing environment for shared purpose, shared vision, compassion and mutual respect in the workplace to improve organizational performance. The study's societal implications include the development of a supportive interpersonal environment that fosters cooperation, respect, and relational vibrancy at work. Originality/Value: This study highlighted the effect of relational climate on organizational performance in the public sector context.

This paper presents a federated learning (FL) approach to train an AI model for SARS-Cov-2 variant classification. We analyze the SARS-CoV-2 spike sequences in a distributed way, without data sharing, to detect different variants of this rapidly mutating coronavirus. Our method maintains the confidentiality of local data (that could be stored in different locations) yet allows us to reliably detect and identify different known and unknown variants of the novel coronavirus SARS-CoV-2. Using the proposed approach, we achieve an overall accuracy of $93\%$ on the coronavirus variant identification task. We also provide details regarding how the proposed model follows the main laws of federated learning, such as Laws of data ownership, data privacy, model aggregation, and model heterogeneity. Since the proposed model is distributed, it could scale on ``Big Data'' easily. We plan to use this proof-of-concept to implement a privacy-preserving pandemic response strategy., Comment: 10 pages, 7 figures, Accepted at 10th International Conference on Information Management and Big Data (SIMBig 2023)

Anderson acceleration (AA) is a well-known method for accelerating the convergence of iterative algorithms, with applications in various fields including deep learning and optimization. Despite its popularity in these areas, the effectiveness of AA in classical machine learning classifiers has not been thoroughly studied. Tabular data, in particular, presents a unique challenge for deep learning models, and classical machine learning models are known to perform better in these scenarios. However, the convergence analysis of these models has received limited attention. To address this gap in research, we implement a support vector machine (SVM) classifier variant that incorporates AA to speed up convergence. We evaluate the performance of our SVM with and without Anderson acceleration on several datasets from the biology domain and demonstrate that the use of AA significantly improves convergence and reduces the training loss as the number of iterations increases. Our findings provide a promising perspective on the potential of Anderson acceleration in the training of simple machine learning classifiers and underscore the importance of further research in this area. By showing the effectiveness of AA in this setting, we aim to inspire more studies that explore the applications of AA in classical machine learning., Comment: Accepted in KDH-2023: Knowledge Discovery in Healthcare Data (IJCAI Workshop)

Understanding the host-specificity of different families of viruses sheds light on the origin of, e.g., SARS-CoV-2, rabies, and other such zoonotic pathogens in humans. It enables epidemiologists, medical professionals, and policymakers to curb existing epidemics and prevent future ones promptly. In the family Coronaviridae (of which SARS-CoV-2 is a member), it is well-known that the spike protein is the point of contact between the virus and the host cell membrane. On the other hand, the two traditional mammalian orders, Carnivora (carnivores) and Chiroptera (bats) are recognized to be responsible for maintaining and spreading the Rabies Lyssavirus (RABV). We propose Virus2Vec, a feature-vector representation for viral (nucleotide or amino acid) sequences that enable vector-space-based machine learning models to identify viral hosts. Virus2Vec generates numerical feature vectors for unaligned sequences, allowing us to forego the computationally expensive sequence alignment step from the pipeline. Virus2Vec leverages the power of both the \emph{minimizer} and position weight matrix (PWM) to generate compact feature vectors. Using several classifiers, we empirically evaluate Virus2Vec on real-world spike sequences of Coronaviridae and rabies virus sequence data to predict the host (identifying the reservoirs of infection). Our results demonstrate that Virus2Vec outperforms the predictive accuracies of baseline and state-of-the-art methods., Comment: 11 Pages 6 Figures Accepted in conference Conference on Health, Inference, and Learning (CHIL) 2023

Cancer is a complex disease characterized by uncontrolled cell growth and proliferation. T cell receptors (TCRs) are essential proteins for the adaptive immune system, and their specific recognition of antigens plays a crucial role in the immune response against diseases, including cancer. The diversity and specificity of TCRs make them ideal for targeting cancer cells, and recent advancements in sequencing technologies have enabled the comprehensive profiling of TCR repertoires. This has led to the discovery of TCRs with potent anti-cancer activity and the development of TCR-based immunotherapies. In this study, we investigate the use of sparse coding for the multi-class classification of TCR protein sequences with cancer categories as target labels. Sparse coding is a popular technique in machine learning that enables the representation of data with a set of informative features and can capture complex relationships between amino acids and identify subtle patterns in the sequence that might be missed by low-dimensional methods. We first compute the k-mers from the TCR sequences and then apply sparse coding to capture the essential features of the data. To improve the predictive performance of the final embeddings, we integrate domain knowledge regarding different types of cancer properties. We then train different machine learning (linear and non-linear) classifiers on the embeddings of TCR sequences for the purpose of supervised analysis. Our proposed embedding method on a benchmark dataset of TCR sequences significantly outperforms the baselines in terms of predictive performance, achieving an accuracy of 99.8\%. Our study highlights the potential of sparse coding for the analysis of TCR protein sequences in cancer research and other related fields., Comment: Accepted at ICONIP 2023

The amount of sequencing data for SARS-CoV-2 is several orders of magnitude larger than any virus. This will continue to grow geometrically for SARS-CoV-2, and other viruses, as many countries heavily finance genomic surveillance efforts. Hence, we need methods for processing large amounts of sequence data to allow for effective yet timely decision-making. Such data will come from heterogeneous sources: aligned, unaligned, or even unassembled raw nucleotide or amino acid sequencing reads pertaining to the whole genome or regions (e.g., spike) of interest. In this work, we propose \emph{ViralVectors}, a compact feature vector generation from virome sequencing data that allows effective downstream analysis. Such generation is based on \emph{minimizers}, a type of lightweight "signature" of a sequence, used traditionally in assembly and read mapping -- to our knowledge, the first use minimizers in this way. We validate our approach on different types of sequencing data: (a) 2.5M SARS-CoV-2 spike sequences (to show scalability); (b) 3K Coronaviridae spike sequences (to show robustness to more genomic variability); and (c) 4K raw WGS reads sets taken from nasal-swab PCR tests (to show the ability to process unassembled reads). Our results show that ViralVectors outperforms current benchmarks in most classification and clustering tasks., Comment: 24 pages, 5 figures, accepted to Springer Medical & Biological Engineering & Computing

This article provides a concise summary of the recent developments that have been achieved in our comprehension of the asymmetric addition processes that are catalysed by native Cinchona alkaloids and their derivatives. This class of reactions includes cycloadditions, 1,4-adds, direct nucleophilic additions across carbon–oxygen or carbon–nitrogen double bonds, and direct nucleophilic additions across carbon–oxygen double bonds. Because of their capacity to catalyse the addition of a wide variety of functional groups to C9, many Cinchona alkaloids have been utilised in these processes as catalysts. These functional groups include amino, alkoxy, hydroxyl, amido, urea, and thiourea, among others. The importance of mechanical variables is emphasised in many different contexts. Additionally, the utilisation of adducts in future synthesis is sometimes broken down into its component steps. Ocimum basilicum was discovered to be mostly consisted of estragol (> 35.71 percent), (E)-ocimene (> 1.47 percent), trans-bergamotene (> 0.83 percent), a-cadinol (> 0.41 percent), eucalyptol (> 0.25 percent), and -caryophyllene (> 0.07 percent), whereas Ocimum sanctum is primarily composed of eucaly There is a greater concentration of chemical components in the leaves of Ocimum basilicum and Ocimum sanctum than there is in the actual inflorescence or flowers of the plant. The genetic distance between the two species was analysed in order to better understand the interspecies relationship, and the results showed that it was 2.86. The small difference in genetic makeup that exists between Ocimum basilicum and Ocimum sanctum is evidence that these two species are related to one another and share similar traits.

This article provides a concise summary of the recent developments that have been achieved in our comprehension of the asymmetric addition processes that are catalysed by native Cinchona alkaloids and their derivatives. This class of reactions includes cycloadditions, 1,4-adds, direct nucleophilic additions across carbon–oxygen or carbon–nitrogen double bonds, and direct nucleophilic additions across carbon–oxygen double bonds. Because of their capacity to catalyse the addition of a wide variety of functional groups to C9, many Cinchona alkaloids have been utilised in these processes as catalysts. These functional groups include amino, alkoxy, hydroxyl, amido, urea, and thiourea, among others. The importance of mechanical variables is emphasised in many different contexts. Additionally, the utilisation of adducts in future synthesis is sometimes broken down into its component steps. Ocimum basilicum was discovered to be mostly consisted of estragol (> 35.71 percent), (E)-ocimene (> 1.47 percent), trans-bergamotene (> 0.83 percent), a-cadinol (> 0.41 percent), eucalyptol (> 0.25 percent), and -caryophyllene (> 0.07 percent), whereas Ocimum sanctum is primarily composed of eucaly There is a greater concentration of chemical components in the leaves of Ocimum basilicum and Ocimum sanctum than there is in the actual inflorescence or flowers of the plant. The genetic distance between the two species was analysed in order to better understand the interspecies relationship, and the results showed that it was 2.86. The small difference in genetic makeup that exists between Ocimum basilicum and Ocimum sanctum is evidence that these two species are related to one another and share similar traits.

This article provides a concise summary of the recent developments that have been achieved in our comprehension of the asymmetric addition processes that are catalysed by native Cinchona alkaloids and their derivatives. This class of reactions includes cycloadditions, 1,4-adds, direct nucleophilic additions across carbon–oxygen or carbon–nitrogen double bonds, and direct nucleophilic additions across carbon–oxygen double bonds. Because of their capacity to catalyse the addition of a wide variety of functional groups to C9, many Cinchona alkaloids have been utilised in these processes as catalysts. These functional groups include amino, alkoxy, hydroxyl, amido, urea, and thiourea, among others. The importance of mechanical variables is emphasised in many different contexts. Additionally, the utilisation of adducts in future synthesis is sometimes broken down into its component steps. Ocimum basilicum was discovered to be mostly consisted of estragol (> 35.71 percent), (E)-ocimene (> 1.47 percent), trans-bergamotene (> 0.83 percent), a-cadinol (> 0.41 percent), eucalyptol (> 0.25 percent), and -caryophyllene (> 0.07 percent), whereas Ocimum sanctum is primarily composed of eucaly There is a greater concentration of chemical components in the leaves of Ocimum basilicum and Ocimum sanctum than there is in the actual inflorescence or flowers of the plant. The genetic distance between the two species was analysed in order to better understand the interspecies relationship, and the results showed that it was 2.86. The small difference in genetic makeup that exists between Ocimum basilicum and Ocimum sanctum is evidence that these two species are related to one another and share similar traits.

The immune system has the ability to provoke inflammation in response to a wide variety of different triggers. Toxic chemicals, infectious diseases, radiation, and cells that have been harmed are some examples of these stimuli. It removes the detrimental stimuli and at the same time initiates the healing process, which is a win-win situation. As a result, the protective reaction of inflammation is essential for ensuring that the body continues to function properly. The majority of the time, cellular and molecular activities and interactions work together to successfully minimise the risk of experiencing damage or infection during acute inflammatory reactions. This is because these activities and interactions are coordinated to function together. This review article was prepared utilising materials written in English, and it has been published in time intervals of 15 years beginning in 1995 and continuing all the way up until the current day. Both systematic reviews and randomised controlled trials (RCTs), which are considered to be the two most reliable types of research, were included in the collection of publications that were pertinent to the goal that we set for ourselves. The first two approaches are the only ones that should be prioritised above the others. Studies with an open label and studies with cohorts are not as essential as those with a case-control design, which are called preclinical trials.

The immune system has the ability to provoke inflammation in response to a wide variety of different triggers. Toxic chemicals, infectious diseases, radiation, and cells that have been harmed are some examples of these stimuli. It removes the detrimental stimuli and at the same time initiates the healing process, which is a win-win situation. As a result, the protective reaction of inflammation is essential for ensuring that the body continues to function properly. The majority of the time, cellular and molecular activities and interactions work together to successfully minimise the risk of experiencing damage or infection during acute inflammatory reactions. This is because these activities and interactions are coordinated to function together. This review article was prepared utilising materials written in English, and it has been published in time intervals of 15 years beginning in 1995 and continuing all the way up until the current day. Both systematic reviews and randomised controlled trials (RCTs), which are considered to be the two most reliable types of research, were included in the collection of publications that were pertinent to the goal that we set for ourselves. The first two approaches are the only ones that should be prioritised above the others. Studies with an open label and studies with cohorts are not as essential as those with a case-control design, which are called preclinical trials.

The immune system has the ability to provoke inflammation in response to a wide variety of different triggers. Toxic chemicals, infectious diseases, radiation, and cells that have been harmed are some examples of these stimuli. It removes the detrimental stimuli and at the same time initiates the healing process, which is a win-win situation. As a result, the protective reaction of inflammation is essential for ensuring that the body continues to function properly. The majority of the time, cellular and molecular activities and interactions work together to successfully minimise the risk of experiencing damage or infection during acute inflammatory reactions. This is because these activities and interactions are coordinated to function together. This review article was prepared utilising materials written in English, and it has been published in time intervals of 15 years beginning in 1995 and continuing all the way up until the current day. Both systematic reviews and randomised controlled trials (RCTs), which are considered to be the two most reliable types of research, were included in the collection of publications that were pertinent to the goal that we set for ourselves. The first two approaches are the only ones that should be prioritised above the others. Studies with an open label and studies with cohorts are not as essential as those with a case-control design, which are called preclinical trials.

In recent times, the Li-CO2 battery has gained significant importance arising from its higher gravimetric energy density (1876 Wh kg(-1)) compared to the conventional Li-ion batteries. Also, its ability to utilize the greenhouse gas CO2 to operate an energy storage system and the prospective utilization on extraterrestrial planets such as Mars motivate to practicalize it. However, it suffers from numerous challenges such as (i) the reluctant CO2 reduction/evolution; (ii) solid/liquid/gas interface blockage arising from the deposition of Li2CO3 discharge product on the cathode; (iii) high overpotential to decompose the stable discharge product Li2CO3; and (iv) instability of the electrolytes. Numerous efforts have been undertaken to tackle these challenges by developing catalysts, improving the stability of electrolytes, protecting the anode, etc. Despite these efforts, due to the lack of a decisive confirmation of the reaction mechanisms of the discharging/charging reactions occurring in the system, the progress of the Li-CO2 battery system has been slow. In situ characterization techniques help overcome ex-situ techniques' limitations by monitoring the processes with the progress of a reaction. The current review focuses on bridging the gap in the understanding of the Li-CO2 batteries by exploring the various in situ/operando characterization techniques that have been employed.

In recent times, the Li-CO2 battery has gained significant importance arising from its higher gravimetric energy density (1876 Wh kg-1) compared to the conventional Li-ion batteries. Also, its ability to utilize the greenhouse gas CO2 to operate an energy storage system and the prospective utilization on extraterrestrial planets such as Mars motivate to practicalize it. However, it suffers from numerous challenges such as (i) the reluctant CO2 reduction/evolution; (ii) solid/liquid/gas interface blockage arising from the deposition of Li2CO3 discharge product on the cathode; (iii) high overpotential to decompose the stable discharge product Li2CO3; and (iv) instability of the electrolytes. Numerous efforts have been undertaken to tackle these challenges by developing catalysts, improving the stability of electrolytes, protecting the anode, etc. Despite these efforts, due to the lack of a decisive confirmation of the reaction mechanisms of the discharging/charging reactions occurring in the system, the progress of the Li–CO2 battery system has been slow. In situ characterization techniques help overcome ex-situ techniques’ limitations by monitoring the processes with the progress of a reaction. The current review focuses on bridging the gap in the understanding of the Li–CO2 batteries by exploring the various in situ/operando characterization techniques that have been employed. © 2022 Wiley-VCH GmbH.

In recent times, the Li-CO2 battery has gained significant importance arising from its higher gravimetric energy density (1876 Wh kg-1) compared to the conventional Li-ion batteries. Also, its ability to utilize the greenhouse gas CO2 to operate an energy storage system and the prospective utilization on extraterrestrial planets such as Mars motivate to practicalize it. However, it suffers from numerous challenges such as (i) the reluctant CO2 reduction/evolution; (ii) solid/liquid/gas interface blockage arising from the deposition of Li2CO3 discharge product on the cathode; (iii) high overpotential to decompose the stable discharge product Li2CO3; and (iv) instability of the electrolytes. Numerous efforts have been undertaken to tackle these challenges by developing catalysts, improving the stability of electrolytes, protecting the anode, etc. Despite these efforts, due to the lack of a decisive confirmation of the reaction mechanisms of the discharging/charging reactions occurring in the system, the progress of the Li–CO2 battery system has been slow. In situ characterization techniques help overcome ex-situ techniques’ limitations by monitoring the processes with the progress of a reaction. The current review focuses on bridging the gap in the understanding of the Li–CO2 batteries by exploring the various in situ/operando characterization techniques that have been employed. © 2022 Wiley-VCH GmbH.

In recent times, the Li-CO2 battery has gained significant importance arising from its higher gravimetric energy density (1876 Wh kg(-1)) compared to the conventional Li-ion batteries. Also, its ability to utilize the greenhouse gas CO2 to operate an energy storage system and the prospective utilization on extraterrestrial planets such as Mars motivate to practicalize it. However, it suffers from numerous challenges such as (i) the reluctant CO2 reduction/evolution; (ii) solid/liquid/gas interface blockage arising from the deposition of Li2CO3 discharge product on the cathode; (iii) high overpotential to decompose the stable discharge product Li2CO3; and (iv) instability of the electrolytes. Numerous efforts have been undertaken to tackle these challenges by developing catalysts, improving the stability of electrolytes, protecting the anode, etc. Despite these efforts, due to the lack of a decisive confirmation of the reaction mechanisms of the discharging/charging reactions occurring in the system, the progress of the Li-CO2 battery system has been slow. In situ characterization techniques help overcome ex-situ techniques' limitations by monitoring the processes with the progress of a reaction. The current review focuses on bridging the gap in the understanding of the Li-CO2 batteries by exploring the various in situ/operando characterization techniques that have been employed.

In recent times, the Li-CO2 battery has gained significant importance arising from its higher gravimetric energy density (1876 Wh kg(-1)) compared to the conventional Li-ion batteries. Also, its ability to utilize the greenhouse gas CO2 to operate an energy storage system and the prospective utilization on extraterrestrial planets such as Mars motivate to practicalize it. However, it suffers from numerous challenges such as (i) the reluctant CO2 reduction/evolution; (ii) solid/liquid/gas interface blockage arising from the deposition of Li2CO3 discharge product on the cathode; (iii) high overpotential to decompose the stable discharge product Li2CO3; and (iv) instability of the electrolytes. Numerous efforts have been undertaken to tackle these challenges by developing catalysts, improving the stability of electrolytes, protecting the anode, etc. Despite these efforts, due to the lack of a decisive confirmation of the reaction mechanisms of the discharging/charging reactions occurring in the system, the progress of the Li-CO2 battery system has been slow. In situ characterization techniques help overcome ex-situ techniques' limitations by monitoring the processes with the progress of a reaction. The current review focuses on bridging the gap in the understanding of the Li-CO2 batteries by exploring the various in situ/operando characterization techniques that have been employed.

In recent times, the Li-CO2 battery has gained significant importance arising from its higher gravimetric energy density (1876 Wh kg-1) compared to the conventional Li-ion batteries. Also, its ability to utilize the greenhouse gas CO2 to operate an energy storage system and the prospective utilization on extraterrestrial planets such as Mars motivate to practicalize it. However, it suffers from numerous challenges such as (i) the reluctant CO2 reduction/evolution; (ii) solid/liquid/gas interface blockage arising from the deposition of Li2CO3 discharge product on the cathode; (iii) high overpotential to decompose the stable discharge product Li2CO3; and (iv) instability of the electrolytes. Numerous efforts have been undertaken to tackle these challenges by developing catalysts, improving the stability of electrolytes, protecting the anode, etc. Despite these efforts, due to the lack of a decisive confirmation of the reaction mechanisms of the discharging/charging reactions occurring in the system, the progress of the Li–CO2 battery system has been slow. In situ characterization techniques help overcome ex-situ techniques’ limitations by monitoring the processes with the progress of a reaction. The current review focuses on bridging the gap in the understanding of the Li–CO2 batteries by exploring the various in situ/operando characterization techniques that have been employed. © 2022 Wiley-VCH GmbH.

The main warning in the area of food preservation and care is on topmost are crop diseases. It has been recognized speedily, but it is not as easy as in any area of the world because no required framework exists. Both the healthy and diseased plant leaves were gathered and collected under the condition and circumstances. For this purpose, a public set of information was used. It was 20,639 images of plants that were infected and healthy. In order to recognize three different crops and 12 diseases, a sequential convolutional neural network from Keras was trained and applied. The perfection and exactness was 98.18 % onset of information of the above trained mentioned model using CNN . It has also indicated the probability and possibility of this strategy and procedure. The over-fitting occurs and neutralizes by putting the dropout value to 0.25.

The main warning in the area of food preservation and care is on topmost are crop diseases. It has been recognized speedily, but it is not as easy as in any area of the world because no required framework exists. Both the healthy and diseased plant leaves were gathered and collected under the condition and circumstances. For this purpose, a public set of information was used. It was 20,639 images of plants that were infected and healthy. In order to recognize three different crops and 12 diseases, a sequential convolutional neural network from Keras was trained and applied. The perfection and exactness was 98.18 % onset of information of the above trained mentioned model using CNN . It has also indicated the probability and possibility of this strategy and procedure. The over-fitting occurs and neutralizes by putting the dropout value to 0.25.

The t-distributed stochastic neighbor embedding (t- SNE) is a method for interpreting high dimensional (HD) data by mapping each point to a low dimensional (LD) space (usually two-dimensional). It seeks to retain the structure of the data. An important component of the t-SNE algorithm is the initialization procedure, which begins with the random initialization of an LD vector. Points in this initial vector are then updated to minimize the loss function (the KL divergence) iteratively using gradient descent. This leads comparable points to attract one another while pushing dissimilar points apart. We believe that, by default, these algorithms should employ some form of informative initialization. Another essential component of the t-SNE is using a kernel matrix, a similarity matrix comprising the pairwise distances among the sequences. For t-SNE-based visualization, the Gaussian kernel is employed by default in the literature. However, we show that kernel selection can also play a crucial role in the performance of t-SNE. In this work, we assess the performance of t-SNE with various alternative initialization methods and kernels, using four different sets, out of which three are biological sequences (nucleotide, protein, etc.) datasets obtained from various sources, such as the well-known GISAID database for sequences of the SARS- CoV-2 virus. We perform subjective and objective assessments of these alternatives. We use the resulting t-SNE plots and k- ary neighborhood agreement (k-ANA) to evaluate and compare the proposed methods with the baselines. We show that by using different techniques, such as informed initialization and kernel matrix selection, that t-SNE performs significantly better. Moreover, we show that t-SNE also takes fewer iterations to converge faster with more intelligent initialization., Comment: Accepted to IEEE BigData 2022

The massive amount of genomic data appearing for SARS-CoV-2 since the beginning of the COVID-19 pandemic has challenged traditional methods for studying its dynamics. As a result, new methods such as Pangolin, which can scale to the millions of samples of SARS-CoV-2 currently available, have appeared. Such a tool is tailored to take as input assembled, aligned and curated full-length sequences, such as those found in the GISAID database. As high-throughput sequencing technologies continue to advance, such assembly, alignment and curation may become a bottleneck, creating a need for methods which can process raw sequencing reads directly. In this paper, we propose Reads2Vec, an alignment-free embedding approach that can generate a fixed-length feature vector representation directly from the raw sequencing reads without requiring assembly. Furthermore, since such an embedding is a numerical representation, it may be applied to highly optimized classification and clustering algorithms. Experiments on simulated data show that our proposed embedding obtains better classification results and better clustering properties contrary to existing alignment-free baselines. In a study on real data, we show that alignment-free embeddings have better clustering properties than the Pangolin tool and that the spike region of the SARS-CoV-2 genome heavily informs the alignment-free clusterings, which is consistent with current biological knowledge of SARS-CoV-2.

Unlearning algorithms aim to remove deleted data's influence from trained models at a cost lower than full retraining. However, prior guarantees of unlearning in literature are flawed and don't protect the privacy of deleted records. We show that when users delete their data as a function of published models, records in a database become interdependent. So, even retraining a fresh model after deletion of a record doesn't ensure its privacy. Secondly, unlearning algorithms that cache partial computations to speed up the processing can leak deleted information over a series of releases, violating the privacy of deleted records in the long run. To address these, we propose a sound deletion guarantee and show that the privacy of existing records is necessary for the privacy of deleted records. Under this notion, we propose an accurate, computationally efficient, and secure machine unlearning algorithm based on noisy gradient descent.

The immune system has the ability to provoke inflammation in response to a wide variety of different triggers. Toxic chemicals, infectious diseases, radiation, and cells that have been harmed are some examples of these stimuli. It removes the detrimental stimuli and at the same time initiates the healing process, which is a win-win situation. As a result, the protective reaction of inflammation is essential for ensuring that the body continues to function properly. The majority of the time, cellular and molecular activities and interactions work together to successfully minimise the risk of experiencing damage or infection during acute inflammatory reactions. This is because these activities and interactions are coordinated to function together. This review article was prepared utilising materials written in English, and it has been published in time intervals of 15 years beginning in 1995 and continuing all the way up until the current day. Both systematic reviews and randomised controlled trials (RCTs), which are considered to be the two most reliable types of research, were included in the collection of publications that were pertinent to the goal that we set for ourselves. The first two approaches are the only ones that should be prioritised above the others. Studies with an open label and studies with cohorts are not as essential as those with a case-control design, which are called preclinical trials.

This article provides a concise summary of the recent developments that have been achieved in our comprehension of the asymmetric addition processes that are catalysed by native Cinchona alkaloids and their derivatives. This class of reactions includes cycloadditions, 1,4-adds, direct nucleophilic additions across carbon–oxygen or carbon–nitrogen double bonds, and direct nucleophilic additions across carbon–oxygen double bonds. Because of their capacity to catalyse the addition of a wide variety of functional groups to C9, many Cinchona alkaloids have been utilised in these processes as catalysts. These functional groups include amino, alkoxy, hydroxyl, amido, urea, and thiourea, among others. The importance of mechanical variables is emphasised in many different contexts. Additionally, the utilisation of adducts in future synthesis is sometimes broken down into its component steps. Ocimum basilicum was discovered to be mostly consisted of estragol (> 35.71 percent), (E)-ocimene (> 1.47 percent), trans-bergamotene (> 0.83 percent), a-cadinol (> 0.41 percent), eucalyptol (> 0.25 percent), and -caryophyllene (> 0.07 percent), whereas Ocimum sanctum is primarily composed of eucaly There is a greater concentration of chemical components in the leaves of Ocimum basilicum and Ocimum sanctum than there is in the actual inflorescence or flowers of the plant. The genetic distance between the two species was analysed in order to better understand the interspecies relationship, and the results showed that it was 2.86. The small difference in genetic makeup that exists between Ocimum basilicum and Ocimum sanctum is evidence that these two species are related to one another and share similar traits.

This article provides a concise summary of the recent developments that have been achieved in our comprehension of the asymmetric addition processes that are catalysed by native Cinchona alkaloids and their derivatives. This class of reactions includes cycloadditions, 1,4-adds, direct nucleophilic additions across carbon–oxygen or carbon–nitrogen double bonds, and direct nucleophilic additions across carbon–oxygen double bonds. Because of their capacity to catalyse the addition of a wide variety of functional groups to C9, many Cinchona alkaloids have been utilised in these processes as catalysts. These functional groups include amino, alkoxy, hydroxyl, amido, urea, and thiourea, among others. The importance of mechanical variables is emphasised in many different contexts. Additionally, the utilisation of adducts in future synthesis is sometimes broken down into its component steps. Ocimum basilicum was discovered to be mostly consisted of estragol (> 35.71 percent), (E)-ocimene (> 1.47 percent), trans-bergamotene (> 0.83 percent), a-cadinol (> 0.41 percent), eucalyptol (> 0.25 percent), and -caryophyllene (> 0.07 percent), whereas Ocimum sanctum is primarily composed of eucaly There is a greater concentration of chemical components in the leaves of Ocimum basilicum and Ocimum sanctum than there is in the actual inflorescence or flowers of the plant. The genetic distance between the two species was analysed in order to better understand the interspecies relationship, and the results showed that it was 2.86. The small difference in genetic makeup that exists between Ocimum basilicum and Ocimum sanctum is evidence that these two species are related to one another and share similar traits.

The immune system has the ability to provoke inflammation in response to a wide variety of different triggers. Toxic chemicals, infectious diseases, radiation, and cells that have been harmed are some examples of these stimuli. It removes the detrimental stimuli and at the same time initiates the healing process, which is a win-win situation. As a result, the protective reaction of inflammation is essential for ensuring that the body continues to function properly. The majority of the time, cellular and molecular activities and interactions work together to successfully minimise the risk of experiencing damage or infection during acute inflammatory reactions. This is because these activities and interactions are coordinated to function together. This review article was prepared utilising materials written in English, and it has been published in time intervals of 15 years beginning in 1995 and continuing all the way up until the current day. Both systematic reviews and randomised controlled trials (RCTs), which are considered to be the two most reliable types of research, were included in the collection of publications that were pertinent to the goal that we set for ourselves. The first two approaches are the only ones that should be prioritised above the others. Studies with an open label and studies with cohorts are not as essential as those with a case-control design, which are called preclinical trials.

The immune system has the ability to provoke inflammation in response to a wide variety of different triggers. Toxic chemicals, infectious diseases, radiation, and cells that have been harmed are some examples of these stimuli. It removes the detrimental stimuli and at the same time initiates the healing process, which is a win-win situation. As a result, the protective reaction of inflammation is essential for ensuring that the body continues to function properly. The majority of the time, cellular and molecular activities and interactions work together to successfully minimise the risk of experiencing damage or infection during acute inflammatory reactions. This is because these activities and interactions are coordinated to function together. This review article was prepared utilising materials written in English, and it has been published in time intervals of 15 years beginning in 1995 and continuing all the way up until the current day. Both systematic reviews and randomised controlled trials (RCTs), which are considered to be the two most reliable types of research, were included in the collection of publications that were pertinent to the goal that we set for ourselves. The first two approaches are the only ones that should be prioritised above the others. Studies with an open label and studies with cohorts are not as essential as those with a case-control design, which are called preclinical trials.

This article provides a concise summary of the recent developments that have been achieved in our comprehension of the asymmetric addition processes that are catalysed by native Cinchona alkaloids and their derivatives. This class of reactions includes cycloadditions, 1,4-adds, direct nucleophilic additions across carbon–oxygen or carbon–nitrogen double bonds, and direct nucleophilic additions across carbon–oxygen double bonds. Because of their capacity to catalyse the addition of a wide variety of functional groups to C9, many Cinchona alkaloids have been utilised in these processes as catalysts. These functional groups include amino, alkoxy, hydroxyl, amido, urea, and thiourea, among others. The importance of mechanical variables is emphasised in many different contexts. Additionally, the utilisation of adducts in future synthesis is sometimes broken down into its component steps. Ocimum basilicum was discovered to be mostly consisted of estragol (> 35.71 percent), (E)-ocimene (> 1.47 percent), trans-bergamotene (> 0.83 percent), a-cadinol (> 0.41 percent), eucalyptol (> 0.25 percent), and -caryophyllene (> 0.07 percent), whereas Ocimum sanctum is primarily composed of eucaly There is a greater concentration of chemical components in the leaves of Ocimum basilicum and Ocimum sanctum than there is in the actual inflorescence or flowers of the plant. The genetic distance between the two species was analysed in order to better understand the interspecies relationship, and the results showed that it was 2.86. The small difference in genetic makeup that exists between Ocimum basilicum and Ocimum sanctum is evidence that these two species are related to one another and share similar traits.

COVID-19 pandemic, is still unknown and is an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona-) viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is an important part of determining host specificity since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among avians, bats, camels, swines, humans and weasels, to name a few. We propose a feature embedding based on the well-known position-weight matrix (PWM), which we call PWM2Vec, and use to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications such as determining protein function, or identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs in the context of host classification from viral sequences to generate a fixed-length feature vector representation. The results on the real world data show that in using PWM2Vec, we are able to perform comparably well as compared to baseline models. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus.

Contains fulltext : 230540.pdf (publisher's version ) (Open Access)

Contains fulltext : 239289.pdf (Publisher’s version ) (Open Access)

3GPP has introduced C-V2X technology in the Rel. 14 for improving road safety. There are two modes of vehicular communication in C-V2X viz., Mode 3 and Mode 4. Since Mode 4 allows vehicles to communicate with each other even outside the cellular coverage, it is acknowledged as the baseline mode. In Mode 4, vehicles select the best available least interfered radio resource by utilising Semi-Persistent Scheduling (SPS) in a distributed fashion. Medium Access Control (MAC) parameters like pKeep (probability of keeping a radio resource) and Resource Counter (RC) affect the duration of radio resource collision, so these need to be configured carefully for efficient C-V2X communication. This paper explicates the impact of pKeep and RC on the overall performance of C-V2X system. A new algorithm called Traffic-Aware SPS (TA-SPS) is then proposed, which optimally configures these parameters according to the current density of road traffic by estimating it from periodic Cooperative Awareness Messages (CAMs) broadcasted by the vehicles. By reducing the number of resource collisions, TA-SPS increases transmission reliability of the C-V2X system. Through extensive simulation studies, we show how TA-SPS outperforms the traditional SPS in mixed road traffic scenarios. © 2021 IEEE.

With the advancement in technology and digitalization of society, the number of smartphone users has increased drastically. According to [1], there will be 5.3 billion Internet users by 2023. Due to this, Wi-Fi penetration rate will also increase. For tracking smartphones present inside the vehicles, the uniqueness of Wi-Fi Medium Access Control (MAC) addresses helps a lot. Road traffic monitoring systems can make use of this information for estimating travel time, average speed, and traffic flow in the areas of interest. This article focuses on utilizing Wi-Fi based road-side sniffers to listen to Wi-Fi probe signals broadcasted by the smartphones present inside vehicles for estimating traffic stats of road segments. Hopping Interval (HI) is the rate at which Wi-Fi radio of the sniffer is changed to one of the available channels in ISM band. A channel hopping algorithm is proposed, which adjusts HI of the sniffer according to the current traffic conditions. The proposed algorithm reduces the estimation error and increases the robustness and efficiency of the traffic monitoring system. To validate the algorithm, simulation experiments have been performed in Indian traffic scenarios, and results show that the proposed algorithm is better than static channel assignment on the sniffer with an improvement of 4%. Also, estimated travel time and the ground truth values are found out to be closely related, which assures the suitability of the Wi-Fi based road-side sniffers for traffic monitoring.

Contains fulltext : 230540.pdf (publisher's version ) (Open Access)

Contains fulltext : 239289.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 230540.pdf (publisher's version ) (Open Access)

3GPP has introduced C-V2X technology in the Rel. 14 for improving road safety. There are two modes of vehicular communication in C-V2X viz., Mode 3 and Mode 4. Since Mode 4 allows vehicles to communicate with each other even outside the cellular coverage, it is acknowledged as the baseline mode. In Mode 4, vehicles select the best available least interfered radio resource by utilising Semi-Persistent Scheduling (SPS) in a distributed fashion. Medium Access Control (MAC) parameters like pKeep (probability of keeping a radio resource) and Resource Counter (RC) affect the duration of radio resource collision, so these need to be configured carefully for efficient C-V2X communication. This paper explicates the impact of pKeep and RC on the overall performance of C-V2X system. A new algorithm called Traffic-Aware SPS (TA-SPS) is then proposed, which optimally configures these parameters according to the current density of road traffic by estimating it from periodic Cooperative Awareness Messages (CAMs) broadcasted by the vehicles. By reducing the number of resource collisions, TA-SPS increases transmission reliability of the C-V2X system. Through extensive simulation studies, we show how TA-SPS outperforms the traditional SPS in mixed road traffic scenarios. © 2021 IEEE.

3GPP has introduced C-V2X technology in the Rel. 14 for improving road safety. There are two modes of vehicular communication in C-V2X viz., Mode 3 and Mode 4. Since Mode 4 allows vehicles to communicate with each other even outside the cellular coverage, it is acknowledged as the baseline mode. In Mode 4, vehicles select the best available least interfered radio resource by utilising Semi-Persistent Scheduling (SPS) in a distributed fashion. Medium Access Control (MAC) parameters like pKeep (probability of keeping a radio resource) and Resource Counter (RC) affect the duration of radio resource collision, so these need to be configured carefully for efficient C-V2X communication. This paper explicates the impact of pKeep and RC on the overall performance of C-V2X system. A new algorithm called Traffic-Aware SPS (TA-SPS) is then proposed, which optimally configures these parameters according to the current density of road traffic by estimating it from periodic Cooperative Awareness Messages (CAMs) broadcasted by the vehicles. By reducing the number of resource collisions, TA-SPS increases transmission reliability of the C-V2X system. Through extensive simulation studies, we show how TA-SPS outperforms the traditional SPS in mixed road traffic scenarios. © 2021 IEEE.

With the advancement in technology and digitalization of society, the number of smartphone users has increased drastically. According to [1], there will be 5.3 billion Internet users by 2023. Due to this, Wi-Fi penetration rate will also increase. For tracking smartphones present inside the vehicles, the uniqueness of Wi-Fi Medium Access Control (MAC) addresses helps a lot. Road traffic monitoring systems can make use of this information for estimating travel time, average speed, and traffic flow in the areas of interest. This article focuses on utilizing Wi-Fi based road-side sniffers to listen to Wi-Fi probe signals broadcasted by the smartphones present inside vehicles for estimating traffic stats of road segments. Hopping Interval (HI) is the rate at which Wi-Fi radio of the sniffer is changed to one of the available channels in ISM band. A channel hopping algorithm is proposed, which adjusts HI of the sniffer according to the current traffic conditions. The proposed algorithm reduces the estimation error and increases the robustness and efficiency of the traffic monitoring system. To validate the algorithm, simulation experiments have been performed in Indian traffic scenarios, and results show that the proposed algorithm is better than static channel assignment on the sniffer with an improvement of 4%. Also, estimated travel time and the ground truth values are found out to be closely related, which assures the suitability of the Wi-Fi based road-side sniffers for traffic monitoring.

With the advancement in technology and digitalization of society, the number of smartphone users has increased drastically. According to [1], there will be 5.3 billion Internet users by 2023. Due to this, Wi-Fi penetration rate will also increase. For tracking smartphones present inside the vehicles, the uniqueness of Wi-Fi Medium Access Control (MAC) addresses helps a lot. Road traffic monitoring systems can make use of this information for estimating travel time, average speed, and traffic flow in the areas of interest. This article focuses on utilizing Wi-Fi based road-side sniffers to listen to Wi-Fi probe signals broadcasted by the smartphones present inside vehicles for estimating traffic stats of road segments. Hopping Interval (HI) is the rate at which Wi-Fi radio of the sniffer is changed to one of the available channels in ISM band. A channel hopping algorithm is proposed, which adjusts HI of the sniffer according to the current traffic conditions. The proposed algorithm reduces the estimation error and increases the robustness and efficiency of the traffic monitoring system. To validate the algorithm, simulation experiments have been performed in Indian traffic scenarios, and results show that the proposed algorithm is better than static channel assignment on the sniffer with an improvement of 4%. Also, estimated travel time and the ground truth values are found out to be closely related, which assures the suitability of the Wi-Fi based road-side sniffers for traffic monitoring.

Agriculture occupation has been the prime occupation in India since the primeval era. Nowadays, the country is ranked second in the prime occupations threatening global warming. Apart from this, diseases in plants are challenging to this prime source of livelihood. The present research can help in recognition of different diseases among plants and help to find out the solution or remedy that can be a defense mechanism in counter to the diseases. Finding diseases among plant DL is considered to the most perfect and exact paradigms. Four labels are classified as “bacterial spot,” “yellow leaf curl virus,” “late blight,” and “healthy leaf.” An exemplar model of the drone is also designed for the purpose. The said model will be utilized for a live report for extended large crop fields. In this exemplar drone model, a high-resolution camera is attached. The captured images of plants will act as software input. On this basis, the software will immediately tell which plants are healthy and which are diseased.

Agriculture occupation has been the prime occupation in India since the primeval era. Nowadays, the country is ranked second in the prime occupations threatening global warming. Apart from this, diseases in plants are challenging to this prime source of livelihood. The present research can help in recognition of different diseases among plants and help to find out the solution or remedy that can be a defense mechanism in counter to the diseases. Finding diseases among plant DL is considered to the most perfect and exact paradigms. Four labels are classified as “bacterial spot,” “yellow leaf curl virus,” “late blight,” and “healthy leaf.” An exemplar model of the drone is also designed for the purpose. The said model will be utilized for a live report for extended large crop fields. In this exemplar drone model, a high-resolution camera is attached. The captured images of plants will act as software input. On this basis, the software will immediately tell which plants are healthy and which are diseased.