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Abstract Cystic fibrosis transmembrane conductance regulator (CFTR) protein is an ion channel found in numerous epithelia and controls the flow of water and salt across the epithelium. The aim of our study to find natural compounds that can improve lung function for people with cystic fibrosis (CF) caused by the p.Gly628Arg (rs397508316) mutation of CFTR protein. The sequence of CFTR protein as a target structure was retrieved from UniProt and PDB database. The ligands that included Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and one Trikafta (R*) reference drug were screened out from PubChem database. Autodock vina software carried out docking, and binding energies between the drug and the target were included using docking-score. The following tools examined binding energy, interaction, stability, toxicity, and visualize protein-ligand complexes. The compounds having binding energies of −6.4, −5.1, −6.6, −5.1, and − 6.5 kcal/mol for Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and R*-drug, respectively with mutated CFTR (Gly628Arg) structure were chosen as the most promising ligands. The ligands bind to the mutated CFTR protein structure active sites in hydrophobic bonds, hydrogen bonds, and electrostatic interactions. According to ADMET analyses, the ligands Armepavine and Quercetin also displayed good pharmacokinetic and toxicity characteristics. An MD simulation for 200 ns was also established to ensure that Armepavine and Quercetin ligands attached to the target protein favorably and dynamically, and that protein-ligand complex stability was maintained. It is concluded that Armepavine and Quercetin have stronger capacity to inhibit the effect of mutated CFTR protein through improved trafficking and restoration of original function.

Abstract Transportation systems primarily depend on vehicular flow on roads. Developed countries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

Abstract Cystic Fibrosis Transmembrane Regulator (CFTR) is a significant protein that is responsible for the movement of ions across cell membranes. The cystic fibrosis (CF) occur due to the mutations in the CFTR gene as it produces the dysfunctional CFTR protein. The sequence of CFTR protein as a target structure was retrieved from UniProt and PDB database. The ligands selection was performed through virtual screening and top 3 ligands choose out of 65 ligands silibinins, curcumin, demethoxycurcumin were selected with a reference drug Trikafta (R*). According to docking, ADMET analyses, the natural ligands (Silibinins and Curcumin) displayed best binding energy, pharmacokinetic and free toxicity than other natural compounds and reference drug (R*). An MD simulation for 200 ns was also established to ensure that natural ligands (Silibinins and Curcumin) attached to the target protein favorably and dynamically, and that protein–ligand complex stability was maintained. It is concluded that silibinins and curcumins have a better capacity to decrease the effect of mutant CFTR protein through improved trafficking and the restoration of original function. In conclusion, in silico studies demonstrate the potential of silibinins and curcumin as therapeutic agents for cystic fibrosis, particularly for the D614G mutated protein. Their ability to increase CFTR function while reducing cellular stress and inflammation, together with their favorable safety profile and accessibility could make them valuable additions to cystic fibrosis treatment options. Further experimental and clinical validation will be required to fully realize their potential and include them into effective therapy regimens.

The pattern recognition receptor (PRR), which drives innate immunity, shields the host against invasive pathogens. Fish and other aquatic species with poorly developed adaptive immunity mostly rely on their innate immunity, regulated by PRRs such as inherited-encoded toll-like receptors (TLRs). The discovery of 21 unique TLR variations in various aquatic animals over the past several years has sparked interest in using TLRs to improve aquatic animal’s immune response and disease resistance. This comprehensive review provides an overview of the latest investigations on the various characteristics of TLRs in aquatic animals. It emphasizes their categorization, insights into 3D architecture, ligand recognition, signaling pathways, TLRs mediated immune responses under biotic and abiotic stressors, and expression variations during several developmental stages. It also highlights the differences among aquatic animals’ TLRs and their mammal counterparts, which signifies the unique roles that TLRs play in aquatic animal’s immune systems. This article summarizes current aquaculture research to enhance our understanding of fish immune systems for effective aquaculture -related disease management.

Abstract With easy access to social media platforms, spreading fake news has become a growing concern today. Classifying fake news is essential, as it can help prevent its negative impact on individuals and society. In this regard, an end-to-end framework for fake news detection is developed by utilizing the power of adversarial training to make the model more robust and resilient. The framework is named "ANN: Adversarial News Net," emoticons have been extracted from the datasets to understand their meanings concerning fake news. This information is then fed into the model, which helps to improve its performance in classifying fake news. The performance of the ANN framework is evaluated using four publicly available datasets, and it is found to outperform baseline methods and previous studies after adversarial training. Experiments show that Adversarial Training improved the performance by 2.1% over the Random Forest baseline and 2.4% over the BERT baseline method in terms of accuracy. The proposed framework can be used to detect fake news in real-time, thereby mitigating its harmful effects on society.

A carbon-based material with a broad scope of favourable developments is called graphene. Recently, a graphene nanoribbon with cove-edged was integrated by utilizing a bottom-up liquid-phase procedure, and it can be geometrically viewed as a hybrid of the armchair and the zigzag edges. It is indeed a type of nanoribbon containing asymmetric edges made up of sequential hexagons with impressive mechanical and electrical characteristics. Topological indices are numerical values associated with the structure of a chemical graph and are used to predict various physical, chemical, and biological properties of molecules. They are derived from the graph representation of molecules, where atoms are represented as vertices and bonds as edges. In this article, we derived the exact topological expressions of cove-edged graphene nanoribbons based on the graph-theoretical structural measures that help reduce the number of repetitive laboratory tasks necessary for studying the physicochemical characteristics of graphene nanoribbons with curved edges.

Abstract Francisella tularensis (Ft) poses a significant threat to both animal and human populations, given its potential as a bioweapon. Current research on the classification of this pathogen and its relationship with soil physical–chemical characteristics often relies on traditional statistical methods. In this study, we leverage advanced machine learning models to enhance the prediction of epidemiological models for soil-based microbes. Our model employs a two-stage feature ranking process to identify crucial soil attributes and hyperparameter optimization for accurate pathogen classification using a unique soil attribute dataset. Optimization involves various classification algorithms, including Support Vector Machines (SVM), Ensemble Models (EM), and Neural Networks (NN), utilizing Bayesian and Random search techniques. Results indicate the significance of soil features such as clay, nitrogen, soluble salts, silt, organic matter, and zinc , while identifying the least significant ones as potassium, calcium, copper, sodium, iron, and phosphorus. Bayesian optimization yields the best results, achieving an accuracy of 86.5% for SVM, 81.8% for EM, and 83.8% for NN. Notably, SVM emerges as the top-performing classifier, with an accuracy of 86.5% for both Bayesian and Random Search optimizations. The insights gained from employing machine learning techniques enhance our understanding of the environmental factors influencing Ft’s persistence in soil. This, in turn, reduces the risk of false classifications, contributing to better pandemic control and mitigating socio-economic impacts on communities.

Quality sleep plays a vital role in living beings as it contributes extensively to the healing process and the removal of waste products from the body. Poor sleep may lead to depression, memory deficits, heart, and metabolic problems, etc. Sleep usually works in cycles and repeats itself by transitioning into different stages of sleep. This study is unique in that it uses wearable devices to collect multiple parameters from subjects and uses this information to predict sleep stages and sleep patterns. For the multivariate multiclass sleep stage prediction problem, we have experimented with both memoryless (ML) and memory-based models on seven database instances, that is, five from the collected dataset and two from the existing datasets. The Random Forest classifier outclassed the ML models that are LR, MLP, kNN, and SVM with accuracy (ACC) of 0.96 and Cohen Kappa 0.96, and the memory-based model long short-term memory (LSTM) performed well on all the datasets with the maximum attained accuracy of 0.88 and Kappa 0.82. The proposed methodology was also validated on a longitudinal dataset, the Multiethnic Study of Atherosclerosis (MESA), with ACC and Kappa of 0.75 and 0.64 for ML models and 0.86 and 0.78 for memory-based models, respectively, and from another benchmarked Apple Watch dataset available on Physio-Net with ACC and Kappa of 0.93 and 0.93 for ML and 0.92 and 0.87 for memory-based models, respectively. The given methodology showed better results than the original work and indicates that the memory-based method works better to capture the sleep pattern.

In recent times, there has been a substantial increase in the annual use of plastics, resulting in a major upward trend. As a result, recycling plastic waste has become a major global issue. The present research aims to explore the feasibility of utilizing Polyethylene terephthalate (PET) as a possible substitute for coarse aggregate in concrete. In addition, the integration of Graphene Nano-platelets (GNPs) into the concrete mixture was conducted using different quantities, aiming to improve its structural integrity. The study employed an experimental research design to carry out its examination. PET was included into concrete at varied ratios, namely 0 %, 5 %, 15 %, 20 %, and 25 %, serving as an additional ingredient alongside coarse aggregate. Additionally, GNPs were introduced into the mixture at various weight percentages, namely 0 %, 0.03 %, 0.05 %, 0.08 %, and 0.1 %. A series of mechanical tests were performed to evaluate various properties of the concrete, including compressive strength (CS), split tensile strength (STS), flexural strength (FS), modulus of elasticity (MoE), ultrasonic pulse velocity (UPV). In addition, an assessment of the concrete's environmental impact was conducted by analysing the carbon content and evaluating its eco-efficiency (ESE). The research study revealed that the incorporation of 5 % PET as a replacement for coarse aggregate, together with the inclusion of 0.1 % GNPs, resulted in the optimal enhancement of CS, STS, FS, MOE, UPV by 9 %, 12.21 %, 4.40 %, 4.40 % and 0.070 % respectively. The Response Surface Methodology (RSM) models were developed, and mathematical equations were generated in order to predict the expected results. The optimization process for all the models was carried out using a multi-objective optimization technique, followed by a subsequent validation process.

Abstract In the context of ARVC, a systematic review of the validation of the ARVC risk score can provide insights into the accuracy and reliability of this score in identifying patients at high risk of ARVC. Digital databases were searched to identify the relevant studies using Medical Subject Headings (MeSH). A total of 8 studies were included in this systematic review. A total of 8 studies were included in this review. The review found that the sensitivity of the ARVC risk scores ranged from 80 to 95%, and the specificity ranged from 31 to 79%. The PPV was 55%, and the NPV was 88%. The ARVC score provided a C-index for a 5-year VA risk prediction of 0.84 [95% CI (0.74–0.93)] and a Harrell C-index of 0.70 (95% CI 0.65–0.75). The calibration slope was 1.01 (95% CI 0.99–1.03). ARVC score demonstrated a significant event 5-year threshold between 15 and 20% and the classical ARVC 5-years/freedom-from-VA rate was 0.76(0.66–0.89) and the non-classical form 5-years/freedom-from-VA rate was 0.58 (0.43–0.78). In conclusion, the validation of ARVC risk scores is an essential step toward improving the accuracy of ARVC diagnosis and risk stratification. Further studies are needed to establish the accuracy and reliability of ARVC risk scores and to address the limitations of the current evidence.

Abstract Nanomaterials feature exceptional, one-of-a-kind qualities that might be used in electronics, medicine, and other industries. Two-dimensional nanomaterials called borophene have a variety of intriguing characteristics, which helped them to leave an indelible impression in the fields of chemistry, material science, nanotechnology, and condensed matter physics. The concept of modelling the structure of a molecule or chemical network to a chemical graph and then quantitatively analysing them with the aid of topological descriptors was a major advance in the fields of mathematics and chemistry, with a wide range of applications. M-polynomial approach is a very versatile and quick method for computing the degree-based descriptors of chemical graphs or networks. The degree-based descriptors of the $$\beta _{12}$$ β 12 -Borophene nanosheet are established in this study utilising the M-polynomial technique. A program code that enables to generate the M-polynomial of any chemical structure was developed in Java platform and the same is displayed. At the conclusion, the numerical and graphical comparison based on the identified analytic expressions is also provided. Additionally, the QSPR analysis was also carried out and the outcoms are presented therein.

Abstract Entropy of a connected network is a quantitative measure from information theory that has triggered a plethora of research domains in molecular chemistry, biological sciences and computer programming due to its inherent capacity to explore the structural characteristics of complex molecular frameworks that have low structural symmetry as well as high diversity. The analysis of the structural order is greatly simplified through the topological indices based graph entropy metrics, which are then utilized to predict the structural features of molecular frameworks. This predictability has not only revolutionized the study of zeolitic frameworks but has also given rise to new generations of frameworks. We make a comparative study of two versatile framework topologies namely zeolites BCT and DFT, which have been widely utilized to create a new generation of frameworks known as metal organic frameworks. We discuss bond-additive topological indices and compute entropy measure descriptors for zeolites BCT and DFT using degree and degree-sum parameters. In addition, we perform bond-wise scaled comparative analysis between BCT and DFT which shows that zeolite BCT has greater entropy values compared to zeolite DFT.

Abstract Pathogenic bacteria present a major threat to human health, causing various infections and illnesses, and in some cases, even death. The accurate identification of these bacteria is crucial, but it can be challenging due to the similarities between different species and genera. This is where automated classification using convolutional neural network (CNN) models can help, as it can provide more accurate, authentic, and standardized results.In this study, we aimed to create a larger and balanced dataset by image patching and applied different variations of CNN models, including training from scratch, fine-tuning, and weight adjustment, and data augmentation through random rotation, reflection, and translation. The results showed that the best results were achieved through augmentation and fine-tuning of deep models. We also modified existing architectures, such as InceptionV3 and MobileNetV2, to better capture complex features. The robustness of the proposed ensemble model was evaluated using two data splits (7:2:1 and 6:2:2) to see how performance changed as the training data was increased from 10 to 20%. In both cases, the model exhibited exceptional performance. For the 7:2:1 split, the model achieved an accuracy of 99.91%, F-Score of 98.95%, precision of 98.98%, recall of 98.96%, and MCC of 98.92%. For the 6:2:2 split, the model yielded an accuracy of 99.94%, F-Score of 99.28%, precision of 99.31%, recall of 98.96%, and MCC of 99.26%. This demonstrates that automatic classification using the ensemble model can be a valuable tool for diagnostic staff and microbiologists in accurately identifying pathogenic bacteria, which in turn can help control epidemics and minimize their social and economic impact.

Cystic fibrosis (CF) is a genetic monogenic disorder inherited in an autosomal recessive manner, marked by persistent airway infections in the endobronchial region. This condition leads to the gradual development of bronchiectasis and, ultimately, respiratory failure, emerging as the primary cause of mortality in individuals diagnosed with CF. Diagnosis is done depending on the patient’s symptoms and lung radiological findings like chest X-rays and CTs. In younger patients and children, diagnosis becomes difficult due to overlapping symptoms with other diseases such as CF which is a rare genetic disease in our population. Diagnosis of CF usually relies on characteristic symptoms, a family history of CF, and an abnormal sweat chloride test, but in children, low sweat production during testing leads to false negative results. In this case report, a suspected patient with ambiguous respiratory symptoms underwent a comprehensive investigation revealing elevated CRP levels, TLC, and characteristic pulmonary manifestations on chest X-ray, suggesting cystic fibrosis. Despite negative sweat chloride tests, the patient was analysed for potential candidate SNPs and was also tested for potential CFTR mutations to rule out CF, genetic analysis confirmed the diagnosis. Genetic testing plays a crucial role in diagnosing cystic fibrosis, even when traditional tests are inconclusive. Specific mutations like Δ508 deletion and rs213950 guide personalized treatment. Consanguinity and family history highlight genetic predisposition, while environmental factors may influence symptom onset. Further research is needed to understand these complexities and improve diagnostic and treatment approaches.

Tuberculosis (TB) is one of the most contagious diseases that has a greater mortality rate than HIV/AIDS and the cases of TB are feared to rise as a repercussion of the COVID-19 pandemic. The pharmaceutical industry is constantly looking for ways to improve drug design processes in order to combat the growth of infections and cure newly identified syndromes or genetically based dysfunctions with the help of QSPR models. QSPR models are mathematical tools that establish relationships between a molecular structure and its physicochemical attributes using structural properties. Topological indices are such properties that are generated from the molecular graph without any empirically derived measurements. This work focuses on developing a QSPR model using distance-based topological indices for anti-tuberculosis medications and their diverse physicochemical features.

The repairing and strengthening of concrete structures using external and internal partial confinements are inevitable in the construction industry due to the new standards and rapid developments. The conventional materials and methods of confinement are unable to meet modern safety and functional standards. The fiber-reinforced polymer (FRP) enhances the strength and ductility of deteriorating and new concrete columns by reducing lateral confinement pressure and resistance against seismic shocks. The precise methods of partial confinement are inevitable for effective FRP-concrete bonding, durability, and cost-effectiveness under different loading conditions and to cope with external environmental factors. Predictive modeling and simulation techniques are pivotal for the optimization of confinement materials and methods by investigating the FRP-concrete novel confinement configurations, stress–strain responses, and failure modes. The novel materials and methods for concrete columns’ partial confinement lack high compressive strength, ductility, chemical attack resistivity, and different fiber orientation impacts. This review provides an overview of recent confinement materials, novel methods, and advanced modeling and simulation techniques with a critical analysis of the research gaps for partial FRP confinement of concrete columns. The current challenges and future prospects are also presented.

In theoretical chemistry, topological indices are commonly employed to model the physico-chemical properties of chemical compounds. Mathematicians frequently use Zagreb indices to calculate a chemical compound's strain energy, melting point, boiling temperature, distortion, and stability. The current global pandemic caused by the new SARS-CoV-2, also known as COVID-19, is a significant public health concern. Various therapy modalities are advised. The issue has become worse since there hasn't been enough counseling. Researchers are looking at compounds that might be used as SARS and MERS therapies based on earlier studies. In several quantitative structure-property-activity relationships (QSPR and QSAR) studies, a variety of physiochemical properties are successfully represented by topological indices, a sort of molecular descriptor that just specifies numerical values connected to a substance's molecular structure. This study investigates several irregularity-based topological indices for various antiviral medicines, depending on the degree of irregularity. In order to evaluate the effectiveness of the generated topological indices, a QSPR was also carried out using the indicated pharmaceuticals, the various topological indices, and the various physiochemical features of these antiviral medicines. The acquired results show a substantial association between the topological indices being studied by the curve-fitting approach and the physiochemical properties of possible antiviral medicines.

Latin American dictator fiction has a long tradition and is widely acclaimed in critical studies, while Pakistani dictator fiction began to emerge in recent years. The study is analytical and comparative research based on the contents of two culturally, historically, and geographically diverse novels—Mario Vargas Llosa’s The Feast of the Goat and Mohammad Hanif’s A Case of Exploding Mangoes so as to locate their common and contrasting aspects. It is a qualitative study and through content analysis, it finds convergences and divergences between a Latin American and a Pakistani dictator novel. A special emphasis of the study is on power, dictator portrayal, torture, resistance, retelling/rewriting history, and representation. The study concludes that where Llosa presents the detailed description of events related to the atrocious regime of Trujillo, Hanif presents a partial and not the whole view of Zia’s dictatorship. Llosa misses no opportunity to create the sentiments of detestation and wrath against the despotic rule and his language throbs with bitterness and becomes satiric while unveiling the dark aspects of the Trujillo regime. However, Hanif, instead of giving a detailed and direct portrayal of certain dark aspects and brutalities, gives just oblique references to them and even the events which he brings to the forefront are presented in a very light and humorous vein which fail to arouse the emotions of abhorrence and denouncement against the despotic rule.

Multi-agent deep reinforcement learning (MDRL) is a popular choice for multi-intersection traffic signal control, generating decentralized cooperative traffic signal strategies in specific traffic networks. Despite its widespread use, current MDRL algorithms have certain limitations. Firstly, the specific multi-agent settings impede the transferability and generalization of traffic signal policies to different traffic networks. Secondly, existing MDRL algorithms struggle to adapt to a varying number of vehicles crossing the traffic networks. This paper introduces a novel Cooperative Multi-Agent Deep Q-Network (CMDQN) for traffic signal control to alleviate traffic congestion. We have considered innovative features such as signal state at the preceding junction, the distance between junctions, visual features, and average speed. Our CMDQN applies a Decentralized Multi-Agent Network (DMN), employing a Markov Game abstraction for collaboration and state information sharing between agents to reduce waiting times. Our work employs Reinforcement Learning (RL) and a Deep Q-Network (DQN) for adaptive traffic signal control, leveraging Deep Computer Vision for real-time traffic density information. We also propose an intersection and a network-wide reward function to evaluate performance and optimize traffic flow. The developed system was evaluated through both synthetic and real-world experiments. The synthetic network is based on the Simulation of Urban Mobility (SUMO) traffic simulator, and the real-world network employed traffic data collected from installed cameras at actual traffic signals. Our results demonstrated improved performance across several key metrics when compared to the baseline model, reducing waiting times and improving traffic flow. This research presents a promising approach for cooperative traffic signal control, significantly contributing to the efforts to enhance traffic management systems.

Health facilities have increased life expectancy, a key factor for the growth of the elderly population. Elderly people are at increased risk of falls, causing physical and psychological damage. Falls occur rarely compared to other activities of daily living. Due to such a class imbalance, supervised techniques are not the solution for fall detection systems. In addition, domain-level features for the fall activity are hard to generalize due to their diversity. In this work, the fall detection problem is formulated as anomaly detection in the time series where deviation from the activities of daily living is computed. On the basis of the deviation score, a fall is detected. We propose TCHA, Temporal Convolutional Hourglass Autoencoder, to learn spatial and temporal features from the videos. Hourglass units in the Temporal Convolutional Encoder help us extract multiscale features by expanding the receptive fields of neurons, reducing the information loss in deep learning methods. The proposed methodology is evaluated on the five data sets, including a compiled data set from publicly available Toyota Smarthome data set and four benchmarked datasets that include the UR-Fall dataset, IMVIA dataset, SDU dataset, and Thermal Fall dataset. Our methodology shows 4.1% superior results to existing state-of-the-art methods for unseen falls.

As wheat is one of the major crops worldwide, therefore, accurate disease detection in wheat plants is critical for mitigating effects and halting disease spread. Nowadays, the detection of diseases through images using machine learning and deep learning has achieved state-of-art results. Yet these models suffer from two shortcomings, one is the data-hungry models of deep learning, and the second is the adaption of new classes with previous ones, i.e., if a new disease arrived, one must retrain the entire model with a new dataset which has led to the origination of few-shot learning that solves both problems. A wheat disease classification network is proposed based on a few-shot learning method for the classification of 18 wheat diseases using EfficientNet as a backbone. Also, the attention mechanism is incorporated to facilitate efficient feature selection. Our proposed network has achieved 93.19% accuracy on the 40 images of 18 disease classes manually collected from the internet while that of our backbone network EfficientNet has gained an accuracy of about 98.5% accuracy on CGIAR Dataset. The evaluation demonstrates that despite the presence of only a few images in both the training and query sets, the proposed model has achieved superior performance in terms of accuracy and computation cost. Furthermore, the evaluation also highlights the effectiveness of our model in terms of data and computation cost. The proposed method can be used for disease detection in wheat crops requiring less amount of data.

Entropy is a thermodynamic function in chemistry that, based on the number of possible configurations for a given system or process, measures the randomness and disorder of molecules in that system or process. Many problems in arithmetic, biology, chemical graph theory, organic and inorganic chemistry, and other disciplines are resolved using distance-based entropy. A topological descriptor also connects certain physical aspects of the underlying chemical substances, in contrast to a topological index, which in chemical graph theory is a numerical representation of a chemical network. When building models for any chemical network, the graph is crucial. Simonraj et al. produced a brand-new category of graphs known as the third kind of hex-derived networks. In our work, we discuss the third class of hex-derived networks, which includes hex-derived networks ( $\mathcal {HDN}_{3}$ ), triangular hex-derived networks ( $\mathcal {THDN}_{3}$ ), rectangular hex-derived networks ( $\mathcal {RHDN}_{3}$ ), and chain hex-derived networks ( $\mathcal {CHDN}_{3}$ ). We compute exact results for newly defined entropies by utilizing their topological indices based on end vertex degrees.

Summarization generates a brief and concise summary which portrays the main idea of the source text. There are two forms of summarization: abstractive and extractive. Extractive summarization chooses important sentences from the text to form a summary whereas abstractive summarization paraphrase using advanced and nearer-to human explanation by adding novel words or phrases. For a human annotator, producing summary of a document is time consuming and expensive because it requires going through the long document and composing a short summary. An automatic feature-rich model for text summarization is proposed that can reduce the amount of labor and produce a quick summary by using both extractive and abstractive approach. A feature-rich extractor highlights the important sentences in the text and linguistic characteristics are used to enhance results. The extracted summary is then fed to an abstracter to further provide information using features such as named entity tags, part of speech tags and term weights. Furthermore, a loss function is introduced to normalize the inconsistency between word-level and sentence-level attentions. The proposed two-staged network achieved a ROUGE score of 37.76% on the benchmark CNN/DailyMail dataset, outperforming the earlier work. Human evaluation is also conducted to measure the comprehensiveness, conciseness and informativeness of the generated summary.

Fusion of internet of things (IoT) and artificial intelligence for smart cities has allowed structured policy making.In particular, traffic jam is one of the serious concern in metropolitan cities. In advanced countries, specific devices are installed to assemble flow of traffic. The state of traffic is streamed to commuters through network. This dependency on internet connection results in inappropriate solution in absence of stable physical infrastructure. The developing countries lack availability of internet on roadway and urban zones face even worst condition. This study proposes a smart framework to enable optimal path selection by communicating with neighbouring devices. The android smart phones serve the purpose of embedded sensors. A temporary network is created for smart transportation system. Data processing and cleaning were conducted on different routes of two urban cities. The proposed system is evaluated on conventional and proposed optimal path selection techniques. Computed results depict that travel times have been decreased to around 23.3% in comparison to conventional methods using MDSP algorithm.

Abstract Coxiella burnetii (Cb) is a hardy, stealth bacterial pathogen lethal for humans and animals. Its tremendous resistance to the environment, ease of propagation, and incredibly low infectious dosage make it an attractive organism for biowarfare. Current research on the classification of Coxiella and features influencing its presence in the soil is generally confined to statistical techniques. Machine learning other than traditional approaches can help us better predict epidemiological modeling for this soil-based pathogen of public significance. We developed a two-phase feature-ranking technique for the pathogen on a new soil feature dataset. The feature ranking applies methods such as ReliefF (RLF), OneR (ONR), and correlation (CR) for the first phase and a combination of techniques utilizing weighted scores to determine the final soil attribute ranks in the second phase. Different classification methods such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and Multi-Layer Perceptron (MLP) have been utilized for the classification of soil attribute dataset for Coxiella positive and negative soils. The feature-ranking methods established that potassium, chromium, cadmium, nitrogen, organic matter, and soluble salts are the most significant attributes. At the same time, manganese, clay, phosphorous, copper, and lead are the least contributing soil features for the prevalence of the bacteria. However, potassium is the most influential feature, and manganese is the least significant soil feature. The attribute ranking using RLF generates the most promising results among the ranking methods by generating an accuracy of 80.85% for MLP, 79.79% for LR, and 79.8% for LDA. Overall, SVM and MLP are the best-performing classifiers, where SVM yields an accuracy of 82.98% and 81.91% for attribute ranking by CR and RLF; and MLP generates an accuracy of 76.60% for ONR. Thus, machine models can help us better understand the environment, assisting in the prevalence of bacteria and decreasing the chances of false classification. Subsequently, this can assist in controlling epidemics and alleviating the devastating effect on the socio-economics of society.

Aniline, a vital component in various chemical industries, is known to be a hazardous persistent organic pollutant that can cause environmental pollution through its manufacturing, processing, and transportation. In this study, the microcosms were established using sediment with a history of aniline pollution as an inoculum to analyze the aniline biodegradation under aerobic conditions through stable isotope probing (SIP) and isopycnic density gradient centrifugation technology. During the degradation assay, aniline that was 13C-labeled in all six carbons was utilized to determine the phylogenetic identity of the aniline-degrading bacterial taxa that incorporate 13C into their DNA. The results revealed that aniline was completely degraded in the microcosm after 45 and 69 h respectively. The bacteria affiliated with Acinetobacter (up to 34.6 ± 6.0%), Zoogloea (up to 15.8 ± 2.2%), Comamonas (up to 2.6 ± 0.1%), and Hydrogenophaga (up to 5.1 ± 0.6%) genera, which are known to degrade aniline, were enriched in the heavy fractions (the DNA buoyant density was 1.74 mg L−1) of the 13C-aniline treatments. Moreover, some rarely reported aniline-degrading bacteria, such as Prosthecobacter (up to 16.0 ± 1.6%) and Curvibacter (up to 3.0 ± 1.6%), were found in the DNA-SIP experiment. Gene families affiliated with atd, tdn, and dan were speculated to be key genes for aniline degradation based on the abundance in functional genes and diversity in different treatments as estimated using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States version 2 (PICRUSt2) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). This study revealed the functional bacteria and possible degradation genes for aniline degradation in simulated polluted environments through SIP. These findings suggest that important degrading bacteria for the transformation of aniline and potential degradation pathways may be useful in the effective application of bioremediation technologies to remediate aniline-contaminated sites.

Abstract Live fluorescence imaging has demonstrated the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine correlates with its functional efficacy. Learning and memory studies have shown that a great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. We have tested the algorithm on in-vitro, in-vivo, and simulated datasets to demonstrate its performance in a wide range of possible experimental scenarios.

In recent years, there has been growing interest in using plant fibers to reinforce materials in modern manufacturing. This study focuses on the development of a novel biocomposite made from an enset fiber (EF) and polylactic acid (PLA) matrix using compression molding at a hot-pressing temperature of 170 °C and pressure of 7 MPa for 7 min. Before preparing the biocomposites, the fibers were chemically modified with different concentrations of sodium hydroxide (NaOH) and cut into shorter fibers with a 40 mm average length in size. Then, the extent of modifications on the mechanical properties, dynamic mechanical behavior, morphology, and water absorption were investigated. The tensile, flexural, and Charpy impact tests were carried out to evaluate the mechanical properties of the samples as per ASTM standards. Moreover, dynamic mechanical analysis (DMA) and the water absorbency of the biocomposites were investigated, and the results were graphically shown and explained. The results indicate that the biocomposite treated with 5% NaOH exhibited significant improvements in tensile strength, flexural strength, and impact strength compared to the untreated composite. The tensile modulus and flexural modulus of 5% NaOH-modified enset fiber biocomposite were also enhanced by 55.8% and 70.3% compared to untreated enset fiber biocomposite. The highest tensile strength, flexural strength, and impact strength found for the PLA composite reinforced by EF treated with 5% w/v NaOH solution were 20.16 MPa, 30.21 MPa, and 12.02 kJ/m2, respectively. In general, the modification of natural fibers improves adhesion at the interface and therefore decreases the water absorption and improves the dynamic mechanical properties of biocomposites.

Cycloarene molecules are benzene-ring-based polycyclic aromatic hydrocarbons that have been fused in a circular manner and are surrounded by carbon–hydrogen bonds that point inward. Due to their magnetic, geometric, and electronic characteristics and superaromaticity, these polycyclic aromatics have received attention in a number of studies. The kekulene molecule is a cyclically organized benzene ring in the shape of a doughnut and is the very first example of such a conjugated macrocyclic compound. Due to its structural characteristics and molecular characterizations, it serves as a great model for theoretical research involving the investigation of π electron conjugation circuits. Therefore, in order to unravel their novel electrical and molecular characteristics and foresee potential applications, the characterization of such components is crucial. In our current research, we describe two unique series of enormous polycyclic molecules made from the extensively studied base kekulene molecule, utilizing the essential graph-theoretical tools to identify their structural characterization via topological quantities. Rectangular kekulene Type-I and rectangular kekulene Type-II structures were obtained from base kekulene molecules arranged in a rectangular fashion. We also employ two subcases for each Type and, for all of these, we derived ten topological indices. We can investigate the physiochemical characteristics of rectangular kekulenes using these topological indices.

Accurate fruit counting is one of the significant phenotypic traits for crucial fruit harvesting decision making. Existing approaches perform counting through detection or regression-based approaches. Detection of fruit instances is very challenging because of the very small fruit size compared to the whole size image of a tree. At the same time, regression-based counting techniques contributes impressive results but presents inaccurate results while number of instances increases. Moreover, most approaches lack scalability and are applicable only on one or two fruit types. This paper proposes a fruit counting mechanism that combines loose segmentation and regression counting that works on six fruit types: Apple, Orange, Tomato, Peach, Pomegranate and Almond. Through relaxed segmentation, fruit clusters are segmented to extract the small image regions which contain the small cluster of fruits. Extracted regions are forwarded for the regression counting of fruits. Relaxed segmentation is achieved through a state-of-the-art deconvolutional network, while modified Inception Residual Networks (ResNet) based nonlinear regression module is proposed for fruit counting. For segmentation, 4,820 original images, including corresponding mask images, of all six fruit types are augmented to 32,412 images through different augmentation techniques, while 21,450 extracted patches are augmented to 89,120 images used for the regression module training. The proposed approach attained a counting accuracy of 94.71% for individual fruit types higher than techniques reported in literature.

News is a source of information to know about progress in the various areas of life all across the globe. However, the volume of this information is high, and getting benefits from the available information becomes difficult. Moreover, the frequency of fake news is increasing significantly and used to fulfill a particular agenda. This led to research on the classification of news to prevent the spread of disinformation. In this work, we use Adversarial Training as a means of regularization for fake news classification. We train two transformed-based encoder models using adversarial examples that help the model learn noise invariant representations. We generate these examples by perturbing the model’s word embedding matrix, and then we fine-tune the model on clean and adversarial examples simultaneously. We train and evaluate the models on the Buzzfeed Political News and Random Political News datasets. Results show consistent improvements over the baseline models when we train models using adversarial examples. Experiments show that Adversarial Training improves the performance by 1.25% over the BERT baseline, 2.05% over the Longformer baseline for the Random Political News dataset, 1.25% over the BERT baseline and 0.9% over the Longformer baseline for Buzzfeed Political News dataset in terms of F1-score.

The connection of Zagreb polynomials and Zagreb indices to chemical graph theory is a bifurcation of mathematical chemistry, which has had a crucial influence on the development of chemical sciences. Nowadays, the study of topological indices has become a vast effective research area in chemical graph theory. In this article, we add up eight different Zagreb polynomials for the Silicate Network and Silicate Chain Network. From these Zagreb polynomials, we catch up on degree-based Zagreb indices. We also provide a graphical representation of the outcome that describes the dependence of topological indices on the given parameters of polynomial structure.

Abstract Acral melanoma (AM) is a rare and lethal type of skin cancer. It can be diagnosed by expert dermatologists, using dermoscopic imaging. It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers. Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma. However, to date, limited research has been conducted on the classification of melanoma subtypes. The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes, such as, AM. In this study, we present a novel deep learning model, developed to classify skin cancer. We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions. Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection. Our custom-built model is a seven-layered deep convolutional network that was trained from scratch. Additionally, transfer learning was utilized to compare the performance of our model, where AlexNet and ResNet-18 were modified, fine-tuned, and trained on the same dataset. We achieved improved results from our proposed model with an accuracy of more than 90 % for AM and benign nevus, respectively. Additionally, using the transfer learning approach, we achieved an average accuracy of nearly 97 %, which is comparable to that of state-of-the-art methods. From our analysis and results, we found that our model performed well and was able to effectively classify skin cancer. Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.

Background: The selection of genotypes in breeding programs relies on accurate estimates of heritability for variables relevant to yield. The better genotypes may be chosen rapidly using statistical estimate. Seventeen advanced breeding lines and three commercial bread wheat (Triticum aestivum L.) genotypes (used as checks) were assessed regarding their genetic characteristics for yield-related traits in the current study. Methods: Grain yield's correlation with several possible qualities was investigated and compared to regional controls. Results: The results showed that the peduncle length was positively correlated with the spike length, plant height, and total number of spikes. There was a favorable correlation between grain yield and spike count. The number of tillers per square meter was favorably connected with days to maturity but negatively correlated with grain weight, moisture percentage, and gluten content. The percentage of protein and gluten also declined with the increasing quantity of grains in each spike. Weight per 1000 grains was favorably connected with moisture percent and negatively correlated with days to maturity. Day of maturity was positively correlated with yield, whereas day of blooming was negatively correlated. The number of tillers m−2 had strong heritability and rapid genetic improvement. The number of grains per spike, plant height, 1000-grain weight, peduncle length, days to maturity, and protein percentage all demonstrated high broad sense heritability and moderate genetic advance, whereas spike length and starch percentage both displayed low heritability and poor genetic advance. The number of spikes per plant, plot yield, days to flowering, moisture%, and gluten% all showed moderate heritability with modest genetic progress. Conclusion: Following from the above, it is recommended that plant features with high heritability and genetic advancement be given preference in selection, while qualities with a negative link to grain output be subjected to cautious selection.

Continuous efforts are being made to improve plain concrete compressive strength and ductility by applying carbon, glass fiber, or hybrid-reinforced epoxy resin composites. The investigation centers on analyzing the axial compressive strength and strain, compressive stress–strain behavior, failure morphology, and crack evolution of the reinforced cylinders. Besides the experiments, non-linear finite element analysis was performed using the finite element (FE) package ABAQUS 2021. The test results indicate that carbon fiber triaxial woven fabric (TWF-C) confinement result in the most significant improvement of 118% in compressive stress than the concrete specimens. On the other hand, glass fiber triaxial woven fabric (TWF-G) confinement shows the highest enhancement of 161% in ductility. The mechanical properties of the sample utilizing glass fiber as the weft yarn and carbon fiber as the warp yarn (TWF-GC2) exhibit superior improvements of 22% in compressive stress and 8% in axial strain compared to the sample using glass fiber as the warp yarn and carbon fiber as the weft yarn (TWF-CG2). Samples with glass fiber as weft yarn show gradual cracks during loading, while carbon fiber as weft yarn show instantaneous damage. The numerical finite element models accurately predict the experimental results of the tested specimens in this study. There were 1.2~3% and 5~10% discrepancies for compressive stress and axial strain, respectively, between experimental and FE results. Overall, the results suggest that Triaxial woven fabric confinement is a valuable technique to improve the strength and strain of concrete and that the type of fibers used could be tailored for appropriate performance characteristics.

Entropy is a thermodynamic function used in chemistry to determine the disorder and irregularities of molecules in a specific system or process. It does this by calculating the possible configurations for each molecule. It is applicable to numerous issues in biology, inorganic and organic chemistry, and other relevant fields. Metal–organic frameworks (MOFs) are a family of molecules that have piqued the curiosity of scientists in recent years. They are extensively researched due to their prospective applications and the increasing amount of information about them. Scientists are constantly discovering novel MOFs, which results in an increasing number of representations every year. Furthermore, new applications for MOFs continue to arise, illustrating the materials’ adaptability. This article investigates the characterisation of the metal–organic framework of iron(III) tetra-p-tolyl porphyrin (FeTPyP) and CoBHT (CO) lattice. By constructing these structures with degree-based indices such as the K-Banhatti, redefined Zagreb, and the atom-bond sum connectivity indices, we also employ the information function to compute entropies.

Background: The main cause for low mungbean [Vigna radiata (L.) R. Wilczek] productivity is mungbean yellow mosaic virus (MYMV). Generally, the management of MYMV relies upon frequent insecticide sprays to control its vector (whitefly) and genetic resistance. However, disease forecast models can help to economize the pesticide sprays. Hence current study was designed to identifying environmental factor that promote disease development and developing a disease prediction model. Methods: One hundred and twenty-seven mungbean accessions were planted for two years (2012 and 2013) and infection was dependent on natural inoculum. Weekly and daily data on disease incidence and environmental variables were collected and analyzed using correlation and stepwise regression analysis. Results: Wind velocity and high temperature had a negative relation with disease occurrence during both years, whereas low temperature, rainfall, and relative humidity, had positive relationship based on linear regression. The environmental conditions responsible for the highest disease incidence were, maximum temperature (32–34 °C), relative humidity (72–75 %), minimum temperature (27–29 °C), rainfall (1.8–2.1 mm) and wind velocity (3–4.5 km/hr) during both growing seasons Overall, five environmental variable multiple regression model encompassing relative humidity, wind speed, rainfall, suboptimum temperature, and optimum temperatures accommodate the data rightly explaining 83 % variation in disease outgrowth. Conclusion: The observed MYMV disease occurrence values for most of the mungbean genotypes, and those predicted by the model were very close. This multi-environmental variable model can be utilized to provide early warning forecasts for the management of MYMV in Pakistan.

The advancement of both the fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has enabled the development of effective automatic systems for analyzing human behavior. It is possible to recognize gestures, which are frequently used by people to communicate information non-verbally, by studying hand movements. So, the main contribution of this research is the collected dataset, which is taken from open-source videos of the relevant subjects that contain actions that depict confidence levels. The dataset contains high-quality frames with minimal bias and less noise. Secondly, we have chosen the domain of confidence determination during social issues such as interviews, discussions, or criminal investigations. Thirdly, the proposed model is a combination of two high-performing models, i.e., CNN (GoogLeNet) and LSTM. GoogLeNet is the state-of-the-art architecture for hand detection and gesture recognition. LSTM prevents the loss of information by keeping temporal data. So the combination of these two outperformed during the training and testing process. This study presents a method to recognize different categories of Self-Efficacy by performing multi-class classification based on the current situation of hand movements using visual data processing and feature extraction. The proposed architecture pre-processes the sequence of images collected from different scenarios, including humans, and their quality frames are extracted. These frames are then processed to extract and analyze the features regarding their body joints and hand position and classify them into four different classes related to efficacy, i.e., confidence, cooperation, confusion, and uncomfortable. The features are extracted using a combination framework of customized Convolutional Neural Network (CNN) layers with Long Short-Term Memory (LSTM) for feature extraction and classification. Remarkable results have been achieved from this study representing 90.48% accuracy with effective recognition of human body gestures through deep learning approaches.

Due to significant advancements being made in the field of drug design, the use of topological descriptors remains the primary approach. When combined with QSPR models, descriptors illustrate a molecule’s chemical properties numerically. Numbers relating to chemical composition topological indices are structures that link chemical composition to physical characteristics. This research concentrates on the analysis of curvilinear regression models and degree-based topological descriptors for thirteen skin cancer drugs. The physicochemical characteristics of the skin cancer drugs are examined while regression models are built for computed index values. An analysis is performed for several significant results based on the acquired data.

In this article, a novel technique to evaluate and compare the neighborhood degree molecular descriptors of two variations of the carbon nanosheet C5C7(a,b) is presented. The conjugated molecules follow the graph spectral theory, in terms of bonding, non-bonding and antibonding Ruckel molecular orbitals. They are demonstrated to be immediately determinable from their topological characteristics. The effort of chemical and pharmaceutical researchers is significantly increased by the need to conduct numerous chemical experiments to ascertain the chemical characteristics of such a wide variety of novel chemicals. In order to generate novel cellular imaging techniques and to accomplish the regulation of certain cellular mechanisms, scientists have utilized the attributes of nanosheets such as their flexibility and simplicity of modification, out of which carbon nanosheets stand out for their remarkable strength, chemical stability, and electrical conductivity. With efficient tools like polynomials and functions that can forecast compound features, mathematical chemistry has a lot to offer. One such approach is the M-polynomial, a fundamental polynomial that can generate a significant number of degree-based topological indices. Among them, the neighborhood M-polynomial is useful in retrieving neighborhood degree sum-based topological indices that can help in carrying out physical, chemical, and biological experiments. This paper formulates the unique M-polynomial approach which is used to derive and compare a variety of neighborhood degree-based molecular descriptors and the corresponding entropy measures of two variations of pent-heptagonal carbon nanosheets. Furthermore, a regression analysis on these descriptors has also been carried out which can further help in the prediction of various properties of the molecule.

Text to face generation is a sub-domain of text to image synthesis. It has a huge impact on new research areas along with the wide range of applications in the public safety domain. Due to the lack of dataset, the research work focused on the text to face generation is very limited. Most of the work for text to face generation until now is based on the partially trained generative adversarial networks, in which the pre-trained text encoder has been used to extract the semantic features of the input sentence. Later, these semantic features have been utilized to train the image decoder. In this research work, we propose a fully trained generative adversarial network to generate realistic and natural images. The proposed work trained the text encoder as well as the image decoder at the same time to generate more accurate and efficient results. In addition to the proposed methodology, another contribution is to generate the dataset by the amalgamation of LFW, CelebA and locally prepared dataset. The dataset has also been labeled according to our defined classes. Through performing different kinds of experiments, it has been proved that our proposed fully trained GAN outperformed by generating good quality images by the input sentence. Moreover, the visual results have also strengthened our experiments by generating the face images according to the given query.

Corona Virus is a pandemic, and the whole world is affected due to it. Apart from the vaccine, the only cure for this drastic disease is to follow the rules and regulations that avoid further spread. There are different mechanisms like (Social Distancing, Mask Detection, Human occupancy etc.) through which we can able to stop the spread of the coronavirus. In this paper, we proposed hotspot zone detection using the computer vision techniques of deep learning. We have defined the hotspot area as the particular region on which the person touches more than some specified threshold. We further mark that area to some specific color to help the authority take necessary action and disinfect that particular place. To implement this algorithm, we have utilized the human-object interaction concept. We have extracted the dataset of person classes from the publicly available dataset for the person detection and the self-generated dataset to train the algorithm. Different experiments on object detection algorithms (YOLO-v3, Faster RCNN, SSD) for person detection have been performed in this work. We achieved the maximum accuracy in real-time on the YOLO-v3 for person detection. Whereas we have marked the specific area using the template matching algorithm of computer vision techniques. Our proposed algorithm detects the persons and extracts the region of interest points on which the user draws the rectangle. Then we find the intersection over union ratio between the detected person and the region of interest of the marked area to make the decision. We have achieved 88.72% accuracy on person detection in the local environment. Whereas, for the whole system of human-object interaction for detecting the hotspot area zone, we have achieved 86.7% accuracy using the confusion matrix.

Automated brain segmentation is an active research domain due to the association of various neurological disorders with different regions of the brain, to help medical professionals in prognostics and diagnostics. Traditional techniques like atlas-based and pattern recognition-based methods led to the development of various tools for automated brain segmentation. Recently, deep learning techniques are outperforming classical state-of-the-art methods and gradually becoming more mature. Consequently, deep learning has been extensively employed as a tool for precise segmentation of brain regions because of its capability to learn the intricate features of the high-dimensional data. In this work, a network for the segmentation of multiple brain regions has been proposed that is based on 3D convolutional neural networks and utilizes residual learning and dilated convolution operations to efficiently learn the end-to-end mapping from MRI volumes to the voxel-level brain segments. This research is focused on the segmentation of up to nine brain regions including cerebrospinal fluid, white matter and gray matter as well as their sub-regions. Mean dice scores of 0.879 and 0.914 have been achieved for three and nine brain regions, respectively by using the data from three different sources. Comparative analysis shows that our network gives better dice scores for most of the brain regions than state-of-the-artwork. Moreover, the mean dice score of 0.903, obtained for eight brain regions segmentation with MRBrains18 dataset, is better than 0.876 which was achieved in the previous work.

The advancements in Edge computing have paved the way for deep learning in real-time systems. One of the beneficiaries is an adaptive traffic control system that responds to real-time traffic observations by governing the signal phase and timings. Reinforcement Learning (RL) is extensively utilized in the literature in order to decrease traffic congestion in a road network. However, most of the previous works leverage centralized and cloud-based RL due to the computational complexity of underlying deep neural networks (DNN). Therefore, a persistent challenge towards adopting Edge learning is in devising a Multi-Agent RL in which agents are simplified, and their state spaces are localized but they perform comparable to the centralized RL. This article presents a Collaborative and Adaptive Signaling on the Edge (CASE), a novel Multi-Agent RL approach to control the traffic signals' phase and timing. Each signalized intersection in the road network is provided with an Edge Learning Platform which hosts an RL-Agent that observes local traffic states and learns an optimum signal policy. Moreover, CASE allows collaboration among RL-Agents by sharing their signal phase and timings to achieve convergence and performance. This collaboration is limited to one's direct neighbours only to minimize the computational complexity. We performed rigorous evaluations in terms of the choice of RL methods and their state space/reward and found that our collaborative state-space has resulted in a performance comparable to a centralized RL yet with a cost similar to the decentralized RL. Finally, a performance comparison of the CASE controller ported to the state-of-the-art Edge learning platforms is presented in this article. The results show that the proposed CASE controller can achieve real-time performance when ported to a general-purpose GPU-based platform. This arrangement achieves more than 8 times improvement in computational time over conventional embedded platforms.

In order to accelerate the performance of various Natural Language Processing tasks for Roman Urdu, this article for the very first time provides 3 neural word embeddings prepared using most widely used approaches namely Word2vec, FastText, and Glove. The integrity of generated neural word embeddings is evaluated using intrinsic and extrinsic evaluation approaches. Considering the lack of publicly available benchmark datasets, it provides a first-ever Roman Urdu public dataset which consists of 3241 sentiments annotated against positive, negative, and neutral classes. To provide benchmark baseline performance over the presented dataset for Roman Urdu sentiment analysis, we adapt diverse machine learning (Support Vector Machine, Logistic Regression, Naive Bayes), deep learning (convolutional neural network, recurrent neural network), and hybrid deep learning approaches. Performance impact of generated neural word embeddings based representation is compared with other most widely used bag of words based feature representation approaches using diverse machine and deep learning classifiers. In order to improve the performance of Roman Urdu sentiment analysis, it proposes a novel precisely extreme multi-channel hybrid methodology which makes use of convolutional and recurrent neural networks along with pre-trained neural word embeddings. The proposed hybrid approach outperforms adapted machine learning approaches by the significant figure of 9% and deep learning approaches by the figure of 4% in terms of F1-score.

Multi-label classification aims to associate multiple labels to a given data/object instance to better describe them. Multi-label data sets are common in a lot of emerging application areas like: Text/Multimedia classification, Bio-Informatics, Medical image annotations and Computer Vision to name a few. There is a growing interest in efficient and accurate multi-label classification. There are two major approaches to perform multi-label classification (i) problem transformation methods and (ii) algorithm adaptation methods. In algorithm adaptation, the traditional classification algorithms are modified to handle multi-label data sets. One classification algorithm which is often modified to do multi-label classification is k- nearest neighbor (kNN). k-nearest neighbor is popular due to its simplicity, easy to implement and seamlessly adaptability. Despite its merits it has several drawbacks like: sensitivity of noisy data, missing values and outliers; feature scaling and often becoming inaccurate for large overlapping solution space. In this paper, a modification to kNN method is suggested for multi-label classification with three improvement strategies (i) selection of local example w.r.t. unknown example – the motivation for this comes from the fact that local and relevant space is vital for the improvement in multi-label classification; (ii) Splitting the input space into multiple sub-spaces for optimal label estimation – the motivation is to estimate label accurately in the presence of noisy labels; And (iii) selection of labels using Mean Average Precision (MAP) estimates – here our motivation is to utilize the training data effectively to maximize the hidden distribution and optimal parameters for the method. The proposed method is implemented and compared with state-of-the-art approaches based on kNN or similar approaches that effectively select and optimize relevant spaces for multi-label classification. Evaluation based on multiple metrics like Hamming loss, Precision/Recall and F-measure are used for evaluation. The suggested approach performed much better than the state-of-the-art on the datasets with strong label cardinalities.

Tessellations of kekulenes and cycloarenes have a lot of potential as nanomolecular belts for trapping and transporting heavy metal ions and chloride ions because they have the best electronic properties and pore sizes. The aromaticity, superaromaticity, chirality, and novel electrical and magnetic properties of a class of cycloarenes known as kekulenes have been the subject of several experimental and theoretical studies. Through topological computations of superaromatic structures with pores, we investigate the entropies and topological characterization of different tessellations of kekulenes. Using topological indices, the biological activity of the underlying structure is linked to its physical properties in (QSPR/QSAR) research. There is a wide range of topological indices accessible, including degree-based indices, which are used in this work. With the total π-electron energy, these indices have a lot of iteration. In addition, we use graph entropies to determine the structural information of a non-Kekulean benzenoid graph. In this article, we study the crystal structure of non-Kekulean benzenoid graph Kn and then calculate some entropies by using the degree-based topological indices. We also investigate the relationship between degree-based topological indices and degree-based entropies. This relationship is very helpful for chemist to study the physicochemical characterization of non-Kekulean benzenoid chemical. These numerical values correlate with structural facts and chemical reactivity, biological activities, and physical properties.

A topological index, which is a number, is connected to a graph. It is often used in chemometrics, biomedicine, and bioinformatics to anticipate various physicochemical properties and biological activities of compounds. The purpose of this article is to encourage original research focused on topological graph indices for the drugs azacitidine, decitabine, and guadecitabine as well as an investigation of the genesis of symmetry in actual networks. Symmetry is a universal phenomenon that applies nature’s conservation rules to complicated systems. Although symmetry is a ubiquitous structural characteristic of complex networks, it has only been seldom examined in real-world networks. The M¯-polynomial, one of these polynomials, is used to create a number of degree-based topological coindices. Patients with higher-risk myelodysplastic syndromes, chronic myelomonocytic leukemia, and acute myeloid leukemia who are not candidates for intense regimens, such as induction chemotherapy, are treated with these hypomethylating drugs. Examples of these drugs are decitabine (5-aza-20-deoxycytidine), guadecitabine, and azacitidine. The M¯-polynomial is used in this study to construct a variety of coindices for the three brief medicines that are suggested. New cancer therapies could be developed using indice knowledge, specifically the first Zagreb index, second Zagreb index, F-index, reformulated Zagreb index, modified Zagreb, symmetric division index, inverse sum index, harmonic index, and augmented Zagreb index for the drugs azacitidine, decitabine, and guadecitabine.