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A topological index is a number that is connected to a chemical composition in order to correlate a substance’s chemical makeup with different physical characteristics, chemical reactivity, or biological activity. It is common to model drugs and other chemical substances as different forms, trees, and graphs. Certain physico-chemical features of chemical substances correlate better with degree-based topological invariants. Predictions concerning the dynamics of the continuing pandemic may be made with the use of the graphic theoretical approaches given here. In Networks, the degree entropy of the epidemic and related trees was computed. It highlights the essay’s originality while also implying that this piece has improved upon prior literature-based realizations. In this paper, we study an important degree-based invariant known as the inverse sum indeg invariant for a variety of graphs of biological interest networks, including the corona product of some interesting classes of graphs and the pandemic tree network, curtain tree network, and Cayley tree network. We also examine the inverse sum indeg invariant features for the molecular graphs that represent the molecules in the bicyclic chemical graphs.

The metric dimension has various applications in several fields, such as computer science, image processing, pattern recognition, integer programming problems, drug discovery, and the production of various chemical compounds. The lowest number of vertices in a set with the condition that any vertex can be uniquely identified by the list of distances from other vertices in the set is the metric dimension of a graph. A resolving function of the graph G is a map ϑ:V(G)→[0,1] such that ∑u∈R{v,w}ϑ(u)≥1, for every pair of adjacent distinct vertices v,w∈V(G). The local fractional metric dimension of the graph G is defined as ldimf(G) = min{∑v∈V(G)ϑ(v), where ϑ is a local resolving function of G}. This paper presents a new family of planar networks namely, rotationally heptagonal symmetrical graphs by means of up to four cords in the heptagonal structure, and then find their upper-bound sequences for the local fractional metric dimension. Moreover, the comparison of the upper-bound sequence for the local fractional metric dimension is elaborated both numerically and graphically. Furthermore, the asymptotic behavior of the investigated sequences for the local fractional metric dimension is addressed.

Entropy is a thermodynamic function in physics that measures the randomness and disorder of molecules in a particular system or process based on the diversity of configurations that molecules might take. Distance-based entropy is used to address a wide range of problems in the domains of mathematics, biology, chemical graph theory, organic and inorganic chemistry, and other disciplines. We explain the basic applications of distance-based entropy to chemical phenomena. These applications include signal processing, structural studies on crystals, molecular ensembles, and quantifying the chemical and electrical structures of molecules. In this study, we examine the characterisation of polyphenylenes and boron (B12) using a line of symmetry. Our ability to quickly ascertain the valences of each atom, and the total number of atom bonds is made possible by the symmetrical chemical structures of polyphenylenes and boron B12. By constructing these structures with degree-based indices, namely the K Banhatti indices, ReZG1-index, ReZG2-index, and ReZG3-index, we are able to determine their respective entropies.

The use of a single-valued neutrosophic set (svns) makes it much easier to manage situations in which one must deal with incorrect, unexpected, susceptible, faulty, vulnerable, and complicated information. This is a result of the fact that the specific forms of material being discussed here are more likely to include errors. This new theory has directly contributed to the expansion of both the concept of fuzzy sets and intuitionistic fuzzy sets, both of which have experienced additional development as a direct consequence of the creation of this new theory. In svns, indeterminacy is correctly assessed in a way that is both subtle and unambiguous. Furthermore, membership in the truth, indeterminacy, and falsity are all completely independent of one another. In the context of algebraic analysis, certain binary operations may be regarded as interacting with algebraic modules. These modules have pervasive and complicated designs. Modules may be put to use in a wide variety of different applications. Modules have applications in a diverse range of industries and market subsets due to their adaptability and versatility. Under the umbrella of the triplet (μ,ν,ω) structure, we investigate the concept of svns and establish a relationship between it and the single-valued neutrosophic module and the single-valued neutrosophic submodule, respectively. The purpose of this study is to gain an understanding of the algebraic structures of single-valued neutrosophic submodules under the triplet structure of a classical module and to improve the validity of this method by analyzing a variety of important facets. In this article, numerous symmetrical features of modules are also investigated, which demonstrates the usefulness and practicality of these qualities. The results of this research will allow for the successful completion of both of these objectives. The tactics that we have devised for use in this article are more applicable to a wide variety of situations than those that have been used in the past. Fuzzy sets, intuitionistic fuzzy sets, and neutrosophic sets are some of the tactics that fall under this category.

Zika virus (ZIKV) pandemic and its implication in congenital malformations and severe neurological disorders had created serious threats to global health. ZIKV is a mosquito-borne flavivirus which spread rapidly and infect a large number of people in a shorter time-span. Due to the lack of effective therapeutics, this had become paramount urgency to discover effective drug molecules to encounter the viral infection. Various anti-ZIKV drug discovery efforts during the past several years had been unsuccessful to develop an effective cure. The NS2B-NS3 protein was reported as an attractive therapeutic target for inhibiting viral proliferation, due to its central role in viral replication and maturation of non-structural viral proteins. Therefore, the current in silico drug exploration aimed to identify the novel inhibitors of Zika NS2B-NS3 protease by implementing an e-pharmacophore-based high-throughput virtual screening. A 3D e-pharmacophore model was generated based on the five-featured (ADPRR) pharmacophore hypothesis. Subsequently, the predicted model is further subjected to the high-throughput virtual screening to reveal top hit molecules from the various small molecule databases. Initial hits were examined in terms of binding free energies and ADME properties to identify the candidate hit exhibiting a favourable pharmacokinetic profile. Eventually, molecular dynamic (MD) simulations studies were conducted to evaluate the binding stability of the hit molecule inside the receptor cavity. The findings of the in silico analysis manifested affirmative evidence for three hit molecules with −64.28, −55.15 and −50.16 kcal/mol binding free energies, as potent inhibitors of Zika NS2B-NS3 protease. Hence, these molecules holds the promising potential to serve as a prospective candidates to design effective drugs against ZIKV and related viral infections.

Entropy is a measure of a system’s molecular disorder or unpredictability since work is produced by organized molecular motion. Shannon’s entropy metric is applied to represent a random graph’s variability. Entropy is a thermodynamic function in physics that, based on the variety of possible configurations for molecules to take, describes the randomness and disorder of molecules in a given system or process. Numerous issues in the fields of mathematics, biology, chemical graph theory, organic and inorganic chemistry, and other disciplines are resolved using distance-based entropy. These applications cover quantifying molecules’ chemical and electrical structures, signal processing, structural investigations on crystals, and molecular ensembles. In this paper, we look at K-Banhatti entropies using K-Banhatti indices for C6H6 embedded in different chemical networks. Our goal is to investigate the valency-based molecular invariants and K-Banhatti entropies for three chemical networks: the circumnaphthalene (CNBn), the honeycomb (HBn), and the pyrene (PYn). In order to reach conclusions, we apply the method of atom-bond partitioning based on valences, which is an application of spectral graph theory. We obtain the precise values of the first K-Banhatti entropy, the second K-Banhatti entropy, the first hyper K-Banhatti entropy, and the second hyper K-Banhatti entropy for the three chemical networks in the main results and conclusion.

Entropy is essential. Entropy is a measure of a system’s molecular disorder or unpredictability, since work is produced by organized molecular motion. Entropy theory offers a profound understanding of the direction of spontaneous change for many commonplace events. A formal definition of a random graph exists. It deals with relational data’s probabilistic and structural properties. The lower-order distribution of an ensemble of attributed graphs may be used to describe the ensemble by considering it to be the results of a random graph. Shannon’s entropy metric is applied to represent a random graph’s variability. A structural or physicochemical characteristic of a molecule or component of a molecule is known as a molecular descriptor. A mathematical correlation between a chemical’s quantitative molecular descriptors and its toxicological endpoint is known as a QSAR model for predictive toxicology. Numerous physicochemical, toxicological, and pharmacological characteristics of chemical substances help to foretell their type and mode of action. Topological indices were developed some 150 years ago as an alternative to the Herculean, and arduous testing is needed to examine these features. This article uses various computational and mathematical techniques to calculate atom–bond connectivity entropy, atom–bond sum connectivity entropy, the newly defined Albertson entropy using the Albertson index, and the IRM entropy using the IRM index. We use the subdivision and line graph of the H3BO3 layer structure, which contains one boron atom and three oxygen atoms to form the chemical boric acid.

A topological index as a graph parameter was obtained mathematically from the graph’s topological structure. These indices are useful for measuring the various chemical characteristics of chemical compounds in the chemical graph theory. The number of atoms that surround an atom in the molecular structure of a chemical compound determines its valency. A significant number of valency-based molecular invariants have been proposed, which connect various physicochemical aspects of chemical compounds, such as vapour pressure, stability, elastic energy, and numerous others. Molecules are linked with numerical values in a molecular network, and topological indices are a term for these values. In theoretical chemistry, topological indices are frequently used to simulate the physicochemical characteristics of chemical molecules. Zagreb indices are commonly employed by mathematicians to determine the strain energy, melting point, boiling temperature, distortion, and stability of a chemical compound. The purpose of this study is to look at valency-based molecular invariants for SiO4 embedded in a silicate chain under various conditions. To obtain the outcomes, the approach of atom–bond partitioning according to atom valences was applied by using the application of spectral graph theory, and we obtained different tables of atom—bond partitions of SiO4. We obtained exact values of valency-based molecular invariants, notably the first Zagreb, the second Zagreb, the hyper-Zagreb, the modified Zagreb, the enhanced Zagreb, and the redefined Zagreb (first, second, and third). We also provide a graphical depiction of the results that explains the reliance of topological indices on the specified polynomial structure parameters.

A topological index is a numerical parameter that is derived mathematically from a graph structure. In chemical graph theory, these indices are used to quantify the chemical properties of chemical compounds. We compute the first and second temperature, hyper temperature indices, the sum connectivity temperature index, the product connectivity temperature index, the reciprocal product connectivity temperature index and the F temperature index of a molecular graph silicate network and silicate chain network. Furthermore, a QSPR study of the key topological indices is provided, and it is demonstrated that these topological indices are substantially linked with the physicochemical features of COVID-19 medicines. This theoretical method to find the temperature indices may help chemists and others in the pharmaceutical industry forecast the properties of silicate networks and silicate chain networks before trying.

Entropy is a thermodynamic function in chemistry that reflects the randomness and disorder of molecules in a particular system or process based on the number of alternative configurations accessible to them. Distance-based entropy is used to solve a variety of difficulties in biology, chemical graph theory, organic and inorganic chemistry, and other fields. In this article, the characterization of the crystal structure of niobium oxide and a metal–organic framework is investigated. We also use the information function to compute entropies by building these structures with degree-based indices including the K-Banhatti indices, the first redefined Zagreb index, the second redefined Zagreb index, the third redefined Zagreb index, and the atom-bond sum connectivity index.

Web contains a vast amount of data, which are accumulated, studied, and utilized by a huge number of users on a daily basis. A substantial amount of data on the Web is available in an unstructured format, such as Web pages, books, journals, and files. Acquiring appropriate information from such humongous data has become quite challenging and a time-consuming task. Trivial keyword-based information retrieval systems highly depend on the statistics of data, thus facing word mismatch problem due to inevitable semantic and context variations of a certain word. Therefore, this marks the desperate need to organize such massive data into a structured format so that information can be easily processed in a large context by taking data semantics into account. Ontologies are not only being extensively employed in the semantic Web to store unstructured information in an organized and structured way but it has also raised the performance of diverse information retrieval approaches to a great extent. Ontological information retrieval systems retrieve data based on the similarity of semantics between the user query and the indexed data. This paper reviews modern ontology-based information retrieval methods for textual, multimedia, and cross-lingual data types. Furthermore, we compare and categorize the most recent approaches used in the above-mentioned information retrieval methods along with their major drawbacks and advantages.

In today's age, a massive amount of videos are produced every day, which contains audio, visual, and textual data. This constant increase is due to the ease of recording service in portable devices, such as mobile phones, tablets, or cameras. The major challenge is to understand the visual semantics and convert it into a condensed format, such as caption or summary to save storage space, enables users to index, navigate, and help gain information in less time. We propose an innovative joint end-to-end solution, Abstractive Summarization of Video Sequences, which uses the deep neural network to generate the natural language description and abstractive text summarization of an input video. This provides a text-based video description and abstractive summary, enabling users to discriminate between relevant and irrelevant information according to their needs. Furthermore, our experiments show that the joint model can attain better results than the baseline methods in separate tasks with informative, concise, and readable multi-line video description and summary in a human evaluation.

Currently, data generated by smart devices connected through the Internet is increasing relentlessly. An effective and efficient paradigm is needed to deal with the bulk amount of data produced by the Internet of Things (IoT). Deep learning and edge computing are the emerging technologies, which are used for efficient processing of huge amount of data with distinct accuracy. In this world of advanced information systems, one of the major issues is authentication. Several techniques have been employed to solve this problem. Face recognition is considered as one of the most reliable solutions. Usually, for face recognition, scale-invariant feature transforms (SIFT) and speeded up robust features (SURF) have been used by the research community. This paper proposes an algorithm for face detection and recognition based on convolution neural networks (CNN), which outperform the traditional techniques. In order to validate the efficiency of the proposed algorithm, a smart classroom for the student's attendance using face recognition has been proposed. The face recognition system is trained on publically available labeled faces in the wild (LFW) dataset. The system can detect approximately 35 faces and recognizes 30 out of them from the single image of 40 students. The proposed system achieved 97.9% accuracy on the testing data. Moreover, generated data by smart classrooms is computed and transmitted through an IoT-based architecture using edge computing. A comparative performance study shows that our architecture outperforms in terms of data latency and real-time response.

The present study determined the effect of the packaging type and aging time on the meat quality of water buffalo (Bubalus bubalis) bulls. A total of n = 36 longissimus lumborum (LL) muscles from n = 18 buffalo bulls were obtained. Half LL muscles were packed in modified atmosphere packaging (Hi-O2 MAP), vacuum packaging (VP), and oxygen-permeable packaging (OP) on day 1, while the other half were aged for 7 days. Meat instrumental color, cooking loss, Warner–Bratzler shear force (WBSF), thiobarbituric acid reactive substances (TBARS), and total volatile basic nitrogen (TVB-N) of the LL steaks were analyzed, both on unaged and aged buffalo meat. Color CIE L* and C* values on all display days and a* on the first 4 days of the simulated retail display under Hi-O2 MAP packaging were significantly higher than those of the VP and OP. WBSF and TBARS values were also higher under Hi-O2 MAP as compared to the other packaging. Steaks under OP exhibited lower cooking loss but higher TVB-N values than the MAP and VP. The 7-day-aged buffalo meat indicated higher instrumental color (L*, a* and C*), cooking loss, and lower WBSF values than fresh meat. This study concluded that Hi-O2 MAP improved the color; however, it negatively influenced the buffalo meat’s WBSF and TBAR values. Furthermore, VP and aging were the most effective in decreasing the WBSF values of buffalo meat.

The hybridization of natural and synthetic fibers leads to composites’ optimum mechanical properties. In this study, an attempt was made to study the effect of the stacking sequence on PBS-based Glass-Jute (GJ) hybrid composites. Six types of hybrid composite, each containing five different layers of jute and glass fabric, were manufactured by the compression molding method. Mechanical properties, such as tensile, flexural, and impact resistance were studied and analyzed in detail. The surface characterization of the composites was performed through scanning electron microscopic images. The moisture absorption properties were also investigated by immersing the composites in distilled water for one week at ambient temperature. The TGA test was conducted to study their thermal properties. The experimental results showed that the stacking sequence of the fiber layers has a significant effect on the overall performance of GJ hybrid composites. Among the hybrid GJ composites, composites with glass fiber layers on their outer surfaces showed optimum mechanical, thermal, and water resistance properties.

About 334 million people worldwide have been affected by asthma and additionally 100 million expected to have asthma by the year 2025. Being the sixth most populous and economically developing country and with the increasing trend of urbanization and due to poor health facilities, lacking the knowledge of the causes of the disease, increased pollutant exposures and consanguineous marriages, Pakistani population has greater chances of asthma and allergic diseases. Almost 20% of overall pediatric population of Pakistan is affected by this disease. In the past, only environmental factors were considered responsible for asthma but now it is a well-known fact that genetic factors have major contribution in the development of the disease. Poor knowledge of the possibility of genetics involved in this disease has been very common in both uneducated as well as educated population of this region. There is no doubt today that genetics have a major role in the exhibition of asthma but unlike other Mendelian disorders it cannot easily be identified by a single or a simple set of associated genes, rather small contributions of multiple genes and environmental exposure to asthmatic patients may be found. Our focus in this article is genetic factors of asthma in Pakistan. More than 100 genes associated with asthma have already been reported in different populations worldwide but generally, due to an ignored disease in this region, possible genetic causes, impacts, management and treatment of the disease has rarely been studied in Pakistan. This article was intended to explore some latest developments in asthma genetics, diagnosis, prognosis and management and suggest some recommendations to control this deadly disease.

In recent years, deep learning approaches have gained great attention due to their superior performance and the availability of high speed computing resources. These approaches are also extended towards the real time processing of multimedia content exploiting its spatial and temporal structure. In this paper, we propose a deep learning-based video description framework which first extracts visual features from video frames using deep convolutional neural networks (CNN) and then pass the derived representations into a long-short term memory-based language model. In order to capture accurate information for human presence, a fine-tuned multi-task CNN is presented. The proposed pipeline is end-to-end, trainable, and capable of learning dense visual features along with an accurate framework for the generation of natural language descriptions of video streams. The evaluation is done by calculating Metric for Evaluation of Translation with Explicit ORdering and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores between system generated and human annotated video descriptions for a carefully designed data set. The video descriptions generated by the traditional feature learning and proposed deep learning frameworks are also compared through the ROUGE scores.

Pakistan is globally sixth most populous, economically developing south Asian country with tremendously increasing trend of urbanization. This increase in urbanization along with consanguineous marriages trend in Pakistani nationals might contribute as most important factors of increasing asthma prevalence. Up-till now, a few studies related to asthma genetics have been conducted in Pakistan. These studies suggest that the SNP variants like (rs2569190, rs2569191), (rs2243250, rs2227284), (rs1800896), (rs1881457, rs20541, rs1800925), (rs2280091, rs2787094, rs528557), (rs1131882) in CD14, IL-4, IL10, IL-13, ADAM33 and TBXA2R genes respectively and the SNPs (rs3816470, rs6503525, rs3859192, rs12603332) on chromosomal region 17q21 are significantly associated in Pakistani population whereas the haplotype “CCTCAG" of SNPs (rs12936231, rs7216389, rs7216558, rs9894164, rs1007654 and rs7212938) in 17q21 and ‘AAGTCG’ of SNPs (rs2280089, rs2280090, rs2280091, rs44707, rs528557, and rs612709) in ADAM-33 region are protective factor against asthma susceptibility. These studies will definitely contribute in understanding genetic basis of asthmatic complications in Pakistan and large population cohort size and sub-ethnic studies in future will give more meaningful conclusions to predict possible asthma susceptible genomic variants in sub-ethnic and general population of Pakistan.

Compute intensive programs generally consume significant fraction of execution time in a small amount of repetitive code. Such repetitive code is commonly known as hotspot code. We observed that compute intensive hotspots often possess exploitable loop level parallelism. A JIT (Just-in-Time) compiler profiles a running program to identify its hotspots. Hotspots are then translated into native code, for efficient execution. Using similar approach, we propose a methodology to identify hotspots and exploit their parallelization potential on multicore systems. Proposed methodology selects and parallelizes each DOALL loop that is either contained in a hotspot method or calls a hotspot method. The methodology could be integrated in front-end of a JIT compiler to parallelize sequential code, just before native translation. However, compilation to native code is out of scope of this work. As a case study, we analyze eighteen JGF (Java Grande Forum) benchmarks to determine parallelization potential of hotspots. Eight benchmarks demonstrate a speedup of up to 7.6x on an 8-core system

After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions.

Uncertain data can be categorized as imprecise data and probabilistic data. In each of these categories, the uncertainty can be found at different granularity levels. Conventional data models are developed for the purpose of storing, manipulating and retrieving certain data. These data models do not extend their support for the management of uncertain data. Thus, a standalone data model is required aimed at storing, manipulating and retrieving certain as well as uncertain data. In this paper we introduce UDM relations, an uncertain data model for the management of uncertain data along with certain data. Vertical partitioning approach is used to translate an uncertain relation into UDM-relations. Our data model supports ALU (Attribute-Level Uncertainty) as well as TLU (Tuple-Level Uncertainty) for the finite sets of possible worlds. It follows the concept of standard relational database technology. With slight modifications to standard relational algebra operators, we have introduced four relational operators that are used to evaluate a query on UDM-relations.

The purpose of this paper is to discuss Intelligent Bus Stops in a special Demand Responsive Transit (DRT), the Flexible Bus System. These Intelligent Bus Stops are more efficient and information rich than Traditional Bus Stops. The real time synchronization of the Flexible Bus System makes it unique as compared to Traditional Bus Systems. The Main concern is to make Bus Stops intelligent and information rich. Buses are informed about the no. of passengers waiting at the upcoming Bus Stops. If there are no passengers to ride or get off on upcoming Bus Stop, the Bus can skip that Bus Stop and head towards the next Bus Stop where passenger is waiting, which will decrease the ride time of the passengers on the Bus and also the wait time of the passengers waiting on the upcoming Bus Stops. Providing more information at Bus Stops about the Destination (Time to Destination, Distance to Destination etc.) and Buses (Bus Location, Arrival Time of Bus etc.) makes it easier for the passengers to decide whether to ride a particular Bus or not.

A data model empowers us to store, retrieve and manipulate data in a unified way. We consider the biological data consists of DNA (De-Oxyribonucleic Acid), RNA (Ribonucleic Acid) and protein structures. In our Bioinformatics Lab (Bioinformatics Lab, Alkhawarizmi Institute of Computer Science, University of Engineering & Technology, Lahore, Pakistan), we have already proposed two data models for DNA and protein structures individually. In this paper, we propose a unified data model by using the data models of TOS (Temporal Object Oriented System) after making some necessary modifications to this data model and our already proposed the two data models. This proposed unified data model can be used for the modeling and maintaining the biological data (i.e. DNA, RNA and protein structures), in a single unified way

This paper addresses the problem of spoken document retrieval under noisy conditions by incorporating sound selection of a basic unit and an output form of a speech recognition system. Syllable fragment is combined with a confusion network in a spoken document retrieval task. After selecting an appropriate syllable fragment, a lattice is converted into a confusion network that is able to minimize the word error rate instead of maximizing the whole sentence recognition rate. A vector space model is adopted in the retrieval task where tf-idf weights are derived from the posterior probability. The confusion network with syllable fragments is able to improve the mean of average precision (MAP) score by 0.342 and 0.066 over one-best scheme and the lattice.