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Objectives: Prevalence of irregular menstrual cycle ranges from 81.7% to 96.3%. Recent research suggested that homeopathy is one of the most popular choices for women with various gynecological disorders. This trial was aimed at differentiating individualized homeopathic medicinal products (IHMPs) from identical-looking placebos in the treatment of menstrual irregularities in early reproductive women. Design: Double-blind, randomized (1:1), two parallel arms, placebo-controlled trial. Setting: D. N. De Homoeopathic Medical College & Hospital, Kolkata, West Bengal, India. Subjects: Ninety-two females with menstrual irregularities. Interventions: Group verum (n = 46; IHMPs plus concomitant care) versus group control (n = 46; placebos plus concomitant care). Outcome Measures: Primary—The proportion of early reproductive females in whom menstrual irregularities can be corrected for consecutive three cycles; Secondary—Menstrual Distress Questionnaire (MDQ) total score; all of them were measured at baseline and every month, up to 4 months. Results: Intention-to-treat sample (n = 92) was analyzed. Group differences were examined by chi-squared tests with categorical outcomes, two-way repeated measure analysis of variance accounting for the time–effect interactions, and unpaired t-tests comparing the mean estimates obtained individually every month. The level of significance was set at p < 0.05 two-tailed. After 4 months of intervention, the group difference in the primary outcome was nonsignificant statistically—IHMPs: 22/46 v/s placebo: 24/46, chi-square (Yates corrected) = 0.043, p = 0.835. The improvement observed in the MDQ total score (F1,90 = 0.054, p = 0.816) and subscales scores were higher in the IHMPs group than in placebos, however statistically nonsignificant in most of the occasions, except for the behavioral change subscale (F1,90 = 0.029, p < 0.001). Pulsatilla nigricans was the most frequently prescribed medicine. Kent's Repertory and Zandvoort's Complete Repertory were the most frequently used repertories. No harm or serious adverse events were reported from either group. Conclusions: The analysis failed to demonstrate clearly that IHMPs were effective beyond placebos in all but one of the outcomes. More appropriate outcome measures may be sought for future trials. Clinical Trial Registration Number: CTRI/2022/04/041659. [ABSTRACT FROM AUTHOR]

The onset of SARS-CoV-2 infection in 2019 sparked a global COVID-19 pandemic. This infection is marked by a significant rise in both viral and host kinase activity. Our primary objective was to identify a pivotal host kinase essential for COVID-19 infection and the associated phenomenon of the cytokine storm, which may lead to long-term COVID-19 complications irrespective of viral genetic variations. To achieve this, our study tracked kinase phosphorylation dynamics in RAW264.7 macrophages following SPIKE transfection over time. Among the kinases surveyed, p70S6K (RPS6KB1) exhibited a 3.5-fold increase in phosphorylation at S418. This significant change prompted the selection of p70S6K for in silico investigation, utilizing its structure bound to M2698 (PDB: 7N93). M2698, an oral dual Akt/p70S6K inhibitor with an IC50 of 1.1 nM, exhibited psychosis side effects in phase I clinical trials, potentially linked to its interaction with Akt2. Our secondary objective was to discover a small-molecule analogue of M2698 that exhibits a distinct binding preference for p70S6K over Akt2 through computational modeling and analysis. The in silico part of our project began with validating the prediction accuracy of the docking algorithm, followed by an OCA analysis pinpointing specific atoms on M2698 that could be modified to enhance selectivity. Subsequently, our investigation led to the identification of an analog of M2698, designated as S34, that showed a superior docking score towards p70S6K compared to Akt2. To further assess the stability of S34 in its protein–ligand (PL) complexes with p70S6K and Akt2, MD simulations were conducted. These simulations suggest that S34, on average, forms two hydrogen bond interactions with p70S6K, whereas it only forms one hydrogen bond interaction with Akt2. This difference in hydrogen bond interactions likely contributed to the observed larger root mean square deviation (RMSD) of 0.3 nm in the S34-Akt2 complex, compared to 0.1 nm in the S34-p70S6K complex. Additionally, we calculated free binding energy to predict the strength of the binding interactions of S34 to p70S6K and Akt2, which showed ~2-fold favorable binding affinity of S34 in the p70S6K binding pocket compared to that in the Akt2 binding pocket. These observations may suggest that the S34-p70S6K complex is more stable than the S34-Akt2 complex. Our work focused on identifying a host kinase target and predicting the binding affinity of a novel small molecule to accelerate the development of effective treatments. The wet bench results specifically highlight p70S6K as a compelling anti-COVID-19 target. Meanwhile, our in silico investigations address the known off-target effects associated with M2698 by identifying a close analog called S34. In conclusion, this study presents novel and intriguing findings that could potentially lead to clinical applications with further investigations. [ABSTRACT FROM AUTHOR]

Generation of rooted trees is a major area of research in graph theory. The number of rooted trees increases as the order of the graph increases. Many researchers around the world have given different algorithms generating rooted trees with different efficiency. Generation of rooted trees of a given order can be used to solve other combinatorial optimization problems. In this article, we have proposed an algorithm, Rooted-Trees, generating all labelled rooted trees of a given order, n and up to a given height, h (where 1 ≤ h ≤ n − 1). The algorithm, Rooted-Trees, generates the rooted trees of our desire, which are the spanning trees of a given complete graph of order n, assuming one of the vertices of the graph as the root. [ABSTRACT FROM AUTHOR]

The learning quality of humans depends on the sense of contemplation. Textual documents are a huge part of the literature on contemplation which effortlessly creates perception. Automatic document class identification or organisation is a machine learning function to understand the psychological and emotional content of the text in a concise way. The problem of identification of documents falls in the field of library science, information science and artificial intelligence. The research progress of class identification of documents has been made in various most spoken languages. Numerous research works have been published in European and Asian languages. However, there is a gap in the literature when it comes to any less resource language, especially Bengali. Consequently, this work portrays an efficient topic modelling approach for Bengali document class identification. It proposes a Dirichlet-polynomial clustering model likelihood corpus distribution (LCD), which is based on a Bayesian numerical prototype. Experiments are done to prove the efficiency of LCD over various topic modelling algorithms, such as latent Dirichlet allocation (LDA), LDA with bag-of-words (LDA-BOW), latent semantic indexing (LSI), and hierarchical Dirichlet process (HDP). For performance evaluation, we considered five real-world datasets of Bengali corpora, such as science, sports, computer, season, and epic in this work. The coherence score of different modelling algorithms is compared to find the best model for each dataset separately. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) having multiple hidden layers are very efficient to learn large volume datasets and applied in a wide range of applications. The DNNs are trained on these datasets using learning algorithms to learn the relationships among different variables. The base method that makes DNNs successful is stochastic gradient descent (SGD). The gradient reveals the way that a function's steepest rate of alteration is occurring. No matter how the gradient behaves, the key issue with basic SGD is that all parameters must adjust in equal-sized increments. Consequently, creating adaptable step sizes for every parameter is an effective method of deep model optimization. Gradient-based adaptive techniques utilize local changes in gradients or the square roots of exponential moving averages of squared previous gradients. However, current optimizers continue to struggle with effectively utilizing optimization curved knowledge. The novel emapDiffP optimizer suggested in this study utilizes the prior two parameters to generate a non-periodic and non-negative function, and the upgrade parameter makes use of a partly adaptive value to account for learning rate adjustability. Thus, the optimization steps become smoother with a more accurate step size for the immediate past parameter, a partial adapting value, and the largest two momentum values as the denominator of parameter updating. The rigorous tests on benchmark datasets show that the presented emapDiffP performs significantly better than its counterparts. In terms of classification accuracy, the emapDiffP algorithm gives the best classification accuracy on CIFAR10, MNIST, and Mini-ImageNet datasets for all examined networks and on the CIFAR100 dataset for most of the networks examined. It offers the best classification accuracy on the ImageNet dataset with the ResNet18 model. For image classification tasks on various datasets, the suggested emapDiffP technique offers outstanding training speed. With MNIST, CIFAR100, and ImageNet datasets, the suggested approach achieves the lowest training times; with CIFAR10, it takes the second-lowest training times. The proposed emapDiffP performs better than existing approaches in the majority of circumstances when used on the Set5 dataset for the image super-resolution challenge. Additionally, compared to previous approaches, the suggested method performs better on a variety of NLP tasks and object detection tasks. [ABSTRACT FROM AUTHOR]

A turning point in cancer research is the introduction of massively parallel sequencing technology which greatly reduced the cost and time for genome sequencing. This enhanced the scope for detecting and analyzing the role of structural alterations in cancer. However, certain bias exists in NGS-based approaches, which badly affects the CNV identification process. Moreover, DNA repeats existing in CNV regions need special attention as they will degrade the performance of majority of the existing CNV detection tools, even after applying generalized bias correction method. This motivated this work, where a novel method has been designed to address the issue of DNA repeats and thereby mappability bias existing in regions of CNV. The method consists of three phases, where the first phase computes the alignment information of uniquely mapped DNA reads, considering the base quality and base mismatch parameters at nucleotide level precision. The second and the third phase use a novel approach to allocate the non-uniquely mapped reads to an optimal region of the DNA repeats based on a probabilistic membership model. The proposed method is capable of identifying CNVs present in coding, as well as non-coding region of the DNA, and is also capable of detecting CNVs existing in DNA repeat regions. The methodology achieves a sensitivity greater than 0. 9 9 during the performed simulations, and on real data, the detected variants are validated with the database of genomic variants, where the percentage overlap is also greater than 95%, and has achieved much better breakpoint prediction, as compared with other popular bias correction CNV detection methods. [ABSTRACT FROM AUTHOR]

Document classification is an open problem in library, information, and computer sciences towards assigning documents to one or more classes. The interest of linguistic researchers in this domain has increased day by day due to interesting applications like language identification, readability assessment, sentiment analysis, spam filtering, etc. However, researchers focussing on natural language processing of resource-scaring languages have faced many hurdles due to the absence of benchmark datasets. Bengali is among the most-spoken resource-scaring or low-resource language. Although Bengali NLP researchers have endeavoured towards creating their own datasets, they are only useful for performance evaluation of their proposed document classification techniques only. Therefore, there is a gap in the literature on the availability of benchmark datasets. To overcome this barrier, this paper presents a benchmark dataset for Bengali document classification, which is publicly accessible and freely available. This dataset consists of a two-tier architecture, the first-tier for hard classification and the second-tier for soft classification techniques. Hard classification techniques follow supervised learning based models for the classification of documents, while on the other hand, soft classification techniques follow unsupervised learning based models for the clustering of documents. The proposed dataset consists of thirteen unique characteristics. This paper also introduces four new feature sets to evaluate the performance of the proposed dataset, namely: location revealing factor, part of speech tagging factor, relative frequency, and prominence factor. [ABSTRACT FROM AUTHOR]

Word sense disambiguation (WSD) is the process of finding out the appropriate meaning of a polysemous word based on any given context. The Bengali language inherently comprises a large number of polysemous words. Recently, researchers in the domain of linguistics have been attracted to the problem of WSD in Bengali text due to its numerous interesting applications, viz. machine translation, opinion polarity identification, question-answering systems, etc. In this paper, lexeme connexion measure of cohesive lexical ambiguity revealing factor has been proposed that takes a decision on the disambiguation of senses of a Bengali polysemous word. All the polysemous words have been treated as target words, and a context window of three different sizes, viz. five, seven, and ten are considered based on these target words. This paper has generated lexeme harmony measure for quantifying heuristically of syntactic belongings of a collection of lexemes in Bengali text. The proposed methodology has been extracted a feature vector by considering the cohesive lexical ambiguity revealing factor or CLARF, depending on frame lexeme harmony (FLH), sense lexeme harmony (SLH), polysemy singularity coherence (PSC), polysemy distribution factor (PDF), and relative polysemy singularity coherence (RPSC) factor of a lexeme. This Bengali WSD technique has been applied max-rule of integrated lexeme connexion measure (LCM) of each lexeme of both the testing and training cases score for sense recognition. The proposed algorithm has succeeded in eliminating the drawback of the Bengali WSD approaches, as it can focus on both the lexical and semantic relationships between words. The performance of this algorithm has been evaluated on a dataset that consists of 100 polysemous words of three/four senses. Various evaluation metrics have been used to analyse the results obtained by the proposed algorithm. The obtained results indicate the robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A digital microfluidic biochip (DMFB) with cyber-physical adaptation implements complex bio-protocols with high precision and high throughput dealing with safety-critical applications including point-of-care diagnosis, personalized medicine, and drug development. Having integrated sensors with network connectivity, a cyber-physical DMFB is undeniably susceptible to attacks. A number of leading research works are carried out to assess various attacks and their impacts. Several defense mechanisms are developed by arranging on-chip monitoring systems through deployment of checkpoints. As checkpoints are external resources imposing a cost-overhead to the system, a cost-effective detection mechanism is of utmost importance. Moreover, after detecting an attack, an efficient recovery process is imperative to execute the associated bioassay in a vulnerable environment. Here, an attack-tolerant synthesis is proposed with two-way security through integrating attack-detection and attack-recovery from various Denial of Service attacks. Moreover, a selective re-synthesis approach has been introduced to allow multiple recovery steps to be executed simultaneously on the biochip. The recovery strategy is closely coupled with the detection process which makes the system adaptive towards attack-tolerance. Experimental results on several benchmarks demonstrate the efficacy of the proposed two-way attack-tolerance strategy. [ABSTRACT FROM AUTHOR]

Stochastic Gradient Decent (SGD) is a very popular basic optimizer applied in the learning algorithms of deep neural networks. However, it has fixed-sized steps for every epoch without considering gradient behaviour to determine step size. The improved SGD optimizers like AdaGrad, Adam, AdaDelta, RAdam, and RMSProp make step sizes adaptive in every epoch. However, these optimizers depend on square roots of exponential moving averages (EMA) of squared previous gradients or momentums or both and cannot take the benefit of local change in gradients or momentums or both. To reduce these limitations, a novel optimizer has been presented in this paper where the adjustment of step size is done for each parameter based on changing information between the 1st and the 2nd moment estimate (i.e., diffMoment). The experimental results depict that diffMoment offers better performance than AdaGrad, Adam, AdaDelta, RAdam, and RMSProp optimizers. It is also noticed that diffMoment does uniformly better for training Convolutional Neural Networks (CNN) applying different activation functions. [ABSTRACT FROM AUTHOR]

Computer-aided documented content analysis is a prominent research area in natural language processing. A realistic implementation of this task is related to the subjectivity of the quantifiable data. One of the most interesting specialisations of this problem is automated document classification, which is a system that can identify the category of a document without human intervention. The problem of document classification has to consider the evaluation of the heart-of-the-matter of the textual material. Being one of the most-spoken languages in the world, a huge number of Bengali documents are present in digital form, and it is increasing rapidly due to the age of the internet. A document classification method is required to organise and categorise these huge documents rapidly and efficiently. In this paper, a decisive dictionary based model has been presented for the classification of documents in the Bengali text. We have introduced the concepts of lexiconid, lexiconaffinity, lexiconunicity, and lexiconassociation to acquire the features. The feature set is integrated with different levels of threshold. The proposed model is supervised, and the entire dataset has been split into testing and training sets. The proposed model has been validated using the k-fold cross-validation strategy. A significant number of dictionary based parameter values have been estimated for each token present in the text. In this paper, the text has been classified using a new rule based classification algorithm, predictive lexicon inference (PLI) classifier. The proposed model has been evaluated on five datasets: ParadiseLost, Iliad, Odyssey, Ramayana, and Mahabharata. In addition to document classification, this algorithm enables name entity classification, and chronology or description classification. [ABSTRACT FROM AUTHOR]

With increasing effectiveness of flow-based microfluidic biochips in the field of biochemical experiments and point-of-care diagnosis, design automation demands enormous attention to integrate the flow-layer design and control-layer design instead of considering them in isolation. Some severe design issues like valve reliability, control-channel delay, pressure skew, etc. are obvious and they must be tactfully dealt along with minimization of several cost-driving issues like schedule length, pin count, and control-channel length while ensuring planarity of the flow-layer components as well as control-layer channel routes. Hence, this paper aims to propose a unified flow-control co-design methodology considering various flow-control cost-driving issues while reducing the design cycles to obtain a low-cost and efficient platform. The performance is evaluated over several benchmark bioassays that show the efficacy of the proposed co-design method which reduces the number of required valves by 30%, control-channel length by 48.5%, and number of flow-channel crossings by 72.2% than the existing works, on an average. [ABSTRACT FROM AUTHOR]

The computation of natural language enables a suitable transmission through the universe by retrieving the correct sense of each word. A word may be monosemous or polysemous. The use of polysemous words in an appropriate context plays a critical role in communication. Over the last 2 decades, a significant amount of research has been done for automatically solving the correct sense of a polysemous word in the context of word sense disambiguation. A word sense disambiguation algorithm identifies the proper sense of a polysemous word by analysing the contextual data. Nevertheless, there is a gap in the contemporary literature regarding the availability of datasets in Asian languages, especially Bengali. Therefore, in this work, we have presented a dataset comprising hundred Bengali polysemous words. Each word in this dataset consists of three or four disjoint senses, and each sense comprises ten paragraphs. Each paragraph describes the sense of a particular polysemous word. We have performed statistical analysis on the basis of seven relevant and important characteristics. A general framework has also been presented for training and testing with possible guidelines for performance analysis. A baseline strategy has been introduced based on four feature sets. Finally, a set of experiments have been performed to analyse the system performance. [ABSTRACT FROM AUTHOR]

In this paper, a two-phase ant colony optimization (ACO) based approach has been presented to solve a single depot multiple travelling salesmen problem (mTSP) in Type-2 Gaussian fuzzy environment. In the single depot mTSP, a set of nodes and a set of salesmen are present, and each of the cities must be visited exactly once by the salesmen such that all of them start and finish at a depot. In this paper, a two-phase algorithm has been devised with ACO algorithm and some features of genetic algorithm (GA) to solve the projected problem. The devised algorithm is working appropriately with single depot mTSP. Here, in the first phase, the ACO algorithm is used for creating complete paths, and after that in the second phase, the GA features are used for optimizing the paths of multiple travellers. Moreover, the travelling cost is considered as Type-2 Gaussian fuzzy in nature and is reduced to its approximate crisp value using the reduction method of critical values. Some benchmark instances from TSPLIB have been used for performance analysis of the proposed algorithm. Computated results show that the devised algorithm is a competitive one for solving mTSP. Computational results with different datasets have been presented and some sensitivity analysis has also been done for fuzzy instances. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a stochastic Knapsack Problem (KP) based mathematical model for small-scale vegetable sellers in India and solved it by an advanced Genetic Algorithm. The knapsack problem considered here is a bounded one, where vegetables are the objects. In this model, we have assumed that different available vegetables (objects) have different weights (that are available), purchase costs, and profits. The maximum weight of vegetables that can be transported by a seller is limited by the carrying capacity of the vegetable carrier and the business capital of the seller is also limited. The aim of the proposed mathematical model is to maximize the total profit of the loaded/traded items, with a set of predefined constraints on the part of the vegetable seller or retailer. This problem has been solved in a Type-2 fuzzy environment and the Critical Value (CV) reduction method is utilized to defuzzify the objective value. We have projected an improved genetic algorithm based approach, where we have incorporated two features, namely refinement and immigration. We have initially considered benchmark instances and subsequently some redefined cases for experimentation. Moreover, we have solved some randomly generated proposed KP instances in Type-2 fuzzy environment. [ABSTRACT FROM AUTHOR]

A 1 nA current reference circuit is designed in semiconductor laboratory (SCL) 0.18 μm standard threshold voltage CMOS technology under 1.8 V power supply. The reference current is extremely immune to supply variation and shows a line sensitivity of 0.002%/V at post layout simulation level, over a wide range of supply variation from 0.5 V to 3.5 V. The line sensitivity is much smaller than any relevant data so far reported. In addition, it has a very low temperature coefficient of 214 ppm/°C over a temperature variation from −50°C to 50°C. The circuit consumes a total power of 588 nW. The reported results are also studied at various process corners and under variable process parameters like oxide thickness and threshold voltage of transistors. A satisfactory behaviour of the circuit is obtained in all cases. The accuracy of the simulation results is validated through quite close matching of the results with that obtained through rigorously developed theoretical models. [ABSTRACT FROM AUTHOR]

Gene regulatory networks are biologically robust, which imparts resilience to living systems against most external perturbations affecting them. However, there is a limit to this and disturbances beyond this limit can impart unwanted signalling on one or more master regulators in a network. Certain disturbances may affect the functioning of other constituent genes of the same network. In most cases, this phenomenon can have some effect on the functioning of the living organism. In this investigation, we have proposed a methodology to mitigate the effects of external perturbations on a genetic network using a proportional-integral-derivative controller. The proposed controller has been used to perturb one or more of the other unaffected master regulators such that the most affected gene/s of the network revert to their normal state. The only required condition of such type of manoeuvring is that there should be multiple master regulators in a network. The proposed technique has been experimented on a 10-gene DREAM4 benchmark network and also on a larger 20-gene network, where only downregulation has been considered due to data constraints. Simulation results indicate that the most vulnerable genes can be reverted to their normal expression levels in 10 out of the 16 simulations performed. [ABSTRACT FROM AUTHOR]

The involvement of Digital Microfluidic Biochips (DMFBs) in the field of disease detection, automated drug discovery, on-chip DNA (Deoxyribonucleic acid) analysis has become well-accepted during last decades. Though trustworthiness of Digital Microfluidic Biochip system plays a vital role in point-of-care diagnosis, defective electrodes are the main reasons for various misleading assay performances. It also affects overall assay completion time; even it leads to discard a chip in the worst scenario. Hence, fault detection is a crucial need. In this paper, an efficient fault detection mechanism is formulated to identify multiple numbers of defective/faulty electrodes on an m × n biochip array, where m and n can be of any positive number. Test droplets are strategically routed throughout an arbitrary sized electrode array and capacitive sensing circuit is deployed to take decision about existence of faults. The basic idea of the proposed technique is to identify actual positions of faulty electrodes through boundary test, row test, and diagonal test. In some complicated cases, instead of finding exact faulty location(s), through an extended testing our proposed technique identifies faulty zone(s) (it is a bounded region where one or more defective electrodes reside) to ensure safe assay performance on a chip. A detailed study evaluates the proposed approach considering different faulty environments. [ABSTRACT FROM AUTHOR]

This paper introduces a time-aware hybrid expertise retrieval (TaHER) system for community question answering (CQA) services. It comprises of a text-based part and a network-based part. The text-based part makes use of the textual and the temporal information associated with questions and answers. Moreover, it assesses the recent interests and the activities of answerers. For a given question, it determines the knowledge of each answerer and identify active answerers with adequate knowledge. The network-based part is composed of several period-dependent networks. It uses the relationships among the answerers along with temporal information. Next, it applies a link analysis technique on the networks to determine the time-aware authority of each answerer in the community. We, nonetheless, propose a fusion strategy for combining the offshoots of these two parts. Using 5 performance measures, TaHER system is compared with 20 state-of-the-art algorithms on 4 real-world datasets. According to our experiments, in 93.75% (375 out of 400) cases, the proposed approach outperforms the comparing approaches. We also experimentally validate the importance of each assumption used by us. [ABSTRACT FROM AUTHOR]

Sudoku is an NP-complete-based mathematical puzzle, which has enormous applications in the domains of steganography, visual cryptography, DNA computing, and so on. Therefore, solving Sudoku effectively can bring revolution in various fields. Several heuristics are there to solve this interesting structure. One of the heuristics, genetic algorithm, is used by many researchers to solve Sudoku successfully, but they face various problems. Genetic algorithm has so many lacunas, and to overcome these, we have hybridised it in a novel way. In this paper, we have developed a hybrid genetic algorithm-based firefly mating algorithm, which can solve Sudoku instances with a greater success rate for easy, medium, and hard difficulty level puzzles. Our proposed method has controlled "getting stuck in local optima", considering a smaller population and lesser generation. [ABSTRACT FROM AUTHOR]

Paper-based digital microfluidic biochips (or P-DMFBs) are becoming highly impelling due to its low-cost and in-place fabrication of electrodes and control wiring on a single piece of paper having an inkjet printer and conductive ink. Despite enormous advantages, several complex design rules also subsist, such as avoidance of induced control interference, minimum separation among the control lines, and congestion-free wiring on a single layer, which is to be correlated leading toward overall feasibility of the design. Several cost raising issues, such as schedule length, control pin count, and wire length, must be considered for attaining a successful fluid-control codesign. Moreover, design gaps exist among the subtasks of the fluid level, control level, and fluid-control design as a whole, which undeniably impose expensive design cycles increasing overall cost. This article builds a machine learning-based model for the pin-constrained P-DMFBs to predict violation in control design beforehand and accordingly guides the fluid-control codesign to tackle important cost-driving issues while attaining congestion- and conflict-free wiring. This model effectively eliminates the design cycles producing a low-cost platform. The predictive model has been evaluated over a balanced data set. Several benchmarks for assessing the performance are studied. [ABSTRACT FROM AUTHOR]

Programmable Microfluidic Devices (PMDs) are revolutionizing the traditional biochemical experiments due to their flexibility of performing various functionalities on a platform without any amendment in the underlying hardware. To enhance the inherent tractability of a PMD, microchannels are frequently shared among the operations; however, this leads to cross-contamination problem due to the residues trapped on the channel. For producing safe outcomes, a flow-level synthesis minimizing contamination as well as an efficient washing strategy become immediate requisites. Moreover, each unit of wash fluid possesses a finite capacity for washing and therefore, cannot clean the entire contaminated area on a chip. Hence, capacity-aware washing scheme is the urgent requirement to fulfil the practical constraints of a flow-layer design. In this paper, a design synthesis minimizing the amount of contamination is proposed which is followed by a model for wash optimization targeting to reduce wash time and total loss of capacity, while removing all the contaminations. The efficacy of the proposed synthesis and the washing scheme has been assessed considering various baseline approaches and the existing works on the same. [ABSTRACT FROM AUTHOR]

Vegetable wholesaling problem has a vital role in the business system. In this problem, a vegetable wholesaler is supposed to supply raw, fresh vegetables to supermarkets in an efficient way by minimizing time but maximizing profit. In this paper, we have presented a multi-resource multi-objective knapsack problem (MRKP) for vegetable wholesalers. This model is beneficial for a vegetable wholesaler who collects different types of vegetables (objects) from different villages (resources) to a market for selling the vegetables (objects). MRKP is an extension of the classical concept of 0–1 multi-dimensional knapsack problem (KP). In this model, precisely a wholesaler has a limited capacity van/trolley by which he/she collects a set of vegetables from different villages/vegetable fields. In this model, we have assumed that all the vegetables are available for each resource. Each type of vegetable is associated with a weight, a corresponding profit, and a collection time (for a particular resource). The profit, weight, and collection time of objects is different for different resources. Here a time slice is considered for each object to collect it from different villages to a particular destination/market. Also, the profit and time are the two objectives. MRKP aims to find the amount of an object and the corresponding resource name from which it is collected. We have solved the proposed problem in the fuzzy environment. In this paper, we have explained two defuzzification techniques, namely fuzzy expectation and total λ -integral value method to solve the proposed problem. We have explained a modified multi-objective genetic algorithm (NSGA-II by Deb et al. in IEEE Trans Evol Comput 6(2):192–197, 2002) that is to maximise the profit and minimise the time to collect the objects. We have considered a multi-objective benchmark test function to show the effectiveness of the proposed Genetic Algorithm. Modification is made by introducing refinement operation. An extensive computational experimentation has been executed that generates interesting results to establish the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The entities of communication have an enormous impact on interaction. Textual data is an important attribute of communication. Textual analysis of this data is carried out by the linguistic researchers in various perspectives. It helps to understand the people's perception by analyzing the contextual data into its various senses. The sense of a polysemous word is varied according to its context. Hence, the process of identifying the proper meaning of a polysemous word with respect to the context is known as word sense disambiguation (WSD). For the extraction of actual meaning, WSD is an essential technique in Natural Language Processing. Over the last two decades, a lot of algorithms have been proposed to solve this linguistic ambiguity problem in various languages. In addition, a number of review papers have been published in various most spoken languages. Even so, it is elevating that there is a discontinuity in the literature when it comes to the techniques of Bengali WSD. This paper confers an extensive survey work regarding approaches of Bengali WSD. It also presents a survey work of the existing dataset of Bengali WSD. [ABSTRACT FROM AUTHOR]

The accurate reconstruction of gene regulatory networks for proper understanding of the intricacies of complex biological mechanisms still provides motivation for researchers. Due to accessibility of various gene expression data, we can now attempt to computationally infer genetic interactions. Among the established network inference techniques, S-system is preferred because of its efficiency in replicating biological systems though it is computationally more expensive. This provides motivation for us to develop a similar system with lesser computational load. In this work, we have proposed a novel methodology for reverse engineering of gene regulatory networks based on a new technique: half-system. Half-systems use half the number of parameters compared to S-systems and thus significantly reduce the computational complexity. We have implemented our proposed technique for reconstructing four benchmark networks from their corresponding temporal expression profiles: an 8-gene, a 10-gene, and two 20-gene networks. Being a new technique, to the best of our knowledge, there are no comparable results for this in the contemporary literature. Therefore, we have compared our results with those obtained from the contemporary literature using other methodologies, including the state-of-the-art method, GENIE3. The results obtained in this work stack favourably against the competition, even showing quantifiable improvements in some cases. [ABSTRACT FROM AUTHOR]

Generation of all possible spanning trees of a graph is a major area of research in graph theory as the number of spanning trees of a graph increases exponentially with graph size. Several algorithms of varying efficiency have been developed since early 1960s by researchers around the globe. This article is an exhaustive literature survey on these algorithms, assuming the input to be a simple undirected connected graph of finite order, and contains detailed analysis and comparisons in both theoretical and experimental behavior of these algorithms. [ABSTRACT FROM AUTHOR]

Confined hydration and conformational flexibility are some of the challenges encountered for the rational design of selective antagonists of G-protein coupled receptors. We present a set of C3-substituted (-)-stepholidine derivatives as potent binders of the dopamine D3 receptor. The compounds are characterized biochemically, as well as by computer modeling using a novel molecular dynamics-based alchemical binding free energy approach which incorporates the effect of the displacement of enclosed water molecules from the binding site. The free energy of displacement of specific hydration sites is obtained using the Hydration Site Analysis method with explicit solvation. This work underscores the critical role of confined hydration and conformational reorganization in the molecular recognition mechanism of dopamine receptors and illustrates the potential of binding free energy models to represent these key phenomena. [ABSTRACT FROM AUTHOR]

Production of correct bioassay outcome is the foremost objective in digital microfluidic biochips (or DMFBs). In high‐frequency DMFBs, continuous actuation of electrodes leads to malfunctioning or even breakdown of the system. The improper functioning of a biochip tends to produce erroneous results. On the other hand, while transporting droplets, the residues may get stuck to electrode walls and cause contamination to other droplets. To ensure proper assay outcome, washing becomes mandatory, whose incorporation may delay the bioassay completion time significantly. Furthermore, each wash droplet possesses a capacity constraint within which the residues can be washed off successfully. Evidently, the design objectives possess a large degree of trade‐offs among themselves and must be attacked to prepare an efficient platform. Here, the authors propose a complete fluid‐level synthesis considering all the essential goals together instead of dealing with them in isolation. The presented approach effectively handles the trade‐off scenarios and provides flexibility to the designer to decide the threshold of the individual optimisation objective leading to the construction of a good‐quality solution as a whole. The performance is evaluated over several benchmark bioassays. [ABSTRACT FROM AUTHOR]

Video steganography is the method of hiding the presence of data within the cover video. In this paper, a novel video steganography technique is revealed, where with the help of Sudoku puzzle of order 9 × 9, extracted image frames of a cover video are utilized to hide encrypted data. The novelties of the work imply that as the input information is first encrypted into cipher text using a new modified substitution and permutation network (SPN)-type block cipher, then, that cipher text is embedded in the video using Sudoku puzzle as a key. This new and robust encryption model is like a block cipher in comparison with Advanced Encryption Standard (AES) that provides enhanced resistance in case of fault attack. This new SPN-type architecture differs with AES architecture in the step of round key mixing function. Its computation complexity is much higher compared to original AES to find 128 bits of tenth round key. Therefore, our proposed work combines both cryptographic and steganographic technique and Sudoku puzzle to enhance the security in maximum level with optimal computational complexity and good embedding capacity rate. [ABSTRACT FROM AUTHOR]

Droplet-based microfluidic biochips (or DMFBs) are rapidly becoming a revolutionizing lab-on-a-chip technology. Numerous application specific protocols bridging the cross-disciplinary fields necessitate DMFBs as their prime need. The main goal at the fluid level is to minimize bioassay schedule length. Also, for a safe assay outcome, contamination among droplet routes must be avoided. Size restriction of a chip and reconfigurable nature of the operational modules in DMFB introduce contaminated cells which necessarily require washing as an urgent need. As the sub-tasks of fluid level possess their own constraints for a successful DMFB design, rip-up and reiteration of sub-tasks may become unavoidable if all of those constraints are not satisfied mutually. To achieve a shorter time for chip realization a crucial need in fluid-level design is to avoid rip-up and re-iteration; hence, design convergence is to be incorporated that collectively considers the fluid-level sub-tasks, instead of solving them individually. Thus, this paper focuses on the fluid level of DMFBs while considering design convergence, contamination avoidance, and washing issues. Obtained results are compared with several existing benchmarks. [ABSTRACT FROM AUTHOR]

Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways. [ABSTRACT FROM AUTHOR]