Spawning frequencies and breeding seasons of some freshwater fishes with special reference to those occurring in the plains of northern India

It is well known that the differences in the spawning behaviour of teleosts can to a certain extent be illustrated on the basis of the condition revealed by • the unspawned eggs in the ovary (Heidrich, 1925; Clark, 1925, 1934; Hickling and Rutenberg, 1936). Many studies on the ova diameter frequencies have been undertaken during recent years and in almost all cases it has been accepted that species which have all the ovarian eggs of similar size spawn them together whereas those which have a wide range in oocyte sizes may have several groups of eggs matured and shed periodically during the breeding seaso

Intuitionistic fuzzy bi-implicator and properties of Lukasiewicz intuitionistic fuzzy bi-implicator

This paper presents axiomatic as well as constructive definitions of intuitionistic fuzzy bi-implicators based on intuitionistic fuzzy t-norms and their intuitionistic fuzzy residual implicators. The inter-relationship among different proposed classes is presented along with a detailed study of the properties of one of these intuitionistic fuzzy bi-implicators called the intuitionistic fuzzy beta-bi-operator constructed using Lukasiewicz intuitionistic fuzzy t-norm and its R-implicator

A weighted companion of Ostrowski's inequality using three step weighted kernel

There are numerous works suggesting linking SSM models to information systems (IS) models for information systems development. In these works links between SSM and IS models are established at the conceptual level i.e. they provide ways to utilise SSM models to identify or to derive IS models. However, most of them have not provided a method for representing the links explicitly in the combined model. Consequently, the links become subtle and untraceable, looking at the combined model only at the representation level. This paper proposes an approach based on meta modelling to achieve a uniform and formalised representation of models that combine SSM and IS models. The approach allows one to represent not only SSM and IS modelling concepts but also relationships between the concepts in a meta model using the conceptual modelling language Telos. Telos supports meta models to be flexibly specified and extended to meet specific modelling requirements of a particular project. An exemplary application is presented to demonstrate the operationalisation of the proposed approach and to illustrate the uniform representation of combined models. The example also shows benefits of the formalised representation in terms of computer support for managing and retrieving a combined model's meta data

An Adaptive Software Fault Tolerant Framework for Ubiquitous Vehicular Technologies

Probability for the occurrence of faults increases manifolds when program Lines of Code (LoC) exceeds a few thousand in ubiquitous applications. Faults mitigation in ubiquitous applications, such as those of autonomous Vehicular Technologies (VTs), has not been effective even with the use of formal methods. Faults in such applications require exhaustive testing for a timely fix, that seems infeasible computationally. This emphasizes the imperative role of Software Fault Tolerance (SFT) for autonomous applications. Several SFT techniques have been proposed but failures revealed in VT applications imply that existing SFT techniques need to be fine-tuned. In this paper, current replication-based SFT techniques have been analyzed and classified with respect to their diversity, adjudication, and adaptivity. Essential parameters (such as Reliability, Time, Variance, etc) for adjudication, diversity, and adaptiveness were recorded. The identified parameters were mapped to different techniques (such as AFTRC, SCOP, VFT, etc) for observing their shortcomings. Consequently, a generic framework named ”Diverse Parallel Adjudication for Software Fault Tolerance (DPA-SFT)” has been proposed. DPA-SFT addresses the shortcomings of existing SFT techniques for VTs with the added value of parallel and diverse adjudication. A prototype implementation of the proposed framework has been developed for assessing the viability of DPA-SFT over modules of VT. An empirical comparison of the proposed framework was performed with prevalent techniques (AFTRC, SCOP, VFT, etc). A thorough evaluation suggests that DPA-SFT performs better than contemporary SFT techniques in VTs due to its parallel and diverse adjudication

Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization

In public vehicles, one of the major concerns is driver's level of expertise for its direct proportionality to safety of passengers. Hence, before a driver is subjected to certain type of vehicle, he should be thoroughly evaluated and categorized with respect to certain parameters instead of only one‐time metric of having driving license. These aspects may be driver's expertise, vigilance, aptitude, experience years, cognition, driving style, formal education, terrain, region, minor violations, major accidents, and age group. The purpose of this categorization is to ascertain suitability of a driver for certain vehicle type(s) to ensure passengers' safety. Currently, no driver categorization technique fully comprehends the implicit as well as explicit characteristics of drivers dynamically. In this paper, machine learning–based dynamic and adaptive technique named D‐CHAITs (driver categorization through hybrid of artificial intelligence techniques) is proposed for driver categorization with an objective focus on driver's attributes modeled in DriverOntology. A supervised mode of learning has been employed on a labeled dataset, having diverse profiles of drivers with attributes pertinent to drivers' perspectives of demographics, behaviors, expertise, and inclinations. A comparative analysis of D‐CHAIT with three other machine learning techniques (fuzzy logic, case‐based reasoning, and artificial neural networks) is also presented. The efficacy of all techniques was empirically measured while categorizing the drivers based on their profiles through metrics of accuracy, precision, recall, F‐measure performance, and associated costs. These empirical quantifications assert D‐CHAIT as a better technique than contemporary ones. The novelty of proposed technique is signified through preprocessing of feature attributes, quality of data, training of machine learning model on more relevant data, and adaptivity This is the peer reviewed version of the following article: Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization, which has been published in final form at https://doi.org/10.1002/ett.3729. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions

Boron nitride nanoscrolls: structure, synthesis, and applications

This is the author accepted manuscriptBoron nitride nanoscrolls (BNS) are open-ended, one-dimensional (1D) nanostructures made by the process of rolling boron nitride nanosheets (BNNS) into a scroll-like morphology. BNS offer a high surface area to volume ratio and possess many unique properties (similar to carbon nanotubes (CNT), carbon nanoscrolls (CNS) and boron nitride nanotubes (BNT)) such as high resistance to oxidation, chemical stability, increased lubrication, high-temperature resistance, electrical insulation, the ability to cap molecules inside and at the ends,and a wide band gap regardless of chirality. Despite these attractive featuresand properties well suited for applications in biotechnology, energy storage, and electronics, the true potential of boron nitride, and BNS as the next ‘miracle material’ is yet to be fully explored. In this critical review, we assess, for the first time, various studies published on the formation, structural and dynamic characteristics of BNS, potential routes for BNS synthesis, and the toxicology of BNS. Finally, the future perspectives of BNS are discussed in view of its unique and exceptional candidacy for many (real-world) applications

A Context-aware and Intelligent Framework for the Secure Mission Critical Systems

Recent technological advancements in pervasive systems have shown the poten-tial to address challenges in the military domain. Research developments in mili-tary-based mission-critical systems have refined a lot as in autopilot, sensing true target behavior, battle damage conditions, acquiring and manipulating command control information. However, the application of pervasive systems in the military domain is still evolving. In this paper, an intelligent framework has been pro-posed for mission-critical systems to incorporate advanced heterogeneous com-munication protocols; service-oriented layered structure and context-aware infor-mation manipulation. The proposed framework addresses the limitation of “time-space” constraints in Mission-critical systems that have been improved signifi-cantly. This improvement is courtesy to enhancing situation-aware tactical capa-bilities such as localization, decision significance, strategic span, strategic inten-tions, resource coordination and profiling concerning the situation. A comprehen-sive use case model has been presented for a typical battle-field scenario followed by a comparison of the proposed framework with existing techniques. It is evi-dent from experiments and analyses that the proposed framework provides more effective and seamless interaction with contextual resources to improve tactical capabilities. This is the peer reviewed version of the following article: A Context-aware and Intelligent Framework for the Secure Mission Critical Systems, which has been published in final form in Transactions on Emerging Telecommunications Technologies. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Version

Efficacy and pitfalls of digital technologies in healthcare services:A systematic review of two decades

The use of technology in the healthcare sector and its medical practices, from patient record maintenance to diagnostics, has significantly improved the health care emergency management system. At that backdrop, it is crucial to explore the role and challenges of these technologies in the healthcare sector. Therefore, this study provides a systematic review of the literature on technological developments in the healthcare sector and deduces its pros and cons. We curate the published studies from the Web of Science and Scopus databases by using PRISMA 2015 guidelines. After mining the data, we selected only 55 studies for the systematic literature review and bibliometric analysis. The study explores four significant classifications of technological development in healthcare: (a) digital technologies, (b) artificial intelligence, (c) blockchain, and (d) the Internet of Things. The novel contribution of current study indicate that digital technologies have significantly influenced the healthcare services such as the beginning of electronic health record, a new era of digital healthcare, while robotic surgeries and machine learning algorithms may replace practitioners as future technologies. However, a considerable number of studies have criticized these technologies in the health sector based on trust, security, privacy, and accuracy. The study suggests that future studies, on technological development in healthcare services, may take into account these issues for sustainable development of the healthcare sector

Servicing Delay Sensitive Pervasive Communication Through Adaptable Width Channelization for Supporting Mobile Edge Computing

Over the last fifteen years, wireless local area networks (WLANs) have been populated by large variety of pervasive devices hosting heterogeneous applications. Pervasive Edge computing encouraged more distributed network applications for these devices, eliminating the round-trip to help in achieving zero latency dream. However, These applications require significantly variable data rates for effective functioning, especially in pervasive computing. The static bandwidth of frequency channelization in current WLANs strictly restricts the maximum achievable data rate by a network station. This static behavior spawns two major drawbacks: under-utilization of scarce spectrum resources and less support to delay sensitive applications such as voice and video.To this point, if the computing is moved to the edge of the network WLANs to reduce the frequency of communication, the pervasive devices can be provided with better services during the communication and networking. Thus, we aim to distribute spectrum resources among pervasive resources based upon delay sensitivity of applications while simultaneously maintaining the fair channel access semantics of medium access control (MAC) layer of WLANs. Henceforth, ultra-low latency, efficiency and reliability of spectrum resources can be assured. In this paper, two novel algorithms have been proposed for adaptive channelization to offer rational distribution of spectrum resources among pervasive Edge nodes based on their bandwidth requirement and assorted ambient conditions. The proposed algorithms have been implemented on a real test bed of commercially available universal software radio peripheral (USRP) devices. Thorough investigations have been carried out to enumerate the effect of dynamic bandwidth channelization on parameters such as medium utilization, achievable throughput, service delay, channel access fairness and bit error rates. The achieved empirical results demonstrate that we can optimally enhance the network-wide throughput by almost 30% using channels of adaptable bandwidths