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The purpose of this paper is threefold: to study the classroom practices followed by the government and private school teachers in handling children with learning disabilities; to study the differences in practice levels of government and private school teachers with respect to learning disabilities among children; and to study the influence of Socio-demographic factors of teachers on the level of their classroom practices toward learning disabilities in children.

As per the questionnaire development, the present study consisted of a self-developed tool (appendix) by the researcher, to assess the classroom practices of the teachers toward learning disabilities in children. In sum, 10 percent of the total sample size was considered for the pilot study, to know the feasibility, reliability and validity of the developed tool. The sampling technique used was convenient sampling. The data were collected through the survey method from 40 government and 40 private school teachers.

Both groups of teachers had low to moderate levels of practice with respect to handling children with learning disabilities in the classrooms. The findings disclosed significant association between demographic variables like monthly income, place of residence, and present school experience and selected features related to learning disabilities. A significant association was observed with teachers’ gender, having special training on handling children with learning disabilities and having children with learning disabilities in the classroom. A significant association was also found between schools offering regular training programs for teachers on teaching, handling and awareness of learning disabilities and practice level of the respondents.

The study is limited to the assessment of classroom Practices of government and private primary school teachers toward learning disabilities in children.

The researcher, on the basis of the findings of the present study, suggests that trainings/workshops/intervention measure for teachers are much needed in order to help them in identifying and handling children with LD in a regular classroom especially at the primary level.

Trainings can be provided in the areas of teaching skills, inclusive education, intervention measures/managing children with learning disabilities in the classroom, special education, counseling, classroom habits of a teacher, etc. This will ensure that teachers are empowered thus becoming powerful, operative and competent in teaching children with different needs and handling the uniqueness of each and every child. The school organizations and the government authorities can be informed on the need for such measures. This study recommends that there is a need for improving the classroom practice of teachers, in order to help children, benefit and get equal opportunities as their non-disabled peers.

This research paper is based on the findings collected from 80 primary teachers from various schools across the city.

This paper aims to present a mosaicking method for underwater robotic applications, whose result can be provided to other perceptual systems for scene understanding such as real-time object recognition.

This method is called robust and large-scale mosaicking (ROLAMOS) and presents an efficient frame-to-frame motion estimation with outlier removal and consistency checking that maps large visual areas in high resolution. The visual mosaic of the sea-floor is created on-the-fly by a robust registration procedure that composes monocular observations and manages the computational resources. Moreover, the registration process of ROLAMOS aligns the observation to the existing mosaic.

A comprehensive set of experiments compares the performance of ROLAMOS to other similar approaches, using both data sets (publicly available) and live data obtained by a ROV operating in real scenes. The results demonstrate that ROLAMOS is adequate for mapping of sea-floor scenarios as it provides accurate information from the seabed, which is of extreme importance for autonomous robots surveying the environment that does not rely on specialized computers.

The ROLAMOS is suitable for robotic applications that require an online, robust and effective technique to reconstruct the underwater environment from only visual information.

The purpose of this paper is to investigate service quality of e-commerce Websites in online platform and their contribution on e-business promotion.

The online survey was performed on a survey portal provided by Nepal Telecom in Nepal.

The findings of this study suggest that information quality and online service quality were the key determinants for user satisfaction and sustainability of e-commerce technology.

Research opportunities of web services and e-commerce area are fruitful and important for both academics and practitioners.

The findings on online service quality of e-commerce technology will be useful for current management practice such as making business policies and strategies and sharing information to managers and organization leaders. This study can be used for e-commerce Website operators wishing to enhance the competitiveness of their Websites in the highly competitive online market.

E-commerce is considered an excellent alternative for individuals and companies to reach new customers. Service quality delivery through Internet is an essential strategy to success, more important than price and web presence. The e-commerce Website has been identified as having a significant impact on business activities in solving the geographical problem. A number of performance problems have been observed for e-commerce Websites, and much work has gone into characterizing the performance of web-servers and Internet applications.

Cyber resilience in cyber supply chain (CSC) systems security has become inevitable as attacks, risks and vulnerabilities increase in real-time critical infrastructure systems with little time for system failures. Cyber resilience approaches ensure the ability of a supply chain system to prepare, absorb, recover and adapt to adverse effects in the complex CPS environment. However, threats within the CSC context can pose a severe disruption to the overall business continuity. The paper aims to use machine learning (ML) techniques to predict threats on cyber supply chain systems, improve cyber resilience that focuses on critical assets and reduce the attack surface.

The approach follows two main cyber resilience design principles that focus on common critical assets and reduce the attack surface for this purpose. ML techniques are applied to various classification algorithms to learn a dataset for performance accuracies and threats predictions based on the CSC resilience design principles. The critical assets include Cyber Digital, Cyber Physical and physical elements. We consider Logistic Regression, Decision Tree, Naïve Bayes and Random Forest classification algorithms in a Majority Voting to predicate the results. Finally, we mapped the threats with known attacks for inferences to improve resilience on the critical assets.

The paper contributes to CSC system resilience based on the understanding and prediction of the threats. The result shows a 70% performance accuracy for the threat prediction with cyber resilience design principles that focus on critical assets and controls and reduce the threat.

Therefore, there is a need to understand and predicate the threat so that appropriate control actions can ensure system resilience. However, due to the invincibility and dynamic nature of cyber attacks, there are limited controls and attributions. This poses serious implications for cyber supply chain systems and its cascading impacts.

ML techniques are used on a dataset to analyse and predict the threats based on the CSC resilience design principles.

There are no social implications rather it has serious implications for organizations and third-party vendors.

The originality of the paper lies in the fact that cyber resilience design principles that focus on common critical assets are used including Cyber Digital, Cyber Physical and physical elements to determine the attack surface. ML techniques are applied to various classification algorithms to learn a dataset for performance accuracies and threats predictions based on the CSC resilience design principles to reduce the attack surface for this purpose.

Pearl millet (Pennisetum glaucum) is a rich source of nutrients as compared to the major cultivated cereal crops. However, major factors which limit its utilization are the presence of anti-nutritional factors (phytate, tannins and polyphenols) which lower availability of minerals and poor keeping quality because of higher lipase activity. Therefore, this paper aims to focus on the impact of different processing methods on the nutrient composition and anti-nutritional components of pearl millet.

This is a literature review study from 1983 to 2017, focusing on studies related to pearl millet processing and their effectiveness in the enrichment of nutritional value through reduction of anti-nutritional compounds.

From the literature reviewed, pearl millet processing through various methods including milling, malting, fermentation, blanching and acid as well as heat treatments were found to be effective in achieving the higher mineral digestibility, retardation of off flavor, bitterness as well as rancidity problems found during storage of flour.

Through this review paper, possible processing methods and their impact on the nutrient and anti-nutrient profile of pearl millet are discussed after detailed studied of literature from journal articles and thesis.

Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance.

A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed.

The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches.

This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

In order to optimize BER and to substantiate performance measures, initially, the filter bank multicarrier (FBMC) quadrature amplitude modulation (QAM) performance metrics are evaluated with the cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) system. The efficiency of CP-OFDM, as well as FBMC/QAM that is transmitting over specific fading channels, is evaluated in terms of quality trade-off metrics over bit error rate (BER) as well as modulation order. When compared with the traditional FBMC systems, the proposed FBMC QAM system shows better performance. The performance metrics of FBMC/QAM with the inclusion of multiuser multiple-input-multiple-output (MUMIMO) is validated with worst case channel environment. The performance penalty gap that exists in CP- OFDM is compared with improved FBMC QAM in terms of both BER and OOB radiation measures. The BER trade off comparison between ML and MMSE optimally determine the prominent signal detection model for high performance FBMC QAM system.

The main objective of this research work is to provide perceptions about performance, co-channel interference avoidance as well as about the techniques that are used for minimizing the complexity of the system that is related to FBMC QAM structure for reducing intrinsic interference with higher spectral features as well as maximal likelihood (ML) detector systems.

This research work also looks at the efficiency of multiuser multiple-input-multiple-output (MU-MIMO) FBMC/QAM over nonlinear channels. Furthermore, when compared with OFDM, it also significantly reduces the penalty gap efficiency, thereby enabling the accessibility of the proposed FBMC QAM system from BER as well as implementation point of view. Finally, the signal detection is facilitated by the sub-detector and is achieved on the downlink side by making use of threshold-driven statistical measures that accurately minimize the complexity trade-off measures of the ML detector over modulation order. The computation of the proposed FBMC method’s BER performance measures was carried out through MATLAB simulation environments, as well as efficiency of the suggested work was demonstrated through detailed analyses.

This research work intend to combine the efficient MU-MIMO based transmission scheme with optimal FBMC/QAM for improved QoS over highly nonlinear channels which includes both delay spread and Doppler effects. And optimal signal detection model is facilitated at the downlink side by making use of threshold-driven statistical measures that accurately minimize the complexity trade-off measures of the ML detector over modulation order. The computation of the proposed FBMC method’s BER performance measures was carried out through MATLAB simulation environments, as well as efficiency of the suggested work was demonstrated through detailed analyses.

The purpose of this paper is to determine the most efficient hotels in the Indian hotel industry, the competitive positioning of these hotels, and the factors that affect their efficiency change.

This study conducts a two-stage analysis and uses data envelopment analysis (DEA) and Global Malmquist productivity index (MPI) approach in the first stage to calculate the managerial performance of a panel of 63 Indian hotels in 2019–2020 and their efficiency change from 2009–2010 to 2019–2020. Bootstrapped generalized least square (GLS) approach is applied in the second stage to evaluate the impact of contextual variables on efficiency change.

Using the results of the first stage analysis, the authors categorized the 63 Indian hotels into 7 distinct clusters. These clusters represent different levels of competitiveness and pace of growth. The GLS regression reveals a U-shaped relationship between hotel size and efficiency change and a negative relationship between pro social investments and efficiency.

This is the first study in the hotel industry that has used global MPI as a measure of efficiency change in the first stage and GLS in the second stage. In the Indian context, to the best of authors’ knowledge, no such study exists.

The purpose of this paper is to investigate international franchise performance. It focuses on how franchisors conceive their international performance, the drivers of their international performance, and how age-at-entry moderates the impact of their resources and capabilities (R&C) on international performance.

Using the lens of the resource-based view of the firm, the authors build on franchisor voice from a qualitative study (n=28) to propose a research model of international franchise performance. A second, quantitative study (n=89) tests the model with PLS structural equation modeling.

Franchisors view international performance in terms of relationship satisfaction with foreign franchisees and performance in comparison to competitors. The empirical results show that relationship satisfaction significantly improves comparative performance. Both franchisor-owned resources, the brand and knowhow, enhance only comparative performance, while all three international relational capabilities, related to knowhow transfer, monitoring, and contract design, and both reconfigurational capabilities, related to organizational responsiveness and innovativeness, improve relationship satisfaction. Only contract design and innovativeness increase comparative performance. Finally, late internationalization reinforces franchisor ability to leverage relational and reconfigurational capabilities for better relationship satisfaction.

This paper contributes to research on international franchise performance. It uses a mixed-method design and offers the first quantitative investigation of the drivers of international franchise performance. This research also integrates the role of franchisor R&C with franchisor strategic choices, through the moderating effect of internationalization timing.

The suggested work examines the latest developments such as the techniques employed for allocation of power, browser techniques, modern analysis and bandwidth efficiency of nonorthogonal multiple accesses (NOMA) in the network of 5G. Furthermore, the proposed work also illustrates the performance of NOMA when it is combined with various techniques of wireless communication namely network coding, multiple-input multiple-output (MIMO), space-time coding, collective communications, as well as many more. In the case of the MIMO system, the proposed research work specifically deals with a less complex recursive linear minimum mean square error (LMMSE) multiuser detector along with NOMA (MIMO-NOMA); here the multiple-antenna base station (BS) and multiple single-antenna users interact with each other instantaneously. Although LMMSE is a linear detector with a low intricacy, it performs poorly in multiuser identification because of the incompatibility between LMMSE identification and multiuser decoding. Thus, to obtain a desirable iterative identification rate, the proposed research work presents matching constraints among the decoders and identifiers of MIMO-NOMA.

To improve the performance in 5G technologies as well as in cellular communication, the NOMA technique is employed and contemplated as one of the best methodologies for accessing radio. The above-stated technique offers several advantages such as enhanced spectrum performance in contrast to the high-capacity orthogonal multiple access (OMA) approach that is also known as orthogonal frequency division multiple access (OFDMA). Code and power domain are some of the categories of the NOMA technique. The suggested research work mainly concentrates on the technique of NOMA, which is based on the power domain. This approach correspondingly makes use of superposition coding (SC) as well as successive interference cancellation (SIC) at source and recipient. For the fifth-generation applications, the network-level, as well as user-experienced data rate prerequisites, are successfully illustrated by various researchers.

The suggested combined methodology such as MIMO-NOMA demonstrates a synchronized iterative LMMSE system that can accomplish the optimized efficiency of symmetric MIMO NOMA with several users. To transmit the information from sender to the receiver, hybrid methodologies are confined to 2 × 2 as well as 4 × 4 antenna arrays, and thereby parameters such as PAPR, BER, SNR are analyzed and efficiency for various modulation strategies such as BPSK and QAM_j (j should vary from 8,16,32,64) are computed.

The proposed hybrid MIMO-NOMA methodologies are synchronized in terms of iterative process for optimization of LMMSE that can accomplish the optimized efficiency of symmetric for several users under different noisy conditions. From the obtained simulated results, it is found, there are 18%, 23% 16%, and 8% improvement in terms of Bit Error Rate (BER), Least Minimum Mean Squared Error (LMMSE), Peak to Average Power Ratio (PAPR), and capacity of channel respectively for Binary Phase Shift Key (BPSK) and Quadrature Amplitude Modulation (QAM) modulation techniques.