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Purpose of this study is to design a compact gap coupled anchor shape patch antenna for wireless local area network/high performance radio local area network and worldwide interoperability for microwave access applications.

An anchor shape microstrip antenna is conceived, designed, simulated and measured. The anchor shape antenna is transformed to its rectangular equivalent by conserving the patch area. Modeling and simulation of the antenna is performed by Ansys high frequency structure simulator (HFSS) electromagnetic solver based on the concept of finite element method. The simulated results are experimentally verified by using Agilent E5071C vector network analyzer. Theoretical analysis of an electromagnetically gap coupled anchor shape microstrip patch antenna has been performed by obtaining the lumped element equivalent of the transformed antenna.

The proposed antenna has a compact conducting patch of dimension 0.26λ × 0.12λ mm² (λ is calculated at lower resonating frequency of 3.56 GHz) with impedance bandwidths of 100 and 140 MHz and antenna gains of 1.91 and 3.04 dB at lower resonating frequency of 3.56 GHz and upper resonating frequency of 5.4 GHz, with omni-directional radiation pattern.

In literature, one does not encounter anchor shape antenna using the concept of gap coupling and parasitic patches. The design has been optimized for wireless local area network/worldwide interoperability for microwave access applications with a relatively low patch area (291.12 mm²) as compared to other reported antennas for wireless local area network/worldwide interoperability for microwave access applications. Transformed antenna and the actual experimental antenna behavior varies, but the resonant frequencies of the transformed antenna as observed by theoretical analysis and simulated results (by high frequency structure simulator) are reasonably close, and the percentage difference between the resonant frequencies (both at lower and upper bands) is within the permissible limit of 1-2.5 per cent. Results confirm the theoretical proposition of transformation of shapes in antenna design, which allows a designer to adapt the design shape according to the application.

In this article, the authors consider the following nonlinear singular boundary value problem (SBVP) known as Lane–Emden equations, −u″(t)-(α/t) u′(t) = g(t, u), 0 < t < 1 where α ≥ 1 subject to two-point and three-point boundary conditions. The authors propose to develop a novel method to solve the class of Lane–Emden equations.

The authors improve the modified variation iteration method (VIM) proposed in [JAAC, 9(4) 1242–1260 (2019)], which greatly accelerates the convergence and reduces the computational task.

The findings revealed that either exact or highly accurate approximate solutions of Lane–Emden equations can be computed with the proposed method.

Novel modification is made in the VIM that provides either exact or highly accurate approximate solutions of Lane-Emden equations, which does not exist in the literature.

The purpose of this study is to identify factors that are important for logistics organizations from the perspective of manpower readiness for digitization of logistics operations. The study also prioritizes the identified factors and also evaluates the readiness index of manpower for the digitalization of logistics processes.

The factors for manpower readiness are identified through literature review and analysis of a case study. Three major categories of factors are identified. These are organizational, behavioural and technological factors. Under these three major categories of factors, 18 sub-factors are identified. Thereafter, with experts' inputs, the factors are prioritized using Fuzzy analytic hierarchy process (AHP). Further, a case illustration of an Indian logistics company has been taken to understand the current processes, technical capabilities, manpower skills and organization culture. After the case analysis and expert inputs, the manpower readiness index has been evaluated by using graph theory matrix approach (GTMA).

The prioritization of manpower readiness factors has been done using Fuzzy AHP. Organizational factors are found to be the most important factors which require quick attention. Sub-factors that are most important for building competencies in the logistics sector are providing the right training on functional skill development (0.129), top management support and commitment for digitalization (0.117), and organizational culture for process digitalization (0.114), etc. Finally, framework for evaluation of manpower readiness index for logistics operations in the digital age has been illustrated for a case company.

Indian logistics companies can benchmark their readiness index with respect to the best in the industry. Based on the readiness index, logistics companies can analyse their position, gaps from best and worst and can also identify potential areas for improvement.

The novelty of the study lies in the development of a framework for manpower readiness for digitalization in the logistics sector. In literature, this field is very less researched and provides the scope for developing strategies for improving manpower competencies for Industry 4.0. Logistics companies can improve their performance by making their manpower ready based on results obtained for readiness index.

The study aims to examine the relationship between retailer's performance and corporate social responsibility (CSR) practices in Indian context. The article introduces a new conceptual model considering the parameters as per the stakeholder theory perspective.

The authors conducted empirical research with CSR practices (CSRP) and other parameters to evaluate the retailers' performance using stakeholder theory. The authors present the results from 292 valid responses from the Indian retailers. The study used structured equation modeling (SEM) to present the analysis and the results.

Findings indicate that CSRP along with additional constructs loyalty (L), reputation (R), customer satisfaction (CS) and competitive advantage (CA) have positive and significant impact on organizational performance (OP).

The framework will serve as a guiding tool to the management of the retail outlets to examine carefully the link among CSRP with other variables presented in the study and subsequently to the retail outlet performance. The retailers must be more focused on the expectations and demands of the customers concerning products and services for better growth of the firms.

The uniqueness of the study lies in “CSR practices enabled integrated model” to examine the performance of the organizations. The proposed theoretical model would add value to the existing literature to help the retailers in process of their performance improvement.

The purpose of this paper is to investigate the influence of turning parameters such as cutting speed, feed rate and depth of cut on tool flank wear and machined surface quality of AISI 304 stainless steel during environment friendly turning under nanofluid minimum quantity lubrication (NMQL) conditions using PVD-coated carbide cutting inserts.

Turning experiments are conducted as per the central composite rotatable design under the response surface methodology. ANOVA and regression analysis are employed to examine significant cutting parameters and develop mathematical models for VB (tool flank wear) and R_a (surface roughness). Multi-response desirability optimization approach is used to investigate optimum turning parameters for simultaneously minimizing VB and R_a.

Optimal input turning parameters are observed as follows: cutting speed: 168.06 m/min., feed rate: 0.06 mm/rev. and depth of cut: 0.25 mm with predicted optimal output response factors: VB: 106.864 µm and R_a: 0.571 µm at the 0.753 desirability level. ANOVA test reveals depth of cut and cutting speed-feed rate interaction as statistically significant factors influencing tool flank wear, whereas cutting speed is a dominating factor affecting surface roughness. Confirmation tests show 5.70 and 3.71 percent error between predicted and experimental examined values of VB and R_a, respectively.

AISI 304 is a highly consumed grade of stainless steel in aerospace components, chemical equipment, nuclear industry, pressure vessels, food processing equipment, paper industry, etc. However, AISI 304 stainless steel is considered as a difficult-to-cut material because of its high strength, rapid work hardening and low heat conductivity. This leads to lesser tool life and poor surface finish. Consequently, the optimization of machining parameters is necessary to minimize tool wear and surface roughness. The results obtained in this research can be used as turning database for the above-mentioned industries for attaining a better machined surface quality and tool performance under environment friendly machining conditions.

Turning of AISI 304 stainless steel under NMQL conditions results in environment friendly machining process by maintaining a dry, healthy, clean and pollution free working area.

Machining of AISI 304 stainless steel under vegetable oil-based NMQL conditions has not been investigated previously.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

The purpose of this paper is to design a sustainable supply chain network (SCN) for omnichannel environment in order to provide better service to customers through flexible distribution. Thus, there is a need to incorporate multiple-channel distribution in the network design of supply chains (SCs).

A multiple-channel distribution supply chain network (MCDSCN) has been proposed under omnichannel environment. This proposed model integrates online giants with local retailer’s distribution network in an uncertain environment with sustainability. To incorporate sustainability, an objective function is added to reduce carbon content along with other objectives of minimization of SC cost. The model turns out to be mixed-integer linear programming model which is coded in GAMS and solved using CPLEX solver.

The proposed MCDSCN model is compared with conventional SCN. Furthermore, it was found that the proposed MCDSCN model has achieved significant saving in SC cost and is also more sustainable than conventional SCN. The proposed model also enables online giants to integrate their distribution network with local retailer’s distribution network.

Through proposed model, customers are free to access product and services as per their choice of channels which increases their convenience, reach and satisfaction.

The proposed MCDSCN model is a novel approach to design flexible distribution systems. This would significantly help organizations to design their distribution network more effectively to meet global competition.

The awareness for protecting the environment has resulted in remanufacturing and recycling policies in manufacturing industries. Carbon emission is one of the most important elements affecting the environment. Carbon emission due to production and transportation creates complicated situations for the manufacturing firms by affecting the manufacturer's carbon quota. The ecological consequences posed in a reverse logistic model are the subject of this study.

The present study explores the fuzzy model of economic production for both remanufacturing and recycling with uncertain cost parameters under the cap-and-trade rule to control the carbon emission due to different modes of transportation. Due to imprecise cost parameters, the hexagonal fuzzy numbers are set to fuzzify the overall cost, which leads to correct decisions in a more confident way. The result is defuzzified by using graded mean integration.

This study offers an explicit condition to control the carbon emission of the manufacturer and reduce the optimum cost. The findings indicate that the collection of used goods that can be remanufactured must be increased. The model is validated numerically. Sensitivity analysis explores the various aspects of different parameters on net cost to accomplish the fuzzy production model.

Under fuzzy inference, the research offers a relevant contribution in the field of recycling with controlling carbon emission by using the cap-and-trade policy. This study provides a trading strategy for a manufacturer's decision to avoid losses.

The purpose of this study is to present Industry 4.0 technologies for advancing the circular economy (CE) adaption in manufacturing industry’s supply chain (SC) network. To pursue the same, Industry 4.0 technological aspects were recognized as solution measures to overcome the challenges for CE implementation in SC.

A new hierarchical framework containing 13 leading CE challenges and eight promising Industry 4.0 technological aspects had been proposed, representing their mutual relationship. The proposed framework was analysed using a hybrid approach of analytic hierarchy process (AHP) and combinative distance-based assessment (CODAS) under interval-valued intuitionistic fuzzy (IVIF) environment. The IVIF-AHP was used to acquire the priority weights of the CE challenges, whereas the IVIF-CODAS was used to attain the preference order of the proposed technological aspects.

The key findings of the present work indicate that “Information disruptions among the SC members due to multiple channels” and “Manpower inability to handle the toxic materials” are the two most critical challenges hindering the adoption of CE practices in SC. Along with, the results also demonstrate that to overcome these challenges, “Smarter equipment to empower flexibility and mass customization” and “Big data driven decision-making system” are the two most significant Industry 4.0 technological solutions, adoption of which might encourage the organizations to align their operations with CE philosophies.

The sample size of the experts engaged in work was limited; however, big data studies could be conducted in future to capture more insights of the stated topic. In addition to this, to understand the implication of CE on Industry 4.0-based manufacturing, a separate study can be synthesised in future.

The proposed work facilitates a new framework consolidating various perspectives associated with CE implementation into a manufacturing industry considering the scenario of Indian rubber industry. This study enables the decision-makers to recognize the challenging factors for CE implementation into their organizations and up-taking the proposed Industry 4.0 practices as technological measures for improving the organization overall performance.

One area in which strategy and organizational ecology converge is organizational change. This essay weaves together salient themes in my (and my co-authors’) various writings on organizational change, and is anchored in the research literature of the last twenty years. Among other ideas developed here, I point out that there is now a convergence of agendas in strategy and ecology, with an important role being played by intraorganizational ecology. I develop the distinction between strong and weak selection approaches to organizational ecology. While the strong selection view does not find empirical support, there is stronger support for the weak selection view. I lay out some key features of an emerging evolutionary synthesis for the study of strategy and organization, and develop an evolutionary approach to organizational change.