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Humanoid robots have complicated dynamics, and they lack dynamic stability. Despite having similarities in kinematic structure, developing a humanoid robot with robust walking is quite difficult. In this paper, an attempt to produce a robust and expected walking gait is made by using an ALO (ant lion optimization) tuned linear inverted pendulum model plus flywheel (LIPM plus flywheel).

The LIPM plus flywheel provides the stabilized dynamic walking, which is further optimized by ALO during interaction with obstacles. It gives an ultimate turning angle, which makes the robot come closer to the obstacle and provide a turning angle that optimizes the travel length. This enhancement releases the constraint on the height of the COM (center of mass) and provides a larger stride. The framework of a sequential locomotion planer has been discussed to get the expected gait. The proposed method has been successfully tested on a simulated model and validated on the real NAO humanoid robot.

The convergence curve defends the selection of the proposed controller, and the deviation under 5% between simulation and experimental results in regards to travel length and travel time proves its robustness and efficacy. The trajectory of various joints obtained using the proposed controller is compared with the joint trajectory obtained using the default controller. The comparison shows the stable walking behavior generated by the proposed controller.

Humanoid robots are preferred over mobile robots because they can easily imitate the behaviors of humans and can result in higher output with higher efficiency for repetitive tasks. A controller has been developed using tuning the parameters of LIPM plus flywheel by the ALO approach and implementing it in a humanoid robot. Simulations and experiments have been performed, and joint angles for various joints are calculated and compared with the default controller. The tuned controller can be implemented in various other humanoid robots

This paper aims to outline and implement a novel hybrid controller in humanoid robots to map an optimal path. The hybrid controller is designed using the Owl search algorithm (OSA) and Fuzzy logic.

The optimum steering angle (OS) is used to deal with the obstacle located in the workspace, which is the output of the hybrid OSA Fuzzy controller. It is obtained by feeding OSA's output, i.e. intermediate steering angle (IS), in fuzzy logic. It is obtained by supplying the distance of obstacles from all directions and target distance from the robot's present location.

The present research is based on the navigation of humanoid NAO in complicated workspaces. Therefore, various simulations are performed in a 3D simulator in different complicated workspaces. The validation of their outcomes is done using the various experiments in similar workspaces using the proposed controller. The comparison between their outcomes demonstrates an acceptable correlation. Ultimately, evaluating the proposed controller with another existing navigation approach indicates a significant improvement in performance.

A new framework is developed to guide humanoid NAO in complicated workspaces, which is hardly seen in the available literature. Inspection in simulation and experimental workspaces verifies the robustness of the designed navigational controller. Considering minimum error ranges and near collaboration, the findings from both frameworks are evaluated against each other in respect of specified navigational variables. Finally, concerning other present approaches, the designed controller is also examined, and major modifications in efficiency have been reported.

This paper aims to design and implement the multiple adaptive neuro-fuzzy inference system (MANFIS) architecture-based sensor-actuator (motor) control technique for mobile robot navigation in different two-dimensional environments with the presence of static and moving obstacles.

The three infrared range sensors have been mounted on the front, left and right side of the robot, which reads the forward, left forward and right forward static and dynamic obstacles in the environment. This sensor data information is fed as inputs into the MANFIS architecture to generate appropriate speed control commands for right and left motors of the robot. In this study, we have taken one assumption for moving obstacle avoidance in different scenarios the speed of the mobile robot is at least greater than or equal to the speed of moving obstacles and goal.

Graphical simulations have designed through MATLAB and virtual robot experimentation platform (V-REP) software and experiments have been done on Arduino MEGA 2560 microcontroller-based mobile robot. Simulation and experimental studies demonstrate the effectiveness and efficiency of the proposed MANFIS architecture.

This paper designs and implements MANFIS architecture for mobile robot navigation between a static and moving obstacle in different simulation and experimental environments. Also, the authors have compared this developed architecture to the other navigational technique and found that our developed architecture provided better results in terms of path length in the same environment.

The purpose of this paper is to recognize and prioritize the critical drivers (CDs) essential for establishing a sustainable foxnut supply chain (SFNSC) aligned with the sustainable development goals (SDGs) set forth by the United Nations. The objective is to make a meaningful contribution to the longevity and well-rounded sustainability of the foxnut industry by scrutinizing pivotal factors that endorse triple bottom line (TBL) sustainability aspect throughout the supply chain.

A systematic approach, integrating literature reviews and government reports, identified potential CDs for a sustainable foxnut supply chain. Expert opinions refined the list with the help of fuzzy-Delphi method (FDM), and the final CDs were analyzed with fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) to establish their causal relationships and hierarchical importance.

The study identifies the top three CDs for a SFNSC: “Branding of the product”, “The Global increase in demand” and “Value addition of the foxnut”. Moreover, “Storage infrastructure”, “Mechanized processing” and “Proper transportation facilities” also contribute to the sustainability of the foxnut supply chain.

The results hold significance for various stakeholders in the foxnut industry, encompassing producers, policymakers and researchers. The identified CDs can guide decision-making and resource allocation to improve the sustainability of the foxnut supply chain. The study's framework and methodology can also be applied to other industries to promote sustainable practices and achieve SDGs.

This study enhances understanding of CDs for an SFNSC. FDM and F-DEMATEL techniques analyze causal relationships and rank key factors. The SFNSC model may help other major foxnut producers to become more sustainable.

The purpose of this paper is to develop an interpretive structural modeling (ISM) model to investigate the critical success factors (CSF) and the extent of CSF's influence in the implementation of lean industry 4.0 in manufacturing supply chain.

The study has been carried out with the help of the latest literature followed by brainstorming sessions with experts. The experts were the managers from the industries, assistant professors, and research scholars from academia working in this domain. Finally, a structured model is formed using ISM methodology for the analysis of the CSFs followed by matrice d'impacts croisés multiplication appliquée á un classment (MIAMAC) Analysis for the validation of the model.

The study identifies robotics, virtual and augmented reality and cloud computing as the main CSFs which are responsible to drive all the identified CSFs. However the CSF professional training and development (PTD) has been identified as the weakest driver but having the highest dependent power.

The study has included nine CSFs and the contextual relationships between the CSFs are based on the knowledge and experience of the experts, which may be biased. Moreover, the paper has covered the ISM approach, and the same thing can be validated using the fuzzy-ISM and other multi-criteria decision-making (MCDM) techniques.

This investigation of the CSFs in the lean industry 4.0 is original and the identified CSFs are the result of the literature reviews and an extensive discussion from the experts. The paper uses the complete experience of the respective experts to make this work more effective and original.

Sustainability is a very important factor to be considered in the supply chain (SC) of any industry. Agricultural industry needs to be addressed even more importantly with the tools of sustainability as it concerns the life of millions. This paper explores the critical barriers (CBs) in the sustainable supply chains (SSCs) of makhana industry located in the northern part of India and seeks to design a model for the researchers and the managers who want to work in this industry.

Initially, the CBs were identified with the help of an extensive literature review of sustainability in SCs for agri-industry and discussion with makhana industry experts (consisting of managers and senior managers) and academicians (consisting of professors and research scholars). The study uses the multi-criteria decision-making (MCDM) technique, namely interpretive structural modeling (ISM) and fuzzy ISM to develop the model. The study finally validates the model using Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis.

The obtained results indicate that, in the SSC of makhana industry, the role of “Lack of adoption of organic agricultural management techniques” (CB2), “Lack of modern techniques (CB4)”, “Multiple intermediaries” (CB5), “Weak socio-economic conditions” (CB7) and “Lack of proper knowledge” (CB1) are very significant. These barriers are needed to be addressed first as they have the highest driving power and other barriers are directly driven by these CBs.

The paper has included seven experts, and the interrelationship between CBs has been developed on the basis of their knowledge and discussion, so the results may be a little bias. Moreover, the paper has obtained the results using the ISM and fuzzy ISM by considering ten CBs; the researchers can explore this research by including more CBs and validate the results using other MCDM techniques like fuzzy-decision making trial and evaluation laboratory (DEMATEL), fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Best Worst Method (BWM).

This study is unique as per industry point of view and may help the researchers and managers to explore the field of makhana.

In time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.

In this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.

The proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.

Research in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.

The proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

The purpose of this paper is to investigate the impact of fairness concerns of the retailer on the pricing policies of the supply chain partners, their individual profits, and the overall performance of a dual-channel supply chain composed of one manufacturer and one retailer. First, the authors model the dual-channel supply chain under retailer’s fairness concern. Second, the authors derive the optimal pricing policies of the channel members. Third, the authors analyze the effects of retailer’s fairness and bargaining power on the pricing strategies and profit functions of the dual-channel supply chain system.

The authors adopt the manufacturer-led Stackelberg game theoretic framework, where the dominant manufacturer’s pricing decisions are based on the retailer’s pricing decision. The paper considers Nash bargaining solution as the fairness reference point to formulate the utility function of the fair-retailer. The paper uses this approach because it endogenously accounts for the competitive power and cooperative contribution of the channel members when they interact.

The authors find that the retailer’s fairness concerns are not always beneficial for its better performance. If the retailer is moderately sensitive towards its fairness, it will positively influence its performance. However, if the fairness concern becomes too high then it will negatively impact the retailer’s performance because it results in customers’ migration towards direct online channel for buying the products. In addition, if the retailer’s fairness concerns are mild, the manufacturer’s prices will decrease in retailer’s bargaining power, which is opposite otherwise.

The authors use Nash bargaining solution model as the fairness reference in the context of dual-channel supply chain, which is comparatively a recent approach and has been used independently from dual-channel supply chain system.

This study aims to compare the short-run performance of construction and non-construction initial public offerings (IPOs) that are offered in India during 2006–2015. The study also attempts to investigate the impact of ownership structure (i.e. concentrated ownership in the hand of promoters and institutional ownership) and market sentiment on the performance of construction sector IPOs in short run.

A total of 281 IPOs were listed at National Stock Exchange, India, during the study period, and 44 of those were from construction sector. The short-run performance of these construction and non-construction IPOs was compared using two indicators, i.e. monthly stock return (SR) and excess return over market benchmark (MAR). To examine the effect of concentrated ownership in the hand of promoters, institutional ownership and market sentiment on IPO performance, systematic dynamic panel regression model was developed.

The IPOs of construction firms perform significantly better than the non-construction firms. The performance of construction IPOs is significantly driven by the lag effect. This suggests a significant informational inefficiency, which results in a delayed reaction by investors. The market sentiment has a positive influence on the performance of construction sector IPOs, whereas the institutional holding has a negative influence.

To the best of the author’s knowledge, this study is the first attempt to examine the performance of construction sector IPOs in short run. The study uses systematic dynamic panel data regression, which provides better and reliable estimates.