Ahmad Ali

Adopting green technologies is crucial for a sustainable supply process and organization development. In this construct, the current study analyzes how green technology adoption (GTA) and knowledge management (KM) processes could minimize supply risk in large manufacturing companies. The current study utilizes research techniques based on the resource-based view and contingency theories to minimize supply chain risk. The present study selected green variables (learning, productivity, raw materials, and resource utilization) along with KM processes (acquisition, sharing, and utilization) to investigate these connections to achieve the research objective. The author collected the data (203) from manufacturing firms in Zhengzhou city and used PLS-SEM, fuzzy set qualitative comparative analysis (fsQCA) to examine the study aims. The relationship between the adoption of green technologies and the KM process, which may reduce supply risk, was verified via PLS-SEM. fsQCA is employed as a combined strategy with green technology and the KM dimension to examine supply risk. The study results showed a significant correlation between the KM process’s ability to reduce supply risk under the GTA. The results of the fsQCA specify the numerous dimensions of green implementation enablers, and the KM process produced superior results in terms of supply risk minimization. This research contributes to bridging gaps and understanding the interrelationship between green measurement and the supply risk process minimization. The current study provides practical and social justification for enlightening the relationship between GTA and the KM process, aiming to minimize the supply risk. Based on the study outcome and ground information, this study reported limitations and future research direction.

Given the critical importance of green technology implementation (GTI) is important for production process improvement and an eco-friendly environment. The present study investigates the connection between GTI and the knowledge management (KM) process to minimize manufacturing risk. This research also validates that an assured combination of green implementation and KM can lead to minimizing manufacturing risk. The sample data (153) was taken from those manufacturing companies that utilize green technologies. Smart PLS 3.2.9 analyzes the relationship between certain variables of GTI and the KM process. Furthermore, fuzzy set qualitative comparative analysis (fsQCA) is used for a combined configurational approach to examine manufacturing risk minimization. The study’s outcome validated that green implementation positively correlated with KM to minimize production risk. fsQCA approach, KM, and green implementation outcome indicated that production risks are minimized. This study contributes to bridging research gaps in the literature and advances understanding of the interrelationship between green implementation and KM processes to minimize manufacturing risk. In addition, research is vital to combine direct and configurational methodologies to highlight two distinct facets of green implementation and the KM process for minimizing manufacturing risk.

The China–Pakistan economic corridor (CPEC) is a collection of different ongoing projects including transportation, infrastructure, the Gwadar seaport, oil pipeline, and internet connection zone projects. The Chinese government has adopted a development strategy for infrastructure growth and investment in Pakistani and Asian territories through the CPEC project. Transportation is considered the backbone of the CPEC. In addition to the CPEC project is the linked “belt and road initiative” (BRI) project, which aims to enhance regional connectivity and is a harbinger of the future in Asia, as well as in European countries. However, uncertain situations, such as a lack of proper planning, security, and political stability, hinder the growth and development of infrastructure. Three corridors of the CPEC road transportation network, namely, the eastern, the western, and future central alignment, have been examined through a master supposition group using the Delphi technique, which has never been applied to the road transportation network in the CPEC plan. The review is designed to draw master conclusions and demonstrate an outcome for round one and two in the present work. Round one and two investigate the impact of stakeholder support, politicians’ roles, terrorism, security situations, poverty, and economic crises. Using the Delphi technique within the host country hinders the construction of the road network. The results obtained through the appraisal have justified the present potential endeavor.

The increase in demand for clean, safe, and environmentally friendly renewable energy sources faces several challenges such as system design and reliable operations. DC microgrid (MG) is a promising system due to higher efficiency and natural interface to renewable sources. In the hierarchical control of DC Microgrid, the V-I droop control is deployed usually in primary control level for common load sharing between converters. However, conventional droop control causes improper current sharing, voltage variations, and circulating current regulation due to the presence of droop and line resistance between converters. The aim of this paper is to presents the primary control level design of buck converters in current mode control according to the concepts of time constant and time delay, and secondary control design for parallel operations in distributed manners by combining methods, namely, low bandwidth communication (LBC), circulating current minimization techniques, and average voltage/current control. Moreover, different time delays are used for two converters to testify the effects of communication delays on current sharing and voltage restoration. The simulation is done for 2 × 2.5 KWdc parallel buck converters in PLECS (a Simulation software used for high speed simulation for power electronics) environment which shows excellent results in minimizing circulation currents, enhancing proportional current sharing, and restoring the grid voltage.

Present research in the domain of wireless sensor network (WSN) has unearthed that energy restraint of sensor nodes (SNs) encumbers their perpetual performance. Of late, the encroachment in the vicinity of wireless power transfer (WPT) technology has achieved pervasive consideration from both industry and academia to cater the sensor nodes (SNs) letdown in the wireless rechargeable sensor network (WRSNs). The fundamental notion of wireless power transfer is to replenish the energy of sensor nodes using a single or multiple wireless charging devices (WCDs). Herein, we present a jointly optimization model to maximize the charging efficiency and routing restraint of the wireless charging device (WCD). At the outset, we intend an unswerving charging path algorithm to compute the charging path of the wireless charging device. Moreover, Particle swarm optimization (PSO) algorithm has designed with the aid of a virtual clustering technique during the routing process to equilibrate the network lifetime. Herein clustering algorithm, the enduring energy of the sensor nodes is an indispensable parameter meant for the assortment of cluster head (CH). Furthermore, compare the proposed approach to corroborate its pre-eminence over the benchmark algorithm in diverse scenarios. The simulation results divulge that the proposed work is enhanced concerning the network lifetime, charging performance and the enduring energy of the sensor nodes.

Classification of Hyperspectral Images (HSIs) has gained attention for the past few decades. In remote sensing image classification, the labeled samples are insufficient or hard to obtain; however, the unlabeled ones are frequently rich and of a vast number. When there are no sufficient labeled samples, overfitting may occur. To resolve the overfitting issue, in this present work, we proposed a novel approach for HSI feature extraction, called robust regularized Block Low-Rank Discriminant Analysis (BLRDA), which is a robust and efficient feature extraction method to improve the HSIs’ classification accuracy with few labeled samples. To reduce the exponentially growing computational complexity of the low-rank method, we divide the entire image into blocks and implement the low-rank representation for each block respectively. Due to the symmetric matrix requirements for the regularized graph of discriminant analysis, the k-nearest neighbor is applied to handle the whole low-rank graph integrally. The low-rank representation and the kNN can maximally capture and preserve the global and local geometry of the data, respectively, and the performance of regularized discriminant analysis feature extraction can be apparently improved. Extensive experiments on multi-class hyperspectral images show that the proposed BLRDA is a very robust and efficient feature extraction method. Even with simple supervised and semi-supervised classifiers (nearest neighbor and SVM) and randomly given parameters, the feature extraction method achieves significant results with few labeled samples, which shows better performance than similar feature extraction methods.

Nowadays, wireless power transfer is ubiquitously used in wireless rechargeable sensor networks (WSNs). Currently, the energy limitation is a grave concern issue for WSNs. However, lifetime enhancement of sensor networks is a challenging task need to be resolved. For addressing this issue, a wireless charging vehicle is an emerging technology to expand the overall network efficiency. The present study focuses on the enhancement of overall network lifetime of the rechargeable wireless sensor network. To resolve the issues mentioned above, we propose swarm intelligence based hard clustering approach using fireworks algorithm with the adaptive transfer function (FWA-ATF). In this work, the virtual clustering method has been applied in the routing process which utilizes the firework optimization algorithm. Still now, an FWA-ATF algorithm yet not applied by any researcher for RWSN. Furthermore, the validation study of the proposed method using the artificial neural network (ANN) backpropagation algorithm incorporated in the present study. Different algorithms are applied to evaluate the performance of proposed technique that gives the best results in this mechanism. Numerical results indicate that our method outperforms existing methods and yield performance up to 80% regarding energy consumption and vacation time of wireless charging vehicle.

Nowadays, the investigation of the Wireless Sensor Network (WSN) has materialized its functional area ubiquitously such as environmental engineering, industrial and business applications, military, feedstock and habitat, agriculture sector, seismic detection, intelligent buildings, smart grids, and predictive maintenance, etc. Although some challenges still exist in the wireless sensor network, in spite of the shortcoming, it has been gaining significant attention among researchers and technologists due to its versatility and robustness. WSN is subject to a high potential technology that has been successfully implemented and tested in real-time scenarios, as well as deployed practically in various applications. In this paper, we have carried out an extensive survey in real-time applications of wireless sensor network deployment in a practical scenario such as the real-time intelligent monitoring of temperature, criminal activity in borders and surveillance on traffic monitoring, vehicular behavior on roads, water level and pressure, and remote monitoring of patients. The application of the Wireless Sensor Network in the assorted field of research areas has been widely deliberated. WSN is found to be the most effective solution in remote areas which are not yet explored due to its perilous nature and unreachable places. Here, in this study, we have cited the recent and updated research on the ubiquitous usage of WSN in diverse fields in an extensive and comprehensive approach.

The TCP/IP streams are pervasive in the fast communication between the sensor nodes over the internet. However, these sensor nodes are dependent upon various sources for provisioning energy and maintaining Quality of Service (QoS). The QoS guarantees the end-to-end reliable communication as it has many constraints that need to be controlled independently. In this article, we propose a methodology to extend the lifetime of these sensor nodes by introducing a novel mobile node as a charger. At the same time, our method introduces an innovative mechanism called initial constant congestion window (ICCW) to handle the congestion of the bottleneck links. The ICCW manages new sending rate for effective utilization of the link capacity by injecting two different threshold values (SST_μ, SST_F) in the TCP procedure. In the end, the simulation work, as compared with the previous TCP techniques, asserts an overall reduction in packet loss, enhancement in throughput, and sending rate, which leads toward the congestion control. Moreover, the continuous recharging technique of the sensor nodes with the help of wireless power transfer makes the WSN as renewable wireless sensor network, which improves its overall lifetime.

Tracking of wireless nodes such as robots in wireless sensor network (WSN) has been widely studied in literature. Most of these studies are based on the Kalman filter (KF) for linear models corrupted by Gaussian noise, the extended Kalman filter (EKF) for non linear models and the particles filter (PF) which does no require the model o be linear nor the noise be Gaussian. In his paper, we present a comparative study of mobile target node (TN) racking via the KF, EKF and PF based on the received power of the signal. A constant velocity model is considered for the motion of TN, depicting an indoor environment. The performance of the filters are compared in terms of the root mean square error (RMSE). Extensive simulations are performed to evaluate the performance of the discussed filters.