Industries strive to prevent ecologically destructive actions in their supply chains. At the same time, the optimization of their resources is a major concern for industries to minimize carbon emissions, boost sustainable practices, and improve a country’s long-term economic development. Therefore, the objective of this study is to examine the impact of Green Supply Chain Management (GSCM) methods on operational performance with the mediation of technological innovation, in the context of Pakistani manufacturing firms. The partial least square-structural modeling (PLS-SEM) method is adopted in this paper. Data were gathered from 223 different manufacturing firms in Pakistan and then analyzed among these variables. The data show good validity and reliability, and structural model explains 61% of the variance in operational performance and 45.4% of the variance in technical innovation, demonstrating its predictive validity. The R-square criteria classify R-square entities of 0.67, 0.33, and 0.19 as considerable, moderate, and weak, respectively. It is demonstrated that all the f-square values are greater than 0.020 and 0.35, indicating a significant effect on the model’s validity. The findings of this study reveal that GSCM practices have a significantly positive effect on both technological innovation and operational performance. Technological innovation has a direct influence on operational performance and has a partial mediating effect on the relationship between GSCM practices and operational performance. Therefore, this research offers managers insight into the importance of technological innovation and GSCM practice adoption to achieve competitive advantages. It further provides the groundwork for managers, practitioners, and environmental management researchers to emphasize the value of GSCM practice in improving operational performance.

Underwater Wireless Sensor Networks (UWSNs) face significant communication and performance challenges due to their harsh and dynamic underwater environment. To address these issues, efficient and robust communication models are essential. This research proposes an energy-aware deployment approach and introduces the Controlled Transmission enhanced by hybrid model scheme with Differential Evolution, Whale Optimization, and Chaotic Adaptation (CT-DEWCA) for relay selection. This model enhances node placement by implementing a “strategic dual transmission” approach, which employs CT for short-distance data transfer and utilizes the DEWCA hybrid algorithm to select optimal relay nodes for long-distance communication. The strategy optimizes energy allocation between storage and regular nodes. Simulations show that the CT-DEWCA model outperforms conventional Vector-Based Forwarding (VBF), Hop-by-Hop Vector-Based Forwarding (HH-VBF), Depth-Based Routing (DBR), and, with recent work, Energy-Efficient Artificial Fish Swarm-based Clustering Cognitive Intelligence Protocol (EAFSCCIP). The results show an improvement by an average of 5.4% in packet delivery ratio, 20% reduction in cumulative energy consumption, 6% increase in throughput, and 12% lower packet-loss rate on average compared to the other protocols. This research offers a robust solution for enhancing UWSNs performance and longevity by addressing critical energy efficiency issues and reliable data transmission in underwater environments. The proposed approach demonstrates the utility and potential of bio-inspired optimization techniques to tackle the unique challenges of underwater communication networks.

We propose the design of a novel fractal antenna that is both unique and performance-driven. Two important antenna design features, miniaturization and wideband operation, are combined in this work. A ring-shaped antenna is designed using the well-known fractal geometry. This hybrid geometry is a fusion of meander and Koch curve shapes. The geometrical construction of the proposed antenna is compared to the standard Koch curve geometry. It is shown that combining the meander and Koch curve shapes increases the effective electrical length. The wider bandwidth is achieved by bringing the higher modes together. The overall dimensions of proposed meander Koch curve fractal ring antenna are 45×25×1.6mm³. The resonance frequency of the antenna is between 4.94 and 6.12GHz ( BW=21.83), which covers the entire 5GHz WLAN band. The prototype has been fabricated and experimentally verified.

The domain of underwater wireless communication (UWC) link is gaining much attention due to an increase in various underwater activities such as offshore hydrocarbon exploration, underwater unmanned vehicles (UUV), and military practices. Increased bandwidth and a reliable data link are mainly required for such activities. Both requirements of the domain are heavily affected by the highly conductive property of the seawater. This paper demonstrates the performance evaluation of radiofrequency-UWC, focusing on surface wave analysis, to propose a reliable solution for offshore activities. A constructive interference scheme can be useful due to the sharp difference in the properties of the two mediums (air and seawater). To that end, an experimental setup is created, and a corresponding finite element method (FEM) based simulation of the radio-based wireless link is run. This is because it has higher bandwidth and speed than acoustic and optical approaches. A conduction current mechanism transmits and receives data in a synthetic water tank containing a prepared conductive media (saltwater). The study of changing depths of transmitter-receiver nodes in saltwater shows that surface waves cause significant noise reception in shallow water (less than dipole length, below water level). During a series of experiments in the tank, the lowest bit error rate (BER) is observed only when the node’s submerged height was greater than dipole length. As a result, it is meant to provide a genuine data channel model. The discovery and analysis will aid in the development of a dependable underwater data link, with applications including short-range diver-to-diver communication, and UUV capability.

A microstrip‐fed hybrid Fractal antenna for WLAN application is presented in this article. The proposed antenna is composed of meander and Koch elements combined to form a longer curve that extends the electrical length. This approach simultaneously reduces the operating frequency and antenna size. The Green's functions and segmentation method are used to calculate the input impedance of the hybrid Fractal antenna. For analysis, the amalgamated antenna shape is decomposed into simple regular segments. The impedance parameters of the newly arranged shapes are calculated by the corresponding Green's functions. The impedance matrix equations of all the segments are presented so as to display in a single matrix whose solution determines the input impedance of the complete antenna geometry. To validate the theory, a prototype of the proposed antenna has been numerically simulated and experimentally measured.

These days, The website Uniform Resource Locator (URL) is widely used for accessing and navigating online information. However, the rise of AI-generated fake content, scams, counterfeit URLs, and other cyberattacks has significantly increased phishing-related threats. These fake URLs are difficult to identify because phishing links often resemble legitimate URLs. Consequently, both known and unknown (zero-day) phishing attacks remain difficult to detect in practice.This paper presents a hybrid deep learning–based intrusion detection system capable of detecting both known and zero-day phishing URLs. The objective is to provide users with absolute URLs while protecting them from fake ones. Another goal is to design an adaptive intrusion detection system (IDS) that combines static analysis, signature-based detection, and heuristic methods to identify phishing URLs. The proposed method, named the Zero-Phishing Convolutional Neural Network and Long Short-Term Memory (ZP-CNN-LSTM) algorithm, consists of three distinct schemes. For instance, static analysis rules are based on regular expressions, signatures for convolutional neural networks (CNNs), and zero-day detection using LSTM and autoencoders. We tested our project in the VR and AR research laboratory on 2 million testbed URLs, determined whether they were real or fake with respect to phishing, and predicted their phishing patterns. Results show that the proposed method achieves 98% higher accuracy than existing phishing detection methods in practice.