Accurate climate condition detection plays a crucial role in understanding long-term environmental changes and predicting future climate behavior. By analyzing variations in temperature, precipitation, and atmospheric trends, it becomes possible to identify global warming patterns and their regional impacts. This paper analyzes global and regional climate anomaly trends using traditional time-series and machine learning models, including Linear Regression, Ridge Regression, Random Forest, ARIMA, and Holt-Winters. The dataset, representing temperature anomalies relative to the 1951–1980 baseline, was used to forecast trends up to 2030. Results show a consistent rise in global temperatures across all models, confirming the persistent impact of climate change. The Holt-Winters model achieved the highest accuracy (MAE = 0.1868, RMSE = 0.2083, MAPE = 13.13%), effectively capturing long-term trends, while ARIMA also performed competitively. Random Forest excelled in capturing non-linear regional patterns, particularly for Australia, Brazil, and Germany, where MAPE values ranged from 15–26%. Overall, integrating statistical and machine learning approaches enhances forecasting accuracy and supports data-driven climate resilience planning.
This study presents a novel UWB antenna featuring optimized radiation and incorporating a metamaterial-inspired design. The antenna utilizes a CPW-fed circular monopole paired with an 8×8 metasurface composed of low-profile metamaterial cells. Each cell has four star-shaped slots with identical arms, which contribute to the antenna's distinct electromagnetic characteristics. Both the antenna and metasurface are organized on an FR4. The results indicate that the design effectively spans from 3 to above 13 GHz, covering the 3.1-10.6 GHz UWB frequency spectra, offering strong frequency response, high radiation efficiency, and satisfactory gain. This antenna design is highly suitable for UWB applications, fulfilling the demanding specifications of contemporary wireless communication systems. This research work directly supports SDG 9 (Industry, Innovation and Infrastructure) by advancing the resilient and innovative communication infrastructure crucial for global connectivity.
This study presents a compact 4-element MIMO antenna array designed for THz and 6G communications. The antenna features a straightforward design, utilizing modified T-shaped monopole elements fed by microstrip lines, positioned on opposite sides of a compact board with overall dimensions of 105 × 105 µm². The proposed MIMO antenna demonstrates broadband functionality, operating effectively from 3.7 THz to 4.3 THz. The array exhibits strong potential in terms of key performance metrics, including S-parameters, radiation patterns, and gain/efficiency levels, making it a promising candidate for use in future 6G networks. This research work directly supports SDG 9 (Industry, Innovation, and Infrastructure).
This paper presents a broadband high-efficiency power amplifier (PA) operating from 2.4 to 3.8 GHz, developed through a design strategy that exploits filter-based impedance matching networks. Instead of relying on conventional narrowband matching circuits, the proposed topology adopts a coupled-line structure derived from bandpass filter theory to achieve smooth impedance transformation, wideband performance, and inherent harmonic attenuation. Both the input and output matching networks are analytically synthesized to emulate bandpass characteristics, ensuring well-controlled impedance trajectories across the operating band. Employing a 10 W GaN HEMT transistor, the designed broadband PA achieves a nearly flat small gain of 11–16 dB, an output power of 38.7–40.9 dBm, and a drain efficiency up to 76.2 %. These results confirm the validity of integrating filter-inspired matching topologies as an effective and compact route toward next-generation broadband PAs. This research work directly supports SDG 9 (Industry, Innovation and Infrastructure) by enabling more capable and energy-efficient communication networks.
 In this work, the complex SEE transform was applied to solve some nonlinear delay differential equations (DDE). It is worth noting that the definitions of the integral transformations used in solving non-linear (DDE) were obtained from previous work. In addition, several examples were solved to demonstrate the possibility of using complex integral transformations to find simplified solutions to nonlinear differential equations. Several examples containing complex SEE transform were discussed in this paper.
This paper provides a comprehensive review of<br />secured network infrastructure, encompassing fundamental networking<br />concepts, prevalent cyber threats, and advanced defense<br />mechanisms. this study elucidates the evolution of network<br />security from basic packet filtering firewalls to sophisticated<br />Next-Generation Firewalls (NGFWs), Intrusion Detection and<br />Prevention Systems (IDS/IPS), and Virtual Private Networks<br />(VPNs). It further examines various network topologies, routing<br />protocols, and the role of modern simulation tools like Packet<br />Tracer and GNS3 in understanding network behavior and<br />vulnerabilities. The paper categorizes common network attacks<br />such as Denial of Service (DoS), Man-in-the-Middle (MitM), SQL<br />Injection, and Zero-Day Exploits, highlighting their impact on<br />network integrity and availability. The objective is to synthesize<br />this information into a cohesive framework that underscores<br />the critical importance of a multi-layered security approach in<br />safeguarding contemporary digital environments. This review<br />aims to serve as a foundational resource for researchers and practitioners<br />seeking to understand and implement robust network<br />security strategies against an ever-evolving threat landscape. In<br />doing so, it aligns with global sustainable development efforts by<br />reinforcing the resilience and reliability of digital infrastructure<br />essential for sustainable industry and innovation.<br />&nbsp;
This paper introduces a compact inset-fed microstrip patch antenna optimized for non-invasive blood pressure monitoring in the 2.4 GHz ISM medical band. The design features a rectangular patch with integrated slot modifications and an oval-defected partial ground, designed to enhance bandwidth and impedance matching. Fabricated on an FR-4 substrate (&epsilon;r = 4.4, tan&delta; = 0.025), the antenna demonstrates a simulated return loss of -70 dB, a bandwidth of 550 MHz, and a simulated peak gain of 4.4 dBi. Experimental measurements closely match the simulated results. On-body evaluations at various arm positions show a consistent downward shift in resonant frequency for an elevated-blood-pressure subject at all tested locations, qualitatively confirming the sensitivity of the antenna response to blood-pressure-induced tissue changes. While the present work focuses on demonstrating this qualitative sensitivity with a limited number of participants, the results highlight the potential of the proposed antenna as a sensing front-end that can be calibrated and integrated into future continuous, cuffless biomedical monitoring systems. This work supports SDG 3 (Good Health and Well-Being)&mdash;notably target 3.4 on reducing premature mortality from non-communicable diseases&mdash;by delivering a low-cost, non-invasive microstrip patch antenna for continuous, cuffless blood-pressure monitoring in the 2.4 GHz ISM band.
<strong>This study examines Batna&rsquo;s urban mobility problems and proposes a data-driven eco-mobility framework using Intelligent Transportation Systems (ITS). It combines field observations with traffic-flow analysis on National Road 77 between the city center and Hamla extension. Results reveal severe congestion, with peak flows reaching 49.2% of capacity, reflecting heavy dependence on private cars and weak public transport. To address this, the paper proposes an integrated plan built on: (1) urban redevelopment with smart materials, (2) an ITS-supported multimodal system including hybrid buses and a tramway, and (3) smart green infrastructure. The strategy is expected to cut congestion, lower environmental impacts, and improve transport efficiency. Overall, the study argues that an ITS-based integrated approach can transform mobility in Batna and serve as a model for similar semi-arid cities, supporting SDG 11 by enabling cleaner, inclusive urban transport.</strong>
The advent of sixth-generation (6G) mobile networks, marked by an exponential proliferation of wireless devices and novel applications, has dramatically intensified the need for intelligent and adaptive spectrum management. Cognitive radio (CR), initially conceived for dynamic spectrum access, is now being fundamentally reshaped for the 6G era through its convergence with artificial intelligence (AI), edge computing, and reconfigurable intelligent environments. This survey presents a comprehensive overview of this transformation, structured around three foundational pillars: the evolution of spectrum sensing, the rise of AI-native resource allocation, and the challenges of multi-domain coexistence. We begin by charting the progression of spectrum sensing from classical techniques like energy detection to advanced, data-driven approaches that harness deep learning, federated intelligence, and generative AI, tailored for the high-frequency regimes of THz and mmWave bands. We then explore the shift in resource allocation from conventional optimization frameworks to end-to-end AI-native paradigms, encompassing deep reinforcement learning, multiagent collaboration, and the use of digital twins for predictive modeling. The survey further investigates the intricate coexistence of terrestrial, non-terrestrial (NTN), and RIS-assisted networks, tackling emergent issues such as cross-technology interference, security vulnerabilities, and dynamic regulatory landscapes. Concluding with a forward-looking perspective, we pinpoint critical open challenges from semantic-aware spectrum sharing and privacy-preserving learning to quantum-resistant security and &rdquo;green sensing&rdquo; that are pivotal for building sustainable 6G ecosystems. By integrating concepts from diverse fields, this work serves as both a definitive reference and a strategic roadmap for researchers and engineers pioneering the next generation of autonomous wireless systems.
<div><strong>Predicting customer churn is an essential part of retention strategy for telecom companies so as to maximize revenue. In this paper, four machine learning models, Random Forest, Gradient Boosting, Logistic Regression, and K-Nearest Neighbors are compared to predict customer churn using a telecom dataset. We use SMOTE-Tomek to cope with class imbalance and optimize models by using GridSearchCV, Optuna, and Grey Wolf Optimizer. Our optimized Random Forest has 85.9% of accuracy beating other models. The study reveals the main churn factors such as type of contract and the usage of services, which are useful in developing targeted retention strategies for telecom providers..</strong></div>
The research examines the potential for adaptive planning in the trajectory construction of a collaborative manipulator in the presence of humans, such as public health service or crowded workplaces . Mathematical models are proposed that integrate the gradient field of potential functions, adaptive weighting of social repulsion, and the change of the motion vector through fuzzy logic. The models provide safe and effective trajectories considering the operator&#39;s proximity, obstructions, and the target. The proposed models are examined through their software implementation in the Python environment. These techniques facilitated the simulation of effector motion, the construction of a gradient field, and the assessment of system behavior under varying environmental configurations. The results indicate successful evasion of physical objects and the human comfort zone while achieving the intended objectives. The developed models possess the capability for incorporation into collaborative robotics systems in alignment with the specifications of Industry 5.0. Subsequent study may focus on incorporating reinforcement learning, extending to three-dimensional space, and conducting real hardware testing.
As generative AI rapidly transforms the digital landscape, it brings with it both unprecedented opportunities and formidable challenges&mdash;particularly in the realm of cybersecurity. This keynote explores how large language models, synthetic media generators, and autonomous agents are reshaping the threat landscape, enabling novel forms of cyberattacks such as hyper-personalized phishing, deepfake social engineering, and automated vulnerability discovery. At the same time, it examines how defenders can leverage the same technologies to build more resilient systems, from AI-augmented threat detection to real-time incident response and security automation.
