Bakhtar University

Software-defined networking (SDN) has transformed network architectures through centralised control and programmability; yet this very centralization quite convincingly a double-edged sword exposes a set of critical vulnerabilities, including controller-targeted distributed denial-of-service attacks, exploits at the southbound interface, and flow rule manipulations that traditional defences continue to struggle to counter effectively. What is more, the rapid proliferation of IoT, 5G, and cloud integrations has only amplified these risks rendering static security mechanisms markedly inadequate in the face of continually evolving threats. This systematic review aims to investigate the efficacy of artificial intelligence techniques particularly machine learning and deep reinforcement learning in enhancing SDN security. Indeed, by adopting a rigorous systematic literature review methodology, the study will synthesise peer-reviewed works from major academic databases published between 2014 and 2025, critically evaluating AI-driven approaches for threat detection, anomaly identification, and automated mitigation while simultaneously identifying integration challenges such as scalability, real-time performance, and adversarial robustness. What is more, expected outcomes include the development of a comprehensive taxonomy of AI applications in SDN security, comparative insights into their reported performance (quite useful for highlighting strengths and weaknesses), and the identification of evidence-based research gaps. For that matter, the review will conclude by proposing future research directions oriented towards resilient and adaptive frameworks capable of safeguarding next-generation software-defined networks.

This study introduces a novel methodology aimed at minimising total harmonic distortion (THD) in grid-connected photovoltaic (PV) systems (GCPVs) through the implementation of a maximum power point tracking (MPPT) approach based on artificial neural networks (ANN). High THD levels in PV systems can lead to inefficiencies, power quality issues, and potential damage to the grid infrastructure. Although traditional MPPT methods effectively optimise the power output, they often fail to address harmonics. The proposed ANN-based MPPT algorithm improves PV power harvesting while actively minimising the harmonic distortions. The ANN was trained using a comprehensive dataset that included various environmental conditions, ensuring robust performance in diverse operational scenarios. Simulation results demonstrate that the ANN-based MPPT approach significantly reduces THD to below 1% across various irradiance levels, in contrast to the 1.18% to 2.72% observed with conventional methods such as perturb and observe (PO), while simultaneously preserving optimal power output. Reducing harmonic distortion improves the power quality, system efficiency, and lifespan of grid-connected components. This study highlights ANN-based control strategies for addressing the challenge of maximising energy harvesting and maintaining power quality in modern PV systems, offering a solution for the sustainable integration of solar energy into the grid.

Abstract Breast cancer is a leading health concern for women globally. Hormonal fluctuations can lead to benign breast abnormalities, such as cysts, such as cysts, which may have potential to become cancerous. Researchers have been proposed various thermal modelling techniques to detect breast cancer by differentiating between healthy and cancerous breast tissues. There is a gap literature regarding the use of thermal models to differentiate between benign and malignant breast disorders in women. This research focuses on the thermal changes caused by benign and malignant breast tumor in fatty tissue of woman’s breast. Experiment was conducted in School of Advanced Sciences and languages, VIT Bhopal University, India. That was approved by King Khalid University, and University of Arizona. A 2D steady-state model was developed using Penne’s bioheat equation, incorporating ket parameters such as blood flow rate, thermal conductivity and metabolic heat generation. The model incorporates adiabatic boundary conditions were defined for different environmental conditions. Solutions were computed using finite element analysis. Simulations yielded results for various spherical cysts and different tissue depths within a hemispherical breast model. Furthermore, this study compared thermal profiles associated with cysts and malignant tumors breast tissue. This study reveals distinct thermal differences between cyst and malignant tumour in breast tissue, potentially aiding in accurate diagnosis and reducing false-positive. This study identified several factors influencing cancer classification and prediction accuracy. The data used here is publicly available on Mammographic Image Analysis Society (MIAS) database [35]. Cysts and malignant tumors exhibit opposite thermal effects in breast tissue. The curve’s slope at the cyst-normal tissue interface is opposite to that at the tumor-normal tissue interface. Thermography can leverage the distinct thermal signatures of cysts and tumors to detect and characterize these abnormalities in breast tissue. Thermography can accurately differentiate cysts and cancerous tumors, reducing false positives. Notably, this technique can detect small lump (2mm diameter/1mm radius) at depths of up to 2.5 cm. This study finds SVM outperforms RF and ANN in breast cancer classification, leveraging computationally extracted features (viz., avg. accuracies of SVM, ANN and RF are 96.66%, 93.88% and 90.55%, respectively). In this study, we classified breast cancer data using three machine learning techniques: Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF). We optimized model performance by training with selected features based on the analysis.

The assessment of the dynamics of spontaneous external respiration in children who passed without hardware respiratory support made it possible to identify a significantly significant increase in breathing in children of group 3 relative to the indicator in group 1 on the first day by 43%, 24%, 11% on days 1,3,4.All patients of the 2nd group needed respiratory prosthetics during the first 7 days.Despite the more pronounced severity of the condition in group 3, in the first week after SCTBI, an increase in respiratory rate of more than 35 per minute was not allowed, oxygen saturation at a level of 98.3 to 99.5% in all injured patients.A more stable level, the longest physiological position of the peak of the acrophase of the circadian rhythm of oxygen saturation was due to the respiratory support of lung ventilation.In group 1, hemodynamic restructuring was revealed according to the hyperdynamic type in conditions of a decrease in the oxygen saturation index.

The assessment of the dynamics of spontaneous external respiration in children who passed without hardware respiratory support made it possible to identify a significantly significant increase in breathing in children of group 3 relative to the indicator in group 1 on the first day by 43%, 24%, 11% on days 1,3,4.All patients of the 2nd group needed respiratory prosthetics during the first 7 days.Despite the more pronounced severity of the condition in group 3, in the first week after SCTBI, an increase in respiratory rate of more than 35 per minute was not allowed, oxygen saturation at a level of 98.3 to 99.5% in all injured patients.A more stable level, the longest physiological position of the peak of the acrophase of the circadian rhythm of oxygen saturation was due to the respiratory support of lung ventilation.In group 1, hemodynamic restructuring was revealed according to the hyperdynamic type in conditions of a decrease in the oxygen saturation index.

Abstract A time-traveling is generated and experimentally confirmed using a relativistic circle constructed by a microstrip add-drop multiplexer. Such a device can operate with both an antenna and electronic device functions, which can be applied to link the two different time events with wired or wireless transmission schemes. A timeline is formed by the Rabi frequency oscillation when entangled states under successive filtering. In this letter, the message can be modulated for the lower and upper sidebands of the Rabi frequency, presenting the past and future traveling times, respectively. The required information can be retrieved in the past or the future via point-to-point transmission terminals. In the experiment, the message is modulated by the required information to the frequency-time function of the present carrier and input into the system performed by a point-to-point link. The merging information is formed on the timeline, and can be retrieved via the device output ports. The band pass filter is applied to obtain the required message. The results have shown that the proposed work can be realized with point-to-point communication, where the collapsed entangled pairs from both sides of the worlds are merged then Bob can communicate with Alice via the timeline.

The group of nations which includes Brazil, Russia, India, China, and South Africa (BRICS) need to address rising energy demand, reduce carbon emissions, and sustain economic growth, necessitating a strategy that integrates environmental sustainability with economic development. The current study delves into the profound impact of energy mix, financial development, digitalization, and national income on carbon emissions in BRICS countries. The study uses a nonlinear panel quantile regression model utilizing panel data spanning from 2000 to 2023, by estimating the asymmetric impact of energy mix, financial development, and digitalization on carbon emissions of BRICS countries. The estimates assert that a higher energy mix ratio, favoring renewable sources for energy generation, serves as a potent catalyst in mitigating carbon emissions. Moreover, the study highlights the pivotal role of financial development in curbing carbon emissions by fostering investment in green energy and encouraging sustainable energy consumption. Furthermore, the study underscores the transformative effect of digitalization in BRICS countries, leading to a reduction in carbon emissions. This exemplifies how BRICS countries have strategically leveraged financial development and digitalization to significantly reduce carbon emissions. However, the study also prompts policymakers to address the challenges posed by increased energy consumption from non-renewable sources amid developmental activities. By providing fresh evidence on the impact of the energy mix, financial development, and digitalization on carbon emissions, this study augments the existing literature and underscores the imperative for concerted action in addressing environmental concerns.

This article aims to analyze the geopolitical dimensions of the transition to renewable energies in light of the international competition over securing access to rare earth elements (REEs), referred to as new geopolitical foundations that give the countries possessing them a geopolitical advantage. The study addressed three elements representing the geopolitics of REEs in renewable energy supply chains, global economic powers’ strategies to ensure access to them based on China’s dominant position in their markets as well as the geopolitics of international competition to secure access to REEs. The article concluded that the rising demand for REEs sparks a worldwide “mineral war,” in which global economic powers line up to secure access to these strategic resources necessary to build a future for clean energy. However, challenges remain significant and complex given their interconnection with the current international energy security mechanisms after many regions with large REEs reserves have become theatres of geopolitical tensions.

Gene selection is an essential step for the classification of microarray cancer data. Gene expression cancer data (deoxyribonucleic acid microarray] facilitates in computing the robust and concurrent expression of various genes. Particle swarm optimization (PSO) requires simple operators and less number of parameters for tuning the model in gene selection. The selection of a prognostic gene with small redundancy is a great challenge for the researcher as there are a few complications in PSO based selection method. In this research, a new variant of PSO (Self-inertia weight adaptive PSO) has been proposed. In the proposed algorithm, SIW-APSO-ELM is explored to achieve gene selection prediction accuracies. This novel algorithm establishes a balance between the exploitation and exploration capabilities of the improved inertia weight adaptive particle swarm optimization. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) algorithm is employed for solution explorations. Each particle in the SIW-APSO increases its position and velocity iteratively through an evolutionary process. The extreme learning machine (ELM) has been designed for the selection procedure. The proposed method has been employed to identify several genes in the cancer dataset. The classification algorithm contains ELM, K-centroid nearest neighbor, and support vector machine to attain high forecast accuracy as compared to the start-of-the-art methods on microarray cancer datasets that show the effectiveness of the proposed method.

Marketing in tourism industry has been done in different eras based on the media available to managers of this industry. With the emergence of new media, the type of marketing activity has also changed. Employees’ skills are also important and the success of the individual or the organization depends on committed and expert human resources. Therefore, the present research aimed to explore the mediating role of knowledge construction process in the relationship between social media and open innovation of employees of Mashhad airline agencies. The research population consisted of all employees of airline agencies in Mashhad. The sample size was estimated using a convenience sampling method. The final size was estimated at 348. The data collection instrument was a standard questionnaire used to check the validity of the construct using confirmatory factor analysis. Cronbach’s alpha coefficient and composite reliability were used to measure reliability. The results substantiated the validity and reliability of the questionnaire. The data analysis was used through a structural equation modeling in Smart PLS. The results showed that the use of social media affected open innovation and knowledge construction. The use of knowledge construction is effective in open innovation. The knowledge construction process mediates the relationship between social media and open innovation.