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"Akhtar, Muhammad"
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Does competitive position matter: Investigating the impact of information risk on COE and corporate investment
The purpose of this study is to find out how firm's competitive position plays the moderating role between the relation of information risk and COE. The study considers the effect of two different types of information risk, i.e., lack of information quality and transparent information.
The data of the study is collected from all the non-financial firms listed on PSX from 2007 to 2022. Two-step system GMM dynamic panel estimators are applied to test the dynamic nature of the proposed model.
The findings show that firms having better competitive position signal their strength through improved information disclosure in order to gain the confidence of shareholders. This competitive environment poses a governance effect by imposing discipline on manager's behavior, reducing information asymmetry and improving the quality of information disclosure, resulting in reduction of the COE. Further, a more competitive environment improves the readability of the annual report and reduces information asymmetry. In addition, by reducing financing frictions, this research provides new and unique insights pertaining to the importance of competitive position in the sensitivity of investment to information risk.
This research extends to the corpus of literature by investigating the unexplored strategic determinants, such as a firm's competitive position, to mitigate the impact of two distinct types of information risk: lower quality and reduced transparency. Additionally, it explores how these risks influence both the cost of equity and corporate investment.
Journal Article
Innovative Adsorbents for Pollutant Removal: Exploring the Latest Research and Applications
The growing presence of diverse pollutants, including heavy metals, organic compounds, pharmaceuticals, and emerging contaminants, poses significant environmental and health risks. Traditional methods for pollutant removal often face limitations in efficiency, selectivity, and sustainability. This review provides a comprehensive analysis of recent advancements in innovative adsorbents designed to address these challenges. It explores a wide array of non-conventional adsorbent materials, such as nanocellulose, metal–organic frameworks (MOFs), graphene-based composites, and biochar, emphasizing their sources, structural characteristics, and unique adsorption mechanisms. The review discusses adsorption processes, including the basic principles, kinetics, isotherms, and the factors influencing adsorption efficiency. It highlights the superior performance of these materials in removing specific pollutants across various environmental settings. The practical applications of these adsorbents are further explored through case studies in industrial settings, pilot studies, and field trials, showcasing their real-world effectiveness. Additionally, the review critically examines the economic considerations, technical challenges, and environmental impacts associated with these adsorbents, offering a balanced perspective on their viability and sustainability. The conclusion emphasizes future research directions, focusing on the development of scalable production methods, enhanced material stability, and sustainable regeneration techniques. This comprehensive assessment underscores the transformative potential of innovative adsorbents in pollutant remediation and their critical role in advancing environmental protection.
Journal Article
Comparative effects of β-cyclodextrin, HP-β-cyclodextrin and SBE7-β-cyclodextrin on the solubility and dissolution of docetaxel via inclusion complexation
Cyclodextrins possess the ability to increase the apparent solubility and dissolution rate of poorly water soluble drugs. The objectives of the study were to investigate the effect of β-cyclodextrin, hydroxypropyl-β-cyclodextrin and sulfobutyl ether7 β-cyclodextrin on solubility and dissolution rate of docetaxel. Four different methods (physical mixture, kneading, freeze drying and solvent evaporation) were employed for inclusion complexation, at varying drug to cyclodextrin ratios 1:1, 1:2 and 1:4. The inclusion complexes of docetaxel with β-cyclodextrin, hydroxypropyl-β-cyclodextrin and sulfobutyl ether7 β-cyclodextrin at molar ratios 1:1 were characterized by fourier transform infrared spectroscopy (FTIR), X-ray diffractometry (XRD), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and proton nuclear magnetic resonance (1H NMR). The dissolution profiles of inclusion complexes were compared with pure drug. The results revealed formation of inclusion complexes between drug and β-cyclodextrin, hydroxypropyl-β-cyclodextrin and sulfobutyl ether7 β-cyclodextrin as confirmed by FTIR. 1H NMR revealed inclusion of drug within cyclodextrin cavity with appearance of proton shifts. Drug crystallinity was reduced with physical mixing and kneading method while amorphous form was attained by freeze drying and solvent evaporation method as revealed by XRD. The DSC and TGA confirmed the formation of inclusion complexes with the absence of melting peak of drug. The effect on solubility and dissolution rate of docetaxel was greater with sulfobutyl ether7 β-cyclodextrin than hydroxypropyl-β-cyclodextrin and β-cyclodextrin when prepared with similar methods. Drug to cyclodextrin ratio 1:1 was the optimum ratio to increase the solubility and dissolution of docetaxel irrespective of the method. It is concluded that sulfobutyl ether7 β-cyclodextrin had greater effect on solubility and dissolution rate of docetaxel than β-cyclodextrin and hydroxypropyl-β-cyclodextrin at molar ratios 1:1.
Journal Article
Malware Analysis and Detection Using Machine Learning Algorithms
One of the most significant issues facing internet users nowadays is malware. Polymorphic malware is a new type of malicious software that is more adaptable than previous generations of viruses. Polymorphic malware constantly modifies its signature traits to avoid being identified by traditional signature-based malware detection models. To identify malicious threats or malware, we used a number of machine learning techniques. A high detection ratio indicated that the algorithm with the best accuracy was selected for usage in the system. As an advantage, the confusion matrix measured the number of false positives and false negatives, which provided additional information regarding how well the system worked. In particular, it was demonstrated that detecting harmful traffic on computer systems, and thereby improving the security of computer networks, was possible using the findings of malware analysis and detection with machine learning algorithms to compute the difference in correlation symmetry (Naive Byes, SVM, J48, RF, and with the proposed approach) integrals. The results showed that when compared with other classifiers, DT (99%), CNN (98.76%), and SVM (96.41%) performed well in terms of detection accuracy. DT, CNN, and SVM algorithms’ performances detecting malware on a small FPR (DT = 2.01%, CNN = 3.97%, and SVM = 4.63%,) in a given dataset were compared. These results are significant, as malicious software is becoming increasingly common and complex.
Journal Article
The shift to 6G communications: vision and requirements
The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more reliable, fast, and can support a massive number of devices with ultra-low latency requirements. The researchers around the globe are proposing cutting edge technologies such as artificial intelligence (AI)/machine learning (ML), quantum communication/quantum machine learning (QML), blockchain, tera-Hertz and millimeter waves communication, tactile Internet, non-orthogonal multiple access (NOMA), small cells communication, fog/edge computing, etc., as the key technologies in the realization of beyond 5G (B5G) and 6G communications. In this article, we provide a detailed overview of the 6G network dimensions with air interface and associated potential technologies. More specifically, we highlight the use cases and applications of the proposed 6G networks in various dimensions. Furthermore, we also discuss the key performance indicators (KPI) for the B5G/6G network, challenges, and future research opportunities in this domain.
Journal Article
Evaluation of Machine Learning Algorithms for Malware Detection
This research study mainly focused on the dynamic malware detection. Malware progressively changes, leading to the use of dynamic malware detection techniques in this research study. Each day brings a new influx of malicious software programmes that pose a threat to online safety by exploiting vulnerabilities in the Internet. The proliferation of harmful software has rendered manual heuristic examination of malware analysis ineffective. Automatic behaviour-based malware detection using machine learning algorithms is thus considered a game-changing innovation. Threats are automatically evaluated based on their behaviours in a simulated environment, and reports are created. These records are converted into sparse vector models for use in further machine learning efforts. Classifiers used to synthesise the results of this study included kNN, DT, RF, AdaBoost, SGD, extra trees and the Gaussian NB classifier. After reviewing the test and experimental data for all five classifiers, we found that the RF, SGD, extra trees and Gaussian NB Classifier all achieved a 100% accuracy in the test, as well as a perfect precision (1.00), a good recall (1.00), and a good f1-score (1.00). Therefore, it is reasonable to assume that the proof-of-concept employing autonomous behaviour-based malware analysis and machine learning methodologies might identify malware effectively and rapidly.
Journal Article
Metal-Based Catalysts in Biomass Transformation: From Plant Feedstocks to Renewable Fuels and Chemicals
The transformation of biomass into renewable fuels and chemicals has gained remarkable attention as a sustainable alternative to fossil-based resources. Metal-based catalysts, encompassing transition and noble metals, are crucial in these transformations as they drive critical reactions, such as hydrodeoxygenation, hydrogenation, and reforming. Transition metals, including nickel, cobalt, and iron, provide cost-effective solutions for large-scale processes, while noble metals, such as platinum and palladium, exhibit superior activity and selectivity for specific reactions. Catalytic advancements, including the development of hybrid and bimetallic systems, have further improved the efficiency, stability, and scalability of biomass transformation processes. This review highlights the catalytic upgrading of lignocellulosic, algal, and waste biomass into high-value platform chemicals, biofuels, and biopolymers, with a focus on processes, such as Fischer–Tropsch synthesis, aqueous-phase reforming, and catalytic cracking. Key challenges, including catalyst deactivation, economic feasibility, and environmental sustainability, are examined alongside emerging solutions, like AI-driven catalyst design and lifecycle analysis. By addressing these challenges and leveraging innovative technologies, metal-based catalysis can accelerate the transition to a circular bioeconomy, supporting global efforts to combat climate change and reduce fossil fuel dependence.
Journal Article
Detection of Malware by Deep Learning as CNN-LSTM Machine Learning Techniques in Real Time
Cyber-attacks on the numerous parts of today’s fast developing IoT are only going to increase in frequency and severity. A reliable method for detecting malicious attacks such as botnet in the IoT environment is critical for reducing security risks on IoT devices. Numerous existing methods exist for mining IoT networks for previously discovered patterns that may be exploited to improve security. This study used a hybrid deep learning approach, namely the CNN-LSTM technique, to detect botnet attacks. Any software that infiltrates a computer system or is installed there without the administrators’ knowledge or permission is malicious. There is a wide range of viruses that cyber-criminals use to further their nefarious ends. A revolutionary deep learning system has been developed to counteract the increasing quantity of harmful programs. The system takes advantage of NLP methods as a baseline, mixes CNNs and LSTM neurons to capture local spatial correlations, and learns from successive long-term dependencies. Spatial invariance, often known as symmetry, is the property wherein the dataset size remains constant throughout iterations of an algorithm while undergoing various transformations. Therefore, automated extraction of high-level abstractions and representations aids in the malware categorization process. When compared to its predecessor research study, the current level of categorization accuracy is significantly greater than 0.81. The proposed CNN-LSTM method obtained an R2 = 99.19% in the dataset, with a correlation coefficient for the CNN-LSTM technique of R2 = 100% utilizing the provided dataset. The symmetry correlation of the CNN-LSTM, which illustrates that the CNN-LSTM method has the highest detection accuracy, at 99%, among the other malware detection methods such as the SVM and DT. The rest of classifiers had an accuracy of 98% for DT, and 95% for SVM. The accuracy of the LSTM model is 99%, the precision of the CNN-LSTM is 99%, recall is 99% and F1 score is 1.
Journal Article
Combining AHP and genetic algorithms approaches to modify DRASTIC model to assess groundwater vulnerability: a case study from Jianghan Plain, China
Accurate identification of vulnerability areas is critical for groundwater resources protection and management. The present study employed the modified DRASTIC model to assess the groundwater vulnerability of Jianghan Plain, a major farming area in central China. DRASTICL model was developed by incorporating the land use factor to the original model. The ratings and weightings of the selected parameters were optimized by analytic hierarchy process (AHP) method and genetic algorithms (GAs) method, respectively. A combined AHP–GAs method was proposed to further develop this methodology. The unity-based normalization process was employed to categorize the vulnerability maps into four types, such as very high (>0.75), high (0.5–0.75), low (0.25–0.5), and very low (<0.25). The accuracy of vulnerability mapping was validated by Pearson’s correlation coefficient between vulnerability index and the nitrate concentration in groundwater and analysis of variance
F
statistic. The results revealed that the modified DRASTIC model had a large improvement over the conventional model. The correlation coefficient increased significantly from 41.07 to 75.31% after modification. Sensitivity analysis indicated that the depth to groundwater with 39.28% of mean effective weight was the most critical factor affecting the groundwater vulnerability. The developed vulnerability model proposed in this study could provide important objective information for groundwater and environmental management at local level and innovation for international researchers.
Journal Article