Search Results (21)

This study focuses on understanding the bioconvection in Jeffery–Hamel (JH) flow, which has valuable applications in areas like converging dies, hydrology, and the automotive industry, which make it a topic of practical importance. This research aims to explore Homogeneous–Heterogeneous (HH) chemical reactions in a magnetic Darcy–Forchheimer model with bioconvection in convergent/divergent channels. To analyze the role of porosity, the Darcy–Forchheimer law is applied. The main system of equations is simplified through similarity transformation into ordinary differential equations solved numerically with the help of the NDSolve technique. The results, compared with previous studies for validation, are presented through graphs and tables. The study reveals that in divergent channels, the velocity decreases with higher solid volume fractions, while in convergent channels, it increases. Furthermore, various physical parameters, such as the Eckert number and porosity parameter, increase skin friction in divergent channels but decrease it in convergent channels. These findings suggest that the parameters investigated in this study can effectively enhance homogeneous reactions, providing valuable insights for practical applications. Full article

Schiff bases synthesized in our laboratory have demonstrated pain-relieving effects through both peripheral and central nervous system pathways. Considering that centrally acting analgesics often affect the muscle tone of the gastrointestinal tract (GIT) and related deep internal organs, this study was conducted to examine potential relaxant effects on blood vessels and GIT smooth muscles. The possible relaxant effects of Schiff bases (SB1 and SB2) on isolated rabbit aortic strips were evaluated. The experiments involved assessing their impact on contractions induced by 80 mM potassium chloride (KCL) and 1 µM norepinephrine (NE). Norepinephrine concentration response curves (N. ECRCs) were constructed in the absence and presence of three different concentrations of SB1 and SB2, using N. ECRCs as a negative control. Terazosin served as a standard α1 receptor blocker. Docking studies were employed to validate the mechanism of action for SB1 and SB2. The study outcomes suggest that SB1 is more potent than SB2, demonstrating lower EC₅₀ values for NE-induced contractions in intact (5.50 × 10⁻⁵ ± 2.23 M) and denuded (5.81 × 10⁻⁵ ± 3.80 M) aortae. For NE-induced contractions, SB1 showed percent relaxation values of 48% and 41% in intact and denuded aortae, respectively. In comparison, SB2 exhibited values of 82.5% and 74%, showing that SB1 is more efficacious than SB2. The rightward shift of N. ECRCs for both SB1 and SB2 confirms their inhibition of α1 receptors. Additive effects of SB1 and SB2 were seen in the presence of verapamil (p < 0.0001). Docking analysis revealed that the compounds can properly bind to the target receptor Gq 1D (P25100). Findings show that both Schiff base SB1 and SB2 produce significant (p < 0.05) vasorelaxation via the α1 receptor blocking mechanism. Full article

Background/Objectives: Metallic NPs have been explored for various therapeutic uses owing to utilitarian physicochemical characteristics such as antibacterial, anti-inflammatory, and healing properties. The objective of this study is to evaluate the therapeutic potential of novel silver carbonate nanostructures in promoting wound healing and their antibacterial activity against Pseudomonas chengduensis and Staphylococcus aureus. Methods: In this work, we prepared Ag₂CO₃ nanoparticles through a two-step methodology that was expected to improve the material’s biomedical performance and biocompatibility. The characterization and assessment of synthesized NPs biocompatibility were conducted using hemolysis assays on the blood of a healthy male human. Further, we performed molecular docking analysis to confirm interactions of silver NPs with biological molecules. Results: In detail, the synthesized NPs showed <5% hemolysis activity at various concentrations, confirming their therapeutic applicability. Additionally, the NPs had good metabolic activities; they raised the T3/T4 hormone content and acted effectively on Insulin-like Growth Factor 1 (IGF-1) in diabetic models. They also facilitated the rate of repair by having the diabetic wounds reach 100% re-epithelialization by day 13, unlike the control group, which reached the same level only by day 16. The synthesized Ag₂CO₃ NPs exhibited high antimicrobial potential against both Pseudomonas chengduensis and Staphylococcus aureus, hence being a potential material that can be used for infection control in biomedical tissue engineering applications. Conclusions: These findings concluded that novel synthesis methods lead to the formation of NPs with higher therapeutic prospects; however, studies of their metaphysical and endocrinological effects are necessary. Full article

Mercury (Hg) is a highly toxic contaminant posing serious ecological and human health risks. This study investigates the Hg transfer characteristics and prediction models in a soil–lettuce system, employing bioconcentration factors (BCF), path analysis (PA), and Freundlich-type functions. A pot experiment was conducted in a greenhouse, where lettuce was grown in a range of Chinese agricultural soils (n = 21) and deliberately spiked with Hg using Hg(NO₃)₂ solution. The results indicated that lettuce grown in Hg-spiked acidic soils (pH < 6.5) accumulated total Hg (THg) levels up to 14.01 µg kg⁻¹, surpassing the safe consumption limit of 10 µg kg⁻¹. The BCF for lettuce THg was less than 1.0, suggesting a low transfer of Hg from soil to lettuce. Notably, BCF values were significantly higher in acidic soils (0.02) compared to alkaline soils (0.005). Path analysis accounted for 82% of the variation in lettuce THg content, identifying soil THg, pH, and amorphous (Amo) Al and Fe oxides as primary direct factors. Additionally, soil-available Hg (AvHg), exchangeable Hg (ExHg), clay, and organic matter (OM) were significant indirect factors affecting lettuce THg content. To validate the findings of the path analysis, an extended Freundlich-type equation was developed using stepwise multiple linear regression (SMLR). This model exhibited high predictive accuracy (R² = 0.82, p ≤ 0.001), with soil pH, THg, and amorphous Al and Fe oxides being the key variables for predicting Hg transfer in the soil–lettuce system. The insights from this study can guide the management of safe lettuce production in Hg-contaminated soils, ensuring the mitigation of Hg exposure through agricultural produce. Full article

In developing countries, like Pakistan, the pursuit of urbanization and economic development disrupts the delicate ecosystem, resulting in additional biogeochemical emissions of heavy metals into the human habitat and posing significant health risks. The levels of these trace elements in humans remain unknown in areas at higher risk of pollution in Pakistan. In this investigation, selected trace metals including Copper (Cu), Chromium (Cr), Lead (Pb) Cadmium (Cd), Cobalt (Co), Nickel (Ni), and Arsenic (As) were examined in human hair, urine, and nail samples of different age groups from three major cities (Muzaffargarh, Multan, and Vehari) in Punjab province, Pakistan. The results revealed that the mean concentrations (ppm) of Cr (1.1) and Cu (9.1) in hair was highest in Muzaffargarh. In urine samples, the mean concentrations (μg/L) of Co (93), As (79), Cu (69), Cr (56), Ni (49), Cd (45), and Pb (35) were highest in the Multan region, while As (34) and Cr (26) were highest in Vehari. The mean concentrations (ppm) of Ni (9.2), Cr (5.6), and Pb (2.8), in nail samples were highest in Vehari; however, Multan had the highest Cu (28) concentration (ppm). In urine samples, the concentrations of all the studied metals were within permissible limits except for As (34 µg/L) and Cr (26 µg/L) in Vehari. However, in nail samples, the concentrations of Ni in Multan (8.1 ppm), Muzaffargarh (9 ppm), Vehari (9.2 ppm), and Cd (3.69 ppm) in Muzaffargarh exceeded permissible limits. Overall, the concentrations of metals in urine, nail, and hair samples were higher in adults (39–45 age group). Cr, Cu, and Ni revealed significantly higher concentrations of metals in hair and water in Multan, whereas As in water was significantly (p < 0.001) correlated with urinary As in Multan, indicating that the exposure source was region-specific. Full article

In smart home environments, the interaction between a remote user and devices commonly occurs through a gateway, necessitating the need for robust user authentication. Despite numerous state-of-the-art user-authentication schemes proposed over the years, these schemes still suffer from security vulnerabilities exploited by the attackers. One severe physical attack is the node capture attack, which allows adversaries to compromise the security of the entire scheme. This research paper advances the state of the art by conducting a security analysis of user-authentication approaches regarding their vulnerability to node capture attacks resulting in revelations of several security weaknesses. To this end, we propose a secure user-authentication scheme to counter node capture attacks in smart home environments. To validate the effectiveness of our proposed scheme, we employ the BAN logic and ProVerif tool for verification. Lastly, we conduct performance analysis to validate the lightweight nature of our user-authentication scheme, making it suitable for IoT-based smart home environments. Full article

Exploiting Radio Frequency Identification (RFID) technology in healthcare systems has become a common practice, as it ensures better patient care and safety. However, these systems are prone to security vulnerabilities that can jeopardize patient privacy and the secure management of patient credentials. This paper aims to advance state-of-the-art approaches by developing more secure and private RFID-based healthcare systems. More specifically, we propose a lightweight RFID protocol that safeguards patients’ privacy in the Internet of Healthcare Things (IoHT) domain by utilizing pseudonyms instead of real IDs, thereby ensuring secure communication between tags and readers. The proposed protocol has undergone rigorous testing and has been proven to be secure against various security attacks. This article provides a comprehensive overview of how RFID technology is used in healthcare systems and benchmarks the challenges faced by these systems. Then, it reviews the existing RFID authentication protocols proposed for IoT-based healthcare systems in terms of their strengths, challenges, and limitations. To overcome the limitations of existing approaches, we proposed a protocol that addresses the anonymity and traceability issues in existing schemes. Furthermore, we demonstrated that our proposed protocol had a lower computational cost than existing protocols and ensured better security. Finally, our proposed lightweight RFID protocol ensured strong security against known attacks and protected patient privacy using pseudonyms instead of real IDs. Full article

The exponential growth of the Internet of Things (IoT) has led to the rapid expansion of interconnected systems, which has also increased the vulnerability of IoT devices to security threats such as distributed denial-of-service (DDoS) attacks. In this paper, we propose a machine learning pipeline that specifically addresses the issue of DDoS attack detection in IoT networks. Our approach comprises of (i) a processing module to prepare the data for further analysis, (ii) a dynamic attribute selection module that selects the most adaptive and productive features and reduces the training time, and (iii) a classification module to detect DDoS attacks. We evaluate the effectiveness of our approach using the CICI-IDS-2018 dataset and five powerful yet simple machine learning classifiers—Decision Tree (DT), Gaussian Naive Bayes, Logistic Regression (LR), K-Nearest Neighbor (KNN), and Random Forest (RF). Our results demonstrate that DT outperforms its counterparts and achieves up to 99.98% accuracy in just 0.18 s of CPU time. Our approach is simple, lightweight, and accurate for detecting DDoS attacks in IoT networks. Full article

Citrus canker (CC), caused by one of the most destructive subfamilies of the bacterial phytopathogen Xanthomonas citri subsp. Citri (Xcc), poses a serious threat to the significantly important citrus fruit crop grown worldwide. This has been the subject of ongoing epidemiological and disease management research. Currently, five different forms have been identified of CC, in which Canker A (Xanthomonas citri subsp. citri) being the most harmful and infecting the majority of citrus cultivars. Severe infection symptoms include leaf loss, premature fruit drop, dieback, severe fruit blemishing or discoloration, and a decrease in fruit quality. The infection spreads rapidly through wind, rain splash, and warm and humid climates. The study of the chromosomal and plasmid DNA of bacterium has revealed the evolutionary pattern among the pathovars, and research on the Xcc genome has advanced our understanding of how the bacteria specifically recognize and infect plants, spread within the host, and propagates itself. Quarantine or exclusion programs, which prohibit the introduction of infected citrus plant material into existing stock, are still in use. Other measures include eliminating sources of inoculum, using resistant hosts, applying copper spray for protection, and implementing windbreak systems. The main focus of this study is to highlight the most recent developments in the fields of Xcc pathogenesis, epidemiology, symptoms, detection and identification, host range, spread, susceptibility, and management. Additionally, it presents an analysis of the economic impact of this disease on the citrus industry and suggests strategies to reduce its spread, including the need for international collaboration and research to reduce the impact of this disease on the global citrus industry. Full article

Malware, short for malicious software, is any software program designed to cause harm to a computer or computer network. Malware can take many forms, such as viruses, worms, Trojan horses, and ransomware. Because malware can cause significant damage to a computer or network, it is important to avoid its installation to prevent any potential harm. This paper proposes a machine learning-based malware detection method called MalwD&C to allow the secure installation of Programmable Executable (PE) files. The proposed method uses machine learning classifiers to analyze the PE files and classify them as benign or malware. The proposed MalwD&C scheme was evaluated on a publicly available dataset by applying several machine learning classifiers in two settings: two-class classification (malware detection) and multi-class classification (malware categorization). The results showed that the Random Forest (RF) classifier outperformed all other chosen classifiers, achieving as high as 99.56% and 97.69% accuracies in the two-class and multi-class settings, respectively. We believe that MalwD&C will be widely accepted in academia and industry due to its speed in decision making and higher accuracy. Full article

Cryptojacking is a type of computer piracy in which a hacker uses a victim’s computer resources, without their knowledge or consent, to mine for cryptocurrency. This is made possible by new memory-based cryptomining techniques and the growth of new web technologies such as WebAssembly, allowing mining to occur within a browser. Most of the research in the field of cryptojacking has focused on detection methods rather than prevention methods. Some of the detection methods proposed in the literature include using static and dynamic features of in-browser cryptojacking malware, along with machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and others. However, these methods can be effective in detecting known cryptojacking malware, but they may not be able to detect new or unknown variants. The existing prevention methods are shown to be effective only against web-assembly (WASM)-based cryptojacking malware and cannot handle mining service-providing scripts that use non-WASM modules. This paper proposes a novel hybrid approach for detecting and preventing web-based cryptojacking. The proposed approach performs the real-time detection and prevention of in-browser cryptojacking malware, using the blacklisting technique and statistical code analysis to identify unique features of non-WASM cryptojacking malware. The experimental results show positive performances in the ease of use and efficiency, with the detection accuracy improved from 97% to 99.6%. Moreover, the time required to prevent already known malware in real time can be decreased by 99.8%. Full article

In recent years, massive development in the malware industry changed the entire landscape for malware development. Therefore, cybercriminals became more sophisticated by advancing their development techniques from file-based to fileless malware. As file-based malware depends on files to spread itself, on the other hand, fileless malware does not require a traditional file system and uses benign processes to carry out its malicious intent. Therefore, it evades conventional detection techniques and remains stealthy. This paper briefly explains fileless malware, its life cycle, and its infection chain. Moreover, it proposes a detection technique based on feature analysis using machine learning for fileless malware detection. The virtual machine acquired the memory dumps upon executing the malicious and non-malicious samples. Then the necessary features are extracted using the Volatility memory forensics tool, which is then analyzed using machine learning classification algorithms. After that, the best algorithm is selected based on the k-fold cross-validation score. Experimental evaluation has shown that Random Forest outperforms other machine learning classifiers (Decision Tree, Support Vector Machine, Logistic Regression, K-Nearest Neighbor, XGBoost, and Gradient Boosting). It achieved an overall accuracy of 93.33% with a True Positive Rate (TPR) of 87.5% at zeroFalse Positive Rate (FPR) for fileless malware collected from five widely used datasets (VirusShare, AnyRun, PolySwarm, HatchingTriage, and JoESadbox). Full article

Smart cities assure the masses a higher quality of life through digital interconnectivity, leading to increased efficiency and accessibility in cities. In addition, a huge amount of data is being exchanged through smart devices, networks, cloud infrastructure, big data analysis and Internet of Things (IoT) applications in the various private and public sectors, such as critical infrastructures, financial sectors, healthcare, and Small and Medium Enterprises (SMEs). However, these sectors require maintaining certain security mechanisms to ensure the confidentiality and integrity of personal and critical information. However, unfortunately, organizations fail to maintain their security posture in terms of security mechanisms and controls, which leads to data breach incidents either intentionally or inadvertently due to the vulnerabilities in their information management systems that either malicious insiders or attackers exploit. In this paper, we highlight the importance of data breaches and issues related to information leakage incidents. In particular, the impact of data breaching incidents and the reasons contributing to such incidents affect the citizens’ well-being. In addition, this paper also discusses various preventive measures such as security mechanisms, laws, standards, procedures, and best practices, including follow-up mitigation strategies. Full article

Trojan Detection—the process of understanding the behaviour of a suspicious file has been the talk of the town these days. Existing approaches, e.g., signature-based, have not been able to classify them accurately as Trojans. This paper proposes TrojanDetector—a simple yet effective multi-layer hybrid approach for Trojan detection. TrojanDetector analyses every downloaded application and extracts and correlates its features on three layers (i.e., application-, user-, and package layer) to identify it as either a benign application or a Trojan. TrojanDetector adopts a hybrid approach, combining static and dynamic analysis characteristics, for feature extraction from any downloaded application. We have evaluated our scheme on three publicly available datasets, namely (i) CCCS- CIC-AndMal-2020, (ii) Cantagio-Mobile, and (iii) Virus share, by using simple yet state-of-the-art classifiers, namely, random forest (RF), decision tree (DT), support vector machine (SVM), and logistic regression (LR) in binary—class settings. SVM outperformed its counterparts and attained the highest accuracy of 96.64%. Extensive experimentation shows the effectiveness of our proposed Trojan detection scheme. Full article

Cancerous tumor cells divide uncontrollably, which results in either tumor or harm to the immune system of the body. Due to the destructive effects of chemotherapy, optimal medications are needed. Therefore, possible treatment methods should be controlled to maintain the constant/continuous dose for affecting the spreading of cancerous tumor cells. Rapid growth of cells is classified into primary and secondary types. In giving a proper response, the immune system plays an important role. This is considered a natural process while fighting against tumors. In recent days, achieving a better method to treat tumors is the prime focus of researchers. Mathematical modeling of tumors uses combined immune, vaccine, and chemotherapies to check performance stability. In this research paper, mathematical modeling is utilized with reference to cancerous tumor growth, the immune system, and normal cells, which are directly affected by the process of chemotherapy. This paper presents novel techniques, which include Bernstein polynomial (BSP) with genetic algorithm (GA), sliding mode controller (SMC), and synergetic control (SC), for giving a possible solution to the cancerous tumor cells (CCs) model. Through GA, random population is generated to evaluate fitness. SMC is used for the continuous exponential dose of chemotherapy to reduce CCs in about forty-five days. In addition, error function consists of five cases that include normal cells (NCs), immune cells (ICs), CCs, and chemotherapy. Furthermore, the drug control process is explained in all the cases. In simulation results, utilizing SC has completely eliminated CCs in nearly five days. The proposed approach reduces CCs as early as possible. Full article