Explore the vast range of titles available.

Malware has emerged as a significant threat to end-users, businesses, and governments, resulting in financial losses of billions of dollars. Cybercriminals have found malware to be a lucrative business because of its evolving capabilities and ability to target diverse platforms such as PCs, mobile devices, IoT, and cloud platforms. While previous studies have explored single platform-based malware detection, no existing research has comprehensively reviewed malware detection across diverse platforms using machine learning (ML) techniques. With the rise of malware on PC or laptop devices, mobile devices and IoT systems are now being targeted, posing a significant threat to cloud environments. Therefore, a platform-based understanding of malware detection and defense mechanisms is essential for countering this evolving threat. To fill this gap and motivate further research, we present an extensive review of malware detection using ML techniques with respect to PCs, mobile devices, IoT, and cloud platforms. This paper begins with an overview of malware, including its definition, prominent types, analysis, and features. It presents a comprehensive review of machine learning-based malware detection from the recent literature, including journal articles, conference proceedings, and online resources published since 2017. This study also offers insights into the current challenges and outlines future directions for developing adaptable cross-platform malware detection techniques. This study is crucial for understanding the evolving threat landscape and for developing robust detection strategies.

The increasing frequency of ransomware attacks necessitates the development of more effective detection methods. Existing image-based ransomware detection approaches have largely focused on static analysis, overlooking specialized ransomware behaviors such as encryption, privilege escalation, and system recovery disruption. Although dynamic and memory forensics-based visualization methods exist in the broader malware domain, they primarily target generic malware families and often rely on memory dumps or system snapshots without transforming behavioral features into spatially meaningful representations. Moreover, traditional machine learning methods such as Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) typically depend on manual feature engineering and large labelled datasets, limiting scalability and adaptability. To address these limitations, we propose a novel behavior-to-image ransomware detection framework that transforms dynamic behavioral features extracted from sandbox-generated JSON reports into two-dimensional (2D) grayscale and color image representations, optimized for transfer learning (TL), enabling effective classification under small-data conditions. Our approach integrates domain-specific feature filtering and impact analysis to ensure the selection of the most ransomware-relevant attributes. TL subsequently automates feature extraction and classification, eliminating the need for separate feature selection procedures and overcoming the time-consuming process of manual feature engineering. Furthermore, by leveraging prior knowledge from large-scale image datasets, TL significantly mitigates the need for extensive labelled data while maintaining high detection accuracy and strong generalization. Experimental results demonstrate that fine-tuned pretrained models, notably ResNet50, achieve up to 99.96% accuracy with a minimal loss factor of 0.0026, even with a small dataset of 500 ransomware and 500 benign samples. We further validated the model’s interpretability through t-SNE visualizations and saliency maps, confirming its ability to focus on class-discriminative behavioral patterns. The low misclassification rate, along with the transparency of the model, highlights its potential for practical deployment in ransomware detection systems.

Antibiotic-loaded bone cement (ALBC) is commonly used in joint replacement therapy for prevention and treatment of bone infection and mechanical properties of the cement is still an important issue. The effects of ciprofloxacin and vancomycin was investigated on mechanical characterization of PMMA bone cement. Different properties of cement containing (0, 2.5, 5 and 10% W/W) antibiotics, including compressive and bending properties, microstructural, porosity and density were evaluated. Both antibiotics significantly reduced the density values and mechanical properties (compressive and flexural strength and modulus) in all groups in comparison to control over first two weeks (p < 0.05). This reduction was due to increased porosity upon antibiotic addition (3.05 and 3.67% for ciprofloxacin and vancomycin, respectively) in comparison to control (2.08%) (p < 0.001) and exposure to aqueous medium. Vancomycin as antibiotic with higher molecular weight (MW = 1485) had significant effect on compressive strength reduction of the cement at high amount compared to ciprofloxacin (MW = 367) (P < 0.01), there was no difference between two antibiotics at lower concentrations (P > 0.05). The effect of antibiotic loading is both molecular weight and drug content dependent. The time is also an important parameter and the second week is the probably optimum time to study mechanical behavior of ALBC.

Very high heat is generated during the polymerization of poly (methyl methacrylate) (PMMA) bone cement, which is used for implant fixation in orthopedic surgery. As such, it has been suggested that irrigating the bone cement layer in the surgical site with a saline solution is a way of cooling the layer. In this study, we aimed to determine the influence of irrigation with a saline solution on the flexural strength and the microstructure of the test specimens of two PMMA bone cement brands: Simplex P and FIX 1. Specimens were assigned to three groups: (1) irrigation with normal saline solution at 25 °C (RS group), (2) irrigation with cold saline at 4 °C (CS group), and (3) no irrigation (control group). For each of the groups, the specimens were tested after various times of aging in phosphate-buffered saline solution (PBS) at 37 °C for 1 h, 24 h, and 7 days. Flexural strength was measured following ISO 5833 protocol, and the surface microstructure was determined using scanning electron microscopy (SEM). The flexural strength results showed that for each of the cement brands, the difference between the groups was not significant, except for Simplex P specimens aged for 24 h, for which flexural strength of the RS and CS group specimens was lower than in the control group. The microstructural features of the surface of the specimens were similar across groups. These findings suggest that in a cemented arthroplasty, irrigation of the bone cement for the purpose of cooling it must only be used after very careful consideration.

Source coding has a rich and long history. However, a recent explosion of multimedia Internet applications (such as teleconferencing and video streaming, for instance) renews interest in fast compression that also squeezes out as much redundancy as possible. In 2009 Jarek Duda invented his asymmetric numeral system (ANS). Apart from having a beautiful mathematical structure, it is very efficient and offers compression with a very low coding redundancy. ANS works well for any symbol source statistics, and it has become a preferred compression algorithm in the IT industry. However, designing an ANS instance requires a random selection of its symbol spread function. Consequently, each ANS instance offers compression with a slightly different compression ratio. The paper investigates the compression optimality of ANS. It shows that ANS is optimal for any symbol sources whose probability distribution is described by natural powers of 1/2. We use Markov chains to calculate ANS state probabilities. This allows us to precisely determine the ANS compression rate. We present two algorithms for finding ANS instances with a high compression ratio. The first explores state probability approximations in order to choose ANS instances with better compression ratios. The second algorithm is a probabilistic one. It finds ANS instances whose compression ratios can be made as close to the best ratio as required. This is done at the expense of the number θ of internal random “coin” tosses. The algorithm complexity is O(θL3), where L is the number of ANS states. The complexity can be reduced to O(θLlog2L) if we use a fast matrix inversion. If the algorithm is implemented on a quantum computer, its complexity becomes O(θ(log2L)3).

The integration of Internet of Things (IoT) and unmanned aerial vehicles (UAVs) in smart farming has revolutionized agricultural practices by enhancing monitoring, automation, and decision-making to improve agricultural productivity and sustainability. However, the widespread use of these technologies has also introduced new security challenges, particularly the risk of interference from unauthorized UAVs. This survey provides an analysis of the threats posed by unauthorized UAVs to smart farms, highlighting potential vulnerabilities such as data interception, communication jamming, and physical damage. This paper first explores recent advancements in IoT and UAV technologies, which are integral to the functioning of smart farms. Then, we present an analysis of unauthorized UAV threats to smart farms and evaluate the current state-of-the-art UAV countermeasure technologies. By examining these emerging threats and potential solutions, this survey aims to inform researchers, engineers, policymakers, and practitioners involved in smart farming about the critical need for enhanced anti-UAV systems. Additionally, it highlights the necessity for airspace management authorities to recognize the risks posed by unauthorized UAVs, invest resources in protective measures, and address the challenges associated with securing smart farms against unauthorized UAV threats.

IntroductionAugmented renal clearance (ARC) defined as creatinine clearance (Clcr) above 130 mL/min/1.73m2 may lead to suboptimal antibacterial treatment. The aim of this study was to determine a strategy for meropenem administration to achieve both pharmacodynamic-pharmacokinetic (PK-PD) target (50%fT > MIC) and better clinical outcomes in patients with VAP and ARC.Materials and methodsIn this randomized clinical trial, patients with VAP and high risk for ARC were recruited. An 8-h urine collection was performed on the 1st, 3rd, and 5th days of study to measure Clcr. Included patients were divided into three groups: (1) 1 g meropenem, 3-h infusion, (2) 2 g meropenem, 3-h infusion, (3) 1 g meropenem, 6-h infusion. On the 2nd, 3rd, and 5th days of treatment, peak and trough blood samples were collected to undergo HPLC assay. MICs were assessed using microdilution method. Patients were also clinically monitored for 14 days.ResultsForty-five patients were included. Group 3 showed significanty higher rate of patients achieving fT > MIC > 50% (100% for group 3 versus 40% for group 2 and 13% for group 1; p = 0.0001). Mean fT > MIC% was significantly higher in group 3 (78.77 ± 5.87 for group 3 versus 49.6 ± 7.38 for group 2 and 43.2 ± 7.98 for group 1; p = 0.0001). Statistical analysis showed no significant differences among groups regarding clinical improvement.ConclusionAccording to the findings of this trial, prolonged meropenem infusion is an appropriate strategy compared to dose elevation among ARC patients.

Ventilator-associated pneumonia (VAP) caused by multidrug-resistant (MDR) is associated with high morbidity and mortality, and optimal antimicrobial dosing strategies remain uncertain. Although ampicillin-sulbactam is increasingly used for MDR infections, limited clinical data exist regarding the efficacy and safety of different dosing regimens when used as part of combination therapy. This study aimed to compare the clinical outcomes of low- versus high-dose ampicillin-sulbactam in combination of meropenem and colistin in patients with MDR associated VAP. In this randomized clinical trial, patients with MDR associated VAP admitted to the intensive care unit were allocated to receive either low-dose ampicillin-sulbactam (6 g IV every 6 h; total 24 g/day) or high-dose ampicillin-sulbactam (9 g IV every 6 h; total 36 g/day). Meropenem and colistin were administered concomitantly in both groups, as they remain commonly used standard therapies for severe MDR infections despite increasing resistance and toxicity concerns. Clinical outcomes, including fever duration, pulmonary secretions, Clinical Pulmonary Infection Score (CPIS), duration of mechanical ventilation, length of ICU and hospital stay, mortality, and adverse drug reactions, were assessed over a 10-day follow-up period. A total of 77 patients were enrolled (39 in the low-dose group and 38 in the high-dose group). The high-dose group demonstrated significantly shorter hospital stay (15.34 ± 4.99 vs 19.46 ± 6.91 days; P = 0.007), ICU length of stay (11.66 ± 6.11 vs 17.08 ± 7.40 days; P < 0.001), and duration of mechanical ventilation (6.39 ± 2.14 vs 7.74 ± 2.60 days; P = 0.003). Among patients receiving combination therapy for MDR associated VAP, higher-dose ampicillin-sulbactam was associated with improved clinical outcomes without increased toxicity. However, the small sample size, short follow-up period, and use of concomitant antibiotics limit attribution of outcomes solely to ampicillin-sulbactam dosing. Larger, well-controlled studies are needed to define the optimal dosing strategy.

Objectives. This study aimed to evaluate the efficacy of a combination of 1% nanocurcumin gel with 0.1% triamcinolone acetonide mouth rinse for oral lichen planus (OLP). Materials and Methods. This double-blind randomized clinical trial was conducted on 31 patients with erosive or ulcerative OLP. All patients received 0.1% triamcinolone mouth rinse and were then randomly divided into two groups for combination therapy with (I) %1 nanocurcumin gel or (II) placebo gel. The reticular-erosive-ulcerative (REU) score was calculated at baseline and at two and four weeks after the intervention. The changes in the mean REU score and the efficacy index were calculated to determine the level of improvement after two and four weeks. Data were analyzed using independent t-test, repeated measures ANCOVA, Mann–Whitney test, and chi-square test. P<0.05 was considered statistically significant. Results. There were 14 patients in the nanocurcumin and 17 patients in the placebo group. A significantly higher decrease in the mean REU score was observed in the nanocurcumin compared with the placebo group (P<0.001). The efficacy index was significantly higher in the nanocurcumin group (P<0.001). Conclusion. Application of 1% nanocurcumin in combination with 0.1% triamcinolone acetonide can serve as an effective treatment strategy to enhance the level of improvement of lesions compared with the use of triamcinolone acetonide alone.

In this work, we have synthesized twenty five new 2-(5-(5-nitrofuran-2-yl)-1,3,4-thiadiazol-2-ylimino)thiazolidin-4-one derivatives bearing an aryl or heteroaryl methylene group on position 5 of thiazolidinone and evaluated their antimicrobial activity against Gram-positive and -negative bacteria as well as three metronidazole resistant Helicobacter pylori strains. Most of the compounds were very potent towards tested Gram-positive bacteria and showed an antibacterial efficacy substantially greater than ampicillin as the reference drug. However, no effectiveness was observed for the Gram-negative microorganisms. The compounds 9, 20 and 29 exhibited strong antimicrobial activity against Helicobacter pylori strains (inhibition zone > 30 mm) in 100 μg/disc and (inhibition zone > 20 mm) in 50 μg/disc. Taking these findings together, it seems that these potent antibacterial derivatives could be considered as promising agents for developing new anti-infectious drugs against microorganisms resistant to currently available antibiotics.