Search Results (10,394)

The constant rise of malicious URLs continues to pose significant threats and challenges in cybersecurity, with attackers increasingly evading classical detection methods like blacklists and heuristic-based systems. While machine learning (ML) techniques such as SVMs and CNNs have improved detection, their accuracy and scalability remain limited for emerging adversarial approaches. Quantum machine learning (QML) is a transformative strategy that relies on quantum computation and high-dimensional feature spaces to potentially overcome classical computational limitations. However, the accuracy of QML models such as QSVM and QCNN for URL detection in comparison to classical models remains unexplored. This study evaluates ML models (SVMs and CNNs) and QML models (QSVMs and QCNNs) on a dataset, employing data preprocessing techniques such as outliers, feature scaling and feature selection with ANOVA and PCA. Quantum models utilized ZZFeatureMap and ZFeatureMap for data encoding, to transfer original data to qubits. The achieved results showed that CNNs outperformed QCNNs and QSVMs outperformed SVMs in the performance evaluation, demonstrating a competitive potential of quantum computing. QML shows promise for cybersecurity, particularly given the QSVM’s kernel advantages, but current hardware limits the QCNN’s practicality. The significance of this research is to contribute to the growing body of knowledge in cybersecurity by providing a comparative analysis of classical and quantum ML models for classifying malicious URLs. Full article

Metallic materials are formed with grain structures which are naturally formed during solidification or modified after any manufacturing process, such as rolling, cutting, machining, welding, etc. Grains with different sizes and geometrical morphologies have a strong influence on the micro- and macro properties of metallic materials. This is the reason why many authors have worked on computer simulations based on numerical methods, applying chaos theory and using graphical techniques for its reproduction. Thus, this manuscript is focused on the comparison between grain structures obtained computationally with grains in real metallic samples. The analyzed structures in this work were polygonal and equiaxed, they resulted from a dynamic evolution in which nucleation and growth procedures were simulated. Then, computational algorithms and standardized procedures were used for characterization. Thus, in order to demonstrate the geometrical similarities and differences in mathematical terms, similarities between sample features were calculated, measuring sizes and populations along different directions which could be used to establish rules and parameters for the appropriate control of nucleation and growth. Furthermore, the influence of the computational methods for simulation and characterization are also described in detail. Full article

The effect of emergent technologies for the starch extraction was studied. The evaluation of conventional extraction (MKS-WMP), ultrasound-assisted extraction (MKS-UAE), and microwave-assisted extraction (MKS-MAE) on chemical, technological, gelling, pasting, and microstructural properties of starch from mango kernel was carried out. The extraction yield was found in the values of 42.05, 50.40, and 47.43% for MKS-WMP, MKS-UAE, and MKS-MAE treatments, respectively. The amylose contents for MKS-UAE and MKS-MAE starches were significantly higher (p < 0.05) than MKS-WMP, with an increase of about 13–18%. The total phenolic content ranged from 84.89 to 90.85 mg GAE/g starch without significant differences (p > 0.05). The technological properties evidence a good water-holding capacity (80.48–90.05 g/100 g of starch) and oil-holding capacity (70.58–83.23 g/100 g of starch). The gelatinization temperature, measured by rheological analysis, ranged between 77 and 82 °C. Microstructural properties showed that ultrasound- and microwave-assisted treatments improved the shape and surface of starch granules, and that they are promising alternatives for starch extraction, providing some characteristics that could increase the applications in the food industry. Full article

Congested airspace conflict resolution during terminal operations is a common air traffic management issue that may produce cascading delays. Vehicles needing emergency clearance to land, at either traditional airports or vertiports, would require others on approach to move out of the way and, in some instances, cause a wave of delay to propagate through all vehicles on approach. Specifically, uncrewed aerial systems utilizing near-maximum arrival rates would be greatly impacted when requested to move off their approach path and may interfere with others. Vertiports further complicate crowded approaches because vehicles can arrive from many different angles at the same time to maximize landing area usage. Traditional air traffic management techniques were studied for vertiport applications specific to high-capacity operations. This work investigated methods of uniformly re-directing vehicles on approach to a vertiport that would be impacted by an emergency vehicle to minimize or avoid cascading delays. A route of time-optimal Bézier curves as well as Dubins paths optimized for interception heading was generated and flown on as an alternate maneuver when an unaccounted-for emergency vehicle initiated a bypass of an air traffic fleet. A comparison to flight on a holding pattern showed that the Bézier and Dubins route improved delay times and mitigated a cascading delay effect. Full article

The most efficient technique for resolving the issue of plastic waste disposal is by converting the wastes into high-quality liquid oils through thermal and catalytic pyrolysis. The objective of this work was to study the composition of liquid oils obtained by thermal and catalytic degradation of plastic wastes containing polystyrene (PS), polyvinyl chloride (PVC), and polyethylene terephthalate (PET). The clay catalysts were characterized by N₂ adsorption–desorption isotherms (BET), Scanning Electron Microscopy (SEM) and Fourier transform Infrared Spectrometry (FTIR), Polarized Optical Microscopy (POM), Atomic Force Microscopy (AFM). The effect of temperature and clay catalyst type on the yields of the end-products resulting in thermo-catalytic degradation of PS has been evaluated. Degradation of PS showed the highest liquid oil production at 86.85% in comparison to other plastic types. The characterization of the liquid oils was performed by comprehensive two-dimensional gas chromatography coupled with single quadrupole mass spectrometry (GC × GC-qMS). In liquid oils of PS, eighteen principal compounds (of groups: linear hydrocarbons, mono-aromatics, and di-aromatics) were identified. In the liquid oils of the plastic waste mixture, twenty-four principal compounds (of groups: linear hydrocarbons, mono-aromatics, oxygen-containing aromatic, di-aromatics, and tri-aromatics) were identified. The liquid oils were investigated in order to reconvert them as styrene monomers or other chemicals in energy recovery. Full article

Halloysite nanoclay (HNC) and as-polymerized polytetrafluoroethylene powder (PTFE) were introduced into biodegradable polylactic acid (PLA) via a melt mixing technique to enhance its mechanical, rheological properties and foaming ability. The synergetic effects of these fillers on the morphological, mechanical, thermal, and foaming properties of PLA were investigated. Results indicated that the tensile properties were improved in comparison to neat PLA. Differential Scanning Calorimetry (DSC) revealed a decrease in PLA crystallization time with increasing filler concentration, indicating a strong nucleating effect on PLA crystallization. Extensional flow tests showed that strain hardening in PLA composites is influenced by fillers, with PTFE particularly exhibiting a more pronounced effect, attributed to nanofibrillation and entanglement during melt processing. The addition of a dual-filler system improved the melt strength and viscosity of PLA, resulting in foams with decreased cell size and increased cell density. Full article

The present investigation utilized artificial neural networks (ANN) and gene expression programming (GEP) in comparison with the two-point method (TPM) to develop a generalized solution for predicting infiltrated water volume (∀_Z) across various soil types under furrow conditions. This work assesses infiltration behavior with respect to experimental data from several temporal contexts. Data distribution and model performance are evaluated via descriptive statistics and correlation tests. Artificial intelligence (AI) models (ANN and GEP) trained and evaluated utilizing input variables—inflow rate ($Q_{in}$); furrow length ($L$); waterfront advance time at the end of the furrow ($T_L$); infiltration opportunity time ($T_o$); and cross-sectional area of the inflow ($A_o$) are compared with TPM performance. More precisely and consistently than the water advance power function, AI-based algorithms hope to be invading water volume. Statistical analysis shows that ANN and GEP have lower error metrics, increased generalizability, and better representation of complex infiltration dynamics. The determination coefficient (R²) of ANN data produced 98.1% for testing and 97.8% for validation, while TPM showed accuracy reductions of 2.5% and 4.6%, respectively. On the other side, the R² of GEP produced 95.7% for testing and 96.1% for validation, while TPM showed accuracy reductions of 0.7% and 3%, respectively. During ANN model computation, TPMs root mean square error (RMSE) of 0.0135 m³/m exceeded all mean values. Errors within 10% relative deviation were displayed using the ANN model ${\forall }_Z$. Particularly, ANN and GEP, the study revealed that AI techniques predict furrow irrigation penetration of water volume better than the water advance power function. These models advance soil and furrow adaptation, extrapolation, and accuracy. Results show that AI-driven modeling may maximize hydrological assessments and irrigation control. Full article

Chitin and chitosan are valuable biopolymers with various applications, ranging from food to pharmaceuticals. Traditionally sourced from crustaceans, the rising demand for chitin/chitosan, paired with the development of the insect sector, has led to the exploration of insect biomass and its byproducts as a potential source. Conventional processes rely on hazardous chemicals, raising environmental concerns. This critical review evaluates emerging “greener” approaches, including biological methods, green solvents, and advanced processing techniques, for chitin/chitosan production from insect-derived materials such as exuviae and cocoons. Two systematic evaluations are included: (1) a cross-comparison of chitin and chitosan yields across insect life stages and byproducts (e.g., up to 35.7% chitin from black soldier fly (BSF) larval exoskeletons can be obtained) and (2) a stepwise sustainability assessment of over 30 extraction workflows reported across 16 studies. While many are labeled as green, only a few, such as bromelain, lactic acid fermentations, or NADES-based processes, demonstrated fully green extraction up to the chitin stage. No study achieved a fully green conversion to chitosan, and green workflows typically required materials with low fat content and minimal pretreatment. These findings will be useful to identify opportunities and underscore the need to refine greener methods, improve yields, reduce impurities, and enable industrial-scale production, while sustainability data need to be generated. Full article

A newly developed series of polylactide (PLA)-based composites filled with hybrid lignin–carbon nanotube (CNTs) particles were studied using thermal and dielectric techniques. The low CNT content (up to 3 wt%) aimed to create conductive networks while enhancing particle–polymer adhesion. For comparison, PLA composites based on lignin and CNTs were also examined. Although infrared spectroscopy showed no significant interactions, calorimetry and dielectric spectroscopy revealed a rigid interfacial PLA layer exhibiting restricted mobility. The interfacial polymer amount was found to increase monotonically with the particle content. The hybrid-filled PLA composites exhibited electrical conductivity, whereas PLA/Lignin and PLA/CNTs remained insulators. The result was indicative of a synergistic effect between lignin and CNTs, leading to lowering of the percolation threshold to 3 wt%, being almost ideal for sustainable conductive printing inks. Despite the addition of lignin and CNTs at different loadings, the glass transition temperature of PLA (60 °C) decreased slightly (softer composites) by 1–2 K in the composites, while the melting temperature remained stable at ~175 °C, favoring efficient processing. Regarding crystallization, which is typically slow in PLA, the hybrid lignin/CNT particles promoted crystal nucleation without increasing the total crystallizable fraction. Overall, these findings highlight the potential of eco-friendly conductive PLA composites for new-generation applications, such as printed electronics. Full article

This study presented a comprehensive comparison of soil gas sampling methodologies to monitor volatile organic compounds (VOCs) at two industrial sites in northern Italy. Utilizing active sampling techniques, such as stainless-steel canisters, vacuum bottles, and sorbent tubes, alongside passive methods like low-density polyethylene (PE) membranes, sorbent pens, and Waterloo Membrane Samplers (WMS), the research examines their effectiveness under varied environmental conditions. Five field campaigns were conducted in two areas of the industrial sites characterized by BTEX and chlorinated solvent contamination. The results highlighted that active sampling, while expensive, provides real-time, high-resolution VOC concentration data, often outperforming passive methods for heavier compounds (e.g., hexachlorobutadiene). However, using the active systems in certain campaigns, challenges such as high soil humidity or atmospheric air infiltration were observed, resulting in an underestimation of the soil gas concentrations. Passive sampling systems demonstrated cost-effective, efficient alternatives, offering consistent spatial and temporal coverage. These methods showed alignment with active techniques for lighter compounds (e.g., TCE and BTEX) but faced limitations in sorbent saturation and equilibrium time for heavier VOCs (e.g., hexachlorobutadiene), requiring adjustments in exposure duration to enhance accuracy. PE samplers provided results comparable to active methods, especially for BTEX and TCE, while WMS and sorbent pens exhibited lower sensitivity for certain analytes. This underscores the importance of optimizing sampler configurations and deployment strategies. The findings emphasize the value of integrating active and passive approaches to achieve robust VOC assessments in heterogeneous subsurface environments. Full article

The generation of High-Dynamic-Range (HDR) images is essential for capturing details at various brightness levels, but current reconstruction methods, using deep learning techniques, often require significant computational resources, limiting their applicability on devices with moderate resources. In this context, this paper presents a lightweight architecture for reconstructing HDR images from three Low-Dynamic-Range inputs. The proposed model is based on Generative Adversarial Networks and replaces traditional convolutions with depthwise separable convolutions, reducing the number of parameters while maintaining high visual quality and minimizing luminance artifacts. The evaluation of the proposal is conducted through quantitative, qualitative, and computational cost analyses based on the number of parameters and FLOPs. Regarding the qualitative analysis, a comparison between the models was performed using samples that present reconstruction challenges. The proposed model achieves a PSNR-$\mu$ of 43.51 dB and SSIM-$\mu$ of 0.9917, achieving competitive quality metrics comparable to HDR-GAN while reducing the computational cost by 6× in FLOPs and 7× in the number of parameters, using approximately half the GPU memory consumption, demonstrating an effective balance between visual fidelity and efficiency. Full article

Background: The bioavailability of a drug depends, among other parameters, on solubility. One of the strategies used to enhance the solubility of sparingly soluble drugs is the use of excipients. Excipients can interact with the drug by increasing its solubility and/or stabilizing supersaturated solutions. Some of the most common excipients are cyclodextrins and hydrophilic polymers. Objectives: The effect of two cyclodextrins (captisol and cavasol) and three hydrophilic polymers (klucel, kollidon and plasdone S630) on the solubility of three ionizable drugs (benzthiazide, isoxicam, and piroxicam) is evaluated at biorelevant pH values, using two complementary techniques. Methods: The solubility enhancement was evaluated by the comparison of the solubility with and without the presence of excipients through the shake-flask and CheqSol methodology. Results: Captisol and cavasol slightly increase the concentration of the neutral species of the drugs in the solution before precipitation begins, although they do not enhance the supersaturation duration nor the thermodynamic solubility of the drugs. The increase in solubility in the presence of cyclodextrins is mainly caused by the ionization state of the drug. Hydrophilic polymers not only improve thermodynamic solubility but also the extent and the duration of the supersaturation. Some metastable forms are observed for benzthiazide and isoxicam in the presence of kollidon and plasdone S630. Conclusions: The shake-flask method enabled the evaluation of thermodynamic solubility both in the absence and presence of excipients. Meanwhile, the CheqSol method provided insights into the presence of supersaturated solutions. Different behavior is observed depending on the nature of the excipient. Full article

Plasma depletions in the low-latitude ionosphere are irregularities of special interest in space weather research, as they are highly detrimental to the operation of satellite-based communication and navigation systems. In this frame, we present the results of a systematic study of the low-latitude topside ionosphere, based on in situ measurements of both electron density ($N_e$) and electric field provided by the Langmuir Probe (LP) and the Electric Field Detector (EFD) onboard the first China Seismo-Electromagnetic Satellite (CSES-01). Specifically, by exploiting in situ measurements from 1 January 2019 to 31 May 2024, we devised two different techniques for the automatic detection of post-midnight plasma depletions at about 500 km of altitude: one using only $N_e$ observations, the other using only electric field measurements. We validated these new techniques against each other and performed a statistical investigation of the main characteristics of the observed plasma irregularities, such as their latitudinal extension, longitudinal distribution, and monthly and seasonal occurrence. To test the robustness and reliability of our algorithms, we also applied them to well-established Swarm B satellite observations. In particular, we first investigated both the monthly and the seasonal occurrences of post-sunset plasma depletions detected between 18:00 and 04:00 local time (LT), by LP onboard the Swarm B satellite at about 500 km of altitude. In addition, we compared ionospheric irregularities detected by Swarm B with those detected by CSES-01. For the comparison, we considered Swarm B LP data collected for the same period as the CSES-01 dataset and under the same conditions by selecting Swarm B observations in the range 01:00 $\le LT<$ 03:00. Our results prove the robustness and reliability of both LP and EFD algorithms in detecting plasma depletions, and their good agreement suggests their complementarity in detecting such kinds of plasma irregularities. Results also confirm consistency between CSES-01 and Swarm B observations (once the same LT orbits have been considered) and with the relevant literature on the topic. Full article

The present work aims to verify whether the incremental hole-drilling technique (IHD), a widely accepted technique, is suitable for determining residual stresses in AISI 316L samples obtained by selective laser melting (SLM). The thermo-mechanical effects of cutting during the application of this technique can induce unwanted residual stresses due to the relatively low thermal conductivity of this material, leading to erroneous results. To accomplish this aim, a hybrid experimental-numerical method was implemented to analyze the ability of IHD to determine an imposed stress state. Experimentally, samples were subjected to a tensile calibration stress using a horizontal tensile test machine. To eliminate pre-existing residual stress, the samples were subjected to differential loads, instead of absolute ones. In this way, experimental strain-depth relaxation curves related to the imposed calibration stress were obtained. Based on the experimental data, IHD was numerically simulated using the finite element method. Numerical strain-depth relaxation curves, related to the same calibration stress used in the experimental study, were obtained. The comparison between the experimental and numerical strain-depth relaxation curves, as well as the stresses calculated using the so-called integral method for determining stresses via IHD, shows that IHD is a suitable technique for measuring residual stresses in additively manufactured AISI 316L samples. Full article

Introduction: Post-tonsillectomy hemorrhage is a serious complication that varies according to the surgical technique used, potentially compromising patient safety and recovery. Even though several techniques were frequently used, including cold steel dissection, coblation, monopolar diathermy, and bipolar diathermy, there were certain discrepancies in hemorrhage rates in the literature. This meta-analysis aims to compare the rates of primary and secondary hemorrhage among these surgical techniques, with a focus on guiding clinical decision-making. Methodology: A total of 12 studies, published between 2005 and 2024, were selected from the PubMed, Web of Science, Scopus, and Cochrane Library databases, comprising 1684 participants from both pediatric and adult groups. Primary and secondary hemorrhage rates, surgical techniques, and study characteristics were extracted as data. Therefore, the aim of performing this meta-analysis with random-effects models was to calculate pooled estimates for hemorrhage rates and the heterogeneity index (I²). The techniques studied included cold steel dissection, coblation, monopolar diathermy, and bipolar diathermy. Results: The pooled primary hemorrhage rate across all techniques was 1.0% (95% Cl: 0.5–1.4%), with insignificant heterogeneity (I² = 0.0%, p < 0.665). By contrast, pooled secondary hemorrhage occurred at a rate of 5.8% (95% CI: 3.9–7.6%). Cold steel tonsillectomy was associated with the lowest secondary hemorrhage rate of 3.7% (95% CI: 0.8–6.6%, I² = 43.558%, p = 0.115), while bipolar diathermy had the highest secondary hemorrhage rate of 8.6% (95% CI: 2.3–15.0%, I² = 86.448%, p < 0.001). Conclusions: This meta-analysis underscores the considerable variability in rates of post-tonsillectomy hemorrhage frequency among various surgical techniques. Cold steel dissection appears to be the safest regarding secondary hemorrhage, while coblation likely minimizes primary bleeding. Bipolar diathermy comes across as the technique with the highest risk for primary hemorrhage and requires special caution during its use. Such results emphasize the need for careful selection of the surgical technique concerning patients’ particular conditions and the need to enhance care periods to reduce the bearing of any hemorrhagic complications. Full article