Search Results (7,126)

This study provides a detailed comparative analysis of a three-hybrid intrusion detection method aimed at strengthening network security through precise and adaptive threat identification. The proposed framework integrates an Autoencoder-Gaussian Mixture Model (AE-GMM) with two supervised learning techniques, XGBoost and Logistic Regression, combining deep feature extraction with interpretability and stable generalization. Although the downstream classifiers are trained in a supervised manner, the hybrid intrusion detection nature of the framework is preserved through unsupervised representation learning and probabilistic modeling in the AE-GMM stage. Two benchmark datasets were used for evaluation: NSL-KDD, representing traditional network behavior, and UNSW-NB15, reflecting modern and diverse traffic patterns. A consistent preprocessing pipeline was applied, including normalization, feature selection, and dimensionality reduction, to ensure fair comparison and efficient training. The experimental findings show that hybridizing deep learning with gradient-boosted and linear classifiers markedly enhances detection performance and resilience. The AE–GMM-XGBoost model achieved superior outcomes, reaching an F1-score above 0.94 ± 0.0021 and an AUC greater than 0.97 on both datasets, demonstrating high accuracy in distinguishing legitimate and malicious traffic. AE-GMM-Logistic Regression also achieved strong and balanced performance, recording an F1-score exceeding 0.91 ± 0.0020 with stable generalization across test conditions. Conversely, the standalone AE-GMM effectively captured deep latent patterns but exhibited lower recall, indicating limited sensitivity to subtle or emerging attacks. These results collectively confirm that integrating autoencoder-based representation learning with advanced supervised models significantly improves intrusion detection in complex network settings. The proposed framework therefore provides a solid and extensible basis for future research in explainable and federated intrusion detection, supporting the development of adaptive and proactive cybersecurity defenses. Full article

This study aims to investigate the predictive value of pre-treatment multi-phasic contrast-enhanced computed tomography (CECT) radiomic features for treatment resistance in patients with rat sarcoma virus (RAS)-mutated colorectal liver metastases (CRLMs) receiving bevacizumab-based chemotherapy. Seventy-three samples with RAS-mutated CRLMs receiving bevacizumab-combined chemotherapy regimens were evaluated. Radiomic features were extracted from arterial phase (AP), portal venous phase (PVP), AP-PVP subtraction image, and Delta phase (DeltaP, calculated as AP-to-PVP ratio) images. Three groups of radiomics features were extracted for each phase, including peritumor, core tumor, and whole-tumor regions. For each of the four phases, a two-sided independent Mann–Whitney U test with the Bonferroni correction and K-means clustering was applied to the remnant features for each phase. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was then applied for further feature selection. Six machine learning algorithms were then used for model development and validated on the independent testing cohort. Results showed peritumoral radiomic features and features derived from Laplacian of Gaussian (LoG) filtered images were dominant in all the compared machine learning algorithms; NB models yielded the best-performing prediction (Avg. training AUC: 0.731, Avg. testing AUC: 0.717) when combining all features from different phases of CECT images. This study demonstrates that peritumoral radiomic features and LoG-filtered pre-treatment multi-phasic CECT images were more predictive of treatment response to bevacizumab-based chemotherapy in RAS-mutated CRLMs compared to core tumor features. Full article

Magmatic activity is crucial for identification of the tectonic framework of the ancient oceanic crust. In this study, systematic investigation, including a field survey, zircon LA-ICP-MS U-Pb dating, and whole-rock geochemical analysis, has been carried out on the intrusive quartz- and granodiorites within the Meso-Tethyan Shiquanhe Ophiolitic Mélange (SQM), Tibet. Zircon U-Pb dating yields the weighted mean ages of 174.7 ± 1.4 Ma (quartz diorite) and 178.9 ± 1.2 Ma (granodiorite), respectively, demonstrating the Early Jurassic formation age. The quartz diorite samples are metaluminous (A/NKC = 0.77–0.95) (molar/Al₂O₃/(CaO + Na₂O + K₂O)), while the granodiorite samples are weakly peraluminous (A/NKC = 0.95–1.21), and both of them exhibit tholeiitic to calc-alkaline geochemical characteristics and can be classified as I-type granites. The right-dipping rare-earth element (REE) patterns, enrichment in large ion lithophile elements (LILEs: Rb, Ba, Th), and depletion in high-field-strength elements (HFSEs: Nb, Ta, Ti), as well as relatively high (La/Yb)_N ratios, are features compatible with an island arc setting. Combined with previous works, we suggest that the Shiquanhe ophiolitic mélange not only preserves records of mid-late Jurassic island arc magmatic activity but also contains evidence of island arc magmatism from the late Early Jurassic. Full article

N-Alkyl deoxynojirimycin-derived drugs, belonging to the class of iminosugars, are well-known for their α-glucosidase inhibitory activity. N-Butyl-deoxynojirimycin (N-butyl-DNJ; NB-DNJ; also known as miglustat or UV-1) has been developed for the treatment of type 1 Gaucher disease and Niemann–Pick disease type C as Zavesca^®. Furthermore, it behaves as a host-targeted glucomimetic that inhibits endoplasmic reticulum α-glucosidase I and II (GluI and GluII, respectively) enzymes, resulting in improper glycosylation and misfolding of viral glycoproteins; thus, it is a potential antiviral agent. It is studied against a broad range of viruses in vitro and in vivo; however, its utility as antiviral has not been fully explored. Other N-alkylated congeners of DNJ are in preclinical and clinical studies for diverse viral infections. The iminosugar N-9′-methoxynonyl-1-deoxynojirimycin (MON-DNJ or UV-4) is probably the most studied and potent inhibitor of α-Glu I and α-Glu II in clinical trials. It is often studied in the form of its hydrochloride salt (UV-4B) and has broad-spectrum activity against diverse viruses, including dengue and influenza. In clinical trials, it was found to be safe at all doses tested up to 1000 mg. In this paper, an overview on N-alkyl derivatives of DNJ is reported, focusing on their antiviral activity. The literature search was carried out by means of three literature databases, i.e., PubMed/MEDLINE, Google Scholar, and Scopus, screened using different keywords. A brief history of the discovery of their usefulness as antivirals is given, as well as the most recent studies on new compounds belonging to this class. Since different names are often used for the same compound, we tried to dissipate confusion and bring some order to this jumble of names. Specifically, in the tables, all the diverse names used to identify each compound, were reported. Full article

Tomato spotted wilt virus (TSWV) is one of the most important plants segmented negative-strand RNA viruses (NSVs). Plants employ the ubiquitin–proteasome system (UPS) and autophagy pathways to degrade viral effector proteins, forming a key antiviral defense layer. SnRK1 functions as a central energy sensor and plays pivotal roles in plant growth and development, as well as immune defense. However, whether SnRK1 modulates the infection of plant segmented NSVs and the underlying regulatory mechanisms remains elusive. In this study, we found that nonstructural protein NSs, a viral suppressor of RNA silencing (VSR) encoded by TSWV, specifically interacts with the catalytic α subunit of host SnRK1 (SnRK1α). NbSnRK1α promotes the degradation of NSs via the 26S proteasome pathway, independently of autophagy. Transient silencing of NbSnRK1α led to increased accumulation of the NSs protein. Furthermore, we found that NbSnRK1α significantly impairs the VSR activity of NSs by promoting its degradation, thereby restoring the host’s RNAi-mediated antiviral defense. Subsequent viral infection assays confirmed that NbSnRK1α inhibits TSWV replication, whereas silencing NbSnRK1α enhances the susceptibility of Nicotiana. benthamiana to TSWV infection and facilitates systemic viral spread and disease symptom development. Our study uncovers a new antiviral defense case by which NbSnRK1α enhances host antiviral immunity through targeting a segmented negative-strand RNA viral effector for 26S proteasomal degradation, broadening the understanding of the NbSnRK1’s role in broad-spectrum antiviral defense. Full article

We present a comprehensive investigation into the emergence of interface-bound states, particularly Majorana zero modes (MZMs), in a lateral heterostructure composed of two three-dimensional topological insulators (TIs), Bi₂Se₃ and Sb₂Te₃, under the influence of proximity-induced superconductivity from niobium (Nb) contacts. We develop an advanced two-dimensional Dirac model for the topological surface states (TSS), incorporating spatially varying chemical potentials and s-wave superconducting pairing. Using the Bogoliubov–de Gennes (BdG) formalism, we derive analytical solutions for the bound states and compute the local density of states (LDOS) at the interface, revealing zero-energy modes characteristic of MZMs. The topological nature of these states is rigorously analyzed through winding numbers and Pfaffian invariants, and their robustness is explored under various physical perturbations, including gating effects. Our findings highlight the potential of this heterostructure as a platform for topological quantum computing, with detailed predictions for experimental signatures via tunneling spectroscopy. Full article

The proliferation of Internet of Things (IoT) devices has created unprecedented security challenges characterized by resource constraints, heterogeneous network architectures, and severe class imbalance in attack detection datasets. This paper presents a comprehensive benchmark evaluation of five Generative Adversarial Network (GAN) architectures for energy-aware intrusion detection: Standard GAN, Progressive GAN (PGAN), Conditional GAN (cGAN), Graph-based GAN (GraphGAN), and Wasserstein GAN with Gradient Penalty (WGAN-GP). Our evaluation framework introduces novel energy-normalized performance metrics, including Accuracy-per-Joule (APJ) and F1-per-Joule (F1PJ), that enable principled architecture selection for energy-constrained deployments. We propose an optimized WGAN-GP architecture incorporating diversity loss, feature matching, and noise injection mechanisms specifically designed for classification-oriented data augmentation. Experimental results on a stratified subset of the BoT-IoT dataset (approximately 1.83 million records) demonstrate that our optimized WGAN-GP achieves state-of-the-art performance, with 99.99% classification accuracy, a 0.99 macro-F1 score, and superior generation quality (MSE 0.01). While traditional classifiers augmented with SMOTE (i.e., Logistic Regression and CNN1D-TCN) also achieve 99.99% accuracy, they suffer from poor minority class detection (77.78–80.00%); our WGAN-GP improves minority class detection to 100.00% on the reported test split (45 of 45 attack instances correctly identified). Furthermore, WGAN-GP provides substantial efficiency advantages under our energy-normalized metrics, achieving superior accuracy-per-joule performance compared to Standard GAN. Also, a cross-dataset validation across five benchmarks (BoT-IoT, CICIoT2023, ToN-IoT, UNSW-NB15, CIC-IDS2017) was implemented using 250 pooled test attacks to confirm generalizability, with WGAN-GP achieving 98.40% minority class accuracy (246/250 attacks detected) compared to 76.80% for Classical + SMOTE methods, a statistically significant 21.60 percentage point improvement ($p<0.0001$). Finally, our analysis reveals that incorporating diversity-promoting mechanisms in GAN training simultaneously achieves best generation quality AND best classification performance, demonstrating that these objectives are complementary rather than competing. Full article

This study provides a quantitative assessment of the combined effects of progressive urbanization and changes in precipitation patterns (PPs) on the urban water cycle. The primary objective was to evaluate historical (1940–2024) and projected (to 2060) changes in total annual surface runoff (TSR) and retention capacity (RC) in the highly urbanized catchment of the Dłubnia River in Cracow, Poland. Simulations were performed using the EPA SWMM hydrodynamic model, supported by digitized historical land-use maps and long-term meteorological records. The results demonstrate that the dominant driver of the observed 6.4-fold increase in TSR and 6.8-fold loss of retention capacity (LRC) over the study period was the progressive increase in impervious surfaces. Although inter-annual variability in the amount and structure of annual precipitation (AP) strongly correlates with annual TSR (r = 0.97), its contribution to the long-term upward trend in TSR is marginal (r = 0.19). Land use and land cover change (LULC) exhibits an extremely strong correlation with the long-term TSR trend (r = 0.998). The study also highlights the high effectiveness of nature-based solutions (NbSs), particularly bioretention cells (BCs)/rain gardens, in mitigating the adverse hydrological effects of excessive surface sealing. Implementation of BCs covering just 3–4% of the total drained roof and road area is sufficient to fully offset the projected combined negative impacts of further urbanization and climate change (CC) in scope Representative Concentration Pathways (RCP4.5 and RCP8.5) projections on catchment retention capacity by 2060. These findings position strategically targeted, relatively small-scale bioretention as one of the most effective and feasible urban adaptation measures in mature, densely developed cities. Full article

Lead-free transparent ferroelectric ceramics with integrated opto-electro-mechanical functionalities are pivotal for next-generation multifunctional devices. In this study, K_0.48Na_0.52NbO₃-xLa₂O₃ (KNN-xLa, x = 0.005 − 0.04) ceramics were fabricated via a conventional solid-state route to investigate the La³⁺-induced multiscale structural evolution and its modulation of optical and electrical properties. La³⁺ substitution drives a critical structural transition from an anisotropic orthorhombic phase (Amm2) to a high-symmetry pseudocubic-like tetragonal phase (P4mm) for x ≥ 0.025, characterized by minimal lattice distortion (c/a = 1.0052). This enhanced structural isotropy, coupled with submicron grain refinement (<1 μm) driven by -mediated solute drag, effectively suppresses light scattering. Consequently, a high-transparency plateau (T₇₈₀ ≈ 53–58%, T₁₇₀₀ ≈ 70–72%) is achieved for 0.025 ≤ x ≤ 0.035. Simultaneously, the system undergoes a crossover from normal ferroelectric (FE) to relaxor (RF) state, governed by an FE–RF boundary at x = 0.015. While x = 0.005 exhibits robust piezoelectricity (d₃₃ ≈ 92 pC/N), the x = 0.015 composition facilitates a transitional polar state with large strain (0.179%) and high polarization (P_m ≈ 33.3 μC/cm², P_r ≈ 15.8 μC/cm²). Piezoresponse force microscopy (PFM) confirms the domain evolution from lamellar macro-domains to speckle-like polar nanoregions (PNRs), elucidating the intrinsic trade-off between optical transparency and piezoelectricity. This work underscores La³⁺ as a potent structural modifier for tailoring phase boundaries and defect chemistry, providing a cost-effective framework for developing high-performance transparent electromechanical materials. Full article

The presence of the brittle β/B2 phase in TiAl alloys often deteriorates their mechanical properties, posing a significant challenge for manufacturing large-sized, high-performance sheets. To address this issue, this study systematically investigates the synergistic effect of pack rolling and subsequent heat treatment on the microstructure evolution and mechanical properties of a Ti-44Al-4Nb-1.5Mo-0.1B-0.1Y alloy. Sheets with two different deformation levels (R7: 69.8% and R11: 83.0% reduction) were prepared via pack rolling. This was followed by a series of heat treatments at different temperatures (1150–1350 °C) and cyclic heat treatments at 1250 °C (3, 6, and 9 cycles). The results demonstrate that the higher deformation level (R11) promoted extensive dynamic recrystallization, resulting in a uniform microstructure of equiaxed γ, α₂, and β phases, while the lower deformation (R7) retained a significant fraction of deformed γ/α₂ lamellae. Heat treatment at 1250 °C was identified as optimal for transforming the microstructure into fine lamellar colonies while effectively reducing the β/B2 phase. Cyclic heat treatment at this temperature further decreased the β-phase content to 4.1% after 9 cycles. The elimination mechanism was determined to follow the β→ α → γ + α₂ phase transformation sequence, driven by the combined effect of rolling-induced defects and cyclic thermal stress. Cyclic heat treatment at this temperature was particularly effective in generating a high density of nucleation sites within the lamellar colonies, leading to significant refinement of the lamellar structure. Consequently, the R11 sheet subjected to 9 cycles of heat treatment exhibited a 15.5% increase in tensile strength and an 8.3% improvement in elongation compared to the hot-isostatically pressed state. This enhancement is primarily attributed to the significant refinement of lamellar colonies and the reduction in interlamellar spacing. This work presents an effective integrated processing strategy for fabricating high-performance TiAl alloy sheets with superior strength and toughness. Full article

Climate change is increasing the frequency and intensity of natural hazards, placing additional stress on critical infrastructure systems. Addressing these challenges requires both robust evaluation frameworks and the inclusion of Nature-Based Solutions (NbSs) alongside conventional protection measures. Building on the RAMSSHEEP concept, originally proposed for risk-driven maintenance, and later further developed and applied in, e.g., previous Horizon projects and COST Action TU1406, this study integrates natural hazard considerations and NbS risk mitigation measures into a comprehensive approach to evaluate the resilience of critical infrastructure. The novel methodology involves a structured expert elicitation process with participants from the Horizon NATURE-DEMO project, to adapt and extend the RAMSSHEEP framework for resilience-oriented transformation. This also includes alignment with established hazard and risk assessment systems to ensure methodological consistency and applicability of the final concept. The resulting framework enables systematic evaluation of infrastructure vulnerability and resilience, explicitly accounting for natural hazards and the contribution of NbSs to risk mitigation. The expected outcome is an objective, repeatable assessment methodology that supports decision-makers in planning, prioritizing, and monitoring resilience-enhancing measures across the infrastructure life cycle. A particular focus of this contribution lies in the methodological approach, ensuring its applicability within interdisciplinary and multi-level decision-making contexts. Full article

In order to improve the wear resistance of titanium alloy and apply it to the high-speed train brake disc, TiNbZrV_x (x = 0, 0.2, 0.4, 0.6, 0.8) refractory medium-entropy alloy coatings were prepared on Ti-6Al-4V (TC4) substrate. The effect of V content on the microstructure, mechanical properties, and friction and wear properties of the coatings was studied. TiNbZrV_x coatings achieved good metallurgical bonding with the substrate, forming BCC and B2 phases and AlZr₃ intermetallic compound (IMC). From TiNbZr coating to TiNbZrV_0.8 coating, V promotes element segregation and new phase formation, which decreased the average grain size from 85.055 μm to 56.515 μm, increased the average hardness from 265.5 HV to 343.4 HV, and reduced the room temperature (RT) wear rate by 97.8%. However, the ductility of the coatings decreased from 15.7% to 5.8% because the grain boundary precipitates changed the dislocation arrangement, and the tensile fracture mode changed from ductile fracture to brittle fracture. Abrasive wear was the main wear mode at RT, and adhesive wear and oxidation wear were the main wear modes at elevated temperature. The COF at elevated temperature was lower than that at RT, because a large number of friction pair components were transferred to the coating surface at high temperature and were repeatedly rolled to form a dense film, which played a certain lubricating role. Full article

Soil corrosion is a critical durability and cost factor for metallic foundations in photovoltaic (PV) power plants, yet it is still addressed with fragmented criteria compared with atmospheric corrosion. This paper reviews the main soil corrosivity drivers relevant to PV installations—moisture and aeration dynamics, electrical resistivity, pH and buffer capacity, dissolved ions (notably chlorides and sulfates), microbiological activity, hydro-climatic variability and geological heterogeneity—highlighting their coupled and non-linear effects, such as differential aeration, macrocell formation and corrosion localization. Building on this mechanistic basis, an engineering-oriented methodological roadmap is proposed to translate soil characterization into durability decisions. The approach combines soil corrosivity classification according to DIN 50929-3 and DVGW GW 9, tiered estimation of hot-dip galvanized coating consumption using AASHTO screening, resistivity–pH correlations and ionic penalty factors, and verification against conservative NBS envelopes. When coating life is insufficient, a traceable steel thickness allowance based on DIN bare-steel corrosion rates is introduced to meet the target service life. The framework provides a practical and auditable basis for durability design and risk control of PV foundations in heterogeneous soils. The proposed framework shows that, for soils exceeding AASHTO mild criteria, zinc corrosion rates may increase by a factor of 1.3–1.7 when chloride and sulfate penalties are considered, potentially reducing coating service life by more than 40%. The methodology proposed enables designers to estimate the penalty factors for sulfates ($f_p\left({SO}_4^{2-}\right)$) and chlorides ($f_p\left({Cl}^{-}\right)$) in each specific project, calculating the appropriate values of $K_{{SO}_4^{2-}}$ and $K_{{Cl}^{-}}$ using electrochemical techniques—ER/LPR and EIS—to estimate the effect of the soluble salts content in the ${Zn}_{Corr Rate}$, not properly catch by the proxy indicator $V_{corr}\left(ER, pH\right)$ when sulfate and chloride content are over AAHSTO limits for mildly corrosive soils. Full article

Newborn screening (NBS) for sickle cell disease (SCD) has been performed in the United States (US) for decades, significantly reducing infant morbidity and mortality. A landmark clinical trial demonstrated that early identification of SCD enabled timely and life-saving prophylactic penicillin; this led to recommendations for universal NBS across the US. Early use of hydroxyurea as a safe and effective treatment for SCD further improved clinical outcomes by preventing acute and chronic disease complications. These advances add to the importance of early diagnosis through NBS, providing an opportunity for early treatment intervention. In recent years, high-resource countries—including those in Europe, the UK, and Canada—have adopted NBS for SCD using diverse strategies. Simultaneously, pilot programs in lower-resource settings such as Africa, Brazil, and India have demonstrated local feasibility and impact through implementation efforts. An overarching equity gap for achieving global NBS for SCD is the variable access to simple, accurate, and affordable testing. Other challenges include timing of NBS testing, targeted populations, laboratory methods, and parental education with genetic counseling. Questions remain about the equitable enrollment of affected infants worldwide into comprehensive care to ensure early treatment. These challenges raise concerns about sustainability, underscore the need for long-term funding and a strategic plan, and highlight persistent inequities from the lack of global NBS standards. Full article

Ultrasonic-assisted laser-directed energy deposition (UA-DED) is a promising combined technology for manufacturing high-value thin-walled Inconel 718 components in aerospace. Nevertheless, the optimal ultrasonic frequency—a key parameter for achieving desirable performance in thin-walled Inconel 718 alloys—remains to be determined. In this study, we systematically investigated the influence of ultrasonic frequency (12–20 kHz) on the microstructure and mechanical properties of thin-walled Inconel 718 fabricated by UA-DED. The results revealed that an ultrasonic frequency of 20 kHz was optimal and can yield significant improvements in the microstructures of the as-deposited sample coordinate planes, manifested by the complete suppression of large pores, three-dimensional refinement of the γ matrix grains, alleviation of Nb and Mo segregation, the reduction of fragmented Laves particles, a decrease in residual macroscopic stresses, and homogeneous distributions of γ′/γ″ phases and γ-grain orientation. Meanwhile, the application of a 20 kHz ultrasonic frequency endows the manufactured thin-walled 718 parts with superior mechanical properties, including a tensile strength of 899 MPa in the laser scanning direction and 877 MPa in the build direction, along with the corresponding elongations of 34.8% and 38.9%. This work demonstrates the potential of modulating ultrasonic frequency to tailor microstructures and produce high-performance thin-walled Inconel 718 aerospace components. Full article