Search Results (1,290)

Magnesium (Mg) alloys are crucial for lightweight automotive design, underscoring the need for effective welding despite their poor weldability and susceptibility to defects such as cracks. The paper was prepared by reviewing available scientific databases of studies and patents using appropriate keywords. It reviews the properties, applications, and welding methods (fusion, friction, diffusion, explosive, and hybrid) of Mg alloys, assessing their advantages, disadvantages, and precautions. The importance of understanding the mechanical behavior and structural integrity of welded joints was highlighted. The impact of process variables on weld microstructure and properties, along with future research areas, is also addressed. AZ-series Mg alloys were found to be favored for weldability. For them, primary welding methods include GTAW, GMAW, LBW, and FSW. Trends emphasize solid-state techniques, advanced dissimilar metal joining, and AI optimization to enhance welding efficiency and quality. Welding of modern Mg-inclusive high-entropy alloys is under intensive development. Full article

Background/Objectives: Covalent inhibitors often demonstrate prolonged pharmacological effects even after their disappearance from the site of action because target recovery depends on target turnover. This disconnect between pharmacokinetics (PK) and pharmacodynamics (PD) complicates the development of such inhibitors since plasma exposure coverage of in vitro potency cannot be used for compound selection and human dose projections. In this study, we describe the development of a PK/PD modeling framework for covalent inhibitors. Methods: The model was developed for KRAS G12C inhibitors using pre-clinical data on sotorasib. The model was validated using data from both internal Amgen compounds and published data for several KRAS G12C inhibitors. The applicability of the framework was extended to EGFR covalent inhibitors by incorporating PK/PD and the efficacy of osimertinib and its active metabolite AZ5104. Results: The model successfully captured the pharmacokinetics, KRAS G12C target occupancy, inhibition of phosphorylation of ERK protein, and tumor growth inhibition following the administration of sotorasib in mice bearing MIA PaCa-2 xenografts. External validation with several internal Amgen compounds as well as publicly available data on KRAS G12C inhibitors showed the robustness of the model. The application of this framework to EGFR inhibitor Osimertinib and AZ5104 captured p-EGFR dynamics and resultant tumor growth inhibition. Conclusions: A modeling framework for covalent inhibitors was developed that links exposure to target occupancy, downstream signaling, and tumor efficacy. This framework could be useful for compound optimization and human dose projections. Full article

In order to verify the reliability and engineering applicability of the AE-CMT welding technology for magnesium alloy joining, AE-CMT welding experiments were conducted at welding speeds ranging from 0.5 to 3.0 m/min on 1.5 mm-thick H24-temper AZ31B magnesium alloy sheets using imported 1.2 mm-diameter WE-33M welding wire. Within the welding speed range of 0.5–3.0 m/min, increasing the welding speed progressively reduces heat input, thereby refining grains and homogenizing the microstructure. The welding heat input of the AE-CMT process ranges from 0.47 KJ/mm to 1.07 KJ/mm, and the grain sizes of the weld zone and HAZ are 9.61–14.18 μm and 6.35–12.22 μm, respectively. In the range of 0.5–2.0 m/min welding speed, increasing welding speed progressively enhances the tensile strength of the welded joint. Notably, joints fabricated at a welding speed of 2.0 m/min deliver the maximum tensile strength, equivalent to 98.0% of the base metal. Well-defined dimples are also detected on the corresponding fracture surfaces. A further increase in welding speed leads to a gradual reduction in the tensile strength of the welded joint. It is demonstrated that welding speed acts as a critical process parameter for tailoring the microstructure and mechanical properties of AE-CMT-welded AZ31B magnesium alloy joints. Full article

Hepatocellular carcinoma (HCC) is an aggressive liver cancer requiring reliable biomarkers, while current approaches are limited by high-dimensional data and complex nonlinear gene interactions. In this study, differentially expressed genes were identified and fed to a deep autoencoder to reduce dimensionality, capture nonlinear interactions, and extract informative latent features. Predictive gene features were selected through mutual information (MI) ranking and LASSO regression and subsequently evaluated using logistic regression (LR), random forest (RF), and support vector machine (SVM) classifiers with 5-fold cross-validation (CV). The top 50 genes underwent enrichment analyses. Protein–protein interaction (PPI) networks were constructed to identify hub genes, followed by gene–drug interaction, transcription factor analysis, and survival validation. Enrichment analysis highlighted critical pathways involved in metabolic, signaling, and cell-cycle, and viral carcinogenesis. CV showed stable and high performance across classifiers (accuracy = 0.969, F1 ≈ 0.97), with RF and SVM achieving the highest AUC values (~0.983 and ~0.982). Independent tests showed excellent performance, with RF achieving perfect performance (1.0) across all evaluation metrics, confirming high feature discriminative power. Survival analysis showed that hub genes, including HSP90AB1, TUBA1B, PKM, H2AZ1, YWHAZ, ACLY, RAN, ILF2, KPNA2, and TXNRD1, were significantly associated with poor prognosis (HR > 1.5, p < 0.05), correlating with reduced overall, relapse-free, and disease-specific survival in HCC. The integrative novel framework effectively identifies biologically relevant biomarkers, providing insights into HCC mechanisms and potential targets for precision therapy. Full article

High-velocity oxy-fuel (HVOF)-sprayed NiCrBSi coatings containing 0, 10, 30, and 50 wt.% WC-Co were evaluated on AZ91D magnesium alloy to determine how the discrete reinforcement level affects surface topography, phase constitution, Vickers microhardness, dry-sliding friction, mass loss, and wear-track microchemistry. As-sprayed surfaces were characterized by three-dimensional profilometry; coating cross-sections and worn surfaces by optical microscopy and SEM/EDS; phase constitution by XRD; and mechanical response by HV_0.1 indentation. Dry-sliding tests were performed at 10, 30, and 50 N over 100–1000 m. Increasing WC-Co content raised Sa from 8.8 ± 0.3 to 13.0 ± 0.5 µm and Vickers microhardness from 776 ± 4 to 959 ± 5 HV_0.1. XRD indicated a γ-Ni-based matrix containing boride/carbide constituents, while WC, W₂C, and Co became increasingly prominent in the reinforced coatings. Boride assignments are based on diffraction evidence, whereas B and C EDS signals were treated semi-quantitatively. The 50 wt.% WC-Co coating exhibited the lowest mass loss and mean coefficient of friction at every load. Its mean friction coefficients were 0.31, 0.35, and 0.41 at 10, 30, and 50 N, corresponding to reductions of 40.1%, 38.9%, and 36.2% relative to AZ91D. At 1000 m, its mass-normalized wear rate indices were 9.0 × 10⁻⁴, 4.0 × 10⁻⁴, and 5.3 × 10⁻⁴ mg N⁻¹ m⁻¹, respectively. Post-wear mapping showed the largest field-scale W-Co-rich fraction in the 50 wt.% coating; however, isolated spectra containing more than 94 wt.% Mg are compatible with local coating penetration/substrate exposure and/or Mg-rich debris. The 50 wt.% composition therefore provided the best combined response among the four tested levels, while intermediate compositions are required to identify a continuous-composition optimum. Full article

The increasing computational demands of artificial intelligence (AI) inference at the edge require hardware accelerators capable of overcoming the von Neumann bottleneck while operating under power constraints. Conventional digital architectures based on multiply–accumulate (MAC) units are limited in energy efficiency and scalability for resource-constrained applications. This work presents a proof-of-concept AI accelerator based on analog frequency–domain computation implemented within a semi-systolic array architecture. The proposed approach exploits frequency mixing to perform multiplication and accumulation operations in hardware, enabling the execution of matrix–matrix operations, which constitute the General Matrix Multiplication (GEMM) methods that dominate the computational workload of convolutional and fully connected neural networks. The proposed system consists of a custom printed circuit board controlled by an ATmega328P microcontroller(Microchip Technology Inc., Chandler, AZ, USA) and a software stack designed to interface with standard machine learning frameworks such as PyTorch. The software layer enables neural network operations, including convolutional and fully connected layers, to be mapped onto hardware-executed matrix–matrix computations through an abstraction analogous to the General Matrix Multiplication (GEMM) functionality provided by Level-3 Basic Linear Algebra Subprograms (BLAS). Matrix multiplication and accumulation are partly performed directly by the hardware processing elements, while the software control unit coordinates data movement and computation scheduling. Although bias operations are not implemented in the current prototype, their comparatively low computational cost makes them less critical to the overall acceleration strategy. A quantization-aware mapping methodology constrained by analog-to-digital and digital-to-analog converter specifications is introduced to translate neural network operations into frequency–domain computations. The paper further describes the hardware architecture, communication protocols, software stack organization, and interactions between system components. In addition, the effects of analog nonidealities and error sources associated with frequency–domain multiplication are investigated, and simulations of the proposed processing elements are presented to evaluate the computational approach. Experimental and simulation results demonstrate the feasibility of performing dense linear algebra operations through analog frequency–domain processing and validate the operation of the processing elements. The study further explores converter resolution, frequency interference, and analog component nonidealities and provides a comparison with conventional digital and other low-power accelerator approaches. The results indicate that exploiting the inherent parallelism of analog computation offers a promising pathway toward ultra-low-power AI inference, making the proposed architecture a potential alternative for energy-constrained edge applications. Full article

Drought research on quinoa has focused almost exclusively on the shoots, leaving the roots, the organ that first senses soil drying, largely unexamined, and its recovery dynamics are still poorly characterized. Here, we show that in the Chilean coastal quinoa ecotype AZ1, recovery from drought is governed belowground, and the root regains hydraulic and metabolomic competence well before the stomata reopen. After 72 h of soil drying, stomatal conductance (g_s) decreased by 98%, whole-plant transpiration declined biphasically (~92% of the loss within the first two hours), and water potential decreased steeply at the soil–root interface (with soil and root water potential declining approximately 10- and 20-fold relative to well-watered plants), while the stem remained near-stable, pinpointing the root as the dominant hydraulic bottleneck. Twenty-four hours after rewatering, root system and whole-plant water potential, osmotic root hydraulic conductance (Lp_rOS), root anatomy, and the polar metabolome were largely restored, yet g_s remained statistically indistinguishable from droughted plants. Strikingly, hydraulic recovery proceeded without rebuilding the osmotic sugar pool; instead, normalization of TCA-cycle intermediates points to an energy-powered and possible aquaporin-mediated transport route that bypasses still-suberized apoplastic barriers. Root system metabolomics, led by GABA and L-alanine, which overshot the control, tracked root rehydration but correlated negatively with g_s, suggesting that nitrogen-rich solutes may act as candidate belowground cues restraining stomatal reopening. These findings suggest that the quinoa root system acts as a pacemaker for drought recovery. Full article

Olive orchards constitute important reservoirs of floristic diversity, including species that are strongly dependent on agroecosystems for their persistence. The conservation of floristic diversity enhances the provision of essential agroecosystem services, contributing to the sustainability of olive orchard agroecosystems. The main aim of this study was to provide a descriptive analysis of the floristic diversity of olive orchards, a topic that has not been investigated extensively in Crete. The study was designed to capture variation associated with management system (MS) and agroecological zone (AZ), providing a comprehensive assessment of floristic diversity. A seven-and-a-half-year floristic survey was conducted in the Messara Valley, Crete, Greece, across nine organic olive orchards implementing Green Infrastructure (GI), eight conventionally managed orchards, and two abandoned orchards. The orchards were located in both plain and hilly agroecological zones. Within each orchard, monthly surveys were conducted in three randomly assigned square sampling stations per hectare, which remained constant throughout the study period. A descriptive analysis and univariate comparisons between the MS and AZ were conducted using six pairs of organic and conventional orchards, with three pairs located in the hilly AZ and three in the plain AZ. In total, 331 species, belonging to 60 families and 28 taxonomic orders, were identified. Most recorded species were characterised as therophytes (55%) and hemicryptophytes (31%), mainly belonging to the families Asteraceae, Fabaceae, Poaceae, and Lamiaceae. Total species richness was higher in organic (257 species, 56 families) than in abandoned (195 species, 45 families) and conventional orchards (192 species, 50 families), however, the mean species richness did not differ statistically significantly among management systems. Mean species and family richness were significantly higher in the hilly AZ than in the plain AZ, and total species richness was also higher in the hilly AZ (316 species, 59 families) than in the plain AZ (128 species, 43 families). This study highlghts the role of olive orchards as reservoirs of floristic diversity in Crete and demonstrates the influence of MS, Green Infrastructure and AZ in shaping the floristic diversity of these agroecosystems. Full article

The rapid expansion of sub-6 GHz 5G and Internet of Things (IoT) networks demands highly miniaturized Multiple-Input Multiple-Output (MIMO) antennas. However, balancing extreme physical compactness with rigorous inter-port isolation introduces severe computational bottlenecks for conventional optimization algorithms. To overcome these multidimensional challenges, this paper proposes a novel Deep Surrogate Active Learning framework for the autonomous design and empirical validation of an ultra-compact dual-band MIMO antenna. By using a surrogate-assisted closed-loop strategy to reduce reliance on repeated full-wave evaluations, the methodology combined a custom-penalized Deep Neural Network with dynamic boundary reduction. After training the initial surrogate model with 440 valid full-wave responses obtained from the offline design-of-experiments (DOE) stage, the best CST-validated candidate was identified at the 83rd active learning cycle. The optimized nested-loop geometry, incorporating a partial defected ground structure (DGS), occupies an extremely confined footprint of only 1634 mm² on a Rogers RO4350B substrate (Rogers Corporation, Chandler, AZ, USA). The selected geometry provided simulated −10 dB impedance bands of 3.35–3.88 GHz and 4.34–5.05 GHz, while the complete two-port model maintained inter-port isolation better than 13.8 dB and 14.9 dB across the lower and upper target passbands, respectively. Measurements of the fabricated prototype showed the intended dual-band behavior, a maximum measured gain of 4.54 dBi, and total radiation efficiencies of approximately 51–63% across both ports at the evaluated frequencies. The simulated Envelope Correlation Coefficient (ECC) remained below 0.035 across the target passbands, supporting the suitability of the compact geometry for the investigated sub-6 GHz MIMO bands. Full article

Driven by the growing demand for quality assurance within the essential oil (EO) industry, this study builds upon prior seasonal chemical and anticholinergic characterizations of Azorean Cryptomeria japonica foliage (Az–CJF) EO by presently evaluating the seasonal variations in its antibacterial, antioxidant, and anti-inflammatory activities. Autumn (Aut–EO) and spring (Spr–EO) samples exhibited a uniform, targeted antibacterial profile exclusively against Gram-positive bacteria, with a MIC of 5.0 mg/mL for Micrococcus luteus and ≥10.0 mg/mL for Bacillus licheniformis, B. subtilis, and Staphylococcus aureus. Antioxidant capacities also remained seasonally consistent within each of the three individual assays, namely the DPPH, ABTS, and β-carotene bleaching assays (EC₅₀ ≤ 10.5, ≤ 6.0, and ≤0.3 mg/mL, respectively), suggesting a stable potential for lipid peroxidation inhibition. In addition, both EOs protected against bovine serum albumin denaturation (84–98%) with no statistically significant differences, generally outperforming diclofenac sodium (70–87%). Notably, anti-inflammatory activity via COX pathways proved to be seasonally dependent: Spr–EO inhibited COX-1 and COX-2 more significantly than Aut–EO, with both EOs showing COX-1 selectivity (IC₅₀ of 284 vs. 560 µg/mL). Although less potent than diclofenac sodium (COX IC₅₀ < 0.2 µg/mL), the superior activity of Spr–EO was supported by molecular docking, suggesting this enhanced effect is driven by higher contents of kaur-16-ene and oxygenated sesquiterpenes (such as α- and β-eudesmol). These bioactivities provide baseline parameters for batch standardization. By merging stable core properties with seasonal anti-inflammatory profiles, Az–CJF EO shows promise as a natural multi-target-directed ligand (MTDL) for therapeutic applications within a circular bioeconomy. Full article

This study presents the design, development, and evaluation of iSign, an Android-based educational mobile application developed to support sign language learning among deaf and hard-of-hearing individuals in rural communities. The project was conducted using a research and development (R&D) approach guided by the ADDIE instructional design framework. A preliminary needs assessment, based on municipal records and field validation, revealed limited access to structured sign language education and assistive learning resources in selected barangays of Odiongan, Romblon. The iSign application was designed as a user-centered and accessible mobile learning tool incorporating alphabet learning (A–Z), number recognition (0–9), a dictionary containing 160 commonly used vocabulary words with definitions and corresponding sign language video demonstrations, and multimedia content such as nursery rhyme songs interpreted in sign language. The system was developed using Android Studio and structured to support offline accessibility to accommodate low-connectivity environments. The application was evaluated in terms of functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. Twenty-one community participants and four information technology experts assessed the system using a five-point Likert scale. The overall weighted mean rating of 4.0 (“Agree”) indicates that the developed application met acceptable software quality standards and user satisfaction levels. The findings demonstrate that iSign is a functional and accessible mobile learning application that can serve as a supplementary tool for sign language education in underserved communities. Full article

Friction stir welding (FSW) is an effective solid-state joining technique for magnesium alloys such as AZ31, owing to its ability to minimize conventional welding defects. Nevertheless, the process generates significant residual stresses that can impair the fatigue performance and dimensional stability of welded structures. In this study, a sequentially coupled thermo-mechanical finite element model was employed to characterize the residual stress distribution in FSW AZ31 Mg alloy. The calculated near-surface longitudinal residual stress was assessed against XRD measurements at five locations on the top surface, giving a root mean square error of about 10.34 MPa. The results revealed an M-shaped longitudinal residual stress profile with marked asymmetry between the advancing and retreating sides, associated with non-uniform heat input and the resulting asymmetric temperature history. Among the three stress components, the longitudinal residual stress was the largest, followed by the transverse component, while the normal stress was the smallest. The thermo-mechanically affected zone and the crown zone exhibited higher residual stresses compared to the heat-affected zone. In addition, the influences of welding speed and tool rotational speed on residual stress evolution were systematically evaluated. The longitudinal residual stress increased with welding speed up to 350 mm/min and subsequently decreased, while a peak value was observed at 1200 rpm. These numerical results provide useful guidance, within the studied parameter range, for welding-parameter selection and residual-stress control in magnesium alloy joints. Full article

The use of five machine-learning regression models, Gaussian Process Regression (GPR), Support Vector Machine (SVM), XGBoost, CatBoost, and LightGBM, was for predicting the ultimate tensile strength (UTS) of friction stir welded (FSW) AZ31B magnesium alloy joints. A controlled, single-source, experimental dataset comprising 99 observations was created on the same FSW machine under the same laboratory conditions. The dataset covered three feed rates, eleven rotational speeds and three tool tilt angles, and each parameter combination was represented by the mean UTS value from triplicate tensile tests. The input variables were the feed rate, rotational speed and tilt angle, and the prediction target was UTS measured using ASTM E8M-04. To create a more challenging and realistic assessment, we implemented blocked-holdout validation, keeping only the previously unseen rotational speed levels for the test set. Hyperparameters were selected via exhaustive grid search, with 5-fold GroupKFold cross-validation used solely on the training data. Among the models that were tested, GPR demonstrated the best overall blocked-holdout performance, with a R² = 0.985 and RMSE = 1.798 MPa. XGBoost (R² = 0.923) and CatBoost (R² = 0.912) also demonstrated competitive performance. Conversely, LightGBM exhibited the poorest generalization performance (R² = 0.817). The findings suggest that kernel and boosting-based approaches have the capacity to adequately simulate the nonlinear relationship between FSW process parameters and tensile performance, while GPR demonstrated the best generalization under the blocked-holdout evaluation strategy. Full article

Driven by the automotive industry’s strategies for energy conservation, emission reduction, and lightweighting, magnesium alloy wheels have emerged as a key focus of research and industrialization efforts, owing to their high specific strength, excellent vibration-damping properties, and superior heat dissipation performance. This paper provides a systematic review of the performance advantages, material systems, forming processes, applications, and industrialization challenges of magnesium alloy automotive wheels. The core advantages of magnesium alloy wheels in terms of weight reduction, vibration damping, and thermal management are elaborated. The compositional characteristics, suitable processes, and performance differences between cast magnesium alloys (e.g., AZ91D, AM60B) and wrought magnesium alloys (e.g., AZ80, ZK61-Y) are outlined. The technical characteristics, microstructural and property evolution, and limitations of casting processes (gravity, high-pressure, low-pressure, and semi-solid casting), plastic forming processes (isothermal extrusion forging, backward extrusion forging, and spin forming), and hybrid processes are discussed. Combined with the case studies of magnesium alloy wheel applications in the automotive sector, this paper analyzes the core bottlenecks of magnesium alloy wheels in terms of corrosion resistance, production cost, and industrial consistency, and outlines future research directions. This paper aims to provide theoretical references and technical support for the design, manufacturing, and large-scale application of lightweight, high-performance magnesium alloy wheels. Full article

Chronic heat stress poses a major challenge to marine aquaculture, yet its sex-associated molecular basis remains poorly understood in Chinese tongue sole (Cynoglossus semilaevis). Juveniles were exposed to a control temperature (24 °C) or elevated temperature (30 °C) for two months, and liver samples from low-temperature females (LF), high-temperature females (HF), low-temperature males (LM), and high-temperature males (HM) were analyzed by RNA sequencing (RNA-seq) and whole-genome bisulfite sequencing (WGBS). Principal component analysis indicated a strong temperature-associated separation, while sex-related separation under heat stress was smaller and was mainly observed along the second principal component. In high-temperature relative to low-temperature comparisons, females showed a broader set of differentially expressed genes (DEGs; LF vs. HF, 1968) than males (LM vs. HM, 506). Differential methylation analyses indicated that cytosine-guanine (CG) methylation was the predominant heat-associated epigenetic signal. Integrative analysis identified 624 overlapping genes between DEGs and CG-associated differentially methylated genes (CG-DMGs) in females and 177 in males, suggesting broader methylation-associated transcriptional remodeling in females. Functional enrichment associated the female overlap genes with immune response, inflammatory signaling, lipid metabolism, and detoxification, whereas male overlap genes were more closely associated with proteostasis, autophagy, and DNA replication/repair. Correlation analyses suggested modest methylation–expression coupling and highlighted candidate W-linked genes, including H2AZ2 and ANKRD13A. Overall, these results should be regarded as a preliminary baseline for understanding sex-associated molecular responses to chronic heat stress in tongue sole and as a source of candidate genes and pathways for future validation and heat-resilience breeding. Full article