Search Results (60)

This study proposes a useful multi-criteria optimization approach for defining the proper fabrication factors for the CNC milling process on the inclined surfaces of SKD 11 material. The method is to be used in mold fabrication technology within the field of mechanical engineering. A combination technique of the Taguchi technique (TM), response surface method (RSM), and Lichtenberg optimization algorithm (LA) was proposed to optimize the fabrication factors for enriching the superiority attributes. In the first stage, several initial experiments of the fabricating parameters were generated by the TM. Secondly, the mathematical equations among the main fabricating parameters, the surface roughness, the flatness, and the CNC milling time were then established by the RSM. Significant influences of fabrication elements on surface roughness, flatness, and CNC milling time were evaluated by variance analysis and sensitivity analysis based on three distinct CNC milling toolpath strategies. Finally, the Lichtenberg optimization algorithm was carried out based on regression equations to define the optimized factors for three cutting strategies. The optimized results showed that the reverse CNC milling toolpath strategy was the best for achieving the three quality responses. Furthermore, the results demonstrated that the inaccuracies among optimized as well as experiment confirmations for the surface roughness, flatness and CNC milling time were 6.54%, 18.182% and 11.972%, respectively. The verifications of experiment results were relatively suitable with the anticipated consequences. The outcomes reveal that an integration optimization methodology is a successful approach to tackling the multi-objective optimal problem of determining the best CNC milling parameters for the cartwheel specimen made of SKD11 material in injection mold technology. It can also be expanded to apply to complicated multi-criteria optimization problems. Full article

Background/Objective: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer (BC) lacking targeted therapies and characterized by high tumor heterogeneity. In this study, we evaluated the anticancer activity and mechanistic profile of Simalikalactone D (SKD), a quassinoid compound derived from the endemic Puerto Rican tree Simarouba tulae, in three TNBC cell lines, MDA-MB-468, MDA-MB-231, and SUM-149. Methods: MDA-MB-468, MDA-MB-231 and SUM-149 TNBC cells were evaluated for cell viability, proliferation and migration following SKD treatment. Phospho-antibody array, proteomics, and Western blot analyses were used to explore the SKD mechanism of action in MDA-MB-468 and MDA-MB-231 cell lines. Molecular docking was performed to assess SKD’s interaction with potential intracellular targets. Results: SKD exerted a concentration-dependent effect on the three cell lines. However, MDA-MB-468 cells exhibited an IC₅₀ of 67 nM, while the IC₅₀ values for MDA-MB-231 and SUM-149 were 422 nM and 598 nM, respectively. In MDA-MB-468 cells, 100 nM of SKD induced apoptosis, evidenced by the activated caspase-3 activity, PARP-1 cleavage and decrease in Bcl-2 and survivin protein levels. Sublethal SKD (25 nM) impaired migration in MDA-MB-231 cells and reduced proliferation and motility in SUM149 cells. A 6 h SKD treatment markedly reduced phosphorylation of apoptosis-related proteins (p53, BAD, DAXX, AKT1, JUN) and Jak/STAT pathway components, indicating early disruption of intracellular signaling prior to phenotypic changes. Proteomic profiling showed distinct pathway alterations in both MDA-MB-468 and MDA-MB-231 cells, with reduced Integrin β1 (ITGB1) levels emerging as a shared effector. This suggests that SKD broadly disrupts cell adhesion and migration independently of apoptosis-driven cell death. Western blot validation confirmed reduced ITGB1 protein levels across all three TNBC cell lines examined. In silico docking confirmed favorable binding affinities of SKD to both EGFR (ΔG = −6.718 kcal/mol) and STAT4 (ΔG = −8.481 kcal/mol). Conclusions: Overall, our findings suggest that SKD is a potent anticancer agent in a subgroup of TNBC cells. Full article

Image restoration tasks such as deraining, deblurring, and dehazing are crucial for enhancing the environmental perception of autonomous vehicles and traffic systems, particularly for tasks like vehicle detection, pedestrian detection and lane line identification. While transformer-based models excel in these tasks, their prohibitive computational complexity hinders real-world deployment on resource-constrained platforms. To bridge this gap, this paper introduces a novel Soft Knowledge Distillation (SKD) framework, designed specifically for creating highly efficient yet powerful image restoration models. Our core innovation is twofold: first, we propose a Multi-dimensional Cross-Net Attention(MCA) mechanism that allows a compact student model to learn comprehensive attention relationships from a large teacher model across both spatial and channel dimensions, capturing fine-grained details essential for high-quality restoration. Second, we pioneer the use of a contrastive learning loss at the reconstruction level, treating the teacher’s outputs as positives and the degraded inputs as negatives, which significantly elevates the student’s reconstruction quality. Extensive experiments demonstrate that our method achieves a superior trade-off between performance and efficiency, notably enhancing downstream tasks like object detection. The primary contributions of this work lie in delivering a practical and compelling solution for real-time perceptual enhancement in autonomous systems, pushing the boundaries of efficient model design. Full article

The crystal structure of kuliokite-(Y), Y₄Al(SiO₄)₂(OH)₂F₅, has been re-investigated using the material from the type locality the Ploskaya Mt, Kola peninsula, Russian Arctic. It has been shown that in contrast to previous studies, the mineral is monoclinic, Im, with a = 4.3213(1), b = 14.8123(6), c = 8.6857(3) Å, β = 102.872(4)°, and V = 541.99(3) ų. The crystal structure was solved and refined to R₁ = 0.030 on the basis of 3202 unique observed reflections. The average chemical composition determined by electron microprobe analysis is (Y_2.96Yb_0.49Er_0.27Dy_0.13Tm_0.07Lu_0.05Ho_0.05Gd_0.01Ca_0.01)_Σ4.04Al_0.92Si_2.04O₈-[(OH)_2.61F_4.42]_Σ7.03; the idealized formula is (Y,Yb,Er)₄Al[SiO₄]₂(OH)_2.5F_4.5. The crystal structure of kuliokite-(Y) contains two symmetrically independent Y sites, Y1 and Y2, coordinated by eight and seven X anions, respectively (X = O, F). The coordination polyhedra can be described as a distorted square antiprism and a distorted pentagonal bipyramid, respectively. The refinement of site occupancies indicated that the mineral represents a rare case of HREE fractionation among two cation sites driven by their coordination numbers and geometry. In agreement with the lanthanide contraction, HREEs are selectively incorporated into the Y2 site with a smaller coordination number and tighter coordination environment. The strongest building unit of the structure is the [AlX₂(SiO₄)₂] chain of corner-sharing AlX₆ octahedra and SiO₄ tetrahedra running along the a axis. The chains have their planes oriented parallel to (001). The Y atoms are located in between the chains, along with the F⁻ and (OH)⁻ anions, providing the three-dimensional integrity of the crystal structure. Each F⁻ anion is coordinated by three Y³⁺ cations to form planar (FY₃)⁸⁺ triangles parallel to the (010) plane. The triangles share common edges to form [F₂Y₂]⁴⁺ chains parallel to the a axis. The analysis of second-neighbor coordination of Y sites allowed us to identify the structural topology of kuliokite-(Y) as the only case of the skd network in inorganic compounds, previously known in molecular structures only. The variety of anionic content in the mineral allows us to identify the potential existence of two other mineral species that can tentatively be named ‘fluorokuliokite-(Y)’ and ‘hydroxykuliokite-(Y)’. Full article

Gallium-based liquid metal is corrosive to steel alloys, forming FeGa₃ surface films which can potentially be applied as a solid lubricant to enhance wear resistance and mitigate liquid metal-induced corrosion. However, the characteristics of these films remain insufficiently explored. In this study, Ga-In-Sn alloy was ultrasonically soldered onto annealed and decarburised substrates, followed by heating in a vacuum chamber to form a 30 μm thick FeGa₃ reaction layer. The film on the annealed samples with an alpha-ferrite microstructure exhibited high porosity and a surface roughness of 1.97 Ra. In contrast, the film on the decarburised samples with a ferritic microstructure showed minimal porosity and a lower surface roughness of 1.29 Ra. Nanoindentation tests revealed Young modulus values of 231 GPa and 242 GPa and hardness values of 11.4 GPa and 12.7 GPa for the annealed and decarburised samples, respectively. The high porosity in the annealed samples is attributed to the suppression of FeGa₃ formation in regions containing chromium carbides. Shear stress for fracture, measured by microcantilever tests at the interface between the substrate and the inner matrix of the surface film, showed lower fracture shear stress in the annealed sample, attributed to the presence of larger pores within its microstructure. Full article

Accurate normalization is crucial for reliable gene expression quantification and depends on stably expressed housekeeping genes (HKGs) as internal controls. However, HKGs expression varies with developmental stage, tissue type, and treatments, potentially introducing bias and compromising data accuracy. Thus, validating candidate reference genes under defined conditions is essential. Reynoutria, also known as giant Asian knotweeds, is a Polygonaceae family genus of several medicinal plants producing a diverse array of specialized metabolites of pharmacological interest. Outside their native range, these plants are also noxious invasive weeds, causing significant environmental and economic threats. Research on stable reference genes in these species is limited, with a primary focus on R. japonica. To enable accurate gene expression analysis related to specialized metabolism and natural product biosynthesis, we aimed to identify the most stable reference genes across the most common species: R. japonica Houtt., R. sachalinensis (F. Schmidt) Nakai, and their hybrid—R. × bohemica Chrtek & Chrtková. In this study, we evaluated twelve candidate HKGs (ACT, TUA, TUB, GAPDH, EF-1γ, UBQ, UBC, 60SrRNA, eIF6A, SKD1, YLS8, and NDUFA13) across three tissue types (rhizomes, leaves, and flowers) from three Reynoutria species sampled at peak flowering. Primer specificity and amplification efficiency were confirmed through standard-curve analysis. We assessed expression stability using ΔCt, geNorm, NormFinder, and BestKeeper, and generated comprehensive rankings with RefFinder. Our integrated analysis revealed organ- and species-dependent stability differences, yet identified up to three reference genes suitable for interspecific normalization in Reynoutria. This represents the first systematic, comparative validation of HKGs across closely related knotweed species, providing a robust foundation for future transcriptomic and functional studies of their specialized metabolism and other biological processes. Full article

The interface between high-performance thermoelectric materials and electrodes critically governs the conversion efficiency and long-term reliability of thermoelectric generators under high-temperature operation. Here, we propose Al_xCoCrFeNi high-entropy alloys (HEA) as barrier layers to bond Cu-W electrodes with p-type skutterudite (p-SKD) thermoelectric materials. The HEA/p-SKD interface exhibited excellent chemical bonding with a stable and controllable reaction layer, forming a dense, defect-free (Fe,Ni,Co,Cr)Sb phase (thickness of ~2.5 μm) at the skutterudites side. The interfacial resistivity achieved a low value of 0.26 μΩ·cm² and remained at 7.15 μΩ·cm² after aging at 773 K for 16 days. Moreover, the interface demonstrated remarkable mechanical stability, with an initial shear strength of 88 MPa. After long-term aging for 16 days at 773 K, the shear strength retained 74 MPa (only 16% degradation), ranking among the highest reported for thermoelectric materials/metal joints. Remarkably, the joint maintained a shear strength of 29 MPa even after 100 continuous thermal cycles (623–773 K), highlighting its outstanding thermo-mechanical stability. These results validate the Al_xCoCrFeNi high-entropy alloys as an ideal interfacial material for thermoelectric generators, enabling simultaneous optimization of electrical and mechanical performance in harsh environments. Full article

Secure autonomous secret key distillation (SKD) systems traditionally depend on external common randomness (CR) sources, which often suffer from instability and limited reliability over long-term operation. In this work, we propose a novel SKD architecture that synthesizes CR by combining a keystream of a shared-key keystream generator $KSG\left(K_G\right)$ with locally generated binary Bernoulli noise. This construction emulates the statistical properties of the classical Maurer satellite scenario while enabling deterministic control over key parameters such as bit error rate, entropy, and leakage rate (LR). We derive a closed-form lower bound on the equivocation of the shared-secret key $K_G$ from the viewpoint of an adversary with access to public reconciliation data. This allows us to define an admissible operational region in which the system guarantees long-term secrecy through periodic key refreshes, without relying on advantage distillation. We integrate the Winnow protocol as the information reconciliation mechanism, optimized for short block lengths ($N=8$), and analyze its performance in terms of efficiency, LR, and final key disagreement rate (KDR). The proposed system operates in two modes: ideal secrecy, achieving secret key rates up to 22% under stringent constraints (KDR < 10⁻⁵, LR < 10⁻¹⁰), and perfect secrecy mode, which approximately halves the key rate. Notably, these security guarantees are achieved autonomously, without reliance on advantage distillation or external CR sources. Theoretical findings are further supported by experimental verification demonstrating the practical viability of the proposed system under realistic conditions. This study introduces, for the first time, an autonomous CR-based SKD system with provable security performance independent of communication channels or external randomness, thus enhancing the practical viability of secure key distribution schemes. Full article

The transformer-based deep learning approach represents the current state-of-the-art in machine translation (MT) research. Large-scale pretrained transformer models produce state-of-the-art performance across a wide range of MT tasks for many languages. However, such deep neural network (NN) models are often data-, compute-, space-, power-, and energy-hungry, typically requiring powerful GPUs or large-scale clusters to train and deploy. As a result, they are often regarded as “non-green” and “unsustainable” technologies. Distilling knowledge from large deep NN models (teachers) to smaller NN models (students) is a widely adopted sustainable development approach in MT as well as in broader areas of natural language processing (NLP), including speech, and image processing. However, distilling large pretrained models presents several challenges. First, increased training time and cost that scales with the volume of data used for training a student model. This could pose a challenge for translation service providers (TSPs), as they may have limited budgets for training. Moreover, CO₂ emissions generated during model training are typically proportional to the amount of data used, contributing to environmental harm. Second, when querying teacher models, including encoder–decoder models such as NLLB, the translations they produce for low-resource languages may be noisy or of low quality. This can undermine sequence-level knowledge distillation (SKD), as student models may inherit and reinforce errors from inaccurate labels. In this study, the teacher model’s confidence estimation is employed to filter those instances from the distilled training data for which the teacher exhibits low confidence. We tested our methods on a low-resource Urdu-to-English translation task operating within a constrained training budget in an industrial translation setting. Our findings show that confidence estimation-based filtering can significantly reduce the cost and CO₂ emissions associated with training a student model without drop in translation quality, making it a practical and environmentally sustainable solution for the TSPs. Full article

Background: Rumex patientia (Polygonaceae), a perennial herbaceous species predominantly found in northern temperate regions, has been historically utilized in traditional Chinese medicine for its hematological regulatory properties, including blood cooling, hemostasis, and detoxification. Despite the pharmacological value of this species, unvalidated reference genes compromise precise gene expression profiling. Methods: We initially selected eight candidate genes (ACT, GAPDH, YLS, SKD1, UBQ, UBC, EF-1α, TUA) from R. patientia transcriptomes and then assessed their transcriptional stability using RT-qPCR across root, stem, and leaf tissues under four abiotic stresses: cold, drought, salinity, and heavy metal exposure. Results: ACT emerged as the most stable reference gene in three specific scenarios: root/leaf tissues under cold stress, stems during drought exposure, and roots subjected to salt treatment, revealing distinct tissue–stress response patterns. TUA emerged as the most stable reference in cold- and salt-challenged stems, while SKD1 exhibited superior stability in drought-affected roots/leaves, heavy-metal-stressed tissues, and salt-treated leaves. Validation via the drought-inducible MYB transcription factor confirmed reference gene reliability. Conclusions: This work pioneers a standardized reference gene framework for R. patientia under multi-stress conditions, offering essential methodological foundations for subsequent molecular research in this medicinal plant. Full article

In this work, we prove that the initial value problem for the Schrödinger–Korteweg–de Vries (SKdV) system is locally well posed in Gevrey spaces for $s>-\frac{3}{4}$ and $k\ge 0$. This advancement extends recent findings regarding the well posedness of this model within Sobolev spaces and investigates the regularity properties of its solutions. Full article

The high-pressure die-casting (HPDC) process involves injecting molten light metal into a steel mold under high pressure, resulting in parts with excellent surface quality and precise dimensions. However, this process subjects the mold to thermal fatigue and mechanical stress, which can lead to damage over time. This study investigated the wear characteristics of two types of inserts made from different steel materials, SKD61 steel and FDAC steel, under HPDC conditions. A thorough approach that combined computer simulations, experiments, and 3D scanning was employed to analyze wear patterns and dimensional changes after up to 300 casting cycles. The results indicate that the SKD61 steel outperformed the FDAC steel in terms of wear resistance and dimensional stability. The maximum deposition values of the SKD61 mold were only 0.009 mm, which was only 25% compared to the FDAC mold, indicating a significantly higher wear resistance. These findings are crucial for selecting and enhancing insert materials in HPDC, ultimately leading to higher-quality and more efficient casting. Full article

This study investigates the reliability of Francis turbines and highlights the critical need for an improved deformation monitoring system for bolts that fasten a hydroturbine head-cover to its casing. During different operational stages of the hydraulic unit, such as start-up, partial load, and full load, the hydroturbine head-cover and its fastening bolts are subjected to static and cyclic loads. The loads generate vibrations and different deformations that must be monitored. Although various measuring instruments, such as vibration sensors and accelerometers, have been developed to monitor hydroturbine vibrations, only two systems—KM-Delta-8-CM and PTK KM-Delta—are currently applied to measure fastening bolt deformation. Furthermore, only one system, SKDS-SISH, was found to monitor the forces inducing this deformation. After analysis, it is evident that the described systems for monitoring the deformation of the fastening bolts do not guarantee the trustworthiness of the measuring sensors and there is a need for their improvement. The implementation of a self-checking function (including metrological features), the development of a digital twin of the sensor, and the application of technologies based on artificial intelligence could solve this problem. Full article

Heavy metal pollution from anthropogenic sources poses significant risks to ecosystems and human health. Biosorption offers a sustainable removal method, but kinetics are poorly captured by traditional neural networks. This study introduces optimized Bidirectional Long Short-Term Memory (Bi-LSTM) networks for multivariate modeling of Ni(II) biosorption on Quercus crassipes acorn shells, trained using experimental (EKD), synthetic (SKD), and combined (CKD) datasets. A two-stage hyperparameter optimization with Optuna yielded models with R² above 0.995 and low RMSE in 5-fold cross-validation. Second-stage models showed high stability, with coefficient of variation (CoV) values below 10% for RMSE. Based on unseen kinetics, production models showed slightly lower performance (R² = 0.89–0.996): EKD1, EKD2, and CKD1 showed the most consistent performance across challenging conditions with R² values of 0.9617, 0.9769, and 0.9415, respectively; SKD models achieved strong results under standard conditions (kinetic 1, SKD1 R² = 0.9963). SHapley Additive exPlanations (SHAP) analysis identified contact time and initial Ni(II) concentration as key variables, with temperature, cation charge, and a salt interference code also contributing to model interpretability. These findings demonstrate that optimized Bi-LSTM networks offer a robust and interpretable data-driven solution for modeling Ni(II) removal under multivariate conditions, including the presence of salts. Full article

This paper presents an autonomous perfectly secure low-bit-rate voice communication system (APS-VCS) based on the mixed-excitation linear prediction voice coder (MELPe), Vernam cipher, and sequential key distillation (SKD) protocol by public discussion. An authenticated public channel can be selected in a wide range, from internet connections to specially leased radio channels. We found the source of common randomness between the locally synthesized speech signal at the transmitter and the reconstructed speech signal at the receiver side. To avoid information leakage about open input speech, the SKD protocol is not executed on the actual transmitted speech signal but on artificially synthesized speech obtained by random selection of the linear spectral pairs (LSP) parameters of the speech production model. Experimental verification of the proposed system was performed on the Vlatacom Personal Crypto Platform for Voice encryption (vPCP-V). Empirical measurements show that with an adequate selection of system parameters for voice transmission of 1.2 kb/s, a secret key rate (KR) of up to 8.8 kb/s can be achieved, with a negligible leakage rate (LR) and bit error rate (BER) of order ${10}^{-3}$ for various communications channels, including GSM 3G and GSM VoLTE networks. At the same time, by ensuring perfect secrecy within symmetric encryption systems, it further highlights the importance of the symmetry principle in the field of information-theoretic security. To our knowledge, this is the first autonomous, perfectly secret system for low-bit-rate voice communication that does not require explicit prior generation and distribution of secret keys. Full article