Search Results (611)

A bioresorbable cannulated bone screw was developed using PLA/PCL-based composites reinforced with hydroxyapatite (HA) and titanium dioxide (TiO₂), two additives previously reported to enhance mechanical compliance, biocompatibility, and molding feasibility in biodegradable polymer systems. The design incorporated a crest-trimmed thread and a strategically positioned gate in the thin-wall zone opposite the hexagonal socket to preserve torque-transmitting geometry during micromolding. To investigate shrinkage behavior, a Taguchi orthogonal array was employed to systematically vary micromolding parameters, generating a structured dataset for training a back-propagation neural network (BPNN). Analysis of variance (ANOVA) identified melt temperature as the most influential factor affecting shrinkage quality, defined by a combination of shrinkage rate and dimensional variation. A hybrid AI framework integrating the BPNN with genetic algorithms and particle swarm optimization (GA–PSO) was applied to predict the optimal shrinkage conditions. This is the first use of BPNN–GA–PSO for cannulated bone screw molding, with the shrinkage rate as a targeted output. The AI-predicted solution, interpolated within the Taguchi design space, achieved improved shrinkage quality over all nine experimental groups. Beyond the specific PLA/PCL-based systems studied, the modeling framework—which combines geometry-specific gate design and normalized shrinkage prediction—offers broader applicability to other bioresorbable polymers and hollow implant geometries requiring high-dimensional fidelity. This study integrates composite formulation, geometric design, and data-driven modeling to advance the precision micromolding of biodegradable orthopedic devices. Full article

Hybrid fiber-reinforced concrete (HFRC), renowned for its significantly enhanced mechanical properties and structural integrity, is widely used in infrastructure construction and has become a key avenue of modern high-performance concrete development. The hybrid application of basalt fiber (BF) and polypropylene fiber (PPF) at optimized ratios generates synergistic effects, improving both mechanical performance and material service reliability. To explore and evaluate the synergistic mechanism of BF-PPF hybrid fibers on concrete’s mechanical properties and performance, this study employs an orthogonal experimental design and mechanical testing methods, measuring the materials’ static compressive strength (loading rate: 0.6 mm/min), splitting tensile strength (loading rate: 0.12–0.14 MPa/s), dynamic elastic modulus (measured by the ultrasonic method), and dynamic compressive strength (loading rates: 0.6 mm/min, 6 mm/min, and 60 mm/min). For these tests, we prepared 100 mm × 100 mm × 100 mm cubic specimens (for static compressive, dynamic compressive, and splitting tensile tests) and 400 mm × 100 mm × 100 mm prismatic specimens (for dynamic elastic modulus tests), with three parallel specimens in each test group. In addition, the microstructure was characterized by scanning electron microscopy (SEM) to observe the fiber-matrix interaction. The results show that when the BF/PPF volume ratio is 1:2 (BF0.05PPF0.1), the concrete’s compressive strength, splitting tensile strength, and elastic modulus increase by 13.7%, 76.3%, and 116.0%, respectively, with corresponding synergistic effect indices (Q) of 0.057, 0.213, and 0.241, indicating obvious positive synergy. Under dynamic loading, hybrid combinations with higher PPF content (e.g., BF0.05PPF0.1) exhibit strain-rate-dependent enhancements in compressive strength and better impact resistance. SEM analysis reveals that fibers inhibit microcrack propagation through fiber bridging, network distribution, and pull-out resistance, while also improving the interfacial transition zone’s structure. These findings provide theoretical support for the engineering application of composite fiber-reinforced concrete materials. Full article

In off-road scenes, segmentation targets exhibit significant scale progression due to perspective depth effects from oblique viewing angles, meaning that the size of the same target undergoes continuous, boundary-less progressive changes along a specific direction. This asymmetric variation disrupts the geometric symmetry of targets, causing traditional segmentation networks to face three key challenges: (1) inefficientcapture of continuous-scale features, where pyramid structures and multi-scale kernels struggle to balance computational efficiency with sufficient coverage of progressive scales; (2) degraded intra-class feature consistency, where local scale differences within targets induce semantic ambiguity; and (3) loss of high-frequency boundary information, where feature sampling operations exacerbate the blurring of progressive boundaries. To address these issues, this paper proposes the ProCo-NET framework for systematic optimization. Firstly, a Progressive Strip Convolution Group (PSCG) is designed to construct multi-level receptive field expansion through orthogonally oriented strip convolution cascading (employing symmetric processing in horizontal/vertical directions) integrated with self-attention mechanisms, enhancing perception capability for asymmetric continuous-scale variations. Secondly, an Offset-Frequency Cooperative Module (OFCM) is developed wherein a learnable offset generator dynamically adjusts sampling point distributions to enhance intra-class consistency, while a dual-channel frequency domain filter performs adaptive high-pass filtering to sharpen target boundaries. These components synergistically solve feature consistency degradation and boundary ambiguity under asymmetric changes. Experiments show that this framework significantly improves the segmentation accuracy and boundary clarity of multi-scale targets in off-road scene segmentation tasks: it achieves 71.22% MIoU on the standard RUGD dataset (0.84% higher than the existing optimal method) and 83.05% MIoU on the Freiburg_Forest dataset. Among them, the segmentation accuracy of key obstacle categories is significantly improved to 52.04% (2.7% higher than the sub-optimal model). This framework effectively compensates for the impact of asymmetric deformation through a symmetric computing mechanism. Full article

Background/Objectives: The prevalence of obesity continues to rise globally, and with this an increase in the use of herbal weight loss supplements (WLS). At present, there is limited evidence to support the efficacy and safety of WLS, and there have been growing reports of adverse events associated with their use. We aimed to determine those WLS that caused toxicity in vitro and to use ¹H nuclear magnetic spectroscopy (NMR) to examine the metabolomic changes induced by these WLS in human hepatic and intestinal cells. Materials and Methods: This study used in vitro methods and ¹H NMR spectroscopy to analyse the metabolomic changes in vitro of WLS available for purchase in Australia. Ten WLS were selected, nine WLS caused significant toxicity in HepG2 human liver cells, and of these, six met the criteria for ¹H NMR analysis, which was based on a 25–50% reduction in cell viability. Results: All 10 WLS caused a significant reduction in viability of Caco-2 human intestinal cells, with seven selected for metabolic profiling. Orthogonal partial least squares discriminant analysis (O-PLS-DA) of ¹H NMR spectral data was used to characterise the metabolites that differed between the untreated and treated cells and the fold changes of the metabolites were determined. The results showed alterations to key metabolites such as amino acids, glucose, carboxylic acids, and amines in all treatment groups compared to untreated controls across both cell lines. Conclusions: Collectively, these biochemical changes represent disturbances to intracellular proteins, energy metabolism, and membrane lipids suggestive of oxidative stress. This study highlights the need for further investigations into the actions of these WLS in vivo, and, as these products were regulated by the Therapeutic Goods Administration (TGA) at the time of purchase, this study suggests improved pre-market screening to ensure consumer health is protected. Full article

In this paper, we investigate the geometric realization of $\mathrm{Spin}\left(8\right)$ triality through vector fields on the octonionic algebra $\mathbb{O}$. The triality automorphism group of $\mathrm{Spin}\left(8\right)$, isomorphic to ${\mathfrak{S}}_3$, cyclically permutes the three inequivalent 8-dimensional representations: the vector representation V and the spinor representations $S_{+}$ and $S_{-}$. While triality automorphisms are well known through representation theory, their concrete geometric realization as flows on octonionic space has remained unexplored. We construct explicit smooth vector fields $X_{\sigma }$ and $X_{{\sigma }^2}$ on $\mathbb{O}\cong {\mathbb{R}}^8$ whose flows generate infinitesimal triality transformations. These vector fields have a linear structure arising from skew-symmetric matrices that implement simultaneous rotations in three orthogonal coordinate planes, providing the first elementary geometric description of triality symmetry. The main results establish that these vector fields preserve the octonionic multiplication structure up to automorphisms in $G_2=\mathrm{Aut}\left(\mathbb{O}\right)$, demonstrating fundamental compatibility between geometric flows and octonionic algebra. We prove that the standard Euclidean metric on $\mathbb{O}$ is triality-invariant and classify all triality-invariant Riemannian metrics as conformal to the Euclidean metric with a conformal factor depending only on the isotonic norm. This classification employs Schur’s lemma applied to the irreducible $\mathrm{Spin}\left(8\right)$ action, revealing the rigidity imposed by triality symmetry. We provide a complete classification of triality-symmetric minimal surfaces, showing they are locally isometric to totally geodesic planes, surfaces of revolution about triality-fixed axes, or surfaces generated by triality orbits of geodesic curves. This trichotomy reflects the threefold triality symmetry and establishes correspondence between representation-theoretic decomposition and geometric surface types. For complete surfaces with finite total curvature, we establish global classification and develop explicit Weierstrass-type representations adapted to triality symmetry. Full article

This paper proposes two all-dielectric transmitarrays operating at Ka-band (26.5–40 GHz), achieving dual-polarization and beam-scanning functionalities. The dual-polarized design employs a cross-shaped dielectric post transmission unit, where the lengths of the two posts can be adjusted to enable independent phase modulation in the two orthogonal polarizations. Both polarizations provide 360° continuous phase coverage. To reduce the design complexity and achieve independent control of polarization, an optimized unit group with 16 states and 2-bit phase quantization is developed. A prototype of the all-dielectric transmitarray with 20 × 20 units is fabricated. The measured x/y-polarized peak gains are 25.3 dBi/25.5 dBi and the 1 dB bandwidths achieve 27% and 22%, respectively. To address feed–array integration, another all-dielectric transmitarray is further designed, which uses the same dual-polarized dielectric units, but replaces the horn feed with a dielectric rod antenna array. The feed array can generate multiple beams, enabling discrete beam-scanning within a 60° angle range. Both the dielectric transmitarray and the feed array can be fabricated by using 3D-printed technology, which greatly enhances the system integration and provides flexibility in generating multiple high-gain beams. Full article

To develop highly nutritious Bangia fusco-purpurea (BFP) vegan sausages, we investigated the effects of BFP, gluten, and xanthan gum–konjac gum–carrageenan complex gel (CG) on the gel strength and sensory quality of the sausages. The formulation process was optimized through single-factor and orthogonal tests, whereas the gel formation mechanism of the key factors was explored. The orthogonal test results showed that the optimal addition levels of BFP, gluten, and CG were 5%, 56%, and 37%, respectively. Variance analysis revealed that both gluten and CG significantly affected gel strength (p < 0.05), with gluten notably influencing the overall sensory quality (p < 0.05). Texture profile analysis (TPA) and rheological properties demonstrated that as gluten (33–37%) and CG (52–56%) concentrations increased, the gel strength and elastic modulus exhibited concentration-dependent enhancement. Further analysis of the sulfhydryl content, disulfide bonds, surface hydrophobicity, and microstructure revealed that higher gluten content promoted intermolecular disulfide crosslinking and hydrophobic group exposure, whereas CG contributed to physical filling via hydrogen and ionic bonds, resulting in a uniform and dense gel network structure. The synergistic effects of gluten and CG enhanced the gel properties of BFP vegan sausages, providing a theoretical foundation for the development of high-quality plant protein-based meat alternatives. Full article

Commercial fruit juices are categorized into juice not from concentrate (JNFC) and juice from concentrate (JFC). Tomato juice is one of the most popular vegetable juices, and its aroma is an important factor in evaluating its quality. However, differences in the aroma characteristics of JNFC and JFC tomato juices have not been clearly identified. This study aimed to investigate the volatile organic compounds (VOCs) involved in distinguishing between JNFC and JFC using commercially available tomato juices. Furthermore, the effect of concentration on the VOC composition was evaluated using different procedures. Twenty-three commercial tomato juices were prepared for analysis of VOCs using headspace solid phase microextraction-gas chromatography mass spectrometry (HS-SPME-GC-MS). Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to discriminate the samples into JNFC and JFC groups. JNFC contained 43 VOCs, which was more than twice that contained in JFC, and the quantitative variation was larger in JNFC than in JFC. In particular, the JNFC group contained significantly more alcohol and phenol compounds. On the other hand, the JFC group contained more formyl pyrrole and Strecker aldehydes. Additional GC-MS/olfactometry (GC-MS/O) and odor active value analyses indicated that (Z)-3-hexenol and 3-methylbutanal were the best VOCs to distinguish between the JNFC and JFC groups. Furthermore, different concentration procedures, including heating concentration (HC), decompression concentration (DC), and freeze drying (FD), were performed, and the corresponding VOCs were compared. HC and DC reduced the levels of most of the compounds to the levels seen in commercial JFC. These results indicate that the concentration procedure is an important processing stage, in addition to the break process, that determines the quality of tomato juice. Full article

Background/Objectives: The components of food and beverages are important elements that determine their palatability. Although the components of sake, a traditional Japanese alcoholic beverage, have been studied for many years, their correlation with sensory characteristics is unclear. Methods: We investigate the correlation with the sake metabolome analysis method developed by our group using ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry. We constructed orthogonal projections to latent structure models to predict sensory evaluation data obtained through the quantitative descriptive analysis method from the sake metabolome data. Results: For two years of study, 8 sensory evaluation models of the 2016 brewing year and 11 sensory evaluation models of the 2017 brewing year, including color, ethyl hexanoate, Hine-ka, Nama hine-ka, ethyl acetate, grainy/sweet aroma, sweetness, sourness, body, astringency, harsh taste/acrid taste, aftertaste, and overall quality, demonstrated a predictive performance with Q² > 0.5. Liquid chromatography-based analytical data indicated that it is possible to predict not only taste but also aroma. Additionally, the generalization performance of the prediction models for sensory evaluation attributes common to both years was verified. Conclusions: These results provide a new option for explaining the sensory characteristics of sake from its components and contribute to a deeper understanding of them. Full article

Background/Objectives: The aim of this study was to explore the effects of diets with different protein levels on the metabolite composition and metabolic pathways of the longest dorsal muscle of Tibetan pigs, in order to provide a metabolic basis for optimizing the nutritional regulation strategy of Tibetan pigs. Methods: A total of 32 healthy 180-day-old depopulated male Tibetan pigs were randomly divided into four groups and fed diets with protein levels of 10%, 12%, 14%, and 16%, respectively, with a feeding cycle of 8 weeks. The longest dorsal muscle samples were collected, and their metabolic profiles were systematically analyzed by LC-MS non-targeted metabolomics. Results: The TIC plots of the quality control samples were highly overlapped, indicating a stable instrumental detection process and good consistency of sample processing. Principal component analysis and orthogonal partial least squares discriminant analysis revealed significant metabolic differences between groups with different protein levels. A total of multiple differential metabolites was obtained based on VIP value and p-value screening, and Venn diagram analysis revealed a total of 11 metabolites among the three comparative groups, suggesting that they may have key roles in the protein regulation process. Volcano plots further clarified the number and trend of significantly up- and down-regulated metabolites in each group. KEGG pathway enrichment analysis showed that, with the elevation of protein level, the metabolic pathway response showed a tendency of expanding from basal energy metabolism to the complex network of amino acid synthesis, steroidogenesis, endocrine signaling, and detoxification pathways, especially in the high-protein-treated group. Conclusions: The study showed that different protein intake levels could significantly regulate the metabolites and key metabolic pathways in the longest muscle of Tibetan pigs, which provided theoretical support for the scientific formulation of a protein supply program to optimize the quality and growth performance of Tibetan pork. Full article

Sugarcane, a key sugar crop in China, is predominantly manually harvested. In the main sugarcane-producing areas of China, typhoons cause canes to become lodged, resulting in high field losses and low harvesting efficiency. This study aimed to reduce these losses by analyzing the causes: ineffective stalk pickup, transfer, and conveyance. The tests showed the stalk–steel static friction coefficient (SFC) was lower than the stalk–soil SFC. Conventional basecutters use raised patterns to enhance friction, but soil adhesion makes them ineffective, hindering lodged stalk pickup. Bent stalks also struggle to enter butt lift rollers or pass through roller trains, increasing losses. The proposed improvements included adding toothed plates on the cutter discs, optimized disc–roller positioning, and using fewer rollers (one butt lift and one feed roller pair). Theoretical analysis confirmed the toothed plates improved pickup via grabbing force, while using fewer rollers stopped the stalks detaching from and blocking the roller train. A prototype was tested via orthogonal experiments, showing a field loss ratio of 1.21%, a feed rate of 13.09 kg/s, and a billet qualification rate of 95.82% with optimal settings (chopper speed: 390 rpm; 10 stalks/group; roller speed: 230 rpm; ground speed: 1.41 m/s). Field tests achieved 2.0% loss, demonstrating effectiveness for severely lodged cane, a significant improvement over the conventional harvesters (15–20% loss). These findings aid low-loss-level harvester development. Full article

The electrocardiogram (ECG), which records variations in surface electrical potential over time, has been widely used in the diagnosis of cardiovascular diseases. In recent years, the artificial intelligence (AI) + ECG paradigm has attracted considerable interest, but the two intrinsic characteristics of the ECG, namely, inter-lead correlations and multi-label classification, are often overlooked. Given that this oversight may constrain the full potential of AI models to enhance diagnostic performance, this study focuses on investigating methods for fusing information from a 12-lead ECG. A series of comprehensive experiments was conducted to evaluate the performance of various lead configurations, that is, 1-, 3-, 6-, 9-, and 12-lead combinations, with different fusion strategies. Innovatively integrating medical theory, we propose a novel five-lead-grouping strategy and develop a neural network architecture named Lead-5-Group Net (L5G-Net). After ranking the 12 leads with the AUC, we found that the aVR, V5, and V6 leads are particularly informative for single-lead ECG diagnosis. Furthermore, in multi-lead ECG classification, adopting an orthogonal lead-selection strategy which is based on the hypothesis of spatial interdependence among ECG leads was shown to enhance performance by ensuring that the information provided by each lead is complementary. Finally, the proposed L5G-Net demonstrates outstanding performance, achieving a macro-AUC of 0.9357 on the PTB-XL multi-label dataset without the use of data augmentation, attention mechanisms, or other strategies. Furthermore, considerable performance gains were observed after the five-lead-grouping strategy was applied to DenseNet and ResNet. These results imply that the proposed strategy can be seamlessly integrated into various network architectures and considerably enhance performance. Full article

Differential privacy (DP) is an important framework to provide strong theoretical guarantees on the privacy and utility of released data. Since its introduction in 2006, DP has been applied to various data types and domains. More recently, the introduction of metric differential privacy has improved the applicability and interpretability of DP in cases where the data resides in more general metric spaces. In metric DP, indistinguishability of data points is modulated by their distance. In this work, we demonstrate how to extend metric differential privacy to datasets representing three-dimensional rotations in SO(3) through two mechanisms: a Laplace mechanism on SO(3), and a novel privacy mechanism based on the Bingham distribution. In contrast to other applications of metric DP to directional data, we demonstrate how to handle the antipodal symmetry inherent in SO(3) while transferring privacy from $S^3$ to SO(3). We show that the Laplace mechanism fulfills $ϵ\varphi$-privacy, where $\varphi$ is the geodesic metric on SO(3), and that the Bingham mechanism fulfills $\widetilde{ϵ}\varphi$-privacy with $\widetilde{ϵ}={\displaystyle \frac{\pi }{4}}ϵ$. Through a simulation study, we compare the distribution of samples from both mechanisms and argue about their respective privacy–utility tradeoffs. Full article

Human personality (HP) is seen as an individual’s consistent patterns of feeling, thinking, and behaving by today’s psychological studies, in which HPs are characterized in terms of traits—in particular, as relatively enduring characteristics that influence human behavior across many situations. In this sense, more generally, artificial personality (AP) is studied in computer science to develop AI agents who should behave more like humans. However, in this paper, we suggest another approach by which the APs of individual agents are distinguishable based on their behavioral characteristics in achieving tasks and not necessarily in their human-like performance. As an initial step toward AP, we propose an approach to extract human decision-making characteristics as a generative resource for encoding the variability in agent personality. Using an application example, we demonstrate the feasibility of grouping APs, divided into several steps consisting of (1) defining a feature space to measure the commonality of decision making between individual and a group of people; (2) grouping APs by using multidimensional orthogonal features in the feature space to guarantee inter-individual differences between APs in achieving for the same task; and (3) evaluating the consistency of grouping APs by performing a cluster-stability analysis. Finally, our thoughts for the future implementation of APs are discussed and presented. Full article

Recently, there has been a growing interest in using agricultural and forestry residues to cultivate Flammulina filiformis. However, there is limited research on cultivating F. filiformis with soybean straw as a substrate. This study systematically optimized the cultivation formula for F. filiformis using soybean straw as the raw substrate and explored the effects of the water content, carbon-to-nitrogen ratio (C/N ratio), substrate particle size, and substrate loading on its growth and development. By replacing corncob, wheat bran, and soybean hulls with soybean straw and increasing the proportion of rice bran, the cultivation formula for growing F. filiformis was optimized. We found that the maximum fruiting body yield of 405 g (330 g dry substrate per bottle) and a biological efficiency of 122.73% were achieved using a substrate mixture of 25% soybean straw, 20% corncob, 20% cottonseed hull, 25% rice bran, 8% wheat bran, 1% CaCO₃, and 1% shellfish powder. The yield and biological efficiency of fruiting bodies cultivated on the substrate containing 25% soybean straw did not show significant differences compared to the control group. However, the cultivation formula containing 25% soybean straw yielded F. filiformis with significantly higher levels of amino acids, essential amino acids, and fat. These findings suggest that the 25% soybean straw substrate formulation can serve as a viable alternative to the control formulation for the cultivation of F. filiformis, although variations in the nutritional composition exist. Based on this optimized formula, an optimal biological efficiency can be achieved with a substrate-to-water ratio of 1:1.7, a wet substrate loading amount of 940 g (in a 1250 mL cultivation bottle), and a soybean straw particle size range of 6–8 mm. The optimal C/N ratio for cultivating F. filiformis using soybean straw ranges from 27:1 to 32:1. Additionally, orthogonal experiments revealed that the nitrogen content significantly affected the fruiting body yield, stipe length, and stipe diameter, while the water content mainly affected the pileus diameter, pileus thickness, and number of fruit bodies. Under defined conditions (dry substrate loading volume of 337 g (in a 1250 mL cultivation bottle), a substrate-to-water ratio of 1:1.6, and a C/N ratio of 26:1), the maximum yield and biological efficiency per bottle reached 395 g and 117.21%, respectively. Our findings indicate that the F. filiformis cultivation using soybean straw as the raw substrate exhibits a promising performance and extensive application potential. Full article