Search Results (56)

This study examines the relationship between learning environments and learning outcomes in acquiring Korean as a language target. We compare two learner groups residing in the United States: English-speaking learners of Korean in foreign language contexts versus Korean heritage speakers. Both groups share English as their dominant language and receive similar tertiary-level instruction, yet differ in their language-learning profiles. We measure two groups’ comprehension behaviour involving Korean subject−predicate honorific agreement, focusing on two conditions manifesting a mismatch between the honorifiable status of a subject and the realisation of the honorific suffix in a predicate. Results from the acceptability judgement task revealed that (1) both learner groups rated the ungrammatical condition as more acceptable than native speakers did, (2) Korean heritage speakers rated the ungrammatical condition significantly lower than English-speaking learners, and (3) overall proficiency in Korean modulated learners’ evaluations of the ungrammatical condition in opposite directions between the groups. No between-group difference was found in the infelicitous-yet-grammatical condition. Results from reaction time measurement further showed that Korean heritage speakers responded considerably faster than English-speaking learners of Korean. These results underscore the critical role of broad usage experience—whether through home language exposure for heritage language speakers or formal instruction for foreign language learners—in shaping non-dominant language activities. Full article

In addressing the design imperatives of automotive headlight miniaturization and energy conservation, this paper puts forth a design methodology for vehicle lighting systems that is predicated on free surface optics and an intelligent optimization algorithm. The establishment of the energy mapping relationship between the light source surface and the target surface is predicated on relevant performance standards. The numerical calculation is then integrated with MATLAB R2022a to obtain the free-form surface coordinate points and establish a three-dimensional model. To optimize the parameter design, a genetic algorithm is employed to fine-tune the design parameter ${\theta }_{max}$, thereby attaining the optimal ${\theta }_{max}$ that strikes a balance between volume and luminous efficiency. The experimental results demonstrate that by integrating the optimal incidence angle into the design of the high beam and low beam, the final simulation results show that the optical efficiency of the low beam is 88.89%, and the optical efficiency of the high beam is 89.40%. This enables the automotive headlamp system to achieve a balance between volume and luminous efficiency. The free-form lamp design framework proposed in this study provides a reference for the compact design and intelligent optimization of the lamp system. Full article

In the context of automatic generation methods for tolerance specifications, the current approach entails designers subjectively selecting geometric tolerances suitable for annotation from the tolerance types generated through automatic reasoning. This process is susceptible to the occurrence of mismatches between tolerance types and product functionality. To address this issue, this paper proposes a similarity calculation and evaluation method for screening tolerance types, which retrieves tolerance types that meet design requirements from resource cases. The proposed method commences with the establishment of a case data structure, predicated on the tolerance design problem. This structure incorporates features for similarity comparison, encompassing tolerance element characteristics that determine tolerance types, part component element characteristics, topological relationship features of component elements, and usage performance. Concurrently, past tolerance specification problems and their design solutions are defined as resource cases, while new tolerance specification problems are defined as target cases. Subsequently, analogous elements for similarity assessment are identified in both resource and target cases. Utilizing these elements, a similarity calculation method is devised, based on feature sets, to compute the similarity between the target case and previous cases. Finally, through case implementation and comparative analysis, the proposed similarity evaluation method is verified to have significant advantages in case discrimination ability. Full article

In the domain of autonomous driving systems, vehicle trajectory prediction represents a critical aspect, as it significantly contributes to the safe maneuvering of vehicles within intricate traffic environments. Nevertheless, a preponderance of extant research efforts have been chiefly centered on the spatio-temporal relationships intrinsic to the vehicle itself, thereby exhibiting deficiencies in the dynamic perception of and interaction capabilities with adjacent vehicles. In light of this limitation, we propose a vehicle trajectory prediction algorithm predicated on a hybrid prediction model. Initially, the algorithm extracts pertinent context information pertaining to the target vehicle and its neighboring vehicles through the application of a two-layer long short-term memory network. Subsequently, a fusion module is deployed to assimilate the characteristics of the temporal influence, spatial influence, and interactive influence of the surrounding vehicles, followed by the integration of these attributes. Ultimately, the prediction module is engaged to yield the predicted movement positions of the vehicles, expressed in coordinate form. The proposed algorithm was trained and validated using the publicly accessible datasets I-80 and US-101. The experimental results demonstrate that our proposed algorithm is capable of generating more precise prediction results. Full article

Background: Hepatocellular carcinoma (HCC), a prevalent form of primary liver malignancy, arises from liver-specific hepatocytes. Senecio scandens Buch.-Ham(Climbing senecio) is a bitter-tasting plant of the Compositae family with anti-tumor properties. This study aims to identify the molecular targets and pathways through which Climbing senecio regulates HCC. Methods: Active ingredients of Climbing senecio were collected from four online databases and mapped to relevant target databases to obtain predicted targets. After recognizing the key pathways through which Climbing senecio acts in HCC. Gene expression data from GSE54238 Underwent differential expression and weighted gene correlation network analyses to identify HCC-related genes. The “Climbing senecio-Hepatocellular Carcinoma Targets” network was constructed using Cytoscape 3.10.1 software, followed by topology analysis to identify core genes. The expression and distribution of key targets were evaluated, and the differential expression of each key target between normal and diseased samples was calculated. Moreover, single-cell data from the Gene Expression Omnibus (GSE202642) were used to assess the distribution of Climbing senecio’s bioactive targets within major HCC clusters. An intersection analysis of these clusters with pharmacological targets and HCC-related genes identified Climbing senecio’s primary targets for this disease. Cell communication, receiver operating characteristic (ROC)analysis, survival analysis, immune filtration analysis, and molecular docking studies were conducted for detailed characterization. Results: Eleven components of Climbing senecio were identified, along with 520 relevant targets, 300 differentially expressed genes, and 3765 co-expression module genes associated with HCC. AKR1B1, CA2, FOS, CXCL2, SRC, ABCC1, and PLIN1 were identified within the intersection of HCC-related genes and Climbing senecio targets. TGFβ, IL-1, VEGF, and CXCL were identified as significant factors in the onset and progression of HCC. These findings underscore the anti-HCC potential and mode of action of Climbing senecio, providing insights into multi-targeted treatment approaches for HCC. Conclusions: This study revealed that Climbing senecio may target multiple pathways and genes in the process of regulating HCC and exert potential drug effects through a multi-target mechanism, which provides a new idea for the treatment of HCC. However, the research is predicated on network database analysis and bioinformatics, offering insights into HCC therapeutic potential while emphasizing the need for further validation. Full article

The effective discrimination of targets from backgrounds in environments characterized by a low signal-to-clutter ratio (SCR) is paramount for the advancement of infrared small target detection (IRSTD). In this work, we propose a novel detection framework predicated on low-rank sparse decomposition (LRSD), incorporating an improved logarithmic norm and a mechanism for sparse structure refinement, herein referred to as the improved logarithmic norm and sparse structure refinement (ILN-SSR). The ILN-SSR framework more precisely characterizes the sparse properties of both the background and the target, enabling a more effective distinction between the target and its background. Initially, our approach entails the utilization of an improved logarithmic norm to precisely estimate the low-rank attributes of the infrared image background. This is followed by the employment of a linear sparse regularization term alongside a target-traits-based sparse regularization term aimed at meticulously identifying targets within sparse regions and refining the sparse structure. Subsequently, we combine these components into the ILN-SSR framework, which formulates IRSTD as an optimization problem. The resolution of this framework is achieved through the implementation of the alternating direction method of multipliers (ADMM). The efficacy of the proposed framework is corroborated through the analysis of six image sequences. Comprehensive experimental assessments affirmed the framework’s substantial robustness in navigating various complex backgrounds. Full article

Accurately quantifying flora and their respective anatomical structures within natural ecosystems is paramount for both botanical breeders and agricultural cultivators. For breeders, precise plant enumeration during the flowering phase is instrumental in discriminating genotypes exhibiting heightened flowering frequencies, while for growers, such data inform potential crop rotation strategies. Moreover, the quantification of specific plant components, such as flowers, can offer prognostic insights into the potential yield variances among different genotypes, thereby facilitating informed decisions pertaining to production levels. The overarching aim of the present investigation is to explore the capabilities of a neural network termed GhP2-YOLO, predicated on advanced deep learning techniques and multi-target tracking algorithms, specifically tailored for the enumeration of rapeseed flower buds and blossoms from recorded video frames. Building upon the foundation of the renowned object detection model YOLO v8, this network integrates a specialized P2 detection head and the Ghost module to augment the model’s capacity for detecting diminutive targets with lower resolutions. This modification not only renders the model more adept at target identification but also renders it more lightweight and less computationally intensive. The optimal iteration of GhP2-YOLOm demonstrated exceptional accuracy in quantifying rapeseed flower samples, showcasing an impressive mean average precision at 50% intersection over union metric surpassing 95%. Leveraging the virtues of StrongSORT, the subsequent tracking of rapeseed flower buds and blossom patterns within the video dataset was adeptly realized. By selecting 20 video segments for comparative analysis between manual and automated counts of rapeseed flowers, buds, and the overall target count, a robust correlation was evidenced, with R-squared coefficients measuring 0.9719, 0.986, and 0.9753, respectively. Conclusively, a user-friendly “Rapeseed flower detection” system was developed utilizing a GUI and PyQt5 interface, facilitating the visualization of rapeseed flowers and buds. This system holds promising utility in field surveillance apparatus, enabling agriculturalists to monitor the developmental progress of rapeseed flowers in real time. This innovative study introduces automated tracking and tallying methodologies within video footage, positioning deep convolutional neural networks and multi-target tracking protocols as invaluable assets in the realms of botanical research and agricultural administration. Full article

Barley is indeed a versatile cereal crop, valued for its uses in food, animal feed, and increasingly in biofuel production. As interest grows in developing new barley genotypes that are better adapted to diverse environmental conditions and production systems, integrating agro-morphological evaluations with molecular marker analyses in barley breeding programs is essential for developing new genotypes. It is necessary to explore the genetic diversity of those germplasm to predicate their responses to targeted environments and regions. The current study explored the phenotypic and genotypic relations among Saudi advanced germplasm to facilitate the development of superior barley cultivars suitable for desert environments. Molecular microsatellites (SSR) markers revealed considerable wide genetic variation among Saudi germplasm and checks. Population structure analyses revealed four main groups. Those groups were validated using similarity analyses and coefficients. As well, principal components analysis (PCA) and heat map analyses separated the studied genotypes into four main groups. The improved Saudi germplasm, selected from the barley breeding program, revealed considerably wide genetic and phenotypic diversities, indicating the feasibility of selection to improve for semi-arid conditions. The improved line KSU-BR-C/G-2 had the highest grain yield and harvest index in the first season. Rihana/Lignee was followed by the KSU-BR-C/G-2 genotype, with a grain yield averaging 6734.07 (kg ha⁻¹), in the first season. KSU-BR-88-29-10 yielded 20,000 kg ha⁻¹ for biomass yield. In the second year, KSU-BR-30-7 had the highest biomass yield, with 27,037.04 kg ha⁻¹. Full article

Large Language Models (LLMs), such as ChatGPT, encounter ‘jailbreak’ challenges, wherein safeguards are circumvented to generate ethically harmful prompts. This study introduces a straightforward black-box method for efficiently crafting jailbreak prompts that bypass LLM defenses. Our technique iteratively transforms harmful prompts into benign expressions directly utilizing the target LLM, predicated on the hypothesis that LLMs can autonomously generate expressions that evade safeguards. Through experiments conducted with ChatGPT (GPT-3.5 and GPT-4) and Gemini-Pro, our method consistently achieved an attack success rate exceeding 80% within an average of five iterations for forbidden questions and proved to be robust against model updates. The jailbreak prompts generated were not only naturally worded and succinct, but also challenging to defend against. These findings suggest that the creation of effective jailbreak prompts is less complex than previously believed, underscoring the heightened risk posed by black-box jailbreak attacks. Full article

The attention mechanism performs well for the Neural Machine Translation (NMT) task, but heavily depends on the context vectors generated by the attention network to predict target words. This reliance raises the issue of long-term dependencies. Indeed, it is very common to combine predicates with postpositions in sentences, and the same predicate may have different meanings when combined with different postpositions. This usually poses an additional challenge to the NMT study. In this work, we observe that the embedding vectors of different target tokens can be classified by part-of-speech, thus we analyze the Natural Language Processing (NLP) related Content-Adaptive Recurrent Unit (CARU) unit and apply it to our attention model (CAAtt) and embedding layer (CAEmbed). By encoding the source sentence with the current decoded feature through the CARU, CAAtt is capable of achieving translation content-adaptive representations, which attention weights are contributed and enhanced by our proposed $L_1\left(exp\left(Nx\right)\right)$ normalization. Furthermore, CAEmbed aims to alleviate long-term dependencies in the target language through partial recurrent design, performing the feature extraction in a local perspective. Experiments on the WMT14, WMT17, and Multi30k translation tasks show that the proposed model achieves improvements in BLEU scores and enhancement of convergence over the attention-based plain NMT model. We also investigate the attention weights generated by the proposed approaches, which indicate that refinement over the different combinations of adposition can lead to different interpretations. Specifically, this work provides local attention to some specific phrases translated in our experiment. The results demonstrate that our approach is effective in improving performance and achieving a more reasonable attention distribution compared to the state-of-the-art models. Full article

In order to enhance the predictive accuracy and control capabilities pertaining to low- and medium-frequency road noise in automotive contexts, this study introduces a methodology for Structural-borne Road Noise (SRN) prediction and optimization. This approach relies on a multi-level target decomposition and a hybrid model combining Convolutional Neural Network (CNN) and Support Vector Regression (SVR). Initially, a multi-level target analysis method is proposed, grounded in the hierarchical decomposition of vehicle road noise along the chassis parts, delineated layer by layer, in accordance with the vibration transmission path. Subsequently, the CNN–SVR hybrid model, predicated on the multi-level target framework, is proposed. Notably, the hybrid model exhibits a superior predictive accuracy exceeding 0.97, surpassing both traditional CNN and SVR models. Finally, the method and model are deployed for sensitivity analysis of chassis parameters in relation to road noise, as well as for the prediction and optimization analysis of SRN in vehicles. The outcomes underscore the high sensitivity of parameters such as the dynamic stiffness of the rear axle bushing and the large front swing arm bushing influencing SRN. The optimization results, facilitated by the CNN–SVR hybrid model, align closely with the measured outcomes, displaying a negligible relative error of 0.82%. Furthermore, the measured results indicate a noteworthy enhancement of 4.07% in the driver’s right-ear Sound Pressure Level (SPL) following the proposed improvements compared to the original state. Full article

The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data. Full article

Diabetes is an independent risk factor for cognitive impairment, with the latter presenting challenges for diabetes self-management and glycemic control in individuals with type 2 diabetes. Predicated on the theory of unpleasant symptoms, the purpose of the current narrative review of the literature was to identify etiologic factors that influence cognitive impairment as a precursor to dementia in individuals with diabetes. Physiological, psychological, and situational factors were recognized as important life course components of cognitive impairment in later adulthood. Developing interventions targeting modifiable factors is warranted in preventing cognitive impairment in adults with diabetes. Full article

Bladder cancer is one of the most commonly diagnosed genitourinary malignancies. For many years, the primary treatment for metastatic urothelial cancer (mUC) was predicated on the use of platinum-based chemotherapy. More recently, immune checkpoint inhibitors (ICIs) were approved by regulatory agencies such as the US FDA for use in both the first- and second-line settings. This review outlines the approved ICIs for mUC in the second-line setting and as an alternative to chemotherapy in the first-line setting, as well as the novel agents that have also been incorporated into the treatment of this malignancy. Single-agent ICIs are often used in second-line settings in mUC, and there are three drugs currently approved for those who progress after receiving platinum-based chemotherapy. In the first-line setting, the preferred treatment regimen remains cisplatin-based chemotherapy. However, single-agent ICI can be an alternative first-line treatment for those who are not candidates for cisplatin-based therapy. There are also clinical trials adding ICIs to chemotherapy as combination regimens. However, treatment for mUC has now expanded even beyond immunotherapy. Newer targeted agents such as erdafitinib, a fibroblast growth factor receptor inhibitor, and two antibody–drug conjugates, enfortumab vedotin and sacituzumab govitecan, have been recently approved. As new drug agents are discovered, it will be important to assess both the treatment outcomes as well as the effects on patients’ quality of life. Furthermore, integrating genetic and molecular information can help guide treatment decisions as next-generation sequencing is more commonly acquired during the evaluation of newly diagnosed patients with advanced and metastatic cancer. Full article

To conduct an accurate and reliable airport delay prediction will provide an important basis for the macro control of an airspace delay situation and the dynamic allocation of airspace system capacity balance. Accordingly, a method of delay prediction for target airports based on the spatio-temporal delay variables of adjacent airports is proposed in this paper. First, by combining the complex network theory, we first extract the topology of the airport network and create airport clusters with comparable network properties. Second, we develop the TS-BiLSTM-Attention mode to predict the delay per hour for airports in the cluster. As the spatio-temporal feature variables, the arrival delay of airport cluster-associated airports and the delay time series of landing airports are utilized to reach the conclusion. The experimental results indicate that the delay prediction predicated on clusters is superior to that based on data from a single airport. This demonstrates that the delay propagation law derived from cluster data based on spatio-temporal feature extraction can generalize the delay propagation characteristics of airports within clusters. Full article