Search Results (189)

Background/Objectives: The complexity of diseases such as cancer and auto-immune disorders drives the need for unique, target-driven therapeutics. A broader arsenal to generate better biologics-based therapeutics is needed to provide more efficient and effective antibody generation technologies. The critical parameter for antibody generation is to generate as much candidate diversity to each target as possible. Method/Results: We present guidelines for having an efficient process using a fully synthetic human single-domain antibody (sdAb) phage display library. Critical milestones for success focused on library quality control (QC) assessments, evaluation of specific biopanning outputs, and construct designs that enabled efficient transition to mammalian expression. The synthetic VHO libraries produced epitope diversity better than an immunized sourced library with candidates possessing nM potencies and monodispersity > 90% via SEC. Conclusions: Synthetic human scaffold sdAb phage display libraries was constructed, biopanned, and selected candidates that could be directly transitioned for mammalian expression. The diverse VHO sets of candidates produced from many targets easily provided opportunities to make a multi-specific biological compound. Both synthetic and immunized phage selection campaign results suggested that these technologies complemented each other to generate therapeutic candidates. Finally, we demonstrated how diverse data produced from a process that used VHO synthetic libraries could accelerate drug discovery. Full article

Current agentic AI frameworks such as LangGraph and AutoGen simulate autonomy via sequential prompt chaining but lack true multi-agent coordination architectures. These systems conflate semantic reasoning with orchestration, requiring LLMs at every coordination step and limiting scalability. By contrast, TB-CSPN (Topic-Based Communication Space Petri Net) is a hybrid formal architecture that fundamentally separates semantic processing from coordination logic. Unlike traditional Petri net applications, where the entire system state is encoded within the network structure, TB-CSPN uses Petri nets exclusively for coordination workflow modeling, letting communication and interaction between agents drive semantically rich, topic-based representations. At the same time, unlike first-generation agentic frameworks, here LLMs are confined to topic extraction, with business logic coordination implemented by structured token communication. This hybrid architectural separation preserves human strategic oversight (as supervisors) while delegating consultant and worker roles to LLMs and specialized AI agents, avoiding the state-space explosion typical of monolithic formal systems. Our empirical evaluation shows that TB-CSPN achieves 62.5% faster processing, 66.7% fewer LLM API calls, and 167% higher throughput compared to LangGraph-style orchestration, without sacrificing reliability. Scaling experiments with 10–100 agents reveal sub-linear memory growth (10× efficiency improvement), directly contradicting traditional Petri Net scalability concerns through our semantic-coordination-based architectural separation. These performance gains arise from the hybrid design, where coordination patterns remain constant while semantic spaces scale independently. TB-CSPN demonstrates that efficient agentic AI emerges not by over-relying on modern AI components but by embedding them strategically within a hybrid architecture that combines formal coordination guarantees with semantic flexibility. Our implementation and evaluation methodology are openly available, inviting community validation and extension of these principles. Full article

Modern aircraft increasingly utilizes highly integrated electronic equipment, driving continuously increasing heat dissipation demands. Vapor compression refrigeration systems demonstrate stronger alignment with future aircraft thermal management trends, leveraging their superior volumetric cooling capacity, high energy efficiency, and independence from engine bleed air. This paper reviews global research progress on aircraft vapor compression refrigeration systems, covering performance optimization, dynamic characteristics, control strategies, fault detection, and international development histories and typical applications. Analysis identifies emerging challenges under large-temperature-range cooling requirements, with comparative assessment establishing zeotropic mixture auto-cascade vapor compression refrigeration systems as the optimal forward-looking solution. Finally, recognizing current research gaps, we propose future research directions for onboard auto-cascade vapor compression refrigeration systems: optimizing refrigerant mixtures for flight conditions, achieving efficient gas-liquid separation during variable overloads and attitude conditions, and developing model predictive control with intelligent optimization to ensure reliability. Full article

Combustion of aviation jet fuel emits a complex mixture of pollutants linked to adverse health outcomes among airport personnel and nearby communities. While epidemiological studies showed the detrimental effects of aviation-derived air pollutants on human health, the molecular mechanisms of the interactions of these pollutants with cellular biomolecules like proteins that drive the adverse health effects remain poorly understood. In this study, we performed molecular docking simulations of 272 pollutant–protein complexes using AutoDock Vina 1.2.7 to characterize the binding strength of the pollutants with the selected proteins. We selected 34 aviation-derived pollutants that constitute three chemical categories of pollutants: volatile organic compounds (VOCs), polyaromatic hydrocarbons (PAHs), and organophosphate esters (OPEs). Each pollutant was docked to eight proteins that play critical roles in endocrine, metabolic, transport, and neurophysiological functions, where functional disruption is implicated in disease. The effect of binding of multiple pollutants was analyzed. Our results indicate that aliphatic and monoaromatic VOCs display low (<6 kcal/mol) binding affinities while PAHs and organophosphate esters exhibit strong (>7 kcal/mol) binding affinities. Furthermore, the binding strength of PAHs exhibits a positive correlation with the increasing number of aromatic rings in the pollutants, ranging from nearly 7 kcal/mol for two aromatic rings to more than 15 kcal/mol for five aromatic rings. Analysis of intermolecular interactions showed that these interactions are predominantly stabilized by hydrophobic, pi-stacking, and hydrogen bonding interactions. Simultaneous docking of multiple pollutants revealed the increased binding strength of the resulting complexes, highlighting the detrimental effect of exposure to pollutant mixtures found in ambient air near airports. We provide a priority list of pollutants that regulatory authorities can use to further develop targeted mitigation strategies to protect the vulnerable personnel and communities near airports. Full article

As the core element of adaptive optical systems, tuneable lenses are essential in adaptive optics. Dielectric elastomer-driven tuneable lenses offer significant advantages in tuning range, response speed, and lightweight design compared to traditional mechanical zoom lenses. This paper systematically reviews the working mechanisms and research advancements of these lenses. Firstly, based on the two driving modes of deformation zoom and displacement zoom, the tuning principle of dielectric elastomer-driven tuneable lenses is analysed in depth. Secondly, the design methodology and current status of the research are systematically elaborated for four typical structures: monolithic, composite, array, and metalenses. Finally, the potential applications of this technology are discussed in the fields of auto-zoom imaging, microscopic imaging, augmented reality display, and infrared imaging, along with an analysis of the key technological challenges faced by this technology, such as material properties, modelling and control, preparation processes, and optical performance. This paper aims to provide a systematic reference for researchers in this field and to help promote the engineering application of dielectric elastomer tuneable lens technology. Full article

Light Detection and Ranging (LiDAR) point clouds are an essential perception modality for artificial intelligence systems like autonomous driving and robotics, where the ubiquity of small objects in real-world scenarios substantially challenges the visual tracking of small targets amidst the vastness of point cloud data. Current methods predominantly focus on developing universal frameworks for general object categories, often sidelining the persistent difficulties associated with small objects. These challenges stem from a scarcity of foreground points and a low tolerance for disturbances. To this end, we propose a deep neural network framework that trains a Siamese network for feature extraction and innovatively incorporates two pivotal modules: the target-awareness prototype mining (TAPM) module and the regional grid subdivision (RGS) module. The TAPM module utilizes the reconstruction mechanism of the masked auto-encoder to distill prototypes within the feature space, thereby enhancing the salience of foreground points and aiding in the precise localization of small objects. To heighten the tolerance of disturbances in feature maps, the RGS module is devised to retrieve detailed features of the search area, capitalizing on Vision Transformer and pixel shuffle technologies. Furthermore, beyond standard experimental configurations, we have meticulously crafted scaling experiments to assess the robustness of various trackers when dealing with small objects. Comprehensive evaluations show our method achieves a mean Success of 64.9% and 60.4% under original and scaled settings, outperforming benchmarks by +3.6% and +5.4%, respectively. Full article

Metabolic enzymes and cancer-driving transcriptions factors are often overexpressed in neoplastic cells, and their exposed cysteine residues are amenable to chemical modification. This review explores cysteine alkylation as a cancer treatment strategy, focusing on Michael acceptors like curcumin and helenalin, which interact with transcription factors NF-κB, STAT3 and HIF-1α. Molecular docking studies using AutoDockFR revealed distinct binding affinities: curcumin showed strong interactions with STAT3 and NF-κB, while helenalin exhibited high affinity for STAT3 and HIF-1α. Synthetic compounds like STAT3-IN-1 and CDDO-Me demonstrated superior binding in most targets, except for CDDO-Me with HIF-1α, suggesting unique structural incompatibilities. Natural products such as zerumbone and umbelliferone displayed moderate activity, while palbociclib highlighted synthetic-drug advantages. These results underscore the importance of ligand−receptor structural complementarity, particularly for HIF-1α’s confined binding site, where helenalin’s terminal Michael acceptor system proved optimal. The findings advocate for integrating computational and experimental approaches to develop cysteine-targeted therapies, balancing synthetic precision with natural product versatility for context-dependent cancer treatment strategies. Full article

Driver factors are the main cause of road traffic accidents. For the research of automotive active safety, an identification method for road hypnosis of a driver of a car with dynamic human–vehicle heterogeneous data fusion calculation is proposed. Road hypnosis is an unconscious driving state formed by the combination of external environmental factors and the psychological state of the car driver. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task. The safety of humans and cars is greatly affected. Therefore, the study of the identification of drivers’ road hypnosis is of great significance. Vehicle and virtual driving experiments are designed and carried out to collect human and vehicle data. Eye movement data and EEG data of human data are collected with eye movement sensors and EEG sensors. Vehicle speed and acceleration data are collected by a mobile phone with AutoNavi navigation, which serves as an onboard sensor. In order to screen the characteristics of human and vehicles related to the road hypnosis state, the characteristic parameters of the road hypnosis in the preprocessed data are selected by the method of independent sample T-test, the hidden Markov model (HMM) is constructed, and the identification of the road hypnosis of the Ridge Regression model is combined. In order to evaluate the identification performance of the model, six evaluation indicators are used and compared with multiple regression models. The results show that the hidden Markov-Ridge Regression model is the most superior in the identification accuracy and effect of the road hypnosis state. A new technical scheme reference for the development of intelligent driving assistance systems is provided by the proposed comprehensive road hypnosis state identification model based on human–vehicle data can provide, which can effectively improve the life recognition ability of automobile intelligent cockpits, enhance the active safety performance of automobiles, and further improve traffic safety. Full article

Cyclin-dependent kinase 1 (CDK1) has emerged as a critical regulator of cell cycle progression, yet its role in liver fibrosis-associated hepatocellular carcinoma (LF-HCC) remains underexplored. This study aimed to systematically evaluate CDK1’s prognostic significance, immune regulatory functions, and therapeutic potential in LF-HCC pathogenesis. Integrated bioinformatics approaches were applied to multi-omics datasets from GEO, TCGA, and TIMER databases. Differentially expressed genes were identified through enrichment analysis and protein–protein interaction networks. Survival outcomes were assessed via Kaplan–Meier analysis, while immune cell infiltration patterns were quantified using CIBERSORT. Molecular docking simulations evaluated CDK1’s binding affinity with pharmacologically active compounds (alvocidib, seliciclib, alsterpaullone) using AutoDock Vina. CDK1 demonstrated significant overexpression in LF-HCC tissues compared to normal controls (p < 0.001). Elevated CDK1 expression correlated with reduced overall survival (HR = 2.41, 95% CI:1.78–3.26, p = 0.003) and advanced tumor staging (p = 0.007). Immune profiling revealed strong associations between CDK1 levels and immunosuppressive cell infiltration, particularly regulatory T cells (r = 0.63, p = 0.001) and myeloid-derived suppressor cells (r = 0.58, p = 0.004). Molecular docking confirmed high-affinity binding of CDK1 to kinase inhibitors through conserved hydrogen-bond interactions (binding energy ≤ −8.5 kcal/mol), with alvocidib showing optimal binding stability. This multimodal analysis establishes CDK1 as both a prognostic biomarker and immunomodulatory regulator in LF-HCC pathogenesis. The enzyme’s dual role in driving tumor progression and reshaping the immune microenvironment positions it as a promising therapeutic target. Computational validation of CDK1 inhibitors provides a rational basis for developing precision therapies against LF-HCC, bridging translational gaps between biomarker discovery and clinical application. Full article

In the context of digital transformation, the varying dimensions of digital maturity significantly influence value creation enhancement for enterprises. Optimizing these dimensions to augment corporate value represents an urgent challenge for manufacturing enterprises. This study examines 355 listed automotive manufacturing enterprises (including auto parts and related businesses) through multi-case analysis, grounded theory, and QCA methodology to investigate the intrinsic mechanisms and pathways linking digital transformation with value enhancement in automotive manufacturing. The sample enterprises were categorized by industry type into capital-intensive, technology-intensive, and labor-technology-intensive manufacturers, and were then further segmented into complete vehicle manufacturers, component manufacturers, and related industry manufacturers. The selection criteria emphasized enterprises with explicit digital transformation strategies, sufficient transformation documentation, complete annual reports, stable core operations, and anomaly-free key data. The key findings include the following: (1) Grounded theory identified service digitalization, environmental digitalization, middleware digitalization, marketing digitalization, and R&D digitalization as critical variables, with enterprise value enhancement requiring multi-dimensional synergies rather than single-factor determinants. (2) Configuration analysis revealed that comprehensive empowerment type (consistency > 0.8, coverage 35.9%) drives high-value enhancement, while service-deficiency, R&D-deficiency, and marketing-deficiency configurations characterize non-high-value scenarios. Service, R&D, and marketing digitalization emerge as core-value-enhancing competencies (consistency 0.817, coverage 75.9%). (3) Heterogeneous driving forces were observed across vehicle manufacturers, component manufacturers, and related industry manufacturers, though service digitalization constitutes a common-value-enhancing element. This research provides theoretical insights into manufacturing digital transformation’s value creation mechanisms and strategic implications, addressing current academic gaps. However, the automotive industry focus limits generalizability despite its concrete exploration of industry-specific digital transformation. Future studies should expand industry coverage and conduct comparative analyses to enhance theoretical robustness. Full article

Human factors are the most important factor in road traffic crashes. Human-caused traffic crashes can be reduced through the active safety system of vehicles. Road hypnosis is an unconscious driving state caused by the combination of external environmental factors and the driver’s psychological state. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task, and driving safety is greatly affected. Therefore, road hypnosis identification is of great significance for the active safety of vehicles. A road hypnosis identification model based on XGBoost—Hidden Markov is proposed in this study. Driver data and vehicle data related to road hypnosis are collected through the design and conduct of vehicle driving experiments. Driver data, including eye movement data and EEG data, are collected with eye movement sensors and EEG sensors. A mobile phone with AutoNavi navigation is used as an on-board sensor to collect vehicle speed, acceleration, and other information. Power spectrum density analysis, the sliding window method, and the point-by-point calculation method are used to extract the dynamic characteristics of road hypnosis, respectively. Through normalization and standardization, the key features of the three types of data are integrated into unified feature vectors. Based on XGBoost and the Hidden Markov algorithm, a road hypnotic identification model is constructed. The model is verified and evaluated through visual analysis. The results show that the road hypnosis state can be effectively identified by the model. The extraction of road hypnosis-related features is realized in non-fixed driving routes in this study. A new research idea for road hypnosis and a technical scheme reference for the development of intelligent driving assistance systems are provided, and the life identification ability of the vehicle intelligent cockpit is also improved. It is of great significance for the active safety of vehicles. Full article

This paper investigates the memory of the Ornstein–Uhlenbeck process (OUP) via three ratios of the OUP increments: signal-to-noise, noise-to-noise, and tail-to-tail. Intuition suggests the following points: (1) changing the noise that drives the OUP from Gauss to Levy will not affect the memory, as both noises share the common ‘independent increments’ property; (2) changing the auto-correlation of the OUP from exponential to slowly decaying will affect the memory, as the change yields a process with long-range correlations; and (3) with regard to Levy driving noise, the greater the noise fluctuations, the noisier the prediction of the OUP increments. This paper shows that intuition is plain wrong. Indeed, a detailed analysis establishes that for each of the three above-mentioned points, the very converse holds. Hence, Levy noise has a significant and counter-intuitive effect on Ornstein–Uhlenbeck memory. Full article

In advanced driver-assistance systems (ADASs), the misalignment of the mounting angle of the automotive radar significantly affects the accuracy of object detection and tracking, impacting system safety and performance. This paper introduces the Automotive Radar Alignment Detection Network (AutoRAD-Net), a novel model that leverages complex-valued convolutional neural network (CV-CNN) to address azimuth misalignment challenges in automotive radars. By utilizing complex-valued inputs, AutoRAD-Net effectively learns the physical properties of the radar data, enabling precise azimuth alignment. The model was trained and validated using mounting angle offsets ranging from −3° to +3° and exhibited errors no greater than 0.15° across all tested offsets. Moreover, it demonstrated reliable predictions even for unseen offsets, such as −1.7°, showcasing its generalization capability. The predicted offsets can then be used for physical radar alignment or integrated into compensation algorithms to enhance data interpretation accuracy in ADAS applications. This paper presents AutoRAD-Net as a practical solution for azimuth alignment, advancing radar reliability and performance in autonomous driving systems. Full article

To address poor real-time performance and low accuracy in car-following risk identification, a model based on autoencoders is proposed. Using the SHRP2 natural driving dataset, this paper constructs a car-following risk identification model in two stages. In Stage 1, a deep feedforward neural network autoencoder reconstructs preprocessed multi-source heterogeneous indicators of human-vehicle-road-environment. The high-dimensional latent space feature representation is used as input for Stage 2, enhancing the basic model’s performance. Eight basic models and sixteen models with autoencoders are compared using multiple evaluation indicators. A simulated driving test verifies the model’s generalization and robustness. Results show improved accuracy in car-following risk identification, with the optimized AutoEncoder_LR performing best at 91.33% for risk presence and 70.14% for risk levels. These findings can aid in safe driving and rear-end accident prevention. Full article

Recent improvements in self-driving and connected cars promise to enhance traffic safety by reducing risks and accidents. However, security concerns limit their acceptance. These vehicles, interconnected with infrastructure and other cars, are vulnerable to cyberattacks, which could lead to severe costs, including physical injury or death. In this article, we propose a framework for an intrusion detection system to protect internal vehicle communications from potential attacks and ensure secure sent/transferred data. In the proposed system, real auto-network datasets with Spoofing, DoS, and Fuzzy attacks are used. To accurately distinguish between benign and malicious messages, this study employed seven distinct supervised machine-learning algorithms for data classification. The selected algorithms encompassed Decision Trees, Random Forests, Naive Bayes, Logistic Regression, XG Boost, LightGBM, and Multi-layer Perceptrons. The proposed detection system performed well on large real-car hacking datasets. We achieved high accuracy in identifying diverse electronic intrusions across the complex internal networks of connected and autonomous vehicles. Random Forest and LightGBM outperformed the other algorithms examined. Random Forest outperformed the other algorithms in the merged dataset trial, with 99.9% accuracy and the lowest computing cost. The LightGBM algorithm, on the other hand, performed admirably in the domain of binary classification, obtaining the same remarkable 99.9% accuracy with no computing overhead. Full article