Dual Mechanism Research Articles

Integrating Blockchain and Homomorphic Encryption to Enhance Security and Privacy in Project Management and Combat Counterfeit Goods in Global Supply Chain Operations

The proliferation of counterfeit goods and data privacy concerns in global supply chain operations poses a significant risk to organizations and end-users alike. This paper explores the integration of blockchain technology and homomorphic encryption as dual mechanisms to enhance security and privacy in project management and supply chain operations. Blockchain offers an immutable, decentralized ledger that provides traceability and authenticity across supply chain nodes, while homomorphic encryption enables secure data processing and analysis without compromising sensitive information. The synergy of these technologies presents a robust framework to address counterfeit risks, facilitating end-to-end product authentication and allowing for secure, real-time collaboration in project management settings. This integration minimizes vulnerabilities in data handling and improves compliance with privacy regulations by securing data at rest and in transit. By evaluating case studies and recent advancements, this paper highlights the potential of blockchain and homomorphic encryption to redefine security protocols, fostering a trustworthy and resilient supply chain ecosystem. Future research directions are recommended to expand the practical applications and address scalability challenges associated with these emerging technologies.

A Magnetic Climbing Robot for Steel Bridge Inspection

Abstract. The maintenance of traditional steel bridges presents many significant challenges due to the fact that bridges of different ages and standards vary in design, structural complexity and technical difficulty. In light of these considerations, it is clear that maintenance personnel must possess a wealth of professional expertise and experience in order to effectively address the diverse and intricate maintenance challenges that arise. Moreover, the maintenance process entails high-risk operations such as high-altitude work and welding, which pose a threat to the safety of construction personnel. Therefore, this study introduces a magnetic adsorption robot designed to address the low efficiency and high costs of traditional maintenance methods. This robot features a dual robotic arm mechanism and modeled using the Denavit Hartenberg (D-H) parameter method. Electromagnetic adsorption is utilized to achieve wall adhesion, and direct power supply is provided through DC power to control weight. The robot can perform flexible remote control operations through the HC-05 Bluetooth module. The experimental results show that the robot exhibits robust adhesion and obstacle crossing ability on steel surfaces, thereby enabling unimpeded movement within the steel structure. In addition, the robot is now capable of performing overhaul procedures via remote control.

FinSafeNet: securing digital transactions using optimized deep learning and multi-kernel PCA(MKPCA) with Nyström approximation.

With the swift advancement of technology and growing popularity of internet in business and communication, cybersecurity posed a global threat. This research focuses new Deep Learning (DL) model referred as FinSafeNet to secure loose cash transactions over the digital banking channels. FinSafeNet is based on a Bi-Directional Long Short-Term Memory (Bi-LSTM), a Convolutional Neural Network (CNN) and an additional dual attention mechanism to study the transaction data and influence the observation of various security threats. One such aspect is, relying these databases in most of the cases imposes a great technical challenge towards effective real time transaction security. FinSafeNet draws attention to the attack and reproductive phases of Hierarchical Particle Swarm Optimization (HPSO) feature selection technique simulating it in a battle for extreme time performance called the Improved Snow-Lion optimization Algorithm (I-SLOA). Upon that, the model then applies the Multi-Kernel Principal Component Analysis (MKPCA) accompanied by Nyström Approximation for handling the MKPCA features. MKPCA seeks to analyze and understand a non-linear structure of data whereas, Nyström Approximation reduces the burden on computational power hence allows the model to work in situations where large sizes of datasets are available but with no loss of efficiency of the model. This causes FinSafeNet to work easily and still be able to make accurate forecasts. Besides tackling feature selection and dimensionality reduction, the model presents advanced correlation measures as well as Joint Mutual Information Maximization to enhance variable correlation analysis. These improvements further help the model to detect the relevant features in transaction data that may present a threat to the security of the system. When tested on commonly used database for testing banks performance, for instance the Paysim database, FinSafeNet significantly improves upon the previous and fundamental approaches, achieving accuracy of 97.8%.

Treatment of Conjunctival Melanoma Cell Lines With a Light-Activated Virus-Like Drug Conjugate Induces Immunogenic Cell Death.

Conjunctival melanoma (CJM) is a rare malignant ocular surface tumor, which often leads to local recurrences and metastases. In murine models of subcutaneous tumors, treatment with a novel virus-like drug conjugate (VDC; Bel-sar) showed a dual mechanism of action with direct tumor cell killing as well as stimulation of an antitumoral immune response. Bel-sar is currently being evaluated for the treatment of primary uveal melanoma and indeterminate nevi in a phase III clinical trial. We determined whether Bel-sar also has direct antitumor efficiency and a potential immunostimulatory capacity in CJM cells. Three human tumor-derived CJM lines were used. Bel-sar's subcellular and intracellular locations were determined with tracers. Following light activation of Bel-sar, cytotoxicity and exposure of damage-associated molecular patterns (DAMPs) were assessed. Treated tumor cells were co-cultured with THP-1 derived macrophages to assess tumor-cell phagocytosis. Bel-sar was bound and internalized by CJM cells and subsequently found in the cell membrane, lysosome, Golgi apparatus, and mitochondria. Bel-sar activation induced near complete cell death with half-maximal inhibitory concentration (IC50) values between 30 pM and 60 pM. Finally, light-activated Bel-sar enhanced exposure of DAMPs, including calreticulin, heat shock protein 90, and stimulated phagocytosis by macrophages. Treatment with a novel VDC (Bel-sar) induced pro-immunogenic cell death in all three CJM cell lines. The in vitro cytotoxicity was accompanied by exposure of DAMPs, suggesting Bel-sar is a potential treatment for CJM by a dual mechanism of action. This dual mechanism may provide a targeted and direct killing of tumor cells and induce an immune response which might decrease local recurrences and metastasis.

Overcoming clay structure challenges in lithium recovery from boron waste using high‐temperature pressure acid leaching

AbstractBoron mines contain significant amounts of lithium along with boron. After boron is extracted, lithium remains in the waste, which has a carbonate‐hosted clay‐type structure, along with other impurities. The scarcity of lithium resources and the increasing need for lithium worldwide make such resources economically important. Although the best hydrometallurgical method for the recovery of lithium trapped within the clay‐structured mineral resources is roasting with chemicals to disrupt the clay structure and acid leaching, the process is quite difficult and costly due to the high energy and chemical addition requirements. To overcome this challenge, this study proposed a high‐temperature–pressure sulphuric acid leaching process to recover lithium from the boron waste. Under the optimized conditions (liquid/solid ratio: 10, acid concentration: 1 M, temperature: 150°C, and contact time: 120 min), 100% of lithium was leached. The leaching mechanism was determined through mineral characterization (X‐ray diffractometry [XRD], X‐ray fluorescence spectrophotometer [XRF], scanning electron microscopy–energy‐dispersive X‐ray spectroscopy [SEM–EDX], Mastersizer), and a shrinking core heterogeneous kinetics model. It was found that high‐temperature–pressure sulphuric acid leaching disrupted clay structure and promoted the leaching of lithium, the leaching kinetics fit the shrinking core heterogeneous kinetics model, and was controlled by a dual mechanism with ash diffusion and chemical reactions on the particle surface. The reaction rate constants increased with increasing temperature, and the activation energy was found to be 32.17 kJ/mol.

Mimicking somatic behavior of neurons using the integrate and fire model of 2D SnS memristive switching characteristics

This Letter presents the fabrication and characterization of a 2D SnS memristor, proposing its integrate and fire (I&F) model as a potential hardware implementation of neuronal somatic behavior. The memristor comprises a thin layer of tin (II) sulfide (SnS) sandwiched between copper (Cu) electrodes on a silicon (Si) substrate. This structure exhibits an impressive Roff:Ron ratio of 103 at a read voltage Vrd of 0.25 V with exceptionally low switching Vsw and set Vset voltages of 0.3 and 0.35 V, respectively, with ∼3 order variation between the maximum Rmax and Rmin resistances offered during single voltage sweep cycle. We have explained the memristive behavior using the dual ionic conduction mechanism in the SnS active layer. We extracted the real-time band diagram of SnS using ultraviolet photoelectron spectroscopy, explaining the low Vsw observed. We propose that the emulation of the I&F artificial neuron model exhibited by the fabricated device could serve as a promising application in the field of artificial neuron spiking.

Exaptation of an evolutionary constraint enables behavioural control over the composition of secreted venom in a giant centipede.

Venoms are biochemical arsenals that have emerged in numerous animal lineages, where they have co-evolved with morphological and behavioural traits for venom production and delivery. In centipedes, venom evolution is thought to be constrained by the morphological complexity of their venom glands due to physiological limitations on the number of toxins produced by their secretory cells. Here we show that the uneven toxin expression that results from these limitations have enabled Scolopendra morsitans to regulate the composition of their secreted venom despite the lack of gross morphologically complex venom glands. We show that this control is probably achieved by a combination of this heterogenous toxin distribution with a dual mechanism of venom secretion that involves neuromuscular innervation as well as stimulation via neurotransmitters. Our results suggest that behavioural control over venom composition may be an overlooked aspect of venom biology and provide an example of how exaptation can facilitate evolutionary innovation and novelty.

Romosozumab for the treatment of osteoporosis - a systematic review.

Romosozumab, a new treatment of osteoporosis, is a monoclonal antibody that targets sclerostin and thereby exhibits a dual mechanism of action by stimulating bone formation and inhibiting bone resorption. This systematic review aims to assess the clinical efficacy and safety of romosozumab for treatment of primary and secondary osteoporosis. A comprehensive literature search was conducted in October 2023 across multiple databases including Embase, PubMed and Cochrane Library. Randomized controlled trials (RCTs) and observational studies evaluating the impact of romosozumab on BMD, bone turnover markers (BTM), fracture outcomes, and its safety profile were included. Data extraction and quality assessment were performed independently by two reviewers in accordance with PRISMA guidelines. A total of 36 articles met the inclusion criteria. Romosozumab significantly increased BMD at the lumbar spine, total hip, and femoral neck compared to placebo and active comparators in patients with primary osteoporosis. Sequential therapy with romosozumab followed by antiresorptives maintained or further increased BMD and reduced fracture risk. Romosozumab was generally well tolerated, however, an imbalance in cardiovascular adverse event was observed in one large clinical trial. Observational studies supported these findings. Specific subgroups of patients with secondary osteoporosis were assessed, demonstrating overall positive outcomes with romosozumab treatment. Romosozumab effectively increases BMD and reduces fracture risk, particularly when used as initial therapy in high fracture-risk patients. Sequential therapy with subsequent antiresorptive treatment optimizes long-term benefits. While generally well-tolerated, its cardiovascular safety profile requires further long-term studies to ensure its safety in clinical practice. Additional studies are needed to confirm efficacy and safety in patients with secondary osteoporosis.

GWO-ANFIS-RD3PG: A reinforcement learning approach with dynamic adjustment and dual replay mechanism for building energy forecasting

Reliable prediction of building energy consumption is essential for effective and sustainable energy management. Traditional forecasting methods, however, face challenges when dealing with sudden changes in energy consumption. To address the above-mentioned issue, we introduce the GWO-ANFIS-RD3PG prediction model in this paper. This model aims to ensure global forecasting accuracy while minimizing prediction errors at sudden change points. A Grey Wolf Optimization (GWO) algorithm with the Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to predict the energy consumption type and its fluctuation magnitude for the next time step. Within the Recurrent Dual Experience Replay Buffer DDPG (RD3PG) prediction module, we employ Long Short-Term Memory (LSTM), as the actor network in the Deep Deterministic Policy Gradient (DDPG), to account for long-term dependencies in time series data. Notably, we introduce an adaptive action modification mechanism that allows the agent to optimize actions based on the output of the aforementioned ANFIS, enabling real-time adjustments during sudden change. To further enhance the model’s performance, we adopt a dual experience replay mechanism to store data samples from sudden change points, capturing insight information during these anomalies. Experimental results on two real-world datasets demonstrate that this method reduces MAE by 5.81% and 13.47%, and RMSE by 5.69% and 15.21% compared to the state-of-art models, showing the significance of the proposed method for energy efficiency improvement in real-world building operations.

The non-canonical RNA binding protein RAN stabilizes the mRNA of intranuclear stress granule assembly factor G3BP1 in nasopharyngeal carcinoma

RNA binding proteins play critical roles in tumor progression by participating in the post-transcriptional regulation of RNA. However, the levels and function of RBPs in nasopharyngeal carcinoma (NPC) remain elusive. Here we identified a non-canonical RNA binding protein RAN that has the most significant role in NPC progression by a small siRNA pool screening. Functionally, RAN facilitates NPC proliferation and metastasis in vitro and in vivo. High levels of RAN are associated with poor prognosis of NPC patients and can be performed as a prognostic biomarker. Mechanistically, RAN increases the nucleus import of TDP43 and enhances TDP43 nuclear distribution. On the other hand, RAN is directly bound to the coding sequence of G3BP1 mRNA and serves as an adapter to facilitate TDP43 interacting with G3BP1 mRNA 3′ untranslated region. These contribute to increasing G3BP1 mRNA stability in the nucleus and lead to up-regulation of G3BP1, which further enhances AKT and ERK signaling and ultimately promotes NPC proliferation and metastasis. These findings reveal that RAN stabilizes intranuclear G3BP1 mRNA by dual mechanisms: recruiting TDP43 into the nucleus and enhancing its interaction with G3BP1 mRNA, suggesting a critical role of RAN in NPC progression and providing a new regulation framework of RBP-RNA.

Large-Scale Group Decision Making with Dual Feedback from Community Residents Based on the Organizational Invisible Field

In China, communities function as grassroots self-governing bodies, and the enhancement of public participation in community governance has remained a central focus of study. This paper applies the Large-Scale Group Decision-Making (LSGDM) method to the process of community self-governance and proposes a dual feedback group consensus decision-making model that takes into account the unique social relations among residents. Firstly, the concept of the Organizational Invisible Field—formed in communities by intangible social capital such as positional power and interpersonal relationships within the organization—is introduced. The definition of Invisible Field Force is utilized to measure the influence of these forms of capital on social relationships. Subsequently, drawing on field dynamic theory, the process by which residents’ preferences within the organization are shaped by the action of Invisible Field Force is explored. Secondly, acknowledging that invisible relationships can be affected by dynamic interactions during the decision-making process, the Invisible Field Force change model is constructed. Building on this, a dual feedback consensus coordination mechanism—encompassing both in-organization members and all residents—is designed. Finally, the validity and utility of the model are verified through case studies and sensitivity analyses.

Intelligent optimization of horizontal wellbore trajectory based on reinforcement learning

Ultra-long horizontal wells are crucial for improving the production and developmental benefits of shale oil and gas wells. Currently, advanced downhole semi-closed-loop steering drilling technology depends on a dual communication mechanism between the surface and downhole, as well as the empirical decisions of human experts. However, it is difficult to acutely control the trajectory of ultra-long horizontal open hole drilling in a reservoir owing to geological uncertainties related to shale oil and gas, the uncertainty of the drilling tool's building capacity, and the lag of measurement information while drilling. This report proposes an adaptive target detection and design method for ultra-long horizontal well trajectories based on reinforcement learning. The developed approach enables dynamic identification of reservoir sequences according to logging-while-drilling data and prediction of horizontal targets in real-time, with a recognition accuracy of 90.1%. Additionally, measurement-while-drilling data are used to accurately characterize the depth, inclination, and azimuth of the target-entering process. The dynamic interaction mechanism between the bit and the target environment is simulated by defining the bit action, reward function, and an update mechanism. The drilling engineering conditions restrict the interactions, such that the bit automatically decides the direction in which to drill the targets, demonstrating 100% accuracy for the wellbore trajectory toward the target. The experimental application results indicate that the developed method can be applied to determine the dynamic target area of a horizontal well in real time and make intelligent downhole decisions regarding ultra-long horizontal section borehole trajectory adjustments while drilling and measuring. The drilling rate of high-quality reservoirs is therefore significantly improved. The results discussed herein provide insights to support further developments of downhole full-closed-loop intelligent autonomous decision-making borehole trajectory control.

How does green technology innovation affect urban carbon emissions? Evidence from Chinese cities

Green technology innovation is regarded as a key catalyst for sustainable urban development. This study mainly utilizes 278 Chinese cities data from the China Urban Statistical Yearbook, adopting ordinary least squares (OLS) and mediation effect model to investigate the dual impact and mechanism of green technology innovation on urban carbon emissions. Besides, spatial Durbin model (SDM) and threshold model are employed to examine spillover and non-linear impact. The results indicate that: (1) Green technology innovation can significantly mitigate carbon emission intensity, but cannot reduce carbon emissions. Thus, both carbon emission reduction effect and rebound effect coexist, and there is heterogeneity across different cities. (2) Green technology innovation has the potential to decrease carbon intensity through optimizing energy intensity, government participation and basic life cleanliness. (3) There is a spatial spillover effect concerning the emission reduction impact of green technology innovation. (4) The relationship between carbon emission intensity and green technology innovation exhibits a non-linear impact under varying levels of energy intensity, government participation, industrial structure rationalization, and environmental regulation. Building upon these findings, policy recommendations outlined in this study encompass the reinforcement of technological utilization, avoidance of excessive governmental intervention, and adoption of strategies for regional connectivity and differentiation.

Graph-enhanced visual representations and question-guided dual attention for visual question answering

Visual Question Answering (VQA) has witnessed significant advancements recently, due to the application of deep learning in the field of vision-language research. Most current VQA models focus on merging visual and text features, but it is essential for these models to also consider the relationships between different parts of an image and use question information to highlight important features. This study proposes a method to enhance neighboring image region features and learn question-aware visual representations. First, we construct a region graph to represent spatial relationships between objects in the image. Then, graph convolutional networks (GCNs) are used to propagate information across neighboring regions, enriching each region’s feature representation by integrating contextual information. To capture long-range dependencies, the graph is enhanced with random walk with restart (RWR), enabling multi-hop reasoning across distant regions. Furthermore, a question-aware dual attention mechanism is introduced to further refine region features at both region and feature levels, ensuring that the model emphasizes key regions that are critical for answering the question. The enhanced region representations are then combined with the encoded question to predict an answer. Through extensive experiments on VQA benchmarks, the study demonstrates state-of-the-art performance by leveraging regional dependencies and question guidance. The integration of GCNs and random walks in the graph helps capture contextual information to focus visual attention selectively, resulting in significant improvements over existing methods on VQA 1.0 and VQA 2.0 benchmark datasets.

Anchor carbon dots inside NH2-MIL-88B via ship-in-a-bottle strategy for dual signal enhancement in colorimetric-fluorescent sensors

The increase in oxytetracycline (OTC) pollution has become a significant risk to ecological stability and human health because of excessive use. Therefore, developing a precise and reliable sensor for trace OTC detection is critical. In this work, a dual-mode sensor of colorimetric-fluorescent (CL-FL) with dual signal-on was developed for OTC detection. Firstly, a novel carbon dots encapsulating in cavities of MOFs composite (CDs@NH2-MIL-88B) was constructed by the ship-in-a-bottle approach, which enhanced peroxidase-like activity and fluorescent intensity. Further, combining with surface molecularly imprinted polymer (MIP), the dual-mode sensor was constructed to develop selectivity (CDs@NH2-MIL-88B@rMIP). Finally, the dual signal enhancement mechanism of the sensor comes from the interaction between OTC and identifying binding site involving strong hydrogen bonding and metal complexation from imprinted cavity and CDs@NH2-MIL-88B, respectively. The prepared sensor has a linear detection range of 1.00 × 10-9 - 1.00 × 10-5M with the limit of detection (LOD) of 1.47 × 10-10M for the CL mode and a detection range of 1.00 × 10-11 -1.00 × 10-7M with the LOD of 1.84 × 10-12M for the FL mode. Meanwhile, the sensor shows high sensitivity, accuracy and reliability in OTC detection. Overall, this work provides a promising CL-FL dual-mode sensing systems with signal-on in the detection of antibiotics and other hazardous pollutants.

Interlayer engineering of V2O5·nH2O by conductive Ni-BTA enabling high-performance aqueous ammonium ion batteries

Aqueous ammonium ion batteries (AAIBs) are of interest due to the low molar mass, small hydration radius, abundant raw materials and high safety of the carrier ammonium-ion. Nevertheless, there are numerous constraints associated with electrode materials that are suitable for ammonium-ion storage. In this study, we design and synthesize a composite comprising of one-dimensional conductive metal-organic skeleton material (1D c-MOF) embedded between layers of hydrated vanadium pentoxide (VOH) with improved ammonium-ion storage. The central ion of the 1D c-MOF is selected to be the nickel ion, while the ligand is 1,2,4,5-benzenetetramine (BTA). The incorporation of Ni-BTA between the vanadium oxide layers results in the formation of a composite (Ni-BTA/VOH) exhibiting enhanced structural stability, augmented layer spacing and elevated conductivity. Furthermore, the dual energy storage mechanisms of VOH and CN rearrangement act in concert to yield a “1+1 > 2” effect, thereby markedly enhancing the ammonium-ion storage. The specific capacity of Ni-BTA/VOH can reach 183 mAh g−1 at 0.2 A g−1, and the retention rate can reach 52.5 % after 500 cycles at 2 A g−1. This work not only proves the potential of Ni-BTA/VOH for widespread application in the field of aqueous batteries, but also provides a new method for structural engineering of VOH with boosted ammonium-ion storage properties.

Drug-Linker Constructs Bearing Unique Dual-Mechanism Tubulin Binding Payloads Tethered through Cleavable and Non-Cleavable Linkers

Antibody-drug conjugates (ADCs) have advanced as a mainstay among the most promising cancer therapeutics, offering enhanced antigen targeting and encompassing wide diversity in their linker and payload components. Small-molecule inhibitors of tubulin polymerization have found success as payloads in FDA approved ADCs and represent further promise in next-generation, pre-clinical and developmental ADCs. Unique dual-mechanism payloads (previously designed and synthesized in our laboratories) function as both potent antiproliferative agents and promising vascular disrupting agents capable of imparting selective and effective damage to tumor-associated microvessels. These payloads have been incorporated into a variety of drug-linker constructs utilizing the clinically relevant cathepsin B cleavable Val-Cit dipeptide linker, employed within several FDA approved ADCs, along with other non-cleavable constructs. Various synthetic strategies were evaluated to prepare these drug-linker constructs. Aniline-based payloads were incorporated utilizing the Val-Cit dipeptide linker similar to FDA approved ADCs such as Adcetris® (brentuximab vedotin). An additional self-immolative group, previously described in the literature for related model systems, was employed to tether the phenolic payloads. A variety of drug-linker constructs (with each bearing a unique dual mechanism payload) were synthesized and evaluated biologically for their enzyme-mediated release of payload and inhibition of tubulin polymerization. Following deactivation of the highly electrophilic maleimido terminus as its corresponding N-acetyl cysteine (NAC) derivative, the most promising construct (NAC-4) demonstrated approximately 90% release of an aniline-functionalized payload (1) upon treatment with cathepsins B or L over 90 minutes. Building on these promising results, future studies will examine the conjugation of drug-linker construct 4 to selected antibodies and engineered proteins and evaluate the biological activity of the resultant antibody-drug conjugates (ADCs).

Parallel-way: Multi-modality-based brain tumor segmentation using parallel capsule network

ABSTRACT Brain tumors present a formidable diagnostic challenge due to their aberrant cell growth. Accurate determination of tumor location and size is paramount for effective diagnosis. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are pivotal tools in clinical diagnosis, yet tumor segmentation within their images remains challenging, particularly at boundary pixels, owing to limited sensitivity. Recent endeavors have introduced fusion-based strategies to refine segmentation accuracy, yet these methods often prove inadequate. In response, we introduce the Parallel-Way framework to surmount these obstacles. Our approach integrates MRI and PET data for a holistic analysis. Initially, we enhance image quality by employing noise reduction, bias field correction, and adaptive thresholding, leveraging Improved Kalman Filter (IKF), Expectation Maximization (EM), and Improved Vibe Algorithm (IVib), respectively. Subsequently, we conduct multi-modality image fusion through the Dual-Tree Complex Wavelet Transform (DTWCT) to amalgamate data from both modalities. Following fusion, we extract pertinent features using the Advanced Capsule Network (ACN) and reduce feature dimensionality via Multi-objective Diverse Evolution-based selection. Tumor segmentation is then executed utilizing the Twin Vision Transformer with dual attention mechanism. Implemented our Parallel-Way framework which exhibits heightened model performance. Evaluation across multiple metrics, including accuracy, sensitivity, specificity, F1-Score, and AUC, underscores its superiority over existing methodologies.

Pharmacologic restoration of GTP hydrolysis by mutant RAS.

Approximately 3.4 million patients worldwide are diagnosed each year with cancers that harbor pathogenic mutations in one of three RAS proto-oncogenes (KRAS, NRAS and HRAS)1,2. These mutations impair the GTPase activity of RAS, leading to activation of downstream signaling and proliferation3-6. Long-standing efforts to restore the hydrolase activity of RAS mutants have been unsuccessful, extinguishing any consideration towards a viable therapeutic strategy7. Here we show that tri-complex inhibitors, that is, molecular glues with the ability to recruit cyclophilin A (CYPA) to the active state of RAS have a dual mechanism of action: not only do they prevent activated RAS from binding to its effectors, but, unexpectedly, they also stimulate GTP hydrolysis. Drug-bound CYPA complexes modulate residues in the switch II motif of RAS to coordinate the nucleophilic attack on the ɣ phosphate of GTP, in a mutation-specific manner. RAS mutants most sensitive to stimulation of GTPase activity were more susceptible to treatment compared to mutants whose hydrolysis could not be enhanced, suggesting that pharmacologic stimulation of hydrolysis potentiates the therapeutic effects of tri-complex inhibitors for specific RAS mutants. This study lays the foundation for developing a new class of therapeutics that inhibit cancer growth by stimulating mutant GTPase activity.

Research on safety control technology of high-speed railway combined test based on threatening event analysis

PurposeSafety management is a key point and poses a challenge in joint testing. To detect and address potential accidents' hidden dangers early, this paper conducts research on the safety control technology for high-speed railway joint tests by incorporating the concept of hazardous events.Design/methodology/approachAiming at ensuring the safety of high-speed railway combined inspections and trials, this paper starts from the dual prevention mechanism. It introduces the concept of threatening events, defines them and analyzes the differences between threatening events and railway accidents. The paper also proposes a cause model for threatening events in high-speed railway combined inspections and trials, based on three types of hazard sources. Furthermore, it conducts research on the control strategies for these threatening events.FindingsThe research on safety control technology for high-speed railway combined operation and testing, based on the analysis of threatened events, offers a new perspective for safety management in these operations. It also provides theoretical and practical support for the transition from passive prevention to active risk pre-control, which holds significant theoretical and practical value.Originality/valueThe innovation mainly includes the following three aspects: (1) Building on the traditional dual prevention mechanism, which includes risk hierarchical management and control as well as hidden danger investigation and management, a triple prevention mechanism is proposed. This new mechanism adds the management of threatening events as the third line of defense. The aim is to more comprehensively identify and address potential security risks, thereby enhancing the efficiency and effectiveness of security management. (2) In this paper, the definition of a railway threatening event is clarified, and the causative model of a high-speed railway threatening event based on three kinds of danger sources is proposed. (3) This paper puts forward the control strategy of the high-speed railway combined operation and trial, which includes five key links: identification, reporting, analysis, rectification and feedback, which provides a new perspective for the safety management of the high-speed railway combined operation and trial and has important theoretical and application value.