An Emergency Disposal Decision-making Method with Human--Machine Collaboration

Rapid developments in artificial intelligence technology have led to unmanned systems replacing human beings in many fields requiring high-precision predictions and decisions. In modern operational environments, all job plans are affected by emergency events such as equipment failures and resource shortages, making a quick resolution critical. The use of unmanned systems to assist decision-making can improve resolution efficiency, but their decision-making is not interpretable and may make the wrong decisions. Current unmanned systems require human supervision and control. Based on this, we propose a collaborative human--machine method for resolving unplanned events using two phases: task filtering and task scheduling. In the task filtering phase, we propose a human--machine collaborative decision-making algorithm for dynamic tasks. The GACRNN model is used to predict the state of the job nodes, locate the key nodes, and generate a machine-predicted resolution task list. A human decision-maker supervises the list in real time and modifies and confirms the machine-predicted list through the human--machine interface. In the task scheduling phase, we propose a scheduling algorithm that integrates human experience constraints. The steps to resolve an event are inserted into the normal job sequence to schedule the resolution. We propose several human--machine collaboration methods in each phase to generate steps to resolve an unplanned event while minimizing the impact on the original job plan.Comment: 15 pages, 16 figure

eIF4A inhibitors suppress cell-cycle feedback response and acquired resistance to CDK4/6 inhibition in cancer

CDK4/6 inhibitors are FDA-approved drugs for estrogen receptor-positive (ER+) breast cancer and are being evaluated to treat other tumor types, including KRAS-mutant non-small cell lung cancer (NSCLC). However, their clinical utility is often limited by drug resistance. Here, we sought to better understand the resistant mechanisms and help devise potential strategies to overcome this challenge. We show that treatment with CDK4/6 inhibitors in both ER+ breast cancer and KRAS-mutant NSCLC cells induces feedback upregulation of cyclin D1, CDK4, and cyclin E1, mediating drug resistance. We demonstrate that rocaglates, which preferentially target translation of key cell-cycle regulators, effectively suppress this feedback upregulation induced by CDK4/6 inhibition. Consequently, combination treatment of CDK4/6 inhibitor palbociclib with the eukaryotic initiation factor (eIF) 4A inhibitor, CR-1-31-B, is synergistic in suppressing the growth of these cancer cells in vitro and in vivo Furthermore, ER+ breast cancer and KRAS-mutant NSCLC cells that acquired resistance to palbociclib after chronic drug exposure are also highly sensitive to this combination treatment strategy. Our findings reveal a novel strategy using eIF4A inhibitors to suppress cell-cycle feedback response and to overcome resistance to CDK4/6 inhibition in cancer.Accepted manuscrip

Packet Classification Algorithms: From Theory to Practice

Abstract—During the past decade, the packet classification problem has been widely studied to accelerate network applications such as access control, traffic engineering and intrusion detection. In our research, we found that although a great number of packet classification algorithms have been proposed in recent years, unfortunately most of them stagnate in mathematical analysis or software simulation stages and few of them have been implemented in commercial products as a generic solution. To fill the gap between theory and practice, in this paper, we propose a novel packet classification algorithm named HyperSplit. Compared to the well-known HiCuts and HSM algorithms, HyperSplit achieves superior performance in terms of classification speed, memory usage and preprocessing time. The practicability of the proposed algorithm is manifested by two facts in our test: HyperSplit is the only algorithm that can successfully handle all the rule sets; HyperSplit is also the only algorithm that reaches more than 6Gbps throughput on the Octeon3860 multi-core platform when tested with 64-byte Ethernet packets against 10K ACL rules. Keywords-algorithm; classification; multi-core; performance I

Lattice-based Public Key Encryption with Authorized Keyword Search: Construction, Implementation, and Applications

Public key encryption with keyword search (PEKS), formalized by Boneh et al. [EUROCRYPT\u27 04], enables secure searching for specific keywords in the ciphertext. Nevertheless, in certain scenarios, varying user tiers are granted disparate data searching privileges, and administrators need to restrict the searchability of ciphertexts to select users exclusively. To address this concern, Jiang et al. [ACISP\u27 16] devised a variant of PEKS, namely public key encryption with authorized keyword search (PEAKS), wherein solely authorized users possess the ability to conduct targeted keyword searches. Nonetheless, it is vulnerable to resist quantum computing attacks. As a result, research focusing on authorizing users to search for keywords while achieving quantum security is far-reaching. In this work, we present a novel construction, namely lattice-based PEAKS (L-PEAKS), which is the first mechanism to permit the authority to authorize users to search different keyword sets while ensuring quantum-safe properties. Specifically, the keyword is encrypted with a public key, and each authorized user needs to obtain a search privilege from an authority. The authority distributes an authorized token to a user within a time period and the user will generate a trapdoor for any authorized keywords. Technically, we utilize several lattice sampling and basis extension algorithms to fight against attacks from quantum adversaries. Moreover, we leverage identity-based encryption (IBE) to alleviate the bottleneck of public key management. Furthermore, we conduct parameter analysis, rigorous security reduction, and theoretical complexity comparison of our scheme and perform comprehensive evaluations at a commodity machine for completeness. Our L-PEAKS satisfies IND-sID-CKA and T-EUF security and is efficient in terms of space and computation complexity compared to other existing primitives. Finally, we provide two potential applications to show its versatility

Post-Quantum Public-key Authenticated Searchable Encryption with Forward Security: General Construction, Implementation, and Applications

Public-key encryption with keyword search was first proposed by Boneh et al. (EUROCRYPT 2004), achieving the ability to search for ciphertext files. Nevertheless, this scheme is vulnerable to inside keyword guessing attacks (IKGA). Public-key authenticated encryption with keyword search (PAEKS), introduced by Huang et al. (Inf. Sci. 2017), on the other hand, is secure against IKGA. Nonetheless, it is susceptible to quantum computing attacks. Liu et al. and Cheng et al. addressed this problem by reducing to the lattice hardness (AsiaCCS 2022, ESORICS 2022). Furthermore, several scholars pointed out that the threat of secret key exposure delegates a severe and realistic concern, potentially leading to privacy disclosure (EUROCRYPT 2003, Compt. J. 2022). As a result, research focusing on mitigating key exposure and resisting quantum attacks for the PAEKS primitive is significant and far-reaching. In this work, we present the first instantiation of post-quantum PAEKS primitive that is forward-secure and does not require trusted authorities, mitigating the secret key exposure while ensuring quantum-safe properties. We extended the scheme of Liu et al. (AsiaCCS 2022), and proposed a novel post-quantum PAEKS construction, namely FS-PAEKS. To begin with, we introduce the binary tree structure to represent the time periods, along with a lattice basis extension algorithm, and SamplePre algorithm to obtain the post-quantum one-way secret key evolution, allowing users to update their secret keys periodically. Furthermore, our scheme is proven to be IND-CKA, IND-IKGA, and IND-Multi-CKA in the quantum setting. In addition, we also compare the security of our primitive in terms of computational complexity and communication overhead with other top-tier schemes and provide implementation details of the ciphertext generation and test algorithms. The proposed FS-PAEKS is more efficient than the FS-PEKS scheme (IEEE TDSC 2021). Lastly, we demonstrate three potential application scenarios of FS-PAEKS

White matter microstructure alterations in idiopathic restless legs syndrome: a study combining crossing fiber-based and tensor-based approaches

IntroductionRestless legs syndrome (RLS) is a common sensorimotor disorder characterized by an irrepressible urge to move the legs and frequently accompanied by unpleasant sensations in the legs. The pathophysiological mechanisms underlying RLS remain unclear, and RLS is hypothesized to be associated with alterations in white matter tracts.MethodsDiffusion MRI is a unique noninvasive method widely used to study white matter tracts in the human brain. Thus, diffusion-weighted images were acquired from 18 idiopathic RLS patients and 31 age- and sex-matched healthy controls (HCs). Whole brain tract-based spatial statistics (TBSS) and atlas-based analyzes combining crossing fiber-based metrics and tensor-based metrics were performed to investigate the white matter patterns in individuals with RLS.ResultsTBSS analysis revealed significantly higher fractional anisotropy (FA) and partial volume fraction of primary (F1) fiber populations in multiple tracts associated with the sensorimotor network in patients with RLS than in HCs. In the atlas based analysis, the bilateral anterior thalamus radiation, bilateral corticospinal tract, bilateral inferior fronto-occipital fasciculus, left hippocampal cingulum, left inferior longitudinal fasciculus, and left uncinate fasciculus showed significantl increased F1, but only the left hippocampal cingulum showed significantly higher FA.DiscussionThe results demonstrated that F1 identified extensive alterations in white matter tracts compared with FA and confirmed the hypothesis that crossing fiber-based metrics are more sensitive than tensor-based metrics in detecting white matter abnormalities in RLS. The present findings provide evidence that the increased F1 metric observed in sensorimotor tracts may be a critical neural substrate of RLS, enhancing our understanding of the underlying pathological changes

City-level air quality improvement in the Beijing-Tianjin-Hebei region from 2016/17 to 2017/18 heating seasons: Attributions and process analysis

With the implementation of clean air strategies, PM_(2.5) pollution abatement has been observed in the “2 + 26” cities in the Beijing-Tianjin-Hebei (BTH) region (referred to as the BTH2+26) and their surrounding areas. To identify the drivers for PM_(2.5) concentration decreases in the BTH2+26 cites from the 2016/17 heating season (HS1617) to the 2017/18 heating season (HS1718), we investigated the contributions of meteorological conditions and emission-reduction measures by Community Multi-Scale Air Quality (CMAQ) model simulations. The source apportionments of five sector sources (i.e., agriculture, industry, power plants, traffic and residential), and regional sources (i.e., local, within-BTH: other cities within the BTH2+26 cities, outside-BTH, and boundary conditions (BCON)) to the PM_(2.5) decreases in the BTH2+26 cities were estimated with the Integrated Source Apportionment Method (ISAM). Mean PM_(2.5) concentrations in the BTH2+26 cities substantially decreased from 77.4 to 152.5 μg m⁻³ in HS1617 to 52.9–101.9 μg m⁻³ in HS1718, with the numbers of heavy haze (daily PM_(2.5) ≥150 μg m⁻³) days decreasing from 17-77 to 5–30 days. The model simulation results indicated that the PM_(2.5) concentration decreases in most of the BTH2+26 cities were attributed to emission reductions (0.4–55.0 μg m⁻³, 2.3–81.6% of total), but the favorable meteorological conditions also played important roles (1.9–25.4 μg m⁻³, 18.4–97.7%). Residential sources dominated the PM_(2.5) reductions, leading to decreases in average PM_(2.5) concentrations by more than 30 μg m⁻³ in severely polluted cities (i.e., Shijiazhuang, Baoding, Xingtai, and Beijing). Regional source analyses showed that both local and within-BTH sources were significant contributors to PM_(2.5) concentrations for most cities. Emission controls in local and within-BTH sources in HS1718 decreased the average PM_(2.5) concentrations by 0.1–47.2 μg m⁻³ and 0.3–22.1 μg m⁻³, respectively, relative to those in HS1617. Here we demonstrate that a combination of favorable meteorological conditions and anthropogenic emission reductions contributed to the improvement of air quality from HS1617 to HS1718 in the BTH2+26 cities

A clinical prognostic model for patients with esophageal squamous cell carcinoma based on circulating tumor DNA mutation features

BackgroundFew predictive models have included circulating tumor DNA (ctDNA) indicators to predict prognosis of esophageal squamous cell carcinoma (ESCC) patients. Here, we aimed to explore whether ctDNA can be used as a predictive biomarker in nomogram models to predict the prognosis of patients with ESCC.MethodsWe included 57 patients who underwent surgery and completed a 5-year follow-up. With next-generation sequencing, a 61-gene panel was used to evaluate plasma cell-free DNA and white blood cell genomic DNA from patients with ESCC. We analyzed the relationship between the mutation features of ctDNA and the prognosis of patients with ESCC, identified candidate risk predictors by Cox analysis, and developed nomogram models to predict the 2- and 5-year disease-free survival (DFS) and overall survival (OS). The area under the curve of the receiver operating characteristic (ROC) curve, concordance index (C-index), calibration plot, and integrated discrimination improvement (IDI) were used to evaluate the performance of the nomogram model. The model was compared with the traditional tumor-nodes-metastasis (TNM) staging system.ResultsThe ROC curve showed that the average mutant allele frequency (MAF) of ctDNA variants and the number of ctDNA variants were potential biomarkers for predicting the prognosis of patients with ESCC. The predictors included in the models were common candidate predictors of ESCC, such as lymph node stage, angiolymphatic invasion, drinking history, and ctDNA characteristics. The calibration curve demonstrated consistency between the observed and predicted results. Moreover, our nomogram models showed clear prognostic superiority over the traditional TNM staging system (based on C-index, 2-year DFS: 0.82 vs. 0.64; 5-year DFS: 0.78 vs. 0.65; 2-year OS: 0.80 vs. 0.66; 5-year OS: 0.77 vs. 0.66; based on IDI, 2-year DFS: 0.33, p <0.001; 5-year DFS: 0.18, p = 0.04; 2-year OS: 0.28, p <0.001; 5-year OS: 0.15, p = 0.04). The comprehensive scores of the nomogram models could be used to stratify patients with ESCC.ConclusionsThe novel nomogram incorporating ctDNA features may help predict the prognosis of patients with resectable ESCC. This model can potentially be used to guide the postoperative management of ESCC patients in the future, such as adjuvant therapy and follow-up

Significant wintertime PM_(2.5) mitigation in the Yangtze River Delta, China, from 2016 to 2019: observational constraints on anthropogenic emission controls

Ambient fine particulate matter (PM_(2.5)) mitigation relies strongly on anthropogenic emission control measures, the actual effectiveness of which is challenging to pinpoint owing to the complex synergies between anthropogenic emissions and meteorology. Here, observational constraints on model simulations allow us to derive not only reliable PM_(2.5) evolution but also accurate meteorological fields. On this basis, we isolate meteorological factors to achieve reliable estimates of surface PM_(2.5) responses to both long-term and emergency emission control measures from 2016 to 2019 over the Yangtze River Delta (YRD), China. The results show that long-term emission control strategies play a crucial role in curbing PM_(2.5) levels, especially in the megacities and other areas with abundant anthropogenic emissions. The G20 summit hosted in Hangzhou in 2016 provides a unique and ideal opportunity involving the most stringent, even unsustainable, emergency emission control measures. These emergency measures lead to the largest decrease (∼ 35 µg m⁻³, ∼ 59 %) in PM_(2.5) concentrations in Hangzhou. The hotspots also emerge in megacities, especially in Shanghai (32 µg m⁻³, 51 %), Nanjing (27 µg m⁻³, 55 %), and Hefei (24 µg m⁻³, 44 %) because of the emergency measures. Compared to the long-term policies from 2016 to 2019, the emergency emission control measures implemented during the G20 Summit achieve more significant decreases in PM_(2.5) concentrations (17 µg m⁻³ and 41 %) over most of the whole domain, especially in Hangzhou (24 µg m⁻³, 48 %) and Shanghai (21 µg m⁻³, 45 %). By extrapolation, we derive insight into the magnitude and spatial distribution of PM_(2.5) mitigation potential across the YRD, revealing significantly additional room for curbing PM_(2.5) levels