Different Regular Black Holes: Geodesic Structures of Test Particles

This paper investigates the metric of previously proposed regular black holes, calculates their effective potentials, and plots the curves of the effective potentials. By determining the conserved quantities, the dynamical equations for particles and photons near the black hole are derived. The analysis encompasses timelike and null geodesics in different spacetimes, including bound geodesics, unstable circular geodesics, stable circular geodesics, and escape geodesics. The findings are presented through figures and tables. Furthermore, the bound geodesics of the four regular black hole spacetimes are analyzed, examining the average distance of particle orbits from the center of the event horizon, the precession behavior of the perihelion, and the probability of particles appearing inside the outer event horizon during motion. Based on these analyses, a general formula is proposed, which yields the existing metrics when specific parameter values are chosen. The impact of parameter variations on the effective potential and geodesics is then computed using this new formula.Comment: 23 pages, 13 figure

Voting System Based on Blockchain

Online ballot box system has the advantages of high efficiency and environmental protection, but the existing network voting technology still has a lot of matter. Almost all electronic voting system could be proved to be intrusion. The administrator of the system could tamper with the data for benefit, and the system may be attacked by hackers. The safety and fairness of the existing network voting system depend entirely on the safety and credibility of the website itself, but these cannot guarantee the fairness of voting. Make full use of blockchain technology, so that voting, even if there are malicious participants, but also to ensure the correctness and safety of the vote. The introduction of block chain technology, block chain has decentralized, data tampering and other characteristics. P2P network is applied in the block chain layer to construct a distributed database, digital signature algorithm and encryption technology are used to ensure that the data cannot be tampered with, consensus network algorithm is used to ensure the consistency of the data in the network, and timestamp technology is applied to save the data blocks in a chain structure connected end to end. It paper focuses on the implementation of P2P network networking mode, node block synchronization, data and block verification mechanism and consensus mechanism to ensure data consistency in the network layer of block chain layer. Using time stamp, Merkle tree, asymmetric encryption and other technologies to design data blocks and use chain structure to store data blocks. Combined with the characteristics of blockchain, a fair and transparent voting system is constructed. Model aims to apply the block chain technology to the voting scenario and design a secure block chain voting architecture. It system is designed and developed based on the block chain system. It makes full use of its decentralization, removes the dependence of electronic voting on trusted third parties, and protects the privacy of voters and candidates. Data cannot be tampered with. Once the data are stored in the block chain, it cannot be tampered with. It provides a real and credible database

Agent Based Simulation of Group Emotions Evolution and Strategy Intervention in Extreme Events

Agent based simulation method has become a prominent approach in computational modeling and analysis of public emergency management in social science research. The group emotions evolution, information diffusion, and collective behavior selection make extreme incidents studies a complex system problem, which requires new methods for incidents management and strategy evaluation. This paper studies the group emotion evolution and intervention strategy effectiveness using agent based simulation method. By employing a computational experimentation methodology, we construct the group emotion evolution as a complex system and test the effects of three strategies. In addition, the events-chain model is proposed to model the accumulation influence of the temporal successive events. Each strategy is examined through three simulation experiments, including two make-up scenarios and a real case study. We show how various strategies could impact the group emotion evolution in terms of the complex emergence and emotion accumulation influence in extreme events. This paper also provides an effective method of how to use agent-based simulation for the study of complex collective behavior evolution problem in extreme incidents, emergency, and security study domains

Glass and glass ceramic electrodes and solid electrolyte materials for lithium ion batteries: A review

Due to its distinct network structure, lack of a grain boundary, and isotropic qualities, glass has been the subject of extensive research. Lithium ion batteries can have their capacity and safety increased by using glassy electrode and electrolyte materials. We discuss the properties and uses of several types of glass and glass ceramic as anodes, including tin oxide glass, vanadium oxide glass, and so on. Metal-organic framework (MOF) materials are also investigated as a new generation of high-performance anode materials. We present the usage of glassy MOF materials to overcome MOF material volume change during charge and discharge, as well as the order and disorder transition of certain MOF materials during charge and discharge. The use of vanadium-based glass as a cathode material is also discussed. These materials have the potential to be employed as electrode materials in the next generation of lithium- ion batteries. In addition, the application of glass, especially sulfide glass, as an all-solid-state battery electrolyte and the effect of mixed anion effect on improving the conductivity of solid electrolyte were introduced.</p

PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection

Masked Autoencoders learn strong visual representations and achieve state-of-the-art results in several independent modalities, yet very few works have addressed their capabilities in multi-modality settings. In this work, we focus on point cloud and RGB image data, two modalities that are often presented together in the real world, and explore their meaningful interactions. To improve upon the cross-modal synergy in existing works, we propose PiMAE, a self-supervised pre-training framework that promotes 3D and 2D interaction through three aspects. Specifically, we first notice the importance of masking strategies between the two sources and utilize a projection module to complementarily align the mask and visible tokens of the two modalities. Then, we utilize a well-crafted two-branch MAE pipeline with a novel shared decoder to promote cross-modality interaction in the mask tokens. Finally, we design a unique cross-modal reconstruction module to enhance representation learning for both modalities. Through extensive experiments performed on large-scale RGB-D scene understanding benchmarks (SUN RGB-D and ScannetV2), we discover it is nontrivial to interactively learn point-image features, where we greatly improve multiple 3D detectors, 2D detectors, and few-shot classifiers by 2.9%, 6.7%, and 2.4%, respectively. Code is available at https://github.com/BLVLab/PiMAE.Comment: Accepted by CVPR2023. Code is available at https://github.com/BLVLab/PiMA

ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation

Despite remarkable advances that large language models have achieved in chatbots, maintaining a non-toxic user-AI interactive environment has become increasingly critical nowadays. However, previous efforts in toxicity detection have been mostly based on benchmarks derived from social media content, leaving the unique challenges inherent to real-world user-AI interactions insufficiently explored. In this work, we introduce ToxicChat, a novel benchmark based on real user queries from an open-source chatbot. This benchmark contains the rich, nuanced phenomena that can be tricky for current toxicity detection models to identify, revealing a significant domain difference compared to social media content. Our systematic evaluation of models trained on existing toxicity datasets has shown their shortcomings when applied to this unique domain of ToxicChat. Our work illuminates the potentially overlooked challenges of toxicity detection in real-world user-AI conversations. In the future, ToxicChat can be a valuable resource to drive further advancements toward building a safe and healthy environment for user-AI interactions

Bridging Trustworthiness and Open-World Learning: An Exploratory Neural Approach for Enhancing Interpretability, Generalization, and Robustness

As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence technologies, it is imperative that we recognize the critical importance of trustworthiness in open-world, which has become ubiquitous in all aspects of daily life for everyone. However, several challenges may create a crisis of trust in current artificial intelligence systems that need to be bridged: 1) Insufficient explanation of predictive results; 2) Inadequate generalization for learning models; 3) Poor adaptability to uncertain environments. Consequently, we explore a neural program to bridge trustworthiness and open-world learning, extending from single-modal to multi-modal scenarios for readers. 1) To enhance design-level interpretability, we first customize trustworthy networks with specific physical meanings; 2) We then design environmental well-being task-interfaces via flexible learning regularizers for improving the generalization of trustworthy learning; 3) We propose to increase the robustness of trustworthy learning by integrating open-world recognition losses with agent mechanisms. Eventually, we enhance various trustworthy properties through the establishment of design-level explainability, environmental well-being task-interfaces and open-world recognition programs. These designed open-world protocols are applicable across a wide range of surroundings, under open-world multimedia recognition scenarios with significant performance improvements observed

Post-marketing safety surveillance of sacituzumab govitecan: an observational, pharmacovigilance study leveraging FAERS database

Background and objective: Sacituzumab govitecan (SG), the first antibody-drug conjugate targeting human trophoblast cell-surface antigen 2 (Trop-2), has been approved by the Food and Drug Administration (FDA) for the treatment of advanced or metastatic breast cancer and urothelial cancer. However, there is currently a dearth of information regarding the safety profiles of SG in a large sample cohort. The objective of the present study is to investigate SG-related adverse events (AEs) in real-world settings leveraging the FDA Adverse Event Reporting System (FAERS) database to guide the safety management of clinical medication.Methods: The FAERS database was retrospectively queried to extract reports associated with SG from April 2020 to March 2023. To identify and evaluate potential AEs in patients receiving SG, various disproportionality analyses such as reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the multi-item gamma Poisson shrinker (MGPS) were employed.Results: Overall, 2069 reports of SG as the “primary suspect” were identified. Noteworthy, SG was significantly associated with an increased risk of blood lymphatic system disorders (ROR, 7.18; 95% CI, 6.58–7.84) and hepatobiliary disorders (ROR, 2.68; 95% CI, 2.17–3.30) at the System Organ Class (SOC) level. Meanwhile, 61 significant disproportionality preferred terms (PTs) simultaneously complied with all four algorithms were adopted. Therein, anemia, thrombocytopenia, neutropenia, leukopenia, diarrhea, asthenia, alopecia, and electrolyte imbalance were consistent with the common AEs described in the clinical trials and specification of SG. Furthermore, unexpected significant AEs include colitis (ROR, 12.09; 95% CI, 9.1–16.08), heart rate increased (ROR, 5.11; 95% CI, 3.84–6.79), sepsis (ROR, 4.77; 95% CI, 3.59–6.34), cholestasis (ROR, 6.28; 95% CI, 3.48–11.36), blood bilirubin increased (ROR, 4.65; 95% CI, 2.42–8.94) and meningitis (ROR, 7.23; 95% CI, 2.71–19.29) were also be detected. The median time to onset of SG-related AEs was 14 [interquartile range (IQR), 7–52] days, with the majority occurring within the initial month of SG treatment.Conclusion: Our study validates the commonly known AEs and also found some potentially emerging safety issues related to SG in real-world clinical practice, which could provide valuable vigilance evidence for clinicians and pharmacists to manage the safety issues of SG

Evaluation of adverse events of CDK4/6 inhibitors: a real-world study based on the FAERS database

Background and purpose: The development and approval of inhibitors of cyclin-dependent kinase 4/6 (CDK4/6) is an essential milestone in treating hormone receptor-positive metastatic breast cancer. The efficacy of these drugs is similar, but the adverse events (AE) are different, directly affecting the physician's choice of drug. There is no systematic study on the safety of CDK4/6 inhibitors in the real world. In this study, we compared the differences in AE of CDK4/6 inhibitors through signal mining in the FDA Adverse Event Reporting System (FAERS) and identified unknown AE signals to provide a reference for the clinical choice of treatment and monitoring AE. Methods: All data in the FAERS database were extracted from the first quarter of 2004 to the first quarter of 2023. After removing duplicates, data were analyzed by the disproportionality method for reports ranking palbociclib, abemaciclib, or ribociclib as the primary suspect. Signals were identified using the reporting odds ratio (ROR) and MHRA methods. Positive signals were required to meet the following criteria: the number of reports ≥3, the lower limit of the 95% confidence interval of the ROR &gt;1, proportional reporting ratios (PRR) &gt;2, and the χ2 &gt;4. Results: A total of 85 562 reports of AE associated with CDK4/6 inhibitors were identified. The highest signal intensity of palbociclib was observed in hematologic and lymphatic AE (leukopenia ROR = 20.01). Palbociclib had lower AE signals in the gastrointestinal, hepatic, and renal systems than the other drugs (diarrhea ROR = 1.95, gamma-glutamyltransferase increased ROR = 0.36, blood creatinine increased ROR = 1.01). Abemaciclib had the strongest signal in the gastrointestinal system (diarrhea ROR = 13.54); it also showed a strong AE signal in the hepatic and renal systems (gamma-glutamyltransferase increased ROR = 2.58, blood creatinine increased ROR = 7.74) and a lower AE signal than the other drugs in the blood and lymphatic systems (leukopenia ROR = 5.34). Ribociclib had a lower AE signal intensity in the blood and lymphatic system than palbociclib (leukopenia ROR = 7.55); however, among hepatic AE, ribociclib had the highest signal intensity of increased gamma-glutamyltransferase (ROR = 4.05). In rare severe hepatic systemic AE, abemaciclib had the strongest signal in hepatic failure (ROR = 3.50) and drug-induced liver injury (ROR = 4.68). Erythema multiforme was a newly identified signal in the abemaciclib reports (ROR = 3.06). Conclusion: The safety profile of CDK4/6 inhibitors varies. Analysis of the FAERS database revealed hematologic and lymphatic system toxicities for palbociclib and ribociclib and gastrointestinal and hepatorenal toxicities for abemaciclib. Erythema multiforme was found as a novel severe AE for abemaciclib. Individualized drug selection and monitoring of AE based on the patient’s physiological status and AE are needed during treatment