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摘要： Computer modeling is the process of creating and using mathematical or computer-based models to understand, analyze and predict the behavior of complex systems. In this context, models serve as abstract representations of the systems under study, which include natural phenomena, physical processes, or social interactions. The publications presented in this study provide an overview of related research ideas from different countries. The aim of this research is to visually map scientific publications on computer modeling, using bibliometric analysis and indexing through Scopus. Scopus application feature analysis was used, and the resulting bibliometric network was visualized using R Studio. This research expanded the Scopus database to include keywords related to computer modeling, covering a collection of 1,800 academic documents published globally between 2013 and 2023. Patterns and trends were identified based on this research, which revealed an increasing number of international publications in the field of computer modeling indexed by Scopus in 2019, 2020 and 2021. The most prolific countries, research institutions, and authors in computer modeling publications were found to be the United States, Georgia Institute of Technology, and Biswas, G. Collaborative networks of researchers associated with computer modeling publications were also observed. The most intensive subjects and document types identified were Computer Science and articles. The findings of this study serve as a valuable reference for future studies conducted in the field of computer modeling, particularly in the context of science education.

关键词： Computer science   Quantum information   Qubits  

摘要： Parameterized quantum circuits are a key component of quantum machine learning models for regression, classification, and generative tasks. Quantum Circuit Born machines produce discrete distributions over bitstrings whose length is exactly the number of qubits. To allow for distributions on continuous variables, new models have been introduced where classical randomness is uploaded into quantum circuits and expectation values are returned with a dimensionality decoupled from qubit number. While these models have been explored experimentally, their expressivity remains underexplored. In this work, we formalize this family and establish its theoretical foundation. We prove the universality of several variational circuit architectures for generating continuous multivariate distributions and derive tight resource bounds to reach universality using tools related to the Holevo bound. Our results reveal a trade-off between the number of qubits and measurements. We further explore relaxed notions of universality and present a practical use case, outlining potential domains for quantum advantage.

摘要： The integration of computer-aided modeling in chemical science has completely revolutionized the way in which complex chemical processes are comprehended and visualized. This extensive literature review aims to explore, in great detail, the remarkable impact of computational tools, such as cutting-edge molecular dynamics simulations, highly advanced process modeling software, and innovative algorithms, thereby significantly enhancing the capability to visualize chemical reactions and processes at both the microscopic molecular and macroscopic levels. Through the provision of highly intricate and meticulous simulations of intricate reaction mechanisms, thermodynamic properties, and kinetic behaviors, these exceptional modeling techniques empower scientists to optimize chemical processes to an unprecedented degree, predict outcomes with remarkable accuracy, and vastly decrease experimental costs. By thoroughly examining the most significant advancements in this rapidly evolving field, including the development of real-time simulation tools that have opened up entirely new frontiers, this paper endeavors to emphasize their astonishing applications in a myriad of vital fields, such as the revolutionary realm of drug discovery, the awe-inspiring domain of materials science, as well as the industrially pivotal world of chemistry. Moreover, this comprehensive review delves into the immense benefits attained through the seamless integration of computer-aided modeling techniques with invaluable experimental data, thereby imbuing the scientific community with an ingeniously holistic and comprehensive approach to dramatically advancing the frontiers of chemical research and fueling unparalleled industrial innovation.

关键词： Environmental science   Natural resources   Computer science  

摘要： Common-pool resources (CPRs), including fisheries and the atmosphere, are critical for ecological, social, and economic sustainability but are easily overused. We use an agent-based model to investigate how social networks shape resource extraction outcomes. Networks with highly visible nodes can create a "majority illusion" in which most users believe high-intensity extraction is dominant, even if it is not. This misperception can push the entire population toward one of two possible states: an abundant, high-welfare resource or a depleted, low-welfare one. Aligning users' environmental impact with their visibility can mitigate this effect and steer the system toward a single, predictable outcome. These results suggest that network-based policies, such as reshaping information flows, particularly targeting high-impact hubs, could help stabilize cooperative behaviors and encourage sustainable management of CPRs, reducing the risk of tragedy-of-the-commons scenarios.

关键词： Computer science   Applied computing  

摘要： Spiking neural networks (SNNs) inspired by biological neurons offer energy-efficient and interpretable computation but is limited by the simplistic structure of point neurons. We introduce a multi-compartment spiking neuron model (MCN) with trainable cross-compartment connections that simulate soma-dendrite interactions. Theoretically, we prove that these connections act as spatiotemporal momentum, guiding learning dynamics toward global optima. To leverage this, we propose a multi-compartment spatiotemporal backpropagation (MCST-BP) algorithm that enhances gradient flow stability. Experimental results for multiple benchmark datasets, including S-MNIST, CIFAR-10, Spiking Heidelberg Digits (SHD), and ECG, show that MC-SNNs outperform traditional SNNs in both convergence speed and accuracy. Our work bridges neurobiological structure and computational modeling, providing a theoretical and practical foundation for highperformance brain-inspired learning systems.

关键词： Physics   Computer science   Engineering  

摘要： Spectroscopic techniques are indispensable for material characterization, yet their weak signals remain highly prone to interference from environmental noise, instrumental artifacts, sample impurities, scattering effects, and radiation-based distortions (e.g., fluorescence and cosmic rays). These perturbations not only significantly degrade measurement accuracy but also impair machine learning-based spectral analysis by introducing artifacts and biasing feature extraction. This review provides a systematic evaluation of critical spectral preprocessing methods-encompassing cosmic ray removal, baseline correction, scattering correction, normalization, filtering and smoothing, spectral derivatives, and advanced techniques like 3D correlation analysis-highlighting their theoretical underpinnings, performance trade-offs, and optimal application scenarios. The field is undergoing a transformative shift driven by three key innovations: context-aware adaptive processing, physics-constrained data fusion, and intelligent spectral enhancement. These cutting-edge approaches enable unprecedented detection sensitivity achieving sub-ppm levels while maintaining >99% classification accuracy, with transformative applications spanning pharmaceutical quality control, environmental monitoring, and remote sensing diagnostics.

关键词： Health sciences   Biological sciences   Computer science  

摘要： Craniocerebral and pulmonary injuries are primary blast-induced damages, assessed via numerical simulations, animal models, and postmortem human surrogates (PMHS). Recent years, the successful development of shock wave cell models and organoid models has provided new research directions for evaluation of blast injuries. Particularly human-derived organoids that can highly simulating the structure and function of human organs, significantly enhancing the physiological relevance of the models. Additionally, AI-based models (machine/deep learning) show promise in blast injury prediction and assessment. This review systematically summarizes the biological effects of explosive shock waves, the application of conventional assessment models and their limitations, and emerging technologies-cell/organoid models and AI applications. The utilization of cell models, human-derived organoid models, and AI models for the assessment of blast-induced biological injuries and subsequent research holds significant importance for understanding the cellular mechanisms of injury, protective research, and injury warning systems.

关键词： Measurement   Correlation   Training   Chatbots   Data collection   Large language models   Electronic mail   Computer science   Predictive models   Annotations   Dialogue and interactive systems   knowledge distillation   large language models (LLMs)   natural language processing (NLP)   resource and evaluation  

摘要： Automatic evaluation metrics not only play a vital role in developing dialogue and interactive systems but also have a great impact on social activities in our daily life. However, previous specialized metrics for evaluating dialogues exhibit a relatively low correlation with human judgments. In addition, today's state-of-the-art (SOTA) evaluators that leverage large language models (LLMs) are challenging to deploy in real-world applications due to their sheer size. To this end, we propose a novel evaluation framework, compact and competitive dialogue evaluation (CCDE), which leverages knowledge distillation of LLMs to generate training data and sequentially learn a multitask evaluator regarding diversified quality dimensions. Specifically, we first employ ChatGPT as teacher to generate a high-quality and rich-annotation corpus, CCDE-data. Then, we implement a student evaluator CCDE (1.3B) via using InstructGPT as the backbone model that is trained and fine-tuned on CCDE-data. We conduct extensive experiments on three public benchmarks: fine-grained evaluation of dialog (FED), PersonaChat, and TopicalChat. The results demonstrate that our model CCDE can outperform the current SOTA model G-Eval which calls GPT-4 (>= 175B) by 4.3 on the FED dataset, 3.5 on the PersonaChat dataset, and 0.3 on the TopicalChat dataset, in terms of the Spearman correlation metric (%). We release the data and code at: https://***/r/ccde-3827.

关键词： Research impact   Innovation   Development   Patents   Repositories  

摘要： What factors determine the impact of a scientific paper? Does its impact extend to patents and software development, or is it primarily confined to academic circles? While current literature predominantly adopts a descriptive approach, emphasizing patent citations as indicators of industry impact, the role of research in driving software development is overlooked. To address this gap, we quantitatively assessed the impact of research papers on both patents and software repositories. With a computational social science approach, we collected, curated, and analyzed a large-scale dataset of 200K papers published between 1980 and 2022 across the research areas of AI, Computer Vision, Data Mining, Databases, HCI, and NLP, including conferences like NeurIPS, ICML, ACL, CVPR, CHI, KDD, and The Web Conference. We found that, on average, 7.1% of papers from these venues became patents and 11.6% went into repositories-significantly higher than top general science journals (3.8% for patents and 0.02% for repositories). Despite being a minority, these papers have received a disproportionate number of citations-4% of AI papers became patents, and 18% went into repositories, yet they have received 29% and 42% of the area's academic citations, respectively. However, after correcting for papers published at different times with survival analysis, we found that there is a significant time lag between patents or repositories and papers (10-15 years for patents, 5 years for repositories in Computer Vision and NLP, and even longer for top general science journals at 30 years). As for consistent trends, Deep Learning has become exponentially popular, and "papers with code" are becoming the norm. Finally, we showed that a paper's publication venue and the extent to which a paper builds upon (un)conventional knowledge determine the impact on patents and repositories, with greater conventionality predicting impact on patents, and lesser conventionality predicting impact on repositories.

关键词： Security   Internet of Things   Encryption   Cloud computing   Data privacy   Access control   Resistance   Servers   Computer science   Standards   Ciphertext-policy attribute-based encryption (CP-ABE)   forward security   Internet of Things (IoT)   learning with errors (LWE)   puncturable encryption (PE)  

摘要： Cloud-assisted Internet of Things (IoT) provides resource-constrained IoT devices with powerful storage capabilities while also raising significant data privacy and security issues. Ciphertext-policy attribute-based encryption (CP-ABE) is widely used to protect the privacy and security of IoT data. However, the leakage of users' long-term keys compromises the confidentiality of previously generated IoT data. In this article, we propose a novel puncturable CP-ABE (Pt-CP-ABE) scheme based on the learning with errors (LWE) assumption. Pt-CP-ABE enables data owners to define access policies, thereby achieving fine-grained ciphertext access control. Furthermore, after decrypting the ciphertext, data users can update their private keys by themselves and revoke the decryption privileges of existing ciphertexts by puncturing the tags within the ciphertext. This ensures that even if the private key is compromised, the previously generated IoT data remains confidential, thereby achieving flexible forward security. We formalize the security definition for Pt-CP-ABE and prove the security of the Pt-CP-ABE scheme in the standard model. To the best of our knowledge, our Pt-CP-ABE scheme is the first CP-ABE scheme that supports flexible puncturing, forward security, and resistance to quantum computer attacks. Furthermore, by incorporating a revocation mechanism into our Pt-CP-ABE, we propose another Pt-CP-ABE scheme with user revocation functionality. Notably, this is the first ABE scheme to simultaneously achieve dual security guarantees of forward security and quantum resistance, along with user revocation.