Hasil untuk "Science"

Xuezhong Fu, Yutong Fu, Xiaotao Yang et al.

To accurately evaluate the dynamic characteristics of helical gear transmission systems under high-speed operating conditions and to address the problem that the lumped parameter model fails to accurately reflect the system’s dynamic performance because its degrees of freedom are imperfectly considered, the full-degree-of-freedom dynamic model of the high-speed shaft-bearing-helical gear transmission system was established. The system’s dynamic equations were derived, and the natural frequencies calculated from the theoretical model, as well as the corresponding mode shapes for each degree of freedom, were compared with the finite element model simulation results. The relationship between the critical rotational speed within the system’s 3 × 104 rpm range of rotational speed and the system frequency was analyzed. The dynamic characteristics of the driving wheel and driven wheel in terms of amplitude and motion trajectory were investigated, and the influence of eccentricity on these characteristics was discussed. Results indicate that the relative deviation of the first six natural frequencies between the proposed model and finite element model ranges from 0.09 % to 13.1 %, and the deviations of the higher-order modes are within 1 %. This effectively validates that the full-degree-of-freedom model can accurately reflect the dynamic characteristics of the system, providing a theoretical basis for the design of high-speed-helical gear transmission system.

Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Ahmed Ali Ahmed et al.

Abstract Vehicular Ad-Hoc Networks (VANETs) have facilitated the massive exchange of real-time traffic and weather conditions, which have helped prevent collisions, reduce accidents, and road congestions. This can effectively enhance driving safety and efficiency in technology-driven transportation systems. However, the transmission of massive and sensitive information across public wireless communication channels exposes the transmitted data to a myriad of privacy as well as security threats. Although past researches has developed many vehicular ad-hoc networks security preservation schemes, several of them are inefficient or susceptible to attacks. This work, introduces an approach that leverages reverse fuzzy extraction, bilinear pairing, and Physically Unclonable Function (PUF) to design an efficient and anonymity-preserving authentication scheme. We conduct an elaborate formal security analysis to demonstrate that the derived session key is secure. The semantic security analyses also demonstrate its resilience against typical VANET attacks such as impersonations, denial of service, and de-synchronization, instilling confidence in its effectiveness. Moreover, our approach incurs the lowest computational overheads at relatively low communication costs. Specifically, our protocol attains a 66.696% reduction in computation costs, and a 70% increment in the supported security functionalities.

Priya Sharma, Talia Vasaturo-Kolodner, Valentina Mancini et al.

Renqi Chen, Haoyang Su, Shixiang Tang et al.

The Science of Science (SoS) explores the mechanisms underlying scientific discovery, and offers valuable insights for enhancing scientific efficiency and fostering innovation. Traditional approaches often rely on simplistic assumptions and basic statistical tools, such as linear regression and rule-based simulations, which struggle to capture the complexity and scale of modern research ecosystems. The advent of artificial intelligence (AI) presents a transformative opportunity for the next generation of SoS, enabling the automation of large-scale pattern discovery and uncovering insights previously unattainable. This paper offers a forward-looking perspective on the integration of Science of Science with AI for automated research pattern discovery and highlights key open challenges that could greatly benefit from AI. We outline the advantages of AI over traditional methods, discuss potential limitations, and propose pathways to overcome them. Additionally, we present a preliminary multi-agent system as an illustrative example to simulate research societies, showcasing AI's ability to replicate real-world research patterns and accelerate progress in Science of Science research.

William F. Godoy, Oscar Hernandez, Paul R. C. Kent et al.

We characterize the GPU energy usage of two widely adopted exascale-ready applications representing two classes of particle and mesh solvers: (i) QMCPACK, a quantum Monte Carlo package, and (ii) AMReXCastro, an adaptive mesh astrophysical code. We analyze power, temperature, utilization, and energy traces from double-/single (mixed)-precision benchmarks on NVIDIA's A100 and H100 and AMD's MI250X GPUs using queries in NVML and rocm_smi_lib, respectively. We explore application-specific metrics to provide insights on energy vs. performance trade-offs. Our results suggest that mixed-precision energy savings range between 6-25% on QMCPACK and 45% on AMReX-Castro. Also, we found gaps in the AMD tooling used on Frontier GPUs that need to be understood, while query resolutions on NVML have little variability between 1 ms-1 s. Overall, application level knowledge is crucial to define energy-cost/science-benefit opportunities for the codesign of future supercomputer architectures in the post-Moore era.

Gavin L. Simpson

Nonlinear relationships between covariates and a response variable of interest are frequently encountered in animal science research. Within statistical models, these nonlinear effects have, traditionally, been handled using a range of approaches including transformation of the response, parametric nonlinear models based on theory or phenomenological grounds, or through fixed degree spline or polynomial terms. If it is desirable to learn the shape of these relationships then generalized additive models (GAMs) are an excellent alternative. GAMs extend the generalized linear model such that the linear predictor includes one or more smooth functions, parameterised using penalised splines. A wiggliness penalty on each function is used to avoid over fitting while estimating the parameters of the spline basis functions to maximise fit to the data. Modern GAMs include automatic smoothness selection methods to find an optimal balance between fit and complexity of the estimated functions. Because GAMs learn the shapes of functions from the data, the user can avoid forcing a particular model to their data. Here, I provide a brief description of GAMs and visually illustrate how they work. I then demonstrate the utility of GAMs on three example data sets of increasing complexity, to show i) how learning from data can produce a better fit to data than that of parametric models, ii) how hierarchical GAMs can be used to estimate growth data from multiple animals in a single model, and iii) how hierarchical GAMs can be used for formal statistical inference in a designed experiment. The examples are supported by R code that demonstrates how to fit each of the models considered, and reproduces the results of the statistical analyses reported here. Ultimately, I show that GAMs are a modern, flexible, and highly usable statistical model that is amenable to many research problems in animal science.

Liqiang Jing, Zhehui Huang, Xiaoyang Wang et al.

Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) have demonstrated impressive language/vision reasoning abilities, igniting the recent trend of building agents for targeted applications such as shopping assistants or AI software engineers. Recently, many data science benchmarks have been proposed to investigate their performance in the data science domain. However, existing data science benchmarks still fall short when compared to real-world data science applications due to their simplified settings. To bridge this gap, we introduce DSBench, a comprehensive benchmark designed to evaluate data science agents with realistic tasks. This benchmark includes 466 data analysis tasks and 74 data modeling tasks, sourced from Eloquence and Kaggle competitions. DSBench offers a realistic setting by encompassing long contexts, multimodal task backgrounds, reasoning with large data files and multi-table structures, and performing end-to-end data modeling tasks. Our evaluation of state-of-the-art LLMs, LVLMs, and agents shows that they struggle with most tasks, with the best agent solving only 34.12% of data analysis tasks and achieving a 34.74% Relative Performance Gap (RPG). These findings underscore the need for further advancements in developing more practical, intelligent, and autonomous data science agents.

Feng Xiong, Xinguo Yu, Hon Wai Leong

This paper explores the transformative role of artificial intelligence (AI) in enhancing scientific research, particularly in the fields of brain science and social sciences. We analyze the fundamental aspects of human research and argue that it is high time for researchers to transition to human-AI joint research. Building upon this foundation, we propose two innovative research paradigms of human-AI joint research: "AI-Brain Science Research Paradigm" and "AI-Social Sciences Research Paradigm". In these paradigms, we introduce three human-AI collaboration models: AI as a research tool (ART), AI as a research assistant (ARA), and AI as a research participant (ARP). Furthermore, we outline the methods for conducting human-AI joint research. This paper seeks to redefine the collaborative interactions between human researchers and AI system, setting the stage for future research directions and sparking innovation in this interdisciplinary field.

Glyn A. Collinson, Heli Hietala, Ferdinand Plaschke et al.

Abstract Shocklets and short large‐amplitude magnetic structures (SLAMS) are steepened magnetic fluctuations commonly found in Earth's upstream foreshock. Here we present Venus Express observations from the 26th of February 2009 establishing their existence in the steady‐state foreshock of Venus, building on a past study which found SLAMS during a substantial disturbance of the induced magnetosphere. The Venusian structures were comparable to those reported near Earth. The 2 Shocklets had magnetic compression ratios of 1.23 and 1.34 with linear polarization in the spacecraft frame. The 3 SLAMS had ratios between 3.22 and 4.03, two of which with elliptical polarization in the spacecraft frame. Statistical analysis suggests SLAMS coincide with unusually high solar wind Alfvén mach‐number at Venus (12.5, this event). Thus, while we establish Shocklets and SLAMS can form in the stable Venusian foreshock, they may be rarer than at Earth. We estimate a lower limit of their occurrence rate of ≳14%.

Zachary Gan

This essay critically examines the biopoliticization of suicide, challenging its framing as a public health issue which obscures its cultural and philosophical significance. Drawing from Michel Foucault’s theories of biopower, this essay argues that suicide is externalized, massified, and medicalized under the discourse of public health, leading to its subjugation to biopower’s rhetoric. At the core of this narrative is a powerful presupposition that suicide is separable from the individual who commits the act. Drawing from Primo Levi’s The Drowned and the Saved and Judith Butler’s essay Violence, Politics, and Mourning, this essay conceives suicide as an intentional act of agency, occurring under particular conditions of emotional duress which are created by a historical relay of societal violence. This essay seeks to dismantle the prevailing narrative of suicide, free suicide from its biopolitical rhetoric, and argues that suicide ought to be understood as a radical act which bears witness against the violence of the biopolitical state.

Parisa Karami-Kolmoti, Hadi Beitollahi, Sina Modiri

This study addressed the use of manganese dioxide nanorods/graphene oxide nanocomposite (MnO₂ NRs/GO) for modifying a glassy carbon electrode (GCE). The modified electrode (MnO₂ NRs/GO/GCE) was used as an electrochemical sensor for the determination of hydroquinone (HQ) in water samples. Differential pulse voltammetry (DPV), cyclic voltammetry (CV), and chronoamperometry were used for more analysis of the HQ electrochemical behavior. Analyses revealed acceptable electrochemical functions with lower transfer resistance of electrons and greater conductivity of the MnO₂ NRs/GO/GCE. The small peak-to-peak separation is an indication of a rapid electron transfer reaction. Therefore, this result is probably related to the effect of the MnO₂ NRs/GO nanocomposite on the surface of GCE. In the concentration range of 0.5 μM to 300.0 μM with the detection limit as 0.012 μM, there was linear response between concentration of HQ and the current. The selectivity of the modified electrode was determined by detecting 50.0 μM of HQ in the presence of various interferent molecules. At the end, the results implied the acceptable outcome of the prepared electrode for determining HQ in the water samples.

Bishnu Adhikari, Alyson G. Myers, Chuanmin Ruan et al.

This study evaluated the effects of a <i>Bacillus</i> direct-fed microbial and microencapsulated calcium butyrate fed individually and in combination, as compared to an antibiotic growth promoter, on growth performance, processing characteristics, intestinal morphology, and intestinal microbiota of Ross 708 broilers reared from 0 to 47 d post-hatch. Dietary treatments included: (1) a negative control with no antimicrobial (NC), (2) a positive control diet containing bacitracin methylene disalicylate (PC), (3) a diet containing a <i>Bacillus</i> direct-fed microbial (CS), (4) a diet containing microencapsulated calcium butyrate (BP), and (5) a diet containing both CS and BP. Treatments were replicated with 10 pens of 20 birds each. From 0 to 15 d post-hatch, the FCR of broilers fed the PC, CS, BP, and CS + BP diets were lower (<i>p</i> < 0.05) than those fed the NC diet, but treatment effects (<i>p</i> > 0.05) were not observed on subsequent performance. BP supplementation improved (<i>p</i> < 0.05) total breast meat weight and yield at processing. Intestinal histology was not influenced (<i>p</i> > 0.05) by the treatment. Analysis of the jejunal microbiota collected at 15 d post-hatch revealed that the genus SMB53 was significantly lower for the CS group, and <i>Sporanaerobacter</i> was lower in the CS and CS + BP groups compared with the NC (<i>p</i> < 0.05). The jejunal microbiota from broilers in the CS + BP group had higher (<i>p</i> < 0.05) alpha and beta diversities compared with broilers fed the NC and CS diets. The results reflected synergistic effects between CS and BP in modulating the jejunal microbiota at 15 d that may have been related to enhanced feed efficiency (i.e., lower FCR) observed during this period.

Xiaoyan Peng, Jiawen Liu, Baoling Li et al.

Acyl carrier protein (<i>ACP</i>) is an acyl carrier in fatty acid synthesis and is an important cofactor of fatty acid synthetase. Little is known about ACP in insects and how this protein may modulate the composition and storage of fatty acids. We used an RNAi-assisted strategy to study the potential function of ACP in <i>Hermetia illucens</i> (Diptera: Stratiomyidae). We identified a <i>HiACP</i> gene with a cDNA length of 501 bp and a classical conserved region of DSLD. This gene was highly expressed in the egg and late larval instars and was most abundant in the midgut and fat bodies of larvae. Injection of <i>dsACP</i> significantly inhibited the expression level of <i>HiACP</i> and further regulated the fatty acid synthesis in treated <i>H. illucens</i> larvae. The composition of saturated fatty acids was reduced, and the percentage of unsaturated fatty acids (<i>UFAs</i>) was increased. After interfering with <i>HiACP</i>, the cumulative mortality of <i>H. illucens</i> increased to 68.00% (<i>p</i> < 0.05). <i>H. illucens</i> growth was greatly influenced. The development duration increased to 5.5 days, the average final body weights of larvae and pupae were decreased by 44.85 mg and 14.59 mg, respectively, and the average body lengths of larvae and pupae were significantly shortened by 3.09 mm and 3.82 mm, respectively. The adult eclosion rate and the oviposition of adult females were also severely influenced. These results demonstrated that <i>HiACP</i> regulates fatty acid content and influences multiple biological processes of <i>H. illucens</i>.

Michael L Brodie

Data science is not a science. It is a research paradigm. Its power, scope, and scale will surpass science, our most powerful research paradigm, to enable knowledge discovery and change our world. We have yet to understand and define it, vital to realizing its potential and managing its risks. Modern data science is in its infancy. Emerging slowly since 1962 and rapidly since 2000, it is a fundamentally new field of inquiry, one of the most active, powerful, and rapidly evolving 21st century innovations. Due to its value, power, and applicability, it is emerging in over 40 disciplines, hundreds of research areas, and thousands of applications. Millions of data science publications contain myriad definitions of data science and data science problem solving. Due to its infancy, many definitions are independent, application specific, mutually incomplete, redundant, or inconsistent, hence so is data science. This research addresses this data science multiple definitions challenge by proposing the development of coherent, unified definition based on a data science reference framework using a data science journal for the data science community to achieve such a definition. This paper provides candidate definitions for essential data science artifacts that are required to discuss such a definition. They are based on the classical research paradigm concept consisting of a philosophy of data science, the data science problem solving paradigm, and the six component data science reference framework (axiology, ontology, epistemology, methodology, methods, technology) that is a frequently called for unifying framework with which to define, unify, and evolve data science. It presents challenges for defining data science, solution approaches, i.e., means for defining data science, and their requirements and benefits as the basis of a comprehensive solution.

N. Werner, J. Řípa, C. Thöne et al.

This is the first in a collection of three papers introducing the science with an ultra-violet (UV) space telescope on an approximately 130~kg small satellite with a moderately fast re-pointing capability and a real-time alert communication system approved for a Czech national space mission. The mission, called Quick Ultra-Violet Kilonova surveyor - QUVIK, will provide key follow-up capabilities to increase the discovery potential of gravitational wave observatories and future wide-field multi-wavelength surveys. The primary objective of the mission is the measurement of the UV brightness evolution of kilonovae, resulting from mergers of neutron stars, to distinguish between different explosion scenarios. The mission, which is designed to be complementary to the Ultraviolet Transient Astronomy Satellite - ULTRASAT, will also provide unique follow-up capabilities for other transients both in the near- and far-UV bands. Between the observations of transients, the satellite will target other objects described in this collection of papers, which demonstrates that a small and relatively affordable dedicated UV-space telescope can be transformative for many fields of astrophysics.

Ahmed Abbasi, Roger H. L. Chiang, Jennifer J. Xu

Data science has been described as the fourth paradigm for scientific discovery. The latest wave of data science research, pertaining to machine learning and artificial intelligence (AI), is growing exponentially and garnering millions of annual citations. However, this growth has been accompanied by a diminishing emphasis on social good challenges - our analysis reveals that the proportion of data science research focusing on social good is less than it has ever been. At the same time, the proliferation of machine learning and generative AI have sparked debates about the socio-technical prospects and challenges associated with data science for human flourishing, organizations, and society. Against this backdrop, we present a framework for "data science for social good" (DSSG) research that considers the interplay between relevant data science research genres, social good challenges, and different levels of socio-technical abstraction. We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems (and other related disciplines) and highlight current impediments. We then use our proposed framework to introduce the articles appearing in the special issue. We hope that this article and the special issue will spur future DSSG research and help reverse the alarming trend across data science research over the past 30-plus years in which social good challenges are garnering proportionately less attention with each passing day.

Yan Long, Hongshan Zhao

Game theory has become an important tool to study the competition between oligopolistic enterprises. After combing the existing literature, it is found that there is no research combining two-stage game and nonlinear dynamics to analyze the competition between enterprises for advertising. Therefore, this paper establishes a two-stage game model to discuss the effect of the degree of firms’ advertising input on their profits. And the complexity of the system is analyzed using nonlinear dynamics. This paper analyzes and studies the dynamic game for two types of application network models: data transmission model and transportation network model. Under the time-gap ALOHA protocol, the noncooperative behavior of the insiders in the dynamic data transmission stochastic game is examined as well as the cooperative behavior. In this paper, the existence of Nash equilibrium and its solution algorithm are proved in the noncooperative case, and the “subgame consistency” of the cooperative solution (Shapley value) is discussed in the cooperative case, and the cooperative solution satisfying the subgame consistency is obtained by constructing the “allocation compensation procedure.” The cooperative solution is obtained by constructing the “allocation compensation procedure” to satisfy the subgame consistency. In this paper, we propose to classify the packets transmitted by the source nodes, and by changing the strategy of the source nodes at the states with different kinds of packets, we find that the equilibrium payment of the insider increases in the noncooperative game with the addition of the “wait” strategy. In the transportation dynamic network model, the problem of passenger flow distribution and the selection of service parameters of transportation companies are also studied, and a two-stage game theoretical model is proposed to solve the equilibrium price and optimal parameters under Wardrop’s criterion.

Xiao Yan, Jianhua Guo, Xinghua Jiang

Abstract Recently, the application and development of flexible microwave-absorption composites based on silicone rubber have gradually become a research hot spot. In this study, methyl vinyl phenyl silicone rubber (MPVQ)/carbonyl iron particles (CIPs)/graphene (GR) composites were prepared by mechanical blending, and the effects of thermal-ageing temperature on the microwave-absorption properties of the composites were investigated. The mechanism of the thermal-ageing temperature’s effects on microwave-absorption behaviour was identified. The results show that unaged composites have superior microwave-absorption properties, with a minimum reflection loss (RL min ) of − 87.73 dB, a lowest thickness of 1.46 mm, and an effective absorption bandwidth (EAB, RL < − 10 dB) reaching 5.8 GHz (9.9–15.7 GHz). With ageing at 240 °C for 24 h, the RL min at a frequency of 5.48 GHz is − 45.55 dB with a thickness of 2.55 mm, and the EAB value reaches 2 GHz (range 4.6–6.6 GHz). In the thermal-ageing process, a crosslinking reaction occurs in MPVQ with an increase in crosslinking density from 5.88 × 10−5 mol g−1 (unaged) to 4.69 × 10−4 mol g−1 (aged at 240 °C). Simultaneously, thermal degradation of the composites leads to a reduction in the rubber concentration. In addition, a small amount of CIPs are oxidized to Fe3O4, and the remaining CIPs aggregate to generate more electrically conductive pathways. Consequently, the dielectric loss of the composites will be significantly improved, resulting in poor impedance matching. The microwave-absorption properties of the composites gradually decrease with increasing thermal-ageing temperature from 200 to 240 °C.

Haosen Ge, In Young Park, Xuancheng Qian et al.

High-quality text data has become an important data source for social scientists. We have witnessed the success of pretrained deep neural network models, such as BERT and RoBERTa, in recent social science research. In this paper, we propose a compact pretrained deep neural network, Transformer Encoder for Social Science (TESS), explicitly designed to tackle text processing tasks in social science research. Using two validation tests, we demonstrate that TESS outperforms BERT and RoBERTa by 16.7% on average when the number of training samples is limited (<1,000 training instances). The results display the superiority of TESS over BERT and RoBERTa on social science text processing tasks. Lastly, we discuss the limitation of our model and present advice for future researchers.

N. Satchell, T. Mitchell, P. M. Shepley et al.

Abstract We report on the electrical transport properties of Nb based Josephson junctions with Pt/Co $$_{68}$$ 68 B $$_{32}$$ 32 /Pt ferromagnetic barriers. The barriers exhibit perpendicular magnetic anisotropy, which has the main advantage for potential applications over magnetisation in-plane systems of not affecting the Fraunhofer response of the junction. In addition, we report that there is no magnetic dead layer at the Pt/Co $$_{68}$$ 68 B $$_{32}$$ 32 interfaces, allowing us to study barriers with ultra-thin Co $$_{68}$$ 68 B $$_{32}$$ 32 . In the junctions, we observe that the magnitude of the critical current oscillates with increasing thickness of the Co $$_{68}$$ 68 B $$_{32}$$ 32 strong ferromagnetic alloy layer. The oscillations are attributed to the ground state phase difference across the junctions being modified from zero to $$\pi $$ π . The multiple oscillations in the thickness range $$0.2~\leqslant ~d_\text {CoB}~\leqslant ~1.4$$ 0.2 ⩽ d CoB ⩽ 1.4  nm suggests that we have access to the first zero- $$\pi $$ π and $$\pi $$ π -zero phase transitions. Our results fuel the development of low-temperature memory devices based on ferromagnetic Josephson junctions.