Hasil untuk "Electronic computers. Computer science"

Yixue Xia, Yongzhang He, Wei Jiao et al.

Monitoring, governing, and guiding public opinion on social media during emergencies is challenging, often leading to mass panic, rumors, and public opinion crises. Understanding the scale evolution of information and subjects within the public opinion ecology of emergencies can aid in achieving macrocontrol. To address this, an information-subject dual-structure model was developed to illustrate the scale development trend. Fitting and prediction methods based on the model were proposed, and general rules were provided through simulation. For empirical research, data from three categories of six emergency events were downloaded from the GsData platform. The linear regression method was used to predict the trend of subjects and information, achieving an F1 fit above 82% for the six datasets. Subsequently, a fine-tuning matrix was established, increasing the F1 values to over 98.30%, thereby confirming the model’s interpretability and universality.

Yu Liang, Lexin Yang, Shuyi Sun et al.

Reliable inspection of power transmission lines is essential for smart grid maintenance, yet manual surveys are slow, costly, and risky, and current detectors still struggle with small insulator defects hidden in complex outdoor scenes. This paper introduces YOLOv11-RAH, a lightweight network framework designed for edge deployment in intelligent inspection systems that pairs the YOLOv11 backbone with a recurrent attention head. The head iteratively refines multi-scale features through depthwise gated convolutions and halts once convergence is reached, strengthening localization without noticeable latency. A four stage augmentation scheme, which are geometric, photometric, Mosaic, and random crop transformations, further improves robustness to viewpoint, illumination, and scale variations. For evaluation, a consolidated insulator corpus is assembled from two cleaned public benchmarks and a newly private UAV dataset, covering diverse outdoor conditions and defect categories. Extensive experiments with other detection algorithms show the consistent accuracy gains of the proposed network improvement, and ablation studies further confirm the benefits of the proposed attention module and augmentation pipeline. In particular, YOLOv11-RAH achieves roughly 5 percentage mAP improvement over YOLOv11n base model on both the consolidated public datasets PTL-AI Furnas and CPLID, and the private UAV dataset AIDD, underscoring its transferability across data sources. These findings suggest that iterative attention combined with targeted data augmentation offers an efficient and reliable solution for automatic insulator condition detection in intelligent inspection networks.

Tijs Karman, Niccolò Bigagli, Weijun Yuan et al.

We develop double microwave shielding, which has recently enabled evaporative cooling to the first Bose-Einstein condensate of polar molecules [Bigagli et al., Nature 631, 289 (2024)]. Two microwave fields of different frequency and polarization are employed to effectively shield polar molecules from inelastic collisions and three-body recombination. Here, we describe in detail the theory of double microwave shielding. We demonstrate that double microwave shielding effectively suppresses two- and three-body losses. Simultaneously, dipolar interactions and the scattering length can be flexibly tuned, enabling comprehensive control over interactions in ultracold gases of polar molecules. We show that this approach works universally for a wide range of molecules. This opens the door to studying many-body physics with strongly interacting dipolar quantum matter.

JIANG Nan

With the explosive growth of data volume in the era of the digital economy, data platforms are facing increasing challenges in data storage, processing, and calculation. This paper proposes multiple Data Lakes under a unified Data Middle Platform, which is applied in industrial big data. The architecture, based on the integration of lake and warehouse technology, aims to build an efficient, flexible, and scalable data management platform to support cross-level, cross-organizational, and cross-regional data-sharing applications. With the centralized deployment of the three-level data lake and the middle platform deployed uniformly, the five core functions are constructed, forming the dataacquisition capabilities of multi-source heterogeneous data, multi-mode data storage, data processing, data governance, data service, and data security protection. Through multi-data-lakes, collaborative interaction, and unified data control ability, to realize the overall management and open sharing of data of super-large group companies, application scenarios in various business fields are supported, and the value of data elements is released.

Wu Zhihua, Peng Chen, Tian Engang et al.

Cyber-physical microgrids, as a representative industrial system, seamlessly integrate computation, communication, control, and physical devices, making it vulnerable to cyber-attacks that can trigger cascading failures and potentially lead to the collapse of the entire grid. This paper aims to develop an efficient detection framework for collusive stealthy attacks in cyber-physical microgrids. First, an ℒ∞ unknown input observer (UIO) is deployed at the control center to monitor communication links between distributed generation units (DGUs). By treating interconnections and secondary control as unknown inputs, the observer gain is designed using only local information. Then, the vulnerability of the ℒ∞ UIO-based monitoring unit is analyzed, and a collusive stealthy attack is devised to disrupt grid operations without alerting the ℒ∞ UIO-based detection mechanism. To counteract the novel attack, a dynamic encoding mechanism is developed for the communication links between DGUs. This mechanism involves encoding control signals prior to transmission and subsequently decoding them at the control center. Furthermore, an in-depth analysis of the feasibility criteria for the encoding matrix has been conducted. Eventually, the efficacy of the proposed detection framework is validated through a series of simulation experiments.

Jingjing Xu

The college English corpus can help us better master English, but how to obtain the desired information from a large number of English corpus has become the focus of information technology. Based on the natural language processing (NLP) technology, a sentiment analysis model is built in this article. An improved term frequency–inverse document frequency (TF-IDF) algorithm is proposed in this article, where the weighted average method is used to determine the emotional value of each emotional word. The inspirational words are used to obtain the English corpus’s emotional tendency and emotional value. The results show that the model has high classification accuracy and operation efficiency when selecting feature words. Compared with the TF-IDF, the improved TF-IDF algorithm added the necessary information weight processing and word density weight processing to two new processing links, which can significantly improve the efficiency of college English learning.

Haokun Wang, Xiaobo Liu, Nuofan Qiu et al.

Besides direct interaction, human hands are also skilled at using tools to manipulate objects for typical life and work tasks. This paper proposes DeepClaw 2.0 as a low-cost, open-sourced data collection platform for learning human manipulation. We use an RGB-D camera to visually track the motion and deformation of a pair of soft finger networks on a modified kitchen tong operated by human teachers. These fingers can be easily integrated with robotic grippers to bridge the structural mismatch between humans and robots during learning. The deformation of soft finger networks, which reveals tactile information in contact-rich manipulation, is captured passively. We collected a comprehensive sample dataset involving five human demonstrators in ten manipulation tasks with five trials per task. As a low-cost, open-sourced platform, we also developed an intuitive interface that converts the raw sensor data into state-action data for imitation learning problems. For learning-by-demonstration problems, we further demonstrated our dataset’s potential by using real robotic hardware to collect joint actuation data or using a simulated environment when limited access to the hardware.

Pengfei Feng, Huiqing Jin, Linfeng Zhao et al.

In order to improve the intelligent vehicle lane-changing performance, an active lane-changing control algorithm is proposed considering the changes of road curvature and vehicle speed. Firstly, the vehicle dynamics model considering vehicle speed variation and lane-changing safety distance is established, and the expected lane-changing trajectory model under the curved road is designed simultaneously. Then, taking the yaw rate and longitudinal speed as the control objectives of lateral and longitudinal motions, respectively, the sliding-mode variable structure control method based on Lyapunov stability condition is adopted, and the trajectory tracking controller is designed by combining the inverted method to track the desired lane-changing trajectory. Finally, the lane-changing trajectory model and trajectory tracking controller are verified in simulation platform of CarSim/Simulink and hardware-in-the-loop (HIL) test bench. The results show that the proposed trajectory tracking control method can perform the lane-changing behavior well under different road curvatures and vehicle speeds while maintaining high trajectory tracking control accuracy.

Kristina Ulicna, Kristina Ulicna, Giulia Vallardi et al.

Single-cell methods are beginning to reveal the intrinsic heterogeneity in cell populations, arising from the interplay of deterministic and stochastic processes. However, it remains challenging to quantify single-cell behaviour from time-lapse microscopy data, owing to the difficulty of extracting reliable cell trajectories and lineage information over long time-scales and across several generations. Therefore, we developed a hybrid deep learning and Bayesian cell tracking approach to reconstruct lineage trees from live-cell microscopy data. We implemented a residual U-Net model coupled with a classification CNN to allow accurate instance segmentation of the cell nuclei. To track the cells over time and through cell divisions, we developed a Bayesian cell tracking methodology that uses input features from the images to enable the retrieval of multi-generational lineage information from a corpus of thousands of hours of live-cell imaging data. Using our approach, we extracted 20,000 + fully annotated single-cell trajectories from over 3,500 h of video footage, organised into multi-generational lineage trees spanning up to eight generations and fourth cousin distances. Benchmarking tests, including lineage tree reconstruction assessments, demonstrate that our approach yields high-fidelity results with our data, with minimal requirement for manual curation. To demonstrate the robustness of our minimally supervised cell tracking methodology, we retrieve cell cycle durations and their extended inter- and intra-generational family relationships in 5,000 + fully annotated cell lineages. We observe vanishing cycle duration correlations across ancestral relatives, yet reveal correlated cyclings between cells sharing the same generation in extended lineages. These findings expand the depth and breadth of investigated cell lineage relationships in approximately two orders of magnitude more data than in previous studies of cell cycle heritability, which were reliant on semi-manual lineage data analysis.

Ņikiforova Oksana, Zabiniako Vitaly, Kornienko Jurijs et al.

Insufficient user involvement, lack of user feedback, incomplete and changing user requirements are some of the critical reasons for the difficulty of IS usage, which could potentially reduce the number of customers. Under the previous authors’ research, the method for analysing the behaviour of IT system users was developed, which was intended to improve the usability of the system and thus could increase the efficiency of business processes. The developed method is based on the use of graph searching algo rithms, Markov chains and Machine Learning approach. This paper focuses on detailing of method output data in the context of definition of their importance based on expert evaluation and demonstration of visual presentation of different UX analysis situations. The paper briefly reminds the essence of the method, including both the input and output data sets, and, with the help of experts, evaluates the expected result in the context of their importance in UX analysis. It also introduces visualization prototype developed to obtain the output data, which allows verifying the input/output data transformation possibilities and expected data acquisition potential.

Adita Rianto, Ahmad Rafie Pratama

COVID-19 was declared a pandemic by the World Health Organization (WHO) in early 2020. The Indonesian government has taken steps to stop COVID-19 from spreading, one of which is vaccination. However, not everyone thinks vaccination is a good idea. Like in other countries, Indonesian people responded in different ways to COVID-19 vaccination posts on social media, be it from government officials or news portals. Public responses can be used to help the government make a better vaccination strategy to end the pandemic in Indonesia. Using the lexicon method in determining the sentiment in COVID-19 vaccination posts on Facebook, this research found that unlike news portals that tended to post a more balanced content (36% positive, 20% negative, and 44% neutral out of 23,623 posts, min score = -19, max score = 24, mean = 0.25, SD = 1.43), government accounts posted much more positive content, both in quality (min score = -15, max score = 40, mean score = 4.16, SD = 6.76 ) and quantity (69% positive) than they did the neutral (15%) and the negative content (16%) out of 723 posts. Subsequent analysis with Two-Way ANOVA tests discovered that COVID-19 vaccination posts by the news portals elicited more varied reactions from the public than government accounts that tended to elicit mostly positive reactions. Also, both the content sentiment of COVID-19 vaccination posts in Indonesia and the account types making those posts, as well as their interaction terms do have an impact on how the public responds to them.

Shane Dooley

In most quantum sensing schemes, interactions between the constituent particles of the sensor are expected to lead to thermalization and degraded sensitivity. However, recent theoretical and experimental work has shown that the phenomenon of quantum many-body scarring can slow down, or even prevent, thermalization. We show that scarring can be exploited for quantum sensing that is robust against certain strong interactions. In the ideal case of perfect scars with harmonic energy gaps, the optimal sensing time can diverge despite the strong interactions. We demonstrate the idea with two examples: a spin-1 model with Dzyaloshinskii-Moriya interaction and a spin-1/2 mixed-field Ising model. We also briefly discuss some nonideal perturbations and the addition of periodic controls to suppress their effect on sensing.

H. G. van der Roest, J. Wenborn, Channah Pastink et al.

Petrovica Sintija, Anohina-Naumeca Alla, Kikans Andris

Nowadays, interoperability of learning management systems is still not very high. The authoring tools can help transfer e-learning content between different learning management systems. However, in this context, they should be able to produce learning content that is compliant with some industry standards. One of the most widely used standards is the SCORM 1.2 release. The research addresses the extension of the functionality of the previously developed content development tool EMMA by incorporating into it the support for the subset of SCORM 1.2 requirements. The paper describes the process of the acquisition, implementation, and validation of the defined requirements. Moreover, it presents the results of the analysis of 33 SCORM authoring tools and 16 SCORM players.

Wensi Wu, Justyna Kosianka, Heather Reed et al.

The present work discusses capabilities within the finite element library CU-BENs. CU-BENs focuses on applying the finite element method to structural mechanics problems encountered within the context of inverse problems and partitioned fluid–structure interaction; thus element formulations are primarily of a structural type — truss, frame, and triangular discrete Kirchhoff theory shells. CU-BENs defaults to the skyline sparse storage scheme for the system matrix, but also supports other storage schemes when using external libraries such as LAPACK and UMFPACK. CU-BENs includes built-in nonlinear solution strategies, such as the Newton Raphson method and modified spherical arc length method, that are available within static analyses as well within the context of transient dynamic analyses involving a generalized-α implementation of the Newmark implicit time integration scheme.

Yasmine Koubaa, Mohamed Boukattaya, Tarak Damak

This article designs a novel adaptive trajectory tracking controller for nonholonomic wheeled mobile robot under kinematic and dynamic uncertainties. A new velocity controller, in which kinematic parameter is estimated, produces velocity command of the robot. The designed adaptive sliding mode dynamic controller incorporates an estimator term to compensate for the external disturbances and dynamic uncertainties and a feedback term to improve the closed-loop stability and account for the estimation error of external disturbances. The system stability is analyzed using Lyapunov theory. Computer simulations affirm the robustness of the designed control scheme.

S. Kidalov, F. M. Shakhov

A major problem challenging specialists in present-day materials sciences is the development of compact, cheap to fabricate heat sinks for electronic devices, primarily for computer processors, semiconductor lasers, high-power microchips, and electronics components. The materials currently used for heat sinks of such devices are aluminum and copper, with thermal conductivities of about 250 W/(m·K) and 400 W/(m·K), respectively. Significantly, the thermal expansion coefficient of metals differs markedly from those of the materials employed in semiconductor electronics (mostly silicon); one should add here the low electrical resistivity metals possess. By contrast, natural single-crystal diamond is known to feature the highest thermal conductivity of all the bulk materials studied thus far, as high as 2,200 W/(m·K). Needless to say, it cannot be applied in heat removal technology because of high cost. Recently, SiC- and AlN-based ceramics have started enjoying wide use as heat sink materials; the thermal conductivity of such composites, however, is inferior to that of metals by nearly a factor two. This prompts a challenging scientific problem to develop diamond-based composites with thermal characteristics superior to those of aluminum and copper, adjustable thermal expansion coefficient, low electrical conductivity and a moderate cost, below that of the natural single-crystal diamond. The present review addresses this problem and appraises the results reached by now in studying the possibility of developing composites in diamond-containing systems with a view of obtaining materials with a high thermal conductivity.

J. Bailey

Ø. Guttersrud, Kjell Sverre Petterson

Aditya Permadi, Mary Handoko

Abstrak—Sukses implementasi ERP di sektor industri dalam satu dekade ini telah mempunyai banyak manfaat yang mendorong sektor pendidikan khususnya pendidikan tinggi untuk mengikuti keberhasilan implementasi ERP.Implementasi ERP sangat kompleks, membutuhkan waktu lama, dan mahal yang biasanya dihadapkan dengan tantangan serius.Kegagalan implementasi ERP adalah kurangnya kesiapan organisasi dalam hal kematangan aspek organisasi, bisnis proses, kultur dan teknologi dan pemilihan vendor yang tepat. Banyak penelitian dilakukan untuk pengembangkan model penilaian kesiapan implementasi ERP di sektor idustri tetapi beberapa penelitian menunjukan bahwa model/praktek terbaik dalam implementasi ERP di sektor industri tidak sesuai untuk pendidikan tinggi.penelitian ini mengusulkan sebuah pengembangan model penilaian kesiapan implementasi ERP untuk menilai kesiapan organisasi khusus di sektor pendidikan tinggi, pemilihan model yang akan dikembangkan dipilih sesuai dengan tatakelola pendidikan tinggi lalu critical success factor sebagai indikator pembentuk dari kausal model. Validsi SEM Confirmatory Factor Analysis terhadap model yang diusulkan. Penelitian ini dapat menunjukan bukti bahwa model penilaian kesiapan implementasi ERP di sektor pendidikan tinggi berbeda dengan model kesiapan ERP di sektor industri.