Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel 6-dimensional force measurement based on the electric 6-DOF loading device

The 6-degrees-of-freedom (6-DOF) platform has gained popularity in industry owing to its advantages, especially in indoor simulation experiment requiring six-dimensional force environments. However, up to date, the measurement of such six-dimensional forces is challenging due to complex structures, difficult decoupling, and high costs. In this study, an electric 6-DOF loading device designed by ourselves is presented, with an ability to load high precision displacement and force. To measure the six-dimensional force of the device’s moving platform, this paper proposes a scheme involving embedding tension-compression sensors in each leg of the device and describes the mechanical model of the device. Then a novel method with a variable transformation matrix is proposed, complemented by a decoupling study that optimizes the variable transformation matrix. Finally, we validate the proposed method through an external force loading experiment. The results indicate that the error in Fy experiment is less than 2.7%, and the error in Fz experiment is less than 1.6%. The novel method exhibits high accuracy, easy installation, fast response, and low cost.
Income Inequality in the United States: Using Tax Data to Measure Long-Term Trends

Top income share estimates based only on individual tax returns (Piketty and Saez, 2003) are biased by tax-base changes, major social changes, and missing income sources. Addressing these issues requires numerous assumptions, especially for broadening income beyond that reported on tax returns. This paper shows the effects of adjusting for technical tax issues and the sensitivity to alternative assumptions for distributing missing income sources. Our estimates of the distribution of national income suggest that since 1980 top income shares are lower and have increased less than other tax-based studies. Since the early 1960s, increasing government transfers and tax progressivity have resulted in little change in after-tax top income shares. data Views and opinions expressed are of the and do official Treasury or policy. This paper embodies the of the Joint Committee on but as members of both and both houses of the Joint Committee
The Thuathe meteorite fall of 21 July 2002 : research letters

The Thuathe meteorite fell on 21 July 2002 between 15:45 SAST (first sightings) and 15:49 (local sightings). The meteorite is classified as an H4 / 5 type ordinary chondrite, confirmed by whole rock and mineralogical analyses. The minerals found in the meteorite were kamacite, troilite, albitic plagioclase, forsteritic olivine, diopside, enstatite, and chromitic spinel. Temperatures were obtained from the orthopyroxene-clinopyroxene, orthopyroxene-olivine and clinopyroxene-olivine systems, that yielded values of 1200-1600°C. With respect to average chondrites, the Thuathe meteorite samples are depleted in Zr, and enriched in Rb, Th, Ta, Ba, La, Sr, Sc, Co and Ni.
SEmigrate: Optimizing Data Protection with VM Introspection

Recently, virtual machines (VMs) with a large amount of memory are widely used. Since it is often difficult to migrate such a large-memory VM to one large destination host, split migration divides the memory of a VM into small fragments and transfers them to multiple destination hosts. The migrated VM exchanges its memory data between the hosts using remote paging. To prevent information leakage from and tampering with the memory data in an untrusted environment, memory encryption and integrity checking can be used. However, the overhead of such data protection affects the performance of the hosts and the VM more largely in faster networks. This paper proposes SEmigrate for optimizing data protection in split migration and remote paging. SEmigrate avoids decrypting memory data and integrity checking at most of the destination hosts to reduce the protection overhead and completely prevent information leakage. Also, it can selectively encrypt only sensitive memory data and check the integrity of only important memory data by analyzing the memory of the guest operating system and applications in a VM. SEmigrate could reduce the time for data-protected split migration by up to 43% and improve the performance of migrated VMs by up to 19% in 100 Gigabit Ethernet.
Analysis of Longitudinal Rail Vehicle Dynamic and Design of Controller for Slip/Slide Protection

This study proposes a simulation design approach using MATLAB/Simulink to develop a rail vehicle dynamic model and to test wheel slip/slide protection (WSP) controllers. Instead of implementing and testing WSP device to the actual rail vehicle, which involves considerable financial investment, making simulation design is an attractive alternative. Two WSP controllers are designed. One is based on PID approach. The other one is hysteresis or bang-bang controller. The results show that PID controller exhibits superior performance with minimal slip and shorter stopping distances than hysteresis controller according to CENELEC standard EN15595 setup. The experiment is also performed under various adhesion conditions namely high and low adhesion simulating normal dry surface and degraded wet or contaminated surface rail conditions.
Face Mask Detection Using Deep Learning of VGG19

With the outbreak of the novel coronavirus in 2019, various industries in the world have been hit hard, and in 2022, the epidemic situation is still severe. To prevent the spread of the epidemic, wearing a mask has become the most effective method. Based on this situation, detecting whether people wear masks has become a very popular research direction in the field of artificial intelligence in recent years. In this study, the author uses the pre-trained haar cascade model to achieve rapid face recognition and then puts the picture into the trained VGG19 model for mask recognition. Finally, the accuracy of the entire recognition is 98.79%, and the loss is 0.04.
Perovskite Topological Lasers: A Brand New Combination

Nanolasers are the essential components of modern photonic chips due to their low power consumption, high energy efficiency and fast modulation. As nanotechnology has advanced, researchers have proposed a number of nanolasers operating at both wavelength and sub-wavelength scales for application as light sources in photonic chips. Despite the advances in chip technology, the quality of the optical cavity, the operating threshold and the mode of operation of the light source still limit its advanced development. Ensuring high-performance laser operation has become a challenge as device size has been significantly reduced. A potential solution to this problem is the emergence of a novel optical confinement mechanism using photonic topological insulator lasers. In addition, gain media materials with perovskite-like properties have shown great potential for lasers, a role that many other gain materials cannot fulfil. When combined with topological laser modes, perovskite materials offer new possibilities for the operation and emission mechanism of nanolasers. This study introduces the operating mechanism of topological lasers and the optical properties of perovskite materials. It then outlines the key features of their combination and discusses the principles, structures, applications and prospects of perovskite topological lasers, including the scientific hurdles they face. Finally, the future development of low-dimensional perovskite topological lasers is explored.
ConvNext: A Contemporary Architecture for Convolutional Neural Networks for Image Classification

Convolutional neural networks (CNNs) have established themselves as the industry standard for image classification jobs. Convolutional neural network architectures have been created and enhanced recently to improve accuracy and decrease computational complexity. Convolutional Neural Networks' effectiveness declined as transformers became more widely used in computer vision. The transformers, such as vision transformers, outperformed the antecedent CNNs in image classification tasks requiring computer vision. However, in 2022, Facebook researchers created a collection of convolutional neural networks that performed better than vision transformers in the Image Classification experiment. A ResNet was used to build this network, subsequently “modernized” to make it more resemble a Vision Transformer. Following this, the curated model was trained and evaluated on the ImageNet1k and ImageNet22k datasets for Image Classification, yielding several novel results. ConvNext was the name of this network, and it has many variations. On the CIFAR-10 dataset, we assess the effectiveness of the ConvNext models in this work. We demonstrate the advantage of the ConvNext model in terms of accuracy by comparing the results with those from other cutting-edge models.
Minichromosome maintenance protein 5 – a promising prognostic marker of oral epithelial dysplasias and oral squamous cell carcinomas

Background: Early diagnosis is the single most effective means of reducing the mortality rate of oral cancer. Aim: This study was undertaken to assess the expression of minichromosome maintenance protein 5 (MCM5) in oral epithelial dysplasias and oral squamous cell carcinomas (OSCCs) and to evaluate their possible role as a biomarker for early diagnosis and prognosis of OSCC. Design: A retrospective cross-sectional study. Materials and Methods: The study was conducted to assess the expression of MCM5 immunohistochemically in the tissue samples of oral epithelial dysplasias (n = 27) and OSCCs (n = 30) diagnosed between 2014 and 2019. Statistical Analysis: The difference in the mean nuclear labelling index (LI) between the groups and the subgroups was analysed statistically using the Kruskal–Wallis test and the post hoc test, and the Dunn–Bonferroni multiple comparison analysis was conducted for pairwise comparison between the four main groups and the subgroups. The association between mean MCM5 LI and clinicopathological parameters was analysed using Spearman's rank correlation coefficient. Results: A progressive increase in the nuclear expression of MCM5 protein (P-value <0.001) was noticed from normal oral mucosa through oral epithelial hyperplasia and oral epithelial dysplasia to OSCC. A significant correlation was also observed between the mean nuclear MCM5 LI of OSCC and TNM staging (R2 = 0.268, P = 0.029). Conclusion: Our findings suggest that MCM5 may be of great value in assessing the malignant potential of dysplastic lesions and may serve as biomarker of utility in the early diagnosis and prognosis of OSCC.
Collaborative and Verifiable VNF Management for Metaverse With Efficient Modular Designs

The metaverse is envisioned to create immersive and virtual worlds for people to experience interoperable 3D applications. However, the real-time, interactive, and multimedia characteristics of the metaverse applications require strict quality-of-service (QoS) on the underlying networking architecture, including high throughput, ultra-low delay, and human-centric service configurations. Network function virtualization (NFV)-enabled networking resource management can provide a promising solution to service-oriented QoS satisfaction for metaverse users. In this paper, we propose a blockchain-based collaborative and verifiable virtualized network function (VNF) management scheme for metaverse, named BVNF+. BVNF+ enables multiple network providers across different trust domains to abstract their services as VNFs and collaboratively manage end-to-end network slices for human-centric network services in metaverse. To address the design challenge of balancing the on-chain and off-chain overheads, we decouple the computations of VNF queries into modular components based on software and hardware verifiable computation (vc) approaches. Our modular strategy can achieve on/off-chain computation and communication efficiency while keeping low usage of the secure hardware. We conduct security analysis and extensive experiments based on a real-world blockchain testing network. The analysis and experimental results demonstrate that BVNF+ is both secure and efficient as compared with the existing works.
Malware Detection By Distributed Systems with Partial Centralization

The paper proposes approach to use new tools for malware detection in corporate networks, which are distributed systems with partial centralization. To make decision about malware presence, the components which includes specified decision-making for the system's center are defined as a decentralized subsystem. To determine the states of the system components, characteristic indicators are proposed, and generalized analytical expressions for their calculation are developed. Such calculations make it possible to assess the state of the components in the system in order to determine its further steps. As a result, the system is the basis for usage of different malware detection methods in combination with the system components as an integral sensor. To test the system, a worm-virus detection method was implemented and experiments were conducted. The results of experimental studies approved the efficiency of the proposed solution.
Research in farm management technologies using the expert method.

The task of the research was to state the most popular peculiarities of farm management technologies depending on the size of the herd in order to use the research results in calculations of greenhouse gas emissions. The research was performed applying the expert methods based on the farm management technologies as they are closely related to the size of the herd and the kind of the obtained farm manure. The expert method can be applied for research in farm management technologies of different animal species and groups, but in the present article only milk cow management technologies will be discussed as they produce the biggest amount of greenhouse gas emissions. The practice shows that on small farms the cows are tied, on medium farms – either tied or loose, but on large farms – only loose. On the farms where the cows are tied solid litter manure is obtained, but where the cows are handled loose – liquid manure is obtained. Besides, on the farms with a small herd the cows are pastured in summer and in this period manure spread in the pastures is produced. Stating the maximal size of the herd that is pastured and the length of the pasture period as well as the marginal size at which the transition from tied to loose handling takes place and additionally using the statistical data on the total number of cows in the country and the proportion of animals according to the size of the herd, it is possible to state from which proportion of milk cows solid litter is produced and from which – liquid manure. Therefore, the experts were given the task to name the marginal values of the above mentioned technology parameters based on the value intervals stated in advance. Thereupon that the experts had to state only one chosen value, it was not possible to apply the traditional expert evaluation methods and this method had to be adapted in accordance to the existing situation. The research results showed that in Latvia the critical size of the milk cow herd at which the transition from tied to loose handling takes place is 85 cows, the herds that are not larger than 90 cows are pastured but the pasture period lasts in average for 165 days.
Reducing The Assessment Cost of "Two Rules" Through Bi-level Programming Configuration of Energy Storage in Wind Farms

The National Energy Administration of China has issued the "Implementation Measures for Power Grid Operation and Management" and the "Implementation Measures for Auxiliary Power Services Management" (Two Rules), which assess the relevant indicators of power generation plants involved in grid safety and stability. Due to the significant uncertainties caused by factors such as climate and environment, the output power of wind farms faces higher assessment costs in terms of accuracy of power prediction and power fluctuations. To reduce the assessment costs for wind farms under the Two Rules, this paper proposes a method for optimizing the allocation of energy storage system (ESS) capacity in wind farms. It generates and reduces the description of uncertainties through wind power output scenarios, and solves the optimal configuration of ESS capacity by utilizing an improved Grey Wolf Optimization (GWO) and the Yalmip toolbox for iterative bilevel programming. The results of the case analysis demonstrate that the proposed method effectively solves the optimal configuration of ESS capacity in wind farms, and exhibits good applicability in reducing assessment costs, improving wind farm prediction accuracy, and mitigating wind power fluctuations.
A Scalable AI-Driven Chatbot for Real-Time Diagnostics in Manufacturing Plants: Merging Google Dialogflow, BERT, and a Self-Learning Module

In this paper, we introduce a scalable, AI-driven chatbot designed for real-time diagnostics in Industry 4.0 manufacturing plants. By incorporating Google Dialogflow and a plant-specific knowledge graph, the chatbot enables seamless interaction with users while requiring minimal reconfiguration when adapting to new environments. The integration of BERT in the back-end empowers the chatbot to efficiently identify errors. Furthermore, a self-learning module continuously scans the manufacturing execution system (MES) database for new errors, generating learning data for the automatic re-training of the BERT-based neural network. Although there are opportunities for refinement, the chatbot exhibits significant potential as a scalable, supplementary first-line support system in the manufacturing sector.
Biodiversity and palaeoclimate of the Middle Jurassic floras from the Tiaojishan Formation in western Liaoning, China

The Middle Jurassic Tiaojishan Formation in Beipiao City, western Liaoning comprises a variety of plant fossils including leaf impressions, compressions, permineralized rhizomes, fossil wood and dispersed spores and pollen grains. Impression and compression data suggest that the flora in the Tiaojishan Formation is dominated by Bermettitales, ferns and Nilssoniales, followed by ginkgophytes; conifers and sphenopsids are less common. Abundant permineralized rhizomes are referable to diverse taxa within fern families such as Osmundaceae and Cyatheaceae. Abundant and upright fossil stumps are common in the Tiaojishan Formation. They are welt preserved with distinct growth rings and are predominatly conifers. The floristic signature of the Tiaojishan Formation indicates that subtropical to temperate warm and humid climates prevailed during the late Middle Jurassic in the Beipiao area. Growth ring pattern analysis of the fossil conifer wood demonstrates a consistent and distinct seasonal climate during this interval. The biodiversity of the remarkable Tiaojishan flora provides essential evidence for understanding the vegetation, pataeoclimate and environmental evolution of western Liaoning during the Middle to Late Jurassic.
Motivation and genre as social action: a phenomenological perspective on academic writing

This article discusses the relationship between motivation and genre in the context of academic writing, aiming to further bridge the gap between information-processing (IP) cognitive approaches and socio-cultural or dialogical approaches to understanding cognition. The author takes one significant recent article bridging the gap, Graham’s Writers Within Communities (WWC) model, as a starting point and attempts to add concepts from genre as social action and Deci and Ryan’s Self-Determination Theory (SDT). The article explores how genre as social action is intimately connected with motivation and how SDT’s principles of competence, autonomy, and relatedness align with the phenomenological perspective on genre and motivation. The author suggests that these theories provide a more comprehensive understanding of writing motivation, emphasizing that the perception of genre as social action is a crucial motivator for writers and that self-determination is vital to authentic self-regulation in academic writing. The article illustrates the uses of the additional theories with an interview-based case study of a dissertation writer. It ends by discussing the possible implications of this theoretical research for empirical research on student motivation from both IP cognitive and sociocultural perspectives.
Unveiling the Power of Simplicity: Two Remarkably Effective Methods for Fingerprint Segmentation

Accurate fingerprint segmentation is crucial for reliable fingerprint recognition systems. This paper presents two novel segmentation methods, GMFS and SUFS, inspired by the KISS (Keep It Simple and Straightforward) principle. Both methods, evaluated on a public benchmark and compared to eighteen state-of-the-art approaches, excel in terms of accuracy, while maintaining simplicity and computational efficiency. GMFS utilizes a single handcrafted feature for straightforward yet effective fingerprint segmentation, achieving superior performance compared to previously reported traditional methods. SUFS employs a simplified U-net architecture for end-to-end segmentation, demonstrating remarkable performance: it achieves an average classification error rate of 1.51% across the entire benchmark, with an improvement of over 40% compared to the previously best-performing method. Furthermore, despite being trained on a relatively small dataset, it exhibits significant generalization capabilities, effectively segmenting fingerprints from very different acquisition technologies without requiring fine-tuning. An open-source Python implementation of both methods is available, fostering further research and development in the field of fingerprint recognition.
Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

Simple Summary Anti-cancer drug response prediction models aim to reduce the time necessary for developing a treatment for patients affected by this complex disease. Their goal is to decrease the number of required biological experiments by computationally weeding out unpromising compounds. In this work, we explore the potential gains of incorporating large-scale applications of classical virtual screening techniques like molecular docking into cutting-edge deep learning models. We demonstrate improvement in performance as well as limitations of our approach. Abstract Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.