Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Timescale Beamforming Optimization for Downlink Multi-User Holographic MIMO Surfaces

Benefiting from the rapid development of metamaterials and metasurfaces, the holographic multiple-input and multiple-output surface (HMIMOS) has been regarded as a promising solution for future wireless networks recently. By densely packing numerous radiation elements together, HMIMOS is capable of realizing accurate beamforming with low hardware complexity. However, enormous radiation elements on the HMIMOS lead to high computational complexity and signaling overhead when applying traditional beamforming schemes relying on instantaneous channel state information (CSI). To tackle this problem, we propose a two-timescale optimization scheme to minimize the required transmission power under the constraint of all users' quality-of-service (QoS). Specifically, the beampatterns at the base station (BS) and the user equippment (UE) are optimized over the slowly changing statistical CSI based on the constrained stochastic successive convex approximation (CSSCA) algorithm. Then, the instantaneous CSI is utilized to design the precoding matrix in order to ensure the system performance without significant increase in computational cost, due to the small number of feeds on the HMIMOS. Simulation results demonstrate the effectiveness of our proposed method compared to other baselines.
The Presence of a Parasite in the Head Tissues of a Threatened Fish (Bidyanus bidyanus, Terapontidae) from South-Eastern Australia

The silver perch, Bidyanus bidyanus (Mitchell) (Terapontidae) is a freshwater fish, endemic to the Murray-Darling river system in south-eastern Australia. Population declines have led to the fish being listed as critically endangered by the Australian Government. Knowledge about parasites and diseases of wild populations of freshwater fish are limited in Australia. During an examination of wild-caught silver perch, digenean mesocercaria were observed in the head tissues. A total of five of the 11 silver perch collected from the Wakool River, New South Wales, were infected with mesocercaria. All mesocercaria were found in the head tissues; no mesocercaria were found encysted in the eye lens. The mesocercaria were found to belong to the family Strigeidae based on the sequences of their internal transcribed spacer (ITS) region. The lack of comparable sequences of strigeid digeneans from Australian hosts precludes being able to determine if the mesocercaria found in this study are a new species or representatives of an already described species. However, genetic results confirm that this is a different species to other digeneans previously described from silver perch, thus increasing the number of digeneans reported from B. bidyanus to three species. The presence of digenean mesocercaria in the head tissues of a wild population of silver perch, as found in the present study, is of potential conservation significance. Given the critically endangered conservation status of B. bidyanus, and previous evidence of strigeid infection altering fish behaviour, ecology, and predation mortality, further research on the potential impacts of infection on wild populations is warranted.
Building a knowledge graph for dietary services targeting specific groups of people

Currently, most research focuses on users' behavioral preferences while ignoring the impact of food properties on human health. This article extracts a large amount of knowledge about the relationship between food properties and human health from multiple heterogeneous data sources. Based on this, a knowledge graph in the field of food is constructed for special populations to help them plan their diet more reasonably and reduce the risk of common diseases. Using background data sources such as Baidu Baike and Wikipedia, the BERT-BiLSTM-MHA-CRF method is proposed to extract food-related attributes from more than 14,731 descriptions of food properties. Combined with the differentiated features of special populations, a knowledge graph in the field of food is constructed. The knowledge graph mainly includes six entity types: food nutrition, efficacy and function, food name, population, dish name, and seasoning, with a total of 11,218 entities and 96,186 relationships. The experiment shows that compared with traditional static word vector models, BERT can generate dynamic word vectors based on context in large-scale corpus, making semantic encoding more accurate. The multi-head self-attention mechanism weights various entities in the food domain to reduce the interference of invalid information, making the model more accurate in capturing entity features. The BERT-BiLSTM-MHA-CRF method proposed in this article achieves P, R, and F1 greater than 90%.
Control, Command and Regulation System Based on Virtual Instrumentation for Three-Phase Alternating High Current Source

The paper presents a fully digital control system based on virtual instrumentation that replaces the physical control, command and regulation panel made up of electronic modules and analogue devices. This enables the transition from primarily manual local control by the operator to remote control and eventually full automation of the heating testing process for medium and low voltage switchgear. Taking advantage of virtual instrumentation, the solution can be easily implemented and enhanced by integrating process control and computerisation applications.
Sports competition tactical analysis model of cross-modal transfer learning intelligent robot based on Swin Transformer and CLIP

Introduction This paper presents an innovative Intelligent Robot Sports Competition Tactical Analysis Model that leverages multimodal perception to tackle the pressing challenge of analyzing opponent tactics in sports competitions. The current landscape of sports competition analysis necessitates a comprehensive understanding of opponent strategies. However, traditional methods are often constrained to a single data source or modality, limiting their ability to capture the intricate details of opponent tactics. Methods Our system integrates the Swin Transformer and CLIP models, harnessing cross-modal transfer learning to enable a holistic observation and analysis of opponent tactics. The Swin Transformer is employed to acquire knowledge about opponent action postures and behavioral patterns in basketball or football games, while the CLIP model enhances the system's comprehension of opponent tactical information by establishing semantic associations between images and text. To address potential imbalances and biases between these models, we introduce a cross-modal transfer learning technique that mitigates modal bias issues, thereby enhancing the model's generalization performance on multimodal data. Results Through cross-modal transfer learning, tactical information learned from images by the Swin Transformer is effectively transferred to the CLIP model, providing coaches and athletes with comprehensive tactical insights. Our method is rigorously tested and validated using Sport UV, Sports-1M, HMDB51, and NPU RGB+D datasets. Experimental results demonstrate the system's impressive performance in terms of prediction accuracy, stability, training time, inference time, number of parameters, and computational complexity. Notably, the system outperforms other models, with a remarkable 8.47% lower prediction error (MAE) on the Kinetics dataset, accompanied by a 72.86-second reduction in training time. Discussion The presented system proves to be highly suitable for real-time sports competition assistance and analysis, offering a novel and effective approach for an Intelligent Robot Sports Competition Tactical Analysis Model that maximizes the potential of multimodal perception technology. By harnessing the synergies between the Swin Transformer and CLIP models, we address the limitations of traditional methods and significantly advance the field of sports competition analysis. This innovative model opens up new avenues for comprehensive tactical analysis in sports, benefiting coaches, athletes, and sports enthusiasts alike.
Transmembrane Helices 7 and 8 Confer Aggregation Sensitivity to the Cystic Fibrosis Transmembrane Conductance Regulator

The Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a large multi-spanning membrane protein that is susceptible to misfolding and aggregation. We have identified here the region responsible for this instability. Temperature-induced aggregation of C-terminally truncated versions of CFTR demonstrated that all truncations up to the second transmembrane domain (TMD2), including the R region, largely resisted aggregation. Limited proteolysis identified a folded structure that was prone to aggregation and consisted of TMD2 and at least part of the Regulatory Region R. Only when both TM7 (TransMembrane helix 7) and TM8 were present, TMD2 fragments became as aggregation-sensitive as wild-type CFTR, in line with increased thermo-instability of late CFTR nascent chains and in silico prediction of aggregation propensity. In accord, isolated TMD2 was degraded faster in cells than isolated TMD1. We conclude that TMD2 extended at its N-terminus with part of the R region forms a protease-resistant structure that induces heat instability in CFTR and may be responsible for its limited intracellular stability.
Does Proximity Matter in the Choice of Partners in Collaborative R&D Projects? – An Empirical Analysis of Granted Projects in Germany

This paper contributes to the discussion on the importance of physical distance in the emergence of cross-region collaborative Research and Development (R&D) interactions. The proximity theory, and its extensions, is used as a theoretical framework. A spatial interaction model for count data was implemented for the empirical analysis of German data from the period from 2005 to 2010. The results show that all tested proximity measurements (geographical, cognitive, social and institutional proximity) have a significant positive influence on collaboration intensity. The proximity paradox, however, cannot be confirmed for geographical, social and institutional proximity, but for cognitive proximity.
Revealing Good Governance in Regional Financial Reporting (Case Study on Banyuwangi Regency Government)

This study aims to determine the factors in the implementation of governance of Banyuwangi Regency so that a Reasonable Without Exception (WTP) regional financial report is obtained. This study used qualitative research methods with a focus on phenomology. Data collection through literature studies, interviews, and surveys within the scope of the Banyuwangi Regency Government. Primary data were obtained from interviews and surveys, while secondary data were obtained from literature reviews. Data analysis using interactive analysis methods. The results of the study found that leaders have a very important role in government. The leadership style applied is able to build a conducive work environment. In addition, leaders are able to motivate employees to increase performance effectiveness and productivity. This has an impact on the quality of the regional financial statements produced.
Structured Trajectories for SLAM

Many mobile robots generate unstructured trajectories and subsume seemingly random motion during SLAM. An extension to the SLAM problem is therefore considered to counter this. It is referred to as SLAM+CPP: Simultaneous, Localization, Mapping Plus Coverage Path Planning in which a robot is required to perform traditional SLAM but with a systematic pattern of movement such that the entire floor surface is traversed (coverage planning is performed) and mapped with structured and well-defined trajectories. This paper gives theoretical foundations and discusses challenges and whether solutions are available and gives results of basic studies that we have performed. A candidate solution is suggested that performs to expectations in simple single-room environments.
On the Tradeoff Between Spatial Resolution and High Curvature Bending in FBG-Based Shape Sensing

Fiber Bragg grating (FBG)-based shape sensing is an emerging technology used for the navigation of flexible medical instruments, such as needles, catheters, and endoscopes. FBG-based sensors operate by measuring strain-induced wavelength shifts in the reflected light, which are used to determine the curvature along a fiber’s length. While an increased number of gratings enhances shape-sensing accuracy, incorporating many within a fixed spectrum can induce interference in wavelength shifts, specifically during high curvature bending, limiting the detectable bending range. Such interference severely compromises shape estimation, posing challenges for instruments requiring high curvature bends. In this article, we study this tradeoff between resolution and bending curvature. Next, we introduce an algorithm that leverages the reflectivity and full-width half maximum (FWHM) of FBG nodes for improved bend estimation, even in high-curvature scenarios. To this end, first, we provide a model of reflectivity of uniform gratings using coupled-mode theory. The model is used to match spectrum measurements with FBG nodes. Second, we develop an algorithm using the proposed model and random walk model to estimate the relative probability of a given reflected wavelength corresponding to each FBG node. This algorithm then uses variants of the Hungarian algorithm to solve an assignment problem and classify FBG nodes. We then collected three datasets with FBGs experiencing varying curvature. One of the datasets validates the proposed model and the latter two demonstrate that relative to a commercially available interrogator, our approach classifies FBG nodes with 14% higher accuracy, 13% higher precision, 35% higher recall, and 4% higher specificity.
Novel role for alpha-2-macroglobulin (A2M) as a disease modifying protein in senile osteoporosis

Introduction: In the rapidly aging U.S. population, age-induced bone loss (senile osteoporosis) represents a major public health concern that is associated with a significant increased risk for low trauma fragility fractures, which are debilitating to patients, cause significant morbidity and mortality, and are costly to treat and manage. While various treatments exist to slow bone loss in osteoporosis patients, these suffer from poor tolerability and label restrictions that limit their overall effectiveness. Over the past decade, skeletal stem/progenitor cells (SSPCs), which are the main precursor of osteoblasts and adipocytes in adult bone marrow (BM), have emerged as important players in osteoporosis. Methods: Age-induced skeletal pathology was quantified in elderly (24-month-old) vs. mature (3-month-old) mice by micro-CT and changes in SSPC abundance in the BM of these mice was quantified by fluorescence-activated cell sorting (FACS). SSPCs from elderly vs. mature mice were also analyzed by RNA-Seq to identify differentially expressed genes (DEGs), and gain and loss-of-function studies were performed in human BM-derived mesenchymal stromal cells (BM-MSCs) to assess A2M function. Results: Elderly mice were shown to exhibit significant age-induced skeletal pathology, which correlated with a significant increase in SSPC abundance in BM. RNA-seq analysis identified alpha-2-macroglobulin (A2M), a pan-protease inhibitor that also binds inflammatory cytokines, as one of the most downregulated transcripts in SSPCs isolated from the BM of elderly vs. mature mice, and silencing of A2M expression in human BM-MSCs induced their proliferation and skewed their lineage bifurcation toward adipogenesis at the expense of osteogenesis thereby recapitulating critical aspects of age-induced stem cell dysfunction. Conclusion: These findings identify A2M as a novel disease modifying protein in osteoporosis, downregulation of which in bone marrow promotes SSPC dysfunction and imbalances in skeletal homeostasis.
‘Beyond policy tourism’: the international lived experience of cycling in the Netherlands and Canada

ABSTRACT As more cities implement cycling infrastructure, there is a growing need to both learn best practices from other places through a detailed understanding of the lived and embodied experiences of cycling. However, this is rarely the case. On the one hand, planners and policymakers rely on incomplete (quantitative) data and policy tours that are unable to document the full extent of cycling or how it is experienced. While recent studies have expanded to include qualitative methods, they are predominantly conducted in one place, limiting our ability to draw international comparisons. The Netherlands is a popular destination for such tours and is generally regarded as one of the best places in the world for cycling. But what about people who, for a variety of reasons, have lived in different countries? Their knowledge, experiences and reflections on cycling are rarely featured in planning. To redress this, we interviewed participants who have international experience, capturing beyond aspects of policy tourism, illuminating how: (1) mixed land-use patterns, (2) incentivizing cycling as a mode choice, and (3) cycle networks and safety are vital to cycling participation. We advance the cycling research agenda by examining these findings and proposing changes best suited to low-cycling cities.
Compactability Regularities Observed during Cold Uniaxial Pressing of Layered Powder Green Samples Based on Ti-Al-Nb-Mo-B and Ti-B

We determined the compactability regularities observed during the cold uniaxial pressing of layered powder green samples, taking into account factors such as composition, height, and number of Ti–B (TiB) and Ti–Al–Nb–Mo–B (TNM) layers. The following composition was chosen for the TNM layer at %: 51.85Ti–43Al–4Nb–1Mo–0.15B, while for the Ti-B layer we selected the composition wt %: Ti-B-(20, 30, 40) Ti. Green samples were made containing both 100 vol % TiB and TNM, and those taken in the following proportions, vol %: 70TiB/30TNM, 50TiB/50TNM, 30TiB/70TNM; multilayer green samples were studied in the following proportions, vol %: 35TiB/30TNM/35TiB, 25TiB/25TNM/25TiB/25TNM, 35TNM/30TiB/35TNM. Based on the obtained rheological data, we determined the rheological characteristics of the layered green samples, including compressibility modulus, compressibility coefficient, relaxation time, and limiting value of linear section deformation. These characteristics were found to vary depending on the composition, height, and number of layers. Our findings revealed that reducing the TNM content in the charge billet composition improves the compaction of powder materials under the given technological parameters of uniaxial cold pressing. Moreover, we observed that increasing the boron content and decreasing the amount of titanium in the Ti–B composition enhances the compactability of powder materials. We also established a relationship between the compaction pressure interval and the density of the layered powder green sample.
Atomic Force Microscopy Characterization of Polyacrylamide Substrate for Traction Force Application

Atomic Force Microscopy (AFM) technology has ushered researchers to directly observe surface topology and the substrate mechanical properties using specialized probe. AFM is one of the microscopic techniques with the highest lateral resolution which can be employed in air or even in liquids. In this experiment, we characterized the local elastic properties of the polyacrylamide (PA) hydrogel using Atomic Force Microscopy (AFM). PA consists of huge units of an organic acrylamide monomers which can be saturated to form a highly water-swollen hydrogel. The hydrogel offers tunable density with a high degree of pliability which depends of its applications. Such applications of PA hydrogel can be in cell substrate studies and measurement of cell-generated forces. Our results with AFM measurement yielded force-distance curves were used to determine the elastic behaviour of the polyacrylamide (PA) hydrogel. Analysis has shown that 15% w/v PA hydrogel concentration has Young’s modulus, Yav=1608.9 ± 1.3 kPa (n=8) and transverse stiffness, Kav=88.7 ± 9.7 μN/nm (n=8) at Thus, elasticity measurements has provided useful insights for the future experiment on traction force microscopy with amoeboid organism.
Statistical Characteristics on Decadal Abrupt Change Process of Time Sequence in 500 hPa Temperature Field

In this paper, we fit the time series, which likely has abrupt change processes(ACP), with piecewise function deducing for describing the mean abrupt change from the logistic model. Thus combined with the theory of probability distribution formed the basis for considering the process in which abrupt change amplitude is maximum, as is the ACP.The parameters reflecting the beginning moment of the abrupt change, abrupt change amplitude, abrupt change rate,persistence time, and instability are determined and analyzed. By testing the time series in a 500 hPa temperature field during the period 1948–2012, we determine the following results:(1) Abrupt changes started in 1956–1959, 1970–1979,1986–1994, and 1994–2004. We considered more probabilities by testing the start moment of the ACP. In these changes,the temperatures of all changes in the means show increases except for that in 1986–1994, and the rate increases if the abrupt change amplitude is large. (2) In spatial distribution of the abrupt change moment, the moment of the grid points in which the abrupt change occurred in 1956–1959 and 1970–1979 above the sea occurred earlier than that in the grid points above Eurasia, whereas the abrupt changes in 1986–1994 and in 1994–2004 show opposite behavior. (3) The abrupt change amplitude of temperature occurring in low latitudes is lower than that occurring in high latitudes. (4) Under the context of global warming, the statistics distribution of the persistence time shows that an increasing number of grid points require shore time to complete an abrupt change. (5) During the abrupt change process, the instability of the system was enhanced significantly.
Computational Imaging Through Atmospheric Turbulence

Seeing through a turbulent atmosphere has been one of the biggest challenges for ground-to-ground long-range incoherent imaging systems. The literature is very rich that can be dated back to Andrey Kolmogorov in the late 40’s, followed by a series of major developments by David Fried, Robert Noll, among others, during the 60’s and 70’s. However, even though we have a much better understanding of the atmosphere today, there remains a gap from the optics theory to image processing algorithms. In particular, training a deep neural network requires an accurate physical forward model that can synthesize training data at a large scale. Traditional wave propagation simulators are not an option here because they are computationally too expensive --- a 256x256 gray scale image would take several minutes to simulate.
Multi-scale Data Fusion and Machine Learning for Vehicle Manoeuvre Classification

Vehicle manoeuvre analysis is vital for road safety as it helps understand driver behaviour, traffic flow, and road conditions. However, classifying data from in-vehicle acquisition systems or simulators for manoeuvre recognition is complex, requiring data fusion and machine learning (ML) algorithms. This paper proposes a hybrid approach that combines multivariate multiscale entropy (MMSE) and one-dimensional convolutional neural networks (1D-CNNs). MMSE is utilised for early feature extraction and data fusion, and the extracted features are classified using 1D-CNNs, achieving an impressive 87% test accuracy in multiclass classification. This paper provides insights into improving vehicle manoeuvre classification using advanced ML techniques and data fusion methods to handle complex data sets effectively. Ultimately, this approach can enhance the understanding of driver behaviour, inform policy decisions, and develop more effective strategies to enhance road safety.