Miracle-dz/UniFAR-FTUs huggingface dataset:
URBAN CLASSIFICATION BASED ON TOP-VIEW POINT CLOUD AND SAR IMAGE FUSION WITH SWIN TRANSFORMER

Abstract. Urban areas are complex scenarios consisting of objects with various materials. This variety poses a challenge to single-data classification schemes. In this paper, we propose a feature fusion and classification network on RGB top-view point cloud and SAR images with swin-Transformer. In this network, the heterogeneous features are learned separately by an asymmetric encoder, and then they are concatenated along the channel dimension and fed into a fusing encoder. Finally, the fused features are decoded by an UperNet for generating the semantic labels. As data we use high-resolution 3D point cloud provided by Hessigheim benchmark which are complemented by TerraSAR-X images. The overall precision and the mean intersection over union (mIoU) achieves 87.25% and 73.56%, respectively, which outperforms the single-data swin-Transformer by 4.08% and 1.91%, respectively.
An Alternative Source Jerk Method Implementation for the Subcriticality Estimation of the VENUS-F Subcritical Core in the FREYA Project

As a prolongation of the FP-6 GUINEVERE (Generation of Uninterrupted Intense NEutron pulses at the lead VEnus REactor) project at the VENUS-F facility at SCK• CEN, the FP-7 FREYA (Fast Reactor Experiments for hYbrid Applications) project was launched in 2011. The FREYA project aims at validating the methodology for subcriticality monitoring of a subcritical core as proposed in the MUSE experiments conclusions. This is needed for the reactivity monitoring of a powerful ADS (Accelerator Driven System) like MYRRHA. Experiments are also needed for sub-critical and critical ADS and LFR (Lead Fast Reactor) core characterisation for benchmarking and licensing purposes. Several levels of sub-criticality of the first subcritical core were measured applying the integral Source Jerk method and compared to the reference results obtained with the MSM (Modified Source Multiplication) method.
Analysis of Two-Fluid-Phase Porous Medium Systems Using Microscale Experiments and Lattice Boltzmann Modeling

Amanda L. Dye: Analysis of Two-Fluid-Phase Porous Medium Systems Using Microscale Experiments and Lattice Boltzmann Modeling (Under the direction of Cass T. Miller) Modeling two-fluid-phase flow in porous medium systems requires the specification of closure relations to produce a solvable system. The thermodynamically constrained averaging theory (TCAT) has been used to formulate a closed model and to yield insights about the underlying microscale processes that must be represented at the macroscale. In this work we evaluate these TCAT developments using a two-dimensional microfluidic cell and an advanced lattice Boltzmann model (LBM). Microfluidic experiments were performed in which the external fluid pressures were varied and the system was allowed to equilibrate for a period of time before the next step change in fluid pressures. LBM simulations of drainage and imbibition were performed to mimic the experiments. Using imaging techniques the fluid distributions, interfacial areas, and interfacial curvatures of the experimental data were compared to the numerical results and analyzed. LBM simulations showed good agreement with the experimental data. The validated LBM was used to investigate the closure relations of the TCAT model. LBM simulations provided support for the existence of a unique relationship between capillary pressure, fluid saturation, and fluid-fluid interfacial area. The simulations also revealed that the approach to an equilibrium state involves a relatively slow process in which fluid interfaces relax to their equilibrium state. Motivated by this physical process, a temporally adaptive domain decomposition algorithm was developed using a iii level-set method to locate interfaces and estimate their rate of advancement. The proposed adaptive algorithm was shown to reduce computational e↵ort by an order of magnitude, while yielding essentially identical solutions to a conventional fully coupled approach. Futhurmore, the microfluidic experiments were modified to study the dynamics of a two-fluid-phase flow. Viscous fingering and Haines jumps were visualized. The validated LBM was used to model the experimental data and showed good agreement for certain aspects of the dynamics. Overall, the results show that the developed experimental and computational techniques can be used to evaluate multiscale theoretical models of porous medium systems accurately.
The effects of R&D and innovation capabilities on the thai automotive industry part’s competitive advantage: a SEM approach

A structural equation model was used to verify both the direct and indirect inﬂuences on research and development and innovation capabilities that aﬀect Thai automotive parts enterprises competitive advantage. Thailand’s automotive sector is a major driver of the economy with a multi-thousand network of both domestic and international companies con-tributing a signiﬁcant amount to both domestic and Asian economic growth. After peaking in 2013 and reaching the Top 10 automotive nations, there has been a slide backwards. Competition is ﬁerce and the pace of change ever quickening. Therefore, the researchers sought to determine how the automotive parts sector needed to improve in capability, skills and infrastructure to maintain a sustainable competitive advantage. From the structural equation model analysis of 220 regional automotive parts sector managers and use of AMOS software, it was determined that research and development combined with innovation plays key roles in the industry’s proﬁtability and survivability. Additionally, there must be support across a wide sector of the economy, including universities, government agencies and institutions. Once championed as the ‘Detroit of Asia’, Thailand’s recognized success as a global automotive hub is a classic case of a well-executed industrial plan but that lead can potentially slip away without embracing new technologies and innovative thinking.
Towards Sustainable Community Development through Renewable Energies in Kyrgyzstan: A Detailed Assessment and Outlook

In rural Kyrgyzstan, the energy need is usually derived from multiple natural energy resources such as firewood, charcoal, agricultural residues, animal dung, and wood branches, which are considered common and predominant practices. Because of the non-sustainable resources and heavy reliance on the environment, Kyrgyzstan is one of the most vulnerable countries to climate change in Central Asia. On the contrary, the great renewable energy potential in Kyrgyzstan is untapped, which could be the most promising solution to ensuring sustainable energy supply in the country. However, because of the lack of scientific knowledge, current energy policies, and the lack of infrastructure, renewable resources are mainly untapped. To exploit the country’s renewable energy potential, there is a need for a systematic diagnosis to develop a strategy to explore renewables in Kyrgyzstan, which is currently missing in the existing literature. In that regard, the presented study aims to assess the current status of renewable energy sources by considering the local energy context from a potential point of view. Further to this, it provides a comparative overview through a matrix of strengths, weaknesses, opportunities, and threats. Such novel analysis would be the fundamental base for formulating policy advice and a national plan to enhance the utilization of renewable energy in Kyrgyzstan. The presented analysis was carried out based on the extensive literature review, the country’s national plan, and the existing energy policies of Kyrgyzstan. The article found out that there is huge potential available for the renewable energy market. As compared to other renewable energy sources, solar energy has great potential and can be considered one of the pioneer sustainable sources for integration into the country’s power generation framework.
Quantifying Motion and ‘T’ Dominance of Elite Squash Players

Advances in video analysis technology expand the realm of possibility for analyzing sport tactics and motion. Video analysis techniques are especially effective in squash due to the small, well-lit, defined court, paired with an ever-growing video collection of elite tournament matches. The availability of matches enables a new methodology that requires no special setup, but only uses video analysis software and the recorded matches that are conveniently filmed for entertainment purposes. This paper presents and evaluates this new methodology that encompasses distance travelled, velocities and speeds attained, court position, and ‘T’ dominance in elite squash. The method was validated when results were compared to previous studies, and a correlation between world rank and court dominance was measured for the first time. A strong correlation (r= 0.64,p <0.001) was found between world rank and average distance from an optimal court position known as the ‘T’ of the squash court.
Research on the Application of Artificial Intelligence Technology in the Visual Communication Design of Shopping Platform

With the improvement of people's living standard, users of commodity experience requirements are getting more and more attention and shopping business to consumers to provide products at the same time also need the service should be paid more attention, so that consumers in the shopping process to obtain the better shopping experience. In this paper, the main purpose of the introduction of artificial intelligence technology in transmission shopping platform website visual design usability analysis, through the relevant theoretical knowledge and visual elements of the shopping website of usability evaluation and improvement, using questionnaire method to analyze the improved website and traditional website. To sum up some of the design methods and principles of shopping website interface.
3D MODELLING OF TRAM TUNNEL FROM TLS POINT CLOUDS FOR TECHNICAL DOCUMENTATION AND EXTRACTION OF CHARACTERISTIC LINES

Abstract. The Strasbourg tramway has only one tunnel 1400 m long built in 1994. It grants the operation of two lines of the transportation network. It also houses the only underground station "Gare centrale" and is the only tunnel operated by the CTS (Compagnie des Transports Strasbourgeois). Due to new guidelines for georeferencing of underground networks, the CTS had to know the precise planimetric and altimetric position of the tunnel, considered as a sensitive network located in urban areas. The regulations also set the criteria for precision which for this specific case requires an accuracy of less than 40 cm. This project on behalf of and in partnership with the CTS therefore aims to set up a method allowing the 3D survey of the tunnel considering the specific constraints of urban transport networks and then to propose different 2D or 3D deliverables allowing the management of the tunnel. Initially, the project focused on extracting the shape of the tunnel characteristic lines, which corresponds to the line tangent to the points of higher altitude and the extreme points of each cross-section, along the axis of the tunnel. In a second step, a 2D plan containing all the technical elements present in the tunnel was realized, as well as a 3D modelling of a significative part of the infrastructure. A video was also derived from the point cloud to inspect the entire tunnel from different points of views. In the future, more advanced 3D models or BIM models could be considered. This would require information on the structure of the walls, the construction materials, or the links between the different elements. A project to survey and model the underground station will make it possible in the future to perfect the acquisition and knowledge of the underground infrastructures belonging to the transport company.
Static Balance Digital Endpoints with Mon4t: Smartphone Sensors vs. Force Plate

Static balance tests are conducted in various clinics for diagnosis and treatment adjustment. As a result of population aging, the accessibility of these tests should be increased, in the clinic, and for remote patient examination. A number of publications have already conducted static balance evaluations using the sensors embedded in a smartphone. This study focuses on the applicability of using smartphone-based balance assessment on a large scale while considering ease of use, safety, and reliability. The Mon4t® app was used to acquire the postural motion using different smartphone devices, different smartphone locations, and various standing postures. The signals derived from the app were compared to the center of pressure displacement derived from a force plate. The results showed moderate to high agreement between the two methods, particularly at the tandem stance (0.69 ≤ r ≤ 0.91). Preliminary data collection was conducted on three healthy participants, followed by 50 additional healthy volunteers, aged 65+. The results demonstrated that the Mon4t app can serve as an accessible and inexpensive static balance assessment tool, both in clinical settings and for remote patient monitoring, which is key for enabling telehealth.
Modification of Ramie Fiber via Impregnation with Low Viscosity Bio-Polyurethane Resins Derived from Lignin

The purpose of this study was to prepare low-viscosity lignin-based polyurethane (LPU) resins for the modification of ramie (Boehmeria nivea (L.) Gaudich) fiber via impregnation to improve the fiber’s thermal and mechanical properties. Low-viscosity LPU resins were prepared by dissolving lignin in 20% NaOH and then adding polymeric 4,4-methane diphenyl diisocyanate (pMDI, 31% NCO) with a mole ratio of 0.3 NCO/OH. Ramie fiber was impregnated with LPU in a vacuum chamber equipped with a two-stage vacuum pump. Several techniques such as Fourier-transform infrared (FTIR) spectroscopy, differential scanning calorimetry, thermogravimetric analysis, pyrolysis-gas chromatography–mass spectroscopy, field emission-scanning electron microscopy coupled with energy dispersive X-ray (EDX), and a universal testing machine were used to characterize lignin, LPU, and ramie fiber. The LPU resins had low viscosity ranging from 77 to 317 mPa·s−1. According to FTIR and EDX analysis, urethane bonds were formed during the synthesis of LPU resins and after impregnation into ramie fibers. After impregnation, the reaction between the LPU’s urethane group and the hydroxy group of ramie fiber increased thermal stability by an average of 6% and mechanical properties by an average of 100% compared to the untreated ramie fiber. The highest thermal stability and tensile strength were obtained at ramie impregnated with LPU-ethyl acetate for 30 min, with a residual weight of 22% and tensile strength of 648.7 MPa. This study showed that impregnation with LPU resins can enhance the thermal and mechanical properties of fibers and increase their wider industrial utilization in value-added applications.
Effects of ACTH-Induced Long-Term Hypercortisolism on the Transcriptome of Canine Visceral Adipose Tissue

Cushing’s syndrome, or hypercortisolism (HC), a common endocrinopathy in adult dogs, is caused by chronic hypercortisolemia. Among different metabolic disorders, this syndrome is associated with enhanced subcutaneous lipolysis and visceral adiposity. However, effects of HC in adipose tissue, especially regarding visceral adipose tissue (VAT), are still poorly understood. Herein, the transcriptomic effects of chronic HC on VAT of dogs were evaluated. For this, subcutaneously implanted ACTH-releasing pumps were used, followed by deep RNA sequencing of the canine VAT. Prolonged HC seems to affect a plethora of regulatory mechanisms in VAT of treated dogs, with 1190 differentially expressed genes (DEGs, p and FDR < 0.01) being found. The 691 downregulated DEGs were mostly associated with functional terms like cell adhesion and migration, intracellular signaling, immune response, extracellular matrix and angiogenesis. Treatment also appeared to modulate local glucocorticoid and insulin signaling and hormonal sensitivity, and several factors, e.g., TIMP4, FGF1, CCR2, CXCR4 and HSD11B1/2, were identified as possible important players in the glucocorticoid-related expansion of VAT. Modulation of their function during chronic HC might present interesting targets for further clinical studies. Similarities in the effects of chronic HC on VAT of dogs and humans are highlighted.
RAMAN SPECTRAL ANALYSIS FROM SHERLOC ONBOARD MARS 2020

Abstract. The Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals (SHERLOC) is one of the instruments mounted on the Perseverance rover’s robotic arm. SHERLOC focuses on searching for mineral species and organics that may contain spectral signatures of hydrous environments and past microbial life. It measures the Raman scattering in a narrow wavelength range (246–357 nm) with a significantly stronger fluorescence around 274–355 nm. Here the 40 measurements of Raman spectra from three detectors of SHERLOC, captured on Sol 4, were analyzed along with Matscam-Z images. Principal component analysis was applied to check the mineralogical diversity. The results include fluorescence signatures with maxima at 276 nm and Raman peaks similar to amorphous silicates.
SARCASTIC v2.0 - High-Performance SAR Simulation for Next-Generation ATR Systems

Synthetic aperture radar has been a mainstay of the remote sensing field for many years, with a wide range of applications across both civilian and military contexts. However, the lack of openly available datasets of comparable size and quality to those available for optical imagery has severely hampered work on open problems such as automatic target recognition, image understanding and inverse modelling. This paper presents a simulation and analysis framework based on the upgraded SARCASTIC v2.0 engine, along with a selection of case studies demonstrating its application to well-known and novel problems. In particular, we demonstrate that SARCASTIC v2.0 is capable of supporting complex phase-dependent processing such as interferometric height extraction whilst maintaining near-realtime performance on complex scenes.
Stepped frequency radar target recognition using 1D-CNN

Real radar returns from four small scale commercial aircraft models are used to train and test a convolutional neural network target recognition system. Many target recognition systems convert the one dimensional stepped-frequency features into two-dimensional using tools such as spectrograms and scalograms, and thereby utilize a two-dimensional CNN. In this paper, a one-dimensional convolutional neural net is used. The unknown target’s azimuth position may be known completely or within a certain range. The recognition performance is compared with that of an optimal Bayesian classifier assuming complete statistical knowledge. A discussion of the advantages and disadvantages of using 1D-CNN is presented.
Research on Conventional Beamforming Based on Compressive Sensing

Compressive sensing, or compressive sampling (for short, CS) is a novel sensing/sampling paradigm. With the rapid development of the theory and algorithms for sparse recovery in finite dimensions, compressive sensing has already inspired some notable investigation in the context of Direction Of Arrival (DOA) estimation. In this paper, we show how CS can be applied in the DOA estimation and be solved by the well-established toolbox, CVX. In order to ensure a high spatial resolution, the conventional compressive beamforming formulation is further extended to a virtual array case. Numerical simulations illustrate the effectiveness of the DOA estimation algorithm based on CS. In addition, numerical tests also show that under some challenging scenarios such as low SNR, coherent arrivals and few snapshots, compressive beamforming based on virtually expanded array (for convenience, called V-CS) can distinguish closely spaced sources and has higher resolving probability than conventional compressive beamforming.
Elevating Design: Building Design as a Dynamic Capability

This thesis focuses on the interaction between design and business, exploring its impact on the success of organizations through two case studies of design managers, Dan Harden, Chief Executive Officer for Whipsaw Inc and Sam Lucente, Global Vice President of Design for Hewlett-Packard. Through an analysis of organizational strategy and design, this thesis proposes a theoretical model that identifies how design becomes a dynamic capability for any organization when its promotion and support shifts from a person to a function. Finally, based on this model, this thesis analyzes the effectiveness of design thinking in supporting design as a dynamic capability and offers conclusions for the elevation of a design function in support of a sustained competitive advantage in organizations. acknowledgements To my family, friends, faculty, and mentors—you are my inspiration. table of contents introduction case studies dan harden sam lucente design as a dynamic capability design dynamic capabilities view building design as a dynamic capability from a person to a function effectiveness of design thinking in supporting design as a dynamic capability creative capabilities design process design thinking design thinking improves collaboration misperception of design thinking foundations of design are as important as the process design management: the critical function in search of talent conclusions
SEX DISCRIMINATION OF INDIVIDUAL TREES USING UAV IMAGERY

Abstract. The sex ratio is the proportion of male to female trees, which has a substantial impact on reproductive success and conservation status. Appropriate sex-related differences in dioecious trees commonly result in leading to a robustly structured population. Fieldwork for sex discrimination is time-consuming and labor-required. Benefiting from the unmanned aerial vehicle (UAV) and SfM techniques, the present study aims to detect male and female Caspian poplar (Populus caspica) trees. In March 2021, a heterogeneous forest in Noor city located in Mazandaran province was photographed, then 3D point clouds were extracted from the images using structure from motion algorithm (SfM) to generate an orthomosaic and a point cloud. The field survey was carried out to record the species, sex, and position of the overstory trees which were identifiable on the orthomosaics. A random forest classification algorithm was applied using R software to classify the trees into male and female. By assessing the producer's accuracy, user's accuracy, and overall accuracy, the classification accuracy for identified trees was computed using 10-fold cross-validation. The results showed an accuracy of 83% for identifying Caspian poplar trees and 52% accuracy for Sex discrimination. Overall, our effort to evaluate sex discrimination of dioecious trees using UAV imagery represents a promising preliminary step in forest data collection.