Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometrical tuning of microdiffuser/nozzle for valveless micropumps

Valveless micropumps require the integration of microdiffusers/nozzles for flow rectification in microfluidic systems. The flow directing capability of a micropump is determined by the efficiency of the diffuser. With the reduction in size of the micropump, conventional microdiffuser geometrical parameters are not suitable for obtaining high flow efficiencies due to several fluidic effects such as channel friction, wall shear stress, vena contracta, etc, and therefore it is important to modify the diffuser geometry according to the requirements of the pressure coefficients in order to obtain improved flow rates. This paper presents a simple and microfabrication friendly geometrical tuning method which offers the user a broad range of dependent tunable geometric parameters to improve the performance of the microdiffuser for valveless micropumps. Herein, for a given flow condition, the flow behaviour and the variation of pressure coefficients of the microdiffuser/nozzle with geometric tuning have been studied for different diffuser angles using finite element modelling (FEM). The results show that the proposed method is highly suitable for tuning the geometry of microdiffusers for a wide range of operating conditions of valveless micropumps. The performances of the best diffuser geometries for different diffuser angles have been experimentally verified, and the test results are used for the validation of the results of the FEM. The comparison between the FEM and experimental results shows a close agreement.
A dual-band and wide-band antenna on flexible substrate for smart clothing

The development of wearable computer systems and smart clothing has been rapid. They are becoming more and more lightweight and quite soon we will see a wide range of unobtrusive wearable and ubiquitous computing equipment integrated to into our everyday wear. Rapid progress in wireless communication promises to replace wired-communication networks in the near future in which antennas are in a more important role. This paper presents an antenna design for wearable applications. Due to wearable system demands this paper discusses mainly physically small planar antennas. A design example is given in which a flexible PIFA (FlexPIFA) structure for single and dual-band operation is introduced to smart clothing in Bluetooth and UMTS neighborhood. To our knowledge this is first time a thin and flexible antenna has been designed for commercial smart clothing. Measured results are compared with simulations and good agreement is observed. In addition the effect of the human body on antenna performance is discussed.
AERO-ACOUSTIC COMPUTATIONS OF WIND TURBINES

A numerical algorithm for acoustic noise generation is extended to 3D flows. The approach involves two parts comprising a viscous incompressible flow part and an inviscid acoustic part. In order to simulate noise generated from a wind turbine, the incompressible and acoustic equations are written in polar coordinates. The developed algorithm is combined with a so-called actuator-line technique in which the loading is distributed along lines representing the blade forces. Computations are carried out for the 500kW Nordtank wind turbine equipped with three LM19 blades.Copyright © 2001 by The American Institute of Aeronautics and Astronautics, Inc. and ASME
A CMOS time-of-flight range image sensor with gates on field oxide structure

This paper presents a new type of CMOS time-of-flight (TOF) range image sensor using single layer gates on field oxide structure for photo conversion and charge transfer. An additional process step to create an n-type buried layer is used. This simple structure allows the realization of a high resolution array with 15 times 15 mum2 pixels in standard CMOS process. To minimize the influence of background light, the proposed pixel structure has charge draining gates. A small duty cycle optical pulse is also used for this purpose. The TOF sensor chip was successfully implemented and tested
The reduction of mimosine content in Leucaena leucocephala (petai belalang) leaves using ethyl methanesulphonate (EMS)

A study was conducted to reduce the mimosine content ofLeucaena leucocephalaleaves by using ethyl methanesulphonate (EMS). The seeds were treated with three different doses of EMS (i.e., 0.1%, 0.3% and 0.6%) aiming at reducingthe mimosine content and improving the nutritive value of L. leucocephala. The mimosine content, crude protein and crude fiber were decreased with the increasing of EMS doses. The effect of 0.6% EMS treatment was much more beneficial as compared to 0.1% EMS or 0.3% EMS in the reduction of the mimosine content in L. leucocephala leaves. Mimosine content was successfully reduced from 1.6% to 0.2% or a reduction of 87.5% obtained in 0.6% EMS treated samples.This result has led to the identification of a new L. leucocephala clone with reduced mimosine contentfrom the 0.6% EMS treated samples.Livestock feed should not contain more than 30% of L. leucocephala as the mimosine can cause hair loss and stomach problems in animals. This limitation can be overcome by using this newly identifiedL. leucocephala clone. The livestock feed now can contain more than 30% of L. leucocephala leaves and still have all the Leucaena benefits. However, treatment with 0.6% EMS decreased the nutritive values of L.leucocephalaleaves, but the crude protein of 0.6% EMS treated samples (18.69%) was still higher than Medicago sativa (alfalfa)with 14.83% crude protein. With this clone, it assists the foresters or farmers to reduce the livestock feed price significantly in future.
High sensitivity piezoresistive cantilever sensor for biomolecular detection

Micro-cantilevers are now gaining popularity in biomolecular sensing. Monitoring and mass detection of biological species can be accurately determined using the micro- or nano- scale cantilevers. The targeted biological molecules are first immobilized onto the microcantilever which is later determined through static or dynamic techniques. Optical detection method is employed to determine the deflection of the cantilever in static analysis or the shift of resonance frequency in dynamic analysis. Though both analyses using optical detection have been highly sensitive and produce accurate results, they required extensive and expensive experimental setup and are not practical for point-of-care application. Piezoresistive sensing has become an alternative approach in biological sensing as changes in mechanical properties can be converted to electrical output. This paper presents the design and simulation of a highly sensitivity piezoresistive based micro-cantilever for biomolecular detection. A series of holes are formed on the silicon based micro-cantilever. These discontinuities form the Stress Concentration Region (SCR) used to increase sensitivity for piezoresistive sensing. Finite Element Analysis (FEA) through ANSYS 8.0 was performed on the structural model to maximize the SCR so as to improve the sensitivity of the cantilever. The optimized number, geometry, radius, and the distance from the first hole to the root of these holes were discussed in this paper.
A Vehicle-Borne Mobile Mapping System Based Framework for Semantic Segmentation and Modeling on Overhead Catenary System Using Deep Learning

Overhead catenary system (OCS) automatic detection is of important significance for the safe operation and maintenance of electrified railways. The vehicle-borne mobile mapping system (VMMS) may significantly improve the data acquisition. This paper proposes a VMMS-based framework to realize the automatic detection and modelling of OCS. The proposed framework performed semantic segmentation, model reconstruction and geometric parameters detection based on LiDAR point cloud using VMMS. Firstly, an enhanced VMMS is designed for accurate data generation. Secondly, an automatic searching method based on a two-level stereo frame is designed to filter the irrelevant non-OCS point cloud. Then, a deep learning network based on multi-scale feature fusion and an attention mechanism (MFF_A) is trained for semantic segmentation on a catenary facility. Finally, the 3D modelling is performed based on the OCS segmentation result, and geometric parameters are then extracted. The experimental case study was conducted on a 100 km high-speed railway in Guangxi, China. The experimental results show that the proposed framework has a better accuracy of 96.37%, outperforming other state-of-art methods for segmentation. Compared with traditional manual laser measurement, the proposed framework can achieve a trustable accuracy within 10 mm for OCS geometric parameter detection.
Procedural 3D Cave Generation

Procedural content generation is the algorithmical construction of models, textures or alterations of objects in virtual worlds. This field has exploded in recent years, taking advantage of the immense parallel computational power of modern videocards. It is especially popular in the form of real-time content generation for video games, but is also used in 3d graphics, simulation systems, virtual reality, and the movie industry. Although procedurally generated content is already being used in a sizeable chunk of the mainstream video game industry, procedural caves in 3D space have remained elusive, with only a few unsophisticated approaches to speak of. Our research goal for this thesis is to explore the possibilities for, and develop an approach to, procedurally generating believable and compelling three-dimensional cave landscapes, which satisfy common gameplay requirements, using the GPU1. To achieve this goal, we employ the use of L-Systems to construct the structure, chambers, and overall flow of the cave systems. We also implement a series of GPU programming layers which use a metaball ”brush” distorted by various noise functions to three-dimensionally sculpt the cave interiors. We present experimental results which show the relationships between our processes and their ability to produce cave landscapes. Furthermore, features such as stalactites and columns are generated through the use of Cellular Automata and inserted as a step in our GPU programming pipeline. In order to display the caves as in-game assets (3D meshes), we apply an isosurface extraction algorithm to our point density data. The geometry is then enhanced further using procedural texturing, lighting, and bumpmapping, through shader programming. Our resulting system is capable of procedurally generating cave networks in real time on low end gaming graphics cards and rendering the resulting geometry as in-game terrain. In addition, our tool allows a certain degree of control over the generation process, which includes features such as confining the caves to a certain volume in the virtual world, guaranteeing full path connectivity, influencing the direction and target of the tunnels, the frequency of the generated landmarks, the amplitude of distortion, and the option to preview, choose and combine different L-system structures.
Numerical Study on the Dynamic Fracture Energy of Concrete Based on a Rate-Dependent Cohesive Model

As an important parameter for concrete, fracture energy is difficult to accurately measure in high loading rate tests due to the limitations of experimental devices and methods. Therefore, the utilization of numerical methods to study the dynamic fracture energy of concrete is a simple and promising choice. This paper presents a numerical investigation on the influence of loading rate on concrete fracture energy and cracking behaviors. A novel rate-dependent cohesive model, which was programmed as a user subroutine in the commercial explicit finite element solver LS-DYNA, is first proposed. After conducting mesh sensitivity analysis, the proposed model is calibrated against representative experimental data. Then, the underlying mechanisms of the increase in fracture energy due to a high strain rate are determined. The results illustrate that the higher fracture energy during dynamic tension loading is caused by the wider region of the damage zone and the increase in real fracture energy. As the loading rate increases, the wider region of the damage zone plays a leading role in increasing fracture energy. In addition, as the strain rate increases, the number of microcracks whose fracture mode is mixed mode increases, which has an obvious effect on the change in real fracture energy.
Pharmacological Activities and Characterization of Phenolic and Flavonoid Compounds in Methanolic Extract of Euphorbia cuneata Vahl Aerial Parts

Euphorbia cuneata Vahl. (Euphorbiaceae) is a plant used in folk medicine for the treatment of pain and inflammation, although the biological basis for these effects has not been thoroughly investigated. The goal of this study was to investigate the pharmacological properties and characterization of phenolic and flavonoid compounds present in the aerial parts of E. cuneata. E. cuneata aerial parts were tested for antioxidant activity (DPPH), antibacterial activity, cell viability and cytotoxic effects, and anti-inflammatory activity. Phenolic and flavonoid contents (HPLC), and volatile constituents (GC-MS) were also characterized. The methanol extract had the highest antioxidant activity, while the ether extract had the lowest. The antioxidant activity of E. cuneata extract increased from (21.11%) at a concentration of 10 µg/mL to (95.53%) at a concentration of 1280 µg/mL. S. aureus was the most sensitive organism with the highest zone of inhibition and lowest MIC, with acetone extract; whereas C. tropicalis was the most resistant, with the lowest inhibition zone. MTT assay revealed that the methanol extract of E. cuneata had significant cytotoxic effects on the A549, Caco-2, and MDA-MB-231 cell lines, respectively. Lower concentrations of methanolic extract gave anti-inflammatory activity, and those effects were compared with indomethacin as a positive control. Pyrogallol was the most abundant phenolic acid, followed by caffeic, p-coumaric, ferulic, syringic, and gallic acids, respectively. The 7-hydroxyflavone and rutin flavonoids were also found in the extract. GC-mass analysis showed that aerial parts of E. cuneata were rich in methyl 12-hydroxy-9-octadecenoate. The volatile components were also composed of considerable amounts of hexadecanoic acid, methyl ester, (9E,12E)-octadeca-9,12-dienoyl chloride, and methyl octadeca-9,12-dienoate as well as a little amount of hexanal dimethyl acetal. It can be concluded that methanolic extract of E. cuneata could be used as an available source of natural bioactive constituents with consequent health benefits.
A technique for dynamic CMOS noise immunity evaluation

While dynamic CMOS logic is considered an attractive circuit technique, it suffers from noise problems. Noise may affect dynamic CMOS circuits in many ways. In this paper, new models have been developed that consider a noise pulse on one of the circuit inputs or on the clock input. These models specify the circuit noise immunity in terms of both the amplitude and the duration of a noise pulse. HSPICE simulations confirm the validity of these models for both long and short-channel MOSFETs. As dynamic circuits are designed around the constraint of noise, the models presented here clearly indicate parameters that affect noise and their influence, and how it could aid digital circuit designs.
Transferring PointNet++ Segmentation from Virtual to Real Plants

One of the biggest challenges of deep neural network to perform segmentation of point clouds is the requirement of large amount of annotated data, which is expensive in terms of manual labour and time. For the case of plants, there is a scarcity of datasets to train the networks to perform organ segmentation for automated phenotyping applications. In this work, we explore how the use of virtual plants as modelled by stochastic L-systems can circumvent this problem. We investigate the effect of point density and how the complexity of the plant model affect the transfer of learning from virtual to real plants on a segmentation task based on PointNet++.
Arti ﬁ cial intelligence and machine learning for the green development of agriculture in the emerging manufacturing industry in the IoT platform

In recent years, greenhouse development has been innovative in agriculture based on information systems guidance with accelerated growth. The IoT provides an intelligent system and remote access technologies such as green infrastructure. The usability of information systems for e ﬀ ective training and producing intelligent systems and predictive models in organizational real-time based on machine learning and arti ﬁ cial intelligence (AI). Therefore, a Remote Sensing Assisted Control System (RSCS) has been proposed for improving greenhouse agriculture requirements. This proposed method utilizes arti ﬁ cial intelligence and machine learning technology for the green development potential industry ’ s ability to manage economic resources and increase innovative agriculture product development patterns. Thus, the key preconditions for increasing healthy food choices and promoting local and global organic farmers ’ potential development are straightforward suggestions for developing an e ﬀ ective marketing strategy. The experimental results RSCS the highest precision ratio of 95.1%, the performance ratio of 96.35%, a data transmission rate of 92.3%, agriculture production ratio of 94.2%, irrigation control ratio of 94.7%, the lowest moisture content ratio of 18.7%, and CO 2 emission ratio of 21.5%, compared to other methods.
Investigation of Inertial Navigation for Localization in Underground Mines

This thesis project considers the potential use of inertial navigation on a consumer grade tablet mounted in a vehicle in an underground mine. The goal is to identify which sensors and techniques are useful and to design a navigation algorithm based on those results. The navigation algorithm is intended to work alongside the current received signal strength indication (RSSI) positioning system. Testing of the gyroscope, accelerometer and magnetometer sensors suggest that, while dead reckoning is likely not precise enough, an orientation filter can be designed that can be used for navigation. A complementary orientation filter using the gyroscope and accelerometer is then designed that shows better results than the default sensor fusion solutions available in Android. The filter is expandable and can come to include magnetometer data in the future. Based on the outputs of this filter, a navigation algorithm based onangle matching with map information is proposed. Precise positioning in an underground mine can be crucial to employee safety, and may also bring production benefits.
ECONOMIC MODELS FOR TMDL ASSESSMENT AND IMPLEMENTATION

The TMDL assessment and implementation process is designed to achieve designated uses for water bodies, which are set by states based on criteria including perceived costs and benefits. Setting water quality goals based on designated uses and plans to achieve these goals have important implications for public welfare. Both treatment and damage costs should be considered in simultaneously determining the desired water quality goal and allocating pollution reductions among sources to achieve that goal. Assessing and implementing TMDL plans are complicated by uncertainties about pollution damages and stakeholder responses. Economic optimization or simulation models linked to water quality models allow water quality impacts and costs of TMDL standards to be assessed. Higher water quality thresholds may be reserved for watersheds with higher estimated benefits. Costs of achieving standards can be reduced by targeting reductions at pollution sources with the lowest costs of achieving reductions. Trading programs can help achieve efficient targeting of pollution reductions while distributing costs equitably. The effectiveness of economic models to assist in setting water quality goals and in TMDL program planning and implementation can be improved by using economic models to analyze costs and benefits of water quality improvements and to assist with pollution targeting and trading programs to minimize costs of reducing pollution. Multi-media impacts of pollution should be included within economic and environmental water quality models. Given uncertainties about benefits and costs of achieving TMDL standards, policymakers and program managers need to collect more data on stakeholder responses to TMDL programs as well as better monitoring data on pollutant levels and functioning of aquatic systems.
Sanguiin H-6 Fractionated from Cloudberry (Rubus chamaemorus) Seeds Can Prevent the Methicillin-Resistant Staphylococcus aureus Biofilm Development during Wound Infection

Staphylococcus aureus is the most common cause of surgical site infections and its treatment is challenging due to the emergence of multi-drug resistant strains such as methicillin-resistant S. aureus (MRSA). Natural berry-derived compounds have shown antimicrobial potential, e.g., ellagitannins such as sanguiin H-6 and lambertianin C, the main phenolic compounds in Rubus seeds, have shown antimicrobial activity. The aim of this study was to evaluate the effect of sanguiin H-6 and lambertianin C fractionated from cloudberry seeds, on the MRSA growth, and as treatment of a MRSA biofilm development in different growth media in vitro and in vivo by using a murine wound infection model where sanguiin H-6 and lambertianin C were used to prevent the MRSA infection. Sanguiin H-6 and lambertianin C inhibited the in vitro biofilm development and growth of MRSA. Furthermore, sanguiin H-6 showed significant anti-MRSA effect in the in vivo wound model. Our study shows the possible use of sanguiin H-6 as a preventive measure in surgical sites to avoid postoperative infections, whilst lambertianin C showed no anti-MRSA activity.
Hypergraph Propagation and Community Selection for Objects Retrieval

Spatial verification is a crucial technique for particular object retrieval. It utilizes spatial information for the accurate detection of true positive images. However, existing query expansion and diffusion methods cannot efficiently propagate the spatial information in an ordinary graph with scalar edge weights, resulting in low recall or precision. To tackle these problems, we propose a novel hypergraph-based framework that efficiently propagates spatial information in query time and retrieves an object in the database accurately. Additionally, we propose using the image graph’s structure information through community selection technique, to measure the accuracy of the initial search result and to provide correct starting points for hypergraph propagation without heavy spatial verification computations. Experiment results on ROxford and RParis show that our method significantly outperforms the existing query expansion and diffusion methods.