Miracle-dz/UniFAR-FTUs huggingface dataset:
Kalman Filter for Hybrid Tracking Technique in Augmented Reality

Augmented reality started to emerge as a promising visualization technique that tracks real objects and adds virtual content into real world context using camera view. Many augmented reality solutions are based on computer vision techniques to identify and track objects. Problems that must be solved are image transformations, chaotic environment, lighting condition and occlusion from users’ or objects in the environment, which causes virtual content to disappear. This has a negative impact for augmented reality usability, therefore, object recognition and tracking in real-time becomes difficult and sometimes an impossible task. In this research orientation-position information acquisition using computer vision and sensor fusion techniques are analysed. Experiments are accomplished with predefined assumptions and simulated orientation-position information. Conditions for optimal orientation-position estimates are introduced. Research results are compared and supplementary properties are presented of a proposed hybrid tracking technique using Kalman filter. DOI: http://dx.doi. org/10.5755/j01.eie. 22.6. 17228
A ML multichannel ATI-SAR technique for measuring ocean surface velocities

Conventional along-track interferometric (ATI) synthetic aperture radars (SAR) derive the ocean surface velocity from an estimate of the phase difference between the SAR processed echoes received from two displaced phase centres with a single time-lag. A maximum likelihood (ML) algorithm is proposed to process the multi-channel SAR data from an array of phase centres with multiple time-lags. The new technique provides sensibly better estimation accuracy than the conventional one. Moreover, it provides unambiguous velocity retrieval and flexibility to ocean coherence time.
Lightweight YOLOv4 with Multiple Receptive Fields for Detection of Pulmonary Tuberculosis

The characteristics of pulmonary tuberculosis are complex, and the cost of manual screening is high. The detection model based on convolutional neural network is an essential method for assisted diagnosis with artificial intelligence. However, it also has the disadvantages of complex structure and a large number of parameters, and the detection accuracy needs to be further improved. Therefore, an improved lightweight YOLOv4 pulmonary tuberculosis detection model named MIP-MY is proposed. Firstly, over 300 actual cases are selected to make a common dataset by professional physicians, which is used to evaluate the performance of the model. Subsequently, by introducing the inverted residual channel attention and the pyramid pooling module, a new structure of MIP is created and used as the backbone extractor of MIP-MY, which could further decrease the number of parameters and fuse context information. Then the multiple receptive field module is added after the three effective feature layers of the backbone extractor, which effectively enhances the information extraction ability of the deep feature layer and reduces the miss detection rate of small pulmonary tuberculosis lesions. Finally, the pulmonary tuberculosis detection model MIP-MY with lightweight and multiple receptive field characteristics is constructed by combining each improved modules with multiscale structure. Compared to the original YOLOv4, the model parameters of MIP-MY is reduced by 47%, while the mAP value is raised to 95.32% and the miss detection rate is decreased to 6%. It is verified that the model can effectively assist radiologists in the diagnosis of pulmonary tuberculosis.
Deterioration of streamflow monitoring in Omo-Gibe basin in Ethiopia

ABSTRACT Poor availability and accuracy of streamflow data constrain research and operational hydrology. We evaluated the status of 40 streamflow stations and the quality of their data in the Omo-Gibe basin, Ethiopia. The method included a three-week field inspection of the stations. Inspection of stations followed common WMO guidelines for appropriate gauging sites. Feedback of observers was collected, and the streamflow data was analysed. Most of the stations were installed on rivers at headwater catchments. Only 17% of the stations were fully operational; the remaining stations require major maintenance. Common problems with the time series data include short observation periods, large numbers of missing records, and inhomogeneity. Nearly all observers expressed dissatisfaction due to lack of supervision, uncertain salary payments and lack of recognition of their contribution. The findings of this study indicate the need to investigate the institutional barriers that affect the homogeneity, completeness, and timeliness of the stream data.
Study on Closely Related Citrus CMMs based on Chemometrics and Prediction of Components-Targets-Diseases Network by Ingenuity Pathway Analysis

As the representatives of closely related Chinese medicinal materials (CMMs) originated from Rutaceae family, Aurantii fructus immaturus (AFI), Aurantii fructus (AF), Citri reticulatae pericarpium viride (CRPV), and Citri reticulatae pericarpium (CRP) have better functions in regulating qi and promoting gastrointestinal motility. However, differences in the quality of closely related Citrus CMMs have not yet been revealed until now. Herein, this study focused on the systematic differentiation and in-depth understanding of closely related Citrus CMMs by a strategy integrating chemometrics and network pharmacology. Determined by ultra performance liquid chromatography, the content of nine flavonoids showed obvious fluctuations in the decoction pieces from different species (Citrus aurantium Linnaeus and Citrus reticulate Blanco) with decreasing levels in the samples of ripe fruits. Decoction pieces from the different species and ripening stages were well distinguished by orthogonal projection to latent structure-discriminate analysis (OPLS-DA) and cluster analysis. As a result, four active components including narirutin, naringenin, hesperidin, and 3,5,6,7,8,3′,4′-heptemthoxyflavone were filtered out by variable importance for the projection (VIP) value (VIP > 1.0), which were regarded as chemotaxonomic markers. Furthermore, a components-targets-diseases network was constructed via ingenuity pathway analysis (IPA), and the correlations were predicted between four chemotaxonomic markers, 223 targets, and three diseases including colitis, breast cancer, and colorectal cancer. The obtained results will be of great significance for identifying closely related Citrus CMMs and conduce to improving the resource utilization of CMMs.
Accurate Dielectric Capacitance Determination from MetalInsulator-Semiconductor Devices Having Bias and Frequency Dependent Conductance Due To Leakage Current

A model for MIS device in accumulation is proposed which includes a bias and frequency dependent conductance in parallel with the insulator capacitance due to the leakage current. The model is validated by experimental results on MOS structures fabricated on n<100>Si having about 9 nm dry thermal oxide. The real and imaginary impedance versus frequency plots of such MOS device having large conductance can be used to determine accurately the oxide capacitance and consequently the oxide thickness without the use of ellipsometer. Greatly reduced MIS dot area can assist in avoiding frequency dispersion in accumulation capacitance, particularly at small-signal frequency of 1 MHz which is commonly used for generating high frequency C-V plots.
Role of vitamin C in preventing of COVID-19 infection, progression and severity

Vitamin C stands as an essential water-soluble vitamin, antioxidant and has been shown to enhance immunity. SARS-CoV-2 has been spreading rapidly across the worldwide, several cellular processes of innate and adaptive immunity are aided by vitamin C, which strengthens the immune system overall. Multiple lines of evidence in the literature associate vitamin C with antioxidant, anti-inflammatory, anticoagulant and immunomodulatory actions. Pneumonia and sepsis patients had poor ascorbic acid status and high oxidative stress, according to many studies. Pneumonia patients who get vitamin C may have less severe symptoms and a longer course of the illness if they do. To standardize plasma levels in sepsis patients, gram measurements of the vitamin must be administered intravenously (IV). This intervention has been shown in a few trials to reduce mortality. COVID-19 management in China and the United States has exhibited remarkable results when using a high percentage of intravenous vitamins C. It's acceptable to include vitamin C in the COVID-19 treatment protocol as a secondary measure based on the current active clinical studies looking at the impact of vitamin C on the management of COVID-19. Patients with hypovitaminosis C or severe respiratory illnesses, such as COVID-19, may benefit from taking vitamin C, due to its good safety profile, simplicity of use, and potential for rapid production scaling. The study's goal was to see whether high dosage intravenous vitamin C had any impact on individuals with severe COVID-19 (HDIVC). Finally we discuss recent research that has been published on the efficacy of vitamin C administration in the treatment of viral infection and life-threatening conditions. The purpose of this manuscript is to summarise existing research on the efficacy of vitamin C as a treatment for COVID-19 and to discuss possible explanations for why it may work in some individuals but not in others.
Optimization of Traffic Congestion Management in Smart Cities under Bidirectional Long and Short-Term Memory Model

To solve the increasingly serious traffic congestion and reduce traffic pressure, the bidirectional long and short-term memory (BiLSTM) algorithm is adopted to the traffic flow prediction. Firstly, a BiLSTM-based urban road short-term traffic state algorithm network is established based on the collected road traffic flow data, and then the internal memory unit structure of the network is optimized. After training and optimization, it becomes a high-quality prediction model. Then, the experimental simulation verification and prediction performance evaluation are performed. Finally, the data predicted by the BiLSTM algorithm model are compared with the actual data and the data predicted by the long short-term memory (LSTM) algorithm model. Simulation comparison shows that the prediction results of LSTM and BiLSTM are consistent with the actual traffic flow trend, but the data of LSTM deviate greatly from the real situation, and the error is more serious during peak periods. BiLSTM is in good agreement with the real situation during the stationary period and the low peak period, and it is slightly different from the real situation during the peak period, but it can still be used as a reference. In general, the prediction accuracy of the BiLSTM algorithm for traffic flow is relatively high. The comparison of evaluation indicators shows that the coefficient of determination value of BiLSTM is 0.795746 greater than that of LSTM (0.778742), indicating that BiLSTM shows a higher degree of fitting than the LSTM algorithm, that is, the prediction of BiLSTM is more accurate. The mean absolute percentage error (MAPE) value of BiLSTM is 9.718624%, which is less than 9.722147% of LSTM, indicating that the trend predicted by the BiLSTM is more consistent with the actual trend than that of LSTM. The mean absolute error (MAE) value of BiLSTM (105.087415) is smaller than that of LSTM (106.156847), indicating that its actual prediction error is smaller than LSTM. Generally speaking, BiLSTM shows advantages in traffic flow prediction over LSTM. Results of this study play a reliable reference role in the dynamic control, monitoring, and guidance of urban traffic, and congestion management.
A Particle Swarm Optimization of Natural Ventilation Parameters in a Greenhouse with Continuous Roof Vents

Although natural ventilation plays an important role in the affecting greenhouse climate, as defined by temperature, humidity and CO2 concentration, particularly in Mediterranean countries, little information and data are presently available on full-scale greenhouse ventilation mechanisms. In this paper, we present a new method for selecting the parameters based on a particle swarm optimization (PSO) algorithm which optimize the choice of parameters by minimizing a cost function. The simulator was based on a published model with some minor modifications as we were interested in the parameter of ventilation. The function is defined by a reduced model that could be used to simulate and predict the greenhouse environment, as well as the tuning methods to compute their parameters. This study focuses on the dynamic behavior of the inside air temperature and humidity during ventilation. Our approach is validated by comparison with some experimental results. Various experimental techniques were used to make full-scale measurements of the air exchange rate in a 400 m 2 plastic greenhouse. The model which we propose based on natural ventilation parameters optimized by a particle swarm optimization was compared with the measurements results. Copyright © 2009 IFSA.
The Effect of Exogenous Lysozyme Supplementation on Growth Performance, Caecal Fermentation and Microbiota, and Blood Constituents in Growing Rabbits

Simple Summary Rabbit farming is increasingly requiring non-antibiotic alternatives to improve rabbit gut health to maintain high feeding efficiency and excellent production index. Thus, using a friendly alternative is an appropriate way to protect rabbits from pathogens while also enhancing their performance and welfare. Lysozyme, an enzyme derived from avian egg white, aids in nutrient digestion and absorption and provides protection against bacterial diseases while lowering pollutants’ excretion, such as ammonia. Thus, the aim of this study was to evaluate the effect of lysozyme supplementation in rabbits’ diets on growth performance, caecal fermentation, bacteria population, and blood constituents. The results demonstrated an improvement in rabbit performance, caecal fermentation, blood lipid profile, and antioxidant status due to an increase in beneficial bacteria in lysozyme-treated rabbits. Therefore, supplementing the rabbit diet with lysozyme up to 150 mg/kg is recommended. Abstract The effects of exogenous lysozyme supplementation (LYZ) on growth performance, caecal fermentation and microbiota, and blood characteristics were investigated in growing rabbits. A total of 420 growing male V-Line rabbits (30 d old; weighing 528 ± 16 g) were randomly divided into four groups of 105 rabbits each, and monitored for 42 days. Experimental groups included a control group (LYZ0) fed a basal diet without LYZ supplementation, and three treated groups fed the same basal diet supplemented with LYZ at 50, 100, and 150 mg/kg diet, respectively. The results showed a quadratic improvement in the final body weight, daily growth rate, FCR, and digestibility of DM, while the digestibility of OM, CP, EE, NDF, and ADF improved linearly when LYZ supplementation was increased. The dressing percentage increased quadratically when LYZ levels were increased in the rabbit diets. In rabbits fed LYZ diets, L. acidophilus counts increased linearly (p < 0.05) and L. cellobiosus, and Enterococcus sp. counts increased quadratically, whereas E. coli counts decreased. In the LYZ-supplemented groups, the caecal pH value and NH3-N concentration declined quadratically, whereas total VFA, acetic, and butyric acids increased. Total lipids decreased linearly, whilst triglycerides and cholesterol decreased quadratically with LYZ supplementation. Total antioxidant capacity, superoxide dismutase, glutathione S-transferase, and catalase increased quadratically, while malondialdehyde decreased linearly in the LYZ-supplemented groups. In conclusion, exogenous lysozyme administration improved rabbit growth performance and antioxidant status while lowering the blood lipid profile, altering the bacterial population, and regulating caecal fermentation. Therefore, LYZ up to 150 mg/kg can be used as a potential supplement in rabbit feed.
Blade angle effect on droplet size spectrum of rotary atomizers used in Brazil

The effect of blade angle on droplet size spectrum of five rotary cage atomizers and one rotary disc atomizer used in Brazil was evaluated in a high speed wind tunnel, with an air speed of 180 km h−1. This study included the characterization of the droplet size spectrum, via laser diffraction instrument, generated by three blades angles, of 45, 60 and 75 degrees. The spray solution was composed of Picoxystrobin+ Cyproconazole fungicide (SC), at 20 mL c.p. L−1, and at a flow rate of 6 L min−1. As the angle decreased from 75 to 45 degrees, the rotational speed of the atomizers increased and the volume median diameter (VMD) decreased. However, increasing the rotational speed also increased the percentage of droplets smaller than 100 µm (% < 100 µm). At the blade angles of 45 and 60 degrees the atomizers showed that % < 100 µm was higher than 20% and VMD was smaller than 198 µm. The disc type atomizer resulted in the lower values of relative span (RS). The use of 75 degrees blade angle produced the highest values of VMD and the lowest values of % < 100 µm; these parameters were also affected by atomizer model and brand.
Identifikasi Dan Evaluasi Risiko Manajemen Rantai Pasok Komoditas Jagung Dengan Pendekatan Logika Fuzzy

To meet the needs of the national corn on the feed industry that requires a continuous supply of raw materials with a definite quantity throughout the year, in the national maize production conditions that is not continuous and fluctuating, it is necessary supply planning and storage methods to avoid the risk of maize corn supply crisis form of food shortages or rising feed prices. One method is to apply the concept of supply chain risk management. The high level of dependence and complexity of networks makes supply chain of agricultural products supply chain is becoming more vulnerable to interference. The risk of supply chain disturbance can occur internally (the relation between the organization with a network of suppliers) and external (between network suppliers with the environment). Therefore it is necessary to identify and evaluate supply chain risks in order to avoid continuing problems that can occur at any point in the supply chain network. The objective of this study is to describe the model of identification and evaluation for maize supply chain risk. This model can be used to identify the dominant risk factors and variables at each level of supply chain so that it will be obtained appropriate recommendations action to anticipate them. Risk identification will be conducted with fuzzy AHP approach and risk evaluation would be done by using fuzzy logic with data input form the opinion of several experts maize supply chain.
Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid

Due to the penetration of renewable energy and load variation in the microgrid, the diagnosis of power quality disturbances (PQD) is important to the operation stability and safety of the microgrid system. Once the power imbalance is present between the generation and the load demand, the fundamental frequency would deviate from the nominal value. As a result, the performance of the power quality classifier based on the neural network would be deteriorated since the deviation of fundamental frequency is not taken into account. In this paper, the regulated two-dimensional (2D) deep convolutional neural network (CNN)-based approach for PQD classification is proposed. In the data preprocessing stage, the IEC-based synchronizer is introduced to detect the deviation of fundamental frequency. In this way, the 2D grayscale image serving as the input of the deep CNN classifier can be accurately regulated. The obtained 2D image can effectively preserve information and waveform characteristics of the PQD signal. The experiment is implemented with datasets containing 14 different categories of PQD. According to this result, it is revealed that the regulated 2D deep CNN can improve the effectiveness of PQD classification in a real-time manner. Furthermore, the proposed method outperforms the methods in previous studies according to the field verification.
Coding‐Feeding Metasurface for Diffusion and Dual‐Band Emission

As 2D metamaterials, metasurfaces are well known due to the distinctive manipulation of the amplitude, phase, polarization, and propagation for electromagnetic (EM) waves. However, the existing metasurfaces are mainly designed to control the incident waves. Herein, a coding‐feeding metasurface (CFMS) with two functions of dual‐band emission and diffusion for incidences simultaneously is proposed. The zigzag emission patch and two arc‐split triangle patches for polarization conversion are nested together on a shared aperture of a unit cell for CFMS. The genetic optimization algorithm is introduced for diffusion. A prototype is fabricated and measured to validate the proposed CFMS with dual‐function. The experimental results agree well with the simulated data, which verify the diffusion and emission for CFMS. The CFMS with the lightweight, compact, and low profile has the potential applications in imaging, detection, and wireless communication systems for stealth aircraft.
Collaborative Project Procurement in the Construction Industry: Investigating the Drivers and Barriers in Malaysia

The traditional construction procurement approach engenders adversarial attitudes and the pursuit of individual organisation’s gain has led to inefficiencies and poor project management. Previous studies suggest collaborative working in the construction supply chain may produce better outcomes. This paper examines the drivers and barriers to embracing collaborative procurement practices pertinent to the construction industry in the developing world; using Malaysia as a base of the study. Drawing on an empirical survey involving construction professionals (contractors, consultants and clients), the significance of the drivers and barriers are prioritised based on mean scores. The most significant drivers are better quality outcome, better cost control, better time control, effective problem solving and increased client satisfaction. As for the barriers, resistance to change current way of working, communication problem, incompatible personalities and organisational cultures, lack of top management support, and inadequate training and guidance rank the highest. Next, Kruskal-Wallis tests were selected to compare the results given by the three groups of respondents. All the drivers are homogeneously perceived except for ‘long-term relationship’. Homogeneous perceptions are observed for all drivers. Results from this study are expected to benefit both construction practitioners and researchers in the exploration, planning and implementation of collaboration-based project procurement arrangements.
A roadmap of manufacturing system evolution - from product competitive advantage towards collaborative value creation

Manufacturing has been experiencing dramatic evolutions in recent decades. The boundary of a manufacturing system are extended from factory towards various types of network relationships. The missions of manufacturing system are also transformed and re-defined. This paper, based upon recent years industry case studies and research, introduces a manufacturing system evolutionary picture from which four main types of manufacturing systems can be identified and analysed in order to understand their characteristics of system and behind strategic drivers or intentions. The paper suggests that the classical manufacturing strategy theory should be adapted into a contingency strategy to respond the manufacturing evolutions and treat different systems interdependently. The paper also suggests a new conceptual framework for further research on new manufacturing evolution and contingency strategy.