Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Asphalt Pavement Health Monitoring With Built-In Sensors: A Novel Application to Real-Time Modulus Evaluation

Modulus is a critical parameter for evaluating the bearing capacity and remaining life of pavements. The prevalent modulus back-calculation method by using the pavement surface deflections captured from falling weight deflectometer (FWD) could characterize the overall bearing capacity of pavements. However, a non-uniqueness issue may occur when identifying the modulus of each layer from the perspective of mathematics. Alternatively, this paper presents a novel application to real-time modulus evaluation based on asphalt pavement health monitoring with built-in sensors. First, the sensor layout is optimized on the basis of the theoretical relationship between modulus and mechanical response inside the pavement. The proposed sensor layout, including input (random loads), output (mechanical responses), and environmental sensing modules, is illustrated in detail. Then, the procedure for real-time modulus evaluation is proposed, and the convergence and uniqueness of modulus evaluation results are investigated. Lastly, a case study of the realistic asphalt pavement health-monitoring system is conducted to demonstrate the effectiveness of the application to real-time modulus evaluation. The results indicate that the proposed modulus evaluation method has a potential to identify the modulus of each pavement structural layer in real time with a convergent and unique solution under a realistic traffic load. The evaluation process has the advantage of not interfering with traffic compared with the prevalent FWD-based modulus evaluation method. The pavement health monitoring with built-in sensors is recommended for newly built roads to facilitate long-term real-time performance assessment and maintenance decision making.
Bipartite Cooperative Coevolution for Energy-Aware Coverage Path Planning of UAVs

The coverage path planning of unmanned aerial vehicles (UAVs) is a complex optimization problem in practice, especially for those involving multiple target areas. It is challenging to comprehensively plan the inter-area visiting order and the intra-area coverage paths simultaneously. Due to the battery limitation, usually the task can hardly be finished by a single UAV, and instead a fleet of UAVs are required. In this article, we first formulate an energy-aware multi-UAV multi-area coverage path planning (EM<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula>CPP) model, in order to characterize the practical path planning requirements of UAVs in complex conditions. Subsequently, to accomplish the optimization task, we propose a bipartite cooperative coevolution (BiCC) algorithm that coevolves an inter-area path planning and an intra-area path planning components to obtain good solutions. The basic operators in BiCC, such as the initialization and the reproduction operators, are tailored for the task of EM<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula>CPP. Besides, we also develop a fast heuristic algorithm for EM<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula>CPP, which is able to produce approximate solutions in a short time. Simulations on real-world datasets validate the good performance of the proposed methods.
Tracking of dynamic gesture fingertips position in video sequence

The ﬁeld of research of this paper combines Human Computer Interface, gesture recognition and ﬁngertips tracking. Most gesture recognition algorithms processing color images are unable to locate folded ﬁngers hidden inside hand contour. With use of hand landmarks detection and localization algorithm, processing directional images, the ﬁngertips are tracked whether they are risen or folded inside the hand contour. The capabilities of the method, repeatibility and accuracy, are tested with use of 3 gestures that are recorded on the USB camera. Fingertips are tracked in gestures presenting a linear movement of an open hand, ﬁnger folding into ﬁst and clenched ﬁst movement. In conclusion, a discussion of accuracy in application to HCI is presented.
Translation, Cultural Adaptation, and Validation of the 10-year Fracture Risk Assessment Tool (FRAX) into Filipino

Introduction : Screening tools for osteoporosis are relatively expensive and inaccessible to the general Filipino population. This study aims to develop a Filipino version of a validated measure, the Fracture Risk Assessment Tool, in order to facilitate improvement of fracture prevention care in the country. Methods : The FRAX was translated and culturally adapted into a Filipino version using established forward and backward translation methods and was succeedingly tested for equivalence to the original. The final version was administered to 120 out-patients and was tested for reliability using BMD measurements of the distal radius. Results : To be better understood and reliably answered by the Filipino population, several qualifiers were added to items such as previous fracture and rheumatoid arthritis. This was to account for the high incidence of high-energy trauma in the country, and the use of the same Filipino term for RA as with other arthritides, respectively. Conclusion : The Filipino version of the FRAX appears to be an acceptable and reliable instrument, serving as a low-cost alternative to BMD and DXA scans which are generally inaccessible and unaffordable by majority of the population
An Assessment of Tourists’ Shopping Experiential and Loyalty Intentions in Malaysian Outlet Shopping Malls

This study was conducted to empirically examine the effects of shopping tourism experiences on visitors’ loyalty intentions at Gateway@KLIA2 and Mitsui Outlet Park, Sepang. Administrative questionnaires were distributed personally to 384 tourists using tablet google form. The data was analysed using the Statistical Program for the Social Science (SPSS) and Partial Least Squares -Structural Equation Modelling (PLS-SEM). Findings revealed that there is a significant relationship between tourists’ shopping experience and loyalty intentions. The study concluded that understanding the tourist experiences based on experiential marketing or Strategic Experience Modules by Schmitt which are sensory, affective, intellectual, behaviour, and relational is beneficial for shopping mall operators and retail industry in Malaysia.
The emotional state through visual expression, auditory expression and physiological representation

As emotional content reflects human behaviour, automatic emotion recognition is a topic of growing interest. During the communication of an emotional message, the use of physiological signals and facial expressions gives several advantages that can be expected to understand a person’s personality and psychopathology better and determine human communication and human-machine interaction. In this article, we will present some notions about identifying the emotional state through visual expression, auditory expression and physiological representation, and the techniques used to measure emotions.
A comparative study to assess surface water's anthropic and natural vulnerability using the DKPR and RUSLE approaches. Case of the watershed of the Joumouaa dam (Coastal basin of Ghiss, North East Morocco)

The salinization of surface water in a coastal context leads to a qualitative degradation of this resource by various sources of anthropogenic and natural pollution. In this context, we present the results of a comparative study using "DKPR" and "RUSLE" models to evaluate the degree of surface water vulnerability against pollution, especially in the sub-watershed of the Joumouaa dam, a hydraulic infrastructure providing drinking water for the Targuist city. The "DKPR" model adopted as a qualitative approach involves four parameters: Accessibility of the aquatic environment (D), Water functioning of the soil and subsoil (K), Physiography watershed (P), Rainfall erosivity (R). The final result is a resource vulnerability map obtained by combining index maps of these four parameters using remote sensing and GIS. The "RUSLE" model applied as a quantitative approach integrates five factors in a multiplying function, namely: rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover-management (C), and soil conservation practices (P) in a remote sensing and GIS environment. The analysis of the final vulnerability maps of the approaches mentioned above will be helpful support for water resource managers and decision-makers better identify areas of high risk and their protection.
Insight to Enhancing the Performance of the Pole Drop Test for Detecting Field Winding Turn Faults in Salient Pole Synchronous Motors

The pole drop test is the most widely applied off-line test for detecting shorted turns in the field winding of salient pole synchronous motors. It is a simple test where the voltage drop (or inductance) of the poles are compared to detect the presence of shorted turns. Although it does not require special test equipment, false indications due to sensitivity problems are common. In this letter, the voltage and inductance distribution between the poles are analyzed under shorted field turn conditions. Some practical insight for enhancing the sensitivity and reliability of the pole drop test are given based on the qualitative analysis and experimental testing on a 30 kVA synchronous motor with shorted field turns. It is shown that the sensitivity of the test is significantly higher when the test is performed with the rotor inserted, which is not reported elsewhere. It is also shown that the reliability of the test can be improved by observing the voltage or inductance in poles adjacent to the pole suspected with shorted turns.
Applied machine learning in recognition of DGA domain names

Recognition of domain names generated by domain generation algorithms (DGAs) is the essential part of malware detection by inspection of network traffic. Besides basic heuristics (HE) and limited detection based on blacklists, the most promising course seems to be machine learning (ML). There is a lack of studies that extensively compare different ML models in the field of DGA binary classification, including both conventional and deep learning (DL) representatives. Also, those few that exist are either focused on a small set of models, use a poor set of features in ML models or fail to secure unbiased independence between training and evaluation samples. To overcome these limitations, we engineered a robust feature set, and accordingly trained and evaluated 14 ML, 9 DL, and 2 comparative models on two independent datasets. Results show that if ML features are properly engineered, there is a marginal difference in overall score between top ML and DL representatives. This paper represents the first attempt to neutrally compare the performance of many different models for the recognition of DGA domain names, where the best models perform as well as the top representatives from the literature.
Influence of Torrefaction Temperature and Climatic Chamber Operation Time on Hydrophobic Properties of Agri-Food Biomass Investigated Using the EMC Method

Due to the tendency for excessive moisture adsorption by raw, unprocessed biomass, various methods of biomass valorization are in use, allowing for the improvement of physical–chemical biomass properties, including hydrophobicity. One of the methods is torrefaction, which changes the hydrophilic properties of the biomass to hydrophobic. Therefore, in this study, the influence of the torrefaction temperature and the exposure time to moisture adsorption conditions on the hydrophobic properties of waste biomass from the agri-food industry (lemon peel, mandarin peel, grapefruit peel, and butternut-squash peel) were analyzed. The torrefaction was carried out at the following temperatures: 200, 220, 240, 260, 280, 300, and 320 °C. The hydrophobic properties were determined by using the EMC (Equilibrium Moisture Content) method, conducting an experiment in the climatic chamber at atmospheric pressure, a temperature of 25 °C, and relative humidity of 80%. The total residence time of the material in the climate chamber was 24 h. It was shown that the torrefaction process significantly improves the hydrophobic properties of waste biomass. Concerning dried raw (unprocessed) material, the EMC (24 h) coefficient was 0.202 ± 0.004 for lemon peels, 0.223 ± 0.001 for grapefruit peels, 0.237 ± 0.004 for mandarin peels, and 0.232 ± 0.004 for butternut squash, respectively. After the torrefaction process, the EMC value decreased by 24.14–56.96% in relation to the dried raw material, depending on the type of organic waste. However, no correlation between the improvement of hydrophobic properties and increasing the torrefaction temperature was observed. The lowest values of the EMC coefficient were determined for the temperatures of 260 °C (for lemon peel, EMC = 0.108 ± 0.001; for mandarin peel, EMC = 0.102 ± 0.001), 240 °C (for butternut-squash peel, EMC = 0.176 ± 0.002), and 220 °C (for grapefruit peel, EMC = 0.114 ± 0.008). The experiment also showed a significant logarithmic trend in the dependence of the EMC coefficient on the operating time of the climatic chamber. It suggests that there is a limit of water adsorption by the material and that a further increase of the exposure time does not change this balance.
Evolution of formaldehyde (HCHO) in a plume originating from a petrochemical industry and its volatile organic compounds (VOCs) emission rate estimation

Large industrial facilities, such as petrochemical complexes, have decisive effects on regional air quality: directly due to their own hazardous volatile organic compounds (VOCs) emissions and indirectly due to their contribution to secondary air pollution. In South Korea, pronounced ozone and particulate matter issues have been reported in industrial areas. In this study, we develop a new top-down VOC emission rate estimation method using in situ airborne formaldehyde (HCHO) observations in the downwind plume of the Daesan Petrochemical Complex (DPC) in South Korea during the 2016 Korea–United States Air Quality (KORUS-AQ) mission. On May 22, we observed a peak HCHO mole fraction of 12 ppb after a transport time of 2.5 h (distance approximately 36 km) under conditions where the HCHO photochemical lifetime was 1.8 h. Box model calculations indicate that this elevated HCHO is mainly due to secondary production (more than 90% after 2 h of plume aging) from various VOC precursors including ethene, propene, and 1,3-butadiene. We estimate a lower limit for yearly DPC VOC emissions of 31 (±8.7) × 103 MT/year for HCHO precursors and 53 (±15) × 103 MT/year for all measured primary VOCs. These estimates are 1.5–2.5 times higher than the latest Korean emission inventories, KORUSv5. This method is beneficial not only by tracking the sources, sinks, and evolution of HCHO but also by validating existing emission inventories.
Optimal Allocation of EV Charging Stations in a Radial Distribution Network Using Probabilistic Load Modeling

Electric vehicle (EV) charging stations (CSs) are the coupling point between the power grid and the transport network. Operational behaviour of EVs will affect both the networks simultaneously. Thus, the optimal placement of CSs in a distribution network plays a vital role. This paper proposes a novel method for optimal placement of EV charging stations considering loss minimization of the electrical grid, as well as EVs’ power loss during travel towards charging station. CS’s utilization factor helps to determine the number of CSs required in a network by ensure the effective usage of the investment in the CS infrastructure. Instead of a constant load/static load demand, the proposed work uses a queueing model for the dynamic behaviour of a CS serviceability. To capture the uncertainty in electrical demand and EV behaviour, a probabilistic load modelling (PLM) method is employed. With the recognized modelling, this work can optimally allocate CSs in a distribution network. The problem is structured as a multi-objective optimization problem. Simulation experiments are conducted on a test system to illustrate the proposed method. The influence on voltage profile, CS utilization, and load uncertainty are analyzed and presented to demonstrate the effectiveness of the proposed method.
In vitro assessment of safety and functional probiotic properties of Lactobacillus mucosae strains isolated from Iranian native ruminants intestine

Abstract The search for host isolated probiotic bacteria from animal intestine may discover new probiotic candidates with promising health properties. This study evaluated the safety and functional probiotic potential of the lactic acid bacteria (LAB) isolated from the Iranian native ruminants intestine under in vitro assays. The isolates were selected according to criteria including survivability in low pH, bile salts, pancreatic enzymes, different temperatures, NaCl concentrations, antibacterial activities, presence of adhesion genes and safety characteristics. The selected LAB were then identified to species level using 16S rRNA gene sequencing. The results showed that out of one hundred and eighty-seven LAB isolates, only six strains (NABRII50, NABRII51, NABRII52, NABRII53, NABRII54 and NABRII55) were tolerant to low pH, bile salt, pancreatin enzyme, 45 °C temperature and 2% sodium chloride. The six selected isolates belonged to Lactobacillus mucosae. Two of the adhesion genes (mub and map) were detected in all strains except NABRII53. The virulence factors were observed in NABRII50, NABRII53 and NABRII55. The tetracycline resistance gene (tet (S)) was detected in NABRII55. This study was the first effort to select Lb. mucosae strains with the probiotic potential from the Iranian ruminants intestine. These results revealed that the ruminant intestinal ecosystem could be considered as a valuable origin of probiotic candidates and all the selected LAB strains except NABRII50, NABRII53 and NABRII55 could be considered as promising probiotics. HIGHLIGHTS Lactobacillus mucosae strains isolated from Iranian native ruminants intestine including NABRII51, NABRII52 and NABRII54 showed probiotic potential under in vitro assays. The Lb. mucosae strains including NABRII51, NABRII52 and NABRII54 were able to survive in intestinal physiological conditions, and carried the adhesion genes, such as mub and map. In vitro biosafety assays confirmed that Lb. mucosae strains (NABRII51, NABRII52 and NABRII54) were safe to further studies.
Enhancing co-stimulation of CAR T cells to improve treatment outcomes in solid cancers

Summary Co-stimulation is a fundamental component of T cell biology and plays a key role in determining the quality of T cell proliferation, differentiation, and memory formation. T cell-based immunotherapies, such as chimeric antigen receptor (CAR) T cell immunotherapy, are no exception. Solid tumours have largely been refractory to CAR T cell therapy owing to an immunosuppressive microenvironment which limits CAR T cell persistence and effector function. In order to eradicate solid cancers, increasingly sophisticated strategies are being developed to deliver these vital co-stimulatory signals to CAR T cells, often specifically within the tumour microenvironment. These include designing novel co-stimulatory domains within the CAR or other synthetic receptors, arming CAR T cells with cytokines or using CAR T cells in combination with agonist antibodies. This review discusses the evolving role of co-stimulation in CAR T cell therapies and the strategies employed to target co-stimulatory pathways in CAR T cells, with a view to improve responses in solid tumours.
Soft-Health: Software-Defined Fog Architecture for IoT Applications in Healthcare

In this article, we propose a software-defined fog architecture, named as Soft-Health, to serve various Internet-of-Things (IoT)-based healthcare applications. The health conditions of the patients fluctuate over time. Further, specialized medical care may not always be available in all healthcare facilities. The use of wireless body area network (WBAN) for continuous patient monitoring addresses the issue to a certain extent. However, as the physiological parameters of a patient are time-critical in nature, any delay, packet loss, and network overhead, may result in deterioration of the patient’s health conditions. Considering this, we design a Software-defined fog-enabled IoT platform for various healthcare applications. We consider that the fog layer comprises SDN switches that allocate the packet to the appropriate fog/cloud depending upon the criticality index (CI) of the data packets originating from patients. We mathematically formulate the CI, based on the physiological parameters sensed and transmitted to the switches. Further, we design an optimization function to obtain the maximum utility of a fog node, for an optimal number of processes executed by that node. We apply the Lagrangian method to simplify the optimization function and solve it using Karush–Kuhn–Tucker (KKT) conditions. We apply the auto-regression model to predict the total delay incurred and the total energy consumed by the proposed scheme. Exhaustive analysis of our proposed scheme, Soft-Health, demonstrates that the delay incurred decreases by 24.57% and 40.1% approximately, compared to the existing schemes, Mobi-Flow, and CARE, respectively.
Rationale and Design Approach for Full-scale Experiential Learning Opportunities in Structural Engineering

steel design, and foundation design courses. The design approach of the experiential learning modules highlights several factors including desired structural behavior, scale, testing capabilities, and implementation feasibility. Some supporting data regarding student perceptions of difficult topics is provided to reinforce faculty observations. The paper also provides brief descriptions of the thirteen experiential learning modules developed for the four courses to improve student understanding of structural behavior and concludes with a brief discussion of project assessment efforts.
Automotive Radar Maximum Unambiguous Velocity Extension Via High-Order Phase Components

Doppler ambiguity in the range-Doppler domain of linear frequency modulated (LFM) continuous wave radars occurs when a target’s Doppler frequency exceeds half of the chirp repetition frequency. Doppler ambiguity is common in a variety of civil and military radar applications, and an extension of the maximal unambiguous Doppler shift is critical for their practical use. This article utilizes previously neglected high-order phase terms in the received LFM radar echo for extension of maximum unambiguous velocity. A computationally feasible velocity estimation algorithm using hypotheses testing with local maximum likelihood is derived. The ability of the proposed low-complexity algorithm to estimate previously ambiguous target velocity is investigated using recorded automotive radar measurements and via simulations. The performance of the proposed algorithm is evaluated using a novel model of the Doppler estimation errors that accounts for Doppler ambiguity. The ability of the derived model to predict the threshold phenomenon is demonstrated via simulations.