Miracle-dz/UniFAR-FTUs huggingface dataset:
The investigation of factors affecting the purchasing decisions of consumers of green products

Goal: The purpose of this study was to investigate the factors affecting the purchasing decisions of consumers of green products. Design / Methodology / Approach: The sample of this study was composed of consumers of disposable products in Isfahan. In this study, Descriptive statistics is used for data analysis and Structural Equation Modeling is used test the research hypotheses and determine the effect of independent variables on the dependent variable. Results: Considering the critical values obtained from the research hypothesis test, it can be concluded that environmental protection, environmental responsibility, green product experience, environmentally friendly brands and social acceptance have a positive and significant effect on green product purchase decisions. Limitations of the investigation: Since the present study is an exploratory study, the findings of the study are limited to the sample and results may be changed if the sample is changed. Also, the sample of green product buyers' decisions was obtained from only one province, so the results may not be generalizable to all provinces of Iran. Practical implications: According to the results, it is recommended that environment-friendly companies differentiate their products from other products and price them appropriately. This should have an impact on consumers and encourage them to buy and use green products. Originality / Value: The design of the products in a recyclable form and making their components and materials reusable will in future cause their raw materials are recycled and reused; thus, buyers are more eager to buy green products with regard to environmental protection.
Sample-size considerations for a study of shorebird nest survival in the 1002 Area, Arctic National Wildlife Refuge, Alaska

Authorization of lease sales for oil development in the 1002 Area of the Arctic National Wildlife Refuge has highlighted gaps in information about biological communities in the area. The U.S. Fish and Wildlife Service, which is planning a study to evaluate spatial variation in the nest survival of tundra-breeding shorebirds to identify hotspots with high nest survival, sought advice from the U.S. Geological Survey on how to predict which sampling designs might be successful in achieving the study goals. I used previously obtained data on nest survival from Alaska’s North Slope to develop a simulation study that explored the statistical consequences of various sampling designs in the context of expected ranges of values of biological parameters. Of all sampling parameters, the number of nests per plot had the strongest influence on the ability to detect hotspots, followed by number of years of monitoring and the age at which nests were found. The benefit of sampling 98 plots instead of 68 was relatively small. Ages of nests cannot be predicted a priori, so I developed a field reference chart to identify when further nest-searching would be useful for a given plot. This simulation study used the best available information, but values of biological parameters will become better defined following subsequent data collection in the 1002 Area. Data from upcoming field seasons could be used to refine this analysis and improve estimation of the expected results of the field study, thus maximizing the likelihood that data from the 1002 Area nest survival study will meet the monitoring goals.
Machine-Learning-Based Hazardous Spot Detection Framework by Mobile Sensing and Opportunistic Networks

This study proposes a framework to detect hazardous spots on roads by combining mobile sensing on commercial-use vehicles with vehicle-to-vehicle (V2V) opportunistic networking. The most likely hazardous spots are the locations where sudden braking or change in direction is necessitated. In the proposed system, a vehicle periodically makes a tentative decision regarding the state of the road using its own sensing data by a machine learning (ML)-based technique; however, the final decision is made by applying the Viterbi algorithm. A hidden Markov model (HMM) was introduced in the Viterbi algorithm for numerically representing the spatial relationship between the condition of the road and sequential output of the local hazardous spot detector. Numerical simulation analyses were conducted by using a real acceleration dataset of more than 25 commercial-use vehicles in Kakogawa City, Japan. It was shown that the Viterbi algorithm significantly improved the accuracy of detecting hazardous spots as compared to the tentative decisions made by support vector machines (SVMs). In addition, our study introduced a V2V cooperative data processing technique to achieve higher accuracy in the detection of perilous spots. The vehicles shared their local decisions through an opportunistic networking technology. Trellis pruning was integrated into the Viterbi algorithm based on the shared decisions for emitting the final decisions. It was demonstrated that the proposed V2V cooperative framework had the potential to enhance the accuracy of detecting hazardous spots for all the vehicles in a community.
RESIDUE ANALYSIS OF IMAGE QUALITY USING TENSORFLOW

Residue analysis is important factor to enhance the quality of the image. Nowadays various image quality assessment and enhancement techniques are available. Layered High range dynamic image processing method is proposed to improve the image quality. The mapping of 8-bit conversion and code is applied for finding residue of images. In this case, each image characteristics are taken into account and create residue block for processing. The local characteristics are verified and find the residue signal ranges. In this paper, we used High dynamic range encoding scheme for calculating each residue block characteristics. This method is used to find different bitrates, spatial activities and dynamic ranges. The experiments are shown the 8-bit conversion method perform well and find the residue analysis. The final results are conducted by using linear and non-linear method. Our proposed method performs better and gives high quality visual images, trained data set and less computational cost.
Allelic profile of Serbian Xanthomonas campestris pv. campestris isolates from cabbage

Xanthomonas campestris pv. campestris (Xcc), the causal agent of black rot disease of cabbage (Brassica oleracea var. capitata L.), is one of the most important bacteria which affect proper cabbage growth, leading to head weight and quality losses and thereby drastically reducing its marketing value. The pathogen is genetically diverse, which is evident from the presence of eleven races worldwide and more than thirty combinations of allelic profiles. Therefore, this study aimed to determine the allelic profiles of Serbian cabbage Xcc strains obtained in 2014. The analysis was done on three selected Xcc strains whose DNA was first amplified using polymerase chain reaction (PCR) with four housekeeping genes - P-XdnaK, fyuA, gyrB, and rpoD, then sequenced, and the obtained sequences were finally used to determine allelic profiles. Allelic profiles were determined by comparison with 33 Xcc strains obtained from different hosts and regions, whose allelic profiles had been determined previously. A non-redundant database (NRDB) from the pubMLST was used for allelic profile determination and Phyloviz software for constructing a minimum spanning tree. The obtained allelic profile of all Serbian Xcc cabbage strains was 1, 3, 1, 1 for the P-X-dnaK, fyuA, gyrB and rpoD genes, respectively. This profile is assigned as sequence type 2 (ST2) and it coincides with a Portuguese B. oleracea Xcc strain, CPBF 213, originating from B. oleracea var. costata. No connection between sequence type (ST) and the host was detected.
Manual versus mechanical compression hemostasis approach after coronary angiography via snuffbox access.

OBJECTIVE Distal radial artery access or trans-snuffbox access (TSA) is a novel, safe, and feasible technique for coronary artery interventions wherein its vascular hemostasis is still concerned. So, this study aimed to compare two homeostasis methods comprising manual and mechanical compression approaches in patients undergoing coronary angiography (CAG) via TSA. METHODS In a prospective nonrandomized clinical trial, a total of 80 patients undergoing diagnostic CAG by TSA were divided into two equal groups: manual compression and mechanical compression (using radial TR band), the main end point of which was primary hemostasis time. Other variables were patient satisfaction, puncture site pain severity, hospitalization time, and local neurovascular complication during the 30-day follow-up. RESULTS The mean age of the patients was 57.1±8.0 years, with 40 of them (54.1%) being male. The primary hemostasis time was significantly shorter in the manual compression approach [15.0±5.9 minutes with median 15 (9-20)] than in the TR band group [25.7±4.9 minutes with median 25 (20-30)] (p<0.001). No significant difference was noted in the patient's satisfaction and puncture site pain severity as well as hospitalization time between the two methods (p>0.050). The neurovascular complication, including hematoma, numbness, and dRA occlusion, rates had also no significant difference between the two groups (p>0.050). CONCLUSION The manual compression approach on the puncture site reduces hemostasis time in patients undergoing CAG via TSA when compared with the mechanical compression method.
2-D Piecewise-Linear Neuron Model

This brief presents a novel two-dimensional (2D) piecewise-linear neuron model to characterize the firing activities of a tabu learning neuron. The neuron model has an alterable equilibrium, and its bifurcation mechanism is effectively analyzed by employing an approximate scheme. Coexisting bi-stable firing patterns are revealed and parameter-related neuron firing activities are demonstrated by several numerical methods. Finally, based on the standard components, an analog electronic neuron circuit is implemented to verify the coexisting bi-stable firing patterns. The presented neuron model with an extremely simple algebraic equation and circuit structure can be taken as a core cell for applications to large-scale neuromorphic circuits.
Approach to Control Permittivity and Shape of Centimeter-Sized Additive Manufactured Objects: Application to Microwave Scattering Experiments

Controlling the electromagnetic properties of dielectric objects is demanded in microwave applications such as radar cross section (RCS) studies, metamaterial design, and antennas prototyping. Additive manufacturing has made the fabrication of desired shapes easier, but there is still room for improvement in controlling the permittivity. This article proposes a novel approach to control the permittivity of 3-D printed objects, in particular the ones with low permittivity contrasts. The effective permittivity is set by locally varying the material density. The object is first meshed using tetrahedral meshing software. Then its air percentage is controlled by adjusting the diameter of cylinders, which are positioned at each edge of the mesh. By tuning the volume fraction, one can achieve the required effective permittivity. Design, manufacturing, and characterization steps are discussed for the specific case of spheres. With such a canonical shape, the effective permittivity can be directly retrieved by comparing far-field electromagnetic scattering measurements with Mie computations. Bistatic RCS measurements and simulations are provided and discussed. They enable us to assess the validity of the proposed methodology, in particular the good adequacy between the adequately chosen unstructured air/material distribution, the desired relative permittivity, and the good homogenization of the object.
On-Chip Silicon Optomechanical Cavities at Low Temperatures

Cryogenic optomechanical cavities fabricated from silicon are promising candidates for quantum information applications and platforms to study mesoscale quantum physics. However, the low temperature behaviour of these devices remains shrouded in mystery. To elucidate this, we have designed and built an optomechanical coupling apparatus inside of a dilution refrigerator, which we use to study on-chip silicon optical microdisks coupled to nanomechanical beams. Using an optomechanically mediated thermal ringdown technique, we measure the dissipation in a half-ring resonator between 10 mK to 10 K, and attribute it to twolevel system defects embedded within the one-dimensional geometry of the device. Modifying the standard tunneling model to describe this damping mechanism, we determine the density of states and deformation potentials of these two-level system ensembles, postulating that they originate from surface defects. We also study a low temperature photothermal backaction force observed in our devices that acts to suppress conventional radiation-pressure effects. Using a photothermal optomechanical model, we find that this interaction can, in principle, be exploited to cool our resonator’s motion into its ground state. Finally, we use a master equation approach to assess the feasibility of using our device geometry to perform nonlinear optomechanical measurements of quantized mechanical energy. In doing so, we set an upper limit on the allowable linear coupling strength of the system, which is significantly less stringent than the single-photon strong coupling regime required in previously studied optomechanical cavities.
A four-unknown refined plate theory for dynamic analysis of FG-sandwich plates under various boundary conditions

The current work, present dynamic analysis of the FG-sandwich plate seated on elastic foundation with various kinds of support using refined shear deformation theory. The present analytical model is simplified which the unknowns number are reduced. The zero-shear stresses at the free surfaces of the FG-sandwich plate are ensured without introducing any correction factors. The four equations of motion are determined via Hamilton\' principle and solved by Galerkin\'s approach for FG-sandwich plate with three kinds of the support. The proposed analytical model is verified by comparing the results with those obtained by other theories existing in the literature. The parametric studies are presented to detect the various parameters influencing the fundamental frequencies of the symmetric and non-symmetric FG-sandwich plate with various boundary conditions.
True-Time-Delay Mechanical Phase Shifter in Gap Waveguide Technology for Slotted Waveguide Arrays in Ka-Band

This article proposes a novel all-metal mechanical phase shifter in gap waveguide technology. The phase shifter is aimed at providing beam-scanning capabilities to conventional slot array antennas along the elevation plane. To validate experimentally the beam-steering functionality, a $4\times 8$ slot-array antenna has been designed and fabricated, along with the phase-shifting mechanism. The whole antenna consists of two pieces: a lower rotatable block, which changes the length of concentric groove gap waveguides, and an upper fixed block, where the slot-array antenna is placed. Experimental results validate the proposed concept, having obtained steering angles of up to 25°, with gain levels around 20 dBi with an antenna efficiency close to 90%. A reflection coefficient below −10 dB is achieved for a wide range of rotation angles from 29.5 to 30.5 GHz. The proposed phase shifter is completely scalable to any array size and its true-time-delay nature enables wide steering ranges for closely-spaced slot arrays with wideband radiation performance.
Predicting Concrete Compressive Strength using Deep Convolutional Neural Network based on Image Characteristics

In this study, we examined the efficacy of a deep convolutional neural network (DCNN) in recognizing concrete surface images and predicting the compressive strength of concrete. A digital single-lens reflex (DSLR) camera and microscope were simultaneously used to obtain concrete surface images used as the input data for the DCNN. Thereafter, training, validation, and testing of the DCNNs were performed based on the DSLR camera and microscope image data. Results of the analysis indicated that the DCNN employing DSLR image data achieved a relatively higher accuracy. The accuracy of the DSLR-derived image data was attributed to the relatively wider range of the DSLR camera, which was beneficial for extracting a larger number of features. Moreover, the DSLR camera procured more realistic images than the microscope. Thus, when the compressive strength of concrete was evaluated using the DCNN employing a DSLR camera, time and cost were reduced, whereas the usefulness increased. Furthermore, an indirect comparison of the accuracy of the DCNN with that of existing non-destructive methods for evaluating the strength of concrete proved the reliability of DCNN-derived concrete strength predictions. In addition, it was determined that the DCNN used for concrete strength evaluations in this study can be further expanded to detect and evaluate various deteriorative factors that affect the durability of structures, such as salt damage, carbonation, sulfation, corrosion, and freezing-thawing.
Half-Air-Filled Ball-Grid-Array-Based Substrate-Integrated Groove-Gap Waveguide and its Transition to Microstrip at W-Band

A half-air-filled substrate-integrated groove-gap waveguide is proposed for millimeter-wave and terahertz applications. The waveguide is constructed by a ball grid array using a flip-chip technique without metallic vias with lightweight and low profile. The possible leakage perpendicular to the groove is prevented by solder balls as modeled by an equivalent circuit. Besides, an inline transition between the proposed waveguide and a microstrip line is proposed, fabricated, and measured. The design concept and working mechanism are explained. Good agreement between simulation and measurement is observed. The simulation shows that the proposed waveguide can cover the entire ${W}$ -band (70–118 GHz), and the measurement demonstrates that the proposed transition can operate at 90–98.8 GHz.
Performance of Micelle-Clay Filters for Removing Pollutants and Bacteria from Tertiary Treated Wastewater

Filters filled with a micelle-clay complex mixed with sand were employed to investigate their purification capability of tertiary treated wastewater with loose UF-membranes. The UF membrane was hollow fiber with a molecular weight cutoff of 100kD. The complex was prepared from the organic cation octadecyltrimethylammonium (ODTMA) and the negatively charged clay-mineral, montmorillonite. This complex has a very large surface area, which includes large hydrophobic domains and is positively charged, about half of the cation exchange capacity of the clay. Two sets of filtration experiments were carried out at flow rates of 1.2 and 50 mL/min, which correspond to flow velocities of 3.7 and 153 cm/h, respectively. In the first case, after a passage of 1 L, the turbidity, total suspended solids (TSS), fecal coliforms (FC), and total coliforms (TC) were reduced to zero from 14 NTU, 6ppm, 350 and 10,000 counts per 100mL, respectively. In the second case, the numbers of FC and TC were reduced from 50,000/100mL to zero after the passage of 14 L. The values of COD and BOD were reduced several-fold. The conclusion is that the incorporation of micelle-clay filters in the sewage treatment system with loose tertiary capability is promising and warrants larger scale experiments for optimization of the overall system.
Deep-Reinforcement-Learning-Based Proportional Fair Scheduling Control Scheme for Underlay D2D Communication

In the last few years, we have witnessed the usage of billions of Internet-of-Things (IoT)-enabled devices in different applications starting from e-healthcare, transportation, agriculture, etc., across the globe. These interconnected devices share information using the Internet to improve the Quality of Service of the end users. There is a requirement of synchronization among the devices to provide scalability, reliability, and connectivity. Despite these advantages, proximity gain, interference, and fairness are various challenges for these devices in IoT which need to be resolved. To overcome these issues, we propose deep reinforcement learning (DRL)-based control scheme in the underlay of device-to-device (D2D) communication. D2D communication reuses the spectrum resources with cellular user equipment (CUE) to improve spectral efficiency. We propose the joint resource block (RB) scheduling and power control scheme to improve the sum rate of the network while considering the users’ fairness among all the links. To solve this problem, first, we transform the nonconvex optimization problem into a multiagent reinforcement learning formulation using the Markov decision process (MDP). Then, to solve the RB allocation, we used the multiagent deep $Q$ -network (DQN) framework to reduce the output dimension and improve the learning efficiency. Then, to convert the stochastic policy into deterministic policy, and to improve the fairness we combine the DQN with deep deterministic policy gradient to form the distributed deep deterministic policy gradient (DDDPG) scheme. Finally, to control the power of both the CUEs and D2D transmitters (DTs), we integrated the conventional optimization scheme with the DDDPG (CO-DDDPG). This combination enhances the convergence speed and reduces the computational complexity of the overall network. Numerical results show that the proposed scheme improves the network sum rate of 11.76% and the fairness 4.21% as compared to the state-of-the-art existing distributed DRL schemes.
SINGLE-LAYER FISHBONE-SHAPED FREQUENCY SELECTIVE SURFACE WITH STABLE PERFORMANCE

A novel single-layer band-pass frequency selective surface (FSS) is proposed in this letter. The unit cell is composed of eight rotationally symmetrical fishbone-shaped structures surrounded by a modified octagonal loop. This fishbone-shaped FSS exhibits stable resonant frequency while the incident angle ranges from 0◦ to 60◦ for both TE and TM polarizations, which means that polarization insensitivity and angular stability are well demonstrated on the proposed FSS. A prototype is fabricated and measured in an anechoic chamber, and good agreements are obtained between measured and simulated results.
Detecting the Early Damages in Structures With Nonlinear Output Frequency Response Functions and the CNN-LSTM Model

Frames, shells, and hybrid structures with early damages, such as early cracks, often behave as extremely weak nonlinear systems, among which the nonlinearity is difficult to be detected, especially if the system response is affected by the noise. To avoid these damages becoming catastrophic failures, developing effective incipient damages detection methods is important. The nonlinear output frequency response functions (NOFRFs) and associated indexes can be considered as one kind of the prospective detection tools, which are usually determined from the established nonlinear autoregressive with exogenous inputs (NARX) model. However, the hyperparameters in the NARX model are difficult to be determined so that the identification accuracy cannot be guaranteed. Therefore, it is important to develop more accurate methods to estimate the NOFRFs and their associated indicators for damage detection. Motivated by the powerful learning abilities of convolutional neural networks (CNN) and long short-term memory (LSTM) networks, a novel method based on NOFRFs and the CNN-LSTM model for detecting the early damages in structures is proposed. By applying the beat excitation, the response of the structure is divided into two components, where the approximately linear component is used to estimate the frequency characteristic of the linear component by the classical linear model and the nonlinear component is used to establish the CNN-LSTM model. By calculating the responses of the two models, the NOFRFs and associated indexes can be accurately estimated, and then the early damage can be detected. Simulation and experimental studies verify the potential and effectiveness of the novel method proposed in this article.