Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-Wide Identification, Structural, and Gene Expression Analysis of BRI1-EMS-Suppressor 1 Transcription Factor Family in Cucumis sativus

BRASSINOSTEROID INSENSITIVE1-EMS-suppressor 1 (BES1) is an essential regulator downstream of brassinosteroid signaling and plays important roles in plant stress response, growth, and development. To date, the regulation mechanisms of BES1 transcription factors have been identified and elucidated in model plants Arabidopsis and rice. However, little information is available regarding the BES1 family in Cucumis sativus. Therefore, this study conducted a genome-wide analysis of BES1 genes in cucumber. In cucumber, a total of six CsBES1 genes were identified, and their phylogenetic relationships, gene structures, and cis-elements in promoters were studied. CsBES1 genes were distributed on four of seven chromosomes. Gene structure analysis showed that the intron–exon model of CsBES1 genes was conserved and the CsBES1 protein contained a DUF822-conserved motif. Promoter cis-element prediction showed that plenty of developmental and stress- and hormone-related elements have been found in promoter regions of CsBES1 genes. Meanwhile, BES1 was divided into three groups (I, II, and III) on the basis of phylogenetic relationship analysis in six plant species. In addition, CsBES1 gene expression patterns were confirmed by transcription database and qRT-PCR analysis; the results showed that the expression of CsBES1 genes had not only tissue-specific expression but also different types of CsBES1 isoform which might respond to specific plant stresses. In summary, genome-wide identification, phylogeny, gene structure, and expression profile analysis of CsBES1 genes in cucumber provided a referable theoretical information for further functional study of CsBES1 genes and further facilitated the molecular breeding of cucumber.
Speech Enhancement Using Dilated Wave-U-Net: an Experimental Analysis

Speech enhancement is a relevant component in many real-world applications such as hearing aid devices, mobile telecommunications, and healthcare applications. In this paper, we investigate on the Dilated Wave-U-Net model: a recently proposed end-to-end neural speech enhancement approach based on the Wave-U-Net architecture. We evaluate the performance of the model on two datasets: the public VCTK dataset, and a contaminated version of Librispeech dataset. In particular, we experiment on using alternative losses based on the MSE loss, L1 norm and on a combination of L1 and MSE losses. Results show that the Dilated Wave-U-Net architecture outperforms other state-of-the-art methods in terms of intelligibility and quality metrics on both datasets and that MSE loss is the most performing one.
COMPARATIVE ANATOMICAL ANALYSIS OF STEM IN FOUR GENERA OF THE TRIBE SALSOLEAE, CHENOPODIACEAE

Ramazannejad Ghadi, R. , Azizian, D. & Assadi, M. 2006 12 31: Comparative anatomical analysis of stem in four genera of the tribe Salsoleae, Chenopodiaceae. –Iran. J. Bot. 12 (2):169-182. Tehran. The stem anatomy of 18 species belong to the genera Anabasis L. , Haloxylon Bge. , Hammada Iljin, Seidlitzia Bge. ex Boiss. of the tribe Salsoleae (Chenopodiaceae) was examined. Anatomical characters with numerical analysis provided some significant data for the delimitation of taxa at the generic and specific levels.
Analysis of Parkinson's disease diagnosis using a combination of Genetic Algorithm and Recursive Feature Elimination

Parkinson's disease is a neurological disorder which affects different body parts. Voice distortion is one of the early symptoms to appear. It is present in most of the Parkinson patients and has a lot of potential in early diagnosis of the disease. In this paper, potential of feature selection techniques is explored in diagnosis of Parkinson's disease using speech features. In this, two-stage feature selection is applied which combines the benefits of Genetic Algorithm and Support Vector Machine-Recursive Feature Elimination techniques into one. Classification is done using SVM and results are compared with SVM without feature selection and individual selection techniques. The two-stage feature selection technique not only reduced the dimensionality of the dataset but also improved the performance of the system.
Evaluating the Potential Antibiotic Resistance Status in Environment Based on the Trait of Microbial Community

The overuse of antibiotics has promoted the propagation and dissemination of antibiotic resistance genes (ARGs) in environment. Due to the dense human population and intensive activities in coastal areas, the health risk of ARGs in coastal environment is becoming a severe problem. To date, there still lacks of a quantitative method to assess properly the gross antibiotic resistance at microbial community level. Here, we collected sediment samples from Hangzhou Bay (HB), Taizhou Bay (TB), and Xiangshan Bay (XB) of the East China Sea for community-level ARGs analysis. Based on the 16S rRNA genes and predictive metagenomics, we predicted the composition of intrinsic ARGs (piARGs) and some related functional groups. Firstly, a total of 40 piARG subtypes, belonging to nine drug classes and five resistance mechanisms, were obtained, among which the piARGs encoding multidrug efflux pumps were the most dominant in the three bays. Secondly, XB had higher relative abundances of piARGs and pathogens than the other two bays, which posed higher potential health risk and implied the heavier impact of long-term maricultural activities in this bay. Thirdly, the co-occurrence network analysis identified that there were more connections between piARGs and some potential pathogenic bacteria. Several piARG subtypes (e.g., tetA, aacA, aacC, and aadK) distributed widely in the microbial communities. And finally, the microbial diversity correlated negatively with the relative abundance of piARGs. Oil, salinity, and arsenic had significant effects on the variations of piARGs and potential pathogenic bacteria. The abundance-weighted average ribosomal RNA operon (rrn) copy number of microbial communities could be regarded as an indicator to evaluate the antibiotic resistance status. In conclusion, this study provides a new insight on how to evaluate antibiotic resistance status and their potential risk in environment based on a quantitative analysis of microbial communities.
Dominance of Autochthonous Phytoplankton-Derived Particulate Organic Matter in a Low-Turbidity Temperate Estuarine Embayment, Gwangyang Bay, Korea

In estuaries, the sources of organic matter, its composition, and its functional role in the coastal ecosystem food web are all influenced by dominant estuarine processes. We investigated seasonal variations in the quantity and composition of dissolved inorganic nutrients, suspended particulate matter (SPM), and phytoplankton across a low-turbidity estuarine–coastal continuum in Gwangyang Bay, South Korea. The narrow estuarine channel has a sharp salinity gradient (from 0 to around 30) and low SPM concentrations (<20 mg L–1). The channel carries river discharge, with a large quantity of nutrients (NO3 and SiO2), into the bay. The especially low SPM concentrations in the estuary (range: 2.0–29.1 mg L–1), and generous nutrient source, create an exceptionally low-turbidity condition in the water column and high primary productivity with no conspicuous spatial or seasonal trends. Gradually increasing δ13C values, low POC:Chla (<200), and low molar C:N (4–8) in the SPM, together with higher Chla concentrations at low SPM locations along the longitudinal transect, suggest that local production of phytoplankton has a strong influence on the distributional patterns of particulate organic matter. Short water-residence times, within the bay system, impede the accumulation of diverse-origin organic matter. The community composition of phytoplankton, distinguished by their size group, also plays a role in characterizing SPM quantity and composition. The results indicate that autochthonous pelagic production governs biogeochemical features in the low-turbidity estuarine embayment, contrasting with a prevalence of allochthonous organic matter, and its active processing, in highly turbid and/or longer residence-time systems.
Risk Assessment of Polluted Glass Insulator Using Leakage Current Index Under Different Operating Conditions

In this article, an innovative approach to assess the risk of uniform/non-uniform pollution and wet high voltage glass insulator is proposed. The assessment is based on a new index, named as <inline-formula> <tex-math notation="LaTeX">$R_{hi}$ </tex-math></inline-formula>, which is derived from the <inline-formula> <tex-math notation="LaTeX">$3^{\mathrm {rd}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$5^{\mathrm {th}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$7^{\mathrm {th}}$ </tex-math></inline-formula> harmonics of the leakage current. Two 33 kV strings of cap and pin glass insulators having three units with different profiles are employed to evaluate the risk level, based on measured critical voltage gradient (<italic>Ec</italic>). Critical voltage stress tests are executed on the contaminated insulators under different ratios of lower to upper surface (<inline-formula> <tex-math notation="LaTeX">$J$ </tex-math></inline-formula>) and wetting rates. Furthermore, the effects of ratio of soluble deposit density (<italic>ESDD</italic>) to non-soluble deposit density (<italic>NSDD</italic>) on the flashover occurrence are analyzed. It was found that the insulator profile has significant influences on the likelihood of flashover which is estimated as 38% and 24% for type A and B insulators, respectively. From the results, it can be concluded that the proposed <inline-formula> <tex-math notation="LaTeX">$R_{hi}$ </tex-math></inline-formula> index is able to predict the risk level of the insulator and the probability of flashover occurrence more accurately than the 5th/3rd index, which is widely published in literature.
Modelling the use of FinTech in Indonesia

Nowadays, the existence of FinTech services has been emerging in many parts of countries including Indonesia. This service combines financial services and mobile technology to serve consumers more effectively and efficiently relating to their needs of financial services. Its market size is also growing giving sign that the service is widely accepted. However, there is still a lack of study especially relating to consumer behavior in in adopting the service. Therefore, the aim of this research is to study what factors influence consumers in adopting FinTech services by surveying Indonesian consumers. From 250 respondents, there are 204 forms filled. By conducting Structural Equation Modeling (SEM) using Lisrel 8.80 software, all consumer's inputs are computed. From this computation, there are three results that can be drawn. First, it revealed that trust has generally influenced not only user's satisfaction but also his continuance use intention, meanwhile, his satisfaction may not influence his continuance use intention. Second, all factors of convenience, performance, cost advantages, referent network size and perceived complementary are all together influencing user' satisfaction. Third, all factors of authentication, non-repudiation, confidentially, privacy protection, and data integrity are all together needed to be considered. Therefore, any Fintech companies are suggested to be aware of these matters to win their customers in this market.
A Hierarchical Optimization Method for Parameter Estimation of Diesel Generators

Diesel generators (DGs) are used to provide electrical power to consumers because their power outputs can be scheduled, and they offer stable operating characteristics in a standalone or microgrid system. The parameters for DGs are set to ensure reliable and accurate simulation for distributed energy resources (DERs), which increases system reliability, maintains power supply quality and reduces operational costs. There are many parameters that must be estimated for a DG. These parameter settings such as system gains and time constants may vary, as facilities are run for a few days. Parameters for a DG must then be re-estimated to ensure accurate simulation in a microgrid system. The proposed method uses a sensitivity analysis to classify parameters into three different categories. A hierarchical optimization method combined with an enhanced whale optimization algorithm (EWOA) is then used to estimate the parameter settings for a DG using actual measurement data. The proposed method is applied to a practical microgrid system, and the results show that the proposed method determines the optimal parameter settings for a DG that enables accurate simulation and robust implementation for a microgrid system.
Label-Free Electrochemical Detection of Dibenzofuran Using MnO₂ Nanofibres

Rapid industrialization is taking a huge toll on environment. Specifically, industrial sites engaged in combustion and carbonization processes of coal-based fuels and its derivative results in the discharge of Dibenzofuran (DBF). It is considered as one of the most hazardous organic pollutants of concern by global environmental protection agencies. DBF is a Persistent, Bioaccumulate and Toxic (PBT) chemical which causes significant damage to human and surrounding environment. In this study an electrochemical sensor with the potential to be integrated with current drinking water dispensing systems to warn individuals about the presence of DBF is developed. Nanofibrous manganese dioxide (<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-MnO<sub>2</sub>) is employed as receptor for the detection of DBF, while the sensing performance has been probed using electrochemical impendence spectroscopy. An equivalent electrical circuit model was developed using the electrochemical impendence data in order to gain insights into the electron transfer kinetics involved. The impedance response shows a linear relationship with DBF concentration over a range from <inline-formula> <tex-math notation="LaTeX">$10^{-2}$ </tex-math></inline-formula> nM - <inline-formula> <tex-math notation="LaTeX">$10^{6}$ </tex-math></inline-formula> nM. The limit of detection and sensitivity were calculated to be 1.22 nM and 0.58 <inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula>/nM/mm<sup>2</sup> respectively. The shelf-life of the sensor was found to be around 45 days.
Privacy and Utility Preserving Trajectory Data Publishing for Intelligent Transportation Systems

Nowadays, the extensive collection and storage of massive personal GPS data in intelligent transportation systems every day provide great convenience for trajectory data analysis and mining research, thus bringing valuable information for real-life applications. Yet, protecting personal privacy is also more challenging in the smart environment. When trajectories of individuals are published together with their sensitive attributes such as disease, income etc., one can use partial trajectory knowledge for identity, sensitive locations, and sensitive values of target individuals. We present <inline-formula> <tex-math notation="LaTeX">$(\alpha, K)_{L}$ </tex-math></inline-formula>-privacy model and an anonymization scheme aimed at <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>dentifying and <inline-formula> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula>liminating <inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula>iolating privacy <inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula>ubtrajectories (IEVS), to prevent privacy disclosure while preserving the accuracy and high quality of published trajectories. In particular, IEVS employs three anonymization techniques, i.e., trajectory splitting, location suppression, and sensitive value generalization to eliminate all subtrajectories violating <inline-formula> <tex-math notation="LaTeX">$(\alpha, K)_{L}$ </tex-math></inline-formula>-privacy principle. Experiments show our scheme is effective to improve the data utility of anonymized trajectories when compared with previous work.
Vehicle Tracking and Speed Estimation From Roadside Lidar

Vehicle speed is a key variable for the calibration, validation, and improvement of traffic emission and air quality models. Lidar technologies have significant potential in vehicle tracking by scanning the surroundings in 3-D frequently, hence can be used as traffic flow monitoring sensors for accurate vehicle counting and speed estimation. However, the characteristics of lidar-based vehicle tracking and speed estimation, such as attainable accuracy, remain as open questions. This research therefore proposes a tracking framework from roadside lidar to detect and track vehicles with the aim of accurate vehicle speed estimation. Within this framework, on-road vehicles are first detected from the observed point clouds, after which a centroid-based tracking flow is implemented to obtain initial vehicle transformations. A tracker, utilizing the unscented Kalman Filter and joint probabilistic data association filter, is adopted in the tracking flow. Finally, vehicle tracking is refined through an image matching process to improve the accuracy of estimated vehicle speeds. The effectiveness of the proposed approach has been evaluated using lidar data obtained from two different panoramic 3-D lidar sensors, a RoboSense RS-LiDAR-32 and a Velodyne VLP-16, at a traffic light and a road intersection, respectively, in order to account for real-world scenarios. Validation against reference data obtained by a test vehicle equipped with accurate positioning systems shows that more than 94% of vehicles could be detected and tracked, with a mean speed accuracy of 0.22 m/s.
A Proactive and Practical COVID-19 Testing Strategy

To reopen the economy safely during the COVID-19 pandemic, governments need the capability to proactively identify new and often asymptomatic infections, as well as contact tracing. Policymakers and public health professionals need a sampling-testing method that can achieve broad population coverage without overwhelming medical workers. We observe that COVID-19 high-risk groups are located in the hubs and cliques of our geo-social network, formed by the close encounters of people during daily life. These individuals are the de facto “canary in a coal mine”. We propose that nations offer free and anonymous testing service to them. With open-source computer algorithms and datasets, only a small fraction of the population selected for COVID-19 testing can cover the majority of high-exposure-risk individuals. A 0.3% sampled testing for a megacity covers 3/4 of its entire population. A 3% sampled testing for a rural town covers 3/4 of its entire population. With government oversight and public consent, this approach can serve each province/state or city/township for decentralized daily testing planning. However, to protect privacy, we recommend constructing the geo-social network of anonymized cellphones, not named individuals. This infrastructure should be dismantled once the pandemic is largely over. This can be achieved by policymakers, health workers, and engineers together in solidarity.
Automatic License Plate Recognition for Parking System using Convolutional Neural Networks

Traditionally, a parking staff enters the license plate of the outgoing vehicle in the system to generate the parking tickets. To improve the efficiency of the system, we propose an automatic vehicle license plate numbers recognition to automatically detect and record the plate numbers to the system eliminating the manual entry. The system consists of a YOLO model to automatically detect the license plate from the image of a vehicle, some preprocessing and image segmentation process to detect the digits in the license plate, and finally, a ResNet model to classify the plate numbers. The license plates used in this research are from Indonesia. From the experiments, we find that the YOLO can accurately detect the license plate with a high degree of confidence. Yet, the ResNet model achieves around 80% accuracy from the validation data. Despite the high accuracy, the model can sometimes wrongly classify plate numbers due to noises from the segmentation process, non-standard or damaged plates, and similarly looking digits.
Improving Energy Efficiency With Content-Based Adaptive and Dynamic Scheduling in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have revolutionized the era of conventional computing into a digitized world, commonly known as “The Internet of Things”. WSN consists of tiny low-cost sensing devices, having computation, communication and sensing capabilities. These networks are always debatable for their limited resources and the most arguable and critical issue in WSNs is energy efficiency. Sensors utilize energy in broadcasting, routing, clustering, on-board calculations, localization, and maintenance, etc. However, primary domains of energy consumption at node level are three i.e. sensing by sensing-module, processing by microprocessor and communication by radio link. Extensive sensing, over-costs processing and frequent communication not only minimize the network life-time, but also affects the availability of these resources for other tasks. To increase life-time and provide an energy-efficient WSN, here we have proposed a new scheme called “A Content-based Adaptive and Dynamic Scheduling (CADS) using two ways communication model in WSNs”. CADS dynamically changes a node states during data aggregation and each node adapts a new state based on contents of the sensed data packets. Analyzer module at the Base-Station investigates contents of sensed data packets and regulates functions of a node by transmitting control messages in a backward direction. CADS minimizes energy consumption by reducing unnecessary network traffic and avoid redundant message-forwarding. Simulation results have been shown that it increases energy-efficiency in terms of network life-time by 9.65% in 100 nodes-network, 11.36% in 150 nodes-network and 0.94% in 300 nodes. The proposed scheme is also showing stability in terms of increasing cluster life by 87.5% for a network of 100 nodes, 94.73% for 150 nodes and 53.9% in 300 nodes.