Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and development of an electronic device for data transmission with universal coverage

This paper presents the design and development of an electronic device with cellular and satellite coverage for localization and tracking of mobile assets in Colombia (public transport, freight, carriers, etc.). The development of this product integrates hardware, software and communication interfaces. It is aligned with a need identified in the market by the company One Solution Position: OSP LTDA. With this technology transfer we intend to improve the offer of wireless services for mobile asset tracking with a solution that covers the following features: dual operation (cellular/satellite), versatile, economical, low-power consumption, and based on international standards FCC and CE Mark.
Highly selective bandpass filter based on substrate integrated waveguide

A novel highly selective six-pole elliptic filter based on a substrate integrated waveguide (SIW) is proposed for the first time. The filter is composed of two planar SIW extend doublets. The modular design approach based on cascading small building blocks is adopted to reduce the effect of manufacturing tolerance on the performance of the filter. The proposed filter can produce four finite transmission zeros, and has an excellent stopband rejection characteristic. Measured results are presented and compared with results simulated by a high frequency structure simulator. Good agreement between simulated and measured results is observed.
A Continuous Taxi Pickup Path Recommendation under The Carbon Neutrality Context

In the context of the carbon neutrality target, carbon reduction in the daily operation of the transportation system is more important than that in productive activities. There are few travel services that can quantify low-carbon travel, with a lack of effective low-carbon travel tools to guide transportation behavior. On-demand access to taxi services can effectively reduce the additional carbon emissions caused by cruising, which in turn increases efficiency in urban mobility with a reduced taxi fleet scale. For individual taxis, they lack macroscopic horizon in their choice of passenger pickup paths. The selected travel path based on personal operational experience or real-time location is limited by local optimization when making path decisions. In this work, we proposed a macro-path recommendation method to assist the taxi pickup path selection to accelerate the transformation of the taxi system towards low-carbon sharing. First, an adaptive learning spatiotemporal neural network was used to predict the coarse-grained distribution of potential trips. Next, the trajectory sharing graph was constructed based on the potential trips distribution to reallocate the taxi orders for the continuous pickup path optimization. As a result, the continuous pickup path balanced the relation between travel demands and taxi supply, improving the economic and environmental benefits of taxi operation and contributing to the goal of carbon neutrality. We conducted experiments on the Chengdu city ride-hailing dataset. Compared with the current status of taxi operations, the solution shows improvements in both the scale of taxi services and order gain.
Area of Specialization and Teaching Performance of the Secondary Science Teachers in Negros Oriental, Philippines

The K to 12 reform and the new Philippine Professional Standard for Teachers have changed the landscape of teacher quality requirements in the Philippines. It is in this context that this study is deemed necessary to determine the area of specialization and the teaching performance of the public secondary school Science teachers in terms of science content knowledge with this new educational schemeThere were 46 respondents in this study from the Department of Education-Guihulngan City Division, Negros Oriental Philippines. Findings reveal that the science teachers’ area of specialization can be categorized as Bachelor of Secondary Education (BSED) major in General Science, BS in Scientific Field (Nursing, Biology, and Chemical Engineering) and the Non-Science Field (English, TLE, Social Science). It indicates that majority of them lack the needed educational qualification to teach all science disciplines in the K12 curriculum. Furthermore, it shows that the BSED-General Science teachers with outstanding rating perform better than the B.S. in Scientific Field group and the non-science teachers with very good and good performance ratings respectively. It is an indication of lack of science content knowledge among the non-science teachers. Furthermore, the result discloses that the area of specialization is a determining factor in their teaching performance. It revealed that among the three groups of respondents, the general science teachers appear to be the most efficient. They are followed by those in the B.S. in Scientific field, and the non-science teachers came out to be the lowest. Hence, it clearly suggests that the BSED-General Science teachers are the most qualified to teach science since they have the sufficient educational preparations. The B.S.in Scientific Field teachers need to undergo more training and enroll in graduate studies to master all the science areas in the K12 curriculum. While the non-science teachers are discouraged to handle science subjects since they lack the necessary educational preparation to teach science.
A fast Poisson solver for realistic semiconductor device structures

Combines the techniques of fast Fourier transforms, Buneman cyclic reduction and the capacity matrix in a finite difference Poisson solver specifically designed for modelling realistic electronic device structures. A solution may be determined on a number of connected rectangular regions which correctly accounts for continuity of the electric displacement at dielectric interfaces. Suggests the method is particularly well suited to problems requiring repeated solution with the same device structure and provides a self‐consistent Monte Carlo simulation as an example of such an application.
Dual‐mode DC‐block semicircular‐patch bandpass filter using slot technique

A simple semicircular‐patch bandpass filter (BPF) is presented in this letter. The slot between two semicircular‐patch resonators provides DC blocking and a strong coupling, while the slotted orthogonally feed lines are used to obtain the dual‐mode characteristics. Measurement demonstrates the proposed design methodology and filter. As we know, this is the first report on the microstrip dual‐mode BPF using semicircular‐patch structure. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2261–2263, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22676
Appraising Lignite Quality Parameters by Linguistic Fuzzy Identification

Abstract Lignite quality parameters have had central importance for power plants. This article addresses a comparative study on fuzzy and regression modeling for estimating the calorific value of lignite, which is one of the quality parameters from the other parameters: moisture, ash, volatile matter, and sulphur content. For the estimations, data driven models were designed based on linguistic fuzzy modeling structures. In addition, estimations of the fuzzy models were compared with linear regression estimations. The great majority of performance evaluations showed that the fuzzy estimations are very satisfactory in estimating calorific value of lignite.
Effects of Ai-Chi Practice on Balance and Left Cerebellar Activation during High Working Memory Load Task in Older People: A Controlled Pilot Trial

Background: Normal aging is associated with balance and working memory decline. From a neurobiological standpoint, changes in cerebellar functional plasticity may mediate the decline in balance and working memory for older adults. Mounting evidence suggests that physical activity is beneficial for decreasing aging effects. Previous studies have focused on land-based physical activity and research concerning the aquatic environment is scarce. This study investigated the effectiveness of Ai-Chi on balance abilities and cerebral activation during a high working memory load task among community-dwelling older people. Methods: A total of 19 people aged 65–86 years were allocated to receive Ai-Chi practice (n = 6), structured on-land Ai-Chi practice (n = 7) or guided-imagery of Ai-Chi practice (n = 6) for a bi-weekly, 30-min exercise session for 12 weeks. Balance was measured by the Tinetti balance sub-test and working memory was measured by the N-back test during functional-MRI scan. Results: The Ai-Chi practice group presented a significant change in balance between pre and post intervention (balance t = −4.8, p < 0.01). In the whole-brain analysis, during high working memory load task, the Ai-Chi practice group presented a decrease in left cerebellar activation. Region of interest analyses yielded similar results by which pre-cerebellar activation was higher than post-intervention (t = 2.77, p < 0.05). Conclusions: Ai-Chi is an available, non-invasive intervention method that may serve as a tool to improve cerebellar activation that in turn might improve balance. In addition, our findings may provide new insights into the neuronal mechanisms that underlie both motor and cognitive abilities.
Mining of Quantitative Association Rules in Agricultural Data Warehouse: A Road Map

Agriculture data is highly broadened in terms of irrigation sources, climate, soil and inputs like fertilizers and pesticides. For sustainable growth of agriculture, these resources need to be monitored, analyzed and allocated optimally. Data Mining techniques may be used in agricultural data for mining the association rules among various inputs and outputs used for cropping. This paper is an endeavour to study the existing data mining algorithms to mine association rules widely used in corporate sector. The paper also presents an idea for mining quantitative multidimensional association rules from Agricultural Data Warehouse based on concise data using data cubes.
Barrier mitigation measures trigger the rapid recovery of genetic connectivity in five freshwater fish species

Rivers are heavily fragmented by man-made instream barriers such as dams and weirs. This hyper-fragmentation is a major threat to freshwater biodiversity and restoration policies are now adopted worldwide to mitigate these impacts. However, there is surprisingly little feedback on the efficiency of barrier mitigation measures in restoring riverine connectivity, notably for non-migratory fish species. Here, we implemented a “before-after genetic monitoring” of the restoration of 11 weirs in France using a dedicated genetic index of fragmentation (the FINDEX), with a focus on five fish species from two genera. We found that most obstacles actually had a significant impact on connectivity before restoration, especially the highest and steepest ones, with an overall barrier effect of about 51% of the maximal theoretical impact. Most importantly, we demonstrated for the first time that mitigation measures such as dam removal or fish pass creation significantly and rapidly improved connectivity, with –for some barriers-a complete recovery of the genetic connectivity in less than twelve months. Our study provides a unique and strong proof-of-concept that barrier removal is an efficient strategy to restore riverine connectivity and that molecular tools can provide accurate measures of restoration efficiency within a few months. Graphical Abstract
Blogging for Learning: Improving Teaching Strategies for Implementing Blogs in Higher Education

Worldwide, instructors in higher education are implementing blogging technology in their classrooms to help students articulate and share their learning with peers and experts. Despite the intended outcomes of deep learning, instructors are often disappointed with the lack of critical analysis and transfer evident in student blog posts. This paper documents pertinent research conducted with a cohort of Australian teacher education students using blogs to describe their teaching experience while on practicum and share their experience with their peers and the instructor. The findings identified several instructional strategies to be considered when implementing blogs in higher education.
Impact of a SLC24A5 variant on the retinal pigment epithelium of a Japanese patient with oculocutaneous albinism type 6

Oculocutaneous albinism (OCA) 6 is a non‐syndromic type of OCA that has distinct ocular symptoms and variable cutaneous hypopigmentation. The causative gene of OCA6 is SLC24A5, which encodes NCKX5, a K+‐dependent Na+/Ca2+ exchanger 5. NCKX5 is involved in the maturation of melanosomes, but its function is still unclear. In this study, we characterized a Japanese patient with OCA6. Genetic analysis revealed compound heterozygous variants in SLC24A5, c.590 + 1dupG, and c.598G>A (p.G200R). To clarify the functional significance of the missense variant, we generated a knock‐in (KI) mouse model carrying the mouse homolog of the G200R variant using the CRISPR/Cas9 system. Chemical analysis showed decreased amounts of eumelanin in the hair and skin of KI mice, while levels of benzothiazine units in pheomelanin were significantly increased in their hair. Retinal pigment was also decreased in KI mice. Notably, a histopathologic study revealed a significant pigment loss in the retinal pigment epithelium (RPE) but not in the choroid. Immunohistochemically, the expression of NCKX5 in the RPE was decreased but was maintained in the choroid of KI mice. These findings could explain the difference in phenotypic severity between eye symptoms and hypopigmentation in the skin/hair.
Estimating global landslide susceptibility and its uncertainty through ensemble modelling

Abstract. This study assesses global landslide susceptibility (LSS) at the coarse 36-km spatial resolution of global satellite soil moisture observations, to prepare for a subsequent combination of a global LSS map with dynamic soil moisture estimates for landslide modelling. Global LSS estimation intrinsically contains uncertainty, arising from errors in the underlying data, the spatial mismatch between landslide events and predictor information, and large-scale model generalizations. For a reliable uncertainty assessment, this study combines methods from the landslide community with common practices in meteorological modelling to create an ensemble of global LSS maps. The predictive LSS models are obtained from a mixed effects logistic regression, associating hydrologically triggered landslide data from the Global Landslide Catalog (GLC) with predictor variables from the Catchment land surface modeling system (incl. input parameters of soil (hydrological) properties and resulting climatological statistics of water budget estimates), geomorphological and lithological data. Road network density is introduced as a random effect to mitigate potential landslide inventory bias. We use a blocked random cross validation to assess the model uncertainty that propagates into the LSS maps. To account for other uncertainty sources, such as input uncertainty, we also perturb the predictor variables and obtain an ensemble of LSS maps. The perturbations are optimized so that the total predicted uncertainty fits the observed discrepancy between the ensemble average LSS and the landslide presence or absence from the GLC. We find that the most reliable total uncertainty estimates are obtained through the inclusion of a topography-dependent perturbation between 15 % and 20 % to the predictor variables. The areas with the largest LSS uncertainty coincide with moderate ensemble average LSS. The spatial patterns of the average LSS agree well with previous global studies and yield areas under the Receiver Operation Characteristic between 0.63 and 0.9 for independent regional to continental landslide inventories.
Credit Risk Early Warning System using Fuzzy Expert Systems

We describe an early warning system (EWS) for credit risk management based on fuzzy expert systems. Modelling process, including knowledge elicitation and univariate analysis is discussed. Several approaches to handling model complexity, model auto tuning and validation are described. We found that such a hybrid model had more predictive power and is more robust than a purely statistical one. Even more important is that the methodological choice facilitated the discussion between stakeholders and increased involvement, acceptance and interdepartmental communication.
Identifying Compiler and Optimization Level in Binary Code From Multiple Architectures

While compiling a native application, different compiler flags or optimization levels can be configured. This choice depends on the different requirements. For example, if the application binary is intended for final release, the flags and optimization settings should be set for execution speed and efficiency. Alternatively, if the application is to be used for debugging purposes, debug flags should be configured accordingly, usually involving minor or no code optimization. However, this information cannot be easily extracted from a compiled binary. Nonetheless, ensuring the same compiler and compilation flags is particularly important when comparing different binary files, to avoid inaccurate or unreliable analyses. Unfortunately, to understand which flags and optimizations have been used, a deep knowledge of the target architecture and the compiler used is required. In this study, we present two deep learning models used to detect both compiler and optimization level in a compiled binary. The optimization levels we study are O0, O1, O2, O3, and Os in the x86_64, AArch64, RISC-V, SPARC, PowerPC, MIPS, and ARM architectures. In addition, for the x86_64 and AArch64 architectures, we also determine whether the compiler is GCC or Clang. We created a dataset of more than 76000 binaries and used it for training. Our experiments showed over 99.95% accuracy in detecting the compiler and between 92% to 98%, depending on the architecture, in detecting the optimization level. Furthermore, we analyzed the change in accuracy when the amount of data was extremely limited. Our study shows that it is possible to accurately detect both compiler flag settings and optimization levels with function-level granularity.
A computer system for predicting noise levels from construction equipment: Examples of how ENM windows software can be applied to solve noise problems in construction

Predicting noise levels from construction equipment is important, yet difficult, because ground and weather conditions interact with the sound in complex ways that cannot be simply modelled and calculated by hand. This paper describes the prediction mechanisms of ENM windows as well as issues in implementation which are also discussed. It is suggested that by using the program, construction companies can reduce the costs incurred due to unnecessary fines and ineffective prevention methods.
Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis

Rotating machinery is one of the major components of industries that suffer from various faults due to the constant workload. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. In this study, noise eliminated ensemble empirical mode decomposition (NEEEMD) was used for fault feature extraction. A convolution neural network (CNN) classifier was applied for classification because of its feature learning ability. A generalized CNN architecture was proposed to reduce the model training time. A sample size of 64×64×3 pixels RGB scalograms are used as the classifier input. However, CNN requires a large number of training data to achieve high accuracy and robustness. Deep convolution generative adversarial network (DCGAN) was applied for data augmentation during the training phase. To evaluate the effectiveness of the proposed feature extraction method, scalograms from related feature extraction methods such as ensemble empirical mode decomposition (EEMD), complementary EEMD (CEEMD), and continuous wavelet transform (CWT) are classified. The effectiveness of scalograms is also validated by comparing the classifier performance using grayscale samples from the raw vibration signals. All the outputs from bearing and blade fault classifiers showed that scalogram samples from the proposed NEEEMD method obtained the highest accuracy, sensitivity, and robustness using CNN. DCGAN was applied with the proposed NEEEMD scalograms to further increase the CNN classifier’s performance and identify the optimal number of training data. After training the classifier using augmented samples, the results showed that the classifier obtained even higher validation and test accuracy with greater robustness. The proposed method can be used as a more generalized and robust method for rotating machinery fault diagnosis.
A Stiffness-Adjusting Method to Improve Thrust Efficiency of a Two-Joint Robotic Fish

Fish are very efficient swimmers. In this paper, we studied a two degree-of-freedom (DOF) propeller that mimic fish caudal fin like locomotion. Kinematics modelling and hydrodynamic CFD analyses of the two DOF propellers were conducted. According to the CFD simulation, we show that negative power was generated within the flapping cycle, and wake flow at different instant was demonstrated. Based on the dynamic model, we compared the thrust efficiency under different stiffness control method. The results show that the thrust efficiency was enhanced under moderate stiffness control strategy.
Recognition of Optical Microscopy Images Based on Shape and Texture

Airborne pollen is the main cause of pollen allergy, so statistic of the amount and distribution of pollen in the air is important. This paper presents a method to identify airborne pollen grains in optical microscopy images. After pollen region is segmented by thresholding, global shape descriptor and Fourier descriptor are used to extract shape features, gray level co-occurrence matrix is employed to extract texture features, and finally pollen grains are classified by a k-nearest neighborhood classifier. In the experiment with 55 cases of Poaceae, 44 cases of Moraceae and 48 cases of Pinaceae, a classification rate of 82.31% can be obtained with an accuracy of 85.45%, 61.36%, and 97.91% for each family.