Miracle-dz/UniFAR-FTUs huggingface dataset:
Interdecadal changes in potential predictability of the summer monsoon in East Asia and South Asia

Interdecadal changes in the potential predictability of the East Asian summer monsoon (EASM) and South Asian summer monsoon (SASM) were investigated using the signal‐to‐noise ratio of daily reanalysis data from 1948 to 2017. Results reveal that variations in the SASM predictability are out of phase with those in EASM predictability, and that SASM predictability is higher (lower) than EASM predictability before (after) the 1980s. The out‐of‐phase relationship of the predictability of the two monsoon systems is attributed mainly to temporal changes in their relationships with El Niño‐Southern Oscillation (ENSO). The relationship between the EASM and ENSO is related to the position of the ENSO heating center, which is located in the central Pacific before the 1980s but shifted to the eastern Pacific after the 1980s, accompanied by a strengthening (weakening) in the relationship between ENSO and the EASM (SASM). The shift in the position of the ENSO heating center was found to have contributions from the modulation of the Pacific decadal oscillation.
POINT-WISE CLASSIFICATION OF HIGH-DENSITY UAV-LIDAR DATA USING GRADIENT BOOSTING MACHINES

Abstract. Point-wise classification of 3D point clouds is a challenging task in point cloud processing, whereas, in particular, its application to high-density point clouds needs special attention because a large number of point clouds affect computational efficiency negatively. Although deep learning based models have been gaining popularity in recent years and have reached state-of-the-art results in accuracy for point-wise classification, their requirements of the high number of training samples and computational resources make those models inefficient for high-density 3D point clouds. However, traditional machine learning classifiers require less training samples, so they are capable of reducing computational requirements, even considering the latest machine learning classifiers, particularly in ensemble learning of gradient boosting machines, the results can compete with deep learning models. In this study, we are studying the point-wise classification of high-density UAV LiDAR data and focusing on efficient feature extraction and a recent state-of-the-art gradient boosting machine learning classifier, LightGBM. Our proposed framework includes the following steps: at first, we are using point cloud sampling for creating sub-sampled point clouds, then we are calculating the features based on those scales implemented on GPU. Finally, we are using the LightGBM classifier for training and testing. For the evaluation of our framework, we used a publicly available benchmark dataset, Hessigheim 3D. According to the results, we achieved an overall accuracy of 87.59% and an average F1 score of 75.92%. Our framework has promising results and scores closer to deep learning models. However, more distinctive features are required to obtain more accurate results.
An Informational Hub that Utilizes Modern Application User Interfaces to Encourage Healthy Lifestyles

The idea for this app is a platform for people and especially athletes to know proper dieting, exercising, and how to recover from workout-related accidents [2]. I had to go online and search up everything I needed to know and sometimes I would get false information from the wrong websites. Athletes should be able to see what is good for them to eat, what kinds of workouts they should do everyday, as well as how to recover from certain injuries that they may sustain. When the user logs in to the app they will see options that say recovery exercise news and nutrition pages that contain all the information they will need. The nutrition page will display some common good foods to eat and their data such as calories, carbs, and more. The exercises page will list several important exercises vital to a good workout. Lastly, the injury page, also referred to as the recovery page, will show users a picture of the human body and allow them to select certain parts of the body to read up on relevant information pertaining to injuries around that area [3]. I also made 2 experiments to help me know and fix the app’s blindspot, the first one is a survey i give to my friends to try my app and fill it out, the second one is i follow the workouts and foods on my app for one day and compare to american’s daily nutrition and workout plans.
Limited Impact of Soil Microorganisms on the Endophytic Bacteria of Tartary Buckwheat (Fagopyrum tataricum)

Soil has been considered the main microbial reservoir for plants, but the robustness of the plant microbiome when the soil resource is removed has not been greatly considered. In the present study, we tested the robustness of the microbiota recruited by Tartary buckwheat (Fagopyrum tataricum Gaertn.), grown on sterile humus soil and irrigated with sterile water. Our results showed that the microbiomes of the leaf, stem, root and next-generation seeds were comparable between treated (grown in sterile soil) and control plants (grown in non-sterile soil), indicating that the plants had alternative robust ways to shape their microbiome. Seed microbiota contributed greatly to endophyte communities in the phyllosphere, rhizosphere and next-generation seeds. The microbiome originated from the seeds conferred clear benefits to seedling growth because seedling height and the number of leaves were significantly increased when grown in sterilized soil. The overall microbiome of the plant was affected very little by the removal of the soil microbial resource. The microbial co-occurrence network exhibited more interactions, and Proteobacteria was enriched in the root of Tartary buckwheat planted in sterilized soil. Our research broadens the understanding of the general principles governing microbiome assembly and is widely applicable to both microbiome modeling and sustainable agriculture.
Petunia dihydroflavonol 4-reductase is only a few amino acids away from producing orange pelargonidin-based anthocyanins

Anthocyanins are responsible for the color spectrum of both ornamental and natural flowers. However, not all plant species produce all colors. For example, roses are not blue because they do not naturally possess a hydroxylase that opens the pathway for delphinidin and its derivatives. It is more intriguing why some plants do not carry orange or scarlet red flowers with anthocyanins based on pelargonidin, because the precursor for these anthocyanins should be available if anthocyanins are made at all. The key to this is the substrate specificity of dihydroflavonol 4-reductase (DFR), an enzyme located at the branch point between flavonols and anthocyanins. The most common example is petunia, which does not bear orange flowers unless the enzyme is complemented by biotechnology. We changed a few amino acids in the active site of the enzyme and showed that the mutated petunia DFR started to favor dihydrokaempferol, the precursor to orange pelargonidin, in vitro. When transferred to petunia, it produced an orange hue and dramatically more pelargonidin-based anthocyanins in the flowers.
PiPSim: A Behavior-Level Modeling Tool for CNN Processing-in-Pixel Accelerators

Convolutional neural networks (CNNs) have been gaining popularity in recent years, and researchers have designed specialized architectures to speed up the inference process. However, despite the promising potential of processing near-/in- sensor architectures actively explored in the visual Internet of Things, there is still a need to develop a behavior-level simulator to model performance and facilitate early design exploration. This article proposes a stand-alone simulation platform for processing-in-pixel (PiP) systems, namely, PiPSim. It offers a flexible interface and a wide range of design options for customizing the efficiency and accuracy of PiP-based accelerators using a hierarchical structure. Its organization spans from the device level, e.g., memory technology, upward to the circuit level, e.g., compute-add on architecture, and then to the algorithm level, e.g., DNN workloads. PiPSim realizes instruction-accurate evaluation of circuit-level performance metrics as well as learning accuracy at run-time. Compared to SPICE simulation, PiPSim achieves over 25 $000\times $ speed-up with less than a 2.5% error rate on average. Furthermore, PiPSim can optimize the design and estimate the tradeoff relationships among different performance metrics.
Three-Dimensional Peri-Implant Tissue Changes in Immediately vs. Early Placed Tapered Implants Restored with Two Different Ceramic Materials—1 Year Results

Background: A prospective multi-center randomized controlled clinical trial was performed to digitally analyze tissue volume changes in immediately and early placed implants with simultaneous bone augmentation restored with two different all-ceramic materials. Methods: A total of 60 patients received 60 bone-level tapered implants (BLT, Straumann AG) immediately (n = 30) or early placed, 8–10 weeks after tooth extraction, (n = 30). Implants were restored with all-ceramic single crowns fabricated out of zirconia (Lava Plus, 3M), or lithium disilicate (E.max CAD, Ivoclar Vivadent AG) bonded to titanium base abutments (Variobase for Cerec, Straumann AG). Impressions were taken at baseline (BL), 6 and 12 months, and STL data were used to define an area of interest (AOI) to analyze peri-implant volume changes and midfacial recessions. Results: For immediate placement, a mean volume loss of −5.56 mm3 (±5.83 mm3) was found at 6 months, and of −6.62 mm3 (±6.56 mm3) at 12 months. For early placement, a mean volume loss of −1.99 mm3 (±5.82 mm3) at 6 months, and of −3.7 mm3 (±5.62 mm3) at 12 months was found. The differences in volume loss at 12 months between the two implant placement protocols were significant (p = 0.005). In both groups, mean midfacial recessions of 0.48 mm (±0.52) occurred. Conclusions: A more pronounced peri-implant volume loss can be expected 12 months after immediate implant placement compared with early placement.
Optimal capital structure of Vietnamese listed firms - finance industry and consumer discretionary industry

Abstract This study makes effort in considering the static structural models with the endogenous bankruptcy boundary in order to maximize the firm’s value. This research starts with highly recognized theoretical frameworks on the capital structure including Modigliani and Miller Theory’s contribution, trade-off theory, agency theory, pecking order theory and equity market timing theory. This paper investigates the experiential implementation of Black-Scholes (1973), Merton (1974), Leland (1994), and Leland and Toft (1996) models on capital structure in accordance with various assumptions. Moreover, this research is based mainly on static structural models to go into depth to determine the optimal debt level of the finance and consumer discretionary industries. From secondary information on stock volatility and drift, this study determines the adjustments in capital structure as well as the changes in the probability of default of 50 listed Vietnamese firms in the last three years from 2018 to 2020. The results indicate that the target capital structure changes proportionally to the tax percentage due to tax benefits (deductible interest payments) and varies inversely to the cost of debt (default and financial distress costs). In addition, instead of static structural models, firms can apply the theories on the debt level to determine the optimal leverage based on the complication of capital structure.
Wideband and Low-Profile High-Resistivity Silicon-Based Dielectric Resonant Antennas by Loading AMC

This communication presents the design of high-resistivity silicon-based dielectric resonant antennas (DRAs) by using microelectromechanical system (MEMS) technology. A stair-stepping-driven patch fed by through-silicon via (TSV) is specifically designed with a U-shaped slot that can reduce the inductive of TSV. An air cavity between dielectric resonator and driven patch is used to enhance the gain and bandwidth of DRA. The measured impedance bandwidth (−10 dB) of the Ka-band DRA is from 27 to 43 GHz with a maximum gain of 6.5 dB and 95% radiation efficiency at 38 GHz. The size of our proposed antenna is only <inline-formula> <tex-math notation="LaTeX">$0.48\lambda _{0}\,\,\times \,\,0.48\lambda _{0}\,\,\times \,\,0.14\lambda _{0}$ </tex-math></inline-formula>. The thickness of our proposed Ku-band DRA can be further reduced from <inline-formula> <tex-math notation="LaTeX">$0.14\lambda _{0}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$0.06\lambda _{0}$ </tex-math></inline-formula> by loading an artificial magnetic conductor (AMC) structure on the top surface of DR. A Ku-band antenna is fabricated as an example. The measured fraction bandwidth (FBW) is 33% (−10 dB) from 14.2 to 19.8 GHz with a maximum gain of 6.5 dB and 94.7% radiation efficiency at 18 GHz. Good agreements between the simulated and measured results are obtained for both DRAs. The size of our proposed Ku-band antenna is only <inline-formula> <tex-math notation="LaTeX">$0.51\lambda _{0}\,\,\times \,\,0.51\lambda _{0}\,\,\times \,\,0.06\lambda _{0}$ </tex-math></inline-formula>. The proposed DRAs can be further 3-D-integrated with other components by using bumps for future millimeter-wave (MMW) phased-array radar and communication systems.
Advanced Skeletal Ossification Is Associated with Genetic Variants in Chronologically Young Beef Heifers

Osteogenesis is a developmental process critical for structural support and the establishment of a dynamic reservoir for calcium and phosphorus. Changes in livestock breeding over the past 100 years have resulted in earlier bone development and increased physical size of cattle. Advanced skeletal maturity is now commonly observed at harvest, with heifers displaying more mature bone than is expected at 30 months of age (MOA). We surmise that selection for growth traits and earlier reproductive maturity resulted in co-selection for accelerated skeletal ossification. This study examines the relationship of single nucleotide polymorphisms (SNPs) in 793 beef heifers under 30 MOA with USDA-graded skeletal maturity phenotypes (A-, B-, C- skeletal maturity). Further, the estrogen content of FDA-approved hormonal implants provided to heifers prior to harvest was evaluated in association with the identified SNPs and maturities. Association tests were performed, and the impact of the implants were evaluated as covariates against genotypes using a logistic regression model. SNPs from the ESR1, ALPL, PPARGC1B, SORCS1 genes, and SNPs near KLF14, ANKRD61, USP42, H1C1, OVCA2, microRNA mir-29a were determined to be associated with the advanced skeletal ossification phenotype in heifers. Higher dosage estrogen implants increased skeletal maturity in heifers with certain SNP genotypes.
Wideband Waveforming for Integrated Data and Energy Transfer: Creating Extra Gain Beyond Multiple Antennas and Multiple Carriers

—When wideband signals propagate in a rich-scatterer environment, we obtain abundant resolvable multiple transmission paths to form a number of virtual antennas. Therefore, substantial spatial gain can be attained by care-fully waveforming in all these resolvable transmission paths without additional antennas. This resultant spatial gain is then exploited for improving the performance of integrated-data-and-energy-transfer (IDET) from a single transmitter to multiple receivers. We aim to maximise the downlink fair-throughput and sum-throughput, while satisfying the energy harvesting requirements by jointly optimising the waveformers at the transmitter and the power splitters at the receivers. A low-complexity fractional-programming (FP) based alternating algorithm is proposed to solve these non-convex optimisation problems. The non-convex wireless energy transfer (WET) constraints are transformed to be convex with a modiﬁed quadratic transform (MQT) method. As a result, the stationary points for both the fair-throughput and the sum-throughput maximisation problems are obtained. The numerical results demonstrate the advantage of our proposed algorithm over a minimum-mean-square-error (MMSE) scheme, a zero-forcing (ZF) scheme and a time-reversal (TR) scheme. Simulation results show that the wireless data transfer (WDT) performance of our scheme outperforms the single-input-single-output orthogonal-frequency-division-multiple-access (SISO-OFDMA) when the output direct current (DC) power requirement is high. When we have a practical individual subcarrier power constraint, the WDT performance of our scheme outperforms multiple-input-single-output orthogonal-frequency-division-multiplex-access (MISO-OFDMA).
Validation of the Accuracy of Automatic Measurement of Blood Volume in Culture Bottles for Blood Culture

Several manufacturers have developed systems that automatically measure the amount of blood in culture bottles. We compared the volumes measured automatically by the Virtuo instrument (bioMérieux, France) (height-based volumes) and those calculated by weighing the bottles. In all, 150 pairs of aerobic and anaerobic blood culture bottles (BacT/ALERT FA/FN Plus, bioMérieux) were randomly selected over two periods to compare the height- and weight-based volumes and analyze the effect of foam. We also estimated the limit of detection (LOD) and the cut-off value for 5 mL equine blood. The mean height-based volume was approximately 0.67 mL greater than the weight-based volume, particularly in anaerobic culture bottles. Foam did not have a significant effect. The LOD for the automatic height-based volume of equine blood was 0.2–0.4 mL. The 5 mL cut-off was 4–4.2 mL. Therefore, when reporting or monitoring blood volume within culture bottles in the laboratory, these performance characteristics should be adequately considered.
Overall Success Factors Affecting the Performances of Hybrid Cloud ERP: A Case Study of Automobile Industries in Thailand

The hybrid cloud ERP system is widely used in automobile companies in Thailand. It is a popular and effective strategic tool that aids in boosting organization’s competitiveness. However, because of their complexity, high risk, high resource requirements, and high investment costs, ERP projects still have a significant failure rate. It is widely acknowledged by academics and practitioners alike to be an extremely challenging endeavour. This study proposes a conceptual paradigm for postmodern ERP implementation across the entire life cycle. Mixed methodologies for this model’s theoretical development included case study observation, literature review, semi-structured interviews with ten IT experts and ERP consultants, and online questionnaires. Based on information gathered from 455 system users from 114 automobile industries sector, it was analysed by using Structural Equation Modelling (SEM). For data analysis, the partial least squares (PLS) method was employed. Out of the eighteen (18) hypotheses, fifteen (15) were supported by the PLS-SEM results. The conceptual model from the study that was presented can be put to use or helpful in the organization’s management, or project managers can utilize it as a framework and direction for hybrid cloud ERP implementation. The findings of the study can also be used to create a conceptual framework for the actual use of ERP systems for automobile industry, such as the incorporation of blockchain and postmodern ERP systems in many sectors of business. There is discussion of the findings’ implications for practical and research, and potential study areas are proposed.
Automated crack segmentation on 3D asphalt surfaces with richer attention and hybrid pyramid structures

ABSTRACT Automated pavement crack detection is crucial to supporting fine pavement maintenance and ensuring safety for road facilities. Due to the complex pavement condition and crack features, it is still a critical challenge in intelligent pavement surveys. This paper proposed a novel pixel-level pavement crack segmentation network, PCSNet, to provide a solution to this challenge. The network has richer attention and hybrid pyramid structures, which implement full-process crack feature fusion and enhancement. The richer attention module consists of cascaded self-attention and attention gate modules. It captures the crack spatial dependence information and prunes the feature response. The hybrid pyramid structures consist of a multistage convolutional pyramid module and a pyramid pooling module. It integrates contextual information at multiple receptive field scales to enhance the potential crack feature representation. The proposed structure enriches the crack details and optimises the scene parsing on the global geometry of the cracks. A sizeable 3D pavement crack dataset is built for training and testing. The proposed network exhibited the best performance, achieving F1-score, mean intersection of union, and mean pixel accuracy of 81.21%, 77.13%, and 87.17%, respectively. The network can reconstruct the complete crack geometry, preserve the crack edges well, and optimises the detection of shallow and complex cracks. The method exhibits superior and robust performance, facilitating accurate pavement technical condition assessment and maintenance decisions.
Satellite passive microwave observations and analysis of Arctic and Antarctic sea ice, 1978–1987

We have recently completed an analysis that examines in detail the spatial and temporal variations in global sea-ice coverage from 26 October 1978, through 20 August 1987. The sea-icemeasurements we analyzed are derived from data collected by a multispectral, dual-polarized, constant incidence-angle microwave imager, the Scanning Multichannel Microwave Radiometer (SMMR) on board the NASA Nimbus 7 satellite. The characteristics of the SMMR have permitted a more accurate calculation of total sea-ice concentrations (fraction of ocean area covered by sea ice) than earlier single-channel instruments and, for the first time, a determination of both multiyear sea-ice concentrations and physical temperatures of the sea-ice pack. An estimate of the SMMR wintertime total ice concentration accuracy of ± 7% in both hemispheres has been obtained. As this is an improvement over the estimated accuracies of previous microwave sensors, we are able to present improved calculations of the sea-ice extents (areas enclosed by the 15% ice concentration boundaries), sea-ice concentrations, and open-water areas within the ice margins. This analysis will be published in a book, Arctic and Antarctic sea ice, 1978–1987: satellite passive microwave observations and analysis, due for publication in1992. Some highlights from the analysis are presented in this paper.
Gaussian of Differences: A Simple and Efficient General Image Fusion Method

The separate analysis of images obtained from a single source using different camera settings or spectral bands, whether from one or more than one sensor, is quite difficult. To solve this problem, a single image containing all of the distinctive pieces of information in each source image is generally created by combining the images, a process called image fusion. In this paper, a simple and efficient, pixel-based image fusion method is proposed that relies on weighting the edge information associated with each pixel of all of the source images proportional to the distance from their neighbors by employing a Gaussian filter. The proposed method, Gaussian of differences (GD), was evaluated using multi-modal medical images, multi-sensor visible and infrared images, multi-focus images, and multi-exposure images, and was compared to existing state-of-the-art fusion methods by utilizing objective fusion quality metrics. The parameters of the GD method are further enhanced by employing the pattern search (PS) algorithm, resulting in an adaptive optimization strategy. Extensive experiments illustrated that the proposed GD fusion method ranked better on average than others in terms of objective quality metrics and CPU time consumption.