Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissolved Organic Matter Controls on Terrestrial Carbon Sequestration and Export in Contrasting California Ecosystems

Summary Dissolved organic carbon (DOC) plays an important role in the upland soil carbon cycle while simultaneously, via the hydrologic cycle, providing a direct link between the terrestrial and aquatic ecosystems. We have monitored changes in DOC fluxes and chemistry as rainwater percolates through the soil and reemerges at the channel head in two contrasting upland watersheds in coastal California: a redwood-Douglas fir forest and a coastal prairie ecosystem. At the soil profile scale when vertically infiltrating flow dominates, significant quantities of C move as DOC into the mineral soil. These fluxes are rapidly attenuated with increasing depth in the mineral soil, primarily due to sorption reactions with these fine-textured soils. This movement and subsequent adsorption of DOC seems to be a critical mechanism in accumulating large stores of organic C in deep soil horizons. Isotopic and spectroscopic data indicate that soil DOC is not composed simply of fresh plant leachates; rather DOC appears to be a mixture of plant-derived compounds and more humified material. Radiocarbon data, in particular, point to the fact that soil DOC is not just fresh litter leachate, but rather it is a mixture of more aged organic constituents. In terms of an input-output budget, DOC losses are minor relative to respiratory CO 2 losses. However, DOC plays a significant role in translocating surfical C to the deeper mineral soil. In support of this notion, we found that DOC flux rates are very similar to model-derived vertical transport velocities. Additionally, DOC-based mean residence times in the mineral soil are of similar magnitude to those derived from carbon-14 measurements of the soil organic C pool. Differences in hydrologic flowpaths impart distinct stream chemical patterns at these two sites. When vertically infiltrating throughflow dominates, DOC and other reactive solutes appear to be governed by exchange reactions in the regolith, resulting in little temporal variation in stream chemistry. However, when shallow saturated flow paths are triggered, large quantities of DOC can be exported from these systems. As discharge increases, we generally find an increase in DOC concentration and a shift in composition towards fresher, less processed organic material.
Brand Positioning untuk Keunggulan Bersaing pada Aplikasi Transportasi Online Menggunakan Sentiment Analysis (Studi pada: Go-Jek dan Grab)

The development of technology and information in the industrial revolution 4.0 an impact on the increasing number of internet users. Social media is a place to express opinions, share and receive information in a short time to produce User Generated Content (UGC). Not only individuals, but companies also use social media to interact with their customers to understand the company's position in the eyes of customers. Through brand positioning on social media, companies can compare with competing companies to measure their competitive advantage on social media. This study aims to determine the brand positioning and competitive advantage of the top brands of online transportation applications in Indonesia, namely Go-Jek and Grab via Twitter. The method used in this research is a mixed method. This study obtained data through crawling data on Twitter using RStudio version 4.2. 2 software. The data processed in this study is UGC in the form of tweets with the keywords "goride" and "grabbike" which are products of each online transportation brand. Data collection in this study was carried out from November 1 2022 to November 30, 2022. Then the data was processed using sentiment analysis and text visualization using Wordcloud to analyze what topics are frequently discussed by users. The results show the position as well as the advantages and disadvantages of each online transportation application.
Data from synoptic water-quality studies on the Colorado River in the Grand Canyon, Arizona, November 1990 and June 1991

Two water-quality synoptic studies were made on the Colorado River in the Grand Canyon, Arizona. Field measurements and the collection of water samples for laboratory analysis were made at 10 mainstem and 6 tributary sites every 6 hours for a 48-hour period on November 5-6, 1990, and again on June 18-20, 1991. Field measurements included discharge, alkalinity, water temperature, light penetration, pH, specific conductance, and dissolved oxygen. Water samples were collected for the laboratory analysis of major and minor ions (calcium, magnesium, sodium, potassium, strontium, chloride, sulfate, silica as SiO2), trace elements (aluminum, arsenic, boron, barium, beryllium, cadmium, cobalt, chromium, copper, iron, lead, lithium, manganese, molybdenum, nickel, selenium, thallium, uranium, vanadium and zinc), and nutrients (phosphate, nitrate, ammonium, nitrite, total dissolved nitrogen, total dissolved phosphorus and dissolved organic carbon). Biological measurements included drift (benthic invertebrates and detrital material), and benthic invertebrates from the river bottom.
Detection Method of Resonant Fiber Optic Gyroscope With Hollow-Core Photonic Crystal Fiber Based on Phase-Shift Keying Modulation

A detection method of resonant fiber optic gyroscope with hollow-core photonic-crystal fiber (HC-RFOG) based on phase-shift keying (PSK) modulation is proposed. The fluctuation of intracavity light intensity and the modulation of the incident light was effectively decoupled, and the influence of the nonlinearity of modulation on the gyro bias stability was reduced. Theoretically, the theoretical gyro bias drift introduced by the nonlinearity of modulation is reduced to below 0.01°/h, without increasing the complexity of the gyro. Compared with the traditional triangle-wave phase modulation method, the method in this paper can effectively suppress the peak difference of the positive and negative first-order harmonics more than 4 times. Ultimately, a HC-RFOG system based on the PSK modulation was built, with which a more than 4 times reduction in the short-term gyro bias drift compared to the traditional triangle modulation and a gyro bias instability of 1.5°/h have been achieved, providing a low-cost and high-performance approach for noise suppression and signal detection in HC-RFOGs.
Spatial Integration of Refugees: Towards a Post-Migrant Approach

In the context of increasing social mobility, extensive global migration flows and the growing importance of understanding the diverse circumstances of urban life, ideas of a homogeneous, and stable social mainstream are decreasingly in line with social reality. Post-migrant studies understand migration as not only a force that shapes society but also as a factor in place-making. This article aims to discuss a different integration paradigm, focusing on the spatial integration dimension from the perspective of the refugees and their experiences of everyday practices. It aims to reflect on the role of the articulation between these practices with local actors that can intermediate and influence the quality of life of the incomers, either positively or negatively. The main research question we address is: Can spatial transformation in the public space foster the integration of and a feeling of belonging by refugees through collaborative processes? This analysis is developed through a critical reflection on the role of institutional actors as potential mediators between everyday practices and long-term solutions and, at the same time, as reproducers of hegemonic power relations. The proposed debate is based on collaborative teaching and research activities conducted in 2021 and 2022 in Berlin, Germany, and Irbid, Jordan, involving different groups of actors—researchers, students, and local and national institutions, as well as refugees and local residents.
CLIPC: Contrastive-Learning-Based Radar Signal Intrapulse Clustering

The radar signal intrapulse clustering (RSIPC) can help achieve unsupervised radar emitter identification, which is of great significance in the field of electronic warfare. In order to address the poor performance of traditional clustering methods in handling RSIPC tasks, we propose a contrastive learning-based RSIPC method called CLIPC. Since the single-domain information of radar signal intrapulses may result in the loss of important features, we integrate the multidomain information of radar signal intrapulses to obtain information fusion samples. By training a contrastive learning network on these information fusion samples, the network can extract deep features of radar signal intrapulses. Subsequently, we realize RSIPC using these deep features. To enhance the adaptability of the used contrastive learning network, we optimize the data augmentation methods in the network through experimental analysis. Additionally, we optimize the dimension of the deep features extracted by the network to reduce information redundancy and improve the efficiency of features clustering. Experimental results demonstrate that our improvements in contrastive learning network lead to better clustering performance and efficiency. We also investigate the clustering performance and reliability of the CLIPC under different signal-to-noise ratios (SNRs) through experiments. When the SNR is 0 dB, the proposed method has a clustering accuracy that is 0.1 higher than the contrastive learning method based on single-domain information, 0.3 higher than the traditional clustering method, and 0.2 higher than the clustering method based on autoencoder.
Allometric models of DBH and crown area derived from QuickBird panchromatic data in Cryptomeria japonica and Chamaecyparis obtusa stands

This study investigated the relationship between the estimates of the parameters of allometric models of diameter at breast height (DBH) obtained from a field survey and crown area derived from high-resolution satellite data in Cryptomeria japonica and Chamaecyparis obtusa stands, and stand variables such as stand age, stand density, mean stand DBH, mean stand tree height and relative spacing. Twenty-six sample plots of 0.04 ha were established in C. japonica and C. obtusa stands of 41 years of age and older. We constructed an allometric model between the crown area, obtained from QuickBird panchromatic data using a watershed method, and DBH measured during field surveys for each sample plot. The model can be expressed as E(y) = axb . The parameters a and b were estimated by the least-squares method for the plots. For C. japonica and C. obtusa, we performed a non-linear regression analysis to investigate the relationship between the estimates, â and , of the parameters of the allometric model and stand variables such as stand age, stand density, mean stand DBH, mean stand tree height and relative spacing, simultaneously. The coefficients of determination for C. japonica and C. obtusa were 0.84 and 0.87, respectively. The coefficients of determination obtained in the regression analysis to investigate the relationship between the DBH from a field survey and the DBH estimated from the predicted curve in the non-linear regression analysis were 0.82 and 0.86, respectively, for C. japonica and C. obtusa. The analysis revealed that the stand variables affected the parameters of the allometric models of the DBH and crown area derived from high-resolution satellite data in mature stands of C. japonica and C. obtusa.
Mazusmotuoensis (Mazaceae), a new species from Xizang, China

﻿Abstract Mazusmotuoensis W.B. Ju, Bo Xu bis & X.F. Gao is a newly described species found in Xizang Autonomous Region, China. Morphologically, this species differs from all the other known Mazus species by having erect perennial herb form with a rhizome, presence of multicellular hairs, without basal leaves, opposite arrangement of stem leaves, and corolla lobes with erose-toothed margins. Molecular phylogenetic analysis using nuclear and cpDNA genes suggests that this new species occupies a basal position within Mazus. In conclusion, both morphological evidence and molecular phylogenetic analyses support that this species belongs to Mazus and represents an as-yet-unreported new species with distinct differences from other species within the genus.
Influence of Grain Size Distribution and Shape on GPR Waves – Study of Aeolian Dunes

Ground penetrating radar (GPR), a geophysical method based on electromagnetic (EM) wave propagation, can provide very detailed and continuous images of the internal structures of aeolian dunes. In order to model and explain the origin of observed reflections, we build a model of electric permittivity which accounts for the grain size distribution and shape. By modelling the propagation of GPR waves in frequency domain, we have shown that grain size and grain shape have an influence on GPR reflectivity and we confirmed it by adjusting the modelled data to the real GPR data acquired in arid zones.
Few-Shot Industrial Meter Detection Based on Sim-to-Real Domain Adaptation and Category Augmentation

Rapid and accurate detection of industrial meters in complex scenarios is an essential step toward inspection robot automatic meter recognition. Deep learning (DL) is a promising solution. However, due to the lack of large-scale public industrial meter image datasets, it is very difficult to train industrial meter detection models based on DL. Therefore, in this article, we combine the image generation technique and sim-to-real domain adaption technique to address the problem of few-shot industrial meter detection in complex scenarios. Specifically, we use Stable Diffusion to generate abundant virtual samples as the source domain dataset by inputting textual prompts. A small number of real samples are used as the target domain dataset. In addition, to attenuate the effect of domain shift, we propose a domain adaptation object detection framework based on category augmentation. This framework introduces domain information into the classifier and combines uncertainty estimation, which not only eliminates the training of domain classifiers in traditional adversarial learning-based domain adaptation algorithms but also facilitates feature alignment between source domain and target domain. Experiments show that the framework achieves 50.8% mAP50:95 and $55.0\% F1$ score, which outperforms the network trained with only real images by 8.3% mAP50:95 and $8.7\% F1$ score. We can achieve close performance with only 25% of the target domain samples with the help of the source domain dataset. Moreover, our method also outperforms other state-of-the-art methods in supervised domain adaptation object detection.
Impact of arithmetic asymmetries on simulated thermodynamic ice-sheet evolution

Abstract Numerical ice-sheet model experiments sometimes exhibit asymmetries in the solutions, despite the symmetric conditions imposed. By first identifying arithmetic asymmetry in the models as one of the reasons for symmetry-breaking through loss of trailing digits, this paper presents a numerical procedure to preserve the symmetries by restructuring the order of the floating-point evaluation of the equations in the numerical ice-sheet model. Re-examination of the series of experiments in the HEINO topic of the ISMIP demonstrates that small perturbations triggered by arithmetic asymmetries significantly amplify and cause qualitative differences in the simulated ice-sheet evolutions. This study shows that it is imperative to apply a symmetric scheme to maintain overall symmetries in the simulation of ice-sheet evolution, at least under a highly idealized configuration.
Quality Factors in Borosilicate Glass Shell Resonators

This article reports the quality factors (<inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-factors) of borosilicate glass shell resonators. Borofloat 33 glass microshell resonators are fabricated by a chemical foaming process and characterized by a laser Doppler vibrometer (LDV). Experimental results show that the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factors of these glass shell resonators operated in the <inline-formula> <tex-math notation="LaTeX">$n = 2$ </tex-math></inline-formula> wineglass modes are 1200–3300. Energy losses in these shell resonators are then analyzed. Analysis results reveal that air damping, anchor loss, thermoelastic damping (TED), and surface loss barely contribute to the total dissipation. By adopting a planar frame resonator (PFR), the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factor due to material dissipation of Borofloat 33 glass is measured to be 2800 ± 300. The <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factors in Borofloat 33 glass shell resonators are attributed to material dissipation. This work is conducive to the design and fabrication of microshell resonators, enabling high-performance gyroscopes. In addition, this work may provide inspiration for the applications of borosilicate glass resonators in micro-optic-electro-mechanical-systems (MOEMSs).
Peran Harga dan Citra Merek dalam Mediasi Pengaruh E-Word to Mouth terhadap Keputusan Pembelian melalui Aplikasi TikTok

Currently, people in Indonesia need a way to shop that is easy and also reliable. The existence of the TikTok application is the answer to this problem in addition to its main function as an entertainment application that can be used with the Android or IOS application operating system. This study aims to determine whether price, brand image affect purchase intention by e-Word of Mouth mediation in using the TikTok application. This research method uses a quantitative approach by distributing questionnaires to 130 consumers as measured by a scale of 1 to 5. Smart PLS (Partial Least Squares) is used in the analysis of this study to test external and internal models. The results of this study indicate that price, brand image have a significant impact on purchase intention. Through mediation variables e-Word of Mouth. So that the TikTok application can answer consumer questions and problems, simplify, and increase daily purchases. Nowadays, people need a way to shop that is easy, with prices and products that are suitable to save time and additional costs.
A Near-Infrared Single-Photon Detector for Direct Time-of-Flight Measurement Using Time-to-Amplitude-Digital Hybrid Conversion Method

A near-infrared (NIR) single-photon detector for direct time-of-flight (dTOF) measurement is proposed based on $0.18 ~\mu \text{m}$ BCD technology. The integrated single-photon avalanche diode (SPAD) adopts a high-voltage p-well (HVPW)/high-voltage buried n+ (HVBN) layer as a deep-junction multiplication region to significantly enhance the NIR photon detection probability (PDP). Meanwhile, a virtual guard ring formed by the low doping p-type epitaxial layer is used to effectively minimize dark count noise. In particular, a compact time-to-amplitude converter (TAC) combined with an asynchronous counter is employed to realize a time-to-amplitude-digital hybrid conversion for gaining higher time resolution and larger dynamic range, simultaneously. The experimental results demonstrate that at 5 V excess bias voltage, the NIR PDP at 905 nm wavelength is up to 7.9%, and the dark count rate (DCR) is below 10 Hz at room temperature. Furthermore, the detector obtains a time resolution of 39 ps and a time full-scale range (FSR) of $2.56 ~\mu \text{s}$ , with a differential nonlinearity (DNL) and integral nonlinearity (INL) less than ±1 LSB, respectively. The proposed single-photon dTOF detector exhibits the significant advantages of high sensitivity, low noise, high time resolution, and large FSR, which is very fit for low-cost and high-precision dTOF measurement systems.
Image inpainting based on double joint predictive filtering and Wasserstein generative adversarial networks

Abstract. Image inpainting is promising but challenging in computer vision tasks; it aims to fill in missing regions of corrupted images with semantically sensible content. By utilizing generative adversarial networks (GAN), state-of-the-art methods have achieved great improvements, but the ordinary GAN generally suffers from difficulties in training and unstable gradients, leading to unsatisfactory inpainting results. Image-level predictive filtering is a widely used restoration method that adaptively predicts the weights of pixels around a target pixel and then linearly combines these pixels to generate the image, but it cannot fill larger missing regions. Thus, we extend image-level predictive filtering to the deep feature level through an encoder–decoder network and embed adaptive channel attention and spatial attention modules in the encoder network. We use Wasserstein GAN instead of normal GAN due to its superior properties and then combine it with image-level predictive filtering and deep feature-level predictive filtering, which ultimately leads to a significant improvement in image inpainting. We validate our method on two public datasets: CelebA-HQ and Places2. Our method demonstrates good performance across four metrics: peak signal-to-noise ratio, L1, structural similarity index measure, and learned perceptual image patch similarity.
Effectiveness of Contextual Schema Therapy for Extreme Perfectionism and Emotion Regulation in Individuals with Perfectionism Disorder

Background: Individuals are consistently influenced by ineffective core beliefs known as maladaptive schemas that develop during childhood and can pave the way for the appearance of problems such as extreme perfectionism. Therefore, therapeutic approaches are needed that can be effective for these schemas. Objectives: The present study aimed to investigate the effectiveness of contextual schema therapy (CST) on perfectionism and emotion regulation in individuals with perfectionism disorder. Methods: In this study, 12 individuals diagnosed with perfectionism disorder visiting the psychological service centers in Tehran in 2022 were purposively selected for this quasi-experimental study with a pre-test, mid-test, and post-test design. The participants underwent CST in 40 one-hour sessions and completed the Positive and Negative Perfectionism Scale and Emotion Regulation Questionnaire in all three stages of the assessment. The data were analyzed using the repeated measures analysis of variance (ANOVA) in SPSS-26. Results: The mean and standard deviation (SD) of perfectionism in the pre-test, mid-test, and post-test phases were 151.08 ± 18.97, 94.25 ± 12.48, and 67.58 ± 5.41, respectively. Moreover, the mean ± SD of emotion regulation in the pre-test, mid-test, and post-test phases were 40.25 ± 8.95, 45.50 ± 8.12, and 50.08 ± 7.94, respectively. The results demonstrated that the CST was effective for perfectionism across all assessment stages (P < 0.001); however, this influence was not significant for emotion regulation during the mid-test stage. Conclusions: Contextual schema therapy led to a reduction in extreme perfectionism and an improvement in emotion regulation in individuals with perfectionism. It is suggested that CST be applied in psychological service centers to reduce perfectionism and emotion regulation strategies in clients.