Miracle-dz/UniFAR-FTUs huggingface dataset:
Grain size bias in cosmogenic nuclide studies of stream sediment in steep terrain

Cosmogenic nuclides in stream sediment are widely used to quantify catchment‐average erosion rates. A key assumption is that sampled sediment is representative of erosion from the entire catchment. Here we show that the common practice of collecting a narrow range of sizes—typically sand—may not yield a representative sample when the grain size distribution of sediment produced on slopes is spatially variable. A grain size bias arises when some parts of the catchment produce sand more readily than others. To identify catchments that are prone to this bias, we used a forward model of sediment mixing and erosion to explore the effects of catchment relief and area across a range of altitudinal gradients in sediment size and erosion rate. We found that the bias increases with increasing relief, because higher‐relief catchments have a larger fraction of high elevations that are underrepresented in the sampled sand when grain size increases with altitude. The bias also increases with catchment area, because sediment size reduction during transport causes an underrepresentation of more distal, higher elevations within the catchment. Our analysis indicates that grain size bias may be significant at many sites where cosmogenic nuclides have been used to quantify catchment‐average erosion rates. We discuss how to quantify and account for the bias using cosmogenic nuclides and detrital thermochronometry in multiple sediment sizes.
Hands-Free Virtual Try-On using Untethered AR Glasses for Everyday Shopping Experiences

In this paper, we present a novel application for the hands-free virtual try-on of upper-body garments using augmented reality (AR) glasses. Our application allows users to capture a source garment and preview it from any angle on their own customized 3D avatar embedded in the real world. We propose a straightforward workflow that can run on lightweight, untethered AR glasses within seconds and does not exceed power consumption restrictions, despite using multiple layers of machine learning (ML) models. We built upon an efficient 2D to 2D garment transfer ML approach and reprojected the resulting garment onto a 3D model as a texture. This approach enables seamless transfer of an arbitrary top garment, such as sweaters, t-shirts, or tank tops, onto the avatar without requiring prior knowledge of clothing type. Users can interact with our application using hand gestures or voice commands, which allows for an empty-handed and, hence, more natural interaction with AR, as opposed to phones or other handheld devices. To the best of our knowledge, this is the first virtual try-on application tailored for AR head-mounted devices (HMDs) that aims to bridge the gap between these industries. Our new approach can help shape the way users engage with fashion in the future and enable developers to create more immersive and personalized experiences.
Visual working memories are abstractions of percepts

Pioneering studies demonstrating that the contents of visual working memory (WM) can be decoded from the patterns of multivoxel activity in early visual cortex transformed not only how we study WM, but theories of how memories are stored. For instance, the ability to decode the orientation of memorized gratings is hypothesized to depend on the recruitment of the same neural encoding machinery used for perceiving orientations. However, decoding evidence cannot be used to test the so-called sensory recruitment hypothesis without understanding the underlying nature of what is being decoded. Although unknown during WM, during perception decoding the orientation of gratings does not simply depend on activities of orientation tuned neurons. Rather, it depends on complex interactions between the orientation of the grating, the aperture edges, and the topographic structure of the visual map. Here, our goals are to 1) test how these aperture biases described during perception may affect WM decoding, and 2) leverage carefully manipulated visual stimulus properties of gratings to test how sensory-like are WM codes. For memoranda, we used gratings multiplied by radial and angular modulators to generate orthogonal aperture biases despite having identical orientations. Therefore, if WM representations are simply maintained sensory representations, they would have similar aperture biases. If they are abstractions of sensory features, they would be unbiased and the modulator would have no effect on orientation decoding. Results indicated that fMRI patterns of delay period activity while maintaining the orientation of a grating with one modulator (eg, radial) were interchangeable with patterns while maintaining a grating with the other modulator (eg, angular). We found significant cross-classification in visual and parietal cortex, suggesting that WM representations are insensitive to aperture biases during perception. Then, we visualized memory abstractions of stimuli using a population receptive field model of the visual field maps. Regardless of aperture biases, WM representations of both modulated gratings were recoded into a single oriented line. These results provide strong evidence that visual WM representations are abstractions of percepts, immune to perceptual aperture biases, and compel revisions of WM theory.
Traffic flow prediction based on transformer

Nowadays, it is an obvious fact that with the double blessing of economic growth and the diversity of human’s life, travel has become an indispensable activity during the leisure time. Meanwhile, due to the convenience, timesaving and accessibility, private cars gradually play a vital role on the road. However, it is conceivable that the increase in vehicles means that there will be inevitable congestion, especially during the peak hours of the day. In order to reverse the situation that private cars are from “convenience” to “inconvenience”, this paper decides to explore how to accurately predict the change of traffic flow over time, perceive the upcoming peak period in advance to provide reliable traffic information for publishing and allocate traffic resources reasonably. To capture the time features in traffic flow data, extract their internal laws and process them more accurately, this research applies a combined model of convolutional neural network (CNN) and transformer, that is, CNN is used for the feature extraction, and the extracted features are passed to the transformer model for further predictive analysis. The experimental dataset in the research comes from the official website of Highway England. The result displays that the iteration speed of this model is rapid, the prediction error is able to be minimized and it can be applied in the case of a huge dataset.
Dual-Period Impedance Loaded Transmission Line Model for Dispersion Relationship of Parallel Plates With Interdigital-Pin Bed of Nails

In this article, a dual-period impedance loaded transmission line model is established for the z-directed parallel plates with the <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>- and <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula>-directed periodic interdigital-pin bed of nails. In the model, the <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula>-directed periodicity is assumed to be smaller than the wavelength, and the height between two parallel plates is smaller than the half-wavelength. If so, for the periodic metallic nails in the <inline-formula> <tex-math notation="LaTeX">$yo\textit {z}$ </tex-math></inline-formula> plane, the zero-order Floquer’s mode as a free wave exists in the structure, and the high-order modes as surface waves are tightly bound to the metallic nails. Thereby, the waveguide can be regarded as an <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-direction transmission line alternately loaded by two types of periodic parallel impedances. Further, the dispersion equation is established to determine the passband and forbidden band through the imaginary part of the propagation constant. Especially if two kinds of parallel impedances are equal, the proposed theory can boil down to the existing dispersion model. Finally, based on the sinusoidal current distribution and the equivalent cross section assumptions, the calculus of variations is adopted to solve the analytical expression of the parallel impedance for the cylinder or rectangular nails. The simulations support the proposed theory, regardless of the periodic metallic rectangular and cylinder nails. Compared to the existing transverse resonance method, the proposed theory not only holds for interdigital (and noninterdigital) rectangular (and cylinder) nails but also shows a higher accuracy for the noninterdigital cylinder nails, greatly enriching the dispersion theory.
Multidisciplinary approach for the prediction of cement paste rheological properties: physical analysis, experimental rheology and microstructural modelling

In this contribution, an interdisciplinary approach was employed to investigate and describe the fresh rheology of cementitious pastes, using analytical techniques, microstructural modelling, and experimental rheometry. The pastes, based on Ordinary Portland Cement and Limestone Calcined Clay Cement at a solid volume fraction of Φ = 0.45, were subjected to various analyses. Physical and chemical analyses were conducted including laser granulometry and isothermal heat flow calorimetry. Dynamic rotational rheometry and static oscillatory rheometry, along with viscoplastic and viscoelastic rheological modelling were used to support the characterization of the pastes. A physics‐based Monte‐Carlo algorithm was developed to numerically describe rheological properties such as structural buildup and yield stress. The results showed that as the physical properties of the pastes became more complex, the correlation of several analysis methods went more challenging. However, the combination of analytical, experimental, and phenomenological rheology allowed for a more distinct characterization of the cement paste. These findings can serve as a basis for further multidisciplinary rheological characterization of cementitious building materials.
Effect of composting and storage on the microbiome and resistome of cattle manure from a commercial dairy farm in Poland

Manure from food-producing animals, rich in antibiotic-resistant bacteria and antibiotic resistance genes (ARGs), poses significant environmental and healthcare risks. Despite global efforts, most manure is not adequately processed before use on fields, escalating the spread of antimicrobial resistance. This study examined how different cattle manure treatments, including composting and storage, affect its microbiome and resistome. The changes occurring in the microbiome and resistome of the treated manure samples were compared with those of raw samples by high-throughput qPCR for ARGs tracking and sequencing of the V3-V4 variable region of 16S rRNA gene to indicate bacterial community composition. We identified 203 ARGs and mobile genetic elements (MGEs) in raw manure. Post-treatment reduced these to 76 in composted and 51 in stored samples. Notably, beta-lactam, cross-resistance to macrolides, lincosamides and streptogramin B (MLSB), and vancomycin-resistance genes decreased, while genes linked to MGEs, integrons, and sulfonamide resistance increased after composting. Overall, total resistance gene abundance significantly dropped with both treatments. During composting, the relative abundance of genes was lower midway than at the end. Moreover, higher biodiversity was observed in samples after composting than storage. Our current research shows that both composting and storage effectively reduce ARGs in cattle manure. However, it’s challenging to determine which method is superior, as different groups of resistance genes react differently to each treatment, even though a notable overall reduction in ARGs is observed.
Work Product Review Process Applied to Test Cases Review for Software Testing

Testing is one of the most important factors in the software development cycle to obtain a quality product. It is essential to carry out effective tests to ensure that the software works as expected, meets all project requirements, and does not present defects after implementation. For this, the test cases must have a well-written and adequate structure, with actions and conditions that verify and cover all the functionalities and behaviors of the system. Even with several changes in requirements, business rules, new features, or adaptations that may occur during product development. This work describes an industrial case study where the work review process established by ISO/IEC 20246:2017 is implemented in the review of software test cases. As a result, a reduction in test execution time was achieved, as well as a reduction in the number of professionals involved as a result of better detection of defects during tests.
Potential use of indigenous arbuscular mycorrhizal fungi to improve soil productivity in tailing of tin mining: a greenhouse study scale

Tin mining is as a critical factor destroying ecosystems and challenging for reclamation. Indigenous arbuscular mycorrhizal fungi (AMF) are well-known for assisting plants growing in harsh environments. This study aimed to (1) isolate indigenous AMF; (2) screen AMF for nutrient uptake and plant biomass; (3) investigate the screened AMF in supporting plant growing in tin tailing. Soils were collected from natural forest and post-tin mining in Bangka. Wet sieving was conducted to obtain spore of AMF. Collected AMF were propagated and screened for phosphorus uptake and plant biomass of tested Sorghum bicolor growing in sterilized sand at greenhouse. Eight best-screened AMF was evaluated using Piper nigrum growing in sterilized tin tailing+10% compost at greenhouse. The new leaf number, phosphorus uptake, and flowering emergence were measured. Trap culture obtained 16 Acaulospora sp. , 1 Glomus sp. , and 1 Scutelospora sp, in post-tin mining, and 9 Acaulospora sp. in natural forest. Screening of AMF resulted in higher shoot P uptake and biomass than controls. Application of screened AMF on tin-talling showed that four AMF had higher new leaf number, phosphorus uptake, and flower emergence of P. nigrum. These results imply that selected indigenous AMF are potential for future land productivity of post-tin mining area.
A comparison of CAPM and Fama-French three-factor model under Machine Learning approaching

With the economy experiencing rapid growth in recent years, more individuals have started venturing into the stock market. Precisely forecasting the rate of return can mitigate investment risks for stock investors and significantly enhance their investment returns. The Capital Asset Pricing Model (CAPM) and the 3-factor Fama-French model (FF3) are widely recognized in academic and practical settings. This model comparison provides frameworks to analyze the relationship between portfolio risk and return in inefficient markets. This research utilized the Support Vector Regression (SVR) algorithm to forecast the returns of a diversified portfolio in the Hanoi stock market (HNX) from 2010 to 2022. Subsequently, the explanatory power of the CAPM and FF3 models were compared using the Ordinary Least Squares (OLS) algorithm. Finally, this research incorporated the SVR algorithm within the FF3 framework to develop a predictive model. The research findings demonstrate that the FF3 model provides a superior explanation to the CAPM model. Additionally, the study reveals that the SVR algorithm outperforms the OLS algorithm in terms of efficiency, as it yields lower Root Mean Square Error (RMSE) values. Consequently, the next research direction entails replacing the FF3 model with a more comprehensive multi-factor model, anticipating obtaining an enhanced predictive model.
Stochastic Dynamic System Implementation to Predict Nisab Values in Zakat Application

Every Muslim is required to donate a specific portion of their assets as zakat when certain circumstances are met. Zakat is paid to be given to those who are eligible to receive it and is one of the pillars of Islam. The nisab is the minimal amount of wealth a person must possess to be obligated to pay zakat. With the development of information and technology, zakat payments can now be easily done, with the prediction of when someone can pay zakat. This research is an implementation to predict to pay zakat according to nisab and haul by using a stochastic dynamic system. In this study, using the Bayesian method with Markov Chain Monte Carlo (MCMC) i.e. the Gibbs Sampling algorithm was applied to estimate the dynamic to convergent stochastic model parameters. Stochastic dynamism is a concept that allows for the fact that the price volatility of testicles based on gold prices varies from time to time and is not constant. This model is used to predict nisab and haul in the Zakat Sukses application. Based on the stochastic dynamic model obtained, the prediction results for zakat are almost close to the actual data. The benefit of this research is that the predicted value obtained can be used as a reference for Zakat Sukses application users to find out the predictions of nisab and haul for zakat payers.
Enhancing Issue Management through the Employment of Inspection Technique: An Experience Report in The Industry

The software industry has been adopting the concept of Global Software Development to release products quicker and with fewer costs, assuring quality by employing software testing techniques which are able to detect issues early. In this context, this paper presents an experience report performed at SIDIA Institute of Science and Technology between the years of 2020 and 2022, during which a process of issue management (IM) was developed to reduce the problems in the issue report, such as: lack of cohesion, absence of confirmation tests and long learning curve time of the testing team. In order to face these challenges, the IM process employs inspection techniques in order to improve the quality of reports, and then, increase its effectiveness, i.e., the number of valid issues. In addition, to support the IM process, it was created an onboarding phase, a template for issue reports and the monitoring of issues until the confirmation test. Through the implementation of this process, there was evaluated a total amount of 11002 issues report, and as a result, it was observed that the issues’ effectiveness rate was increased from to (raising by ) in the period in which this research was performed. In sequence, it was detected that around of issues had problems related to issue report writing. Furthermore, qualitative research revealed that of the interviewed testers consider the process effective. The authors hope that lessons learned described in this paper can contribute to the academic and industry communities by regarding the employment of inspection techniques to evaluate issue reports.
Social imaginaries methods and socio-engineering competences in sustainable river management (a case study in the living lab Upper Citarum)

The Living Lab approach has become popular and developed in the past decade. It could provide a configuration to pursue a shared vision of integrated water resources management of the Citarum River in West Java - Indonesia. The multi-stakeholder situation and the growing recognition of interdependencies among stakeholders foster the complexity of addressing sustainable river management for the Upper Citarum River. To gain insights on essential competencies and adaptations in higher education curricula, the Environmental Engineering Department of the Faculty of Civil and Environmental Engineering-ITB, Telkom University Indonesia, and Van Hall Larenstein University of Applied Sciences, Netherlands, joined hands in a collaborative research project. This study aims to develop a socio-engineering aspect for sustainable river water quality management in the Environmental Engineering Field and Curricula. The methods used are social imaginaries of Participatory Mapping and a Poetry Route that allowed the involved river bank communities to activate their role and take positions in the living lab. Institutional stakeholders, acting in a facilitating role, learned to gain and share information from and with the community. The result concludes that social imaginaries methods enable a new perspective in developing community-based programs and advocate further exploring the socio-engineering competencies of environmental professionals.
Rheological Investigation of a Set of PCE Superplasticizers in Cement

The importance of polycarboxylate ethers (PCE) as concrete superplasticizers has significantly increased in recent decades, both technically and commercially. At the least, three descriptors describe the average PCE structure: the side chain length (P), the charge density (N), and the number of repeating units (n). The synthesis parameters determine the average composition of the PCEs and, consequently, their performance in cementitious materials. This study examines the rheological properties of 27 different PCE structures, which were synthesized using redox‐initiated free‐radical copolymerization. We independently varied the parameters P and N and produced three PCEs with different molar weights for each combination of P and N. Cement pastes with low PCE dosages of 0.035 wt% were tested in a rheometer using a plate‐plate geometry. It turns out that when the PCE is added directly to the mixing water, the PCE has a complex influence on the very early hydration. Consequently, the obtained rheology data is not entirely in line with available structure‐activity relationships. Additionally, we attempted to model the observed rheology data based on three structural PCE descriptors. While a multivariate linear regression model failed to accurately describe the relationship, a random forest model delivered a better model. In summary, we conclude that the impact of PCEs on early hydration significantly affects the rheology of the paste. The changed hydration pathway causes a deviation of our results from existing structure‐activity relationships and makes it challenging to statistically model the sparse rheology data with a minimal set of three structural descriptors.
Multi-radar interference experiment and performance evaluations on algorithm-based and learning-based schemes

The demand for high-resolution automotive radar operating in the 77GHz band is on the rise, especially as we approach the practical implementation of autonomous driving technology. With the increasing prevalence of in-vehicle Chirp Sequence (CS) radars in the future, there is a growing concern about the potential for broadband inter-radar interference. This interference poses a significant risk, potentially leading to a higher likelihood of undetected targets. To address this problem, various algorithm-based and learning-based schemes have been proposed to suppress the inter-radar interference, e.g., iterative threshold based zero suppression method and RNN (Recurrent Neural Network) based interference suppression method. However, they only demonstrate their effectiveness based on simulation results. In this paper, we conducted a multi-radar interference experiment with up to four interference sources and compared the performance of different schemes by using the collected real data.