Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of the Hoabinhian Techno-Complex of Tam Hang Rock Shelter in Northeastern Laos

Tam Hang rock shelter was excavated in 1934 by the French geologist Jacques Fromaget who discovered 13 skeletons and a lot of stone tools. But after the second War World, the lithic assemblages were lost. Then the site was forgotten since 2003, when the department of archaeology of the Lao Ministry of Culture relocated it and made with a French team a new prospection. In 2007 and after seven campaigns of excavations, a lot of archaeological materials had been found in two new areas opened at Tam Hang South and Tam Hang Central. More than 9000 specimens of stone have been found. This article proposes to give a new approach of the whole stratigraphy with C14 dating. It also makes a characterization of the lithic assemblage which belongs to the Hoabinhian techno-complex.
Pyroxenite-rich mantle formed by recycled oceanic lithosphere: Oxygen-osmium isotope evidence from Canary Island lavas

Plate tectonic processes result in recycling of crust and lithosphere into Earth's mantle. Evidence for long-term preservation of recycled reservoirs in the mantle comes from the enriched isotopic character of oceanic island basalt (OIB) lavas. Although recycled constituents can explain much of the geochemical variation in the OIB-source mantle, it has been shown that direct melting of these components would lead to magmas with evolved compositions, unlike OIB. Instead, it has been argued that either metasomatic pyroxene-rich peridotite that has inherited the trace element and isotopic character of subducted materials, or high-temperature intramantle metasomatism of lithosphere can explain OIB compositions. To test these models, we present new oxygen and osmium isotope data for lavas from the Canary Islands of El Hierro and La Palma. These islands have distinct 18O/16O and 187Os/188Os compositions that can be explained through melting of pyroxenite-enriched peridotite mantle containing <10% recycled oceanic lithosphere. We also assess O-Os isotope systematics of lavas from Hawai‘i and the Azores and show that they also conform to addition of distinct recycled oceanic components, including lithosphere and pelagic sediment. We conclude that enriched isotopic signatures of some OIBs are consistent with pyroxenite-rich mantle sources metasomatized by recycled components.
Sense as you go: A context-aware adaptive sensing framework for on-road driver profiling

The realization of next-generation intelligent transportation services primarily depends on the rich data that could effectively capture the on-road and in-vehicle conditions in real time. Road safety being one of the most critical objectives for such services, received significant research interest in the past. Smartphone-based inertial sensing has proved to be quite promising in identifying potential anomalies in road conditions or driving patterns. However, the data generated from the onboard Inertial Measurement Unit are exposed to high noise due to mobility and subjective diversity. Moreover, a large amount of data produced at a higher sampling rate directly impacts on the battery life of such resource-constrained devices as well as the privacy of the concerned user. To address this set of challenges, we introduce a novel framework for mobile devices with the objective to support ‘sensing on a need’ basis. The proposed framework AdaSift has three key features: (1) detects the relevant context signifying a window of important data points in real-time, (2) filters out irrelevant data at the source itself and thus reduces the associated overhead significantly; and (3) effectively identify a driver’s profile based on the unique behavioral signature traced by the filtered data. We evaluated the performance of AdaSift by implementing the framework on an Android smartphone and conducting a user study with 8 drivers. The experimental results show that the use of AdaSift could achieve up to accuracy in on-road driver profiling while reducing the energy consumption footprint of the smartphone by up to .
Historic and recent trends of cyanobacterial harmful algal blooms and environmental conditions in Clear Lake, California: A 70-year perspective

Clear Lake is a large, natural lake in northern California, USA, with many beneficial uses but also substantive environmental issues. The lake has a long history of water quality problems including mercury contamination, pesticide usage, invasive species, and high rates of primary production. In recent years, an increase in cyanobacterial harmful algal blooms (cyanoHABs) has been documented in the lake, adding to the environmental issues faced by aquatic species present in the lake and the local community. Extensive observations of various physical, chemical, and biological parameters in Clear Lake began in the mid-1900s. The most pertinent of these data sets and findings have been reviewed and analyzed with the intent of improving our understanding of the causes and drivers of cyanoHABs, toxin production, and identifying data gaps. Several parameters including average annual water temperature have remained relatively constant over the past 70 years, although the seasonally averaged water temperatures have shifted in a manner that may now favor cyanobacterial dominance. Clear Lake has also witnessed recent changes in several environmental variables such as total phosphorus concentrations that might contribute to blooms. An analysis of lake conditions prior to and following the enactment of a total maximum daily load (TMDL) for phosphorus in 2007 indicates little measurable influence on total phosphorus concentrations in Clear Lake. The present trajectory of lake chemistry suggests that additional research and management efforts will be needed to address the recurrence of cyanoHABs in the future. Future lake management strategies should include consideration of the role of internal nutrient loads to lessen cyanoHABs. Furthermore, a better understanding of cyanobacterial community interactions and top-down effects on bloom formation within the lake can help guide future cyanoHAB management strategies.
A Trauma-Informed Care Model: Addressing Adverse Childhood Experiences in Patients with End-Stage Kidney Disease

In the healthcare field, there has been increased recognition of the long-term consequences that adverse childhood experiences (ACEs) have on the health and well-being of people. It is imperative that a trauma-informed care (TIC) approach is utilized in the treatment of patients with end-stage kidney disease (ESKD) because of high rates of non-adherence and health risk behaviors. The purpose of this article is to explore current nephrology practices and TIC approaches utilized in the healthcare field. A TIC approach, through a health-belief model lens specific to the ESKD population, can be introduced by providers to better assess and treat patients. Implications for practice include standardization of TIC in social work practice, better outcomes for patients, and ongoing research considerations.
What's The Point: AutoEncoding Building Point Names

Deciphering the point names of various equipment within commercial buildings remains a vexing challenge. Both the lack of standards, and the usual idiosyncrasies in naming conventions, add to the complexity faced by engineers as they attempt to analyse and optimise a building’s systems. For example, T, Tmp, Temp, C, and Celsius could all be used to denote a temperature sensor. Likewise, the delimiters (e.g., ., -, or _) that apportion parts of a point name varies considerably. This results in the possibility of both f1-AHU_tmp and Floor02.AHU. Temperature appearing in the same dataset (and often even within the same building). The initial work here explores foundational deep-learning techniques to help a human expert understand previously unseen building point names. We’ve applied a simplified yet effective neural-network architecture called AutoEncoders (AE) to the task, with tests done against an expansive dataset comprising of point names gathered from publicly available Brick and Mortar datasets, as well as a private corpus. This novel implementation is augmented by a unique sequential tokenising method that applies tri-grams in a sliding window over the point names, which preserves both the linearity and delimiters within point names. This is diametrically opposed to prior approaches that have either ignored the character sequences and/or removed the presence of delimiting characters. Early results show promising 91% accuracy levels achieved against previously unseen point names.
A Semi‐Analytical Model for Water Vapor, Temperature, and Surface‐Albedo Feedbacks in Comprehensive Climate Models

Radiative feedbacks govern the Earth's climate sensitivity and elucidate the geographic patterns of climate change in response to a carbon‐dioxide forcing. We develop an analytical model for patterned radiative feedbacks that depends only on changes in local surface temperature. The analytical model combines well‐known moist adiabatic theory with the radiative‐advective equilibrium that describes the energy balance in high latitudes. Together with a classic analytical function for surface albedo, all of the non‐cloud feedbacks are represented. The kernel‐based analytical feedbacks reproduce the feedbacks diagnosed from global climate models at the global, zonal‐mean, and seasonal scales, including in the polar regions, though with less intermodel spread. The analytical model thus provides a framework for a quantitative understanding of radiative feedbacks from simple physics, independent of the detailed atmospheric and cryospheric responses simulated by comprehensive climate models.
Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification

Computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Therefore, chest CT images should provide more information for COPD diagnosis, such as COPD stage classification. This paper proposes a features combination strategy by concatenating three-dimension (3D) CNN features and lung radiomics features for COPD stage classification based on the multi-layer perceptron (MLP) classifier. First, 465 sets of chest HRCT images are automatically segmented by a trained ResU-Net, obtaining the lung images with the Hounsfield unit. Second, the 3D CNN features are extracted from the lung region images based on a truncated transfer learning strategy. Then, the lung radiomics features are extracted from the lung region images by PyRadiomics. Third, the MLP classifier with the best classification performance is determined by the 3D CNN features and the lung radiomics features. Finally, the proposed combined feature vector is used to improve the MLP classifier's performance. The results show that compared with CNN models and other ML classifiers, the MLP classifier with the best classification performance is determined. The MLP classifier with the proposed combined feature vector has achieved accuracy, mean precision, mean recall, mean F1-score, and AUC of 0.879, 0.879, 0.879, 0.875, and 0.971, respectively. Compared to the MLP classifier with the 3D CNN features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.8% (accuracy), 5.3% (mean precision), 5.8% (mean recall), 5.4% (mean F1-score), and 2.5% (AUC). Compared to the MLP classifier with lung radiomics features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.0% (accuracy), 5.1% (mean precision), 5.0% (mean recall), 5.1% (mean F1-score), and 2.1% (AUC). Therefore, it is concluded that our method is effective in improving the classification performance for COPD stage classification.
Speckle contrast reduction through the use of a modally-specific photonic lantern for optical coherence tomography

A few-mode optical coherence tomography (FM-OCT) system was developed around a 2 × 1 modally-specific photonic lantern (MSPL) centered at 1310 nm. The MSPL allowed FM-OCT to acquire two coregistered images with uncorrelated speckle patterns generated by their specific coherent spread function. Here, we showed that averaging such images in vitro and in vivo reduced the speckle contrast by up to 28% and increased signal-to-noise ratio (SNR) by up to 48% with negligible impact on image spatial resolution. This method is compatible with other speckle reduction techniques to further improve OCT image quality.
A Signal-Level Fusion Distributed Radar Localization Method Based on Wideband Synthesis Technology

Traditional distributed radar systems face challenges like limited localization precision and stringent time synchronization demands in implementing signal-level fusion algorithms. This article presents a novel distributed radar localization method based on wideband (WB) signal synthesis technology to address these issues. In this method, each node of the distributed radar simultaneously transmits a set of narrowband stepped linear frequency-modulated signals. After de-chirping and filtering separation, the echo signals from each node are calibrated and synthesized into an equivalent WB signal. This process improves the range resolution and localization accuracy of single nodes. However, WB synthesis necessitates precise distance data, and the estimation error from narrowband echoes can be substantial, affecting the synthesis’s efficiency. To overcome this, we propose a grid search WB synthesis algorithm. The algorithm establishes a search area centered on the distance estimation derived from the narrowband signals, with a diameter determined by the resolution of these signals. Within this search area, the algorithm conducts a grid search, utilizing the peak magnitude of the synthesized signal’s fast Fourier transform (FFT) spectrum as a criterion to find the most accurate synthesis result. This method can be integrated with traditional fusion algorithms, enhancing localization precision further. Theoretical analysis and simulation results demonstrate that our method outperforms conventional techniques in terms of localization accuracy. Notably, the grid search algorithm reduces the requirement for precise time synchronization in signal-level fusion algorithms, improving the method’s adaptability.
Crafting and Analyzing Multi-Structured Aramid Materials and Their Pyrolytic Transformations: A Comprehensive Exploration

Gradient porous materials, particularly carbon-based materials, hold immense potential in the fields of batteries, energy storage, electrocatalysis, and sensing, among others, by synergistically combining the attributes associated with each pore size within a unified structural framework. In this study, we developed a gradient porous aramid (GP-Aramid) by incorporating cellulose acetate as a porosity promoter in the polymer casting solution in different proportions. These GP-Aramids were subsequently transformed into their pyrolyzed counterparts (GP-Pyramids), retaining their original structures while displaying diverse cellular or dense microstructures inherited from the parent aramid, as confirmed via scanning electron microscopy. X-ray diffraction spectra provided evidence of the conversion of aramids into carbonaceous materials. The materials showed structural defects observed through the intensity ratio of the G and D bands (ID/IG = 1.05) in the Raman spectra, while X-ray photoelectron spectra (XPS) revealed that the carbonization process yielded pyrolyzed carbon materials unusually rich in nitrogen (6%), oxygen (20%), and carbon (72%), which is especially relevant for catalysis applications. The pyrolyzed materials showed bulk resistivities from 5.3 ± 0.3 to 34.2 ± 0.6 depending on the meta- or para-orientation of the aramid and the porous structure. This work contributes to understanding these gradient porous aromatic polyamides’ broader significance and potential applications in various fields.
Research on the Recognition and Tracking of Group-Housed Pigs’ Posture Based on Edge Computing

The existing algorithms for identifying and tracking pigs in barns generally have a large number of parameters, relatively complex networks and a high demand for computational resources, which are not suitable for deployment in embedded-edge nodes on farms. A lightweight multi-objective identification and tracking algorithm based on improved YOLOv5s and DeepSort was developed for group-housed pigs in this study. The identification algorithm was optimized by: (i) using a dilated convolution in the YOLOv5s backbone network to reduce the number of model parameters and computational power requirements; (ii) adding a coordinate attention mechanism to improve the model precision; and (iii) pruning the BN layers to reduce the computational requirements. The optimized identification model was combined with DeepSort to form the final Tracking by Detecting algorithm and ported to a Jetson AGX Xavier edge computing node. The algorithm reduced the model size by 65.3% compared to the original YOLOv5s. The algorithm achieved a recognition precision of 96.6%; a tracking time of 46 ms; and a tracking frame rate of 21.7 FPS, and the precision of the tracking statistics was greater than 90%. The model size and performance met the requirements for stable real-time operation in embedded-edge computing nodes for monitoring group-housed pigs.
Analysis of Observed Global Mean Land-Surface Precipitation of the Period 1951 to 2000

Precipitation is a major branch of the global water cycle. All evaporated water is to fall back on earth sooner or later. The average residence time of water in the atmosphere is about 7 days. Evaporation itself is closely linked to surface air temperature which itself is driven by natural and anthropogenic external forcing and internal climate variability. Therefore also the global water cycle should reflect the forcing that drives global mean surface air temperature.
Surface Treatments Effect on Rainfall Canopy Interception and Runoff-Rainfall Ratio for in-Field Rainwater Harvesting

The purpose of this study was to ascertain the effectiveness of surface treatments to quantify the partitioning of rainwater falling on the runoff strips and basins as well as to determine the fraction of rainwater available to intercept by maize canopy and infiltrate into the root zone. The rainfall canopy interception (RCI) was estimated as a function of basin leaf area ratio per rain event. The runoff rainfall (RR) ratio was determined for both a single rainfall event and the whole growing season. Infiltration ratio of basin to runoff area was analysed for every unit millimeter of water that infiltrate in the runoff section, some additional of water will infiltrate in the basin area. The plateau value of RCI-rainfall relationships rendered about double in the wider (1.0-1.1 mm) compared to the narrow runoff strips (0.5-0.6 mm). Statistically, the combined surface treatments (RSL  ML) affected the RR ratio with higher efficiency in bare 1 m runoff (27%) and the lower efficiency group (< 10%) is associated with the widest runoff length covered with mulch. Variations in fractions of rainwater that can infiltrate into basins and runoff areas can lead one to select alternative strategies for water harvesting techniques.
Muscle Strength and Biochemical Markers as Predictors of Depression in Hemodialysis Patients: A Cross-Sectional Study

Patients with chronic renal failure, many of which treated with hemodialysis, present a high prevalence of impaired muscle strength which suggest that muscle mass parameters may be used as markers for changes in muscle in these patients. Measurement of handgrip strength (HGS) is a common, simple, and quick measure of muscle function an indicator of overall muscle strength which has been associated with physical activity and several anthropometric traits. Intercellular adhesion molecule-1 (ICAM-1) and insulin-like growth factor-1 (IGF-1) are biochemical markers associated with inflammatory processes which are a common consequence of dialysis. Additionally, hemodialysis patients frequently present signs of malnutrition and depression. This cross-sectional study aimed to evaluate if muscle and biochemical markers could be used to predict the risk of depression in hemodialysis patients. Several anthropometric parameters, nutrient intake, depression state and the serum levels of ICAM-1 and IGF-1 were determined and Pearson’s correlation coefficient and/or Spearman’s correlation coefficient were used to test the correlation between them. Our results do not show a correlation between HGF, IGF-1 and ICAM-1 with the depression status of the patients, but mid-arm muscle circumference (MAMC) was statistically and positively correlated with depression. Additionally, ICAM-1 levels were negatively correlated with HGS, MAMC, and IGF-1. Overall, the results of the present study suggest that HGS may be used as an indicator of cardiovascular diseases and MAMC may be a good predictor of the level of depression in hemodialysis patients, although further studies are required.
Enhancing the Reliability of SC PUF Through Optimal Capacitor Configuration

Switched-capacitor (SC) PUF has been used in the protection of security chips from invasive attacks due to their capacitive sensitivity. However, non-volatile memory cells are often used to bypass the unstable SC PUF units, which then become insensitive to outside attacks, creating a vulnerability that can be exploited by probing or Focus Ion Beam (FIB) attacks, thereby posing a potential security risk if these units are used to cover sensitive areas of the security chip. This paper proposes a method to increase stability while maintaining anti-invasive-attack capabilities by utilizing the configurability of the four capacitors in each SC PUF unit, and selecting the configuration that provides a larger mismatch of capacitor ratios, resulting in a larger output voltage mismatch. By doing so, the variation of offset for the latch-styled sense amplifier (LSSA) is suppressed, and the stability of the SC PUF is significantly increased. Additionally, changing the polarity of the LSSA’s inputs to match its offset also increases the effective input voltage of the LSSA. The proposed SC PUF using optimal capacitor configuration is fabricated using a 12 nm FinFET CMOS process. Test results demonstrate that the bit error rate of the SC PUF is reduced from about 17% to about 1.5%, and most of the SC PUF units can serve as anti-invasive-attack shields of the security chip.