Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised Triple Macrosynchronized Lockstep (STMLS) Architecture for Multicore Processors

In various fields, such as those with high-reliability requirements, there is a growing demand for high-performance microprocessors. Whereas commercial microprocessors offer a good trade-off between cost, size, and performance, they often need to be adapted to meet the reliability demands of safety-critical applications. To address this challenge, a Supervised Triple Macrosynchronized Lockstep architecture for multicore processors is presented in this work. Multiple recovery mechanisms, including rollback and roll-forward, have been implemented to harden the system. By integrating these mechanisms, the microprocessor becomes more robust and capable of mitigating potential errors or failures that may occur during operation. A quad-core ARM Cortex-A53 processor has been used as a case study, and an extensive fault injection campaign in the register file has been conducted to evaluate the effectiveness of our proposed approach. The results show that the hardened system exhibits high reliability, with 100% error coverage and error correction capabilities of up to 86.40%.
Effects of H2SO4, GA3, and cold stratification on the water content, coat composition, and dormancy release of Tilia miqueliana seeds

Introduction Tilia miqueliana is an endemic species whose population is declining. The permeability barrier and mechanical constraint of the pericarp (seed coat) are important causes of its seed dormancy. Although there has been considerable research on this subject, questions remain regarding how the permeability barrier and mechanical constraint of the seed coat are eliminated during dormancy release and how water enters the seed. Therefore, protecting the species by improving its germination/dormancy breaking in the laboratory is urgent Methods In this study, the changes in the cellular structure, mechanical properties, and components of the Tilia miqueliana seed coat after an H2SO4-gibberellic acid (GA3) treatment were analyzed during dormancy release. Various analyses (e.g., magnetic resonance imaging, scanning electron microscopy, and paraffin section detection) revealed the water gap and water channel. Results The H2SO4 treatment eliminated the blockage at the micropyle and hilum of the seeds. Water entered the seeds through the water gap (micropyle) rather than through the hilum or seed coat, after which it dispersed along the radicle, hypocotyl, and cotyledon to the endosperm. During the cold stratification period, the cellular structure was damaged and an increasing number of holes appeared on the inner and outer surfaces of the seed coat. Vickers hardness tests showed that GA3 decreased the seed coat hardness. Additionally, the seed coat lignin and total phenol contents continuously decreased during the cold stratification period. Notably, the Liquid chromatography–mass spectrometry (LC–MS) analysis of the seed coat detected polyethylene glycol (osmoregulator), which may have destabilized the water potential balance inside and outside the seed and increased the water content to levels required for germination, ultimately accelerating seed dormancy release. Discussion This sophisticated and multi-level study reveals how H2SO4 and GA3 eliminate the permeability barrier and mechanical constraints of the seed coat during dormancy release of Tilia miqueliana seeds. This will be beneficial to artificially assist the natural regeneration and population expansion of Tilia miqueliana.
Alteration in the molecular structure of the adenine base exposed to gamma irradiation: An ESR study

Abstract Adenine polycrystals were obtained from their powder form under effective crystallization conditions by adjustment of the concentration of chemical solutions. The adenine samples were irradiated with a 60Co gamma-ray source at room temperature for 12, 24, 48, and 72 h, and then these samples were investigated between 240 and 400 K using an electron spin resonance (ESR) spectrometer. No signals were observed in the non-irradiated samples irradiated for 12, 24, and 48 h; however, when irradiated for 72 h, the samples exhibited complex ESR spectra. The analysis indicated the presence of a radical structure, NH ̇ \dot{{NH}} . The hyperfine splitting and g-values were calculated from the spectra by simulation programs. Also, during the measurements, it was observed that the shapes of the ESR lines changed slightly with the temperature.
A New Technique to Measure Infiltration rate for Assessing Infiltration of BMPs

Infiltration is an essential process of most stormwater best management practices and measurements of the infiltration rate applied to a design storm are needed to determine performance, schedule maintenance and meet regulatory requirements. Infiltration rates, however, have great spatial variation, and infiltration measurement techniques are relatively slow. The Modified Philip-Dunne (MPD) infiltrometer is a new technique to measure the saturated hydraulic conductivity (Ksat) of surface soil. It is a fast, simple and inexpensive falling head device; suitable for infiltration practices because it can be performed relatively quickly to capture the large spatial variability that occurs with infiltration rates. A userfriendly spreadsheet program and a manual have made the application of the MPD infiltrometer straight-forward so that the infiltration rate of soil can be obtained quickly. A comparison between the value of Ksat obtained from MPD and from the numerical simulations of the Richards equation for homogeneous and non-homogeneous soil will be presented.
Aggregated Operation Scheme for Distributed Photovoltaic and Energy Storage System Enabling Multi-Service Provision

The aggregated entity formed by the distributed photovoltaic (DPV) and energy storage system has the capability to offer multiple services in the electricity markets, reaping the advantages of both energy arbitrage and frequency regulation. This article focuses on developing a bidding strategy and operation plan for an aggregated entity from a profit pursuit perspective. We propose a multi-stage stochastic programming optimization model that divides the original problem into three stages, allowing for feasible and more realistic decision-making while maintaining the relationships among decisions. The proposed model includes a new multi-service portfolio that balances the profit of the two markets and accounts for uncertainties in market prices, DPV output, and regulation signals. To motivate collaboration among stakeholders, we also develop a profit allocation model rooted in the principles of Nash bargaining theory. This model serves as a compelling incentive for all parties involved, encouraging mutually beneficial cooperation. Finally, a numerical case study demonstrates the validity of the proposed models.
Plant growth detection based on binocular multispectral imaging

Three-dimensional vision contains richer information about objects than traditional two-dimensional images, to make it more desirable for many applications within measurement, inspection and localization. We design a binocular multispectral imaging system, composed of two multispectral cameras and external light source. Incorporates spectral information at the level of reconstruction algorithm to improve feature matching. The system is capable of observing the growth of plants such as chlorophyll through multi-spectral information, and the depth calculation based on the stereo vision algorithm can detect the growth height of plants. Therefore, this work will be favorable for crop growth observation and provide an efficient and accurate measurement method for 3D morphometry, making it of great importance for the development of biological, agricultural and production research fields.
Dynamic Dimerization of Chemokine Receptors and Potential Inhibitory Role of Their Truncated Isoforms Revealed through Combinatorial Prediction

Chemokine receptors play crucial roles in fundamental biological processes. Their malfunction may result in many diseases, including cancer, autoimmune diseases, and HIV. The oligomerization of chemokine receptors holds significant functional implications that directly affect their signaling patterns and pharmacological responses. However, the oligomerization patterns of many chemokine receptors remain poorly understood. Furthermore, several chemokine receptors have highly truncated isoforms whose functional role is not yet clear. Here, we computationally show homo- and heterodimerization patterns of four human chemokine receptors, namely CXCR2, CXCR7, CCR2, and CCR7, along with their interaction patterns with their respective truncated isoforms. By combining the neural network-based AlphaFold2 and physics-based protein–protein docking tool ClusPro, we predicted 15 groups of complex structures and assessed the binding affinities in the context of atomistic molecular dynamics simulations. Our results are in agreement with previous experimental observations and support the dynamic and diverse nature of chemokine receptor dimerization, suggesting possible patterns of higher-order oligomerization. Additionally, we uncover the strong potential of truncated isoforms to block homo- and heterodimerization of chemokine receptors, also in a dynamic manner. Our study provides insights into the dimerization patterns of chemokine receptors and the functional significance of their truncated isoforms.
Short-term exposure to ultrafine and fine particulate matter with multipollutant modelling on heart rate variability among seniors and children from the CorPuScula (coronary, pulmonary, sanguis) longitudinal study in Germany

Background Short-term exposure particulate matter with a diameter of 10 µm or less (PM10) and fine particulate matter (PM2.5) has been associated with heart rate variability (HRV), but exposure to ultrafine particles (UFP) has been less well examined. We investigated the associations between the HRV outcomes and short-term exposure to UFP, PM10 and PM2.5 among school-aged children and seniors. Methods CorPuScula (Coronary, Pulmonary and Sanguis) is a longitudinal, repeated-measure panel study conducted in 2000–2002 in Munich, Germany including 52 seniors (58–94 years old) with 899 observations and 50 children (6–10 years old) with 925 observations. A 10-min resting electrocardiogram was performed to assess resting HRV outcomes [Standard Deviation of Normal to Normal Intervals (SDNN), Root Mean Square of Successive Differences between Normal Heartbeats (RMSSD), Low Frequency power (LF), High Frequency power (HF), ration between low and high frequency (LF/HF)]. UFP and PM exposures were measured near the care home and school yard for seniors and children, respectively. Mean exposures during the day of examination (9–21 h) as well as 3-h, 12-h, 24-h, one-day, and two-day lags were assessed. Linear mixed-effect models were used to investigate the associations between short-term air pollution and HRV outcomes separately in children and seniors. The models were adjusted for sex, age, weather conditions (temperature, precipitation, and water vapor pressure), BMI, lifestyle and medical information. Two and multipollutant models adjusted for NO2 and O3 were performed. Results Among seniors, we observed increases in SDNN, LF, HF and LF/HF ratio after short-term exposure to UFP (hourly and daily lags) in contrast to decreases in SDNN and RMSSD after exposure to PM10. Associations were generally robust to two- and multipollutant adjustment. Among children, we observed increases of the LF/HF ratio after short-term exposures to UFP at lags 12 and 24 h. In contrast, we observed decreases of the ratio after exposure to PM2.5 and PM10. Results were largely unchanged for multipollutant modelling, however we found a more pronounced increase in SDNN and LF/HF (UFP lag 12 and 24 h) after adjusting for NO2. Conclusions Overall, among seniors, we observed associations of UFP and PM10 exposure with sympathetic responses of the ANS, which play an important role in sudden heart attacks or arrhythmia. Among children we found more inconsistent associations between UFP and a delayed increase in HRV. Adjusting for co-pollutants including NO2 and O3 yielded robust results.
Conception et développement d'une application degestion de données dans un réseau de capteurs mobiles

Le travail presente dans ce rapport traite la mise en œuvre d'une application de gestion et d'analyse de l'information envoyee par differents capteurs mobiles tels que la temperature, l'humidite et la pollution. En effet, l'utilisation des capteurs mobiles pour l'evaluation environnementale a atteint un haut niveau technique ; notre but principal est par consequent de mettre en œuvre une solution avancee dans le but de controler des nœuds de capteur environnementaux mobiles afin de transferer, en temps reel, les informations d'aide a la decision.
Flux and modification of sinking particles: Three field studies in the Southern Ocean

We recorded vertical profiles of size distributions of particles (ranging from 0.052 to several mm in equivalent spherical diameter) in the natural iron fertilized bloom southeast of Kerguelen Island (Southern Ocean) and in surrounding High Nutrient Low Chlorophyll (HNLC) waters with an Under Water Video Profiler (UVP) during the KEOPS cruise (Jan.-Feb 2005). Total particle numerical abundance (TPN) and total particle volume (TPV) in the 0-200 m layer were respectively 3-fold and 20-fold higher in the bloom, and integrated TPV was correlated to integrated chlorophyll concentration. The difference persisted well into the ocean interior with a 10-fold higher TPV at 400 m depth beneath the natural iron fertilized bloom. Below 400 m, increases in TPV values at the bloom stations reflect the suspension of bottom sediments. Bloom waters had a greater proportion of large particles from the surface to 400 m and also exhibited an increase of this proportion with depth compared to HNLC waters. Multiple visits to the bloom reference station, A3, suggest preferential removal of large particles as the bloom declined. Comparing our particle abundance size spectra with those observed previously in polyacrylamide gel filled sediment traps allows us to estimate mesopelagic particle sinking rates. These results suggest that particles sink faster in the HNLC waters than beneath the bloom. The fact that sinking speeds were not a simple monotonic function of particle size and varied spatially highlights the need to go beyond parameterisations of sinking rate as a function of size alone.
Harmonizing minds and machines: survey on transformative power of machine learning in music

This survey explores the symbiotic relationship between Machine Learning (ML) and music, focusing on the transformative role of Artificial Intelligence (AI) in the musical sphere. Beginning with a historical contextualization of the intertwined trajectories of music and technology, the paper discusses the progressive use of ML in music analysis and creation. Emphasis is placed on present applications and future potential. A detailed examination of music information retrieval, automatic music transcription, music recommendation, and algorithmic composition presents state-of-the-art algorithms and their respective functionalities. The paper underscores recent advancements, including ML-assisted music production and emotion-driven music generation. The survey concludes with a prospective contemplation of future directions of ML within music, highlighting the ongoing growth, novel applications, and anticipation of deeper integration of ML across musical domains. This comprehensive study asserts the profound potential of ML to revolutionize the musical landscape and encourages further exploration and advancement in this emerging interdisciplinary field.
Wireless Capsule Endoscope Localization Using LSTM Network

In this letter, we propose a method of wireless capsule endoscope (WCE) localization using long short-term memory (LSTM) recurrent neural network. We use the ultra-wideband (UWB) and medical implant communication service (MICS) band path loss of the signals received by a set of sensor receivers as the input features for WCE localization. We apply local weighted linear regression (LWLR) for path loss smoothing minimizing the deviations caused by the human body tissue dielectric properties. Then, the LSTM network is applied on the scattered and smoothed path loss features to localize the WCE in 2443 traveling positions in the small intestine trajectory. We also analyze the impact of the number of sensor receivers, network topology, and human body dimensions on the accuracy of localization. It is observed that using LSTM network on the UWB and MICS smoothed path loss, significantly high accuracy of localization with root mean square error (RMSE) of 0.24 and 0.27 mm is obtained using only 16 sensor receivers in a computational time of 467.77 and 532.23 s, respectively.
Analysis of classification algorithms for crop detection using LANDSAT 8 images

Remote sensing the acquisition of information about an object or phenomenon without making physical contact with the object is applied in a multitude of different areas, ranging from agriculture, forestry, cartography, hydrology, geology, meteorology, aerial traffic control, among many others. Regarding agriculture, an example of application of this information is regarding crop detection, to monitor existing crops easily and help in the region’s strategic planning. In any of these areas, there is always an ongoing search for better methods that allow us to obtain better results. For over forty years, the Landsat program has utilized satellites to collect spectral information from Earth’s surface, creating a historical archive unmatched in quality, detail, coverage, and length. The most recent one was launched on February 11, 2013, having a number of improvements regarding its predecessors. This project aims to compare classification methods in Portugal’s Ribatejo region, specifically regarding crop detection. The state of the art algorithms will be used in this region and their performance will be analyzed.
High Resolution Processing and Imaging for Subtle Geological Features

We show that by extending bandwidth prior to migration the spatial resolution (lateral and vertical) can be improved, and that velocity analysis can give a more detailed velocity profile which in turn improves image quality. An increase in bandwidth prior to migration reduces both vertical and lateral wavelet widths, and, therefore, increases spatial resolution. Improved spatial resolution allows a more detailed velocity field to be defined both laterally and vertically. This approach can be applied to both time and depth migration flows.
Time Series Modeling of Global Average Absolute Sea Level Change

This study aimed to demonstrate the effectiveness of time series models in modeling long-term records of global average absolute sea level changes from 1880 to 2014. Following the Box–Jenkins methodology, the ARIMA(0,1,2) model with drift was identified as the best-fit model for the time series due to its lowest AIC value. Using the LM algorithm, the results revealed that the NARNN model with 7 neurons in the hidden layer and 7 time delays exhibited the best performance among the nonlinear autoregressive neural network models, as indicated by its lower MSE. While ARIMA models excel in modeling linear problems within time series data, NARNN models are better suited for nonlinear patterns. However, a HYBRID model was explored, which combines the strengths of both ARIMA and NARNN models, offering the capability to address both linear and nonlinear aspects of time series data. The comparative analysis of this study demonstrated that the HYBRID model, with 6 neurons in the hidden layer and 7 time delays, outperformed the NARNN model with 7 neurons in the hidden layer and 7 time delays, as well as the ARIMA(0,1,2) model, with the lowest MSE in this study. These findings represent a significant step in time series forecasting by leveraging the strengths of both statistical and machine learning methods.
Geospatial characteristics of allotment garden provision in Tokyo

Abstract Allotment gardens (AGs), one of the most popular forms of urban agriculture (UA), have attracted social attention because of the ecosystem services they provide to citizens. However, the services and availability of AGs may be unevenly distributed, owing to their geographic location. The patterns underlying the provision of AG plots and facilities to users in Tokyo are unclear. Thus, this study quantitatively examines the characteristics of different types of AG provision and their determinants in the metropolitan region of Tokyo. We classified a sample of 313 AGs gathered from governmental open data via a non-hierarchical cluster analysis of AG provision patterns based on their properties, including number of plots, plot size, contract price and duration, and facilities such as agricultural equipment and access to instructors. Moreover, we examined the influence of urban development and residential characteristics on these classes using the Kruskal–Wallis test. The analysis identifies six AG provision patterns based on their properties. It also revealed that AG provision in Tokyo was differentiated by the percentage of agricultural land and the socio-demographic characteristics of residents, including population, percentage of young population, and income levels from the city center to the suburban areas, corresponding to urban sprawl. These findings could provide valuable insights to help local governments, farmers, and non-profit organizations address the challenges and opportunities arising from each AG provision pattern and to make AG plots and facilities more adaptable to upcoming urban shrinkage, business opportunities, and possible excessive subdivision and price hikes.
Boosting Light Field Image Super Resolution Learnt From Single-Image Prior

In recent years, many deep learning networks are proposed for light field super resolution (LFSR). LFSR problem is essentially ill-posed since unknown detail information need to be predicted. Hence LFSR networks require plentiful content information (e.g., shape, color, texture) learned from sufficiently diverse scenarios. However, due to the high collection cost, existing light field datasets are in small size and have few scenarios, which could not meet the requirement and limit the performance of LFSR networks. To solve this problem, we proposed a novel framework to significantly boost their performance. Our main idea is to introduce valuable and plentiful content information from single images into LFSR networks as prior. Specifically, first, a view synthesis method is applied to add unreal disparity into single images, increasing the dimensionality of single images, hence to solve the problem of inconsistent data modalities. Then, we design the Scenarios-Content Introduction Module (SCIM) to effectively extract content feature from synthesized data. Finally, due to the added unreal disparity in the first stage, the features have severely pseudo information. Hence the Feature Attention Module (FAM) is proposed to discriminately select valuable information and combine this information into the LFSR network. Extensive experiments on six datasets validate the effectiveness of the proposed method, leading to a maximum gain of 0.439 dB. Our method can even boost SOTA networks to achieve higher performance.