Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulation of Radial Higher-Order Orbital Angular Momentum Mode Based on Helically-Twisted Elliptic Fiber

Based on mode coupling theory and angular momentum matching principle, the generation process and mechanism of radial higher-order orbital angular momentum (OAM) based on helically-twisted elliptic fiber (HTEF) is analyzed. High-precision HTEFs are successfully fabricated using the arc discharge technology of a fusion splicer. By changing the twisted rate of the HTEF, OAM $-2n$ with radial order $n$ of 2, 3, 4 and 5 are successfully generated at 1550 nm. Subsequently, we compare the differences in twisted rates between simulations and experiments.
PixelVerse: Secure and Efficient File Storage and Transfer through Video Embedding

PixelVerse is a novel approach for secure file transfer, which uses video embedding to ensure data integrity and avoid compression. This paper presents an in-depth evaluation of the PixelVerse methodology, consisting of video encoding, compression prevention, data extraction, and importing. We display the effectiveness of PixelVerse using three unique modes: best compression resistance, Enhanced compression resistance, and maximum performance. Furthermore, we explore the applications and target markets of PixelVerse and provide real-life use cases. The research findings and assessments indicate that PixelVerse proves to be a proficient and dependable solution for secure file-sharing and storage, as evidenced by performance measures and the results. PixelVerse finds practical applications in various industries such as healthcare, finance, and legal services, where safeguarding data confidentiality and privacy through secure file storage and transfer are of utmost importance.
ESTIMATING ROUTE TRAVEL TIME VARIABILITY FROM LINK DATA BY MEANS OF CLUSTERING

Accurate route travel time estimation is today one of the most challenging problems in traffic theory. This research proposes a novel method for the estimation of route travel time distributions, based on historical link travel time observations. Central in the development of this framework is the distinction between (cheap) off-line storage and computations and (expensive) on-line computations. For that it is important to minimize the on-line computational effort of calculating a route travel time histogram. The key elements in the method are correlations in link travel time fluctuations and a clustering algorithm. Tests on the Belgian road network show that 1) the clustering method is on-line computationally efficient while keeping the off-line storage under control, 2) that it can accurately estimate route travel time distributions and 3) that it can be applied successfully for route reliability estimation.
Regenerative Braking Control Strategy for Electric Vehicles based on PSO Optimization of BLDC Motor Speed Control

This research study intends to propose a novel approach to enhance the speed control of a Brushless Direct Current (BLDC) motor in Electric Vehicles (EVs) through the integration of a Particle Swarm Optimization (PSO) algorithm with a Proportional-Integral-Derivative (PID) controller. The objective is to optimize the regenerative braking performance of the EV, leading to improved energy efficiency and extended battery life. The proposed system consists of a BLDC motor connected to the wheels of the EV, controlled by a PID controller that regulates the motor's speed. The PID controller's gains are optimized using the PSO algorithm, which efficiently searches the parameter space to find the optimal PID coefficients that minimize the speed tracking error and maximize the regenerative braking efficiency. The simulation results demonstrate the effectiveness of the PSO-optimized PID controller with respect to conventional PID controllers. PS O's ability to fine-tune the PID gains leads to better speed tracking performance during both acceleration and deceleration phases. Additionally, the optimized regenerative braking control enables the efficient recovery of kinetic energy during braking, thereby improving the overall energy efficiency and extending the EV's driving range.
The Multi-Dimensional Landscape of Graph Drawing Metrics

—Any graph drawing can be characterised by a range of computational aesthetic metrics. For example, a given drawing might be described as having eight crossings, a mean angular resolution of 0.34, and an edge orthogonality value of 0.72. However, without knowing the distribution of these metrics it is hard to compare the quality of drawings of different graphs, nor know whether a given drawing is typical or an outlier within the space of all possible drawings. This paper explores the range and distribution of ten normalised graph drawing layout metrics, based on graphs created by six graph generation algorithms and drawings created by six popular layout algorithms. We include the “Rome" and “North" graph repositories in our analysis. Our exploration of the multidimensional aesthetics space allows for comparisons between the graph drawing algorithms, highlighting those that cover larger or smaller volumes of the aesthetics space. We calculate the correlation coefficients between the metrics, indicating those that may conflict with each other (negatively correlated), and those that may be redundant (positively correlated). Our results will be useful as the basis for simulated annealing or gradient descent layout algorithms, for identifying the best layout algorithms for producing a specified combination and range of aesthetics, and for informing experimental controls in human empirical studies.
Soil Organic Carbon Lateral Movement Processes Integrated Into a Terrestrial Ecosystem Model

Lateral movement of soil organic carbon (SOC) induced by soil erosion and runoff changes spatial distributions of SOC, and further changes the land‐atmosphere CO2 exchange and terrestrial carbon budget. However, current ecosystem models do not or only poorly integrate the process of SOC lateral movement and cannot simulate the impacts of soil erosion on the carbon cycle. This study integrated SOC erosion and deposition processes into a process‐based ecosystem model (i.e., Integrated BIosphere Simulator (IBIS)), and separately simulated the lateral movements of dissolved organic carbon (DOC) and particulate organic carbon (POC). The model was evaluated in three river basins in Northeast China that are dominated by cropland, forest, and grassland. The results showed that the model reproduced well the production, erosion, and deposition of DOC and POC. The annual SOC lateral movement (1.34–7.22 g C m−2 yr−1) induced by erosion in the three tested basins was 0.27%–1.45% of the annual net primary production. The model developed in this study has great implications for simulating the lateral movements of SOC in terrestrial ecosystems, which can improve model performance in projecting the terrestrial carbon budget.
Estimation of Air Quality Index using AI and ML Techniques

The air pollution has caused a critical threat to the environment and human health, making accurate and timely air quality monitoring very imperative. This research work is presenting an innovative approach of estimation of Air Quality Index (AQI) based on the Artificial Intelligence and Machine Learning techniques implemented in MATLAB. The AQI is a metric which quantifies the air pollution level to guide public health policies. The research begins by gathering the secondary air quality data from available sources. This data covers various parameters viz. SO2, NO2, CO, O3, PM10, and PM2. 5. These datasets have been preprocessed to cope up with outliers and missing values, guaranteeing the reliability of the analysis to be done. The decision tree is used in MATLAB as a method to establish a non-linear prediction model for AQI estimation. Additionally, a GRNN model is implemented in MATLAB as an alternative way of AQI estimation. This technique is based on artificial neural networks, and has proved to be accomplishing a variety of regression tasks successfully. For evaluating the performance of developed models, the testing is conducted using RMSE (Root Mean Square Error) metric. The comparative analysis presented reveals the accuracy of each model.
Afrontamiento comunal, crecimiento postraumático colectivo y bienestar social en el contexto del terremoto del 27 de febrero de 2010 en Chile Coping community, collective posttraumatic growth and social well-being in context february 27 earthquake in Chile, 2010

The earthquake and tsunami in Chile on February 27, 2010 constituted a context of disaster which mobilized collective strategies to cope with its effects. One of them corresponds to the communal coping (CC), which involves activating social resources to solve shared collective problems. Another aspect is the reevaluation associated with positive changes that people perceive after a disaster and it is defined as post-traumatic growth (PTG). This paper explores the use of different forms of CC and PTG after the earthquake, in a sample of 80 people in Chile. Four dimensions of communal coping were identified: direct coping, distraction, emotional expression and avoidance. Furthermore, PTG structure was defined as composed of individual, relational and collective levels. Direct coping, distraction and emotional sharing significantly correlated with CPT and social well-being. The results and their implications are discussed in a context of psychosocial studies of natural disasters.
Stiffness Model for an Overhead PUU

In this paper, a stiffness model for an overhead PUU is proposed based on the concept of series and parallel springs. The mechanism was considered to be composed of three elastic linkages and a rigid end effector. Among them, each elastic linkage consists of two revolute joints 1, two revolute joints 2, and a link. The link is composed of two U-shaped structures and a round bar. In this method, the stiffness of each component is first calculated, and then the calculated value is transformed into the global coordinate system. Finally, the converted results were combined to obtain the stiffness model of the overall mechanism. Additionally, a finite element software was used to validate the stiffness model. The correctness of the stiffness model transformation process can be verified with the same variations as the analyzed results. However, some differences in geometric settings can cause numerical discrepancies.
Computer-aided detection of wall motion abnormalities in cardiac MRI

Objective: To evaluate the performance of an automated regional wall motion abnormality (RWMA) detection method given the combination of rest and dobutamine-induced stress cardiac MR data and to correlate the automated RWMA results with infarct trans-murality assessments from contrast-enhanced MRI. Materials and Methods: The automated RWMA method is built upon a statistical model of normokinetic myocardial contours. Forty-one rest cine-MRI of healthy volunteers were collected to build the model. Independent Component Analysis (ICA) was used to generate the probability distributions of normokinetic myocardium after several registration steps. Three normokinetic ICA models were built independently: base, mid-ventricular and apex. Twelve patients with the presence of myocardial infarction were included in the experiment. Their rest, dobutamine-induced stress and contrast-enhanced MR images in short-axis view were semi-automatically analyzed. Results: A total of 192 myocardial segments were analyzed; 116 scar and 76 non-scar (normal) segments. For scar tissue detection, adding stress data signiﬁcantly improved the performance compared to rest data only. Mean RWMA probability value differences between scar and non-scar regions with rest-stress data were wider and signiﬁcant differences ( p < 0.001, CI = 99.9%) were present in all ICA models. Combined sensitivity was 79% (base: 90%, mid: 79%, apex: 67%) and speciﬁcity was 80% (base: 83%, mid: 85%, apex: 70%). Correlated with CE-MRI, RWMA probability values decrease progressively as infarct transmurality increases. Conclusion: Combining rest and stress MR data using automated RWMA assessment method detects scar regions more accurately than using resting MRI alone. The presented automated abnormality detector correlates well with infarct transmurality.
Turning Databases Into Generative AI Machines

Data is no more the commodity oil. Today, it is an asset for any enterprise. However, turning data into intelligence remains a challenge for most people. In this paper, we explore whether databases can be turned into generative AI machines that can talk to anyone. We identify three core challenges when applying generative AI on data, namely accuracy, scale, and privacy, and show how a generative large data model could solve all of these. We describe our conceptual framework of generative AI on databases, the GOD machine , and ground it in production workloads at SmartApps. Our results promise new directions in fusing AI with data.
combined with therapeutic hypothermia is not neuroprotective after severe hypoxia-ischemia in neonatal rats. PLoS

Background Therapeutic hypothermia (TH) is standard treatment following perinatal asphyxia in new-born infants. Experimentally, TH is neuroprotective after moderate hypoxia-ischemia (HI) in seven-day-old (P7) rats. However, TH is not neuroprotective after severe HI. After a moderate HI insult in newborn brain injury models, the anesthetic gas xenon (Xe) doubles TH neuroprotection. The aim of this study was to examine whether combining Xe and TH is neuroprotective as applied in a P7 rat model of severe HI.
Growth dynamics of black spruce (Picea mariana) in a rapidly thawing discontinuous permafrost peatland

High‐latitude warming has led to radical changes in abiotic conditions influencing forest growth. In the North American boreal forest, widespread declines in forest productivity (particularly in western regions) and changing climate‐growth relationships have been documented. Previous studies have proposed that this decline can be attributed to drought stress as increasing temperatures may cause evapotranspirative demand to exceed available moisture. We used tree ring studies to document growth dynamics of black spruce, one of the most dominant boreal tree species, in a boreal peatland experiencing rapid permafrost thaw. We specifically look at how changing permafrost conditions influence growth. Growth of black spruce at this site has declined steadily since the mid‐1900s and exhibited a shift from positive responses to temperature pre‐1970 to predominantly negative responses in recent decades, despite precipitation increasing over time at this site. Our results show that there is no apparent effect of landscape position or rate of lateral permafrost thaw on growth trends of black spruce, despite gradients in soil moisture and active layer thickness across the mosaic of wetlands and drier permafrost plateaus at this site. However, this does not imply no effect of permafrost thaw on growth; our results support growing evidence that vertical permafrost thaw (i.e., active layer thickening) is causing drought stress in these slow‐growing, shallow‐rooted trees. To our knowledge, this study is the first to investigate permafrost as a driver of within‐site variability in growth‐climate responses, and we provide insight into the widespread growth declines and divergence of climate‐growth relationships in high‐latitude forests.
The Impact of Learning Management System on Learning Process in Myanmar: Student Perspective

: The digital transformation of education, accelerated by the Covid-19 pandemic, has spurred widespread adoption of e-learning platforms globally, with Learning Management Systems (LMS) at the forefront. Despite the enhanced communication channels they offer, LMS implementation can introduce unique challenges in the educational arena. This study explores the impact of LMS on the learning process in Myanmar's education system from a student's perspective, focusing on five key factors: convenience, emotional engagement, technological barriers, assessment effectiveness, and cost. A total of 400 questionnaires were distributed to students who had used an LMS, with 264 valid responses received. These were analyzed using SPSS software, revealing significant correlations between all five factors and the learning process, with assessment effectiveness demonstrating the strongest relationship. The results provide valuable insights into how LMS can be optimized to support the learning process in the context of Myanmar's education system, indicating a need for educators and LMS developers to consider these factors for more effective e-learning experiences.
Investment booms, diverging competitiveness and wage growth within a monetary union: An AB-SFC model

This paper proposes a 2-country stock-ﬂow consistent agent-based model of a monetary union and exposes it to three supposed drivers of imbalances in the build-up to the Great Recession: unequal developments in investment, competitiveness and wages. The model has some innovative features: It does not rely on given propensities to import, with agents from diﬀerent regions instead all being part of the same market and import shares emerging endogenously as a result of the geographical distance between agents. The model also features labor hoarding by ﬁrms and a banking sector that bears some Minskyan features. The model is able to replicate the low unemployment rates in the North that were paralleled with falling unemployment rates in the South together with trade imbalances in favor of the North.
Mapping the Geometry of Law Using Natural Language Processing

: Judicial documents and judgments are a rich source of information about legal cases, litigants, and judicial decision-makers. Natural language processing (NLP) based approaches have recently received much attention for their ability to decipher implicit information from text. NLP researchers have successfully developed data-driven representations of text using dense vectors that encode the relations between those objects. In this study, we explore the application of the Doc2Vec model to legal language to understand judicial reasoning and identify implicit patterns in judgments and judges. In an application to federal appellate courts, we show that these vectors encode information that distinguishes courts in time and legal topics. We use Doc2Vec document embeddings to study the patterns and train a classifier model to predict cases with a high chance of being appealed at the Supreme Court of the United States (SCOTUS). There are no existing benchmarks, and we present the first results at this task at scale. Furthermore, we analyze generic writing/judgment patterns of prominent judges using deep learning-based autoencoder models. Overall, we observe that Doc2Vec document embeddings capture important legal information and are helpful in downstream tasks.
Yellowbrick: An Elastic Data Warehouse on Kubernetes

The Yellowbrick Data Warehouse delivers efficient, scalable and resilient data warehousing in public clouds and in private data centers. The database management system is composed of a set of Kubernetes-orchestrated microservices. Kubernetes provides the single-source-of-truth for system configuration and state, and manages all data warehouse lifecycle operations, including the creation, expansion, contraction and destruction of elastic compute resources and shared services. The common runtime provided by Kubernetes enabled us to port to three different cloud providers in under a year. We created a SQL interface to Kubernetes to hide the details of the underlying microservices implementation from the end user. We also developed our own reliable network protocol based on the Data Plane Development Kit (DPDK) for efficient data exchange between nodes in the public cloud. In this paper, we provide an overview of Yellowbrick and its microservices approach to delivering elasticity, scalability and separation of compute and storage. We also describe the optimizations we have implemented in the operating system and in our software to drive efficiency and performance, supported by benchmark results. We conclude with lessons learned and discuss future developments.
La résistance à la pourriture des cabosses due à Phytophthora spp., recherche des composantes de la résistance

La pourriture des cabosses du cacaoyer, due a des #Phytophthora#, sevit dans toutes les zones de production. Avec plus de 50 % de pertes de cabosses, l'Afrique centrale est la region la plus affectee par cette maladie. Le controle de cette maladie represente donc un enjeu majeur pour l'avenir de la cacaoculture mondiale et la selection de materiel resistant constitue l'un des themes de recherche prioritaire pour de nombreux pays producteurs. Un projet international sur ce sujet, recevant un support financier de Caobisco, a debute en juillet 1995. Ce projet, d'une duree de 5 ans, a pour objectifs : d'identifier les facteurs intervenants dans la resistance a cette maladie, de mettre au point et de valider des tests precoces de resistance, de detecter d'eventuels QTLs associes a la resistance et d'effectuer une premiere selection de materiel resistant.Cet article presente les principaux resultats obtenus apres 3 ans de fonctionnement. (Resume d'auteur)
Immune checkpoint inhibitor response in sarcomas associates with immune infiltrates and increased expression of transposable elements and viral response pathways

Response to immune checkpoint inhibition (ICI) in sarcoma is overall low and heterogeneous. Understanding determinants of ICI outcomes may improve efficacy and patient selection. One potential mechanism is epigenetic de-repression of transposable elements (TEs), which stimulates antitumor immunity. Here, we used transcriptomic data to assign immune-hot versus immune-cold status to 67 pre-treatment biopsies of sarcomas from patients treated on ICI trials. Progression-free survival and overall response was superior in the immune-hot group. Expression of TEs and epigenetic regulators significantly predicted immune-hot status in a regression model in which specific TE subfamilies and IKZF1, a chromatin-interacting transcription factor, were significantly contributory. TE and IKZF1 expression positively correlated with tumor immune infiltrates, inflammatory pathways, and clinical outcomes. Key findings were confirmed in a validation cohort (n=190). This work suggests that TE and IKZF1 expression warrant investigation as predictive biomarkers for ICI response and as therapeutic targets in sarcomas.
Les peuplements associés à l'hydrothermalisme océanique du snake Pit (dorsale médio-atlantique; 23°N, 3480 m): composition et microdistribution de la mégafaune

Des peuplements hydrothermaux ont ete etudies au cours de la campagne Hydrosnake menee sur le snake Pit (dorsale medio-atlantique). Pres de 50 especes, en majorite vagiles, se repartissent autour de 4 sites composes de cheminees actives et de diffuseurs. Les crevettes, principaux consommateurs primaires, presentent des adaptations trophiques originales. Des hypotheses sont emises pour tenter d'expliquer les differences que ces peuplements presentent avec ceux du Pacifique