Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Semi-Automatic Approach of Building an Ontology: A Case Study on Material Handling Data

: InnoSale project aims to improve sales processes for complex industrial equipment and services using AI technologies. The project addresses the challenges of time-consuming back-office support and interpreting customer requests using different vocabularies. As partners involved in the project, we are developing a semi-automated approach to the creation of an ontology for the material handling domain by merging existing terminology from leading companies in the industry. This ontology will serve as the basis for a semantic search engine to improve the generation of quotations and the matching of customer requirements. Through the use of historical data and advanced machine learning techniques, the search engine streamlines the sales process, reducing manual effort and improving response times. The results showcases how the utilization of machine learning and NLP techniques can aid in constructing an ontology in a semi-automatic fashion. The study demonstrates the effectiveness of extracting terms, identifying synonyms, and uncovering various relationships, contributing to the development of an ontology. These approaches offer potential for improving the ontology construction process and enhancing semantic search capabilities, leading to more effective information retrieval. This position paper, being concise in nature, presents our initial findings and progress in this endeavor. It’s important to note that, based on new sources of information and ongoing research in the future, the results and conclusions may evolve or differ.
A real-time trajectory classification module

Nowadays, massive volumes of mobility data are being generated from thousands of tracking devices, such as GPS devices, RFID sensors, location-based services, satellites, and wireless communication technologies. This phenomenon can be strongly observed in the maritime domain and as a result, today's industry is flooded with tracking data originating from vessels across the globe that transmit their position at frequent intervals. Automated methodologies able to extract meaningful information and identify mobility patterns from such tracking data are of utmost importance since they can reveal abnormal or illegal vessel activities in due time. To this end, we present a demo of a trajectory classification methodology that is able to classify vessels' trajectories into activities that the vessels are engaged in from AIS data streams in real-time. The goal is to provide maritime authorities with a visualization tool and an API of the vessel trajectories and their activities in real-time. The trajectory classification methodology that is used in this demo achieves a classification performance of over 95%.
Quantifying Fairness Disparities in Graph-Based Neural Network Recommender Systems for Protected Groups

: The wide acceptance of Recommender Systems (RS) among users for product and service suggestions has led to the proposal of multiple recommendation methods that have contributed to solving the problems presented by these systems. However, the focus on bias problems is much more limited. Some of the most successful and recent methods, such as Graph Neural Networks (GNNs), present problems of bias amplification and unfairness that need to be detected, measured, and addressed. In this study, an analysis of RS fairness is conducted, focusing on measuring unfairness toward protected groups, including gender and age. We quantify fairness disparities within these groups and evaluate recommendation quality for item lists using a metric based on Normalized Discounted Cumulative Gain (NDCG). Most bias assessment metrics in the literature are only valid for the rating prediction approach, but RS usually provide recommendations in the form of item lists. The metric for lists enhances the understanding of fairness dynamics in GNN-based RS, providing a more comprehensive perspective on the quality and equity of recommendations among different user groups.
Utility of Univariate Forecasting for Workload Metrics Predictions in Enterprise Applications

: Modern enterprise IT systems are complex solutions that require careful planning of computational capacities and placement, especially in the cloud environments where the total cost of ownership directly depends on provisioned resources. The decision process on infrastructure transformation or capacity sizing of existing IT landscapes can be supported by collecting and analyzing the workload data of the running systems. However, the scope and length of this data are limited, as its collection is often an expensive and lengthy process. Therefore, within this work, we empirically evaluate multiple techniques for extending the workload data by employing various univariate time series forecasting algorithms. We analyze a use case of SAP-based enterprise applications and rely on real-world workload data collected from various running SAP system landscapes. Our analysis demonstrates that XGBoost is best suited for univariate forecasting SAP-speciﬁc key performance indicators for both stationary and trending time series. However, the shape of the workload proﬁle has a high degree of inﬂuence on the results of the forecasting. Enterprise applications’ workload data that represent regular day-to-day operations without irregular events is a prerequisite for accurate forecasting.
COLOUR PSYCHOLOGY POTENTIAL IN LITHUANIAN ADVERTISING

The study examines the potential of colour psychology in Lithuanian advertising, acknowledging that the application of colour psychology may vary among countries. The theoretical overview outlines the concept of colour psychology and evaluates its use in advertising. The research objective is to assess how colour psychology can be effectively implemented in Lithuanian advertising campaigns. The study aims to explore the potential of colour psychology in Lithuanian advertising. The analysis will investigate the use of colour psychology in Lithuanian advertising. Methodology of the study included a review of the scientific literature and two structured questionnaire surveys of both consumers and industry experts. The study showed that when comparing user and expert surveys, both groups agreed that properly selected colours that evoke positive emotions have a strong influence on consumer emotions and even actions. However, when comparing the theory with the results obtained in the user questionnaire, it appears that Lithuanians assign slightly different associations to corresponding colours than foreign authors. Carefully selected colours that evoke positive emotions in advertisements can attract consumers' attention and potentially encourage purchases of the depicted brand. In order to choose the right colours to communicate in advertisements, it is important to analyse the audience to which the advertisement will be presented and to clarify the values that the brand cherishes and what the specific colour used in the advertisement is intended to tell consumers about the product or service.
Optimized test pattern selection with machine learning method

The technology node shrinks years after years. To guarantee the functionality and yield of IC production, the resolution enhancement technology becomes more and more important. Both optical proximity correction and inverse lithography technique need a precisely calibrated lithographic model. A mask of test patterns needs to be prepared and the lithographic experiment has to be done with it to obtain the CD SEM data for the model fitting. It is beneficial to select the test pattern efficiently. Fewer number of test patterns should be selected without compromising their coverage capability and the accuracy of the lithographic model. We present a machine learning method based on the convolutional autoencoder and core set selection method to achieve above goal. We optimize the existing test pattern mask by selecting parts of gauges out. The OPC models calibrated with the selected data are compared with the models calibrated with original test patterns to evaluate our method.
Holocene environmental changes in Lixiahe area

During the performance of the regional geological survey project,the authors,based on several typical geological profiles and drill holes,discussed the distribution regularity of Holocene strata and studied the distribution characteristics of landform in Lixiahe area.Then the vegetation change and palaeoclimatic and palaeoenvironmental variations in the area since 10 ka BP were analyzed and investigated.According to the results obtained,the natural environment changes in Holocene in Lixiahe area went through four stages:(1) From 10 to 7.5 ka BP,lakes and swamps were widely distributed,and some parts of the area were affected by the sea water. (2) From 7.5 to 4.0 ka BP,a transgression occurred in this area,and the natural environment was mainly composed of tidal flat,shallow marine foreshore and lagoons.The greatest transgression could reach the area west of the Great Canal,but a part of the area was not influenced by this transgression. (3) From 4.0 ka BP to 1128 AD,the regression occurred,the coastline stayed for a long time along the Fangong embankment,and the shell bar of Xigang was formed.Consequently,the west part of Lixiahe area was often impacted by the sea water. (4) From 1128 AD,because the Yellow River captured the Huaihe area,the surface water system in Lixiahe area changed,and the shoreline in the area withdrew continuously eastwards.Since then,the area has not been impacted by the sea water again.
Composition-dependent freezing nucleation rates for HNO3/H2O aerosols resembling gravity-wave-perturbed stratospheric particles

Laboratory measurements are presented for the freezing kinetics of H 2 O/HNO 3 aerosols over the temperature range of 188-204 K. For 2:1 H 2 O:HNO 3 aerosols crystallizing to NAD we observed a maximum nucleation rate of J= 9.3 x 10 9 cm -3 s -1 at 194 K. This temperature is between the glass point of 161 K [Ji et al., 1993] and the melting point of 235.5 K [Ji et al., 1996]. This can be compared to a previous measurement of J =6.7 x 10 9 cm -3 s -1 at 193 K [Disselkamp et al., 1996] and lower temperature measurements of J 10 10 - 10 12 cm -3 s -1 at 178.8 - 175.8 K [Bertram and Sloan, 1998a]. Measured nucleation rates decrease as the aerosol becomes dilute, but NAD formation is still observable for 2.5:1 H 2 O:HNO 3 at temperatures near 195 K. In contrast, freezing of 3:1 H 2 O:HNO 3 aerosol was not observed for constant temperature experiments throughout this temperature range, yielding an upper limit of J < 1.5 x 10 9 cm -3 s -1 . This is the lowest experimental value determined for 3:1 H 2 O:HNO 3 freezing rates at these temperatures. From the measured freezing rates and knowledge of the free energy of diffusion the average interfacial free energy for NAD in a 2:1 H 2 O:HNO 3 solution was determined to be σ = 25.2 ergs cm -2 . A limit for the interfacial free energy was placed on 3:1 H 2 O:HNO 3 particles, for which freezing was not observed. These data imply that if aerosols reach compositions more concentrated than 3:1 H 2 O:HNO 3 in the atmosphere, NAD may play a role in polar stratospheric cloud formation.
Anomalous Indoor Human Trajectory Detection Based on the Transformer Encoder and Self-Organizing Map

Anomalous human trajectory detection is a critical task in security surveillance in working areas. To identify anomalous human trajectories, understanding features of their movement plays an important role. Therefore, in this work, a Transformer encoder and self-organizing map-based model called TENSO is proposed to learn trajectory characteristics for detecting anomalies. In particular, the proposed model learns the internal characteristics of normal trajectories and clusters of normal trajectory representations in a latent space. To learn the internal characteristics of normal trajectories, the encoder of Transformer with a self-attention mechanism first encodes trajectories into sequences of embedding vectors of trajectory points in the latent space. Then, a decoder reconstructs the trajectories from the latent space. In addition, to learn clusters of normal trajectory representations in the latent space, the self-organizing map (SOM) layer is used, which gets its input as the output of the Transformer encoder. In the training phase, the TENSO model is trained using a total loss of trajectory reconstruction and SOM losses. In the anomaly detection phase, a test trajectory is evaluated to determine whether it is an anomaly based on trajectory reconstruction errors and the quantization error on the SOM. In this phase, a new metric is proposed which, namely WS, is the weighted sum of recall and precision to choose the appropriate threshold for detecting anomalies. The TENSO model-based framework is evaluated using two real trajectory datasets: MIT Badge and sCREEN. Experimental results show that the proposed framework identifies anomalies effectively and outperforms the baselines.
EXPERIMENTAL SIMULATION OF FAST ELECTRON BOMBARDMENT OF METHANOL ICE AND ITS IMPLICATIONS IN ASTROCHEMISTRY

In this work, we experimentally simulate the methanol (CH3OH) ice behavior (12 K) through the bombarded by fast electrons (4.9 keV) in an attempt to simulate radiation chemistry induced by radiation in space environments. The sample analysis by infrared spectroscopy reveals the appearance of new species, including CO2, CO, H2O, and CH4, during the sample bombardment. We have quantified the effective destruction cross-section of methanol (5.5 × 10-19 cm2) and determined the formation cross-section for these newly produced species. Additionally, we have characterized the chemical equilibrium (CE) phase, which becomes evident at higher fluences. We have also calculated molecular abundances and assessed the desorption yield induced by fast electrons within the same sample. Furthermore, we estimated the timescale required to achieve chemical equilibrium in specific astrophysical environments impacted by electrons. This study significantly contributes to our comprehension of electron bombardment behavior in astrophysical ices and allows for meaningful comparisons with organic-rich ices in space environments
Consideraciones generales del dolor lumbar agudo

Introduccion: La lumbalgia representa un sintoma muy frecuente en la practica medica, las causas que la producen, los mecanismos fisiopatologicos y el modo de enfrentarla. Objetivo. Actualizar al lector sobre las consideraciones generales del dolor lumbar agudo. Desarrollo: Ademas de las estructuras intrinsecas y extrinsecas que provocan dolor en la region lumbar, existen otros elementos relacionados con la fisiopatologia como son los factores: anatomico, compresivo, vascular, inmunitario, inflamatorio y neural, que seran bien descritos en este articulo a modo de conocer como funciona cada uno en la aparicion del dolor lumbar agudo y cronico, para posteriormente asumir nuestra responsabilidad como medicos en el tratamiento de este mal que aqueja a nuestra poblacion. Conclusiones: el sindrome doloroso lumbar es de presentacion frecuente en la consulta medica. El diagnostico incluye la busqueda de los signos de alarma que traducen enfermedades sistemicas, cancer, infecciones, fracturas, entre otras. Sin embargo en la mayoria de los pacientes la lumbalgia es de tipo vertebral mecanica, con un curso benigno y puede ser resuelta favorablemente con un tratamiento adecuado en la atencion primaria de salud.
A Numerical Study on the Capacity Region of a Three-Layer Wiretap Network

In this paper, we study a three-layer wiretap network including the source node in the top layer, N nodes in the middle layer and L sink nodes in the bottom layer. Each sink node recovers the message generated from the source node correctly via the middle layer nodes that it has access to. Furthermore, it is required that an eavesdropper eavesdropping a subset of the channels between the top layer and the middle layer learns absolutely nothing about the message. For each pair of decoding and eavesdropping patterns, we are interested in finding the capacity region consisting of (N+1)-tuples, with the first element being the size of the message successfully transmitted and the remaining elements being the capacity of the N channels from the source node to the middle layer nodes. This problem can be seen as a generalization of the secret sharing problem. We show that when the number of middle layer nodes is no larger than four, the capacity region is fully characterized as a polyhedral cone. When such a number is 5, we find the capacity regions for 74,222 decoding and eavesdropping patterns. For the remaining 274 cases, linear capacity regions are found. The proving steps are: (1) Characterizing the Shannon region, an outer bound of the capacity region; (2) Characterizing the common information region, an outer bound of the linear capacity region; (3) Finding linear schemes that achieve the Shannon region or the common information region.
Design of 30 A Load Current and 93% Peak Efficiency for Active Clamping Forward Converter With Complete Protection

A chip-level design of 30 A load current and 93% peak efficiency for active clamping forward (ACF) converter is presented in this paper to improve the system performance and compactness for all load range and different input voltages. The topology of the low side ACF and self-driven synchronous rectification is adapted to low output voltage and large load current applications. A homologous IC controller with proposed technique of adaptive maximum duty cycle, slope compensation, and optional driver logic is designed. The IC controller is turned into different working states to cooperate different states of the system and functions for complete protection of overcurrent protection, over temperature protection, under voltage lockout, and soft shutdown. The controller chip is fabricated in 0.18 μm BCD process. The experiment results show that 30 A full load current with a compact volume of 39 cm3 and complete protection has been realized. The overshoot and undershoot of transient response are less than 200 mV at 5 A–25 A within 120 μs and the peak efficiency of 93.0% is achieved.
Uterine Arteries Resistance in Pregnant Women with Gestational Diabetes Mellitus, Diabetes Mellitus Type 1, Diabetes Mellitus Type 2, and Uncomplicated Pregnancies

Background: The examination of the uterine arteries using Doppler in the first trimester of pregnancy serves as a valuable tool for evaluating the uteroplacental circulation. Diabetes mellitus is associated with altered placental implantation and pregnancy-related pathologies, such as preeclampsia. The aim of this study was to compare the uterine arteries’ pulsatility indices (UtA PI) in women with diabetes mellitus type 1 (DM1), diabetes mellitus type 2 (DM2), gestational diabetes mellitus (GDM), and uncomplicated pregnancies. Methods: This was a retrospective case–control trial including pregnant women with DM1, DM2, GDM, and uncomplicated pregnancies, presenting for first-trimester ultrasound screening in two tertiary university hospitals between 2013 and 2023. The first-trimester UtA pulsatility index (PI), expressed in multiples of medians (MoMs), was compared between the four groups. Results: Out of 15,638 pregnant women, 58 women with DM1, 67 women with DM2, 65 women with GDM, and 65 women with uncomplicated pregnancies were included. The mean UtA PI were 1.00 ± 0.26 MoMs, 1.04 ± 0.32 MoMs, 1.02 ± 0.31 MoMs, and 1.08 ± 0.33 MoMs in pregnant women with DM1, DM2, GDM, and uncomplicated pregnancies, respectively (p > 0.05). Conclusions: Potential alterations in the implantation of the placenta in pregnant women with diabetes were not displayed in the first-trimester pulsatility indices of the uterine arteries, as there were no changes between the groups.
Erfahrungen mit Diospyros kaki Creveld, dem Kakibaum

Zusammenfassung Vor über 20 Jahren wurde Diospyros kaki Creveld von Marijke Creveld als neue Arznei in die Homöopathie eingeführt. In der Zwischenzeit konnten einige klinische Erfahrungen gesammelt werden, und auch der phythopharmakologische Kenntnisstand hat sich erweitert. Es wird eine Kasuistik einer Patientin mit Brustkrebs vorgestellt, die im Verlauf der Behandlung eine schwere Beeinträchtigung erfuhr, die mit dieser Arznei gut behandelt werden konnte.
Lightweight Pixel2mesh for 3-D Target Reconstruction From a Single SAR Image

Three-dimensional target reconstruction from 2-D synthetic aperture radar (SAR) images can reduce the difficulty of data acquisition when compared to 3-D imaging technologies. Pixel2mesh was well designed for 3-D target reconstruction from 2-D optical images. However, when it is directly applied to small SAR datasets, overfitting is prone to occur. In this letter, we propose a lightweight Pixel2mesh for 3-D target reconstruction from a single 2-D SAR image. Based on the original Pixel2mesh, we improve two subnetworks: feature extraction and 3-D deformation. First, we lightweight the graph residual network (G-ResNet) module in the 3-D deformation subnetwork to avoid overfitting. Second, we add a decoder after the feature extraction subnetwork to reconstruct the input image and then obtain the image reconstruction loss to guide the training of the whole network. In addition, we modify the Laplace regularization term for the training of the proposed network, aiming to make the deformation more reasonable. Experiments are implemented on the Gotcha dataset, where 2-D images of seven cars are obtained by performing 2-D imaging. The 3-D labels of these cars are generated by using their CAD models downloaded from public websites. Experimental results verify that our network can achieve better 3-D reconstruction results than the original Pixel2mesh.
Risk of arrhythmias following COVID-19: nationwide self-controlled case series and matched cohort study

Abstract Aims COVID-19 increases the risk of cardiovascular disease, especially thrombotic complications. There is less knowledge on the risk of arrhythmias after COVID-19. In this study, we aimed to quantify the risk of arrhythmias following COVID-19. Methods and results This study was based on national register data on all individuals in Sweden who tested positive for SARS-CoV-2 between 1 February 2020 and 25 May 2021. The outcome was incident cardiac arrhythmias, defined as international classification of diseases (10th revision) codes in the registers as follows: atrial arrhythmias; paroxysmal supraventricular tachycardias; bradyarrhythmias; and ventricular arrhythmias. A self-controlled case series study and a matched cohort study, using conditional Poisson regression, were performed to determine the incidence rate ratio and risk ratio, respectively, for an arrhythmia event following COVID-19.A total of 1 057 174 exposed (COVID-19) individuals were included in the study as well as 4 074 844 matched unexposed individuals. The incidence rate ratio of atrial tachycardias, paroxysmal supraventricular tachycardias, and bradyarrhythmias was significantly increased up to 60, 180, and 14 days after COVID-19, respectively. In the matched cohort study, the risk ratio during Days 1–30 following COVID-19/index date was 12.28 (10.79–13.96), 5.26 (3.74–7.42), and 3.36 (2.42–4.68), respectively, for the three outcomes. The risks were generally higher in older individuals, in unvaccinated individuals, and in individuals with more severe COVID-19. The risk of ventricular arrhythmias was not increased. Conclusion There is an increased risk of cardiac arrhythmias following COVID-19, and particularly increased in elderly vulnerable individuals, as well as in individuals with severe COVID-19.