Miracle-dz/UniFAR-FTUs huggingface dataset:
VirtualButcher: Coarse-to-fine Annotation Transfer of Cutting Lines on Noisy Point Cloud Reconstruction

Robotics and automation are rapidly becoming part of meat processing operations. Current automation of breaking down a carcass into primals relies on guidance from X-ray, inter-connected with robotised band-saws. While yielding very accurate cutting lines, the use of vision systems for guidance would be significantly more affordable. This work proposes a novel method that solves the annotation transfer between a 3D noise-free cut-ting line annotated on a CT acquired canonical model and a noisy target in the form of a point cloud acquired by RGB-D cameras. The proposed coarse-to-fine method initially aligns the posture of each body using a non-rigid deformation algorithm and then performs a local search to solve the surface correspondence which is later used to morph the template non-rigidly. We quantitatively assess the approach by benchmarking with multiple state-of-the-art algorithms on a public available human pose dataset. We also present a proof of concept evaluation on lamb carcasses.
Analysis of the Effect of Variability on the Blood Glucose Prediction Accuracy*

In the artificial pancreas (AP) concept physiological models prove to play an important role. The high complexity of the physiological processes, intrapatient variability, data scarcity and other factors (physical activity, stress, quality of meal) pose difficulties to achieve an accurate, and virtual representation of the patient. In this pilot study, experiment-and patient-related considerations were taken into account during the identification to restrict the parameter space in a realistic way. Effects of the variabilites have been investigated on the prediction accuracy by using sensitivity analysis.
Adjusting Local Conformational Sampling For Fragment Assembly Protein Structure Prediction Based On Secondary Structure Complexity

Fragment assembly protein structure prediction is one of the most successful methods whenever reliable templates (for homology-based approaches) and/or massive computational resources (for physics-based approaches) are not available. However, it suffers from important limitations: tremendous search space, energy scores inaccuracy, and consequently the large number of decoys which are needed to be generated. Taking advantage of the different protein sequence-structure complexity shown by the various types of secondary structure, - using Rosetta - we propose to customize the diversity of fragments for each region of the conformation being built. By eventually reducing the size of search space, this approach permits better exploitation of promising areas. Experiments demonstrate the value of the proposed strategy: compared to standard Rosetta's performance in terms of first model, accuracy improves significantly (~6%), respectively drastically (~24%), when using 20,000, resp. 2,000, decoy-based predictions. Furthermore, performance using 2,000 decoys is equivalent to that of standard Rosetta using 20,000 decoys, which means that predictions can be executed on a standard PC instead of a highperformance computing system.
Type-level programming with match types

Type-level programming is becoming more and more popular in the realm of functional programming. However, the combination of type-level programming and subtyping remains largely unexplored in practical programming languages. This paper presents match types, a type-level equivalent of pattern matching. Match types integrate seamlessly into programming languages with subtyping and, despite their simplicity, offer significant additional expressiveness. We formalize the feature of match types in a calculus based on System F sub and prove its soundness. We practically evaluate our system by implementing match types in the Scala 3 reference compiler, thus making type-level programming readily available to a broad audience of programmers.
Development of a method for assessing the accessibility of the urban environment for low-mobility groups of the population based on Google Maps reviews

Today, social networks play a huge role in the life of a modern person Moreover, people can not only communicate in this way, but also share tips, professional skills, promote their services, buy goods, and so on. Every day, a huge amount of information appears in social networks. People ask questions about their future trips, share their travel experiences, talk about local attractions, post photos, praise or scold restaurants, museums and any other objects of the urban environment. In this paper, we consider a method for assessing the availability of urban improvement facilities for low-mobility groups of the population based on data from their social networks The study was conducted on the example of the Petrogradsky district of St. Petersburg. 25 objects of urban improvement (parks, gardens, squares) were selected, and reviews from Google Maps were uploaded for each object. A dictionary describing the accessibility of objects was prepared and a database search was conducted to find problems related to accessibility for low-mobility groups of the population that users write about. All reviews were depersonalized and depersonalized. Such data can be used by the city authorities when planning new facilities, or when implementing targeted programs for the development of improvement facilities.
Considering the Parameters of Pulse Width Modulation Voltage to Improve the Signal-to-Noise Ratio of Partial Discharge Tests for Inverter-Fed Motors

The improvements in signal-to-noise ratio (SNR) are indispensable to obtain the desired output signal of partial discharge (PD) under square wave pulsewidth modulation (PWM) voltage. In this article, the effect of complex PWM voltage parameters (rise time, fall time, frequency, duty cycle) on the SNR of PD tests is investigated. The results revealed that the sensor's high-frequency gain should be increased to improve the SNR under faster switching. PD impulses are more likely to be submerged in the residual interference under high-frequency voltage. The SNR can be improved by only detecting the PD at the rising front because the PD signal overlaps with commutation noise at the falling front of short duty cycle voltage, such as 0.03% duty cycle. The above results can reference the PWM parameters and improve the SNR in PD tests for rotating machines controlled by power electronics.
Air Pollution Mapping with Sensor-based Methodology

the purpose of this study is to develop a sensor-based methodology(S-BM) for mapping air pollution (AP) related to Gaseous Composition of the Atmosphere in a specific area. It uses a drone equipped with sensors to identify the current composition of the air. After self-identifying the locations with specific distances in a specific area, the drone can go to those locations automatically and obtain sensor readings related to the gas percentages at those locations. After that the data is then transmitted to a computer program which analyzes (cluster analysis methodology), the data and then maps the air pollution in that specific area. Our results provide important informa-tion on how to measure, manage and atmospheric pollution mapping (APM). It also helps to identify air-polluted areas that need to be addressed quickly, and, thereby, it helps to save the atmosphere. We hope to program to get the sensor reading sand analyze the data with a suitable methodology and predict the condition of the atmosphere in the specific area. We hope to use cluster analysis and other analysis methodologies and technologies to this function. We need a dataset to train the model that can do the air quality prediction (AQP) of the relevant area. For that, we surfed the internet and found some datasets regarding the air pollution level of some major countries and their capitals. We think we will be able to make the model by using these datasets and predict the air pollution level of a specific area clearly. In addition, we are going to predict the future AP levels in a specific area by analyzing the current gas percentages of some specific gas components in the atmosphere like CO, CO2, SO2 and NH3 etc.
An Implantable 8-Contact Sense and Stimulation System for Continuous Recording

Neuromodulation systems currently provide therapy for many diseases. Advancing therapy capabilities is an important goal in the design of next generation devices, but consideration of user concerns needs to be included in design updates. To address both issues, a system capable of providing neuromodulation therapy and biological sensing in support of next generation therapies was designed. This report describes the design considerations for a chronic implant and presents the architecture of the stimulation and sensing circuitry along with experimental results of the first-generation circuit.
Moving Target Defense Strategy in Critical Embedded Systems: A Game-theoretic Approach

Moving Target Defense (MTD) is a promising de-fense technique that aims to break the asymmetry between attacker and defender by reconfiguring a system's assets. When used in a critical embedded system, non-functional constraints limit the set of usable MTD techniques, therefore limiting the entropy of reconfigurations. It is therefore necessary to compute an optimal moving target strategy in order to reduce the time between reconfigurations, while managing their impact on the quality of service provided to users. In this paper, we propose a game-theoretic approach to define an optimal moving target defense strategy. Our approach translates risk analysis parame-ters into a Bayesian Stackelberg game, which we translate to a mixed integer linear program. We validate our approach on an industrial case study from the automotive domain, showing the practical usability of our approach. In further experiments, we assess the scalability of our method, as well as the solution's stability in case new vulnerabilities are discovered after the deployment of the system.
Database Scaling on Kubernetes

Kubernetes is a hot topic in the field of Software Engineering and Distributed Computing. When compared to previous methods, the principle underlying Kubernetes, which is containerization, has altered how applications are created and delivered. However, when considering the state, particularly the databases, with Kubernetes, there is a scalability and data synchronization barrier. The most frequently used approach is to host the database outside of Kubernetes and maintain connectivity with the cluster. Kubernetes inherent capabilities are sufficient for hosting databases. But that requires high domain knowledge to do the configurations and maintain the databases on Kubernetes. The purpose of this research is to fulfil that gap by introducing a solution for managing highly available databases on Kubernetes. The solution is limited to managing PostgreSQL databases on Kubernetes using auto-scaling. A novel algorithm is proposed for auto-scaling, as previous algorithms do not take database requests into account when determining the scaling need. The drawbacks of data synchronization and auto-scaling will be solved in this research, and the end user will be able to access the service without interruption for the majority of the time, as the final solution makes the database cluster highly available for the service layer.
Bacterial Evolutionary Algorithm-based Feature Selection for Word Sentiment Interpolation in Hungarian Language

With the advancement of social artificial agents the need for correct understanding of sentiment is growing. In this paper we propose a method for building a context-less word-level emotional model of words in the Hungarian language based on Russell's Circumpex model of affect. By utilizing Bacterial Evo-lutionary Algorithm for feature selection, a method for efficient web-based annotation is proposed. Using the latent information of word embeddings multi-layer perceptron networks are trained to realize an interpolative function of two-dimensional emotion vectors over the embedding space. Dimensionality reduction via correlation analysis is also discussed.
Validation of a Radar Sensor Model under non-ideal Conditions for Testing Automated Driving Systems

Testing of advanced driver-assistance systems (ADAS) is complex, time-consuming and expensive. Therefore, new methods for the validation of such applications are required. A common solution is the use of virtual validation via environment simulation tools, but their reliability must first be confirmed. For this purpose, a comparison of a real and a simulated radar sensor under adverse weather conditions is performed in this work.To quantify the deviation between reality and the virtual test environment, a complex scenario with multiple traffic objects is set up on the proving ground and in the simulation. The data is measured for clear weather, rain and fog and afterwards compared to validate the performance of the sensor model under those non-ideal conditions.The implemented method shows that both sensors neglect the same traffic objects. Compared to the real radar, the variation of the measured parameters according to changing weather conditions in the simulation tends to be correct, but the values are not completely realistic.Though, the validation method is implemented successfully, in further work the comparison of different sensors is recommended. Furthermore, an in-depth examination of the impact due to varying intensity of rain and fog has to be undertaken.
Design and Implementation of PID-Based Steering Control for 1/10-Scale Autonomous Vehicle

Due to the rising popularity and interest in autonomous vehicles, it is beneficial to build a low-cost prototype autonomous vehicle for educational purposes. Thus, more students would be able to acquire hands-on experiences in designing and implementing critical smart navigational functionalities for autonomous vehicles. In this study, Proportional-Integral-Derivative (PID) control and steering control algorithms are developed to maneuver a 1/10-scale autonomous vehicle in a real-world scaled-down driving environment. An obstacle detection system is also designed. The state-of-the-art Robot Operating System (ROS) is employed in the software development and vehicle control to communicate between components. This paper, as part of a few related papers on autonomous vehicle from our research group, focuses on the control algorithms design and implementation, which incorporates continuous real-time feedback to generate a correction value to keep the vehicle on track. The algorithm combines the lane tracking, stop sign detection, and obstacle detection of the vehicle and sends data values to motors. Experimental results suggest the efficacy of our developed approaches. The future work of this study is discussed.
Small Blubber Samples (50 mg) Sufficient for Analyses of 10 Stress and Reproductive Steroid Hormones in Gray and Fin Whales via Liquid Chromatography Mass Spectrometry

Information on stress, reproductive fitness, and health is difficult to obtain in wild cetaceans but critical for conservation and management. The goal of this study was to develop a methodology requiring minimal blubber mass for analysis of reproductive and stress steroid hormones and, hence, suitable for cetacean biopsies. Blubber biopsies and samples were collected from free-ranging and stranded gray and fin whales. Steroid hormones were extracted from blubber samples as small as 50 mg using liquid-liquid extraction methodology developed to handle the high fat content of blubber. Samples were analyzed via liquid chromatography with tandem mass spectrometry for 10 hormones: aldosterone, androstenedione, cortisol, cortisone, corticosterone, 17β-estradiol, estrone, 17α-hydroxyprogesterone, progesterone, and testosterone. As part of the optimization, homogenization via bead beating and blade dispersion were compared, and the former found superior. To investigate optimal yet minimal tissue mass required, hormone panels were compared among paired 50, 150, and 400 mg samples, the latter two being commonly reported masses for hormone blubber analysis. Results indicated that 50 mg of blubber was suitable and sometimes superior. Additionally, significant differences in precision values were observed between species, possibly stemming from differences in blubber composition, and relevant to homogenization technique selection and calibration methods that use blubber matrix matches obtained from a species other than the study species. Based on recovery and precision values, our methodology was accurate and precise in the measurement of spiked known quantities for all 10 hormones, confirming the methodology capabilities in 50 mg blubber mass in both species. Altogether, and in our specific sample sets, all endogenous hormones, except corticosterone, were identified above the detection limit in 50 mg gray whale blubber samples while all endogenous hormones, except aldosterone, cortisone, estrone, and progesterone, were detected in 50 mg fin whale blubber samples. We present a robust methodology for the analysis of multiple reproductive and stress steroid hormones in minimal masses of cetacean blubber compatible with small biopsies. Finally, we identified statistically significant differences in corticosteroid concentrations between stranded and free ranging animals.
Autoimmune Regulator (AIRE) Deficiency Does Not Affect Atherosclerosis and CD4 T Cell Immune Tolerance to Apolipoprotein B

Atherosclerosis is a chronic, lipid-driven disease of medium sized arteries which causes myocardial infarction and stroke. Recently, an adaptive immune response against the plaque-associated autoantigen Apolipoprotein B100 (ApoB), the structural protein component of low-density lipoprotein, has been implicated in atherogenesis. In healthy individuals, CD4+ T cells responding to ApoB mainly comprised regulatory T cells, which confer immune tolerance and atheroprotection. Mice and patients with atherosclerosis harbor increased numbers of proatherogenic ApoB-reactive T-helper cell subsets. Given the lack of therapies targeting proatherogenic immunity, clarification of the underlying mechanisms is of high clinical relevance. T cells develop in the thymus, where strong autoreactive T cells are eliminated in the process of negative selection. Herein, we investigated whether the transcription factor autoimmune regulator (AIRE), which controls expression of numerous tissue-restricted self-antigens in the thymus, is involved in mediating tolerance to ApoB and whether Aire deficiency might contribute to atherogenesis. Mice deficient for Aire were crossbred to apolipoprotein E-deficient mice to obtain atherosclerosis-prone Aire−/− Apoe−/− mice, which were fed a regular chow diet (CD) or western-type diet (WD). CD4+ T cells responding to the ApoB peptide p6 were analyzed by flow cytometry. We demonstrate that Aire deficiency influences neither generation nor activation of ApoB-reactive T cells and has only minor and overall inconsistent impacts on their phenotype. Furthermore, we show that atherosclerotic plaque size is not affected in Aire−/− Apoe−/− compared to Aire+/+ Apoe−/−, irrespective of diet and gender. In conclusion, our data suggests that AIRE is not involved in regulating thymic expression of ApoB or atherosclerosis. Alternative mechanisms how ApoB-reactive CD4 T cells are selected in the thymus will have to be investigated.
Post-hoc Analysis of Pharmacodynamics and Single-Agent Activity of CD3xCD123 Bispecific Antibody APVO436 in Relapsed/Refractory AML and MDS Resistant to HMA or Venetoclax Plus HMA

APVO436 is a recombinant bispecific antibody designed to direct host cytotoxic T-cells to CD123-expressing blast cells in patients with hematologic malignancies. APVO436 showed promising tolerability and single-agent activity in relapsed or refractory (R/R) acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). The primary purpose of this post-hoc analysis was to evaluate the therapeutic and pharmacodynamic effects of APVO436 in 14 R/R AML/MDS patients who had failed treatment with hypomethylating agents (HMA) or venetoclax plus HMA prior to being enrolled in the APVO436 Phase 1 dose-escalation study that was recently completed. Eight of these 14 patients had R/R AML and had failed treatment with HMA (N=2) or venetoclax plus HMA (N=6). The remaining 6 patients had R/R MDS and had also failed treatment with HMA (N=5) or venetoclax plus HMA (N=1). They were treated with APVO436 at submicrogram dose levels >0.08 mcg/kg that were active in preclinical NOD/SCID mouse xenograft models of AML. APVO436 activated patients’ T-cells as evidenced by reduced numbers of circulating CD123+CD34+ and CD33+CD34+ peripheral blasts. Single-agent activity was observed at dose levels ranging from 0.1 mcg/kg to 0.7 mcg/kg in 4 R/R AML patients (50%), including 3 patients with prolonged stable disease (SD) and one patient with complete remission (CR). Likewise, 3 MDS patients had SD (50%) and 3 additional MDS patients (50%) had a marrow CR at dose levels ranging from 0.1 mcg/kg to 0.8 mcg/kg. The median survival for the combined group of 14 R/R AML/MDS patients was 282 days. This early evidence of single-agent activity of APVO436 in R/R AML/MDS patients who failed HMA with or without venetoclax provides proof of concept supporting its in vivo immunomodulatory and anti-leukemic activity and warrants further investigation of its clinical impact potential.
Chemoenzymatic Synthesis of Complex Phenylpropanoid Derivatives by the Botrytis cinerea Secretome and Evaluation of Their Wnt Inhibition Activity

In this study, a series of complex phenylpropanoid derivatives were obtained by chemoenzymatic biotransformation of ferulic acid, caffeic acid, and a mixture of both acids using the enzymatic secretome of Botrytis cinerea. These substrates were incubated with fungal enzymes, and the reactions were monitored using state-of-the-art analytical methods. Under such conditions, a series of dimers, trimers, and tetramers were generated. The reactions were optimized and scaled up. The resulting mixtures were purified by high-resolution semi-preparative HPLC combined with dry load introduction. This approach generated a series of 23 phenylpropanoid derivatives, 11 of which are described here for the first time. These compounds are divided into 12 dimers, 9 trimers (including a completely new structural scaffold), and 2 tetramers. Elucidation of their structures was performed with classical spectroscopic methods such as NMR and HRESIMS analyses. The resulting compound series were analyzed for anti-Wnt activity in TNBC cells, with several derivatives demonstrating specific inhibition.
Estimated Biomass Loss Caused by the Vaia Windthrow in Northern Italy: Evaluation of Active and Passive Remote Sensing Options

Windstorms are a major disturbance factor for European forests. The 2018 Vaia storm, felled large volumes of timber in Italy causing serious ecological and financial losses. Remote sensing is fundamental for primary assessment of damages and post-emergency phase. An explicit estimation of the timber loss caused by Vaia using satellite remote sensing was not yet undertaken. In this investigation, three different estimates of timber loss were compared in two study sites in the Alpine area: pre-existing local growing stock volume maps based on lidar data, a recent national-level forest volume map, and an novel estimation of AGB values based on active and passive remote sensing. The compared datasets resemble the type of information that a forest manager might potentially find or produce. The results show a significant disagreement in the different biomass estimates, related to the methods used to produce them, the study areas characteristics, and the size of the damaged areas. These sources of uncertainty highlight the difficulty of estimating timber loss, unless a unified national or regional European strategy to improve preparedness to forest hazards is defined. Considering the frequent impacts on forest resources that occurred in the last years in the European Union, remote sensing-based surveys targeting forests is urgent, particularly for the many European countries that still lack reliable forest stocks data.