Miracle-dz/UniFAR-FTUs huggingface dataset:
Crash Simulation Exploration with Energy Absorption Features and Graph Algorithms

We propose to represent data from finite element car crash simulations in a graph database to empower machine learning algorithms. The industrial perspective of this work is on narrowing the gap between the uptake of modern machine learning methods and the current computer aided engineering-based vehicle development workflow. The main goals are to achieve searchability and to enable trend investigations in the product development history. We introduce features for car crash simulations to enrich the graph and to provide a summary overview for the development stages. These features are based on the energy output of the finite element solver and enable filtering of the input data by identifying essential components of the vehicle. Additionally, based on these features we propose a so-called platform/vehicle fingerprint, which assists in summarizing the exploration of the design space and facilitates cross-platform and/or various load-case comparisons. Furthermore, we predict similarities between simulations using graph algorithms, which allows the identification of outliers and ranks simulations according to their similarities. We use a car sub-model for the illustration of the similarity ansatz and present results on data from real-life development stages of an automotive company.
Nominal countability in Kaingang and beyond

This paper is the first investigation of nominal countability in Kaingang, a Jê language spoken in Brazil. The main claim of this paper is that all Kaingang nouns are lexically count. This hypothesis is supported by a number of morphosyntactic and semantic properties of nouns in the language. Among them two crucial properties emerge: (i) Kaingang allows numerals and other count quantity expressions to combine directly with individual and substance nouns, and (ii) in quantity judgement tasks (Barner & Snedeker 2005) comparisons with both types of nouns are cardinality-based. I analyze this generalized counting strategy as a direct effect of the lexical semantics of nouns. Building on Krifka’s approach (1989; 2007; 2008), I argue that all Kaingang nouns are born quantized, i.e., they are lexically equipped with a context-sensitive built-in counting function that measures quantities in terms of individual-or portion-units. This paper contributes with additional crosslinguistic evidence to two claims: (i) that the mass/count distinction in the nominal domain isn’t a language universal (Wiltschko 2012), and (ii) that the defining property of count nouns is quantization, rather than atomicity (Krifka 1989; 2007).
Methylation proﬁles of endometrioid and serous endometrial cancers

Promoter methylation is a gene-and cancer type-speciﬁc epigenetic event that plays an important role in tumour development. As endometrioid (endometrioid endometrial carcinoma, EEC) and serous endometrial cancers (uterine papillary serous carcinoma, UPSC) exhibit different clinical, histological and molecular genetic characteristics, we hypothesized that these differences may be reﬂected in epigenetic phenomena as well. Identiﬁcation of a panel of methylation biomarkers could be helpful in a correct histological classiﬁcation of these two subtypes, which solely on the basis of morphology is not always easy. Methylation-speciﬁc multiplex ligation-dependent probe ampliﬁcation was used to assess the extent of promoter methylation of different tumour suppressor genes in EEC and UPSC. Methylation results were correlated with histology and survival. The median cumulative methylation index of all genes was signiﬁcantly higher in EEC (124) than in UPSC (93) ( P ! 0.001). Promoter methylation of CDH13 and MLH1 was more frequently present in EEC, while CDKN2B and TP73 were more frequently methylated in UPSC. Almost 90% of EEC and 70% of UPSC could be predicted by CDH13 and TP73. In EEC, methylation of MLH1 was associated with a shorter disease-free survival (DFS; P ! 0.0001) and overall survival (OS; P Z 0.005). In a multivariate model, MLH1 methylation emerged as an additional prognostic factor to stage for DFS ( P Z 0.002). In conclusion, promoter methylation is more common in EEC than UPSC. A panel of methylation biomarkers could be useful to distinguish between the two histological subtypes of endometrial cancer. Furthermore, methylation of MLH1 may have prognostic value in EEC.
Boundary conditions at a gel-ﬂuid interface

Hydrogels consist of a polymer skeleton hydrated by an aqueous solvent, and their hydrodynamics is often described by a coarse-grained poroelasticity model where the boundary conditions between the hydrogel and a surrounding solvent require careful consideration. Young et al. [Phys. Rev. Fluids 4 , 063601 (2019)] used the energy dissipation principle to derive a set of boundary conditions regarding the velocity jumps at the interface. However, when applied to an external shear ﬂow over a gel layer, these conditions predict no entrained ﬂow inside the gel, in contrast to the prediction of a previous model by Minale [Phys. Fluids 26 , 123102 (2014)]. We adapt the procedure of Young et al. to derive an alternative set of boundary conditions that does allow an external shear ﬂow to induce shear inside the gel and compare the velocity proﬁle to that of Minale. We also derive the limiting form of the boundary conditions in a Darcy medium.
Role of the water balance constraint in the long short-term memory network: large-sample tests of rainfall-runoff prediction

While deep learning (DL) models are effective in rainfall-runoff modelling, their dependence on data and lack of physical mechanisms can limit their use in hydrology. As there is yet no consensus on the consideration of the fundamental water balance for DL models, this paper presents an in-depth investigation of the effects of water balance constraint on the long-short term memory (LSTM) network. Specifically, based on the Catchment Attributes and Meteorology for Large-sample 10 Studies (CAMELS) dataset, the LSTM and its architecturally mass-conserving variant (MC-LSTM) are trained basin-wise to provide rainfall-runoff prediction and then the robustness of the LSTM and MC-LSTM against data sparsity, random parameters initialization and contrasting climate conditions are assessed across the contiguous United States. Through large-sample tests, the results show that the water balance constraint evidently improves the robustness of the basin-wise trained LSTM. On the one hand, as the amount of training data increases from 1 year to 15 years, the incorporation of the water 15 balance constraint into the LSTM network decreases the sensitivity from 95.0% to 32.7%. On the other hand, the water balance constraint contributes to the stability of the LSTM for 450 (85%) basins when there are 3 years’ training data. In the meantime, the water balance constraint improves the transferability of the LSTM from the driest years to the wettest years for 318 (67%) basins. Overall, the in-depth investigations of this paper facilitate insights into the use of DL models for rainfall-runoff modelling. 20
Assessing the Drivers of Steady State Economic Growth in Nigeria

This study assesses the effects of foreign direct investment (FDI), government expenditures on education and health, trade openness, and initial income on steady state economic growth in Nigeria. The study utilised the Nonlinear Autoregressive Distributed Lag model with data obtained from the world bank development indicator database covering the period 2000Q1 to 2021Q4. The long run results showed evidence of asymmetric response of steady state economic growth to shocks emanating from FDI and health expenditure only. The impact of positive changes in FDI is greater than that of negative changes, while positive changes in health expenditure, although statistically insignificant, exert lesser influence than negative changes. Also, education expenditure is found to influence steady state economic growth positively. These findings are consistent with endogenous growth theory, which highlights the importance of technological innovation and human capital accumulation for sustainable growth. Furthermore, the estimated parameter of initial income is positive, indicating a possibility of convergence, however, trade openness has a marginal negative effect on steady state economic growth, reflecting the effect of unequal international trade competition. The study emphasized the need to intensify effort in attracting and retaining FDI, as well as re-evaluating trade policies to foster sustainable growth in Nigeria.
Effects on mother and fetus of epidural and combined spinal-epidural techniques for labor analgesia.

OBJECTIVE Epidural (EA) and combined spinal-epidural (CSE) techniques have both been utilized for labor analgesia. This study compared the effects on the mother and newborn of these techniques in labor analgesia and anesthesia. METHODS Forty pregnant women received epidural analgesia with 15 mL of 0.125% ropivacaine (EA group) and 5 microg of sufentanil plus 2.5mg bupivacaine in the subarachnoid space (CSE group). Pain intensity, sensory blockade level, latency time, motor block intensity, labor analgesia duration, epidural analgesia duration, maternal hypotension, and pruritus were evaluated. The newborns were evaluated by Apgar and the neurological and adaptive capacity score (NACS) developed by Amiel-Tison. RESULTS There were no significant statistical differences between groups for pain scores, latency time, sensory blockade level, and Apgar score. Motor block, labor analgesia duration, and epidural analgesia duration were greater in the CSE group, whose seven mothers had mild pruritus. The NACS were greater in the EA group after half, two, and 24 hours. Ninety five percent of EA group newborns and 60% of CSE group newborns were found to be neurologically healthy at the 24 hour examination. CONCLUSION EA and CSE analgesia relieved maternal pain during obstetric analgesia, but CSE mothers had pruritus and a longer labor. Newborns of mothers who received epidural analgesia showed the best NACS.
Effect of adding protected methionine to the ration on hematological indices and biochemical blood parameters in Awassi lambs

Alkhashab, A. Th. , Kassim, H. W. & Abdelalbaki, Th. M. (2023). Effect of adding protected methionine to the ration on hematological indices and biochemical blood parameters in Awassi lambs. Bulg. J. Agric. Sci., 29 (4), 709–713 This experiment was undertaken to study the effect of protected Methionine supplementation on hematological and bio - chemical parameters in growing Awassi lambs. For that, ten male Awassi lambs 8 months old, with 34 ± 0.5 kg initial bwt, were randomly assigned to two groups of equal size, the first one represented as a control group(c) and the second was the treatment group(T). Lambs of control group were fed basal ration consisting of a concentrated diet consisting of barely seeds, wheat brand, soyabean meal, salt, urea, limestone and wheat straw as a roughage diet. The treatment group lambs were fed a basal control ration containing 5 g/ head/day (RPM) rumen protected methionine. Blood samples were collected once monthly and the experiment lasted 3 months. Results about blood parameters showed a significant (P ≤ 0.05) decrease in WBC and a significant (P ≤ 0.05) increase in blood glucose, cholesterol levels in methionine treated group than control group, but there are no significant effect of treatment with protected methionine on other hematological values and biochemical parameters. It con - cluded that supplementing the ration with protected Methionine led to non-significant effect on hematological and biochemical values except WBC, glucose and cholesterol in growing Awassi
LDPC Code’s Decoding Algorithms for Wireless Sensor Network: a Brief Review

: As an effective error correction technology, the Low Density Parity Check Code (LDPC) has been researched and applied by many scholars. Meanwhile, LDPC codes have some prominent performances, which involves close to the Shannon limit, achieving a higher bit rate and a fast decoding. However, whether these excellent characteristics are suitable for the resource-constrained Wireless Sensor Network (WSN), it seems to be seldom concerned. In this article, we review the LDPC code’s structure brief.ly, and them classify and summarize the LDPC codes’ construction and decoding algorithms, finally, analyze the applications of LDPC code for WSN. We believe that our contributions will be able to facilitate the application of LDPC code in WSN.
How Important is the Temporal Context to Anticipate Oncoming Vehicles at Night?

Driving at night is a challenging task for humans due to low-light conditions and often low concentration caused by drowsiness. Here, advanced driver assistance systems can come to the rescue and support the driver to increase comfort as well as safety. For that, the vehicle needs a sound and complete understanding of its environment, especially other road participants. The earlier this information is available, the more proactive decisions can be made by the system. To detect on-coming vehicles as soon as possible even before they are actually directly visible, the light reflections caused by their headlamps can be leveraged. Previous work showed that algorithms can perform this task on rural land roads. However, for more complex urban scenarios no approach exists so far. Yet, before starting the dataset and algorithm development for a computer vision system able to solve the task, a sound understanding of the problem is required. In a recent study we already showed that humans can anticipate oncoming vehicles based on their light reflections also in urban scenarios. Still, it remains unclear whether spatial information alone is sufficient to perform the task, or if the temporal context is required. This understanding is essential when designing labeling pipelines for a dataset, as well as algorithm development. Therefore, in this paper we perform a large experiment to evaluate the importance of temporal context for the human ability to anticipate oncoming vehicles at night in urban scenarios. We present participants scenes with different numbers of frames and measure their anticipation performance. We show that providing temporal context significantly increases the human detection accuracy as well as decision confidence. With this we provide additional insights into the task of anticipatory vehicle detection at night which can be taken into consideration when designing a dataset as well as algorithms.
An Approach Towards Predicting the Computational Runtime Reduction from Discrete-Event Simulation Model Simplification Operations

Model simplification is the process of developing a simplified version of an existing discrete-event simulation (DES) to study the performance of specific system subcomponents relevant to the analysis. The simplified model is referred to as a ‘metasimulation’. A widely used model simplification operation is abstraction, which involves replacing the subcomponents, not core to the analysis, from the parent DES model with random variables representing the lengths of stay in said subcomponents. However, the one-time computational cost of developing metasimulations via abstraction can itself be considerable, as the approach necessitates executing the parent model for generating the necessary data for developing the metasimulation. Thus, this study proposes a queuing-theoretic approach for estimating the computational runtime reduction (CRR) achieved through abstraction, wherein the prediction of CRR precedes the development of the metasimulation. Towards this, we present preliminary results from applying this approach for simplification of DES models made up of M/M/n workstations.
Impact of Tap Cell on Single Event Transient in 28-nm CMOS Technology

In advanced integrated circuit (IC) technologies at the 28-nm node and below, tap cells are used for well and substrate contact in actual VLSI design in pursuit of higher device density. To investigate the difference in SET sensitivity between different placements in designs using tap cells as well and substrate contacts, in this paper, SET vulnerability of inverter cells placed at different distances to tap cells are studied. Results show that in 28-nm CMOS technology, n-MOSFETs are not sensitive to the variation in placement, whereas the p-MOSFET shows regular variations in SET widths if placed with gradual changes in distance from the tap cell. Therefore, the parasitic bipolar amplification effect in the p-MOSFET is also studied. It is shown that this effect is the main reason for the variation in SET sensitivity in p-MOSFETs with different placements. This study provides guidance to circuit designers on how to determine the physical location of standard cells during the large-scale advanced process circuit design.
CA_MobileNetV2 for Underwater Acoustic Target Recognition

In response to the hardware limitation and realtime requirement in the underwater acoustic target recognition, this paper proposes a coordinate attention-based end-to-end MobileNet (CA_MobileNetV2) with fewer parameters and high recognition accuracy. The acoustic signal is mapped into LOFAR images and input into the backbone MobileNetV2. We utilize depth-wise separable convolution to reduce model complexity. However, insufficient feature extraction of lightweight models may lead to accuracy loss. CA_MobileNetV2 embeds Bottleneck's acoustical time-frequency characterized Coordinate Attention (CA) mechanism. This mechanism captures long-term temporal dependencies and preserves the precise positional relationships in the frequency domain to represent the target signal. Additionally, the cosine annealing algorithm is used to enhance the convergence speed of the model during training, preventing the model from getting stuck in local optima and reducing the model's accuracy. Experimental results demonstrate that the CA_MobileNetV2 reduces the parameter size by 80.05% compared to ResNet18 and achieves an accuracy of 98.16% with a more pronounced inter-class gap in underwater acoustic target recognition.
Harnessing multimodal clinical predictive models for childhood tumors

Neuroblastoma, the most common solid cancer in early childhood, exhibits significant heterogeneity, ranging from benign to aggressive forms. This paper presents a comprehensive study of a multimodal clinical predictive model for childhood neuroblastoma.The dataset, collected within the PRIMAGE project, includes 1056 patients with neuroblastoma, featuring clinical, molecular, genetic, and imaging data at diagnosis and follow-up. Radiomics features were extracted from T1/T2-weighted MR and CT images. Diffusion-weighted imaging provided apparent diffusion coefficient maps used to assess malignancy. Dynamic contrast-enhanced MR imaging was used to calculate semi-quantitative parameters for lesion characterization. Customized multiscale tumor growth models integrated initial cell density distribution, tumor vasculature, and geometry from clinical MR imaging.A multimodal clinical predictive model was developed. The integration of clinical, molecular, genetic, radiological, radiomics, and multiscale tumor growth data led to a remarkable 15% improvement in predictive accuracy and a 20% boost in precision compared to models using individual data sources.The proposed multimodal methodology offers a more nuanced and personalized understanding of childhood neuroblastoma, promising improved outcomes and advancing research in the field of pediatric oncology.
Effect of Different Driving Styles on the Energy Consumption and CO2 Emission

Paper analyses the effect of the driving style induced by a supervising device on the total vehicle consumption. The study is performed by computing the energy and fuel consumption, and the CO2 emission of several cars associated to five vehicle segments. The driving profiles are derived from WLTC for low, medium, and extra high cycles by introducing acceleration events typical of three driving styles. From simulation results, the driving style shows significant impact on energy consumption and CO2 emissions in particular for urban scenario where acceleration induced power request is predominant with respect to running resistances that are predominant at higher speeds typical of extra-urban and highway scenarios.
Effect of the Silvopastoral System on Growth of Chinese Quality Broiler and Environment

This experiment determined the effect of growth and environments of Chinese quality broilers in the Silvopastoral System. The results showed that the quality broilers of low density group had fine growth and development and high survival rate;the low stocking density can increase the herbage intake and feed conversion ratio of quality broilers.The degradation degree of grassland in the low density group was significantly lower than that of high density group.The growth rate of trees and the soil available P and K in the high density group,was significantly higher than that of the low density,but the forage resources of the high density group was lack.In order to obtain optimization in the comprehensive benefit,these factors will be considered as ecological environment,the stocking density,pasture growing for the silvopastoral system
Analysis of Neutral Point Balancing Scheme for Back-to-Back Converter in PMSG-Based Wind Energy Conversion Systems

For the high and medium power level grid integration of permanent magnet synchronous generator (PMSG)-based wind energy conversion systems (WECS), the industry utilizes back-to-back multilevel converters. The three-level neutral point clamped (NPC) converter among them, is a popular choice due to its advantages, such as lower voltage stress, reduced switching losses, and improved power quality. However, practical NPC-based back-to-back converters often suffer from neutral point voltage imbalance, which results in significant ripple in the grid current and torque of PMSG-based WECS. This research paper focuses on the a zero sequence voltage (ZSV) injection method for carrier-based pulse width modulation of a three-level NPC back-to-back converter, aiming to mitigate above issues and discusses the different mode of operation based on ZSV injection on machine side converter and grid side converter. This paper also concludes that the phase current from the converter side with higher magnitude has a greater effectiveness in achieving neutral point balance when utilizing the zero sequence voltage (ZSV) method. If ZSV is injected into both side of back-to-back converter, it will give better result for neutral point balancing for low and high modulation indices. The efficacy of this method is presented using simulation study.
Energy-Efficient Unified Multi-Hash Coprocessor for Securing IoT Systems Integrating Blockchain

SHA-256, BLAKE-256, and BLAKE2s are cryptographic hash functions widely used in data security for Blockchain-enabled IoT systems to ensure the integrity of data and transactions. However, previous works have focused solely on implementing individual hash functions from these three cryptographic hash functions. Therefore, we propose a unified hardware solution named a B2$HA coprocessor that integrates all three hash functions and offers high speed, low power consumption, and high flexibility. Our proposed approach involves three novel optimizations: a multi-level pipeline architecture, a reused-register technique, and an adder-sharing method. The theoretical evaluation demonstrates that B2$HA coprocessor shares 76.5% of 32-bit registers (104 out of 136) and 84.6% of 32-bit adders (44 out of 52) for SHA-256, BLAKE-256, and BLAKE2s computations. Additionally, the implementation results on the 45 nm CMOS ASIC show that the unified efficiency of B2$HA is approximately 82%. The B2$HA coprocessor is implemented and verified at the system-on-chip level on the Xilinx Zynq UltraScale+ MPSoC ZCU102 FPGA. Our experimental results on multiple FPGAs show that the B2$HA coprocessor exhibits superior flexibility and outperforms stand-alone hashing architectures in terms of throughput and area efficiency by 1.938. 4 times and 1.4-4.5 times, respectively.