Miracle-dz/UniFAR-FTUs huggingface dataset:
The Support of MLIR HLS Adaptor for LLVM IR

Since the emergence of MLIR, High-level Synthesis (HLS) tools started to design in multi-level abstractions. Unlike the traditional HLS tools that are based on a single abstraction (e.g. LLVM), optimizations in different levels of abstraction could benefit from cross-layer optimizations to get better results. Although current HLS tools with MLIR can generate HLS C/C++ to do synthesis, we believe that a direct IR transformation from MLIR to LLVM will keep more expression details. In this paper, we propose an adaptor for LLVM IR, which can optimize the IR, generated from MLIR, into HLS readable IR. Without the gap of unsupported syntax between different versions, developers could focus on their specialization. Our preliminary results show that the MLIR flow via our adaptor can generate comparable performance results with the version by MLIR HLS tools generating HLS C++ codes. The experiment is performed with Xilinx Vitis and HLS tools.
Explainable AI Prediction of Cooking Oil Prices Over Time

Cooking Oil is an essential Commodity in household needs, so an uncertain price increase will significantly impact the Community’s Scope. This study aimed to determine the factors of high and low prices of Cooking Oil Based on Other Commodities. Therefore, this research was conducted based on predicting an increase in cooking oil prices from time to time determined by other commodities. We use Long Short-Term Memory (LSTM), one of the Machine Learning methods that can predict based on time series data efficiently (remembering a collection of information that has been stored for a long time and deleting information that is no longer relevant). Then it is implemented into the Explainable Artificial Intelligence (XAI) model, namely SHapley Additive ExPlanations (SHAP) using Random Forest, which is used in the explanation problem of machine learning models that can handle data sets containing Continue Variables as in the case of regression and categorical variables as in the case of classification. On Commodities that affect the Increase in Cooking Oil Prices.
Assessment measures for URANS/DES/LES: an overview with applications

A survey of quality assessment methods is presented covering a range of possible errors in applications of CFD. Beside modeling and numerical errors further uncertainties in unsteady flow simulations are related to inlet conditions and statistical averaging. The main focus is on measures that can be applied to unsteady Reynolds averaged Navier–Stokes (URANS) solvers, hybrid large eddy simulations (HLES), detached eddy simulations (DES), and mostly LES applications. The quantification of uncertainty in LES (large eddy simulations) is difficult for two reasons: (i) substantial grid refinement is too expensive and time consuming; (ii) the modeling errors and discretization errors are convoluted in such a nonlinear manner that it is difficult to segregate one from the other. In hybrid URANS/LES this task is even more difficult because the transitional regions cannot be identified a priori. A brief review of attempts to quantify the accuracy of LES focusing on Richardson extrapolation (RE) is presented. Selected measures are applied to URANS, DES and LES cases from the literature. An attempt is made to separate the modeling contribution from the numerical errors in LES. The methods proposed for the quantification of LES and DES accuracy are still in their infancy stage. Early results are however, promising.
A Comparative Study of Palonosetron with Ondansetron for Prophylaxis of Postoperative Nausea and Vomiting (PONV) Following Laparoscopic Gynaecological Surgeries

Abstract Background Incidence of postoperative nausea and vomiting (PONV) in susceptible patients can be unacceptably high (70–80% reported incidence). This study was designed to evaluate the effect of palonosetron and ondansetron in preventing PONV in high-risk patients undergoing gynaecological laparoscopic surgery. Methodology In this randomised, controlled, double-blind trial, nonsmoking females 18–70 years and weighing 40–90 kg, scheduled for elective laparoscopic gynaecological surgeries, were enrolled into the ondansetron (Group A, n=65) or palonosetron (Group B, n=65) group. Palonosetron (1 mcg/kg 4) or ondansetron (0.1 mg/kg 4) were administered just before induction. Postoperatively, incidence of nausea, vomiting, PONV (scored on a scale of 0–3), need for rescue antiemetic, complete response, patient satisfaction, and adverse effects were evaluated for up to 48 h following surgery. Results The overall PONV scores and postoperative nausea score during 0–2 h and 24–48 h were comparable, but PONV scores (P=0.023) and postoperative nausea scores (P=0.010) during 2–24 h were significantly lesser in Group B compared to Group A. There was no statistically significant difference in the postoperative vomiting score or retching during 0–48 h. The amount of first-line rescue antiemetic used during 2–24 h was significantly higher in Group A (56%) than in Group B (31%) (P=0.012; P<0.05). Complete response to the drug during 2–24 h was significantly higher (P=0.023) in Group B (63%) compared to Group A (40%), whereas response was comparable during 0–2 h and 24–48 h. Both groups had comparable incidences of adverse effects and patient satisfaction scores. Conclusion Palonosetron has superior antinausea effect, less need of rescue antiemetics, and lesser incidence of total PONV in comparison to ondansetron during 2–24 h and comparable effect to ondansetron during the 0–2 h and 24–48 h postoperative periods in high-risk patients undergoing gynaecological laparoscopic surgery.
Domestic Investment Laws and State Capitalism

Abstract State capitalism and the liberal economic order have had an antagonistic relationship. While the international economic law rules have sought to reduce the role of the state in the economy, state-controlled entities have more recently increased in size and importance – both domestically, as well as internationally. In this connection, the article analyses the effects of state capitalism's expansion simultaneously with the domestic investment law of States. The article analyses the underlying principles of state capitalism in an effort to answer the question of whether domestic laws promoting investment – as defined in the special issue – are positive, negative, or neutral to state capitalists. The article further interprets the trends spawned by the propagation of the liberal international economic order as states realize their development targets and envisage to actively contribute to the regulation of international trade and cross-border transactions globally.
ODNA: identification of organellar DNA by machine learning

Motivation Identifying organellar DNA, such as mitochondrial or plastid sequences, inside a whole genome assembly, remains challenging and requires biological background knowledge. To address this, we developed ODNA based on genome annotation and machine learning to fulfill. Results ODNA is a software that performs organellar DNA sequence classification of a genome assembly by machine learning based on a pre-defined genome annotation workflow. We trained our model with 829,769 DNA sequences from 405 genome assemblies and achieved very high predictive performance (e.g., MCC of 0.61) on independent validation data, thus outperforming existing approaches significantly. Availability Our software ODNA is freely accessible as a web service at https://odna.mathematik. uni-marburg.de and can also be run in a docker container. The source code can be found at https://gitlab.com/mosga/odna and the processed data at Zenodo (DOI: 10.5281/zenodo.7506483).
The QseB response regulator imparts tolerance to positively charged antibiotics by controlling metabolism and minor changes to LPS

The modification of lipopolysaccharide (LPS) in Escherichia coli and Salmonella spp. is primarily controlled by the two-component system PmrAB. LPS modification allows bacteria to avoid killing by positively charged antibiotics like polymyxin B. We previously demonstrated that in uropathogenic E. coli (UPEC), the sensor histidine kinase PmrB also activates a non-cognate transcription factor, QseB, and this activation somehow augments polymyxin B tolerance in UPEC. Here, we demonstrate – for the first time – that in the absence of the canonical LPS transcriptional regulator, PmrA, QseB can direct some modifications on the LPS. In agreement with this observation, transcriptional profiling analyses demonstrate regulatory overlaps between PmrA and QseB in terms of regulating LPS modification genes. However, both PmrA and QseB must be present for UPEC to mount robust tolerance to polymyxin B. Transcriptional and metabolomic analyses also reveal that QseB transcriptionally regulates the metabolism of glutamate and 2-oxoglutarate, which are consumed and produced during the modification of lipid A. We show that deletion of qseB alters glutamate levels in the bacterial cells. The qseB deletion mutant, which is susceptible to positively charged antibiotics, is rescued by exogenous addition of 2-oxoglutarate. These findings uncover a previously unknown mechanism of metabolic control of antibiotic tolerance that may be contributing to antibiotic treatment failure in the clinic. IMPORTANCE Although antibiotic prescriptions are guided by well-established susceptibility testing methods, antibiotic treatments oftentimes fail. The presented work is significant, because it uncovers a mechanism by which bacteria transiently avoid killing by antibiotics. This mechanism involves two closely related transcription factors, PmrA and QseB, which are conserved across Enterobacteriaceae. We demonstrate that PmrA and QseB share regulatory targets in lipid A modification pathway and prove that QseB can orchestrate modifications of lipid A in E. coli in the absence of PmrA. Finally, we show that QseB controls glutamate metabolism during the antibiotic response. These results suggest that rewiring of QseB-mediated metabolic genes can lead to stable antibiotic resistance in subpopulations within the host, thereby contributing to antibiotic treatment failure.
Evolutionary history of Castanea sativa in the Caucasus driven by Middle and Late Pleistocene paleoenvironmental changes

Due to global climate cooling and aridification since the Paleogene, the members of the Neogene flora were extirpated from the Northern Hemisphere or were confined to a few refugial areas. For some species, the final reduction/extinction came in the Pleistocene, but some others have survived climatic transformations up to the present. This has occurred in Castanea sativa, a species of high commercial value in Europe and a significant component of the Caucasian forests’ biodiversity. In contrast to the European range, neither the historical biogeography nor the population genetic structure of the species in the isolated Caucasian range has been clarified. Here, based on a survey of 21 natural populations from the Caucasus and a single one from Europe, we provide likely biogeographic reconstruction and genetic diversity details. By applying Bayesian inference, species distribution modelling, and fossil pollen data, we estimated (1) the time of the Caucasian - European divergence during the Middle Pleistocene (436.5 ka), (2) the time of divergence among Caucasian lineages, and (3) outlined the glacial refugia for species. The climate changes related to the Early Middle Pleistocene Transition and the alpine orogenic uplift in the region are proposed as the major drivers of the intraspecific divergence and European-Caucasian disjunction, while the impact of the last glacial cycle was of marginal importance.
EVALUATION OF VOLUME TRACKING ALGORITHMS FOR GAS-LIQUID TWO-PHASE FLOWS

Qualitative and quantitative evaluations of volume tracking algorithms such as DA, FLAIR, MARS, CIP and VTEMC were conducted. Wide variety of two-dimensional test problems including (1) a circle transported in simple translation and rotational field, (2) a bubble rising in 45° slanted gravity field, (3) zigzag motion of a bubble in a vertical channel, and (4) a bubble rising in a stagnant liquid in axissymmetric cylindrical coordinate were chosen in the present study. As a result of these tests, the superiority of the cell-centered piecewise linear algorithm with the divergence treatment in operator split and especially with embedded micro cells was confirmed. Applicability of these algorithms to three-dimensional problems has to be examined in the future works.Copyright © 2003 by ASME
The Appearance of Primeval Galaxies

The appearance of protogalaxies expected by current theories of structure formation is discussed. A review of when and how galaxies form is given, and the results of two numerical simulations of inhomogeneous galaxy formation are presented. These models are used to predict the abundance of primeval galaxies in observational surveys. 67 references.
Regional homogeneity changes in prelingually deafened patients: a resting-state fMRI study

Resting-state functional magnetic resonance imaging (fMRI) is a technique that measures the intrinsic function of brain and has some advantages over task-induced fMRI. Regional homogeneity (ReHo) assesses the similarity of the time series of a given voxel with its nearest neighbors on a voxel-by-voxel basis, which reflects the temporal homogeneity of the regional BOLD signal. In the present study, we used the resting state fMRI data to investigate the ReHo changes of the whole brain in the prelingually deafened patients relative to normal controls. 18 deaf patients and 22 healthy subjects were scanned. Kendall's coefficient of concordance (KCC) was calculated to measure the degree of regional coherence of fMRI time courses. We found that regional coherence significantly decreased in the left frontal lobe, bilateral temporal lobes and right thalamus, and increased in the postcentral gyrus, cingulate gyrus, left temporal lobe, left thalamus and cerebellum in deaf patients compared with controls. These results show that the prelingually deafened patients have higher degree of regional coherence in the paleocortex, and lower degree in neocortex. Since neocortex plays an important role in the development of auditory, these evidences may suggest that the deaf persons reorganize the paleocortex to offset the loss of auditory.
Analysis of Failure Time Data Arising From Studies With Alternating Treatment Schedules

We develop statistical methods for designing and analyzing studies in which treatments are deliberately interrupted and reinitiated, but where interest lies in making inferences about continuous treatment use. We refer to such designs as alternating designs, because subjects alternate between periods in which they are taking the treatment of interest and periods when they are not. Our goals are to determine how to estimate the distribution of time to an event if the treatment were given continuously, to compare the distributions of two such continuously given treatments, and to assess the effects of covariates on the distribution of a continuously given treatment. We examine a nonparametric estimator of the cumulative hazard function for continuous treatment using data from an alternating design and show it to be uniformly consistent and asymptotically normal under certain conditions relating to the effects of interrupting the treatment. We then introduce nonparametric tests for comparing the distributions corresponding to two such continuously given treatments and derive their asymptotic properties under general alternatives to the null and under various conditions related to the interruption of treatment. We compare the properties of the alternating treatment design and the classical parallel group design and present results from a simulation study that assesses the size and power of the test procedures introduced. Finally, we examine partial likelihood methods for assessing the effects of covariates and continuous treatment on time until an event under a proportional hazards model. We illustrate the proposed methods using the results from a recent study in which subjects alternate between taking an active drug and placebo on an annual basis.
Study the Effect of Buried P-base in GP-MOS on Device UIS, SC and Gate Oxide Electric field Characteristics

In this paper, based on the TCAD simulation platform, the single-shot unclamped inductive switching (UIS) characteristics, short-circuit (SC) characteristics and gate oxide electric field reliability of the BP-GPMOS structure which has a buried P-base in GP-MOS structure are studied. The simulation results show that compared with the traditional GP-MOS devices, the SC tolerance time of BP-GPMOS is increases by 30%, the UIS tolerance time increases by 37.5%, the maximum electric field of the gate oxide reduces by 27.3%. The BP-GPMOS has significantly improved SC, UIS characteristics and gate oxide electric reliability of device.
A New IDS for Smart Home based on Machine Learning

IoT environments are highly diverse with regard to devices, applications, and communications protocols. Consequently, they are highly vulnerable to many new attacks specific to this particular network. Existing Intrusion detection systems have shown their inefficiency in loT and this is for many reasons related basically to the limited computation capability of the devices, their mobility, the inherent Internet connectivity, and the large scale of the IoT network. Thus, a lightweight and efficient intrusion detection system designed for loT is required. Inspired by the success of Machine Learning in many fields and its potential in attack detection, we propose in this paper an intrusion detection system for Smart Home. The main goal is to design a model that detects different attacks on different Smart Home devices. We aim to enhance the detection capabilities of our IDS by exploring different features allowing us to classify the input traffic as benign or malicious. More importantly, we seek to develop specific models to predict the type of attacks per device. To this end, we trained two ML and two DL models using an loT dataset named 10T/IIoT. The obtained results show that the ML algorithms trained after applying a feature selection technique outperforms the model where all features are used in the training. Besides, the ML models allow reaching accuracy values competing with the accuracy of DL models, further, they outperform DL models for some devices/attacks.
Surface-State Transitions of Silicon in Electron Energy-Loss Spectra

Surface-state transitions on ordered silicon (100)2 x 1 and (111)7 x 7 surfaces have been observed by electron energy-loss spectroscopy. The surface transitions were identified by comparing the clean ordered surfaces with clean disordered surfaces, and with disordered surfaces due to submonolayer quantities of absorbed oxygen. Three transitions are observed corresponding to surface states near the top, near the middle, and at the bottom of the valence band. These surface-state transitions are stronger on the (111)-7 x 7 surface than on the (100)2 x 1 surface. (auth)
Identifying Coexisting Bluetooth and Zigbee Technologies Employing Dynamic Mode Decomposition

Advancements in wireless technologies and ever-increasing growth in telecommunication systems mean that devices operating the license-free Industrial, Scientific, and Medical (ISM) band have become increasingly inexpensive and widely available. Employing Bluetooth and Zigbee-based wireless devices in the realm of the Internet of Things (IoT), smart cities, and medical applications has simultaneously intensified during recent years. Because these devices share the 2.4 GHz ISM band, achieving successful coexistence to mitigate interference and enhance spectrum sharing have become a vital concern. In this paper, we develop novel techniques based on dynamic mode decomposition (DMD) modeling to identify Bluetooth and Zigbee technologies coexisting in an experimental heterogeneous network. The proposed technique is based on identifying the time domain signature of time series raw power measurements by capturing embedded periodicity features transmitted within the signal. The advantage of the proposed technique over comparable techniques is that it does not require channel estimation, time/frequency synchronization, or long observation intervals. Our work evaluated the performance of the proposed methods to detect and differentiate between targeted signals in terms of accuracy and processing time required to identify a signal. In addition, the performance was compared with deep learning models for validation and evaluation.
Lung Cancer Detection using 3D-Convolution Neural Network

Our paper uses an innovative 3D Convolutional Neural Network to determine lung cancer from patients' Computed Tomography (CT) scans because Convolutional Neural Networks (CNN) are useful for extracting important characteristics from images. This project aims to analyze CT scan slices (the data) and create a machine learning model based on the analysis. In this case, 3D Convolutional Neural Networks determine whether a person has cancer by evaluating the data and using a preprocessing technique. By utilizing this device, one can identify and eliminate cancerous cells at their earliest stages
On Outage Performance for Aeronautical Satellite Communications OFDM System

Satellite mobile communication is playing an important role in aviation that can help solve the coverage and availability all over the globe. This study investigates the interference effect between terrestrial aeronautical communications and Orthogonal Frequency Division Multiplexing (OFDM) satellite systems-based when sharing the same frequency band. To help predict and prevent the interference impact on the system performance, a system outage probability model of the satellite link in the presence of terrestrial interference under the Nakagami-m fading channel is proposed. The theoretical results illustrate the proposed model’s effectiveness and the interference impact on the system performance under low SIR.
Segmentation of the Germ from Corn Kernel CT Image Based on the Mask-RCNN

The drying cracks were predominantly generated in the endosperm and there was no crack in the germ, thus the germ should be segmented from corn kernel computed tomography (CT) images for studying the formation and propagation of the drying crack. The CT images of the corn kernel were often manually segmented, which was time-consuming and poor in accuracy. A segmentation model based on a Mask region convolutional neural network was developed for segmenting the CT images of the germ. The average precision and mean of pixel accuracy were 1 and 0.985 respectively, and the processing rate was 3-4images/s, which indicated that the segmentation model could segment effectively the germ with high segmentation efficiency.