Miracle-dz/UniFAR-FTUs huggingface dataset:
Robotic Platform for Precise Mechanical Stress Induction in Greenhouses Cultivation*

This paper presents an autonomous robotic platform for research of mechanically induced stress in plants growing in controlled greenhouse conditions. The platform provides a range of possibilities for mechanical stimuli including motion type, frequency, speed, and torque. The motions can be tailored for a single pot, making study of mechanical plant stress versatile, rapid and precise. We evaluate the performance of the platform for a use-case of basil plant cultivation. An eight week experiment was performed in greenhouse conditions on 220 basil plants. We show that the induction of mechanical stress by the platform significantly affects plant morphology, such as shortening stem length by 30 % -40 % and inter-node length by 50 % -80 %, while preserving leaf weight which is the main part of the basil plant used for culinary purposes. Results also show that variations in types of mechanical stimuli motions provides significant differences in the effect on plant morphology. Finally we show that decreasing the mechanical stimuli frequency to rates feasible to be performed manually significantly reduces the effect, stressing the need for autonomous systems capable of providing continuous stimuli during day and night. These results validate previously published findings in research of mechanical stress induction, and therefore implies the platform can be used for research of this phenomena.
Mobility Prediction via Sequential Learning for 5G Mobile Networks

Here, we present a mobility prediction framework for 5G mobile systems. Our work stems from the intuition that mobility in vehicular networks is highly correlated, and such correlation can be captured by advanced neural network designs to anticipate the users’ point of attachment. To prove this, we combine Markov chains with recurrent and convolutional neural networks, training them on mobility trajectories estimated by the received radio signal from mobile millimeter-wave devices. The proposed framework is decentralized, i.e., user trajectories are independently learned by each base station. In this paper, various problems are pragmatically tackled and solved, such as dealing with imbalanced datasets, as some trajectories are under represented, and obtaining a mobility classifier whose accuracy increases as new mobility samples are collected.The proposed technique is assessed using emulated traces obtained through the SUMO mobility simulator for the city of Cologne. Numerical results show accuracies higher than 88% in the prediction of the next serving base station from 4 seconds before the handover is performed. Mobility (next base station) predictors like the ones presented here are key for network management purposes within 5G networks, e.g., to proactively allocate communication and edge computing resources.
The Processing Of Synchronization In Bistatic Synthetic Aperture Radar

The time and phase synchronization are the important issues that must be addressed for bistatic synthetic aperture radar (BiSAR). The processing of synchronization is discussed in the paper. First, the synchronization scheme of LuTan-1 system is introduced in detail. The test results of ground validation system are illustrated to verify the synchronization scheme in LuTan-l. Then, an algorithm based on frequency offset estimation and iteration processing is also proposed for the noncooperative BiSAR. The proposed algorithms have great application potential for future BiSAR system.
Visual Computing-based Perception System for Small Autonomous Vehicles: Development on a Lighter Computing Platform

Recently, perception system for autonomous vehicle has seen a tremendous growth. Most of the recent works employ sensor fusion with complementary properties to produce a robust and accurate perceptive system for vehicle. However, this comes at a high price, requires high computing power and consumes more energy. In this study a perceptive system is designed to tackle the above issues while maintaining its accuracy and robustness. The proposed perceptive system is using only a pair of vision sensors. A Convolution Neural Network is used to detect and identify objects in the field of vision. A pair of cameras are then used to form a stereovision which is used to measure the distance of the objects detected. A disparity map from stereovision images was constructed first, then from the region of interest, a single disparity value was extracted to calculate the distance. The system is employed on a single board computer system StereoPi with the help of Intel Neural Compute Stick 2 to run deep neural network inference. An experiment was then conducted to test the perceptive system’s robustness, accuracy, and runtime. Results show that the proposed system is capable of a detection accuracy of 71.7% with an average error of 0.37% up to a distance of 1.3m.
A 110 GHz Hybrid Integrated Transmitter Design

This paper presents a design of a millimeter wave (mm-wave) transmitting device, which integrates components fabricated in various technologies. It is composed of a transmitter front-end integrated circuit, an antenna array, and a silicon lens. The design is outlined with particular emphasis on accommodation of contrary requirements of individual elements and integration of the antenna array with other parts of the circuitry into a single device. The low temperature co-fired ceramics (LTCC) multilayer structure was chosen as a platform for integration. LTCC demonstrates good electrical parameters (low losses and relatively low permittivity) in the mm-wave range which facilitates design of an efficient antenna. Additional beam focusing and the gain enhancement is achieved by means of the silicon lens suspended over the antenna on an adjustable fixture. The problem of optimal lens placement for two types of antennas is considered.
On the Latency of Multipath-QUIC in Real-time Applications

Recently, the ubiquity of broadband wireless networks has encouraged the growth of real-time network applications. Their strict latency requirements present a challenge for traditional Internet protocols which tend to be designed to optimize mean performance. Two recent developments that show promise for addressing the challenges of real-time applications are QUIC, a UDP-based protocol that has many features of TCP, and multipath transmission, an extension allowing transport layer protocols to transmit data simultaneously over multiple network paths and interfaces. In this paper, we present MAppLE (MPQUIC Application Latency Evaluation platform), which provides the instrumentation needed to evaluate and develop MPQUIC stream multiplexers, stream schedulers, and multipath packet schedulers. We also present our NineTails scheduler, a multipath MPQUIC scheduler that utilizes selective multipath redundancy to control tail loss and near-tail loss latencies. Our experimental results show that in a lossy asymmetric heterogeneous wireless network, our proposed scheduler reduces outlier latencies compared to other existing scheduling algorithms, and improves Quality of Experience (QoE) in video streaming by inducing fewer playback rebuffering events.
Cell Culturing on Electrical Circuit with Printed Electronics Technics

The goal of this study is developing a tactile sensor with receptors capable of self-organization in response to mechanical stimuli. Living organisms exhibit self-organization abilities such as self-reproduction, self-repair, and high adaptability. To embed those abilities directly, we have developed bio-machine hybrid systems. In this study, we focused on the C2C12 cell due to mechanobiological properties of Calcium ion levels increased when the cells were subjected to mechanical stimuli. Calcium ion changes can be detected with electrodes. In this study, we successfully confirmed that we could culture the cells on the thin membrane with electrical circuits by printed electronics.
Machine Learning Prediction of New York Airbnb Prices

The sharing economy emerged just twenty years ago. It was hard to envision its importance for many industries back then, but today, it is closely incorporated into our daily lives. Airbnb is a prime example of this phenomenon; it brought a brand new business model to the hospitality industry. The Airbnb platform has created millions of micro-businesses with a distinctive pricing strategy. Understanding the pricing strategy will provide insights into this new business model.This paper analyzes a sample of 48 896 listings in New York City from Airbnb.com, and builds a price prediction model with natural language processing and machine learning techniques. Different methods ranging from linear regression, to a generalized additive model, to a deep neural network, to random forest, to XGBoost and bagging (merging the previous five methods) were explored for the creation of the final prediction model. Of these, bagging, XGBoost and random forest demonstrated the strongest performance with the test data collected from the Airbnb website on January 28th, 2020.
Iraqi Plate Number Detection Based on Deep Learning and Recognize by SURF

License plate recognition (LPR) is an abstraction of the image or a series of images from the car license plate details. The camera which takes photos, the quality of the images obtained is an important factor in the LPR's achievement. LPR processes license plates below diverse environmental conditions with efficiency. Lighting, gravel, and towing devices inside the car will partially occlude the L.P. In this paper, a comprehensive evaluation of state-of-the-art LPR procedures was offered which detected a licensing plate using deep learning(DL) with the convolutional neural network (CNN) algorithm. Then used the speeded-up robust feature (SURF) algorithm to recognized this license plate. The goal behind this paper to predict the traffic flow by detecting and recognize the license plate cars. The accuracy was giving for detection of training 99.2% and loss of 0.8% and for testing 96% and loss of 4%. For recognition testing, given 96% by the SURF algorithm and using a database to save the personal information if this plate exists can be entered to private park-cars otherwise not enter. The waiting for cars in the gate was considered as The traffic flow.
Performance Analysis of Silicon and III-V Channel Material for Junctionless-Gate-All-Around Field Effect Transistor

The scaling down of device dimension beyond the Moore’s Law era have introduce the use of new material and device architecture. This paper reports a comparative study between Silicon and III-V junctionless-gate-all-around (JGAA) transistor as an alternative approach to overcome the short channel effects (SCEs). The device is simulated and characterized using TCAD Sentaurus. The III-V semiconductor channel material applied are Gallium Nitrite (GaN) and Gallium Arsenide (GaAs). The device electrical performance was compared in terms of the threshold voltage (Vth), subthreshold slope (SS), drain current (Ion) and leakage current (Ioff) extracted from the current-voltage characteristics. The results demonstrated a feasibility of using advanced device architecture using non-silicon material for future nanoelectronics application.
A rigorous method to compare interpretability of rule-based algorithms

Interpretability is becoming increasingly important in predictive model analysis. Unfortunately, as mentioned by many authors, there is still no consensus on that idea. The aim of this article is to propose a rigorous mathematical definition of the concept of interpretability, allowing fair comparisons between any rule-based algorithms. This definition is built from three notions, each of which being quantitatively measured by a simple formula: predictivity, stability and simplicity. While predictivity has been widely studied to measure the accuracy of predictive algorithms, stability is based on the Dice-Sorensen index to compare two sets of rules generated by an algorithm using two independent samples. Simplicity is based on the sum of the length of the rules deriving from the generated model. The final objective measure of the interpretability of any rule-based algorithm ends up as a weighted sum of the three aforementioned concepts. This paper concludes with the comparison of the interpretability between four rule-based algorithms.
METABOLIC: High-throughput Pro�ling of Microbial Genomes for Functional Traits, Biogeochemistry, and Community-scale Metabolic Networks

Background: Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent, however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and contributions to biogeochemical cycling. Results: We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale work�ow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, identi�cation of metabolism markers, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale work�ow supplements genome-scale analyses with determination of genome abundance in the community, potential microbial metabolic handoffs and metabolite exchange, and calculation of microbial community contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or from single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, and community-scale metabolic networks using a newly de�ned metric ‘MN-score’ (metabolic network score). METABOLIC takes ~3 hours with 40 CPU threads to process ~100 genomes and metagenomic reads within which the most compute-demanding part of hmmsearch takes ~45 mins, while it takes ~5 hours to complete hmmsearch for ~3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface
minimum description length in boolean matrix factorization

– During the past few years Boolean matrix factorization (BMF) has become an important direction in data analysis. The minimum description length principle (MDL) was successfully adapted in BMF for the model order selection. Nevertheless, an BMF algorithm performing good results from the standpoint of standard measures in BMF is missing. In this thesis, we propose a novel from-below Boolean matrix factorization algorithm based on formal concept analysis. The algorithm utilizes the MDL principle as a criterion for the factor selection. On various experiments we show that the proposed algorithm outperforms—from diﬀerent standpoints—existing state-of-the-art BMF algorithms.
Virtual bidding: a mechanism to mitigate market power in electricity markets Some evidence from New York market

Electricity markets are prone to the exercise of market power due to specific characteristics of this commodity. Indeed, different studies have found evidence of market manipulation on liberalized electricity markets (Some evidence might be found for instance in Weiss (1975), Schmalensee and Golub (1984), Green and Newbery (1992), Borenstein, Bushnell & Wolak, (2000 & 2002) Joskow & Kahn (2001) and Hildebrandt (2001)). Nevertheless, some market designs and market rules may increase the efficiency of these markets and mitigate market power. This paper explores the impact of financial trading on spot electricity markets, as a market rule that mitigates market power. This is also referred to as “virtual bidding. ” In order to provide evidence on the impact of this policy, we estimate a structural model based on Bresnahan (1982) and Lau (1982) using data from the New York market. Results show that financial trading has a positive impact on the reduction of market power. From a public policy perspective, one may therefore argue for the allowance of financial trading in electricity markets, not only to improve liquidity but also to mitigate market power.
A PILOT STUDY TO ASSESS EMOTIONAL DISTRESS AND QUALITY OF LIFE AMONG TRANSGENDERS IN SOUTH INDIA P Maithreyi and Nambi

*Corresponding Author Email: maithreyipoguri@yahoo.com ABSTRACT Background and aims: Transgenders face several social disadvantages in the Indian cultural context, and often live on the fringe of the society. Presence of adverse life circumstances predisposes them to have psychological morbidity. In the absence of previous systematic evidence, this pilot study aimed to assess emotional distress and quality of life among transgenders attending a clinic in south India. Methods: This hospital based cross-sectional study assessed transgenders attending a specialized multi-speciality clinic. Participants were assessed for relevant demographic and clinical information. Anxiety and depression were assessed using Hospital Anxiety and Depression Scale (HADS), while General Health Questionnaire (GHQ) was used to assess for general emotional distress. WHOQOL Bref was used to assess quality of life. Results: The pilot study included 15 transgenders with a median age of 30 years. All the participants had experienced sexual abuse in childhood, and 11/15 (73.3%) had a lifetime suicide attempt. The median HADS anxiety, HADS depression and GHQ scores were 9, 14 and 8 respectively. About 66.7% of the sample had significant anxiety symptoms while 100% had significant depressive symptoms as per HADS cut-off. The quality of life scores were particularly low for the ‘social’ domain. Conclusion: Transgenders experience considerable emotional distress and have poor social quality of life. Medical professionals need to be sensitive to the health-care needs of transgenders to improve their psychological well being.
Superior Specimen and Diagnostic Accuracy with Endoscopic Ultrasound-Guided Liver Biopsies Using 19-Gauge versus 22-Gauge Core Needles

Background/Aims Endoscopic ultrasound-guided liver biopsy (EUS-LB) is an effective and safe method of procuring liver tissue. The aims of this study were to assess and compare the outcomes and tissue adequacy of a single-pass, single-actuation, wet suction technique between 19 G and 22 G needles in patients undergoing EUS-LB. Methods We performed a prospective case series study of 20 patients undergoing EUS-LB at a single center between September 2017 and April 2020. The primary objective was to evaluate differences in sample adequacy via a single actuation wet suction technique between a 19 G core needle and a 22 G core needle. Adequacy was gauged by cumulative core biopsy length and the number of portal tracts visualized. Results The 19 G needle provided a longer core length (2.5 cm vs. 1.2 cm, p<0.0001), more complete portal tracts (5.8 vs. 1.7, p<0.0001), more total tracts (8.8 vs. 3, p<0.0001), and a longer, intact, fragment length (0.75 cm vs. 0.32 cm, p<0.0006). The 19 G needle was superior in providing adequate (60% vs. 5%, p<0.001) and diagnostic pathologic samples (85% vs. 10%, p<0.001). Conclusions A single-pass, single-actuation, wet suction technique using a 19 G needle is superior to that using a 22 G needle for tissue acquisition and sample adequacy in EUS-LB.
The Diagnostic Value of ischemia-modified albumin (IMA) and signal peptide-CUB-EGF domain-containing protein-1 (SCUBE-1) in an Experimental Model of Strangulated Mechanical Bowel Obstruction

Abstract Background Mechanical bowel obstruction (MBO) is one of the principal pathologies requiring emergency surgery and a significant worldwide cause of morbidity. The identification of patients in whom bowel obstruction resolves spontaneously is important in terms of preventing unnecessary surgical interventions and future potential adhesions. The decision-making process is difficult in patients presenting without classic examination findings. Methods 36 female Sprague-Dawley rats randomly divided into six experimental groups. In Group 1, 3 and 5, laparotomy was performed, with blood and tissue specimens being collected after 1, 2 and 6 h, respectively. In Group 2, 4 and 6, the ileum segment was ligated following laparotomy, and blood and tissue specimens were collected after 1, 2 and 6 h, respectively. The ileum specimens were examined macroscopically, after which 1-cm sections were taken and examined in terms of histopathological changes. IMA and SCUBE-1 levels were determined for each group, and macro- and microscopic tissue examination findings were compared between the groups. Results Comparison within the groups exposed to waiting times of 1 h (groups 1 and 2), 2 h (groups 3 and 4) and 6 h (groups 5 and 6) revealed higher mean IMA and SCUBE-1 levels in rats undergoing ligation together with incision (groups 2, 4, and 6) compared to those undergoing laparotomy only (groups 1, 3, and 5). Correlation analysis was applied to determine the relationship between total scores obtained from histopathological examination and IMA and SCUBE-1 values. The analysis revealed strong, significant and positive correlation between histopathological examination scores and IMA (r=0.643, p=0.000) and SCUBE-1 (r=0.509, p=0.002) values. Conclusion The study findings showed that both IMA and SCUBE-1 values increased in a strangulated MBO model in rats. We think that IMA and SCUBE-1 values can be used as a markers of damage in the early period in strangulated MBO, and that the patient's surgery requirement can thus be determined in the early period.
PD-L1过表达对CLL-1 CAR-T细胞抗急性髓系白血病作用的影响

目的 探讨程序性死亡受体配体1（PD-L1）对CLL-1 CAR-T细胞抗急性髓系白血病（AML）作用的影响。 方法 通过构建PD-L1表达载体、制备慢病毒、转导、单克隆筛选技术获得稳定表达PD-L1的THP-1单克隆细胞株（THP1-PDL1），然后以前期制备的CLL-1 CAR-T细胞为效应细胞，以THP-1、THP1-PDL1单克隆细胞株作为靶细胞，分别通过LDH检测、CBA法、CFSE法评价PD-L1过表达对CLL-1 CAR-T细胞裂解功能、炎性因子释放、细胞增殖等功能的影响。 结果 ①成功制备了PD-L1慢病毒，并筛选获得了稳定表达PD-L1的THP1-PDL1单克隆细胞株，流式细胞术及PCR验证成功。 ②PD-L1过表达抑制了CLL-1 CAR-T细胞裂解THP-1细胞的能力；效靶比为10∶1时，CLL-1CAR-T细胞对THP1-PDL1细胞的杀伤效率明显低于对THP-1细胞的杀伤效率［（15.70±9.90）％对（51.95±2.52）％，P<0.05］。 ③PD-L1过表达减弱了CLL-1 CAR-T细胞释放细胞因子的能力［与THP1-PDL1细胞共培养时对与THP-1细胞共培养时：IFN-γ（115.66±3.13）pg/ml对（1708.16±26.76）pg/ml，P<0.05；IL-6（17.37±0.72）pg/ml对（124.92±4.26）pg/ml，P<0.05；IL-10（5.69±0.13）pg/ml对（124.12±3.02）pg/ml，P<0.05］；同时抑制了CLL-1 CAR-T细胞的增殖能力。 结论 成功构建了表达PD-L1的THP1-PDL1单克隆细胞株，同时证实了PD-L1过表达对CLL-1 CAR-T细胞抗AML的不利的影响，为通过PD-1/PD-L1通路调控CLL-1 CAR-T细胞功能提供了一定的理论基础。