Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-Fungal Analysis of Bacillus subtilis DL76 on Conidiation, Appressorium Formation, Growth, Multiple Stress Response, and Pathogenicity in Magnaporthe oryzae

In recent years, biological control has gained more attention as a promising method to combat plant disease. Such severe diseases cited include rice blasts caused by Magnaporthe oryzae. However, more effective microbial strains with strong adaptability still need to be identified. Therefore, we sought to assess the conidia germination, and formation of appressorium of DL76 in Magnaporthe oryzae. Besides, we also aimed at understanding the growth, multiple stress response and pathogenicity in Magnaporthe oryzae. We isolated Bacillus subtilis DL76 from a rice farm, which observed a strong antimicrobial effect on M. oryzae. The sterilized culture filtrate of DL76 inhibited the growth of M. oryzae, which motivated us to deduce the influence of DL76 on the pathogenicity of M. oryzae. We screened the effect of Bacillus subtilis DL76 on M. oryzae guy11. It demonstrated that sterilized culture filtrate (1 × 107 CFU/mL) of DL76 can delay and even suppress the germination of conidia and (1 × 107 and 1 × 106 CFU/mL) prevent the formation of appressorium in vitro and in vivo. DL76 became hypersensitive to osmotic, oxidative, and cell wall degrading agents. In addition, the relative transcript levels of stress-responsive genes oxidative and osmotic were down-regulated by DL76 except for sod1, cat1, and cat2. In vivo assessment of the antifungal activity of Bacillus subtilis using conidia suspension of DL76 reduced the incidence and severity of rice blast. Conclusively, our results show that DL76 is essential for controlling rice blast by inhibiting conidiation, growth, multiple stress tolerance, and pathogenicity in M. oryzae.
Decentralized Control Architecture for UAV-UGV Cooperation

— We present a decentralized control architecture for an heterogeneous group of mobile robots made of one Unmanned Aerial Vehicles (UAV) and several Unmanned Ground Vehicles (UGVs) performing collaborative tasks (area inspection, object transportation, etc.). The UAV is used to help a human operator to supervise and guide a group of UGVs by providing an aerial coverage view of the navigation area. Our control scheme is based on minimalistic computation and communication requirements, as well as an architecture complexity kept at a simple level regardless of the deployed number of grounds robots. Simulation and experimentations are performed and show the efficiency of our proposed control architecture.
A Multivariate Temporal Convolutional Attention Network for Time-Series Forecasting

Multivariate time-series forecasting is one of the crucial and persistent challenges in time-series forecasting tasks. As a kind of data with multivariate correlation and volatility, multivariate time series impose highly nonlinear time characteristics on the forecasting model. In this paper, a new multivariate time-series forecasting model, multivariate temporal convolutional attention network (MTCAN), based on a self-attentive mechanism is proposed. MTCAN is based on the Convolution Neural Network (CNN) model, using 1D dilated convolution as the basic unit to construct asymmetric blocks, and then, the feature extraction is performed by the self-attention mechanism to finally obtain the prediction results. The input and output lengths of this network can be determined flexibly. The validation of the method is carried out with three different multivariate time-series datasets. The reliability and accuracy of the prediction results are compared with Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Long Short-Term Memory (ConvLSTM), and Temporal Convolutional Network (TCN). The prediction results show that the model proposed in this paper has significantly improved prediction accuracy and generalization.
Investigation of Defects in New Buildings in Malaysia

Malaysia is among one of the development countries that growing rapidly in all sectors which include the sector in construction. Nevertheless, some of the new buildings are poorly constructed. One of the components that need attention is the defects in new buildings. The aim of this study is to evaluate the causes of defects and the objectives of this study are to determine the causes of defects in new buildings, to determine the types of defects in new buildings and to determine the solutions of defects in new buildings. A qualitative research was conducted by interviewing nine interviewees who have experienced in high-rise residential buildings. The results are analyzed by interpretation. According to the study, the causes of defects in new buildings are poor workmanship, work executed are not in proper manner, poor material usage and not according to specification, lack of supervision, surrounding temperature and environment, limited time, manpower allocation and not follow the method statement given. The types of defects are structural cracks like wall cracks, hairline cracks at joint of beams and columns, architectural cracks like plastering fall off and peeling paint, honeycomb, pipe clogged and cause leakage at toilet, roof leakage, hollowness at columns and floor tiles. The solutions to overcome the defects in new buildings are engage third party or an expert, revise back the method statement, ensure good workmanship, have a rehearsal before doing anything, give proper training to workers and ensure good quality of materials before usage. A conclusion was made and recommendations are included for future studies.
Structural Study of Sulfur-Added Carbon Nanohorns

In the past few decades, nanostructured carbons (NCs) have been investigated for their interesting properties, which are attractive for a wide range of applications in electronic devices, energy systems, sensors, and support materials. One approach to improving the properties of NCs is to dope them with various heteroatoms. This work describes the synthesis and study of sulfur-added carbon nanohorns (S-CNH). Synthesis of S-CNH was carried out by modified chemical vapor deposition (m-CVD) using toluene and thiophene as carbon and sulfur sources, respectively. Some parameters such as the temperature of synthesis and carrier gas flow rates were modified to determine their effect on the properties of S-CNH. High-resolution scanning and transmission electron microscopy analysis showed the presence of hollow horn-type carbon nanostructures with lengths between 1 to 3 µm and, diameters that are in the range of 50 to 200 nm. Two types of carbon layers were observed, with rough outer layers and smooth inner layers. The surface textural properties are attributed to the defects induced by the sulfur intercalated into the lattice or bonded with the carbon. The XRD patterns and X-ray microanalysis studies show that iron serves as the seed for carbon nanohorn growth and iron sulfide is formed during synthesis.
LSTM Network Learning for Sentiment Analysis

: The strong economic issues (e-reputation, buzz detection .) and political ( opinion leaders identification .) explain the rapid rise of scientists on the topic of sentiment classification. Sentiment analysis focuses on the orientation of an opinion on an entity or its aspects. It determines its polarity which can be positive, neutral, or negative. Sentiment analysis is associated with texts classification problems. Deep Learning (machine learning technique) is based on multi-layer artificial neural networks. This technology has allowed scientists to make significant progress in data recognition and classification. What makes deep learning different from traditional machine learning methods is that during complex analyses, the basic features of the treatment will no longer be identified by human treatment in a previous algorithm, but directly by the deep learning. In this article we propose a Twitter sentiment analysis application using a deep learning algorithm with LSTM units adapted for natural language processing.
Experimental task analysis

Rigorous analysis and evaluation of real implemented robotic systems for intelligent tasks are rarely performed. Such systems are often extremely complicated, depending not only on 'interesting' theoretical parameters and models, but on many assumptions and constants which may be set almost arbitrarily. We view all task implementations as particular parameterizations of the task goals they represent. Through fractional factorial experiments we establish the statistically significant parameters and parameter interactions for a 'sensorless' model-based push-orienting task. This type of analysis is a necessary step to understanding integrated intelligent systems. It reveals aspects of system implementations which cannot easily be predicted in advance, and gives a clear picture of the task requirements, given the strengths and weaknesses of the observed system.
A Scalable Blockchain-Based Integrity Verification Scheme

Ensuring the integrity of remote data is the prerequisite for implementing cloud-edge computing. Traditional data integrity verification schemes make users spend a lot of time regularly checking their data, which is not suitable for large-scale IoT (Internet of Things) data. On the other hand, the introduction of a third-party auditor (TPA) may bring about greater privacy and security issues. We use blockchain to address the problem of TPA. However, implementing dynamic integrity verification with blockchain is a bigger challenge due to the low throughput and poor scalability of blockchain. More importantly, whether there is a security problem with blockchain-based integrity verification is not yet known. In this paper, we propose a scalable blockchain-based integrity verification scheme that implements fully dynamic operations and blockless verification. The scheme builds scalable homomorphic verification tags based on ZSS (Zhang-Safavi-Susilo) short signatures. We exploit smart contract technology to replace TPA for integrity verification tasks, which not only eliminates the risk of privacy leakage but also resists collusion attacks. Furthermore, we formally define a blockchain-based security model and prove that our scheme is secure under the security assumption of cryptographic primitives. Finally, the mathematical analysis of our scheme shows that both the communication complexity and the communication complexity of an audit are O c , in which c is the number of challenge blocks. We compare our scheme with other schemes, and the results show that our scheme has the lowest time consumption to complete an audit.
Marine Plankton-Derived Whitlockite Powder-Based 3D-Printed Porous Scaffold for Bone Tissue Engineering

Powder-based 3D printing is an excellent technique for the fabrication of complex structural shapes. The outstanding bone remodeling capacity of calcium phosphate bioceramics is a desirable characteristic for such fabrication. Whitlockite (WH) is a calcium phosphate-based ceramic that contains Mg ions and possesses good mechanical properties, rapid resorbability, and promotes osteogenesis. The aim of this study was to fabricate 3D-printed scaffolds using marine plankton-derived WH (MP-WH) powder. The surface morphology and composition of the fabricated scaffolds were characterized by scanning electron microscopy and X-ray diffraction. The biocompatibility and osteogenic effects were evaluated using human mesenchymal stem cells. We successfully obtained a 3D porous scaffold using MP-WH. The MP-WH 3D scaffold showed improved compressive strength compared to the tricalcium phosphate (TCP) 3D scaffold. The in vitro results showed that compared with TCP 3D scaffolds, MP-WH 3D scaffolds were biocompatible and enhanced cell proliferation and adhesion. In addition, alkaline phosphatase activity and real-time polymerase chain reaction assays demonstrated that osteoblast differentiation was improved on the MP-WH scaffold. These results suggest that marine plankton-derived WH is useful for fabricating 3D-printed scaffolds for bone tissue engineering applications.
Enhancement of the flexural and impact properties laminated biocomposite by new natural fibres for artificial lower limb socket

ABSTRACT For several purposes, involving sickness, injuries, and genetic abnormalities, artificial limbs may be necessitated. Artificial limbs must be altered and modified regularly since the human physique vagaries with time owing to gaining or changing body mass. The utilisation of natural fibres as a potential substitute for fabricating a below-knee prosthesis socket will be highlighted in this study. To construct a below-knee socket, a vacuum bagging process was utilised. Woven flax, sisal, cotton, carbon, and glass fibre have formed in the laminates. The effect of various fibre stacking patterns on certain physical and mechanical properties was investigated. Multiple mechanical tests, including flexural, maximum shear stress, and impact tests, were performed as part of the experimental portion of this work. The findings of this study revealed that ingesting various kinds of reinforcing materials had a significant impact on the characteristics of fabricated composites, with the virtues of (flexural strength, flexural modulus, max. Shear stress, impact strength, and fracture toughness) improving as the volume fraction of materials increased. This review focuses on the assets of flax, sisal, and cotton fibre hybrid strengthened PMMA composites (hybridised in a variety of volume proportions and lamination layups) that have yet to be tested as a superior alternative for the creation of prosthetic sockets.
Surveillance for Ixodes scapularis and Ixodes pacificus ticks and their associated pathogens in Canada, 2019

Background The primary vectors of the agent of Lyme disease in Canada are Ixodes scapularis and Ixodes pacificus ticks. Surveillance for ticks and the pathogens they can transmit can inform local tick-borne disease risk and guide public health interventions. The objective of this article is to characterize passive and active surveillance of the main Lyme disease tick vectors in Canada in 2019 and the tick-borne pathogens they carry. Methods Passive surveillance data were compiled from the National Microbiology Laboratory Branch and provincial public health data sources. Active surveillance was conducted in selected sentinel sites in all provinces. Descriptive analysis of ticks submitted and infection prevalence of tick-borne pathogens are presented. Seasonal and spatial trends are also described. Results In passive surveillance, specimens of I. scapularis (n=9,858) were submitted from all provinces except British Columbia and I. pacificus (n=691) were submitted in British Columbia and Alberta. No ticks were submitted from the territories. The seasonal distribution pattern was bimodal for I. scapularis adults, but unimodal for I. pacificus adults. Borrelia burgdorferi was the most prevalent pathogen in I. scapularis (18.8%) and I. pacificus (0.3%). In active surveillance, B. burgdorferi was identified in 26.2% of I. scapularis; Anaplasma phagocytophilum in 3.4% of I. scapularis, and Borrelia miyamotoi and Powassan virus in 0.5% or fewer of I. scapularis. These same tick-borne pathogens were not found in the small number of I. pacificus tested. Conclusion This surveillance article provides a snapshot of the main Lyme disease vectors in Canada and their associated pathogens, which can be used to monitor emerging risk areas for exposure to tick-borne pathogens.
Robot-assisted gait training in patients with Parkinson's disease: Implications for clinical practice. A systematic review.

BACKGROUND Gait impairments are common disabling symptoms of Parkinson's disease (PD). Among the approaches for gait rehabilitation, interest in robotic devices has grown in recent years. However, the effectiveness compared to other interventions, the optimum amount of training, the type of device, and which patients might benefit most remains unclear. OBJECTIVE To conduct a systematic review about the effects on gait of robot-assisted gait training (RAGT) in PD patients and to provide advice for clinical practice. METHODS Asearchwas performed on PubMed, Scopus, PEDro, Cochrane library,Web of science, and guideline databases,following PRISMA guidelines.We included English articles if they used a robotic system with details about the intervention, the parameters, and the outcome measures. We evaluated the level and quality of evidence. RESULTS We included twenty papers out of 230 results: two systematic reviews, 9 randomized controlled trials, 4 uncontrolled studies, and 5 descriptive reports. Nine studies used an exoskeleton device and the remainders end-effector robots, with large variability in terms of subjects' disease-related disability. CONCLUSIONS Despite an association of WMH volume with CSF Aβ 42 RAGT showed benefits on gait and no adverse events were recorded. However, it does not seem superior to other interventions, except in patients with more severe symptoms and advanced disease.
Sneaking Through Security: Mutating Live Network Traffic to Evade Learning-Based NIDS

Machine learning based network intrusion system (NIDS) is known to be vulnerable to evasions. Attackers conceal intrusion activities to make them undetected. Researching evasion techniques contributes to evaluating and increasing the robustness of NIDS. Previous evasion approaches modify feature values or packets of an offline network trace as a whole. However, in real scenarios, attackers are constrained to manipulate only outbound packets on the fly. To bridge this assumption gap, we present the first evasion solution for live network traffic against learning based NIDSs. The solution consists of three components: a devised Kalman filter based algorithm to predicate the feature values of live flows, a set of formally constructed atomic packet mutation operators, and a proposed Strength Enhanced Deep Q-learning (SE-DQN) to determine effective mutation operators on outbound packets according to the predicted features. A defense scheme based on adaptive decision threshold adjustment is also provided. Experimental evaluation is presented on various NIDS classifiers and cyber attacks. Results show that SE-DQN achieves an evasion rate of at least 64.2% on most classifiers and even more than 90% on certain ones, and it is three times faster than DQN on learning mutation policy. The defense scheme shows an improvement of at least 76.4% on recall measurement.
Tabular Learning-Based Traffic Event Prediction for Intelligent Social Transportation System

Accurate forecasting of future traffic is a critical contemporary problem for transportation research. However, it is difficult to understand the feature patterns of traffic events due to the complexity of the traffic environment, heterogeneous factors, and lack of abnormal samples. This article proposes a framework to integrate the social traffic data and use the TabNet model to facilitate the representation learning task in traffic event prediction. With the tabular learning and model interpretability analysis, the importance of common traffic external factors toward traffic events is studied. The study has practical significance for regulating traffic planning and the development of the operational boundary for autonomous driving systems.
Real Time Monitoring and the Internet of Things: Reshaping How we Collect Data

In the burgeoning Internet of Things, new end-use metering data collection methods are available that may revolutionize the way evaluators collect usage and behavior information. For lighting metering in particular, connected lamps may allow for a high degree of certainty regarding the on/off status and other characteristics of the lamp, such as light color or brightness, because the status data is continuously communicated to a centralized hub or controlling device. While there are concerns regarding product connectivity performance and network security, these issues are not insurmountable. This paper presents a proof of concept of the methodology and a brief overview of illustrative findings from a recently completed assessment of residential, wirelessly-controllable LEDs, as well as potential EM&V implications brought on by new, connected energy efficiency equipment.
How Eco-Efficiency Is the Forestry Ecological Restoration Program? The Case of the Sloping Land Conversion Program in the Loess Plateau, China

The Forestry Ecological Restoration Program (FERP) aims to restore the world’s degraded forest landscapes to restore biodiversity and mitigate climate change. Scientific evaluation of eco-efficiency of forestry restoration programs (EEoFERPs) is the basis for developing and implementing inclusive and sustainable development policy measures. We take the world’s largest FERP—China’s Sloping Land Conversion Program (SLCP)—as an example. Using 314 county-level panel data in the Loess Plateau, the core area for the implementation of the SLCP, during 2002–2015, this study aims to evaluate the eco-efficiency of the Sloping Land Conversion Program (EEoSLCP) based on a DEA model and to measure the eco-efficiency dynamic changes through a Malmquist index model. The results show that: (1) The overall EEoSLCP of the Loess Plateau is at a low level, with an average efficiency of only 0.357 from 2002 to 2015. EEoSLCP is on an overall upward trend, mainly influenced by technical efficiency. (2) There are significant spatial differences in EEoSLCP among counties on the Loess Plateau, with an overall decreasing distribution trend from south to north and from southeast to northwest. (3) The Malmquist index of the EEoSLCP grew at an average annual rate of 17.7%, with technical efficiency changes being the most important factor driving its growth. Our results suggest that in the future, when implementing or designing FERPs, it is necessary not only to select the appropriate restoration plan precisely while respecting the laws of nature, but also to improve the management and technical level of FERPs accordingly.