Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular basis of parental contributions to the behavioural tolerance of elevated pCO2 in a coral reef fish

Knowledge of adaptive potential is crucial to predicting the impacts of ocean acidification (OA) on marine organisms. In the spiny damselfish, Acanthochromis polyacanthus, individual variation in behavioural tolerance to elevated pCO2 has been observed and is associated with offspring gene expression patterns in the brain. However, the maternal and paternal contributions of this variation are unknown. To investigate parental influence of behavioural pCO2 tolerance, we crossed pCO2-tolerant fathers with pCO2-sensitive mothers and vice versa, reared their offspring at control and elevated pCO2 levels, and compared the juveniles' brain transcriptional programme. We identified a large influence of parental phenotype on expression patterns of offspring, irrespective of environmental conditions. Circadian rhythm genes, associated with a tolerant parental phenotype, were uniquely expressed in tolerant mother offspring, while tolerant fathers had a greater role in expression of genes associated with histone binding. Expression changes in genes associated with neural plasticity were identified in both offspring types: the maternal line had a greater effect on genes related to neuron growth while paternal influence impacted the expression of synaptic development genes. Our results confirm cellular mechanisms involved in responses to varying lengths of OA exposure, while highlighting the parental phenotype's influence on offspring molecular phenotype.
The forbidden bandgap characteristic of EBG structures

The bandgap characteristics of several electromagnetic bandgap (EBG) structures are compared, and parametric studies of a wideband uniplanar compact photonic bandgap (UC‐PBG) structure are carried out. It has been presented that the bandgap bandwidth of the mushroom type EBG structure increases when a lower dielectric constant substrate is used or when a larger substrate thickness is used. However, for the UC‐PBG structures, which do not have shorting vias, the bandgap characteristic does not follow the trend as it is in the EBG structure with shorting via. Moreover, it is demonstrated that the bandgap frequency varies with the dielectric constant of the substrate, but the bandgap bandwidth does not have particular trend with the dielectric constant of the substrate. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 2965–2967, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23804
Antenna array geometry optimization for a passive coherent localisation system

Passive Coherent Localisation (PCL), also known as Passive Radar, making use of RF sources of opportunity such as Radio or TV Broadcasting Stations, Cellular Phone Network Base Stations, etc. is an advancing technology for covert operation because no active radar transmitter is required. It is also an attractive addition to existing active radar stations because it has the potential to discover low-flying and low-observable targets. The CORA (Covert Radar) experimental passive radar system currently developed at Fraunhofer-FHR features a multi-channel digital radar receiver and a circular antenna array with separate elements for the VHF- and the UHF-range and is used to exploit alternatively Digital Audio (DAB) or Video Broadcasting (DVB-T) signals. For an extension of the system, a wideband antenna array is being designed for which a new discone antenna element has been developed covering the full DVB-T frequency range. The present paper describes the outline of the system and the numerical modelling and.
Remote, proactive, telephone based management of toxicity in outpatients during adjuvant or neoadjuvant chemotherapy for early stage breast cancer: pragmatic, cluster randomised trial

Abstract Objective To evaluate the effectiveness of remote proactive management of toxicities during chemotherapy for early stage breast cancer. Design Pragmatic, cluster randomised trial. Setting 20 cancer centres in Ontario, Canada, allocated by covariate constrained randomisation to remote management of toxicities or routine care. Participants All patients starting adjuvant or neoadjuvant chemotherapy for early stage breast cancer at each centre. 25 patients from each centre completed patient reported outcome questionnaires. Interventions Proactive, standardised, nurse led telephone management of common toxicities at two time points after each chemotherapy cycle. Main outcome measures The primary outcome, cluster level mean number of visits to the emergency department or admissions to hospital per patient during the whole course of chemotherapy treatment, was evaluated with routinely available administrative healthcare data. Secondary patient reported outcomes included toxicity, self-efficacy, and quality of life. Results Baseline characteristics of participants were similar in the intervention (n=944) and control arms (n=1214); 22% were older than 65 years. Penetration (that is, the percentage of patients who received the intervention at each centre) was 50-86%. Mean number of visits to the emergency department or admissions to hospital per patient was 0.91 (standard deviation 0.28) in the intervention arm and 0.94 (0.40) in the control arm (P=0.94); 47% (1014 of 2158 patients) had at least one visit to the emergency department or a hospital admission during chemotherapy. Among 580 participants who completed the patient reported outcome questionnaires, at least one grade 3 toxicity was reported by 48% (134 of 278 patients) in the intervention arm and by 58% (163 of 283) in the control arm. No differences in self-efficacy, anxiety, or depression were found. Compared with baseline, the functional assessment of cancer therapy trial outcome index decreased by 6.1 and 9.0 points in the intervention and control participants, respectively. Conclusions Proactive, telephone based management of toxicities during chemotherapy did not result in fewer visits to the emergency department or hospital admissions. With the rapid rise in remote care because of the covid-19 pandemic, identifying scalable strategies for remote management of patients during cancer treatment is particularly relevant. Trial registration ClinicalTrials.gov NCT02485678.
An Approach to Natural Language Intention Understanding of Civil Aviation Passengers Based on DIET Architecture

With the development of science and technology, more and more products appeared in the airport terminal to provide services for the passenger in civil aviation. However, due to the lack of understanding of natural language by scientific and technological equipment, the repeated consultation of the same question often fails to give satisfactory answers to the passengers, which reduces the service efficiency and friendliness. In order to provide better services for passengers in civil aviation, we aim at the common problems of passengers in the airport environment, simulate the possible service demands of passengers, analyze the key feature data in the demands, and combine the joint identification model of DIET to realize the accurate understanding of the intention in the natural language of passengers. In this paper, Python tools are used for making simulation experiments of natural language and intention understanding to verify the feasibility of the implementation method in this paper.
Attention Mechanism Driven YOLOv3 on FPGA Acceleration for Efficient Vision Based Defect Inspection

In this study, an efficient vision-based industry defect inspection system using attention mechanism driven YOLOv3 on FPGA acceleration is proposed. First, an attention mechanism is employed to improve YOLOv3 for the target defect inspection application. Image preprocessing named CZS (Cut, Zoom, and Splice) operation is used to reconstruct product images for selectively concentrating on the pre-defined detection regions. Then we optimize the backbone network of YOLOv3 according to defect size in images. Second, we use the PYNQ-Z2 FPGA board to deploy the proposed defect inspection system. The optimized YOLOv3 is deployed on the programmable logic through Xilinx DNNDK, which is a low-latency, low-cost, and low-power consumption hardware platform for industrial defect inspection. Experimental results showed that the achieved defect inspection accuracy was 99.2% with a processing speed of 1.54 FPS.
Chinese Herbal Recognition Databases Using Human-In-The-Loop Feedback

Traditional Chinese medicine identification plays an important role in the development of traditional Chinese medicine. Traditional Chinese medicine identification mostly relies on researchers' experience, so traditional Chinese medicine identification is still challenging. Using the computer identification of traditional Chinese medicine seems an effective method, but no dataset can train models. The lack of a dataset is the challenge of traditional Chinese medicine identification by use computers. This paper proposes a method for constructing a Chinese medicine dataset based on human-in-the-loop. This method uses a manual intervention labeling method to realize a labeling mode that saves labour resources. First, we use a web crawler to collect data from the Internet, then use a pre-model to remove some irrelevant data, next, we iterative data annotation based on the classification confidence, finally, we will obtain a dataset named CH42 that annotation by human-computer collaboration. Besides, we designed a backbone network for explicitly modeling interdependencies between channels. The CH42 contains 42 types of Chinese medicine data, a total of 6,112 pictures, the model automatically labeled about 64% of the data. We sampled 6 sets of data and found 6 mislabeled data from 1458 pictures. The model labeling accuracy rate is about 98.6%.
Multimodal Emotion Recognition with Factorized Bilinear Pooling and Adversarial Learning

With the fast development of social networks, the massive growth of the number of multimodal data such as images and texts allows people have higher demands for information processing from an emotional perspective. Emotion recognition requires a higher ability for the computer to simulate high-level visual perception understanding. However, existing methods often focus on the single-modality investigation. In this work, we propose a multimodal model based on factorized bilinear pooling (FBP) and adversarial learning for emotion recognition. In our model, a multimodal feature fusion network is proposed to encode the inter-modality features under the guidance of the FBP to help the visual and textual feature representation learn from each other interactively. Beyond that, we propose an adversarial network by introducing two discriminative classification tasks, emotion recognition and multimodal fusion prediction. Our entire method can be implemented end-to-end by using a deep neural network framework. Experimental results indicate that our proposed model achieves competitive performance on the extended FI dataset. Progressive results prove the ability of our model for emotion recognition against other single- and multi-modality works respectively.
Study on Bio Efficacy of Insecticides in the Predator Management of Katki Lac Crop

The Bio efficacy of insecticides for predator management of Kerria lacca was studied on the natural stand of Butea monosperma trees of 10 women lac growers of village Malhara Seoni district Madhya Pradesh during the July- October 2012. A combination of Cartap hydrochloride + Mancozeb(T1),Emamectin benzoate + Dithane M-45(T2) and Control(T3) was evaluated against the predators of the Lac insect. Pesticides application significantly reduced the incidence of major predators- E amabilis and P pulverea. In comparison to T3 there was a reduction in the population of E amabilis by 90 and 87 per cent respectively with (T2) and (T1). In case of P pulvera reduction in the population was 90 and 86.18 percent with T1 and T2 respectively over the control (T3).
Porting and FPGA Implementation of LXDE Desktop Environment Based on RISC-V

As a new generation of open and reduced instruction set architecture, RISC-V has the characteristics of concise instruction set, modularity and scalability, and has been quickly and widely used due to its advantages of agile development mode and complete tool chains, but there are few desktop systems based on RISC-V at present. Based on the open source RISC-V BOOM core, this paper constructs a desktop system which can start the LXDE graphical interface on Xilinx VU440 FPGA. Specifically, we implement a variety of peripheral modules such as bus conversion and VGA on the boom core, and port the open source bootloader, Linux 4.15 kernel and LXDE desktop environment. Under the desktop system, standard test sets, such as CoreMark can be run normally, and the test results show that the performance of the system is at a high level. By extending peripherals, this RISC-V prototype system can be used to realize SoC in many special areas.
Research on Data Governance Framework for Fire Department

This paper analyzes data governance elements, models and frameworks, provides a clear plan for data governance for fire department. Using the method of literature research, network investigation and conclude data system of fire departments, the china domestic and foreign research status of data governance is reviewed. We build the framework of data governance for fire department, including Data resource directory system, Data technology support system and Data standardization system. This paper preliminarily forms the framework of data governance for fire department. This framework was applied to the fire information planning work. The results indicate that based on the status and characteristics of fire industry, the implementation of this framework is effective and feasible, and it is also the basis of standard fire control data governance in future.
Research on Mixed-Precision Quantization and Fault-Tolerant of Deep Neural Networks

As deep neural networks become more and more common in mission-critical applications, such as smart medical care, drones, and autonomous driving, ensuring their reliable operation becomes critical. The data in the hardware memory is susceptible to bit-flip due to external factors, which leads to a decrease in the inference accuracy of the deep neural network deployed on the hardware. We solve this problem from the perspective of the deep neural network itself, We use a reinforcement learning algorithm to search for the optimal bit width for the weights of each layer of the deep neural network. According to this bit width strategy, the deep neural network is quantified, which maximizes the limitation of data fluctuations caused by bit-flip and improves the fault-tolerance of the neural network. The fault-tolerance of the network model compared with the original model, the solution proposed in this paper improves the fault-tolerance of LeNet5 model by 8.5x , the fault tolerance of MobileNetV2 model by 15.6x , the fault tolerance of VGG16 model by 14.5x , and the accuracy decreases negligibly.
Linking Organizational Culture to Lean Implementation in the Malaysian Construction Industry: A Conceptual Framework

This paper examines the relationship and role of organizational culture in the implementation of Lean Construction. Being a conceptual paper, it provides a framework to identify relevant organizational culture attributes that have the potential to impact lean implementation in the construction industry. It is expected that through this framework, a step forward could be made towards culture as a useful explanatory concept in lean research. The framework is expected to be tested empirically using data from CIDB G7 contractors in the Malaysian Construction Industry. This paper contributes to the theories about the role of culture in lean implementation by developing a framework to investigate a more comprehensive but relevant set of cultural attributes, that would help achieve greater success in lean transformation initiative.
Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models

Abstract Intrusion Detection Systems (IDSs) utilise deep learning techniques to identify intrusions with maximum accuracy and reduce false alarm rates. The feature extraction is also automated in these techniques. In this paper, an ensemble of different Deep Neural Network (DNN) models like MultiLayer Perceptron (MLP), BackPropagation Network (BPN) and Long Short Term Memory (LSTM) are stacked to build a robust anomaly detection model. The performance of the ensemble model is analysed on different datasets, namely UNSW-NB15 and a campus generated dataset named VIT_SPARC20. Other types of traffic, namely unencrypted normal traffic, normal encrypted traffic, encrypted and unencrypted malicious traffic, are captured in the VIT_SPARC20 dataset. Encrypted normal and malicious traffic of VIT_SPARC20 is categorised by the deep learning models without decrypting its contents, thus preserving the confidentiality and integrity of the data transmitted. XGBoost integrates the results of each deep learning model to achieve higher accuracy. From experimental analysis, it is inferred that UNSW_ NB results in a maximal accuracy of 99.5%. The performance of VIT_SPARC20 in terms of accuracy, precision and recall are 99.4%. 98% and 97%, respectively.
A Review on Privacy Requirements and Application Layer Security in Internet of Things (IoT)

Abstract Internet of Things (IoT) is the predominant emerging technology that targets on facilitating interconnection of internet-enabled resources. IoT applications concentrate on automating different tasks that facilitate physical objects to act autonomously without any human interventions. The emerging and current IoT applications are determined to be highly significant for improving the degree of efficiency, comfort and automation for its users. Any kind of security breach on the system will directly influences the life of the humans In this paper, a comprehensive review on Privacy requirements and application layer Security in Internet of Things (IoT) is presented for exploring the possible security issues in IoT that could be launched over the individual layers of IoT architecture. This review explores different challenges of classical security solutions that are related to authentication, key management and cryptographic solutions.It also presents the details of existing access control and device authentication schemes with their pros and cons.
Colloidal characteristics of gelatin-treated clay: Intercalation, stability, wettability, and rheological properties

In this paper, sodium montmorillonite was modified with gelatin of different concentrations, and various colloidal characteristics of the gelatin-treated clays were measured and analyzed in detail. First, the influence of gelatin on the interlayer space of Mt layers was investigated by X-ray diffraction analysis. Moreover, the aggregation of Mt particles was examined using a combination of electron microscopy and particle size distribution experiments, while the variation of the electrical property of Mt was measured using ζ potential test. Gelatin of different concentrations can increase the particle size of Mt in different degrees. The addition of 4% gelatin could improve the ζ potential of Mt from −30.65 to −15.55 mV. The wettability change of modified Mt induced by the adsorption of gelatin was followed by measurements of water contact angle and observations of the morphology of Mt/gelatin membrane through SEM images. 4% gelatin could improve the water contact angle of Mt to 81.3°. Finally, the rheological properties of Mt/gelatin dispersion including shear viscosity and shear stress were measured using a stress-controlled rheometer. All of the results were consistent by showing that the overall colloidal characteristics and behavior of the gelatin-treated Mt strongly varied depending on the gelatin concentration used in the modification process. These results can provide a deep and comprehensive understanding of the colloidal properties of clay/gelatin systems and give important guidance for the performance design and improvement of Mt/gelatin composite materials. Furthermore, this study can also be expanded the application of gelatin and its composites to other fields.
Investigating the Influence of Mentor–Mentee Race and Gender Pairs on Public Health Persistence

Background. Mentoring and immersive experiences through internships are important means of increasing underrepresented (UR) students’ persistence in public health. However, while the positive effects of mentoring are well established, studies on the effect of race/ethnicity and gender mentor matching on persistence have produced mixed results. Aims. This article investigates the impact of homogeneous and heterogeneous mentor–mentee pairs on UR intern persistence for Project Imhotep, a summer internship program at Morehouse College. Method. The authors employ multivariable logistic regression analyses to examine the relationship between mentor and mentee race/ethnicity and gender characteristics on undergraduate intern academic persistence and career attainment. Results. Mentor demographics and institution type are predictors associated with intern academic and career persistence; however, the predictive importance of model attributes varied by outcome. Mentees paired with UR mentors (female or male) were more likely to attend graduate school, and mentees mentored by female mentors (UR female or White female) were more likely to pursue a graduate degree in public health. Finally, mentees mentored by UR females had the highest likelihood of either pursuing a graduate degree in public health or a public health career. Discussion. This article advances our understanding of how mentor–mentee race/ethnicity and gender affects the recruitment and retention of undergraduate students from racially and ethnically UR populations into public health. Conclusions. The findings suggest that the degree of shared racial/ethnic and/or gender identity between a public health intern and mentor influenced the likelihood of the intern pursuing further education or a public health career.
Managing Yourself and Others

Oxfam's Pick-up-and-Go packs provide a structured, interactive learning system for geographically dispersed NGO staff, enabling basic training to be delivered where and when learners most need it. The materials are designed for use by facilitators who have presentation and management skills but not necessarily any specialist expertise. Each pack consists of a printed manual and an integral CD ROM, containing a short introduction to the approach and objectives of the course, together with detailed notes for facilitators, flipchart presentations, handouts for photocopying, and (in some cases) supplementary information and PowerPoint presentations. The CD ROM contains the entire English-language text, with translations in French, Spanish, and Portuguese.