InP and GaAs characterization with variable stoichiometry obtained by molecular spray

Both InP and GaAs surfaces were studied in parallel. A molecular spray technique was used to obtain two semiconductor surfaces with different superficial compositions. The structures of these surfaces were examined by electron diffraction. Electron energy loss was measured spectroscopically in order to determine surface electrical characteristics. The results are used to support conclusions relative to the role of surface composition in establishing a Schottky barrier effect in semiconductor devices

TECHNICAL NOTE ON THE INTRODUCTION OF PARTRIDGE COLOURED HUNGARIAN CHICKEN IN THE MEKONG DELTA OF VIETNAM

The paper aims to provide a brief agricultural profile of Tra Vinh province, informative adaptation results of Partridge coloured Hungarian chicken (PH) in Mekong Delta and describe the procedure to introduce PH into Tra Vinh province. During the introducing process, flexibility, consideration of the local condition (temperature, humidity, daily sunlight…), and availability of local resources such as bamboo blind, rice husk is essential for introducing a new chicken breed into Mekong Delta. For this, practical examples are given in the study. Regarding adaptation results, relatively high survival rate (89.6%) of PH was recorded at the end of 8th week. Although the recorded data of PH in Tra Vinh is limited, their performance is expected to be equally good or even better in comparison with that obtained in the sub-tropical climatic zone (North Vietnam). Further studies of PH adaptability in Mekong Delta for sustainable, traditional production and crossing purposes, as well as the involvement of chicken caravans to free range farming are recommended

UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering

In recent years, artificial intelligence has played an important role in medicine and disease diagnosis, with many applications to be mentioned, one of which is Medical Visual Question Answering (MedVQA). By combining computer vision and natural language processing, MedVQA systems can assist experts in extracting relevant information from medical image based on a given question and providing precise diagnostic answers. The ImageCLEFmed-MEDVQA-GI-2023 challenge carried out visual question answering task in the gastrointestinal domain, which includes gastroscopy and colonoscopy images. Our team approached Task 1 of the challenge by proposing a multimodal learning method with image enhancement to improve the VQA performance on gastrointestinal images. The multimodal architecture is set up with BERT encoder and different pre-trained vision models based on convolutional neural network (CNN) and Transformer architecture for features extraction from question and endoscopy image. The result of this study highlights the dominance of Transformer-based vision models over the CNNs and demonstrates the effectiveness of the image enhancement process, with six out of the eight vision models achieving better F1-Score. Our best method, which takes advantages of BERT+BEiT fusion and image enhancement, achieves up to 87.25% accuracy and 91.85% F1-Score on the development test set, while also producing good result on the private test set with accuracy of 82.01%.Comment: ImageCLEF2023 published version: https://ceur-ws.org/Vol-3497/paper-129.pd

Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.Comment: 12 page

Identifying hotspots for antibiotic resistance emergence and selection, and elucidating pathways to human exposure: Application of a systems-thinking approach to aquaculture systems

Aquaculture systems are highly complex, dynamic and interconnected systems influenced by environmental, biological, cultural, socio-economic and human behavioural factors. Intensification of aquaculture production is likely to drive indiscriminate use of antibiotics to treat or prevent disease and increase productivity, often to compensate for management and husbandry deficiencies. Surveillance or monitoring of antibiotic usage (ABU) and antibiotic resistance (ABR) is often lacking or absent. Consequently, there are knowledge gaps for the risk of ABR emergence and human exposure to ABR in these systems and the wider environment. The aim of this study was to use a systems-thinking approach to map two aquaculture systems in Vietnam – striped catfish and white-leg shrimp – to identify hotspots for emergence and selection of resistance, and human exposure to antibiotics and antibiotic-resistant bacteria. System mapping was conducted by stakeholders at an interdisciplinary workshop in Hanoi, Vietnam during January 2018, and the maps generated were refined until consensus. Thereafter, literature was reviewed to complement and cross-reference information and to validate the final maps. The maps and component interactions with the environment revealed the grow-out phase, where juveniles are cultured to harvest size, to be a key hotspot for emergence of ABR in both systems due to direct and indirect ABU, exposure to water contaminated with antibiotics and antibiotic-resistant bacteria, and duration of this stage. The pathways for human exposure to antibiotics and ABR were characterised as: occupational (on-farm and at different handling points along the value chain), through consumption (bacterial contamination and residues) and by environmental routes. By using systems thinking and mapping by stakeholders to identify hotspots we demonstrate the applicability of an integrated, interdisciplinary approach to characterising ABU in aquaculture. This work provides a foundation to quantify risks at different points, understand interactions between components, and identify stakeholders who can lead and implement change

A Compact Dual Bandpass Filter Using Dual Composite Right-/Left-Handed and Open-Loop Ring Resonators for 4G and 5G Applications

In this paper a very compact design of a dual-band band pass filter (D-BPF) using dual composite right-/left-handed (D-CRLH) and open-loop ring (OLR) resonators is presented. To overcome the frequency ratio limitations of D-CLRH resonators technique, the D-BPF design combines D-CRLH and OLR resonators to finally perform a D-BPF. The filter covers the 2.6 and 3.5 GHz spectrums for 4G and 5G applications, respectively. The reported D-BPF is designed and optimized using ADS software, and is implemented on a Rogers RO5880 substrate with a relative dielectric constant of 2.2 and thickness of 0.787 mm. The overall compact size is 8×8×0.787 mm^3. To our knowledge, this design is considered as the most compact and smallest size dual-bandpass filters

Diurnal variability of atmospheric O-2, CO2, and their exchange ratio above a boreal forest in southern Finland

The exchange ratio (ER) between atmospheric O(2 )and CO2 is a useful tracer for better understanding the carbon budget on global and local scales. The variability of ER (in mol O(2 )per mol CO2) between terrestrial ecosystems is not well known, and there is no consensus on how to derive the ER signal of an ecosystem, as there are different approaches available, either based on concentration (ERatmos) or flux measurements (ERforest). In this study we measured atmospheric O-2 and CO2 concentrations at two heights (23 and 125 m) above the boreal forest in Hyytiala, Finland. Such measurements of O-2 are unique and enable us to potentially identify which forest carbon loss and production mechanisms dominate over various hours of the day. We found that the ERatmos signal at 23 m not only represents the diurnal cycle of the forest exchange but also includes other factors, including entrainment of air masses in the atmospheric boundary layer before midday, with different thermodynamic and atmospheric composition characteristics. To derive ERforest, we infer O(2 )fluxes using multiple theoretical and observation-based micro-meteorological formulations to determine the most suitable approach. Our resulting ERforest shows a distinct difference in behaviour between daytime (0.92 +/- 0.17 mol mol(-1)) and nighttime (1.03 +/- 0.05 mol mol(-1)). These insights demonstrate the diurnal variability of different ER signals above a boreal forest, and we also confirmed that the signals of ERatmos and ERforest cannot be used interchangeably. Therefore, we recommend measurements on multiple vertical levels to derive O-2 and CO2 fluxes for the ERforest signal instead of a single level time series of the concentrations for the ERatmos signal. We show that ERforest can be further split into specific signals for respiration (1.03 +/-; 0.05 mol mol-1) and photosynthesis (0.96 +/- 0.12 molmol(-1)). This estimation allows us to separate the net ecosystem exchange (NEE) into gross primary production (GPP) and total ecosystem respiration (TER), giving comparable results to the more commonly used eddy covariance approach. Our study shows the potential of using atmospheric O-2 as an alternative and complementary method to gain new insights into the different CO2 signals that contribute to the forest carbon budget.Peer reviewe