Activated biochars used as adsorbents for dyes removal

Adsorption represents one of the most interesting technique for the removal of pollutants from wastewaters. Activated carbons show the best performances on this kind of processes but their high production costs limit their applications. In this context a big challenge is to find new materials having characteristic similar to those of commercial activated carbons but being environmental friendly and cheaper. In this work the adsorption efficiency of activated biochars produced from pine wood was investigated on the removal of dyes from water. An innovative method for the activation of the biochar using deep eutectic solvents (DES) was tuned and the characteristics of the obtained adsorbent material were compared with those of biochar activated with traditional method and non-activated biochar. The best adsorption capacities were obtained with the DES activated biochar, reaching a value of 480 mg/g for the methylene blue adsorption. Adsorption isotherm and kinetic models were applied to experimental data in order to understand the adsorption mechanism of the process

On Autonomous Agents in a Cyber Defence Environment

Autonomous Cyber Defence is required to respond to high-tempo cyber-attacks. To facilitate the research in this challenging area, we explore the utility of the autonomous cyber operation environments presented as part of the Cyber Autonomy Gym for Experimentation (CAGE) Challenges, with a specific focus on CAGE Challenge 2. CAGE Challenge 2 required a defensive Blue agent to defend a network from an attacking Red agent. We provide a detailed description of the this challenge and describe the approaches taken by challenge participants. From the submitted agents, we identify four classes of algorithms, namely, Single- Agent Deep Reinforcement Learning (DRL), Hierarchical DRL, Ensembles, and Non-DRL approaches. Of these classes, we found that the hierarchical DRL approach was the most capable of learning an effective cyber defensive strategy. Our analysis of the agent policies identified that different algorithms within the same class produced diverse strategies and that the strategy used by the defensive Blue agent varied depending on the strategy used by the offensive Red agent. We conclude that DRL algorithms are a suitable candidate for autonomous cyber defence applications.Comment: Presented at the 2nd Internation Workshop on Adaptive Cyber Defence, 202

Deep blue light amplification from a novel triphenylamine functionalized fluorene thin film

The development of high performance optically pumped organic lasers operating in the deep blue still remains a big challenge. In this paper, we have investigated the photophysics and the optical gain characteristics of a novel fluorene oligomer functionalized by four triphenylamine (TPA) groups. By ultrafast spectroscopy we found a large gain spectral region from 420 to 500 nm with a maximum gain cross-section of 1.5 × 10-16 cm2 which makes this molecule a good candidate for photonic applications. Amplified Spontaneous Emission measurements (ASE) under 150 fs and 3 ns pump pulses have revealed a narrow emission at 450 nm with a threshold of 5.5 μJcm-2 and 21 μJcm-2 respectively. Our results evidence that this new fluorene molecule is an interesting material for photonic applications, indeed the inclusion of TPA as a lateral substituent leads to a high gain and consequently to a low threshold blue organic ASE

Deep penetrating nevus: a case report and brief literature review

BACKGROUND -: Deep penetrating nevus (DPN) is a distinct variant of melanocytic nevus and remains a histopathologic challenge to pathologists because of its resemblance to blue nevus, malignant melanoma, pigmented Spitz nevus, and congenital melanocytic nevus. It often goes unrecognized due to its relative rarity. CASE PRESENTATION -: Here we report a case of DPN of the left anterior leg in a 51-year old female. A brief review of the literature shows that these lesions have a distinct growth pattern and cellular morphology that can differentiate these lesions from other entities including malignant melanoma. CONCLUSION -: It is important to recognize these features because DPN carries a better prognosis than malignant melanoma

Exploring algorithms to recognize similar board states in Arimaa

The game of Arimaa was invented as a challenge to the field of game-playing artificial intelligence, which had grown somewhat haughty after IBM\u27s supercomputer Deep Blue trounced world champion Kasparov at chess. Although Arimaa is simple enough for a child to learn and can be played with an ordinary chess set, existing game-playing algorithms and techniques have had a difficult time rising up to the challenge of defeating the world\u27s best human Arimaa players, mainly due to the game\u27s impressive branching factor. This thesis introduces and analyzes new algorithms and techniques that attempt to recognize similar board states based on relative piece strength in a concentrated area of the board. Using this data, game-playing programs would be able to recognize patterns in order to discern tactics and moves that could lead to victory or defeat in similar situations based on prior experience

A Social Network Image Classification Algorithm Based on Multimodal Deep Learning

The complex data structure and massive image data of social networks pose a huge challenge to the mining of associations between social information. For accurate classification of social network images, this paper proposes a social network image classification algorithm based on multimodal deep learning. Firstly, a social network association clustering model (SNACM) was established, and used to calculate trust and similarity, which represent the degree of similarity between users. Based on artificial ant colony algorithm, the SNACM was subject to weighted stacking, and the social network image association network was constructed. After that, the social network images of three modes, i.e. RGB (red-green-blue) image, grayscale image, and depth image, were fused. Finally, a three-dimensional neural network (3D NN) was constructed to extract the features of the multimodal social network image. The proposed algorithm was proved valid and accurate through experiments. The research results provide a reference for applying multimodal deep learning to classify the images in other fields