Features of the Supramolecular Cell-Genetic Organization of E. Coli in Engineering Aspects of Biotechnology

Recently, there has been a keen interest in the physicochemical features of self-organizing spatio-temporal, heteropolymer-supramolecular assemblies, in which the system of components of the fluctuation dynamics of surface protein groups is evolutionarily selected for the implementation of morphogenetic processes of ontogenesis.That is, evolution created chemical compounds, the exceptional organization of which ensured the fulfillment of the most complex and precise tasks.In this research, the bacterial cell ofE. coli was considered in the concept of supramolecular science, where, in accordance with the informational development program based on the principles of molecular recognition, phase ensembles appear, which are characterized by a certain organization, depending on the phase growth of the population culture. In this respect, proteomic super-molecular physicochemistry can be considered as physicochemical or molecular informatics.Arginine is of interest because almost all of its molecule is active and undergoes obligatory interactions both with DNA and with other histones and non-histones. The results of this study demonstrated the super-protein surface of supramolecular assemblies, the flexible system PPCС-E.coli, active zones, dynamics of continuity, positioning topologicalspatio-temporal Arg-protease-processing, local areas of the nucleoid system, and interrelations at the level of: Bp-liquid crystal-bacterioplasma; NsCo-fragile, PsCo-tightly bound to the cell remainder; and in the Co-cell remainder itself. These data may be of practical interest in various engineering aspects of biotechnology. Keywords: arginine protease processing, supramolecules, E.coli, phase protein, super-molecules.

Emotion recognition from syllabic units using k-nearest-neighbor classification and energy distribution

In this article, we present an automatic technique for recognizing emotional states from speech signals. The main focus of this paper is to present an efficient and reduced set of acoustic features that allows us to recognize the four basic human emotions (anger, sadness, joy, and neutral). The proposed features vector is composed by twenty-eight measurements corresponding to standard acoustic features such as formants, fundamental frequency (obtained by Praat software) as well as introducing new features based on the calculation of the energies in some specific frequency bands and their distributions (thanks to MATLAB codes). The extracted measurements are obtained from syllabic units’ consonant/vowel (CV) derived from Moroccan Arabic dialect emotional database (MADED) corpus. Thereafter, the data which has been collected is then trained by a k-nearest-neighbor (KNN) classifier to perform the automated recognition phase. The results reach 64.65% in the multi-class classification and 94.95% for classification between positive and negative emotions.

Enhanced Face Recognition Using Adaptive Local Tri Weber Pattern with Improved Deep Learning Architecture

Effective face recognition is accomplished using the extraction of features and classification. Though there are multiple techniques for face image recognition, full face recognition in real-time is quite difficult. One of the emerging and promising methods to address this challenge in face recognition is deep learning networks. The inevitable network tool associated with the face recognition method with deep learning systems is convolutional neural networks (CNNs). This research intends to develop a new method for face recognition using adaptive intelligent methods. The main phases of the proposed method are (a) data collection, (b) image pre-processing, (c) normalization, (d) pattern extraction, and (e) recognition. Initially, the images for face recognition are gathered from CPFW, Yale datasets, and the MIT-CBCL dataset. The image pre-processing is performed by the Gaussian filtering method. Further, the normalization of the image will be done, which is a process that alters the range of pixel intensities and can handle the poor contrast due to glare. Then a new descriptor called adaptive local tri Weber pattern (ALTrWP) acts as a pattern extractor. In the recognition phase, the VGG16 architecture with new chick updated-chicken swarm optimization (NSU-CSO) is used. As the modification, VGG16 architecture will be enhanced by this optimization technique. The performance of the developed method is analyzed on two standards face database. Experimental results are compared with different machine learning approaches concerned with noteworthy measures, which demonstrate the efficiency of the considered classifier.

Memorisation and implicit perceptual learning are enhanced for preferred musical intervals and chords

Is it true that we learn better what we like? Current neuroaesthetic and neurocomputational models of aesthetic appreciation postulate the existence of a correlation between aesthetic appreciation and learning. However, even though aesthetic appreciation has been associated with attentional enhancements, systematic evidence demonstrating its influence on learning processes is still lacking. Here, in two experiments, we investigated the relationship between aesthetic preferences for consonance versus dissonance and the memorisation of musical intervals and chords. In Experiment 1, 60 participants were first asked to memorise and evaluate arpeggiated triad chords (memorisation phase), then, following a distraction task, chords’ memorisation accuracy was measured (recognition phase). Memorisation resulted to be significantly enhanced for subjectively preferred as compared with non-preferred chords. To explore the possible neural mechanisms underlying these results, we performed an EEG study, directed to investigate implicit perceptual learning dynamics (Experiment 2). Through an auditory mismatch detection paradigm, electrophysiological responses to standard/deviant intervals were recorded, while participants were asked to evaluate the beauty of the intervals. We found a significant trial-by-trial correlation between subjective aesthetic judgements and single trial amplitude fluctuations of the ERP attention-related N1 component. Moreover, implicit perceptual learning, expressed by larger mismatch detection responses, was enhanced for more appreciated intervals. Altogether, our results showed the existence of a relationship between aesthetic appreciation and implicit learning dynamics as well as higher-order learning processes, such as memorisation. This finding might suggest possible future applications in different research domains such as teaching and rehabilitation of memory and attentional deficits.

Applying Automatic Translation for Optical Music Recognition’s Encoding Step

Optical music recognition is a research field whose efforts have been mainly focused, due to the difficulties involved in its processes, on document and image recognition. However, there is a final step after the recognition phase that has not been properly addressed or discussed, and which is relevant to obtaining a standard digital score from the recognition process: the step of encoding data into a standard file format. In this paper, we address this task by proposing and evaluating the feasibility of using machine translation techniques, using statistical approaches and neural systems, to automatically convert the results of graphical encoding recognition into a standard semantic format, which can be exported as a digital score. We also discuss the implications, challenges and details to be taken into account when applying machine translation techniques to music languages, which are very different from natural human languages. This needs to be addressed prior to performing experiments and has not been reported in previous works. We also describe and detail experimental results, and conclude that applying machine translation techniques is a suitable solution for this task, as they have proven to obtain robust results.

Curiosity Enhances Incidental Object Encoding in 8-month-old Infants

Recent research with adults indicates that curiosity induced by uncertainty enhances learning and memory outcomes, and that the resolution of curiosity has a special role in curiosity-driven learning. However, the role of curiosity-based learning in early development is unclear. Here we presented 8-month-old infants with a novel looking time procedure to explore: 1) whether uncertainty-induced curiosity enhances learning of incidental information; and 2) whether uncertainty-induced curiosity leads infants to seek uncertainty resolution over novelty. In Experiment 1, infants saw blurred images to induce curiosity (Curiosity sequence) or a clear image (Non-curiosity sequence) followed by presentation of incidental objects. Despite looking equally to the incidental objects in both sequences, in a subsequent object recognition phase infants looked longer to incidental objects presented in the Non-Curiosity than in the Curiosity condition, indicating that curiosity induced by blurred pictures enhanced processing of the incidental object, leading to a novelty preference for the incidental object in the Non-Curiosity condition. In Experiment 2, a blurred picture of a novel toy was first presented, followed by its corresponding clear picture paired with a clear picture of a new novel toy side-by-side. Infants showed no preference for either image, providing no evidence for a drive to resolve uncertainty. Overall, the current studies suggest that curiosity in infants has a broad attention-enhancing effect which contrasts with a more focused function of curiosity in adults..

Real-Time Human Detection and Gesture Recognition for On-Board UAV Rescue

Unmanned aerial vehicles (UAVs) play an important role in numerous technical and scientific fields, especially in wilderness rescue. This paper carries out work on real-time UAV human detection and recognition of body and hand rescue gestures. We use body-featuring solutions to establish biometric communications, like yolo3-tiny for human detection. When the presence of a person is detected, the system will enter the gesture recognition phase, where the user and the drone can communicate briefly and effectively, avoiding the drawbacks of speech communication. A data-set of ten body rescue gestures (i.e., Kick, Punch, Squat, Stand, Attention, Cancel, Walk, Sit, Direction, and PhoneCall) has been created by a UAV on-board camera. The two most important gestures are the novel dynamic Attention and Cancel which represent the set and reset functions respectively. When the rescue gesture of the human body is recognized as Attention, the drone will gradually approach the user with a larger resolution for hand gesture recognition. The system achieves 99.80% accuracy on testing data in body gesture data-set and 94.71% accuracy on testing data in hand gesture data-set by using the deep learning method. Experiments conducted on real-time UAV cameras confirm our solution can achieve our expected UAV rescue purpose.

Metamemory and memory discrepancies in directed forgetting of emotional information

Directed Forgetting (DF) studies show that it is possible to exert cognitive control to intentionally forget information. The aim of the present study was to investigate how aware individuals are of the control they have over what they remember and forget when the information is emotional. Participants were presented with positive, negative and neutral photographs, and each photograph was followed by either a Remember or a Forget instruction. Then, for each photograph, participants provided Judgments of Learning (JOLs) by indicating their likelihood of recognizing that item on a subsequent test. In the recognition phase, participants were asked to indicate all old items, irrespective of instruction. Remember items had higher JOLs than Forget items for all item types, indicating that participants believe they can intentionally forget even emotional information—which is not the case based on the actual recognition results. DF effect, which was calculated by subtracting recognition for Forget items from Remember ones was only significant for neutral items. Emotional information disrupted cognitive control, eliminating the DF effect. Response times for JOLs showed that evaluation of emotional information, especially negatively emotional information takes longer, and thus is more difficult. For both Remember and Forget items, JOLs reflected sensitivity to emotionality of the items, with emotional items receiving higher JOLs than the neutral ones. Actual recognition confirmed better recognition for only negative items but not for positive ones. JOLs also reflected underestimation of actual recognition performance. Discrepancies in metacognitive judgments due to emotional valence as well as the reasons for underestimation are discussed.

Mutual Recognition, Transnational Legal Relationships and Regulatory Models

The evolution of the EU legal system reveals a generalisation of mutual recognition variations. On the one hand, these variations are always based on the same structuring elements: mutual trust, equivalence and country-of-origin. Depending on the subject (e.g.taking into account whether harmonisation exists and the EU freedom concerned), each of these structuring elements acquires greater or lesser significance, ultimately determining the degree of conditionality or automaticity at recognition phase. On the other hand, the function of any of those variations creates the legal conditions to establish transnational legal relationships subject to different national legal orders. All these consequences are the result of two fundamental aspects: 1) The EU option by relational regulatory model which ensures the connection between equivalent national rules, using conflict of laws with special techniques. 2) The conferral of transnational effectiveness to national rules and administrative actions to allow the exercise of freedoms granted by EU law.

Spiral Tip Recognition via Deterministic Learning

The spiral tip is vital to the understanding of the spiral wave behaviors. Most studies of spiral tip dynamics focused on the prevention, control, and elimination of spiral wave, while few studies focused on the recognition of spiral wave. In real systems with the spiral wave, the recognition of the spiral wave should be before control or elimination. In the paper, we study the recognition of the spiral tip via deterministic learning. It mainly consists of two phases: the identification phase and the recognition phase. In the identification phase, the dynamics of spiral tips of the training set is accurately identified by using deterministic learning. In the recognition phase, a set of errors is obtained for a test spiral tip by employing an estimator model. Finally, the recognition of test spiral tip is achieved according to the smallest error principle. Numerical simulations based on the spiral tip generated by the Barkley model are performed to demonstrate the effectiveness and feasibility of the proposed method.