Main Modules Research Articles

ScabyNet, a user-friendly application for detecting common scab in potato tubers using deep learning and morphological traits

Common scab (CS) is a major bacterial disease causing lesions on potato tubers, degrading their appearance and reducing their market value. To accurately grade scab-infected potato tubers, this study introduces “ScabyNet”, an image processing approach combining color-morphology analysis with deep learning techniques. ScabyNet estimates tuber quality traits and accurately detects and quantifies CS severity levels from color images. It is presented as a standalone application with a graphical user interface comprising two main modules. One module identifies and separates tubers on images and estimates quality-related morphological features. In addition, it enables the extraction of tubers as standard tiles for the deep-learning module. The deep-learning module detects and quantifies the scab infection into five severity classes related to the relative infected area. The analysis was performed on a dataset of 7154 images of individual tiles collected from field and glasshouse experiments. Combining the two modules yields essential parameters for quality and disease inspection. The first module simplifies imaging by replacing the region proposal step of instance segmentation networks. Furthermore, the approach is an operational tool for an affordable phenotyping system that selects scab-resistant genotypes while maintaining their market standards.

Local to global purification strategy to realize collaborative camouflaged object detection

The purpose of camouflaged object detection is to detect objects in images that are not easily perceived by human eyes. Aiming at the problems of low recognition performance and unsatisfied texture information extraction in the complex environment in the current camouflaged object detection algorithms, we propose to improve the accuracy by simultaneously detecting a group of images containing the same camouflaged category. Therefore, we put forward a novel method termed local to global purification network (LGPNet) for collaborative camouflaged object detection. Our method comprises two main modules: the Local Detail Mining module (LDM) and the Global Intra-group Feature Extraction module (GIFE). The LDM is designed to exploit diversified detail information via different adaptive kernels and receptive field mechanisms locally, and the GIFE module is invented for feature enhancement and multi-level information aggregation. Specifically, the GIFE first utilizes channel attention and spatial attention mechanisms to enhance high-level semantic information and then aggregates the intra-group characteristics by level. Extensive experiments on CoCOD8K dataset and 4 COD benchmark datasets illustrate the effectiveness and superiority of our method compared to SOTAs.

An integrated microfluidic platform for on-demand single droplet dispenser with high accuracy by electrohydrodynamic (EHD) printing technique

Droplet microfluidics has revolutionized single-cell analysis, allowing for ultra-high-throughput sorting of droplets to meet the demands of single-cell sorting. However, accurately and efficiently dispensing target droplets into specific containers for downstream analysis has remained a challenge. Here, we present an integrated microfluidic droplet dispenser based on the electrohydrodynamic (EHD) principle, capable of on-demand single droplet dispensing with high accuracy. Our system uses optical signal activation and EHD actuation to isolate and dispense droplets. The microfluidic droplet auto dispenser consists of four main modules: a pump-drive chip module, an optical automatic signal recognition module, a high voltage control module, and an automated X-Y translation stage module. By optimizing the chip structure and voltage parameter, this platform successfully isolates single Escherichia coli (E. coli) cells onto a Petri dish for downstream cultivation and heterogeneity analysis. In addition, the dispenser microfluidic system dispenses the sorted droplets in a "one droplet, one well" manner, providing an automated method for droplet interface from the micro- to macro- platform. This dispenser system provides a platform for phenotypic screening of single colony pick and single cell analysis

Learning Neighbor User Intention on User–Item Interaction Graphs for Better Sequential Recommendation

The task of sequential recommendation aims to predict a user’s preference by analyzing the user’s historical behaviours. Existing methods model item transitions through leveraging sequential patterns. However, they mainly consider the target user’s behaviours and dynamic characteristics, while often ignoring high-order collaborative connections when modelling user preferences. Some recent works try to use graph-based methods to introduce high-order collaborative signals for sequential recommendation. However, these methods are flawed by two problems: the sequential patterns cannot be effectively mined and their way of introducing high-order collaborative signals is not suitable for sequential recommendation. To address these problems, we propose to fully exploit sequence features and model high-order collaborative signals for sequential recommendation. We propose a N eighbor user I ntention-based S equential Rec ommender (NISRec), which utilizes the intentions of high-order connected neighbor users as high-order collaborative signals in order to improve recommendation performance for the target user. The NISRec contains two main modules: the neighbor user intention embedding module (NIE) and the fusion module. The NIE module describes both the long-term and short-term intentions of neighbor users and aggregates them separately. The fusion module uses these two types of aggregated intentions to model high-order collaborative signals in both the embedding process and user preference modelling phase for recommendations of the target user. Experimental results show that our new approach outperforms the state-of-the-art methods on both sparse and dense datasets. Extensive studies further show the effectiveness of the diverse neighbor intentions introduced by the NISRec.

Fraction of C. d. collilineatus venom containing crotapotin protects PC12 cells against MPP + toxicity by activating the NGF-signaling pathway.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease. There is no effective treatment for neurodegenerative diseases. Snake venoms are a cocktail of proteins and peptides with great therapeutic potential and might be useful in the treatment of neurodegenerative diseases. Crotapotin is the acid chain of crotoxin, the major component of Crotalus durissus collilineatus venom. PD is characterized by low levels of neurotrophins, and synaptic and axonal degeneration; therefore, neurotrophic compounds might delay the progression of PD. The neurotrophic potential of crotapotin has not been studied yet. We evaluated the neurotrophic potential of crotapotin in untreated PC12 cells, by assessing the induction of neurite outgrowth. The activation of the NGF signaling pathway was investigated through pharmacological inhibition of its main modulators. Additionally, its neuroprotective and neurorestorative effects were evaluated by assessing neurite outgrowth and cell viability in PC12 cells treated with the dopaminergic neurotoxin MPP+ (1-methyl-4-phenylpyridinium), known to induce Parkinsonism in humans and animal models. Crotapotin induced neuritogenesis in PC12 cells through the NGF-signaling pathway, more specifically, by activating the NGF-selective receptor trkA, and the PI3K/Akt and the MAPK/ERK cascades, which are involved in neuronal survival and differentiation. In addition, crotapotin had no cytotoxic effect and protected PC12 cells against the inhibitory effects of MPP+ on cell viability and differentiation. These findings show, for the first time, that crotapotin has neurotrophic/neuroprotective/neurorestorative potential and might be beneficial in Parkinson's disease. Additional studies are necessary to evaluate the toxicity of crotapotin in other cell models.

Flow modulation mechanism in a cylinder with corrugated surfaces

Flow modulation mechanism in a cylinder with corrugated surfaces is investigated by wind tunnel experiments at Reynolds number Re=25 600. Experimental results include surface pressure measurement for the cylinder wall and particle image velocimetry (PIV) for the wake flow. A pair of corrugated surfaces are symmetrically installed on the cylinder wall. Corrugated surfaces are distributed at three different locations on the cylinder wall, i.e., the windward part (case 1), leeward part (case 2), and lateral part (case 3). Experimental results show that corrugated surfaces can modify surface pressure, aerodynamic forces, and vorticity evolution of the cylinder flow. Compared with the natural cylinder (baseline case), the mean drag and fluctuating lift forces of case 1 are reduced by 58% and 82%, which are optimal among all test cases. Flow modulation effects of case 2 on global cylinder wake flow are unobvious and that of case 3 are between cases 1 and 2. Corrugated surfaces can also modify modal properties of proper orthogonal decomposition (POD) of the cylinder wake. Moreover, characteristics of recirculation bubbles, velocity deficits, turbulence kinetic energy, and Reynolds stresses in the wake are all modulated. The main flow modulation mechanism is that shear-layer shapes and streamline distributions near the corrugated surfaces are changed based on zoom-in PIV results on the cylinder near-wall region.

See Carbon: Real-time IoT Monitoring System for Motorcycle Vehicle Carbon Emissions

This research introduces a comprehensive solution for monitoring Carbon emissions from motorcycle vehicles. Utilizing specialized gas sensors for Carbon Monoxide (CO), Carbon Dioxide (CO₂), and Hydrocarbon (HC), coupled with an Arduino Nano main microcontroller, GSM module, and GPS module encased in CFRP casing, the system ensures real-time monitoring. A dedicated website and mobile companion enable users to easily track and analyze emissions history, providing suggestions for reduction. This implementation aims to empower motorcycle drivers to monitor their vehicle's health and reduce their Carbon footprint.

Theoretical terahertz spectroscopy of free radical solutes in solution: an EPR spin probe in water.

Free radical species are used as spin labels in electron paramagnetic resonance (EPR) spectroscopy of biomolecular systems in water, for instance in the frame of Overhauser dynamic nuclear polarization (ODNP) relaxometry to probe the local hydration water dynamics close to protein surfaces in aqueous environments. Widely used in this context are nitroxide spin probes such as TEMPO, PROXYL or MTSL derivatives. Here, we study the THz spectroscopy of HMI (2,2,3,4,5,5-HexaMethylImidazolidin-1-oxyl) in water at ambient conditions which has been recently investigated as to how its EPR properties depend on its solvation pattern in water. To enable theoretical THz spectroscopy of molecular radicals in solution, we have generalized well-established methodologies for THz spectral decomposition of closed-shell systems, namely the supermolecular solvation complex (SSC) and cross-correlation analysis (CCA) techniques, to open-shell polyatomic solute species in water. Based on this methodological advance, we have decomposed and assigned the THz response of HMI including its solvation shell by employing the generalized SSC and CCA methods to cope with the open-shell character of this free radical solute, in particular its unpaired electron localized at the nitroxy group. We reveal that the main modulations of the far-IR spectrum of HMI are dominated by the low-frequency intramolecular modes of the spin probe molecule itself while the solvation of its two hydrogen bonding sites contribute much less intensely in this spectral window. Finally, we have computed THz spectra of HMI with its local solvation water with an aim to provide a theoretical analogue of experimental THz difference spectroscopy. Beyond the specific case, our decomposition methodology that now is able to include open shells can be applied in future work to analyze the low-frequency vibrational response of the solvation shell of other free radicals in solution.

Low Noise Operational Amplifier using Current Driving Bulk by CMOS Technology

A reverse substrate bias operational amplifier using existing driver technology has been designed and implemented using 0.25μm CMOS process to achieve high noise performance. Op amps are efficient and versatile devices. Its applications include signal conditioning, custom conversion, analog equipment, competitive simulation, and various electronic industries meeting custom design requirements. As the trend of low voltage devices continues, designing high-performance analog circuits becomes increasingly difficult. The main module of analog circuits is the work amplifier. In large electronic devices, there is a trade-off between speed, power, and gain, among other drawbacks. Additionally, in order to reduce costs and integrate analog and digital circuits on a single die, analog designers must face the challenges of using the CMOS process. Design and implementation are done in 0.25μm technology using TSMC libraries and with the help of Tanner tools.

Development of a Prototype of an Automated Dog Feeding System

In large kennels, canine services have difficulties in feeding dogs. Known dog feeding systems are studied and the development of an automated system using Omron PLC is described. The developed technological scheme is given. For simplification of installation, the equipment is divided into modules. A table of signal addresses used in Omron series industrial controllers is given. The system of logical equations for controlling the main modules of the equipment device drive is given. Purpose of the research: development of the project of the automated system of dog feeding in kennels. Materials and methods: the theory of finite automata, the theory of synthesis of logical equations and ladder diagram technique were used. The software was developed using Software CX’, ’One. Results: a laboratory bench prototype of the automated feeding system was developed. Practical significance: implementation of this system in departmental organizations will reduce the time spent on food dosing, reduce the probability of error associated with the human factor, as well as provide the process of feeding dogs while reducing the labor input of service personnel.

Research and implementation of virtual reality technology to build 3D application that simulates the disassembly and assembly of equipment and weapons (DASim)

Disassembly and assembly of equipment and weapons (E&W) is a regular work of great importance in the military. Implementing the right procedures and quickly will help increase efficiency and ensure the safety for the citizens and weapons. However, the training and training of cadres, technical staff, trainees and soldiers to master the skills of disassembling equipment and weapons takes a lot of time and resources, and there are also many risks in the practice process. Virtual reality (VR) application is a time-saving and effective solution for training knowledge and training weapons disassembly skills, not only allowing users to interact with a virtual environment like reality, but also minimize potential risks in the process of practicing with real equipment and weapons, without mastering the process of disassembly and maintenance.
 In this paper, the research team will present the approach, build an algorithm and design a software program to simulate the disassembly of weapons equipment using 3D virtual reality technology. The software consists of three main modules: theory, practice, testing, helping officials, technical staff, trainees, and soldiers to master knowledge from theory to practice process, forming skills before operating directly on real weapons. The study concretizes through the example of establishing the content of the structure, disassembly process and practice of disassembling AK submachine guns, 7.62 mm PKMS. In addition, the paper will also evaluate the effectiveness of the use of VR in training and training personnel to disassemble and assemble weapons.

Integrating Industrial Property Education into the Curricula of Technical Studies

The paper presents some aspects and examples of handling intellectual property (IP) at universities or R&D institutes regarding how to promote and to protect innovative results or products. The first example is an outline of a strategy for the implementation of a university policy in the field of IP. The second example is the European IPEDU project and its implications in the continuing education of librarians. The project is an original initiative with ethical implications for and considerations of IP. The paper focuses on the main result of the project: the postgraduate course with five modules aimed at librarians and recent graduates to strengthen their knowledge in the field of IP and industrial property. The structure of the course and the main modules are outlined. This course is considered essential for librarians and non-law students, as understanding the wide range of relevant information and practical implications of IP is critical in today’s world of information-based careers. As a freshly accredited programme, it needs evaluation, because there has not yet been any feedback from the learners.

The Specifics of the Elective “School of Leadership” Using Repositories of Online Resources

Objective. Modern online library resources play an important role in the organisation and conduct of the School of Leadership elective. The purpose of this study is to consider the peculiarities of conducting the elective “School of Leadership” for applicants using online resource repositories. Methods. A comprehensive approach to the study of the problem led to the use of such theoretical research methods as the analysis of philosophical, pedagogical, psychological and sociological literature to identify the specifics of leadership and leadership competence, the analysis of repositories of online resources, synthesis, comparison, generalization of various scientific approaches to the basic concepts of the study and determination of the specifics of the organization and conducting the elective “School of leadership”. Results. The work clarifies the essence of the concepts “leadership competence”, “elective”, “elective class”, discloses the peculiarities of organization and conduct, main tasks, content modules, organizational and pedagogical conditions of the elective “School of leadership”. The work describes the knowledge and skills that students will be able to improve and develop thanks to a properly planned and organized elective using the resources of online repositaries. Particular attention is paid to the features of ensuring the effectiveness and efficiency of the elective for the formation of students’ leadership competence and the content of the elective “School of leadership”, which depends on the achievement of the set goal, aims and tasks for the development of student-leaders. Conclusions. The main results that were obtained within the framework of this scientific research should be considered the substantiation of the concept of “elective” and the description of the specifics of the organization and the conducting of the elective “School of leadership” for students. The results of this scientific research, as well as the conclusions formulated on their basis, are of practical value for scientists, teachers of educational institutions, psychologists and other specialists who are engaged in the study of leadership problems and the formation of leadership competence and the search for effective methods, means and forms of work for the development of applicants’ leadership potential.

DIGITALIZATION OF THE HOSPITALITY SPHERE AS THE BASIS FOR CREATING A COMPETITIVE BUSINESS

The competitiveness of modern business is determined by the level of consumer accessibility to the final product. The sphere of hospitality is specific, since accessibility to a service or services determines the level of interest of guests in receiving them. Innovation has become the basis of the work of modern enterprises and a tool for their adaptation to functioning in crisis situations. The intensity of saturation of enterprises of hotel and restaurant business with digital innovations determines the convenience of the guest in obtaining services and in cooperation. The variety of software products available on the market of digital tools challenges the enterprise to choose the most convenient and appropriate software. It is important to investigate the level of need for digital tools for hospitality businesses and explore the available and most accessible of the entire range. To this end, the article assesses the dynamics of temporary accommodation services and catering provided by hospitality enterprises in Ukraine. In the process of studying the dynamics of the structure of services for temporary accommodation and catering provided by hospitality enterprises in Ukraine, positive changes were noted. In the process of studying software products on the market, it is noted that it is worth paying attention to Servio software products, which serve as the main tool for managing the hotel and restaurant business, and have a number of specialized modules. It is proposed to integrate the SERVIO POS software product into the business processes of the hotel and restaurant business, when using which the enterprise will receive an important tool for organizing a restaurant, cafe or bar management system. The article explores the functionality, possibilities and advantages of integrating the SERVIO POS software product into the activities of hospitality enterprises.
 In the course of the study, it was found that the use of mobile applications of the Poket-line from SERVIO will provide an opportunity to improve the business activities of hospitality enterprises. The algorithm of using the SERVIO POS Pocket mobile application in the work of hospitality institutions is presented.
 An important innovative tool for hotel management has been proposed for the operation of hotels - the multifunctional software product HMS. Main modules and functionality of HMS software are presented.
 One of the types of digital settings for hospitality enterprises is the online module SERVIO HMS RESERVATION, which can be installed free of charge on the website of a hotel or other hospitality institution.
 The article offers an algorithm for the use and functionality of the SERVIO HMS RESERVATION online module in the service system of hotel business enterprises.
 It has been studied that the availability of online service forms an additional segment of the market and provides for additional economic benefits for hospitality enterprises.

EMViT-Net: A novel transformer-based network utilizing CNN and multilayer perceptron for the classification of environmental microorganisms using microscopic images

Environmental microbes are certainly present in our surroundings since they are essential to the growth and survival of human advancement. The detailed analysis of environmental microorganisms (EMs) is very important to recognize, understand and make use of microbes as well and prevent damage. Extracting the discriminatory features from a limited-size dataset is very challenging for a deep learning model and a pure transformer-based network cannot achieve good classification results on a limited-size dataset due to the lack of muti-scale features. In this study, a novel vision transformer-based deep neural network is proposed by integrating the transformer with CNN for the classification of EM using microscopic images. The proposed network EMViT-Net has three main modules: a transformer module, a CNN module and a multilayer perceptron module. The transformer model extracted multiscale features to generate more discriminatory information from the images. A new separable convolutional parameter-sharing attention (SCPSA) block is integrated with the CNN module in the core of EMViT-Net, which makes the model robust to capture the local and global features, and simultaneously reduces the computational complexity of the model. The data augmentation is performed to introduce the variability in the dataset and counter the problem of overfitting and data imbalance. After extensive experiments and detailed analysis, it has been determined that the proposed model EMViT-Net outperforms the other existing methods and achieves state-of-the-art results with an accuracy of 71.17% which proves the effectiveness of the model for the classification of environmental microbes.

Construction of Online Interactive Teaching Platform for College English Based on Multilayer Perceptual Machine Modeling

Abstract Most of the existing English online teaching platforms are just digital replicas of offline teaching, which cannot fully utilize the advantages of information technology. Based on the learning theory of constructivism, this paper builds the functional architecture of a personalized English interactive teaching platform and completes the design of the main functional modules. According to the quantitative results of the platform’s user proposition requirements, a matrix description method for English test papers is proposed, and an algorithm is designed to realize the automatic generation of test papers. Meanwhile, the resource recommendation mechanism of the English platform is optimized using a multilayer perceptual machine. Unstructured English learning resources are processed based on the word vector representation method, and for the problem of insufficient processing of nonlinear data in the DN-CBR model, multilayer training is carried out using the MLP to improve the perceptual degree of this part of the data, and the design goals are verified through the test of the teaching platform. The correlation coefficient between self-assessment difficulty and topic difficulty is 0.368, and the correlation coefficient between self-assessment difficulty and positive error is 0.163, indicating that there is a moderate correlation between learners’ self-assessment difficulty and the difficulty of the system topics and between learners’ self-assessment difficulty and positive error. The MLP-based pushed questions’ difficulty is adapted to the learner’s personalization level by dynamically revising it based on their learning process data.

Un’introduzione al concetto di apocalisse dialogica. Frantumi, cocci, relitti e scorie degli scambi di battute nella poesia italiana del Novecento

The expression «dialogical apocalypse» defines the modalities of dysfunctional communication in twentieth-century Italian poetry, where dialogue does not manifest itself as adherence to mutual communication, but refers to the malfunctioning of communication itself. In lyrical texts, verbal interactions are constantly inserted in a complex network of communication pathologies and dysfunctions. The anti-dialogical function acts on multiple levels: structural (for example, by skipping a turn), pragmatical (as the answers that do not respond), thematic (in particular, the tendency to return to intractable topics, such as death), and relational (the exchange is constituted in conflict and antagonism). The present article aims to frame the anti-dialogical function by proposing a metaphorical juxtaposition between the dialogic process and the Fifteen puzzle, through the comparison between some textual samples (from Gozzano, Caproni, Sereni and Sanguineti) and the main modules of pragmatic incongruity.

Mapping of Deep Neural Network Accelerators on Wireless Multistage Interconnection NoCs

In the last few decades, the concept of Wireless Network-on-chip (WiNoC) has emerged as a promising alternative for Multiprocessor Systems on Chip (MPSOC) to achieve reliable and scalable communication. Worth recalling in this regard is that our research team has already designed, verified and evaluated Multistage Interconnection Networks (MIN) in this field. With respect to the present work, we consider proceeding with further exploring our thoughts on this research area. Firstly, we propose the design and performance evaluation of a hybrid (wireless/wired) MIN, analysing how this augmented network can potentially improve not only the average delay, but also energy consumption. Secondly, we continue with examining the implementation of our advanced DELTA-based MIN architecture on Deep Neural Network (DNN) accelerators, while accounting for its potential regularity and scalability in simultaneously maintaining an effective power efficiency and lower latency throughout the DNN operating process. In this context, several metrics have been evaluated in regard to three DNN application cases through implementation of their main respective modules.

A locational false data injection attack detection method in smart grid based on adversarial variational autoencoders

Stealthy FDIA (False Data Injection Attack) is a serious cyber threat that can modify state estimation of smart grid through maliciously altering the measurement data, but can’t be detected by traditional bad data detection system in smart grid. There exist two weakpoints for numerous deep neural networks (DNNs) based data-driven schemes against FDIA. First, they mainly focus on detecting the presence of FDIA, but fail to localize the specific bus/nodes affected. Second, their performance is not sufficiently desirable under small attack, i.e., anomalies caused by the attack closely resemble normal data. To address the above issues, this paper proposes an effective locational FDIA detection framework based on data reconstruction, AT-GVAE, which seamlessly integrates the variational autoencoders (VAEs) and generative adversarial network paradigm. Specifically, our contributions are threefold. First, the proposed AT-GVAE framework is novelly composed of two main modules: the generative VAE_G and discriminative VAE_D that both play dual roles: reconstruct data from jointly learning distributions of data and latent feature space, and meanwhile play Minmax game with adversarial way. Second, multiple-layer gated recurrent units (GRUs) are utilized as the basic structure of the encoder and decoders in both VAEs, to characterize the temporal correlations of measurement data sequence. Additionally, the self-attention mechanism is used to enhance the expressive ability of GRU based VAEs. Then, the anomaly score for each busbar in the smart grid is determined by comparing the residual between the observed measurement and the outputs of VAE_G and VAE_D, enabling the localization of FDIA. Finally, thorough experiments on multiple power systems with series data of total 3456 measurements demonstrate that, in terms of multiple typical metrics, including false alarm rate vs. detection probability, AUC-ROC, and AUC-PR, our proposed framework outperforms the state-of-the-art GRU, VAE and adversarial training based FDIA detection schemes.

COHERENT AMPLITUDE MODULATION IN A LASER GAS SENSOR

The article is devoted to a current topic in science and technology -determining the type and concentration of gas using a laser. At close values of green laser radiation intensity and bias voltage, interference patterns of the time series of the photodiode output signal were obtained. The degree of coherence (more than ~0.1) made it possible to distinguish between the types of gases and their concentrations. Signal coherence was controlled by Allan deviation values.The novelty of the research method is the choice of the harmonic of the electrical network asthe main modulation frequency. It is shown that the correlation functions and the corresponding power spectra are sensitive to low-frequency fluctuations of molecules and their clumps. This allows the results and methods of this work to be used in specific cases instead of large-sized and expensive complexes of optical instruments at room pressure and temperature.