Processing System Research Articles

Some m-fold symmetric bi-univalent function classes and their associated Taylor-Maclaurin coefficient bounds

The Ruscheweyh derivative operator is used in this paper to introduce and investigate interesting general subclasses of the function class Σm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\Sigma_{m}$\\end{document} of m-fold symmetric bi-univalent analytic functions. Estimates of the initial Taylor-Maclaurin coefficients |am+1|\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\vert a_{m+1} \\vert $\\end{document} and |a2m+1|\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\vert a_{2 m+1} \\vert $\\end{document} are obtained for functions of the subclasses introduced in this study, and the consequences of the results are discussed. Additionally, the Fekete-Szegö inequalities for these classes are investigated. The results presented could generalize and improve some recent and earlier works. In some cases, our estimates are better than the existing coefficient bounds. Furthermore, within the engineering domain, the utilization of the Ruscheweyh derivative operator can encompass a broad spectrum of engineering applications, including the robotic manipulation control, optimizing optical systems, antenna array signal processing, image compression, and control system filter design. It emphasizes the potential for innovative solutions that can significantly enhance the reliability and effectiveness of engineering applications.

FPGA Implementation of Power-efficient Multipliers for Digital Signal Processing Applications

Background:: Higher power consumption raises chip temperature because it draws more current from the power source, which directly affects how long the batteries survive in portable devices. High temperature affects the dependability and functionality of a circuit, requiring more complex packaging and cooling strategies. One of the most significant challenges in VLSI design is power consumption. The power consumption of the circuit rises with both transistor density and chip complexity. In addition, one of the essential building blocks of hardware in the majority of VLSI applications and digital signal processing systems is the multiplier. Aims and objective:: This study aimed to design and compare array multiplier, Vedic multiplier, and Wallace tree multiplier using variable bit lengths. Methodology:: In this paper, authors designed array multiplier, Vedic multiplier, and Wallace tree multiplier using variable bit lengths. For comparison, the VIVADO tool was used to simulate and synthesize multiplier outputs. Results:: Wallace tree multipliers resulted in 31.153mW, 13.220mW, 4.099mW, and 0.988 mW of power dissipation for 16-bit, 8-bit, 4-bit, and 2-bit, respectively. The best multiplier was designed using different logic like AOI, OAI, NAND-NAND, and NOR-NOR and was compared based on power dissipation. It was observed that 2.256mW power dissipation was observed for NOR-NOR logic, which was minimal among other logics. Conclusion:: The 4-bit Wallace multiplier using NOR-NOR logic was used for FPGA implementation, which can be used in digital signal processing applications.

Token-modification adversarial attacks for natural language processing: A survey

Many adversarial attacks target natural language processing systems, most of which succeed through modifying the individual tokens of a document. Despite the apparent uniqueness of each of these attacks, fundamentally they are simply a distinct configuration of four components: a goal function, allowable transformations, a search method, and constraints. In this survey, we systematically present the different components used throughout the literature, using an attack-independent framework which allows for easy comparison and categorisation of components. Our work aims to serve as a comprehensive guide for newcomers to the field and to spark targeted research into refining the individual attack components.

Feasibility and sustainable development of the medicinal plant sector in the Republic of Moldova

The medicinal plants sector is one of perspective in the Republic of Moldova, which in the past had a good development (1980-1995) and which is currently in a recovery phase with increased interest from agricultural producers and regional markets. The article provides a comprehensive analysis of the international trade in medicinal plants, budgeting of income and expenses for their cultivation, which allows understanding the main players in the target markets and being able to integrate domestic products emerging from international trends. Based on the research and analyses conducted, prominent deficiencies have been identified that extend to the modernization of production and processing technologies of raw materials, marketing strategies for products derived from medicinal plants, the creation of added value, the implementation of ecological systems for production and processing, as well as the practical management of business operations. These are essential for the development of an efficient operational management system, the increased competitiveness of products and enterprises to ensure access to markets. Additionally, strategic conclusions have been formulated for the sustainable development of the medicinal plant sector, necessary for implementation by value chain actors in the industry.

YOLO-ET: A Machine Learning model for detecting, localising and classifying anthropogenic contaminants and extraterrestrial microparticles optimised for mobile processing systems

Imminent robotic and human activities on the Moon and other planetary bodies would benefit from advanced in situ Computer Vision and Machine Learning capabilities to identify and quantify microparticle terrestrial contaminants, lunar regolith disturbances, the flux of interplanetary dust particles, possible interstellar dust, β-meteoroids, and secondary impact ejecta. The YOLO-ET (ExtraTerrestrial) algorithm, an innovation in this field, fine-tunes Tiny-YOLO to specifically address these challenges. Designed for coreML model transference to mobile devices, the algorithm facilitates edge computing in space environment conditions. YOLO-ET is deployable as an app on an iPhone with LabCam® optical enhancement, ready for space application ruggedisation. Training on images from the Tanpopo aerogel panels returned from Japan’s Kibo module of the International Space Station, YOLO-ET demonstrates a 90% detection rate for surface contaminant microparticles on the aerogels, and shows promising early results for detection of both microparticle contaminants on the Moon and for evaluating asteroid return samples. YOLO-ET’s application to identifying spacecraft-derived microparticles in lunar regolith simulant samples and SEM images of asteroid Ryugu samples returned by Hayabusa2 and curated by JAXA’s Institute of Space and Astronautical Sciences indicate strong model performance and transfer learning capabilities for future extraterrestrial applications.

Surprisal From Language Models Can Predict ERPs in Processing Predicate-Argument Structures Only if Enriched by an Agent Preference Principle.

Language models based on artificial neural networks increasingly capture key aspects of how humans process sentences. Most notably, model-based surprisals predict event-related potentials such as N400 amplitudes during parsing. Assuming that these models represent realistic estimates of human linguistic experience, their success in modeling language processing raises the possibility that the human processing system relies on no other principles than the general architecture of language models and on sufficient linguistic input. Here, we test this hypothesis on N400 effects observed during the processing of verb-final sentences in German, Basque, and Hindi. By stacking Bayesian generalised additive models, we show that, in each language, N400 amplitudes and topographies in the region of the verb are best predicted when model-based surprisals are complemented by an Agent Preference principle that transiently interprets initial role-ambiguous noun phrases as agents, leading to reanalysis when this interpretation fails. Our findings demonstrate the need for this principle independently of usage frequencies and structural differences between languages. The principle has an unequal force, however. Compared to surprisal, its effect is weakest in German, stronger in Hindi, and still stronger in Basque. This gradient is correlated with the extent to which grammars allow unmarked NPs to be patients, a structural feature that boosts reanalysis effects. We conclude that language models gain more neurobiological plausibility by incorporating an Agent Preference. Conversely, theories of human processing profit from incorporating surprisal estimates in addition to principles like the Agent Preference, which arguably have distinct evolutionary roots.

Advancing zero-emission maritime solutions: Case study of an ammonia-powered fuel cell system implementation

The international scientific community asserts that ammonia will play an essential role in decarbonizing ships, primarily due to its abundance, availability in ports, and potential for production from renewable sources (green ammonia). Moreover, ammonia can serve as an energy carrier for the hydrogen economy. The paper considers using PEM fuel cell technology as the primary power generation system on board ships to achieve zero-emission conditions. Additionally, it examines the refitting of a mega-yacht as a case study. As a result, the ammonia processing system for hydrogen generation is heavier and bulkier than a conventional Genset, and certain installation constraints must be addressed. However, this study confirms that this installation does not require significant modifications to the original design, and the flotation and trim are verified. However, the limited available space for ammonia storage tanks reduces autonomy to a range of 3–6 days, depending on operating conditions.

A systematic review of in-memory database over multi-tenancy

The significant cost and time are essential to obtain a comprehensive response, the response time to a query across a peer-to-peer database is one of the most challenging issues. This is particularly exact when dealing with large-scale data processing, where the traditional approach of processing data on a single machine may not be sufficient. The need for a scalable, reliable, and secure data processing system is becoming increasingly important. Managing a single in-memory database instance for multiple tenants is often easier than managing separate databases for each tenant. The research work is focused on scalability with multi-tenancy and more efficiency with a faster querying performance using in-memory database approach. We compare the performance of a row-oriented approach and column-oriented approach on our benchmark human resources (HR) schema using Oracle TimesTen in-memory database. Also, we captured some of the key advantages on optimization dimension(s) are the traditional approach, late-materialization, compression and invisible join on column-store (c-store) and row-base. When compression and late materialization are enabled in a query set; it improves the overall performance of query sets. In particular, the paper aims to elucidate the motivations behind multi-tenant application requirements concerning the database engine and highlight major designs over in-memory database for the tenancy approach on cloud.

Design and Application of Electronic Toll Collection Special Situation Processing System

<div>In 2018, the state explicitly proposed to “promote the cancellation of expressway toll stations at provincial boundaries.” Electronic toll collection has become the main toll collection method on expressways. With the construction of ETC toll lanes, the proportion of ETC vehicles in the expressway traffic flow is increasing, and the rapid processing of vehicle special situations is facing challenges. At present, various provinces have adopted various methods to improve the traffic efficiency and transaction success rate of ETC from the issuance link, customer service link, and lane transaction link. According to statistical data, the average transaction success rate of ETC lane is not higher than 99% at present. As of October 2021, the number of ETC users nationwide has reached 256 million, and there are an average of 40 million ETC transactions per day across the network, that is, about 400,000 special cases need to be processed. How to efficiently deal with special vehicles in the new mode and avoid ramp toll stations becoming traffic bottlenecks is a new problem facing at this stage. In order to fit the application scenarios of ETC lanes and solve the problems of time-consuming, labor-intensive, and high safety risks of traditional special situation processing methods, this article sorts out the classification and application scenarios of expressway ETC special situations under the new model. The development status and existing problems of ETC special situation processing are studied and analyzed. This article also establishes a more rapid and convenient special situation handling mechanism, which simplifies the processing process of ETC lane special situations to the greatest extent, and avoids traffic congestion and potential safety problems caused by ETC special situations. Finally, the self-service processing and unattended lanes are realized, which improves the efficiency of special situation processing and ensures fast passage.</div>

Using Natural Language Processing to Identify Home Health Care Patients at Risk for Diagnosis of Alzheimer's Disease and Related Dementias.

This study aimed to: (1) validate a natural language processing (NLP) system developed for the home health care setting to identify signs and symptoms of Alzheimer's disease and related dementias (ADRD) documented in clinicians' free-text notes; (2) determine whether signs and symptoms detected via NLP help to identify patients at risk of a new ADRD diagnosis within four years after admission. This study applied NLP to a longitudinal dataset including medical record and Medicare claims data for 56,652 home health care patients and Cox proportional hazard models to the subset of 24,874 patients admitted without an ADRD diagnosis. Selected ADRD signs and symptoms were associated with increased risk of a new ADRD diagnosis during follow-up, including: motor issues; hoarding/cluttering; uncooperative behavior; delusions or hallucinations; mention of ADRD disease names; and caregiver stress. NLP can help to identify patients in need of ADRD-related evaluation and support services.

Forecasting Maximum Water Level Data for Post Sangkuliman using An Artificial Neural Network Backpropagation Algorithm

Neural Network (NN) is an information processing system that has characteristics similar to biological neural networks. One of the algorithms in NN is Backpropagation Neural Network (BPNN). BPNN is an excellent method for dealing with complex pattern recognition problems. In this research, maximum water level forecasting was carried out at Sangkuliman Post using a Backpropagation Neural Network. This research aims to obtain modeling for forecasting maximum water level, as well as forecasting results using the best model. The research results show that the best model is five neurons in hidden layer 1 and 3 neurons in hidden layer 2 with the backpropagation algorithm, the activation function used is binary sigmoid, the learning rate is 0.001, and the maximum iteration is 10,000,000 with the smallest RMSE result being 1.816. The forecast results for the following 12 periods are 1.672, 1.779, 1.523, 1.271, 1.752, 1.692, 1.335, 1.479, 1.750, 1.779, 1.340, 1.269, and 1.754. Forecasting results can be used by various parties in decision-making and planning in multiple fields, as an example to see the patterns of biological and vegetable life around Sangkuliman Post. Based of forecasting results, certain months show an increase in maximum water levels.

Comparative study on personalized open-domain generative dialogue systems

Developing personalized dialogue systems is a monumental challenge in the rapidly evolving domain of Natural Language Processing (NLP). These systems are engineered to facilitate more natural, intuitive, and user-friendly interactions between humans and machines. Their theoretical significance extends to real-world applications, making them an invaluable asset in today's digital age. Over recent years, this area has garnered immense attention from researchers and academics, becoming a hotbed for innovation and study. This paper aims to contribute to this burgeoning field by conducting an in-depth comparative analysis of the current leading personalized language models. Specifically, this paper will scrutinize models grounded in deep learning algorithms and those that employ reinforcement learning techniques. This research objective is to dissect each approach's unique advantages and limitations. By doing so, the paper hopes to identify actionable avenues for improvement and optimization. Furthermore, the paper will offer a forward-looking perspective, outlining potential advancements and innovations that could shape the future landscape of personalized dialogue systems in Natural Language Processing.

A framework for interpreting the relationship between risk-taking and self-control in adolescence

Adolescent risky behaviors are often interpreted as products of self-control failures stemming from a developmental mismatch between reward processing and cognitive control systems. However, adolescents – much like adults – may also engage in risky behaviors because of conscious and deliberate (even if objectively poor) decisions. It is not easy to distinguish between these two scenarios because when people fail in self-control they actually do things they want to do. We build on philosopher Harry Frankfurt’s classical work on free will to provide a framework for determining when and why a given risky behavior stems from a failure of self-control. This framework enables the proposal of clear and reasonable criteria that can be used to clarify the relationship between adolescent risk-taking and self-control.

Competencies in Higher Education and the Labor Market: A Sociocognitive Analysis of the Problem of Integration

Introduction. Professional competencies developed by the education system do not provide the high degree of integration with the real economy at the Russian labor market. The sociocognitive approach to social institutions as information processing systems suggests that this may be partly due to different languages used by employers and educational structures for describing competencies. The purpose of this study is to test the hypothesis of this mismatch drawing on the example of the applied sociology sector. Materials and Methods. The case study method was applied to the material of the archive of correspondence on personnel issues of a company from the field of applied sociology, which worked in 2012–2019 (46 papers). The hypothesis that employers and educational system use a different language for describing competencies was tested. Content analysis method was applied to the largest Russian job portals: hh.ru, superjob.ru and Avito.Rabota. The hypothesis of non-use of educational standards for describing competencies in the labor market was tested. Results. Both hypotheses were confirmed. Based on the results of the correspondence archive analysis, it was proved that employers use a language different from that of the education system when describing requirements to employees (including their competencies). The results of content analysis revealed the phenomenon of almost complete isolation of the labor market from the formal standards of higher education (educational standards for describing the competencies of sociologists were not used in a single vacancy or a resumé out of more than 32 thousand found on the portals). Discussion and Conclusion. To overcome the lack of demand for graduates in the labor market, it is necessary to overcome the existing language gap between the education system and employers. To this end, the system of validation of competencies should be changed. The source of their description should not be the education system itself, the texts and ideas that are born in it, but the requirements of employers, verbalized by them in the description of vacancies. Descriptions of competencies that do not meet the widely accepted requirements of employers should be automatically excluded from professional education standards as invalid. The results contribute to searching critical problems in the educational system and adapting it to the current needs of the labor market.

Development of the logical system for forecasting wind characteristics in the urban conditions

This study is devoted to the development of a logical system to improve the efficiency of wind turbines with the identification of patterns and relationships between wind speeds and directions in urban conditions. The results of the study made it possible to identify approaches to use wind energy efficiently to produce electricity with minimal losses for the power supply of urban facilities. Data Mining methods were applied, the dependencies between weather data were examined. Machine Learning algorithms have also been applied to forecast wind speed and direction in order to increase the efficiency of power generation. A thorough exploratory data analysis was carried out, including visualization of meteorological data and the study of their statistical indicators. To predict wind speed, numerical indicators such as air temperature, wind direction, pressure tendency and atmospheric pressure were taken. Linear Regression, Decision Tree and Support Vector Machine were taken as Machine Learning models. The results revealed that the Random Forest turned out to be the most effective, with a mean squared error of 0.302. According to the results of the research, it was found that the initial data on the characteristics of the wind are highly distorted when it circulates in the urban environment due to the influence of urban development. However, the use of a logical system made it possible to predict and adapt the operation of wind turbines to changes in wind characteristics with minimal losses. A pivotal outcome of this study is the creation of a real-time data processing system, enabling accurate predictions of wind characteristics from speed and direction measuring devices. Additionally, a MATLAB Simulink-based computer model was developed to explore the logical system's influence on wind turbine performance. The findings underscore the efficacy of employing forecasting within a logical system framework to harness wind energy efficiently, contributing to sustainable urban energy solutions

Compact rover surveying and laser scanning for BIM development.

This paper presents a custom made small rover based surveying, mapping and building information modeling solution. Majority of the commercially available mobile surveying systems are larger in size which restricts their maneuverability in the targeted indoor vicinities. Furthermore their functional cost is unaffordable for low budget projects belonging to developing markets. Keeping in view these challenges, an economical indigenous rover based scanning and mapping system has developed using orthogonal integration of two low cost RPLidar A1 laser scanners. All the instrumentation of the rover has been interfaced with Robot Operating System (ROS) for online processing and recording of all sensorial data. The ROS based pose and map estimations of the rover have performed using Simultaneous Localization and Mapping (SLAM) technique. The perceived class 1 laser scans data belonging to distinct vicinities with variable reflective properties have been successfully tested and validated for required structural modeling. Systematically the recorded scans have been used in offline mode to generate the 3D point cloud map of the surveyed environment. Later the structural planes extraction from the point cloud data has been done using Random Sampling and Consensus (RANSAC) technique. Finally the 2D floor plan and 3D building model have been developed using point cloud processing in appropriate software. Multiple interiors of existing buildings and under construction indoor sites have been scanned, mapped and modelled as presented in this paper. In addition, the validation of the as-built models have been performed by comparing with the actual architecture design of the surveyed buildings. In comparison to available surveying solutions present in the local market, the developed system has been found faster, accurate and user friendly to produce more enhanced structural results with minute details.

Algorithm-Driven Tele-otoscope for Remote Care for Patients With Otitis Media.

The COVID-19 pandemic has spurred a growing demand for telemedicine. Artificial intelligence and image processing systems with wireless transmission functionalities can facilitate remote care for otitis media (OM). Accordingly, this study developed and validated an algorithm-driven tele-otoscope system equipped with Wi-Fi transmission and a cloud-based automatic OM diagnostic algorithm. Prospective, cross-sectional, diagnostic study. Tertiary Academic Medical Center. We designed a tele-otoscope (Otiscan, SyncVision Technology Corp) equipped with digital imaging and processing modules, Wi-Fi transmission capabilities, and an automatic OM diagnostic algorithm. A total of 1137 otoscopic images, comprising 987 images of normal cases and 150 images of cases of acute OM and OM with effusion, were used as the dataset for image classification. Two convolutional neural network models, trained using our dataset, were used for raw image segmentation and OM classification. The tele-otoscope delivered images with a resolution of 1280 × 720 pixels. Our tele-otoscope effectively differentiated OM from normal images, achieving a classification accuracy rate of up to 94% (sensitivity, 80%; specificity, 96%). Our study demonstrated that the developed tele-otoscope has acceptable accuracy in diagnosing OM. This system can assist health care professionals in early detection and continuous remote monitoring, thus mitigating the consequences of OM.

Hierarchical Principal Components for Data-Driven Multiresolution fMRI Analyses.

Understanding the organization of neural processing is a fundamental goal of neuroscience. Recent work suggests that these systems are organized as a multiscale hierarchy, with increasingly specialized subsystems nested inside general processing systems. Current neuroimaging methods, such as independent component analysis (ICA), cannot fully capture this hierarchy since they are limited to a single spatial scale. In this manuscript, we introduce multiresolution hierarchical principal components analysis (hPCA) and compare it to ICA using simulated fMRI datasets. Furthermore, we describe a parametric statistical filtering method developed to focus analyses on biologically relevant features. Lastly, we apply hPCA to the Human Connectome Project (HCP) to demonstrate its ability to estimate a hierarchy from real fMRI data. hPCA accurately estimated spatial maps and time series from networks with diverse hierarchical structures. Simulated hierarchies varied in the degree of branching, such as two-way or three-way subdivisions, and the total number of levels, with varying equal or unequal subdivision sizes at each branch. In each case, as well as in the HCP, hPCA was able to reconstruct a known hierarchy of networks. Our results suggest that hPCA can facilitate more detailed and comprehensive analyses of the brain's network of networks and the multiscale regional specializations underlying neural processing and cognition.

Breast Tumour Segmentation Using Advanced UNet with Saliency, Channel, and Spatial Attention Models

Cancers are getting pretty common these days and in that the second most common cancer in the world after lung cancer is breast cancer. The primary screening techniques for early diagnosis of cancerous nodules in women breast are Ultrasound, Mammography, and MRI analysis. However, accurately identifying and outlining the boundaries of tumor regions remains challenging due to their unpredictable shapes, random variations, and blurry outlines. Currently, the CAD based image analysis systems have gained huge attention in biomedical image processing systems along with Machine Learning (ML) and Deep Learning (DL) methods. The conventional machine learning methods suffer from accuracy related issues therefore deep learning-based schemes have been adopted. Specifically, UNet based approach is widely employed for biomedical image segmentation. This research focuses on development of a UNet based deep learning architecture for breast cancer image analysis and segmentation. The proposed model improves the segmentation accuracy by adding saliency model, channel and spatial attention model to consider the low- and high-level features. These modules help to achieve the clear boundary during segmentation. The performance of proposed model is evaluated by using publicly available mammogram dataset known as DDSM dataset.

Exploration, detection, and mitigation: Unveiling gender bias in NLP

Natural Language Processing (NLP) systems have a mundane impact, yet they harbour either obvious or potential gender bias. The automation of decision-making in NLP models even exacerbates unfair treatment. In recent years, researchers have started to notice this issue and have made some approaches to detect and mitigate these biases, yet no consensus on the approaches exists. This paper discusses the interdisciplinary field of linguistics and computer sciences by presenting the most common gender bias categories and breaking them down with ethical and artificial intelligence approaches. Specific methods for detecting and minimizing bias are shown around biases present in raw data, annotator, model, and the linguistic gender system. In this paper, an overview of the hotspots and future perspectives of this research topic is presented. Limitations of some detection methods are pinpointed, providing novel insights into future research.