Customizable System Research Articles

Personalization of thoracoabdominal CT examinations using scanner integrated clinical decision support systems – Impact on the acquisition technique, scan range, and reconstruction type

ObjectivesThis study evaluates the impact of a scanner-integrated, customized clinical decision support system (CDSS) on the acquisition technique, scan range, and reconstruction in thoracoabdominal CT. Materials and MethodsWe applied CDSS in contrast–enhanced examinations of the trunk with various clinical indications on a recent scanner with the capability of dual-energy CT (DECT), anatomic landmark detection (ALD), and iterative metal-artifact reduction (MAR). Simple and comprehensive questions about the patient’s breath hold capability, the anatomical region of interest, and metal implants can be answered after the localizer. The acquisition technique (single energy, SECT, or dual energy), scan range (chest-abdomen-pelvis or chest-abdomen), and reconstruction technique (with or without MAR) were then automatically adapted in the examination protocols in coherence with these selections. Retrospectively, we compared the usage rates for these techniques in 624 examinations on the study scanner with 740 examinations on a comparable scanner without CDSS. Subgroup analysis of effective dose (ED), scan duration, and image quality (IQ) was performed in the study group. ResultsCDSS leads to an increased usage rate of DECT (64.4% vs. 2.8%) and MAR (75.4% vs. 44.0%). All scan range adaptations by ALD were successful. The resulting subjective IQ between single energy and DECT acquisitions was comparable (all p > 0.05). Scan duration was significantly longer in DECT than in SECT (16.9 s vs. 6.5 s; p < 0.001). However, the objective IQ was significantly higher in DECT (CNRD 2.1 vs. 1.8; p < 0.01), and the ED significantly lower (6.7 mSv vs. 7.6 mSv; p = 0.004). ConclusionCDSS for thoracoabdominal CT leads to a substantially increased usage rate of innovative techniques during acquisition and reconstruction. Patients with adapted protocols benefit from improved image quality and increased post-processing options at lower radiation doses.

Module-based scientific physics engine architecture

The ever-evolving landscape of digital simulations demands innovative frameworks to achieve both realism and efficiency. The research highlights the issues of modern software architecture for physics simulation. The new architecture was developed to mitigate scalability and flexibility issues. The essence of the proposed architecture resides in the convergence of three pivotal concepts: the modular physics engine, the physics pipeline, and the Entity Component System (ECS) pattern. The modular physics engine represents a paradigm shift in simulation design. Compartmentalizing functionalities into modules, this approach fosters flexibility and reusability, enabling efficient construction of specialized simulations. The physics pipeline orchestrates simulations through structured stages, analogous to graphics pipelines. It guides dynamic forces, collisions, and interactions, optimizing resource use and integrating custom systems for accuracy. Complementing these, the ECS pattern decouples data and behavior, facilitating the construction of user defined physical pipeline comprised of loosely coupled modules. Combined with the modular physics engine and physics pipeline, ECS forms a comprehensive approach for complex physics simulations.

A Study on the Effects of Local Police's Perception of Crime Prevention Activities on Police Trust

Purpose: The purpose of this study is to examine how the crime prevention activities of the autonomous police, centering on Suwon City, have an impact on police trust, and to suggest ways to secure the reliability of the autonomous police activities in the future.&#x0D; Method: In this study, 320 copies of the meaningful results obtained through the survey conducted on the citizens of Suwon were analyzed. For the analysis, the crime prevention activities of the autonomous police were set as an independent variable and trust in the police was set as a dependent variable.&#x0D; Results: As a result of analysis on the impact of crime prevention activities(patrol, crime prevention environment creation, order maintenance) of autonomous police on police trust, it was found that patrol and crime prevention environment creation had a significant effect, and order maintenance No significant effect was found.&#x0D; Conclusion: In order to actively prevent crime, it is necessary to support visible police activities, such as strengthening intensive patrols using foot patrols, motorcycles, and patrol cars, as well as supporting technical equipment for effective arrest. CCTV has a very useful value in that it can be used as an efficient means of arresting criminals by filling the public security vacuum caused by insufficient police manpower at the present time and converting data into a database. In addition, more practical equipment support should be provided by securing the police's own budget to expand the development and expansion of a customized work system tailored to the local situation and to strengthen cooperative public order such as autonomous security guards and child guards.

Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated Environment

The technical issue of congestion, which is predominantly found in deregulated power systems, is caused by the failure of transmission networks to satisfy load power demands. This failure is primarily caused due to an increase in loads or loss of transmission lines or generators in modern restructured power networks. This work introduces a CM approach using Deep Convolution Neural Network (DCNN) for minimizing congestion and supporting Independent System Operators (ISOs). The purpose of the work is to generate enhanced prediction outputs for congestion management with reduced error values. These objectives were achieved through the actual power rescheduling of generators. The proposed work adopts DCNN which is optimized using an Improved Lion Algorithm (LA) and aids in providing significant outcomes for congestion management with reduced error. By implementing customized IEEE 57-bus, IEEE 30-bus, and IEEE 118-bus test systems, the suggested approach has been successfully verified for its performance on test systems of varied sizes. This analysis incorporates restrictions such as line loads, bus voltage influence, generator, line limits, etc. The most important results for the test system indicating convergence profile, congestion cost, and change in real-power and voltage magnitude are obtained by the simulation in MATLAB, and on the basis of the obtained simulation outcomes, it is evident that the proposed Improved Lion Algorithm optimized Deep Convolution Neural Network displays phenomenal computation performance in minimizing congestion losses at minimum congestion costs. When compared to several contemporary optimization techniques, the suggested technique performs better in terms of congestion cost and losses by generating improved prediction outputs with reduced errors.

A Master Multi-Slave System Based on Structural Modal Frequencies

Structural Health Monitoring (SHM) is a process where, through sensors-based electronic systems and output data analysis, the structural integrity of a building or an infrastructure is observed and determined through periodically sampled measurements for material and geometric variations, ensuring safety and minimizing risk factors. Over the years, the approach to this problem evolved both in terms of system technology, with the use of more accurate sensors, and the parameters used to determine the building status, i.e., the extraction of the damage index. In these terms, structural modal harmonics-based analysis is acquiring more and more relevance. In this paper, we propose a fully custom master multi-slave system used for SHM purposes, capable of acquiring data from six different channels coming from two independent accelerometers suitably configured as to synchronously produce spectrograms and static trim of the monitored structure. A test of the system has been performed on a modular structure subject to random perturbation. Time and frequency domain results were analyzed before and after structural alterations. Their effects were studied in a 500 Hz bandwidth, showing a drift in the resonant frequency of the structure from 145 Hz to 152 Hz, and, additionally, a harmonic displacement: medium-frequency components experienced a 50 Hz drift, while low-frequency components collapsed into two harmonics at 60 Hz and 90 Hz. These tests proved the system’s functionality and the feasibility of this method for structural integrity analysis and, eventually, damage evaluation.

Rational Design and Multicomponent Synthesis of Lipid-Peptoid Nanocomposites towards a Customized Drug Delivery System Assembly.

Nanotechnology has assumed a significant role over the last decade in the development of various technologies applied to health sciences. This becomes even more evident with its application in controlled drug delivery systems. In this context, peptoids are a promising class of compounds for application as nanocarriers in drug delivery systems. These compounds can be obtained efficiently and with highly functionalized structural diversity via the Ugi 4-component reaction (U-4CR). Herein, we report the design of the process control strategy for the future development of lipid-peptoid-based customized drug delivery system assemblies. Over 20 lipid-peptoid nanocomposites were synthesized via the U-4CR in good to excellent yields. These products were successfully submitted to the nanoparticle formation by the emulsification-evaporation process from lipophilic solution and analyzed via Dynamic Light Scattering (DLS). Several molecules generated nanoparticles with a size ≤200 nm, making them good candidates for drug delivery systems, such as in cancer treatment.

A Customized Deep Sleep Recommender System Using Hybrid Deep Learning.

This paper proposes a recommendation system based on a hybrid learning approach for a personal deep sleep service, called the Customized Deep Sleep Recommender System (CDSRS). Sleep is one of the most important factors for human life in modern society. Optimal sleep contributes to increasing work efficiency and controlling overall well-being. Therefore, a sleep recommendation service is considered a necessary service for modern individuals. Accurate sleep analysis and data are required to provide such a personalized sleep service. However, given the variations in sleep patterns between individuals, there is currently no international standard for sleep. Additionally, service platforms face a cold start problem when dealing with new users. To address these challenges, this study utilizes K-means clustering analysis to define sleep patterns and employs a hybrid learning algorithm to evaluate recommendations by combining user-based and collaborative filtering methods. It also incorporates feedback top-N classification processing for user profile learning and recommendations. The behavior of the study model is as follows. Using personal information received through mobile devices and data, such as snoring, sleep time, movement, and noise collected through AI motion beds, we recommend sleep and receive user evaluations of recommended sleep. This assessment reconstructs the profile and, finally, makes recommendations using top-N classification. The experimental results were evaluated using two absolute error measurement methods: mean squared error (MSE) and mean absolute percentage error (MAPE). The research results regarding the hybrid learning methods show 13.2% fewer errors than collaborative filtering (CF) and 10.2% fewer errors than content-based filtering (CBF) on an MSE basis. According to the MAPE, the methods are 14.7% more accurate than the CF model and 9.2% better than the CBF model. These results demonstrate that CDSRS systems can provide more accurate recommendations and customized sleep services to users than CF, CBF, and combination models. As a result, CDSRS, a hybrid learning method, can better reflect a user's evaluation than traditional methods and can increase the accuracy of recommendations as the number of users increases.

Sistem Pendukung Keputusan Penentuan Pemberian Reward Terbaik Terhadap Customer Dengan Metode SMART

Online Shop Shaqueena is an independent business sector engaged in selling beauty masks and various types of skincare products. For the sake of smooth business, the Shaqueena Store maintains good relations with consumers. One of the things that is done to establish good relations with consumers is to provide rewards or awards to the best customers. Based on observations, the process of selecting and recommending the best customers at the Shaqueena store is still done manually and randomly without using the help of the system. This manual and random selection is ineffective because the selection process itself is time-consuming and results in a lack of accuracy on which customers will be rewarded. This study aims to implement the SMART method in the reward system for customers. In its application the system is built using web-based programming and the PHP programming language with MySQL as the storage database. The method used in this study is the SMART method. Where SMART has flexibility in weighting, so it is suitable to be applied to the selection of rewards where there is no assessment weight. The result of this research is a website-based decision support system.

Construction of Customized Teaching System of Higher Vocational Colleges in the Era of "Internet +"

In the current "Internet +" era, all kinds of information technologies are becoming increasingly mature, which provides the basic support for the transformation and development of higher education. Under the background of information, higher education needs to pay more attention to students 'personalized learning needs and students' comprehensive development, and customized teaching arises at the historic moment. This has important practical value for improving the quality of higher education and deepening the reform of higher education. In order to establish and improve the customized teaching system of higher vocational colleges, higher vocational colleges should realize the role change of customized teaching elements, accelerate the integration and development of teaching modules, and improve the design of customized teaching system.

A mosquito mouthpart-like bionic neural probe

Advancements in microscale electrode technology have revolutionized the field of neuroscience and clinical applications by offering high temporal and spatial resolution of recording and stimulation. Flexible neural probes, with their mechanical compliance to brain tissue, have been shown to be superior to rigid devices in terms of stability and longevity in chronic recordings. Shuttle devices are commonly used to assist flexible probe implantation; however, the protective membrane of the brain still makes penetration difficult. Hidden damage to brain vessels during implantation is a significant risk. Inspired by the anatomy of the mosquito mouthparts, we present a biomimetic neuroprobe system that integrates high-sensitivity sensors with a high-fidelity multichannel flexible electrode array. This customizable system achieves distributed and minimally invasive implantation across brain regions. Most importantly, the system’s nonvisual monitoring capability provides an early warning detection for intracranial soft tissues, such as vessels, reducing the potential for injury during implantation. The neural probe system demonstrates exceptional sensitivity and adaptability to environmental stimuli, as well as outstanding performance in postoperative and chronic recordings. These findings suggest that our biomimetic neural-probe device offers promising potential for future applications in neuroscience and brain-machine interfaces.A mosquito mouthpart-like bionic neural probe consisting of a highly sensitive tactile sensor module, a flexible microelectrode array, and implanted modules that mimic the structure of mosquito mouthparts. The system enables distributed implantation of electrode arrays across multiple brain regions while making the implantation minimally invasive and avoiding additional dural removal. The tactile sensor array can monitor the implantation process to achieve early warning of vascular damage. The excellent postoperative short-term recording performance and long-term neural activity tracking ability demonstrate that the system is a promising tool in the field of brain-computer interfaces.

LungElast—an open-source, flexible, low-cost, microprocessor-controlled mouse lung elastometer

The study of mouse lung mechanics provides essential insights into the physiological mechanisms of pulmonary disease. Consequently, investigators assemble custom systems comprising infusion-withdrawal syringe pumps and analog pressure sensors to investigate the lung function of these animals. But these systems are expensive and require ongoing regulation, making them challenging to use. Here I introduce LungElast, an open-source, inexpensive, and self-contained instrument that can experimentally determine lung elasticity and volumes even in immature mice. It is assembled using custom 3D printed parts and readily available or easily constructed components. In this device, a microprocessor-controlled stepper motor automatically regulates lung volume by precisely driving a syringe piston whose position is determined using time-of-flight LIDAR technology. The airway pressures associated with the lung volumes are determined using compact sensor-on-chip technology, retrieved in a digital format, and stored by the microcontroller. The instrument software is modular, which eases device testing, calibration, and use. Data are also provided here that specify the accuracy and precision of the elastometer’s sensors and volume delivery and demonstrate its use with lung models and mouse pups. This instrument has excellent potential for research and educational work.

The politics of mediation: subjectivity, value and power in the digital grid of Aadhaar

ABSTRACT Digital data infrastructures act as mediating technologies that shape and manipulate flows and visibilities. Focusing on the case of the Indian digital ID Aadhaar, this article proposes a theory of mediation as a conceptual framework for understanding the role of digital technology in projects for datafication of state governance and national economy. In 2016, the Indian biometric ID Aadhaar was instrumentalised in a nation-wide move towards digital monetary transfers, a campaign launched under the banner of ‘demonetisation’. The digital state ID was integrated into Know Your Customer (KYC) systems of financial identification complicating government practices of recording and subjectivation. This process, during which government discourse explicitly drew parallels between political subjectivation and financial control, indicates the growing significance of the digital medium in shaping the relationship between states and their subjects. The notion of mediation proposed here addresses the mutual articulation between monetary transfers, practices of government recording and data valorisation in a context where state projects for digitalisation are increasingly integrated in global digital networks and data platforms.

A Study on the Smart City Project Evaluation Method based on Demand for Proptech Technology using Fuzzy Measurement

This study established an evaluation system for preliminary evaluation of projects combined with smart city based on demand for proptech technology, and suggested a customized evaluation method that can be used for future regional projects. As the main result, first, the evaluation system was established by dividing it into three areas for the establishment of the evaluation system: proptech technology field, smart city living lab field, and smart city business plan field. In addition, the analysis results for this were presented as 0.609 for the internal evaluation field in the proptech technology field, 0.420 for the evaluation field of participation factors in the smart city living lab field, and 0.410 for sustainability in the smart city business planning field. Finally, a customized evaluation system was proposed with an emphasis on methodological application and provision of alternatives, and its utilization method was verified.

Modeling and analysis of a new production methodology for achieving mass customization

In this paper, we address a Stochastic-Demand Assembly Job Shop Scheduling Problem (SD-AJSSP) in the presence of the commonality of sub-assemblies across products. We propose a new production methodology, named Assemble-to-Order with Commonality of Sub-Assemblies (ATO-CS) to not only solve the SD-AJSSP, but also, achieve a successful implementation of a mass customisation system by collectively aiming to (1) keep the production costs low by leveraging upon commonality of sub-assemblies in products’ BOM and producing sub-assemblies on a mass scale during one of the two stages of production, (2) minimise the loss due to excess inventory build-up in anticipation of stochastic demand of products by postponing the production of certain apex sub-assemblies in products’ BOM until the actual demand is realised, and (3) reduce the time of the products’ delivery to customers. The ATO-CS method determines optimum production levels as well as schedules assembly operations/jobs over the machines at each stage of production, where the second stage is an assembly job shop and is shown to outperform commonly-used production methodologies. We also develop an algorithm for its implementation and show its efficacy over the use of the state-of-the-art commercial solver CPLEX® in obtaining a lower solution cost and smaller optimality gap.

Vibrotactile Alerting to Prevent Accidents in Highway Construction Work Zones: An Exploratory Study.

Struck-by accidents are the leading cause of injuries in highway construction work zones. Despite numerous safety interventions, injury rates remain high. As workers' exposure to traffic is sometimes unavoidable, providing warnings can be an effective way to prevent imminent threats. Such warnings should consider work zone conditions that can hinder the timely perception of alerts, e.g., poor visibility and high noise level. This study proposes a vibrotactile system integrated into workers' conventional personal protective equipment (PPE), i.e., safety vests. Three experiments were conducted to assess the feasibility of using vibrotactile signals to warn workers in highway environments, the perception and performance of vibrotactile signals at different body locations, and the usability of various warning strategies. The results revealed vibrotactile signals had a 43.6% faster reaction time than audio signals, and the perceived intensity and urgency levels on the sternum, shoulders, and upper back were significantly higher than the waist. Among different notification strategies used, providing a moving direction imposed significantly lower mental workloads and higher usability scores than providing a hazard direction. Further research should be conducted to reveal factors that affect alerting strategy preference towards a customizable system to elicit higher usability among users.

A Reverse Blending based supply chain for mass customization of green fertilizers

Reverse Blending is a new product design and manufacturing approach based on finding the optimal chemical composition of a very small number of semi-finished common products (composites) whose possible blends may satisfy a wide variety of green fertilizers. As these fertilizers are adapted to the ecological requirements of crops and soils, Reverse Blending allows for both efficient and sustainable agriculture. Its implementation involves a supply chain redesign that calls for a shift from sequential batch production of different products to a hybrid system consisting of: i) make-to-stock of the composites within the chemical plant and ii) make-to-order of customized fertilizers within small blending units to be located near the farmer. This mass customization system brings new opportunities not yet exploited in the context of continuous production (granules, liquids …). This paper aims to highlight these opportunities and demonstrate the managerial and logistical consequences of a Reverse Blending based mass customization through several analysis studies.

An End-to-End Musical Instrument System That Translates Electromyogram Biosignals to Synthesized Sound

Abstract This article presents a custom system combining hardware and software that senses physiological signals of the performer's body resulting from muscle contraction and translates them to computer-synthesized sound. Our goal was to build upon the history of research in the field to develop a complete, integrated system that could be used by nonspecialist musicians. We describe the Embodied AudioVisual Interaction Electromyogram, an end-to-end system spanning wearable sensing on the musician's body, custom microcontroller-based biosignal acquisition hardware, machine learning–based gesture-to-sound mapping middleware, and software-based granular synthesis sound output. A novel hardware design digitizes the electromyogram signals from the muscle with minimal analog preprocessing and treats it in an audio signal-processing chain as a class-compliant audio and wireless MIDI interface. The mapping layer implements an interactive machine learning workflow in a reinforcement learning configuration and can map gesture features to auditory metadata in a multidimensional information space. The system adapts existing machine learning and synthesis modules to work with the hardware, resulting in an integrated, end-to-end system. We explore its potential as a digital musical instrument through a series of public presentations and concert performances by a range of musical practitioners.

MARKETING MECHANISM IN THE SPHERE OF CUSTOMS SERVICES

The article is devoted to the study of the peculiarities of the functioning of the marketing mechanism in the field of customs services. The definition of customs marketing as a management concept in market conditions is given. It is noted that the modern concept of customs services marketing is aimed at solving the problems of economic security and promoting trade through the use of customs tools at the level of international standards. The purpose of customs marketing is to increase the quality and competitiveness of customs administration, to ensure the prerequisites and optimal conditions for promoting international trade, to accelerate foreign trade turnover and to ensure the comfort of conducting foreign economic activities without reducing the effectiveness of customs control. It is emphasized that the marketing of customs services is a doctrine, a business philosophy, a tool for assessing the spheres of foreign economic activity, monitoring customs services and the activities of customs authorities, a technology for identifying requirements, demand and active promotion of customs services at the business and state level for the formation, search and service of meeting needs clients and participants in foreign economic activity. It has been proven that the ultimate goal of the functioning and development of the sphere of customs services is the effective coordination of all areas of activity of customs authorities with the requirements of the modern market. Practical solutions for the development of marketing in the customs sphere are offered. The main measures necessary for the further development of the marketing system in the customs sphere and ensuring the quality and high efficiency of customs services have been determined. Provisions have been formulated regarding the formation of an effective balanced customs system, ensuring its effectiveness and improving the quality of the range of services provided.

Designing technology systems to detect and prevent financial crime

Almost all early-stage financial services businesses are motivated to have a comprehensive compliance programme. However, they often need help knowing where to begin. There is much literature on enhancing existing compliance programmes and specific topics such as know your customer or transaction monitoring systems (TMS). This paper delves into the critical components required to build a functioning compliance programme and the fundamental building blocks that will set up a FinTech for long-term success. This paper is comprehensive enough to allow any company focused on financial services to pick and apply key concepts explained in the paper to build a compliance programme. At the same time, it is specific enough to help explain each component in detail so that companies can use this as a guiding principle to set up a compliance programme.

Customization of an atomic force microscope for multidimensional measurements under environmental conditions.

Atomic force microscopy (AFM) is an analytical surface characterization tool that reveals the surface topography at a nanometer length scale while probing local chemical, mechanical, and even electronic sample properties. Both contact (performed with a constant deflection of the cantilever probe) and dynamic operation modes (enabled by demodulation of the oscillation signal under tip-sample interaction) can be employed to conduct AFM-based measurements. Although surface topography is accessible regardless of the operation mode, the resolution and the availability of the quantified surface properties depend on the mode of operation. However, advanced imaging techniques, such as frequency modulation, to achieve high resolution, quantitative surface properties are not implemented in many commercial systems. Here, we show the step-by-step customization of an atomic force microscope. The original system was capable of surface topography and basic force spectroscopy measurements while employing environmental control, such as temperature variation of the sample/tip, etc. We upgraded this original setup with additional hardware (e.g., a lock-in amplifier with phase-locked loop capacity, a high-voltage amplifier, and a new controller) and software integration while utilizing its environmental control features. We show the capabilities of the customized system with frequency modulation-based topography experiments and automated voltage and/or distance spectroscopy, time-resolved AFM, and two-dimensional force spectroscopy measurements under ambient conditions. We also illustrate the enhanced stability of the setup with active topography and frequency drift corrections. We believe that our methodology can be useful for the customization and automation of other scanning probe systems.