Customizable System Research Articles

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.

Save the kids: The need for regulation of cryptocurrency to protect adolescents from fraud

AbstractCryptocurrency has become the new hot investment for young investors. Everyone wants to get in on the next cryptocurrency bubble, and there are those that want to exploit this excitement and exploit children. Influencers who have young audiences are pushing cryptocurrency investments onto their audiences and not fully educating them on the risks. To avoid this exploitation, The Securities and Exchange Commission should actively regulate decentralized exchanges where children purchase cryptocurrency, put age limit regulations into place, and require know your customer systems to verify that minors are not purchasing cryptocurrency without the consent of a guardian.

HEGEMONY IN HAMKA’S NOVEL TENGGELAMNYA KAPAL VAN DER WIJCK

The research discusses the issue of hegemony in Hamka’s novel Tenggelamnya Kapal Van Der Wijck. The work is apparently coded with custom system in its society and can be seen as a reflection of social reality. The novel explores the custom system in Minangkabau society and reveals that nearly every member of the society is bound by the existing cultural norms. From one side, the cultural system gives a good guideline for the people; but on the other side, the hegemony within the cultural systems brings the people into rigidity. No one is brave enough to stand against the hegemony of the existing norms. The research focuses solely on the form of hegemony practices done by the elders in Minangkabau culture. This research is done by descriptive qualitative method through literary sociological approach by using the theory of power of hegemony proposed by Antonio Gramsci. The data are obtained by using reading and noting technique. The data source is the novel Tenggelamnya Kapal Van Der Wijck by Hamka. The results of the research consist of two points; (1) hegemony in the form of ideology namely authoritarianism. (2) hegemony in the forms of culture namely kinship system and social stratification in Minangkabau.

A machine-learning digital-twin for rapid large-scale solar-thermal energy system design

In many industrialized regions of the world, large-scale photovoltaic systems now contribute a significant part to the energy portfolio during daylight operation. However, as energy demands peak shortly before sunset and persist for several hours afterwards, the integration of solar-thermal systems is extremely advantageous as a green “bridge” energy source. Accordingly, this work develops a digital-twin model to track and optimize the flow of incoming solar power through a complex solar-thermal storage system, consisting of a large array of adaptable mirrors, an optical-receiver and a power distribution system for customers to extract energy. Specifically, the solar power flow is rapidly computed with a reduced order model of Maxwell’s equations, based on a high-frequency decomposition of the irradiance into multiple rays that experience mirror reflections, losses and ultimately receiver absorption and customer delivery. The method allows for rapid testing (in microseconds) of the performance of large numbers of mirror-receiver layout configurations in design space, over extremely long time periods, such as weeks, months and years, using a genetic-based machine-learning digital-twin framework, which integrates submodels for: •optics and tracking of the Fresnel multi-mirror system,•thermal absorption of the optical energy by the receiver and•optimal operating temperatures balancing radiative losses with heat storage. The overall machine-learning digital-twin optimizes the configuration layout to balance meeting customer demands and operational efficiency. Numerical examples are provided to illustrate the approach. Finally, a deep-learning algorithm is developed and applied to the create an Artificial Neural-Net representation, which allows for even further simulation speedup.

The African continental free trade area: the road ahead for the continent’s bold integration project

PurposeThis paper aims to examine the road ahead for the African Continental Free Trade Area (AfCFTA), focusing on its potential opportunities and challenges. It is intended to help the AfCFTA’s effective implementation by highlighting the major areas of intervention for State Parties.Design/methodology/approachThe paper analyses relevant economic, political and legal research sources on regional integration in Africa and offers some personal views of the author to evaluate the past, present and future of the AfCFTA.FindingsThe paper shows that the AfCFTA can support its State Parties’ industrialization and diversification, better integrate micro-, small- and medium-sized enterprises (MSMEs) to regional value chains, create jobs, encourage sustainable investments and help its State Parties have common positions on global issues and achieve development. But, it also shows the challenges facing the AfCFTA, which include infrastructure gap, revenue and job losses, overlapping membership of State Parties in Regional Economic Communities, cumbersome customs systems, difficulty to cross African borders, fledgling MSMEs and inadequate technical capacity on trade policy. Accordingly, it recommends that State Parties continuously take various actions to address these challenges and maximize the multiple benefits of the AfCFTA.Originality/valueThe paper provides a comprehensive and up-to-date appraisal of the opportunities and challenges of the AfCFTA, both in the context of the history of regional integration in Africa and the recent global shocks that adversely impacted the continent (COVID-19 and the war in Ukraine).

Cloud Services User’s Recommendation System Using Random Iterative Fuzzy-Based Trust Computation and Support Vector Regression

Cloud computing is now a fundamental type of computing due to technological innovation and it is believed to be a benefit for mid-scale enterprises. The use of cloud computing is increasing daily, which improves service quality but also gives rise to security concerns. Finding trustworthy service can be very challenging, take a great deal of time, or produce subpar services. Due to these difficulties, the client needs a service that is dependable, suitable, time-saving, and trustworthy. As a result, from the end user’s perspective, adopting a cloud service’s trustworthiness becomes crucial. Trust is a measure of how well users’ expectations about a service’s capabilities are realized. In this research, a recommendation system for cloud service customers based on random iterative fuzzy computation (RIFTC) is proposed. RIFTC focuses on the assessment of trust using Quality of Service (QoS) characteristics. RIFTC calculates trust using the machine learning approach Support Vector Regression (SVR). RIFTC can helpfully recommend a cloud service to the end user and anticipate the trust values of cloud services.. Precision (97%), latency (51%), throughput (25.99 mbps), mean absolute error (54%), and re-call (97%) rates are used to assess how well this recommendation system performs. RIFTC’s average F-measure rate is calculated by adjusting the number of users from 200 to 300, and it is 93.46% more accurate on average with less time spent than the current methodologies.