Current System Research Articles

A Shunt DC Electric Spring Control Strategy for MVDC Bus Voltage Stability Onboard AES

Background: The recent trend in the all-electric ship (AES) electrical systems, especially in military vessels, is to move towards medium voltage direct current (MVDC) distribution. Bus voltage instability is a major issue in direct current (DC) distribution systems. Nowadays, direct current electric springs (DCES) are extensively used in low-voltage direct current (LVDC) microgrids to address voltage instability issues. This paper extends the use of a shunt DCES to stabilize the bus voltage in an MVDC grid. The work proposes an addition to the MVDC onboard ship distribution system architecture, described in IEEE 1709, by integrating a shunt DCES with a novel control strategy to stabilize the bus voltage under various loading conditions, including propulsion motor (PM) and online pulsed power load (PPL). Methods: The shunt DCES is designed to provide current into the MVDC bus, which reduces the bus current ripple to attain a stable bus voltage with reduced ripple. A dual loop control with a battery management system (BMS) is proposed for the shunt DCES and simulated in MATLAB/Simulink. BMS is designed based on the state of charge (SOC) of the battery and bus current ripple extracted from the system's source and load side currents. The current supplied by the shunt DCES and the extracted ripple current validate the effectiveness of the proposed control. Total harmonic distortions (THDs) as a measure of voltage ripple of the MVDC bus voltage at different intervals are measured and compared for both systems, with and without shunt DCES. Results: It was observed that the shunt DCES could reduce the voltage ripple well below the permissible limit, which is 5% as per IEEE 1709. Conclusion: The proposed control strategy could attain a reduction of 68-85% in THD under peak to off-peak loading conditions with the addition of shunt DCES.

Mapping the Nanotechnology Patent Landscape in the Field of Cancer.

Currently, cancer is still a significant disease that seriously endangers human health. Therefore, advanced diagnostic technology and treatment protocols are urgently needed. The rapid development of nanotechnology is expected to provide new ideas for cancer diagnosis and treatment. The research aims to comprehensively demonstrate the hotspots of nanotechnology applications in cancer. In this study, an International Patent Classification codes co-occurrence network is constructed to visualize the technology landscape by simultaneously locating and ranking technologies that play an integral role in nanotechnology diffusion and bridging in the field of cancer. In addition, community identification and topic modeling highlight the latent topics in patent documents. The visualization results of the patent network yield five main clusters: Cluster 0 is a nanoparticle composition delivery system with liposomes as the primary carrier. Cluster 1 is mainly represented by nano-immunotherapy with immune checkpoint inhibitors. Cluster 2 is nano phototherapy based on photodynamic therapy and photothermal therapy. Cluster 3 is diagnostic imaging involving nanotechnology. Cluster 4 is a drug delivery system with nanovesicles and albumin nanoparticles as carriers. It was found that carriers represented by liposomes, vesicles, and albumin nanoparticles are essential nanomaterials in the current anticancer drug delivery systems. Integrating next-generation immunosuppressants and nanotechnology will become an important development direction for future immunotherapy. Organic/inorganic nanomaterials are pivotal in cancer imaging diagnosis and phototherapy.

Hetero-associative Memory Based New Iraqi License Plate Recognition

نتيجة للتطورات الأخيرة في أبحاث الطرق السريعة بالإضافة إلى زيادة استخدام المركبات، كان هناك اهتمام كبير بنظام النقل الذكي الأكثر حداثة وفعالية ودقة (ITS) في مجال رؤية الكمبيوتر أو معالجة الصور الرقمية، يلعب تحديد كائنات معينة في صورة دورًا مهمًا في إنشاء صورة شاملة. هناك تحدٍ مرتبط بالتعرف على لوحة ترخيص السيارة (VLPR) بسبب الاختلاف في وجهة النظر، والتنسيقات المتعددة، وظروف الإضاءة غير الموحدة في وقت الحصول على الصورة والشكل واللون، بالإضافة إلى الصعوبات مثل ضعف دقة الصورة ، الصورة الباهتة ، الإضاءة السيئة، التباين المنخفض، يجب التغلب عليها. اقترحت هذه الورقة نموذجًا باستخدام تعديل الذاكرة الترابطية ثنائية الاتجاه (MBAM)، وهي نوع واحد من الذاكرة الترابطية غير المتجانسة، وتعمل MBAM على مرحلتين)مرحلتي التعلم والتقارب) للتعرف على اللوحة، ويمكن لهذا النموذج المقترح التغلب على تلك الصعوبات بسبب قدرة الذاكرة الترابطية لـ MBAM على قبول الضوضاء وتمييز الصور المشوهة، وكذلك سرعة عملية الحساب نظرًا لصغر حجم الشبكة. نتيجة دقة تحديد منطقة اللوحة هي 99.6٪، ودقة تجزئة الأحرف 98٪، والدقة المحققة للتعرف على الأحرف هي100 ٪ في ظروف مختلفة.

KSTAR Grounding System and the Operation Result of the Ground Current Detection System

KSTAR Grounding System and the Operation Result of the Ground Current Detection System

Design and research of grounding current monitoring device for converter transformer core and clamp

The effective monitoring of the grounding current of the core and clamp of the converter transformer can prevent partial overheating, gassing of oil, and other abnormal conditions caused by the grounding fault of the core and clamp, ensuring the safety and stability of the power system. At present, the main detection and diagnosis method is to use the clamp current meter to detect the effective value of the grounding current. However, due to the larger capacity, higher voltage level, more complex structure and electromagnetic field distribution of the converter transformer, there is no clear standard and detection diagnosis method. Firstly, this paper analyzes the causes of grounding current and related calculations. And the design of the monitoring system for the grounding current of the converter transformer core and clamp design of the hardware system for the grounding current of the converter transformer is studied and the design process of the main circuit is given. Secondly, aiming at the problem of amplitude measurement deviation and non-integer frequency of harmonic generated by the grounding current of the core and clamp, a harmonic current detection and analysis scheme based on four Blackman-harris algorithms and the specific design process are proposed. Third, a diagnostic method based on probabilistic neural network is proposed, and a software platform is built to display and diagnose the grounding current state according to it. Finally, a prototype was manufactured and experimental study was carried out to verify the correctness of the proposed scheme.

Thickness Measurement of Thermal Barrier Coating Based on Mutual Inductance of Eddy Current System

Thickness Measurement of Thermal Barrier Coating Based on Mutual Inductance of Eddy Current System

Validation and Implementation in the Cloud of Cell-Level Models of a Digital Twin Simulation Platform

The monitoring and modelling of the Li-Ion Batteries behaviour is still a major technical challenge due to the non-linearities and coupled phenomena that determine their operation. These Li-Ion Batteries can consist of thousands of cells with series/parallel connections, which suffer from operating and degradation deviations. The pursuit of new, increasingly intelligent and heavier state estimation algorithms requires a significant amount of data and computational power, which can be challenging to deploy in current Battery Management System solutions. To solve this problem, this paper proposes a Digital Twin Simulation Platform that considers all the individual cells based on the Cloud to extend the computational power and data storage capacity. This work presents validated cell models, a module-level modelling approach, and an experimental validation platform is suggested. In addition, the first results obtained when implementing the Digital Twin Simulation Platform in the Cloud are presented.

Predictability of the emergency department triage system during the COVID-19 pandemic.

Emergency department (ED) triage systems are used to classify the severity and urgency of emergency patients, and Korean medical institutions use the Korean Triage and Acuity Scale (KTAS). During the COVID-19 pandemic, appropriate treatment for emergency patients was delayed due to various circumstances, such as overcrowding of EDs, lack of medical workforce resources, and increased workload on medical staff. The purpose of this study was to evaluate the accuracy of the KTAS in predicting the urgency of emergency patients during the COVID-19 pandemic. This study retrospectively reviewed patients who were treated in the ED during the pandemic period from January 2020 to June 2021. Patients were divided into COVID-19-screening negative (SN) and COVID-19-screening positive (SP) groups. We compared the predictability of the KTAS for urgent patients between the two groups. From a total of 107,480 patients, 62,776 patients (58.4%) were included in the SN group and 44,704 (41.6%) were included in the SP group. The odds ratios for severity variables at each KTAS level revealed a more evident discriminatory power of the KTAS for severity variables in the SN group (P<0.001). The predictability of the KTAS for severity variables was higher in the SN group than in the SP group (area under the curve, P<0.001). During the pandemic, the KTAS had low accuracy in predicting patients in critical condition in the ED. Therefore, in future pandemic periods, supplementation of the current ED triage system should be considered in order to accurately classify the severity of patients.

"It feels terrible that people are making decisions for me": Reflections and experiences of individuals with psychiatric disability who have substitute decision makers for treatment.

In Canada and elsewhere, making treatment decisions for a person with serious mental illness (SMI) who was found incapable for treatment decisions via a substitute decision maker (SDM) is the norm. This practice is often called into question from a rights-based perspective. The literature on the views of affected individuals is limited. We explore the experiences of adults with SMI who have had SDMs to gain more in-depth understanding. We conducted semistructured interviews with 11 consumers of psychiatric services who have had experiences with SDM (range 1-12 years) at an urban hospital in Toronto, Canada. Thematic analysis showed five main themes and related subthemes, including: (1) strong dissatisfaction with and rejection of the SDM's role and purpose; (2) pervasive sense of stigma associated with having a SDM; (3) ongoing struggles to gain autonomy; (4) mixed changes in relationship with and views about SDM; and (5) views on how to improve SDM process. Our study highlights substantial dissatisfaction with the current SDM system and approaches among adults with SMI who have had SDMs. Issues of stigma and struggles to regain autonomy are prevalent. We discuss the personal, clinical, and social-legal contexts in which they occur, particularly in light of the United Nations' Convention on the Rights of Persons with Disabilities that calls for replacing SDMs with supported decision making. Rights-based approaches to care carry substantial practice implications and call for thoughtful change management. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study.

Electrocardiography (ECG) is a non-invasive tool for predicting cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show promising performance owing to the rapid development of deep learning techniques. However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models. Addressing them in a unified framework remains a significant challenge. To this end, we propose a multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs simultaneously with limited supervision. In the ECGMatch, an ECGAugment module is developed for weak and strong ECG data augmentation, which generates diverse samples for model training. Subsequently, a hyperparameter-efficient framework with neighbor agreement modeling and knowledge distillation is designed for pseudo-label generation and refinement, which mitigates the label scarcity problem. Finally, a label correlation alignment module is proposed to capture the co-occurrence information of different CVDs within labeled samples and propagate this information to unlabeled samples. Extensive experiments on four datasets and three protocols demonstrate the effectiveness and stability of the proposed model, especially on unseen datasets. As such, this model can pave the way for diagnostic systems that achieve robust performance on multi-label CVDs prediction with limited supervision.

Pharmacy practice and First Peoples health equity: a scoping review protocol.

The objective of this review is to examine and describe global pharmacy practice strategies and interventions designed to achieve health equity for First Peoples. Access to medicines and quality use of medicines is critical to achieving health equity for First Peoples. Pharmacists are uniquely placed to lead the charge in transforming current health systems, reducing health disparities, and bolstering the movement toward health equity. Global studies describing pharmacy practice strategies and interventions designed to achieve health equity for First Peoples will be considered for inclusion in the review. Studies relating to all areas of pharmacy practice, including community and clinical pharmacy, social, administrative, pharmaceutical sciences, practice, teaching, research, advocacy, or service relevant to the review's objective will also be considered for inclusion. The types of studies to be included are qualitative, quantitative, and mixed methods systematic reviews, scoping reviews, literature reviews, and gray literature. This review will be conducted in accordance with JBI methodology for scoping reviews. Embase, MEDLINE, Scopus, CINAHL, and gray literature sources will be searched from 1998 to the present. Titles, abstracts, and full texts will be screened against the inclusion criteria. Strategies and interventions identified in the included reviews will be mapped to a published framework, outlining actionable strategies for pharmacy practice inclusion in sustainable efforts to achieve health equity. Qualitative content analysis and descriptive statistics will be utilized with data presented in tables, accompanied by a narrative. Open Science Framework osf.io/qa64b.

Fumos: Neural Compression and Progressive Refinement for Continuous Point Cloud Video Streaming.

Point cloud video (PCV) offers watching experiences in photorealistic 3D scenes with six-degree-of-freedom (6-DoF), enabling a variety of VR and AR applications. The user's Field of View (FoV) is more fickle with 6-DoF movement than 3-DoF movement in 360-degree video. PCV streaming is extremely bandwidth-intensive. However, current streaming systems require hundreds of Mbps bandwidth, exceeding the bandwidth capabilities of commodity devices. To save bandwidth, FoV-adaptive streaming predicts a user's FoV and only downloads point cloud data falling in the predicted FoV. But it is difficult to accurately predict the user's FoV even 2-3 seconds before playback due to 6-DoF. Misprediction of FoV or network bandwidth dips results in frequent stalls. To avoid rebuffering, existing systems would cause incomplete FoV and degraded experience, deteriorating the user's quality of experience (QoE). In this paper, we describe Fumos, a novel system that preserves interactive experience by avoiding playback stalls while maintaining high perceptual quality and high compression rate. We find a research gap in inter-frame redundant utilization and progressive mechaism. Fumos has three crucial designs, including (1) Neural compression framework with inter-frame coding, namely N-PCC, which achieves both bandwidth efficiency and high fidelity. (2) Progressive refinement streaming framework that enables continuous playback by incrementally upgrading a fetched portion to a higher quality (3) System-level adaptation that employs Lyapunov optimization to jointly optimize the long-term user QoE. Experimental results demonstrate that Fumos significantly outperforms Draco, achieving an average decoding rate acceleration of over 260×. Moreover, the proposed compression framework N-PCC attains remarkable BD-Rate gains, averaging 91.7% and 51.7% against the state-of-the-art point cloud compression methods G-PCC and V-PCC, respectively.

Multidecadal Phase Changes in the Thermodynamic State of the System: Ocean–Atmosphere–Continent

The present-day climate (the recent 100–150 years) obviously constitutes the structure of a global intra-system rhythmic process with an individual rhythm of about 60 years. In turn, each of the rhythms is presented by the two climate phases of about 25–35 years characterized by qualitative differences: one phase is relatively continental, while the other is humid. Globality and quasi-synchronism of environmental changes are accompanied by planetary structures: the Global Atmospheric Oscillation (GAO) in the atmosphere and the Multidecadal Oscillation of the Heat content in the Ocean (MOHO) discovered relatively recently. Unexpected and rapid qualitative phase changes in the climate, which first focused attention in the mid-1970s of the last century, were titled “climate shifts”. The revealed features of the present-day climate are of exceptional scientific and practical interest and deserve the development of methods for predicting the timing of the forthcoming climate shift. Arising unexpectedly and accompanied by rapid significant changes, these shifts identified the problem of understanding the nature and establishing the processes and mechanisms causing them. First of all, of interest are phase changes in the thermodynamic state of the climate system components: the ocean, atmosphere, and continents. As a result of the World Ocean (WO) thermohydrodynamics numerical modelling, it is shown that MOHO is localized in the layer of the main thermocline, where the most important elements of the WO circulation are located. The performed study based on observational data allows us to conclude that, during the phase of the WO thermal discharge (1975–1999), the two key systems of currents, the Kuroshio and the Gulf Stream, were under similar thermodynamic conditions.

Data-Driven Modeling and Operation Optimization With Inherent Feature Extraction for Complex Industrial Processes

In response to the tenets of Industry 4.0, operation optimization in industrial processes has become a significant research topic. However, the uncertainties prevailing in the process pose challenges to production operations, especially the feedstock properties. In this work, the operation optimization study is performed on a distillation unit (DU), a typical plant in the industrial process. To enhance production performance, a modeling and operation optimization strategy based on feedstock property and production features is presented. One of the difficulties is how to uncover features from high-dimensional and imperfect data, where imperfect data refers to product quality data that is unavailable online. In the strategy, we inject the inherent characteristic of the process into the data-driven method to extract the feedstock property in a data-based and knowledge-oriented manner. Further, optimal feature representation and process modeling can be achieved by customizing the network structure. The operation optimization problem is formulated to adjust the top temperature of the distillation column (TTDC) to achieve satisfactory production under varying feedstock properties. Experimental results illustrate that the process model based on feedstock property and production features (PM-FP-PF) can better fit the physical process mechanism even based on incomplete information in industrial data. Industrial experiments have shown the proposed strategy has advanced generalization ability to the different feedstock properties. The proposed operation optimization strategy (OOS) improves the product qualification rate and has broad application prospects in industrial processes with similar features. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Industrial processes suffer from a variety of disturbances that interrupt the smooth operation of the system, such as varying feedstock properties. How to deal with them is the key to improve the product qualification rate. In this work, we propose a data-driven modeling and operation optimization framework to improve product quality under varying feedstock properties. The dynamic variation characteristics of the feedstock properties can be obtained by analyzing the physical properties of the production unit. This is used as a basis for representing and extracting feedstock properties in a data-driven way. Further, a process model based on feedstock property and production features (PM-FP-PF) is built to predict product quality. An operation optimization strategy with production capacity consideration is established. It can determine the optimal operation action required by the current system, mitigating the uncertainty of feedstock property. This operation optimization system has been applied to a distillation unit in the hydrofining process. The application results show that the process model achieves satisfactory estimation accuracy, and the operation optimization strategy has improved production performance.

Design of Real Time Intelligent System for Women Safety

Background: Even the technology has advanced a lot in these modern times, women's safety is still a massive issue. Women are not safe anywhere. Their safety is an essential and decisive issue in today’s world. They face extreme troubles when they are alone, travelling on lonely roads and at workplace. Education is essential for everyone and especially for women. It is a powerful weapon to end poverty. Government and NGOs have made many rules and regulations but the crime against women has not ended. There are many instances of harassment, abuse, eve-teasing, kidnapping, domestic violence and rape. Women are working equally as men but still their safety is a big challenge. With the boom in the IT and corporate sector, many women are now working and even during nightshifts. Objectives: A Real Time Intelligent System for Women Safety has been proposed to overcome these limitations. Methods: In this paper, many women's safety systems that were proposed or developed have been discussed. Major drawbacks in the current system, devices or applications have been highlighted. Latest technologies like machine learning, artificial intelligence or data science have been ignored. Results: The proposed system will be highly sophisticated with the latest techniques and women will feel safe in all situations. It will predict the safe route while moving alone. Conclusion: Moreover, it will record the evidence and will also suggest the rights of women.

A Novel Lightweight Navigation System for Oral and Maxillofacial Surgery Using an External Curved Self-Identifying Checkerboard

This paper presents a novel lightweight navigation system for oral and maxillofacial surgery (OMS). An external curved checkerboard with self-identifying markers is set as the reference object around the surgical scene. A customized oral clip with a micro camera is designed for oral localization by tracking the external checkerboard. Similarly, the dental handpiece is also equipped with a micro camera, which can be localized like the clip. The spatial model of the markers is provided by binocular stereoscopic reconstruction. A front surface mirror is taken up for the registration between the oral cavity and the camera on the clip. The pivot calibration of the dental handpiece is accomplished by our proposed calibration method. We set up an experimental group and a control group for evaluation. The surgical tools of the experimental group were approximately 30% lighter, 35% less bulky, and 90% cheaper than those of the control group. Our system yielded the comprehensive navigation accuracy of 0.92 mm whereas the accuracy of the control group was 0.87 mm. Results revealed that our system can achieve similar accuracy compared with a prevailing system at a lighter weight, a more compact volume, and a lower cost. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this work is to reduce the burden on patients and surgeons during OMS. Current commercial navigation systems are still limited by the burden of extra cumbersome fiducial markers and high hardware costs. Their high accuracy benefits from the large size of fiducial markers. To take full advantage of the camera’s localization effect, we propose the concept of “marker-camera inverse projection”, i.e., reversing the roles of the camera and the markers. In this way, cameras on surgical tools detect more points with a more uniform distribution. Our proposed system achieves a decent balance between navigation accuracy and hardware cost, which facilitates the development of surgical tools to be lighter and more economical, and involves tremendous potential for commercialization.

Implementing Blockchain in the Management of Organ Donation and Transplantation

Abstract: Current organ donation and transplantation systems suffer from vulnerabilities to scams and lack of transparency, hindering trust and efficiency. This proposal introduces a groundbreaking blockchain-based solution that combats both issues. By leveraging the blockchain's security and immutability, the system detects and predicts potential scams through AI analysis of realtime data, notifying authorities immediately. Additionally, it strengthens security and trust by ensuring secure participant identities, controlled access to sensitive information, and automated processes via smart contracts. This not only reduces the risk of illegal activity but also fosters fairness and participation, ultimately transforming the system into a life-saving model of transparency and trust. But that's not all. Blockchain securely stores identities, encrypts sensitive information, and automates tasks with smart contracts, eliminating the human error and shady dealings that plague the current system. This creates a web of trust, empowering donors, recipients, and medical professionals alike. The result? A system that's not just secure, but fair and efficient, saving lives and restoring faith in this vital field. It's time to transplant trust into organ donation, and blockchain is the fertile ground for its growth

Air Writing Word Recognition and Translation

Abstract: Project develops a system that can recognize the air-written words in free space, and then classify the recognized word. Air-writing is the new way of writing the linguistic words in free area using hand or finger movements. This project could be a combination of computer vision hand chase and handwriting recognition machine learning. The air writing recognition system uses the web camera of a pc to trace word written within the air by the user and then uses a convolutional neural network to classify the word into one of the possible classes. Several current systems use advanced and high-priced chase setups to realize gesture recognition, however, we tend to get to form a system that may attain a similar work with a far cheaper setup.

A review of wastewater-based epidemiology for antimicrobial resistance surveillance

Antimicrobial resistance (AMR) is recognized as one of the most serious threats to public health. Unparalleled population growth and accelerated rates of AMR emergence and dissemination have resulted in both novel resistance in pathogenic organisms and the re-appearance of infections that were formerly under control. Consequently, this has led to an increased quantity of infectious diseases. One of the main drivers of antimicrobial overuse is inappropriate prescribing in human and veterinary medicine. The ability to rapidly survey the spread of antimicrobial resistance within human populations is key for its prevention, intervention, and control. However, many constraints are present for current clinical surveillance systems and their capacity to determine AMR dynamics in the microbiome of healthy individuals as well as in clinical pathogens causing infections. Wastewater-based epidemiology (WBE) is an emergent technique that has the capacity to act as a supplementary measure for current infectious disease surveillance systems and as an early warning system for infectious disease outbreaks. The development of disease outbreaks to the community level can be monitored in real time through the analysis of population pooled wastewater. This review provides an introduction to using wastewater-based epidemiology to monitor AMR bacteria, as well as an overview of wastewater-based epidemiology and its components.

Light-Mediated Polymerization Catalyzed by Carbon Nanomaterials.

Carbon nanomaterials, specifically carbon dots and carbon nitrides, play a crucial role as heterogeneous photoinitiators in both radical and cationic polymerization processes. These recently introduced materials offer promising solutions to the limitations of current homogeneous systems, presenting a novel approach to photopolymerization. This review highlights the preparation and photocatalytic performance of these nanomaterials, emphasizing their application in various polymerization techniques, including photoinduced i) free radical, ii) RAFT, iii) ATRP, and iv) cationic photopolymerization. Additionally, it discusses their potential in addressing contemporary challenges and explores prospects in this field. Moreover, carbon nitrides, in particular, exhibit exceptional oxygen tolerance, underscoring their significance in radical polymerization processes and allowing their applications such as 3D printing, surface modification of coatings, and hydrogel engineering.