Development Of System Research Articles

In Situ Characterization Techniques for Electrochemical Nitrogen Reduction Reaction.

The electrochemical reduction of nitrogen to produce ammonia is pivotal in modern society due to its environmental friendliness and the substantial influence that ammonia has on food, chemicals, and energy. However, the current electrochemical nitrogen reduction reaction (NRR) mechanism is still imperfect, which seriously impedes the development of NRR. In situ characterization techniques offer insight into the alterations taking place at the electrode/electrolyte interface throughout the NRR process, thereby helping us to explore the NRR mechanism in-depth and ultimately promote the development of efficient catalytic systems for NRR. Herein, we introduce the popular theories and mechanisms of the electrochemical NRR and provide an extensive overview on the application of various in situ characterization approaches for on-site detection of reaction intermediates and catalyst transformations during electrocatalytic NRR processes, including different optical techniques, X-ray-based techniques, electron microscopy, and scanning probe microscopy. Finally, some major challenges and future directions of these in situ techniques are proposed.

Low-temperature hydrogen release exceeding 7 wt% from LiBH4-mannitol composites

Among conventional hydrogen storage materials, LiBH4 has been regarded as one of the best hydrogen transporters due to its relatively large hydrogen capacities. However, because of its extraordinarily high dehydrogenation temperatures, LiBH4 was not suitable for hydrogen storage due to its stable thermodynamic features. Herein, we present a novel approach to LiBH4 that uses solid-state multi-hydroxyl mannitol as a reactant, regulating hydrogen release below 69.9 °C. More than 7 wt% H2 could be released with ultra-fast rates at 140 °C from the LiBH4-mannitol composite. XRD, FTIR, and SEM tests revealed that the reaction between mannitol (Hδ+) and LiBH4 (Hδ-) to produce hydrogen was accompanied by the generation of large amounts of heat in situ, which further promoted the decomposition of LiBH4 for hydrogen production, exceeding the theoretical dehydrogenation amount of Hδ+- Hδ- reaction. This work indicates possibilities for the development of fast and easy high-capacity hydrogen generation systems based on complex hydrides.

Development of a Digital Patient Management System for Improved Structural Heart Patient Outcomes

Development of a Digital Patient Management System for Improved Structural Heart Patient Outcomes

Advances in the diagnosis of ocular diseases: an innovative approach through an expert system

In the context of ophthalmic care, where early diagnosis of eye disorders plays a crucial role in patients' quality of life, this study focused on the development and evaluation of an expert system based on SWI Prolog. The main objective of this research was to provide an effective method for the preliminary diagnosis of ocular disorders, including cataract, trachoma, uveitis, glaucoma, and presbyopia. For the evaluation of the system, a confusion matrix was implemented and accuracy, sensitivity and specificity were calculated using a sample of 30 cases, of which 20 were positive and 10 negatives. The findings revealed an outstanding accuracy of 95%, with a sensitivity and specificity of 90%. This highlights the potential of the tool as an effective means of early detection of visual problems. In conclusion, this expert system represents a significant advance in ophthalmologic diagnosis, with important implications for clinical care and patients' quality of life, although expansion and validation of the tool in further clinical studies is suggested for its wider and more successful implementation in the field of ophthalmology.

Effects of humic acid fertilizer on the growth and microbial network stability of Panax notoginseng from the forest understorey.

Humic acid (HA) can substantially enhance plant growth and improve soil health. Currently, the impacts of HA concentrations variation on the development and soil quality of Panax notoginseng (Sanqi) from the forest understorey are still unclear. In this study, exogenous HA was administered to the roots of Sanqi at varying concentrations (2, 4, and 6ml/L). Subsequently, the diversity and community structure of bacteria and fungi were assessed through high-throughput sequencing technology. The investigation further involved analyzing the interplay among the growth of sanqi, soil edaphic factors, and the microbial network stability. Our finding revealed that moderate concentrations (4ml/L) of HA improved the fresh/dry weight of Sanqi and NO3--N levels. Compared with control, the moderate concentrations of HA had a notable impact on the bacterial and fungal communities compositions. However, there was no significant difference in the α and β diversity of bacteria and fungi. Moreover, the abundance of beneficial bacteria (Bradyrhizobium) and harmful bacteria (Xanthobacteraceae) increased and decreased at 4ml/L HA, respectively, while the bacterial and fungal network stability were enhanced. Structural equation model (SEM) revealed that the fresh weight of Sanqi and bacterial and fungal communities were the factors that directly affected the microbial network stability at moderate concentrations of HA. In conclusion, 4ml/L of HA is beneficial for promoting Sanqi growth and soil quality. Our study provides a reference for increasing the yield of Sanqi and sustainable development of the Sanqi-pine agroforestry system.

Fostering Students’ Development of Productive Representation Systems for Infinite Series

Fostering Students’ Development of Productive Representation Systems for Infinite Series

Echocardiographic Predictors of Surgical Outcomes in Pulmonary Atresia With Intact Ventricular Septum and Development of a Discriminatory Scoring System: An Australian Tertiary Institution Experience

Echocardiographic Predictors of Surgical Outcomes in Pulmonary Atresia With Intact Ventricular Septum and Development of a Discriminatory Scoring System: An Australian Tertiary Institution Experience

Light-responsive Polymers: Developments in Drug Delivery Systems

Abstract: Photoresponsive polymers have emerged as innovative tools in the domain of drug delivery, presenting advanced solutions for controlled and targeted release of therapeutic agents. This review explores recent advances in the design and application of photoresponsive polymers, focusing on their pivotal role in light-triggered drug delivery systems. It also encompasses organic synthesis methodologies, key advancements in polymer design, and the integration of photoresponsive elements into drug carriers. Moreover, this review also focuses on the applications, challenges, and future prospects, providing a comprehensive overview of the evolving landscape of light-responsive drug delivery technologies. The information about the synthesis presented herein aims to contribute to the understanding and advancement of this dynamic field, offering insights for researchers and practitioners engaged in the development of next-generation drug delivery systems.

CycleInSight: An enhanced YOLO approach for vulnerable cyclist detection in urban environments

As urbanization continues to reshape transportation, the safety of cyclists in complex traffic environments has become a pressing concern. In response to this challenge, our research introduces a CycleInSight framework, which harnesses advanced deep learning and computer vision techniques to enable precise and efficient cyclist detection in diverse urban settings. Utilizing you only look once version 8 (YOLOv8) object detection algorithm, the proposed model aims to detect and localize vulnerable cyclists near vehicles equipped with onboard cameras. Our research presents comprehensive experimental results demonstrating its effectiveness in identifying vulnerable cyclists amidst dynamic and challenging traffic conditions. With an impressive average precision of 90.91%, our approach outperforms existing models while maintaining efficient inference speeds. By effectively identifying and tracking cyclists, this framework holds significant potential to enhance urban traffic safety, inform data-driven infrastructure planning, and support the development of advanced driver assistance systems and autonomous vehicles.

Development of a noninvasive olfactory stimulation fMRI system in marmosets.

Olfactory dysfunction is associated with aging and the earliest stages of neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases; it is thought to be an early biomarker of cognitive decline. In marmosets, a small non-human primate model used in brain research, olfactory pathway activity during olfactory stimulation has not been well studied because of the difficulty in clearly switching olfactory stimuli inside a narrow MRI. Here, we developed an olfactory-stimulated fMRI system using a small-aperture MRI machine. The olfactory presentation system consisted of two tubes, one for supply and one for suction of olfactory stimulants and a balloon valve. A balloon valve installed in the air supply tube controlled the presentation of the olfactory stimulant, which enabled sharp olfactory stimulation within MRI, such as 30s of stimulation repeated five times at five-minute intervals. The olfactory stimulation system was validated in vivo and in a simulated system. fMRI analysis showed a rapid increase in signal values within 30s of olfactory stimulation in eight regions related to the sense of smell. As these regions include those associated with Alzheimer's and Parkinson's diseases, olfactory stimulation fMRI may be useful in clarifying the relationship between olfactory dysfunction and dementia in non-human primates.

Bone marrow edema detection for diagnostic support of axial spondyloarthritis using MRI.

This study proposes a process for detecting slices with bone marrow edema (BME), a typical finding of axSpA, using MRI scans as the input. This process does not require manual input of ROIs and provides the results of the judgment of the presence or absence of BME on a slice and the location of edema as the rationale for the judgment. First, the signal intensity of the MRI scans of the sacroiliac joint was normalized to reduce the variation in signal values between scans. Next, slices containing synovial joints were extracted using a slice selection network. Finally, the BME slice detection network determines the presence or absence of the BME in each slice and outputs the location of the BME. The proposed method was applied to 86 MRI scans collected from 15 hospitals in Japan. The results showed that the average absolute error of the slice selection process was 1.49 slices for the misalignment between the upper and lower slices of the synovial joint range. The accuracy, sensitivity, and specificity of the BME slice detection network were 0.905, 0.532, and 0.974, respectively. This paper proposes a process to detect the slice with BME and its location as the rationale of the judgment from an MRI scan and shows its effectiveness using 86 MRI scans. In the future, we plan to develop a process for detecting other findings such as bone erosion from MR scans, followed by the development of a diagnostic support system.

Extracorporeal Cardiopulmonary Resuscitation: CME Review.

Extracorporeal cardiopulmonary resuscitation (eCPR) is increasingly being used for refractory cardiac arrest for both in-hospital and out-of-hospital cardiac arrests. The term eCPR refers to cannulating a patient to an extracorporeal membrane oxygenation (ECMO) circuit to provide perfusion after cardiac arrest refractory to standard cardiopulmonary resuscitation. Extracorporeal cardiopulmonary resuscitation has been shown to offer increased survival benefit among a select group of adult and pediatric patients experiencing refractory cardiac arrests, both in hospital and out of hospital. Extracorporeal cardiopulmonary resuscitation should be considered when (1) the cardiac arrest is witnessed, (2) the patient receives high-quality cardiopulmonary resuscitation, (3) the patient is at or in close proximity to an ECMO center, (4) there is a reversible cause for the cardiac arrest where the perfusion from the ECMO circuit serves as a bridge to recovery, and (5) the treating facility has a robust multidisciplinary system in place to facilitate rapidly moving patients from site of arrest to site of cannulation to intensive care unit. To develop an eCPR system of care, a multidisciplinary team consisting of prehospital, emergency medicine, in-hospital, proceduralist, perfusionist, and intensive care medical professionals must be established who support the use of eCPR for refractory cardiac arrest. The future of eCPR is the development of systems of care that use eCPR for a narrow subset of pediatric out-of-hospital cardiac arrests.

A multisensory Interaction Framework for Human-Cyber–Physical System based on Graph Convolutional Networks

Human-Cyber-Physical Systems (HCPS), as an emerging paradigm centered around humans, provide a promising direction for the advancement of various domains, such as intelligent manufacturing and aerospace. In contrast to Cyber-Physical Systems (CPS), the development of HCPS emphasizes the expansion of human capabilities. Humans no longer solely function as operators or agents working in collaboration with computers and machines but extend their roles to include system design and innovation management. This paper proposes a Multisensory Interaction Framework for HCPS (MS-HCPS) that leverages human senses to facilitate system creation and management. Additionally, the introduced Multisensory Graph Convolutional Network (MS-GCN) model calculates recommendation values for multiple senses, elucidating their relevance to system development. Furthermore, the effectiveness of the proposed framework and model is validated through three practical engineering scenarios. This study explores the research on multisensory interaction in HCPS from a human sensory perspective, aiming to facilitate the progress and development of HCPS across various domains.

Ameliorating Gonorrhea: Recent Therapeutic Adaptations and Scope to Improve its Prevailing Condition.

Gonorrhea is a sexually transmitted infection (STI) caused by the bacteria Neisseria gonorrhoeae. According to recent research, the prevalence of gonorrhea has been increasing in many parts of the world, with some areas reporting high rates of antibiotic resistance. In the United States, the Centers for Disease Control and Prevention (CDC) reported that the number of reported gonorrhea cases increased by 56% between 2015 and 2019. Globally, the World Health Organization (WHO) estimated that there were 87 million new cases of gonorrhea in 2016, with the highest burden of infection in low- and middle-income countries. Research has also shown that gonorrhea is becoming increasingly resistant to conventional antibiotics, increasing the prevalence of gonorrhea. This raises concerns and challenges in disease management. The present review gives updated insight on the current state of the disease, challenges, and shortcomings of existing approaches along with the modern and alternative direction like vaccine development, its challenges, and scope to confront the existing state of drug resistance and increased rate of incidence. Alternative strategies like immunotherapy and phage therapy along with recent antibiotics researched for the treatment of gonorrhea. The review provides a thorough insight into the current state of the disease and various available methods used currently and recommended by WHO. To overcome disease prevalence, various alternate therapies are coming into the limelight. However, scientists and researchers show a lack of interest in the drug development and research of gonorrhea, due to less commercial scope, lack of funding, and limited scope in the scientific scenario. These hurdles need to be overcome to meet the WHO vision of reducing gonorrhea by 90% by 2030. So, there is a need to optimize the drug therapy (optimizing dosing schedule, and precision monitoring) to reduce the chance of drug resistance. Also, there is a wide scope for drug and therapeutic system development.

The gut microbiota-brain axis in neurological disorders.

Previous studies have shown a bidirectional communication between human gut microbiota and the brain, known as the microbiota-gut-brain axis (MGBA). The MGBA influences the host's nervous system development, emotional regulation, and cognitive function through neurotransmitters, immune modulation, and metabolic pathways. Factors like diet, lifestyle, genetics, and environment shape the gut microbiota composition together. Most research have explored how gut microbiota regulates host physiology and its potential in preventing and treating neurological disorders. However, the individual heterogeneity of gut microbiota, strains playing a dominant role in neurological diseases, and the interactions of these microbial metabolites with the central/peripheral nervous systems still need exploration. This review summarizes the potential role of gut microbiota in driving neurodevelopmental disorders (autism spectrum disorder and attention deficit/hyperactivity disorder), neurodegenerative diseases (Alzheimer's and Parkinson's disease), and mood disorders (anxiety and depression) in recent years and discusses the current clinical and preclinical gut microbe-based interventions, including dietary intervention, probiotics, prebiotics, and fecal microbiota transplantation. It also puts forward the current insufficient research on gut microbiota in neurological disorders and provides a framework for further research on neurological disorders.

Development of hyaluronic acid hydrogel containing prednisolone-encapsulated nonphospholipid liposomes for the treatment of rheumatoid arthritis.

Rheumatoid arthritis (RA) requires therapeutic approaches that alleviate symptoms and inhibit the progression of joint damage. Glucocorticoids (GCs) have been a cornerstone of RA treatment, yet their use is often limited by side effects. Recent advancements suggest that liposome-based delivery systems can improve GC biodistribution, minimizing toxicity. This study introduces an innovative tool for RA treatment using prednisone-encapsulated nonphospholipid liposomes (NPLs) in combination with a hyaluronic acid (HA) hydrogel. Our methodology involved incorporating prednisone (PR) with palmitic acid and cholesterol to formulate stable NPLs using a thin-film hydration technique. The synthesized PR-NPLs, characterized by a mean size of 150 nm, demonstrated uniform distribution and higher drug encapsulation in comparison with conventional phospholipid liposomes. In vitro assays revealed that PR-NPL markedly reduced inflammatory responses in macrophages. Additionally, we successfully incorporated PR-NPL into an HA hydrogel, employing a photoinitiated cross-linking process. This novel composite offered modulable PR release, governed by the degree of hydrogel cross-linking. The developed system presents a promising advancement in RA management, especially suited for intraarticular injections. It potentially enables targeted, controlled drug release with a reduced risk of side effects, signifying a significant improvement over existing RA therapies.

A home-based tele-rehabilitation exercise system for patients after knee replacement surgery

BackgroundKnee arthritis is a destructive disorder that affects the knee joints and causes pain and reduced mobility. Drug treatments, weight loss, and exercise control the symptoms of the disease, but these methods only delay the disease process and eventually, knee joint replacement surgery will be needed. After surgery, with the help of a proper physiotherapy program, full recovery takes an average of 6–12 months. However, currently, there is no similar tool to facilitate this process in Iran.ObjectiveThe purpose of this research is to design and develop the prototype of a rehabilitation system for patients after knee replacement surgery, which provides patients with information and appropriate physiotherapy programs.MethodsThis study was development-applied and was done in three stages. In the first stage, the needs and content of education and therapeutic exercises were prepared in the form of a checklist, and then the necessity of each item was checked in the evaluation sessions. In the second stage, the prototype of the system was developed using Adobe XD software and based on the requirements approved from the previous stage. In the third stage, the usability of the program was analyzed from the point of view of experts using the exploratory evaluation method with Nielsen’s 13 principles of usability.ResultsAt first, the system requirements were extracted and prepared in two checklists of content (51 exercises) and capabilities (60 items). Then after a survey of experts based on the Delphi technique, content information (43 exercises) and functional and non-functional requirements (53 items) were obtained. A survey of experts helped to finalize the information elements, categorize them, and prepare the final version of the checklists. Based on this, the system requirements were classified into 11 categories, and the training content was classified into 3 training periods. Finally, the design and development of the system was done. This system has admin, physician, and patient user interfaces. The result of usability showed that this system is efficient and there are only a few problems in the feature of helping users to detect and recover from errors.ConclusionIt seems necessary to develop a system based on the principles of usability by therapists and rehabilitation specialists to train and monitor the remote rehabilitation process of patients after knee joint replacement at home. And the importance of involving stakeholders in the design and development of remote rehabilitation systems is not hidden from anyone. Kara system has all of the above.

Design & Development of smart scrap management system for a manufacturing organisation

Scrap management is handled through manual processes in many manufacturing organizationsleading to poor efficiencies. This paper discusses design, development, and deployment of a suitable scrap management system leveraging Industry 4.0 in a lean manner with traceability using ANPR technologyand digital weighbridge setup aided by logistics trucks, digital handshake, and virtual stores in SAP. The complete processes are digitally photographed leaving behind a digital thread for future forensics

Pengembangan Digitalisasi Inventaris Berbasis Web Untuk Meningkatkan Manajemen Aset Tetap Pada PT ANTAM Tbk UBPE Pongkor

PT Aneka Tambang (ANTAM) Tbk. operates several business units, including the Pongkor Gold Mining Business Unit (UBPE Pongkor). Currently, UBPE Pongkor relies on the SAP (System Application and Products) system and manual reconciliation for fixed asset management. The current system, relying on SAP and manual reconciliation, lacks automated tracking of physical location, asset ownership, and depreciation calculations. To optimize fixed asset management, ANTAM is implementing a QR-Code-based system for asset identification. This research aims to investigate how a digital inventory system can enhance the company's efficiency, accuracy, and operational performance. Specifically, it evaluates the effectiveness of inventory and asset reconciliation activities to ensure data consistency between the system and physical conditions in the field. The research employs a qualitative approach using a descriptive method with data collection techniques of observation, interviews, and documentation. Additionally, it utilizes the Waterfall development methodology. The findings of this research project report indicate that the fixed asset management through digital inventory at PT ANTAM Tbk. UBPE Pongkor operates effectively and aligns with the overall operational process. This includes system planning, system design, implementation, system development, testing, system compatibility testing, and usage and maintenance phases.

An Intelligent Vehicle Price Estimation Approach Using a Deep Neural Network Model

In recent years, the market for used-vehicle trade in the Kingdom of Saudi Arabia has grown significantly. This is due to the high cost of new vehicles that are not affordable by most buyers and lifting the ban on women drivers. Recently, several online websites for selling vehicles are available with different functions. However, estimating the vehicle price is based on traditional calculation methods, and this is inaccurate in several selling situations, as there are many factors that may affect the vehicle price, and these factors must be taken into consideration when estimating the vehicle’s price. Therefore, there is high demand to develop an automated vehicle price estimation system through adopting artificial intelligence (AI) technologies. Hence, this paper proposes an efficient vehicle price estimation system through developing an efficient deep neural network (DNN) model. The developed DNN model has been trained using a recent collected dataset for used-vehicle prices in the Kingdom of Saudi Arabia. The developed system has been validated using a recent vehicle price dataset, and the obtained results are compared with seven different machine learning models and showed a promising regression accuracy. In addition, we developed a reliable graphical user interface (GUI) for the purpose of allowing the user to estimate the price of any vehicle using the pre-trained DNN model.