Current Methods Research Articles

Design and development of control and monitoring hydroponic system

The global agriculture system faces significant challenges in meeting the growing demand for food production, particularly given projections that the world's population will reach 70% by 2050. Hydroponic farming is an increasingly popular technique in this field, offering a promising solution to these challenges. This paper will present the improvement of the current traditional hydroponic method by providing a system that can be used to monitor and control the important element in order to help the plant grow up smoothly. This proposed system is quite efficient and user-friendly that can be used by anyone. This is a combination of a traditional hydroponic system, an automatic control system and a smartphone. The primary objective is to develop a smart system capable of monitoring and controlling potential hydrogen (pH) levels, a key factor that affects hydroponic plant growth. Ultimately, this paper offers an alternative approach to address the challenges of the existing agricultural system and promote the production of clean, disease-free, and healthy food for a better future.

Machine-learning-guided Directed Evolution for AAV Capsid Engineering.

Target gene delivery is crucial to gene therapy. Adeno-associated virus (AAV) has emerged as a primary gene therapy vector due to its broad host range, long-term expression, and low pathogenicity. However, AAV vectors have some limitations, such as immunogenicity and insufficient targeting. Designing or modifying capsids is a potential method of improving the efficacy of gene delivery, but hindered by weak biological basis of AAV, complexity of the capsids, and limitations of current screening methods. Artificial intelligence (AI), especially machine learning (ML), has great potential to accelerate and improve the optimization of capsid properties as well as decrease their development time and manufacturing costs. This review introduces the traditional methods of designing AAV capsids and the general steps of building a sequence-function ML model, highlights the applications of ML in the development workflow, and summarizes its advantages and challenges.

Constant On/Off-Time Digital Current Control and Design Methods in Three-Level Flying-Capacitor Boost Converters for Fast Transient and Voltage Balancing

Constant On/Off-Time Digital Current Control and Design Methods in Three-Level Flying-Capacitor Boost Converters for Fast Transient and Voltage Balancing

Recent progress in per- and polyfluoroalkyl substances (PFAS) sensing: A critical mini-review

Per- and polyfluoroalkyl substances (PFAS) are a class of fluorinated pollutants found widely in numerous industrial and consumer products. Their excellent heat, oil, and water resistance and slow degradation rate in nature lead to their persistent environmental accumulation with potential adverse impacts on various organisms, including humans. Although the current EPA-approved PFAS detection method is elegant and ultrasensitive, its broader application is greatly limited due to the associated high costs, lengthy detection times, and skilled personnel requirements. Hence, there is a strong demand for rapid, robust, low-cost, and accessible PFAS detection methods to expedite the treatment of contaminated media and control exposure to these emerging substances. Since the publication of our first PFAS sensing review in 2021, numerous new PFAS sensors have been developed and reported. Consequently, this critical review primarily focuses on recent advancements in PFAS sensing platforms, encompassing optical-based, electrochemical-based, and other novel sensing principle-based systems, as well as those that complement liquid chromatography coupled with tandem mass spectrometry, the gold standard for PFAS detection. The underlying detection mechanisms, sensing performances, and potential areas for improvement are thoroughly discussed. We hope that this article offers readers a review of alternative PFAS detection systems developed in recent years and inspires future innovations in field-deployable PFAS sensing technology.

A Dexterous Hand-Arm Teleoperation System Based on Hand Pose Estimation and Active Vision.

Markerless vision-based teleoperation that leverages innovations in computer vision offers the advantages of allowing natural and noninvasive finger motions for multifingered robot hands. However, current pose estimation methods still face inaccuracy issues due to the self-occlusion of the fingers. Herein, we develop a novel vision-based hand-arm teleoperation system that captures the human hands from the best viewpoint and at a suitable distance. This teleoperation system consists of an end-to-end hand pose regression network and a controlled active vision system. The end-to-end pose regression network (Transteleop), combined with an auxiliary reconstruction loss function, captures the human hand through a low-cost depth camera and predicts joint commands of the robot based on the image-to-image translation method. To obtain the optimal observation of the human hand, an active vision system is implemented by a robot arm at the local site that ensures the high accuracy of the proposed neural network. Human arm motions are simultaneously mapped to the slave robot arm under relative control. Quantitative network evaluation and a variety of complex manipulation tasks, for example, tower building, pouring, and multitable cup stacking, demonstrate the practicality and stability of the proposed teleoperation system.

Time-dependent fatigue reliability analysis of heavy-haul railway steel bridges

Fatigue of heavy-haul railway bridges has been an ongoing concern due to the excessive stresses and frequent cyclic effects of freight trains. Current fracture-based fatigue research, which promotes fatigue reliability analysis caused by crack propagation, is challenged by the dilemma between the detailed modeling of cyclic stress and the reflection of temporal characteristics of loading conditions. In addition, due to the strong non-Gaussian nature of the fatigue limit state function and the computational inefficiency of integrating finite element analysis, current time-dependent reliability methods need to be further developed. To evaluate the fatigue reliability of heavy-haul railway steel bridges considering the temporal dependence of cyclic train loading, a time-dependent fatigue reliability methodology is developed in this study with two innovations. Firstly, a cyclic stress range model is proposed based on the load analysis at the bridge level, which can capture the time-dependent characteristics of the incurred stress range throughout service life. Then, an improved outcrossing rate method for strong non-Gaussian fatigue limit state functions is developed to reduce the computational costs associated with reliability analysis integrated with the finite element model. The developed time-dependent fatigue reliability methodology is then applied to a heavy-haul railway steel truss bridge located in Shandong, China, which proves its efficiency and accuracy in evaluating the time-dependent fatigue reliability of heavy-haul railway steel bridges. Furthermore, the developed methodology is able to provide insight into the balance between bridge fatigue damage and freight volume, and it is found that the combination of small train axle weight and high operation frequency is the optimal solution to achieve the fatigue reliability of the steel bridge while fulfilling the freight demand.

Incremental Embedding Learning With Disentangled Representation Translation.

Humans are capable of accumulating knowledge by sequentially learning different tasks, while neural networks fail to achieve this due to catastrophic forgetting problems. Most current incremental learning methods focus more on tackling catastrophic forgetting for traditional classification networks. Notably, however, embedding networks that are basic architectures for many metric learning applications also suffer from this problem. Moreover, the most significant difficulty for continual embedding networks is that the relationships between the latent features and prototypes of previous tasks will be destroyed once new tasks have been learned. Accordingly, we propose a novel incremental method for embedding networks, called the disentangled representation translation (DRT) method, to obtain the discriminative class-disentangled features without reusing any samples of previous tasks and while avoiding the perturbation of task-related information. Next, a mask-guided module is specifically explored to adaptively change or retain the valuable information of latent features. This module enables us to effectively preserve the discriminative yet representative features in the disentangled translation process. In addition, DRT can easily be equipped with a regularization item of incremental learning to further improve performance. We conduct extensive experiments on four popular datasets; as the experimental results clearly demonstrate, our method can effectively alleviate the catastrophic forgetting problem for embedding networks.

Equivalent Reduced DC Network Models With Nonlinear Load Functions: A Data-Driven Approach

Nonconvex AC power flow models feature larger computation burden and lack optimality guarantees to be considered in industry expansion and operational planning activities. Additionally, the consideration of full network models, in general, leads to undesirable levels of computational burden, even under the standard DC approximations. Notwithstanding, current reduction methods fail in accurately reproducing real system responses for a wide range of operating points. In this context, we propose a novel data-driven network reduction method to generate equivalent reduced DC power flow models with nonlinear load functions. Our method aims at providing a high-quality representation of the internal system by minimizing the mismatch between the response of the equivalent DC model and the complete AC power flow (or real measurements) under multiple operating-point data. Thus, the approach can be interpreted as a physics-informed machine-learning method as it trains (or estimates) some parameters of the reduced DC power-flow model to fit pre-calculated data. The method determines: 1) a reduced external equivalent network model (topology and parameters), 2) the optimal allocation of external power injections to the boundary buses, and 3) the coefficients determining artificial nonlinear load functions of the operating-point data. Out-of-sample evaluation tests corroborate the performance of our model against modified Ward reduction benchmark models.

MicroPSCal: A MicroStation package for storage calculation of pumped storage power station during planning and designing

Pumped storage power plants can effectively guarantee the healthy development of energy and promote energy transformation and green development. The calculation accuracy is directly related to the flood control safety of the reservoir. The current storage calculation method of storage capacity is inefficient and complicated resulting in deviations between calculated values and actual storage capacity. The paper is devoted to the problem of efficiency and quality of capacity calculation in the planning and design stage of pumped storage power plants. A toolkit MicroPSCal is developed based on MicroStation software to simulate and calculate the corresponding storage capacity of different elevations and draw the storage capacity curve, which can be applied in the actual planning and design process of pumped storage power stations. The results of the calculation showed that this method can make full use of terrain accuracy to achieve high-precision calculation of reservoir capacity of different elevations and automatically draw high-quality storage curves. Compared with traditional storage capacity calculation methods, calculation time was reduced from 18 hours to 45 minutes. This method has obvious advantages and provides a new technical idea for storage capacity calculation during the planning and design of pumped storage power plants.

Toward Direct Regeneration of Spent Lithium-Ion Batteries: A Next-Generation Recycling Method.

The popularity of portable electronic devices and electric vehicles has led to the drastically increasing consumption of lithium-ion batteries recently, raising concerns about the disposal and recycling of spent lithium-ion batteries. However, the recycling rate of lithium-ion batteries worldwide at present is extremely low. Many factors limit the promotion of the battery recycling rate: outdated recycling technology is the most critical one. Existing metallurgy-based recycling methods rely on continuous decomposition and extraction steps with high-temperature roasting/acid leaching processes and many chemical reagents. These methods are tedious with worse economic feasibility, and the recycling products are mostly alloys or salts, which can only be used as precursors. To simplify the process and improve the economic benefits, novel recycling methods are in urgent demand, and direct recycling/regeneration is therefore proposed as a next-generation method. Herein, a comprehensive review of the origin, current status, and prospect of direct recycling methods is provided. We have systematically analyzed current recycling methods and summarized their limitations, pointing out the necessity of developing direct recycling methods. A detailed analysis for discussions of the advantages, limitations, and obstacles is conducted. Guidance for future direct recycling methods toward large-scale industrialization as well as green and efficient recycling systems is also provided.

Current update on anaphylaxis: anaphylaxis management in recent guidelines.

Anaphylaxis is a potentially fatal hypersensitivity reaction but frequently underrecognized. Although its incidence rates vary according to geographical location, it seems clear that there has been a general increase in recent years, either because of greater recognition of this entity or because it is progressing proportionally to the presence of allergic diseases in the world. The development of anaphylaxis management guidelines adapted to local or regional needs seems of utmost importance. Furthermore, it is necessary to assess their implementation and their positive effect regarding diagnosing and treating anaphylaxis. In this review we explore the currently existing definitions of anaphylaxis and its epidemiology, the potential triggers of anaphylaxis and guideline recommendations in terms of diagnosis and management, proposing a novel anaphylaxis calculator and reviewing the current scoring methods for anaphylactic episodes.

An Overview of the Need for Circular Economy on Electric Vehicle Batteries

Batteries are a widely utilized and simple method for powering electronic devices, particularly given the prevalence of individuals traveling to all gadgets. The escalating adoption of electric vehicles and portable electronic devices has led to a surge in the demand for lithium-ion batteries. Consequently, this has given rise to supply uncertainties in acquiring essential minerals such as lithium and cobalt, along with concerns about the proper disposal of dead batteries. The existing methods for battery recycling exhibit variations based on the individual chemistries of the batteries, hence influencing both cost factors and greenhouse gas emissions. Simultaneously, there exists a possibility for repurposing depleted batteries for low-tier energy storage applications. The absence of legislation pertaining to the secure storage and handling of waste streams contributes to the accumulation of refuse in exposed environments and the release of hazardous substances from landfills. In addition, contemporary battery manufacturing methods necessitate the utilization of innovative substances, such as ionic liquids for electrolytes and nanostructures for cathodes, to enhance the energy characteristics and longevity of batteries. The presence of uncertainties regarding the accurate assessment of the environmental consequences associated with novel battery chemicals has the potential to impede efforts aimed at recycling and containment. The objective of this analysis is to consolidate the existing knowledge regarding battery pollutants, both those that are recognized and those that remain uncertain, and to assess their potential environmental impacts. Additionally, this research aims to examine the current strategies and methods employed for the recycling of batteries in the circular economy.

Investigation of the Effect of Aerosol Deposition by Applying Electrostatic Fields

Particulate matter (PM) constitutes a significant risk factor for environmental health regarding ambient air quality. An epidemiological investigation has determined that inadequate air quality is associated with the development of lung and cardiovascular diseases, chronic ailments, respiratory infections, and a significant number of fatalities on a global scale. According to the World Health Organization (WHO), the annual mortality rate due to air pollution is around 7 million individuals. The necessity for researching air quality, climate change, and the challenges posed by particulate matter (PM) is widely recognized. While reducing ambient air quality, it is essential to consider the limitations of current technical control methods. This article focuses on developing and implementing advanced technology to mitigate particle pollution in urban environments. The comparison of empirical data and computational simulations has demonstrated the efficacy of utilizing pulsed radio waves to reduce particulate matter. The control technique exhibits a threefold increase in the rate of PM concentration reduction compared to gravity settling. The approach's efficacy was evaluated through controlled trials conducted in controlled chambers and urban environments, demonstrating up to 50% reductions. The validation of the implemented test case results of the control technology was performed using historical data while considering the existence of radio waves. The coagulation process demonstrated and verified the effectiveness of reducing particle matter. The employed methodology has been shown to encourage outcomes concerning mitigating particulate matter contamination within urban and industrial environments.

Development of personalized non-invasive ventilation masks for critically ill children: a bench study

BackgroundObtaining a properly fitting non-invasive ventilation (NIV) mask to treat acute respiratory failure is a major challenge, especially in young children and patients with craniofacial abnormalities. Personalization of NIV masks holds promise to improve pediatric NIV efficiency. As current customization methods are relatively time consuming, this study aimed to test the air leak and surface pressure performance of personalized oronasal face masks using 3D printed soft materials. Personalized masks of three different biocompatible materials (silicone and photopolymer resin) were developed and tested on three head models of young children with abnormal facial features during preclinical bench simulation of pediatric NIV. Air leak percentages and facial surface pressures were measured and compared for each mask.ResultsPersonalized NIV masks could be successfully produced in under 12 h in a semi-automated 3D production process. During NIV simulation, overall air leak performance and applied surface pressures were acceptable, with leak percentages under 30% and average surface pressure values mostly remaining under normal capillary pressure. There was a small advantage of the masks produced with soft photopolymer resin material.ConclusionThis first, proof-of-concept bench study simulating NIV in children with abnormal facial features, showed that it is possible to obtain biocompatible, personalized oronasal masks with acceptable air leak and facial surface pressure performance using a relatively short, and semi-automated production process. Further research into the clinical value and possibilities for application of personalized NIV masks in critically ill children is needed.

Ultrasimple size encoded microfluidic chip for rapid simultaneous multiplex detection of DNA sequences

Simultaneous multiplexed analysis can provide comprehensive information for disease diagnosis. However, the current multiplex methods rely on sophisticated barcode technology, which hinders its wider application. In this study, an ultrasimple size encoding method is proposed for multiplex detection using a wedge-shaped microfluidic chip. Driving by negative pressure, microparticles are naturally arranged in distinct stripes based on their sizes within the chip. This size encoding method demonstrates a high level of precision, allowing for accuracy in distinguishing 3–5 sizes of microparticles with a remarkable accuracy rate of up to 99%, even the microparticles with a size difference as small as 0.5 μm. The entire size encoding process is completed in less than 5 min, making it ultrasimple, reliable, and easy to operate. To evaluate the function of this size encoding microfluidic chip, three commonly co-infectious viruses’ nucleic acid sequences (including complementary DNA sequences of HIV and HCV, and DNA sequence of HBV) are employed for multiplex detection. Results indicate that all three DNA sequences can be sensitively detected without any cross-interference. This size-encoding microfluidic chip-based multiplex detection method is simple, rapid, and high-resolution, its successful application in serum samples renders it highly promising for potential clinical promotion.

Graph oscillators: Physics-guided graph modeling of mass–spring–damper systems for trajectory prediction and damage localization

Recognizing that a multiple-degree-of-freedom mass–spring–damper system can be viewed as a group of connected nodes, this paper presents a novel computational framework that learns such coupled oscillations by a graph, a mathematical structure describing pairwise connections between objects. Further reinforced by an incomplete equation of motion, distributed neural networks and an anti-symmetric coupling constraint, we can learn unknown coupling mechanisms (or nodal interactions in the language of graph models) using limited data. The proposed physics-data-hybrid method demonstrates a promising predictive capability even for unseen control inputs, outperforming three black-box and grey-box counterparts in synthetic and experimental studies. In addition to its predictive capability, the distributed parameterization of nodal interactions allows us to identify local anomalies by evaluating updates after short transfer learning. While limits of the current method exist, it displays the potential of a generalizable and interpretable meta-model for predicting nonlinear trajectories and identifying the system’s status.

Intelligent decision-making in healthcare telemonitoring via forward-backward chaining and IoT

Healthcare telemonitoring has emerged as a promising approach to remotely monitor patients remotely, enabling timely intervention and personalized care. Internet of things (IoT) device-generated patient data necessitates innovative solutions for intelligent healthcare decision-making, as current methods struggle to provide timely, context-aware, and data-driven recommendations, resulting in suboptimal patient care. This study aims to develop an intelligent decision-making framework for healthcare telemonitoring by leveraging forward-backward chaining and IoT technology. The research focuses on a system using forward-backward chaining algorithms to analyze real-time patient data from IoT devices. It utilizes machine learning models to adapt to changing conditions and refine decision-making, demonstrating its ability to provide real-time context-aware recommendations. Temperature, blood pressure, oxygen level, and heart rate measurement errors are 2.01%, 1.74 to 2.13%, 0.61%, and 1.45%, respectively. The success rate of early disease diagnosis using an expert system is 81%, with an average application interface responsiveness time of 4.978 s. The integration of IoT data with intelligent decision-making algorithms in healthcare telemonitoring has the potential to revolutionize patient care. However, future work should focus on scalability and interoperability for diverse healthcare settings.

Prediction of Electromagnetic Performance for IPMSM Based on Improved Analytical Model Considering Saturation Effects

Unlike surface-mounted permanent magnet synchronous motors (SPMSM), the rotor structure of interior permanent magnet synchronous motors (IPMSM) is irregular and relatively complex. This makes the analytical calculation for IPMSM quite complicated. Except for the use of some equivalent methods to make irregular permanent magnets (PMs) regular, existing subdomain models often cannot effectively and directly account for the complex rotor structure and cannot be easily applied to IPMSM. For this reason, an improved analytical model applicable to IPMSM is presented, studied, and developed in this article. First, a novel subdomain model division method is innovatively introduced. Complex rotor structure can be well accounted for. This not only facilitates the expression of vector potential and satisfies the establishment of subdomain model, but also recovers the original rotor topology to the maximum extent and effectively avoids many uncertainties and unreliability caused by over-equivalence. Afterwards, the magnetic equivalent circuit and equivalent surface current method are combined to fully consider the saturation effects of magnetic isolation bridges, rotor ribs, and cores. Model analysis accuracy is well guaranteed. Further, the precise subdomain method is investigated to achieve fast prediction of key electromagnetic performance. Both finite element analysis (FEA) and prototype experiment strongly highlight the validity of the improved analytical model. In addition, compared to FEA, the improved analytical model does not require tens of thousands of computation cells, which saves a lot of time and storage while ensuring the same prediction accuracy. This opens up unlimited possibilities for the design and optimization of IPMSM.

Handling time-varying treatments in observational studies: A scoping review and recommendations.

Time-varying treatments are common in observational studies. However, when assessing treatment effects, the methodological framework has not been systematically established for handling time-varying treatments. This study aimed to examine the current methods for dealing with time-varying treatments in observational studies and developed practical recommendations. We searched PubMed from 2000 to 2021 for methodological articles about time-varying treatments, and qualitatively summarized the current methods for handling time-varying treatments. Subsequently, we developed practical recommendations through interactive internal group discussions and consensus by a panel of external experts. Of the 36 eligible reports (22 methodological reviews, 10 original studies, 2 tutorials and 2 commentaries), most examined statistical methods for time-varying treatments, and only a few discussed the overarching methodological process. Generally, there were three methodological components to handle time-varying treatments. These included the specification of treatment which may be categorized as three scenarios (i.e., time-independent treatment, static treatment regime, or dynamic treatment regime); definition of treatment status which could involve three approaches (i.e., intention-to-treat, per-protocol, or as-treated approach); and selection of analytic methods. Based on the review results, a methodological workflow and a set of practical recommendations were proposed through two consensus meetings. There is no consensus process for assessing treatment effects in observational studies with time-varying treatments. Previous efforts were dedicated to developing statistical methods. Our study proposed a stepwise workflow with practical recommendations to assist the practice.

A Dual-Branch Framework With Prior Knowledge for Precise Segmentation of Lung Nodules in Challenging CT Scans.

Lung cancer is one of the deadliest cancers globally, and early diagnosis is crucial for patient survival. Pulmonary nodules are the main manifestation of early lung cancer, usually assessed using CT scans. Nowadays, computer-aided diagnostic systems are widely used to assist physicians in disease diagnosis. The accurate segmentation of pulmonary nodules is affected by internal heterogeneity and external data factors. In order to overcome the segmentation challenges of subtle, mixed, adhesion-type, benign, and uncertain categories of nodules, a new mixed manual feature network that enhances sensitivity and accuracy is proposed. This method integrates feature information through a dual-branch network framework and multi-dimensional fusion module. By training and validating with multiple data sources and different data qualities, our method demonstrates leading performance on the LUNA16, Multi-thickness Slice Image dataset, LIDC, and UniToChest, with Dice similarity coefficients reaching 86.89%, 75.72%, 84.12%, and 80.74% respectively, surpassing most current methods for pulmonary nodule segmentation. Our method further improved the accuracy, reliability, and stability of lung nodule segmentation tasks even on challenging CT scans.