Automatic Adjustment Research Articles

Self‐Adaptive and Electric Field‐Driven Protective Layer with Anchored Lithium Deposition Enable Stable Lithium Metal Anode

Lithium metal battery has great development potential because of its lowest electrochemical potential and highest theoretical capacity. However, the uneven deposition of Li+ flux in the process of deposition and stripping induces the vigorous growth of lithium dendrites, which results in severely battery performance degradation and serious safety hazards. Here, the tetragonal BaTiO3 polarized by high voltage corona was used to build an artificial protective layer with uniform positive polarization direction, which enables uniform Li+ flux. In contrast to traditional strategies of using protective layer, which can guide the uniform deposition of lithium metal. The ferroelectric protective layer can accurately anchor the Li+ and achieve bottom deposition of lithium due to the automatic adjustment of the electric field. Simultaneously, the huge volume changes caused by Li+ migration change of the lithium metal anode during charging and discharging is functioned to excite the piezoelectric effect of the protective layer, and achieve seamless dynamic tuning of lithium deposition/stripping. This dynamic effect can accurately anchor and capture Li+. Finally, the layer‐modified Li anode enables reversible Li plating/stripping over 1500 h at 1 mA cm−2 and 50 °C in symmetric cells. In addition, the assembled Li‐S full cell exhibits over 300 cycles with N/P ≈ 1.35. This work provides a new perspective on the uniform Li+ flux at the Li‐anode interface of the artificial protective layer.

Nutrient Film Technique-Based Hydroponic Monitoring and Controlling System Using ANFIS

Most people are now aware of the importance of a healthy lifestyle, including the importance of consuming vegetables. As a result, the demand for vegetables has increased, and so their production needs to be increased. Currently, most plantations use soil as a growing medium, which is time-consuming and requires a significant amount of space. To modernize cultivation, hydroponic techniques should be adopted. However, implementing hydroponics can be challenging as it requires precise pH and nutrient adjustments. The previous research has proposed the hydroponic pH and nutrient control using the Sugeno fuzzy method. However, in Sugeno fuzzy method, there is no systematic procedure in designing the fuzzy controller, thus, the design relies on hydroponic expert knowledge. To address this issue, a smart hydroponic system was developed using the adaptive neuro-fuzzy inference system (ANFIS) method, which allows for automatic adjustments based on the collected dataset and remote control through internet of things (IoT) technology. This study showed that the system could accurately adjust pH and nutrient levels, allowing plants to grow better. Furthermore, the fuzzy controller created using ANFIS is 67% more accurate than creating the fuzzy controller using the Sugeno fuzzy method. Finally, the web application dashboard of the proposed system is also presented in this paper.

Context-aware workflow management for smart manufacturing: A literature review of semantic web-based approaches

Smart Manufacturing Systems (SMS) are software systems that identify opportunities for automating manufacturing operations by using Internet of Things (IoT) devices and services connected to machines. An active challenge of SMS is to satisfy the ever-changing conditions of industries, supply networks, and customer needs. To operate effectively, SMS should be flexible enough to perform automatic or semi-automatic adjustments to manufacturing processes in response to unexpected changes, a feature called context awareness. Recent advances in interpreting context data in the semantic web have permitted SMS to understand the active situation of manufacturing processes. This paper presents a literature analysis of context-aware workflow management approaches in the smart manufacturing domain, with a particular focus on semantic web-based approaches published from 2015 to 2022. A Systematic Literature Review (SLR) methodology was applied to analyze the state-of-the-art via the PICOC method. The contributions of this work are (1) an SLR about context-aware workflow management for smart manufacturing systems focusing on semantic web-based approaches, (2) a systematic taxonomy to break down the approaches in conformity based on content and main workflow management function area, and (3) identification of opportunities for improvement in technical features such as context awareness, use case implementation, tools employed, licensing, security, and scalability. A novel architecture and components are also proposed to address the identified active challenges.

Adaptive control for downhole nuclear magnetic resonance excitation

Nuclear magnetic resonance (NMR) measurements are performed with the pulse sequence and acquisition parameters set by the operator, which cannot be adjusted in real time according to sample characteristics. In one acquisition cycle, usually thousands of high-power pulses are transmitted and thousands of echo points are acquired. The power consumption cause the RF amplifier to overheat, and large amounts of acquired data may be invalid. Therefore, the optimization of excitation and acquisition processes is necessary to improve measurement efficiency. We explore a scheme for the real-time measurement of the samples by adaptively regulating the pulse sequence, which adapts the variable TE pulse sequence as the reconnaissance mode. The appropriate pulse sequence and reasonable parameters (NE, TE) can be selected according to the relaxation characteristics of the samples.This adaptive control strategy has great significance in guiding both dynamic and static measurements, and it is especially suitable for occasions where low magnetic field gradients and diffusion terms can be ignored. We also design a test circuit for adaptive control, which has the function of automatic parameter adjustment. By adjusting parameters such as the number of refocusing pulses, echo spacing, etc., the effective measurement of the samples can be achieved in practice.

Influence of Traditional Cross-border Investment Vehicles on Capital Market Performance: Empirical Evidence from Nigeria and Kenya

This study evaluated the influence of traditional cross-border investment vehicles on the performance of the Nigerian and Kenyan capital markets, for the period spanning from January 2011 to December 2020. Exchange rate, monetary policy rate (otherwise known as the central bank rate in Kenya), inflation rate, crude oil price and credit rating were used as the traditional cross-border investment vehicles while capital market performance was proxied by All Share Index. Ex post facto research design was used in a multiple regression analysis framework to determine the partial effects of the endogenous variables on the explained variable. Pretesting was done using the Augmented Dickey-Fuller method to establish the stationarity of the model variables. The test showed that the data series were of mixed order of integration which necessitated the application of the Autoregressive Distributed Lag (ARDL) model. The ARDL bounds test indicated that the influence of traditional cross border investment vehicles on the performance of the Nigerian and Kenyan Capital markets was bound by a long-run relationship. Specifically, the long-run estimates showed that, whereas the Nigerian capital market was significantly affected by the dynamics of exchange rate and monetary policy more than Kenya, the Kenyan capital market was affected the most by the crude oil price changes and inflation than Nigeria. In terms of credit rating, the Nigerian capital market was largely negatively affected whereas Kenya seemed less volatile. Regarding the speed of adjustment to disequilibrium, it was observed that all the models exhibited an automatic adjustment to equilibrium after very short-run shock. The study concluded that traditional cross border investment vehicles had huge impact on the performance of the two markets and therefore recommended among others that, all the stakeholders should make concerted efforts towards improving the investment climates of East and West African leading stock markets.

The Development of the IS-LM-EE model and its Implications for Economic Policy

The macroeconomic activity must consider the effects of the ecological environment as ecological sustainability increasingly constrains humans’ economic behaviour. Therefore, how to use fiscal and monetary policy tools to achieve harmonious economic and environmental development has become a hot topic of research among scholars. As a result, this study begins by distinguishing between the IS-LM-EE family of models and the various impacts of the same combination of monetary and fiscal policies on economic growth. It is discovered that the link between environment and capital is not clearly described in the models, which results in a variety of policy consequences. For example, tight fiscal and expansionary monetary policy would boost the economy in the Heyes and Sim IS-LM-EE models, whereas the Decker and Wohar model would experience an economic downturn as a result of the same policy combination. Meanwhile, the models of Heyes, Lawn, Decker and Warhar lack further automatic adjustment methods, and although the Sim model features an adjustment mechanism, its lag can significantly affect its effectiveness. Secondly, the study employs Heyes' model as a theoretical basis to simulate the economic impact of government policies including carbon emission quotas and concludes by summarising the limitations of the existing models.

Research on Automatic attitude adjustment Technology of Suspension rail of the Unfolding frame

Spacecraft solar wings, antennas, and other space deployment mechanisms need to be unloaded by gravity during the ground launch test. At present, the most widely used is the guide rail type Micro-gravity unfolding frame, and the rail needs to be adjusted horizontally before the test is carried out. Aiming at the shortcomings of the current rail debugging relying on labor, difficult high-altitude operation, and low debugging efficiency, a guide rail automatic attitude adjustment device is proposed, which can quickly realize the efficient adjustment and attitude maintenance of the rail level. After test verification, the system can increase the local level of the guide rail from the traditional 0.02 mm/m to 0.01 mm/m, the overall level from the original 0.1 mm/m to 0.02 mm/m, and shorten the commissioning time from the original 4 h to 1 h. The power-off holding ability is strong, which greatly improves the efficiency of the ground test. The research results have been applied to other spatial deployment institutions that conduct tests based on rails.

A novel videoscope and tool kit for percutaneous pericardial access under direct visualization

BackgroundPericardial access is necessary for the application of epicardial cardiac therapies including ablation catheters, pacing and defibrillation leads, and left atrial appendage closure systems. Pericardial access under fluoroscopic guidance is difficult in patients without pericardial effusions and may result in coronary artery damage, ventricular injury, or perforation with potentially life-threatening pericardial bleeding in up to 10% of cases. There is a clinical need for a pericardial access technique to safely deliver epicardial cardiac therapies.MethodsIn this paper, we describe the design and evaluation of a novel videoscope and tool kit to percutaneously access the pericardial space under direct visualization. Imaging is performed by a micro-CMOS camera with an automatic gain adjustment software to prevent image saturation. Imaging quality is quantified using known optical targets, while tool performance is evaluated in pediatric insufflation and pericardial access simulators. Device safety and efficacy is demonstrated by infant porcine preclinical studies (N = 6).ResultsThe videoscope has a resolution of 400 × 400 pixels, imaging rate of 30 frames per second, and fits within the lumen of a 14G needle. The tool can resolve features smaller than 39.4 µm, achieves a magnification of 24x, and has a maximum of 3.5% distortion within the field of view. Successful pericardial access was achieved in pediatric simulators and acute in vivo animal studies. During in vivo testing, it took the electrophysiologist an average of 66.83 ± 32.86 s to insert the pericardial access tool into the thoracic space and visualize the heart. After visualizing the heart, it took an average of 136.67 ± 80.63 s to access the pericardial space under direct visualization. The total time to pericardial access measured from needle insertion was 6.7 × quicker than pericardial access using alternative direct visualization techniques. There was no incidence of ventricular perforation.ConclusionsPercutaneous pericardial access under direct visualization is a promising technique to access the pericardial space without complications in simulated and in vivo animal models.

Improving drum sieve performance in lightweight packaging waste recycling by automatic parameter adjustment through 3D laser triangulation-based process monitoring

Current lightweight packaging (LWP) sorting plants are underperforming, and one of the most important reasons is the fluctuation in LWP input materials. Drum sieving is an essential process in LWP waste processing and recycling, as it is one of the first preconditioning steps and could influence all subsequent processes. If the parameter settings of a drum sieve can be automatically adjusted, the sorting performance of the whole plant could be improved and more recyclates could be fed back into the material loop. Therefore, this study investigated the influence from different parameter settings (inclination angles, rotation speed and throughput/material feeding) on drum sieving efficiency using 3D laser triangulation (3DLT) sensors. The results confirmed the correlation between two parameters (inclination angles, rotation speed) and sieving efficiency. Besides, 3DLT sensors enable inline determination of filling levels, which can be used as a basis for an automatic parameter control concept.

Modeling Production-Living-Ecological Space for Chengdu, China: An Analytical Framework Based on Machine Learning with Automatic Parameterization of Environmental Elements.

Cities worldwide are facing the dual pressures of growing population and land expansion, leading to the intensification of conflicts in urban productive-living-ecological spaces (PLES). Therefore, the question of "how to dynamically judge the different thresholds of different indicators of PLES" plays an indispensable role in the studies of the multi-scenario simulation of land space changes and needs to be tackled in an appropriate way, given that the process simulation of key elements that affect the evolution of urban systems is yet to achieve complete coupling with PLES utilization configuration schemes. In this paper, we developed a scenario simulation framework combining the dynamic coupling model of Bagging-Cellular Automata (Bagging-CA) to generate various environmental element configuration patterns for urban PLES development. The key merit of our analytical approach is that the weights of different key driving factors under different scenarios are obtained through the automatic parameterized adjustment process, and we enrich the study cases for the vast southwest region in China, which is beneficial for balanced development between eastern and western regions in the country. Finally, we simulate the PLES with the data of finer land use classification, combining a machine learning and multi-objective scenario. Automatic parameterization of environmental elements can help planners and stakeholders understand more comprehensively the complex land space changes caused by the uncertainty of space resources and environment changes, so as to formulate appropriate policies and effectively guide the implementation of land space planning. The multi-scenario simulation method developed in this study has offered new insights and high applicability to other regions for modeling PLES.

Development of a Pancreatic Fistula Prediction Model After Pancreaticoduodenectomy Based on a Decision Tree and Random Forest Algorithm.

The incidence of postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) is high. We sought to develop a POPF prediction model based on a decision tree (DT) and random forest (RF) algorithm after PD and to explore its clinical value. The case data of 257 patients who underwent PD in a tertiary general hospital from 2013 to 2021 were retrospectively collected in China. The RF model was used to select features by ranking the importance of variables, and both algorithms were used to build the prediction model after automatic adjustment of parameters by setting the respective hyperparameter intervals and resampling as a 10-fold cross-validation method, etc. The prediction model's performance was assessed by the receiver operating characteristic curve (ROC) and the area under curve (AUC). Postoperative pancreatic fistula occurred in 56 cases (56/257, 21.8%). The DT model had an AUC of .743 and an accuracy of .840, while the RF model had an AUC of .977 and an accuracy of .883. The DT plot visualized the process of inferring the risk of pancreatic fistula from the DT model on independent individuals. The top 10 important variables were selected for ranking in the RF variable importance ranking. This study successfully developed a DT and RF algorithm for the POPF prediction model, which can be used as a reference for clinical health care professionals to optimize treatment strategies to reduce the incidence of POPF.

High-Precision Dual-Channel Electrochemical POCT Device With Automatic Adjustment of Current Detection Sensitivity

Electrochemical point-of-care testing (POCT) technology is widely used in clinical testing, health management, epidemic control, food safety monitoring, and drug detection. Existing electrochemical POCT devices are mostly single-channel tests with a simple detection index, fixed sensitivity, and limited test range. In this article, a dual-channel, high-precision electrochemical device with automatic analytic software is developed. Two independent channels with consistent performance can be operated at the same time, multiplying the detection efficiency. The digital potentiometer is adopted as the feedback resistor for the operational amplifier, and an automatic adjustment program is set up on the smartphone app to achieve automatic adjustment of current detection sensitivity. An automatic peak-seeking function is proposed, which can quickly locate and record the peak voltage and current. The current detection range from ±10 nA to ±2 mA with a sensitivity up to 10−8A and a current detection resolution as low as 0.062% (6.19 pA in the ±10 nA range) are obtained. The device has been carried out for the simultaneous detection of ascorbic acid (AA), uric acid (UA), and dopamine (DA) ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}^{2} &gt;0.977$ </tex-math></inline-formula> ). The results indicate that the dual-channel device is of wide linear detection range, high precision, and consistency. It provides a technical means and a test platform for accurate and timely diagnosis of relevant diseases and health monitoring.

Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics.

The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features.

Role of Feeding and Novel Ripening System to Enhance the Quality and Production Sustainability of Curd Buffalo Cheeses

The buffalo dairy sector is extending its boundaries to include new buffalo cheese productions beyond mozzarella, overcoming some barriers that make cheeses expensive and unsustainable. This study aimed to evaluate the effects of both the inclusion of green feed in the diet of Italian Mediterranean buffaloes and an innovative ripening system on buffalo cheese quality, providing solutions capable of guaranteeing the production of nutritionally competitive and sustainable products. For this purpose, chemical, rheological, and microbiological analyses were carried out on cheeses. Buffaloes were fed with or without the inclusion of green forage. Their milk was used to produce dry ricotta and semi-hard cheeses, ripened according to both respective traditional (MT) and innovative methods (MI); these are based on automatic adjustments of climatic recipe guided by the continuous control of pH. Green feed enhances the nutritional profile of the final products (high content of MUFAs and PUFAs). As far as the ripening method is concerned, to our knowledge, this is the first study that tests aging chambers, commonly used for meat, for the maturing of buffalo cheeses. Results pointed out the MI validity also in this field of application, as it shortens the ripening period without negatively compromising any of desirable physicochemical properties and the safety and hygiene of the final products. Conclusively, this research highlights the benefits of diets rich in green forage on productions and provides support for the ripening optimization of buffalo semi-hard cheeses.

A study on a new fiscal risk‐sharing system to make Eurozone an OCA

AbstractThis article seeks to add a new element to the 5‐decade debate over the theory of Optimal Currency Area. In the euro area, the accumulation of economic imbalances caused by the loss of the automatic adjustment functions of the exchange rate have served as a structural background to limit its self‐sustaining resilience during financial shocks. To enhance the stability of Optimal Currency Area (OCA) through the revitalization of those functions, this article argues that if the euro area had adopted some form of fiscal risk‐sharing mechanisms, which could have generated a similar economic effect as if they had maintained a flexible F/X system as that which had existed in pre‐euro days, the fiscal crisis hypothetically could have been more easily overcome within a shorter period with less financial support. The result suggests that if ‘Adjustment Fund for Foreign Exchange rate Loss (AFFEL)’, originally devised within these pages, had operated, the interest rates in crisis‐hit members could have been significantly lower than actual levels during the crisis, while safely maintaining the euro system.

Automated Adjustment of PPE Masks Using IoT Sensor Fusion.

The COVID-19 pandemic has led to a dramatic increase in the use of PPE by the general public as well as health professionals. Scientists and health organizations have developed measures to protect people and minimize the catastrophic outcomes of COVID, including social distancing, frequent and periodic sanitizing, vaccinations, protective coverings, and face masks. During this time, the usage of protective face masks has increased dramatically. A mask only provides full safety to the user if it is a proper fit on their face. The aim of this paper is to automatically analyze and improve the fit of a face mask using IoT sensors. This paper describes the creation of a 3D-printed smart face mask that uses sensors to determine the current mask fit and then automatically tightens mask straps. This is evaluated using adjustment response time and the quality of fit achieved using the automatic adjustment approach with a range of sensor types.

The role of new staplers in reducing the incidence of air leak.

Surgical staplers play an important role in the contemporary minimally invasive thoracic surgery including resection of lung tissue. However, staple line failure resulting in postoperative air leaks is a common complication after lung surgery, that if persist more than five days are defined as prolonged air leaks (PALs). PALs are associated with increased length of stay, patient morbidity and mortality, and hospital costs. To reduce the incidence of PALs, stapler devices underwent in the last years ongoing development aimed at improving device-to-tissue interaction. This clinical practice review explores the most important aspects of the evolution of surgical staplers, based on the review of the available literature. Modern staple cartridges entail small bumps to engage tissue and minimize tissue movement during compression and firing. Staplers with graduated staple heights are advocated to generate less stress on tissue during compression and clamping, thus affording greater perfusion into the staple line. However, air leaks may occur from an appropriate staple line with complete pleural coverage and perfusion due to enlarged staple canals after lung inflation, particularly in case of emphysema. To buttress staple line, thus prevent air leaks in high-risk patients, several types of tissue coverage (bovine pericardium, polytetrafluoroethylene, knitted calcium alginate, bioabsorbable polyglycolic acid) have been successfully developed in the last years. Finally, the most advanced stapler technology is represented by the new energy powered staplers, able to eliminate the manual firing force, monitor tissue compression during firing, and making automatic adjustments to optimize the staple line.

Optimize Injection-Molding Process Parameters and Build an Adaptive Process Control System Based on Nozzle Pressure Profile and Clamping Force.

The injection-molding process is a non-linear process, and the product quality and long-term production stability are affected by several factors. To stabilize the product quality effected by these factors, this research establishes a standard process parameter setup procedure and an adaptive process control system based on the data collected by a nozzle pressure sensor and a tie-bar strain gauge to achieve this goal. In this research, process parameters such as the V/P switchover point, injection speed, packing pressure, and clamping force are sequentially optimized based on the characteristics of the pressure profile. After the optimization process, this research defines the standard quality characteristics through the optimized process parameters and combines it with the adaptive process control system in order to achieve the purpose of automatic adjustment of the machine and maintain high-quality production. Finally, three different viscosity materials are used to verify the effectiveness of the optimization procedure and the adaptive process control system. With the system, the variation of product weight was reduced to 0.106%, 0.092%, and 0.079%, respectively.

How Automatic Adjustment Mechanism Works in Practice – A VECM Analysis of Monetary Transmission Mechanism in Bosnia and Herzegovina

ABSTRACT This paper takes stock of the literature on the monetary transmission mechanism in currency boards and analyzes the process of monetary transmission in Bosnia and Herzegovina. The transmission of monetary signals through the economy is based on the automatic adjustment mechanism of the currency board. Three channels of monetary transmission are discussed in this paper: the interest rate channel, the narrow credit channel, and the exchange rate channel. The empirical analysis shows how exogenous monetary shocks are transmitted through different channels and how long it takes for the automatic adjustment mechanism to restore equilibrium in the economy.

Automatic crossbow control in industrial hot-dip galvanizing lines

In industrial hot-dip galvanizing lines, the crossbow flatness defect of steel strips constitutes one of the main sources for coating thickness deviations and inhomogeneities. In this work, a novel algorithm for online minimization of the crossbow flatness defect based on the automatic adjustment of the pot roll intermesh is developed. The devised model-free control algorithm can be easily implemented on an industrial programmable logic controller, even with very limited computational resources. The controller uses measurement data from an electromagnetic strip stabilization device and avoids the use of the delayed measurement data from the downstream radiometric coating thickness gauges. Assuming that a hot-dip galvanizing line is already equipped with an electromagnetic strip stabilization device, additional hardware is not required for implementation. The control algorithm can also be easily adapted to processing lines without an electromagnetic strip stabilizer. A detailed stability analysis allows to derive some basic guidelines to guarantee the convergence of the overall control scheme. A short- and long-term evaluation of measurement results from an industrial hot-dip galvanizing line demonstrates the effectiveness and robustness of the proposed control scheme.