Automatic Program Research Articles

Robustness of storm water management model parameter sets for dry and wet hydroclimatic conditions

The Storm Water Management Model (SWMM) is an urban watershed model for simulating urban runoff quantity and quality. Although SWMM has been widely used, little to no research has explored the effect of fitting the same SWMM dataset to multiple hydrologic conditions. This paper assesses the impact of best-fit parameter estimates on the hydrologic responses of dry and wet hydroclimatic conditions, evaluates parameter uncertainty, and identifies the physical processes that govern parameter changes during dry and wet conditions. We compared the ability of SWMM to predict flows when independently calibrated to a dry and a wet year, respectively, using an automatic calibration program for SWMM, OSTRICH-SWMM (Optimization Software Toolkit for Research Involving Computational Heuristics). The model developed using SWMM calibrated to a wet year performed better during the model assessment period. The best fit estimates of SWMM parameters differed significantly between dry and wet years. For instance, Manning's roughness coefficient for overland flow was higher in a dry year, as less runoff meant less flow depth on surfaces. Some parameters, e.g., % effective imperviousness, exhibited an expanded posterior probability distribution, increasing the uncertainty of the parameter estimate. However, other parameters, such as Manning's roughness coefficient for streams were the least sensitive and well-defined as the width of their posterior distribution only spanned a relatively narrow range which did not change drastically between the two cases. The robust assessment conducted in this study demonstrated that the hydrologic behavior of SWMM was different between models developed and calibrated for dry and wet hydroclimatic conditions. The different optimal parameter values suggest that although the processes and mechanisms driving hydrologic processes in dry and wet years are similar, their magnitude differs greatly. This study highlights that precipitation patterns affect the selection of optimal parameter values and the water budgets differed markedly between dry and wet years, as expected. The results of this study can assist urban watershed planners in selecting the most appropriate model to use for prediction purposes or when there is a paucity of data available for model calibration. This study recommends changing SWMM model parameters on an annual basis when projecting the effects of new conditions outside of current experience (i.e., land use and climate change) on urban runoff quantity and quality.

Automation architecture for harnessing the demand response potential of aqueous parts cleaning machines

To reduce global greenhouse gas emissions, numerous new renewable power plants are installed and integrated in the power grid. Due to the volatile generation of renewable power plants large storage capacity has to be installed and electrical consumer must adapt to periods with more or less electrical generation. Industry, as one of the largest global consumers of electrical energy, can help by adjusting its electricity consumption to renewable production (demand response). Industrial aqueous parts cleaning machines offer a great potential for demand response as they often have inherent energy storage potential and their process can be adapted for energy-flexible operation. Therefore, this paper presents a method for implementing demand response measures to aqueous parts cleaning machines. We first determine the potential for shifting electrical consumption. Then, we adapt the automation program of the machine so that submodules and process steps with high potential can be energy-flexibly controlled. We apply the method to an aqueous parts cleaning machine in batch process at the ETA Research Factory.

Experimental Research on Motion Analysis Model and Trajectory Planning of GLT Palletizing Robot

To improve wood structure processing efficiency, a palletizing robot suitable for loading and unloading glued laminated timber (GLT) has been developed. The robot comprises a six-axis connecting rod mechanism and a sponge sucker as a grasping actuator, which can enable the intelligent automatic loading and unloading and palletizing operations for small-sized GLT. Matlab robotics was used to construct the kinematic model of the GLT loading and unloading robot. Based on Matlab and Monte Carlo methods, the robot workspace was simulated and analyzed to determine the scope of the robot workspace. Using the high-order quintic and sixtic polynomial curve interpolation method, the trajectory of wood structure parts in the process of loading and unloading operations was planned, respectively, under the two conditions of staying and not staying. Tests verified that the simulation results of the pose of the end-effector were consistent with the actual pose of the robot. The robot’s working range could be analyzed intuitively and effectively. The robot’s operation trajectory planning provides data support and a parameter basis for the automatic control and program design of a loading, unloading and palletizing robot.

Fast Access Method of IoT Smart Terminal Based On C/S Architecture

In order to achieve efficient access and fast access of intelligent terminals, this paper proposes a fast access method for intelligent terminals of the Internet of Things based on C/S architecture. First, a terminal auto configuration program is developed; The automatic configuration program sends application configuration information to the configuration server; Then, the server verifies the identity of the terminal and feeds back the corresponding configuration information. Finally, the automatic configuration program automatically configures the terminal according to the feedback information and accesses the Internet of Things management platform. The experimental results show that this method can realize remote distribution of terminal configuration information and automatic configuration of terminals. Compared with traditional methods, the terminal access time can be reduced by about 70%.

Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence

BackgroundThe purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis.MethodsReconstructed lateral cephalograms (RLCs) from cone-beam computed tomography (CBCT) in 85 patients were selected. Computer-assisted manual analysis (Dolphin Imaging 11.9) and AI automatic analysis (Planmeca Romexis 6.2) were used to locate 19 landmarks and obtain 23 measurements. Mean radial error (MRE) and successful detection rate (SDR) values were calculated to assess the accuracy of automatic landmark digitization. Paired t tests and Bland‒Altman plots were used to compare the differences and consistencies in cephalometric measurements between manual and automatic analysis programs.ResultsThe MRE for 19 cephalometric landmarks was 2.07 ± 1.35 mm with the automatic program. The average SDR within 1 mm, 2 mm, 2.5 mm, 3 and 4 mm were 18.82%, 58.58%, 71.70%, 82.04% and 91.39%, respectively. Soft tissue landmarks (1.54 ± 0.85 mm) had the most consistency, while dental landmarks (2.37 ± 1.55 mm) had the most variation. In total, 15 out of 23 measurements were within the clinically acceptable level of accuracy, 2 mm or 2°. The rates of consistency within the 95% limits of agreement were all above 90% for all measurement parameters.ConclusionAutomatic analysis software collects cephalometric measurements almost effectively enough to be acceptable in clinical work. Nevertheless, automatic cephalometry is not capable of completely replacing manual tracing. Additional manual supervision and adjustment for automatic programs can increase accuracy and efficiency.

How do Developers Really Feel About Bug Fixing? Directions for Automatic Program Repair

Automatic program repair (APR) is a rapidly advancing field of software engineering that aims to supplement or replace manual bug fixing with an automated tool. For APR to be successfully adopted in industry, it is vital that APR tools respond to developer needs and preferences. However, very little research has considered developers' general attitudes to APR or developers' current bug fixing practices (the activity APR aims to replace). This paper responds to this gap by reporting on a survey of 386 software developers about their bug finding and fixing practices and experiences, and their instinctive attitudes towards APR. We find that bug finding and fixing is not necessarily as onerous for developers as has often been suggested, being rated as more satisfying than developers' general work. The fact that developers derive satisfaction and benefit from bug fixing indicates that APR adoption is not as simple as APR replacing an unwanted activity. When it comes to potential APR approaches, we find a strong preference for developers being kept in the loop (for example, choosing between different fixes or validating fixes) as opposed to a fully automated process. This suggests that advances in APR should be careful to consider the agency of the developer, as well as what information is presented to developers alongside fixes. It also indicates that there are key barriers related to trust that would need to be overcome for full scale APR adoption, supported by the fact that even those developers who stated that they were positive about APR listed several caveats and concerns. We find very few statistically significant relationships between particular demographic variables (for example, developer experience, age, education) and key attitudinal variables, suggesting that developers' instinctive attitudes towards APR are little influenced by experience level but are held widely across the developer community.

Comparison of Automatic Speech Recognition System for School-aged Children’s Narratives: Naver Clova Speech and Google Speech-to-Text

Objectives: Language sample analysis (LSA) is a critical component of child language assessment. However, most clinicians consider LSA to be time consuming work. In particular, transcription is seen as an overwhelming task. Due to rapid technological advances, various automatic speech recognition systems have been developed. This study aimed to investigate the accuracy and the characteristics of two automatic speech recognition programs, Naver Clova Speech (Naver Clova) and Google Speech-to-Text (STT).Methods: A total of 40 school-aged children with typical development (TD) and children with language learning disabilities (LLD) participated in the study. Each child was asked to generate two fictional narratives. In total, 72 narratives produced by 36 children were used. To examine the accuracy of Naver Clova and Google STT, syllable error rate was analyzed and compared to reference transcripts. For the detailed analysis, types of error such as substitution, deletion and insertion were examined.Results: Results showed that Naver Clova was significantly lower than Google STT in error rate of transcription. But the transcription error rate of the two child groups was not significantly different. Additionally, the Naver Clova error rate was higher in substitution, deletion, and insertion respectively. The Google STT error rate, on the other hand, was higher in deletion, substitution and insertion respectively.Conclusion: Naver Clova were more accurate than Google STT in transcribing children’s narratives. But the transcription accuracy of two child groups was not different. This suggests that recently developed automatic speech recognition systems have clinical utility. These systems can reduce clinician’s workload in regards to LSA and this would contribute to qualitatively enhanced language assessment.

Investigating the best automatic programming method in predicting the aerodynamic characteristics of wind turbine blade

Automatic programming (AP) is a subfield of artificial intelligence (AI) that can automatically generate computer programs and solve complex engineering problems. This paper presents the accuracy of four different AP methods in predicting the aerodynamic coefficients and power efficiency of the AH 93-W-145 wind turbine blade at different Reynolds numbers and angles of attack. For the first time in the literature, Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods are used for such predictions. In addition, Airfoil Tools and JavaFoil are utilized for airfoil selection and dataset generation. The Reynolds number and angle of attack of the wind turbine airfoil are input parameters, while the coefficients CL, CD and power efficiency are output parameters. The results show that while all four methods tested in the study accurately predict the aerodynamic coefficients, Multi Gene GP (MGGP) method achieves the highest accuracy for RTrain2 and RTest2 (R2 values in CD Train: 0.997-Test: 0.994, in CL Train: 0.991-Test: 0.990, in PE Train: 0.990-Test: 0.970). By providing the most precise model for properly predicting the aerodynamic performance of higher cambered wind turbine airfoils, this innovative and comprehensive study will close a research gap. This will make a significant contribution to the field of AI and aerodynamics research without experimental cost, labor, and additional time.

Structural performance assessment of RC flexural members through crack-based fibre beam-column model updating

For reinforced concrete (RC) structures in service, original finite element models (FEMs) based on design information usually deviate from actual structures due to structural damage such as cracks, displacement and material degradation. Although conventional FEMs can be updated to give results that match the response data used during the updating process, they can hardly predict the subsequent structural performance. In this study, an innovative fibre beam-column model updating method based on static deflection and crack width is proposed. First, the self-developed fibre beam-column finite element program COMPONA-MARC is modified to efficiently simulate the nonlinear cracking behaviour of RC flexural members. Second, sensitivity studies using the discrete central difference method based on the Lagrange interpolation function are conducted to select updating parameters. Third, with the combination of modified COMPONA-MARC and an automation program written in Python, plenty of sample data are obtained, and artificial neural networks (ANNs) are trained by Bayesian Regularization Backpropagation (BRB). Finally, the updated parameters are acquired by inputting the measured static deflections and crack widths. Typical RC beams and RC slabs from the literature are utilized to validate the proposed methodology and explore potential implementation scenarios. The results show that updated FEMs can satisfactorily predict mechanical behaviour and assess structural performance. The proposed method offers enlightening insights about combining structural health monitoring and inspection (SHMI) results with model updating, which can promote the applications of digital twins in performance assessments of RC structures.

Quantitative analysis of lung parenchymal involvement in Behçet's disease by the novel automatic program

We aimed to quantitatively analyze lung parenchymal changes in Behçet's patients and to detect early parenchymal quantitative changes that occur in the absence of positive visual radiological findings. In our study, a total of 31 patients with Behçet's disease, 17 with positive radiological findings and 14 patients without positive radiological findings, and a control group of 33 individuals were evaluated. The automatic program determined lung volumes, lung densities, and opacity volume percentages by evaluating the contrast-enhanced lung computed tomography scans. The total lung volume was 3632.98± 1100.53 mL in the Behçet's disease patient group and 4925.70 ± 1098.88 mL in the control group, and there was a significant decrease in the total lung volume in Behçet's disease patients (P< 0.0001). The mean lung density was -723.73 ± 65.16 in the Behçet's disease patient group and -767.35 ± 41.17 in the control group, and there was a significant increase in the mean density in the Behçet's patients (P= 0.0023). A significantly higher correlation was observed between lung density and lung volume in Behçet's patients (P< 0.0001, r= -0.795). The lung volume of Behçet's disease patients with negative radiological findings was significantly lower than that of the control group (P= 0.0342). Lung volumetric changes in Behçet's disease patients are the quantitative data most affected by the disease. In addition, lung volume decreases in Behçet's patients who do not have visual parenchymal involvement.

ДЕТЕРМИНИРОВАННЫЙ КОНЕЧНЫЙ АВТОМАТ ПОИСКА КОНТЕЙНЕРОВ В ШТАБЕЛЕ

This paper considers the prospects of digitalization in the warehouse cargo handlingsystem, as well as the&#x0D; impact of modern IT technologies on the logistics of a sea container terminal. The technological process of&#x0D; container transshipment in the operational areas of the terminal and formation of auxiliary stacks for further&#x0D; transfer of cargo between different transport means, their temporary storage and transformation (from containerized&#x0D; form to general cargo and back), as well as the implementation of secondary operations (checking,&#x0D; weighing, etc.) are the object of research. From an operational point of view, the formation of an auxiliary stack&#x0D; is a time-taking technological operation, as it requires the selective search and withdrawal from the general&#x0D; stack of a certain number of containers that make up the logistic flow of cargo moved to the relevant operational&#x0D; zone. The method of research is automatic programming of the simulated technological process. In this case,&#x0D; the simulation object is described by a finite automaton which is then converted into code. The main purpose&#x0D; of the automaton is to implement the control function. In the context of this research work, the control actions&#x0D; are aimed at the reloader and program the sequence of its actions to find the target containers in the stack. A&#x0D; mathematical model of the search and withdrawal of the target containers from the stack in the terminal storage&#x0D; area by means of a computerized control system, as well as a transition graph of the finite automaton with&#x0D; control rules (codes) are the results of this research. The application of above mathematical facility to modeling&#x0D; of technological processes makes it possible to present a unified logic of management of the prestacking process&#x0D; in operational areas of the terminal

Identification and counting of fish targets using adaptive resolution imaging sonar.

Fish are a critical component of marine biology; therefore, the accurate identification and counting of fish are essential for the objective monitoring and assessment of marine biological resources. High-frequency adaptive resolution imaging sonar (ARIS) is widely used for underwater object detection and imaging, and it quickly obtains close-upvideo of free-swimming fish in high-turbidity water environments. Nonetheless, processing the massive data output using imaging sonars remains a major challenge. Here, the authors developed an automatic image-processing programme that fuses K-nearest neighbour background subtraction with DeepSort target tracking to automatically track and count fish. The automatic programme was evaluated using four test data sets with different target sizes and observation ranges and differently deployed sonars. According to the results, the approach successfully counted free-swimming fish targets with an accuracy index of 73% and a completeness index of 70%. Under appropriate conditions, this approach could replace time-consuming semi-automatic approaches and improve the efficiency of imaging sonar data processing, while providing technical support for future real-time data processing.

Recent Progress and Perspectives of Direct Ink Writing Applications for Mass Transfer Enhancement in Gas-Phase Adsorption and Catalysis.

Conventional adsorbents and catalysts shaped by granulation or extrusion have high pressure drop and poor flexibility for chemical, energy, and environmental processes. Direct ink writing (DIW), a kind of 3D printing, has evolved into a crucial technique for manufacturing scalable configurations of adsorbents and catalysts with satisfactory programmable automation, highly optional materials, and reliable construction. Particularly, DIW can generate specific morphologies required for excellent mass transfer kinetics, which is essential in gas-phase adsorption and catalysis. Here, DIW methodologies for mass transfer enhancement in gas-phase adsorption and catalysis, covering the raw materials, fabrication process, auxiliary optimization methods, and practical applications are comprehensively summarized. The prospects and challenges of DIW methodology in realizing good mass transfer kinetics are discussed. Ideal components with a gradient porosity, multi-material structure, and hierarchical morphology are proposed for future investigations.

24-Hour Monitoring of Intraocular Pressure Fluctuations Using a Contact Lens Sensor: Diagnostic Performance for Glaucoma Progression.

The purpose of this study was to compare 24-hour intraocular pressure (IOP) related fluctuations monitoring between 2 groups of visual field progression rates in patients with open angle glaucoma (OAG). Cross-sectional study performed at Bordeaux University Hospital. Twenty-four-hour monitoring was performed using a contact lens sensor (CLS; Triggerfish; SENSIMED, Etagnières, Switzerland). Progression rate was calculated using a linear regression of the mean deviation (MD) parameter of the visual field test (Octopus; HAAG-STREIT, Switzerland). Patients were allocated into two groups: group 1 with an MD progression rate <-0.5dB/year and group 2 with an MD progression rate ≥-0.5dB/year. An automatic signal-processing program was developed and a frequency filtering of the monitoring by wavelet transform analysis was used to compare the output signal between the two groups. A multivariate classifier was performed for prediction of the faster progression group. Fifty-four eyes of 54 patients were included. The mean progression rate was -1.09 ± 0.60 dB/year in group 1 (n = 22) and -0.12 ± 0.13 dB/year in group 2 (n = 32). Twenty-four-hour magnitude and absolute area under the monitoring curve were significantly higher in group 1 than in group 2 (group 1: 343.1 ± 62.3 millivolts [mVs] and 8.28 ± 2.10 mVs, respectively, group 2: 274.0 ± 75.0 mV and 6.82 ± 2.70 mVs respectively, P < 0.05). Magnitude and area under the wavelet curve for short frequency periods ranging from 60 to 220 minutes were also significantly higher in group 1 (P < 0.05). The 24-hour IOP related fluctuations characteristics, as assessed by a CLS, may act as a risk factor for progression in OAG. In association with other predictive factors of glaucoma progression, the CLS may help adjust treatment strategy earlier.

Extrasolar Planet Candidates Identified by Single Transit from TESS

The discovery of planets around stars other than Sun was a grand milestone to understand origin and evolution of life in Universe. 2019 Nobel Prize for Physics was awarded to the first discovery of exoplanet around Sun-like stars, Peg 51b, in 1995. With development of 25 years, the current number of confirmed exoplanets have reached 4,000. The population of exoplanets presents great diversity and complexity beyond the planets in Solar system, severely challenging our understanding on the origin and evolution of planets. Moreover, clues of extrasolar life have been partially disclosed by detection of exoplanets in habitable zone with temporal surface temperature. In recent years, several promising surveys of exoplanets have been carried out to search for more exoplanets, compiling more complete sample of exoplanets and detecting Earth-like exoplanets. Transiting Exoplanet Survey Satellite (TESS) launched in 2018, is the most import facility for this purpose. It monitors the brightness of nearby stars in all sky with high cadences, searching for exoplanetary transit events. The survey is sensitive to detect terrestrial exoplanets. In this work, we reanalyzed the high quality light curves of TESS with algorithm developed by ourselves and visual examination. Our work was designed complementary to the automatic pipeline developed by TESS science team. TESS pipeline is not optimal for long-period exoplanets, for which only one transit event occurring in the observation sectors of 27.4 days, appearing as single transit single. Those long-period planets are more likely to be habitable with liquid water on their surface in relative large orbits. We took efforts to recover exoplanets with single transit in TESS. First, we identified candidates with significant narrow dimming in light curves uniformly by our automatic program. The singles were further examined visually and fitted by transit profile, selecting transit events. More precise modeling was carried out by MCMC analysis for transit exoplanet candidates. As a preliminary attempt, we only processed the light curves in sector 1 only. Full TESS dataset in all 26 sectors will be studied in future. In summary, we detected 25 exoplanet candidates in publicly accessible light curves in TESS sector 1. Among them, 14 candidates are known exoplanets, and 6 are candidates identified by TESS pipeline in catalog of Tess Objects of Interest (TOI). 5 exoplanet candidates with single transit signal are newly identified by their profiles, and the properties of these system were inferred in this work. The orbital parameters and physical properties derived by us are consistent with those in literature for all the 14 known exoplanets. 2 among 6 of exoplanet candidates in TOI catalog are single transit objects, our results are consistent with values reported in TOI catalog. For the new exoplanet candidates, we report their inferred orbits and physical sizes. We plan to implement our analysis for all light curves in 26 sectors of TESS, Over 100 exoplanet candidates are expected to be recovered, significantly contributing to the current population of 4,000 known exoplanets.

PHASE FORMATION DURING ALUMINOTHERMAL REDUCTION OF Ti, Nb, Gd (Y) FROM OXIDES

Alloys based on titanium and aluminum with additions of niobium and rare-earth metals have unique mechanical and heat-resistant properties, and also it likely that such alloys would have increased corrosion resistance. The method of thermodynamic modeling using the HSC program was used to study a system with aluminum consumption varying in the range from 0 to 100% of the mass of the initial charge. The features of phase formation in Al–[50TiO2–5Nb2O5–1Y2O3 (Gd2O3)] systems have been studied. The calculation of the heat balance of the process at 1600°C and 44% of Al was – 0.196 MJ per 1 kg of charge, which indicates the possibility of its occurrence with only the aluminothermic reactions. The reduction of titanium and niobium can proceed by reactions through the formation of their oxides of lower valency – TiO, NbO2, NbO. The aluminothermic reduction of gadolinium is thermodynamically possible only at temperatures below 1200°C. The reduction of yttrium through the interaction of Y2O3 with aluminum with the formation of AlY, Al2Y3 AlY2 compounds for the range of 1000–1800°C is thermodynamically impossible. The results of thermodynamic modeling of interactions correlated well with the data of differential thermal and X-ray phase analysis using STA 449 F3 Jupiter (NETZSCH) synchronous thermal analysis and XRD-7000 diffractometer (Shimadzu) with automatic program control, respectively. It was found that the process enters the active phase after the appearance of liquid aluminum and, apparently, is accompanied by exothermic effects with the formation of double and triple intermetallic compounds of aluminum with rare (Nb, Ti) and rare earth (Gd, Y) metals. Transformation of titanium dioxide and niobium pentoxide in the process of transformations is likely carried out through successive and parallel stages of formation of simple and complex oxides with low oxidation states. At the initial stages of the interaction of aluminum with oxides, niobium and titanium aluminides are mainly formed. At subsequent stages, the formation of more complex compounds is observed. At temperatures above 1300°C, ternary intermetallic compounds Al43Nb4Gd6, Ti4Al20Gd and Ti4Al3Y6, Al3Ti, Al0.23Nb0.07Ti0.7 are formed. Gadolinium and yttrium tend to form complex intermetallic compounds in such systems.

Research on Cutting Parameters Database Based on Spacecraft Product Characteristics

Cutting parameters affect the production and processing efficiency and the quality of products. The characteristics of single-piece and small-batch production of spacecraft products result in a large number of cutting parameters. Due to the lack of centralized management methods, cutting parameters in the production site are not unified, which seriously affects the processing efficiency and quality of products. In response to a large number of cutting parameters and the lack of centralized management, the production resource library is completed by establishing a spacecraft product feature pedigree. Taking the product characteristics as the carrier, the production resources as the optional factors, and the forces in the cutting process as the evaluation criteria, the whole life management mechanism of cutting parameters is established to unify the cutting parameters in the production process and complete the establishment of the cutting parameter database. The interface between the programming platform and the cutting parameters library is developed for a low reuse rate of cutting parameters. With typical features as the carrier, the cutting parameters are packaged and transmitted to the programming platform to provide reliable cutting parameters for feature automatic programming, which improves the reuse rate of cutting parameters and simplifies the numerical control programming operation. The results show that the reuse times of cutting parameters are more than 8000 times, effectively ensuring the machining efficiency and quality consistency of products.

Mechanical properties and automatic design method for rural residences with hinged frames and cold-formed thin-walled stiffened walls

At present, the overall construction quality of rural residences is still relatively poor, and the seismic performance of their structural systems is insufficient. In this study, quasi-static tests were carried out on three cold-formed thin-walled stiffened steel plate wall (CFSW) specimens infilled in a single-span two-story 1/2 scale hinged special-shaped column frame. The seismic performance of each specimen was evaluated by comparing the bearing capacity, ductility, stiffness degradation, energy dissipation capacity, and strain. The results show that the filling of concrete and gypsum in the column helps improve the bearing capacity of the structure but has no notable effect on the stiffness. The brittle failure phenomenon in the filled concrete specimens is obvious, and those specimens either without filler or whose columns are filled with gypsum show better ductility and energy dissipation capacities. A finite element (FE) model was established and validated, and the influence of parameters such as the connection method between the wall and frame, the shear-span ratio of the wall, the thickness of the stiffened wall, and the number of hat-section cold-formed steel (HCFS) stiffeners upon the structural system was analyzed. In combination with the test and the FE analysis results, the calculation method for horizontal bearing capacity of CFSW restrained by HCFS stiffeners and engineering design suggestions were put forward. In addition, an automatic program for the forward generation of the CFSW was designed, and the application of the system in practical engineering was verified using Midas software.

Neural Machine Translation in Translation and Program Repair

Translation is a challenge for humans since it needs a good command of two or more languages. When it comes to computer programs, it is even more complex as it is difficult for computers to imitate human translators. With the emergence of deep learning algorithms, especially neural network architectures, neural machine translation (NMT) models gradually outperformed previous machine translation models and became the new mainstream in practical machine translation (MT) systems. Nowadays, NMT has been developing for several years and has been applied in many fields. This paper is focused on studies on four different application categories of NMT models: 1) Text NMT; 2) Automatic program repair (based on NMT); 3) Simultaneous translation. Our work provides a summary of the latest research on different applications of NMT and makes comments on their development in the future. This paper also mentioned the shortcomings of existing studies in this essay and pointed out some possible research directions.

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.