Actual Value Research Articles

Sensor anomaly detection for small modular reactors utilizing improved autoencoder

The anomaly detection of widely distributed sensors in small modular reactors (SMRs) is critical to the safe operation of systems. Existing methods of sensor anomaly detection for transient conditions are less studied and have some shortcomings, necessitating further research in this context. Therefore, this paper presents an unsupervised deep-learning framework for a gated recurrent autoencoder with the Luong attentional mechanism and the residual connection, which is a multiple-input and multiple-output model. A single model can monitor SMR systems equipped with many sensors, and the training of the model does not require an anomaly label. The general threshold-setting method of the autoencoder for anomaly detection is also improved. The Bayesian estimation algorithm sets a dynamic threshold value in the residual evaluation stage. Finally, simulation tests are performed on experimental data sets, and the residual series obtained from the output of this framework are examined for anomalous sensors using Bayesian estimation methods. The mean squared error (MSE), that is, the average squared difference between the reconstructed value and the actual standard value, is used as the evaluation index. The experimental results demonstrate that the MSE of the improved autoencoder is smaller than that of the unimproved model in both transient and steady-state conditions. In addition, the dynamic threshold method reduces the false alarm rate of anomaly detection.

ASSESSMENT OF STEC ESTIMATION QUALITY USING GNSS PPP FIXED

Abstract. GNSS (Global Navigation Satellite System) data can be used for geodetic remote sensing, particularly for monitoring the ionosphere in the context of Space Weather. One of the important parameters derived from GNSS measurements for ionospheric analysis is the Slant Total Electron Content (STEC). By utilizing GNSS data from multiple frequencies or even a single frequency, the STEC can be computed using an appropriated linear combination, like geometry free. However, when computing an ionospheric gradient between two IPP (Ionospheric Pierce Point) from the same satellite, the precision of the STEC estimate can become a limiting factor. In some cases, the uncertainty in the estimate may be greater than the actual gradient value itself. This poses challenges, especially for augmentation systems like GBAS (Ground Based Augmentation System), where accurate ionospheric gradients are crucial. An alternative approach to improve these limitations is to estimate the STEC using a different approach, like Precise Point Positioning (PPP). For such case, the coordinates of the GNSS stations are constrained to known values (PPP-Fixed), while other parameters such as clock biases, tropospheric delays, and ionospheric delays (including STEC) can be estimated. The results of an experiment carried out to assess the quality of STEC for such application are presented and have shown good results. Ionospheric gradients are agreed in the mm level.

Coach Versus Goldlion: The Effect of Socially Versus Personally Oriented Motives on Consumer Preference for Foreign and Domestic Masstige Brands in Emerging Markets

The fast-growing middle class in emerging markets is leading luxury brands to adopt an affordable luxury (aka “masstige”) approach to reach the mass market. However, academic research in this promising area is scarce. Focusing on China, the world's largest emerging market, this research shows a novel pattern of masstige brand consumption: Instead of always preferring foreign masstige brands (e.g., Coach) as implied in previous international marketing literature, consumers in emerging markets prefer domestic masstige brands (e.g., Goldlion) when their personally oriented motives are made salient. In contrast, consumers prefer foreign masstige brands when their socially oriented motives are made salient. This stems from domestic masstige brands better manifesting self-focused intangible attributes (i.e., the actual value to please oneself), whereas foreign masstige brands are superior in other-focused intangible attributes (i.e., the symbolic value to impress others). Three studies using a multimethod approach provide converging results that support this phenomenon. The findings bring significant contributions to the literature and offer actionable implications for managers, including positioning and price promotion strategies for masstige brands in emerging markets.

Increasing sensitivity of the electrostatic field mill sensor by determining its optimal configuration

The object of research is the process of measuring the strength of the electrostatic field for a low dynamic range (from 0 to 1 kV/m). This study is aimed at increasing the sensitivity of the sensor of the electrostatic field mill (EF) by determining its optimal geometric configuration, which will reduce the error of measuring the electrostatic field strength. To establish the actual value of the induced current, a computer model was built and simulation modeling of the EF sensor was carried out. On the basis of the constructed computer model, studies of the EF sensor were carried out to determine the numerical value of the induced current. As a result, it was established that the occurrence of edge effects leads to the appearance of methodological error, which occurs due to the fact that the average induced current is smaller compared to the calculated value. As a result of computer modeling of the EF sensor to determine the value of the optimal number of sectors, it was established that for the proposed design of the EF sensor, the optimal number of sectors is six. It was established that the optimal value of the distance between the sensitive plates and the shielded rotor should be in the range of 2.5–3 mm to ensure the maximum sensitivity of the EF sensor and its safe use. The determined optimal parameters of the EF geometric configuration will allow to form the necessary requirements for the construction of improved electrostatic field strength meters in a low dynamic range (from 0 to 1 kV/m). A promising direction of application of such devices in production will be the development of an additional system for monitoring the strength of the electrostatic field, which will allow to prevent the occurrence of a dangerous situation.

Extent of κ-casein hydrolysis during renneting of bovine milk: A critical assessment of the analytical and estimation approaches.

Renneting is an enzymatic process that turns milk into curd which is then transformed into cheese. Rennet-induced coagulation of caseins (CNs) is the critical step during this process and the key is the primary hydrolysis of κ-CN's Phe105-Met106 bond by chymosin. This article comprehensively reviews the existing data on the extent/degree of κ-CN hydrolysis during renneting of bovine milk and critically evaluates its determination methods. The data show that under normal cheese-making conditions, milk gelation occurs at a degree of κ-CN hydrolysis <80%, which varies due to several factors including analytical and estimation approaches. The common approach involves isolating the macropeptides released, by precipitating whey proteins and residual CN in 1%-12% trichloroacetic acid (TCA), then assuming that the maximum amount obtained is 100% κ-CN hydrolysis. The drawback is that the estimated degree of κ-CN hydrolysis may be higher than the actual value as TCA partially precipitates the macropeptide fractions. Moreover, macropeptide isolation seems unnecessary based on current advances in chromatographic and electrophoretic techniques. The present work proposes a simple mass balance-based approach that will provide accurate estimates in future studies. The accuracy of measuring the degree of κ-CN hydrolysis has implications on the precision of the data in relation to its partitioning (% distribution between the curd and whey) which is essential for improving whey quality.

A novel AI-based combustion diagnostic technology for the identification of chemical source information via flame images: Fuel type and reaction condition

The improvement of combustion diagnostics was an important basis for developing combustion-related research and technologies. Based on the flame image as the carrier, this work proposed a novel combustion diagnostic technique for the identification of chemical source information through the analysis of abundant flame features contained in the image using a random forest algorithm. Five types of flames under 16 reaction conditions were adopted as experimental cases to examine the accuracy of the proposed technique. Results indicated that the random forest algorithm could well “learn” the correlation mechanism between flame features and chemical source information, and identify the fuel type and reaction condition of unknown flame according to this mechanism. For identifying the fuel type, the accuracy on ethylene, 20 % ethyl acetate, 40 % ethyl acetate, 20 % ethyl butyrate, and 40 % ethyl butyrate doped flames reached 86.2 %, 84.4 %, 100 %, 63.6 %, and 64.3 %, respectively. For identifying the reaction condition, the mean absolute error, max error, and mean absolute percentage error between the predicted O2 content and the actual value were 0.96 %, 2.53 %, and 3.46 %, respectively. Moreover, the feature analysis result indicated that the importance degree of each feature was not equivalent between the different identification assignments. The flame features such as rectangularity and the variance of G and B in the blue region were the key features for identifying the fuel type. However, the variance of R and R/B of the blue region played a significant role in identifying the reaction condition.

Performance Evaluation of Logistics Service Provider (LSP) in FMCG Companies Using Physical Distribution Service Quality (PDSQ) Dimension: Case Study

Increasingly fierce business competition requires companies to be able to meet customer desires on time, in the right amount and in the right quality. FMCG XXX Company is a skin care product manufacturing company that focuses on production activities. Therefore, the company transfers product distribution activities to customers (distributors) to Logistics Service Providers (LSP). FMCG XXX hold consumer’s satisfaction in the highest regard. Along with the increasing number of activities being diverted, FMCG XXX companies aim to evaluate LSP performance through appropriate indicators. The indicators evaluated in this study adopt dimensions in Physical Distribution Service Quality (PDSQ) which consist of three dimensions, namely timeliness, availability and condition. Based on the results of the study, 13 indicators were obtained that FMCG XXX Company used to evaluate LSP performance. In general, around 69% of LSP's actual performance has been able to meet the targets set by the company, where two indicators are able to exceed the target and seven indicators are in accordance with the target. However, the company still has to focus on four indicators that have negative deviations because the actual performance value is less than the target set by the company. Negative indicators come from the availability and condition dimensions. The company coordinates with LSP to design improvement projects in order to improve actual performance according to the company's targets.

A Fuzzy-Based Proportional–Integral–Derivative with Space-Vector Control and Direct Thrust Control for a Linear Induction Motor

In this study, the analysis and control of a multi-phase linear induction motor loaded with a variable mechanical system are carried out. Mathematical models are established, and simulation results are analyzed for an improved proportional–integral–derivative controller with closed-loop vector control for PLIM. To make the PID controller more responsive to load thrust disturbances, a fuzzy PID load thrust observer was developed. The FPID is similarly based on space-vector modulation DTC technology to regulate the PLIM’s speed, flux, and thrust. The FPID output is used to calculate the reference thrust force, which is compared to the actual thrust value to calculate the second error. To maintain the linear speed of the PLIM at the specified reference values and at different load values, the FPID controller settings are adjusted. Four indicators were used to compare the capabilities of the FPID controller with those of the conventional PID controller in order to evaluate the performance of PLIM in both cases. These indices represent the individual SSE for each operational phase and the total SSE for the entire loading period. According to the simulation results, the FPID works better than a regular PID when used to adjust the operation of DTC-SVM to drive a PLIM to improve the overall system performance. The simulation results using MATLAB Simulink for a PLIM-drive system show that the proposed FPID control provides improved control behavior and operating performance with fast and accurate speed tracking.

Dynamic assessment of dust hazard risk in the reconstruction of old industrial buildings: coupling effects of dust distribution and personnel trajectories.

Since the old industrial buildings bear many functions of industrial production and storage in the service stage, the dust generated by the regeneration construction is often accompanied by industrial pollutants, causing irreversible damage to the personal safety of construction workers. However, little consideration has been given to the uncertainty of dust emissions and the dynamics of construction personnel movement. Therefore, reducing the risk of dust hazards during the regeneration of old industrial buildings is imperative. This study draws on the trace intersecting theory to analyse the cause of the hazard risk associated with reconstruction dust and explore the impact of the spatiotemporal distribution characteristics of reconstruction dust and its coupling effect with construction on-site personnel activity trajectories and uses the risk matrix method to assess the dust hazard risk in the reconstruction of old industrial buildings. Finally, the renovation and reinforcement process of the first floor of a printing building in Xi'an was considered as an example for verification. The results indicate that the risk assessment model results were highly consistent with the actual situation and risk value for the entire area was 6.05, indicating a risk level of IV. Immediate measures should be implemented to reduce dust concentrations or the frequency of construction personnel activity, thereby minimising potential harm.

Consideration and Resolution of Family and Corporate Disputes: Procedural Problems

The common property of the spouses may include shares or shares in the authorized capital of the company, therefore, various procedural problems related to family and corporate law arise in the practice of the courts. Purpose of the work: on the basis of doctrinal and empirical analysis to resolve some procedural problems in the consideration and resolution of family-corporate disputes. Brief conclusions. When considering a dispute on the division of a share in a company, the court should bring up for discussion the issue of the possibility of obtaining compensation for it, which can prevent further corporate disputes. The fact of the presence of a prohibitive clause in the charter of a legal entity should not be included in the subject of proof in a family dispute on the division of a shares. The presence of a court decision on the recognition of ownership of a part of the share and the disagreement of other participants in the corporation serves as the basis for paying the actual value of the shares. The spouse has the right to apply to the arbitration court to challenge the corporate decision if he violates his property right, but in this case an increased standard of proof will apply, since the plaintiff is obliged to provide indisputably evidence of the disagreement at the time of the legal actions, their economic inexpediency. &amp;Keywords: family dispute, corporate dispute, jurisdiction, proof, shares

Moisture content online measurement in the sludge by ultrasonic reflection method

Moisture content is an important parameter of solid waste degradation in landfill. The traditional gravimetric method used for determining the moisture content of sludge is very time-consuming and cannot achieve online measurement of sludge moisture content. This paper proposes an ultrasonic reflection method to measure the moisture content of sludge on-line, the sludge only needs to be dried once, and then the online measurement can be realized. The specific process is as follows: by analyzing the ultrasonic characteristic parameter under different moisture content, the quantitative relationship between them can be obtained, and then the sludge moisture content can be deduced by the ultrasonic characteristic parameter. In this paper, the relationship between the ultrasonic characteristic parameter of the three sludge and the moisture content was analyzed, and the quantitative relationship was determined. The sludge moisture content calculated by this method is very close to the actual value. This method provides a new research idea for the online measurement of sludge moisture content.

An ensemble model for monthly runoff prediction using least squares support vector machine based on variational modal decomposition with dung beetle optimization algorithm and error correction strategy

In order to enhance the runoff prediction accuracy, an ensemble prediction model based on least squares support vector machine (LSSVM) is proposed by including variational mode decomposition (VMD), dung beetle optimization algorithm (DBO), and error correction (EC) strategy. First, the monthly runoff time series is decomposed using DBO-optimized VMD (DVMD), yielding a series of intrinsic mode functions (IMF) series and a residual (Res). Then, the LSSVM based on DBO optimization predicts each sub-series column and residual. The final forecast results are achieved after the preliminary forecast results have been stacked and corrected by the DBO-LSSVM prediction error. To verify the reliability of the proposed model, it is applied to the monthly runoff prediction of the Xiajiang hydrological station in the Ganjiang River Basin, the Hongshanhe hydrological station in the Heihe River Basin, and the Jiayugaun hydrological station in the Heihe River Basin. The proposed model is evaluated using four evaluation indicators: RMSE, MAPE, NSEC, and R, and is compared with SVM, LSSVM, PSO-LSSVM, DBO-LSSVM, EEMD-LSSVM, CEEMDAN-LSSVM, DVMD-LSSVM, EEMD-DBO-LSSVM, CEEMDAN-DBO-LSSVM, and DVMD-DBO-LSSVM. Results show that the DVMD-DBO-LSSVM-EC model has the highest accuracy. During the test period, the NSEC of Xiajiang hydrological station is 0.9829, R is 0.9921, the NSEC of Hongshanhe hydrological station is 0.9981, R is 0.9991, and the NSEC of Jiayugaun hydrological station is 0.9772, R is 0.9897. The prediction effect of the model on the extreme value of the three stations after adding the error correction strategy has increased by 45.14%, 62.22%, and 29.49%, respectively, compared with the previous, which is closer to the actual value. The developed combination model offers a new approach to forecasting monthly runoff and extreme values.

STUDY OF A DISTRICT HEATING SYSTEM WATER BOILER AS A OBJECT OF CONTROL

&#x0D; &#x0D; The analysis of the operating conditions of a typical district heating system, in particular, a hot water gas boiler, was carried out. It is shown that the boiler, as the main control object, operates under conditions of constant change in external heat load, which, due to their random nature, leads to a number of uncertainties. The expediency of the mathematical description of uncertainties using the stochastic method as the most tested in practical conditions is substantiated. According to the results of the passive experiment on the hot water gas boiler KVG6.5-150 of the district heating system of one of the districts of Kharkov, an array of hourly experimental data was obtained, reflecting the main performance indicators of the hot water boiler. As a result of data processing by the least squares method, a mathematical model of the boiler was obtained in the form of a linear regression equation, reflecting the relationship between the temperature of the coolant at the boiler outlet and the ambient air temperature, the temperature of the coolant at the inlet to the boiler and with the flow rates of natural gas and coolant to the boiler. The obtained regression equation was verified by Student's t-test, which confirmed the significance of all coefficients of the regression model. The practical significance of the multiple regression equation was assessed using the coefficient of determination. The quality of the multiple regression equation as a whole was assessed using Fisher's F-test. Since parallel surveys were not conducted, instead of checking the adequacy, the quality of the approximation of the experimental points by the accepted regression equation was assessed, that is, it was checked whether this equation makes sense. Such a test was carried out by comparing the residual variance and the variance relative to the mean. The calculation results showed that the value of the coefficient of determination significantly exceeds the allowable value, and the actual value of the Fisher criterion significantly exceeds the table value. The obtained indicators led to the conclusion that the relationship between the variables in the regression model is significant, and the proposed stochastic method and the multiple linear regression equation can be used to make decisions in the process of synthesizing the technical structure of a computer-integrated control system for objects of a district heating system.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;

Recommender systems for teachers: The relation between social ties and the effectiveness of socially-based features

Open Educational Resources (OER) repositories provide teachers with a wide range of learning resources (LRs), enabling them to design various learning sequences. However, search & select in large OER repositories can be a daunting task for teachers. Incorporating peer recommendations, as is common in online marketplaces, is becoming a popular solution that seeks to exploit the wisdom of the crowd for this task. However, teachers are often reluctant to take a contributory role and provide social recommendations. In addition, little is known about the actual value of social recommendations as a search aid. In this research, we implemented a “light-weight” socially-based recommender system (RS) within a large OER repository that includes social network features. We examined two aspects of the socially-based recommendation mechanisms. First, their utility as search aids that assist teachers in searching and selecting suitable LRs, and second, their impact on teachers' incentives to share recommendations that can assist fellow teachers. To study these two aspects, we examined two science teacher communities using this repository. The results demonstrated the incentivising power of social rewards, and the value of social recommendations as means for search & select. However, we also observed a heterogeneous effect of social features on teachers' behaviour. To explore the factors that may explain these differences, we employed a mixed-method approach, combining qualitative, quantitative, and Social Network Analysis methods. Triangulation of the findings underline the relation between the strength of the social ties within the teachers’ community and the effectiveness of socially-based features.

Fault diagnosis for multiple current sensors in grid‐connected inverter based on average modulation voltage

AbstractGrid‐connected inverters are the core equipment in the renewable power system. There are multiple current sensors which may affect the driving module of the switch in inverter. In multiple current sensor fault diagnosis, the coupling between fault components makes fault diagnosis difficult. This paper presents an offset fault diagnosis method of multiple current sensors based on the average modulation voltage model. Based on the influence of current sensor offset fault in the system, the average modulation voltage model is established in three‐phase stationary coordinates. The difference between the measured value of the current and the actual value is estimated through the model when the offset fault occurs. And then the fault is located. Experimental results show that the fault can be located accurately and fault tolerant control can be performed by this method when there are offset faults in multiple sensors simultaneously.

Optimization of extraction of total flavonoid from leafs of Helicteres hirsuta Lour. based on bath ultrasound-assisted and testing some biological activities

Helicteres hirsuta Lour. contains highly bioactive compounds that have therapeutic effects on many diseases, hence are used to make medicines from most parts of the plant. We optimize using the RSM surface response method based on Design Expert 11 software (Stat-Ease Inc, Minneapolis, USA) and analyze LC-MS/MS method and some other biochemical methods of methanol extraction from leaves of H. hirsuta L. collection Thua Thien Hue, Vietnam. The results identified the ideal parameters for extracting the greatest total flavonoids from leaves of H. hirsuta L. as follows: Times (30 minutes); methanol solvent concentration (52.2407%); ultrasonic frequency (12.4253); and material/solvent ratio (1:19.9145 (w/v)). The total flavonoid value obtained was 7.78425 (mg Catechin/g extract). The actual experiment obtained an actual value of 7.538 (mg Catechin/g extract). The results obtained 22 compounds of a flavonoid nature (61.111%) appearing in the purified methanol fractional extract. These compounds can inhibit DPPH free radicals at 50% with an IC50 value of 521.498 g/mL and they also have inhibitory activity against the growth of cancer cell lines under study with IC50 values ranging from 104.02 to 188.48 g/mL. The best cytotoxic effect of methanol extract from leaves of H. hirsute L. on human leukemia cell line (HL-60) with IC50 was 104.02 μg/mL. The results obtained indicate that leaves of H. hirsute L. have prospective uses as therapeutic herbs in antioxidant and cancer cell-inhibiting properties.

Explaining Returns Through Valuation

This article develops an analytically coherent yet parsimonious framework which explains market returns in terms of contemporaneous information. It anchors on the idea that valuation (static perspective) can be connected to the dynamics that explain returns, and vice versa. The framework requires two components. First, an explicit function that maps information to an estimate of value—a valuation heuristic. Second, the framework assumes that the difference between a firm’s actual value and value-per-heuristic follows an autoregressive stochastic process with a contraction parameter and no intercept. The contraction parameter can be estimated efficiently and nonparametrically. This modeling suffices to derive implied returns. Using scaled Earnings Per Share (``EPS'') forecasts as valuation heuristics, we empirically evaluate the framework’s validity and robustness. Its explanatory power compares favorably to that of traditional ordinary least squares (``OLS'') regressions, despite only requiring a single parameter. In a setting with pooled annual data, the implied and realized returns correlations range between 64% and 73%.

Editorial Presentation - Perspectives on innovation governance: challenges and dilemmas

Editorial Presentation - Perspectives on innovation governance: challenges and dilemmas

Evaluating surface roughness of curved surface with circular profile based on arithmetic circular arc fitting

A procedure based on Kåsa fit for evaluating the surface roughness of a curved surface with a circular arc profile was suggested in this paper. To investigate the effectiveness of the method, some curved surface profiles with preset surface roughness were numerically generated, and their surface roughness was evaluated by the procedure based on Kåsa fit. And the surface roughness of two experimental surface profiles evaluated by the method based on Kåsa fit was compared with those evaluated by linear fitting and quadratic polynomial fitting to demonstrate both the effectiveness and the preservation of Kåsa fit-based procedure. Results show that the evaluated value of surface roughness by Kåsa fit-based procedure only deviated from the actual value by less than 10%, even in the case of considerable fitting parameters’ error (&amp;gt;30%). Also, the Kåsa fit can report a reasonable value of surface roughness with better preservation of shift and rotation motion of the surface profile compared to both linear fit and quadratic polynomial fitting, especially for the curved surface with a short radius. This procedure based on Kåsa fit can be used in evaluating surface roughness of curved surface with circular arc profile no matter what pose of the object under observed.

The implied views of bond traders on the spot equity market

This study delves into the temporal dynamics within the equity market through the lens of bond traders. Recognizing that the riskless interest rate fluctuates over time, we leverage the Black-Derman-Toy model to trace its temporal evolution. To gain insights from a bond trader's perspective, we focus on a specific type of bond: the zero-coupon bond. This paper introduces a pricing algorithm for this bond and presents a formula that can be used to ascertain its real value. By constructing an equation that juxtaposes the theoretical value of a zero-coupon bond with its actual value, we can deduce the risk-neutral probability. It is noteworthy that the risk-neutral probability correlates with variables like the instantaneous mean return, instantaneous volatility, and inherent upturn probability in the equity market. Examining these relationships enables us to discern the temporal shifts in these parameters. Our findings suggest that the mean starts at a negative value, eventually plateauing at a consistent level. The volatility, on the other hand, initially has a minimal positive value, peaks swiftly, and then stabilizes. Lastly, the upturn probability is initially significantly high, plunges rapidly, and ultimately reaches equilibrium.