Adjustment Coefficient Research Articles

Improving the performance of self-organizing map using reweighted zero-attracting method

In this paper, we introduce a novel approach to enhance the accuracy and convergence behavior of Self-Organizing Maps (SOM) by incorporating a reweighted zero-attracting term into the loss function. We evaluated two SOM versions: conventional SOM and robust adaptive SOM (RASOM). The enhanced versions, reweighted zero-attracting SOM (RZA-SOM) and reweighted zero-attracting RASOM (RZA-RASOM), include an l1 norm in the error function to add a zero-attractor term, which improves weight coefficient adjustments while preserving topology. The models were assessed for convergence speed and misadjustment under sparsity assumptions of the true coefficient matrix, and their robustness was tested under conditions of increased non-zero taps. Using six different datasets, we compared the performance of RZA-SOM and RZA-RASOM against conventional SOM and RA-SOM in terms of accuracy, quantization error, and topology preservation. Experimental results consistently demonstrated that RZA-SOM and RZA-RASOM surpassed the performance of conventional SOM and RA-SOM.

Joint guarantee rate index assessment of the contribution of agricultural water-saving measures to river ecological flow in water shortage areas

Reconciling higher ecological water demands and economic purposes with finite freshwater resources remains one of the great management dilemmas. Low water efficiency and large water consumption in agricultural irrigation hinder the protection of river ecological flow in the water shortage areas of Northwest China. By analyzing the surplus and shortage of river ecological protection targets, the intensity guarantee rate index is innovatively proposed to quantitatively evaluate the important contribution of improved water-saving measures for river flow restoration. It compensates for the shortcomings of the duration guarantee rate index in evaluation that may mask ecological problems. Considering ecological baseflow, fish species, and landscape water demand, the ecological flow interval (6.00–12.42 m3/s) was determined. Three scenarios are highlighted for different typical years and irrigation regimes in combination with planting structure adjustment and irrigation water utilization coefficient. In 75 %, 90 %, and 95 % typical years, the maximum total annual water-savings (Scenario 3) are 57.3, 57.1, and 87.2 million m3 (sufficient irrigation); 32.2, 33.9, and 35.5 million m3 (deficit irrigation) respectively. The guarantee degree of river ecological flow under both irrigation regimes increased from the previous 65.6 % to a maximum of 91.7 % and 81.2 %. The increase in the daily guarantee rate and decrease in the intensity guarantee rate under the aforementioned three scenarios indicate a reduction of damage depth and duration of ecological flow. By using a quantitative study, it is pointed out that improving agricultural water-saving is a reasonable and necessary way to protect the river flow.

A modified A* algorithm combining remote sensing technique to collect representative samples from unmanned surface vehicles

Ensuring representativeness of collected samples is the most critical requirement of water sampling. Unmanned surface vehicles (USVs) have been widely adopted in water sampling, but current USV sampling path planning tend to overemphasize path optimization, neglecting the representative samples collection. This study proposed a modified A* algorithm that combined remote sensing technique while considering both path length and the representativeness of collected samples. Water quality parameters were initially retrieved using satellite remote sensing imagery and a deep belief network model, with the parameter value incorporated as coefficient Q in the heuristic function of A* algorithm. The adjustment coefficient k was then introduced into the coefficient Q to optimize the trade-off between sampling representativeness and path length. To evaluate the effectiveness of this algorithm, Chlorophyll-a concentration (Chl-a) was employed as the test parameter, with Chaohu Lake as the study area. Results showed that the algorithm was effective in collecting more representative samples in real-world conditions. As the coefficient k increased, the representativeness of collected samples enhanced, indicated by the Chl-a closely approximating the overall mean Chl-a and exhibiting a gradient distribution. This enhancement was also associated with increased path length. This study is significant in USV water sampling and water environment protection.

A short-circuit protection scheme for active distribution networks based on improved adaptive current reliability coefficient

Abstract Addressing the issue of misoperation in traditional three-zone current protection caused by the assisted, drawdown, or reverse current due to the large-scale integration of distributed generation (DG) into distribution networks (DNs), an adaptive current short-circuit protection scheme based on local information is proposed for active DNs (ADNs). Firstly, the adaptive current protection reliability coefficients are improved for the setting of protection zones I and II of the lines in the ANDs. On the DG side, an improved reliability coefficient is constructed using an e-exponential function. Subsequently, the distance between the fault point and the busbar is calculated by analyzing the relation between the positive-sequence voltage and positive-sequence current at the measuring point when faults occur at various locations. Based on this distance parameter, the reliability coefficient is adjusted in real time. Finally, the modified adjustable reliability coefficient, along with the online-calculated equivalent impedance and equivalent electromotive force, is used to coordinate the setting of short-circuit protection zones I and II of the lines in the ADNs. Simulation results demonstrate that, compared with traditional adaptive current protection schemes, this proposed scheme is independent of the location of short-circuit fault points, the grid-connected capacity, or the number of DGs, exhibiting better selectivity and reliability.

Seismic behavior of resilient concrete column-base connection with SMA bolts and RSPs

This paper presented a novel resilient concrete column-base connection with shaped memory alloy (SMA) bolts and rectangular slotted plates (RSPs) to achieve the demountable and recoverable capabilities in precast concrete columns. Quasi-static tests were conducted on one monolithic and three resilient concrete connections to investigate the effects of RSP number and yield strength on seismic behavior. Results showed that the resilient connection with both flange and web RSPs had superior lateral capacity, ultimate drift ratio, and energy dissipation compared to the monolithic connection. The resilient connection also exhibited minimal concrete damage and smaller residual deformation. However, low-yield-strength flange RSPs significantly reduced lateral capacity, initial stiffness, and energy dissipation, which should be considered in engineering design. Additionally, adjustment coefficients for rocking interface rotations were determined from digital image correlation (DIC) data using a fitting method. A formula was proposed to predict the flexural capacity of resilient specimens, assuming a plane section in the compression zone. The predictions closely matched the test data, with differences within 10 %, demonstrating the formula's effectiveness. The length, diameter, and material yield strength of the anti-shear steel bar (ASB) had a significant impact on the shear capacity of the interface.

The Factors Affecting to Government’s Expenditure of Lao PDR

This paper aims to analyze the factors affecting the government’s expenditure of Lao PDR. Using the time series data from 2000-2022 which retried from the bank of Lao PDR and implied with the ARDL model. The empirical results found that there is a long-term relationship and ARDL (2, 2, 1, 2, 2) model were used. The empirical results found that in the long run, the tax revenue and economic growth have a positive relationship with the government’s expenditure with a statistical significance level of 0.01 and 0.1 respectively. While inflation and money supply are not statistically significant. In the short term, tax revenue, economic growth rate and money supply affect the government’s expenditure with statistically significant effect, while the inflation rate does not have a statistically significant. Tax revenue and economic growth rate affect the government’s expenditure in the opposite direction with a significance level of 0.05 and contrary to the hypothesis. The money supply affects the government’s expenditure in the same direction with a statistically significant level of 0.05 and according to the hypothesis. Which the coefficient of the spread adjustment to the long run equilibrium is equal to 90.97%.

A negative-carbon planning method for agricultural rural industrial park integrated energy system considering biomass energy and modern agricultural facilities

Biomass energy, recognized as a “zero-carbon” energy, has gradually become a crucial approach to promoting the low-carbon transformation of agricultural rural industrial parks (ARIP). Meanwhile, modern agricultural facilities (MAF) can play a significant role in adjusting agricultural load. Therefore, this paper proposes a negative-carbon planning method for the ARIP integrated energy system (IES). Firstly, carbon activities occurring during the operation of ARIP are sorted out and modeled based on the life cycle assessment (LCA) method, especially modeling the “zero-carbon” and “negative-carbon” benefits of biomass energy. At the same time, the models of both agricultural irrigation systems and straw bundling direct combustion heating are constructed in the ARIP planning. Then, a negative-carbon planning model for ARIP IES is established with the goal of minimizing the annualized total cost, where carbon emission reduction is integrated into the optimization objectives through carbon trading. On this basis, a two-stage distributionally robust optimization (DRO) planning model is constructed, where a box-like set and comprehensive norm constraints are used to model the seasonal differences of agricultural industry loads and the uncertainty of probability distribution in photovoltaic (PV) scenarios. Especially, a box set with adjustable uncertainty coefficients is introduced to simulate fluctuations of output in PV scenario. Finally, the CPLEX solver is used to solve the planning model. verify the effectiveness of the proposed planning method in enhancing the economic and environmental effects of ARIP. Results indicate that the proposed planning model can reduce the total cost by 49.584 × 104 $. Besides, the annual carbon emissions can be reduced from 37533.98 t CO2-eq to −15834.5 t CO2-eq. Especially, the carbon sequestration effect of biochar is the key link in achieving “negative carbon” in ARIP.

Automated Diagnosing of Melanoma Based on the Stolz Algorithm

The article discusses an algorithm for automating melanoma diagnosing using computer vision and a neural classifier. The aim of the study is to develop an algorithm for efficient classification of melanoma in images, as well as the influence of dermoscopic features on diagnosing. New growths in the image are highlighted. Calculation of parameters for the Stolz formula and calculation of TDS (calculation of diagnosis) were carried out. Based on the calculations performed, the optimization problem was solved to clarify the boundaries of the neoplasm differentiation areas, and the Stolz formula coefficients adjustment, and a set for neural network training was generated; MLP was selected as a classifier. The average accuracy of the algorithm on two independent data sets is 86%. Classification by a neural network of two sets of images according to Stolz parameters is given. Based on the results, a diagnosing was made. Based on the Stolz algorithm, a set of images with skin neoplasms is pre-processed: the neoplasm is isolated, ABCD signs and the overall dermoscopic score are calculated from the image. The proposed algorithm for automated melanoma diagnosing based on the Stolz dermoscopic method showed good results on real sets of images. The reduced accuracy of the model on the HAM10000 set is due to the class imbalance (ratio of classes 1 to 9). A high F-measure value indicated a good balance between sensitivity and specificity of the model, with the model tending to be better at detecting melanoma true positive (TP) cases. High sensitivity is desirable for problems, where reducing false negatives (missing positive cases) is critical, even at the expense of increasing Type I errors. The completeness of the model was 88.54% and 95% and suggests that the model minimizes cases of missing diseases. It is concluded that the more accurately the ABCD parameters are calculated, the more accurately the model will be able to classify the disease; in the future, refining and dividing parameter D into several components can provide the model with additional features for classification.

Computational Methods of Ruin Probability: Actuarial Comparison of De-Vylder and Tijim’s Models

The underwriting operation of insurance firms is to assume the risk of the insured in return of premium received. In order to shield itself against extreme losses and avoid the risk of insolvency, it becomes necessary to examine how the portfolio is expected to perform over a long-time horizon. The surplus process is connected with the excess of the premium received over claims outgo of the insurer’s portfolio to enable it predict the level at which the insurer could survive. When the surplus approaches a defined lower limit irrespective of the initial reserve, then the insurer is ruined. The probability of ruin apparently defines the volatility embedded in underwriting process as a useful tool of risk measurement in long range planning of premium rate. This paper is anchored on the following objectives: solve the adjustment co-efficient using the moment generating function, compute the De-Vylder’s ruin, compute the Tijim’s ruin approximation and then compare the two. Although it was verified that as the initial capital increases, the probability of ruin decreases under the two models, computational evidence from our results in tables 2 and 3 shows that the Tijim’s approximation to ruin probability is higher than the De-Vylder’s approximation at the same level of initial capital and safety loading. The implication is that the De-Vylder’s approximation to ruin probabilities is an improvement over Tijim’s ruin model and hence De-Vylder’s approximation is recommended for the insurer’s ruin assessment

Synergetic effect of diamond particle size on thermal expansion of Cu-B/diamond composite

Diamond particles reinforced Cu matrix (Cu/diamond) composites have promising applications for heat dissipation of high-power electronic devices because of their high thermal conductivity and suitable coefficient of thermal expansion. The effect of diamond particle size on thermal conductivity has been addressed; however, the effect of diamond particle size on thermal expansion still needs to be clarified. In this study, Cu-B/diamond composites with various diamond particle sizes ranging from 66 μm to 701 μm were fabricated to assess the impact of diamond particle size on the thermal expansion behavior. The composites exhibit low and adjustable coefficient of thermal expansion (CTE) values of 4.58–6.63 × 10−6 K−1, which align with 4–8 × 10−6 K−1 of widely employed semiconductors. The CTE of the Cu-B/diamond composites first decreases and then increases with increasing diamond particle size, which arises from a synergetic effect of interfacial bonding strength and matrix strengthening effect. As the diamond particle size is smaller than 272 μm, the interfacial bonding strength rises with increasing particle size, enabling the diamond particles to restrain the expansion of the Cu matrix more effectively and to reduce the CTE. As the diamond particle size exceeds 272 μm, the dislocation density in the Cu matrix continually decreases with increasing particle size, reducing the strength increment in the Cu matrix and increasing the CTE. The effect of thermal cycling on the thermal expansion of the Cu-B/diamond composites was also investigated, and all the composites show an increase in the CTE after 100 thermal cycles. Notably, the composite with 272 μm diamond particle size exhibits the lowest CTE increment. It shows a CTE value of 5.29 × 10−6 K−1 after thermal cycling, still compatible with the semiconductors for electronic packaging applications.

Solvent‐Induced Dynamic Bond Regulation for Constructing Adaptive Lubricating Hydrogels with Switchable Tribological Performance and Environmental Adaptability

AbstractAdaptive lubricating hydrogels (ALH) show promise for reducing friction and energy consumption in various industrial applications. However, retarded switchable tribological performance and poor environmental adaptability limit their integration into application devices. Herein, a dynamic bond regulating strategy based on solvent conversion for producing an ALH with highly switchable tribological performance and decent environmental tolerance is proposed. By incorporating dynamic B─O bonds into the sodium alginate (SA) framework, these bonds transition between associative and dissociative states, facilitated by solvent conversion between glycerol and ethanol. This process modulates the mechanical modulus of the SA network, allowing for achieving highly switchable tribological performance. Furthermore, the inclusion of glycerol not only as a modulus regulator but also serves as an antifreeze component to impart the ALH with superior antifreeze and heat resistance properties, thus allowing the ALH to demonstrate excellent environmental adaptability. As a result, the resulting ALH exhibits highly switchable lubrication behavior, with a dynamically adjustable coefficient of friction (COF) between 0.022 and 0.157, and maintains consistent lubrication performance under a harsh environment (−40–60 °C), representing the state‐of‐the‐art in reported ALHs. This advancement allows for the development of intelligent brake devices (IBD), demonstrating potential applications in controlling drone speed.

Experimental validation of an efficient strategy for FE model updating and damage identification in tubular structures

ABSTRACT Early identification of damages in tubular structures is crucial for their long-term safety and functionality, as they are essential in various modern life applications. Experimental and numerical modal data may slightly differ due to unknown structural characteristics and uncertainties, which are typically addressed using finite element (FE) model updating procedures. Instead of using the Euler-Bernoulli beam element, this paper utilises the semi-rigidly connected frame element (S-RCFE). By incorporating extra design parameters, such as the end fixity factor of all connections, the S-RCFE offers a unique opportunity to establish a strong agreement between experimental and numerical models through an optimisation-based FE model updating procedure. A well-calibrated FE model represents the actual behaviour of the structure and leads to achieving accurate results in the damage detection step. This paper employs the improved grey wolf optimiser (IGWO) and weIghted meaN oF vectOrs (INFO) to minimise 11 objective functions with adjustable coefficients. The statistical investigations reveal that the IGWO effectively minimised five out of six objective functions, which were defined based on the modified total modal assurance criterion (MTMAC). The rest of the objective functions based on the modal assurance criterion (MAC), natural frequency vector assurance criterion (NFVAC), differences in natural frequencies, and a combination of the MAC and NFVAC could not obtain accurate outcomes for the model updating problem. The statistical comparison indicates that the INFO algorithm is unreliable for the FE model updating despite achieving at least one successful result in ten independent runs. The INFO algorithm and the IGWO algorithm demonstrate comparable performance in damage detection. The analysis also shows that the coefficients of MTMAC, alpha and beta, should be adjusted to 0.65 and 1, respectively, to achieve the most accurate damage detection result.

Improved PID based Adaptive Controllers for Denoising Biomedical Signals

Biomedical signal processing is one of the most popular research domains. Very fine features in biomedical signals carry important information regarding patient’s health. So, it is necessary to have noise free biomedical signals for the correct diagnosis. The major trouble for biomedical equipment is Power Line Interference (PLI) which impairs the signals. An adaptive filter can be one of the possible solutions for the removal of non-stationary noise, but maintaining the system stability along with a high convergence rate is a critical issue. The adaptive algorithm works on the principle of minimization of error for optimized coefficients updating while PID controller attempts to minimize the error over time by adjusting the control variables. In this paper, these two different approaches are combined to get an efficient solution for adaptive PLI cancellation and two new algorithms namely PID-based Response Adjustment for Reducing Error (PID-RARE) and PID-based Coefficient Adjustment for Reducing Error (PID-CARE) are proposed. The integration of NSLMS adaptive algorithm with PID controller in the proposed algorithms are found to be an effective solution to adaptive PLI cancellation and have shown quite better performance in terms of SNRout, correlation coefficient, mean square error thereby providing more cleaner signal at lesser convergence rate.

Experimental assessment of a multilayered packed-sphere La–Fe–Si active magnetic regenerator

This study investigates the performance of a multilayered packed-bed active magnetic regenerator (AMR) using spheroidal particles with first-order magnetocaloric properties. The hydraulic performance is assessed via the interstitial friction factor, showing significant underestimation by the Ergun Equation at high mass flow rates. Coefficient adjustments are made to accurately represent the AMR pressure drop, considering particle nonuniformity and structural components, such as layer mesh dividers. This facilitates the pressure drop modeling and provides a means to check the AMR integrity on a routine basis, without requiring AMR disassembly. The thermal performance, evaluated in terms of the regenerator effectiveness, shows a satisfactory cooling potential for practical applications but emphasizes the need for flow control to prevent effectiveness imbalance between hot and cold flows, crucial for optimal operation. The cooling capacity and maximum temperature span are also evaluated, demonstrating that higher mass flow rates yield higher cooling capacities with lower temperature spans, while lower rates achieve higher spans. Varying the blow fraction shows that regenerators at 50% blow fraction achieve 10% higher cooling capacities than at 37.5%. Increasing operational frequency improves cooling by increasing the number of cycles and reducing losses, resulting in a 15% capacity increase between 0.25 and 0.50 Hz. However, this trend may reverse at higher frequencies beyond the experimental limits. While this study improves the understanding of the hydraulic and thermal performance of packed-bed AMRs, its findings underscore the importance of flow balance and frequency in achieving optimal performance, thus providing insights for future system improvements.

The Contribution of Green, Blue, and Energy Sources to Economic Development in Central Asia

Central Asia (CA) is a young integrated region formed after the collapse of the Soviet Union, with most of its infrastructure based on fossil fuels. The traditional energy and water infrastructure is facing huge inefficiency and technical losses. This study investigates the transition of the green, blue, and energy economies in Central Asia using a small-dimensional panel dataset on five countries, Uzbekistan, Kazakhstan, Kyrgyzstan, Tajikistan, and Turkmenistan, over the period 1995–2018. The authors analyze the impact of ecological footprint, water withdrawal, and energy consumption on gross domestic product. In applying the Panel Vector Error Correction Model, evidence was found supporting the long-running association between variables. Furthermore, the adjustment coefficients suggest that only GDP growth will adjust toward equilibrium. Overall, the findings suggest a more effective role of green transition compared to blue and energy transitions.

Density-guided and Adaptive Update Strategy for Multi-objective Particle Swarm Optimization

Abstract As a powerful optimization technique, multi-objective particle swarm optimization (MOPSO) has been paid more and more attention by scientists. However, in more complex problems, MOPSO faces the challenges of weak global search ability and easy to fall into local optimality. To address these challenges and obtain better solutions, people have proposed many variants. In this study, a density-guided and adaptive update strategy for multi-objective particle swarm optimization (DAMOPSO) is proposed. First, an adaptive grid is used to determine the mutation particles and guides. Then the Cauchy mutation operator is performed for the poorly distributed particles to expand the search space of the population. Additionally, the strategy of non-dominated sorting and hyper-region density are devised for maintaining external archives, which contribute to the uniform distribution of optimal solutions. Finally, an adaptive detection strategy based on the adjustment coefficient and conversion efficiency is designed to update the flight parameters. These approaches not only speed up the convergence of algorithms, but also balance exploitation and exploration more effectively. The proposed algorithm is compared with several representative multi-objective optimization algorithms on 22 benchmark functions; meanwhile, statistical tests, ablation experiments, analysis of stability and complexity are also performed. The experimental results demonstrate DAMOPSO is more competitive than other comparison algorithms.

AlN/ScAlN composite films-based spurious free A1 mode lamb wave resonator with adjustable effective electromechanical coupling coefficient

AlN/ScAlN composite films-based spurious free A1 mode lamb wave resonator with adjustable effective electromechanical coupling coefficient

Effects of Transmission Mechanism of Monetary Policy Channels on Economic Growth in Ethiopia: Cointegration and Causality Analysis Approach

The study aimed at investigating and analyzing factors those effects of transmission mechanism of monetary policy channels on economic growth in Ethiopia using a 36 years’ time series data. To this end, variables such as economic growth (dependent variable), and other regress variables such as real lending rate, real effective exchange rate, credit for private sector, consumer price index, trade of opens, gross capital formation, and money supply are considered. An empirical model linking the real GDP to its theoretical effects is then specified. This study had employed the co-integration and vector error correction model (VECM) analysis with impulse response and variance decomposition analysis to provide robust long run effects and short run dynamic effects on the real GDP. All variables under consideration are integrated of order one I (1) and also co-integrated. Vector Error Correction Model/VECM/ results show that real GDP was positively and significantly affected by the real effective exchange rate, money supply, gross capital formation (investment), credit for private sector, trade of openness over a period of long-run; while real leading interest rate and consumer price index (inflation) have significant negative effect. The estimate of the speed of adjustment coefficient found in this study indicates that about a 31 percent of the variation in the real GDP from its equilibrium level is corrected within a year. The study suggests that lowering the lending interest rate can encourage more private investment because it will encourage private investors to borrow more, which will increase investment in Ethiopia. Since investment is one of the factors that determine the gross domestic product, this will result in an increase in the GDP.

Study on seismic failure mechanism and damage model of unconstrained adobe block walls

Through the quasi-static cyclic in-plane loading test of three kinds of unconstrained adobe block walls, namely plain adobe wall, modified mud adobe wall and modified adobe wall, the failure mode and seismic performance of each specimen were clarified. Using residual deformation and cumulative energy dissipation as failure parameters, a two-parameter seismic damage model of unconstrained adobe block walls was established. and a calculation method for the adjustment coefficients of energy consumption items was provided. The results showed that the failure mode of the typical unconstrained adobe block wall under earthquake action was not affected by the modification of adobe materials, and the final failure mode was shear failure along the mud crack and the inclined crack extending through the block. Furthermore, the proposed seismic damage model can well reflect the evolution law of earthquake damage of adobe block walls, and the recommended damage index boundary values for five performance levels were given, providing a reference for performance-based seismic design and damage assessment of adobe block walls.

МОДЕЛЮВАННЯ АСИНХРОННИХ МАШИН У СКЛАДІ ЕЛЕКТРОМЕХАНІЧНИХ СИСТЕМ З УРАХУВАННЯМ ВТРАТ У СТАЛІ СТАТОРА

A comparative study has been conducted on methods for accounting for the influence of losses in the stator iron of squirrel-cage induction motors on the operating parameters. A comparison was made between reference data and calculation results of the nominal mode parameters of an asynchronous motor without considering losses in the stator iron and with their consideration using equivalent resistances connected in parallel to the motor, as well as equivalent circuits of losses in the stator. The effect of the ratio of active and inductive components of the load of equivalent stator loss circuits on the results of mathematical modeling has been investigated. The study was carried out based on a universal mathematical model of an asynchronous motor of electromechanical systems with an arbitrary stator winding scheme. An algorithm for determining and adjusting the parameters of the stator loss circuits during the integration of the differential equations of electrical and mechanical balance of the asynchronous motor to a steady-state value has been developed, based on the values of losses in the stator iron (depending on the induction and frequency) and the effective value of the current of the equivalent circuit. To ensure solution stability, the use of an iteration coefficient and discretization of resistance adjustment has been justified. The application of stator iron loss circuits in the mathematical model of an asynchronous machine in generator mode has been justified, when the use of parallel connected equivalent resistances contradicts the machine's energy diagram. References 10, figure 1, table 1.