Linear Method Research Articles

Prediction of second-hand sailboat prices based on multivariate linear interpolation methods

According to the requirements of the topic, our team has established a mathematical model based on a large amount of relevant data and conducted a quantitative analysis of factors such as the price of sailboats and their length, types, materials, etc. In the process of data analysis and model building, we mainly use interpolation method and error variance analysis, and display these results through charts and figures. In addition, we have used the obtained model to analyze and estimate the price of sailboats in Hong Kong.

Modular linear circuit matching method for a 110GHz balanced frequency doubler

Modular linear circuit matching method for a 110GHz balanced frequency doubler

Orientation Regulation of Class-switch Recombination in Human B Cells.

We developed a linear amplification-mediated high-throughput genome-wide translocation sequencing method to profile Ig class-switch recombination (CSR) in human B cells in an unbiased and quantitative manner. This enables us to characterize CSR junctions resulting from either deletional recombination or inversion for each Ig class/subclass. Our data showed that more than 90% of CSR junctions detected in peripheral blood in healthy control subjects were due to deletional recombination. We further identified two major CSR junction signatures/patterns in human B cells. Signature 1 consists of recombination junctions resulting from both IgG and IgA switching, with a dominance of Sµ-Sγ junctions (72%) and deletional recombination (87%). Signature 2 is contributed mainly by Sµ-Sα junctions (96%), and these junctions were almost all due to deletional recombination (99%) and were characterized by longer microhomologies. CSR junctions identified in healthy individuals can be assigned to both signatures but with a dominance of signature 1, whereas almost all CSR junctions found in patients with defects in DNA-PKcs or Artemis, two classical nonhomologous end joining (c-NHEJ) factors, align with signature 2. Thus, signature 1 may represent c-NHEJ activity during CSR, whereas signature 2 is associated with microhomology-mediated alternative end joining in the absence of the studied c-NHEJ factors. Our findings suggest that in human B cells, the efficiency of the c-NHEJ machinery and the features of switch regions are crucial for the regulation of CSR orientation. Finally, our high-throughput method can also be applied to study the mechanism of rare types of recombination, such as switching to IgD and locus suicide switching.

Factors Influencing Farmers’ Willingness to Pay for NPK Fertilizer in Specialty Coffee: Insights from Pagar Alam, Indonesia

Robusta coffee cultivation in Pagar Alam City has not been carried out optimally because most farmers do not practice precision farming patterns in the use of production inputs to increase coffee plant productivity, including the use of unsubsidised coffee-specific fertilisers. In order to be accepted by farmers, fertiliser companies must pay attention to the willingness to pay of farmers for these fertilisers. For this reason, the study aims to: (1) determine farmer perceptions of nutrient content, ease of use and quality of fertiliser, (2) determine the value of farmers’ willingness to pay for unsubsidised NPK fertiliser for coffee in Pagar Alam City, and (3) determine the effect of nutrient content, ease of use and quality of fertiliser and other factors on farmers' WTP in Pagar Alam City. The research location was determined purposively with the consideration that Pagar Alam City is one of the highest coffee producing areas in Indonesia. A total of 100 coffee farmers were used as samples and interviewed using a prepared questionnaire. Data on farmers' willingness to pay was obtained by using the contingent valuation method (CVM). Descriptive analysis was used to determine the level of farmers' perception of nutrient content, ease of use and quality of fertiliser. Multiple linear regression methods was used to analyze the effect of nutrient content, ease of use and fertiliser quality and other factors on farmers' WTP. The results showed that the average value of farmers' WTP for unsubsidised NPK fertiliser for coffee was IDR 11,160 per kg which is lower than the price of unsubsidised NPK fertiliser commonly paid by coffee farmers. The study found that fertiliser quality, ease of fertiliser uses and fertiliser nutrient content, farm income level, land size, age, farming experience, household size and farmer membership status in farmer groups significantly affect the farmers’ WTP.

A 4-Stage Runge-Kutta Type Method for Solution of Stiff Ordinary Differential Equations

In this paper, a 2 step implicit block hybrid linear multistep method was reformulated into a 4-stage block hybrid Runge-Kutta Type Method via the butcher analysis. The method can be used to solve first order stiff ordinary differential equations. A numerical example solved with the proposed method showed a better result in comparison with an existing method.

Applying Holt’s linear method to estimate future trends of HIV prevalence among individuals aged 15-49 years in Cabo Verde

Applying Holt’s linear method to estimate future trends of HIV prevalence among individuals aged 15-49 years in Cabo Verde

KINETIC AND THERMODYNAMIC ADSORPTION STUDIES ON THE REMOVAL AND TREATMENT OF TEXTILES EFFLUENTS USING ACTIVATED CARBON DERIVED FROM DIOSPYROS MESPILLIFORMIS SEEDS.

An agricultural waste Diospyros mespiliformis seeds were treated, and activated with 30% of 0.3M sodium hydroxide base, to obtain an activated carbon employed in an adsorption process. It was utilize to treat effluent textile water sample from African Textile Manufacturers Limited in Dala, Kano State, Nigeria. Various techniques, including scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and proximate analysis were employed to investigate the adsorption process. The adsorbent was able to remove the dark red color to colorless found in textile effluents and shown a higher removal affinity for zinc ( 99.4%) and least affinity for nickel (0.11%), also Chromium (Cr) reduced by 39.4%, Copper (Cu) reduced by 20.9%, Iron (Fe) reduced by 91.9% , Manganese (Mg) reduced by 79.3%, are reduced significantly, the PH change from acidic PH 6.4 to neutral PH 7.1, turbidity decrease from 131 to 14 NTU , electrical conductivity change from 723 to 110 ?s·cm?1 .Diospyros mespiliformis seeds activated carbon was used for removal of congo red and malachite green from an aqueous solution. The effect of adsorbent dose, adsorbate concentration and contact time on the process of adsorption was investigated. The optimum dosage was found to be 0.5g had a percentage removal of 98.1% for congo red and 94.4% for malachite green. 10mg/l and 80minute was found to optimum concentration and optimum time in adsorption of congo red and malachite green. The equilibrium, kinetic and thermodynamic properties of the dyes removal were also investigated. The experimental data were modeled using linear regression method of analysis. The correlation coefficient was used as a criterion for model adequacy and acceptance. The pseudo second-order kinetic model was found to best correlate the experimental data with R2 of 0.9523 for CR and 0.9297 for MG. The experimental data were investigated at three different temperature, 305k, 325k and 335k were found to follow the Langmuir model with R2 of 0.9679, 0.9662 and 0.989 for CR and 0.9529, 0.9662 and 0.994 for MG. also were found to follow Temkinp model with with R2 of 0.9549, 0.9318 and 0.9952for CR and 0.9885, 0.9443 and 0.9209 for MG. The negative free gribbs energy indicated that the adsorption processes were spontaneously feasible. The positive values of +31.66KJ/mol and +92.3KJ/mol on both CR and MG indicate that adsorption process has been found to be endothermic in nature. The removal of congo red was found to be more spontaneous and feasible than the removal of malachite green on the adsorbent. From the study, it was deduced that the Diospyros mespiliformis seeds activated with sodium hydroxide was found to be a good adsorbent for the treatment of textile wastewater containing Congo red and Malachite green.

Bi-Level Optimal Configuration of Electric Thermal Storage Boilers in Thermal–Electrical Integrated Energy System

Electric thermal storage boilers (ETSBs) are important devices in enhancing the electric–thermal decoupling ability and spatiotemporal transfer of integrated energy system (IES), which is beneficial for improving system flexibility and energy utilization efficiency. In order to obtain more accurate and comprehensive results, a bi-level optimal model is proposed to study the site selection and capacity configuration of ETSB in IES based on the established mathematical model of ETSB. The objective of upper-level optimization of the model is obtaining the lowest energy supply cost when configuring the location and capacity of ETSB, while the lower-level model optimizes the operation scheduling with the goal of obtaining the lowest operational cost. The mixed-integer linear programming method and the genetic algorithm method are selected to obtain the optimal model. To illustrate the effectiveness and advantages of the proposed method, case studies are carried out. The optimal configuration scheme for an ETSB is obtained by comparing the lowest energy supply cost under different configuration parameters. Furthermore, the impact of an ETSB on the system is also analyzed based on the variations in energy balance, abandoned energy, and energy allocation before and after configuring the ETSB.

A Stepwise Multifactor Regression Analysis of the Interactive Effects of Multiple Climate Factors on the Response of Vegetation Recovery to Drought

In this study, a stepwise multifactor vegetation regression analysis (SMVRA) approach was proposed to investigate the interaction of multiple climate factors on vegetative growth in the study area from 2000 to 2020. It was developed by integrating the stepwise linear regression method, Standardized Precipitation Evapotranspiration Index (SPEI), Normalized Difference Vegetation Index (NDVI), and Pearson correlation coefficient. SMVRA can be used to intuitively understand the interactive effects of multiple correlated factors (e.g., temperature, precipitation, potential evapotranspiration, and the drought index) upon vegetation. The results show that the resilience of vegetation in the BLR basin is influenced by the severity of drought. Annual changes in SPEI over the BLR basin show an increasing trend, with rates of 3.12 × 10−2. Precipitation and NDVI had a strong positive correlation (p < 0.05), found for 34.93% of the total pixels in the study area. In the BLR basin, vegetation growth is inhibited in the 4 years following a drought event. The area near 800 m is most sensitive to drought events. It provides a theoretical basis for future drought response and effective vegetation restoration in the region.

PENGARUH SOSIAL MEDIA MARKETING, E-SERVICE QUALITY, DAN REVIEW CONTENT TERHADAP MINAT BELI KONSUMEN BOX OUTLET SINGARAJA

This study aims to examine the influence of Social Media Marketing, E-Service Quality, and Review Content on consumer buying interest in Singaraja Box Outlet Stores. The population in this study includes all Singaraja Box Outlet Store consumers from various walks of life, both men and women. The sample used in this study was 85 consumers selected through a questionnaire technique distributed using Google Form. This study used a quantitative approach and the data was analyzed by multiple linear regression method using IBM SPSS Statistics 25 application. The results showed that Social Media Marketing, E-Service Quality and Review Content proved to have a positive and significant influence on consumer buying interest in Singaraja Box Outlet Stores

Resource Allocation and Performance of Microfinance Institutions in Machakos County, Kenya

This research study sought to determine the impact of resource allocation on the performance of MFIs in Machakos County. The study adopted a descriptive research design. The target population for the study included 63 staff members from the 21 registered MFIs in the county. The study utilized a purposive sampling technique in an effort to identify the most suited respondents for the study. A structured questionnaire was utilized as the data collection tool. The questionnaire was divided into two parts containing the demographic data relating to the respondent's background as part one and the second part covering resource allocation. The questionnaire was structured with a 5-point Likert scale. Descriptive and Inferential statistics were used in the analysis of the data. The linear regression analysis method was used to assess the associations between resource allocation and performance. The study established that resource allocation positively influenced the performance of MFIs in Machakos County. The study concluded that the manner in which resources are allocated significantly affects the performance of microfinance institutions in Machakos County. The study recommends that policymakers and microfinance institution (MFI) leaders should prioritize developing clear guidelines and frameworks for efficient resource allocation

Iterative UKF under generalized maximum correntropy criterion for intermittent observation systems with complex non-Gaussian noise

The traditional unscented Kalman filters (UKFs) under the maximum correntropy criterion provide a powerful tool for nonlinear state estimation with heavy-tailed non-Gaussian noise. Nevertheless, the above-mentioned filters may yield biased estimates because the Gaussian kernel function can only handle certain types of non-Gaussian noise. Additionally, the use of statistical linearization methods can result in approximation errors when solving linear observation equations, while the system may also experience observation data loss. Therefore, a new iterative UKF with intermittent observations under the generalized maximum correntropy criterion is proposed for systems with complex non-Gaussian noise, called GMCC-IO-IUKF. Firstly, the connection between the UKF with and without intermittent observations is established by designing a coefficient matrix including intermittent observation variables, so as to derive the UKF with intermittent observations under the maximum correntropy criterion. Secondly, for the measurement update of GMCC-IO-IUKF, a nonlinear regression augmented model that can deal with both prediction and observation errors is established using the coefficient matrix and the nonlinear function. To better adapt to different types of non-Gaussian noise, the generalized Gaussian kernel function is substituted for the traditional Gaussian kernel function. Theoretically, GMCC-IO-IUKF can achieve better estimation performance by directly employing the nonlinear function and the latest iteration value. Finally, a classical target tracking model is used to evaluate the estimation performance and feasibility of our proposed GMCC-IO-IUKF algorithm. It appears from the experiment results that our proposed GMCC-IO-IUKF can not only promote estimation precision but also handle complex non-Gaussian noise flexibly.

Determination of Reactivity Ratios of N-butyl Acrylate/methyl Methacrylate Copolymerization by NMR Spectroscopy

In this study, the reactivity ratios of n-butyl acrylate (BA) and methyl methacrylate (MMA) were investigated. The copolymerization reactions were performed in toluene at 70 oC, using azobisisobutyronitrile as the initiator. The composition of the initial monomer mixture and copolymers was easily determined with the help of proton nuclear magnetic resonance (1H-NMR) spectroscopy. The molar fractions of BA and MMA in the feed were varied to apply the linear methods of Fineman–Ross and Kelen - Tudos. The calculation gave the reactivity ratios and . The computed copolymer composition was relatively close to the real copolymers obtained, with a 5% deviation.

A linear compensation method for inference accuracy improvement of memristive in-memory computing

Memristive computing system (MCS), with the feature of in-memory computing capability, for artificial neural networks (ANNs) deployment showing low power and massive parallelism, is a promising alternative for traditional Von-Neumann architecture computing system. However, because of the various non-idealities of both peripheral circuits and memristor array, the performance of the practical MCS tends to be significantly reduced. In this work, a linear compensation method (LCM) is proposed for the performance improvement of MCS under the effect of non-idealities. By considering the effects of various non-ideal states in the MCS as a whole, the output error of the MCS under different conditions is investigated. Then, a mathematic model for the output error is established based on the experimental data. Furthermore, the MCS is researched at the physical circuit level as well, in order to analyze the specific way in which the non-idealities affect the output current. Finally, based on the established mathematical model, the LCM output current is compensated in real time to improve the system performance. The effectiveness of LCM is verified and showing outstanding performance in the residual neural network-34 network architecture, which is easily affected by the non-idealities in hardware. The proposed LCM can be naturally integrated into the operation processes of MCS, paving the way for optimizing the deployment on generic ANN hardware based on the memristor.

Design and Experiment of an Independent Leg-Type Chassis Vehicle Attitude Adjustment System

In response to the current low work efficiency of soil ridge-working machinery, as well as its poor stability, passability, and adaptability, this paper designs an independent leg-type working platform that can autonomously adjust its vehicle attitude through LiDAR scanning in a soil ridge-working environment. The platform, in terms of its mechanism and structural design, adopts dual parallelogram mechanisms, dual lead screw mechanisms, and independent column leg mechanisms, with a maximum adjustable ground clearance of 107 mm and a maximum wheelbase adjustment of 150 mm. A gyroscope is mounted at the center of the platform for attitude adjustment, ensuring the accurate data collection of the ultrasonic ranging module. Moreover, the platform adopts an adaptive adjustment method based on vehicle attitude and soil ridge shape parameters, obtaining soil ridge parameters through LiDAR and combining ultrasonic ranging module data with stepper motor pulse signals to obtain the absolute vehicle attitude parameters, using first and second linear regression methods to adjust the vehicle attitude and other working parameters. A prototype was also created, and the test data from the soil obtained through experiments show that, after leveling with the gyroscope leveling algorithm, the average value of the pitch angle is up to 0.6154°, and the average value of the roll angle is up to 0.9989°, with the maximum variance of the pitch angle being 0.0474° and the maximum variance of the tilt angle being 0.1320°. After the ultrasonic ranging module data are filtered by the Kalman filter, the maximum variance is 0.0304, and after applying the final fusion algorithm, the maximum variance is only 0.0085. The LiDAR measurement width value deviates from the actual width value by no more than 1.0 cm, and the LiDAR measurement height value deviates from the actual height value by no more than 1.0 cm. The platform’s actual adjusted width deviates from the actual soil ridge width by no more than 2.0 cm, and the platform’s actual adjusted height deviates from the actual soil ridge height by no more than 1.2 cm. This platform can improve the passability, adaptability, and stability of agricultural machinery in soil ridge work and provide technical references for subsequent related research.

Modeling and compensation of a hollow XY stage based on hybrid reluctance actuators

In the field of beam pointing and optoelectronic imaging, there is a growing demand for control mechanisms that offer large stroke, and high bandwidth. These are precisely the characteristics of hybrid reluctance actuator, which therefore is increasingly being used in XY stage to replace piezo-actuator and voice coil motor. This study presents a hollow XY stage utilizing hybrid resistance actuators, aiming to adapt to optical applications with inherent nonlinearity and biaxial coupling, which are further modeled and compensated to mitigate their impacts. Firstly, the basic composition and working principle of the hollow XY stage are described, and the impact of its nonlinearity and biaxial coupling characteristics is elucidated. Secondly, the sources of nonlinearity and biaxial coupling are analyzed, and an analytical model of hybrid reluctance actuators is established. The magnetic leakage coefficient of the model is determined, and the accuracy of the model is verified by Finite Element Method (FEM) and experiment, demonstrating consistency with the analytical model's results. Furthermore, based on the nonlinear and biaxial coupling model, a compensator based on feedback linearization method is established, and verified through experiment. The results show that this method has a significant suppression effect on nonlinear and biaxial coupling, and the tracking error and cross-talk is obviously reduced, which proves the effectiveness of this compensation method.

Debt is Not Always Bad Even in Difficult Economic Conditions

The problem faced by the company is the use of debt that is not according to plan. The purpose of this study is to determine whether debt policy affects company performance. This study was conducted on manufacturing sector companies listed on the Indonesia Stock Exchange in 2018–2023 with a population of 296 companies, the sample used was 73 companies for six years. Thus, the number of samples used in this study was 438. The company performance variable is proxied by (ROE) while the independent variables in this study are debt policy proxied by (DER and DER*KE), investment decisions (KI), managerial ownership (MNJR), and economic conditions (KE). The data analysis method uses multiple linear regression methods with SPSS software version 26. The results of the study show that the debt policy variable (DER) has a negative and significant effect on company performance (ROE) and the debt policy variable*economic conditions (DER*KE) has a positive and significant effect on company performance (ROE). This means that when economic conditions are supportive, companies that use debt to finance operational activities or business expansion tend to experience increased performance. Funds obtained from debt can be used for investment, new product development, or market expansion. Proper use of debt can increase a company's return on equity (ROE), as long as the rate of return on the investment financed by debt is higher than the cost of debt. Timing the debt decision is crucial. In improving economic conditions, companies can take advantage of low interest rates and increase production capacity.

Evaluating the strength of subgrade soil for pavement design: an analysis using support vector regression model optimized by bayesian algorithm

Determining the strength of subgrade soil, crucial for flexible pavement design, traditionally relies on time-consuming and labour-intensive California Bearing Ratio (CBR) tests. The limitations of linear and non-linear regression-based prediction methods have led to an increased importance of soft computing techniques in providing flexible and consistent CBR predictions for practical engineering. This paper proposes a hybrid model that combines the Bayesian Optimization algorithm (BOA) with support vector regression (SVR) to predict CBR for subgrade soil in pavement design. A database of 238 soil test datasets from northern Algeria was utilised to develop the BOA-SVR model. Using the BOA technique to optimize hyperparameters can improve the model’s performance. The developed hybrid model closely matched experimental results, with a coefficient of determination of 89% and a mean squared error of 5.77, indicating high predictive accuracy. Comparative analysis shows that the BOA-SVR model performs better than conventional and other machine learning models in predicting CBR in tested subgrade soils, in terms of accuracy and robustness. Finally, the optimal subset of features from the initial dataset combined with the BOA-SVR model is determined using an exhaustive feature selection method that includes cross-validation. The results of the experiment indicate that the accuracy of the hybrid model that has been developed is improved by a subset of only seven features from the twelve initial features, this allows for the reduction of computing time and storage space.

Experimentally validated passive nonlinear capacitor in piezoelectric vibration applications

Abstract Piezoelectric vibration isolation and energy harvesting applications have been extensively studied in the literature. The studies include linear and nonlinear approaches. Linear methods are simpler but possess inherent limitations. On the other hand, nonlinear ones could perform better over a broader operating frequency range. Nonlinearity can be introduced in the mechanical domain or electrical domain actively or passively. Since electrical components can be on smaller scales compared to mechanical counterparts, inducing nonlinearity on the mechanical system through the electrical domain can be more practical. Moreover, passive structures require no energy supply and controller therefore they are simpler and more reliable than active ones. In this paper, a novel way to attain passive hardening stiffness was suggested by introducing an electrical component in a shunt circuit for passive nonlinear piezoelectric vibration isolation or energy harvesting applications and the induced structural non-linearity is demonstrated experimentally. A passive nonlinear component is suggested to be a hardening capacitor obtained by the P–N junction. An analytic model is derived for parallel connected macro-fiber composite (MFC) piezoelectric material attached bimorph configuration on a cantilever beam and the model is solved numerically. MFC integrated bimorph model, and P–N junction approximate model are presented. The frequency response of the coupled system is obtained by using numerical models and experiments. Both numerical analysis and experiments validated the hardening stiffness effect of the P–N junction. To the best of the authors’ knowledge, this study is the first study to demonstrate that nonlinear capacitance of P–N junctions can be used to attain nonlinearity in a mechanical system.

STAN, a computational framework for inferring spatially informed transcription factor activity.

Transcription factors (TFs) drive significant cellular changes in response to environmental cues and intercellular signaling. Neighboring cells influence TF activity and, consequently, cellular fate and function. Spatial transcriptomics (ST) captures mRNA expression patterns across tissue samples, enabling characterization of the local microenvironment. However, these datasets have not been fully leveraged to systematically estimate TF activity governing cell identity. Here, we present STAN ( S patially informed T ranscription factor A ctivity N etwork), a linear mixed-effects computational method that predicts spot-specific, spatially informed TF activities by integrating curated TF-target gene priors, mRNA expression, spatial coordinates, and morphological features from corresponding imaging data. We tested STAN using lymph node, breast cancer, and glioblastoma ST datasets to demonstrate its applicability by identifying TFs associated with specific cell types, spatial domains, pathological regions, and ligand‒receptor pairs. STAN augments the utility of STs to reveal the intricate interplay between TFs and spatial organization across a spectrum of cellular contexts.