Adjustment Technique Research Articles

A New ECG Steganography Based on Enhanced Coefficient Adjustment Technique and Least Significant Bit Substitution

A New ECG Steganography Based on Enhanced Coefficient Adjustment Technique and Least Significant Bit Substitution

Optimal design and control of hybrid synchronous generator regulating turbine angular speed for turbine protection

In this paper is presented a methodology of systemic design and control of a synchronous generator with hybrid excitation for the generation of wind energy. The design of the generator is made by the analytical method taking into account the interactions of the global system. Indeed, the regulation of the speed of the turbine is ensured by adjustment of the excitation current to regulate the electromagnetic torque and in this case the excitation current is limited to a fixed value since the increase of excitation current leads to an increase generator’s phase currents magnitude. The increase of the phase’s currents of the generator is linked to the increase of the amplitude of the electromotive forces depending on the excitation current. Therefore, to limit the amplitude of the phase’s currents to the value of the design current fixed by the note book specifications for a given power of the wind turbine, it is necessary to limit the excitation current by limiting the supply voltage of the DC-DC converter used to vary the excitation current for the regulation of the electromagnetic torque. This technique of speed adjustment is simpler and less expensive than techniques using mechanical and hydraulic braking systems.

Models for survey nonresponse and bias adjustment techniques

Survey statisticians have been dealing with the issues of nonresponse in sample surveys for many years. Due to the complex nature of the mechanism, so far it has not been easy to find a general solution to this problem. In this paper, several aspects of this topic will be elaborated on: the survey unit nonresponse bias has been examined alternatively by taking response amounts which are fixed initially and also by taking the response amounts as random variables. An overview of the components of the bias due to nonresponse will be performed. Nonresponse bias components are illustrated for each alternative approach and the amount of bias was computed for each case.

High throughput LC/ESI-MS/MS method for simultaneous analysis of 20 oral molecular-targeted anticancer drugs and the active metabolite of sunitinib in human plasma

Many types of oral molecular-targeted anticancer drugs are clinically used in cancer genomic medicine. Combinations of multiple molecular-targeted anticancer drugs are also being investigated, expecting to prolong the survival of patients with cancer. Therapeutic drug monitoring of oral molecular-targeted drugs is important to ensure efficacy and safety. A liquid chromatography/electrospray ionization-tandem mass spectrometry (LC/ESI-MS/MS) has been used for simultaneous determination of these drugs in human plasma. However, the sensitivity of mass spectrometers and differences in the therapeutic range of drugs have rendered the development of simultaneous LC/ESI-MS/MS methods difficult. In this study, a simultaneous quantitative method for 20 oral molecular-targeted anticancer drugs and the active metabolite of sunitinib was developed based on the results of linear range shifts of the calibration curves using four ion abundance adjustment techniques (collision energy defects, in-source collision-induced dissociation, secondary product ion selected reaction monitoring, and isotopologue selected reaction monitoring). The saturation of the detector for the seven analytes was resolved by incorporating optimal ion abundance adjustment techniques. Furthermore, the reproducibility of this method was confirmed in validation tests, and plasma from patients was measured by this method to demonstrate its usefulness in actual clinical practice. This analytical method is expected to make a substantial contribution to the promotion of personalized medicine in the future.

Improved SNS-PFEM framework with dual mortar method to model geotechnical large deformation contact problems

This work proposes an improved stable node-based strain smoothing particle finite element method (SNS-PFEM) with the dual mortar method to model the geotechnical large deformation contact problems. The proposed SNS-PFEM framework requires only basic length parameters for constructing stabilization terms, thus yielding a simpler implementation and less computational cost than the existing methods. Based on this framework, an efficient dual mortar contact algorithm is introduced for finite frictional sliding analysis, utilizing the segment-to-segment discretization and dual Lagrange multiplier method. The resulting stiffness matrix is well-conditioned and positive-defined, leading to higher accuracy and less computational cost than penalty-type methods. Furthermore, a dynamic mesh adjustment technique is proposed to ensure high mesh quality throughout the large deformation process. Since all variables are stored on the node in our SNS-PFEM framework, the process of mesh adjustment is both straightforward and computationally efficient. The precision and robustness of our methods are validated through three classical benchmarks and two representative geotechnical problems. Results show that the proposed SNS-PFEM framework effectively eliminates the over-soft issue seen with the NS-PFEM; the dual mortar method offers an accurate, and efficient performance for nonlinear contact analysis; and the dynamic mesh adjustment technology is effective in preventing mesh quality degradation during large deformations.

A novel AC flatness adjustment technique for wideband resistive voltage dividers

Resistive voltage dividers (RVDs) are commonly used as AC attenuation networks in measurement circuits; however, their high-frequency gain is always disturbed by nasty parasitic elements and deviates significantly from the desired design value. This paper proposes a closed-loop adjustment technique for adjusting the frequency response flatness of wideband RVDs (WRVDs). In the proposed adjustment scheme, the frequency response flatness of the WRVD is determined by a voltage-controlled compensation capacitor, whereas the corresponding control voltage is provided by a digital potentiometer. The settling time and the adjustment error of the loop are analyzed to evaluate the adjustment performance. To verify the concept, the adjustment loop is incorporated into a 100:1 WRVD for demonstration. Final experiments show that the proposed technique improves gain flatness by 81 times compared with uncompensated flatness, with 1.15% up to 2MHz, and that the compensated voltage divider has an excellent linearity of 36 ppm up to 100V.

Meteorological Driving Datasets for the U.S. Midwest and Great Lakes Region Incorporating Precipitation Gauge Undercatch Corrections

Abstract Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16° resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915 to 2021 at a daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the United States due to missing station metadata and lack of local wind speed (WS) measurements. We developed and tested several different postprocessing adjustment techniques using regridded WS obtained from the NCEP–NCAR reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of wind-shielded reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations and corrected Global Historical Climatology Network stations with in situ WS, showing good overall agreement. Observed monthly streamflow at 40 river basins was also compared to hydrologic model simulations forced by datasets with and without PUC corrections. The best PUC-corrected dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (r2, Nash–Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region. Significance Statement Many applications in hydrology require in situ precipitation (P) measurements, which are known to have a systematic low bias due to the effects of wind, also known as precipitation undercatch (PUC). Addressing PUC is problematic in the United States due to limited access to detailed station metadata (SMD) and local wind speed (WS) measurements. In this paper we develop a set of procedures to create gridded precipitation datasets for the U.S. Midwest region that incorporate corrections for PUC without needing either (i) detailed SMD or (ii) local WS measurements. Among other tests, results in 40 test basins throughout the Midwest show substantial improvements in simulated streamflow in 32 out of 40 basins when PUC corrections are included in meteorological driving datasets.

Phase change heat transfer in a vertical metal foam-phase change material thermal energy storage heat dissipator

A metallic foam heat dissipator for cooling electronic components was addressed. A heat dissipator is a partitioned aluminum container loaded along with aluminum metallic foam and saturated with paraffin wax. A heat flux at a surface contains a basic uniform flux and the step transient raise, which should be managed by a heat dissipator and a Phase Change Material (PCM). The regulating equations for a melting/solidification transfer of heat & momentum transport in a heat dissipator were instituted into a structure of partial differential equations. Then, the vital monitoring equations were converted into a general dimensionless type and solved by the Finite Element Method. A mesh adjustment technique & automated time-step control was employed to control the accuracy & convergence of the result automatically. An adaptation technique controls the mesh resolution at the melting/solidification interface. The dimensionless temperature of fusion is a vital factor in the control of the surface temperature and heat dissipator efficiency. Considering a fixed amount of material for walls, a heat dissipator with thick sidewalls and thin top and bottom walls results in slightly better thermal performance. Using a PCM heat sink could reduce the heated surface temperature by >175 % during the pulse load.

Online Search-Based Collision-Inclusive Motion Planning and Control for Impact-Resilient Mobile Robots

This article focuses on the emerging paradigm shift of collision-inclusive motion planning and control for impact-resilient mobile robots, and develops a unified hierarchical framework for navigation in unknown and partially observable cluttered spaces. At the lower level, we develop a deformation recovery control and trajectory replanning strategy that handles collisions that may occur at run time, locally. The low-level system actively detects collisions (via embedded Hall effect sensors on a mobile robot built in-house), enables the robot to recover from them, and locally adjusts the postimpact trajectory. Then, at the higher level, we propose a search-based planning algorithm to determine how to best utilize potential collisions to improve certain metrics, such as control energy and computational time. Our method builds upon A* with jump points. We generate a novel heuristic function, and a collision checking and adjustment technique, thus making the A* algorithm converge faster to reach the goal by exploiting and utilizing possible collisions. The overall hierarchical framework generated by combining the global A* algorithm and the local deformation recovery and replanning strategy, as well as individual components of this framework, are tested extensively both in simulation and experimentally. An ablation study draws links to related state-of-the-art search-based collision-avoidance planners (for the overall framework), as well as search-based collision-avoidance and sampling-based collision-inclusive global planners (for the higher level). Results demonstrate our method's efficacy for collision-inclusive motion planning and control in unknown environments with isolated obstacles for a class of impact-resilient robots operating in 2-D.

Real-Time Globally Consistent 3D Reconstruction with Semantic Priors.

Maintaining global consistency continues to be critical for online 3D indoor scene reconstruction. However, it is still challenging to generate satisfactory 3D reconstruction in terms of global consistency for previous approaches using purely geometric analysis, even with bundle adjustment or loop closure techniques. In this paper, we propose a novel real-time 3D reconstruction approach which effectively integrates both semantic and geometric cues. The key challenge is how to map this indicative information, i.e. semantic priors, into a metric space as measurable information, thus enabling more accurate semantic fusion leveraging both the geometric and semantic cues. To this end, we introduce a semantic space with a continuous metric function measuring the distance between discrete semantic observations. Within the semantic space, we present an accurate frame-to-model semantic tracker for camera pose estimation, and semantic pose graph equipped with semantic links between submaps for globally consistent 3D scene reconstruction. With extensive evaluation on public synthetic and real-world 3D indoor scene RGB-D datasets, we show that our approach outperforms the previous approaches for 3D scene reconstruction both quantitatively and qualitatively, especially in terms of global consistency.

On the Weights for Characteristics and Comparables for Property Valuation using Quality Rating Valuation Estimation

This study considers the problem of finding weights for building characteristics and compares buildings in property valuation to provide a more rigorous analytical foundation for a simple yet practical valuation technique knowns as Quality Rating Valuation Estimation (QRVE). Mathematically, we prove that the “best” characteristic weights can be obtained from Multiple Linear Regression Analysis (MRA) coefficients. Furthermore, by applying the Gower Similarity index and the Partition Around Medoid (PAM) clustering technique, the proposed algorithm provides an appropriate similarity of the weighing of compared buildings. The case studies illustrate a way to select a subset of characteristics when there are many of them with two numerical examples, as well as a complete modification of QRVE in conjunction with the grid adjustment technique. The modified QRVE proposal results in a very reasonable and high valuation performance of the building value estimate.

Evaluation of surface reflectance bandpass adjustment techniques

There is an increasing number of on-orbit earth observation sensors in the solar domain (visible to middle infrared). While most the sensors are designed with apparent analogous bandpasses, they often exhibit differences in terms of Relative Spectral Response (RSR). Although the differences are well characterized, there is a need for techniques to adjust for the effect of these differences on surface reflectance after accounting for calibration, directional, and atmospheric effects. This research evaluated eight bandpass adjustment techniques using synthetic and actual remote sensing observations on seven widely used sensors. The scope of the study is global.The eight evaluated techniques were: a) spectral band adjustment factor technique (SBAF, i.e., one-coefficient regression), b) linear fit technique (i.e., two-coefficient regression), c) three Adaptive SBAF techniques (ASBAF), which are defined according to three different cost functions and apply a target-dependent SBAF, d) two artificial neural network approaches using (i) a single band (ANN-1B) and (ii) all bands (ANN-NB) as inputs, and e) a Classification and Regression Tree method (CART) using all bands as inputs. All these techniques require a calibration phase, which was carried out separately using three hyperspectral data sets: (i) USGS spectral measurements, (ii) simulated reflectance by the ProSail radiative transfer model, and (iii) real-world Hyperion observations. The full data set is publically available.The evaluation using the three hyperspectral data sets alone showed that the techniques with a single band as input (SBAF, LIN, and ANN-1B) generated higher uncertainty than the techniques with all bands as input (ASBAF, ANN-N, CART). The ASBAF techniques performed well with a few differences among the three ASBAF variants. The ANN-N was the technique that performed the best. The evaluation based on actual remote sensing data sets (Landsat-8 and Sentinel-2 data were used) suggested that all techniques reduced the deviation between the two data sets. Nonetheless, the deviation between the two data sets caused by factors other than RSR differences did not permit a clearcut comparison of the techniques. Some recommendations regarding the techniques to select for a particular application are provided.Finally, this research introduces the concept of RSR difference index (RDI) to quantify the spectral differences between two spectral bands.

Forecasting of Post-Graduate Students’ Late Dropout Based on the Optimal Probability Threshold Adjustment Technique for Imbalanced Data

The purpose of this research article was to contrast the benefits of the optimal probability threshold adjustment technique with other imbalanced data processing techniques, in its application to the prediction of post-graduate students’ late dropout from distance learning courses in two universities in the Ibero-American space. In this context, the optimization of the Logistic Regression, Random Forest, and Neural Network classifiers, together with different techniques, attributes, and algorithms (Hyperparameters, SMOTE, SMOTE_SVM, and ADASYN) resulted in a set of metrics for decision-making, prioritizing the reduction of false negatives. The best model was the Neural Network model in combination with SMOTE_SVM, obtaining a recall index of 0.75 and an f1-Score of 0.60. Likewise, the robustness of the Random Forest classifier for imbalanced data was demonstrated by achieving, with an optimal threshold of 0.427, very similar metrics to those obtained by the consensus of the three best models found. This demonstrates that, for Random Forest, the optimal prediction probability threshold is an excellent alternative to resampling techniques with different optimal thresholds. Finally, it is hoped that this research paper will contribute to boost the application of this simple but powerful technique, which is highly underrated with respect to data resampling techniques for imbalanced data.

An Efficient Methodology for Detecting the Vertical Movement of Structures

Details regarding the public safety of engineering structures can be gleaned from measurements and monitoring. The development of a methodology for monitoring and analyzing structures' vertical displacement is explained in this paper. The developed methodology aims to add a new dimension to geometric leveling, and leveling routing, by applying a least squares solution for level network adjustment and performing statistical analysis to assess the change in vertical displacement. To monitor and analyze the vertical deformation of a building in Cairo, Egypt, the proposed methodology was utilized. Twenty monitoring points, five auxiliary points, and three local reference stations were utilized. All the measurements were taken with a geodetic invar staff and an automatic level with an attachment of a parallel plate micrometer. The observations were made for an interval of 81 months. The least squares adjustment technique was applied to obtain the adjusted levels and observations and to generate the required statistical data. The results of the subsequent epochs were compared to the results of the first epoch to determine the vertical movement of the monitoring points for each epoch. In addition, the significance of the present displacement was ascertained by comparing the values of vertical displacement to the determined 95% corresponding confidence intervals. The findings demonstrated that the building remained stable throughout the monitoring period. The case study demonstrates how effectively geometric leveling with least square adjustment can be used to monitor the vertical displacement of structures.

Detection of microplastics in human saphenous vein tissue using μFTIR: A pilot study.

Microplastics (MPs) are ubiquitous in the environment, in the human food chain, and have been recently detected in blood and lung tissues. To undertake a pilot analysis of MP contamination in human vein tissue samples with respect to their presence (if any), levels, and characteristics of any particles identified. This study analysed digested human saphenous vein tissue samples (n = 5) using μFTIR spectroscopy (size limitation of 5 μm) to detect and characterise any MPs present. In total, 20 MP particles consisting of five MP polymer types were identified within 4 of the 5 vein tissue samples with an unadjusted average of 29.28 ± 34.88 MP/g of tissue (expressed as 14.99 ± 17.18 MP/g after background subtraction adjustments). Of the MPs detected in vein samples, five polymer types were identified, of irregular shape (90%), with alkyd resin (45%), poly (vinyl propionate/acetate, PVAc (20%) and nylon-ethylene-vinyl acetate, nylon-EVA, tie layer (20%) the most abundant. While the MP levels within tissue samples were not significantly different than those identified within procedural blanks (which represent airborne contamination at time of sampling), they were comprised of different plastic polymer types. The blanks comprised n = 13 MP particles of four MP polymer types with the most abundant being polytetrafluoroethylene (PTFE), then polypropylene (PP), polyethylene terephthalate (PET) and polyfumaronitrile:styrene (FNS), with a mean ± SD of 10.4 ± 9.21, p = 0.293. This study reports the highest level of contamination control and reports unadjusted values alongside different contamination adjustment techniques. This is the first evidence of MP contamination of human vascular tissues. These results support the phenomenon of transport of MPs within human tissues, specifically blood vessels, and this characterisation of types and levels can now inform realistic conditions for laboratory exposure experiments, with the aim of determining vascular health impacts.

Efficacy and outcomes of pulmonary artery banding: Our experience in Azerbaijan

Aim: We conducted a retrospective analysis of 35 patients with various congenital heart defects who underwent pulmonary artery banding (PAB) between January 2018 and September 2023. Our objective was to evaluate the outcomes of PAB and discuss its application in a staged approach for these patients. Material and Methods: The median age at PAB was 5 months (range: 2 to 20 months) with a median weight of 5.2 kg (range: 3 to 9 kg). Results: The hospital and total mortality rates were 11.4% (4). The median intensive care unit stay was 2 days (range: 1 to 22 days), and the median hospital stay was 5 days (range: 5 to 36 days). The second stage (either total correction or cava-pulmonary anastomosis) was achieved in 40% (14/35) of patients at a median age of 15 months. The mean interval to repair was 8 months, with 12 out of 14 patients undergoing complete biventricular repair. Conclusion: Our study demonstrates that PAB could be performed with acceptable outcomes, serving as a viable palliative measure for neonates and extremely small infants who are at high risk for cardiopulmonary bypass. Our study results exhibit a comparable trend to previously reported findings, emphasizing the importance of PAB in managing congenital heart malformations characterized by increased pulmonary blood flow. While the effectiveness of PAB has been validated in our study, future investigations could delve into refining surgical protocols, optimizing band adjustment techniques, and exploring potential preventive measures for common postoperative complications.

Development of Robot Educational Tools Using LEGO DUPLO and Bread Board, and Robot Practice through Trial-and-Error Process

Robots are used in many industries, and it is important to educate students about robot technology. In order to utilize robots in industry, it is necessary to construct robot systems that combine arms, sensors, and other components. In addition, programming and sensor adjustment techniques are required to operate the robot. Therefore, we developed the educational tools using LEGO DUPLO and bread board that allow students to practice building robotic systems through a trial-and-error process. We report on the high educational effect of this tools in robot practice.

Performance evaluation of different seasonal adjustment techniques

The central Bank of Bangladesh publishes financial data on weekly, quarterly, monthly, yearly basis. Among the published data, GDP, production, export, import, balance payment, wage earners remittance, Stock etc. are time-based which often subject to seasonal variation. But like most of the developed country's central bank such as USA, Australia, Singapore, UK, French etc. Bangladesh Bank does not publish seasonal adjusted data. These published time series are used by economists, decision-makers, and consumers to inform decisions. They look for key characteristics of economic series like trend, turning points, and coherence with other economic data. Seasonal shifts might occasionally make it challenging to see these features. Therefore, the research aimed to explore the best seasonal adjustment technique for different financial data published by Bangladesh Bank. Here, non-filter-based seasonal adjustment technique: Ratio to moving average, Ratio to trend, and SARIMA and filter-based technique: X-11 and X-13-ARIMA-SEATS was applied on monthly import and export data published by Bangladesh Bank. The seasonal adjustment method that produces fewer forecasting errors (MAPE, MASE, MAE, and RMSE) were decided as best technique for seasonal adjustment and X-13-ARIMA-SEATS method founds the appropriate seasonal adjustment method for our considered data. Therefore, X-13-ARIMA-SEATS method can be recommended to forecast monthly export and import of Bangladesh published by Bangladesh Bank.

Generation of Optimal Load Paths for Corroded Reinforced Concrete Beams Part I: Automatic Stiffness Adjustment Technique

Generation of Optimal Load Paths for Corroded Reinforced Concrete Beams Part I: Automatic Stiffness Adjustment Technique

Impact of off-farm employment on cooking fuel choices: Implications for rural-urban transformation in advancing sustainable energy transformation

Off-farm employment is a prominent manifestation of rural-urban transformation in developing countries, and it theoretically plays a critical role in rural sustainable energy transformation. Exploiting the nationally representative household-level panel data from the China Labor-force Dynamics Survey (CLDS), this study employs a multi-dimensional fixed-effects (MDFE) model, an inverse probability weighted regression adjustment (IPWRA) technique and a propensity score matching (PSM) approach to investigate the impact of off-farm employment on cooking fuel choices. The results show that off-farm employment exerts a positive and significant impact on the probability of adopting clean fuels for cooking, with specific reductions in biomass and coal and increases in electricity and gas. Furthermore, this effect is mainly realized by reducing the amount of available labor, the number of family meals, and increasing kitchen equipment purchases. However, the relationship between off-farm employment and cooking fuel choices varies considerably across the region, village and household groups. Off-farm employment mainly benefits the eastern, central and western regions of China, villages in plain areas, with brick-concrete houses and ethnic Han Chinese, households with children or the elderly, with poor health and more females, while it is less beneficial for others. The study highlights the important policy implications of rural-urban transition for sustainable energy transition through the creation of off-farm jobs.