Improved Method Research Articles

Evaluation of Soil Quality of Pingliang City Based on Fuzzy Mathematics and Cluster Analysis

Pingliang City has a complex topography and diverse soil types. To realize the improvement of soil according to local conditions and the reasonable and sustainable use of soil resources, an evaluation of soil quality in Pingliang City was carried out, based on the soil distribution situation in Pingliang City, adopting a method combining fuzzy mathematics and cluster analysis of the main evaluation factors, such as soil organic matter, topsoil depth, soil erosion intensity, soil moisture regime, effective soil thickness, soil texture, soil profile structure, soil nutrient status and topographical parts, to carry out a comprehensive evaluation. A comprehensive evaluation of soil quality was conducted in seven counties under the jurisdiction of Pingliang City, and the evaluation results were compared and analyzed against the national standard, “Cultivated land quality grade”, to provide a basis for the selection of scientific soil improvement methods. The results of the arable land quality grades indicate that the quality of farmland in Pingliang City is divided into three to ten grades, and the average quality grade of farmland is 6.83, which is in the middle–lower level, and the overall grade distribution shows the characteristics of low in the middle and high in the east and west. The results of fuzzy mathematics combined with cluster analysis indicated the following trends in soil quality for the 12 soil genera: Chuan black gunny soil > yellow moist soil > sandy soil > silt soil > mulching helilu soil> loessal soil> loamy soil > slope loessal soil > arenosol > tillage leaching gray cinnamon soil > calcareous gray cinnamon soil > red clay soil. The results of the combination of fuzzy mathematics and clustering were significantly correlated with the results of the evaluation of the soil quality of arable land; the correlation coefficient was 0.884. This indicates that the method can accurately and objectively review the advantages and disadvantages of arable land soil and can be effectively applied to the evaluation of the soil quality of agricultural soils in other regions. It is a complement to the existing evaluation of the soil quality of arable land and at the same time provides a reference for the improvement of soil quality in agricultural regions.

Stabilization of Pavement Subgrade Clay Soil Using Sugarcane Ash and Lime

Soft to medium clay soil possesses major sources of damages to the pavement layers overlying them because of their potential failure under moisture changes and external heavy traffic load. In such situations, soil stabilization methods can be used to improve the soil properties and satisfy the desired engineering requirements. This study presents the use of sugarcane bagasse ash (SBA) and lime as chemical stabilizers for a clay soil subbase. Sugarcane bagasse ash and lime are used individually and as mixtures at varying percentages to stabilize a clay soil from Taxila, Pakistan. Various geotechnical laboratory tests such as Atterberg limits, compaction test, and California Bearing Ratio (CBR) are carried out on both pure and stabilized soils. These tests are performed at 2.5%, 5%, and 7.5% of either SBA or lime by weight of dry soil. In addition, mixtures of lime and SBA in ratios of 1:1, 2:1, 3:1, 1:2, and 1:3 are used in 5%, 7.5%, and 10% of dry soil weight, respectively. Results indicate that soil improved with 7.5% SBA showed a 28% increase in the liquid limit, while soil mixed with 2.5% lime in combination with 7.5% SBA showed an increase of 40% in the plastic limit. For the plasticity index, the soil mixed with 7.5% SBA showed an increase of 42%. Moreover, 2.5% lime in combination with 2.5% SBA showed the best improvement in soil consistency as this mixture reduced the soil plasticity from high to low according to the plasticity chart. Furthermore, 2.5% SBA in combination with 5% lime demonstrated the largest improvement on the CBR value, which is about a 69% increase above that of the pure soil. Finally, the cost analysis indicates a promising improvement method that reduces pavement cost, increases design life, and mitigates issues of energy consumption and pollution related to SBA as a solid waste material.

Cognitive testing of the Children’s Palliative Outcome Scale (C-POS) with children, young people and their parents/carers

Background: The Children’s Palliative Outcome Scale (C-POS) is being developed using best methodological guidance on outcome measure development, This recommends cognitive testing, an established method of item improvement, prior to psychometric testing. Aim: To cognitively test C-POS within the target population to establish comprehensibility, comprehensiveness, relevance and acceptability. Design: Cross-sectional cognitive interview study following COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methodology and Rothrock guidance on outcome measure development. Cognitive interviews were conducted using ‘think aloud’ and verbal probing techniques. Setting/participants: Children 5–⩽17 years old with life-limiting conditions and parents/carers of children with life-limiting conditions were recruited from 14 UK sites. Results: Forty-eight individuals participated (36 parents; 12 children) in cognitively testing the five versions of C-POS over two to seven rounds. Content and length were acceptable, and all questions were considered important. Refinements were made to parent/carer versions to be inclusive of non-verbal children such as changing ‘share’ to ‘express’ feelings; and ‘being able to ask questions’ to ‘having the appropriate information’. Changes to improve comprehensibility of items such as ‘living life to the fullest’ were also made. Parents reported that completing an outcome measure can be distressing but this is anticipated and that being asked is important. Conclusion: Cognitive interviewing has facilitated refinement of the C-POS, especially for non-verbal children who represent a large proportion of those with a life-limiting condition. This study has enhanced the face and content validity of the measure and provided preliminary evidence for acceptability for use in routine practice.

Choc-Tadalafil Fusion: Unlocking Solubility and Taste Harmony with ß-CD-Infused Medicated Chocolate.

The primary limitations of tadalafil in treating erectile dysfunction are its low solubility and unpleasant bitter taste, which ultimately result in inadequate patient adherence. The present study aimed to develop and characterize a medicated chocolate formulation containing Tadalafil and ß-CD (solubility enhancer) employing the concept of Design of Experiment (DoE) using chocolate as a user-friendly excipient. An inclusion complex was formulated by incorporating the drug into ß-CD using the kneading method for solubility improvement and also as a taste masker for Tadalafil. The ratio of drug: ß-CD inclusion complex was selected based on a phase solubility study. The inclusion complex was molded into a chocolate base and optimized using the DoE approach. Further, drug excipient interaction was evaluated by DSC and FTIR study. Phase solubility study suggested a 1:1 ratio of Tadalafil: β-CD for better solubility. DSC spectra suggested the conversion of crystalline structure into an amorphous state which indicates improvement of the drug solubility. DSC and FTIR studies revealed that there was no significant interaction between drug and excipients. Next, %CDR (cumulative drug release) at 30 min revealed the immediate effect of Tadalafil from chocolate formulation and free drug analysis (an unbound drug with ß-CD) proved reduced bitterness of the drug in the complex. Additionally, the medicated chocolate was found to be stable at room temperature as per stability study. β-CD was found to be a promising multifunctional excipient as a solubility enhancement carrier and taste masker for bitter-tasting drugs.

Genomic prediction of growth traits in autotetraploid Pacific oyster (Crassostrea gigas) using whole-genome resequencing

Autotetraploid Crassostrea gigas plays an important role in C. gigas artificial breeding because of their ability to stably produce triploid C. gigas with a fast growth rate, delicious meat, and abundant nutrients. There is an urgent need for autotetraploid C. gigas germplasm preservation and genetic improvement methods to ensure the sustainable development of the autotetraploid population. Recently, genomic selection (GS) has been widely applied in autotetraploid plants, significantly shortening the breeding cycle and promoting genetic improvement, but it has not been applied to autotetraploid C. gigas. This study is the first to apply GS methods for genomic prediction of five growth traits (e.g., shell length, shell height, shell width, total weight, and soft tissue weight) in autotetraploid C. gigas real dataset using whole-genome resequencing. Compared to diploid C. gigas, the optimal genotyping of whole-genome resequencing and the SNP encoding model of autotetraploid C. gigas are more complex. Thus, we investigated the SNP calling abilities of two alignment methods (e.g., BWA and Bowtie 2) and two genotyping methods (e.g., GATK and Freebayes) for whole-genome resequencing. Then, we also evaluated the ability of four potential SNP encoding models (e.g., additive, diploidized additive, simplex dominant, and duplex dominant) to estimate the heritability and genomic prediction accuracy of the growth traits in autotetraploid C. gigas, finding that the additive model was the most reliable SNP encoding model. Lastly, four linear models (e.g., Bayes A, Bayes B, Bayes C, and GBLUP), as well as one nonlinear model (e.g., RKHS), were tested to estimate the accuracy of breeding values of growth traits in autotetraploid C. gigas. Overall, GBLUP outperformed all the other four methods on C. gigas growth traits. The impact of varied SNP densities on genomic prediction accuracy was investigated as well. Our results will provide evidence that supports germplasm protection and molecular breeding in autotetraploid C. gigas.

Reducing IV Antibiotic Duration for Neonatal UTI Using a Clinical Standard Pathway.

Urinary tract infections (UTIs) are the most common bacterial infections in young infants and are traditionally treated with longer intravenous (IV) antibiotic courses. A growing body of evidence supports shorter IV antibiotic courses for young infants. Our primary aim was to decrease the IV antibiotic treatment to 3 days over 2 years for neonates aged 0 to 28 days who have been hospitalized with UTIs. Using quality improvement methods, our primary intervention was to implement a revised clinical pathway recommending 3 (previously 7) days of IV antibiotics. Our primary outcome measure was IV antibiotic duration, and the secondary outcomes were length of stay (LOS) and costs. The balancing measure was readmission within 30 days of discharge. Neonates were identified by using International Classification of Diseases diagnosis codes and excluded if they were admitted to the ICU or had a LOS >30 days. We used statistical process control to analyze outcome measures for 4 years before (baseline) and 2 years after the pathway revision (intervention) in February 2020. A total of 93 neonates were hospitalized with UTIs in the baseline period and 41 were hospitalized in the intervention period. We found special cause variation, with a significant decrease in mean IV antibiotic duration from 4.7 (baseline) to 3.1 days (intervention) and a decrease in mean LOS from 5.4 to 3.6 days. Costs did not differ between the baseline and intervention periods. There were 7 readmissions during the baseline period, and 0 during the intervention period. The implementation of a revised clinical pathway significantly reduced IV antibiotic treatment duration and hospital LOS for neonatal UTIs without an increase in hospital readmissions.

Fairness Improvement Method Using Explicit Congestion Notification for QUIC

Fairness Improvement Method Using Explicit Congestion Notification for QUIC

Improved vigenere using affine functions surrounded by two genetic crossovers for image encryption

This paper presents an improved method for encrypting color images, surpassing the effectiveness of genetic crossover and substitution operations. The technique incorporates dynamic random functions to enhance the integrity of the resulting vector, increasing temporal complexity to thwart potential attacks. The improvement involves integrating genetic crossover and utilizing two extensive pseudorandom replacement tables derived from established chaotic maps in cryptography. Following the controlled vectorization of the original image, our approach initiates with a first genetic crossover inspired by deoxyribonucleic acid (DNA) behavior at the pixel level. This genetic crossover is succeeded by a confusion-diffusion lap, reinforcing the connection between encrypted pixels and their neighboring counterparts. The confusion-diffusion process employs dynamic pseudorandom affine functions at the pixel level. Then a second genetic crossover operator is applied. Simulations conducted on a diverse set of images with varying sizes and formats showcase the robustness of our method against statistical, brute-force, and differential attacks.

Implementing Inpatient Social Needs Screening in an Urban Tertiary Care Children's Hospital.

The American Academy of Pediatrics recommends screening for unmet social needs, and the literature on inpatient screening implementation is growing. Our aim was to use quality improvement methods to implement standardized social needs screening in hospitalized pediatric patients. We implemented inpatient social needs screening using the Model for Improvement. An interprofessional team trialed interventions in a cyclical manner using plan-do-study-act cycles. Interventions included a structured screening questionnaire, standardized screening and referrals workflows, electronic health record (EHR) modifications, and house staff education, deliberate practice, and feedback. The primary outcome measure was the percentage of discharged patients screened for social needs. Screening for social needs was defined as a completed EHR screening questionnaire or a full social work evaluation. Process and balancing measures were collected to capture data on screening questionnaire completion and social work consultations. Data were plotted on statistical process control charts and analyzed for special cause variation. The mean monthly percentage of patients screened for social needs improved from 20% at baseline to 51% during the intervention period. Special cause variation was observed for the percentage of patients with completed social needs screening, EHR-documented screening questionnaires, and social work consults. Social needs screening during pediatric hospitalization can be implemented by using quality improvement methods. The next steps should be focused on sustainability and the spread of screening. Interventions with greater involvement of interdisciplinary health care team members will foster process sustainability and allow for the spread of screening interventions to the wider hospitalized pediatric population.

Electrosorption-Driven Selective Thorium Removal from Radioactive Wastewater with Phosphate – Incorporated g-C3N4 Electrode

This research introduces a novel approach for developing an improved method for removing thorium ions (Th(IV)), termed as electrosorption, by fusing the principles of electricity and adsorption. The methodology involves utilizing a phosphate – incorporated graphitic carbon nitride (g-C3N4/P) electrode, synthesized via a hydrothermal – calcination process with urea as a precursor and bis (2-ethylhexyl) phosphoric acid (D2EHPA) as a dopant. Static batch experiments were conducted to explore the influence of various parameters, including voltages, initial Th(IV) concentrations and operational time, on the electrosorption of Th(IV). Characterization analysis including XRD, FT-IR, FESEM-EDS and XPS were conducted to validate the successful integration of phosphate into the g-C3N4 structure. The g-C3N4/P electrode demonstrated impressive adsorption capacity for Th(IV) electrosorption in an aqueous solution containing an initial concentration of Th(IV) at 100mg.L-1. It was observed that applying a potential as low as -0.2V resulted in significantly increase in the Th(IV) removal with 92.23% (> 300mg.g-1) within 30minutes of duration, compared to cases without the electrochemical assistance (65.41mg.g-1). Through FT-IR and XPS analyses, the adsorption mechanism was clarified as follows: (1) the negatively charged g-C3N4/P electrode attracted Th(IV) ions through electrostatic interaction; and (2) Th(IV) ions was coordinated with exposed oxygen (O), nitrogen (N) and phosphorus (P) atoms via complexation. In the context of real wastewater scenarios involving rare earth residue, encompassing Ce(III), La(III), Nd(III) and Pr(III) ions, the g-C3N4/P electrode showcased good capabilities in selectively adsorbing Th(IV). Additionally, the electrode synthesized in this study displayed considerable regenerative capabilities, sustaining a commendable removal ratio of approximately 70% over five successive cycles. These findings underscore the promising potential of g-C3N4/P electrode for thorium removal through electrosorption applications.

Optimization and Improvement of Three-phase Asynchronous Motor Soft Starter Control Circuit

Abstract The soft starters of ABB company are used in a pumped storage power station. Sometimes, a phase absent fault was reported during the process of the motor starting and then led to starting failure. To solve this problem, this article first explained the working principle of the device, and then analyzed the design of the control circuit and the characteristics of the components. Combined with the actual inspection results to find the crux of the problem, It proposed optimization and improvement methods for the soft starter control circuit, and achieved the desired results.

Research on optimization and improvement method of new energy access grid stability based on transient stability margin index

Abstract This paper focuses on the potential local voltage stability risks and transient stability margin decline in the power grid after the large-scale integration of new energy. Based on the transient stability margin index of the power grid, a method for evaluating and optimizing the transient stability margin after the integration of wind power into the power grid is proposed. This paper provides guidance for the grid connection points and voltage level determination of the wind power access system during the planning phase, as well as the configuration of reactive power compensation devices such as SVGs.

ISight - Navigable Path Detection for Visually Impaired using Semantic Segmentation

Abstract: This project introduces a new educational service that uses machine learning algorithms in Python and the OpenCV library to detect and prevent tampering, providing blind people with better freedom and personal security. Thanks to the integration of real-time detection equipment, the system can identify various hazards in different locations without using conventional equipment. Topics covered include optimizing algorithms for immediate use and maximizing efficiency for portable devices. Python supports rapid development, while OpenCV's modularity enables scalable development. This approach performs existing processes accurately, quickly and efficiently, using flexible methods for continuous improvement. Future developments include the integration of advanced learning models and user insights to provide more accurate feedback and pave the way for new products.

Performance evaluation of MeteoTracker mobile sensor for outdoor applications

Abstract. The morphological complexity of urban environments results in a high spatial and temporal variability of the urban microclimate. The consequent demand for high-resolution atmospheric data remains a challenge for atmospheric research and operational application. The recent widespread availability and increasing adoption of low-cost mobile sensing offer the opportunity to integrate observations from conventional monitoring networks with microclimatic and air pollution data at a finer spatial and temporal scale. So far, the relatively low quality of the measurements and outdoor performance compared to conventional instrumentation has discouraged the full deployment of mobile sensors for routine monitoring. The present study addresses the performance of a commercial mobile sensor, the MeteoTracker (IoTopon Srl), recently launched on the market to quantify the microclimatic characteristics of the outdoor environment. The sensor follows the philosophy of the Internet of Things technology, being low cost, having an automatic data flow via personal smartphones and online data sharing, supporting user-friendly software, and having the potential to be deployed in large quantities. In this paper, the outdoor performance is evaluated through tests aimed at quantifying (i) the intra-sensor variability under similar atmospheric conditions and (ii) the outdoor accuracy compared to a reference weather station under sub-optimal (in a fixed location) and optimal (mobile) sensor usage. Data-driven corrections are developed and successfully applied to improve the MeteoTracker data quality. In particular, a recursive method for the simultaneous improvement of relative humidity, dew point, and humidex index proves to be crucial for increasing the data quality. The results mark an intra-sensor variability of approximately ± 0.5 °C for air temperature and ± 1.2 % for the corrected relative humidity, both of which are within the declared sensor accuracy. The sensor captures the same atmospheric variability as the reference sensor during both fixed and mobile tests, showing positive biases (overestimation) for both variables. Through the mobile test, the outdoor accuracy is observed to be between ± 0.3 to ± 0.5 °C for air temperature and between ± 3 % and ± 5 % for the relative humidity, ranking the MeteoTracker in the real accuracy range of similar commercial sensors from the literature and making it a valid solution for atmospheric monitoring.

Theory-based cryopreservation mode of mesenchymal stromal cell spheroids

Cryopreservation of spheroids requires development of new improved methods. The plasma membranes permeability coefficients for water and cryoprotectants determine time characteristics of mass transfer through the cell membranes, and therefore the optimal modes of cells cryopreservation. Here we proposed an approach to cryopreservation of multicellular spheroids which considers their generalized characteristics as analogues of the membranes’ permeability coefficients of the individual cells. We have determined such integral characteristics of spheroids from mesenchymal stromal cells (MSCs) as osmotically inactive volume; permeability coefficients for water and Me2SO molecules and the activation energy of their penetration. Based on these characteristics, we calculated the osmotic behavior of multicellular spheroids under cooling conditions to select the optimal cooling rate. We also determined the optimal cooling rate of spheroids using the probabilistic model developed based on the two-factor theory of cryodamage. From the calculation it follows that the optimal cooling rate of the MSC-based spheroids is 0.75°С/min. To verify the obtained theoretical estimates, we conducted experiments on freezing MSC-based spheroids under different modes. The obtained results of primary viability screening indicate that freezing at a constant linear cooling rate of 0.75–1.0°С/min gives a good result. Theoretical prediction of the spheroid osmotic behavior during cooling provided the basis for experimental verification of varying the temperature to which slow cooling should be carried out before immersion in liquid nitrogen. Slow freezing of spheroids to −40 °C followed by immersion in liquid nitrogen was shown to preserve cells better than slow freezing to −80 °C. Obtained data allow more effective use of MSC-based spheroids in drug screening and regenerative medicine.

Field test of machine-learning based mean radiant temperature estimation methods for thermal comfort-integrated air-conditioning control improvement and energy savings

Advanced control strategies for heating, ventilation, and air-conditioning (HVAC) systems aim to enhance both buildings' energy efficiency and occupant thermal comfort. Despite their potential, real-world demonstration studies on such advanced technologies are still lacking. This study presents a field demonstration of a real-time predicted mean vote (PMV)-based HVAC control strategy in a typical residential building under hot and dry climate conditions. This study introduces an advanced thermal comfort-based controller (TCC) for the PMV-based HVAC control. TCC continually assesses indoor and outdoor thermal conditions and adjusts the HVAC setpoint temperature to optimize real-time energy use while ensuring satisfactory indoor thermal comfort. Machine learning models for the PMV-based HVAC control system are employed to estimate a mean radiant temperature (MRT) point, which is one of the variables used to calculate real-time PMV values. Three machine learning models (i.e., linear regression, regression trees, and artificial neural network) are adopted in this study with non-stationary real-time input values, including times, pre-determined setpoints, and indoor and outdoor temperatures. The developed TCC is installed in a full-scale experimental house in Kuwait, which is under hot and dry climate conditions, to assess the house's indoor thermal comfort and AC energy efficiency performance. Results indicate that the TCC provides better thermal comfort performance compared to the non-TCC case, including up to a 60% improvement in PMV. The proposed TCC controlled by the machine learning methods demonstrates HVAC energy savings potential of over 20% while meeting the desired thermal comfort levels in the experimental building. These findings are expected to be valuable, as they can contribute to reducing cooling energy in residential buildings in hot and dry climate conditions.

Assessing and segmenting salt-affected soils using in-situ EC measurements, remote sensing, and a modified deep learning MU-NET convolutional neural network

A study revealed that the Siwa Oasis faces high soil salinity, which negatively impacts agricultural areas and crop productivity, despite its significant economic and agricultural importance. The current research proposes an approach to detect and segment salinity and vegetation areas at Siwa Oasis, Egypt, by combining remote sensing and building a deep learning neural network model-based U-NET algorithm to detect salinity change areas, anticipate further degradation, and predict soil quality indicators. To locate changes among the available images, standard image improvement, classification, and change detection methods have been used. We applied a deep learning modified U-Net (MU-NET) algorithm to segment and produce salinity maps. The MU-NET architecture is a two-level nested U-structure merged with a residual U-block (RUb), which consists of an encoder and a decoder. We applied RUb, which consists of several layers and skip connections. Different combinations of the salinity and vegetation indices were added to the original image to improve segmentation precision. The model was validated and trained using actual data samples collected over a 10-year period from the Landsat 8 satellite, which can monitor and analyse present land cover changes. The dataset consisted of 91 OLI and TIRS spectral images. Each one consists of eleven bands with a spatial resolution of 30 m for bands 1 to 7 and 9. A field survey was used as the main source of data for comparing the proposed model's outputs to assess the error rate. The study region is experiencing an increase in soil salinity in all directions, particularly with regard to the spatial distribution of saline soils, not just the quantitative increase in salt-affected soils. These findings supported the acceleration of soil salinization and vegetation death. The proposed model achieved the highest performance results among the other models and literature and was based on applying method 12 using 13 image layers, with the highest accuracies of 91.27% and 90.83% for salinity and vegetation, respectively.

Collapse analysis and robustness assessment of dropped-story steel frame structures based on structural vulnerability theory

ABSTRACT This paper proposes an improved analysis method based on the theory of structural vulnerability, which incorporates structural redundancy and comprehensively analyzes structural vulnerability and robustness by considering the changes in structural stiffness and redundancy after damage to structural members under conventional loading. The improved method takes into account the changes in the strain energy of the structure after the damage of the structural members, which can be more in line with the actual situation of continuity collapse, and solves the complex process of traditional structural vulnerability theory that requires clustering and unclustering, greatly reducing the workload as well as solving the problem of not being able to find the critical members of the structure. The improved method is used to analyze the vulnerability of a 5-story dropped-story steel frame structure, to find the critical members of the structure based on the test data simulated by ABAQUS, to evaluate the robustness, and to propose improvement measures. The results show that the improved vulnerability assessment method is more reasonable and effective and can be applied to different steel frame structures.

Evaluating past and future building operational emissions: improved method

Evaluating past and future building operational emissions: improved method

The vertical accuracy improvement method considering gravitational anomaly for SINS/GNSS

Vertical information (vertical velocity and altitude) is important in three-dimensional navigation. In order to improve the vertical accuracy of strapdown inertial navigation system/global navigation satellite system integrated navigation (SINS/GNSS) while reducing costs, firstly, the propagation laws of vertical error sources in two types of SINS/GNSS (vertical velocity set to 0 and not set to 0) are systematically analyzed. Furthermore, a vertical accuracy improvement method considering the gravitational anomaly is proposed. In this method, the gravitational anomaly is considered as one of the vertical error sources. Then, the processing method of error sources in integrated navigation is referenced, and two processing modes of gravitational anomaly are designed. The first method is to represent the gravitational anomaly as one of the system states of SINS/GNSS. The number of dimensions for the system is expanded from 15 to 16. The corresponding mathematical model is derived to ‘absorb’ the vertical errors caused by the gravitational anomaly. The second is to represent the gravitational anomaly as one of the vertical system noises. Thereby, the Kalman filter is adjusted in real time using the above adaptive method to improve the accuracy of the estimated state. The corresponding errors are then suppressed. Field experiments show that both modes of the proposed method can effectively improve the vertical accuracy.