Adjustment Technique Research Articles

Tilt adjustment strategy and applicability of four-bucket jacket foundation for offshore wind turbines

With the rapid development of offshore wind turbine industry, the development of sea areas will gradually move from offshore to deep sea. In this process, multi-bucket jacket foundation will become a highly potential infrastructure solution in the water depth of about 50 m due to its unique advantages. The closed space is formed inside the bucket foundation during the penetration process, so the suction sinking method can be used for penetration, the accurate grasp of the critical suction during the penetration process of the four-bucket jacket foundation determines whether the penetration process can be carried out smoothly. At the same time, appropriate tilt adjustment strategies need to be adopted promptly after the inclination occurs during the penetration process. In this paper, the tilt adjustment technique in the penetration process of the four-bucket jacket foundation was taken as the research object. Firstly, the model test was used to study the critical suction of the foundation at three different penetration depths, and the test results are compared with the theoretical calculation results. Subsequently, foundation on the above research results, the applicability of the tilt adjustment strategy when the tilt angles of 1°, 2° and 3° occurred at three penetration depths was analyzed, which provides technical guidance for the development of practical projects.

Effect of the application of digital technology-assisted optimization in the process of adjusting jaw position.

The aim of this study was to demonstrate a novel jaw position adjustment technique derived from digital twins and evaluate the application effect of digital technology-assisted optimization in the process of adjusting jaw position on patients with temporomandibular disorders (TMD). A total of 74 patients with TMD who attended the Department of Temporomandibular Joint, West China Hospital of Stomatology, Si-chuan University, between June 2022 and May 2023 were selected. The patient's initial computed tomography (CT) and bilateral temporomandibular joint data obtained by magnetic resonance imaging (MRI) were collected. The 148 joints were divided into the normal disc-condyle relationship (N) group, disc displacement with reduction (DDWR) group, and disc displacement without reduction (DDWoR) group. Assisted by digital technology, the patient's CT data were reconstructed, and a personalized reference plane was established to adjust the jaw position. A three-point bite guiding splint was designed by the adjusted occlusal space and then fabricated by 3D printing technology. It was worn by the patients and then reviewed by MRI. Before and after the adjustment of jaw position, the amount and direction of condyle and disc displacement and the angle between condyle and disc were measured as the evaluation indexes of the effect of the adjustment. The correlation with condylar displacement was evaluated. In the N group, the disc moved backward and downward along the X and Z axes by (-0.60±0.62) and (0.51±0.71) mm, respectively. In the DDWR group, the disc moved backward and upward along the X and Z axes by (-1.33±1.38) and (-0.09±1.31) mm, respectively. In the DDWoR group, the disc moved forward and downward along the X and Z axes by (0.49±1.76) and (1.35±1.76) mm, respectively. The angle between the condyle and the disc decreased after adjustment of the jaw position in all three groups. All patients showed improvement in symptoms after adjustment. Digital technology-assisted jaw position adjustment can simplify the process, reduce the sensitivity of the technique, and improve patients' disc-condyle structure and symptoms. Therefore, its application in the treatment of patients with TMD is of great clinical significance.

HyperScout-1 inflight calibration and product validation

ABSTRACT Earth observation aims at monitoring the Earth in increasingly more detail by improving the spatial, spectral and temporal resolution of image acquisitions. This can now be achieved in more cost-efficient ways by the miniaturization of instruments and platforms. New initiatives have emerged to accommodate hyperspectral instruments on CubeSats despite inherent platform limitations in terms of volume, power consumption, computing and downlink capacity. However, the adoption of CubeSat data for operational and scientific applications requires sufficient trust in dependability and data quality. Achieving reliable data quality from CubeSats remains challenging and requires advanced geometric, radiometric, and spectral calibration techniques. We present the successful in-orbit calibration of the HyperScout-1 mission. This In-Orbit Demonstration (IOD) mission, equipped with a miniaturized hyperspectral instrument based on Linear Variable Filter technology (LVF), was launched in 2018 by the European Space Agency (ESA). Rigorous inflight calibration has been applied to HyperScout-1 to ensure the accuracy of its data. The radiometric calibration of the HyperScout-1 instrument was performed vicariously based on modelled spectral radiances over a desert site. Independent validation using data from a CEOS RadCalNet site showed good radiometric accuracy with absolute radiometric errors <5%. Geometric errors due to inaccuracies in attitude measurements and sensor focal plane distortions were corrected using a bundle adjustment technique combined with matching to accurate Ground Control Points. This resulted in excellent geometric performance, with sub-pixel average absolute geolocation errors (~0.48 pixels across track and 0.51 pixels along track). The very limited data budget was a major challenge, forcing us to make some trade-offs in the calibration. Despite this, our robust vicarious calibration strategy allowed effective instrument calibration. As a result, the HyperScout-1 demonstrated the capability of an LVF Hyperspectral instrument on a CubeSat to deliver high-quality imagery and serve applications like land cover classification, change detection and disaster monitoring.

Abstract 4978: Genomic characterization of metastatic pancreatic cancer according to tumor sample sites

Abstract Background: Our research aimed to evaluate and compare the genetic variations in the primary and metastatic sites of patients with metastatic pancreatic cancer. Methods: In this study, we selected individuals with metastatic pancreatic cancer from the data provided by the American Association for Cancer Research Project GENIE (version 14.1). The comparison of genetic mutations between primary and metastatic tumor sites was conducted using the Fisher exact test, augmented with the Benjamini-Hochberg adjustment technique. Results: In a study of 3,108 patients with metastatic pancreatic cancer, 38.9% had metastatic site tumor samples, while 61.1% had samples from the primary tumor. The most frequent mutations were KRAS (89.4%), TP53 (72.9%), SMAD4 (21.2%), and CDKN2A (17.4%). Metastatic samples showed higher rates of CDKN2A, CDKN2B, and MTAP mutations compared to primary site samples (40.1% vs. 29.1%; 20.3% vs. 10.4%; and 20.8% vs. 8.9%, respectively, all with p&amp;lt;0.001 and q&amp;lt;0.001). In patients with young-onset pancreatic cancer (younger than 50 years), there was no notable difference in gene mutations between the two sample groups. Conclusion: Our findings corroborate the notion that identifying the precise genes that contribute to the development of metastatic pancreatic cancer is vital for gaining a more comprehensive understanding of the biological mechanisms. Moreover, genomic profiling could be a key in pinpointing potential biomarkers for diagnosis and therapy. Citation Format: Suleyman Y. Goksu, Muhammet Ozer, Busra Bacik Goksu, Nina N. Sanford, Amy Jones, Nilesh Verma, Syed Kazmi. Genomic characterization of metastatic pancreatic cancer according to tumor sample sites [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4978.

Iterative nearest neighbour age-height curve adjustment: Addressing the impact of spatial heterogeneity on longitudinal forestry provenance trial data

Spatial heterogeneity in long term forestry genetics trials can obscure genetic variation and influence conclusions made based on the data. Nearest neighbour adjustment methods have been employed to account for spatial patterns, however such methods are limited in their applicability to longitudinal provenance trial data because of the need to account for repeated measures as well as the interest in preserving among-site variation. In this study, a novel approach combining nearest neighbour adjustment techniques with longitudinal data analysis concepts was developed to adjust lodgepole pine (Pinus contorta var. latifola Douglas) provenance trial data for spatial heterogeneity. Individual height-age logistic growth curves were fit to each tree to age 35, and the horizontal asymptote parameters of the height-age curves were adjusted for spatial patterns using iterative nearest neighbour adjustments. The methods developed in this study successfully removed positive spatial correlation and reduced the interaction between block and provenance, thus reducing changes in population ranking among blocks within sites. The adjustments applied caused large shifts in population ranks at many sites, indicating that seed selection decisions based on data not adjusted for spatial heterogeneity could lead to selecting non-optimal populations for a given site. This research provides a methodological framework for adjusting longitudinal genetics trial data for underlying site conditions and spatial patterns. Additionally, a spatially adjusted version of a large and extensively utilized provenance trial dataset was created for use in future analyses.

ADC-Free ReRAM-Based In-Situ Accelerator for Energy-Efficient Binary Neural Networks

With the ever-increasing parameter size of deep learning models, conventional ASIC-based accelerators in mobile environments suffer from low energy budget due to limited memory capacity and frequent data movements. Binary neural networks (BNNs) deployed in ReRAM-based in-situ accelerators provide a promising solution, and various related architectures have been proposed recently. However, their performances are largely compromised by the tremendous cost of domain conversion via analog-to-digital converters (ADCs), essential for mixed-signal processing in ReRAM. This paper identifies two root causes of the need for such ADCs and proposes effective solutions to address them. First, we minimize redundant operations in BNNs and reduce the number of ReRAM arrays with ADCs approximately by half. We also propose a partial-sum range adjustment technique based on a layer remapping to deal with the remaining ADCs. Proper handling of the partial-sum distribution allows ReRAM-based in-situ processing without domain conversion, completely bypassing the need for ADCs. Experimental results show that the proposed architecture achieves a 3.44x speedup and 91.5% energy savings, making it an attractive solution for on-device AI at the edge.

Investigation into the Co-Phase Detection Methodology for Segmented Plane Mirrors Utilizing Grazing Incidence Interferometry

Segmented plane mirrors constitute a crucial component in the self-aligned detection process for large-aperture space optical imaging systems. Surface shape errors inherent in segmented plane mirrors primarily manifest as tilt errors and piston errors between sub-mirrors. While the detection and adjustment techniques for tilt errors are well-established, addressing piston errors poses a more formidable challenge. This study introduces a novel approach to achieve long-range, high-precision, and efficient co-phase detection of segmented plane mirrors by proposing a segmented plane mirror shape detection method based on grazing incidence interferometry. This method serves to broaden the detection range of piston errors, mitigate the issue of the 2π ambiguity resulting from piston errors in co-phase detection, and extend the detection capabilities of the interferometer. By manipulating the incident angle of the interferometer, both rough and precise adjustments of the segmented plane mirrors can be effectively executed.

Adaptive chessboard-like high-frequency projection method for three-dimensional measurement of shiny surfaces

Abstract Shiny surface is challenging for the structured light fringe projection three-dimensional measurement technique, since the image saturation caused by highlight results in incorrect intensities in captured images of fringe patterns, and leads to serious phase errors and measurement errors. To address the issue, an adaptive chessboard-like high-frequency projection intensity adjustment technique is proposed. The proposed method alleviates image saturation by adaptively adjusting the intensity of the high-frequency chessboard-like projection pattern. And the complementary patterns are projected to suppress reflections and enhance the robustness of decoding. The experimental results demonstrate that the proposed method achieve high measurement accuracy for shiny surfaces.

Reducing the Bias in Online Reviews Using Propensity Score Adjustment

Online hotel reviews on platforms like TripAdvisor are crucial in shaping customer choices and steering business strategies in the hospitality sector. However, the effectiveness of these platforms is partially hindered by the self-selection bias found in voluntary reviews. This bias can create false expectations and unsatisfactory experiences, mainly as the feedback generally comes from a non-representative group of self-motivated reviewers (SMRs). A common strategy to mitigate this bias is increasing the number of reviews through customer surveys, generating retailer-prompted reviews (RPRs). However, these RPRs, despite reducing selection bias, tend to lack the depth and insight of SMRs, resulting in a credibility gap and reduced representativeness. To address this, our study presents a novel approach using the propensity score adjustment (PSA) technique. This method leverages the distribution of RPRs to refine the accuracy of text data from SMRs, aiming to enhance the reliability and representativeness of online reviews. By combining the strengths of both RPRs and SMRs, we aim to create an online review environment that is both accurate and reliable. In conclusion, this research marks an important step toward improving online review platforms, aiming for a more transparent and trustworthy environment for reviews.

Penalty Coefficient Adjustment Technique for Extended Ising Machines

Penalty Coefficient Adjustment Technique for Extended Ising Machines

Forecasting Monthly Export of Readymade Garments by Removing Seasonal Impact

Various Bangladeshi authorities release time series data that takes seasonal effects into account. However, no adjusted series is offered. The developed world has completely different conditions since they broadcast seasonally adjusted series. Many seasonal adjustment techniques, such as classical and X-based techniques, are available; however they cannot be used in practice in accordance with Bangladesh's seasonal time series. So here we have executed X-11 and X-12-ARIMA which are known as X-based seasonal adjustment methods and some classical methods like SARIMA and MA to seasonal time series data collected from secondary source as economic trend revealed by Bangladesh Bank. We will use export of readymade garments which are monthly data and have seasonal impacts. The entire data collection must first be divided into training and test data. Next, the data was de-seasonalized, and future values were predicted using training and test data sets, respectively, to compute various forecasting errors such as MAPE, PMAD, MAD, and RMSE. We utilize several X-based approaches and traditional methods to compare the errors. We conclude that the seasonal adjustment strategy performs better and has fewer forecasting mistakes. In conclusion, we suggest the optimal seasonal adjustment technique for ready-to-wear exports and project certain future values based on that technique.

Size adjustment suture technique for lymphaticovenular anastomosis.

In this report, we describe a super microsurgical technique that enables rapid and accurate anastomosis while adjusting for caliber differences when anastomosing a small-caliber lymphatic vessel and a vein with a larger caliber, which is frequently encountered in surgeries such as lymphaticovenous anastomosis (LVA). The suture size adjustment technique was performed in 30 anastomoses of lymphatic vessels and veins, whose diameter of lymph duct was at least two times smaller than that of the vein. The type of lymphedema, caliber of lymphatic vessels and veins anastomosed, caliber ratio, vein wall thickness, modified caliber ratio after vein wall thickness subtracted, presence of additional anastomosis, and anastomosis time were examined. On average, the lymphatic vessels had a diameter of 0.61 mm, while the veins were 1.43mm in diameter. The mean caliber ratio of vein to lymphatic vessel was 2.3, while the modified caliber ratio of vein-to-lymphatic vessel was 1.5 on average. The average venous wall thickness was 0.51. The average anastomosis time was 9.1min and no additional anastomosis due to leakage was necessary in any case. We successfully performed an anastomosis of lymphatic vessels and veins with different calibers, which can maintain long-term patency while adjusting the caliber difference and suppressing leakage at the anastomosis site. Finally, the caliber of the vein is commonly larger than that of the lymphatic vessel to be anastomosed in many cases of LVA surgery, indicating that the proposed anastomosis method could be of therapeutic use in many cases.

Dynamic adaptation of contention window boundaries using deep Q networks in UAV swarms

In flying ad hoc networks (FANET), medium access layer (MAC) protocols play an essential role in ensuring better network performance. The effective utilization of network resources and providing access fairness are key research issues in this area, and the contention window size directly influences these factors. This paper mainly focuses on addressing the upper and lower boundaries of the contention window in Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) with a binary exponential back-off algorithm. Existing solutions include static and dynamic window adjustment techniques only to update the upper boundary of the contention window size. However, the static window adjustment technique is unsuitable for changing network conditions, while the dynamic window adjustment technique may need to be more efficient in dense network environments. The proposed solution uses a Deep Q-learning algorithm(DQN) framework to adjust the contention window adaptively based on the reward function, resulting in efficient and dynamic adaptation to changing network conditions among unmanned aerial vehicles (UAV). The proposed methodology is evaluated through simulations, and performance is measured using channel utilization, efficiency, delay, and collision rate. The simulation findings show that, in comparison to generic MAC protocols, the proposed algorithm is 5% more efficient in throughput and incurs a four-fold reduction in delay.

AUTOMATED FINE-SCALE FOREST INVENTORY USING BACKPACK LIDAR – A STRATEGY BASED ON FEATURE EXTRACTION, MATCHING, AND TRACKING FROM INTEGRATED SCANS

Abstract. Backpack LiDAR systems are gaining popularity due to their rapid data acquisition, portability, and cost-effectiveness. However, using backpack LiDAR in forest poses challenges, such as GNSS signal attenuation under the canopy, leading to inaccurate trajectory estimates and misregistered point clouds. This paper aims at presenting a novel method that addresses this challenge by leveraging the integrated scans (IS) concept to enhance point cloud quality for automated forest inventory. The proposed method for forest inventory using ISs consists of four key steps: (i) Integrated scans (IS) generation, (ii) feature extraction, matching, and tracking, (iii) trajectory enhancement, and (iv) tree biometric extraction. Firstly, IS point clouds are generated based on the initial GNSS/INS trajectory. Secondly, reliable forest features such as tree trunks and ground patches are extracted, matched, and tracked across ISs. These features are then utilized in the trajectory enhancement step, where a non-linear Least Squares Adjustment (LSA) technique is used to minimize discrepancies among the features to enhance the trajectory. The resulting point clouds, based on the improved trajectory, are used to extract tree biometric information. The proposed method was evaluated using two distinct datasets collected with different systems. The evaluation results, both qualitatively and quantitatively, validate the effectiveness of the proposed method, showcasing its potential for fine-scale forest inventory applications.

Does adjustment for non-differential misclassification of dichotomous exposure induce positive bias if there is no true association?

This article is a response to an off-the-record discussion that I had at an international meeting of epidemiologists more than decade ago. It centered on a concern, perhaps widely spread, that adjustment for exposure misclassification can induce a false positive result. I trace the possible history of this supposition and test it in a simulated case-control study under the assumption of non-differential misclassification of binary exposure, in which a Bayesian adjustment is applied. Probabilistic bias analysis is also briefly considered. The main conclusion is that adjustment for the presumed non-differential exposure misclassification of dichotomous does not “induce” positive associations, especially if the focus of the interpretation of the result is taken away from the point estimate. The misconception about positive bias induced by adjustment for exposure misclassification, if more clearly explained during the training of epidemiologists, may promote appropriate (and wider) use of the adjustment techniques. The simple message that can be derived from this paper is: “Exposure misclassification as a tractable problem that deserves much more attention than just a typical qualitative throw-away discussion”.

Estimation in Semi-Varying Coefficient Heteroscedastic Instrumental Variable Models with Missing Responses

This paper studies the estimation problem for semi-varying coefficient heteroscedastic instrumental variable models with missing responses. First, we propose the adjusted estimators for unknown parameters and smooth functional coefficients utilizing the ordinary profile least square method and instrumental variable adjustment technique with complete data. Second, we present an adjusted estimator of the stochastic error variance by employing the Nadaraya–Watson kernel estimation technique. Third, we apply the inverse probability-weighted method and instrumental variable adjustment technique to construct the adaptive-weighted adjusted estimators for unknown parameters and smooth functional coefficients. The asymptotic properties of our proposed estimators are established under some regularity conditions. Finally, numerous simulation studies and a real data analysis are conducted to examine the finite sample performance of the proposed estimators.

A blind recovery technique with integer wavelet transforms in image watermarking

The development of internet technology has simplified the sharing and modification of digital image information. The aim of this study is to propose a new blind recovery technique based on integer wavelets transform (BRIWT) by utilizing their image content. The LSB adjustment technique on the integer wavelet transform is used to embed recovery data into the two least significant bits (LSB) of the image content. Authentication bits are embedded into the current locations of the LSB of the image content, while the recovery information is embedded into different block locations based on the proposed block mapping. The embedded recovery data is securely placed at random locations within the two LSBs using a secret key. A three-layer embedding of authentication bits is used to validate the integrity of the image contents, achieving high precision and accuracy. Tamper localization accuracy is employed to identify recovery bits from the image content. This research also investigates the image inpainting method to enhance recovery from tampered images. The proposed image inpainting is performed by identifying non-tampered pixels in the surrounding tamper localization. The results demonstrate that the proposed scheme can produce highly watermarked images with imperceptibility, with an average SSIM value of 0.9978 and a PSNR value of 46.20 dB. The proposed scheme significantly improves the accuracy of tamper localization, with a precision of 0.9943 and an accuracy of 0.9971. The proposed recovery technique using integer wavelet transforms achieves high-quality blind recovery with an SSIM value of 0.9934 under a tampering rate of 10%. The findings of this study reveal that the proposed scheme improves the quality of blind recovery by 14.2 % under a tampering rate of 80 %.

Evaluating the impact of improved crop varieties in the Sahelian farming systems of Niger

Most people in Niger still rely heavily on agriculture as a source of income. However, low productivity, climate change, soil infertility, pests, and diseases are challenges faced by this sector. As a result, the nation suffers from a severe problem of food insecurity. Many investigations indicate that adopting improved crop varieties(ICVs) increases agricultural productivity. Using information gathered from 1784 farmers, this study assesses the effects of adopting improved crop varieties (ICVs) on household welfare. To analyze the data, we employ endogenous switching regression (ESR) and inverse probability-weighted regression adjustment (IPWRA) techniques. The analysis shows that the ICVs adoption significantly improves household income and food access in Niger's Sahelian region. The ESR model's average treatment effects estimate shows that the ICVs adoption raised per capita income, food expenditure, and household dietary diversity score (HDDS) by 75 %, 1.81 %, and 36.49 %, respectively. The IPWRA model yields similar results.Therefore we conclude that adopting ICVs has substantial dynamic benefits that improve household welfare in Sahel Niger by increasing their probability of escaping poverty, food insecurity, and malnutrition. The farmer's knowledge of improved crop varieties significantly influenced favorably the decision to adopt, suggesting that intensifying dissemination and encouraging the promotion of drought-tolerant crop varieties among farmers, development agencies, researchers, and policymakers could be a crucial plan of action to combat poverty, food insecurity, and malnutrition in the Sahelian region.

Intelligent biomedical image classification in a big data architecture using metaheuristic optimization and gradient approximation

Medical imaging has experienced significant development in contemporary medicine and can now record a variety of biomedical pictures from patients to test and analyze the illness and its severity. Computer vision and artificial intelligence may outperform human diagnostic ability and uncover hidden information in biomedical images. In healthcare applications, fast prediction and reliability are of the utmost importance parameters to assure the timely detection of disease. The existing systems have poor classification accuracy, and higher computation time and the system complexity is higher. Low-quality images might impact the processing method, leading to subpar results. Furthermore, extensive preprocessing techniques are necessary for achieving accurate outcomes. Image contrast is one of the most essential visual parameters. Insufficient contrast may present many challenges for computer vision techniques. Traditional contrast adjustment techniques may not be adequate for many applications. Occasionally, these technologies create photos that lack crucial information. The primary contribution of this work is designing a Big Data Architecture (BDA) to improve the dependability of medical systems by producing real-time warnings and making precise forecasts about patient health conditions. A BDA-based Bio-Medical Image Classification (BDA-BMIC) system is designed to detect the illness of patients using Metaheuristic Optimization (Genetic Algorithm) and Gradient Approximation to improve the biomedical image classification process. Extensive tests are conducted on publicly accessible datasets to demonstrate that the suggested retrieval and categorization methods are superior to the current methods. The suggested BDA-BMIC system has average detection accuracy of 94.6% and a sensitivity of 97.3% in the simulation analysis.

The Role of Rural Credit in Agricultural Technology Adoption: The Case of Boro Rice Farming in Bangladesh

Rice agriculture provides millions of households with a steady source of income and employment. However, for small and marginal farmers, the exorbitant cost of production inputs presents a formidable obstacle in their pursuit of acquiring it. Credit constraints are a significant impediment to the adoption of agricultural technologies. Therefore, this paper identifies the determinant of access to rural credit and its impact on Boro rice production technology adoption in Bangladesh using cross-sectional data. The study employed probit regression, propensity score matching (PSM), inverse probability weighting (IPW), and inverse probability weighted regression adjustment (IPWRA) techniques. The findings indicate that age, family size, working members, and involvement in safety net programs negatively and significantly influence access to rural credit, while earning persons in the family, literacy, rice farming experience, remittance, and total income positively influence access to rural credit. The positive and significant ATT values suggested that access to rural credit has a positive and significant effect on technology adoption and the level of technology use. It was also found that access to rural credit has a heterogeneous effect. In particular, non-government organization (NGO) credit has a more significant impact on technology adoption than formal bank credit. Access to credit and the adoption of agricultural technologies can be greatly improved with the help of a location-specific rural credit policy and strong monitoring from the formal banking sector.