The task of determining the distance from one object to another is one of the important tasks solved in robotics systems. Conventional algorithms rely on an iterative process of predicting distance estimates, which results in an increased computational burden. Algorithms used in robotic systems should require minimal time costs, as well as be resistant to the presence of noise. To solve these problems, the paper proposes an algorithm for Kalman combination filtering with a Goldschmidt divisor and a median filter. Software simulation showed an increase in the accuracy of predicting the estimate of the developed algorithm in comparison with the traditional filtering algorithm, as well as an increase in the speed of the algorithm. The results obtained can be effectively applied in various computer vision systems.

Sedimentary microfacies division is the basis of oil and gas exploration research. The traditional sedimentary microfacies division mainly depends on human experience, which is greatly influenced by human factor and is low in efficiency. Although deep learning has its advantage in solving complex nonlinear problems, there is no effective deep learning method to solve sedimentary microfacies division so far. Therefore, this paper proposes a deep learning method based on DMC-BiLSTM for intelligent division of well-logging—sedimentary microfacies. First, the original curve is reconstructed multi-dimensionally by trend decomposition and median filtering, and spatio-temporal correlation clustering features are extracted from the reconstructed matrix by Kmeans. Then, taking reconstructed features, original curve features and clustering features as input, the prediction types of sedimentary microfacies at current depth are obtained based on BiLSTM. Experimental results show that this method can effectively classify sedimentary microfacies with its recognition efficiency reaching 96.84%.

A critical analysis of the classical definition of outsiders is given. Some examples show that this notion is not universal and has at least two drawbacks. Particularly, the set of outsiders may have the probability arbitrary close to one half. On the other hand, the deleting of the set of outsiders may dramatically change the value of the median.

Colorectal cancer (CRC) is the third most common cancer, with rising incidence due to lifestyle and diet. 40% of CRC cases are found to have KRAS mutations. In this study, we investigate the survival outcome of metastatic Colorectal cancer mCRC) patients in Brunei Darussalam restrospectively. Chi-squared test was used to compare the survival outcomes of mCRC patients, and Mann-Whitney U test was used to compare the median ages of both groups. Kaplan-Meier survival curves were drawn and logrank test was used to compare the survival outcome between two groups. There was a total of 105 patients with stage IV CRC being treated during the study period. 81.6% (n=62) of mCRC patients were found to have the primary tumours on the left side of the colon. 19 of these 26 (73.1%) mutant KRAS mCRC patients died, while 23 of 50 (46.0%) wild-type KRAS mCRC patients died at the end of the study period, contributing to death rates of 45.2% and 54.8%, correspondingly. 30.3% (n=23) of the study population had a single metastatic site detected (either liver, or lung or any other organs), while 69.7% (n=53) of the 76 mCRC patients had two (double) or more metastatic sites. 69.2% (n=18) and 30.8% (n=8) of the mutant KRAS mCRC patients had mutations within codons 12 and 13, respectively. To our knowledge, this is the first study in Brunei Darussalam to analyse both the survival outcomes of metastatic CRC patients and those of mutant KRAS mCRC patients. Chi-squared analysis showed a significant difference between the survival outcomes of wild-type KRAS and mutant KRAS mCRC patients (p-value = 0.024). There was a significant difference in the survival outcome between the mutant KRAS mCRC patients with RCC and mutant KRAS mCRC with LCC patients. There was no significant difference between the survival outcomes of mutant KRAS patients with mutations in either codon 12 or 13 of the KRAS gene (Table 3). However, there is a significant difference in the median survival periods between the mutant KRAS mCRC patients with mutations in codon 12 and those with mutation in codon 13 of the KRAS gene (p-value = 0.003). In conclusion, we found that mutant KRAS mCRC patients had a significantly poorer OS, which was shown to be worse when the primary tumours were found at the left side of the colon. Mutant KRAS mCRC patients with mutations in codon 12 were found to have shorter survival median periods than those with mutations within codon 13.

Notion of median in one dimension is a foundational element in nonparametric statistics. It has been extended to multi-dimensional cases both in location and in regression via notions of data depth. Regression depth (RD) and projection regression depth (PRD) represent the two most promising notions in regression. Carrizosa depth DC is another depth notion in regression. Depth induced regression medians (maximum depth estimators) serve as robust alternatives to the classical least squares estimator. The uniqueness of regression medians is indispensable in the discussion of their properties and the asymptotics (consistency and limiting distribution) of sample regression medians. Are the regression medians induced from RD, PRD, and DC unique? Answering this question is the main goal of this article. It is found that only the regression median induced from PRD possesses the desired uniqueness property. The conventional remedy measure for non-uniqueness, taking average of all medians, might yield an estimator that no longer possesses the maximum depth in both RD and DC cases. These and other findings indicate that the PRD and its induced median are highly favorable among their leading competitors.

Speckle noise is one of the most difficult noises to remove especially in medical applications. It is a nuisance in ultrasound imaging systems which is used in about half of all medical screening systems. Thus, noise removal is an important step in these systems, thereby creating reliable, automated, and potentially low cost systems. Herein, a generalized approach MFNR (Multi-Frame Noise Removal) is used, which is a complete Noise Removal system using KDE (Kernal Density Estimation). Any given type of noise can be removed if its probability density function (PDF) is known. Herein, we extracted the PDF parameters using KDE. Noise removal and detail preservation are not contrary to each other as the case in single-frame noise removal methods. Our results showed practically complete noise removal using MFNR algorithm compared to standard noise removal tools. The Peak Signal to Noise Ratio (PSNR) performance was used as a comparison metric. This paper is an extension to our previous paper where MFNR Algorithm was showed as a general purpose complete noise removal tool for all types of noises

Laser scanning systems make use of Light Detection and Ranging (LiDAR) technology to acquire accurately georeferenced sets of dense 3D point cloud data. The information acquired using these systems produces better knowledge about the terrain objects which are inherently 3D in nature. The LiDAR data acquired from mobile, airborne or terrestrial platforms provides several benefit over conventional sources of data acquisition in terms of accuracy, resolution and attributes. However, the large volume and scale of LiDAR data have inhibited the development of automated feature extraction algorithms due to the extensive computational cost involved in it. Moreover, the heterogeneously distributed point cloud, which represents objects with varying size, point density, holes and complicated structures pose a great challenge for data processing. Currently, geospatial database systems do not provide a robust solution for efficient storage and accessibility of raw data in a way that data processing could be applied based on optimal spatial extent. In this paper, we present Global LiDAR and Imagery Mobile Processing Spatial Environment (GLIMPSE) system that provides a framework for storage, management and integration of 3D LiDAR data acquired from multiple platforms. The system facilitates an efficient accessibility to the raw dataset, which is hierarchically represented in a geographically meaningful way. We utilise the GLIMPSE system to automatically extract road median from Airborne Laser Scanning (ALS) point cloud. In the first part of this paper, we detail an approach to efficiently retrieve the point cloud data from the GLIMPSE system for a particular geographic area based on user requirements. In the second part, we present an algorithm to automatically extract road median from the retrieved LiDAR data. The developed road median extraction algorithm utilises the LiDAR elevation and intensity attributes to distinguish the median from the road surface. We successfully tested our algorithms on two road sections consisting of distinct road median types based on concrete and grass-hedge barriers. The use of GLIMPSE improved the efficiency of the road median extraction in terms of fast accessibility to ALS point cloud data for the required road sections. The developed system and its associated algorithms provide a comprehensive solution to the user's requirement for an efficient storage, integration, retrieval and processing of large volumes of LiDAR point cloud data. These findings and knowledge contribute to a more rapid, cost-effective and comprehensive approach to surveying road networks.

Nutrient regulation in aquaponic environments has been the topic of research for many years. Most have focused on appropriate control of nutrients in an aquaponic set-up, but very little research has been done on commercial scale applications. In our model, the input data was sourced on a weekly basis from three commercial aquaponic farms in South-East Texas over the course of a year. Due to limited number of data points, dimensionality reduction techniques like pair-wise correlation matrix was used to remove the highly correlated predictors. Feature selection techniques like the XGBoost classifier and Recursive Feature Elimination with ExtraTreesClassifier were used to rank the features in order of their relative importance. Ammonium and calcium were found to be the top two nutrient predictors and based on the months in which lettuce was cultivated, the median of these nutrient values from the historical dataset served as the optimal concentrations to be maintained in the aquaponic solution. To accomplish this, Vernier sensors were used to measure the nutrient values and actuator systems were built to dispense the appropriate nutrient into the ecosystem via a closed loop.

Patients often need the use of their arms to assist with functional activities, but after bone disruption, pushing is frequently limited to less than 4.5 kg. No method exists to measure arm weight bearing objectively in clinical settings. This project aimed to design, construct, and test a walker for patients who need to limit arm force to prevent excessive bone stress during post-fracture (iatrogenic or traumatic) ossification. First, a qualitative study was conducted to obtain critiques of a Clinical Force Measuring (CFM) walker prototype from rehabilitation professionals. Key statements and phrases were coded that allowed “themes” to emerge from transcribed interviews, which guided device revisions. Next, a second CFM Walker prototype was designed based on the qualitative data and device criteria/constraints and finally tested. The result was fabrication of a new lightweight, streamlined, and cost-effective prototype walker with a simple visual display and auditory cue with upper limit alarms. Key features included attachments for medical equipment and thin film force-sensing resistors integrated into the walker handles that progressively activated 3 LEDs and a buzzer when arm force exceeded programmed thresholds. The innovative CFM Walker will help patients with restricted arm weight bearing, especially elderly adults, recover safer and faster in the future.

Correlations between the air permeability coefficient and various pore structure indicators in cementitious materials were examined to determine the pore structure indicator that best evaluated air permeability using data from previous studies of air permeabilities and pore structures. The determination coefficients of air permeability with total pore volume, critical pore diameter, and ordinary threshold pore diameter were low, although these have often been used as indicators. The median and threshold pore diameters obtained by percolation theory showed high determination coefficients. The equation using the threshold pore diameter better estimated the air permeability coefficient than the Katz–Thompson equation.

Patients recovering from bone disruption due to trauma or surgery need to limit movement to minimize shear force, thereby protecting callus formation and osteogenesis. Patients often use their arms to assist with functional activities, but pushing is frequently limited to <10 lb (4.5 kg). With only verbal instructions, patients’ ability to accurately limit weight-bearing (WB) force is poor. A therapeutic intervention to improve patient adherence with upper extremity (UE) WB guidelines during functional mobility using an instrumented walker could be beneficial. Therefore, the purpose of this article is to describe a feedback training protocol to improve the ability to modulate weight-bearing force in older adults and then provide an overview of the efficacy of this protocol and subsequent development of a Clinical Force Measuring Walker. An instrumented walker was used to measure UE WB during functional mobility in older healthy subjects (n = 30) before, during, and after (immediately and 2 hours) a visual and auditory concurrent feedback training session. During feedback training, force was significantly reduced with all 3 sessions as compared to baseline. When using the front wheeled walker, UE WB force during the second and third feedback training trials went down compared to the first trial. During the third feedback training trial, force was greater than the two previous trials while transferring sit-to-stand and stand-to-sit. After completion of practice with feedback, UE WB force was significantly reduced and remained so 2 hours later. These findings suggest that feedback training is effective for helping patients to modulate UE WB. Use of an instrumented walker and feedback training would be beneficial in clinical practice, especially with older patients. A more intensive feedback training with additional trials and or simultaneous visual and auditory cues during whole-practice may be needed to get UE WB below a 10 lb threshold.

Measuring bias is important as it helps identify flaws in quantitative forecasting methods or judgmental forecasts. It can, therefore, potentially help improve forecasts. Despite this, bias tends to be under-represented in the literature: many studies focus solely on measuring accuracy. Methods for assessing bias in single series are relatively well-known and well-researched, but for datasets containing thousands of observations for multiple series, the methodology for measuring and reporting bias is less obvious. We compare alternative approaches against a number of criteria when rolling-origin point forecasts are available for different forecasting methods and for multiple horizons over multiple series. We focus on relatively simple, yet interpretable and easy-to-implement metrics and visualization tools that are likely to be applicable in practice. To study the statistical properties of alternative measures we use theoretical concepts and simulation experiments based on artificial data with predetermined features. We describe the difference between mean and median bias, describe the connection between metrics for accuracy and bias, provide suitable bias measures depending on the loss function used to optimise forecasts, and suggest which measures for accuracy should be used to accompany bias indicators. We propose several new measures and provide our recommendations on how to evaluate forecast bias across multiple series.

Investigations reporting positive effects of Extracorporeal Shock Wave Therapy (ESWT) on nerve regeneration are limited to the rat sciatic nerve model. The effects of ESWT on muscle-in-vein conduits (MVCs) have also not been investigated yet. This study aimed to evaluate the effects of ESWT after repair of the rat median nerve with either autografts (ANGs) or MVCs. In male Lewis rats, a 7-mm segment of the right median nerve was reconstructed either with an ANG or MVC. For each reconstructive technique, one group of animals received one application of ESWT while the other rats served as controls. Animals were observed for 12 weeks and nerve regeneration was assessed via computerized gait analysis, the grasping test, electrophysiological evaluations and histological quantification of axons, blood vessels and lymphatic vasculature. Here we provide for the first time a comprehensive analysis of ESWT effects on nerve regeneration in a rat model of median nerve injury. Furthermore, this study is among the first reporting the quantification of lymphatic vessels following peripheral nerve injury and reconstruction in vivo. While we found no significant direct positive effects of ESWT on peripheral nerve regeneration, results following nerve repair with MVCs were significantly inferior to those after ANG repair.