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This study focuses on the measurement of concrete cracks in three dimensions (3D) using the particle swarm optimization-based projection algorithm, OpenCV technique, and X-ray computerized tomography (CT) images. The primary objective is to visualize the development of cracks in fire-damaged concrete. The data for X-ray CT imaging were collected, consisting of 3,011 CT images obtained from damaged specimens. The sampling criteria employed ensured a convenient sampling process with a 95% confidence level and 5% limits of errors in the 50–50 category. The results reveal that the undamaged specimens exhibited an average of 406 smaller-sized pores, whereas the fire-damaged specimens, which were subjected to a temperature of 900°C for 1 h, displayed a significant increase in the number of pores and cracks. The proposed method successfully enables the plotting of 3D measurements of concrete crack development. This achievement has practical implications for practitioners, as it allows for (1) visualizing crack development as an initial step toward analyzing deterioration patterns in concrete, and (2) conducting an analysis of concrete failure in structures following fire scenario simulations. Overall, the findings of this study contribute to a better understanding of concrete crack development and offer valuable insights for practitioners in the field. [ABSTRACT FROM AUTHOR]

Automatic learning feedback monitoring and analysis are becoming essential in modern education. We present a video analytic system capable of monitoring in-class student’s learning behaviors and providing feedback to the instructor. It is a common practice nowadays for students to take electronic notes or browse online using laptops and cellphones in class. However the use of technology can also impact student concentration and affect learning behaviors, which can seriously hinder their learning progress if not controlled properly. In this pioneering study, we propose a non-intrusive deep-learning based computer vision system to monitor student concentration by extracting and inferring high-level visual behavior cues, including their facial expressions, gestures and activities. Our system can automatically assist instructors with situational awareness in real time. We assume only RGB color images as input and runable system on edge devices for easy deployment. We propose two video analytic components for student behavior analysis: (1) The facial analysis component operates based on Dlib face detection and facial landmark tracking to localize each student and analyze their face orientations, eye blinking, gazes, and facial expressions. (2) The activity detection and recognition component operates based on OpenPose and COCO object detection can identify eight types of in-class gestures and behaviors including raising-hand, typing, phone-answering, crooked-head, desk napping, etc. Experiments are performed on a newly collected real-world In-Class Student Activity Dataset (ICSAD), where we achieved nearly 80% activity detection rate. Our system is view-independent in handling facial and pose orientations with average angular error < 10°. The source code of this work is at: https://github.com/YiZengHsieh/ICSAD. [ABSTRACT FROM AUTHOR]

Bridge structures deteriorate due to various factors, and their maintenance can be made more efficient by utilizing relevant information. This paper describes an algorithm called Self-Organizing Map-based Cluster Merging (SOMCM) using a multi-dimensional matrix composite neural network, and a surface image identification system. The algorithm is designed to investigate the relevance between the main components of bridges and their types of deterioration. 6140 records on bridge maintenance were collected for all bridges in the Taoyuan region of Taiwan in 2021. The SOMCM algorithm involves finding the winner neuron through merging processes. 61 clusters were merged into 8 clusters after a predetermined number of iterations. Clustering analysis of the final 8 clusters revealed 9 major bridge maintenance association rules. Follow-up studies can apply the algorithm to integrate more technologies including GIS coordinates, material availability, real-time traffic conditions, and weather information to be of benefit to engineering practitioners. • The study is to develop a new algorithm, SOM-based Cluster Merging (SOMCM). • The paper is to investigate the relevance for bridge main components and their deterioration types. • The initial result involves 61 clusters being merged to 8 clusters with the predetermined iterations. • The clustering analysis for the final 8 clusters shows 9 major bridge maintenance association rules. [ABSTRACT FROM AUTHOR]

Pb is a toxic heavy metal in contaminated soil and water, resulted from industrial activities, mine exploration, etc. Phosphate solubilizing bacteria are able to secrete organic acids and further to enhance the solubility of phosphates. Enterobacter. sp and geological fluorapatite (FAp) were applied to investigate the biotransformation of Pb 2+ in solution with SO 4 2− , PO 4 3− , and Cl − species by ICP-OES, ATR-IR, XRD, and SEM. Enterobacter. sp can lower pH of the medium to ∼4. Meanwhile, >90% mobile Pb (declining from 1000 to 30 ppm) was immobilized via the combination of Enterobacter. sp and FAp. With the addition of FAp and Pb, pyromorphite was precipitated, but with relatively low content. In contrast, abundant anglesite mineral was formed in such weakly acidic system. These anglesite crystals can even absorb phosphates particles onto their surface. Additionally, geochemical modeling confirms the formation of anglesite and cerussite under weekly acidic and alkalic condition respectively, especially when H 2 PO 4 − concentration <10 −8 mM. Furthermore, the presence of Cl − in solution leads to the formation of chloropyromorphite when H 2 PO 4 − concentration >10 −12 mM, especially under neutral environment. This study explored the biotransformation of Pb in SO 4 -PO 4 -Cl aqueous system and hence provided guidance on bioremediation of Pb by bacteria and FAp. [ABSTRACT FROM AUTHOR]

Most of phosphorus (P) is present as insoluble phosphorus-bearing minerals or organic forms in soil. Geological fluorapatite (FAp) is the dominant mineral-weathering source of P. In this study, FAp was added into water and soil under elevated CO 2 to investigate the pathway of P release. Two types of soils (an acidic soil from subtropical China and a saline-alkali soil from Tibet Plateau, China) with similar total P content were studied. In the solution, increased CO 2 in air enhanced the dissolution of FAp, i.e., from 0.04 to 1.18 ppm for P and from 2.48 to 13.61 ppm for Ca. In addition, release of Ca and P from FAp reached the maximum (2.14 ppm for P and 13.84 ppm for Ca) under the combination of elevated CO 2 and NaCl due to the increasing ion exchange. Consistent with the results from the solution, CO 2 elevation promoted P release more significantly (triple) in the saline-alkali soil than in the acidic soil. Therefore, saline-alkali soils in Tibet Plateau would be an important reservoir of available P under the global CO 2 rise. This study sheds the light on understanding the geological cycle of phosphorus. [ABSTRACT FROM AUTHOR]

The current deterioration inspection method for bridges heavily depends on human recognition, which is time consuming and subjective. This research adopts Self Organizing Map Optimization (SOMO) integrated with image processing techniques to develop a crack recognition model for bridge inspection. Bridge crack data from 216 images was collected from the database of the Taiwan Bridge Management System (TBMS), which provides detailed information on the condition of bridges. This study selected 40 out of 216 images to be used as training and testing datasets. A case study on the developed model implementation is also conducted in the severely damage Hsichou Bridge in Taiwan. The recognition results achieved high accuracy rates of 89% for crack recognition and 91% for non-crack recognition. This model demonstrates the feasibility of accurate computerized recognition for crack inspection in bridge management. [ABSTRACT FROM AUTHOR]

In this paper, we treat optimization problems as a kind of reinforcement learning problems regarding an optimization procedure for searching an optimal solution as a reinforcement learning procedure for finding the best policy to maximize the expected rewards. This viewpoint motivated us to propose a Q-learning-based swarm optimization (QSO) algorithm. The proposed QSO algorithm is a population-based optimization algorithm which integrates the essential properties of Q-learning and particle swarm optimization. The optimization procedure of the QSO algorithm proceeds as each individual imitates the behavior of the global best one in the swarm. The best individual is chosen based on its accumulated performance instead of its momentary performance at each evaluation. Two data sets including a set of benchmark functions and a real-world problem-the economic dispatch (ED) problem for power systems-were used to test the performance of the proposed QSO algorithm. The simulation results on the benchmark functions show that the proposed QSO algorithm is comparable to or even outperforms several existing optimization algorithms. As for the ED problem, the proposed QSO algorithm has found solutions better than all previously found solutions. [ABSTRACT FROM AUTHOR]

Financial crisis has raised concerns for years and its effect on companies influence economies globally. The ability to accurately identify the features responsible for business failure is an important issue in financial decision-making. There is clear need for accurate decision support for both credit granting and monitoring of ongoing health of credit customers. The financial ratios involved provide useful quantitative financial information to both investors and analysts so that they can evaluate the operation of a firm and analyze its position within a sector. This research brings awareness to managers as to which features they have to focus on. All the ratios involved each play a crucial role. In this paper, the Swarm Inspired Projection (SIP) algorithm as a new analysis tool is combined with the Principal Component Analysis (PCA) to determine the weights of the features and to adjust these weights to suit the profitability of these construction companies. The study made use of 1615 effective financial reports from 55 construction companies over the last decade. Based on the 25 ratios used, the PCA incorporating the SIP algorithm gives us an average accuracy rate of 90%. This method provides better reliability in the identification of the principal features in bankruptcy analysis. Corporate financial distress is a major concern to business sectors worldwide; therefore using both clustering and statistical techniques is a better basis in mitigating bankruptcy to both practitioners and researchers. [ABSTRACT FROM AUTHOR]

A computer-vision-based method is widely employed to support the development of a variety of applications. In this vein, this study uses a computer-vision-based method to develop a playful learning system, which is a robot-based learning companion named RobotTell. Unlike existing playful learning systems, a user-centered design (UCD) approach is applied to assess the interface design of the RobotTell System so that the human–robot interaction can be enhanced during the design process. More specifically, the interface of the RobotTell system is assessed based on Nielsen's heuristics. Based on the results of Nielsen's heuristic evaluation, three versions of the RobotTell system were produced. The implications for the design of these three versions of the RobotTell system are discussed. [ABSTRACT FROM PUBLISHER]

Balanced translocation between chromosomes 3q26 and 8q24 is a very rare event. Here we report six patients with t(3;8)(q26;q24) either as a sole or as a part of genetic abnormalities. Five of the six patients were men with ages ranging from 41 to 84 years old. One patient had a long history of granulocyte colony stimulating factor (G-CSF) treatment. Three of the patients were initially diagnosed with acute myeloid leukemia, two with myelodysplastic syndrome and one with chronic myelogenous leukemia with blast crisis. The peripheral blood in all patients showed severe to moderate anemia; one had absolute neutropenia, one with neutrophilia; four had thrombocytopenia, two with thrombocytosis. The bone marrows from all patients showed dysmegakaryopoiesis with additional erythroid (three patients) and granulocytic (two patients) dysplasia. Cytogenetics revealed t(3;8)(q26;q24) as the sole abnormality in three patients. The majority of patients (4/6) had a poor clinical course, with an average survival of 10 months. [ABSTRACT FROM AUTHOR]

Typhoon Morakot has been the most severe typhoon disaster to strike Taiwan in recent decades causing tremendous damage to bridge surroundings in 2009. However, we still lack a means of assessing post-typhoon damage for follow-up rebuilding. This paper presents an integrated model that automatically measures changes in rivers, areas of damage to bridge surroundings, and changes in vegetation. The proposed model is based on a neurofuzzy mechanism enhanced by the self-organising map optimisation algorithm and also includes the particular functions of dilation, erosion, and skeletonisation to deal with river imagery. High resolution FORMOSAT-2 satellite imagery from before and after the invasion period is adopted. A bridge is randomly selected from the 129 destroyed due to the typhoon for applications of the model. The recognition results show that the river average width has increased 66% with a maximum increase of over 200%. The ruined segment of the bridge is located exactly in the most scoured region. There has also been a nearly 10% reduction in the vegetation coverage. The results yielded by the proposed model demonstrate a pinpoint accuracy rate of 99.94%. This study successfully develops a tool for large-scale damage assessment as well as for precise measurement after disasters. [ABSTRACT FROM AUTHOR]

• Phosphate-solubilizing fungi promoted P release from apatite mainly by secreting oxalic acid. • Calcium carbonate inhibited fungal growth via pH buffering rather than sorption of P. • Montmorillonite contributed either positively or negatively to P release. • Goethite inhibited fungal growth and can adsorb most available P. Phosphorus (P) deficiency limits agricultural productivity in many tropical and subtropical soils. The aim of this study was to investigate the influences of typical mineral components and microorganisms on P availability in soil. Three representative soil minerals, i.e., montmorillonite (Mt), calcium carbonate (Cal) and goethite (Gt) were incubated with phosphate-solubilizing fungus (PSF, Aspergillus niger) and apatite (the dominant P source on Earth) in soil. In the control treatment, the fungus promoted phosphate dissolution and P release (from < 1 to 370.40 mg kg−1), primarily via secreting oxalic acid. Mt addition promoted fungal respiration and up-regulated oxaloacetate acetylhydrolase (regulating organic acid secretion, based on RNA sequencing analysis). However, strong adsorption of organic acids by Mt finally caused decline of P availability (from ~ 400 to 24 mg kg−1). Cal addition decreased fungal counts (down to < 5% of the value for the control treatment). Additionally, the consumption of organic acids (by Cal dissolution) further reduced P release. Gt stimulated fungal respiration, whereas its toxicity significantly decreased fungal abundance. Moreover, Gt demonstrated the maximal P adsorption capacity, i.e., 1.266 mg g−1. Therefore, Gt demonstrated evident negativity on P release. Despite the strong sorption of organic acids, Mt showed high potential to promote P release in the long-term due to the enhanced fungal bioactivity. Our results hence indicated that different minerals had totally distinct effects on P release, which should be addressed when investigating biogeochemistry in soil. [ABSTRACT FROM AUTHOR]

• This study aims to develop an augmented reality assisted system on smart-glasses. • The system uses the user's pointing gesture to show an object's relevant information. • The result shows a high object detection rate both in outdoor and indoor lightings. This paper is aimed at the usage of an augmented reality assisted system set up on the smart-glasses for training activities. Literature review leads us to a comparison among related technologies, yielding that Mask Regions with Convolutional Neural Network (R-CNN) oriented approach fits the study needs. The proposed method including (1) pointing gesture capture, (2) finger-pointing analysis, and (3) virtual tool positioning and rotation angle are developed. Results show that the recognition of object detection is 95.5%, the Kappa value of recognition of gesture detection is 0.93, and the average time for detecting pointing gesture is 0.26 seconds. Furthermore, even under different lighting, such as indoor and outdoor, the pointing analysis accuracy is up to 79%. The error between the analysis angle and the actual angle is only 1.32 degrees. The results proved that the system is well suited to present the effect of augmented reality, making it applicable for real world usage. [ABSTRACT FROM AUTHOR]

Abstract: In this study we develop an automatic detection model for discovering erroneous tax reports. The model uses a variety of neural network applications inclusive of the Multi-Layer Perceptrons (MLPs), Learning Vector Quantization (LVQ), decision tree, and Hyper-Rectangular Composite Neural Network (HRCNN) methods. Detailed taxation information from construction companies registered in the northern Taiwan region is sampled, giving a total of 5769 tax reports from 3172 construction companies which make up 35.98% of the top-three-class construction companies. The results confirm that the model yields a better recognition rate for distinguishing erroneous tax reports from the others. The automatic model is thus proven feasible for detecting erroneous tax reports. In addition, we note that the HRCNN yields a correction rate of 78% and, furthermore, generates 248 valuable rules, providing construction practitioners with criteria for preventing the submission of erroneous tax reports. [Copyright &y& Elsevier]

Abstract: In most hospitals, operating rooms are the most costly facilities and consume a large percentage of the hospital recourses. Therefore, an efficient and effective operating room scheduling system is highly demanded for hospitals. In this paper, a SOMO-based approach to solving the operating room scheduling problem is proposed. Computational experiments performed on 100 randomly generated simulations are conducted to test whether the proposed scheduling algorithm can provide appealing arrangements in a reasonable computation time. [Copyright &y& Elsevier]

Abstract: To improve the accurate rate of mapping multi-spectral remote sensing images, in this paper we construct a class of HyperRectangular Composite Neural Networks (HRCNNs), integrating the paradigms of neural networks with the rule-based approach. The supervised decision-directed learning (SDDL) algorithm is also adopted to construct a two-layer network in a sequential manner by adding hidden nodes as needed. Thus, the classification knowledge embedded in the numerical weights of trained HRCNNs can be extracted and represented in the form of If-Then rules. The rules facilitate justification on the responses to increase accuracy of the classification. A sample of remote sensing image containing forest land, river, dam area, and built-up land is used to examine the proposed approach. The accurate recognition rate reaching over 99% demonstrates that the proposed approach is capable of dealing with image mapping. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to constructing a neural tree to integrate the advantages of decision trees and neural networks. The proposed neural tree, called a quadratic-neuron-based neural tree (QUANT), is a tree-structured neural network composed of neurons with quadratic neural-type junctions for pattern classification. A quadratic neuron is capable of forming a hyper-ellipsoid that can be varied in sizes and in locations on the space spanned by the input variables. Via a batch-mode training algorithm, the QUANT grows a neural tree containing quadratic neurons in its nodes. These quadratic neurons recursively partition the feature space into hyper-ellipsoidal-shaped sub-regions. The QUANT has the partial incremental capability so that it does not need to re-construct a new neural tree to accommodate new training data whenever new data are introduced to a trained QUANT. To demonstrate the performance of the proposed QUANT, one pattern recognition problem and the spam e-mail detection problem were tested. [ABSTRACT FROM AUTHOR]

Construction time matters for activities where rental equipment must be used. The building of a secant pile wall requires the rental of equipment and finding the optimal sequence to minimize the construction time is one way to lower construction costs. In this study we develop an effective and efficient optimization algorithm, which we call self-organizing feature map (SOM)-based optimization (SOMO), to minimize the construction time. The algorithm is applied to a case study to obtain the optimal sequences for both primary and secondary bored piles for a secant pile wall. The new SOMO algorithm is developed based on the ability of the human brain to produce topologically ordered mapping, so as to exploit better solutions via updating the weighting vectors of the neurons in a self-organizing topological way that occurs in the evolution of the feature map for optimization. Given detailed building time of the 16 activities of each bored pile, we find that 143.92 h or 27.21% of the original construction can be saved. The optimal sequences for both primary and secondary bored piles are also determined. The practicability of the SOMO algorithm is substantiated. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a new data projection algorithm which was inspired by the foraging behaviors of doves is proposed. We name the new data projection the swarm-inspired projection (SIP) algorithm. The algorithm allows us to visually estimate the number of clusters existing in a data set. Based on the projection result, we may then partition the data set into the corresponding number of clusters. The SIP algorithm regards each data pattern in a data set as a piece of crumb which will be sequentially tossed to a flock of doves on the ground. The doves will adjust their physical positions to compete for crumbs. Gradually, the flock of doves will be divided into several groups according to the distributions of the crumbs. The formed groups will naturally correspond to the underlying data structures in the data set. By viewing the scatter plot of the final positions of the doves we can estimate the number of clusters existing in the data set. Several data sets were used to demonstrate the effectiveness of the proposed SIP algorithm. [Copyright &y& Elsevier]

The conventional self-organizing feature map (SOM) algorithm is usually interpreted as a computational model, which can capture main features of computational maps in the brain. In this paper, we present a variant of the SOM algorithm called the SOM-based optimization (SOMO) algorithm. The development of the SOMO algorithm was motivated by exploring the possibility of applying the SOM algorithm in continuous optimization problems. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited by the SOMO algorithm. In our opinion, the SOMO algorithm not only can be regarded as a biologically inspired computational model but also may be regarded as a new approach to a model of social influence and social learning. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates variations in performance, postures and strains on the hand–arm–shoulder musculature during the operation of a wireless mouse, trackpad and a new input device. The device is held between the flexed index and middle fingers with the palm facing sideways. The buttons and wheels are activated by flexion and/or rolling of the thumb. Eleven males and nine females participated in the study. All subjects performed an aiming task to test the pointing and dragging functions. The results of this study reveal that the new pointing device allowed users to adopt more ergonomic postures and has the advantage of reduced muscular loadings of the upper extremities. Mean (SD) muscular activities (%RVC) using the wireless mouse, the trackpad and the new input device were as follows: trapezius: 3.0 (1.7), 4.4 (2.9) and 1.4 (1.0), and extensor carpi ulnaris: 7.3 (4.4), 14.5 (8.4) and 5.6 (3.1), respectively. The device was used in a variety of hand positions, alternatively. The size of the working area was far greater when the new input device was used than when the two conventional analogues were used. Although reasonable performance was not achieved, the results support recommendations concerning the redesign of the device. The ergonomic efforts in the design of the input device are of heuristic value, providing a basis for future development. [Copyright &y& Elsevier]

Abstract: Many man-made objects are composed of a number of some simple geometric shapes such as lines, circles, rectangles, etc. Therefore, the detection of rectangular objects is an important issue to some practical applications such as the detection of buildings and vehicles in aerial imagery, the detection of license plates in car images, etc. Several methods have been proposed for solving the problem of the detection of rectangular objects. While some approaches are based on the detecting lines, some approaches are based on the Hough transform. Each approach has its own advantages and disadvantages (e.g., computational load). In this paper, we propose a class of neural networks with a special type of neural junctions for the detection of rectangular objects. The proposed neural networks can be trained in either an unsupervised mode or a batch mode. In contrast to some popular clustering algorithms such as the fuzzy -means algorithm and the fuzzy -rectangular shells algorithm, our approach is not based on minimizing an objective function but based on the idea of competitive learning. Based on the idea of competitive learning, the computational load can be decreased. Several data sets were tested to illustrate the effectiveness of our proposed approach. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to incrementally constructing a neural network that is capable of learning new information without forgetting old knowledge. The proposed neural network, called hyper-spherical ARTMAP network (HS-ARTMAP network), is a synthesis of an RBF-network-like module and an ART-like module. The HS-ARTMAP network is trained via a training algorithm similar to the training algorithm for the fuzzy ARTMAP system. To demonstrate the performance of the proposed HS-ARTMAP network, several pattern recognition and function approximation problems were tested. [Copyright &y& Elsevier]

Abstract: A classifier system is a machine learning system that learns syntactically simple string rules (called classifiers) through a genetic algorithm to guide its performance in an arbitrary environment. In a classifier system, the bucket brigade algorithm is used to solve the problem of credit assignment, which is a critical problem in the field of reinforcement learning. In this paper, we propose a new approach to fuzzy classifier systems and a neuro-fuzzy system referred to as ACSNFIS to implement the proposed fuzzy classifier system. The proposed system is tested by the balancing problem of a cart pole and the back-driving problem of a truck to demonstrate its performance. [Copyright &y& Elsevier]

Abstract: In this paper we propose a clustering algorithm to cluster data with arbitrary shapes without knowing the number of clusters in advance. The proposed algorithm is a two-stage algorithm. In the first stage, a neural network incorporated with an ART-like training algorithm is used to cluster data into a set of multi-dimensional hyperellipsoids. At the second stage, a dendrogram is built to complement the neural network. We then use dendrograms and so-called tables of relative frequency counts to help analysts to pick some trustable clustering results from a lot of different clustering results. Several data sets were tested to demonstrate the performance of the proposed algorithm. [Copyright &y& Elsevier]

A rare case of rapidly growing osteosarcoma that developed in the uterine corpus of a 62-year-old woman is presented. The tumor occupied almost the entire pelvic cavity and extended into the abdominal cavity, with marked involvement of the intestines. Histopathologically, the tumor was composed of an osteoblastic component, accompanied by conspicuous bone formation, and a fibroblastic component. The tumor cells were positive for vimentin and osteocalcin, as well as desmin, α-smooth muscle actin and muscle-specific actin, but negative for h-caldesmon. The results indicated myofibroblastic differentiation in a part of the tumor. A review of 14 reported cases and our case of uterine osteosarcoma revealed that this tumor has a biologically aggressive nature, although its histopathological and immunohistochemical features are similar to those of osteosarcomas in soft tissue and bone. As the prognosis of patients with this tumor is poor, it is of importance to differentiate this tumor from other types of tumors arising from the uterine corpus. [ABSTRACT FROM AUTHOR]

Features a study on the model of self-organizing neural networks. Details on the self-organizing feature map algorithm; Background of applying feature maps in clustering data; Description of the self-organizing neural network model; Simulation results; Conclusion.

Presents a study which proposed an approach to forming feature map algorithm. Goal of the Kohonen self-organizing map algorithm; Simulation results; Conclusion.

Carbonate in soil from karst region is a substantial carbon sink on Earth. Many karst regions are covered by P-deficient soil. This study evaluated the influences of phosphate addition on fungal weathering (by typical phosphate-solubilizing fungus Aspergillus niger) of carbonate in the soil with red color from karst region. Two weathering pathways were recognized, i.e., biochemical and biomechanical deterioration. The biochemical pathway was performed by dissolving carbonate via secreting organic acids. Meanwhile, the dominant organic acid, i.e., oxalic acid, induced the formation of calcium oxalate, which prevented the loss of Ca2+ cations. It was estimated that the ideal carbonate solubilization driven by geological fluorapatite and fungal weathering is up to 3.3% per year, based on the equation of 12 × (R Base + R PSF) × m × (A real /A PSF). Moreover, fungal weathering of carbonate is very sensitive to the solubility of phosphates. Phosphates supply essential P source for the fungal growth and subsequently raise water-soluble P content in the soil. The addition of bioapatite (a variety of natural apatite with relatively high solubility) elevated the value to 4.6% (a ~ 40% enhancement compared with FAp). This research hence elucidated the tight correlation between carbonate weathering and P supply. Inorganic C release driven by P availability and microbial weathering should be addressed in karst region. Unlabelled Image • A. niger can release abundant oxalic acids to prevent Ca2+ loss via the formation of Ca-oxalate. • Phosphate addition significantly enhances (up to ~5%) the fungal weathering rate of carbonate in soil. • The low solubility of FAp is the major drawback of P supply and bioweathering of minerals. • Both of biochemical and biomechanical deterioration contributes to fungal weathering of carbonate rocks. • C flux driven by P availability and microbial weathering should be addressed in karst region. [ABSTRACT FROM AUTHOR]

Provides information on a study which developed a novel class of fuzzy hyperrectangular composite neural networks which utilize synchronized phasor measurements. Hybrid training algorithm; Simulation results; Conclusion of the study.

Demonstrates how neuro-fuzzy networks can be constructed and used on-line for monitoring voltage security. Details on the proposed layers of Fuzzy Hyperrectangular Composite Neural Networks (FHRCNN); Features of neuro-fuzzy networks; How the efficacy of the network was tested.

The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user's head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user's head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems. [ABSTRACT FROM AUTHOR]

Most of P is fixed in pedosphere and lithosphere, i.e., not directly available to ecosystem. Interactions between phosphate-solubilizing bacteria and clay minerals contribute heavily to P biogeochemistry. In this study, hydroxylapatite (HAp), montmorillonite (Mt.) and Enterobacter sp. were applied to investigate their influences on P release, sorption, and transformation. Enterobacter sp. promoted the dissolution of HAp via secreting formic acid (pH declined from neutral to ~5.0). However, respiration of the bacteria with addition of Mt. was decreased by ~15.0% after their logarithm growth. SEM analysis also indicated that the number of Enterobacter sp. cells was significantly reduced on Mt. surface. Mt. was hence able to inhibit the activity of the adsorbed bacteria on its surface. Meanwhile, the mobile bacteria helped to transform HAp mineral to nanoparticles via partial dissolution. Despite the low sorption of mobile P anions (< 0.2 mg L−1), Mt. was able to adsorb plenty of HAp nanoparticles. The subsequently enhanced adsorption of P nanoparticles by Mt. protects the environment from P losses. Therefore, interaction of mobile bacteria and clay minerals have a positive feedback in regulating P transportation in long-term. This study elucidated the different roles of mobile and fixed bacterial cells in P biogeochemistry influenced by Mt. • Montmorillonite inhibits activity of the adsorbed bacterial cells on its surface. • Enterobacter sp. promotes hydroxylapatite dissolution via secreting formic acid. • Enterobacter sp. can also transform P minerals to nanoparticles. • The sorption of mobile PO 4 anions by montmorillonite was limited, i.e., < 0.2 mg g−1. • Montmorillonite can adsorb abundant P nanoparticles and hence prevent P losses. [ABSTRACT FROM AUTHOR]

Despite abundant published research on the volatile characterization of mango germplasm, the aroma differentiation of Chinese cultivars remains unclear. Using headspace solid phase microextraction (HS-SPME) coupled with gas chromatography–mass spectrometry (GC-MS), the composition and relative content of volatiles in 37 cultivars representing the diversity of Chinese mango germplasm were investigated. Results indicated that there are distinct differences in the components and content of volatile compounds among and within cultivars. In total, 114 volatile compounds, including 23 monoterpenes, 16 sesquiterpenes, 29 non-terpene hydrocarbons, 25 esters, 11 aldehydes, five alcohols and five ketones, were identified. The total volatile content among cultivars ranged from 211 to 26,022 μg/kg fresh weight (FW), with 123-fold variation. Terpene compounds were the basic background volatiles, and 34 cultivars exhibited abundant monoterpenes. On the basis of hierarchical cluster analysis (HCA) and principal component analysis (PCA), terpinolene and α-pinene were important components constituting the aroma of Chinese mango cultivars. Most obviously, a number of mango cultivars with high content of various aroma components were observed, and they can serve as potential germplasms for both breeding and direct use. [ABSTRACT FROM AUTHOR]

Background: It is generally believed that DNA methylation, as one of the most important epigenetic modifications, participates in the regulation of gene expression and plays an important role in the development of cancer, and there exits epigenetic heterogeneity among cancers. Therefore, this study tried to screen for reliable prognostic markers for different cancers, providing further explanation for the heterogeneity of cancers, and more targets for clinical transformation studies of cancer from epigenetic perspective.Methods: This article discusses the epigenetic heterogeneity of cancer in detail. Firstly, DNA methylation data of seven cancer types were obtained from Illumina Infinium HumanMethylation 450 K platform of TCGA database. Then, differential methylation analysis was performed in the promotor region. Secondly, pivotal gene markers were obtained by constructing the DNA methylation correlation network and the gene interaction network in the KEGG pathway, and 317 marker genes obtained from two networks were integrated as candidate markers for the prognosis model. Finally, we used the univariate and multivariate COX regression models to select specific independent prognostic markers for each cancer, and studied the risk factor of these genes by doing survival analysis.Results: First, the cancer type-specific gene markers were obtained by differential methylation analysis and they were found to be involved in different biological functions by enrichment analysis. Moreover, specific and common diagnostic markers for each type of cancer was sorted out and Kaplan-Meier survival analysis showed that there was significant difference in survival between the two risk groups.Conclusions: This study screened out reliable prognostic markers for different cancers, providing a further explanation for the heterogeneity of cancer at the DNA methylation level and more targets for clinical conversion studies of cancer. [ABSTRACT FROM AUTHOR]

Computed tomography-guided radiofrequency ablation (CT-RFA) and laparoscopic RFA (L-RFA) have been used to treat intrahepatic recurrent small hepatocellular carcinoma (HCC) against the diaphragmatic dome. However, the therapeutic safety, efficacy, and hospital fee have never been compared between the two techniques due to scarcity of cases. In this retrospective study, 116 patients were divided into two groups with a total of 151 local recurrent HCC lesions abutting the diaphragm. We compared overall survival (OS), local tumor progression (LTP), postoperative complications, and hospital stay and fee between the two groups. Our findings revealed no significant differences in 5-year OS (36.7% vs. 44.6%, p = 0.4289) or 5-year LTP (73.3% vs. 67.9%, p = 0.8897) between CT-RFA and L-RFA. The overall hospital stay (2.8 days vs. 4.1 days, p < 0.0001) and cost (¥ 19217.6 vs. ¥ 25553.6, p < 0.0001) were significantly lower in the CT-RFA in comparison to that of L-RFA. In addition, we elaborated on the choice of percutaneous puncture paths depending on the locations of the HCC nodules and 11-year experience with CT-RFA. In conclusion, CT-RFA is a relatively easy and economic technique for recurrent small HCC abutting the diaphragm, and both CT-RFA and L-RFA are effective techniques. [ABSTRACT FROM AUTHOR]

Exceptional preservation of fossils has often been attributed to the actions of bacteria that aid in the preservation of soft tissues that normally decay rapidly. However, it is well known that fungi play a major role in organic matter decomposition, biogeochemical cycling of elements, and metal-mineral transformations in modern ecosystems. Although the fungal fossil record can be traced back over a billion years, there are only a few recorded examples of fungal roles in fossilization. In this research, we have carried out a detailed geobiological investigation on early Pleistocene hyena coprolites (fossilized dung) in an attempt to ascertain possible fungal involvement in their formation. Using an advanced microscopic and mineralogical approach, we found that numerous hydroxyapatite nanofibers (25–34 nm on average), interwoven to form spheroidal structures, constituted the matrix of the coprolites in addition to food remains. These structures were found to be extremely similar in texture and mineral composition to biominerals produced during laboratory culture of a common saprophytic and geoactive fungus, Aspergillus niger , in the presence of a solid source of calcium (Ca) and phosphorus (P). This observation, and our other data obtained, strongly suggests that fungal metabolism can provide a mechanism that can result in fossil biomineralization, and we hypothesize, therefore, that this may have contributed to the formation of well-preserved fossils (Lagerstätten) in the geological record. The characteristic polycrystalline nanofibers may also have served as a potential biosignature for fungal life in early Earth and extraterrestrial environments. • Abundant hydroxyapatite nanofibers in 1- to 2-million-year-old fossil hyena coprolites • A biomineralization process is proposed for nanofiber formation and fossil preservation • A geoactive fungus could precipitate nanofibers similar to those found in coprolites • Fungal-mediated biomineralization of soft tissues is involved in fossilization Combining micro-textural analysis of fossilized feces (coprolites) and biomineral precipitates produced by the geoactive fungus Aspergillus niger , Luo et al. demonstrate that a fungal biomineralization process could underpin the formation of well-preserved fossils in the geological record. [ABSTRACT FROM AUTHOR]

Luminal breast cancer (BC) accounts for a large proportion of patients in BC, with high heterogeneity. Determining the precise subtype and optimal selection of treatment options for luminal BC is a challenge. In this study, we proposed an MSBR framework that integrate DNA methylation profiles and transcriptomes to identify immune subgroups of luminal BC. MSBR was implemented both on a key module scoring algorithm and "Boruta" feature selection method by DNA methylation. Luminal A was divided into two subgroups and luminal B was divided into three subgroups using the MSBR. Furthermore, these subgroups were defined as different immune subgroups in luminal A and B respectively. The subgroups showed significant differences in DNA methylation levels, immune microenvironment (immune cell infiltration, immune checkpoint PD1/PD-L1 expression, immune cell cracking activity (CYT)) and pathology features (texture, eccentricity, intensity and tumor-infiltrating lymphocytes (TILs)). The results also showed that there is a subgroup in both luminal A and B that has the benefit from immunotherapy. This study proposed a classification of luminal BC from the perspective of epigenetics and immune characteristics, which provided individualized treatment decisions. [ABSTRACT FROM AUTHOR]

Studies on the recycling of uranium, such as its usage as new materials, can promote the safe treatment of nuclear waste. In this work, with the direction of transition-metal complex cations, three uranyl–organic frameworks (UOFs) with different dimensions have been synthesized via a hydrothermal reaction, i.e. [Cu(phen)2(SO4)(UO2)(TDC)·H2O]n (1), {[Zn(phen)3][(UO2)2(TDC)3(TDC)]·6H2O}n (2), and {[Cd(phen)3][(UO2)2(TDC)2(OX)·2H2O]}n (3), (TDC = thiophene-2,5-dicarboxylic acid, OX = oxalate, and phen = 1,10-phenanthroline). With the presence of SO42−, 1 presents a rare neutral 1-D Cu/U heterometallic chain. Under the similar [Zn(phen)3]2+ and [Cd(phen)3]2+ templates, 2 is a 2-D [(UO2)2(TDC)3]n planar non-interpenetrated honeycomb layer, but 3 presents an interesting twofold interpenetrated (6,3) sheet structure, which is led by the larger cavity of the hexagonal motif with the presence of the second ligand OX. This trend could be helpful for the oriented construction of UOFs. Intra- and inter-layer hydrogen bonds between the honeycomb structure and guest cations and π–π stacking interactions contribute to the stabilization of the overall framework. Complete quenching of uranyl luminescence via nonradiative decay can be observed in 1, and 2 and 3 emit yellow fluorescence with well-resolved vibronic fine structures. The interpenetrated arrangements could give rise to weakened uranyl emission. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To control the workflow for surgical patients, we in-cooperate radio-frequency identification (RFID) technology to develop a Patient Advancement Monitoring System (PAMS) in operation theater. Methods: The web-based PAMS is designed to monitor the whole workflow for the handling of surgical patients. The system integrates multiple data entry ports Across the multi-functional surgical teams. Data are entered into the system through RFID, bar code, palm digital assistance (PDA), ultra-mobile personal computer (UMPC), or traditional keyboard at designated checkpoints. Active radio-frequency identification (RFID) tag can initiate data demonstration on the computer screens upon a patient''s arrival at any particular checkpoint along the advancement pathway. Results: The PAMS can manage the progress of operations, patient localization, identity verification, and peri-operative care. The workflow monitoring provides caregivers’ instant information sharing to enhance management efficiency. Conclusion: RFID-initiate surgical workflow control is valuable to meet the safety, quality, efficiency requirements in operation theater. [Copyright &y& Elsevier]

Abstract: Prevention of financial risk is one of the major tasks that construction companies have to pay attention to. Using derivatives to avoid such risks is a practical strategy, but is heavily dependent on the traders’ skills and accuracy of predictions. The purpose of this study is to develop an automatic expert model using a rule extraction based approach that provides practitioners with a prediction tool for the hedging of financial risks through the use of derivatives. Data for the study include 780 quarterly financial statements collected from 2002 to 2006, based on public information from 39 listed construction companies in Taiwan. Statements with incomplete and missing data are eliminated, leaving 672 with which to construct the rule extraction based model, the Hyper Rectangular Composite Neural Networks (HRCNNs). After factor dimension reduction, only 16 financial ratios out of all revealed ratios are left to be used as input variables. The HRCNNs yield an 80.6% successful classification rate. With these 16 financial ratios and the proposed model, derivative use to hedge financial risk can be established for the benefit of the construction practitioners. [Copyright &y& Elsevier]

Abstract: Optimization of construction time helps practitioners save time and money. In this study we compare two optimization methods — self organizing map based optimization (SOMO) and particle swarm optimization (PSO) — for minimizing the construction time of a secant pile wall. The comparison is based on data from 207 primary and secondary bored piles for a secant pile wall. The detailed construction time is measured in minutes and broken down into 16 work activities for each unit of the wall which is then used to yield the optimal construction sequence and time. Both optimization methods can be computed quickly but, SOMO yields a more efficient construction sequence with a shorter construction time. The comparison leads to several findings. Particles with randomly selected velocities and positions may lead to convergence at a local optimization. The PSO comparison mechanism for the yielding of optimization limits the process to the winner''s neighbors, which may also converge to a local optimization. The case study shows the superiority of SOMO and provides answers to the dilemmas using the smallest hyper-plane, the entire comparison mechanism to yield the optimization, and the mechanism for weight adjustment between the winner neuron and its neighbors. [Copyright &y& Elsevier]

The article focuses on the study of Dinesh Rakheja and colleagues related to the incidence of isocitrate dehydrogenase (IDH) mutations in acute myeloid leukemia (AML) with cuplike nuclei. It mentions that the study is undertaken through a review of Wright-Giemsa-stained peripheral blood, hematoxylin-eosin-stained bone marrow dot, and bone marrow aspirate smears. The results of the study show that IDH mutations is associated with outcome in AML patients.

Summary: Successful application of microorganisms to heavy metal remediation depends on their resistance to toxic metals. This study contrasted the differences of tolerant mechanisms between Pb2+ and Cd2+ in Enterobacter sp. Microbial respiration and production of formic acid showed that Enterobacter sp. had a higher tolerant concentration of Pb (>1000 mg l−1) than Cd (about 200 mg l−1). Additionally, SEM confirmed that most of Pb and Cd nanoparticles (NPs) were adsorbed onto cell membrane. The Cd stress, even at low concentration (50 mg l−1), significantly enlarged the sizes of cells. The cellular size raised from 0.4 × 1.0 to 0.9 × 1.6 μm on average, inducing a platelet‐like shape. In contrast, Pb cations did not stimulate such enlargement even up to 1000 mg l−1. Moreover, Cd NPs were adsorbed homogeneously by almost all the bacterial cells under TEM. However, only a few cells work as 'hot spots' on the sorption of Pb NPs. The heterogeneous sorption might result from a 'self‐sacrifice' mechanism, i.e., some cells at a special life stage contributed mostly to Pb sorption. This mechanism, together with the lower mobility of Pb cations, caused higher microbial tolerance and removal efficiency towards Pb2+. This study sheds evident contrasts of bacterial resistance to the two most common heavy metals. [ABSTRACT FROM AUTHOR]

Phosphorus (P) is a major limiting nutrient for plant growth. Clay minerals are able to work as active centers in soil system due to their high surface area and CEC. Yet, effects of clay minerals on P biogeochemical cycle driven by microorganisms are still unclear. In this study, hydroxylapatite and three typical clay minerals (kaolinite, palygorskite, and montmorillonite) were incubated with Aspergillus niger to investigate microbial influences on P release and adsorption. Due to the mineral particles, hyphae wrapped small montmorillonite particles (<10 μm, confirmed by SEM and TEM), which promoted microbial bioactivities, e.g., respiration and acid production. P consumption by the fungus lowered the available P from 143 to 68 ppm. Meanwhile, ATR-IR spectra and HPLC analysis confirmed the intense adsorption of oxalic acid (the primary microbial secretion) onto montmorillonite. Despite the higher acid production, both the high adsorption capability of the clay and the acid consumed by phosphate dissolution caused that pH values increased from ~2 to ~4 after montmorillonite addition. In contrast, low CEC, dispersibility, and surface area of kaolinite and palygorskite limited their ability to enhance microbial activities and the subsequent interactions with hyphae. Therefore, clay minerals, especially montmorillonite, can drive P transport with the favor from fungi in ecosystem. • Mon buffers the solution via adsorption of organic acids. • Considerably large Kao and Pal particles (>10 μm) are surrounded by hyphae. • Hypha-wrapped small Mon particles (<<10 μm) can promote microbial activities. • Mon play a dominant role in regulating the P biogeochemical cycle. [ABSTRACT FROM AUTHOR]

Background: Gallbladder cancer (GBC) is the main pathological type of biliary tract cancers. Due to its aggressive nature, GBC is usually diagnosed at advanced stages with limited therapeutic options and poor outcome, especially after failure of chemotherapy. Case presentation: Herein, we report a recurrent metastatic GBC patient with strong programmed death-ligand 1 (PD-L1) expression (≥50%) who obtained a significant response to radiotherapy combining nivolumab treatment. Conclusions: To our knowledge this is the first case presenting significant nivolumab response in a Chinese GBC patient. This remarkable response was most likely associated with the strong PD-L1 expression, and indicated that PD-L1 expression could be considered as a biomarker for nivolumab treatment in metastatic gallbladder cancer. However, more studies are needed for validation. [ABSTRACT FROM AUTHOR]

Application of biochar in heavy metal remediation suffers from lack of long-term stability. Phosphate-solubilizing bacteria (PSB) are able to elevate P release and the subsequent reaction with Pb to form stable pyromorphite. This study investigated the feasibility of applying PSB modified biochar to enhance immobilization of Pb2+. An alkaline biochar produced from rice husk (RB) and a slightly acidic biochar produced from sludge (SB) were selected. It showed that the biochars can effectively remove Pb2+ via adsorption, i.e., aqueous Pb concentrations after RB and SB addition were reduced by 18.61 and 53.89% respectively. The addition of PSB increased the Pb2+ removal for both biochars (to 24.11 and 60.85%, respectively). In particular, PSB significantly enhanced the formation of stable pyromorphite on surface of SB. This is due to that the evenly distributed PSB enhanced P release and regulated pH on the biochar surface. Moreover, small particles (<0.074 mm) showed their higher ability to induce the formation of pyromorphite, for both RB and SB. Nevertheless, SB demonstrated higher capability of sorption, together with its more abundant P content, which provided a more suitable platform to attract PSB to immobilize heavy metals. Therefore, the combination of biochar and PSB is a promising candidate material for heavy metal remediation. However, the types and particle size distribution of biochar should be addressed. Unlabelled Image • The combination of biochar and phosphate-solubilizing bacteria (PSB) successfully removed Pb2+ from organic medium. • XRD, ATR-IR and SEM/EDS show the formation of pyromorphite on biochar by PSB. • The decomposition of phosphorus compounds by bacteria and the increase of pH value were helpful in the solubilization of P and the subsequent formation of pyromorphite • P release from medium and biochar significantly promotes formation of pyromorphite. • PSB contribute to the decreased formation of cerussite on biochar surface. [ABSTRACT FROM AUTHOR]

Summary: Environmental microorganisms have been widely applied in heavy metal remediation. This study explored the mechanisms of lead tolerance of two typical filamentous fungi, Aspergillus niger and Penicillium oxalicum. It is shown that the mechanisms of reducing Pb toxicity by these two fungi have three major pathways. The secreted oxalic acid can react with Pb (II) to form insoluble Pb minerals, primarily lead oxalate. Then, the enhanced biosorption via forming new border of cell wall prevents the transportation of Pb (II) into hypha. In addition, the fungal activity could be maintained even at high Pb concentration due to the intracellular accumulation. It was confirmed that A. niger has the higher Pb tolerance (up to 1500 mg l−1 Pb level) compared with P. oxalicum (up to 1000 mg l−1). Meanwhile, Pb levels below 1000 mg l−1 partially stimulate the bioactivity of A. niger, which was confirmed by its elevated respiration (from 53 to 63 mg C l−1 medium h−1). This subsequently enhanced microbial functions of A. niger to resist Pb toxicity. A better understanding of Pb tolerance of these two fungi sheds a bright future of applying them to remediate lead‐contaminated environments. [ABSTRACT FROM AUTHOR]

Bioapatite (BAp) is regarded as an effective material to immobilize lead (Pb 2+ ) via the formation of stable pyromorphite. However, when applied in contaminated soil, due to its low surface area and low adsorption capacity, BAp might not sufficiently contact and react with Pb 2+ . Biochar, a carbon storage material, typically has high surface area and high adsorption capacity. This study investigated the feasibility of using biochar as a reaction platform to enhance BAp immobilization of Pb 2+ . An alkaline biochar produced from wheat straw pellets (WSP) and a slightly acidic biochar produced from hardwood (SB) were selected. The results of aqueous adsorption showed the combination of biochar (WSP or SB) and BAp effectively removed Pb 2+ from the aqueous solution containing 1000 ppm Pb 2+ . XRD, ATR-IR, and SEM/EDX results revealed the formation of hydroxypyromorphite on both biochars’ surfaces. This study demonstrates that biochars could act as an efficient reaction platform for BAp and Pb 2+ in aqueous solution due to their high surface area, porous structure, and high adsorption capacity. Therefore, it is mechanistically feasible to apply biochar to enhance BAp immobilization of Pb 2+ in contaminated soil. [ABSTRACT FROM AUTHOR]

Purposes: The purpose of this study was to evaluate a three-dimensional, virtual reality system for vestibular rehabilitation in patients with intractable Ménière's disease and chronic vestibular dysfunction. Methods: We included 70 patients (36 for study, 34 as control) with a chronic imbalance problem caused by uncompensated Ménière's disease. The virtual reality vestibular rehabilitation comprised four training tasks (modified Cawthorne-Cooksey exercises: eye, head, extension, and coordination exercises) performed in six training sessions (in 4 weeks). Measurements of the task scores and balance parameters obtained at the baseline and after final training sessions were compared. Results: A significant improvement was observed in extension and coordination scores. Patients in the early stages of Ménière's disease had a significantly greater improvement in the center of gravity sway and trajectory excursion in the mediolateral direction than did patients in the late stages of Ménière's disease. Mild functional disability attributable to Ménière's disease was a predictor of improvement in the statokinesigram and maximum trajectory excursion in the anteroposterior direction after rehabilitation. The control group showed no significant improvement in almost all parameters. Conclusion: Virtual reality vestibular rehabilitation may be useful in patients with Ménière's disease, particular those in the early stages or having mild functional disability. [ABSTRACT FROM AUTHOR]