Efficient Reduction Method Research Articles

A novel optimised method for speckle reduction in medical ultrasound images

The advancement of medical imaging techniques evolving from X-ray to PET images and the medical image analysis helped medical experts to detect, diagnose and offer treatments for complex disorders and deadly diseases in the human body. Among the various modalities used, Ultrasound imaging is the most widely accepted modality because of its affordability, non-invasive nature and various other features. But the presence of speckle noise in ultrasound image lowers the image quality and reduces diagnostic value. This article states an improved hybrid speckle noise reduction method, a combined application of Kuan and non-local means filters. In this method, Kuan filter is used to sharpen the edges and thereafter the speckle noise elimination is done by using the non-local means. In addition, the performance of the proposed hybrid filter and its design parameters are optimised by using a meta-heuristic called grey wolf optimiser. The performance of hybrid method is evaluated by analysing a chosen set of well-known post filtering methods used for speckle reduction with given ultrasound B-mode images. The comparison of test results using remarkable performance metrics and computation time demonstrate that the hybrid method can be used as the efficient speckle reduction method for image analysis.

Introducing oxygen vacancies to NiFe LDH through electrochemical reduction to promote the oxygen evolution reaction.

The transition metal hydroxide NiFe LDH is a promising oxygen evolution reaction (OER) catalyst. Surface engineering, such as the introduction of oxygen vacancies into NiFe LDH, has been reported to further improve the OER performance; however, searching a facile approach remains an issue. In this work, we report a novel and efficient electrochemical reduction method for in situ introduction of oxygen vacancies into NiFe LDH laminates by applying a constant negative voltage. The results show that the reduced NiFe LDH (denoted as r-NiFe LDH) exhibits enhanced OER performance versus its counterpart NiFe due to the increase of oxygen vacancy density, the electrochemically active surface area, wetting ability, and the significant electron transfer rate. In 1 M KOH, the r-NiFe LDH shows a high current density of 110 mA cm-2 at 1.60 V (vs. RHE), which is 2.8 times the current density of NiFe LDH (40 mA cm-2), as well as the long-term stability of 100 h. This electroreduction method is also applicable to other LDH materials loaded by different current substrates or synthesized by various methods, demonstrating its universality for the enhancement of the OER activity of LDH electrocatalysts.

Effect of carbon on the co-presence of metallic tungsten as a nucleation agent and Eu2+ in glass: crystallization of CaO-Al2O3-SiO2 glass probed with Eu2+ luminescence.

This study demonstrated simple redox control in glasses by improving the method used to added glass raw materials. Specifically, the effect of carbon on the co-presence of metallic tungsten (W) particles as nucleation agents and Eu2+ ions in CaO-Al2O3-SiO2 (CAS) glass was investigated via their crystallization to form CAS glass-ceramics (GCs). In this study, the glass specimens were prepared by mixing glass cullet containing metallic W particles and Eu2+ ions, respectively, with a glass batch containing carbon. Whereas the glass specimen was yellowish because of the presence of Eu2+ when carbon was not added during the remelting process, the glass specimen prepared with carbon was black because of the presence of metallic W particles. In addition, this specimen displayed the 470 nm emission band in its fluorescence spectrum recorded under 393 nm excitation, which was attributed to the presence of Eu2+. According to the fluorescence and transmission spectra, the glass specimen showed a darker coloration and more intense 470 nm emission band compared with the specimen prepared by the conventional melting method that included a remelting process. These results indicated that metallic W and Eu2+ were reduced with greater efficiency by the melting method that involved mixing the glass cullet and batch. In addition, the heat-treated glass specimen prepared by the aforementioned mixing method contained a greater amount of metastable CaAl2Si2O8 with increasing heat treatment time as revealed by X-ray diffraction analysis and scanning electron microscopy observation. The intensity of the 470 nm emission band decreased with increasing intensity of the band at 420 nm because of the incorporation of Eu2+ into the crystalline phase, and the increase in intensity of the 420 nm band was lineally proportional to the volume fraction of the crystallized glass specimens. The results therefore indicated that the co-presence of metallic W particles as nucleation agents and Eu2+ as a probe for tracking the crystallization process was achieved by the addition of carbon during the remelting process of mixed cullet containing W and Eu2+ through crystallization of the CAS glass. The results thus demonstrate the importance of improving the method used to added glass raw materials.

Data Analytics for Noise Reduction in Optical Metrology of Reflective Planar Surfaces

On-line data collection from the manufactured parts is an essential element in Industry 4.0 to monitor the production’s health, which required strong data analytics. The optical metrology-based inspection of highly reflective parts in a production line, such as parts with metallic surfaces, is a difficult challenge. As many on-line inspection paradigms require the use of optical sensors, this reflectivity can lead to large amounts of noise, rendering the scan inaccurate. This paper discusses a method for noise reduction and removal in datapoints resulting from scanning the reflective planar surfaces. Utilizing a global statistic-based iterative approach, noise is gradually removed from the dataset at increasing percentages. The change in the standard deviation of point-plane distances is examined, and an optimal amount of noisy data is removed to reduce uncertainty in representing the workpiece. The developed algorithm provides a fast and efficient method for noise reduction in optical coordinate metrology and scanning.

Characteristics of postoperative pain after mini invasive and conventional mitral surgery in Hanoi Heart Hospital

Introduction: Mini invasive mitral valve surgery had some advantages over conventional surgery in recovery and cosmestic results. Postoperative pain was an interested factor for comparing these two methods, with reported results remained unclear. We realized this study for evaluate the characteristics of postoperative pain between mini invasive and conventional mitral surgery in Hanoi Heart Hospital.
 Methods: A cross sectional study on patient underwent mitral valve replacement with mini invasive and conventional sternotomy approach.
 Results: There were 66 patients in the study, divided into 2 group: 43 patient underwent conventional surgery and 23 patient underwent mini invasive surgery. Mean age was 50,7 ± 9,3, female was 78,8% and mean BMI was 20,9 ± 2,7. Erector spinae plane (ESP) block was performed on 16 patients (24,2%) of conventional surgery group. The VAS score at 3 days, 7 days , and 1 month after surgery was 7,7 ± 1,2; 5,2 ± 1,5; 0,9 ± 1,1 in sternotomy group and 6,6 ± 1,6; 3,8 ± 1,5; 1,7 ± 0,9 in mini invasive group, respectively. In patients with ESP block, mean dose of morphine administered in first two days was 0 and 2,3 ± 5,0 mg/kg comparing to the dose of 19,7 ± 5,5 và 17,7 ± 7,1 in sternotomy patients without ESP block.
 Conclusions: Postoperative pain reduction may not be an advantage of mini invasive mitral surrgery comparing to conventional surrgery, but the time for pain suffering in this group was shorter. ESP was an efficient method for early pain reduction after conventional surgery.

The development of a brief and practical work safety climate measure

BackgroundSafety climate represents employees’ shared perceptions of the value an organisation places on safety. Frequently safety climate measures are lengthy to comprehensively assess critical work safety factors, which makes their completion time consuming, particularly when used in conjunction with other work or performance measures. Consequently, organisations only employ such measures during safety crises, compromising their usefulness as a leading work safety indicator for identifying and remediating emerging safety issues before they become critical. ObjectiveThis study used statistical and practical methodological procedures to develop a brief safety climate measure for the regular monitoring and remediation of safety issues. MethodAn existing comprehensive and valid work safety climate measure (Nordic Occupational Safety Climate Questionnaire [NOSACQ-50]; Kines et al., 2011), was administered to disability support workers (N = 366) and hospitality employees (N = 111). Appropriate statistical procedures and practical usefulness measures including expert opinions of work health and safety researchers (N = 5) and practitioners (N = 14), correlations with physical and mental health measures, and item readability contributed to the selection of the most reliable and practically useful items for the brief measure. ResultsUtilising statistical and practical usefulness methods, a brief 24-item safety climate questionnaire was developed. ConclusionStudy results support the usefulness of this brief 24-item work safety climate measure for both practice and research purposes. The study also demonstrated a procedurally sound and practically efficient item reduction method that considers both statistical findings and methods that enhance the practical usefulness of the measure in applied environments.

Co-hydrothermal carbonization of sewage sludge and polyvinyl chloride: Hydrochar properties and fate of chlorine and heavy metals

An efficient method of synergetic dechlorination and reduction of heavy metals risk is crucial for the clean resource utilization of sewage sludge (SS) and polyvinyl chloride (PVC). In this work, SS was upgraded to hydrochar using co-hydrothermal carbonization (HTC) with PVC at different mixing ratio (30─70%) at 230 °C. Transformation behaviors of chlorine and heavy metals in hydrochars, along with fuel properties, were evaluated. The results illustrated that Mn and Zn could be effectively removed during co-HTC and low ratio addition of PVC was beneficial for reducing the toxicity of Ni and As. Meanwhile, compared with the HTC of single PVC, co-HTC with SS could further improve the dechlorination efficiency (69.49─81.35%). Particularly, the blending ratio of SS to PVC at 7:3 showed high fuel ratio and HHV (13.58MJ/kg) with desirable combustion performance. These findings offered a potential approach to convert SS and PVC into clean solid fuel.

An innovative, highly efficient and expertise-free mechanism reduction method: important species identification through species’ participation in element fluxes

An innovative kinetic mechanism reduction method, Species’ Participation in Element Fluxes (SPEF), is developed in this study. It utilises species’ participation in element fluxes to evaluate the importance of the species during the combustion process. The SPEF method has two main benefits: (1) it targets at C, H and O elements instead of pre-selected target species and has only one variable threshold on species importance, which makes it feasible to obtain satisfactory skeletal mechanisms for researchers even without any mechanism reduction expertise; (2) it shows significantly low time complexity, which helps to achieve high computational efficiency in the reduction process. Three mechanisms with different sizes were reduced and utilised to investigate the reduction capability of the SPEF method. It has been observed that the reduction performance of the SPEF method is competitive with other widely used directed relation graph with error propagation (DRGEP), path flux analysis (PFA) and global pathway selection (GPS) methods. The effectiveness of the SPEF method was further demonstrated by extensively validating the SPEF-reduced mechanisms against the detailed mechanisms. In addition to the application to static reduction, this method could also be easily utilised for dynamic reduction to save computational efforts in reactive flow simulation while retaining satisfactory accuracy. An example was given by integrating the SPEF method into the KIVA4 code package to dynamically reduce chemical mechanisms in 3-D engine simulations. It has been proved that even for 3-D engine simulations with a highly compact mechanism, computational cost can still be saved in a considerable scale using the SPEF method.

Graphitization with Suppressed Carbon Loss for High-Quality Reduced Graphene Oxide

An efficient reduction method to obtain high-quality graphene sheets from mass-producible graphene oxide is highly desirable for practical applications. Here, we report an in situ deoxidation and graphitization mechanism for graphene oxide that allows for high-quality reduced graphene oxide sheets under the low temperature condition (<300 °C) by utilizing a well-known Fischer-Tropsch reaction catalyst (CuFeO2). By applying modified FTR conditions, where graphene oxide was reduced on the catalyst surface under the hydrogen-poor condition, deoxidation with much suppressed carbon loss was possible, resulting in high-quality graphene sheets. Our experimental data and density functional theory calculations proved that reduction which occurred on the CuFeO2 surface preferentially removed adsorbed oxygen atoms in graphene oxide sheets, leaving dissociated carbon structures to be restored to a near-perfect few-layer graphene sheet. TGA-mass data revealed that GO with catalysts released 92.8% less carbon-containing gases than GO without catalysts during the reduction process, which suggests that this process suppressed carbon loss in graphene oxide sheets, leading to near-perfect graphene. The amount of oxygen related to the epoxide group in the basal plane of GO significantly decreased to near zero (from 43.84 to 0.48 at. %) in catalyst-assisted reduced graphene oxide (CA-rGO). The average domain size and the density of defects of CA-rGO were 4 times larger and 0.1 times lower than those for thermally reduced graphene oxide (TrGO), respectively. As a result, CA-rGO had a 246 and 8 times lower electrical resistance than TrGO and CVD-graphene. With these performances, CA-rGO coated paper connected to a coin-cell battery successfully lit an LED bulb, and CA-rGO itself acted as an efficient catalyst for both the hydrogen evolution reaction and the oxygen evolution reaction.

Efficient Dimensionality Reduction Methods in Reservoir History Matching

Production forecasting is the basis for decision making in the oil and gas industry, and can be quite challenging, especially in terms of complex geological modeling of the subsurface. To help solve this problem, assisted history matching built on ensemble-based analysis such as the ensemble smoother and ensemble Kalman filter is useful in estimating models that preserve geological realism and have predictive capabilities. These methods tend, however, to be computationally demanding, as they require a large ensemble size for stable convergence. In this paper, we propose a novel method of uncertainty quantification and reservoir model calibration with much-reduced computation time. This approach is based on a sequential combination of nonlinear dimensionality reduction techniques: t-distributed stochastic neighbor embedding or the Gaussian process latent variable model and clustering K-means, along with the data assimilation method ensemble smoother with multiple data assimilation. The cluster analysis with t-distributed stochastic neighbor embedding and Gaussian process latent variable model is used to reduce the number of initial geostatistical realizations and select a set of optimal reservoir models that have similar production performance to the reference model. We then apply ensemble smoother with multiple data assimilation for providing reliable assimilation results. Experimental results based on the Brugge field case data verify the efficiency of the proposed approach.

Dimensionality reduction and unsupervised clustering for EELS-SI

A novel combination of machine learning algorithms is proposed for the differentiation of distinct spectra in a large electron energy loss spectroscopy spectrum image (EELS-SI) dataset. For clustering of the EEL spectra including similar fine structures in an efficient space, linear and nonlinear dimensionality reduction methods are used to project the EEL spectra onto a low-dimensional space. Then, a density-based clustering algorithm is applied to distinguish the meaningful data clusters. By applying this strategy to various experimental EELS-SI datasets, differentiation of several groups of EEL spectra representing specific fine structures was achieved. It is possible to investigate particular fine structures by averaging all of the spectra in each cluster. Also, the spatial distributions of each cluster in the scanning regions can be observed, which enables investigation of the locations of different fine structures in materials. This method does not require any prior knowledge, i.e., it is a data-driven analysis; therefore, it can be applied to any hyperspectral image.

Downhole circulating cooling device

This paper presents a kind of downhole circulating cooling device in high temperature environment. The device consists of the following components: compressor, condenser, expansion valve, and heat exchanger. The heat exchanger consists of an inner chamber located around the instrument to be cooled and an outer chamber located around the inner chamber. As the cooling fluid passes through the inner chamber, it absorbs heat from the instrument, and then the cooling fluid enters the outer chamber to absorb heat from the wellbore. After absorbing heat, the cooling liquid can be pressurized by the compressor, condensed by the condenser, and then selectively released back to the inner chamber after cooling by the expansion valve, so as to realize the continuous circulation cooling device, so as to realize the thermal insulation and cooling of the instrument. This paper provides an efficient, recyclable and time-saving method for temperature reduction in underground high-temperature environment.

Electroselective and Controlled Reduction of Cyclic Imides to Hydroxylactams and Lactams.

An efficient and practical electrochemical method for selective reduction of cyclic imides has been developed using a simple undivided cell with carbon electrodes at room temperature. The reaction provides a useful strategy for the rapid synthesis of hydroxylactams and lactams in a controllable manner, which is tuned by electric current and reaction time, and exhibits broad substrate scope and high functional group tolerance even to reduction-sensitive moieties. Initial mechanistic studies suggest that the approach heavily relies on the utilization of amines (e.g., i-Pr2NH), which are able to generate α-aminoalkyl radicals. This protocol provides an efficient route for the cleavage of C-O bonds under mild conditions with high chemoselectivity.

Study on influencing factors and mechanism of high-quality tungsten carbide nanopowders synthesized via carbothermal reduction

In this study, high-quality WC powders with 100 nm were synthesized using a simple and efficient carbothermal reduction method. The effects of carbon source and temperature on the synthesis process, nucleation and growth mechanism of WC nanopowders were investigated in detailed. The results show that acetylene black with small particle size and good dispersion is the optimal carbon source to prepare WC nanopowders. The uniform precursor synthesized by acetylene black can significantly increase the contact probability of WO3 and C particles, greatly reduce the activation energy, and effectively improve the reaction efficiency during subsequent carbothermal reduction process. Moreover, the formation temperatures of WC are reduced to 1000 °C compared with other three carbon source. Further increasing carbothermal reduction temperature can provide the driving force of diffusing atoms, which promotes the carbothermal reduction reaction, but also significantly increases the diffusion rate of grain boundary resulting in rapid grain growth.

An efficient feature reduction method for the detection of DoS attack

Feature selection or reduction is a significant process for intrusion detection system (IDS) in finding optimal features. Irrelevant features present in the dataset increase load on computing resources and affect the performance of the system. The present study proposes a feature reduction method based on the combination of filter-based feature reduction algorithms, namely Information Gain Ratio (IGR), Correlation (CR), and ReliefF (ReF). The system initially obtains feature subsets for each classifier based on average weight and further Subset Combination Strategy (SCS) is applied. The proposed feature reduction method results in 24 reduced features for CICIDS 2017 DoS dataset. The proposed method shows an improved performance compared to the current state-of-the-art systems on CICIDS 2017 dataset. The proposed method has also been tested and compared with the current state-of-the-art systems on KDD Cup 99 dataset.

Hot injection-induced synthesis of ZnCdS–rGO/MoS2 heterostructures for efficient hydrogen production and CO2 photoreduction

A highly efficient hybrid ZnCdS–rGO/MoS2 heterostructure is successfully synthesized through a hot injection method for efficient photocatalytic hydrogen evolution and CO2 reduction.

HB-PLS: A statistical method for identifying biological process or pathway regulators by integrating Huber loss and Berhu penalty with partial least squares regression

Gene expression data features high dimensionality, multicollinearity, and non-Gaussian distribution noise, posing hurdles for identification of true regulatory genes controlling a biological process or pathway. In this study, we integrated the Huber loss function and the Berhu penalty (HB) into partial least squares (PLS) framework to deal with the high dimension and multicollinearity property of gene expression data, and developed a new method called HB-PLS regression to model the relationships between regulatory genes and pathway genes. To solve the Huber-Berhu optimization problem, an accelerated proximal gradient descent algorithm with at least 10 times faster than the general convex optimization solver (CVX), was developed. Application of HB-PLS to recognize pathway regulators of lignin biosynthesis and photosynthesis in <i>Arabidopsis thaliana</i> led to the identification of many known positive pathway regulators that had previously been experimentally validated. As compared to sparse partial least squares (SPLS) regression, an efficient method for variable selection and dimension reduction in handling multicollinearity, HB-PLS has higher efficacy in identifying more positive known regulators, a much higher but slightly less sensitivity/(1-specificity) in ranking the true positive known regulators to the top of the output regulatory gene lists for the two aforementioned pathways. In addition, each method could identify some unique regulators that cannot be identified by the other methods. Our results showed that the overall performance of HB-PLS slightly exceeds that of SPLS but both methods are instrumental for identifying real pathway regulators from high-throughput gene expression data, suggesting that integration of statistics, machine leaning and convex optimization can result in a method with high efficacy and is worth further exploration.

UV-L/sulfite advanced reduction process as an efficient method for chlorate reduction in water

UV-L/sulfite advanced reduction process as an efficient method for chlorate reduction in water

A Novel Single Fuzzifier Interval Type-2 Fuzzy C-Means Clustering With Local Information for Land-Cover Segmentation

In the process of land cover segmentation from remote sensing image, there are some uncertainties such as “significant difference in class density”, “different objects with same spectrum”, and “same object with different spectra”. Existing fuzzy c-means clustering is not sufficient to describe the high-order fuzzy uncertainties and cannot achieve accurate segmentation. Type-2 fuzzy set is perfect for handling with interclass multiple uncertainties, and clustering algorithm can suppress the noise of remote sensing image effectively by incorporating local information. Therefore, on the basis of integrating local information, this article proposes a robust single fuzzifier interval type-2 fuzzy local C-means clustering based on adaptive interval-valued data for land cover segmentation. First, interval-valued data modeling is performed for remote sensing data, and remote sensing features are represented as interval-valued vectors, and the robust interval-valued distance measure that can maximize the distance between interval-valued numbers is used to generate an interval type-2 fuzzy set through robust fuzzy clustering. Second, this article adopts an efficient type reduction method to seek equivalent type-1 fuzzy set adaptively, and realizes the segmentation of land cover by the principle of maximum type-1 fuzzy membership. The test results of multispectral remote sensing images show that the segmentation performance of this proposed algorithm outperforms existing state of the art adaptive interval type-2 fuzzy clustering algorithms, and it is beneficial to the interpretation of remote sensing image.

Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup.

Multi‐institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure‐based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI—rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure‐based analysis showed widespread DTI abnormalities in FEP and rs‐fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof‐of‐concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub‐groups.