Good Application Performance Research Articles

Load balancing for heterogeneous traffic in datacenter networks

In modern datacenter networks (DCNs), the overwhelming heterogeneous flows have various stringent demands, ranging from delay-sensitive short flows, throughput-sensitive long flows to best-effort flows without deadline. Recently, many load balancing schemes are proposed to deliver good performance for datacenter applications. However, the existing solutions cannot meet all the above requirements simultaneously. Especially, the short flows experience head-of-line blocking due to queued behind the long and best-effort flows. The long flows often suffer from throughput degradation due to bursty congestion. To solve these issues, we present LBT, a traffic-differentiated load balancer. The key design point of LBT is to adaptively adjust the switching granularity of long flows and best-effort flows according to the two calculated switching thresholds based on the traffic strength. Specifically, under heavy load, the switching granularity is increased to guarantee required bandwidth capacity for delay-sensitive short flows to finish quickly. In contrary, the switching granularity is reduced to enable the long flows to make full of parallel equal-cost paths. Moreover, we adopt different routing strategies for the three different categories flows. We conduct NS-2 simulations to evaluate the effectiveness of LBT. The experimental results show that LBT significantly reduces the average flow completion time of short flows by up to 55.9% ∼ 65.4% compared to the state-of-the-art solutions and achieves high throughput for long flows concurrently.

An enhanced African Vulture Optimization Algorithm for solving the Unmanned Aerial Vehicles path planning problem

In recent years, research on Unmanned Aerial Vehicles has become one of the most interesting topics for industry and academics. UAVs path planning is one of the most important issues in terms of guaranteeing good performance in real-world applications. Its main objective is to determine and ensure an optimal and collision-free trajectory (path) between two positions from a starting point (source) to a destination point (target), while dealing with some requirements (e.g. safety, environment complexity, obstacle avoidance, energy consumption, etc.). In view of this topic’s complexity, an efficient path planning algorithm is required. In this paper, we propose an improvement of the meta-heuristic African Vulture Optimization Algorithm (AVOA), named Chaotic Cauchy Opposition-based AVOA (CCO-AVOA), for solving the UAVs path planning problem in a 3D environment. The effectiveness of the proposed CCO-AVOA is validated in different environments with various numbers of waypoints and threats taking into account the fitness value, path cost, height cost, obstacles cost, UAV’s angle cost, and execution time metrics. Compared to ten well-known meta-heuristics, simulation results demonstrate the efficiency of the proposed CCO-AVOA approach in most cases by obtaining a short, smooth, least costly, and collision-free path with better stability for UAVs in complex environments.

Detection method of limb movement in competitive sports training based on deep learning

Human posture detection is easily affected by the external environment, resulting in blurred results of limb feature extraction. In order to improve the accuracy and speed of human motion detection, this paper proposes a deep learning-based motion detection method in competitive sports training. The double parallel convolution network algorithm in the depth learning algorithm is used to process the collected action information, extract the body action features, and greatly reduce the operation scale; With the help of the theory of motion mechanics, the mechanical parameters in the motion process are calculated to eliminate outliers and reduce feature dimensions; With the help of motion inertial sensors and joint degrees of freedom, the limb motion detection results are obtained. The experimental results show that the average recognition rate of the method for different motion actions is 99.5%, and the average detection time is 148 ms, with good application performance.

Mechanochemical synthesized amino-functionalized ultramicroporous ZIF based mixed-matrix membranes for CO2 separation

Mixed-matrix membranes (MMMs) have arisen as an energy-saving and efficient candidate for CO2 separation that can mitigate the greenhouse effect. However, challenges still remain in achieving good filler-polymer compatibility and high separation performance. Herein, a mechanochemistry method is proposed to synthesize an amine-functionalized ultramicroporous ZIF-62 (ZIF-UC-6) with good CO2 affinity. Amino functional groups and open metal sites are introduced into ZIF-UC-6, which significantly improve its CO2 adsorption capacity of ZIF-62. According to the ideal adsorption solution theory (IAST) calculation, the CO2/CH4 and CO2/N2 selectivity of ZIF-UC-6 is ∼80 and ∼140, respectively. Besides, when ZIF-UC-6 is incorporated into Pebax, hydrogen bonds can form between filler and polymer, which can increase the interfacial compatibility in the MMMs. The optimized membrane possesses a CO2 permeability of ∼560 barrer, together with high CO2/CH4 and CO2/N2 selectivity of 41.8 and 86.2, surpassing the corresponding upper bounds. Moreover, the prepared MMMs have good pressure resistance and long-term stability. This work demonstrates an effective route for developing MMMs with good interfacial compatibility and high CO2 separation performance for potential industrial applications.

Study of Physicochemical and Gelation Properties of Fish Gelatin from Different Sources

Fish gelatin has been increasingly used as a safe alternative to cattle and pig gelatin due to its similar structure, avoiding the health and socio-cultural issues associated with the use of materials of mammalian origin. Fish gelatin can be produced from processed fish products to achieve a high yield at a low cost. Recent studies show that although fish gelatin comes from a wide range of sources, the protein content and amino acid composition of fish gelatin from different sources are different, and some fish gelatin is soft and unstable transglutaminase (TGase) can catalyze the γ-amide group of glutamine residues and the ε-amino group of lysine residues in proteins to form covalent bonds to form a stable protein network structure, improve the strength of the gel so that it can be applied in a more special environment. In this experiment, after screening the raw materials of cold-water fish gelatin M06 and M08, warm-water fish gelatin M03 and M04, a strong fish gelatin was successfully prepared by catalytic modification of cold-water fish gelatin by transglutaminase (TGase), and the excellent performance of TG enzyme-catalyzed modified gelatin was proved through the application effect of chicken salt soluble protein. In this experiment, the protein content of cold-water fish M08 was the highest, which was up to 99.9%, 1.09 times that of warm-water fish. The gelatin content of cold-water fish M08 was the highest of the four kinds of fish gelatin, with a wide proportion of components and rich amino acid composition. Cold-water fish M08 gelatin-derived gel had the highest strength of 253 ± 1 g/cm at 4 °C. It was found that fish gelatin with protein molecular weight distribution and rich amino acid composition had higher gel strength. M08 gelatin is cross-linked by transglutaminase (TGase), which increases the strength of enzyme gels by approximately 200% compared to self-assembled gels. Fish gelatin catalyzed by the TG enzyme improves the gel strength of raw material and makes it more applicable. M08 gelatin also showed good application performance at low temperatures in compound chicken salt-soluble protein gel, with a water retention rate of 95.84% and gel strength of 198.5 g/cm. This study expanded the application range of fish gelatin by TG enzyme and improved the application potential of fish gelatin.

Study on strategies for reducing training samples for accurate estimation of wind-induced structural response of LSTM networks

Long short-term memory (LSTM) neural network is an efficient method to analyze the nonlinear dynamical response of structures. However, once it is applied to a long-duration response prediction task, the demand on the computational resources will become very severe. In order to address this limitation, various strategies based on sample truncation and down-sampling were proposed in this study to reduce the sample size for predicting wind-induced structural response, i.e., to intercept representative short-duration samples from long-duration samples or down sample the long-duration sample to train the LSTM neural network. These novel strategies can achieve the goal of reducing computational burdens without loss of accuracy compared to the long-duration training samples. Based on a single-degree-of-freedom (SDOF) system, the advantages in terms of the time efficiency of the proposed methods on the training phase of LSTM neural networks have been studied, and the accuracy of the predicted wind-induced structural response was investigated. It showed that the Duhamel integral-based approach (DI-BA) of sample truncation presented the best comprehensive performance in time and accuracy. Furthermore, the DI-BA was applied to a four-degree-of-freedom (4DOF) system with different structural parameters to demonstrate its good performance in more general applications involving multiple degrees of freedom.

Shape-stabilized composite phase change material for thermal insulation of cotton fabrics with sandwich structure

Disodium phosphate dodecahydrate was hybrid with n-octadecane as the phase change material (PCM) by reversed-phase emulsification. The cellulose sponge obtained based on cellulose nanocrystals was used as the support material to fabricate a shape-stable composite phase change material (CPCM) through impregnation. It was laminated with cotton fabric to obtain a sandwich structure thermal insulation fabric. An infrared spectrometer, scanning electron microscope, differential thermal scanning calorimeter, and other equipment were used to characterize the morphology, chemical groups, and latent heat properties of CPCMs and thermal insulation fabrics. The results displayed that the cellulose sponge had a porosity of 99.03%, a density of 0.012 g/cm3, and could withstand 1900 times its weight. When the mass proportion of PCM in the composite system was 67.5%, its latent heat energy was 84.44 J/g, which also revealed optimal shape stability. Compared to the original fabric, the thermal insulation efficiency of the sandwich structured fabric was increased by 17.7%∼18.2%, and the photothermal conversion rate reached 25.88%, with good application performance.

Density-Convoluted Support Vector Machines for High-Dimensional Classification

The support vector machine (SVM) is a popular classification method which enjoys good performance in many real applications. The SVM can be viewed as a penalized minimization problem in which the objective function is the expectation of hinge loss function with respect to the standard non-smooth empirical measure corresponding to the true underlying measure. We further extend this viewpoint and propose a smoothed SVM by substituting a kernel density estimator for the measure in the expectation calculation. The resulting method is called density convoluted support vector machine (DCSVM). We argue that the DCSVM is particularly more interesting than the standard SVM in the context of high-dimensional classification. We systematically study the rate of convergence of the elastic-net penalized DCSVM under general random design setting. We further develop novel efficient algorithm for computing elastic-net penalized DCSVM. Simulation studies and ten benchmark datasets are used to demonstrate the superior classification performance of elastic-net DCSVM over other competitors, and it is demonstrated in these numerical studies that the computation of DCSVM can be more than 100 times faster than that of the SVM.

Soil Moisture Inversion Based on Data Augmentation Method Using Multi-Source Remote Sensing Data

Soil moisture is an important land environment characteristic that connects agriculture, ecology, and hydrology. Surface soil moisture (SSM) prediction can be used to plan irrigation, monitor water quality, manage water resources, and estimate agricultural production. Multi-source remote sensing is a crucial tool for assessing SSM in agricultural areas. The field-measured SSM sample data are required in model building and accuracy assessment of SSM inversion using remote sensing data. When the SSM samples are insufficient, the SSM inversion accuracy is severely affected. An SSM inversion method suitable for a small sample size was proposed. The alpha approximation method was employed to expand the measured SSM samples to offer more training data for SSM inversion models. Then, feature parameters were extracted from Sentinel-1 microwave and Sentinel-2 optical remote sensing data, and optimized using three methods, which were Pearson correlation analysis, random forest (RF), and principal component analysis. Then, three common machine learning models suitable for small sample training, which were RF, support vector regression, and genetic algorithm-back propagation neural network, were built to retrieve SSM. Comparison experiments were carried out between various feature optimization methods and machine learning models. The experimental results showed that after sample augmentation, SSM inversion accuracy was enhanced, and the combination of utilizing RF for feature screening and RF for SSM inversion had a higher accuracy, with a coefficient of determination of 0.7256, a root mean square error of 0.0539 cm3/cm3, and a mean absolute error of 0.0422 cm3/cm3, respectively. The proposed method was finally used to invert the regional SSM of the study area. The inversion results indicated that the proposed method had good performance in regional applications with a small sample size.

Synthesis of uniform Pickering microspheres doped with quantum dot by microfluidic technology and its application in tumor marker

Tumor markers play a significant role in early cancer diagnosis, evaluation of the extent of the disease, and monitoring of therapy response. In this study, we described the Pickering emulsion polymerization method to synthesize uniform magnetic/fluorescent microspheres. A Pickering-structure composed of a lot silica nanoparticle closely covered onto the quantum dot-encoded magnetic microbeads is designed and synthesized. The uniform magnetic/fluorescent microspheres were prepared using a microfluidic device and the performance of the microspheres synthesized by the instruments was evaluated by flow cytometry. To avoid fluorescence quenching and intrinsic toxicity, CdSe/ZnS core-shell quantum dot and Fe3O4 nanoparticle were successfully encapsulated into MFM microspheres using the microfluidic technology. Using this structure enables the facile realization of a theoretical 4 × 4 barcoding matrix combining two colors and four fluorescence intensity levels. Then, different optical codes were prepared by simple changing the emission wavelength and the intensity of the quantum dots. The resulting microsphere are combined with flow cytometer using two lasers for decoding of multiplex tumor markers. Moreover, the stability testing of microspheres demonstrated good performance for further application in detection of tumor markers as well. When applied for the high-throughput ultrasensitive detection of three tumor markers (CEA, CA125 and CA199) in a single sample, the detection limits of 0.027 ng/mL for CEA, 1.48 KU/L for CA125 and 1.09 KU/L for CA199 are achieved, which exhibit superior detection performance. Thus, Pickering-structure magnetic/fluorescent microspheres are promising for application in tumor markers.

Application of active noise control for plenum window in a domestic setting

Plenum windows, also known as ventilation windows allow for natural ventilation in living spaces while providing noise reduction to improve the health and living comfort of occupants, especially in noise polluted and tropical/sub-tropical cities. However, while plenum windows can provide acceptable levels of natural ventilation, the passive noise performance of plenum windows when fully opened is still lacking compared to the conventional closed windows at low frequencies. Active noise control (ANC) has been shown to provide good performance in several applications in low-frequencycontrol. Thus, the application of active noise control through a single-channel and dual-channel ANC implementation is to be developed and demonstrated in a field test for a plenum window set in a domestic home built to reproduce the environment of a high-rise apartment. The experimental application will be based on the optimal configurations for single and dual-channel ANC informed by prior simulation studies.

Portable dual-mode biosensor based on smartphone and glucometer for on-site sensitive detection of Listeria monocytogenes

Contamination of Listeria monocytogenes (L. monocytogenes) in the environment and food can pose a serious threat to human health, and there is an urgent need to establish sensitive on-situ detection methods to mitigate its hazards. In this study, we have developed a field assay that combines magnetic separation technology with antibody-labeled ZIF-8 encapsulating glucose oxidase (GOD@ZIF-8@Ab) to capture and specifically identify L. monocytogenes while GOD catalyzes glucose catabolism to produce signal changes in glucometers. On the other side, horseradish peroxidase (HRP) and 3,3′,5,5′-tetramethylbenzidine (TMB) were added to recombined with the H2O2 generated by the catalyst to form a colorimetric reaction system that changes from colorless to blue. The smartphone software was used for RGB analysis to complete the on-site colorimetric detection of L. monocytogenes. This dual-mode biosensor showed good detection performance for the on-site application of L. monocytogenes in lake water and juice samples, both with a limit of detection up to 101 CFU/mL and a good linear range of 101–106 CFU/mL. Therefore, this dual-mode on-site detection biosensor has a promising application for the early screening of L. monocytogenes in environmental and food samples.

Psychometric properties of patient-reported outcome measures assessing self-efficacy in patients with inflammatory bowel disease: A systematic review.

The aim of this study is to conduct a comprehensive evaluation of the patient-reported outcome measure assessing self-efficacy in patients with inflammatory bowel disease, and recommend the most robust measurement. A systematic review of psychometric properties. We performed systematic electronic searches in the following databases from inception to 26 May 2022: PubMed, Embase, CINAHL, Web of Science, Cochrane Library, and PsycINFO via OVID. This review evaluated the tools' quality in accordance with the Selection of Consensus-Based Health Measurement Instruments 2018 system Evaluation guidelines. Three patient-reported outcome measures were identified in the five included studies. The "IBD-yourself" questionnaire revealed very low evidence for a sufficient hypothesis test for construct validity, moderate evidence for insufficient internal consistency, and very low evidence for uncertain measurement error. Evidence from Inflammatory Bowel Disease Self-Efficacy Scale validated internal consistency, structural validity, criterion validity, and hypothesis test for construct validity. However, evidence of moderate quality corroborated this reliability. The Adolescents and Young Adults Inflammatory Bowel Disease Self-Efficacy Scale demonstrated very low evidence for sufficient internal consistency, moderate evidence for a sufficient hypothesis test for construct and content validity, and very low evidence for uncertain reliability. Compared with the other two measures, the Inflammatory Bowel Disease Self-Efficacy Scale has higher quality evidence of higher overall ratings for some of its psychometric properties, but there were some methodological problems that must be further studied to determine their quality. It is uncertain whether a scale to assess self-efficacy in patients with inflammatory bowel disease has good measurement performance in clinical applications. This study first presents the methodological quality and psychometric properties of the self-efficacy scale used to assess patients with inflammatory bowel disease, which is based on the Consensus-Based Health Measurement Instruments standard. This study can help researchers and physicians decide which scale is the most suitable and reliable for patients with inflammatory bowel disease. Because this was a systematic review based on synthesizing information from previous studies, no human participants were included.

Self-healable and super-stretchable conductive elastomeric nanocomposites for efficient thermal management characteristics and electromagnetic interference shielding

Due to the pressing need to mitigate current electromagnetic wave pollution (connecting to the 5 G era), lightweight, stretchable, thermally, and electrically conductive nanocomposite materials have attracted significant scientific interest. Herein, self-healable carboxylated nitrile butadiene elastomeric (XNBR) based nanocomposites were fabricated via sequential wet cum melt mixing technique utilizing multi-walled carbon nanotubes (MWCNTs). The metal-ligand complexes in the XNBR elastomer chain endow a reversible network that leads to self-healing ability. The higher aspect ratio of MWCNTs assists in constructing interconnected conductive pathways inside the XNBR matrix. The nanocomposite with MWCNTs (15 phr) provided an excellent EMI shielding effectiveness of − 27.4 dB, whereas the nanocomposite exhibited a thermal conductivity of 0.85 W.m−1. K−1. Interestingly, the interconnected networking pathway of MWCNTs in the XNBR matrix prepared a tunable analogical structure providing an absorption-dominant shielding mechanism. Under severe practical and environmental testing conditions, the nanocomposites showed a minute change in electromagnetic effectiveness, demonstrating their good performance for outdoor applications. These ultra-lightweight, self-healable, recyclable MWCNTs/ XNBR EMI shields are promising materials for next-generation super-stretchable and portable smart electronics.

Cleaner preparation of high blueness cobalt blue composite pigments using dolomite and kaolin tailing sand

Cleaner and value-added resourceful utilization of the tailings or natural mineral resources has gained increasingly attention under the advanced concept of green and sustainable development. To develop an environmental-friendly preparation process for cobalt blue pigments with high chroma and low cobalt consumption, a dolomite-induced mechanochemical technology was developed for fabrication of cobalt blue composite pigments with controllable high blue intensity in the presence of kaolin tailing sand without using and discharging waste liquids. It revealed that the excellent color performance (L* = 37.63, a* = 10.68, b* = −54.87, C* = 55.91) benefitted from the Ca/Mg species and the lamellar morphology of dolomite by the mechanical force, which could facilely adjust the color performance and in-situ load pigment nanoparticles, respectively. The composite pigments presented a flaky morphology decorated with about 10 nm of nanoparticles, and consisted of calcium aluminate, anorthite, quartz and spinel magnesium cobalt aluminates. It is worth mentioning that the as-prepared composite pigments exhibited excellent environmental stability and good application performance as painting ink on rice paper. Therefore, this novel and cleaner technology is expected to produce high-performance cobalt blue pigments with lower cobalt consumption, as well as simultaneously realize the resource utilization of kaolin tailing sand and dolomite.

A generative adversarial network with “zero-shot” learning for positron image denoising

Positron imaging technology has shown good practical value in industrial non-destructive testing, but the noise and artifacts generated during the imaging process of flow field images will directly affect the accuracy of industrial fault diagnosis. Therefore, how to obtain high-quality reconstructed images of the positron flow field is a challenging problem. In the existing image denoising methods, the denoising performance of positron images of industrial flow fields in special fields still needs to be strengthened. Considering the characteristics of few sample data and strong regularity of positron flow field image,in this work, we propose a new method for image denoising of positron flow field, which is based on a generative adversarial network with zero-shot learning. This method realizes image denoising under the condition of small sample data, and constrains image generation by constructing the extraction model of image internal features. The experimental results show that the proposed method can reduce the noise while retaining the key information of the image. It has also achieved good performance in the practical application of industrial flow field positron imaging.

Compact front module with integrated miniaturised antenna in package for parabolic reflector antenna in satellite ground terminals

A compact front module with integrated miniaturised antenna in package for parabolic reflector antenna is presented for GEO-KOMPSAT-2A (GK-2A) satellite ground terminal application at 1692 MHz. The active feeder antenna module consists of four parts: a pair of miniaturised slot antennas, a pair of differential feeding lines, a metal shielding box, and front circuits, including a low noise amplifier (LNA) and a surface acoustic wave bandpass filter (SAW BPF). To reduce the lateral size of the feeder antennas and obtain required radiation patterns, meandered radiation slots are suggested and fed by a pair of differential coplanar waveguide (CPW) lines. The antenna substrate serves as the module cover and the electromagnetic shielding with a lateral size of 48 × 48 mm2 (0.27 × 0.27 λ 2 ${\lambda }^{2}$ ). Additionally, the feeder antenna module is proven to work with the specified parabolic reflector, both in theory and simulation. The front module with the packaged antenna is fabricated, and the measured output gain exceeds 20 dBi within a desired narrow band. Moreover, the module also achieves excellent performance in the GK-2A satellite signal-receiving experiment when used to feed the parabolic reflector antenna. The entire front module exhibits good package integration and application performance.

Design and analysis of a compact microstrip lowpass–bandpass diplexer with good performance for wireless applications

AbstractThis paper presents the design of a novel lowpass–bandpass diplexer with compact size and good performance using microstrip cells. For this purpose, two microstrip lowpass and bandpass filters are separately designed and mathematically analyzed. The proposed microstrip diplexer has a novel and simple structure. It occupies a compact area of 0.037 λg2 (722 mm2), and its insertion loss and S11 at both channels are low. The insertion loss at the lower and upper channels is only 0.047 and 0.16 dB, respectively. Meanwhile, both channels are flat with the maximum group delay of 1.68 ns which is the lowest compared to the previously reported diplexers mentioned in this paper. To design the proposed diplexer, first, a lowpass diplexer with good performance is designed which has low losses and a good figure of merit. It has a flat passband with a sharp roll-off. Then, to achieve the proposed diplexer, a bandpass resonator is added to the lowpass filter without any extra matching circuit, which saves the overall size. The proposed diplexer is designed, simulated, and fabricated, where the simulation and measurement results are close.

Benchmarking of human Y-chromosomal haplogroup classifiers with whole-genome and whole-exome sequence data

In anthropological, medical, and forensic studies, the nonrecombinant region of the human Y chromosome (NRY) enables accurate reconstruction of pedigree relationships and retrieval of ancestral information. Using high-throughput sequencing (HTS) data, we present a benchmarking analysis of command-line tools for NRY haplogroup classification. The evaluation was performed using paired Illumina data from whole-genome sequencing (WGS) and whole-exome sequencing (WES) experiments from 50 unrelated donors. Additionally, as a validation, we also used paired WGS/WES datasets of 54 individuals from the 1000 Genomes Project. Finally, we evaluated the tools on data from third-generation HTS obtained from a subset of donors and one reference sample. Our results show that WES, despite typically offering less genealogical resolution than WGS, is an effective method for determining the NRY haplogroup. Y-LineageTracker and Yleaf showed the highest accuracy for WGS data, classifying precisely 98% and 96% of the samples, respectively. Yleaf outperforms all benchmarked tools in the WES data, classifying approximately 90% of the samples. Yleaf, Y-LineageTracker, and pathPhynder can correctly classify most samples (88%) sequenced with third-generation HTS. As a result, Yleaf provides the best performance for applications that use WGS and WES. Overall, our study offers researchers with a guide that allows them to select the most appropriate tool to analyze the NRY region using both second- and third-generation HTS data.

Study on load estimation method of HVAC system in large public gymnasium

This paper proposes a load estimation method for HVAC system of large public gymnasium. The operation data of HVAC system in large public gymnasium are collected and processed by principal component analysis. According to the data processed, this paper analyses the influencing factors of HVAC system, constructs the linear regression estimation model of system load, and optimises the objective function of load estimation by using genetic algorithm to obtain the optimal solution. Experiments show that the method has strong robustness good adaptability to the model parameters, high fitting degree between the load estimation results and the actual values, and the maximum running time is less than 12.8 s. It can complete the load estimation efficiently and has good performance in practical application.