Multiple Performance Research Articles

Implementing a Machine Learning based Hybrid Model to Counter Attacks in Mobile Edge Computing

This study focuses on the security of 5G mobile network major technology called Multi-Access Edge Computing (MEC) and its susceptibility to distributed denial of service (DDoS) attacks. The goal of the research is to address the effects of DDoS attacks and implement effective mitigation techniques. Several supervised Machine Learning (ML) techniques, which include Random Forest (RF), Decision Tree, Naïve Bayes, K-Nearest Neighbour, Logistics Regression, and Blending/Stack Model, are evaluated using multiple performance metrics such as accuracy, detection/recall, F1-Measure, Matthew’s correlation coefficient, Receiver Operating Characteristic, and Area Under Receiver Operating Characteristic. According to literature, ML algorithms achieve the best performance in mitigating DDoS attacks, therefore, they can be optimized to enhance their effectiveness. The research provides an overview of the existing mitigation schemes in the MEC and proposes a DDoS mitigation scheme. The findings show that hybrid models outperformed traditional ML models. Among the mitigation techniques evaluated, RF proved to be the most effective in mitigating DDoS attacks in MEC.

Influence of Magnetic Pole Stepping Combined with Auxiliary Stator Slots on the Stability of Dual-Rotor Disc Motors

With the growing utilization of disc motors, the enhancement of their operational stability has become a critical research area. The existing studies usually focus on improving the pole structure of the rotor or the stator structure to optimize one performance of the motor and less on optimizing multiple performances. This paper simultaneously improves the rotor pole structure and stator tooth structure of the motor in order to optimize the sinusoidal waveform of the no-load back electromotive force and the cogging torque at the same time to achieve the goal of reducing the vibration and noise of the permanent-magnet synchronous dual-rotor statorless magnetically coupled disc motor and improve its operational stability. A finite element simulation model of a 20-pole, 24-slot permanent-magnet synchronous dual-rotor statorless magnetically coupled disc motor is established to analyze the influence of various factors, including the number of magnetic pole steps, the opening position, depth, and width of the stator auxiliary slot, on the motor performance. The results show that this stator–rotor combination improvement method effectively reduces the total harmonic distortion (THD) and attenuates multiple harmonics, and the peak cogging torque pulsation is significantly improved while other properties of the motor meet the technical requirements, and the motor performance is improved.

K2vTune: A workload-aware configuration tuning for RocksDB

The explosive growth in data has triggered the emergence of NoSQL databases, which can be used to efficiently deal with unstructured and semi-structured data. RocksDB is a widely used persistent key–value store developed by Facebook to support high-throughput storage environments. It provides flexible configuration knobs that can be tuned to optimize performance. However, optimizing the RocksDB performance for specific workloads is challenging because the knobs have complicated inner relationships and different impacts according to workload type. In addition, RocksDB performance is measured using multiple metrics, such as latency and space amplification, which should be simultaneously considered. To address these issues, we designed a workload-aware configuration tuning framework called K2vTune, which can recognize the configuration knobs of RocksDB according to the workload type and effectively consider multiple performance metrics using our knob2vec method. The knob2vec method learns feature vectors for knobs that can capture both the latent relationships between knobs and workload-specific features to develop an attention neural network for accurate configuration-based performance prediction. K2vTune converts multiple metrics into a single problem and optimizes the problem using a genetic algorithm. We evaluated the proposed method using six different RocksDB workloads and confirmed that K2vTune achieved significant tuning performance improvement compared to baselines, showing 8.83% improvements over the state-of-the-art baseline.

Lightweight multi-objective evolutionary neural architecture search with low-cost proxy metrics

Multi-Objective Evolutionary Neural Architecture Search (MOENAS) methods employ evolutionary algorithms to approximate a set of architectures representing optimal trade-offs between network performance and complexity. Directly estimating network performance via error rates or losses incurs long runtimes due to the computationally expensive network training procedure. Instead, low-cost metrics that require no network training have been proposed as a proxy for network performance. However, these metrics might exhibit inconsistent correlations with network performance across different search spaces. The influences of training-based and training-free metrics on the effectiveness and efficiency of MOENAS are still under-explored.We introduce the Enhanced Training-Free MOENAS (E-TF-MOENAS) that employs the widely-used NSGA-II as the search algorithm and optimizes multiple training-free performance metrics as separate objectives. Experiments on NAS-Bench-101 and NAS-Bench-201 show that E-TF-MOENAS outperforms training-free methods that use a single training-free performance metric and could obtain comparable results to training-based methods but with approximately 30 times less computation cost. E-TF-MOENAS obtains architectures in NAS-Bench-201 with state-of-the-art mean accuracies of 94.37%, 73.50%, and 46.62% for CIFAR-10, CIFAR-100, and ImageNet16-120, respectively, within less than 3 GPU hours. It is beneficial to utilize multiple training-free proxy metrics simultaneously and E-TF-MOENAS provides a convenient framework for building such an efficient NAS approach. The source code can be found at https://github.com/ELO-Lab/E-TF-MOENAS.

The Hubungan Kemahiran Membaca Kritis dengan Kemahiran Menulis Teks Berita Siswa Kelas VIII Sekolah Menengah Pertama Negeri 7 Tanjungpinang Tahun Pelajaran 2022/2023

This study aims to see the relationship between critical reading skills and the ability to write news texts for Grade VIII students of Tanjungpinang 7 Public Middle School in the 2022/2023 academic year. This research method uses a quantitative approach with correlational research techniques. Data collection techniques using multiple choice objective tests and performance. The location of this research is in Tanjungpinang. The population in this study amounted to 448 students and the sample in this study amounted to 46 students. The results of the study: 1) from testing the data it was obtained that the students' critical reading proficiency had an average score of 64.97 with a total of 2989 with sufficient qualifications. 2) and proficiency in writing news texts has an average value of 81.34 with a total of 3742 good qualifications. 3) so, it can be concluded that there is a relationship between critical reading skills and the ability to write news texts for class VIII students of Tanjungpinang 7 Public Middle School in the Academic Year 2022/2023 with a result of 0.444 and the correlation value is in the interval 0.40-0.599 with the strength of the correlation relationship currently.

Empirical Analysis of Electron Beam Lithography Optimization Models from a Pragmatic Perspective

Electron Beam (EB) lithography is a process of focussing electron beams on silicon wafers to design different integrated circuits (ICs). It uses an electron gun, a blanking electrode, multiple electron lenses, a deflection electrode, and control circuits for each of these components. But the lithography process causes critical dimension overshoots, which reduces quality of the underlying ICs. This is caused due to increase in beam currents, frequent electron flashes, and reducing re-exposure of chip areas. Thus, to overcome these issues, researchers have proposed a wide variety of optimization models, each of which vary in terms of their qualitative & quantitative performance. These models also vary in terms of their internal operating characteristics, which causes ambiguity in identification of optimum models for application-specific use cases. To reduce this ambiguity, a discussion about application-specific nuances, functional advantages, deployment-specific limitations, and contextual future research scopes is discussed in this text. Based on this discussion, it was observed that bioinspired models outperform linear modelling techniques, which makes them highly useful for real-time deployments. These models aim at stochastically evaluation of optimum electron beam configurations, which improves wafer’s quality & speed of imprinting when compared with other models. To further facilitate selection of these models, this text compares them in terms of their accuracy, throughput, critical dimensions, deployment cost & computational complexity metrics. Based on this discussion, researchers will be able to identify optimum models for their performance-specific use cases. This text also proposes evaluation of a novel EB Lithography Optimization Metric (EBLOM), which combines multiple performance parameters for estimation of true model performance under real-time scenarios. Based on this metric, researchers will be able to identify models that can perform optimally with higher performance under performance-specific constraints.

Experimental investigation and multi-performance optimization of the leachate recirculation based sustainable landfills using Taguchi approach and an integrated MCDM method

Landfill leachates contain harmful substances viz. chemicals, heavy metals, and pathogens, that pose a threat to human health and the environment. Unattended leachate can also cause ground water contamination, soil pollution and air pollution. This study focuses on management of leachate, by recirculating the rich, nutrient-filled fluid back into the landfills, turning it to a bioreactor, thereby maximising the performance parameters of landfills favourable for electricity production by the waste to energy plants. This study demonstrates a sustainable alternative method for utilising the fluid, rather than treating it using an extremely expensive treatment process. Further, it also experimentally investigates the effect of varying levels of five input parameters of the landfill including waste particle size, waste addition, inorganic content in waste, leachate recirculation rate, and landfill age, each at five levels, on the multiple performance of the landfill using Taguchi’s L25 standard orthogonal array. Experimental results are analysed using an integrated MCDM approach i.e. MEREC-PIV method and statistical techniques such as analysis of mean (ANOM) and analysis of variance (ANOVA). The results indicate that the optimal setting of the input parameters is waste particle size at 9 ppm, waste addition at 80 Ktoe, inorganic content in waste at 2%, leachate recirculation rate at 250 l/day and landfill age at 3 years. Further, inorganic content waste is found to be the most significant parameter for the multiple performance of the landfill. This study presents a novel approach to produce input parameters for power plants which may enhance their profitability and sustainability.

A hybrid RSA-IPA optimizer for designing an artificial neural network to study the Jeffery-Hamel blood flow with copper nanoparticles: Application to stenotic tapering artery

In this study, the effects of copper nanoparticles are taken into account to create a new hybrid metaheuristic for solving the nonlinear MHD Jeffery-Hamel blood flow problem. Since copper nanoparticles are effective in lowering the hemodynamics of stenosis, the subject has scientific and biological implications. The coupled partial differential equations are converted to ordinary differential equations using suitable transformations. Considering an artificial neural network as a solution, the error function has been formed for the differential equations. A hybrid of the reptile search algorithm (RSA) and the interior point algorithm (IPA) is used to minimise the error function (≤10−05). The initial weights are first updated using the RSA algorithm (maximum 20 iterations), which is used as a tool for global search; later, IPA is used for quick local convergence. To validate the proposed technique’s accuracy, comparison studies are conducted using the fourth-order Runge-Kutta method and the hybrid PSO-IPA algorithm. Statistical analysis is provided using multiple performance indices to demonstrate the suggested approach’s precision, efficiency, and reliability. The research findings indicate that the outcomes achieved by the developed RSA-IPA technique exhibit superior performance compared to previously established methodologies, with an accuracy level of 10−04. The findings indicate that a rise in the volume fraction of cu-nanoparticles results in a decrease in the rate of blood flow. The research advances studies of blood flow in the medical sciences.

Facile preparation of CoMoO4/NiCo2O4 nano-sphere anode materials and their performance in asymmetric supercapacitors

CoMoO4/NiCo2O4 electrodes with a three-dimensional nano-sphere structure have been successfully prepared by a combination of hydrothermal and calcination methods. The electrode material exhibits a high specific capacitance of 1175 F g−1 at 1 A/g, excellent multiplicative performance (74.2 % capacity at 15 A/g) and good cycling stability (85.9 % specific capacitance retention after 10,000 cycles). The asymmetric supercapacitor was constructed using activated carbon (AC) as the negative electrode, showing excellent energy density (27.7 wh kg−1) and power density (806 W kg−1). It also has an extremely long operating life, maintaining a specific capacitance of 78.3 % after 5000 cycles at current densities up to 6 A/g. The results indicate that the synthesized mixed transition metal oxides CoMoO4/NiCo2O4 are promising as composite electrode materials for high-performance asymmetric supercapacitors.

81 An Examination of a Multiple Cutoff Performance Validity Index

Objective:Recent consensus guidelines have advocated for the use of multivariate performance validity assessment on ability-based measures such those used in neuropsychological assessment. Further, previous research has demonstrated that aggregating performance validity indicators may produce superior classification accuracy. The present study builds upon this research by aggregating data from three of the most commonly used performance validity measures (Test of Memory Malingering [TOMM], Rey Fifteen Item Test with recognition trial [FIT plus recognition], and Reliable Digit Span [RDS]) to create a performance validity composite measure in a veteran mild traumatic brain injury (mTBI) population.Participants and Methods:Data of patients evaluated at a VA hospital who had completed the RDS, FIT plus recognition, and TOMM as part of their clinical neuropsychological evaluation were analyzed (n = 20). Two composite performance validity indexes were created: a Single Cutoff Performance Validity Index (SC-PVI), which measures the quantity of failures across performance validity measures (PVMs) by summing the total number of PVM failures, and a Multiple Cutoff Performance Validity Index (MC-PVI) which measures the number of failures as well as degree of failure(s) across measures of performance validity (e.g., a participant would attain a score of 3 if their PVM performance failed to reach a conservative cut point; they would obtain a score of 1 if they met conservative cut point, yet failed to reach a liberal cut point).Results:Only one participant (5%) attained a score of 0 on the SC-PVI (i.e., passing all PVTs using standard cutoffs) and MC-PVI (i.e., passing the most liberal cut points on all three PVMs). Conversely, eight participants (40%) attained a score of 3 on the SC-PVI (i.e., failed all three PVMs) and four participants (20%) attained a score of 9 (i.e., failed the most conservative cut points on all three PVMs). Results showed a significant (p < .001) ordinal association between the two indices (G = .984); however, there was no significant agreement between SC-PVI and MC-PVI models (k = -.087; p = .127).Conclusions:Data revealed discordant findings between the three PVMs utilized. The majority of participants (75%) scored between 2-8 on the MC-PVI, meaning that they did not exceed all liberal cut points or fail all conservative cut points. These “grey area” scores suggest an indeterminate range of performance validity, which cannot be captured by a solitary cut point or neatly classified as pass or fail. The utility of multiple cutoff performance validity models (i.e., aggregating PVMs to consider the severity of failure and number of failures) is that they capture the nuance of these data when determining and discussing the credibility of a profile. Multiple cut point data also highlight how the choice of cutoff influences the outcome of performance validity research and clinical decision making. As such, future research on the classification accuracy of this MC-PVI is needed.

A swap-based sparse array design method in personal sound zone.

Sparse array design (SAD) optimizes the geometrical configuration of the loudspeaker array. It provides possibilities for further improving the performance of personal sound zone (PSZ). In this paper, the SAD problem in PSZ is formulated as a sparse multi-objective optimization problem, which takes multiple performance metrics into consideration during the sparsity promotion procedure. A swap-based group search algorithm is designed and proposed for solving the formulated non-convex and combinatorial problem to near-optimality. An approximate form is also proposed for reducing computational burden when there exist multiple target sound fields in bright zone. Simulation and experimental results suggest that the proposed method is more suitable in PSZ for controlling over trade-offs and achieving the same good or even better performances, and the proposed approximate form is effective.

A MACHINE LEARNING APPROACH FOR INTRUSION DETECTION

In the rapidly evolving digital landscape, ensuring the security of computer systems and networks has become a paramount concern. Traditional methods often struggle to keep pace with the increasing complexity and diversity of cyber threats. In the context of cybersecurity and network analysis, the crucial tools for identifying and responding to unauthorized access, malicious activities, and potential threats within a network or system cannot take place. The significance of intrusion detection is underscored by the escalating frequency and sophistication of cyberattacks that can lead to data breaches, service disruptions, and financial losses. The XGBoost algorithms ability to handle complex, imbalanced datasets and its exceptional performance in various domains make it a promising candidate for improving intrusion detection accuracy. To evaluate the efficacy of the XGBoost algorithm, extensive experiments are conducted using the KDDCup99 dataset. This dataset represents diverse network traffic scenarios and intrusion types, providing a comprehensive evaluation environment. The Random Forest algorithm, known for its robustness in handling classification tasks, is selected as a baseline for comparison. The performance of the XGBoost algorithm is assessed using multiple performance metrics, including accuracy, precision, recall, F1-score. The outcomes highlight the potential advantages of utilizing XGBoost for intrusion detection. KEYWORDS: Cybersecurity, Network Analysis, Intrusion Detection, XGBoost Algorithm, Random Forest Algorithm, KDDCup99 Dataset.

Design and Fabrication of a 2-Port Multiple Antenna System for mmWave Application

The next-generation wireless communication technology heading towards the mmWave band requires multiple antenna systems to achieve high diversity, high data rates, and reliability. The design of multiple antenna systems is always associated with the problem of mutual coupling between antenna elements. In order to achieve better system performance, the mutual coupling between the elements has to be minimized. This work basically aims to minimize the mutual interaction among the antenna elements. A 2-port multiple antenna system is presented, with the defected ground structure as a decoupling technique, in order to minimize the mutual coupling. The single microstrip patch antenna is initially designed to operate at 24 GHz; later, it is transformed into a 2-port multiple antenna system. The transformed 2-port multiple antenna is loaded with the defective ground structure. The designed antenna system is fabricated, and practical measurements are carried out. The isolation achieved with defective ground structure through simulation is −27dB, and the practical measured value is −40dB. The 2-port multiple antenna system performance is assessed through the following metrics: Envelope Correlation Coefficient(ECC), Diversity Gain(DG), and Channel Capacity Loss(CCL). For the proposed 2-port multiple antenna the obtained values of ECC, DG and CLL are 0.004, 9.99dB and 0.4 bit/s/Hz. The results of the simulated structure are in good agreement with the fabricated structure. The result shows that defective ground is the better technique to achieve isolation between the closely placed antennas. The defective ground structure is the simplest technique in comparison with other techniques, such as metamaterial, energy band gap etc. The major novelty of the work includes, the design of a 2-port multiple antenna system with single slot and achieving the isolation more than −47dB without affecting the antenna resonating frequency.

Numerical study of Newtonian fluid flow characteristics in triangle enclosure considering various obstacle geometries

This article investigates the effect of cylindrical obstacles with different dimensions inside the triangular cavity on natural convection heat transfer. This study considers three cavities, each of which has one, two, and three-cylinder barriers embedded inside it. The use of one or two cylindrical barriers has been investigated in many research, but in this research, in addition to the number of cylindrical barriers, their dimensions and sizes have been changed, and an alternating temperature function has been used at the base of the cavity. The considered nanofluid is Al2O3H2O, which contains aluminum oxide nanoparticles with a concentration of 5 %. In all three cavities, the velocity of the nanofluid in the walls is zero. The temperature in the lateral sides of the cavity has low-temperature faces. Instead of being constant or adiabatic, the temperature at its base is an alternating function (sinusoidal function with a frequency of 1.0 Hz.). In contrast, it is considered that the surfaces of the obstacles of the cylinders have zero velocity and are adiabatic. In this study, the governing equations of nanofluid flow behavior have been solved using the Galerkin finite element method and analyzed in the Rayleigh number range of 103 to 105. Because triangular cavities with different ratios are used to optimize multiple objectives and increase thermal performance in microchannels, the results show that the multiplicity of cylindrical obstacles in the triangular cavity causes the expansion of the nanofluid velocity range in the whole area, which itself causes the increase of the heat transfer surface, and with the increase of the Rayleigh number and the buoyancy force, the magnitude of the velocity inside the triangular cavity increases. In the triangular cavity containing three cylindrical barriers, the nanofluid's velocity range is low, but according to the velocity contours, its extent is greater than the other two cavities. Also, the average Nusselt number in this cavity is higher than other cavities.

29 Quick-Reference Criteria for Identifying Clinically Significant Multivariate Change in Older Adult Cognition: An ADNI Study

Objective:Accurately interpreting change in cognitive functioning is an essential aspect of clinical care for older adults. Several approaches to identifying ‘true’ cognitive change in a single cognitive measure are available (e.g., reliable change methods, regression-based norms); however, neuropsychologists in clinical settings often rely on simple score differences rather than advanced analytical procedures especially since they examine multiple test performances. This study sought to establish quick-reference normative criteria to help neuropsychologists identify how frequently significant change occurs across multiple cognitive measures in cognitively normal older adults.Participants and Methods:Data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Participants were 401 older adults who were classified as cognitively normal at baseline and at 24-month follow-up. In ADNI, these clinical classifications are made separately from the assessment of cognitive performance, including cognitive change. The sample was 50.1% female, 93.5% non-Hispanic White, 4.0% non-Hispanic Black, 1.5% Asian American, and 1.0% other race/ethnicity, with a mean age of 76.0 years (SD = 4.9). Mean education was 16.4 years (SD = 2.7). The cognitive battery included: Boston Naming Test, Category Fluency Test, Trails A & B, Clock Drawing Test, and Auditory Verbal Learning Test, Trial 1-5 Total and Delayed Recall. Change scores between baseline performance and 24-month follow-up were calculated for each measure. The natural distribution of change scores was examined for each measure and cut points representing the 5th and 10th percentile were applied to each distribution to classify participants who exhibited substantial declines in performance on a given measure. We then examined the multivariate frequency of statistically rare change scores for each individual.Results:As expected in a normal sample, overall cognitive performance was generally stable between baseline and 24-month followup. Across cognitive measures, 43.6% of participants had at least one change score fall below the 10th percentile in the distribution of change scores, and 21.9% had at least one score below the 5th percentile. 13.0% of participants had two or more change scores that fell below the 10th percentile, in comparison to 4.5% with two or more below the 5th percentile. 3.2% of participants had three or more change scores below the 10th percentile, versus 0.5% of participants who had three change scores below the 5th percentile.Conclusions:Among cognitively normal older adults assessed twice at a 24-month interval with a battery of seven measures, it was not uncommon for an individual to have at least one score fall below the 10th percentile (43% of the sample) or even the 5th percentile (21%) in the natural distribution of change scores. However, only 3.2% of normals had more than two declines in test performance below the 10th percentile, and less than 1% of the sample at more than one change score at the 5th percentile. This suggests that individuals who exhibit more multivariate changes in performance than these standards are likely experiencing an abnormal rate of cognitive decline. Our findings provide a preliminary quick-reference approach to identifying clinically significant cognitive change. Future studies will explore additional batteries and examine multivariate frequencies of change in clinical populations.

Assessment of ChatGPT’s performance on neurology written board examination questions

Background and objectivesChatGPT has shown promise in healthcare. To assess the utility of this novel tool in healthcare education, we evaluated ChatGPT’s performance in answering neurology board exam questions.MethodsNeurology board-style...

Increase of Bacterial Leaf Streak in Hard Red Spring Wheat in North Dakota and Yield Loss Considerations.

Bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa, has increased in both prevalence and severity in the major hard red spring wheat (HRSW)-producing state North Dakota. The disease is readily observed after flag leaf emergence and can quickly lead to defoliation and severe yield loss. The objectives of this research were to document the prevalence and incidence of BLS in North Dakota and provide estimations of yield and economic losses. Trained field scouts determined the incidence and prevalence of BLS in ND on HRSW plants between Feekes growth stage (FGS) 8 and FGS 11.2 from 2015 to 2021, and data were used to determine BLS-affected hectares. Yield data in combination with BLS ratings were obtained from HRSW performance trials to estimate the impact of BLS on yield. The combination of variety identity, hectarage data, BLS-affected hectarage estimates, and yield loss estimates was used to estimate economic losses from BLS in 2019 and 2020. Our data suggest that BLS-affected hectares ranged from 747 to 141,680 between 2015 and 2021. Yield loss was observed at multiple HRSW performance trial locations, with estimated yield losses as high as 60% on susceptible varieties. The amount of BLS-affected hectares was the highest in 2019 and 2020, and direct economic losses for North Dakota HRSW producers were estimated to be as high as $4.7 and $8.0 million, respectively. These data highlight the importance of BLS in HRSW and the need to procure resources for breeding efforts and grower education on management of BLS.

Following Affirmative and Negated Rules.

Rules are often stated in a negated manner ("no trespassing") rather than in an affirmative manner ("stay in your lane"). Here, we build on classic research on negation processing and, using a finger-tracking design on a touchscreen, we show that following negated rather than affirmative rules is harder as indicated by multiple performance measures. Moreover, our results indicate that practice has a surprisingly limited effect on negated rules, which are implemented more quickly with training, but this effect comes at the expense of reduced efficiency. Only affirmative rules are thus put into action efficiently, highlighting the importance of tailoring how rules are communicated to the peculiarities of the human mind.

A synergistic strategy of dual-crosslinking and loading intelligent nanogels for enhancing anti-coagulation, pro-endothelialization and anti-calcification properties in bioprosthetic heart valves.

Currently, glutaraldehyde (GA)-crosslinked bioprosthetic heart valves (BHVs) still do not guarantee good biocompatibility and long-term effective durability for clinical application due to their subacute thrombus, inflammation, calcification, tearing and limited durability. In this study, double-modified xanthan gum (oxidized/vinylated xanthan gum (O2CXG)) was acquired from xanthan gum for subsequent double crosslinking and modification platform construction. Sulfonic acid groups with anticoagulant properties were also introduced through the free radical polymerization of vinyl sulfonate (VS) and vinyl on O2CXG. Taking advantage of the drug-loading function of xanthan gum, the treated pericardium was further loaded with inflammation-triggered dual drug-loaded nanogel (heparin (Hep) and atorvastatin (Ator)). Mechanical properties of O2CXG-crosslinked porcine pericardium (O2CXG-PP) were significantly improved via the first network formed by Schiff base bonds and the second C-C bonds network. Due to the presence of sulfonic acid groups as well as the dual drug release from nanogels under the stimulation of H2O2, the hemocompatibility, anti-inflammatory, pro-endothelialization and anti-calcification properties of the crosslinked pericardium modified with nanogels loaded with Hep and Ator (O2CXG+VS+(Hep+Ator) nanogel-PP) was significantly better than that of GA-crosslinked PP (GA-PP). The collaborative strategy of double crosslinking and sequential release of anticoagulant/endothelium-promoting drugs triggered by inflammation could effectively meet the requirement of enhanced multiple performance and long-term durability of bioprosthetic heart valves and provide a valuable pattern for multi-functionalization of blood contacting materials. STATEMENT OF SIGNIFICANCE: Currently, glutaraldehyde-crosslinked bioprosthetic heart valves (BHVs) are subject to subacute thrombus, inflammation, calcification and tearing, which would not guarantee good biocompatibility and long-term effective durability. We developed a cooperative strategy of double crosslinking and surface modification in which double-modified xanthan gum plays a cornerstone. The mechanical properties of this BHV were significantly improved via the first network formed by Schiff base bonds and the second C-C bonds network. Inflammation-triggered combination delivery of heparin and atorvastatin has been demonstrated to enhance anticoagulation, anti-inflammatory and pro-endothelialization of BHVs by utilizing local inflammatory response. The collaborative strategy could effectively meet the requirement of enhanced multiple performance and long-term durability of BHVs and provide a valuable pattern for the multi-functionalization of blood-contacting materials.

METSM: Multiobjective energy-efficient task scheduling model for an edge heterogeneous multiprocessor system

Along with the growth of computing requirements, edge devices, such as satellites and unmanned aerial vehicles, are equipped with heterogeneous multiprocessor systems to cope with complicated missions, including attitude control, signal processing, and objective detection. Concerning the extremely limited power on these devices, reducing the energy and time overhead of task execution evolves into a crucial challenge. Therefore, a multiobjective energy-efficient task scheduling technique (METSM) is proposed. First, a mathematical model is established for the energy-efficient task scheduling problem on edge heterogeneous multiprocessor systems. In this model, both makespan and total energy consumption are the optimization objectives. The decision variables include the task execution sequence, processor assignment, and dynamic voltage and frequency scaling level for each processor. Second, a problem-specific algorithm, namely, iterated greedy-based multiobjective optimizer (IMO), is proposed. Specifically, destruction–reconstruction and local search are redesigned for optimizing task scheduling and resource allocation. Considering local optima avoidance, a probabilistic mutation operation is developed. In addition, multiobjective-oriented strategies of optimal solution selection and acceptance criteria are adopted to accelerate the convergence. Finally, our proposed IMO is compared with several of the latest algorithms through multiple performance metrics. The experimental results show that IMO can obtain optimal Pareto fronts among several multiobjective methods. Savings of approximately 10% and 12% in time and power consumption, respectively, can be achieved by IMO. Moreover, in comparison with classic list-based heuristics for solving test cases, while IMO maintains a similar makespan, energy is reduced by nearly 90%.