Optimal Pair Research Articles

Detection of TNF-α using the established ab-MPs-CLIA

Tumor necrosis factor alpha (TNF-α) is a key cytokine in inflammation and immune responses, making its rapid and accurate detection essential for disease diagnosis and management. In this study, we developed a highly sensitive chemiluminescence immunoassay (CLIA) using antibody-coated magnetic particles (Ab-MPs-CLIA) for TNF-α detection. From nine candidate antibodies, we identified an optimal pair through epitope competition and affinity assessments, significantly improving assay performance. The Ab-MPs-CLIA achieved a detection limit of 0.25 pg/mL, 6.8 times more sensitive than Siemens commercial kits, with a broad linear range of 9.2–1077 pg/mL. The method demonstrated excellent stability, both under accelerated conditions at 37 °C for 7 days and long-term storage at 4 °C for 12 months. It showed no cross-reactivity with common interfering substances in human serum, ensuring high specificity. Notably, the entire process, from sample preparation to result, takes just 25 min, compared to 3–4 h for both ELISA and RIA, and CLIA typically offers 10–100 times higher sensitivity than these methods. These advantages make the Ab-MPs-CLIA an ideal option for clinical laboratories, providing superior sensitivity, specificity, broader dynamic range, and greater operational efficiency than existing TNF-α detection technologies.

Optimization of thermal shock response spectrum as infrared thermography post-processing methodology using Latin hypercube sampling and analytical thermal N-layer model

In this work, we continue to develop and investigate the Thermal Shock Response Spectrum (TSRS) method as an alternative data processing method for infrared thermography (IRT). We focus on improving the current TSRS algorithm and present an optimization methodology for finding the optimal thermal Q-factor and characteristic frequency pair, which is based on the widely applied random sampling method. We show the qualitative relationship between the determined optimal characteristic frequency and the corresponding maximum difference in diffusion length between reference and defective models, as calculated by selecting a specific one-dimensional thermal N-layer model The investigations were performed on an inhomogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial square defects at different depths. Furthermore, two different heat sources were used: a xenon flash lamp and a laser. These sources are not only distinct by their underlying physics but also generate inherently different pulse shapes. To quantitatively estimate the contrast between defect and non-defect areas, and to compare these results with commonly used infrared thermography (IRT) data post-processing methods such as Pulse Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR), the Tanimoto criterion (TC) and signal-to-noise ratio (SNR) were used.

Eliminating noise from a speech signal based on a pair of filters

Today, the increase in the number of devices working based on speech interfaces increases the importance of speech quality. However, even a minimal noise level can seriously affect the accuracy of speech signal recognition and processing. Therefore, denoising speech signals is an important task in signal processing, and it also serves to improve speech quality in telecommunications, speech recognition systems, and other speech-related applications. Applying existing noise reduction filters separately may not always be effective. Therefore, in this research, a noise reduction approach based on the sequential application of filters is proposed. Based on literature analysis, filters such as low-pass, band-pass, Kalman, Butterworth, and elliptic filters were selected, and pairs were formed based on them. Pairs of filters were applied to speech signals with different levels of noise, and the resulting filtered speech signals were evaluated based on the PESQ evaluation criterion. The purpose of this study is to determine the optimal pair of filters that can minimize the impact of noise on the quality of speech signals using the PESQ evaluation criterion. The results of the experiments showed that it is optimal to use a pair of band-pass and Butterworth filters at a low level of noise, a pair of low-pass and elliptic filters at a medium level, and a pair of band-pass and elliptic filters at a high level of noise. The results obtained are important and practical in the development of other hybrid methods of noise reduction.

Development of Three Recombinase Polymerase Amplification Assays for the Rapid Visual Detection of Spiroplasma eriocheiris.

Spiroplasma eriocheiris is a pathogen in the Chinese mitten crab Eriocheir sinensis and is associated with tremor disease. S.eriocheiris infects virtually all artificially bred crustaceans, resulting in significant economic losses to the aquaculture industry in China. Recombinase polymerase amplification (RPA) is considered a promising technology for the rapid and sensitive detection of disease. For the rapid detection of S. eriocheiris, this study established basic RPA, RPA-EXO and RPA-LFD, which were visualised via agarose gel electrophoresis, SYBR Green I, fluorescence signals and test lines on lateral flow dipsticks. Specific primers and probes were designed based on the 16S ribosomal RNA sequence of S. eriocheiris. The optimal primer pairs for the basic RPA, RPA-EXO and RPA-LFD were RPA-F3/R3, RPA-F3/R3 and RPA-LFD-F3/R3, respectively. The optimal temperatures and times of basic RPA, RPA-EXO and RPA-LFD were 37°C for 25 min, 39°C for 30 min and 39°C for 10 min, respectively. The specificity test indicated that in addition to the positive test results for S. eriocheiris, the other five DNA templates tested negative, demonstrating strong specificity. The results of the sensitivity test revealed that the detection limits of RPA, RPA-EXO and RPA-LFD were 5.77 × 10-2, 5.77 × 10-4 and 1.52 × 10-6 ng/μL, respectively, indicating high sensitivity. This study established three methods for the detection of S. eriocheiris that are characterised by a constant temperature, rapid response, high sensitivity and strong specificity.

Comprehensive and Robust Anti-Jamming Dual-Electrode Pair Sensor.

Capacitive flexible sensors often encounter instability caused by temperature fluctuations, electromagnetic interference, stray capacitance effects, and signal noise induced by ubiquitous vibrations. The challenge lies in achieving comprehensive anti-jamming abilities while preserving a simplistic structure and manufacturing process. To tackle this dilemma, a straightforward and effective design is utilized to achieve comprehensive and robust anti-jamming properties in capacitive sensors. Electrospinning thermoplastic polyurethane (TPU) fiber mats soak with ionic liquid (IL) to create a co-continuous structure (TPU@IL) with high ionic conductivity and dielectric constant, which acts as the sensing units. The sensing mechanism of the TPU@IL with multiple electrode pairs encapsulated by polyethylene terephthalate (PET) is systematically elucidated. The optimal dual-electrode pair design for capacitive and resistive sensors, which have different sensitivities to temperature and stress, simultaneous realizes temperature-stress dual-mode sensing. Remarkably, the sensitivity curve of the TPU@IL/PET capacitive sensor exhibits an intriguing rarely reported S-shape with an adjustable step stress point. No liquid leakage even during extensive stress-strain cycling (>4000 cycles). Despite a slight compromise in sensitivity and response time, the TPU@IL/PET sensor demonstrates exceptional electromechanical stability, reliability, and powerful anti-jamming abilities against various interferences. A simple yet innovative sensor design enhances the performance and applicability of capacitive sensors in challenging environments.

A Hybrid Multiobjective Control Strategy Based on Combination of Modulated Model Predictive Control and Phase‐Shifted Modulation for a Unidirectional Five‐Level Rectifier

ABSTRACTThe unidirectional multilevel rectifier has immense potential in high‐power medium‐voltage industrial applications. This paper proposes a multiobjective control method based on the combination of optimal‐vector‐pair modulated model predictive control (OVP‐M2PC) and a phase‐shifted modulation strategy for a unidirectional five‐level rectifier. This strategy takes both of the inductor current tracking and capacitor voltage balancing as the control objectives. First, an optimal vector pair is determined based on the AC‐side current variation rate, and then, through combining the calculated action duration of OVP with carrier phase‐shifted modulation, inductor current tracking is achieved. Second, capacitor voltage balancing control is carried out by compensating for the duty cycles which also decouples the input current tracking and output capacitor voltage balance control. Compared with traditional FCS‐MPC, under the proposed control strategy, complex cost function calculation and weighting factor tuning are not required, which much reduced computational burden. Multiobjective control is achieved with better performance in both steady‐state and dynamic conditions. The superiority of the proposed strategy over traditional FCS‐MPC are validated through simulations and hardware experiments.

Novel FRET-Based Biosensors for Real-Time Monitoring of Estrogen Receptor Dimerization and Translocation Dynamics in Living Cells.

Estrogen receptors (ERs), comprising ER α and ER β, are crucial for regulating cell growth and differentiation via homo- and hetero-dimer formation. However, accurately detecting ER dimerization with precise spatiotemporal resolution remains a significant challenge. In this study, fluorescence resonance energy transfer-based biosensors to monitor ER dynamics in real-time, are developed and optimized.This approach involves comprehensive structural analysis, linker comparison, and the selection of optimal fluorescent protein pairs, resulting in three distinct biosensors capable of detecting all ER homo- and hetero-dimerizations within the nucleus. These biosensors are utilized to reveal interactions between ER α/β and calmodulin during dimer formation.Furthermore, by leveraging the ligand-binding domain (LBD) of ER β, ER ββ LBD biosensor is designed for real-time analysis of ER ββ homodimerization in the cytoplasm, enhancing the ability to screen ER dimerization-related drugs.Additionally, we developed a novel ER ββ translocation biosensor, which enables real-time observation of ER ββ translocation to the nucleus-a capability previously unavailable, is developed. This spatiotemporal analysis demonstrates the relevance of ER translocation in response to drug binding efficacy and extracellular matrix changes. Our biosensors offertransformative tools for studying ER dynamics, providing valuable insights for drug screening and the investigation of ER-related cellular processes.

Modified approach for predicting seismic-induced deformation of landfills considering strength parameters of GMB-GCL interface within the liner system

This study presents an innovative methodology for predicting seismic-induced sliding displacement, a key determinant in evaluating the seismic stability of landfills. The novelty of this research lies in the incorporation of the softening characteristics of the geosynthetic interface within the liner system, a factor that has been largely overlooked in previous studies. A dynamic stability analysis was performed on a landfill using the ABAQUS software, with an emphasis on the impact of coupled parameters, particularly the strength of the interface. The results highlight PGA (Peak Ground Acceleration), PGV (Peak Ground Velocity), Ia (Arias Intensity), and residual interface shear strength (μ) as effective predictors. The study further identifies the combination of PGA and μ as the optimal parameter pairing for predicting the seismic permanent deformation of the landfill. A multi-dimensional data regression approach was employed to propose a calculation formula for seismic permanent deformation, taking into account liner damage. To enhance the seismic design methodology for landfills, the probability density function was integrated into the study. This innovative approach provides a new perspective on seismic stability assessment in landfill engineering and designs.

Formation of interior hollow nanocubes derived from cobalt-based metal-organic frameworks via one-step etching for efficient battery desalination

Battery desalination (BD) offers a sustainable solution for simultaneous energy storage and desalination, addressing the global need for clean water access. However, current BD technologies require improvements in salt removal capacity, charge efficiency and energy consumption to become practical. In this study, we present a BD system utilizing interior hollow nanocubes derived from a cobalt-based zeolitic imidazolate framework (Co-TA HC) as the cathode and silver nanoparticle on reduced graphene oxide (Ag@rGO) as the anode. The regular and hollow nanocubes, with enhanced specific surface area and abundant redox active sites, are prepared via a simple one-step mild tannic acid (TA) etching process, holding great promise for scalable manufacturing. The unique structure of the Co-TA HC significantly shortens the diffusion length of the Na+ and enhances the sodium intercalation kinetics, as indicated by the galvanostatic intermittent titration technique. The optimal pairing of the Co-TA HC and Ag@rGO enables the full discharge with voltage window of 0–1.4 V in the BD system, achieving high volumetric salt adsorption capacity (SAC) of 102.3 mg cm−3 and gravimetric SAC of 143.2 mg g−1 with a current density of 100 mA g−1. This suggests that the practical application of BD systems is a tangible possibility and paves the way for scaling-up emerging desalination technologies.

Optimal Pair Matching Combined with Machine Learning Predicts a Significant Reduction in Myocardial Infarction Risk in African Americans Following Omega-3 Fatty Acid Supplementation.

Conflicting clinical trial results on omega-3 highly unsaturated fatty acids (n-3 HUFA) have prompted uncertainty about their cardioprotective effects. While the VITAL trial found no overall cardiovascular benefit from n-3 HUFA supplementation, its substantial African American (AfAm) enrollment provided a unique opportunity to explore racial differences in response to n-3 HUFA supplementation. The current observational study aimed to simulate randomized clinical trial (RCT) conditions by matching 3766 AfAm and 15,553 non-Hispanic White (NHW) individuals from the VITAL trial utilizing propensity score matching to address the limitations related to differences in confounding variables between the two groups. Within matched groups (3766 AfAm and 3766 NHW), n-3 HUFA supplementation's impact on myocardial infarction (MI), stroke, and cardiovascular disease (CVD) mortality was assessed. A weighted decision tree analysis revealed belonging to the n-3 supplementation group as the most significant predictor of MI among AfAm but not NHW. Further logistic regression using the LASSO method and bootstrap estimation of standard errors indicated n-3 supplementation significantly lowered MI risk in AfAm (OR 0.17, 95% CI [0.048, 0.60]), with no such effect in NHW. This study underscores the critical need for future RCT to explore racial disparities in MI risk associated with n-3 HUFA supplementation and highlights potential causal differences between supplementation health outcomes in AfAm versus NHW populations.

Long-Term Outcomes of Antegrade Thoracic Stent Grafting During Repair of Acute DeBakey I Dissection

BackgroundWe aim to evaluate the impact of antegrade stenting of the distal arch and proximal descending aorta combined with non-total arch procedures in acute type A aortic dissection. MethodsFrom 2005 to 2022, 733 nonsyndromic patients presented with acute DeBakey type I aortic dissection and underwent non-total arch procedure. Ninety-five patients underwent antegrade stenting and 638 did not. Propensity-score analysis was performed, and 95 optimal pairs were created. Survival was estimated using the Kaplan-Meier method and cumulative incidence of reintervention with death as a competing event was calculated and compared using Gray’s method. ResultsSurvival estimates at 10 years after propensity score matching were similar between both groups, 58.9% (95% CI, 46.5%-74.5%) vs 58.4% (95% CI, 48.3%-70.6%) (P = .6) in the non-stented vs stented group. Cumulative incidence of reintervention with competing risk of death at 10 years after propensity matching was 27% (95% CI, 17%-37%) vs 22% (95% CI, 14%-32%) (P = .44), respectively. ConclusionsAntegrade thoracic endovascular aortic repair may be beneficial for remodeling and facilitating future endovascular reinterventions and reduces the occurrence of reintervention for malperfusion.

Sandwich enzyme-linked aptamer-based assay for the detection of Trichomonas vaginalis

Trichomoniasis is the most prevalent curable, non-viral sexually transmitted infection (STI), with an estimated 156 million new infections in 2020. It can potentially result in adverse birth outcomes as well as infertility in men, whilst it also increases the risk of acquiring HIV and contracting other vaginal infections. It is mostly prevalent among women in low-income countries and especially in Africa and the Americas. This STI is caused by Trichomonas vaginalis (TV) and a robust, cost-effective, sensitive, specific and rapid diagnostic test is urgently required. We report the screening of 6 full-length and 4 truncated aptamers previously selected in our group for use in a microplate-based sandwich assay. The combination of dual aptamers comprising a short 14-mer truncated capture aptamer (termed A1_14mer) and a full-length non-truncated reporter aptamer (A6) was elucidated to be the optimum pair for a sensitive sandwich enzyme-linked aptamer assay (ELAA) for the detection of TV achieving a detection limit of 3.02 × 104 TV cells/mL. The results obtained with the A1_14mer-A6 ELAA correlate excellently with wet-mount microscopy for the detection of TV in clinical specimens, cervicovaginal lavages and vaginal swabs, highlighting the potential clinical application of this assay for cost-effective population screening and subsequent prevention of the onset of complications associated with undiagnosed and untreated TV.

Machine-learning-aided method for optimizing beam selection and update period in 5G networks and beyond

Finding the optimal beam pair and update time in 5G systems operating at mmWave frequencies is time-intensive and resource-demanding. This intricate procedure calls for the proposal of more intelligent approaches. Therefore, this work proposes a machine learning-based method for optimizing beam pair selection and its update time. The method is structured around three main modules: spatial characterization of beam pair service areas, training of a machine learning model using collected beam pair data, and an algorithm that uses the decision function of the trained model to compute the optimal update time for beam pairs based on the spatial position and velocity of user equipment. When the machine learning model is deployed in a network with a single gNB equipped with a 8×8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$8\ imes 8$$\\end{document} UPA and one UE equipped with a 1×2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\ imes 2$$\\end{document} UPA in an mmWave scenario simulated in NS3, improvements in SINR and throughput up to 407%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$407\\%$$\\end{document}, were observed. Improvements are gathered because of a reduction of 85.7%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$85.7\\%$$\\end{document} in beam pair selections because of an increase of approximately 1543%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1543\\%$$\\end{document} in the effective time between successive beam pair searches. This method could offer real-time optimization of the beam pair procedures in 5G networks and beyond.

Optimal control problem governed by a kind of Kirchhoff-type equation

In this paper, we consider an optimal control problem governed by a kind of Kirchhoff-type equation, which plays an important role in the phenomenon of beam vibration. Firstly, the existence of solution to the state equation is proved by the variational method. Secondly, for the given cost functional, we get that there exists at least an optimal state-control pair via the Sobolev’s embedding theorem under the constraint of the state equation. Next, the necessary optimality condition for the optimal solution is derived by using the cone method. Finally, we give the pointwise variational inequality, minimum principles and an equivalent necessary condition for the optimal control problem according to the discussion of the variational inequality.

Pairs trading with costly short-selling

We study an optimal pairs trading model with costly short-selling. When the investor has logarithm utility function, we derive the solution in closed form, which shows that: the optimal allocation functions are piece-wise linear in the pair's relative price; stock borrowing fees asymmetrically reduce the optimal size of the long/short position; and for risk-hedging purpose, it can be optimal to short sell even when the stock borrowing fees outweigh the expected return earned from short selling. When the investor has power utility function, we propose analytical allocation functions adopting which only causes a small utility loss. When the investor is constrained from borrowing funds, the margin requirement for short selling can significantly affect the trading strategy. Empirically, we demonstrate the importance of incorporating short selling costs when trading pairs in China's stock market and verify the model-implied relation between short selling costs and profitability of pairs trading.

Efficient Optimization for Time-Constrained Encounter of Spacecraft with Multirevolution Phasing

“Spacecraft encounter” refers to a close flyby or fast proximity between two or more spacecraft in space. It involves the approach of one spacecraft to another, whether for observation, communication, capture, or interception purposes. This paper presents a novel approach to planning orbital encounter missions for spacecraft within a specified time constraint. The proposed method takes advantage of a unique encounter process where the vehicle performs only two in-plane impulses and matches the target location at the encounter position using multirevolution phasing. To ensure the minimum maneuvering fuel consumption from the selection of the encounter position and analyze the optimality of the proposed encounter maneuver, the determination method of the minimal orbital intersection distance is first given by constructing constraint equations in polynomial form. Then, the algorithm framework for determining the optimal encounter maneuvers is established. This scheme allows us to derive the optimal maneuver position, direction, and impulse magnitude, thereby creating a detailed two-layer solution framework for an arbitrary target. Concurrently, the strategy accommodates orbital phase deviations by proffering a rapid correction method that factors in the perturbation environment. To expand this optimal encounter maneuver planning method to multitarget missions, we develop a reduced-order allocation procedure for optimal pairing between vehicles and targets. Through the use of numerical simulations exemplified by one single-target and two multitarget missions, it is evident that the proposed method is both highly efficient and effective for encountering any given target.

Optimal Market-Neutral Multivariate Pair Trading on the Cryptocurrency Platform

Optimal Market-Neutral Multivariate Pair Trading on the Cryptocurrency Platform

Integration of Distributed Generations and electric vehicles in the distribution system

Power quality challenges are a major problem these days because of the significant increase in load demand and the existence of various reasons for disruptions in the distribution system. A voltage sag or a decrease in the magnitude of the line voltage can be brought on by rapid fluctuations in load or distribution system issues. Enhancing the grid current profile, reducing grid voltage sag, and improving other performances can all be achieved through the combination of electric vehicles with distribution generation systems. In this paper, the integration of Distributed Generation with Electric Vehicles in the distribution system has been done to enhance system performances such as true power loss index, imaginary power loss index, voltage deviation index, short circuit current index, cost function, and environmental impact reduction index for 16 bus distribution system in constant impedance, constant current, and constant power load models, or ZIP load models through proper location, sizing, coordinated controlling, and types of Distributed Generation and Electric Vehicle pairs for both charging and discharging modes by adopting Hybrid Genetic Algorithm - Monte Carlo Simulation optimization methodologies. This study's primary goal is to determine the optimal distribution generation and electric vehicle pairing to improve the aforementioned parameters, which will assist researchers in their future planning. Researchers, industry professionals, scientists, and individuals working with Distributed Generation and Electric vehicles in the smart grid will find this statement to be of great use.

A dynamic uncertainty-aware ensemble model: Application to lung cancer segmentation in digital pathology

Ensemble models have emerged as a powerful technique for improving robustness in medical image segmentation. However, traditional ensembles suffer from limitations such as under-confidence and over-reliance on poor performing models. In this work, we introduce an Adaptive Uncertainty-based Ensemble (AUE) model for tumor segmentation in histopathological slides. Our approach leverages uncertainty estimates from Monte Carlo dropout during testing to dynamically select the optimal pair of models for each whole slide image. The AUE model combines predictions from the two most reliable models (K-Net, ResNeSt, Segformer, Twins), identified through uncertainty quantification, to enhance segmentation performance. We validate the AUE model on the ACDC@LungHP challenge dataset, systematically comparing it against state-of-the-art approaches. Results demonstrate that our uncertainty-guided ensemble achieves a mean Dice score of 0.8653 and outperforms traditional ensemble techniques and top-ranked methods from the challenge by over 3 %. Our adaptive ensemble approach provides accurate and reliable lung tumor delineation in histopathology images by managing model uncertainty.

New Engineering Science Insights into the Electrode Materials Pairing of Electrochemical Energy Storage Devices.

Pairing the positive and negative electrodes with their individual dynamic characteristics at a realistic cell level is essential to the practical optimal design of electrochemical energy storage devices. However, the complex relationship between the performance data measured for individual electrodes and the two-electrode cells used in practice often makes an optimal pairing experimentally challenging. Taking advantage of the developed tunable graphene-based electrodes with controllable structure, experiments with machine learning are successfully united to generate a large pool of capacitance data for graphene-based electrode materials with varied slit pore sizes, thicknesses, and charging rates and numerically pair them into different combinations for two-electrode cells. The results show that the optimal pairing parameters of positive and negative electrodes vary considerably with the operation rate of the cells and are even influenced by the thickness of inactive components. The best-performing individual electrode does not necessarily result in optimal cell-level performance. The machine learning-assisted pairing approach presents much higher efficiency compared with the traditional trial-and-error approach for the optimal design of supercapacitors. The new engineering science insights observed in this work enable the adoption of artificial intelligence techniques to efficiently translate well-developed high-performance individual electrode materials into real energy storage devices.