Comprehensive Parameter Research Articles

Multiple face images encryption based on a new non-adjacent dynamic coupled mapping lattice

There have been limited research studies on encryption algorithms specifically designed for certain encryption scenarios. Aiming at different types of face images, this paper presents a multiple face images encryption algorithm based on a new non-adjacent coupled mapping lattice with dynamic parameter (NCMLP). NCMLP has a more comprehensive parameter range, better chaos, and more complex dynamic behavior Compared to classical non-adjacent coupled lattice mapping, which is suitable for encryption. The encryption algorithm firstly uses face detection technology to extract multiple face information and to generate a double-layer key, then scrambles the face information by three-dimensional bit-level cyclic shift, and finally encrypts the global information using 3D pixel level scrambling and 3D double index diffusion. It is worth mentioning that the algorithm has high security and can be applied to ordinary images. The experimental results and safety analysis show that the information entropy can exceed 7.9992, the histogram variance can be less than 1072, the pixel number change rate can reach 99.6323%, and the unified average change intensity can reach 33.5495%, all of which are very close to the ideal values.

Investigation on hardness and tensile strength of friction stir processing of Al6061/TiN surface composite

In this article, the hardness and tensile strength of friction-stir-processing of Al6061/TiN surface composites using various process parameters. The experiments were performed by using Taguchi design of experimentation and influences of parameters on the responses have been examined. The study examined the impact of process parameters on hardness and tensile strength using the Taguchi design of experimentation. This approach explored a comprehensive parameter space, revealing the intricate relationship between parameters and material responses. Optimal hardness was achieved at 134.718 HV by employing a combination of the best-confirmed parameters and the predictive model. This optimal setting (RS = 1000 rpm, FR = 15 mm/min, AT = 4 KN, RP = 10%) demonstrates the successful integration of experimental and predictive approaches to material optimization. Similarly, an optimal tensile strength of 407.021 MPa was attained using the best-confirmed parameters (RS = 1150 rpm, FR = 45 mm/min, AT = 4 KN, RP = 5%). This article discusses optimizing composite properties through friction stir processing using a Taguchi design of experimentation. Approach optimizes hardness and tensile strength, improving material performance and paving the way for diverse industries.

Stress redistribution in a multilayer chamber for compressed air energy storage in abandoned coalmine: Elastic analytical insights and material choice

AbstractCompressed air energy storage (CAES) is attracting attention as one of large‐scale renewable energy storage systems. Its gas storage chamber is one of key components for its success. A successful utilization of an abandoned coalmine roadway depends on the stability of the gas storage chamber. The chamber is a multilayer structure and the redistribution of the stress and displacement in each layer is critical to the chamber stability. So far, this redistribution mechanism under any lateral pressure coefficient and internal air pressure has been unclear and thus a quick and easy evaluation on the CAES chamber stability becomes difficult. In this study, the redistributions of stress and displacement in each layer are analytically solved based on complex variable function theory. First, the stress and displacement in three CAES multilayer structures are obtained under any lateral pressure coefficient and internal air pressure. Then, a comprehensive parameter b1 is obtained to describe the redistribution of stress and displacement in rock mass, lining layer, and grout layer. Its linkage with lateral pressure coefficient, internal air pressure, and material properties is analytically expressed. Thirdly, an analytical expression is obtained for the influence range of internal air pressure on chamber stress. Finally, the role and selection of lining and grouting layers are explored at different lateral pressure coefficient and internal air pressure. It is found that the CAES chamber stability can be effectively and quickly evaluated by these analytical solutions and comprehensive parameters and enhanced by a material with low bulk modulus but high shear modulus.

Comprehensive performance assessment and multi-objective optimization of a new combined cooling, heating and power system with CO2 working fluid

In order to utilize the medium-and-low grade energy sources more efficiently, a new combined cooling, heating and power system using CO2 working fluid is proposed in this paper. The system is coupled by supercritical CO2 Brayton cycle and transcritical CO2 refrigeration cycle, and is characterized by the clever arrangement of some heat exchangers which could recover overall waste heat to generate useful heating capacity. Firstly detailed mathematical models are established to investigate the thermodynamic and exergoeconomic performance of the system. Under the preliminary design conditions with 170 °C heat source, the system could achieve values of 115.49%, 41.37% and 16.84$‧GJ−1 in thermal efficiency, exergy efficiency, and cost of unit product exergy respectively. Then comprehensive parameter analysis are conducted and reveal that within some certain ranges, the decrease of turbine inlet pressure and the increase of turbine inlet temperature, turbine outlet pressure, evaporation pressure and ratio of CO2 mass flow rates would lead to higher exergy efficiency and lower unit product exergy cost. Afterwards multi-objective optimizations performed under two operation modes demonstrate the everlasting high-efficiency of the system at different modes. Finally a performance comparison with a similar system in the literature further proves the superiority of the proposed system.

Study on the effect of aging on cracking resistance of virgin asphalt binder and its evolution model

The cracking resistance of asphalt pavement is significantly affected by the aging of asphalt binder. A better understanding of the effect of aging on the cracking resistance of virgin asphalt binders can help to determine the optimum maintenance time. In this study, the rolling thin film oven test (RTFOT) and pressure aging vessel (PAV) test were conducted to simulate short-term aging and long-term aging of asphalt binders, respectively. Frequency sweep tests were conducted on two asphalt binders at different aging conditions. The Glover-Rowe (G-R) parameter, stiffness modulus (S), creep rate (m), continuous grading temperature (Tc), critical temperature difference (ΔTc), relaxation time (λ), dissipation energy ratio (Wd/Ws), low-temperature indicator ratio (m/S), low-temperature comprehensive compliance parameter (Jc) were obtained. Meanwhile, the sensitivities of different indicators to aging were analyzed and the correlations between G-R parameter and various low-temperature indicators were discussed. Subsequently, a nonlinear model of asphalt binder aging was established by a rheological indicator. The results showed that aging had a significant impact on the low-temperature properties of asphalt binder. G-R parameter, Tc, λ increased with the growth of aging severity, whereas m, ΔTc, Wd/Ws were contrary to this trend. S, m/S, Jc had no significant relationship with the degree of aging. Moreover, G-R parameter and ΔTc were very sensitive to aging. The G-R parameter had a good correlation with m, Tc, λ, ΔTc and Wd/Ws, respectively. It indicated that G-R parameter was closely related to the low-temperature properties of asphalt binders. The aging nonlinear model containing G-R parameter could exactly describe the aging evolution of virgin asphalt binder. It is helpful for the prediction of cracking resistance of asphalt binder and determination of maintenance plan.

Eruptivity Criteria for Solar Coronal Flux Ropes in Magnetohydrodynamic and Magnetofrictional Models

We investigate which scalar quantity or quantities can best predict the loss of equilibrium and subsequent eruption of magnetic flux ropes in the solar corona. Our models are initialized with a potential magnetic arcade, which is then evolved by means of two effects on the lower boundary: first, a gradual shearing of the arcade, modeling differential rotation on the solar surface; and second, supergranular diffusion. These result in flux cancellation at the polarity inversion line and the formation of a twisted flux rope. We use three model setups: full magnetohydrodynamics (MHD) in cartesian coordinates, and the magnetofrictional (MF) model in both cartesian and polar coordinates. The flux ropes are translationally invariant, allowing for very fast computational times and thus a comprehensive parameter study, comprising hundreds of simulations and thousands of eruptions. Similar flux rope behavior is observed using either magnetofriction or MHD, and there are several scalar criteria that could be used as proxies for eruptivity. The most consistent predictor of eruptions in either model is the squared current in the axial direction of the rope, normalized by the relative helicity, although a variation on the previously proposed eruptivity index is also found to perform well in both the MF and MHD simulations.

Experimental analysis on applicability degree and influence factors of MWD parameters for anchor drilling

In the intelligent identification of surrounding rock state of geotechnical engineering, monitoring while drilling (MWD) technology for anchor drilling plays an extremely important role. Through monitoring multiple drilling parameters of granite below sample group and cyan stone below sample group, this study discusses the distinguish degree of selected parameters on the samples with different properties. The experimental results show that it is more suitable to analyze the drilling process with comprehensive multiple parameters rather than a single one. In addition, the sample location, fixed rotation speed and drill bit type may affect the experimental results, which should be considered in the laboratory or field data analysis. The results of this study have certain reference value for parameter determination, field application, laboratory research and result analysis of MWD for anchor drilling.

An analytical solution for thermomechanical buckling of variable angle tow composite laminates with elastically restrained edges

The inherent anisotropy and variable stiffness characteristics exhibited by Variable Angle Tow (VAT) composite materials offer significant possibilities for enhancing the performance of corresponding structures. Through appropriate selection of the curved fiber paths, it becomes feasible to design structures that are both lightweight and efficient. However, conducting mechanical analysis on VAT composite structures is of considerable challenges, particularly in seeking analytical solutions. In view of this, this paper aims to investigate the thermomechanical buckling behavior of VAT composite laminates with elastically restrained edges and presents an analytical solution for the studied problem. The analysis is based on the assumption that the fiber orientation angle of each lamina varies linearly or nonlinearly along the x direction, and the fundamental equations for thermomechanical buckling of VAT laminates are derived using the classical laminated plate theory. The analytical solution is obtained through the combined utilization of Taylor series and Frobenius series. Furthermore, a comprehensive parameter analysis is conducted to assess the impact of different factors, such as fiber paths and elastic support stiffness, on the critical thermomechanical buckling load and mode of VAT laminates. The resulting findings serve as a valuable reference for VAT laminate design, while the proposed analytical solution can be employed as a benchmark for validating other numerical results.

Ruling Out New Physics at Low Redshift as a Solution to the H_{0} Tension.

We make the case that there can be no low-redshift solution to the H_{0} tension. To robustly answer this question, we use a very flexible parametrization for the dark energy equation of state such that every cosmological distance still allowed by data exists within this prior volume. To then answer whether there exists a satisfactory solution to the H_{0} tension within this comprehensive parametrization, we constrained the parametric form using different partitions of the Planck cosmic microwave background, SDSS-IV/eBOSS DR16 baryon acoustic oscillation, and Pantheon supernova datasets. When constrained by just the cosmic microwave background dataset, there exists a set of equations of state which yields high H_{0} values, but these equations of state are ruled out by the combination of the supernova and baryon acoustic oscillation datasets. In other words, the constraint from the cosmic microwave background, baryon acoustic oscillation, and supernova datasets together does not allow for high H_{0} values and converges around an equation of state consistent with a cosmological constant. Thus, since this very flexible parametrization does not offer a solution to the H_{0} tension, there can be no solution to the H_{0} tension that adds physics at only low redshifts. This is directly related to the expansion history of the Universe and its geometrical properties and would include models beyond those parametrized by w(z).

Deformation warning index for reinforced concrete dam based on structural health monitoring data and numerical simulation

The material mechanical parameters of the dam body and foundation will change when a dam is reinforced during the aging process. This causes significant changes in the structural state of the project and makes it difficult to ensure its structural safety. In this study, a new deformation warning index for reinforced concrete dams was developed according to the prototype monitoring data, statistical models, three-dimensional finite element model (FEM) numerical simulation, and the critical conditions of the dam structure. A statistical model was established to separate the water pressure component. Then, a three-dimensional FEM of the reinforced concrete dam was constructed to simulate the water pressure component. Furthermore, the deformation components that affected the mechanical parameters of the dam under the same amount of reservoir water level change were separated and quantified accurately. In addition, the method for inversion of comprehensive mechanical parameters after dam reinforcement was used. The influence mechanisms of the deformation behavior of concrete dams under the reservoir water level and temperature changes were investigated. A new deformation warning index was developed by combining the forward-simulated critical water pressure component and temperature component in the period of extreme temperature decrease with the aging component separated by the statistical model. The new deformation warning index considers the structural state of the dam before and after reinforcement and links the structural strength criterion and the deformation evolution mechanisms. It provides a theoretical foundation and decision support for long-term service and operation management of reinforced dams.

On the optimisation of detector threshold for energy efficient CSS over fading channels

ABSTRACT Non-cooperative scenarios in cognitive wireless sensor network (CWSN) are often encountered with shadowing and hidden terminal issues. Cooperative spectrum sensing (CSS) can solve these issues but at the expense of large overhead. It is necessary to optimise the energy of battery-operated unlicensed users, also known as secondary users (SUs). The effect of fading and noise uncertainty is often overlooked when determining CSS performance. Energy efficiency (EE) is a comprehensive parameter that gives a complete picture of the overall performance of the CSS. There are several parameters that affect the EE of CSS. In this paper, the detector threshold is optimised to maximise the EE of the system. A system model is proposed to determine the EE of centralised CSS over different fading channels in noisy reporting conditions. An iterative algorithm is presented which determines the optimum detector threshold for which the EE is maximum. Results show that the optimum value of the detector threshold from the analytical model matches with simulation data.

DFQIoV: Design of a Dynamic Fan-Shaped-Clustering Model for QoS-aware Routing in IoV Networks

Internet of Vehicles (IoV) is a steadily growing field of research that deals with highly ad-hoc wireless networks. These networks require design of high-speed & high-efficiency routing models, that can be applied to dynamically changing network scenarios. Existing models that perform this task are highly complex and require larger delays for estimation of dynamic routes. While, models that have faster performance, do not consider comprehensive parameters, which limits their applicability when used for large-scale network scenarios. To overcome these limitations, this text proposes design of a novel dynamic fan-shaped clustering model for QoS-aware routing in IoV networks. The model initially collects network information sets including node positions, & energy levels, and combines them with their temporal packet delivery & throughput performance levels. These aggregated information sets are processed via a hybrid bioinspired fan shaped clustering model, that aims at optimization of routing performance via deployment of dynamic clustering process. The model performs destination-aware routing process which assists in reducing communication redundances. This is done via a combination of Elephant Herding Optimization (EHO) with Particle Swarm Optimization (PSO), which integrates continuous learning for router level operations. The integrated model is able to reduce communication delays by 5.9%, while improving energy efficiency by 8.3%, throughput by 4.5%, and packet delivery performance by 1.4% under different network scenarios. Due to which the proposed model is capable of deployment for a wide variety of dynamic network scenarios.

Anionic Solid Solution MXene for Low‐Dosage NIR‐II Tumor Photothermal Therapy

AbstractDue to the deep biological penetrability and therapeutic depth, the photothermal therapy over second near‐infrared region (NIR‐II) is booming against deep‐seated tumors. Intensive endeavors are committed to looking for suitable photothermal agents (PTAs), but the progress seems not so satisfied toward the choice of PTA dosage. Herein, a comprehensive parameter, incident photon‐to‐thermal conversion coefficient (IPTCE), is used to evaluate the overall conversion of PTAs at different dosage, which will benefit for determining the optimized dosage of PTAs in pursuit of complete healing together with reduced long‐term damages of nanodrugs. To prove the possibility, a series of anionic solid solution MXenes are chosen as hosts due to their versatile chemical compositions and correspondingly tunable light response. By deconvoluting fundamental structure–composition–property relationships, anionic regulation with extra electron injection leads to tunable free carrier densities and enhanced NIR‐II harvesting. Ti3C1.23N0.77 with high‐level nitrogen exhibits extraordinary extinction coefficient (43.5 L g−1 cm−1) than other MXenes. The parameter of IPTCE can guide the choice of PTA concentration for complete photothermal healing in vitro and vivo. This proof‐of‐principle demonstration highlights synthetically tailoring of the light harvesting over NIR‐II biowindow for a given host material by anionic regulation and further optimizes tumor photothermal therapy at low dose.

Bioreceptor-inspired soft sensor arrays: recent progress towards advancing digital healthcare

Recent advances in soft sensor technology have pushed digital healthcare toward life-changing solutions. Data reliability and robustness can be realised by building sensor arrays that collect comprehensive biological parameter data from several points on the underlying organs simultaneously, a principle that is inspired by bioreceptors. The rapid growth of soft lithography and printing, three-dimensional (3D) printing, and weaving/knitting technologies has facilitated the low-cost development of soft sensors in the array format. Advances in data acquisition, processing, and visualisation techniques have helped with the collection of meaningful data using arrays and their presentation to users on personal devices through wireless communication interfaces. Local- or cloud-based data storage helps with the collection of adequate data from sensor arrays over time to facilitate reliable prognoses based on historical data. Emerging energy harvesting technologies have led to the development of techniques to power sensor arrays sustainably. This review presents developmental building blocks in wearable and artificial organ-based soft sensor arrays, including bioreceptor-inspired sensing mechanisms, fabrication methods, digital data-acquisition techniques, methods to present the results to users, power systems, and target diseases/conditions for treatment or monitoring. Finally, we summarise the challenges associated with the development of single and multimodal array sensors for advanced digital healthcare and suggest possible solutions to overcome them.

Design approach and its optimization for bolted steel plate strengthened RC beams considering the effect of connection bolt slip

The use of bolted steel plates (BSP) has been found to be an effective method for increasing the flexural capacity of reinforced concrete (RC) beams. However, slip in bolt connections is an inevitable occurrence that affects the mechanical behavior of the BSP beam. While prior studies have thoroughly examined the effects of bolt connection slip on the flexural capacity of BSP beams, assuming unlimited slip, experimental results revealed that bolt connection failure takes place when slip surpasses a specific threshold. The current design methodology does not accommodate this finding. To address this issue, a simplified design method for practical applications is proposed. Comprehensive parameter studies were conducted on the enhanced moment capacity ratio and maximum bolt slip, involving a total of 13,824 and 276,480 calculations, respectively. It is found that several parameters (e.g., the aspect ratio of RC beam, the tensile longitudinal reinforcement ratio, the height of steel plate) affect the two above-mentioned indexes. Equations were then developed to determine the enhanced moment capacity ratio and maximum slip. It is noteworthy that the recommended effective yield slip (0.55 mm) is obtained based on adhesive anchor, which should be checked when other types of anchors would be used. Relying on these equations, an optimized design approach for BSP beams is introduced that avoids complex iterative processes. The effectiveness of the optimized design methodology is substantiated through a practical example.

Storage Duration Prediction for Long-Expired Frozen Meat Exceeding State Reserve Time via Swept-Source Optical Coherence Tomography (SS-OCT) under Low-Frequency Electric Field

Storage duration detection for frozen meat, especially meat exceeding the state reserve time several times, has always been a big challenge in food safety inspection. Under long freezing times, the physical and chemical properties of meat change complexly. In this paper, the SS-OCT detection method under a low-frequency electric field is firstly (to our knowledge) applied to the predict storage durations of long-expired frozen meat. The average normalized cross-correlation (ANCC) is put forward as a comprehensive parameter to reflect both the electric–kinetic and optical properties of meat’s biological changes. A monotonically increasing inversion rule between ANCC and the storage duration of frozen meat is found after investigating 3840 pork samples, the frozen storage durations of which were from 1 to 13 months. To verify the correctness and accuracy of our method, nine groups of long-expired frozen pork samples were investigated. The maximum relative error for their storage durations is less than 5.71%, which means that our SS-OCT method under a low-frequency electric field is promising in providing a rapid on-site storage duration detection method without any complicated laboratory pretreatments for food safety inspection.

Morpho-mechanical mapping of human dura mater microstructure

The human dura mater is known to impact vastly traumatic brain injury mechanopathology. In spite of this involvement, dura mater is typically neglected in computational and physical human head models. The lack of location-dependent microstructural and related mechanical data of dura mater may be considered a rationale behind this simplification. The anisotropic nature of dura mater under various loading conditions so far remains unelucidated. Furthermore, principal collagen fiber orientation is yet to be quantified for a morpho-mechanically-informed material model on the dura mater. This study aims to assess how location-dependent mechanical anisotropy is linked to principal collagen fiber orientation. Uniaxial extension tests were performed in a heated tissue bath for 60 samples from six individuals and correlated to the three-dimensional collagen structure in four individuals using second-harmonic generation (SHG) imaging. Failure stress and stretch at failure, elastic modulus, and a microstructurally motivated material model were integrated to examine local differences in dura mater morpho-mechanics. The quantitative observation of collagen fiber orientation and dispersion confirmed that collagen is highly aligned in the human dura mater and that both fiber orientation and dispersion differ depending on the location investigated. This observation provides a possible explanation for the previously observed isotropic mechanical behavior, as the main collagen fiber direction is not oriented along the anterior-posterior or medial-lateral direction at most of the mapped locations. Additionally, these site-dependent structural properties have implications for the mechanical load response and therefore potentially for the regional functions dura mater has to fulfill. The here chosen non-symmetrical fiber dispersion material model fits the data well and provides a comprehensive parameter base for further studies and future finite element models. Statement of significanceThe human dura mater greatly affects traumatic brain injury mechanisms, but it is often ignored in computational and physical head models. This is because there is a lack of detailed microstructural and mechanical data specific to the dura mater. Its anisotropic nature and collagen fiber orientation have not been fully understood, hindering the development of an accurate material model. Hence, this study combines morphological data on collagen fiber orientation and dispersion at multiple locations of human cranial dura mater, and links microstructure to location-specific load-displacement behavior. It provides microstructurally informed mechanical information towards realistic head models for predicting location-dependent tissue behavior and failure for assessing brain injury and graft material development.

A highly sensitive six-conjoined-tube anti-resonance optical fiber temperature sensor based on surface plasmon resonance

ObjectOwing to advances in optical fiber sensing, sensing techniques based on surface plasmon resonance (SPR) have received much attention. In this work, an SPR temperature sensor based on the anti-resonant fiber (ARF) is designed and analyzed. By filling the core with a thermosensitive liquid, the ambient temperature in the range of 5–55 °C can be detected. MethodsThe proposed ARF was simulated utilizing the finite element analysis software COMSOL, followed by systematic and comprehensive structural parameter optimization, and the basic characteristics of the ARF-SPR temperature sensor were analyzed. Filling with a gold medium instead of the traditional coating process reduces the difficulty in manufacturing the ARF-SPR temperature sensor. ResultsThe average temperature sensitivity of the ARF-SPR sensor is 16.055 nm/°C, the minimum structural parameter sensitivity is 1.0 nm/μm, and the maximum FOM is 360.8 °C−1 at 5 °C. The ARF sensor with excellent properties has large commercial potential in biochemical analysis, temperature monitoring, as well as long-distance sensing.

Event Impact Analysis Using Machine Learning

The "Event Impact Analysis using Machine Learning" project presents an innovative and comprehensive framework for evaluating the repercussions of significant events. This study presents a machine learning model designed to classify feedback into positive or negative sentiments based on relevant parameters, accompanied by comprehensive parameter visualizations. The aim is to create an effective system that accurately categorizes feedback sentiment while providing insightful graphical representations of key parameters. The model employs diverse machine learning algorithms, including advanced natural language processing techniques, to extract informative features from textual feedback. These features are then utilized to train a sentiment classification model capable of distinguishing between positive and negative sentiments. Key parameters contributing to sentiment classification are identified and visually presented through informative graphs and charts. These visualizations offer an intuitive understanding of the relationships between parameters and sentiment, enhancing the interpretability of the model's decision-making process. Real-world feedback datasets are utilized for model training and evaluation. Standard metrics such as accuracy, precision, recall, and F1-score are employed to assess the model's performance in sentiment classification. This study contributes to the field of sentiment analysis by combining accurate sentiment classification with parameter visualization. The resulting insights offer businesses a deeper understanding of customer feedback, aiding in improved decision-making and enhanced user experiences. Key Words: Event Analysis, Feedback Classification, Parameter Visualization, Natural Language Processing, Decision-Making.

LED array microscopy system correction method with comprehensive error parameters optimized by phase smoothing criterion.

LED array microscopy is a novel computational imaging technique that can achieve two-dimensional (2D) phase imaging and three-dimensional (3D) refractive index imaging with both high resolution and a large field of view. Although its experimental setup is simple, the errors caused by LED array position and light source central wavelength obviously decrease the quality of reconstructed results. To solve this problem, comprehensive error parameters optimized by the phase smoothing criterion are put forward in this paper. The central wavelength error and 3D misalignment model with six freedom degree errors of LED array are considered as the comprehensive error parameters when the spatial positional and optical features of arbitrarily placed LED array are unknown. Phase smoothing criterion is also introduced to the cost function for optimizing comprehensive error parameters to improve the convergence results. Compared with current system correction methods, the simulation and experimental results show that the proposed method in this paper has the best reconstruction accuracy, which can be well applied to an LED array microscope system with unknown positional and optical features of the LED array.