Optimal Load Research Articles

A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO model

Air conditioning systems are widely used to provide thermal comfort in hot and humid regions, but they also consume a large amount of energy. Therefore, accurate and reliable load demand forecasting is essential for energy management and optimization in air conditioning systems. Within the current paper, a novel model on the basis of machine learning has been presented for dynamic optimal load demand forecasting in air conditioning systems. The model is based on using an optimized design of Gated recurrent unit (GRU) network and an enhanced metaheuristic algorithm, named Improved Alpine Skiing Optimizer (IASO). GRU is a recurrent neural network that has the ability to comprehend intricate temporal relationships within the input data. On the other hand, the IASO technique has been considered to be a population-based optimization technique emulating the downhill skiing behavior of skiers. The proposed GRU/IASO model is trained and tested utilizing data of real-world obtained through a commercial complex situated within an area characterized by high humidity and hot climate. By comparing the proposed method with some other commonly used techniques, including ---, the advantage of the suggested model regarding accuracy and robustness has been defined.

The optimal annual case volume for acute type A aortic dissection surgery in relation to long-term outcomes.

Previous analyses of the volume-outcome relationship have focused on short-term outcomes such as early mortality. The current study aims to update a novel statistical methodology, facilitating the evaluation of the relation between procedural volume and time-to-event outcomes such as long-term survival, using surgery for acute type A aortic dissection as an illustrative example. This study employed an existing dataset of type A dissection outcomes, retrieved from literature. Studies were included when reporting on annual case load and long-term survival, which served as the primary outcome of interest. Individual patient data were reconstructed from the included studies, and a hazard ratio was determined per study in relation to overall survival, after which the calculated hazard ratios were incorporated in a restricted cubic spline model, facilitating the application of the elbow-method. Fifty-two studies were included (n = 14878 patients), with a median follow-up of 5 years. One-, 3-, 5-, and 10-year survival of the overall cohort were 82% (95% CI 82-83%), 79% (95% CI 78-80%), 74% (95% CI 74-75%), and 60% (95% CI 59-62%) respectively. A significant non-linear volume-outcome relation for long-term survival was observed in both the unadjusted and adjusted analyses (p = 0.030 and p = 0.002), with an optimal annual case load of 32 cases/year (95% CI 31-33). Based on the available data, these findings imply that the annual case volume to achieve optimal long-term survival is located near a procedural volume of 32 cases/year. After accrual of more annual procedures, long-term survival may no longer significantly improve any further.

Path planning and optimization for transmission line barrier operations in complex terrain based on multimachine collaborative control

Abstract To address the challenges of path planning for transmission line barrier operations in complex terrains, this paper proposes a solution based on multimachine collaborative control. A path-planning algorithm that integrates multiple classical algorithms and incorporates multiobjective optimization methods is constructed to handle the complexities of challenging terrains. Auction algorithm and genetic algorithm are adopted to achieve optimal load balance by dynamically adjusting task allocation. Simulation results show that the proposed method effectively enhances task execution efficiency, optimizes path planning, and reduces energy consumption in complex terrains and dynamic environments, while maintaining high-obstacle avoidance success rates and communication stability.

The effect of the load of bushes with shoots on the stress resistance of grapes of the varieties Vladimir, Dmitry, Kurchansky

Due to the currently observed global and local climate changes, there is an imbalance in the biological cycles of plants, a change in their resistance to biotic and abiotic stressors. The level of realization of the potential of economic productivity of grape varieties in the Krasnodar Territory is low and amounts to 60%. At the heart of its increase is the optimization of the placement of grape plantations, taking into account varietal characteristics and variety-oriented, zone-oriented technologies of grape cultivation. Varietal agrotechnology, which provides for the establishment of an optimal load of grape bushes with shoots, plays an important role in regulating the growth of the grape bush and its individual parts, the formation of plant resistance to stressors and the quality of viticulture products. Due to the more frequent manifestation of stressful weather conditions during the growing season of grapes, namely, insufficient soil moisture in combination with high temperature and low humidity, an urgent area of research is the study of stress resistance indicators under different loads of bushes with shoots. The purpose of the research is to study the effect of the load of bushes with shoots on the stress resistance of grape varieties of domestic selection Vladimir, Dmitry, Kurchansky. Indicators characterizing the stress resistance of plants, such as electrolyte yield, hydration, water deficiency and loss of water by leaves (water retention capacity), have been studied. A variety-specific reaction of grape plants to changes in the load of bushes with shoots under stressful conditions (insufficient precipitation and high air temperature) has been established. Increasing the load in Dmitry and Kurchansky grape varieties improves stress resistance indicators, such as tissue hydration, water deficiency and water retention capacity. The Vladimir variety had the best performance with a minimum and average load of bushes with shoots.

Improved Efficiency of Weakly Coupled Wireless High‐Power Transfer Systems by Loss‐Separation Strategy

ABSTRACTIn the equivalent T‐model of the loosely coupled transformers, the small mutual inductance can lead to higher conducting currents, which cause high losses in the primary circuit and significantly reduce the overall transfer efficiency under weak‐coupling states. To overcome this challenge, this paper proposes a strategy to separate the primary loss components from the weak‐coupling stage. In the proposed strategy, a gyrator in the form of a double‐resonance T‐block is added just before the weak‐coupling stage to improve the overall efficiency. Then, the strategy is realized by various topologies such as compensating circuits, added coils, isolated transformers, and integrated‐/split‐ inductors/coils. Also, the optimized designs and component selection for resonance and the mathematical derivation for optimal load resistances were investigated. ANSYS comparative analyses of the topologies are presented by considering practical aspects, including component designs, power flows, transfer efficiency, resonance frequency shifting, and optimal loads. Finally, the analyses were validated using the fabricated experimental setups and demonstrated an efficiency improvement of about 5% for a case with a coupling factor of 0.1. The proposed strategy offers suggestions for industrial designs of high‐power wireless battery charging systems using resonant inductive coils.

An Intelligent Electric Vehicle Charging System in a Smart Grid Using Artificial Intelligence

ABSTRACTThe rapid combination of the electric vehicles into the recent transportation prefers very efficient charging systems involved in grid conditions. This increasing adoption of electric vehicles demands a dynamic and intelligent framework to control charging, confirming optimal grid performance, load balancing, and cost efficiency. In this work, we developed an optimized deep learning framework using the combined structure of Whale‐Optimized Neuro‐Fuzzy Classification for controlling electric vehicle charging within the grid. The increasing involvement of electric vehicle's produces new difficulties, involving overload of grid, and energy management, in real‐time and optimized decision making process. The Whale‐Optimized Neuro‐Fuzzy Classification method uses the hybrid abilities of neuro‐fuzzy systems, integrates the capability of the neural networks and also optimize using a Whale Optimization Algorithm for improved accuracy and efficiency. This proposed method maintains the charging and discharging process of electric vehicles, which have several factors like grid load, priorities of the vehicle and preferences of the user. The neuro‐fuzzy system combines deep convolutional neural network and fuzzy logic to predict charging patterns, considering user preferences, grid demand, renewable energy availability, and so on. This method contribute to the enhancement of energy systems, denotes the future requirement of future smart cities. Evaluation metrics included power usage, energy loss, cost, and processing speed. Analysis of experimental results revealed the accuracy of 99% for the proposed method compared to existing techniques.

Optimizing Muscle Performance in Young Soccer Players: Exploring the Impact of Resisted Sprint Training and Its Relationship with Distance Covered.

Speed training with resisted sprints has been shown to positively affect neuromuscular performance in soccer players. Various loads, ranging from 10% to 120% of body mass, have demonstrated performance improvements across the spectrum. However, the impact of sprint distance with optimal load on these adaptive responses has yet to be thoroughly described. To analyze the influence of sprint distance in resisted sprints on muscle performance in young soccer players. This quantitative study utilized a pre-post experimental design. The sample consisted of 24 young soccer players (15.3 ± 0.68 years; 61.4 ± 7.08 kg; 1.60 ± 0.06 m) randomized into three groups (10, 20, and 30 m) and subjected to 12 sessions of resisted sprint training over six weeks. The volume was homogenized across groups, with a total distance of 120 m for each. The intervention's effect was analyzed through performance in the isometric mid-thigh pull (IMTP), countermovement jump (CMJ), modified 505 agility test (505 m), and linear sprint tests. Differences were analyzed using a mixed ANOVA, incorporating a between-subjects factor (training group) and a within-subjects factor (pre- and post-intervention). Time-dependent differences were observed in all groups for peak force (PF) (p < 0.001; η2p = 0.62), time to PF (TPF) (p < 0.001; η2p = 0.53), impulse at 50 (p < 0.001; η2p = 0.57), 100 (p < 0.001; η2p = 0.60), and 200 ms (p < 0.001; η2p = 0.67) in IMTP; jump height by impulse-momentum (p < 0.001; η2p = 0.64), rate of force development (p = 0.04; η2p = 0.14), yielding impulse (p < 0.001; η2p = 0.49), and concentric impulse (p = 0.01; η2p = 0.19) in CMJ; time (p < 0.001; η2p = 0.46) in 505 m; and average speed in linear sprint (p = 0.003; η2p = 0.36), with moderate to large effect sizes, regardless of the distance covered. No differences were observed for the interaction between the time* and group or between groups. Performance improvements were independent of the sprint distance, with no differences between training groups. Distances between 10 and 30 m may enhance muscle performance in young soccer players.

Boosting Thermoelectric Performance of Semicrystalline Conducting Polymers by Simply Adding Nucleating Agent.

Controlling the microstructure of semiconducting polymers is critical for optimizing thermoelectric performance, yet remains challenging, requiring complex processing techniques like alignment. In this study, a straightforward strategy is introduced to enhance the thermoelectric properties of semi-crystalline polymer films by incorporating minimal amounts of nucleating agents, a method widely used in traditional polymer industries. By blending less than 1 wt% of N,N'-(1,4-phenyl)diisonicotinamide (PDA) into poly(2,5-bis(3-alkylthiophen-2-yl)thieno[3,2-b]thiophene) (PBTTT-C14), controlled modulation of crystallization behavior is achieved, resulting in reduced structural disorder and enhanced charge carrier mobility. Systematic investigations reveal that an optimal PDA loading of 0.9 wt% increases the crystallization degree by 45% compared to pristine PBTTT-C14 films. Under these optimized conditions, the PDA-modified PBTTT-C14 films exhibit a maximum electrical conductivity of 1,894Scm-1 and a maximum power factor of 176µWm-1K-2, showing improvements of 96% and 433%, respectively, over doped pristine PBTTT-C14 films. These gains are attributed to the synergistic effects of polymer chain extension and reduced grain boundary resistance, which collectively enhance charge transport efficiency. Additionally, ion exchange doping is found to maintain a high charge carrier concentration while preserving the crystallinity introduced by PDA, paving the way for advanced thermoelectric materials and next-generation polymer-based electronics.

Research on the Sustainability Strategy of Cogeneration Microgrids Based on Supply-Demand Synergy

With the continuous adjustment of energy structure and the improvement of environmental protection requirements, combined heat and power microgrids (CHP-MG) have received widespread attention as an efficient and economical way of utilizing energy. The complexity of energy supply relationships and energy coupling within the microgrid system necessitates optimizing the power output of each equipment unit. In this paper, an optimization strategy for a multi-energy microgrid system is proposed based on the efficient energy supply of cogeneration microgrids: decoupling the thermoelectric connection by using the energy storage equipment on the supply side, utilizing the flexibility of the electrical loads and the diversity of the system’s heating methods, and reducing the electrical loads and changing the selection of the heating methods on the demand side. The optimization model in the paper is mainly based on mixed-integer linear programming and demand-side management theory, which simulates the system operation under different scenarios so as to find the optimal equipment output and load management strategies. Simulation results show that the optimized CHP-MG system can ensure a reliable power supply while effectively reducing operating costs, improving energy utilization and promoting sustainable operation of the energy system. The optimized microgrid system offers significant advantages in terms of economic efficiency and energy management when compared to conventional CHP systems. These findings provide actionable insights for policymakers, system operators, and researchers aimed at driving the development of efficient and sustainable energy management solutions.

Middle-throughput LC-MS-based platelet proteomics with minute sample amounts using semi-automated positive pressure FASP in 384-well format (PF384).

Comprehensive characterization of platelets requires various functional assays and analysis techniques, including omics-disciplines, each requiring an individual aliquot of a given sample. Consequently, the sample material per assay is often highly limited rendering downscaling a prerequisite for effective sample exploitation. Here we present a transfer of our recently introduced 96-well-based proteomics workflow (PF96) into the 384-well format (PF384) allowing for a significant increase in sensitivity when processing minute platelet protein amounts. In addition, the 4-fold higher throughput (1500 samples per lab worker per week) allows to easily meet the throughput capacities of modern LC-MS instruments. We determined optimal sample loads followed by highlighting the strengths in comparison to our previous sample preparation approach by processing only 3 µg of purified platelet protein from 22 healthy donors. Major advantages are: (I) improved identification and analyte recovery, especially of low copy number proteins, with signal intensity gains of +130 % and +107 % (peptide and protein level, respectively) (II) substantial intensity gains for key-players in platelet activation including the membrane receptors PAR4, P2X1, GPVI, GPV, GPIX and the downstream mediators AKT, PKA, Rap1, Lyn (III) improved reproducibility with a reduction of technical variance from 22 / 25 % down to 16 / 19 % for detection of lower / higher abundant disease markers and (IV) a 4-fold increase in sample preparation throughput. Taken together, these advantages render PF384 a promising future in clinical proteomics and might pave the way of platelet proteomics with minute sample amounts into molecular diagnostics.

Investigation of networked SSHI configurations for plate-based piezoelectric energy harvesters

Piezo-patch energy harvesters attached to plate-like structures can be used to extract multimodal vibrational energy. For each piezo-patch, SSHI circuits can boost the harvested power compared to standard rectifier circuits. However, the effect of using different configurations of the SSHI network for multiple piezo-patches on plate structures has not yet been understood. This study aims to assess the performance of the networked SSHI configurations compared to networked rectifier configurations and investigate the optimum configurations for energy harvesting purposes. For this, three piezo-patches are bonded to an aluminum plate and connected to single and/or multiple circuits. The equivalent circuit model (ECM) of the electromechanical system is developed from the experimentally validated analytical model and utilized in simulation software LTspice. The performances of considered configurations are evaluated regarding peak power outputs, and the optimal load resistances are obtained. Based on the experimental findings, the best configuration is determined and verified by simulations. The results show that by using respective SSHIs, the power output can be improved by preventing charge cancellation despite the cost of increased diode loss. This study contributes to our understanding of optimal energy harvesting techniques by investigating the effectiveness of networked SSHI configurations for multiple piezo-patches on plate structures.

Intelligent Saturation Power Limit Load Distribution Algorithm (ISPLLDA) for Cooperative Manipulators Applications

Cooperative manipulators face challenges related to an inadequate distribution of external loads and a decrease in Dynamic Load Carrying Capacity (DLCC). Understanding the impact of optimal load distribution on power consumption, load carrying capacity, and gripper error is crucial. This paper presents the Intelligent Saturation Power Limit Load Distribution Algorithm (ISPLLDA), a novel method that achieves optimal external load distribution. ISPLLDA dynamically distributes the external load among manipulators based on torque-bearing capacity and actuator position. Additionally, nonlinearity in system dynamics introduces uncertainties, leading to incorrect DLCC evaluation, increased error, and higher actuator power consumption. To address this, a Radial Basis Function Neural Network (RBFNN) accurately determines actuator saturation limits in the presence of uncertainty, enabling correct estimation of system dynamics and external disturbances. ISPLLDA ensures near-simultaneous saturation of all manipulators' actuators, maximizing their capacity utilization. The proposed method is validated through simulations and experimental tests on cooperative manipulators. Results demonstrate a 17% increase in load-carrying capacity, as well as more than 35% improvement in error and torque indexes compared to the Lagrange multipliers method.

Stability Study of Distributed Drive Vehicles Based on Estimation of Road Adhesion Coefficient and Multi-Parameter Control

In order to improve the driving stability of distributed-drive intelligent electric vehicles under different roadway attachment conditions, this paper proposes a multi-parameter control algorithm based on the estimation of road adhesion coefficients. First, a seven-degree-of-freedom (7-DOF) vehicle dynamics model is established and optimized with a layered control strategy. The upper-level control module calculates the desired yaw rate and sideslip angle using the two-degree-of-freedom (2-DOF) vehicle model and estimates the road adhesion coefficient by using the singular-value optimized cubature Kalman filtering (CKF) algorithm; the middle-level utilizes the second-order sliding mode controller (SOSMC) as a direct yaw moment controller in order to track the desired yaw rate and sideslip angle while also employing a joint distribution algorithm to control the torque distribution based on vehicle stability parameters, thereby enhancing system robustness; and the lower-level controller performs optimal torque allocation based on the optimal tire loading rate as the objective. A Speedgoat-CarSim hardware-in-the-loop simulation platform was established, and typical driving scenarios were simulated to assess the stability and accuracy of the proposed control algorithm. The results demonstrate that the proposed algorithm significantly enhances vehicle-handling stability across both high- and low-adhesion road conditions.

Association between anthropometric, biomechanical and strength parameters and throwing velocity in elite water polo players

ABSTRACT This study investigated the association between shoulder biomechanics, anthropometric variables and isometric and dynamic forces in the pullover exercise and throwing speed in professional water polo players. 30 elite male players (age: 20 ± 2.7 years; height: 180 ± 5.3 cm, body mass: 80 ± 7.0 kg) from the Spanish top division league were assessed. Bayesian bivariate correlations revealed significant positive correlations with lean mass, trunk length, wingspan, shoulder isometric strength, optimal power load, and pullover from 20% to 80% 1RM and a significant negative correlation with the shoulder internal rotation range of movement. The significance was determined when the percentage of the posterior distribution within the region of practical equivalence (ROPE) was <1%. Predictive projection based on leave-one-out cross-validation identified a submodel with three key predictors: shoulder isometric strength, shoulder internal rotation range of motion, and trunk length. The results suggest that coaches should integrate the identified correlation values related to conditional variables into training programs to optimize the throwing speed. Additionally, they should emphasize the role of key determinants such as lean mass, trunk length, and wingspan in throwing performance. Integrating these findings into training regimens could yield more effective strategies for improving throwing performance.

Study of mechanical properties of Al-based hybrid nanocomposites reinforced with MoS2 nanoflakes and graphite nanoplatelets: an investigation of the synergistic effect

In the present study, Al-based hybrid nanocomposites were developed by powder metallurgy technique using binary hybrid nanofillers consisting of exfoliated MoS2 and GnP as nanofillers. The impact of the MoS2-GnP hybrid nanofiller on the microstructure, mechanical and tribological properties of the Al-based hybrid nanocomposites have been studied. Initially, bulk MoS2 was exfoliated by milling in a high energy planetary ball mill for 30 h and the GnP was synthesized by subjecting the graphite intercalation compound to a thermal shock and subsequently ultrasonicating the thermally exfoliated GnP. The effective exfoliation and structural refinement of both MoS2 and GnP have been confirmed by X-ray diffraction and scanning electron microscopy analysis. Exfoliated MoS2 and GnP were later blended in different ratios of their weight fraction by ultrasonication in an acetone medium. The Al matrix was then reinforced with the various binary hybrid nanofillers consisting of MoS2 and GnP. Wear analysis revealed mechanisms such as abrasion, adhesion, ploughing, delamination, microcracks, deep grooves, and nanofiller pullout in the case of all the nanocomposites. The Al-1 wt.% MoS2(0.3)GnP(0.7) nanocomposite shows superior properties, including the highest relative density (~93.15%), hardness (~476.28 MPa), compressive strength (~337.76 MPa) and outstanding wear resistance among all the Al-MoS2-GnP nanocomposites. Notably, it was observed that straying from the optimal reinforcement loading level can have a detrimental effect on the physical, mechanical, and wear properties of the nanocomposites, resulting in diminished performance and reduced material integrity. The significant improvement in the wear properties of the Al-1 wt.% MoS2(0.3)GnP(0.7) nanocomposite can be attributed to the self-lubricating properties of MoS2 and GnP.

A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation

The correlation analysis between current surface cracks of structures and external loads can provide important insights into determining the structural residual bearing capacity. The classical regression assessment method based on experimental data not only relies on costly structure experiments; it also lacks interpretability. Therefore, a novel load estimation method for RC beams, based on correlation analysis between detected crack images and strain contour plots calculated by FEM, is proposed. The distinct discrepancies between crack images and strain contour figures, coupled with the stochastic nature of actual crack distributions, pose considerable challenges for load estimation tasks. Therefore, a new correlation index model is initially introduced to quantify the correlation between the two types of images in the proposed method. Subsequently, a deep neural network (DNN) is trained as a FEM surrogate model to quickly predict the structural strain response by considering material uncertainties. Ultimately, the range of the optimal load level and its confidence interval are determined via statistical analysis of the load estimations under different random fields. The validation results of RC beams under four-point bending loads show that the proposed algorithm can quickly estimate load levels based on numerical simulation results, and the mean absolute percentage error (MAPE) for load estimation based solely on a single measured structural crack image is 20.68%.

Pt@ZnCo2O4 Microspheres as Peroxidase Mimics: Enhanced Catalytic Activity and Application for L-Cysteine Detection.

Compared to natural enzymes, the development of efficient artificial simulated enzymes, such as those based on bimetallic materials with high catalytic activity and good stability, is an important way until now. Herein, we employed ZnCo2O4 microspheres as carriers to synthesize Pt-doped composites with different amounts using a one-pot method. The morphology and structure of the synthesized materials were characterized using XRD, SEM, BET, FT-IR, XPS, and Zeta potential techniques. It was found that Pt0 adhered well to the surface of ZnCo2O4 microspheres, with a 12.5% Pt doped ratio exhibiting abundant oxygen vacancies, excellent substrate affinity, and high peroxidase-like activity. Using fluorescent probes and electrochemical methods, the peroxidase-like catalytic mechanism has been explored that Pt@ZnCo2O4 microspheres can accelerate the electron transfer between H2O2 and 3,3',5,5'-tetramethylbenzidine (TMB). Based on the optimal loading ratio of 12.5% of Pt@ZnCo2O4, a colorimetric sensor for visual detection of L-cysteine (L-Cys) was constructed, exhibiting a wide linear range of 0.1~50 µM and a low detection limit of 0.0163 µM. The sensor possesses good selectivity, reusability, and usage stability, which can be well applied to the determination of L-Cys in health product capsules with recovery rates of 96.9%~103.7% and RSD of 1.07%~6.50%. This work broadens the application prospects of spinel materials such as ZnCo2O4 in the field of biological analysis and also provides inspiration for the development of new artificial simulated enzymes.

Osteogenic differentiation of mesenchymal stem cell on poly sorbitol sebacate scaffold under shear stress in a bioreactor.

Mechanical loading plays a pivotal role in regulating bone anabolic processes. Understanding the optimal mechanical loading parameters for cellular responses is critical for advancing strategies in orthopedic bioreactor-based bone tissue engineering. This study developed a poly (sorbitol sebacate) (PSS) filmscaffold with a sorbitol-to-sebacic acid molar ratio of 1:4. The scaffold underwent extensive characterization, including physical and mechanical property evaluations, biocompatibility assessments, and cell adhesion analysis. The Young's modulus of the PSS polymer was determined to be 6.81 ± 0.44 MPa under dry conditions, 6.37 ± 1.09 MPa in a wet state, and 6.38 ± 0.71 MPa after ethanol washing (70 %). The average contact angle of the PSS film was measured at 88.806 ± 1.644°, indicating moderate hydrophilicity. To investigate the osteogenic potential, a fluid flow inducing a shear stress of 1 Pa at a frequency of 1 Hz was applied to mesenchymal stem cells (MSCs) cultured on the PSS scaffold. Cells were exposed to dynamic fluid flow for one hour daily on days 4, 5, 6, and 7 of culture, followed by a static culture period of 14 days. The expression of osteogenic differentiation markers, including osteopontin (OPN), osteocalcin (OCN), type I collagen, and calcium deposition, was significantly elevated under dynamic conditions compared to static culture. This study highlights the importance of mechanical stimulation in enhancing MSC osteogenesis and underscores the osteoconductive properties of the PSS scaffold. These findings provide valuable insights into scaffold design and mechanical loading strategies for laboratory-based bone tissue engineering applications.

THE EFFECTS OF SEPIOLITE LOADINGS ON MECHANICAL, THERMAL, AND MORPHOLOGICAL CHARACTERISTICS OF NATURAL RUBBER/EPOXIDISED NATURAL RUBBER (NR/ENR-25) BLENDS

Sepiolite has been used widely as nanofillers to improve the properties and compatibility of rubber blends, and due to its magnesium silicate hydrates contents, the sepiolite may also function as a thermal stabilizer. These works investigate the effects of sepiolite loadings on curing, mechanical, thermal, and morphological characteristics of natural rubber/epoxidized natural rubber (NR/ENR-25) blends. The blends were made using a two-roll mill technique using standard vulcanization reagents of sulfur and carbon black as the main filler, and their vulcanization characteristics were investigated using the rheometry technique. Furthermore, the thermal, mechanical, and morphological characteristics of the blends were evaluated using thermogravimetry, tensile tests, and electron microscopy, respectively. It was found that an increase in the sepiolite loadings decreased scorch times (ts2 and t90), whereas optimum sepiolite loading with higher tensile strength was 3 phr, which lowered down when the sepiolite loading was increased up to 9 phr. The thermogram of blend specimen with 3 phr sepiolite loading also exhibited superior thermal stability, that is, has higher degradation temperature at 5% weight loss. Fracture surface SEM photographs of the blend specimens showed finely distributed sepiolite particles at optimum sepiolite loading 3 phr and exhibited agglomerations when the loadings were increased up to 9 phr.

Compact and Gain-Enhanced Vivaldi Antenna Design Using Nonuniform Slot Profile Optimization and Aperture Dielectric Loading

Compact and Gain-Enhanced Vivaldi Antenna Design Using Nonuniform Slot Profile Optimization and Aperture Dielectric Loading