High-performance Systems Research Articles

Temperature-dependent charge transport measurements unveil morphological insights in non-fullerene organic solar cells

The morphological analysis of bulk heterojunction (BHJ) active layer stands as a critical imperative for advancing the performance of future organic solar cells. Conventional characterization tools employed for morphological investigation often require substantial resources, both in cost and physical space, thereby imposing restraints on research endeavors in this domain. Here, we extend the application of charge carrier transport characterization beyond conventional mobility assessments, utilizing it as a table-top method for preliminary morphological screening in organic thin films. The investigation focuses on several high-performance BHJ systems that utilize typical “Y” non-fullerene acceptors. It involves in-depth transport studies, including temperature- and field-dependent transport characterizations. The resulting transport data are analyzed in detail using the Gaussian disorder model to extract key transport parameters, specifically the high-temperature limited mobility (μ∞) and positional disorder (∑). Integrating these transport parameters with morphological insights obtained through various characterization tools—including x-ray scattering, sensitive spectroscopy, and quantum chemistry simulation—provides a deep understanding of the intricate interplay between charge transport properties and morphological characteristics. The results reveal explicit relationships, associating μ∞ with the degree of molecular stacking in BHJs and ∑ with the structural disorder in molecule skeleton. Our findings point to the promising potential of utilizing a simple transport characterization technique for the early stage evaluation of thin film packing and geometric properties of organic materials.

Towards Integrating Automatic Emotion Recognition in Education: A Deep Learning Model Based on 5 EEG Channels

In a technologically advanced world, artificial intelligence has impacted all fields of activity. The augmentation of online learning by means of emotion recognition systems raises new challenges in terms of obtaining high-performance systems and in interpreting the results. The paper aims to investigate the usage of automated emotion recognition in learning and to develop a deep learning model based on physiological data to recognize emotions often encountered in classrooms. So, an 1D-CNN model based on physiological data is used to recognize seven emotions: boredom, confusion, frustration, curiosity, excitement, concentration, and anxiety. These emotions are described according to the PAD model and the 5 EEG signals, FP1, AF3, F7, T7, FP2, are taken from the DEAP dataset to train and to test the convolutional neural network model. The high accuracy we obtained (i.e. boredom—99.64%, confusion—99.70%, frustration—99.66%, curiosity—99.80%, excitement—99.91%, concentration—99.70%, anxiety—99.21%) proves that the use of signals obtained via only five channels is sufficient to recognize the presence of emotions. Furthermore, an improved method of analysis based on LIME is proposed and used to obtain reliable explanations for the predictions of our model.

Infantry Weapon Systems Used by Special Operations Forces to Carry Out Assigned Missions

Abstract Special Operations Forces (SOF) operate in dynamic, challenging environments, requiring accurate and versatile infantry weapon systems to effectively perform their assigned missions. This article provides a comprehensive review of the diverse range of infantry weapon systems used by SOF units in different theatres of operations. Through a synthesis of empirical data, operational perspectives and expert analysis, the study aims to elucidate the critical role of specialised weaponry in increasing the success rate of SOF missions. The analysis covers a broad spectrum of weapon platforms, each tailored to meet distinct mission requirements. In addition, the article explores the integration of advanced technologies into weapon construction, such as high-performance optical systems, silencers and other modular accessories, which enhance the lethality, accuracy and stealth capabilities of special forces units.

Numerical analysis of free convection under the influence of radiation and inclined MHD in a triangular cavity filled with hybrid nanofluid and a porous fin

This study presents a novel investigation into the thermal behavior of a hybrid nanofluid (MgO − Ag − H2O) in natural convection around a porous fin in a triangular enclosure under the influence of radiation and magnetohydrodynamic effects. The research uniquely combines these complex phenomena, addressing a significant gap in the literature. This configuration has potential applications in advanced solar thermal collectors, electronic cooling systems for high-power devices, and compact heat exchangers in various industries. The main objectives are to understand how various parameters influence heat transfer and fluid flow behavior and to optimize the design for enhanced thermal performance. The study considers a range of variables including Rayleigh number (103 − − 106), Hartmann number (0 – 50), Aspect ratio (0.3 – 0.6), radiation parameters (Rd = 1 − − 5, λ = 1 − 5), and volume concentration (0- 0.05), which have been numerically analyzed using the finite element method (FEM). The findings reveal that increasing the Darcy number (Da) enhances heat transfer at low Rayleigh numbers (Ra = 103,104). However, at higher Ra (Ra = 106), the impact of Da becomes more complex, with a critical Da beyond which heat transfer efficiency decreases due to an increase in flow resistance. The nanoparticle volume concentration plays a vital role, as higher concentrations lead to improved heat transfer efficiency, especially at higher Ra, through enhanced thermal conductivity and thermal dispersion. The length of the porous fin greatly impacts fluid flow patterns and heat transfer rates, with longer fins creating more complex flow patterns, promoting enhanced heat transfer and stronger thermal plumes. Thermal radiation, represented by the radiation parameters (Rd and λ), significantly influences both the heat transfer rate and the convective flow patterns within the enclosure. This study also incorporates a comprehensive entropy generation analysis, providing novel insights into system irreversibilities and optimization potential. The entropy analysis reveals the complex interplay between various parameters and their impact on system efficiency, offering valuable guidance for designing high-performance thermal management systems.

Effect of Ce-Doping on the Structural, Morphological, and Electrochemical Features of Co3O4 Nanoparticles Synthesized by Solution Combustion Method for Battery-Type Supercapacitors

This study investigates the potential of cerium (Ce) doping to improve the performance of cobalt oxide (Co₃O₄) nanoparticles as battery-type supercapacitor electrodes. Pure Co₃O₄ nanoparticles were synthesized via a solution combustion method and then doped with 2.5% (Ce-Co3O4) and 5% Ce (CeO2-Co3O4). Comprehensive characterization, including X-ray diffraction (XRD), Raman spectroscopy, and field emission scanning electron microscopy (FESEM), was used to analyze the impact of Ce doping on the material properties. XRD analysis confirmed the successful incorporation of Ce into the Co₃O₄ structure, with distinct CeO2 phases forming at higher doping levels. Ce doping resulted in decreased crystallite size and peak intensity, indicating reduced crystallinity and increased defect concentration. Raman spectroscopy corroborated these findings, showing a redshift that suggests weakened metal-oxygen bonds and smaller grain sizes due to Ce³⁺ incorporation. FESEM images demonstrated that Ce doping effectively reduced nanoparticle agglomeration, with 2.5% doping leading to smaller particles and 5% doping promoting a 2D flake-like morphology with increased porosity. Nitrogen adsorption-desorption measurements revealed a significant increase in surface area and pore volume for CeO2-Co₃O₄, facilitating improved electrolyte diffusion and reduced resistance, thereby enhancing electrochemical performance. Evaluation of the electrochemical properties of undoped and Ce-doped Co₃O₄ materials revealed a battery-like response in a three-electrode configuration. Notably, the CeO2-Co3O4 exhibited a superior specific capacity of 603.3 C g-1 at a current density of 1 A g-1, significantly exceeding the values of 368.5 C g-1 and 127.1 C g-1 achieved by Ce-Co3O4 and undoped Co3O4, respectively. Furthermore, the CeO2-doped Co3O4 demonstrated exceptional cyclic stability, retaining 87% of its initial capacity after undergoing 5000 charge-discharge cycles at a high current density of 10 A g-1. These results suggest that Ce doping is a promising strategy for optimizing Co₃O₄-based battery-type electrode materials, potentially leading to the development of high-performance and cost-effective energy storage systems.

An UHF band planar resonator temperature sensor constructed from high-performance titanium dioxide system microwave dielectric ceramics: Toward integrated ceramic-based sensor devices

With the rapid fusion of temperature sensing technology and microwave technology, microwave temperature sensors have become the protagonist of competing research. We propose a planar resonator temperature sensor that combines substrate material modifications with sensor structure design. To realize this concept, high-performance TiO2-xwt. % ZnO (0 ≤ x ≤ 3) microwave dielectric ceramics are prepared. The various factors influencing dielectric properties, including crystal structure, phase composition, Raman vibration, microstructure, element valence, and oxygen vacancy, are completely investigated. The TiO2-0.7 wt. % ZnO ceramic exhibiting exceptional properties (εr = 106.6, Qf = 46 000 GHz, τf = 426.0 ppm/°C) is selected for substrate fabrication. The frequency and temperature dependence of εr and tan δ are analyzed at 2–4.5 GHz from −50 to 100 °C, revealing a good linearity between εr and temperature. A CSRR temperature sensor employing this substrate material is designed, simulated, fabricated, and validated from −50 to 90 °C. This sensor generates two resonance frequencies (around 0.5 and 1.4 GHz) in the UHF band, demonstrating sensitivities of 2.2 MHz/10 °C and 6.3 MHz/10 °C at the first and second resonance frequencies, along with an outstanding normalized sensitivity of approximately 0.045. Through a comprehensive analysis of the physical mechanisms affecting the sensor's sensitivity and quality factor, the design of the sensor is strengthened from the perspective of optimizing the performance of microwave dielectric ceramics. The regulation mechanism of dielectric characteristics is enriched and clarified, thereby achieving a synergistic improvement in sensor performance. This work expands the application scope of microwave dielectric ceramics and provides an innovative approach to environmental monitoring.

NEURAL NETWORKS AND DEEP LEARNING: ENHANCING AI THROUGH NEURAL NETWORK OPTIMIZATION

Neural networks and deep learning have profoundly impacted artificial intelligence (AI), driving advancements across numerous applications. However, optimizing these networks remains a critical challenge, necessitating sophisticated techniques and methodologies. This article explores the state-of-the-art in neural network optimization, delving into advanced gradient descent variants, regularization methods, learning rate schedulers, batch normalization, and cutting-edge architectures. We discuss their theoretical underpinnings, implementation complexities, and empirical results, providing insights into how these optimization strategies contribute to the development of high-performance AI systems. Case studies in image classification and natural language processing illustrate practical applications and outcomes. The article concludes with an examination of current challenges and future directions in neural network optimization, emphasizing the need for scalable, interpretable, and robust solutions.

Application of bionic topology to latent heat storage devices

Latent heat storage is employed to address the intermittent supply of renewable energy, and enhancing heat transfer in phase change materials (PCMs) is pivotal for achieving effective heat storage. Currently, the predominant method for improving heat transfer is through the integration of high thermal conductivity fins into heat storage devices. This study introduces an innovative design of a three-tube phase change heat storage device created through bionic topology optimization. The impact of parameters such as penalty factors and filtering radius on the topology structure is analyzed, and the optimal range of fin volume fraction is determined based on energy storage density and energy storage time. Considering the influence of natural convection on heat transfer properties, four distinct heat storage models, including topology-optimized fins, are compared. The heat transfer and flow properties during the processes of melting and solidification, as well as their field synergy, are investigated. The study reveals that the optimized fins exhibit more bionic fractal structures and a larger, more uniform heat transfer contact area. Compared to traditionally optimized fin structures, this research reduces the heat storage time of the three-tube heat storage device by 21.96 % and the heat release time by 38.13 % without compromising the maximum heat storage capacity. The topology-optimized fins demonstrate a fractal dimension similar to natural fractal structures such as branches, leaf veins, antlers, and snowflakes. This study presents a novel bionic structural design approach for high-performance latent heat storage systems.

Scalable Microservices Architecture: Leadership Approaches for High-Performance Retail Systems

The rapid evolution of retail systems in the digital age necessitates the adoption of scalable microservices architectures to meet the demands of high-performance and responsive platforms. As the retail sector increasingly relies on technology to drive customer engagement, operational efficiency, and competitive advantage, the role of leadership in designing and implementing scalable microservices architectures becomes critical. This paper explores leadership approaches that are essential for guiding the successful deployment of microservices in high-performance retail systems. The shift from monolithic to microservices architectures represents a fundamental change in how retail systems are built and maintained. Microservices, characterized by their modularity, independence, and scalability, offer significant advantages over traditional monolithic architectures, particularly in handling the dynamic demands of modern retail environments. However, the successful implementation of microservices requires more than just technical expertise; it demands strategic leadership that understands the complexities of system design, cross-functional collaboration, and long-term maintenance.

A garment-integrated textile stitch-based strain sensor device, IoT-Enabled for enhanced wearable sportswear applications

The field of wearable technology is undergoing a transformative shift with the integration of IoT-enabled, AI-assisted sensors into sportswear, bringing sophisticated performance analysis within reach for a broader audience. Despite advancements in stitch strain sensors, there is a significant research gap in developing high-performance, stable, and IoT-connected complete stitch strain wearable sensor systems. These advanced sensors are essential for enhancing wearable technology and meeting the growing demands for improved user experience and functionality. In this study by utilizing conductive sewing threads and innovative stitching techniques, we fabricated textile strain sensors that offer high sensitivity, flexibility, and durability. Extensive calibration tests revealed strong linear relationships between resistance and tensile strain, with highest sensor sensitivity (2.08 Ω/mm during loading and 2.72 Ω/mm during unloading). These sensors demonstrate reliable performance with minimal hysteresis, particularly in shorter lengths. The wearable sportswear device, tested in gym environments, demonstrated the sensor's capability to provide real-time feedback on knee flexing/bending and body movement during exercise. This functionality supports injury prevention and enhances workout efficiency. The sensor system is IoT-enabled, powered by an ESP8266 microcontroller, facilitating data transmission to the Thing Speak platform for advanced analysis and remote monitoring. This comprehensive approach not only advances wearable technology but also opens new avenues for remote health monitoring and smart e-textiles, contributing significantly to the fields of sports science and fitness technology.

Time-resolved modeling of dropwise condensation patterns formed on a nanopillared substrate

Instantaneous condensation patterns formed over a vertical nanopillared copper surface are modeled by including drop level details including nucleation, growth, coalescence, droplet jumping and gravitational instability. The vapor phase is taken to be saturated and condensation is driven by a colder wall that imposes a given degree of subcooling. The mathematical model is setup in the form of a condensation cycle, starting from drop nucleation all the way to instability followed by fresh nucleation. In view of the condensing surface being pillared, a variety of droplet morphologies are possible, including Cassie, Wenzel, and partially-wetting that depend on the prevailing condensation conditions. The degree of subcooling is varied over 1 – 28 K, covering a wide range of condensation characteristics from Cassie jumping to Wenzel flooding with increasing temperature difference. Coalescence-based droplet jumping for Cassie and partially-wetting droplet configurations are incorporated as well. Gravitational instability of the largest drop leads to sweeping along the substrate followed by surface renewal and renucleation. With this overall approach, it is possible to explicitly determine the drop-size distribution as a function of time. The entire model is multiscale in space and time, making it a computationally demanding simulation. A domain decomposition framework is used and computations are carried out in a high-performance computing system with a parallel architecture employing message passing interface (MPI). The time-resolved model has been extensively tested against experimental data reported in the literature. The partially-wetting configuration for a subcooling of 8 K has a larger average droplet size and fewer jumping events generating similar heat fluxes as for Cassie droplets at a lower degree of subcooling (ΔT = 5 K) where the average droplet size is smaller and jumping events are frequent. In addition, the Cassie state yields an enhancement factor of around 2.5 relative to a flat surface while Wenzel droplets cause flooding and need subcooling levels as high as 24 K for similar heat flux values.

Shared high-performance work system perceptions as a competitive advantage: mediating role of trust in management in the HPWS-performance link

Shared high-performance work system perceptions as a competitive advantage: mediating role of trust in management in the HPWS-performance link

High-performance Work Systems and Recovery Experiences: An Empirical Study

High-performance Work Systems and Recovery Experiences: An Empirical Study

Low-cost and scalable projected light-sheet microscopy for the high-resolution imaging of cleared tissue and living samples.

Light-sheet fluorescence microscopy (LSFM) is a widely used technique for imaging cleared tissue and living samples. However, high-performance LSFM systems are typically expensive and not easily scalable. Here we introduce a low-cost, scalable and versatile LSFM framework, which we named 'projected light-sheet microscopy' (pLSM), with high imaging performance and small device and computational footprints. We characterized the capabilities of pLSM, which repurposes readily available consumer-grade components, optimized optics, over-network control architecture and software-driven light-sheet modulation, by performing high-resolution mapping of cleared mouse brains and of post-mortem pathological human brain samples, and via the molecular phenotyping of brain and blood-vessel organoids derived from human induced pluripotent stem cells. We also report a method that leverages pLSM for the live imaging of the dynamics of sparsely labelled multi-layered bacterial pellicle biofilms at an air-liquid interface. pLSM can make high-resolution LSFM for biomedical applications more accessible, affordable and scalable.

Electronically Conductive Polymer Enhanced Solid-State Polymer Electrolytes for All-Solid-State Lithium Batteries

All-solid-state lithium batteries (ASSLBs) have gained enormous interest due to their potential high energy density, high performance, and inherent safety characteristics for advanced energy storage systems. Although solid-state ceramic (inorganic) electrolytes (SSCEs) have high ionic conductivity and high electrochemical stability, they experience some significant drawbacks, such as poor electrolyte/electrode interfacial properties and poor mechanical characteristics (brittle, fragile), which can hinder their adoption for commercialization. Typically, SSCE-based ASSLBs require high cell stack pressures exerted by heavy fixtures for regular operation, which can reduce the energy density of the overall battery packages. Polymer–SSCE composite electrolytes can provide inherently good interfacial contacts with the electrodes that do not require high cell stack pressures. In this study, we explore the feasibility of incorporating an electronically and ionically conducting polymer, polypyrrole (PPy), into a polymer backbone, polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP), to improve the ionic conductivity of the resultant polymer–SSCE composite electrolyte (SSPE). The electronically conductive polymer-incorporated composite electrolyte showed superior room temperature ionic conductivity and electrochemical performance compared to the baseline sample (without PPy). The PPy-incorporated polymer electrolyte demonstrated a high resilience to high temperature operation compared with the liquid-electrolyte counterpart. This performance advantage can potentially be employed in ASSLBs that operate at high temperatures. In our recent development efforts, SSPEs with optimal formulations showed room temperature ionic conductivity of 2.5 × 10−4 S/cm. The data also showed, consistently, that incorporating PPy into the polymer backbone helped boost the ionic conductivity with various SSPE formulations, consistent with the current study. Electrochemical performance of ASSLBs with the optimized SSPEs will be presented in a separate publication. The current exploratory study has shown the feasibility and benefits of the novel approach as a promising method for the research and development of next-generation solid composite electrolyte-based ASSLBs.

Barrier Lyapunov Based Adaptive Control for Hydraulic Servo Systems with Parametrical Nonlinearities and Modeling Uncertainties

Load variations, friction, and external disturbance degrade the control performance of hydraulic servo systems. To attain the high precision trajectory tracking performance for the hydraulic servo systems, a barrier Lyapunov based adaptive controller (BLBAC) is proposed in this paper. For the controller, the adaptive law is developed in each step of backstepping design such that the tracking error can converge to a prescribed accuracy. The state-constrained control of hydraulic servo systems is another issue worthy of attention. Hence, a novel time-varying asymmetric barrier Lyapunov function (TABLF) are adopted to guarantee that the states are not to violate their constraints. The closed-loop signals stability is proved by Lyapunov theory. Comparative simulation results verify the effectiveness of the proposed controller in comparison with the other two controllers for hydraulic servo systems.

New control model for autonomous vehicles using integration of Model Predictive and Stanley based controllers

In this paper, a robust control method is introduced for autonomous vehicle control in different scenarios. Dual controllers have been used in this method to ensure high performance and low errors during the vehicle’s trip. The new control system is called Model Predictive and Stanley based controller (MPS), which is an integration of a model predictive controller and a Stanley controller. Each of these two controllers has its drawbacks and weaknesses. The proposed method tries to overcome these points and come up with a high-performance control system. This hybrid way of combining two of the famous controllers has the benefit of using the best part of each one and trying to enhance the other part. The MPS is tested for both path-following and vehicle control in different scenarios and on both straight and curved roads. This controller has shown high performance and flexibility to deal with different scenarios of autonomous driving. The results are compared to previous types of controllers, and the proposed system outperformed these types.

Development of an IoT-Based Device for Data Collection on Sheep and Goat Herding in Silvopastoral Systems.

To evaluate the ecosystem services of silvopastoral systems through grazing activities, an advanced Internet of Things (IoT) framework is introduced for capturing extensive data on the spatial dynamics of sheep and goat grazing. The methodology employed an innovative IoT system, integrating a Global Navigation Satellite System (GNSS) tracker and environmental sensors mounted on the animals to accurately monitor the extent, intensity, and frequency of grazing. The experimental results demonstrated the high performance and robustness of the IoT system, with minimal data loss and significant battery efficiency, validating its suitability for long-term field evaluations. Long Range (LoRa) technology ensured consistent communication over long distances, covering the entire grazing zone and a range of 6 km in open areas. The superior battery performance, enhanced by a solar panel, allowed uninterrupted operation for up to 37 days with 5-min interval acquisitions. The GNSS module provided high-resolution data on movement patterns, with an accuracy of up to 10 m after firmware adjustments. The two-part division of the device ensured it did not rotate on the animals' necks. The system demonstrated adaptability and resilience in various terrains and animal conditions, confirming the viability of IoT-based systems for pasture monitoring and highlighting their potential to improve silvopastoral management, promoting sustainable practices and conservation strategies. This work uniquely focuses on documenting the shepherd's role in the ecosystem, providing a low-cost solution that distinguishes itself from commercial alternatives aimed primarily at real-time flock tracking.

BRMECQN: Design of an efficient integration of Bioinspired Routing Model with Energy efficient Clustering for high QoS Wireless Networks

In this dynamic domain of communication in wireless network technology, the need for advanced models that boost the Quality of Service (QoS) parameters concerning energy efficiency, speed, throughput, packet delivery ratio, and minimization of jitter is of prime concern. Traditional routing and clustering methods in wireless networks are often confronted with limitations such as suboptimal resource utilization, reduced energy efficiency, and lower QoS metrics. In this regard, the present work introduces an innovative integration of bioinspired routing models with energy-efficient clustering to enhance the performance of wireless networks. A novel Fan-Shaped Clustering approach is employed in the proposed model for a proper grouping of the network nodes, which can reduce the energy consumption and enhance the load balancing across the network. The model performs a further integration of Elephant Herding Optimization and Bat Optimization algorithms following the clustering. This integration has been done to develop an optimal route finding mechanism between the clustered nodes through its bioinspired approach, to utilize the global search capability of Elephant Herding Optimization, and the proficiency of Bat Optimizations in local searches. The proposed model, when tested empirically on different configurations of networks, presents significant improvements over methods in practice. The results suggest a 10.5% improvement in energy efficiency, an improvement in speed of 8.3%, a rise in throughput of 3.5%, an improvement in Packet Delivery Ratio of 4.5%, and a reduction in jitter of 1.9%. These have been substantial improvements indicating that the proposed model overcomes the limitations of the existing ones and at the same time places a new benchmark in the performance of wireless networks. The impact of this work is immense, providing a scalable and efficient solution for next-generation wireless networks. This integration of bioinspired algorithms in the routing and clustering of the network shall open new avenues for research and development into the field with the potential of more resilient, efficient, and high-performing wireless communication systems. This work, therefore, adds immense impetus to the development of wireless network technologies to uphold higher QoS and better resource management in an era of ever-growing digital communication demands.

Radio U-Net: a convolutional neural network to detect diffuse radio sources in galaxy clusters and beyond

ABSTRACT The forthcoming generation of radio telescope arrays promises significant advancements in sensitivity and resolution, enabling the identification and characterization of many new faint and diffuse radio sources. Conventional manual cataloguing methodologies are anticipated to be insufficient to exploit the capabilities of new radio surveys. Radio interferometric images of diffuse sources present a challenge for image segmentation tasks due to noise, artifacts, and embedded radio sources. In response to these challenges, we introduce Radio U-Net, a fully convolutional neural network based on the U-Net architecture. Radio U-Net is designed to detect faint and extended sources in radio surveys, such as radio haloes, relics, and cosmic web filaments. Radio U-Net was trained on synthetic radio observations built upon cosmological simulations and then tested on a sample of galaxy clusters, where the detection of cluster diffuse radio sources relied on customized data reduction and visual inspection of Low-Frequency Array Two metre Sky Survey (LoTSS) data. The 83 per cent of clusters exhibiting diffuse radio emission were accurately identified, and the segmentation successfully recovered the morphology of the sources even in low-quality images. In a test sample comprising 246 galaxy clusters, we achieved a 73 per cent accuracy rate in distinguishing between clusters with and without diffuse radio emission. Our results establish the applicability of Radio U-Net to extensive radio survey data sets, probing its efficiency on cutting-edge high-performance computing systems. This approach represents an advancement in optimizing the exploitation of forthcoming large radio surveys for scientific exploration.