Search Results (2,693)

The China Seismo Electromagnetic Satellite (CSES) mission provides in situ measurements of the electromagnetic field, plasma, and charged particles in the topside ionosphere. Each CSES spacecraft carries several different scientific payloads delivering a wealth of information about the ionospheric plasma dynamics and properties, as well as measurement about energetic particles precipitating in the ionosphere. In this work, we introduce CSESpy, a Python package designed to provide an interface to CSES data products, with the aim of easing the pathway for scientists to carry out analyses of CSES data. Beyond simply being an interface to the data, CSESpy aims to provide higher-level analysis and visualization tools, as well as methods for combining concurrent measurements from different instruments, so as to allow multipayload studies in a unified framework. Moreover, CSESpy is designed to be highly flexible as such, it can be extended to interface with datasets from other sources and can be embedded in wider software ecosystems. We highlight some applications, also demonstrating that CSESpy is a powerful visualization tool for investigating complex events involving variations across multiple physical observables. Full article

A critical aspect in the design of power electronics packages is the prediction of their mechanical response under severe thermomechanical loads and the consequent structural damage. For this purpose, finite element (FE) simulations are used to estimate the mechanical performance and reliability under operational conditions, typically alternate high voltages/currents resulting in thermal gradients. When simulations are performed, it is common practice to consider the as-received package to be in a stress-free state. Namely, residual stresses and plastic deformation induced by the manufacturing processes are neglected. In this study, an advanced FE modeling approach is proposed to assess the structural consequences of the encapsulating resin curing, typical in the production of silicon carbide (SiC)-based power electronics modules for electric vehicles. This work offers a general modeling framework that can be further employed to simulate the effects of thermal gradients induced by the production process on the effective shape and residual stresses of the as-received package for other manufacturing stages, such as metal brazing, soldering processes joining copper and SiC, and, to lower extents, the application of polyimide on top of passivation layers. The obtained results have been indirectly validated with experimental data from literature. Full article

Densely integrated microarchitectures spanning three-dimensional integrated circuits (3D-ICs), chiplet-based designs, and system-in-package (SiP) assemblies make heat a first-order security concern rather than a mere reliability issue. This review consolidates the landscape of thermal side-channel attacks (TSCAs) on densely integrated microarchitectures: we systematize observation vectors and threat models, clarify core concepts and assumptions, compare the most credible evidence from the past decade, and distill the main classes of defenses across the hardware–software stack. We also explain why hardening against thermal leakage is integral to cyber–physical system (CPS) security and outline the most promising research directions for the field. The strategic relevance of this agenda is reflected in current policy and funding momentum, including initiatives by the United States Department of Homeland Security and the Cybersecurity and Infrastructure Security Agency (DHS/CISA) on operational technology (OT) security, programs by the National Science Foundation (NSF) on CPS, and Canada’s Regional Artificial Intelligence Initiative and Cyber-Physical Resilience Program (RAII, >CAD 35 million), to bridge advanced microelectronics with next-generation cybersecurity. This survey offers a clear, high-level map of the problem space and a focused baseline for future work. Full article

Seafood fraud, such as mislabeling low-cost rainbow trout as premium salmon, poses serious food safety risks and damages consumer rights. To address this growing concern, this study develops a deep learning-based, smartphone-compatible sensing system for fish meat identification and salmon freshness grading. By providing consumers with real-time, image-based verification tools, the system supports informed purchasing decisions and enhances food safety. The system adopts a two-stage design: first classifying fish meat types, then grading salmon freshness into three levels based on visual cues. An improved DenseNet121 architecture, enhanced with global average pooling, dropout layers, and a customized output layer, improves accuracy and reduces overfitting, while transfer learning with partial layer freezing enhances efficiency by reducing training time without significant accuracy loss. Experimental results show that the two-stage method outperforms the one-stage approach and several baseline models, achieving robust accuracy in both classification and grading tasks. Sensitivity analysis demonstrates resilience to blur and camera tilt, though real-world adaptability under diverse lighting and packaging conditions remains a challenge. Overall, the proposed system represents a practical, consumer-oriented tool for seafood authentication and freshness evaluation, with potential to enhance food safety and consumer protection. Full article

Optimising spraying operations in coffee cultivation can enhance both application efficiency and effectiveness. However, no studies have specifically assessed droplet deposition on leaves adjacent to the spray application band—fraction of droplet deposition referred to as ‘transfer’ in this study. Therefore, this study aimed to quantify droplet deposition and transfer resulting from different application rates and nozzle types in coffee trees. The experiment was conducted in a factorial design including three application rates (200, 400, and 600 L ha⁻¹) and two nozzle types (hollow cone and flat fan), with four replicates. Deposition was quantified at multiple positions: two application sides (left and right), three sections of the plant (upper, middle, and lower), and two branch positions (inner and outer). Thus, all measurements across sides, plant sections, and branch positions were nested, resulting in correlated data that were analysed using linear mixed-effects models (lme4 package), with parameters estimated using the restricted maximum likelihood method. The flat fan nozzle achieved the highest reference deposition, particularly on outer canopy thirds, while spray transfer (~29% of total deposition) was mainly driven by operational factors. Hollow cone nozzles at 200 L ha⁻¹ minimized transfer while maintaining adequate deposition. Optimizing applications requires maximizing reference deposition and minimizing transfer, which can be achieved through operational adjustments, airflow management, and complementary strategies such as adjuvants, electrostatic spraying, or tunnel sprayers. Full article

Corona treatment is commonly used in industry to enhance the surface-free energy of plastic films. However, corona treatment may cause some undesirable effects affecting further processing, such as sealing. In this paper, we deeply analyze the corona treatment effect on selected properties of various polymer films commonly used in packaging applications. The films were treated at two power levels (100 W and 300 W), and the experimental design included surface characterization and mechanical testing to assess changes in wettability, chemical structure, and seal strength. The Owens–Wendt approach confirmed the corona treatment effect by static contact angle measurement and surface free energy calculation. Next, their seal strength was evaluated in relation to surface energy and chemical structure changes. FTIR spectroscopy was used to identify functional groups potentially affected by corona treatment. The results indicate that the impact of corona treatment is material-dependent. In general, corona treatment at a lower level increases the seal strength, while corona treatment at a higher power level is related to a decrease in seal strength. The study highlights the importance of optimizing corona treatment parameters for specific materials to enhance seal performance without compromising surface integrity. Full article

Reducing material usage in plastic products is a key lever for improving resource efficiency and minimizing environmental impact. In thin-walled structures subjected to mechanical loading, material efficiency must be achieved without compromising structural performance. In particular, resistance to buckling, a critical failure mode, must be taken into account during product development. Due to the large number of design and process variables, many of which are interdependent, optimization approaches are uncommon in the blow-molded packaging industry. This paper presents a sensitivity-based optimization approach to improve buckling resistance by modifying the product’s material distribution. Since the sensitivity is nonlinear and depends on the product’s deformation state, various methods are developed and tested to reduce the frame-wise sensitivity data to a single sensitivity vector suitable for optimization. These methods are then tested on common extrusion blow-molded products, achieving improvements in buckling load of up to 60%. This approach is transferable to other thin-walled structures across various engineering domains, offering a pathway toward lightweight yet load-compliant designs. Full article

Within the endeavour of describing and analysing delays and their propagations in air transport, a major limitation is represented by the validation of the obtained results. While this can be overcome through synthetic models, those available in the literature mostly aim at simulating the system in a detailed and realistic way, resulting in high complexity and substantial computational costs. We here present SynthATDelays, a minimalist and modular Python package designed to simulate a virtual customisable air transport system and to provide synthetic delay data under tuneable conditions; it is thus designed to support the validation of data-based studies and pipelines. We describe its internal structure and provide examples about how scenarios can be designed and executed. We further show how it can be used to tackle two relevant questions, i.e., the role of operational buffer times in the absorption of delays and the comparison and optimisation of causality tests to detect the propagation thereof. Full article

Background: Genetic improvement in wheat yield is the most focused research area for the breeding community to ensure sustainable production. Wheat kernel traits and biomass are considered key contributors to enhance crop yield. Methods: This study was designed to explore the genetic diversity of kernel and biomass traits in popular wheat varieties from the US Southern Great Plains using 264 doubled haploid (DH) lines mainly derived from TAM 114 or TAM 204. This population was evaluated in two field environments planted in alpha lattice design during the 2020 crop season. Kernel traits were collected using the hp Scanjet G4010 photo scanner for image capturing and GrainScan v3. software for image analysis. Biomass parameters were collected and processed manually. For genotyping genomic libraries were prepared and sequenced on Illumina NovaSeq 6000 to generate paired end reads of 150 bp. Sequences were aligned to the IWGSC RefSeq genome assembly v2.1 using the Burrows Wheeler Aligner for SNP calling. Results: A total of 59,482 polymorphic SNP markers were retained for genetic analysis after the filtration at 50% missing data and 5% minor allele frequency. To investigate the marker–trait association and the genomic regions, four genome-wide association study models were implemented using the R package GAPIT version 3.5. Based on the Bonferroni correction <8.41 × 10⁻⁷ was used as a threshold to declare marker-trait associations (MTAs) significant. The BLINK model identified 12 MTAs on chromosomes 1A, 2A, 2B, 4A, 4B, and 6B. Conclusions: The identified MTAs can be used to develop diagnostic markers for efficient selection and utilization in recombination breeding and cultivar development process. Full article

Dielectric covers are generally used to provide external protection to antenna systems by providing electromagnetic transparency. They are utilized in ground applications as well as for protecting airborne, Sat Com, terrestrial and underwater antenna installations. This paper presents a unique and universal design of dielectric sandwich-layered cover that can effectively protect antennas operating in a large frequency band from 1 GHz to 28 GHz, including millimeter-wave and microwave ranges, with minimum insertion loss for various incident angles. The proposed single dielectric cover may give sufficient protection for an entire tower or chimney housing multiple antennas, ranging from first-generation to fifth-generation microwave base-station antennas, as well as other wireless/broadcast antennas in millimeter or lower frequency ranges. In the first step, optimum dielectric constant and thickness of the dielectric cover are calculated numerically through a MATLAB (R2015a) code. In the second step, a floquet port analysis is performed to observe the insertion loss through the transmission coefficient against various frequency band-spectrums in microwave and millimeter-wave ranges for validation of the proposed synthesis. The ANSYS 18.2 HFSS tool is used for the purpose. In the third step, fabrication of the dielectric-layered structure is completed with the optimum design parameters. In the final step, the dielectric package is tested under various fabricated antennas in different frequency ranges. Full article

Against the backdrop of the rapid proliferation of electric vehicles (EVs), gender-oriented behavioral mechanisms remain underexplored, particularly the unique pathways of female users in usage experience, value assessment, and purchase decision-making. This study constructs an integrated framework based on the Stimulus–Organism–Response (SOR) model, leveraging social media big data to analyze in depth how gender differences influence EV users’ purchase intentions. By integrating natural language processing techniques, grounded theory coding, and structural equation modeling (SEM), this study models and analyzes 272,083 pieces of user-generated content (UGC) from Chinese social media platforms, identifying key functional and emotional factors shaping female users’ perceptions and attitudes. The results reveal that esthetic value, safety, and intelligent features more strongly drive emotional responses among female users’ decisions through functional cognition, with gender significantly moderating the pathways from perceived attributes to emotional resonance and cognitive evaluation. This study further confirms the dual mediating roles of functional cognition and emotional experience and identifies a masking (suppression) effect for the ‘intelligent perception’ variable. Methodologically, it develops a novel hybrid paradigm that integrates data-driven semantic mining with psychological behavioral modeling, enhancing the ecological validity of consumer behavior research. Practically, the findings provide empirical support for gender-sensitive EV product design, personalized marketing strategies, and community-based service innovations, while also discussing research limitations and proposing future directions for cross-cultural validation and multimodal analysis. Full article

To monitor large deformations in dovetail tenon joints of Dong ethnic wooden drum towers, this study designs a large-range Fiber Bragg Grating (FBG) strain sensor based on the FBG sensing principle. The NSGA-II algorithm is utilized to optimize the packaging structure of FBG strain sensors. Consequently, an adaptive optimization methodology for its packaging configuration is proposed. This study sets the optimization objectives as a 5000 με measurement range and 0.1 pm/με sensitivity. It employs the NSGA-II algorithm to optimize the structural dimensions and material properties of the large-range FBG strain sensor. This process yields three combinations that meet the requirements for monitoring large deformations in dovetail tenon joints of Dong wooden drum towers. Subsequent linearity experiments were conducted to verify the sensitivity stability and measurement range of the three large-range FBG strain sensors. The results show that within the measurement range of 0–6000 με, all three sensors achieve a strain sensitivity of 0.099 pm/με, with a fitted linear correlation coefficient of 0.999. Full article

Personnel selection represents a two-sided matching problem in which firms compete for qualified candidates by designing job-offer packages. While traditional models assume fully rational agents, real-world decision-makers often face bounded rationality due to limited information and cognitive constraints. This study develops a matching framework that incorporates bounded rationality through the Quantal Response Equilibrium, where firms and candidates act as probabilistic rather than perfect optimizers under uncertainty. Using Maximum Likelihood Estimation and organizational hiring data, we validate that both sides display bounded rational behavior and that rationality increases as the selection process advances. Building on these findings, we propose a two-stage stochastic optimization approach to determine optimal job-offer packages that balance organizational policies with candidate competencies. The optimization problem is solved using particle swarm optimization, which efficiently explores the solution space under uncertainty. Data analysis reveals that only 23.10% of low-level hiring decisions align with rational choice predictions, compared to 64.32% for high-level positions. In our case study, bounded rationality increases package costs by 26%, while modular compensation packages can reduce costs by up to 25%. These findings highlight the cost implications of bounded rationality, the advantages of flexible offers, and the systematic behavioral differences across job levels. The framework provides theoretical contributions to matching under bounded rationality and offers practical insights to help organizations refine their personnel selection strategies and attract suitable candidates more effectively. Full article

This study presents a comprehensive assessment of the energy systems in eight Eastern European countries—Bulgaria, Croatia, the Czech Republic, Hungary, Poland, Romania, Slovakia, and Slovenia—focusing on their energy transition, security of supply, decarbonisation, and energy efficiency. Using principal component analysis (PCA) and clustering techniques, we identify three different energy profiles: countries dependent on fossil fuels (e.g., Poland, Bulgaria), countries with a balanced mix of nuclear and fossil fuels (e.g., the Czech Republic, Slovakia, Hungary), and countries focusing mainly on renewables (e.g., Slovenia, Croatia). The sectoral analysis shows that industry and transport are the main drivers of energy consumption and CO₂ emissions, and the challenges and policy priorities of decarbonisation are determined. Regression modelling shows that dependence on fossil fuels strongly influences the use of renewable energy and electricity consumption patterns, while national differences in per capita electricity consumption are influenced by socio-economic and political factors that go beyond the energy structure. The Decarbonisation Level Index (DLI) indicator shows that Bulgaria and the Czech Republic achieve a high degree of self-sufficiency in domestic energy, while Hungary and Slovakia are the most dependent on imports. A typology based on energy intensity and import dependency categorises Romania as resilient, several countries as balanced, and Hungary, Slovakia, and Croatia as vulnerable. The projected investments up to 2030 indicate an annual increase in clean energy production of around 123–138 TWh through the expansion of nuclear energy, the development of renewable energy, the phasing out of coal, and the improvement of energy efficiency, which could reduce CO₂ emissions across the region by around 119–143 million tons per year. The policy recommendations emphasise the accelerated phase-out of coal, supported by just transition measures, the use of nuclear energy as a stable backbone, the expansion of renewables and energy storage, and a focus on the electrification of transport and industry. The study emphasises the significant influence of European Union (EU) policies—such as the “Clean Energy for All Europeans” and “Fit for 55” packages—on the design of national strategies through regulatory frameworks, financing, and market mechanisms. This analysis provides important insights into the heterogeneity of Eastern European energy systems and supports the design of customised, coordinated policy measures to achieve a sustainable, secure, and climate-resilient energy transition in the region. Full article

This research is based on the acoustic package design of new energy vehicles, investigating the application of the hybrid Finite Element–Statistical Energy Analysis (FE-SEA) model in predicting the high-frequency dynamic response of automotive structures, with a focus on the modeling and correction methods for hybrid point connections. New energy vehicles face unique acoustic challenges due to the special nature of their power systems and operating conditions, such as high-frequency noise from electric motors and electronic devices, wind noise, and road noise at low speeds, which directly affect the vehicle’s ride comfort. Therefore, optimizing the acoustic package design of new energy vehicles to reduce in-cabin noise and improve acoustic quality is an important issue in automotive engineering. In this context, this study proposes an improved point connection correction factor by optimizing the division range of the decision circle. The factor corrects the dynamic stiffness of point connections based on wave characteristics, aiming to improve the analysis accuracy of the hybrid FE-SEA model and enhance its ability to model boundary effects. Simulation results show that the proposed method can effectively improve the model’s analysis accuracy, reduce the degrees of freedom in analysis, and increase efficiency, providing important theoretical support and reference for the acoustic package design and NVH performance optimization of new energy vehicles. Full article