A structurally simple three-layer optical absorber is proposed and systematically investigated, consisting of a continuous Ti ground plane, a SiO2 dielectric spacer, and a Ti tetrahedral nanostructure. The absorber is constructed on a periodic square unit cell, where the lateral dimension directly determines the base width and sidewall inclination angle of the tetrahedral structure, thereby enabling effective modulation of the optical response. Full-wave electromagnetic simulations performed using COMSOL Multiphysics (version 6.0) are employed to evaluate the influence of geometric parameters on broadband absorption behavior. The optimized structure achieves a near-unity absorptivity of 0.9999 at 200 nm and maintains an effective absorption bandwidth (absorptivity > 0.9) spanning 200–3000 nm, covering the ultraviolet, visible, and near-infrared spectral regions. Parametric analysis reveals that the tetrahedral height primarily governs long-wavelength extension through enhanced optical path length, graded-index transition, and improved electromagnetic field confinement, while the unit cell width strongly influences impedance matching and localized field localization. In contrast, the Ti ground layer thickness exhibits minimal influence once it exceeds the optical skin depth, confirming its primary role as a transmission-blocking reflective substrate. Impedance retrieval analysis shows that the real part of the normalized impedance remains close to unity and the imaginary part approaches zero over most of the operating range, demonstrating that the ultrabroadband absorption behavior is dominated by effective impedance matching rather than isolated narrowband resonances. Furthermore, electric and magnetic field distribution analyses reveal that electromagnetic energy dissipation is concentrated near the tetrahedral apex and metal–dielectric interfaces, indicating the coexistence of localized plasmonic modes, cavity-assisted absorption, and multi-scale optical confinement.

In this study, we propose an integrated evaluation framework for mobile cooling products by combining environmental sensing, the analytic hierarchy process (AHP), and a fuzzy technique for order preference by similarity to ideal solution (Fuzzy-TOPSIS) to assess product performance and user experience under realistic usage conditions. Four representative product design types-turbo type, ice crystal type, folding type, and ice-crystal/folding hybrid type- were selected for empirical investigation. Environmental sensing was first employed to acquire microclimate parameters, including ambient temperature, relative humidity, and air velocity, enabling context-aware performance assessment. AHP was subsequently applied to determine the relative weights of two primary evaluation dimensions, namely, sensory attributes and sustainable design factors. Subjective sensory ratings and fuzzy linguistic evaluations collected from 60 participants were then integrated using Fuzzy-TOPSIS to obtain overall performance rankings across different usage scenarios. The results indicate comparable importance between sensory and sustainability dimensions (0.515 vs 0.485), reflecting users' simultaneous emphasis on thermal comfort and energy efficiency. The overall ranking was ice-crystal/folding hybrid type > ice-crystal type > turbo type > folding type. Regression analysis further reveals that tactile comfort and perceived airflow cooling significantly enhance emotional satisfaction, while noise negatively affects user experience in quiet environments. The proposed framework effectively integrates environmental sensing with multicriteria decision-making and sensory evaluation, offering a scalable approach for the design and assessment of portable and wearable climate-adaptive products.

Building upon the work of Liu et al., who successfully synthesized the Ca(3)Ga(4)O9+0.01 Bi2O3 +0.07 ZnO phosphor exhibiting green emission at approximately 500nm, this study further investigates the influence of CaO content on both the crystal structure and luminescent behavior of this material system. A series of phosphors with compositions Ca2+xGa4O8+X+0.01 Bi2O3 +0.07 ZnO (where x=0-1.0) were synthesized via the conventional solid-state reaction method. The photoluminescence excitation (PLE) and emission (PL) intensities exhibited a distinct nonlinear dependence on CaO concentration, rising with increasing CaO content, reaching a maximum at x=0.4, and then gradually declining at higher x values. After identifying x=0.4 as the optimal CaO composition yielding the strongest PLE and PL responses, the study proceeded to examine the effect of varying Bi2O3 content on the luminescent characteristics of this optimized Ca2.4Ga(4)O8.4+0.07 ZnO phosphor. To elucidate the relationship between structural evolution and optical performance, X-ray diffraction (XRD) was employed to analyze the crystal structure and phase composition, while photoluminescence (PL) spectroscopy was utilized to determine excitation and emission behaviors. The PLE analysis results revealed that all samples exhibited a consistent excitation peak near 340nm, with PL emission peaks slightly shifting within the 474-477nm range. This study deepens the understanding of how compositional modulation affects the crystal structure and luminescent efficiency of Bi2O3- and ZnO-co-doped Ca2+xGa4O8+X phosphors, offering meaningful insight into the underlying mechanisms governing their photoluminescent properties.

This study utilizes COMSOL Multiphysics (version 6.0) to design a planar ultra-broadband optical absorber with a multilayer configuration. The proposed structure consists of seven stacked layers arranged from bottom to top: W (h1, acting as a reflective substrate and transmission blocker), WSe2 (h2), SiO2 (h3), Ni (h4), SiO2 (h5), Mo (h6), and SiO2 (h7). One key finding of this study is that, when all other layer thicknesses are fixed, variations in the Mo layer thickness systematically induce a redshift in both the short- and long-wavelength cutoff edges. Notably, the long-wavelength cutoff exhibits a larger shift than the short-wavelength edge, resulting in an increased absorption bandwidth where absorptivity remains above 0.900. The second contribution is the demonstration that this planar structure can be readily engineered to achieve ultra-broadband absorption, spanning from the near-ultraviolet and visible region (360 nm) to the mid-infrared (6300 nm). An important characteristic of the proposed design is that the thickness of the h7 SiO2 layer influences the cutoff wavelength at the short-wavelength edge, while the thickness of the h6 Mo layer governs the cutoff position at the long-wavelength edge. This dual modulation capability allows the proposed optical absorber to flexibly tune both the spectral range and the bandwidth in which absorptivity exceeds 0.900, thereby enabling the realization of a wavelength- and bandwidth-tunable optical absorber.

Europium-doped phosphors, including CaTiO3 (red, R), Ca2MgSi2O7 (green, G), and BaMgAl10O17 (blue, B), with varying concentrations of Eu2O3, were prepared using the solid-state reaction method. The morphological characteristics, crystal structures, and luminescence emission properties of the R, G, and B phosphors were investigated. The CIE chromaticity coordinates for CaTiO3:0.085 Eu3+, Ca2MgSi2O7:0.025 Eu2+, and BaMgAl10O17:0.06 Eu2+ phosphors were located at (x = 0.343, y = 0.618), (x = 0.285, y = 0.547), and (x = 0.153, y = 0.938), corresponding to bright red, green, and blue colors, respectively. Additionally, a white light-emitting phosphor was successfully fabricated with the molar ratio of 40:5:1 for [CaTiO3:0.085 Eu3+]/[Ca2MgSi2O7:0.025 Eu2+]/[BaMgAl10O17:0.06 Eu2+]. The CIE chromaticity coordinates of the white light-emitting phosphor were (0.325, 0.291), located in the vicinity of the pure white light coordinates ([0.33, 0.33]).

In this study, a planar multilayer metamaterial absorber capable of optical sensing across an ultra-broadband spectrum was designed and numerically analyzed using the finite element method implemented in the simulation software COMSOL Multiphysics (V6.0). The absorber consists of alternating metal-dielectric layers, with SiO2 serving as the scattering medium to enhance electromagnetic coupling and impedance matching. A six-layer planar structure composed of V, Ge, Bi, and Co was first developed, achieving an average absorptivity of 94.56% within the 500-3160 nm wavelength range, though a slight absorption dip was observed between 400-500 nm. By introducing additional SiO2 and Co layers to form an eight-layer configuration, the absorption bandwidth was broadened to 400-3700 nm with an average absorptivity of demonstrating enhanced optical adaptability. These findings indicate a design trade-off between absorption efficiency and bandwidth coverage, offering valuable insights for the development of high-performance optoelectronic and sensing devices based on planar metamaterial architectures.

Conventional manufacturing execution system (MES) reporting systems often suffer from fragmented sensor data management, labor-intensive report preparation, delayed decision-making, and limited capability for the intelligent interpretation of heterogeneous manufacturing information. Therefore, the objective of this study is to develop a sensor-assisted MES framework integrating large language models (LLMs) and multi-modal collaborative processing (MCP) to improve manufacturing information transparency, report generation efficiency, and intelligent operational decision-making. In this study, we propose a sensor-assisted MES framework integrating LLMs with MCP to accelerate digital transformation and improve intelligent decision-making in smart manufacturing environments. Conventional MES reporting processes often rely on manual data collection, fragmented information analysis, and delayed operational feedback, which reduce management responsiveness and limit enterprise operational performance. By combining advanced natural language processing, heterogeneous sensor data fusion, and Industrial Internet of Things (IIoT)-based monitoring, the proposed LLM+MCP framework enables the automated extraction, analysis, visualization, and synthesis of manufacturing information into intelligent executive reports. The framework integrates multiple sensing modalities, including time-series production signals, equipment operation records, process parameters, tabular manufacturing data, and unstructured operational logs, thereby supporting real-time monitoring, anomaly identification, and adaptive management decisions. Through the designed intelligent reporting and sensing architecture, manufacturing managers can rapidly obtain operational insights and optimize production scheduling, resource allocation, and quality control strategies. Experimental results demonstrate that the proposed system achieves more than 90% reduction in report generation time, including reductions from 60 to 5 min for daily reports, 180 to 15 min for weekly reports, and 480 to 30 min for monthly reports, while maintaining 99.9% system availability and stable response latency ranging from 5 to 60 s. From the results, we can confirm that the proposed sensor-integrated LLM+MCP framework effectively enhances manufacturing information transparency, accelerates enterprise digital transformation, improves intelligent decision-making mechanisms, and ultimately strengthens operational efficiency, management responsiveness, and overall enterprise performance in smart manufacturing systems. Although challenges remain regarding heterogeneous sensing-data quality, data consistency, and the reliability of AI-generated content, the proposed framework addresses these issues through multi-modal sensing integration, structured data preprocessing, and low-rank adaptation (LoRA)-based domain adaptation, thereby improving the robustness and practicality of intelligent manufacturing analytics.

In the current era of digital transformation, enterprises are increasingly reliant on intellectual capital and collaborative efficiency. Traditional human resource management systems (HRMSs) are facing significant challenges in adapting to these evolving demands. The advent of IoT presents promising opportunities for the enhancement of HRMS functionalities. While IoT technologies have been widely applied in industrial manufacturing, their systematic integration into the domain of HRM remains underexplored. In this study, we provide a comprehensive analysis of the potential benefits and challenges associated with IoT adoption in HRMSs. We propose an optimized framework grounded in a four-layer IoT architecture, incorporating sensing devices, blockchain, and AI technologies. The framework highlights sensor-based data acquisition as a fundamental enabler for real-time, human-centric information sensing in HRMSs. The proposed model addresses critical issues such as employee interaction, knowledge integration, and data privacy, with the goal of improving the real-time responsiveness and accuracy of HR operations. Although in this study we focus on framework design rather than experimental implementation, the proposed architecture is built on mature IoT and sensing technologies, suggesting practical feasibility in real organizational settings. By presenting a cohesive architecture, we offer practical insights for HRMS practitioners, system implementers, and technology developers. Moreover, in this study, we will serve as a reference for deploying IoT technologies within complex organizational ecosystems. This work is relevant to readers interested in sensing concepts, as it extends sensor-enabled systems from physical environments to organizational and human-centered applications. The main limitation of this study lies in the lack of empirical validation through prototype deployment. Future work will focus on system implementation and experimental evaluation to further verify applicability and performance. Ultimately, this work aims to foster innovation in HRMSs and strengthen the digital capabilities of modern enterprises.

Ga2O3 has emerged as a promising material for deep-ultraviolet (DUV) photodetectors owing to its ultrawide bandgap, intrinsic solar-blind response, low dark current, and high breakdown electric field, making it highly suitable for high-sensitivity DUV sensing applications. However, despite these advantages, several challenges remain in Ga2O3-based DUV photodetectors, including limited carrier transport efficiency, defect-related trap states, and the need for simplified and scalable fabrication routes for doped conductive films. However, the relatively low electron mobility and limited intrinsic carrier transport capability of pristine Ga2O3 restrict further improvements in device performance. To address these limitations, impurity doping has been recognized as an effective strategy to modulate the electrical properties and enhance carrier transport in Ga2O3-based devices. In this work, NiO-doped Ga2O3 films were developed and systematically investigated for application in DUV photodetectors. NiO was introduced as a functional dopant with a fixed atomic ratio of Ga:Ni = 100:12 (12 at%), and NiO-doped Ga2O3 films were deposited using an electron-beam evaporation technique. This approach aims to provide a simplified fabrication pathway while simultaneously improving electrical transport properties without compromising the intrinsic solar-blind characteristics of Ga2O3. The structural, optical, and electrical properties of the doped films were characterized to evaluate the effects of NiO incorporation on material quality and carrier transport behavior. The results demonstrate that NiO doping effectively modifies the electronic characteristics of Ga2O3 while preserving its ultrawide bandgap and solar-blind detection capability. The fabricated photodetectors exhibit reduced dark current and enhanced photoresponse under DUV illumination. Although the present study demonstrates improved photoresponse and stable device performance, further optimization of dopant concentration, defect control, and long-term operational stability remains necessary to fully realize the potential of NiO-doped Ga2O3 for practical large-scale DUV sensing applications. These findings indicate that NiO-doped Ga2O3 films are a promising material platform for high-performance DUV photodetectors and provide valuable insights into the dopant-assisted performance optimization of ultrawide-bandgap oxide semiconductors.

In this study, we developed an intelligent food packaging design and evaluation framework integrating AI-generated design, sensing materials, craft aesthetics, and a multi-attribute decision model. A craft-art dataset containing patterns, colors, and compositions was first established. Visual features were extracted using a Convolutional Neural Network, whereas Generative Adversarial Network and Latent Diffusion Model algorithms generated diverse patterns that preserved cultural symbols while extending modern visual appeal. Three sensing materials, namely, temperature-humidity sensing films, photochromic films, and eco-friendly substrates, were experimentally tested for response time, color difference (Delta E), mechanical strength, and food safety to ensure real-time responsiveness and stability. A user study with 30 participants compared three prototypes: (1) conventional packaging, that is, packaging without AI-generated design or sensing-interactive functionality, (2) algorithm-generated packaging, and (3) algorithm sensor-interactive packaging. Participants evaluated visual aesthetics, interactivity, and cultural identity. By using Analytic Hierarchy Process and Fuzzy-Technique for Order Preference by Similarity to Ideal Solution for multi-attribute analysis, the sensor-interactive packaging achieved the best performance across Structural Similarity Index Measure, Frechet Inception Distance, color harmony, and user rating (correlation coefficient = 0.670), outperforming the others. The findings confirm that integrating AI-driven generative design with sensor-based materials enhances the aesthetic and interactive experience of packaging. The proposed framework provides a quantifiable and practical evaluation model for cultural-creative design, offering broad applicability to food, cultural, and sustainable packaging industries and supporting the advancement of smart packaging and cultural innovation.

Building upon the work of Liu et al., who successfully synthesized the Ca 3 Ga 4 O[Formula: see text] Bi 2 O[Formula: see text] ZnO phosphor exhibiting green emission at approximately 500[Formula: see text]nm, this study further investigates the influence of CaO content on both the crystal structure and luminescent behavior of this material system. A series of phosphors with compositions Ca[Formula: see text]Ga 4 O[Formula: see text] Bi 2 O[Formula: see text] ZnO (where [Formula: see text]–1.0) were synthesized via the conventional solid-state reaction method. The photoluminescence excitation (PLE) and emission (PL) intensities exhibited a distinct nonlinear dependence on CaO concentration, rising with increasing CaO content, reaching a maximum at [Formula: see text], and then gradually declining at higher x values. After identifying [Formula: see text] as the optimal CaO composition yielding the strongest PLE and PL responses, the study proceeded to examine the effect of varying Bi 2 O 3 content on the luminescent characteristics of this optimized Ca[Formula: see text]Ga 4 O[Formula: see text] ZnO phosphor. To elucidate the relationship between structural evolution and optical performance, X-ray diffraction (XRD) was employed to analyze the crystal structure and phase composition, while photoluminescence (PL) spectroscopy was utilized to determine excitation and emission behaviors. The PLE analysis results revealed that all samples exhibited a consistent excitation peak near 340[Formula: see text]nm, with PL emission peaks slightly shifting within the 474–477[Formula: see text]nm range. This study deepens the understanding of how compositional modulation affects the crystal structure and luminescent efficiency of Bi 2 O 3 - and ZnO-co-doped Ca[Formula: see text]Ga 4 O[Formula: see text] phosphors, offering meaningful insight into the underlying mechanisms governing their photoluminescent properties.

As science communication increasingly transitions from traditional text-based formats to multimedia content, short videos have emerged as a dominant form, particularly on IoT-enabled platforms that facilitate rapid information dissemination. However, the relative effectiveness of text versus short videos in communicating scientific knowledge remains underexplored. In this empirical study, we investigated how text and short videos perform in conveying information about climate change within IoT platform environments. A mixed-method experiment was conducted with 63 participants (32 men, 31 women), who were randomly assigned to receive content in either text or video format. A comprehensive, multidimensional evaluation framework was developed, grounded in dual-channel theory, cognitive load theory, and the technology acceptance model, and implemented via structured questionnaires. Descriptive statistics were analyzed using IBM Statistical Package for the Social Sciences (version 25), employing t-tests, one-way analysis of variance, Bartlett's test of sphericity, and the Kaiser-Meyer-Olkin test. Results indicate that short videos outperform text in terms of memory retention, reduced cognitive load (p < 0.05), perceived readability, usefulness, and viewer satisfaction. Text-based content, however, was rated higher in perceived academic rigor and credibility. Willingness to share content showed no significant difference between the two formats (p = 0.134 > 0.05). These findings underscore the effectiveness of short videos as powerful tools for science communication on IoT-enabled platforms, while also highlighting the continued value of text in contexts requiring academic depth and trust.

This study aims to develop a simple and effective method for synthesizing Nb-doped Ga 2 O 3 films as an n-type semiconductor. Initially, Ga 2 O 3 powder doped with 12 at% Nb 2 O 5 (Ga:Nb=100:12) was used as the starting material and was pre-calcined at 950°C to promote its transformation into the more stable β-phase. The calcined powder was then formed into a pellet-shaped target and deposited onto substrates using electron beam evaporation to fabricate Nb-doped Ga 2 O 3 films. Subsequent annealing of the Nb-doped Ga 2 O 3 films was performed at 500°C under three conditions: without any gas flow, in ambient air, and in a reducing atmosphere composed of 95% N 2 and 5% H 2 . The reducing atmosphere was intended to enhance the film's conductivity by increasing the oxygen vacancy concentration. The electrical properties of the three types of Nb-doped Ga 2 O 3 films-namely, as-deposited, annealed in air, and annealed in the reducing gas-were compared using a semiconductor parameter analyzer. Additionally, Hall effect measurements were carried out to determine the carrier concentration, carrier mobility, and resistivity of the films. The results revealed that both the as-deposited film and the film annealed in air at 500°C remained highly resistive, with resistivity exceeding 10 8 Ω˙cm, indicating insulating behavior. In contrast, the film annealed at 500°C in the reducing atmosphere exhibited significantly improved electrical conductivity. Based on these findings, further analysis focused on the as-deposited film and the film annealed in the reducing atmosphere. Optical transmittance spectra were recorded, and the optical bandgap was estimated using Tauc plot analysis derived from UV-visible spectroscopy. X-ray photoelectron spectroscopy (XPS) was also employed to investigate the chemical bonding states, oxidation states, and the presence of oxygen vacancies in the Ga 2 O 3 films.

A complete solar tracking system consists of several key components: a concentrator, a receiver, and the electromechanical equipment that together convert sunlight into electrical energy. In addition, a tracking structure is necessary to allow solar panels to follow the sun's path, maximizing the amount of sunlight they can capture throughout the day. This structure must also incorporate sensor elements that precisely track the sun's position to ensure optimal performance. Lastly, a control system is needed to efficiently manage and utilize the collected energy. In this research, we focused on reducing manufacturing costs and increasing solar power output while ensuring the system's safety and stability. The goal was to provide a straightforward method for constructing the system, using both functional and strength design principles to create a simple yet effective dual-axis auto solar tracking mechanism. The development process began with designing the tracking mechanism using AutoCAD to generate detailed engineering drawings that specified dimensions and tolerances. A bill of materials was also created, and an initial cost was estimated. The manufacturing of the mechanism was then outsourced to the most suitable supplier. Before assembly, a series of functionality tests were conducted, followed by continuous revisions and adjustments. These iterative tests ensured the mechanism's safety and performance. Once the system met all the design requirements outlined in this research, the production process was considered complete.

In this study, Mn2+ and Eu2+-codoped Sr3−xBaxMgSi2O8 (x = 0–1.5) phosphors were synthesized at 1400 °C under a reducing atmosphere composed of 5% H2 and 95% N2 to produce materials with blue light emission. The resulting powders were characterized using several analytical techniques: X-ray diffraction (XRD) was employed to identify the crystalline phases, scanning electron microscopy (SEM) was used to observe the microstructure, and photoluminescence excitation (PLE) and emission (PL) spectra were measured using a fluorescence spectrophotometer. The results revealed several key findings. XRD analysis showed that the Sr3MgSi2O8 (Sr3−xBaxMgSi2O8) phase coexisted with secondary phases of Sr2SiO4 and Sr2MgSi2O7. SEM observations indicated that the synthesized powders exhibited a distinctive needle-like structure anchored on the surfaces of the particles. The PL and PLE intensities increased sharply as the BaO content increased from x = 0 to x = 0.6, followed by a more gradual increase, reaching a peak at x = 1.2. Additionally, as the value of x increased, the wavelengths corresponding to maximum PL and PLE intensities exhibited a blue shift, moving to shorter wavelengths. Further investigation focused on the excitation behavior by replotting the PLE spectra using energy (eV) as the x-axis. A Gaussian fitting function was applied to deconvolute the excitation bands, enabling an in-depth analysis of how compositional variations influenced the Stokes shift.

The mixed-flow production line has realized a production method featuring multiple varieties and small batches, retaining the advantages of large scale, high efficiency, and low cost of flexibility but also escalates the cost and demands of production management for the enterprise. To efficiently process a variety of products in small batches on the same assembly line, the issue of production sequencing for different products on the mixed-flow assembly line needs to be addressed. In this study, we utilize the FlexSim platform to simulate the workshop production activities of J Machinery Factory over one production cycle. By establishing a model and employing the OPTQUEST optimization module, the optimal solution is rapidly determined without increasing machine equipment, thereby maximizing total output profit. This provides a scientific quantitative basis for enterprise management and decision-making, ultimately enhancing competitiveness.