White photovoltaic modules are attractive for building-integrated photovoltaics (BIPV) because they can improve architectural compatibility, whereas the strong visible-light scattering required for whitening inevitably reduces photovoltaic power generation. In this study, the coupled optical, electrical, and thermal effects of whitening were experimentally investigated using rutile TiO2 particle scattering layers formed on glass substrates and placed over silicon heterojunction (SHJ) solar cells. TiO2 particles with an average diameter of 508 nm were dispersed in an acrylic matrix and coated onto glass substrates with nominal TiO2 volume fractions of 1-10 vol%. Increasing the TiO2 loading increased visible reflectance and CIELAB lightness L*, while reducing optical transmission, short-circuit current, and maximum power output. The improvement in lightness became relatively small above approximately 5 vol%, indicating diminishing visual benefits at higher particle loadings. When referenced to bare glass at the same incidence angle, the relative electrical loss showed only a modest incidenceangle dependence over the investigated angular range. Under continuous 1-sun illumination, whitening substantially reduced the thermal response of the cell. At 10 vol% TiO2, the maximum output power decreased by 54.5% relative to the bare-glass reference, whereas the steady-state cell backside temperature decreased by 8.3 degrees C and the local backside heat flux decreased by 50.0%. These results quantitatively demonstrate an electrical-thermal trade-off in white photovoltaic structures and highlight the need to optimize scattering-layer design to balance architectural appearance, photovoltaic power generation, and backside thermal transport in BIPV applications.
Motor imagery (MI)-based brain–computer interfaces (BCIs) using electroencephalogram (EEG) signals have received particular attention due to their potential to assist in motor function rehabilitation and communication support, thereby improving the quality of life for individuals with physical impairments. MI-BCI classification methods based on deep learning architectures have gained significant popularity in recent years. To further enhance classification performance, we propose EEG-CLT, a sequential architecture that combines three deep learning modules: a Convolutional Neural Network (CNN) to extract local spatiotemporal features, scalar Long Short-Term Memory (sLSTM) networks to capture temporal dependencies, and a Transformer for integrating global context through the self-attention mechanism. The proposed model achieved robust classification performance in within-subject and cross-subject evaluations. In the within-subject evaluation, the accuracies were 75.8% and 87.1% on the BCI Competition IV 2a and 2b datasets, respectively. In the cross-subject evaluation, the accuracies were 61.7%, 74.4%, and 63.1% on the BCI Competition IV 2a, 2b, and PhysioNet EEG datasets, respectively. These accuracies were higher than or comparable to those of the baseline deep learning models. These results suggest that EEG-CLT, which incorporates sLSTM into a CNN–Transformer-based sequential pipeline, provides a useful and competitive design choice for MI-EEG classification across multiple public datasets.
High-performance porous copper (Cu)-based electrochemical sensors could potentially be achieved using GHz-burst femtosecond laser sintering, which allows enhanced control over the oxidation state and surface area. In this study, porous Cu-based electrodes were fabricated by GHz-burst femtosecond laser sintering and their electrochemical performances were evaluated in the non-enzymatic detection of D-glucose. Electrodes were produced under different intra-pulse numbers (Nint = 1, 2, 5, and 10), allowing control over both the extent of oxidation and the phase composition. At higher intra-pulse numbers, oxidation was effectively suppressed because of the reduced pulse intensity, whereas lower intra-pulse numbers promoted copper(I) oxide (Cu2O) formation through excessive heating. Electrochemical studies revealed that the Cu2O-containing electrode fabricated in the non-burst mode (Nint = 1) exhibited superior catalytic activity, demonstrating enhanced sensitivity toward D-glucose compared with that achieved using Cu-rich electrodes. These results confirm that GHz-burst femtosecond laser sintering provides precise control over the Cu phases and porosity, offering a promising strategy for developing efficient non-enzymatic glucose sensors.
In this work, a silica-based adsorbent incorporating two types of extractants was synthesized through vacuum-assisted impregnation. The synergistic adsorption behaviors of the prepared (TEHTDGA + TAMIA-EH)/SiO2–P composite toward 14 types of representative metal ions in simulated high-level liquid waste were systematically investigated under different contact durations, nitric acid concentrations, and solution temperatures, revealing the strong affinity of the (TEHTDGA + TAMIA-EH)/SiO2–P composite toward Pd(II), Ru(III), Re(VII), Mo(VI), and Zr(IV). Particularly, Pd(II) rapidly adsorbed on the composite, reaching equilibrium within 2h, with a removal efficiency exceeding 95
Headache disorders cause work productivity and activity impairment (WPAI). There are two distinct types of burden caused by headache disorders: interictal burden measured by Migraine Interictal Burden Scale-4 (MIBS-4) and ictal burden measured by Headache Impact Test-6 (HIT-6). However, the impact of interictal burden on WPAI remains unclear. This study aimed to investigate whether MIBS-4 score (interictal burden) is associated with WPAI among individuals with headache disorders, in contrast to HIT-6 score (ictal burden). We conducted a school-based online survey of students’ parents in Tsubame City, Japan, in 2024. The questionnaire included age, sex, headache characteristics, MIBS-4 and HIT-6 scores, and overall work productivity impairment (OWPI) assessed using the WPAI questionnaire. A structural equation model (SEM) evaluated the effects of MIBS-4 and HIT-6 scores on OWPI. The headache diagnosis was solely based on the questionnaire. Among 5,227 households, 21.6
