Advancing Open Science

Using efficient voice alarms to ensure safe evacuation is important during emergencies, especially for the elderly. Factors that have important influence on speech perceptions have been investigated for several years. However, relatively few studies have specifically explored the key factors influencing perceptions of voice alarms in emergency situations. This study investigated the combined effects of speech rate (SR), signal-to-noise ratio (SNR), and reverberation time (RT) on older people’s perception of voice alarms. Thirty older adults were invited to evaluate speech intelligibility, listening difficulty, and perceived urgency after hearing 48 different voice alarm conditions. For comparison, 25 young adults were also recruited in the same experiment. The results for older adults showed that: (1) When SR increased, speech intelligibility significantly decreased, and listening difficulty significantly increased. Perceived urgency reached its maximum at the normal speech rate for older adults, in contrast to young adults, for whom urgency was greatest at the fast speech rate. (2) With the rising SNR, speech intelligibility and perceived urgency significantly increased, and listening difficulty significantly decreased. In contrast, with the rising RT, speech intelligibility and perceived urgency significantly decreased, while listening difficulty significantly increased. (3) RT exerted a relatively stronger independent influence on speech intelligibility and listening difficulty among older adults compared to young adults, which tended not to be substantially moderated by SR or SNR. The interactive effect of SR and RT on perceived urgency was significant for older people, but not significant for young people. These findings provide referential strategies for designing efficient voice alarms for the elderly.

The weak hydrogen bond with methylammonium iodide (MAI) dominates the formation of methylammonium lead iodide (MAPbI₃) during its nucleation and growth process. Herein, a weak hydrogen bond involving iodide is designed between the MAI and glycerol molecule in mixed solvents containing N, N-dimethylformamide (DMF) and dimethyl sulfoxide (DMSO) to delay the growth of MAPbI₃ film. Incorporation of glycerol into the perovskite film indicates a larger grain size and suppressed nonradiative recombination of carriers in the film. Finally, the glycerol-doped perovskite solar cells (PSCs) achieve a champion power conversion efficiency (PCE) of up to 16.84%, with excellent stability to retain 92.05% of their initial PCE after 30 days of storage. The above results unveil a deep understanding of weak hydrogen bonds in high-performance perovskite photovoltaics.

This review integrates current knowledge on how greenhouse conditions regulate the nutritional quality and shelf life of tomato, cucumber, and sweet pepper. Preharvest environmental factors jointly shape fruit composition, firmness, and storage performance through their control of photosynthesis, assimilate partitioning, and structural stability. Across all variables, light intensity and fruit temperature emerge as the dominant determinants of overall quality and shelf life potential. Relative air humidity (RH), irrigation regime, and nutrient balance primarily affect firmness, water loss, and physiological disorders, while CO₂ enrichment, shading, and mineral or biostimulant inputs exert secondary yet consistent effects. Comparative evaluation shows that tomato is most sensitive to temperature and RH, cucumber to water status and epidermal stress, and sweet pepper to radiation for color and antioxidant development. These distinctions confirm that no single climatic optimization can be universally applied, and management must therefore target species-specific physiological constraints to sustain both nutritional excellence and storage performance. Major knowledge gaps remain, particularly regarding the combined effects of interacting environmental drivers and the integration of physiological responses with postharvest behavior. Future research should adopt multifactorial designs and predictive modeling to support climate-smart greenhouse strategies that optimize quality and storability under variable growing conditions.

Deep learning-based respiratory sound classification (RSC) has emerged as a promising non-invasive approach to assist clinical diagnosis. However, existing methods often face challenges, such as sub-optimal feature representation and limited model expressiveness. To address these issues, we propose an Attention-based Dual-stream Feature Fusion Network (ADFF-Net). Built upon the pre-trained Audio Spectrogram Transformer, ADFF-Net takes Mel-filter bank and Mel-spectrogram features as dual-stream inputs, while an attention-based fusion module with a skip connection is introduced to preserve both the raw energy and the relevant tonal variations within the multi-scale time–frequency representation. Extensive experiments on the ICBHI2017 database with the official train–test split show that, despite critical failure in sensitivity of 42.91%, ADFF-Net achieves state-of-the-art performance in terms of aggregated metrics in the four-class RSC task, with an overall accuracy of 64.95%, specificity of 81.39%, and harmonic score of 62.14%. The results confirm the effectiveness of the proposed attention-based dual-stream acoustic feature fusion module for the RSC task, while also highlighting substantial room for improving the detection of abnormal respiratory events. Furthermore, we outline several promising research directions, including addressing class imbalance, enriching signal diversity, advancing network design, and enhancing model interpretability.

We present a differentiable, curriculum-based optimization workflow for the engineering-oriented design of large-aperture reflective optical systems. The method integrates physics-informed differentiable ray tracing with a progressive, two-stage optimization strategy that evolves from simple Ritchey–Chrétien (RC) foundations to complex four-mirror architectures. Without relying on pretrained models or large datasets, the workflow optimizes geometric and physical parameters under field-weighted RMS, focal length, and dynamic obscuration constraints while maintaining minimal perturbation to primary and secondary mirrors. Validated through Zemax-based analysis, the optimized systems achieve high imaging quality with improved RMS uniformity and stable convergence across varying aperture scales. This approach provides a practical and scalable pathway for the design and optimization of reflective optical instruments, offering strong robustness and adaptability for diverse imaging applications.

The increasing penetration of electric vehicle (EV) fast-charging stations (FCSs) into distribution networks and microgrids poses considerable operational challenges, including voltage deviations, increased power losses, and peak load stress. This work proposes a novel hybrid optimization framework that integrates Lagrangian relaxation (LR) with adaptive sheep flock optimization (ASFO) to address the resource scheduling issues when EVs are penetrated and their impact on net load demand, total cost. Besides the impact of EV uncertainty on energy exchange cost and operational costs, voltage profile deviations were also studied. LR is employed to decompose the original problem and manage complex operational constraints, while ASFO is employed to solve the relaxed subproblems by efficiently exploring the high-dimensional, non-convex solution space. The proposed method is tested on an IEEE 33-bus distribution system with integrated PV and BESS under 24 h dynamic load and renewable scenarios. Results establish that the hybrid LR-ASFO method significantly outperforms conventional methods. Compared to standalone metaheuristics, the proposed framework reduces total cost by 5.6%, improves voltage profile deviations by 2.4%, and minimizes total operational cost by 4.3%. Furthermore, it safeguards constraint feasibility while avoiding premature convergence, thereby accomplishing better global optimality and system reliability.

As an important branch of the lipoxygenase (LOX) metabolism pathway, hydroperoxide lyase (HPL) is involved in regulating plant development and defense responses. However, the upstream regulatory mechanism of HPL remains unclear in soybean. In the present study, by analyzing the upstream promoter region of the GmHPL gene, cis-elements such as MYB motifs, G-box motifs, ERE motifs and W-box motifs were predicted, which were related to the stress response. Yeast one-hybrid was employed and two transcription factors were identified, GmERF36 and GmILR3. The orthologs of ERF36 and ILR3 in Arabidopsis were involved in pathogen stress. A dual-luciferase reporter assay verified the yeast one-hybrid results and indicated that GmERF36 and GmILR3 suppressed the expression of the GmHPL protein. The qRT-PCR results indicated that GmHPL and GmERF36 initially displayed inverse expression patterns within 24 h after Colletotrichum truncatum treatment (GmERF36 was upregulated while GmHPL was downregulated); then, both of them were upregulated before decreasing. The results indicated that the response of GmHPL to pathogen stress partially depended on GmERF36. Our study gives rise to new insights into the upstream regulatory network of the GmHPL gene.

Due to the convergence of decarbonisation imperatives, digitalisation, automation, and safety assurance, the maritime sector is undergoing an unprecedented transformation, redefining how ships are designed, operated, and regulated [...]