Search Results (1,018)

To further enhance the economic performance of fuel cell vehicles (FCVs), this study develops a model-adaptive model predictive control (MPC) strategy. This strategy leverages the dynamic relationship between proton exchange membrane fuel cell (PEMFC) output characteristics and temperature to track its optimal operating temperature (OOT), addressing challenges of temperature control accuracy and high energy consumption in the PEMFC thermal management system (TMS). First, PEMFC and TMS models were developed and experimentally validated. Subsequently, the PEMFC power–temperature coupling curve was experimentally determined under multiple operating conditions to serve as the reference trajectory for TMS multi-objective optimization. For MPC controller design, the TMS model was linearized and discretized, yielding a predictive model adaptable to different load demands for stack temperature across the full operating range. A multi-constrained quadratic cost function was formulated, aiming to minimize the deviation of the PEMFC operating temperature from the OOT while accounting for TMS parasitic power consumption. Finally, simulations under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) conditions evaluated the OOT tracking performance of both PID and MPC control strategies, as well as their impact on stack efficiency and TMS energy consumption at different ambient temperatures. The results indicate that, compared to PID control, MPC reduces temperature tracking error by 33%, decreases fan and pump speed fluctuations by over 24%, and lowers TMS energy consumption by 10%. These improvements enhance PEMFC operational stability and improve FCV energy efficiency. Full article

The effect of periodical heatwaves and related thermal stratification in freshwater aquatic ecosystems has been a hot research issue. A large dataset of samples was generated from samples exposed to temporary thermal stratification in mesocosms mimicking shallow eutrophic freshwater lakes. Temperature regimes were based on IPCC climate warming scenarios, enabling simulation of future warming conditions. Surface oxygen levels reached 19.37 mg/L, while bottom layers dropped to 0.07 mg/L during stratification. Analysis by FlowCAM revealed dominance of Cyanobacteria under ambient conditions (up to 99.2%), while Cryptophyta (up to 98.9%) and Chlorophyta (up to 99.9%) were predominant in the A2 and A2+50% climate scenarios, respectively. We identified temperature changes and shifts in nutrient concentrations, particularly phosphate, as critical factors in microbial community composition. Furthermore, five distinct Microcystis morphospecies identified by FlowCAM-based analysis were associated with different microbial clusters. The combined use of imaging flow cytometry, which differentiates phytoplankton based on morphological parameters, and nanopore long-read sequencing analysis has shed light into the dynamics of microbial communities associated with different Microcystis morphospecies. In our observations, a peak of algicidal bacteria abundance often coincides with or is followed by a decline in the Cyanobacteria. These findings highlight the importance of species-level classification in the analysis of complex ecosystem interactions and the dynamics of algal blooms in freshwater bodies in response to anthropogenic effects and climate change. Full article

The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this study proposes a cross-modal 3D face generation framework based on single-view semantic masks. It utilizes Swin Transformer for multi-level feature extraction and combines with NeRF for illumination decoupled rendering. We utilize physical rendering equations to explicitly separate surface reflectance from ambient lighting to achieve robust adaptation to complex lighting variations. In addition, to address geometric errors across illumination scenes, we construct geometric a priori constraint networks by mapping 2D facial features to 3D parameter space as regular terms with the help of semantic masks. On the CelebAMask-HQ dataset, this method achieves a leading score of SSIM = 0.892 (37.6% improvement from baseline) with FID = 40.6. The generated faces excel in symmetry and detail fidelity with realism and aesthetic scores of 8/10 and 7/10, respectively, in a perceptual evaluation with 1000 viewers. By combining physical-level illumination decoupling with semantic geometry a priori, this paper establishes a quantifiable feedback mechanism between objective metrics and human aesthetic evaluation, providing a new paradigm for aesthetic quality assessment of AI-generated content. Full article

Stargardt’s disease (STGD1) is an autosomal recessive juvenile macular degeneration caused by mutations in the ABCA4 gene, impairing clearance of toxic retinoid byproducts in the retinal pigment epithelium (RPE). This leads to lipofuscin accumulation, oxidative stress, photoreceptor degeneration, and central vision loss. Over 1200 pathogenic/likely pathogenic ABCA4 variants highlight the genetic heterogeneity of STGD1, which manifests as progressive central vision loss, with phenotype influenced by deep intronic variants, modifier genes, and environmental factors like light exposure. ABCA4 variants also show variable penetrance and geographical prevalence. With no approved treatment, investigational therapies target different aspects of disease pathology. Small-molecule therapies target vitamin A dimerization (e.g., ALK-001), inhibit lipofuscin accumulation (e.g., soraprazan), or modulate the visual cycle (e.g., emixustat hydrochloride). Gene therapy trials explore ABCA4 supplementation including strategies like RNA exon editing (ACDN-01) and bioengineered ambient light-activated OPSIN. RORA gene therapy (Phase 2/3) addresses oxidative stress, inflammation, lipid metabolism, and complement system dysregulation. Trials like DRAGON (Phase 3, tinlarebant), STARLIGHT (phase 2, bioengineered OPSIN) show promise, but optimizing efficacy remains challenging. With the key problem of establishing genotype–phenotype correlations, the future of STGD1 therapy may rely on approaches targeting oxidative stress, lipid metabolism, inflammation, complement regulation, and genetic repair. Full article

Background: Reliable and non-invasive methods for assessing welfare in poultry are essential for improving evidence-based welfare monitoring and advancing management practices in commercial production systems. The iris-to-pupil (IP) ratio, previously validated by our group in primates and cattle, reflects autonomic nervous system balance and may serve as a physiological indicator of stress in laying hens. This study evaluated the utility of the IP ratio under field conditions across diverse commercial layer housing systems. Materials and Methods: In total, 296 laying hens (Lohmann Brown, n = 269; White Leghorn, n = 27) were studied across four locations in Canada housed under different systems: Guelph (indoor; pen), Spring Island (outdoor and scratch; organic), Ottawa (outdoor, indoor and scratch; free-range), and Toronto (outdoor and hobby; free-range). High-resolution photographs of the eye were taken under ambient lighting. Light intensity was measured using the light meter app. The IP ratio was calculated using NIH ImageJ software (Version 1.54p). Statistical analysis included one-way ANOVA and linear regression using GraphPad Prism (Version 5). Results: Birds housed outdoors had the highest IP ratios, followed by those in scratch systems, while indoor and pen-housed birds had the lowest IP ratios (p < 0.001). Subgroup analyses of birds in Ottawa and Spring Island farms confirmed significantly higher IP ratios in outdoor environments compared to indoor and scratch systems (p < 0.001). The IP ratio correlated weakly with ambient light intensity (r² = 0.25) and age (r² = 0.05), indicating minimal influence of these variables. Although White Leghorn hens showed lower IP ratios than Lohmann Browns, this difference was confounded by housing type; all White Leghorns were housed in pens. Thus, housing system but not breed was the primary driver of IP variation. Conclusions: The IP ratio is a robust, non-invasive physiological marker of welfare assessment in laying hens, sensitive to housing environment but minimally influenced by light or age. Its potential for integration with digital imaging technologies supports its use in scalable welfare assessment protocols. Full article

Circadian rhythms are intrinsic, with roughly 24 h oscillations that coordinate many physiological functions and are increasingly recognized as key determinants of human health. When these rhythms become misaligned, there is an increased risk for neurodegenerative and psychiatric disorders, metabolic syndrome, sleep disturbances, and even certain cancers. The hormones, melatonin that rises in the evening and cortisol that peaks shortly after awakening, represent crucial biochemical markers of the circadian phase. This review systematically evaluates contemporary techniques for quantifying melatonin and cortisol, comparing biological matrices (blood, saliva, urine) alongside analytical platforms. Special focus is placed on two clinically informative markers: Dim Light Melatonin Onset (DLMO) and the Cortisol Awakening Response (CAR). We compared immunoassays with liquid chromatography tandem mass spectrometry (LC MS/MS), highlighting differences in sensitivity, specificity, and laboratory feasibility. Potential confounders, including ambient light, body posture, and exact sampling times—are discussed in detail, to show the capacity of providing the most reliable results. By emphasizing the need for standardized protocols and controlled sampling conditions, this review provides essential guidance for researchers and clinicians aiming to assess the circadian biomarkers melatonin and cortisol with precision since they can be used in clinical practice as diagnostic and prognostic tools for assessing numerous pathologies. Full article

We demonstrate an all-fiber, high-sensitivity, dual-parameter sensor for humidity and temperature. The sensor consists of a symmetrical, non-adiabatic, tapered, single-mode optical fiber, operating at the wavelength near the dispersion turning point, and a cascaded fiber Bragg grating (FBG) for temperature compensation. At one end of the fiber’s tapered region, part of the fundamental mode is coupled to a higher-order mode, and vice versa at the other end. Under the circumstances that the two modes have the same group index, the transmission spectrum would show an interference fringe with uneven dips. In the tapered region of the sensor, some of the light transmits to the air, so it is sensitive to changes in the refractive index caused by the ambient humidity. In the absence of moisture-sensitive materials, the humidity sensitivity of our sensor sample can reach −286 pm/%RH. In order to address the temperature and humidity crosstalk and achieve a dual-parameter measurement, we cascaded a humidity-insensitive FBG. In addition, the sensor has a good humidity stability and a response time of 0.26 s, which shows its potential in fields such as medical respiratory dynamic monitoring. Full article

The automotive industry is advancing toward fully automated driving, where perception systems rely on complementary sensors such as LiDAR and cameras to interpret the vehicle’s surroundings. For Level 4 and higher vehicles, redundancy is vital to prevent safety-critical failures. One way to achieve this is by using data from one sensor type to support another. While much research has focused on reconstructing LiDAR point cloud data using camera images, limited work has been conducted on the reverse process—reconstructing image data from LiDAR. This paper proposes a deep learning model, named LiDAR Generative Camera (LiGenCam), to fill this gap. The model reconstructs camera images by utilizing multimodal LiDAR data, including reflectance, ambient light, and range information. LiGenCam is developed based on the Generative Adversarial Network framework, incorporating pixel-wise loss and semantic segmentation loss to guide reconstruction, ensuring both pixel-level similarity and semantic coherence. Experiments on the DurLAR dataset demonstrate that multimodal LiDAR data enhances the realism and semantic consistency of reconstructed images, and adding segmentation loss further improves semantic consistency. Ablation studies confirm these findings. Full article

This study proposes a novel heart rate estimation method that detects subtle cardiac-induced vibrations propagated through the cardiovascular system based on the ballistocardiography (BCG) principle, using a six-axis heart rate sensing device that integrates a three-axis accelerometer and a three-axis gyroscope. To validate the effectiveness of the proposed method, a comparative analysis was conducted against heart rate measurements obtained from photoplethysmography (PPG) sensors, which are widely used in conventional heart rate monitoring. Experiments were conducted on 20 adult participants, and frequency domain analysis was performed using different time windows of 30 s, 20 s, 8 s, and 4 s. The results showed that the 4 s window provided the highest accuracy in heart rate estimation, demonstrating that the proposed method can effectively capture fine cardiac-induced vibrations. This approach offers a significant advantage by utilizing inertial sensors commonly embedded in wearable devices for heart rate monitoring without the need for additional optical sensors. Compared to optical-based systems, the proposed method is more power-efficient and less affected by environmental factors such as ambient lighting conditions. The findings suggest that heart rate estimation using the six-axis heart rate sensing device presents a reliable, continuous, and non-invasive alternative for cardiovascular monitoring. Full article

See-through augmented reality glasses, such as HoloLens 2, are increasingly adopted in medical settings; however, their efficacy as medical display devices remains unclear, as current evaluation protocols are designed for traditional monitors. This study examined whether the established display-evaluation techniques apply to HoloLens 2 and whether it meets standards for primary and secondary medical displays. HoloLens 2 was assessed for overall image quality, luminance, grayscale consistency, and color uniformity. Five participants rated the TG18-OIQ pattern under ambient lighting conditions of 2.4 and 138.7 lx. Minimum and maximum luminance were measured using the TG18-LN12-03 and -18 patterns, targeting ≥ 300 cd/m² and a luminance ratio ≥ 250. Grayscale conformity to the standard grayscale display function allowed deviations of 10% for primary and 20% for secondary displays. Color uniformity was measured at five screen positions for red, green, and blue, with a chromaticity limit of 0.01 for primary displays. HoloLens 2 satisfied four of the ten primary and four of the seven secondary overall-quality criteria, achieving a maximum luminance of 2366 cd/m² and a luminance ratio of 1478.75. Grayscale uniformity was within tolerance for 10 of the 15 primary and 13 of the 15 secondary measurements, while 25 of the 30 color uniformity values exceeded the threshold. The adapted evaluation methods facilitate a systematic assessment of HoloLens 2 as a medical display. Owing to inadequate grayscale and color representation, the headset is unsuitable as a primary diagnostic display; for secondary use, requirements must be assessed based on specific application requirements. Full article

Stimuli-responsive polymers (SRPs) have garnered significant attention in recent decades due to their immense potential in biomedical and environmental applications. When these SRPs are grafted onto magnetic nanoparticles, they form multifunctional nanocomposites capable of various complex applications, such as targeted drug delivery, advanced separations, and magnetic resonance imaging. In this study, we employed a one-step hydrothermal method using 3-aminopropyltrimethoxysilane (APTES) to synthesize APTES-modified Fe₃O₄ nanoparticles (APTES@Fe₃O₄) featuring reactive terminal amine groups. Subsequently, via two consecutive surface-initiated atom transfer radical polymerizations (SI-ATRP), pH- and temperature-responsive polymer blocks were grown from the Fe₃O₄ surface, resulting in the formation of poly(itaconic acid)-block-poly(N-isopropyl acrylamide) (PIA-b-PNIPAM)-grafted nanomagnetic particles (PIA-b-PNIPAM@Fe₃O₄). To confirm the chemical composition and assess how the particle morphology and size distribution of these SRP-based nanocomposites change in response to ambient pH and temperature stimuli, various characterization techniques were employed, including transmission electron microscopy, differential light scattering, and Fourier transform infrared spectroscopy. The results indicated successful synthesis, with PIA-b-PNIPAM@Fe₃O₄ demonstrating sensitivity to both temperature and pH. Full article

Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing efficient water use, given that aeroponics intermittently delivers water in mist form rather than maintaining continuous root zone moisture. However, aeroponics faces critical challenges in irrigation management due to non-standardized structures and limited real-time control. A key limitation is the inability to dynamically respond to temperature (T), relative humidity (RH), light intensity (L_i), electrical conductivity (EC), pH, and photosynthesis rate (P_n), resulting in suboptimal crop yields and resource wastage. Despite growing interest, there remains a research gap in integrating internet of things (IoT) and machine learning technologies into aeroponic systems for adaptive control. IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in P_n and environmental inputs. These technologies are particularly well suited to address the dynamic, data-intensive nature of aeroponic environments. This review purposes a novel, standardized IoT–ML framework to control irrigation by emphasizing IoT sensing and ML-based decision making in aeroponics. This integrated approach is essential for minimizing water loss, enhancing resource efficiency, and advancing the sustainability of controlled-environment agriculture. Full article

The increasing demand for sustainable and renewable energy solutions has made radio frequency energy harvesting (RFEH) a promising technique for powering low-power electronic devices. RFEH captures ambient RF signals from wireless communication systems, such as mobile networks, Wi-Fi, and broadcasting stations, and converts them into usable electrical energy. This approach offers a viable alternative for battery-dependent and hard-to-recharge applications, including streetlights, outdoor night/security lighting, wireless sensor networks, and biomedical body sensor networks. This article provides a comprehensive review of the RFEH techniques, including state-of-the-art rectenna designs, energy conversion efficiency improvements, and multi-band harvesting systems. We present a detailed analysis of recent advancements in RFEH circuits, impedance matching techniques, and integration with emerging technologies such as the Internet of Things (IoT), 5G, and wireless power transfer (WPT). Additionally, this review identifies existing challenges, including low conversion efficiency, unpredictable energy availability, and design limitations for small-scale and embedded systems. A critical assessment of current research gaps is provided, highlighting areas where further development is required to enhance performance and scalability. Finally, constructive recommendations for future opportunities in RFEH are discussed, focusing on advanced materials, AI-driven adaptive harvesting systems, hybrid energy-harvesting techniques, and novel antenna–rectifier architectures. The insights from this study will serve as a valuable resource for researchers and engineers working towards the realization of self-sustaining, battery-free electronic systems. Full article

Elevated [CO₂] enhances light interception and carboxylation efficiency in plants. The combined effects of [CO₂] and photosynthetic photon flux density (PPFD) on stomatal morphology, leaf anatomy, and photosynthetic capacity in tomato seedlings remain unclear. This study subjected tomato seedlings (Solanum lycopersicum Mill. cv. Jingpeng No.1) to two [CO₂] (ambient [a[CO₂], 400 µmol·mol⁻¹] and enriched [e[CO₂], 800 µmol·mol⁻¹]) and three PPFD levels (L; low[Ll: 200 µmol·m⁻²·s⁻¹], moderate[Lm: 300 µmol·m⁻²·s⁻¹], and high[Lh: 400 µmol·m⁻²·s⁻¹]) to assess their interactive impacts. Results showed that e[CO₂] and increased PPFD synergistically improved relative growth rate and net assimilation rate while reducing specific leaf area and leaf area ratio. Notably, e[CO₂] decreased stomatal aperture (−13.81%) and density (−27.76%), whereas elevated PPFD promoted stomatal morphological adjustments. Additionally, Leaf thickness increased by 72.98% under e[CO₂], with Lm and Lh enhancing this by 10.79% and 41.50% compared to Ll. Furthermore, photosynthetic performance under e[CO₂] was further evidenced by improved chlorophyll fluorescence parameters (excluding non-photochemical quenching). While both e[CO₂] and increased PPFD Photosynthetic performance under e[CO₂] was further evidenced by improved chlorophyll fluorescence parameters (excluding non-photochemical quenching). Moreover, e[CO₂]-Lh treatment maximized total dry mass and seedling health index. Correlation analysis indicated that synergistic optimization of stomatal traits and leaf structure under a combination of e[CO₂] and increased PPFD enhanced light harvesting and CO₂ diffusion, thereby promoting carbon assimilation. These findings highlight e[CO₂]-Lh as an optimal strategy for tomato seedling growth, providing empirical guidance for precision CO₂ fertilization and light management in controlled cultivation. Full article

Visible light communication (VLC) is an emerging optical wireless technology capable of delivering high data rates for both indoor and outdoor environments. When combined with multiple-input, multiple-output (MIMO) systems, VLC demonstrates enhanced capacity, extended transmission range, and improved reliability. However, VLC systems are susceptible to ambient light interference, which can degrade performance. This paper investigates the performance of MIMO-VLC systems using three modulation techniques: non-return to zero (NRZ), return to zero (RZ), and quadrature phase shift keying (QPSK). The study evaluates the VLC systems in terms of bit error rate (BER), quality factor (Q-factor), and received power over varying link distances. The obtained results show that MIMO-based systems outperform single-input, single-output (SISO) systems in terms of transmission range, with MIMO achieving up to 1450 m using QPSK, compared to 1125 m for SISO. Under ambient light noise, MIMO-based systems experience a greater reduction in transmission distance (13.6%) compared to SISO (6.2%), but the overall performance gain of MIMO compensates for this degradation. Among the modulation schemes, NRZ and QPSK provide the best performance, showing greater resilience to ambient light interference. The findings confirm that MIMO–VLC systems, particularly with NRZ and QPSK, offer a robust solution for overcoming interference and maximizing transmission distance in real-world applications. Full article