Lights doi: 10.3390/lights2030008

Authors: Ibrahim Olojoku Mustapha Abdul Halim Abdul Latiff Alidu Rashid Dejen Teklu Asfha Abdul Rahim Md Arshad Bamidele Abdulhakeem Adeniyi John Oluwadamilola Olutoki Muhammad Rafi

Distributed Acoustic Sensing (DAS) has revolutionized ambient noise interferometry, yet the reliability of retrieved empirical Green’s functions (EGFs) remains highly sensitive to non-diffuse noise fields and anthropogenic transients. In complex or noisy environments, global metrics frequently misclassify signal convergence due to out-of-band environmental noise, scattered coda, and non-stationary directional transients. Using both 30 min and 4 h passive recordings, this study presents a physics-guided quality control framework for evaluating interferometric diagnostics. Specifically, this study employs the signal-to-trailing noise ratio (STRN), signal-to-precursory noise ratio (SPNR), and spectral signal-to-noise ratio (SSNR) within the surface-wave arrival window t=x/v. Global phase metrics remain heavily suppressed (x¯≈0.05) regardless of stacking duration, whereas the surface-windowed SPNR exhibits an extraordinary statistical shift (p < 0.001), reaching 0.870 ± 0.106 at 30 min and 0.967 ± 0.034 at 4 h. We implement one-to-one correspondence between surface-windowed indicator values and fundamental-mode Rayleigh wave dispersion sharpness. In severely noise-contaminated segments, unwindowed global metrics yield distorted dispersion ridges with severe energy leakage, but the surface-wave window results in an increase in the SPNR above 0.70, fully reconstructing continuous dispersion trajectories (250–500 m/s). Grounded in these results, we formalize a standardized four-step quality control workflow (from velocity windowing to metric calculation, automation, and data output) and outline tailored adaptation guidelines for urban, mountainous, and industrial DAS deployments. This framework provides an automated, physically sound protocol that eliminates manual selection, optimizes computational efficiency, and ensures reliable dispersion extraction for passive DAS imaging.

Lights doi: 10.3390/lights2030007

Authors: Nowrin Jannat Saleha Nasrin Mishu Prithwiraj Biswas Pallab Md. Atik Hasan Nishat Md. Firoz Ahmed M. Hasnat Kabir

Accurate modeling of photovoltaic (PV) cell behavior under varying operational conditions is essential for optimizing energy yield and system reliability. This study presents an extended simulation-based methodology for analyzing monocrystalline silicon PV cells using a double-diode model (DDM) with a physics-based, temperature- and irradiance-dependent parameterization. Building on a SPICE-equivalent circuit formulation, the governing implicit DDM equation is solved numerically to regenerate every current–voltage (I–V) and power–voltage (P–V) curve, and all circuit, block and flow diagrams are redrawn as vector-quality figures. Beyond the deterministic analysis, the manuscript introduces two extensions: (i) a quantitative statistical analysis of the influence of temperature (T), irradiance (G) and series resistance (Rs) on open-circuit voltage, short-circuit current, maximum power and fill factor, using linear/log-linear regression, a multiple linear regression model and a Pearson correlation analysis; and (ii) a machine-learning (ML) validation study in which a random-forest surrogate model is trained on a 600-point physics-consistent synthetic dataset spanning the full (T, G, Rs) operating envelope and evaluated with a held-out test split and 5-fold cross-validation. The surrogate reproduces the DDM outputs with cross-validated coefficients of determination above 0.98 for maximum power, open-circuit voltage, short-circuit current and fill factor, confirming that the DDM response surface is smooth, learnable and suitable for fast surrogate-based design optimization and maximum-power-point-tracking (MPPT) algorithm testing. Simulated outputs at standard test conditions (25 °C, 1000 W/m2, AM 1.5) are compared against manufacturer datasheet values, and residual errors are analyzed and attributed to specific modeling assumptions.

Lights doi: 10.3390/lights2030006

Authors: Dimitar Slavkov Svetoslava Troyanova-Slavkova Petranka Troyanova

Technological advances in optical imaging and artificial light technologies have substantially transformed modern neurosurgical practice by improving intraoperative visualization and surgical precision. This narrative review evaluates current applications of fluorescence-guided surgery, optical navigation systems, near-infrared imaging, augmented visualization platforms, and artificial intelligence-assisted intraoperative imaging in neurosurgery. Particular attention is given to clinically established fluorophores, including 5-aminolevulinic acid, fluorescein sodium, and indocyanine green, which are increasingly used for tumor delineation, vascular assessment, and real-time tissue perfusion analysis. Emerging technologies such as Raman spectroscopy, multispectral imaging, holographic navigation, and nerve-specific fluorescent probes are also discussed in the context of precision and minimally invasive neurosurgery. Current evidence demonstrates that advanced optical systems improve surgical orientation, maximize extent of resection, and support preservation of critical neurovascular structures, particularly in neuro-oncology, vascular neurosurgery, and skull base surgery. However, limitations related to fluorescence specificity, standardization, cost, and technological accessibility remain significant challenges. The continued integration of multimodal optical imaging, computational navigation, and AI-assisted visualization is expected to further enhance intraoperative decision-making, surgical safety, and postoperative outcomes, contributing to the future development of precision image-guided neurosurgery.

Lights doi: 10.3390/lights2020005

Authors: Jackson Eli Hanus Arpan Guha

Visual performance (VP) models have played a foundational role in architectural lighting design, informing illuminance standards intended to support safety, efficiency, and task performance across diverse occupant populations. This paper provides a critical historical review of VP models, tracing their development from early retinal response research and threshold visibility functions to contemporary applications in lighting standards. Key physiological and perceptual factors, including retinal illuminance, contrast, task size, and observer characteristics such as age, are examined through landmark studies that shaped suprathreshold VP modeling. Attention is given to the evolution and adoption of the Relative Visual Performance (RVP) model, which remains central to current Illuminating Engineering Society (IES) illuminance recommendations. The review further synthesizes theory-based, applied, and human-centered experimental approaches, highlighting how VP research expanded to include reaction time, reading performance, chromatic contrast, spectral power distribution, mesopic vision, and virtual reality environments. Despite this extensive body of work, VP models have seen limited revision in response to advances in lighting technology, digital displays, and LED spectral control. Based on gaps identified in prior research, this paper proposes a future modeling framework using linear mixed-effects models to independently assess and assign weights to factors influencing VP. Such an approach may support updated illuminance standards better aligned with modern lighting conditions, occupant needs, and energy efficiency goals.

Lights doi: 10.3390/lights2020004

Authors: Justyna Waśniowska Andrzej Sioma

This article presents one possible method for modelling the electromagnetic radiation spectrum. This work aims to model a radiation source by combining two spectrums and obtaining a different spectrum as a result. To achieve this, the spectrum of a commercially available LED and a filter that transmits electromagnetic radiation at wavelengths above 475 nm were used. The article presents a methodical approach to designing the resulting spectral irradiance graph and the individual component spectral irradiance graphs. The originality of this method lies in transmitting the spectrum of one diode and combining it with an unfiltered diode. The irradiance values obtained on the sensor array depend on the electromagnetic radiation power of the light source and the distance between the sensor array and the light source’s centre. The simulation results for the combined spectrum were 13 W/m2. This work may influence the design of new industrial illuminators.

Lights doi: 10.3390/lights2020003

Authors: Lieke Dorine van Putten Ivan Veleslavov Ayman Ahmed Aristide Mathieu Simon Wegerif

Remote photoplethysmography (rPPG) enables non-contact vital sign measurements using standard smart device cameras, opening up the potential of scalable health applications on consumer smart devices. However, rPPG signal quality is highly sensitive to camera sensor characteristics and image processing pipelines, which can vary between devices. This variation limits reproducibility and generalisation of rPPG-based algorithms beyond specific hardware platforms. This work presents a reproducible test environment for the validation of the camera sensor response in the context of rPPG signals. A microcontroller-driven illumination system and mechanically constrained setup are used to generate controlled, repeatable optical signals. Two characterisation tests are introduced: a time domain morphology analysis and a frequency domain attenuation analysis. Pulse timing consistency, pulse waveform morphology and normalised frequency responses are compared to assess sensor similarity. This method is applied to selected consumer devices and demonstrates consistent camera response patterns under the controlled test conditions. By explicitly addressing validation of the camera sensor and image processing pipeline, this work supports the development of more robust and transferable rPPG-based vital sign applications across a wider range of consumer devices.

Lights doi: 10.3390/lights2010002

Authors: Mario García-González Francisco Vidal-Negreira Antonio González-Cantalapiedra

Photobiomodulation (PBM) has emerged as a noninvasive therapeutic tool with promising clinical applications in veterinary clinical surgery. Its mechanism of action is based on the stimulation of cellular processes through low-intensity light, promoting adenosine triphosphate production, inflammatory modulation, and tissue regeneration. This narrative review examines the current state of knowledge on the use of PBM in veterinary surgical contexts, with an emphasis on its clinical application in wound healing, postoperative pain control, and functional recovery. The physiological foundations of the technique, the main technical parameters that determine its effectiveness (wavelength, dose, frequency, and mode of application), and the available clinical evidence from different specialties such as soft tissue surgery, orthopedics, dentistry, and neurosurgery are analyzed. Current limitations, such as the lack of standardized protocols and their limited inclusion in clinical guidelines, are also addressed, as are future opportunities related to treatment personalization, the development of specific veterinary devices, and integration with emerging technologies. PBM represents a safe and effective adjuvant therapeutic strategy with the potential to become an integral part of veterinary postoperative management.

Lights doi: 10.3390/lights2010001

Authors: Fatema A. Albalooshi M. R. Qader Mazen Ali Yasser Ismail

Fluorescence microscopy is a cornerstone technique in biological research, offering unparalleled insights into cellular and subcellular structures. However, inherent limitations such as photobleaching, phototoxicity, and low signal-to-noise ratios (SNR) often hinder its full potential. This paper introduces DeepFluoNet, a novel deep learning framework designed to significantly enhance the analysis of fluorescence microscopy data. DeepFluoNet leverages a sophisticated convolutional neural network architecture, meticulously optimized for denoising, segmentation, and classification tasks in fluorescence images. DeepFluoNet achieved a 98.5% accuracy in cell nucleus classification, a 95.2% F1-score in mitochondrial segmentation, and a 25% improvement in SNR for low-light images, surpassing state-of-the-art methods by an average of 7.3% in overall performance metrics. Furthermore, the inference time of DeepFluoNet is optimized to be 0.05 s per image, making it suitable for high-throughput analysis. This research bridges critical gaps in existing methodologies by providing a robust, efficient, and highly accurate solution for fluorescence microscopy data analysis, paving the way for more precise biological discoveries.

Lights doi: 10.3390/lights1010005

Authors: Roberto Morandotti

With advances in photonics, light research is at a turning point in its history, as technological advances and breakthroughs shift from fundamental research to transformative applications [...]

Lights doi: 10.3390/lights1010004

Authors: Christopher Small

Anthropogenic night light (ANL) provides a unique observable for the spatially explicit mapping of human-modified landscapes in the form of lighted infrastructure. Since 2013, the Visible Infrared Imaging Radiometer Suite (VIIRS) Day Night Band (DNB) on the Suomi NPP satellite has provided more than a decade of near-daily observations of anthropogenic night light. The objective of this study is to quantify changes in ANL in developed eastern China post-2013 using VIIRS DNB monthly mean brightness composites. Specifically, to constrain sub-annual and interannual changes in night light brightness to distinguish between apparent and actual change of ANL sources, and then conduct a spatiotemporal analysis of observed changes to identify areas of human activity, urban development and rural electrification. This analysis is based on a combination of time-sequential bitemporal brightness distributions and quantification of the spatiotemporal evolution of night light using Empirical Orthogonal Function (EOF) analysis. Bitemporal brightness distributions show that bright (>~1 nW/cm2/sr) ANL is heteroskedastic, with temporal variability diminishing with increasing brightness. Hence, brighter lights are more temporally stable. In contrast, dimmer (<~1 nW/cm2/sr) ANL is much more variable on monthly time scales. The same patterns of heteroskedasticity and variability of the lower tail of the brightness distribution are observed in year-to-year distributions. However, year-to-year brightness increases vary somewhat among different years. While bivariate distributions quantify aggregate changes on both subannual and interannual time scales, spatiotemporal analysis quantifies spatial variations in the year-to-year temporal evolution of ANL. The spatial distribution of brightening (and, much less commonly, dimming) revealed by the EOF analysis indicates that most of the brightening since 2013 has occurred at the peripheries of large cities and throughout the networks of smaller settlements on the North China Plain, the Yangtze River Valley, and the Sichuan Basin. A particularly unusual pattern of sequential brightening and dimming is observed on the Loess Plateau north of Xi’an, where extensive terrace construction has occurred. All aspects of this analysis highlight the difference between apparent and actual changes in night light sources. This is important because many users of VIIRS night light attribute all observed changes in imaged night light to actual changes in anthropogenic light sources—without consideration of low luminance variability related to the imaging process itself.

Lights doi: 10.3390/lights1010003

Authors: Wei Sun Dawei Yuan Zhe Zhang Jiayong Zhong Gang Zhao

Laboratory astrophysics is an emerging interdisciplinary field bridging high-energy-density plasma physics and astrophysics. Optical diagnostic techniques offer high spatiotemporal resolution and the unique capability for simultaneous multi-field measurements. These attributes make them indispensable for deciphering extreme plasma dynamics in laboratory astrophysics. This review systematically elaborates on the physical principles and inversion methodologies of key optical diagnostics, including Nomarski interferometry, shadowgraphy, and Faraday rotation. Highlighting frontier progress by our team, we showcase the application of these techniques in analyzing jet collimation mechanisms, turbulent magnetic reconnection, collisionless shocks, and particle acceleration. Future trajectories for optical diagnostic development are also discussed.

Lights doi: 10.3390/lights1010002

Authors: Songzhi Tian Zijia Feng Hao Zhang Qiaofeng Tan Liqun Sun

Computer holography enables precise modulation of optical fields, facilitating advanced applications such as optical manipulation, micro-/nanofabrication, and high-resolution three-dimensional displays. However, noise remains one of the most critical challenges, as it significantly reduces the accuracy and visual quality of the reconstructed optical fields. Over the past decades, substantial research has been devoted to identifying noise sources and developing a wide range of suppression techniques. In this article, we present a systematic analysis of the origins and characteristics of noise in computer holography, structured based on computational methods, device characteristics, and system configurations. The representative suppression strategies aimed at enhancing holographic reconstruction quality are investigated. This study aims to deepen the understanding of noise characteristics and provide valuable insights and guidance for future developments in hologram optimization, system integration, and high-performance holographic reconstruction techniques.

Lights doi: 10.3390/lights1010001

Authors: Dimitar Rangelov Sierd Waanders Kars Waanders Maurice van Keulen Radoslav Miltchev

Three-dimensional (3D) reconstruction has become a fundamental technology in applications ranging from cultural heritage preservation and robotics to forensics and virtual reality. As these applications grow in complexity and realism, the quality of the reconstructed models becomes increasingly critical. Among the many factors that influence reconstruction accuracy, the lighting conditions at capture time remain one of the most influential, yet widely neglected, variables. This review provides a comprehensive survey of classical and modern 3D reconstruction techniques, including Structure from Motion (SfM), Multi-View Stereo (MVS), Photometric Stereo, and recent neural rendering approaches such as Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS), while critically evaluating their performance under varying illumination conditions. We describe how lighting-induced artifacts such as shadows, reflections, and exposure imbalances compromise the reconstruction quality and how different approaches attempt to mitigate these effects. Furthermore, we uncover fundamental gaps in current research, including the lack of standardized lighting-aware benchmarks and the limited robustness of state-of-the-art algorithms in uncontrolled environments. By synthesizing knowledge across fields, this review aims to gain a deeper understanding of the interplay between lighting and reconstruction and provides research directions for the future that emphasize the need for adaptive, lighting-robust solutions in 3D vision systems.