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The Jiangmen Underground Neutrino Observatory (JUNO) experiment is the next-generation neutrino experiment that aims at exploring the neutrino mass hierarchy problem. Located 700 m underground in Jiangmen, China, JUNO's central detector is an acrylic sphere filled with 20 kt of liquid scintillator with linear alkylbenzene (LAB) as solvent. To achieve the unprecedented energy resolution of σ E / E ⩽ 3 % , the LAB used in JUNO is required for excellent transparency at the wavelength around 430 nm. In cooperation with Jinling Petrochemical (Nanjing), the newly developed LAB shows an improved attenuation length. The resulting transparency of the LAB requires higher sensitivity in measurement. We have, therefore, upgraded the apparatus and the analysis method for higher precision. In this article, we present the upgraded apparatus and the analysis on improving the measurement error. Among the many new samples, the type NJ66 yields an attenuation length of approximately 30 m. The analysis has applied statistical methods with Monte Carlo simulations. A new model is proposed to account for the deviations between data and the Beer–Lambert model. The long attenuation length of LAB observed in this study suggests feasibility to reach the goal on energy resolution required by JUNO. [ABSTRACT FROM AUTHOR]

High transparency Ce3+-doped dense gadolinium aluminum borosilicate (GS x) glass scintillator was synthesized in reducing atmosphere by melt-quenching process. The transmittance of the glasses exceeds 80%, and the light attenuation length is between 5-20 cm within the range of 400–600 nm. GS x glass shows an photoluminescence (PL) in range of 350–550 nm, with the strongest emission peak around 420 nm. The PL decay time of GS x glasses is around 37 ns, with a maximum PL quantum yield of 44.05%. The integrated X-ray excited luminescence (XEL) intensity of GS S glass is 52.5% compared with BGO crystal. For X-ray imaging, GS L glass based imaging system shows a high spatial resolution of 9 lp/mm, which is comparable to CsI:Tl X-ray detectors. Under γ-ray, the highest light yield of GS S glass is 1235 ph/MeV with an energy resolution of 24.0% at 662 keV, close to that of BSO crystal. The scintillation decay time of GS x glasses is consisted of fast (∼100 ns) and slow (∼560 ns) components. In thermally stimulated luminescence (TSL), GS S glass exhibits a strong TSL peak at approximately 440 K, with low intensity shoulders at about 520 K, implying different types of defects and traps. Therefore, GS x glass has promising prospects for X-ray imaging and high-energy physics applications. [ABSTRACT FROM AUTHOR]

The underwater imaging environment is very different from land, and some common land image enhancement methods are often not applicable to the underwater environment. This paper proposes a two-step underwater image enhancement method. White balance is a commonly used color correction method. In underwater environments, the traditional white balance method has certain limitations and results in severe color bias. This is caused by the faster attenuation of red light in underwater environments. We develop a new white balance method based on the assumption of the gray world method. A red correction module is embedded in the method, which is more suitable for underwater environments. For contrast correction, we design an illuminance correction method based on the Retinex model. The method significantly reduces the computational burden compared to traditional methods, while enhancing the brightness and contrast of the images. In addition, most of the current underwater image enhancement methods deal with color and contrast issues separately. However, these two factors influence each other, and processing them separately may lead to suboptimal results. Therefore, we investigate the relationship between color and contrast and propose a trade-off method. Our method integrates color and contrast within a histogram framework, achieving a balanced enhancement of both aspects. To avoid chance, we utilized four datasets, each containing 800 randomly selected images for metric testing. On the five non-referential metrics, three firsts and two seconds were ranked. Our method ranked second on two referenced metrics. Superior results were also achieved in runtime comparisons. Finally, we further demonstrate the superiority of our method through detailed demonstrations and ablation experiments. [ABSTRACT FROM AUTHOR]

Underwater images typically exhibit low quality due to complex imaging environments, which impede the development of the Space-Air-Ground-Sea Integrated Network (SAGSIN). Existing physical models often ignore the light absorption and attenuation properties of water, making them incapable of resolving details and resulting in low contrast. To address this issue, we propose the attenuated incident optical model and combine it with a background segmentation technique for underwater image restoration. Specifically, we first utilize the features to distinguish the foreground region of the image from the background region. Subsequently, we introduce a background light layer to improve the underwater imaging model and account for the effects of non-uniform incident light. Afterward, we employ a new maximum reflection prior in the estimation of the background light layer to achieve restoration of the foreground region. Meanwhile, the contrast of the background region is enhanced by stretching the saturation and brightness components. Extensive experiments conducted on four underwater image datasets, using both classical and state-of-the-art (SOTA) algorithms, demonstrate that our method not only successfully restores textures and details but is also beneficial for processing images under non-uniform lighting conditions. [ABSTRACT FROM AUTHOR]

Underwater target information is submerged in background scattered light due to the scattering effect of suspended particles in turbid water. In order to improve the quality of target imaging in turbid water, this paper proposes a method for achieving clear underwater target imaging using the second Lorentz depolarization index based on a dual division of focal plane Mueller matrix imaging system. This method can directly quantify the attenuation of light in water, and then solve a clear image of the target. Experimental results on underwater targets of different materials (plastic, paper, metal) show that the method proposed can effectively remove background scattering light while enhancing image contrast, ultimately improving the imaging quality of targets in turbid water. [ABSTRACT FROM AUTHOR]

LED packaging miniaturization has raised more requirements for LED materials. As a material contacting the LED chip directly, the reliability of LED non-conductive adhesive has also garnered increasing attention. This study optimized the formula for non-conductive adhesives for an imidazole curing system. The optimized composition of the adhesive is 25%wt for the curing agent and 30%wt for the silica. The prepared non-conductive adhesive has a 7-day pot life and 9-month storage stability. The shear strength reached 87 g and 72 g at 25 °C and 160 °C, respectively. The reliability of the LED modules packaged with the non-conductive adhesive was researched. The green and blue light intensity change was 4.7%, 43.7%, respectively, indicating good anti-aging properties. The blue light decay was mainly due to adhesive aging. The non-conductive adhesive effectively prevented "caterpillar" growth. This provides useful and practical guidelines for industry for applications of adhesive in different packages. [ABSTRACT FROM AUTHOR]

Underwater images suffer from blurred details and color distortion due to light attenuation from scattering and absorption. Current underwater image enhancement (UIE) methods overlook the effects of forward scattering, leading to difficulties in addressing low contrast and blurriness. To address the challenges caused by forward and backward scattering, we propose a novel variational-based adaptive method for removing scattering components. Our method addresses both forward and backward scattering and effectively removes interference from suspended particles, significantly enhancing image clarity and contrast for underwater applications. Specifically, our method employs a backward scattering pre-processing method to correct erroneous pixel interferences and histogram equalization to remove color bias, improving image contrast. The backward scattering noise removal method in the variational model uses horizontal and vertical gradients as constraints to remove backward scattering noise. However, it can remove a small portion of forward scattering components caused by light deviation. We develop an adaptive method using the Manhattan Distance to completely remove forward scattering. Our approach integrates prior knowledge to construct penalty terms and uses a fast solver to achieve strong decoupling of incident light and reflectance. We effectively enhance image contrast and color correction by combining variational methods with histogram equalization. Our method outperforms state-of-the-art methods on the UIEB dataset, achieving UCIQE and URanker scores of 0.636 and 2.411, respectively. [ABSTRACT FROM AUTHOR]

We propose a framework for classifying acoustic scenes utilizing distributed sound sensor devices capable of sound-to-light conversion, which we term as Blinkies. These Blinkies can convert acoustic signals into varying intensities of light via an inbuilt light-emitting diode. By using Blinkies, we can aggregate the spatial acoustic information across a wide region by recording the fluctuating light intensities of numerous Blinkies distributed throughout the region. Nonetheless, the signal communicated is subject to the bandwidth limitation imposed by the frame rate of the video camera, typically capped at 30 frames per second. Our objective is to refine the process of transforming sound into light for the purpose of acoustic scene classification within these bandwidth confines. While traversing through the air, a light signal is affected by inherent physical limitations such as the attenuation of light and interference from noise. To account for these factors, we have integrated these physical constraints into differentiable physical layers. This approach enables us to jointly train a pair of deep neural networks for the conversion of sound to light and for the classification of acoustic scenes. Our simulation studies, which employed the SINS database for acoustic scene classification, demonstrated that our proposed framework outperforms the previous one that utilized Blinkies. These findings emphasize the effectiveness of Blinkies in the field of acoustic scene classification. [ABSTRACT FROM AUTHOR]

Photocatalytic membranes (PM) have been investigated as an antifouling strategy for membrane separation processes. Coating ceramic membranes with photocatalytic layers can provide a highly active surface capable of degrading foulants and smaller molecules improving the membrane's performance when the surface is irradiated by a suitable light. Nevertheless, the coating process often leads to pore blockage due to the formation or deposition of thick layers of photocatalyst on membrane surfaces, which modifies the original membranes' average pore size and reduces membrane permeability. A facile sol-gel dip coating process was used to produce PM without modifying the original surface morphology of alumina microfiltration membranes. A 3.7-fold increase in permeate volume after 90 min of permeation of an acetaminophen solution in continuous filtration mode under UV light (λ = 365 nm LED, 10W) using titania as photocatalyst compared to the bare alumina membrane without irradiation. Furthermore, fouling modelling proved a reduction in the fouling constant, while fouling mechanisms were not modified. Raman analysis showed 100% anatase formed on the membrane surface. Although membranes could remove up to 87% TOC for oily wastewater filtration, antifouling capabilities for this type of effluent were not observed for the photocatalytic membranes mainly due to fouling inside the pores and light attenuation due to the thick fouling layer on the membrane surface. [ABSTRACT FROM AUTHOR]

As large‐area and optically transparent photon harvesting devices, luminescent solar concentrators (LSCs) are promising candidates for building‐integrated photovoltaics owing to their high transmittance and resistance to shadowing effects existing in solar cells. Up to now, there are still many challenges in the practical application of LSCs: 1) Reabsorption loss is inevitable during photoluminescence transmission due to indirect illumination of solar cells in LSC system. 2) Satisfactory energy harvesting cannot be achieved in rainy conditions due to the substantial attenuation of the incident light intensity. 3) Evaporation residue on the surface of LSCs leads to device performance degradation. Pioneering researches and feasible strategies for reabsorption loss suppression, energy harvesting in rainy days as well as self‐cleaning property are still lacking demonstration. In this work, reabsorption loss is suppressed based on advanced structural LSCs with universally applicable optical spacer layer. Then the integrated LSCs with droplet‐based electricity generator (DEG) are proposed for the first time. Such DEG‐LSCs not only realize synergistic harvesting of solar and raindrop energy, but also possess self‐cleaning properties. Finally, a self‐powered temperature and humidity sensing system is designed and demonstrated to provide feasible ideas for the practical application of DEG‐LSCs in self‐powered intelligent buildings. [ABSTRACT FROM AUTHOR]

Currently, Underwater Optical Wireless Communication (UWOC) systems employing Light-Emitting Diodes (LEDs) as transmitters face significant challenges due to the absorption and scattering properties of underwater environments. This paper addresses these challenges by optimizing communication performance through a detailed link budget analysis for LED-based UWOCs. Specifically, this study focuses on selecting the optimum Lambertian order (OLO) for LED beam shaping to maximize the received optical power and minimize signal attenuation and lost light energy. The methodology involves theoretical modeling and simulation to explore various Lambertian orders within a Diffuse Line-of-Sight (D-LoS) scenario. The key parameters include the LED characteristics, underwater channel properties, and receiver parameters. This study investigated the influence of underwater channel scatterings and absorption, as well as the impact of different water types on the communication range. Additionally, various transmitter parameters (beam divergence angle, optical power) and receiver parameters (incident angle, field of view angle, PD active area) were examined. The results demonstrate the critical role of Lambertian order selection in enhancing the communication range of UWOCs. A more realistic power link budget is developed, demonstrating the impact of the optimized OLO on the received optical power. By comparing the received power with the required receiver sensitivity, this study determines the feasible communication range, providing valuable insights into improving UWOC systems. This research significantly contributes to the field by identifying the optimal Lambertian order that maximizes the received optical power while minimizing energy loss, thereby enhancing the efficiency and range of underwater optical wireless communication. [ABSTRACT FROM AUTHOR]

This study focuses on how the chemical makeup of the glass' basic components affects the glass' structure and optical characteristics. Four glass samples have been created for this purpose using the chemical formula; 94 mol% Na2B4O7—(6-x) mol% CeO2 – x mol% Ce(NO3)3, where 0 ≤ x ≤ 6. The Ce cations in this formula have two separate chemical sources, CeO2 and Ce(NO3)3, with CeO2 eventually being replaced by Ce(NO3)3. The standard melt-quenching technique was used to prepare the studied glasses. While X-ray direction (XRD), differential scanning calorimetry (DSC), and UV–vis were used for the structural and optical characterizations. XRD patterns revealed the short-range order glass networks for the prepared samples, while DSC thermograms showed that when CeO2 was replaced with Ce(NO3)3, the glass transition temperature (Tg) decreased, causing the glass stability to improve. The optical characterization resulted in the finding that when CeO2 was replaced with Ce(NO3)3, the Urbach's energy increased with a decrease in bandgap energies, which reflects an increase in the glass homogeneity. Finally, the results may imply that Ce(NO3)3-based glasses can be proposed for usage in applications for UV blockers, radiation shielding, light attenuation, and n-type semiconductors. [ABSTRACT FROM AUTHOR]

Underwater image enhancement technology is crucial for the human exploration and exploitation of marine resources. The visibility of underwater images is affected by visible light attenuation. This paper proposes an image reconstruction method based on the decomposition–fusion of multi-channel luminance data to enhance the visibility of underwater images. The proposed method is a single-image approach to cope with the condition that underwater paired images are difficult to obtain. The original image is first divided into its three RGB channels. To reduce artifacts and inconsistencies in the fused images, a multi-resolution fusion process based on the Laplace–Gaussian pyramid guided by a weight map is employed. Image saliency analysis and mask sharpening methods are also introduced to color-correct the fused images. The results indicate that the method presented in this paper effectively enhances the visibility of dark regions in the original image and globally improves its color, contrast, and sharpness compared to current state-of-the-art methods. Our method can enhance underwater images in engineering practice, laying the foundation for in-depth research on underwater images. [ABSTRACT FROM AUTHOR]

Turbidity, as a key indicator of water quality linked to underwater light attenuation, is crucial for evaluating water quality. Control in high-turbidity water environments plays a critical role in navigable rivers. For this purpose, our study proposed a framework for analyzing the spatio-temporal variation of turbidity and its driving factors in a navigable and turbid river using in situ measurement data, satellite data, socioeconomic data, a power index function model, and correlation analysis. The results show that the proposed model is feasible for quantitative turbidity monitoring of the Xitiaoxi River. Its upstream turbidity is lower than downstream, with seasonal averages for spring, summer, autumn, and winter of 93.9, 111.3, 113.5, and 120.9 NTU, respectively. Furthermore, the turbidity in the middle and lower reaches of the Xitiaoxi River continuously increased before 2005 and began to decline after 2005 due to the policy of mining moratorium. This trend is especially noticeable at monitoring points along the main stream of the Xitiaoxi River, such as downstream of the Xitiaoxi River (S1), Gangkou station (S2), middle reaches of the Xitiaoxi River (S4), Hengtangcun station (S6), upper stream of the Xitiaoxi River (S7), and Huxi River (S8). Mining and shipping have significantly contributed to the turbidity of the target river. This framework offers a practical approach for assessing the environmental impacts of both natural and anthropogenic factors, thereby providing valuable insights for river management practices. [ABSTRACT FROM AUTHOR]

The recovery of submerged macrophytes is crucial for lake restoration. However, Myriophyllum spicatum usually shows an overgrowth and inhibits the growth of Vallisneria denseserrulata via light shading in many restored shallow lakes after the plant transplantation. So far, harvesting M. spicatum is the primary method to alleviate these shading effects in post-restoration lakes. Nevertheless, the effects of harvesting on the growth of V. denseserrulata and water quality are poorly elaborated. In this study, we conducted a mesocosm experiment, including both monoculture and polyculture groups, to investigate the response of V. denseserrulata growth, light climate, and nutrient concentrations in the water with M. spicatum harvesting. Moreover, the growth and morphology of M. spicatum were also examined. We hypothesized that M. spicatum harvesting would enhance the growth of V. denseserrulata and improve both the light climate and water quality. Our results showed that harvesting M. spicatum in the polyculture mesocosms substantially enhanced the relative growth rate (RGR) of V. denseserrulata compared to the non-harvesting controls. Moreover, harvesting M. spicatum reduced the light attenuation coefficient at 30 cm depth; however, the concentrations of chlorophyll-a, total nitrogen, and total phosphorus did not change significantly. As for M. spicatum, harvesting inhibited the growth of main stem and root but did not significantly affect the cumulative weight and RGR of M. spicatum. In contrast, the presence of V. denseserrulata decreased cumulative weight and RGR while promoting the root parameters of M. spicatum. Our findings imply that harvesting overgrowth nuisance submerged macrophyte species (e.g., M. spicatum) can improve the light climate and reduce its root growth, thereby enhancing the growth of target macrophyte species like V. denseserrulata without changes in the water quality which provides valuable insights for post-restoration lake management. [ABSTRACT FROM AUTHOR]

Light penetration depth, as a characteristic parameter reflecting light attenuation and transmission in biological tissues, has been applied in nondestructive detection of fruits and vegetables. Recently, with emergence of new optical detection technologies, researchers have begun to explore methods evaluating optical properties of double-layer or even multilayer fruit and vegetable tissues due to the differences between peel and pulp in the chemical composition and physical properties, which has gradually promoted studies on light penetration depth. A series of demonstrated research on light penetration depth could ensure the accuracy of the optical information obtained from each layer of tissue, which is beneficial to enhance detection accuracy for quality assessment of fruits and vegetables. Therefore, the aim of this review is to give detailed outlines about the theory and principle of light penetration depth based on several emerging optical detection technologies and to focus primarily on its applications in the field of quality evaluation of fruits and vegetables, its future applicability in fruits and vegetables and the challenges it may face in the future. [ABSTRACT FROM AUTHOR]

Variable fluorescence measurements provide automatic and rapid estimates of electron-based photosynthesis. However, electron transport rates (ETR) to phytoplankton production vary greatly due to biological physiology and environmental stress along the Changjiang River to the adjacent seawater, where the turbid riverside, nutrient-rich river plume, and seawater are divided by the sediment front and plume front. We assessed the light responses of ETR, 18O-labeled gross primary production (GPP), and O2-based net community production (NCP) during the bloom season at the Changjiang River Estuary. The distribution of phytoplankton and potential photosynthesis efficiency were positively correlated, exhibiting offshore decreasing trends and peaking around the upwelling areas. The conversion factors of ΦGPP (mol e- per mol O2 production) ranged from 0.8 to 31.8, and ΦNCP ranged from 0.9 to 36.9, respectively. Φe: O increased from the nutrient-rich Changjiang diluted water (CDW) to the nutrient-poor seawater. Although ΦGPP and ΦNCP were linearly related to non-photochemical quenching (R² > 0.5) in the CDW, this relationship was decoupled in offshore seawater due to P-limitation, light stress, and shifts in the phytoplankton community. A better relationship between ΦGPP and ΦNCP was quantified usingmultiple linear analyses with the light attenuation coefficient (Kd), euphotic depth (Zeu), salinity, nutrients, and temperature (p< 0.001). This improvement in accuracy represents a major step forward for widespread and accurate fluorometry-based GPP and NCP applications at high temporal and spatial resolutions in the future. [ABSTRACT FROM AUTHOR]

Mechanical load imparted to tissue, for example via handheld imaging probes, leads to tissue deformation, altering the distribution of tissue microstructure and, consequently, attenuation of light and image formation in optical imaging. In mechanically heterogeneous tissue, the load can result in spatially varying deformation and, therefore, spatially varying changes in the attenuation of light, which may provide additional image contrast. To investigate this potential, an assessment of the spatially resolved impact of mechanical deformation of the tissue on optical imaging is critical; however, it is challenging to incorporate stress mapping into optical imaging without obscuring the detection of photons. To address this, we present the novel integration of stress imaging using optical palpation with attenuation imaging based on optical coherence tomography (OCT). The method was implemented using a compliant silicone sensor incorporated into a custom handheld OCT probe, providing two-dimensional stress imaging with concurrent attenuation imaging. Attenuation imaging with varying mechanical loads was demonstrated on 19 tissue regions acquired from eight freshly excised human breast specimens. The results demonstrated distinct characteristics for different breast tissue types: benign stroma showed relatively large increases in attenuation (e.g., ∼0.3 to 0.4 mm−1/kPa) over a low stress range (∼2 to 10 kPa), while cancerous tissue showed markedly small increases in attenuation (e.g., ∼0.005 to 0.02 mm−1/kPa) mainly over a medium to high stress range (∼10 to 90 kPa). The integration of stress imaging with attenuation imaging provided a pilot assessment of the spatially resolved impact of tissue mechanical heterogeneity on optical attenuation, providing novel image contrast by encoding variations in mechanical properties on optical attenuation in tissue. [ABSTRACT FROM AUTHOR]

To address the issue of large amount of computation, slow arithmetic speed of the current defogging video model that can only process single image, this article proposed a defogging simplified model for vehicular video based on guide filtering. Firstly, the law of atmospheric attenuation index of light energy and physical model of haze degradation were introduced, then the defogging simplified model was obtained by correcting the attenuation law and the degradation model, and two parameters of the fog concentration factor and ambient air light were determined with the gray scale method and guided filtering method. Finally, the model was verified by computer simulation. The simulation results show that the defogging simplified model works effectively to defog for continuous video. [ABSTRACT FROM AUTHOR]

Prochlorococcus marinus, the smallest picocyanobacterium, comprises multiple clades occupying distinct niches, currently across tropical and sub-tropical oligotrophic ocean regions, including Oxygen Minimum Zones. Ocean warming may open growth-permissive temperatures in new, poleward photic regimes, along with expanded Oxygen Minimum Zones. We used ocean metaproteomic data on current Prochlorococcus marinus niches, to guide testing of Prochlorococcus marinus growth across a matrix of peak irradiances, photoperiods, spectral bands and dissolved oxygen. MED4 from Clade HLI requires greater than 4 h photoperiod, grows at 25 μmol O2 L-1 and above, and exploits high cumulative diel photon doses. MED4, however, relies upon an alternative oxidase to balance electron transport, which may exclude it from growth under our lowest, 2.5 μmol O2 L-1, condition. SS120 from clade LLII/III is restricted to low light under full 250 μmol O2 L-1, shows expanded light exploitation under 25 μmol O2 L-1, but is excluded from growth under 2.5 μmol O2 L-1. Intermediate oxygen suppresses the cost of PSII photoinactivation, and possibly the enzymatic production of H2O2 in SS120, which has limitations on genomic capacity for PSII and DNA repair. MIT9313 from Clade LLIV is restricted to low blue irradiance under 250 μmol O2 L-1, but exploits much higher irradiance under red light, or under lower O2 concentrations, conditions which slow photoinactivation of PSII and production of reactive oxygen species. In warming oceans, range expansions and competition among clades will be governed not only by light levels. Short photoperiods governed by latitude, temperate winters, and depth attenuation of light, will exclude clade HLI (including MED4) from some habitats. In contrast, clade LLII/III (including SS120), and particularly clade LLIV (including MIT9313), may exploit higher light niches nearer the surface, under expanding OMZ conditions, where low O2 relieves the stresses of oxidation stress and PSII photoinhibition. [ABSTRACT FROM AUTHOR]

Aquatic birds are notable among the global avifauna for living in environments exposed to large amounts of light. Despite growing evidence that visual adaptations to light underly the ecology and evolution of the avian tree of life, no comprehensive comparative analysis of visual acuity as approximated by eyes size exists for the global aquatic avifauna. Here, I use Stanley Ritland's unpublished dataset of measurements for axial length collected from museum specimens to explore the ecology and evolution of eye size variation for half of the aquatic avifauna (N = 464 species). After correcting for body mass allometry and incorporating phylogenetic relationships, aquatic species had significantly smaller eyes compared to terrestrial species. Furthermore, species using hyperopic foraging manoeuvres, exhibiting carnivorous and insectivorous diets, and displaying nocturnal behaviour had larger eyes. Plunge‐divers (e.g. boobies and tropic birds) and stalkers (e.g. herons) had the largest relative eye sizes, especially species identifying prey at higher altitudes or longer distances. Underwater pursuit‐divers foraging at greater depths had larger eyes, likely due to the dramatic attenuation of light in the deep ocean. Overall, residual eye size was phylogenetically conserved (l = 0.94), with phylogeny alone explaining 62% of residual eye size variation. Collectively, these results suggest that the relatively bright environments found in aquatic ecosystems negate the adaptive benefits of costly metabolic investments associated with developing and maintaining larger eyes, while also reducing the potential occurrence of disability glare. Strong correlations between eye size and foraging ecology in different aquatic environments corroborate similar comparative studies of terrestrial birds and underscore the central role that vision has played in driving the ecology and evolution of the global avifauna. [ABSTRACT FROM AUTHOR]

Artificial light can be used to deter unwanted non‐target catch (bycatch) from fishing gear, which is thought to be achieved by repelling bycatch, or highlighting escape routes on nets. To select for responses in bycatch species, light should (1) cause the bycatch species to avoid capture, and (2) not invoke the same reaction in target species. One way to maximise the chance of a bycatch species responding to light is to ensure the light colour used is more visible to bycatch species. Some studies have considered the visual sensitivity of certain species to address this. In particular, the wavebands of light that a species is sensitive to. However, using this measurement alone is incomplete as it does not consider other factors that affect visibility, such as the ambient light spectrum, and wavelength‐dependant light attenuation in different water types and depths. To account for these variables, and to more accurately predict how both target and bycatch species view light colours in a fishing context, we used a model of the vision of commercially relevant species in fisheries across the world. From this, we show whether a light colour is more visible to a bycatch species compared to a target species in a particular depth and water type, and how modelling can be used to make informed assessments of the selection of relevant light colours in fishing. We also discuss the limitations of using vision models alone and the need for corresponding behaviour and/or fishing trials with lights. [ABSTRACT FROM AUTHOR]

A decrease in atmospheric CO2 partial pressure (pCO2) is considered an important prerequisite for the onset and intensification of Northern Hemisphere Glaciation (NHG). However, how the ocean sequestered missing CO2 during the NHG is still uncertain. Changes in surface productivity and deep ventilation in the Southern Ocean (SO) have been proposed to explain the variations in atmospheric pCO2 over the last eight glacial cycles, but it is unclear whether these mechanisms contributed to the decrease in atmospheric pCO2 during the NHG. Using titanium-normalized contents and mass accumulation rates of biogenic opal and total organic carbon from the International Ocean Discovery Program (IODP) Expedition 374 Site U1524A, we reconstruct the productivity in the Ross Sea, Antarctica, from 3.3 Ma to 2.4 Ma. The productivity records exhibit a long-term decreasing trend and several distinct phased evolutionary features. Specifically, the local productivity fluctuated dramatically during 3.3–3.0 Ma, decreased gradually during 3.0–2.6 Ma, and remained relatively constant during 2.6–2.4 Ma. By comparing productivity with its potential influences, we infer that the phased and long-term evolutions of productivity were mainly controlled by changes in deep ocean ventilation. Sea ice expansion might have decreased productivity during 3.3–3.0 Ma by light attenuation. Changes in eolian dust input have little effect on productivity. Further analysis revealed no coupling linkage between productivity and atmospheric pCO2, indicating that the productivity in the SO Antarctic Zone (AZ) was not the main factor controlling the atmospheric CO2 decrease during the NHG. To improve our understanding of the role of SO processes in the NHG, further studies should focus on the potential influences of deep ocean ventilation on atmospheric pCO2 in the AZ, and similar studies should also be extended to the sea area in the Subantarctic Zone. [ABSTRACT FROM AUTHOR]

Over the past decades, changes in the light conditions of coastal waters, induced by darkening due to water browning, has been gaining momentum within the scientific community. Although there is a general agreement that the darkening of coastal water is caused by shifts in climatic conditions, the actual links between trends in light attenuation caused by water browning and drivers are still being debated, as causality relationships are often assessed through the use of indirect evidence. In this study, we employed algorithmically processed remote-sensing and modelled data with substantially higher resolution than in-situ data as well as in-situ data. Two decades (1998-2007 and 2008-2018) were compared to test impact of parameters known to be altered by climate change, such as freshwater runoff, mobilisation of soil dissolved organic carbon, and alteration of freshwatersaline water balance in coastal waters, on darkening of coastal waters of the Gulf of Riga, Baltic Sea. The study results indicated that the most likely cause of the observed darkening of coastal waters in the study region was the mildening of winter conditions. At the same time, the study results highlighted the urgent need for detailed, time-sensitive data, since none of the conclusions drawn from the analyses of available data were unequivocal. [ABSTRACT FROM AUTHOR]

Vision-based underwater exploration is crucial for marine research. However, the degradation of underwater images due to light attenuation and scattering poses a significant challenge. This results in the poor visual quality of underwater images and impedes the development of vision-based underwater exploration systems. Recent popular learning-based Underwater Image Enhancement (UIE) methods address this challenge by training enhancement networks with annotated image pairs, where the label image is manually selected from the reference images of existing UIE methods since the groundtruth of underwater images do not exist. Nevertheless, these methods encounter uncertainty issues stemming from ambiguous multiple-candidate references. Moreover, they often suffer from local perception and color perception limitations, which hinder the effective mitigation of wide-range underwater degradation. This paper proposes a novel NUAM-Net (Novel Underwater Image Enhancement Attention Mechanism Network) that addresses these limitations. NUAM-Net leverages a probabilistic training framework, measuring enhancement uncertainty to learn the UIE mapping from a set of ambiguous reference images. By extracting features from both the RGB and LAB color spaces, our method fully exploits the fine-grained color degradation clues of underwater images. Additionally, we enhance underwater feature extraction by incorporating a novel Adaptive Underwater Image Enhancement Module (AUEM) that incorporates both local and long-range receptive fields. Experimental results on the well-known UIEBD benchmark demonstrate that our method significantly outperforms popular UIE methods in terms of PSNR while maintaining a favorable Mean Opinion Score. The ablation study also validates the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Underwater imaging presents unique challenges, notably color distortions and reduced contrast due to light attenuation and scattering. Most underwater image enhancement methods first use linear transformations for color compensation and then enhance the image. We observed that linear transformation for color compensation is not suitable for certain images. For such images, non-linear mapping is a better choice. This paper introduces a unique underwater image restoration approach leveraging a streamlined convolutional neural network (CNN) for dynamic weight learning for linear and non-linear mapping. In the first phase, a classifier is applied that classifies the input images as Type I or Type II. In the second phase, we use the Deep Line Model (DLM) for Type-I images and the Deep Curve Model (DCM) for Type-II images. For mapping an input image to an output image, the DLM creatively combines color compensation and contrast adjustment in a single step and uses deep lines for transformation, whereas the DCM employs higher-order curves. Both models utilize lightweight neural networks that learn per-pixel dynamic weights based on the input image's characteristics. Comprehensive evaluations on benchmark datasets using metrics like peak signal-to-noise ratio (PSNR) and root mean square error (RMSE) affirm our method's effectiveness in accurately restoring underwater images, outperforming existing techniques. [ABSTRACT FROM AUTHOR]

Owing to the high cost of commercial optical sensors, there is a need to develop low‐cost optical sensing packages to expand monitoring of aquatic environments, particularly in under‐resourced regions. Visual methods to monitor the optical properties of water, like the Secchi disk and Forel‐Ule color scale, remain in use in the modern era owing to their simplicity, low‐cost and long history of use. Yet, recent years have seen advances in low‐cost, electronic‐based optical sensing. Here, the designs of a miniaturized hand‐held device (mini‐Secchi disk) that measures the Secchi depth and Forel‐Ule color are updated. We then extend the device by integrating a small electronic sensing package (Arduino‐based) into the Secchi disk, for vertical profiling, combining historic and modern methods for monitoring the optical properties of water into a single, low‐cost sensing device, that measures positioning (GPS), light spectra, temperature, and pressure. It is charged and transfers data wirelessly, is encased in epoxy resin, and can be used to derive vertical profiles of spectral light attenuation and temperature, in addition to Secchi depth and Forel‐Ule color. We present data from a series of deployments of the package, compare its performance with commercially available instruments, and demonstrate its use for validation of satellite remotely sensed data. Our designs are made openly available to promote community‐based development and have potential in communicating and teaching science, participatory science, and low‐cost monitoring of aquatic environments. [ABSTRACT FROM AUTHOR]

Optical fiber is increasingly used for both communication and distributed sensing of temperature and strain in environmental studies. In this work, we demonstrate the viability of unreinforced fiber tethers (bare fiber) for Raman-based distributed temperature sensing in deep ocean and deep ice environments. High-pressure testing of single-mode and multimode optical fiber showed little to no changes in light attenuation over pressures from atmospheric to 600 bars. Most importantly, the differential attenuation between Stokes and anti-Stokes frequencies, critical for the evaluation of distributed temperature sensing, was shown to be insignificantly affected by fluid pressures over the range of pressures tested for single-mode fiber, and only very slightly affected in multimode fiber. For multimode fiber deployments to ocean depths as great as 6000 m, the effect of pressure-dependent differential attenuation was shown to impact the estimated temperatures by only 0.15 °K. These new results indicate that bare fiber tethers, in addition to use for communication, can be used for distributed temperature or strain in fibers subjected to large depth (pressure) in varying environments such as deep oceans, glaciers and potentially the icy moons of Saturn and Jupiter. [ABSTRACT FROM AUTHOR]

A decrease in atmospheric CO2 partial pressure (pCO2) is considered an important prerequisite for the onset and intensification of Northern Hemisphere Glaciation (NHG). However, how the ocean sequestered missing CO2 during the NHG is still uncertain. Changes in surface productivity and deep ventilation in the Southern Ocean (SO) have been proposed to explain the variations in atmospheric pCO2 over the last eight glacial cycles, but it is unclear whether these mechanisms contributed to the decrease in atmospheric pCO2 during the NHG. Using titanium-normalized contents and mass accumulation rates of biogenic opal and total organic carbon from the International Ocean Discovery Program (IODP) Expedition 374 Site U1524A, we reconstruct the productivity in the Ross Sea, Antarctica, from 3.3 Ma to 2.4 Ma. The productivity records exhibit a long-term decreasing trend and several distinct phased evolutionary features. Specifically, the local productivity fluctuated dramatically during 3.3–3.0 Ma, decreased gradually during 3.0–2.6 Ma, and remained relatively constant during 2.6–2.4 Ma. By comparing productivity with its potential influences, we infer that the phased and long-term evolutions of productivity were mainly controlled by changes in deep ocean ventilation. Sea ice expansion might have decreased productivity during 3.3–3.0 Ma by light attenuation. Changes in eolian dust input have little effect on productivity. Further analysis revealed no coupling linkage between productivity and atmospheric pCO2, indicating that the productivity in the SO Antarctic Zone (AZ) was not the main factor controlling the atmospheric CO2 decrease during the NHG. To improve our understanding of the role of SO processes in the NHG, further studies should focus on the potential influences of deep ocean ventilation on atmospheric pCO2 in the AZ, and similar studies should also be extended to the sea area in the Subantarctic Zone. [ABSTRACT FROM AUTHOR]

Lakes are a crucial source of drinking water, provide ecological services from fisheries and aquaculture to tourism and are also a critical part of the global carbon cycle. Therefore, it is important to understand how lakes are changing over time. The ESA Ocean Colour Climate Change Initiative (OC-CCI) database allows to study changes in the largest lakes over 1997–2023 period. The Caspian Sea and ten next largest lakes were under investigation. Changes in the phytoplankton biomass (Chl-a), the concentration of particulate matter (bbp(555)), the colored dissolved organic matter, CDOM (adg(412)), and the light diffuse attenuation coefficient in water (Kd(490)) were analyzed. Both increasing and decreasing trends (or no significant trend at all) of studied parameters were observed in these lakes over the study period. In some of the Laurentian Great Lakes the changes in CDOM over the study period were found to be in accordance with the lake water level changes i.e. with the inflow from the catchment. There was difference between the trends of Chl-a and bbp(555) in lakes Michigan and Huron indicating that there may have been shift in phytoplankton community that took place around 2005. The study demonstrated that remote sensing products, like the ones created by ESA OC-CCI, are valuable tools to study behavior of large lakes ecosystems over time. [ABSTRACT FROM AUTHOR]

Photothermal microscopy is a highly sensitive pump-probe method for mapping nanostructures and molecules through the detection of local thermal gradients. While visible photothermal microscopy and mid-infrared photothermal microscopy techniques have been developed, they possess inherent limitations. These techniques either lack chemical specificity or encounter significant light attenuation caused by water absorption. Here, we present an overtone photothermal (OPT) microscopy technique that offers high chemical specificity, detection sensitivity, and spatial resolution by employing a visible probe for local heat detection in the C-H overtone region. We demonstrate its capability for high-fidelity chemical imaging of polymer nanostructures, depth-resolved intracellular chemical mapping of cancer cells, and imaging of multicellular C. elegans organisms and highly scattering brain tissues. By bridging the gap between visible and mid-infrared photothermal microscopy, OPT establishes a new modality for high-resolution and high-sensitivity chemical imaging. This advancement complements large-scale shortwave infrared imaging approaches, facilitating multiscale structural and chemical investigations of materials and biological metabolism. The authors develop overtone photothermal microscopy that leverages a pump-probe detection of second overtone vibrations within the shortwave-infrared (SWIR) window. This technique complements existing large-scale SWIR imaging approaches, offering enhanced resolution and sensitivity for bioimaging applications. [ABSTRACT FROM AUTHOR]

Body colouration, a trait under strong selection, is influenced by the visual background of the environment. The stable influence of depth on visual background dynamics is due to light attenuation along the water column. Depth is also a key factor driving diversification in Sebastes rockfishes, influencing variations in several biological traits. Comparisons between closely related species suggest that brightly coloured species (red, orange, or yellow) tend to inhabit deeper waters and have slower growth rates compared to their shallow-water counterparts with dominance of dark colours (black, brown, or grey). Here, we used 377 photos from 100 Sebastes species, along with recently developed methods of colour quantification and phylogenetic comparative analyses, to assess this trend. Our analyses confirmed the separation of body colouration regarding depth, which was accompanied by differences in growth rates and morphological traits. This indicated that variations in body colourations are included in the ongoing correlational selection process dictated by depth. Analyses of closely related species indicated that depth is an initial driver of colour differentiation and that colour differences do not progressively increase with genetic divergences. We hypothesized that the bright-coloured rockfishes are found in deeper waters because in shallow environments they are more vulnerable to the potential negative effects of UV radiation and higher predation risk, while their predation successes are lessened, in comparison to dark-coloured rockfishes. Overall, this study emphasizes the intricate relationship between genetics, environment, adaptation, and the striking diversity of body colourations observed in Sebastes rockfishes. [ABSTRACT FROM AUTHOR]

Methods based on light field information have shown promising results in depth estimation and underwater image restoration. However, improvements are still needed in terms of depth estimation accuracy and image restoration quality. Previous work on underwater image restoration employed an image formation model (IFM) that overlooked the effects of light attenuation and scattering coefficients in underwater environments, leading to unavoidable color deviation and distortion in the restored images. Additionally, the high blurriness and associated distortions in underwater images make depth information extraction and estimation very challenging. In this paper, we refine the light propagation model and propose a method to estimate the attenuation and backscattering coefficients of the underwater IFM. We simplify these coefficients into distance-related functions and design a relationship between distance and the darkest channel to estimate the water coefficients, effectively suppressing color deviation and distortion in the restoration results. Furthermore, to increase the accuracy of depth estimation, we propose using blur cues to construct a cost for refocusing in the depth direction, reducing the impact of high signal-to-noise ratio environments on depth information extraction, and effectively enhancing the accuracy and robustness of depth estimation. Finally, experimental comparisons show that our method achieves more accurate depth estimation and image restoration closer to real scenes compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Decades of advances in understanding and simulating the polymerization kinetics and structural evolution that arises in free‐radical photopolymerizations of multifunctional monomers are combined into a single, first‐principles 3D model. The model explicitly accounts for polymerization features including diffusion‐controlled kinetics, oxygen inhibition, light attenuation, chain‐length dependent termination, reaction‐diffusion termination, heat transfer, composition and conversion‐dependent material properties, crosslinking effects, and species diffusion. Using the homopolymerization of 1,6‐hexanediol diacrylate as a model system, a minimum of two kinetics experiments performed at different initiation rates are required to fit model parameters. The model accurately predicts known relationships regarding oxygen inhibition, light intensity, and curing temperature for samples of different geometries and boundary conditions. The emphasis of the results herein is placed on the interactions between polymerization features, motivating the importance of a model that accommodates these features all in one simulation. The model is shown to be robust in its handling of thermal boundary conditions, alternative polymerization techniques or mechanisms, and characteristics of 3D voxel formation. The model in this work provides a useful tool for property prediction in a wide variety of applications, most notably coatings, dental materials, industrial photocuring processes, additive manufacturing, and holography, where complex interactions of the various features of polymerization play a substantial role. [ABSTRACT FROM AUTHOR]

Underwater vision technology is crucial for marine exploration, aquaculture, and environmental monitoring. However, the challenging underwater conditions, including light attenuation, color distortion, reduced contrast, and blurring, pose difficulties. Current deep learning models and traditional image enhancement techniques are limited in addressing these challenges, making it challenging to acquire high-quality underwater image signals. To overcome these limitations, this study proposes an approach called adaptive multi-scale multi-color space underwater image enhancement with GAN-physics fusion (AMSMC-UGAN). AMSMC-UGAN leverages multiple color spaces (RGB, HSV, and Lab) for feature extraction, compensating for RGB's limitations in underwater environments and enhancing the use of image information. By integrating a membership degree function to guide deep learning based on physical models, the model's performance is improved across different underwater scenes. In addition, the introduction of a multi-scale feature extraction module deepens the granularity of image information, learns the degradation distribution of different image information of the same image content more comprehensively, and provides useful guidance for more comprehensive data for image enhancement. AMSMC-UGAN achieved maximum scores of 26.04 dB, 0.87, and 3.2004 for PSNR, SSIM, and UIQM metrics, respectively, on real and synthetic underwater image datasets. Additionally, it obtained gains of at least 6.5%, 6%, and 1% for these metrics. Empirical evaluations on real and artificially distorted underwater image datasets demonstrate that AMSMC-GAN outperforms existing techniques, showcasing superior performance with enhanced quantitative metrics and strong generalization capabilities. [ABSTRACT FROM AUTHOR]

Forest plantations most likely promote exotic plant invasion. Using an in situ monitoring method, this study investigated the traits correlated with growth and reproduction of an understory invader, Phytolacca americana L., and ecological factors including understory irradiance, soil stoichiometry and microbial patterns associated with these traits in different exotic plantations of Robinia pseudoacacia L. and Pinus thunbergii Parl. at Mount Lao, Qingdao, China. We found that the traits of P. americana underneath the R. pseudoacacia stand might be situated at the fast side of the trait economic spectrum. The R. pseudoacacia stand appeared to "nurse" P. americana. Furthermore, we intended to explain the nurse effects of R. pseudoacacia stands by examining their ecological factors. First, the R. pseudoacacia stand created understory light attenuation, which matched the sciophilous feature of P. americana. Second, the soil beneath the R. pseudoacacia stand might benefit P. americana more since the soil has greater resource availability. Third, a higher microbial diversity was found in the soil derived from P. americana underneath the R. pseudoacacia stand. A greater abundance of plant pathogens was detected in the soil derived from P. americana in the R. pseudoacacia stand, while more abundant mycorrhizal fungi were detected in the P. thunbergii stand. We speculate that plant pathogens can defend P. americana from aggression from other understory competitors. The mycorrhizal fungi in the P. thunbergii stand might benefit P. americana while simultaneously benefiting other understory plants. Intensive competition from other plants might interfere with P. americana. The potential relationships between plant performance and ecological factors may explain the invasion mechanism of P. americana. The present study provides a novel insight on the facilitative effects of exotic tree plantation on an exotic herb through the modification of soil biota, with implications for the biocontrol of invasive species and forest management and conservation. [ABSTRACT FROM AUTHOR]

This work addresses the modelling of oxygenic photogranules (OPGs), by introducing a mathematical model which describes both the genesis and growth of photogranules and the related treatment process. The photogranule has been modelled as a free boundary domain with radial symmetry, which evolves over time as a result of microbial growth, attachment, and detachment processes. Hyperbolic and parabolic PDEs have been considered to model at mesoscale the transport and growth of sessile biomass and the diffusion and conversion of soluble substrates. The macroscale behavior of the system has been modelled through first order impulsive ordinary differential equations (IDEs), which reproduce a sequencing batch reactor (SBR) configuration. Phototrophic biomass has been considered for the first time in granular biofilms, and cyanobacteria and microalgae have been accounted separately, to model their metabolic differences. To describe the key role of cyanobacteria in the photogranulation process, the attachment velocity of all suspended microbial species has been modelled as a function of the cyanobacteria concentration in suspended form. The model takes into account the main biological processes involved in OPG-based systems: metabolic activity of cyanobacteria, microalgae, heterotrophic and nitrifying bacteria, microbial decay, extracellular polymeric substances (EPS) secretion, symbiotic and competitive interactions between different species, light-dark cycle, light attenuation across the granule, and photoinhibition phenomena. The model has been integrated numerically, and the results show its consistency in describing the photogranule evolution and ecology, and highlight the advantages of the OPG technology, analyzing the effects of the influent wastewater composition and light conditions on the process. [ABSTRACT FROM AUTHOR]

Underwater images suffer from severe color attenuation and contrast reduction due to the poor and complex lighting conditions in the water. Most mainstream methods employing deep learning typically require extensive underwater paired training data, resulting in complex network structures, long training time, and high computational cost. To address this issue, a novel ZeroReference Parameter Estimation Network (Zero-UAE) model is proposed in this paper for the adaptive enhancement of underwater images. Based on the principle of light attenuation curves, an underwater adaptive curve model is designed to eliminate uneven underwater illumination and color bias. A lightweight parameter estimation network is designed to estimate dynamic parameters of underwater adaptive curve models. A tailored set of non-reference loss functions are developed for underwater scenarios to fine-tune underwater images, enhancing the network's generalization capabilities. These functions implicitly control the learning preferences of the network and effectively solve the problems of color bias and uneven illumination in underwater images without additional datasets. The proposed method examined on three widely used real-world underwater image enhancement datasets. Experimental results demonstrate that our method performs adaptive enhancement on underwater images. Meanwhile, the proposed method yields competitive performance compared with state-of-the-art other methods. Moreover, the Zero-UAE model requires only 17K parameters, minimizing the hardware requirements for underwater detection tasks. What'more, the adaptive enhancement capability of the Zero-UAE model offers a new solution for processing images under extreme underwater conditions, thus contributing to the advancement of underwater autonomous monitoring and ocean exploration technologies. [ABSTRACT FROM AUTHOR]

Urbanization drives multiple environmental changes that influence critical ecosystem processes. Factors such as salinization by deicing road salts, reduced water clarity (and greater light attenuation) from eutrophication and sediment loading, and warming constrain not only the biodiversity of ponds, but also their physical mixing (with consequences for oxygen availability and the provision of ecosystem services). Leveraging an extensive urban gradient in the Greater Toronto Area, we collected summertime depth profiles from 50 stormwater retention ponds to investigate their vertical stratification. We found that water columns were generally stratified but contrary to expectations, we found relatively minor roles of basin area and depth. Instead, we discovered an overwhelming effect of salinity along with significant impacts of temperature and water clarity on water density gradients. Findings extend our fundamental understanding of mixing regimes in small, shallow waterbodies and indicate increasing risks to pond functioning in a warmer and saltier future. [ABSTRACT FROM AUTHOR]

Underwater image enhancement has become the requirement for more people to have a better visual experience or to extract information. However, underwater images often suffer from the mixture of color distortion and blurred quality degradation due to the external environment (light attenuation, background noise and the type of water). To solve the above problem, we design a Divide-and-Conquer network (DC-net) for enhancing underwater image, which mainly consists of a texture network, a color network and a refinement network. Specifically, the multi-axis attention block is presented in the texture network, which combine different region/channel features into a single stream structure. And the color network employs an adaptive 3D look-up table method to obtain the color enhanced results. Meanwhile, the refinement network is presented to focus on image features of ground truth. Compared to state-of-the-art (SOTA) underwater image enhance methods, our proposed method can obtain the better visual quality of underwater images and better qualitative and quantitative performance. The code is publicly available at https://github.com/zhengshijian1993/DC-Net. [ABSTRACT FROM AUTHOR]

Aquatic image segmentation is complex due to the inherent complexity of light attenuation, color shifts, and vision limits in underwater settings. In order to improve underwater image segmentation and the delineation of objects in such conditions, this research study suggests a novel semantic technique. The suggested approach uses cutting-edge deep learning methods and a U-Net architecture with DenseNet-201 at its core. The dense connectivity patterns of DenseNet-201 make it possible to extract useful features, increasing gradient flow and feature reuse across the network. The model uses a combination of Dice Loss and Categorical Focal Loss is the loss function to enhance segmentation performance. It utilizes evaluation metrics like the IoU Score and F-Score, which give detailed insights into the model's performance on underwater picture segmentation tasks. According to the evaluation metrics, the "DenseNet201" architecture is the most effective one for underwater picture segmentation. On the test dataset, it received the highest Mean IoU Score (79.77/%) and F1 Score (84.85)%. Furthermore, it obtained competitive results for each class-wise IoU score, demonstrating strong segmentation performance for different object categories. [ABSTRACT FROM AUTHOR]

Underwater optical imaging for information acquisition has always been an innovative and crucial research direction. Unlike imaging in the air medium, the underwater optical environment is more intricate. From an optical perspective, natural factors such as turbulence and suspended particles in the water cause issues like light scattering and attenuation, leading to color distortion, loss of details, decreased contrast, and overall blurriness. These challenges significantly impact the acquisition of underwater image information, rendering subsequent algorithms reliant on such data unable to function properly. Therefore, this paper proposes a method for underwater image restoration using Stokes linearly polarized light, specifically tailored to the challenges of underwater complex optical imaging environments. This method effectively utilizes linear polarization information and designs a system that uses the information of the first few frames to calculate the enhanced images of the later frames. By doing so, it achieves real-time underwater Stokes linear polarized imaging while minimizing human interference during the imaging process. Furthermore, the paper provides a comprehensive analysis of the deficiencies observed during the testing of the method and proposes improvement perspectives, along with offering insights into potential future research directions. [ABSTRACT FROM AUTHOR]

A high‐resolution wave model is crucial for accurate modeling of sediment and organic material transports, but its computational costs hinder direct coupling to an ecosystem model. We developed a machine learning model using long short‐term memory to simulate large‐scale, high‐resolution waves. Trained with numerical wave model (NWM) outputs and wind data from nine locations, our model successfully replicates NWM results for daily mean significant wave height and period in Chesapeake Bay with identical spatial resolution. Compared to the NWM, the data‐driven model has root‐mean‐square errors below 6 cm for daily mean significant wave height and 1 s for the wave period in the bay. It demonstrates excellent model skills and can accurately forecast daily mean significant wave height and period at NOAA wave stations comparable to NWMs. Using minimal wind data and having a short runtime, our data‐driven model shows promise as an alternative for wave forecasting and coupling with sediment and ecological models. Plain Language Summary: Accurately modeling sediment and organic transport necessitates the use of a high‐resolution numerical wave model (NWM). However, the computational expenses of running an NWM within an ecosystem model pose significant challenges. We conducted a study using a machine learning approach based on long short‐term memory to replicate the results of an NWM. The data‐driven wave model (DWM) was trained with the NWM outputs and wind data from nine locations. The trained DWM can replicate the NWM results of daily mean significant wave height and period in Chesapeake Bay with identical spatial resolution and the same predictive skill with a root‐mean‐square error of less than 6 cm and 1 s, respectively for the model domain. The model successfully forecasted daily mean significant wave height and period at NOAA wave stations, showing great forward prediction capability. Notably, the DWM only relied on a minimal amount of wind data from selected locations and had a short runtime. These findings indicate that machine learning is promising as an alternative approach to NWM for wave forecasting. Moreover, it can serve as an alternative wave model and be coupled to ecological models for simulating sediment transport, light attenuation, and resuspension of organic materials. Key Points: A machine learning‐based wave model reproduces the numerical wave model (NWM) results at the same spatial resolution with good accuracyThe data‐driven model, trained on daily significant wave height and period using NWM outputs, has comparable wave prediction skillThe fast‐running data wave model, driven by wind data from nine locations, can forecast waves and couple with sediment and ecological models [ABSTRACT FROM AUTHOR]

Underwater object detection is crucial in marine exploration, presenting a challenging problem in computer vision due to factors like light attenuation, scattering, and background interference. Existing underwater object detection models face challenges such as low robustness, extensive computation of model parameters, and a high false detection rate. To address these challenges, this paper proposes a lightweight underwater object detection method integrating deep learning and image enhancement. Firstly, FUnIE-GAN is employed to perform data enhancement to restore the authentic colors of underwater images, and subsequently, the restored images are fed into an enhanced object detection network named YOLOv7-GN proposed in this paper. Secondly, a lightweight higher-order attention layer aggregation network (ACC3-ELAN) is designed to improve the fusion perception of higher-order features in the backbone network. Moreover, the head network is enhanced by leveraging the interaction of multi-scale higher-order information, additionally fusing higher-order semantic information from features at different scales. To further streamline the entire network, we also introduce the AC-ELAN-t module, which is derived from pruning based on ACC3-ELAN. Finally, the algorithm undergoes practical testing on a biomimetic sea flatworm underwater robot. The experimental results on the DUO dataset show that our proposed method improves the performance of object detection in underwater environments. It provides a valuable reference for realizing object detection in underwater embedded devices with great practical potential. [ABSTRACT FROM AUTHOR]

The images captured under water are typically deteriorated due to non uniform attenuation of traversing light in underwater medium. The studies on underwater environment mainly depends on the techniques for the enhancement of underwater images. Many state-of-the-arts compensate the losses due to scattering of light in underwater images but still it is a challenging task to compensate the color distortion due to absorption of light. A new approach is introduced in this article for the enhancement of underwater images by balancing the attenuation disparity between the color channels. This approach reduces the color dominancy based on the prior that the non-uniform attenuation of RGB channels of light in water which results in severely absorbed, moderately absorbed and informative color channels. The difference between the maximum pixel intensity level of an image and its color channels is considered as the attenuation disparity of color channels. So, this leads to the statistical classification of underwater images into five major types: haze, bluish, greenish, greenish blue and bluish green images and two special types such as yellowish image and dark image subjectively. The pixel intensity of severely and moderately absorbed color channels are manipulated in accordance with the informative color channel by adding the value of concerned attenuation disparity globally. It increases the pixel intensity level of weak color channels and restores the channel information but does not improve the visual quality. The Gray World (GW) method or color equalization (CE) method is applied to remove the color imbalance and the contrast is improved by saturating the high level and low level pixel intensities. Finally, the conventional multiscale image fusion is employed to improve the image quality. The experimental results show that the proposed method outperforms qualitatively and quantitatively in terms of underwater color image quality evaluation (UCIQE) metrics and underwater image quality measures (UIQM) compared to the existing image enhancement methods.

Laser shadowgrams of exploding metal wires in a vacuum show the anomalous transparency of a completely evaporated wire core for a probe beam with a wavelength of 1064 nm. Simultaneous shadowgrams at 532 nm show significant light attenuation. The anomalous transparency for laser radiation at a wavelength of 1064 nm is highly likely due to the Rayleigh scattering of the probing beam by metal atoms. Our experimental results can be explained under the assumption of a strongly non-equilibrium state of an expanding gas cylinder with a non-uniform density. [ABSTRACT FROM AUTHOR]

Seasonal variations of floating-leaved macrophyte Nuphar lutea chlorophyll content were studied in lakes with different water quality. The aim was to investigate whether chlorophyll content of the plants could be used as an indicator of brownification. The relationship between chlorophyll a and b (CHL a:b) of the petioles decreased with increasing water colour. The relationship between CHL a:b and turbidity was less clear. The sum concentration of chlorophyll a and b of the petioles did not respond to water colour consistently. The strong effect of water colour on petiole CHL a:b was probably due to the effect of colour on the attenuation of short-wavelength light. CHL b absorbs short-wavelength light more effectively than CHL a. To maximize the harvesting of short-wavelength light, macrophytes increase their CHL b concentration relative to CHL a. The results suggested that petiole CHL a:b of N. lutea can be used as an indicator of brownification. High phytoplankton biomass may, however, disturb the relationship between water colour and CHL a:b. Therefore, the most reliable results are obtained by sampling several times during the growing season. The floating leaves cannot be recommended as brownification indicators because the relationship between their CHL content and water colour is weak. [ABSTRACT FROM AUTHOR]

Sea ice algae play an important role in the Arctic Ocean ecosystem, driving primary production in the spring and sequestering carbon to the deep ocean. Up to 45% of Arctic Ocean primary production occurs in ice‐covered areas; photosynthetically active radiation (PAR) is fundamental to driving this production. Sea ice, and particularly snow, strongly scatter and reflect light, reducing the amount of PAR transmitted to the ice‐ocean interface. The effect that varying thicknesses of sea ice (0.2–3.5 m) and snow (0.01–1 m) have on the value of PAR transmittance at the ice‐ocean interface are considered for a Winter, Spring, and Summer scenario. When characteristic Arctic Ocean conditions (2 m sea ice and ∼0.2 m snowpack) are modeled, there is roughly a two‐fold difference in PAR transmittance at the ice‐ocean interface between the Winter (0.003) and Spring (0.007) scenarios and an order of magnitude difference with the Summer scenario (0.04). The modeled values correlate within one standard deviation of measured values and show good agreement with extended pan‐Arctic Ocean field campaign measurements. The results also indicate that simple exponential decay methods may lead to inaccurate results, and radiative‐transfer modeling is required to accurately predict PAR transmittance at the ice‐ocean interface. Therefore, this study offers a novel mathematical technique to predict the value of PAR transmittance at the ice‐ocean interface. Coupled with year‐round near‐real‐time sea ice and snow thickness remote sensing data, this technique may improve understanding of primary production and carbon budgets in the changing Arctic Ocean. Key Points: An algorithmic procedure to calculate photosynthetically active radiation (PAR) transmittance at the ice‐ocean interface for different types and thicknesses of sea ice and snowPAR transmittance under the ice requires careful radiative‐transfer modeling; simple light attenuation models give poor resultsApparent two‐fold difference in PAR values between Winter and Spring scenarios, and order of magnitude difference with the Summer scenario [ABSTRACT FROM AUTHOR]

The extragalactic high-energy γ-ray sky is dominated by blazars, which are active galactic nuclei with their jets pointing towards us. Distance measurements are of fundamental importance yet for some of these sources are challenging because any spectral signature from the host galaxy may be outshone by the non-thermal emission from the jet. In this paper, we present a method to constrain redshifts for these sources that relies only on data from the Large Area Telescope on board the Fermi Gamma-ray Space Telescope. This method takes advantage of the signatures that the pair-production interaction between photons with energies larger than approximately 10 GeV and the extragalactic background light leaves on γ-ray spectra. We find upper limits for the distances of 303 γ-ray blazars, classified as 157 BL Lacertae objects, 145 of uncertain class, and 1 flat-spectrum-radio quasar, whose redshifts are otherwise unknown. These derivations can be useful for planning observations with imaging atmospheric Cherenkov telescopes and also for testing theories of supermassive black hole evolution. Our results are applied to estimate the detectability of these blazars with the future Cherenkov Telescope Array , finding that at least 21 of them could be studied in a reasonable exposure of 20 h. [ABSTRACT FROM AUTHOR]

The thermal expansion occurring when a laser beam is incident on the surface of a solid can be used to excite ultrasonic waves in the solid medium, without causing damage to the material. The resulting wavefield is characterized by a dominant shear wave and a weak compressional wave. This Letter demonstrates the possibility of generating a dominant compressional wavefield by coating the surface with a layer of clear ice. This is achieved by exploiting a minimum in the attenuation spectrum of light in ice, which occurs at around 500 nm and renders the coating transparent to green laser radiation. Ice coatings could, therefore, provide a path for the development of more sensitive laser-based nondestructive testing methods that have traditionally been affected by the poor excitability of compressional waves, especially along the direction orthogonal to the surface. [ABSTRACT FROM AUTHOR]