Natural Light Conditions Research Articles

Early-Stage Identification of Powdery Mildew Levels for Cucurbit Plants in Open-Field Conditions Based on Texture Descriptors

Constant monitoring is necessary for powdery mildew prevention in field crops because, as a fungal disease, it modifies the green pigments of the leaves and is responsible for production losses. Therefore, there is a need for solutions that assure early disease detection to realize proactive control and management of the disease. The methodology currently used for the identification of powdery mildew disease uses RGB leaf images to detect damage levels. In the early stage of the disease, no symptoms are visible, but this is a point at which the disease can be controlled before the symptoms appear. This study proposes the implementation of a support vector machine to identify powdery mildew on cucurbit plants using RGB images and color transformations. First, we use an image dataset that provides photos covering five growing seasons in different locations and under natural light conditions. Twenty-two texture descriptors using the gray-level co-occurrence matrix result are calculated as the main features. The proposed damage levels are ’healthy leaves’, ’leaves in the fungal germination phase’, ’leaves with first symptoms’, and ’diseased leaves’. The implementation reveals that the accuracy in the L * a * b color space is higher than that when using the combined components, with an accuracy value of 94% and kappa Cohen of 0.7638.

The effect of light emitting diode (LED) spectrum and light duration on growth and yield of Brassica sinensis L. grown on floating raft hydroponic system

Recent technology in agriculture allows vegetable cultivation in controlled environment by utilizing energy derived from an artificial lighting. Light Emitting Diodes (LED) is a source of artificial light which produce light with the wavelength ranged from ultraviolet - 100 nm to infrared - 3,000 nm. Under natural or artificial lighting condition, plants require energy for photosynthesis from light between 380 nm to 700 nm. In this study we examined the use of two types of 45-watt LED (red and blue – C1, full spectrum – C2) with three different light durations (12 h – D1, 16 h – D2, and 18 h – D3) on the growth and yield performance of brassica under hydroponic technique. Research result showed that full spectrum LED provided better performance in plant height, leaves number and width, fresh yield, root length, and biomass in all combinations with light duration as compared to red and blue LED. The combination of full spectrum LEDs with a duration of 16 h showed the best fresh yield (3.96 kg m-2) followed by 18 h (3.57 kg m-2) and 12 h (3.22 kg m-2). Although the highest production was resulted from the combination of full spectrum LED and 16 h light duration, the most efficient in electric energy utilization was showed in the combination of full spectrum LED and 12 h light duration. All in all, we concluded that the use of full spectrum LED in combination with 16 h light duration feasible to be implemented in brassica cultivation under controlled environment.

Evaluating non-photochemical quenching (NPQ) kinetics and photosynthetic efficiency in cassava (Manihot esculenta) subjected to variable high light conditions.

Light intensity is a critical environmental factor influencing plant growth and development. To survive high light conditions, plants have evolved various protective mechanisms, including non-photochemical quenching (NPQ). However, NPQ can limit effective photosynthetic yield when transitioning to low light conditions. This phenomenon is underexplored in cassava (Manihot esculenta ), a starchy storage root crop known for its high biological efficiency and climate resilience. To address this knowledge gap, we assessed the photoprotective abilities and growth responses of six cassava varieties under natural environmental light conditions (control) and intermittent high light (IHL) conditions by adding 900μmolm-2 s-1 using full-spectrum LED lights, on top of the natural ambient daylight. Our results demonstrated a significant impact of light treatment on aboveground biomass, total crop biomass, chlorophyll a and b content, photosynthetic rate, and NPQ values during transitions from low to high light and vice versa. Notably, cassava variety 'Sree Suvarna' exhibited the highest yield under both control and IHL conditions. These findings suggest that screening cassava varieties for their ability to postpone photoinhibition and recover quickly from photoinhibition may enhance photosynthetic performance. Such strategies have important implications for improving the efficiency and resilience of cassava crops, ultimately contributing to sustainable agricultural productivity.

SCIENTIFIC SUBSTANTIATION OF THE PRODUCTIVITY OF SOWING ALFALFA DEPENDING ON SOIL AND CLIMATIC CONDITIONS OF THE FOREST-STEPPE OF UKRAINE

The article presents a review of literature sources on the study of alfalfa productivity depending on the influence of sowing dates, mineral phosphorus-potassium nutrition, soil liming, pre-sowing seed treatment with bacterial preparations and modes of grass stand use when grown for fodder purposes. It was found that the change of photoperiod in the year of sowing was a signal in alfalfa plants to pass through the microstages of growth and development, which was determined by the length of daylight hours and sowing dates, regardless of hydrothermal resources. Observations showed that the crop reached the beginning of flowering phase (code 61-62 of the BBSN) at daylight hours of 14:53-16:12 h, while at 12:48 h it reached the budding phase (code 49 of the BBSN). Regardless of climate change, which has been observed for more than a decade, sowing alfalfa provided the highest productivity indicators with early spring sowing and long-term use of the grass stand. Over the three years of vegetation, its leaf mass yield averaged 47.03 t/ha, dry matter yield was 11.09 and 2.07 t/ha of crude protein, and gross and metabolizable energy were 204.72 and 104.52 GJ/ha, respectively. At the same time, with the recommended summer sowing date (20 July), the indicators decreased by 9.1, 11.5, 13.1, 12.2 and 11.7%, respectively. Analysing the agro-ecological resources of Ukraine, given the natural soil fertility, moisture, temperature and light conditions, they are quite favourable for the maximum realisation of the biological potential of the fodder productivity of sowing alfalfa within the yield of fodder units of 10-12 t/ha and 1.8-2.2 t/ha of digestible protein. Among the elements of intensifying the cultivation of seed alfalfa for the production of plant raw materials and harvesting of various types of fodder, fertilisation is the most effective factor influencing their quantity and quality. To calculate the amount of mineral fertiliser, we take into account the biological characteristics of the crop, its cyclical growth and development during the growing season, the method of sowing and the duration of grassland use. Regardless of the geographical origin of the seed alfalfa, for two years of using the grass stand and observing technological measures of cultivation (lime, phosphorus-potassium mineral fertilisers, seed inoculation), alfalfa provided the highest yield of crude protein of 5.83-5.97 t/ha when mowing the first mowing in the flowering phase, the second - at the beginning of flowering and the third in the budding phase. Mowing of three mowings of sowing alfalfa at the beginning of the flowering phase contributes to the effective use of the light regime of the growing season as the main factor in increasing productivity. Innovative technologies for growing seed alfalfa use growth-regulating agents of various origins, microfertilisers and biological seed treatment products to increase yields and dry matter quality for foliar feeding. The complex application of these preparations provides an increase in the yield of leaf mass by 16.6-18.2%. Thus, in the context of modern agricultural development, alfalfa is one of the components of reducing the anthropogenic load on the agro-ecological system and increasing the production of environmentally friendly high-protein feed and livestock products for the purpose of efficient use of hydrothermal resources and environmental protection.

Deep learning prediction of photocatalytic water splitting for hydrogen production under natural light based on experiments

Harnessing hydrogen through photocatalytic water splitting represents a pivotal stride towards carbon neutrality. Evaluating ongoing experiments under natural light conditions is critical for scaling this technology. Given the inherent instability of natural light, devising precise hydrogen production forecasts has emerged as a core focus to streamline project timelines and financial governance. This research introduces a robust system for continuous photocatalytic water splitting, leveraging natural light for hydrogen production. Analysis and prognostication of hydrogen generation are conducted using experimental data from 21 distinct groups. Critical factors, including radiation intensity, reaction temperature, reactant concentration, solar concentrator specifics, and hydrogen production rate (HPR), undergo thorough correlation scrutiny. Employing a deep-learning framework, gated recurrent units (GRU) neural network is utilized for nuanced feature apprehension, enhancing hydrogen output forecasting precision. The results illustrate that the mean daily solar-to-hydrogen (STH) energy conversion efficiency across the 21 groups is approximately 0.25 %. Radiation intensity prevails as a significant influencer, with reaction temperature also playing a substantial role. Solar concentrator utilization proves beneficial for augmenting HPR. Nevertheless, the unevenness of solar concentrator presents adverse effects, necessitating consideration during system conceptualization. The optimized prediction model's outcomes align closely with experimental observations, and with a test loss of 2.61 × 10−3 and 682 epochs, the model's predictive superiority is affirmed. The continuous hydrogen production experiments leveraging natural light provide the feasible reference for the large-scale application of this technology. The correlation analysis of critical factors provides the optimization direction for practical engineering design. The hydrogen production prediction model aims to provide scientific basis for the decision-making of this technology.

Investigation into the degradation of air and runoff pollutants using nano g-C3N4 photocatalytic road surfaces

Exhaust emissions and road runoff pollution pose significant environmental challenges, leading to severe air pollution and irreversible soil damage, respectively. The adoption of self-cleaning road surfaces with photocatalytic properties has emerged as an effective approach to combat both types of pollution. Currently, nano titanium dioxide (TiO2) stands as the most widely used semiconductor for photocatalytic pavements. However, its limited light/radiation utilization remains a concern. To address this, the present study focuses on the use of the green nanomaterial graphene-like phase carbon nitride (g-C3N4), which has a smaller band gap to improve its light utilization. Comprehensive characterizations were performed to elucidate its physical/chemical properties. Subsequently, photocatalytic road coatings with various active substance contents were prepared by incorporating g-C3N4 into epoxy resin, and the functional pavement's performances were evaluated. A laboratory assessing method for the degradation of runoff pollution and NO contaminant was devised, enabling the evaluation of the redox capacity of semiconductive photocatalysts. Experimental results demonstrate that the nano g-C3N4 photocatalytic coating exhibits significant enhancement in degradation efficiency for a typical water pollutant, methylene blue (MB), being 2.76 times that of the nano TiO2 photocatalytic coating. Additionally, its ability to degrade NO gaseous contaminant under visible-light irradiation is 7 times more than the control sample of TiO2 coating. Moreover, the photocatalytic coating has good anti-slip property and long-term performance. Thus, nano g-C3N4 proves to be a more suitable semiconductor material for self-cleaning road surfaces, holding tremendous potential to mitigate diverse pollution arising from transportation under natural light conditions.

Multifunctional characteristics of biosynthesized CoFe2O4@Ag nanocomposite by photocatalytic, antibacterial and cytotoxic applications

Carissa carandas, a traditional medicinal herb with a high concentration of antioxidant phytochemicals, has been used for thousands of years in the Ayurveda, Unani, and homoeopathic schools of medicine. By employing Carissa carandas bark extract as a reducing and capping agent in green biosynthesis, we extend this conventional application to produce CoFe2O4 and CoFe2O4@Ag nanocomposite. A variety of techniques have been used to characterize the synthesised nanocomposite, including UV–Vis, FTIR, XRD, FESEM, EDX, and BET. The CoFe2O4 and CoFe2O4@Ag nanocomposite demonstrated promising antibacterial action against human bacterial pathogens like B. subtilis and S. aureus as gram positive and P. aeruginosa and E. coli as gram negative with inhibition zones of 24.3 ± 0.57, 17.4 ± 0.75 and 20.5 ± 0.5, 19.8 ± 1.6 mm respectively, and the obtained results were superior to the nanocomposite without silver. Moreover, in-vitro cytotoxicity effects of biosynthesized CoFe2O4 and CoFe2O4@Ag were performed on the human breast cancer cell MCF-7. It was found that the MCF-7 cells' 50% inhibitory concentration (IC50) was 60 μg/mL. Additionally, biosynthesized CoFe2O4 and CoFe2O4@Ag nanocomposite was used to demonstrate the photocatalytic eradication of Rhodamine Blue (RhB). Due to the addition of Ag, which increases surface area, conductivity, and increased charge carrier separation, the CoFe2O4@Ag nanocomposite exhibits a high percentage of photocatalytic degradation of ⁓ 98% within 35 min under UV light irradiation. The photocatalytic performance of as-synthesised nanocomposite was evaluated using dye degradation-adsorption in both natural light and dark condition. Under dark conditions, it was found that 2 mg mL−1 CoFe2O4@Ag in RhB aqueous solution (5 ppm) causes dye adsorption in 30 min with an effectiveness of 72%. Consequently, it is anticipated that the CoFe2O4@Ag nanocomposite will be a promising photocatalyst and possibly a noble material for environmental remediation applications.

ОСВІТЛЕННЯ У КВАРТИРАХ ПРИСТОСОВАНИХ ДЛЯ ГАРМОНІЙНОГО СПІВІСНУВАННЯ ЛЮДЕЙ ТА ТВАРИН

Interior lighting in pet-friendly apartments is a multifaceted and important aspect of creating a harmonious living environment for both humans and their companions. Research will include the importance of lighting in pet-friendly interiors, delving into design features, technologies and benefits that promote the well-being of both pets and their owners. Research sheds light on the art and science of pet-focused lighting, from providing comfort and safety to improving overall quality of life. We begin by recognizing the deep connection between people and their pets. Lighting in pet-friendly apartments serves not only practical purposes, but also helps create an environment that nurtures this unique bond. Natural lighting is a cornerstone of pet-friendly interiors. We investigate the benefits of using sufficient natural light, including its positive effects on animal behavior, circadian rhythms and general well-being. Different pets have different lighting needs. Whether it's providing adequate UVB light for reptiles or creating cozy nooks for feline sunbathing, we explore lighting settings for different animal species. Lighting is adapted to specific areas of pet activity, such as feeding, play and rest areas. We discuss how task lighting and LED lights adapt to different tasks and activities related to pets. Lighting is key to keeping pets safe. We examine how the right lighting can prevent accidents, such as tripping over pets in low-light conditions. We investigate the effects of lighting color on both pets and their owners. The psychology of color is studied, it is emphasized how lighting can affect mood and behavior. Intelligent lighting systems are increasingly being integrated into pet-friendly apartments. Consider the convenience of remote control, scheduling, and motion-activated lighting for pets and owners. Lighting is an integral part of the overall well-being and health of pets. We discuss how natural light conditions and well-planned artificial lighting can positively affect the physical and emotional health of animals. It is important to take care of the safety of pets and lighting. We outline measures to protect pets from electrical hazards and prevent damage to lighting fixtures caused by curious animals. The Future of Pet Lighting: We conclude with a look at the future of pet-centric lighting, including innovations in smart pet monitoring systems and advanced lighting technologies that will further enrich the lives of pets and their owners. In summary, this abstract highlights the key role of lighting in pet-friendly apartment interiors. By taking into account the special needs of pets, taking advantage of natural light and implementing smart lighting solutions, we can create living spaces that prioritize the well-being, safety and happiness of both the occupants and their beloved companion animals.

Lunar Age-Dependent Oscillations in Expression of the Genes for Kisspeptin, GnIH, and Their Receptors in the Grass Puffer during the Spawning Season.

Grass puffer is a semilunar-synchronized spawner: spawning occurs on beaches only for several days of spring tide around new moon (lunar age 0) and full moon (lunar age 15) every 2 weeks from spring to early summer. To investigate the role of kisspeptin and gonadotropin-inhibitory hormone (GnIH) in the semilunar-synchronized spawning, lunar age-dependent expression of the genes encoding kisspeptin (kiss2), kisspeptin receptor (kissr2), GnIH (gnih), GnIH receptor (gnihr), gonadotropin-releasing hormone 1 (GnRH1) (gnrh1), and three gonadotropin (GTH) subunits (gpa, fshb, lhb) was examined in the male grass puffer, which was kept in an aquarium under natural light condition in a lunar month during the spawning period. In the brain, both kiss2 and kissr2 showed lunar variations with a peak at lunar age 10, while both gnih and gnihr showed semilunar variations with two peaks at lunar age 0 and 20. On the other hand, gnrh1 showed semilunar variation with two peaks at lunar age 0 and 15. In the pituitary, kiss2, kissr2, gnih, and gnihr showed similar variations to those shown in the brain. The fshb and lhb mRNA levels showed semilunar variations with two peaks at lunar age 0 and 15. The present study shows lunar and semilunar oscillations of kiss2/kissr2 and gnih/gnihr expressions, respectively, with their peaks around spring tide in the brain and pituitary along with the semilunar expressions of gnrh1 and the pituitary GTH subunit genes. These results suggest that the lunar age-dependent expressions of the kisspeptin, GnIH, and their receptor genes may be primarily important in the control of the precisely timed semilunar spawning of the grass puffer.

Hyperspectral imaging through scattering media via physics-informed learning

Overcoming light scattering in natural light conditions is a challenging but rewarding problem. For this problem, both physics-based and data-driven methods encounter bottlenecks to recover large amounts of hyperspectral data encoded in the scattered light spot. In this paper, we present a physics-informed learning method to solve the light scattering problem with the neural network and the incoherent light transmission matrix method. With the proposed method, we achieve single-shot hyperspectral imaging through scattering media exceeding the memory effect range. Hyperspectral images with a maximum resolution of 3.2-megapixel are recovered in 64 wavebands, which represents an order of magnitude improvement over the memory effect range. No additional imaging or dispersion elements are required in the system except for the scatter medium, thus ensuring a compact and inexpensive hyperspectral imaging system. Our recovery method outperforms other physics-based and data-driven methods in terms of recovery quality and time cost.

Determination of the sizes and structural elements of wounds based on the computer planimetry. A photo protocol in assessing the course of wound healing process

The article substantiates the relevance of computer planimetry as an integral indicator in assessing the course of wound process. The authors have concluded that currently, there is no any “off-line” computer program that not only measure wound area, but also determine its structural elements. An original author’s work is presented in the article – AnalyRan program (Russia, 2022). This program is designed for medical specialists involved in the treatment of simple and complex clean, conditionally infected and infected acute and chronic wounds of various etiology. The discussed program can quickly and accurately measure the wound area and provide the information on wound structural elements, such as necrosis, fibrin, granulation and epithelium. The information obtained contributes to a differentiated approach to the choice of curative tactics and allows to make an objective comparison of various local agents, namely, their effectiveness at the wound process. The flexibility of the program allows to have fine-tuned settings for a specific camera, smartphone, video camera with frame captures, with flash or its lack, natural or artificial lighting conditions. Potentials of the presented program contribute to its widespread routine use.

Fate, risk and sources of antibiotic resistome and its attenuation dynamics in the river water-sediment system: Field and microcosm study

Antibiotic resistance genes (ARGs) in rivers have received widespread attentions. Deciphering the fate and spread mechanisms of ARGs in river system can contribute to the design of effective strategies for reducing resistome risk in the environment. Although some studies have reported the prevalence and distribution of ARGs in rivers worldwide, few have systematically explored their fates, sources and risks in river water-sediment system. Also, the role of natural sunlight on the attenuation and fate of ARGs in river remains to be demonstrated. To fill the gaps, field investigation and microcosm experiment have been conducted in this study to reveal the fate, risk, source-sink relationship and attenuation dynamics of ARGs in an urban river water-sediment system, by utilizing high-throughput sequencing-based metagenomic assembly analysis and machine-learning-based source tracking tool. In all, 527 unique ARGs belonging to 29 antimicrobial classes were identified in the river. Relatively, the level of ARGs in the sediments were significantly higher (p < 0.05) than that in the waters. Variance partitioning analysis indicated the biotic and abiotic factors co-governed the riverine resistome, totally explaining 76% and 83% variations of ARGs in the waters and sediments, respectively. Microcosm experiment under natural light and dark condition showed that light induced the decay of ARGs in the waters and might promote their transfers from waters to sediments, which were also confirmed by the attenuation dynamics of bacteria in the experimental water-sediment system. Notably, the co-occurrences of ARGs with MGEs and VFs on the same contigs suggested resistome risk in the river, and relatively, the risk scores in the sediments were significantly higher (p < 0.05) than those in the waters. Source apportionment with metagenomic resistome signatures showed the Wenyu River was the most dominant contributor of ARGs in the downstream, with average contributions of 44.5% and 40.8% in the waters and sediments, respectively.

Pengaruh Pencahayaan Alami Terhadap Kenyamanan Visual Pengguna Studi Kasus: Bening Coffee &amp; Space

Light is an important element in human life. Without lighting, humans cannot see an object visually. It is from the reflection of the light of these objects that humans visualize shapes clearly and see comfortably. The coffee shop as a public space certainly requires adequate lighting so that visual comfort is maintained. Visual comfort will be achieved if the terms and conditions for achieving visual comfort have been fulfilled, including the brightness level according to the recommended standards and the distribution of lighting according to the spatial layout according to the standards. With the aim to determine the natural and artificial lighting conditions in coffee shops and lighting standards for visual comfort, this study will use a comparative research method. Where this method compares lighting standards with research results. The results of this research are that the level of light intensity found outside and inside the building meets the SNI standard at 250 lux by taking data that has an average lux during the day and lux in the afternoon. The conclusion from this study is that BENING coffee &amp; space on one of the visual comfort factors has met the visual comfort standards set by the SNI.

Polymer Photocatalysts Containing Segregated π‐Conjugation Units with Electron‐Trap Activity for Efficient Natural‐light‐driven Bacterial Inactivation

AbstractDevelopment of highly efficient and metal‐free photocatalysts for bacterial inactivation under natural light is a major challenge in photocatalytic antibiosis. Herein, we developed an acidizing solvent‐thermal approach for inserting a non‐conjugated ethylenediamine segment into the conjugated planes of 3,4,9,10‐perylene tetracarboxylic anhydride to generate a photocatalyst containing segregated π‐conjugation units (EDA‐PTCDA). Under natural light, EDA‐PTCDA achieved 99.9 % inactivation of Escherichia coli and Staphylococcus aureus (60 and 45 min), which is the highest efficiency among all the natural light antibacterial reports. The difference in the surface potential and excited charge density corroborated the possibility of a built‐in electron‐trap effect of the non‐conjugated segments of EDA‐PTCDA, thus forming a highly active EDA‐PTDA/bacteria interface. In addition, EDA‐PTCDA exhibited negligible toxicity and damage to normal tissue cells. This catalyst provides a new opportunity for photocatalytic antibiosis under natural light conditions.

Polymer Photocatalysts Containing Segregated π-Conjugation Units with Electron-Trap Activity for Efficient Natural-light-driven Bacterial Inactivation.

Development of highly efficient and metal-free photocatalysts for bacterial inactivation under natural light is a major challenge in photocatalytic antibiosis. Herein, we developed an acidizing solvent-thermal approach for inserting a non-conjugated ethylenediamine segment into the conjugated planes of 3,4,9,10-perylene tetracarboxylic anhydride to generate a photocatalyst containing segregated π-conjugation units (EDA-PTCDA). Under natural light, EDA-PTCDA achieved 99.9 % inactivation of Escherichia coli and Staphylococcus aureus (60 and 45 min), which is the highest efficiency among all the natural light antibacterial reports. The difference in the surface potential and excited charge density corroborated the possibility of a built-in electron-trap effect of the non-conjugated segments of EDA-PTCDA, thus forming a highly active EDA-PTDA/bacteria interface. In addition, EDA-PTCDA exhibited negligible toxicity and damage to normal tissue cells. This catalyst provides a new opportunity for photocatalytic antibiosis under natural light conditions.

Color calibration of moist soil images captured under irregular lighting conditions

Soil color is a valuable indicator that visually reflects soil properties. In the field of soil science, soil color has been widely used for soil identification and property assessment. Soil color is influenced not only by soil properties but also by lighting conditions; therefore, adequately considering the effect of lighting conditions for the color-based soil assessment is essential. This paper proposes a color calibration method for moist soil images captured under irregular lighting conditions. Four samples of weathered granite soil with varying water contents were captured under nine different lighting conditions. The effect of lighting conditions on the color of moist soil was analyzed using linear regression analysis in the CIELAB color space. The findings demonstrate a linear relationship between soil color and lighting conditions. Furthermore, the slopes of the linear regression equations remained consistent across all moist soil samples, irrespective of soil type and water content. Based on this, calibration equations were derived to adjust the color of moist soil images captured under arbitrary lighting conditions to those under the desired lighting conditions. The proposed method was validated using soil images captured under natural lighting conditions, which show high accuracy in color calibration except for cases with illuminance exceeding 70,000 lux. This color calibration method provides a reliable approach to evaluating soil properties through accurate color representation and presents new possibilities for developing digital image-based soil property prediction techniques that are less sensitive to lighting conditions.

Effects of Exogenous Melatonin on Colon Glial Cells in Experimental Desynchronosis

The effect of exogenous and endogenous melatonin on GFAP and S-100 positive cells of the colon mucosa of rats kept under different lighting conditions was studied.The aim was to estimate the effect of melatonin on the structural and functional characteristics of colon glial cells in normal conditions and in experimental desynchronosis.Material and methods. The study was conducted on laboratory male Wistar rats (n=72), 2-months old, weighing 180–200 g, divided into 6 groups 12 rats in each. Group I consisted of animals treated in natural light conditions; Group II – rats treated in natural light conditions, receiving synthetic melatonin (Melaxen, Unipharm, Inc., USA) ad libitum at a concentration of 4 mg/l with drinking water; Group III – animals treated in constant darkness; Group IV – animals treated in constant darkness and received melatonin; Group V consisted of animals treated under constant lighting conditions; Group VI included rats treated in constant lighting conditions and received melatonin. Astrocytes were identified using an indirect immunohistochemical method using polyclonal antibodies to glial fibrillary acidic protein (Dako, Germany 750 µg/ml) and protein S-100 (Spring Bio Science USA 1:300). Cells were counted using a Carl Zeiss Axio Scope A1 microscope at 400 magnification and SigmaScan Pro 5 software. Statistical processing was carried out using the MS Excel and Statistica 17 programs.Results. The number of GFAP positive cells decreased in groups III and V. Conditions II and VI resulted in a decrease in the number of gliocytes. In group IV, the administration of melatonin led to an increase in the number of studied cells. A significant increase in the optical density of GFAP in colon gliocytes was observed only when rats were treated under constant lighting conditions. The number of S-100 positive cells decreased in groups III and II. Conditions V and IV led to an increase in the number of S-100 positive cells. The optical density of S-100 does not depend on photoperiod.Conclusion. S-100B and GFAP positive glial cells of the colon respond both to changes in photoperiod and to the administration of exogenous melatonin. In particular, under constant dark conditions, the number of GFAP and S-100B positive cells decreases. The obtained data can be used in the elaboration of new therapeutic approaches to the treatment of intestinal diseases.

Iris

We propose a novel Visible Light Positioning (VLP) method, called Iris, that leverages light spectral information (LSI) to localize individuals in a completely passive manner. This means that the user does not need to carry any device, and the existing lighting infrastructure remains unchanged. Our method uses a background subtraction approach to accurately detect changes in ambient LSI caused by human movement. Furthermore, we design a Convolutional Neural Network (CNN) capable of learning and predicting user locations from the LSI change data. To validate our approach, we implemented a prototype of Iris using a commercial-off-the-shelf light spectral sensor and conducted experiments in two typical real-world indoor environments: a 25 m2 one-bedroom apartment and a 13.3m × 8.4m office space. Our results demonstrate that Iris performs effectively in both artificial lighting at night and in highly dynamic natural lighting conditions during the day. Moreover, Iris outperforms the state-of-the-art passive VLP techniques significantly in terms of localization accuracy and the required density of light sensors. To reduce the overhead associated with multi-channel spectral sensing, we develop and validate an algorithm that can minimize the required number of spectral channels for a given environment. Finally, we propose a conditional Generative Adversarial Network (cGAN) that can artificially generate LSI and reduce data collection effort by 50% without sacrificing localization accuracy.

Photophysiological Characterization of Phytoplankton by Measuring Pigment Production Rates: A Description of Detail Method and a Case Study

Each phytoplankton species has intrinsic pigments, which result in different photophysiological characteristics in response to natural light conditions. Therefore, phytoplankton pigments provide important information on the photosynthetic activity that produces the basic food source for marine ecosystems. This study addresses the challenge of accurately measuring pigment production rates in phytoplankton communities. Two strategies are proposed for improving measurement sensitivity. Firstly, increasing the injection of 13C substrate into incubation bottles up to 15% of the total dissolved inorganic carbon is recommended, with minimal impact on pigment production rate determinations. Secondly, optimizing sample injection volume for high-performance liquid chromatography balances analysis time and dilution effects. The in situ field experiments conducted in this study for pigment production measurements revealed diminished activity of photoprotective mechanisms involving zeaxanthin and diatoxanthin during the study period. Furthermore, the results showed that the notable production rates of chl-b (0.069–0.105 ng C L–1 h–1, 74–89% of total accessary pigment production rates), an accessory pigment mainly attributed to prasinophytes, potentially due to restricted light availability. Prioritization of chl-b production over primary production (negative correlation between primary and chl-b production; R2 = 0.6662) highlights the potential impact of compensatory pigment-related activities on overall phytoplankton productivity. In conclusion, this study underscores the significance of directly quantifying pigment production rates to enhance our comprehension of phytoplankton photophysiology and the production mechanisms specific to various pigments.

SiamC Transformer: Siamese coupling swin transformer Multi-Scale semantic segmentation network for vegetation extraction under shadow conditions

The accuracy of fractional vegetation cover (FVC) estimation has important significance in high-precision agricultural and ecological environment assessments. However, the presence ofshadowsunder natural light conditions can cause problems such as poor vegetation distinguishability and blurred boundaries during vegetation segmentation. This leads to missing or over-segmented vegetation inshadedareas, thus reducing the accuracy of FVC. Polarizationinformation can reflect the texture structure features, edgecharacteristics and surface state information, providing important supplementary information to light intensity information. To address the issue of shadows interfering with vegetation segmentation, a Siamese coupling swin transformer (SiamC Transformer) is proposed in this study. In this network, a dual-flow structure is used to simultaneously retrieve the feature information of vegetation degree of linear polarization (DoLP) images and light intensity images simultaneously to obtain multi-dimensional global semantic information of multi-scale vegetation. In the feature fusion stage, two fusion modules are proposed: an adaptive fusion module (AFM) and an adaptive fusion plus module (AFPM), which can fuse shallow spatial information, texture information and deep semantic information. The AFM and AFPM enable the network to better achieve object localization, region activation and edge sharpening, while improving the segmentation accuracy of small objects. The experimental results show that the mean intersection over union (mIoU) of this network is as high as 97.46% on vegetation datasets consisting of light intensity and polarization images, which is better than that of other algorithms. This network has higher accuracy and adaptability for shadow scenes and improves the accuracy of FVC calculation under shadow conditions.