Light Levels Research Articles

Feasibility of Deploying Home-Based Digital Technology, Environmental Sensors, and Web-Based Surveys for Assessing Behavioral Symptoms and Identifying Their Precipitants in Older Adults: Longitudinal, Observational Study.

Apathy, depression, and anxiety are prevalent neuropsychiatric symptoms experienced by older adults. Early detection, prevention, and intervention may improve outcomes. We aim to demonstrate the feasibility of deploying web-based weekly questionnaires inquiring about the behavioral symptoms of older adults with normal cognition, mild cognitive impairment, or early-stage dementia and to demonstrate the feasibility of deploying an in-home technology platform for measuring participant behaviors and their environment. The target population of this study is older adults with normal cognition, mild cognitive impairment, or early-stage dementia. This is an observational, longitudinal study with a study period of up to 9 months. The severity of participant behavioral symptoms (apathy, depression, and anxiety) was self-reported weekly through web-based surveys. Participants' digital biomarkers were continuously collected at their personal residences and through wearables throughout the duration of the study. The indoor physical environment at each residence, such as light level, noise level, temperature, humidity, or air quality, was also measured using indoor environmental sensors. Feasibility was examined, and preliminary correlation analysis between the level of symptoms and the digital biomarkers and between the level of symptoms and the indoor environment was performed. At 13 months after recruitment began, a total of 9 participants had enrolled into this study. The participants showed high adherence rates in completing the weekly questionnaires (response rate: 275/278, 98.9%), and data collection using the digital technology appeared feasible and acceptable to the participants with few exceptions. Participants' severity of behavioral symptoms fluctuated from week to week. Preliminary results show that the duration of sleep onset and noise level are positively correlated with the anxiety level in a subset of our participants. This study is a step toward more frequent assessment of older adults' behavioral symptoms and holistic in situ monitoring of older adults' behaviors and their living environment. The goal of this study is to facilitate the development of objective digital biomarkers of neuropsychiatric symptoms and to identify in-home environmental factors that contribute to these symptoms.

Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt

In recent years, a great deal of research has been done on the identification, monitoring, and classification of human activities. Human activity recognition (HAR) is a term commonly used to describe the automatic identification of physical activities. For activity recognition, there are primarily vision-based and sensor-based methods available. The computer vision-based method is generally effective in lab settings, but because of clutter, fluctuating light levels, and contrast, it may not perform well in real-world scenarios. Continuous monitoring and analysis of physiological signals obtained from heterogeneous sensors attached to an individual’s body is required to realise sensor-based HAR systems. Most of the previous research in human activity recognition (HAR) is biased along with feature engineering and pre-processing which requires a good amount of domain knowledge. Application-specific modelling and time-taking methods are involved in these approaches. In this work, the multi-head convolutional neural network-based human activity recognition framework is proposed where automatic feature extraction and classification are involved in the form of an end-to-end classification approach. Experiments of this approach are performed by taking raw wearable sensor data with few pre-processing steps and without the involvement of a handcrafted feature extraction technique. 99.23% and 93.55% accuracy are obtained on the WISDM and UCI-HAR datasets which denoted the much improvement in the assessment of HAR over other similar approaches. The model is also tested on locally collected data from a chest mounted belt with fabric sensors and an accuracy of 87.14% has been achieved on that data.

Effects of nocturnal celestial illumination on high-flying migrant insects.

Radar networks hold great promise for monitoring population trends of migrating insects. However, it is important to elucidate the nature of responses to environmental cues. We use data from a mini-network of vertical-looking entomological radars in the southern UK to investigate changes in nightly abundance, flight altitude and behaviour of insect migrants, in relation to meteorological and celestial conditions. Abundance of migrants showed positive relationships with air temperature, indicating that this is the single most important variable influencing the decision to initiate migration. In addition, there was a small but significant effect of moonlight illumination, with more insects migrating on full moon nights. While the effect of nocturnal illumination levels on abundance was relatively minor, there was a stronger effect on the insects' ability to orientate close to downwind: flight headings were more tightly clustered on nights when the moon was bright and when cloud cover was sparse. This indicates that nocturnal illumination is important for the navigational mechanisms used by nocturnal insect migrants. Further, our results clearly show that environmental conditions such as air temperature and light levels must be considered if long-term radar datasets are to be used to assess changing population trends of migrants. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

The effect of developmental care on the length of hospital stay and family centered care practice for very low birth weight infants in neonatal intensive care units: A cluster randomized controlled trial

BackgroundDespite previous studies suggesting that developmental care can provide benign stimulation to promote neural development of newborns, more evidence is needed regarding the other clinical benefits of developmental care. ObjectiveTo evaluate the effect of implementing developmental care on the length of hospital stay, the improvement of care practice in neonatal intensive care units, as well as the short-term outcome of very low birth weight infants. DesignCluster-randomized controlled trial. Setting(s) and participantsFrom March 1, 2021 to March 1, 2022, 1400 very low birth weight infants were recruited from 14 tertiary neonatal intensive care units in China. MethodsWe assigned 14 neonatal intensive care units to either developmental care or standard care. The length of hospital stay of the infants was the primary outcome analyzed at the individual level. Secondary outcomes were family centered care practice including parental involvement, the skin to skin care, exclusive breast milk, oral immune therapy and breastfeeding. The environmental management (noise and light) and the short-term outcomes were also evaluated. ResultsThe length of hospital stay for the developmental care group was 65 % as long as that for the control group (HR: 0.65, 95 % CI, 0.451–0936, p = 0.021). After controlling the covariables, the adjusted HR = 0.755 (95 % CI, 0.515 to 1.107, p = 0.150). When compared to the control group, the developmental care group had greater access to SSC, with 22 infants (3.8 %) in the developmental care group compared to 13 infants (1.7 %) in the standard care group (p = 0.013). A greater proportion of infants in the developmental care group were fed at the breast, than those in the standard care group (136 [23.6 %] vs 9 [1.1 %]; p = 0.029). Compared to the control group, exclusively breast milk was significantly more favorable in the developmental care group (435 [75.6 %] vs 114 [15.0 %]; p = 0.001). The difference remained significant even after adjusting for covariates. However, the rate of oral immune therapy and parental involvement was similar in the two groups. The average noise and light levels in the developmental care group were significantly lower than those in the standard care group. After adjusting for confounders, the difference remained significant. There were no significant differences among groups in the mortality and major morbidity. ConclusionsDevelopmental care might have developed an accumulated effect over time on the length of hospital stay among very low birth weight infants. The implementation of developmental care can greatly improve family centered care practices and the neonatal intensive care unit environment. RegistrationClinicalTrials.govNCT05166720. Registration date: 1 March, 2021.

IoT ENBALED SMART AQUARIUM FOR MONITORING THROUGH SENSORS

The development of an IoT-enabled smart aquarium system offering real-time monitoring and automated control of crucial environmental parameters. The core of the system lies in the ESP8266 microcontroller, facilitating seamless internet connectivity and data processing. Water quality assessment is achieved through a turbidity sensor, detecting suspended particles indicative of potential pollution. Environmental conditions are monitored using a DHT11 sensor, providing data on both temperature and humidity – essential factors for maintaining a healthy habitat for specific fish species. For automated lighting control mimicking natural day/night cycles and preventing excessive algae growth, a Light-Dependent Resistor (LDR) is implemented. The LDR adjusts aquarium lighting based on ambient light levels. Finally, a relay module controls the operation of a water pump, ensuring optimal water circulation and distribution of nutrients throughout the aquarium. While an I2C LCD connected to an Arduino Nano provides local data visualization within the aquarium setup, the ESP8266 transmits sensor readings to a user-friendly platform accessible remotely through a mobile application or web interface. This allows for convenient real-time monitoring of water quality, temperature, humidity, and lighting conditions. The system can be further programmed to generate alerts when critical parameters deviate from the ideal range, enabling proactive user intervention to maintain a healthy environment. This project demonstrates the potential of IoT technology to create a more automated, efficient, and user-friendly system for maintaining a thriving aquatic ecosystem. KEYWORDS: IoT (Internet of Things), Smart Aquarium, ESP8266, Turbidity Sensor, DHT11 Sensor, LDR(Light-Dependent Resistor), Relay Module, Water Pump, I2C LCD, Arduino Nano, Real-Time Monitoring, Automated Control, Remote Access, Mobile Application, Web Interface, Water Quality, Temperature, Humidity, Light Control, Aquatic Environment.

Translation of paclitaxel-induced peripheral neurotoxicity from mice to patients: the importance of model selection.

Paclitaxel-induced peripheral neurotoxicity (PIPN) is a potentially dose-limiting side effect in anticancer chemotherapy. Several animal models of PIPN exist, but their results are sometimes difficult to be translated into the clinical setting. We compared 2 widely used PIPN models characterized by marked differences in their methodologies. Female C57BL/6JOlaHsd mice were used, and they received only paclitaxel vehicle (n = 38) or paclitaxel via intravenous injection (n = 19, 70 mg/kg) once a week for 4 weeks (Study 1) or intraperitoneally (n = 19, 10 mg/kg) every 2 days for 7 times (Study 2). At the end of treatment and in the follow-up, mice underwent behavioral and neurophysiological assessments of PIPN. At the same time points, some mice were killed and dorsal root ganglia, skin, and sciatic and caudal nerve samples underwent pathological examination. Serum neurofilament light levels were also measured. The differences in the neurotoxicity parameters were analyzed using a nonparametric Mann-Whitney test, with significance level set at P < 0.05. Study 1 showed significant and consistent behavioral, neurophysiological, pathological, and serological changes induced by paclitaxel administration at the end of treatment, and most of these changes were still evident in the follow-up period. By contrast, study 2 evidenced only a transient small fiber neuropathy, associated with neuropathic pain. Our comparative study clearly distinguished a PIPN model recapitulating all the clinical features of the human condition and a model showing only small fiber neuropathy with neuropathic pain induced by paclitaxel.

Augmented dataset for multidimensional ballast segmentation and evaluation

Ballast plays an important role as a railway track supporting structure against repeated loading. Ballast degradation results in poor drainage, lateral instability, excessive settlement, service interruptions, and safety concerns. Therefore, utilizing efficient methods for evaluating ballast fouling is important to ensure safe railroad operations. Emerging computer vision approaches have been effectively incorporated into the ballast inspection processes to enhance their accuracy and robustness. Owing to the inherent data-driven nature of deep learning approaches, the efficacy of the developed model heavily depends on the quality of the training dataset, revealing an urgent need for a comprehensive and high-quality annotated ballast aggregate dataset. In this study, a multidimensional ballast aggregate dataset has been established which contains realistic data collected from field and laboratory setups, augmented by data produced by a synthetic ballast particle generator. In the dataset, the 2D data comprised ballast images with 2D annotations (masks) for localizing the particles in the images. The 3D data comprises height maps, point clouds, and 3D annotations for particle localization. The collected data included various environmental lighting levels and fouling conditions to ensure adequate coverage and diversity in the training dataset. A 2D ballast particle segmentation model previously developed by the authors was trained using an augmented dataset and demonstrated reliable and accurate results in field ballast inspections. The proposed dataset will be utilized in future studies, including 3D ballast particle segmentation and shape completion, to aid in the inspection and investigative methods for effective ballast maintenance.

Light competition drives species replacement during secondary tropical forest succession

Light competition is thought to drive successional shifts in species dominance in closed vegetations, but few studies have assessed this for species-rich and vertically structured tropical forests. We analyzed how light competition drives species replacement during succession, and how cross-species variation in light competition strategies is determined by underlying species traits. To do so, we used chronosequence approach in which we compared 14 Mexican tropical secondary rainforest stands that differ in age (8–32 year-old). For each tree, height and stem diameter were monitored for 2 years to calculate relative biomass growth rate (RGR, the aboveground biomass gain per unit aboveground tree biomass per year). For each stand, 3D light profiles were measured to estimate individuals’ light interception to calculate light interception efficiency (LIE, intercepted light per unit biomass per year) and light use efficiency (LUE, biomass growth per intercepted light). Throughout succession, species with higher RGR attained higher changes in species dominance and thus increased their dominance over time. Both light competition strategies (LIE and LUE) increased RGR. In early succession, a high LIE and its associated traits (large crown leaf mass and low wood density) are more important for RGR. During succession, forest structure builds up, leading to lower understory light levels. In later succession, a high LUE and its associated traits (high wood density and leaf mass per area) become more important for RGR. Therefore, successional changes in relative importance of light competition strategies drive shifts in species dominance during tropical rainforest succession.

Adaptive display white point under various ambient conditions

AbstractDisplay technologies have been developed to adjust the display brightness and color under different viewing conditions, providing better viewing experiences to users. Solutions have been proposed to adjust the display white point according to the ambient light condition, which roots into the underlying chromatic adaptation mechanism in the human visual system. This study was carefully designed to reveal the necessity to consider the ambient light level and display luminance together, which affects the viewing mode, when adjusting the adaptive display white point. The results clearly suggested that when the display luminance was greater than the ambient light diffuse white luminance, the display was viewed in the self‐luminous mode and the display white point was not significantly affected by the ambient light chromaticities. In contrast, when the display luminance was lower than the ambient light diffuse white luminance, the display was viewed in the surface mode and the ambient light chromaticities had a significant effect on the display white point, especially when the surround ratio was high.

The Efficacy of Horizontal Adjustable Bent Louver Shading for Enhancing Daylighting Performance in Walk-Up Apartment Buildings

This paper addressed the challenges posed by the limited windows and restricted areas in walk-up flats in Indonesia, resulting in improper indoor daylight performance. The study proposes using outdoor horizontally bent, adjustable louver screens as daylight reflectors to overcome this issue and improve daylighting performance in apartment buildings. The quasi-experimental technique implemented the Radiance Desktop Plug-in Version 1.02 and Ecotect 2011 lighting software. Variables, such as slat shading geometry, i.e., flat, one-bent, two-bent slats, and rotation opening angles, i.e., 30°, 45°, 60°, and 90°, were examined for interior daylight performance regarding daylight intensity and distribution. The findings indicate that although shading can reduce the average lighting level in a room, shading types with bents can improve daylight performance in another important aspect, namely achieving a more even distribution. The most effective shading technique for harvesting and distributing daylight evenly in tropical walk-up apartments was a two-bent slat type, particularly with opening angles of 90°. The results also demonstrate that louvers with multiple bent slat geometry are particularly effective in reflecting high altitudes of incident light often encountered in tropical regions and scattering the incoming light evenly into the innermost area of the room.

Neither Corpus Nor Edition: Building a Pipeline to Make Data Analysis Possible on Medieval Arabic Commentary Traditions

We have built a suite of tools in Python to proficiently analyze text reuse and intertextuality for a specific kind of set of medieval Arabic texts (commentaries) available in print. We take these printed editions, scan them, pre-process the images, give it to an OCR engine, clean the results, and store it in a data structure that mimics the explicit intertextual relation the texts have, and continue to perform data analysis on it. Digital approaches to medieval Arabic texts have either been at the micro-level in what has become known as a ‘digital edition’, i.e. the digital representation of one text, densely annotated, most commonly in TEI-XML, or it has been done at the macro-level in what is called a ‘digital corpus’, consisting of thousands of loosely encoded and sparsely annotated plain text files, accompanied by an entire infrastructure and high-performing software to perform broadly scoped queries. The micro-level generally is at the level of tens of thousands of words while the macro-level can be at the level of over a billion words. The micro-level is explicitly designed to be human readable first, while the macro-level is built to be machine readable first. At the micro-level, every little detail needs to be correct and in order, while at the macro-level a fairly large margin of error is still negligible as a mere rounding error. Amidst these levels we have been seeking a meso-level of digital analysis: neither edition nor corpus, but rather a group of texts at the level of hundreds of thousands to millions of words, with a small but perceptible margin of error, and a light but noticeable level of annotations, principally geared towards machine readability, but with ample opportunity for visual inspection and manual correction. In this paper we explain the rationale for our approach, the technical achievements it has led us to, and the results we so far obtained.

YOLOv5s-CEDB: A robust and efficiency Camellia oleifera fruit detection algorithm in complex natural scenes

To solve the problem of poor recognition accuracy caused by various colors, uneven distribution, and occlusion by branches and leaves of Camellia oleifera fruits under natural growth conditions, this study proposes an improved deep learning network, YOLOv5s-CEDB, for Camellia oleifera fruit detection based on YOLOv5s. Coordinate Attention Mechanism (CoordAtt), Deformable Convolution (DConv), and Explicit Visual Center (EVC) are introduced to enhance the network’s local and global feature extraction performance. To improve the detection performance for small and dense targets, the feature fusion module of the network was replaced with the designed light-bidirectional feature pyramid (light-BiFPN) structure. GhostConv was used to reduce the parameters and inference speed of the structure. A dataset with different light conditions, colors, and density levels of Camellia oleifera fruits was established, and performance evaluation experiments were conducted. Experimental results showed that the mean Average Precision (mAP) and F1-score of the designed YOLOv5s-CEDB network reached 91.4 % and 89.6 %, respectively, which were 2.6 % and 3.6 % higher than those of the original YOLOv5s model, respectively, and the influencing frame rate arrived at 37.6 FPS. Under different colors, distribution densities, occlusion scenarios, and light intensities, the detection accuracy of the YOLOv5s-CEDB network model was significantly better than those of the YOLOv5s, YOLOv8s and Faster-RCNN networks. It was verified that the proposed YOLOv5s-CEDB network could significantly improve the accuracy and stability of Camellia oleifera fruit detection, satisfying the requirements of yield estimation and intelligent harvesting.

Spectroscopic Observations of the Solar Corona during the 2017 August 21 Total Solar Eclipse: Comparison of Spectral Line Widths and Doppler Shifts between Open and Closed Magnetic Structures

The spectroscopic observations presented here were acquired during the 2017 August 21 total solar eclipse (TSE) with a three-channel partially multiplexed imaging spectrometer operating at extremely high orders (>50). The 4 R ⊙ extent of the slit in the north–south direction scanned the corona starting from the central meridian out to approximately 1.0 R ⊙ off the east limb throughout totality. The line widths and Doppler shifts of the Fe x (637.4 nm) and Fe xiv (530.3 nm) emission lines, characteristic of 1.1 × 106 K and 1.8 × 106 K electron temperatures, respectively, varied across the different coronal structures intercepted by the slit. Fe xiv was the dominant emission in the closed fields of an active region and the base of a streamer, with relatively constant 20–30 km s−1 line widths independent of the height. In contrast, Fe x emission exhibited broader (>40 km s−1) line widths in open fields, which increased with height, in particular in the polar coronal hole. Inferences of line widths and Doppler shifts were consistent with extreme ultraviolet (EUV) observations from the Hinode/EUV Imaging Spectrograph, as well as with the near-infrared Fe xiii 1074 nm line observed by Coronal Multichannel Polarimeter. The differences in the spectral line widths between distinct coronal structures are interpreted as an indication of the predominance of wave heating in open structures versus localized heating in closed structures. This study underscores the unparalleled advantages and the enormous potential of TSE spectroscopy in measuring line widths simultaneously in open and closed fields at high altitudes, with minimal exposure times, stray light levels, and instrumental widths.

Smart Streetlight Energy Saving System Based on mmWave Radar

Streetlights serve as fundamental infrastructure to meet the lighting needs of people on every road. However, their extensive deployment often results in unnecessary energy waste, with many streetlights maintaining high brightness despite minimal usage during the night. This study aims to develop a smart energy-efficient streetlight system that automatically adjusts lighting levels based on the absence of vehicles and pedestrians, detected after a 3-minute countdown. Specifically, the study utilizes mmWave radar to collect point cloud data, which undergoes denoising through Doppler, DBSCAN, and XYZ techniques. Additionally, the mmWave radar assists in training an LSTM model to identify pedestrian pathways. The implementation of the proposed system significantly reduces energy consumption and annual costs by automatically dimming or turning off streetlights in areas with minimal pedestrian activity during nighttime.

Leakage Current Harmonic Index as A Diagnostic Technique for Tool to Assess the Surface Condition Assessment of Aged Insulator Surface Under Varying Humidity

Leakage current has been widely utilized as an effective means to monitor the pollution severity of outdoor insulators. The indices of the odd harmonic components of the leakage current have proven to be reliable indicators of insulator performance, particularly in the third to fifth harmonic orders. However, significant challenges exist in using these indices to monitor and classify the leakage current of aged insulators under varying harmonic and pollution conditions. This paper introduces the harmonic index K((3/(5+7) ) which employs third up to seventh odd harmonic component of the leakage current as a diagnostic tool to determine the pollution severity of aged insulators under light, medium and heavy contamination levels. The experiments were conducted using 12 different sets of 11kV insulators with varying degrees of aging. The results indicate that the harmonic index Ki for new and moderately aged insulators decreases with an increase in contamination and is lower than that of heavily aged insulators.

Mechanical properties and microstructure of lime-treated shield tunnel muck improved with carbide slag and soda residue

Shield tunnel muck are usually discarded due to high water content and poor engineering properties, resulting in occupation of land sources and waste of soil sources. Meanwhile, large amounts of industrial waste such as carbide slag (CS) and soda residue (SR) are landfilled with a low reuse rate, which poses a threat to the natural environment. This study aims to improve waste shield tunnel muck using CS and SR and traditional lime, and the improved tunnel muck is expected to be used in subgrade filling to provide a new approach to solve this dilemma. A series of physical, mechanical, subgrade property, and microcosmic tests were conducted on shield tunnel muck improved by CS, SR and lime. The effects of different mixing proportions on the properties of improved tunnel muck were examined. The micro-improvement mechanisms of CS and SR on tunnel muck were explored. Results indicate that the addition of CS or SR can effectively improve the physical and mechanical properties of shield tunnel muck. CS plays a significantly role than SR in improving physical and mechanical properties of tunnel muck. A synergistic enhancement is observed as the combined CS and SR are added, and the optimal mixing proportion of tunnel muck to CS to SR is found to be 100:6:2 with a fixed lime content of 4 %. The alkaline environment created by the synergistic action of CS and SR promotes the dissolution of the active ions in soils, and the generated crystals and gelling products of hydration significant contribute to soil improvement. The tunnel muck improved with appropriate CS or SR content could meet the requirements for light or medium traffic load levels and can be effectively utilized as subgrade filling.

Association of serum glial fibrillary acidic protein with progression independent of relapse activity in multiple sclerosis

ObjectiveInsidious disability worsening is a common feature in relapsing–remitting multiple sclerosis (RRMS). Many patients experience progression independent of relapse activity (PIRA) despite being treated with high efficacy disease-modifying therapies. We prospectively investigated associations of body-fluid and imaging biomarkers with PIRA.MethodsPatients with early RRMS (n = 104) were prospectively included and followed up for 60 months. All patients were newly diagnosed and previously untreated. PIRA was defined using a composite score including the expanded disability status scale, 9-hole peg test, timed 25 foot walk test, and the symbol digit modalities test. Eleven body fluid and imaging biomarkers were determined at baseline and levels of serum neurofilament light (sNfL) and glial fibrillary acidic protein (sGFAP) were also measured annually thereafter. Association of baseline biomarkers with PIRA was investigated in multivariable logistic regression models adjusting for clinical and demographic confounding factors. Longitudinal serum biomarker dynamics were investigated in mixed effects models.ResultsOnly sGFAP was significantly higher in PIRA at baseline (median [IQR] 73.9 [60.9–110.1] vs. 60.3 [45.2–79.9], p = 0.01). A cut-off of sGFAP > 65 pg/mL resulted in a sensitivity of 68% and specificity of 61%, to detect patients at higher risk of PIRA. In a multivariable logistic regression, sGFAP > 65 pg/mL was associated with higher odds of developing PIRA (odds ratio 4.3, 95% CI 1.44–12.86, p = 0.009). Repeated measures of sGFAP levels showed that patients with PIRA during follow-up had higher levels of sGFAP along the whole follow-up compared to stable patients (p < 0.001).ConclusionDetermination of sGFAP at baseline and follow-up may be useful in capturing disability accrual independent of relapse activity in early RRMS.

Blue light regulated lignin and cellulose content of soybean petioles and stems under low light intensity.

To improve light harvest and plant structural support under low light intensity, it is useful to investigate the effects of different ratios of blue light on petiole and stem growth. Two true leaves of soybean seedlings were exposed to a total light intensity of 200μmolm-2 s-1 , presented as either white light or three levels of blue light (40μmolm-2 s-1 , 67μmolm-2 s-1 and 100μmolm-2 s-1 ) for 15days. Soybean petioles under the low blue light treatment upregulated expression of genes relating to lignin metabolism, enhancing lignin content compared with the white light treatment. The low blue light treatment had high petiole length, increased plant height and improved petiole strength arising from high lignin content, thus significantly increasing leaf dry weight relative to the white light treatment. Compared with white light, the treatment with the highest blue light ratio reduced plant height and enhanced plant support through increased cellulose and hemicellulose content in the stem. Under low light intensity, 20% blue light enhanced petiole length and strength to improve photosynthate biomass; whereas 50% blue light lowered plants' centre of gravity, preventing lodging and conserving carbohydrate allocation.

Multilevel sensor for monitoring external and internal environment of eggs

Although it is well known that incubation environment has a great influence on embryogenesis and post-hatching performance of birds, not much is known about how external thermal, sound and light stimuli are isolated by eggshells and perceived by embryos. In this context, this study aimed to develop, calibrate and evaluate a multilevel sensor for integrated monitoring of the external (incubator) and internal environment of eggs. The variables of interest for the external environment were air temperature and relative humidity. For the internal environment, shell temperature, internal temperature, luminosity and sound pressure level were considered. The sensor was developed with an ATmega328 microcontroller, in open-source prototyping, using electronic components which are compatible with the egg's physical structure. Calibrations were carried out in a controlled environment, comparing the multilevel sensor with commercial equipment, obtaining coefficients of determination of R 2 > 0.90 for all variables studied. The multilevel sensor was also validated, simulating a commercial incubation situation and comparing eggs with 2 shell colors (white and brown) and internal volume (intact and empty). Validation results showed that white-shelled eggs insulate less external light (P < 0.001) and full eggs presented higher internal temperatures, greater light and lower sound pressure levels compared to empty eggs (P < 0.001). The multilevel sensor developed here is an innovative proposal for monitoring, simultaneously and in real time, different variables of interest in the commercial incubation environment.

Too Warm and Too Dry—Decline and Threat of the Subarctic-Subalpine Liverwort Hygrobiella laxifolia in a Low Mountain Range in Central Europe under the Conditions of Climate Change

Using the liverwort Hygrobiella laxiflora as an example, this study aims to investigate the impact of climate change on montane species and their ecosystems. The study area is the Elbe Sandstone Mountains in Germany and the Czech Republic, where Hygrobiella laxifolia has an isolated occurrence in deeply carved gorges characterized by a specific microclimate (cellar climate). The focus is on determining the rate at which populations are affected. After little change in the population over a long period, a sudden sharp decline has been observed in recent years. The decline correlates with the mass proliferation of the European spruce bark beetle (Ips typographus) observed since 2017, which has led to a strong thinning of the forests in the area. In many areas of occurrence, the forests now have a much sparser tree layer or none at all. Competition from other, more competitive species also plays an important role as a cause of decline, as these have spread more widely on the sites due to the now higher light levels. The severe bark beetle infestation and the large-scale forest fire of 2022 both represent events that led to the almost complete deforestation of large parts of the study area, which was previously unknown on this scale. Since both the bark beetle infestation and the forest fire are strongly intensified by climate change, we assume that climate change is the main reason for the decline of Hygrobiella laxifolia in the area. The populations of Hygrobiella laxifolia of the Elbe Sandstone Mountains show some morphological peculiarities. As two new species have recently been described within Hygrobiella, we have integrated a sample from the area into a molecular phylogeny of Hygrobiella based on the plastid sequences of trnL-trnF intergenic spacer and matK to clarify the relationships. The sample from Elbe Sandstone Mountains fits well into the clade of Hygrobiella laxifolia.