Time Of Day Research Articles

Context-Aware Recommender Systems: Exploring the Role of Time, Location, and Social Influence in Personalized Recommendations

Recommender systems have become a cornerstone of personalized user experiences across various domains, from e-commerce to entertainment. While traditional models focus on user-item interactions, they often overlook critical contextual information that could significantly enhance prediction accuracy. In this paper, we explore the concept of context-aware recommender systems, which integrate temporal, geospatial, and social context to improve recommendation quality. Temporal context considers factors such as time of day, seasonality, or the user's activity history, while geospatial context leverages the user's location or proximity to items, enhancing the relevance of recommendations. Additionally, social context incorporates user interactions with social networks or communities, enriching the understanding of preferences through peer influence and shared interests. We examine various techniques for incorporating these contexts into recommendation algorithms, including hybrid models, deep learning approaches, and context-sensitive matrix factorization. Furthermore, we address the challenges in balancing the complexity of these models with the need for real-time recommendations and scalability. Finally, we present empirical evaluations on real-world datasets, demonstrating that context-aware models significantly outperform traditional recommender systems in terms of prediction accuracy, diversity, and user satisfaction. This paper aims to provide a comprehensive framework for developing context-aware recommender systems and outline key areas for future research, such as integrating emerging contextual dimensions like sentiment and emotional state.

Low-level turbulence risk assessment and visualization using temporal rate of change of headwind of an aircraft

In this study, we focused on the temporal rate of change of headwind, which is one of the recorded parameters in the aircraft’s in-flight quick access recorder data. We selected the Laplace distribution and utilized the scaling parameter to construct a low-level turbulence risk assessment model. Using this model, we calculated the risk of low-level turbulence occurrence at five airports in Japan based on the month, time of day, and wind speed. We visualized how the geographical conditions at each airport influenced risk in relation to airport wind speeds. We developed a low-level turbulence visualization site linked to weather conditions using these results to enable pilots to easily verify low-level turbulence risk and incorporate this information into their flight routines. These findings are anticipated to significantly enhance aircraft safety.

Use of HANTS algorithm in Sentinel-2 images for interpolation of missing data: a case-study for soy agricultural areas in periods of growth and harvesting

Abstract. Information about the current state of an agricultural culture can be fundamental in decision making, varying from quantity of fertilizers to be used in an area to estimating the productivity of a harvest in a period to establish credits payment. Satellite images with reasonable spatial resolution like Sentinel-2 are a powerful tool to visualize agricultural areas production, especially with the application of multispectral indexes like the Normalized Difference Vegetation Index (NDVI). Yet, due to the limitation of temporal resolution of satellites and periods with large cloudy weather can make the analysis difficult. This study proposed the use of the harmonic analysis time series algorithm (HANTS) to aid the incompleteness of time series obtained by the available satellite images in a case study of soy culture in Brazil. By applying the algorithm, a harmonic interpolation is obtained to produce a full daily NDVI time series from the beginning to end of the study period, facilitating further analysis to obtain metrics of interest. In this study, time series with gaps on specified periods are created based on full time series, then interpolated and compared with the real ones based on root mean squared error (RMSE) to access its accuracy.

Progesterone and Estradiol Levels Associated with Concussion and Clinical Outcomes and Recovery in Female Athletes and Cadets.

Introduction: Female athletes are underrepresented in concussion research, and few studies have investigated associations of ovarian hormones with concussion outcomes. This study explored associations of concussion with levels and variability of progesterone, estradiol, and their ratio (P/E) and examined relationships of hormone levels with clinical measures and recovery after concussion in CARE Consortium female athletes and cadets.Methods: Female participants enrolled (n = 749) at pre-injury baseline. Participants with concussion (mean age 19.34 years; n = 130, 90 athletes, 40 non-athlete cadets) completed one or more visits at non-standardized times of day: immediately post-injury, 24-hours post-injury, upon initiating the return-to-play protocol (Init RTP), and seven days following unrestricted return-to-play (PRTP). Controls (mean age 19.85 years; n = 67, 61 athletes, 6 non-athlete cadets) completed similar visits. Linear mixed models and general linear models tested associations of hormone levels and/or variation with concussion status, symptoms, and recovery, controlling for self-reported birth control use at pre-injury baseline.Results: Female participants with concussion had higher progesterone levels relative to controls on average across all visits (mean difference (ln ng/mL) (standard error) MD = 0.26(0.08), t(193) = 3.03, p = 0.003). Those with concussion had elevated estradiol at 24 hours (MD = 0.27(0.09), t(506) = 3.04, p = 0.02), Init RTP (MD = 0.38(0.09), t(508) = 4.29, p < 0.001), and PRTP (MD = 0.30(0.09), t(515) = 3.25, p = 0.01) relative to pre-injury baseline, and compared to controls at Init RTP (MD = 0.35(0.12), t(429) = 2.78, p = 0.006). Concussed participants had a lower range of estradiol over 7-28 days than controls (B(SE) = -0.24(0.09), F(1,145) = 6.43, p = 0.01). Acutely after concussion, estradiol was positively associated with Brief Symptom Inventory Global Severity Index scores (B(SE) = 0.29(0.12), F(1,102) = 5.60, p = 0.02). No significant relationships were found between hormones and recovery.Conclusions: These results, which warrant further research, suggest ovarian hormones may be associated with concussion and psychological symptom severity post-concussion.

The Efficiency Assessment of the Blockchain Networks Adaptive Algorithm as Part of a Multi-Consensus System on Communication Networks

The relevance of the approaches and solutions proposed in the work is due to the desire for distributed computing and decentralization of system modules using blockchain technology in order to increase the autonomy, security and independence of system components. The implementation of blockchain technology for decentralization is hampered by the lack of flexibility in terms of the algorithm for making a single decision in the asynchronous system ‒ consensus, which affects the amount of generated network traffic and the requirements for the hardware component. Due to the changing nature of the transmitted traffic, the load on the communication channel, the time of day of traffic transmission, the equipment, and the network protocols used, the development of a single universal consensus algorithm is not possible, since it depends on a very wide range of variable and specific parameters characteristic of different tasks. The introduction of adaptation mechanisms to current network conditions and a change in the consensus algorithm without losing information and with a satisfactory level of delays would provide sufficient flexibility for further implementation in existing telecommunication systems. The purpose of this paper is to evaluate the effectiveness of the proposed adaptive algorithm for choosing a consensus for blockchain networks.The essence of the presented algorithm is to change the consensus algorithm on a section of the blockchain network upon reaching certain network conditions, which allows regulating the amount of generated load on the communication network to reduce the loss of transaction blocks and the subsequent delay in their processing. The proposed efficiency assessment model is based on mathematical modeling methods, differential equation analysis, queueing theory and network graphs. The analysis of the results showed the efficiency of the proposed model when comparing the results of the analytical calculation and the experiment. The scientific novelty of the proposed approach is to developing an adaptive consensus selection algorithm, as opposed to developing a universal consensus algorithm, and the efficiency assessment model of the proposed algorithm on the communication network, taking into account a number of parameters characterizing the devices and the section of the communication network.The theoretical significance is in the universality of the proposed efficiency assessment model for calculating the synchronization speed of all nodes of the blockchain network with specified network parameters.The practical significance of the proposed algorithm and efficiency assessment method lies in the formation of new approaches and opportunities for integrating blockchain technology into modern communication networks, abstracting from the problem of consensus selection in changing conditions of the communication network section.

Time allocation among different sorts of activities: An overview of mathematical models

The history of time use research dates back more than 100 years. Russian scientists were among the first to conduct large-scale surveys in this field. Nowadays, this sphere has become interesting among scientists again. Rosstat has rebegun to conduct surveys on the daily time use, large-scale independent surveys have appeared, and media have increased their interest. The amount of empirical microdata that has emerged has made it possible to examine a complex concept as the household time use in ever more numbers. The purpose of this methodological overview is to describe the most significant mathematical models that have been used and are being used to study the time budgets of the population. This article overviews the first models in which time was considered as one of the means of production along with other goods, and households tried to maximize the utility function solely based on financial considerations. Then, modern models, where time is no less an important factor of well-being than material status, are represented. The article demonstrates how the new model classes that have recently emerged adapt to the data that is accessible in certain circumstances, modify themselves to accomplish fresh tasks, and become less costly when looking at analytical estimations. In order to categorize the population’s time budget structure under different research activities, the paper examines and systemsatizes a number of mathematical models

To ride-hail or not to ride-hail? Complementarity and competition between public transit and transportation network companies through the lens of app data

AbstractTransportation Network Company (TNC) services have become a prominent factor in urban transportation in recent years, and there is an ongoing debate regarding their relationship with public transit. While many argue that TNCs draw passengers away from public transportation, others believe the two modes complement each other. However, due to the inadequate sample size of rider surveys as primary data sources, our understanding of how riders choose between these two modalities remains limited. This study uses nine months of trip planning data generated by the Transit App, which captures how travelers engage with multiple options in real time, including TNC and public transit services. We extract measures from Transit describing the travel options and the habits of each individual user for sessions in which the user “tapped” on one of these two modes, indicating consideration of it as an option. Machine learning models predict the likelihood of a rider tapping TNC based on features of the available public transit options and other contextual factors (e.g., time of day, weather conditions). The models find that these taps are driven by factors that highlight the convenience of TNC, such as the waiting time, walking distance, and the number of transfers for public transportation trips. We also find that the majority of TNC trips tapped by app users combine the two modes when using the Transit App, with TNC acting as a connection to or from public transit. These results provide detailed additional evidence for current arguments for both competition and complementarity between TNC and public transit from a population that uses an app to navigate public transit.

Myopia Progression in Children: A Comparative Analysis of Pre-Pandemic and Pandemic Periods

Background: The COVID-19 pandemic brought about significant changes in children's lifestyles, including increased screen time and reduced outdoor activities due to school closures and lockdowns. These changes raised concerns about potential myopia progression in children. Methods: This study compared the spherical equivalent (SE) measurements of children aged 8-17 years from two time visits: one before the pandemic (September 2019 - February 2020) and one during the pandemic (March 2020 - February 2022). Data on family history of myopia, screen time, and outdoor activities were also collected. Paired t-tests were used to analyze the differences in SE between the two visits. Results: A total of 11 children participated in the study. The mean change in SE was -1.55 D, indicating a significant increase in myopia during the pandemic (p = 0.046). The average daily screen time was 201 minutes, while outdoor activity time was 42 minutes. Conclusion: The findings suggest a potential corelation between increased screen time, reduced outdoor activities, and myopia progression in children during the pandemic. It is crucial to encourage healthy visual habits, including limiting screen time and promoting outdoor activities, to mitigate the impact of the pandemic on children's vision.

Relationship between physical activity and abdominal obesity and metabolic markers in postmenopausal women

This study aimed to investigate the impact of physical activity indicators monitored by the POLAR accelerometer on body obesity indicators, metabolic syndrome parameters, and energy metabolism hormones in postmenopausal women. Was included 71 participants from this study program (68.8 ± 4.3 years). We divided participants into LPA and MVPA groups based on their level of moderate to vigorous physical activity per week; Physical activity levels over 7 days were assessed using a POLAR accelerometer, with daily step counts and sedentary time recorded. Measurements included waist circumference, visceral fat volume, body fat percentage, blood pressure, fasting blood glucose, triglyceride levels, HDL cholesterol, and energy metabolism hormone levels (leptin, resistin, adiponectin). The MVPA group displayed lower waist circumference, body fat percentage, abdominal fat, and BMI compared to the LPA group (p < 0.05). A significant negative correlation was observed between daily step count and obesity indicators, including waist circumference (r = -0.301), body fat percentage (r = -0.295), abdominal fat (r = -0.318), and BMI (r = -0.238). Conversely, sedentary time showed a positive correlation with obesity indicators such as waist circumference (r = 0.258), body fat percentage (r = 0.239), and abdominal fat (r = 0.244). Moreover, daily step count exhibited a significant negative correlation with leptin levels (r = -0.245), while sedentary time was positively correlated with the energy metabolic factor leptin (r = 0.279). Waist circumference demonstrated significant positive correlations with triglycerides, blood glucose, adiponectin, resistin, and leptin levels. Postmenopausal women who engage in at least 150 min of MVPA weekly show lower obesity indices. There is a significant correlation between physical activity levels and obesity indicators, which relate to metabolic syndrome and energy metabolism factors. Thus, increased physical activity may help prevent metabolic syndrome and cardiovascular diseases in this population.

Novel assessment of CPAP adherence data reveals distinct diurnal patterns.

Obstructive sleep apnea (OSA) is a prevalent condition effectively treated by continuous positive airway pressure (CPAP) therapy. CPAP adherence data, routinely gathered in clinical practice, include detailed information regarding both duration and timing of use. The purpose of the present study was to develop a systematic way to measure the diurnal pattern of CPAP adherence data and to see if distinct patterns exist in a clinical cohort. Machine learning techniques were employed to analyze CPAP adherence data. A cohort of 200 unselected patients was assessed and a cluster analysis was subsequently performed. Application of this methodology to 17 patients with different visually noted patterns was carried out to further assess performance. Each 30-day period of CPAP use for each patient was characterized by four variables describing the time of day of initiation and discontinuation of CPAP use, as well as the consistency of use during those times. Further analysis identified six distinct clusters, reflecting different timing and adherence patterns. Specifically, clusters with relatively normal timing versus delayed timing were identified. Finally, application of this methodology showed generally good performance with limitations in the ability to characterize shift worker and non-24 rhythms. This study demonstrates a methodology for analysis of diurnal patterns from CPAP adherence data. Furthermore, distinct timing and adherence patterns are demonstrated. The potential impact of these patterns on the beneficial effects of CPAP requires elucidation.

A population-level study reveals hidden patterns in resting site choice of free-ranging dogs

Abstract Rest is an essential biological activity for all animals. Urban animals encounter multiple anthropogenic disturbances while resting. Since free-ranging dogs (FRDs) have survived as a population in the Global South for centuries, they provide a unique opportunity to study adaptation to the human-dominated landscape. In a field-based study over 3 years, we documented resting behaviour and site selection in 66 dog groups (284 dogs) in India. Eleven physical parameters of the resting sites, along with biological activities such as mating and pup-rearing, season, and the time of day, affected their choice of resting sites. The frequency-rank distribution of the unique combinations of the 11 parameters followed a power-law distribution, suggesting underlying biological reasons for the observed preferences. Seven of these parameters were most consistently involved in site choice, explaining around 24% of the observations. FRDs prefer to rest near resources at the centre of their territory, where they have maximum visibility and minimal anthropogenic disturbance. They prefer concrete surfaces with low insect presence. Our results suggest that the FRDs choose resting sites that enable better resource and territory defence, foraging effectiveness, and insect avoidance. Urban planners can use these insights to reduce situations of conflict and build more inclusive urban areas.

Cooling down is as important as warming up for a large-bodied tropical reptile.

An ectotherm's performance and physiological function are strongly tied to environmental temperature, and many ectotherms thermoregulate behaviourally to reach optimum body temperatures. Tropical ectotherms are already living in environments matching their thermal tolerance range and may be expected to conform to environmental temperatures. We tracked the body temperatures (Tb) of 163 estuarine crocodiles across 13 years and compared Tb of 39 crocodiles to water temperature gathered using fish-borne sensors (Tw) across 3 years (2015-2018). While Tb largely conformed closely to Tw, we found inter- and intra-individual differences in relative body temperature (Tb-Tw) that depended on sex and body size as well as the time of day and year. Deviations from Tw, especially during the warm parts of the year, suggest that thermoregulatory behaviour was taking place: we found patterns of warming and cooling events that seemed to mediate this variation in Tb. Thermoregulatory behaviour was observed most frequently in larger individuals, with warming events common during winter and cooling events common during summer. By observing free-ranging animals across multiple years, we found that estuarine crocodiles show yearly patterns of active cooling and warming behaviours that modify their body temperature, highlighting their resilience in the face of recent climate warming. Our work also provides the first evidence for thermal type in large-bodied reptiles.

Sleep, Fatigue, and Recovery Profiles of the Longest Solo Unsupported One-Way Polar Ski Journey Across Antarctica.

Antarctic expeditions are exceptional physiological challenges. Sleep plays a critical role in athletic performance, recovery, and wellness, with sleep disturbances having a negative impact on health and performance. The authors investigated sleep, fatigue, and recovery profiles of the longest solo unsupported one-way polar ski journey across Antarctica. A 33-year-old woman covered 1484.53km from Hercules Inlet to the South Pole, finishing at the Ross Ice Shelf, in 70days and 16hours. Questionnaires on sleep (Pittsburgh Insomnia Rating Scale and Karolinska Sleepiness Scale), fatigue (Subjective Assessment of Fatigue), recovery (Total Quality Recovery), and wellness were completed at different time points (before, during, and after the expedition). Average daily sleep time was between 4 and 5hours, increasing to 7hours for the final part of the expedition. Satisfaction of sleep and lack of energy deteriorated as the expedition progressed, alongside signs of clinical insomnia. Fatigue and muscle soreness increased with increasing milage, with extreme levels and very poor recovery toward the end of the expedition. Despite this, the adventurer continued to perform on extremely high levels. Postexpedition scores returned to baseline, demonstrating the incredible adaptation and ability to recovery. The postexpedition interview showed that prior experience of an Antarctic expedition may have prepared the athlete and made her more resilient for this challenge. The data provide unique insights into Antarctic expeditions and may help us understand the limits of human performance when planning future expeditions of this nature. Female athletes are capable of extreme challenges, breaking established performance boundaries.

Enhancing the performance of runoff prediction in data-scarce hydrological domains using advanced transfer learning

Accurate hydrological predictions are often hindered by the lack of stream gauges in data-scarce regions, where traditional transfer learning (TL) models like Long Short-Term Memory (LSTM) networks often face limitations due to reduced accuracy and adaptability. To enhance runoff prediction in such regions, we developed DAformer, a novel TL approach that integrates domain adversarial neural networks with the Informer model. Trained on comprehensive runoff data from U.S. basins, DAformer was applied to three basins in Chile and the Chaersen basin in China, demonstrating an effective transfer from data-rich to data-scarce environments. Results show that DAformer significantly outperforms LSTM-based models, improving forecast accuracy by 16.1% for 1-day lead time and by 100.5% for 5-day lead time. These improvements indicate that the DAformer model not only enhances prediction accuracy but also holds substantial practical implications for flood risk management and water resource planning in regions with limited data availability. By clustering basins based on Shuttle Radar Topography Mission (SRTM) and other geographical data, we found that relying on multiple source basins further enhances the performance. DAformer, therefore, serves as a robust and scalable method for enhancing runoff prediction for regions with limited data.

Reinforcement Learning for EV Fleet Smart Charging with On-Site Renewable Energy Sources

In 2020, the transportation sector was the second largest source of carbon emissions in the UK and in Newcastle upon Tyne, responsible for about 33% of total emissions. To support the UK’s target of reaching net zero emissions by 2050, electric vehicles (EVs) are pivotal in advancing carbon-neutral road transportation. Optimal EV charging requires a better understanding of the unpredictable output from on-site renewable energy sources (ORES). This paper proposes an integrated EV fleet charging schedule with a proximal policy optimization method based on a framework for deep reinforcement learning. For the design of the reinforcement learning environment, mathematical models of wind and solar power generation are created. In addition, the multivariate Gaussian distributions derived from historical weather and EV fleet charging data are utilized to simulate weather and charging demand uncertainty in order to create large datasets for training the model. The optimization problem is expressed as a Markov decision process (MDP) with operational constraints. For training artificial neural networks (ANNs) through successive transition simulations, a proximal policy optimization (PPO) approach is devised. The optimization approach is deployed and evaluated on a real-world scenario comprised of council EV fleet charging data from Leicester, UK. The results show that due to the design of the rewards function and system limitations, the charging action is biased towards the time of day when renewable energy output is maximum (midday). The charging decision by reinforcement learning improves the utilization of renewable energy by 2–4% compared to the random charging policy and the priority charging policy. This study contributes to the reduction in battery charging and discharging, electricity sold to the grid to create benefits and the reduction in carbon emissions.

Influence of sedentary behavior and physical activity in leisure and work on sleep duration: data from NHANES 2017–2018

ObjectiveTo evaluate the association between sedentary behavior (SB), moderate to vigorous physical activity (MVPA), and sleep duration.MethodsData from the 2017–2018 National Health and Nutrition Examination Survey (NHANES) was analyzed. SB was assessed based on the average daily sitting time, while MVPA was estimated by the frequency and duration of leisure and work-related activities. The ratio of time spent in MVPA to time in SB was analyzed, and a thresholds of ≥ 1.0, 2.5 and 10 min of MVPA per sedentary hour was used to determine sufficiency for mitigating the effects of a sedentary lifestyle. Sleep duration was measured by the average hours slept on weekdays and weekends, classified according to National Sleep Foundation guidelines. The measures of SB, MVPA, and sleep were self-reported. Descriptive statistics were used to characterize the sample, and multivariate logistic regression was applied to assess the associations between movement behaviors and sleep duration.ResultsThe study included 5,533 participants, with 51.8% women, predominantly aged 26–64 years (66.1%). Insufficient physical activity was reported by 59.6% at work and 62.5% during leisure time. Recommended sleep duration was observed in 84.4% of the sample. Adjusted multivariate analysis revealed that individuals engaging in ≥ 2.5 min of MVPA during leisure-time for each sedentary hour were 38.9% less likely to experience short-term sleep (OR:0.72;95%CI:0.53–0.97). Conversely, those who performed the same amount of MVPA at work were 57.0% more likely to have short-term sleep (OR:1.57;95%CI:1.16–2.12).ConclusionMeeting the MVPA threshold during leisure-time reduces the likelihood of short-term sleep, while higher MVPA levels at work increase the likelihood of short-term sleep.

Energy Consumption and Energy Costs Based on Time-of- Day Tariffs in Seafood Processing Units in Kerala, India

Energy conservation in seafood processing is crucial due to the highly energy-intensive nature of its main stages such as pre-processing, processing, and storage, which must operate under cold chain conditions. The variability of energy tariffs based on Time of Day (TOD) significantly impacts the economic sustainability of these operations. This study evaluates the electrical energy consumption patterns of seafood processing units in South India, categorized by production capacity. Primary data were collected from 10 seafood processing units, assessing energy consumption during Normal Load Time (NLT), Peak Load Time (PLT), and Off-Peak Load Time (OLT) over a one-year period. Resultsrevealed that smaller capacity units (&lt;30 T/D) accounted for 56% of total production during PLT, despite energy tariffs being highest during this time.These low-capacity units also exhibited higher mean energy consumption and costs compared to larger units, suggesting inefficiencies. The findings highlight the need for optimized energy management strategies, such as rescheduling production to lower-tariff periods and adopting more energy-efficient technologies. Implementing such measures could reduce energy costs and improve the overall profitability of seafood processing operations, particularly for smaller units.

Examining Circumstances Surrounding Fatal Pediatric Heatstroke in the United States

Fatal Pediatric Vehicular Heatstroke (PVH) is a preventable tragedy that claims the lives of an average of 38 children each year. The purpose of this article is to explore the relationship between fatal Pediatric Vehicular Heatstroke incidents and various circumstantial factors. Data was collected through customized online searches of electronic media using tools such as Google News and Lexis-Nexus. A set of linear regression analyses were conducted to examine the relationship between fatal PVH and three circumstantial variables: geographic region, time of day, and outside temperature. The finding show that specific U.S. Regions, Time of Day, and Outside Temperature significantly predicted fatal Pediatric Vehicular Heatstroke (PVH). Understanding these relationships can aid in the development of more targeted prevention technologies, legislation, and public education efforts aimed at reducing the risk of fatal PVH within the United States.

Protection of the quiet and nocturnal landscape

Motives: We associate landscape primarily with the presence of vistas that change with the time of day, season and weather conditions. Landscape is shaped by light and darkness, sound and silence, all of which influence its perception. Quiet and nocturnal landscapes, however, remain on the margins of scientific interest in traditional landscape studies.Aim: The main aim of the study was to identify the younger generation’s awareness of the importance of nocturnal landscapes, light and noise pollution, and their preferences for protection against these threats. For this purpose, a diagnostic survey method was used. The results of the survey were referred to the 2015 TNS Poland report prepared for the Ministry of the Environment. In addition, the legal conditions of landscape protection in Poland were identified, with reference to protection against noise and light pollution. National and international experiences in the protection of quiet and nocturnal landscapes were also presented.Results: The survey demonstrated that public awareness of light pollution is increasing. A nocturnal (and quiet) landscape is of high value to the respondents. Light pollution causes, among other things, energy waste and landscape degradation (change of landscape characteristics), while noise negatively affects human and animal health. Therefore, the nocturnal (and quiet) landscape should be protected by law. Currently, there are no direct instruments for the protection of nocturnal landscapes in Poland. On the other hand, in the case of quiet landscapes, there are some instruments (e.g. quiet zones), but they have not been used yet. It is encouraging that public awareness of light and noise pollution is increasing. It is necessary to link landscape protection with the protection of the night sky and quiet zones, for example through advertising resolutions and the establishment of cultural parks. It is also important to educate the public about the risks involved.

Differential regulation of sleep by blue, green, and red light in Drosophila melanogaster

IntroductionExposure to blue-enriched light from electronic devices is an emergent disruptor of human sleep, especially at particular times of day. Further dissection of this phenomenon necessitates modeling in a tractable model organism.MethodsThus, we investigated the effects of light color on sleep in Drosophila melanogaster. We measured sleep in red-eyed Canton-S (CS) and white-eyed w1118 flies in baseline 12:12 light/dark conditions and experimental conditions with light-color (blue, red, or green) exposure for all 12 h of daylight or 3 h in the morning or evening.ResultsBlue light reduced daytime and nighttime sleep in CS but not in w1118, potentially indicating a role for the compound eye in blue light’s effects on fruit fly sleep. Red light, especially in the evening, reduced sleep during exposure in both strains. Green light had minimal effects on sleep in CS flies, but evening exposure reduced sleep in w1118 flies, mimicking red light’s effects.DiscussionIn conclusion, light’s effects on sleep in D. melanogaster are dependent on wavelength and time-of-day. Future studies will aim to dissect these mechanisms genetically.