Treatments for chronic pain are inadequate, and new options are needed. Nonpharmaceutical approaches are especially attractive with many potential advantages including safety. Light therapy has been suggested to be beneficial in certain medical conditions such as depression, but this approach remains to be explored for modulation of pain. We investigated the effects of light-emitting diodes (LEDs), in the visible spectrum, on acute sensory thresholds in naive rats as well as in experimental neuropathic pain. Rats receiving green LED light (wavelength 525 nm, 8 h/d) showed significantly increased paw withdrawal latency to a noxious thermal stimulus; this antinociceptive effect persisted for 4 days after termination of last exposure without development of tolerance. No apparent side effects were noted and motor performance was not impaired. Despite LED exposure, opaque contact lenses prevented antinociception. Rats fitted with green contact lenses exposed to room light exhibited antinociception arguing for a role of the visual system. Antinociception was not due to stress/anxiety but likely due to increased enkephalins expression in the spinal cord. Naloxone reversed the antinociception, suggesting involvement of central opioid circuits. Rostral ventromedial medulla inactivation prevented expression of light-induced antinociception suggesting engagement of descending inhibition. Green LED exposure also reversed thermal and mechanical hyperalgesia in rats with spinal nerve ligation. Pharmacological and proteomic profiling of dorsal root ganglion neurons from green LED-exposed rats identified changes in calcium channel activity, including a decrease in the N-type (CaV2.2) channel, a primary analgesic target. Thus, green LED therapy may represent a novel, nonpharmacological approach for managing pain.

The optimal combination of wavelengths of light (spectral quality) for single leaf photosynthesis has been well characterized, but spectral quality is not well characterized in whole plants in long-term studies. Here we report the effects of eight light spectra at two photosynthetic photon fluxes (200 and 500 µmol m-2 s-1) on dry mass, leaf area index and net assimilation of seven species in replicate 21-day studies. The combination of treatments allowed us to separately assess the effects of blue and green light fraction among species and PPF. At a PPF of 500, increasing blue light from 11 to 28 % significantly decreased dry mass in tomato, cucumber, and pepper, but there was no significant effect on soybean, lettuce and wheat. At a PPF of 200, dry mass significantly decreased only in tomato across the blue light range. Effects on leaf area paralleled effects on dry mass in all species at both PPFs, indicating that the effects of blue light on dry mass were mediated by changes in leaf area. Contrary to predictions of net assimilation based on blue light response of single leaves, there was no evidence of decreasing net assimilation with increasing blue light. In contrast to the significant effect of blue light dry mass and leaf area, increasing green light fraction from zero to 30 % resulted in few significant differences. Contrary to several reports on significant green light effects on growth (both increases and decreases), we found no consistent effect of green light among species on growth, leaf area or net assimilation. Collectively, these results indicate significant differences among species in sensitivity to blue light and less sensitivity to green light, and that the effect of blue light on dry mass is primarily determined by changes in leaf area.

In the United States, intersections are among the most frequent locations for crashes. One of the major problems at signalized intersection is the dilemma zone, which is caused by false driver behavior during the yellow interval. This research evaluated driver behavior during the yellow interval at signalized intersections and compared different dilemma zone countermeasures. The study was conducted through four stages. First, the driver behavior during the yellow interval were collected and analyzed. Eight variables, which are related to risky situations, are considered. The impact factors of drivers' stop/go decisions and the presence of the red-light running (RLR) violations were also analyzed. Second, based on the field data, a logistic model, which is a function of speed, distance to the stop line and the lead/follow position of the vehicle, was developed to predict drivers' stop/go decisions. Meanwhile, Cellular Automata (CA) models for the movement at the signalized intersection were developed. In this study, four different simulation scenarios were established, including the typical intersection signal, signal with flashing green phases, the intersection with pavement marking upstream of the approach, and the intersection with a new countermeasure: adding an auxiliary flashing indication next to the pavement marking. When vehicles are approaching the intersection with a speed lower than the speed limit of the intersection approach, the auxiliary flashing yellow indication will begin flashing before the yellow phase. If the vehicle that has not passed the pavement marking before the onset of the auxiliary flashing yellow indication and can see the flashing indication, the driver should choose to stop during the yellow interval. Otherwise, the driver should choose to go at the yellow duration. The CA model was employed to simulate the traffic flow, and the logistic model was applied as the stop/go decision rule. Dilemma situations that lead to rear-end crash risks and potential RLR risks were used to evaluate the different scenarios. According to the simulation results, the mean and standard deviation of the speed of the traffic flow play a significant role in rear-end crash risk situations, where a lower speed and standard deviation could lead to less rear-end risk situations at the same intersection. High difference in speed are more prone to cause rear-end crashes. With Respect to the RLR violations, the RLR risk analysis showed that the mean speed of the leading vehicle has important influence on the RLR risk in the typical intersection simulation scenarios as well as intersections with the flashing green phases' simulation scenario. Moreover, the findings indicated that the flashing green could not effectively reduce the risk probabilities. The pavement marking countermeasure had positive effects on reducing the risk probabilities if a platoon's mean speed was not under the speed used for designing the pavement marking. Otherwise, the risk probabilities for the intersection would not be reduced because of the increase in the RLR rate. The simulation results showed that the scenario with the pavement marking and an auxiliary indication countermeasure, which adds a flashing indication next to the pavement marking, had less risky situations than the other scenarios with the same speed distribution. These findings suggested the effectiveness of the pavement marking and an auxiliary indication countermeasure to reduce both rear-end collisions and RLR violations than other countermeasures.

No description available.

The Green Light letter from President Franklin Roosevelt to Major League Baseball Commissioner Kenesaw Mountain Landis gave MLB permission to continue throughout World War II. The team owners felt relief that MLB is the only professional sport to survive during the years of World War II (1941-1945). MLB became a primary contributor toward the war effort. While war-supporting efforts were conducted, team owners positioned themselves to benefit from the bond between baseball and the American people. MLB portrayed itself through the commissioner’s office policy as a patriotic partner by providing entertainment for American factory workers and contributing equipment to servicemen overseas. MLB also remained a monopoly without Congressional inquiries or public challenge. Since MLB was exempt from anti-trust laws, team owners operated within MLB’s anti-trust exemption and strengthened position for the post war period.

The circadian system controls the timing of most of the physiological functions and behaviour within approximately 24 hours period. The light/dark cycle aligns this circadian period with the exact length of the solar day. Light information is conveyed via ipRGC cells in the retina, with maximum sensitivity in the blue part light (460-480nm wavelength). The information proceeds to the main circadian clocks located in the suprachiasmatic nuclei. This clock regulates the synthesis of neurohormone melatonin in the pineal gland whose high night level can be rapidly downregulated by the light at night. The major aim of this study was to test the effectiveness of the blue or green light-emitting device, potentially utilized in clinical practice, to suppress the night melatonin level and provide thus the first evidence that this device may affect the circadian system. The results show that the light emitted by this device has the potential to lower melatonin levels in most subjects with the higher efficiency in the blue range. Key words: circadian rhythms, melatonin, light pulse, eyelids, blue and green light

Promoting cycling is a crucial solution to improve the livability of urban environments. A non-invasive way to promote cycling is to enhance the bike ride experience via Green Light Optimal Speed Advisory (bike-GLOSA). This work presents such a system for Hamburg, making bike-GLOSA practical in real-world urban environments and evaluating its impact on bike rides. GLOSA systems ingest data from traffic lights to predict their switching behavior and provide speed advisories to users. In the first challenge, we need to find whether the data is reliable, overseeing thousands of traffic lights throughout the city area. We develop a quality assurance framework and monitor the prediction quality over a longer period. Affected by data outages, we achieve a median prediction availability of 55% (IQR: 28%) and prediction quality of 86%. We identify concrete weak points, enhancing prediction stability by 11% to 66% (IQR: 17%) measured in 2024. Furthermore, based on four weeks of data for 18,009 traffic lights, we find that traffic adaptivity may be less problematic for traffic light prediction than previously envisioned. Afterward, we develop a novel method to match traffic lights along the user's trajectory. Instead of using the user's location or camera, our method matches traffic lights along a precalculated bike route geometry. However, errors and inaccuracies in bike routes challenge this approach, requiring an advanced model that employs spatial reasoning to find which associated traffic light turn geometries match the given route geometry. The final model achieves matching F1 scores of 92% and 86% validated on a separate dataset, requiring a median extra time of 1.4 seconds during bike route calculation. Building on these results, we focus on reducing bike routing errors and enhancing route alignment with actual bike paths. Our solution involves an authoritative bike routing dataset and cross-border routing to OpenStreetMap. Apart from a more consistent surface coverage, we find better alignment with actual cycling infrastructure, traffic lights, and user trajectories than with other routing providers. Enhanced alignment and 37% fewer routing errors lead to a 4.74% increase in traffic light matching F1 score. A route-based distance-to-signal estimation is proposed, showing a more stable distance estimation than the over-the-air distance from related work. We combine the developed components in a smartphone app and conduct an unsupervised long-term test throughout 2023 with Hamburg citizens. Survey responses suggest a twofold effect of the speed advisory: rolling out in anticipation of red and accelerating to catch green. These effects are also visible in the recorded data. Approaches with adherence to the speed advisory have 15.32% fewer stops but a 3.3% increase in energy expenditure to catch the green phase by cycling 2.92 km/h faster. Approaches without adherence have a 12.85% higher chance of stopping and a 3.39 km/h decrease in speed while saving 5.5% in energy and rolling out earlier. Combined, these cases cancel out each other, with a 0.74 km/h slower traffic light passing speed, 1.4% estimated energy savings, 0.73% increased stop rate, and 1.46 seconds increased waiting time when stopped. Based on the survey, users report a System Usability Scale of 73, with improvable reliability and coverage of speed advisories. Among many ways to improve our developed solution, users see enhanced informedness, reduced stops, and increased comfort as key benefits. We thoroughly analyze these findings and outline potential directions for future research.

This thesis project presents a traffic micro simulation study that investigates the state-of-the-art in traffic management "Green Light Optimal Speed Advisory (GLOSA)" for vehicles in a smart city. GLOSA utilizes infrastructure and vehicles communication through using current signal plan settings and updated vehicular information in order to influence the intersection approach speeds. The project involves simulations for a mixed traffic environment of conventional and automated vehicles both connected to the intersection control and guided by a speed advisory traffic management system. Among the project goals is to assess the effects on traffic performance when human drivers comply to the speed advice. The GLOSA management approach is also accessed for its potential to improve traffic efficiency in a full market penetration of connected automated vehicles with enhanced capabilities such as having shorter time head ways.

This thesis project investigates the state-of-the-art in traffic management "Green Light Optimal Speed Advisory (GLOSA)" for vehicles in a smart city. GLOSA utilizes infrastructure and vehicles communication through using current signal plan settings and updated vehicular information in order to influence the intersection approach speeds. The project involves traffic microscopic simulations for a mixed traffic environment of conventional and automated vehicles (AVs) both connected to the intersection control and guided by a speed advisory traffic management system. Among the project goals is to assess the effects on traffic performance when human drivers comply to the speed advice. The GLOSA management approach is accessed for its potential to improve traffic efficiency in a full market penetration of connected AVs with absolute compliance. The project also aims to determine the possible outcome resulting from enhancing the AVs capabilities such as implementing short time headways between vehicles in the future.  The best traffic performance results achieved by operating GLOSA goes for connected AVs with the lowest simulated time headway (0.3 sec). The waiting time reduction reaches 95% and trip delay lessens to 88 %.

Road network of a region is of a paramount importance in the overall development. Management of road traffic is a key factor for the city authority and reducing the road traffic congestion is a significant challenge in this perspective. In this thesis, a Wireless Sensor Network (WSN) based road-traffic monitoring scheme with dynamic mathematical traffic model is presented that will not necessarily include all adjacent intersections of a block; rather the important major intersections of a city. The objective of this scheme is to reduce the congestion by re-routing the vehicles to better performing road-segments by informing the down-stream drivers through broadcasting the congestion information in a dedicated radio channel. The dynamic model can provide with the instantaneous status of the traffic of the road-network. The scheme is a WSN based multi-hop relay network with hierarchical architecture and composed of ordinary nodes, Cluster-Head nodes, Base Stations, Gateway nodes and Monitoring and Control Centers (MCC) etc. Through collecting the traffic information, MCC will check the congestion status and in defining the congestion, threshold factors have been used in this model. For the congested situation of a road-segment, a cost function has been defined as a performance indicator and estimated using the weight factors (importance) of these selected intersections. This thesis considered a traffic network with twelve major intersections of a city with four major directions. Traffic arrivals in these intersections are assumed to follow Poisson distribution. Model was simulated in Matlab with traffic generated through Poisson Random Number Generator and cost function was estimated for the congestion status of the road-segments over a simulation period of 1440 minutes starting from midnight. For optimization purpose we adopted two different approaches; in the first approach, performance of the scheme was evaluated for all threshold factor values iteratively one at a time, applying a threshold factor value to define threshold capacities of all the road segments; traffic was generated and relative cost has been estimated following the model specifications with the purpose of congestion avoidance. In the second approach, different values of threshold factor have been used for different road segments for determining the optimum set-up, and exhaustive search technique has been applied with a smaller configuration in order to keep computations reachable. Simulation results show the capacity of this scheme to improve the traffic performance by reducing the congestion level with low congestion costs.