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Driving fatigue cost is a major component of vehicle drivers' travel costs in an intercity or regional network. The charging behavior of electric vehicle (EV) drivers, which is generally synchronized with drivers' resting behavior, can contribute to the mitigation of drivers' fatigue, especially after a prolonged driving period. Overlooking the impact of driving fatigue on the travel cost may overestimate the side-effects of the charging behavior for EV drivers, and result in biased flow and charging demand distribution. In this study, by considering the fatigue as part of the travel cost, we make the first attempt to investigate the impact of EV drivers' charging and resting behaviors on their fatigue cost, and thus their travel plans and resultant flow distribution across the network. To this end, a novel network equilibrium modeling framework is first developed to capture the interaction among EV drivers' travel plans, which specify the routing, recharging, and resting plans on a general intercity or regional network where charging stations and rest stops are deployed. When traveling between their origins and destinations, EV drivers are assumed to determine their travel plans to minimize their individual travel cost composed of driving time, rest time, charging cost, and fatigue cost, while preventing their batteries from being exhausted. The equilibrium model is then formulated as a variational inequality and transformed into a nonlinear optimization problem. An efficient solution algorithm integrating column generation and Benders decomposition approach is proposed to solve the established optimization problem. Numerical examples are presented to demonstrate the performance of the proposed models and solution algorithm. Numerical results validate that considering the impact of driving fatigue on the travel cost emphasizes the need for en-route charging for EV drivers with long-distance trips, and has an appreciable impact on the network flow and charging demand distribution. In addition, large-sized batteries and fast chargers may not necessarily reduce drivers' travel costs for long-distance travel since charging behavior can be synchronized with drivers' resting behavior and thus contribute to the mitigation of drivers' fatigue. • Driving fatigue cost is a major component of vehicle drivers' travel costs. • Charging time can contribute to the mitigation of electric vehicle drivers' fatigue. • Drivers' travel plan specifies their routing, recharging, and resting plans. • A novel network equilibrium model is developed to delineate drivers' travel plans. • An efficient solution algorithm is proposed. [ABSTRACT FROM AUTHOR]

• Aedes aegypti is the predominant vector in urban areas of the Colombo district. • Ae. aegypti adult density correlates with climatic factors such as monthly rainfall and mean temperature. • Ae. aegypti adult density positively correlates with the one-month lagged dengue case number. • Ae. aegypti preferred indoor resting areas in urban areas were bedrooms and living rooms. Understanding local geographical variation in vector density and bionomics related to virus transmission are critical for planning effective vector control programs to control dengue virus transmission. This study investigated the prevalence and resting behaviour of Aedes aegypti and Ae. albopictus in three dengue high-risk areas in the Colombo District, Sri Lanka. Monthly sampling of resting adult mosquitoes was conducted from August 2019 to February 2020. Thirty-seven percent of the households (289/776) harboured Aedes mosquitoes, and 603 Aedes mosquitoes were collected. The proportion of Ae. aegypti was higher in the overall collection during the collection period (94% [569/603]), and 62% (352/569) were females. Significant monthly variations in Ae. aegypti were observed with respect to the indices: number of females per surveyed house (F/SH; p = 0.001), number of females per Aedes positive house (F/PH; p = 0.029), adult house index (AHI; p = 0.001), adult density (AD; p = 0.005) and resting ratio (RR; p = 0.001). AHI, AD, and RR had statistically significant positive correlations with monthly rainfall (p = 0.001, p = 0.011, and p = 0.002 respectively) and one-month lagged dengue cases (p = 0.002, p = 0.005, and p < 0.001 respectively). A statistically significantly higher proportion of Ae. aegypti females were caught resting indoors (N = 309, 88%) than outdoors (N = 43, 12%; p < 0.001). The most common resting areas were bedrooms (51%) & living/dining rooms (37%), and places were under or on furniture (47%) & hangings (34%) for Ae. aegypti. Conversely, 74% of female Ae. albopictus were collected outdoors. Results of this study could have strong implications to improve vector surveillance and control by early detection of dengue to detect outbreaks and minimization of disease transmission. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this study was to obtain variables that enabled estimation of variation in stall occupancy times based on the cow-to-stall ratio. First, stall use by Holstein–Friesian cows was analyzed on 16 farms in Galicia, Northwest Spain, by continuously recording cow behavior for 24h. Intervals of 17s were recorded every 3min and 30s. The video recordings were watched to count the number of simultaneously occupied stalls. Before actually reducing the number of stalls in practice, the variation in the average occupancy time per stall was estimated by using a variable termed ‘predictably altered time of occupancy’ for stall-to-cow ratios of 0.9, 0.8 and 0.7 (111%, 125% and 143% stocking density, respectively). Such an estimate was based on data for an occupancy level of 1.00stall/cow. The following variables were determined based on data obtained from the initial sample of 16 farms: maximum stall occupancy, average occupancy time per stall, average stall occupancy time per cow, and predictably altered time of occupancy. Three farms were selected from the initial sample. On such farms, 0.8 and 0.7stall/cow ratios were actually applied to verify the estimates of variation. Other additional variable was determined for the three selected farms: occupancy time at different levels of stall occupancy. The results for a stall-to-cow ratio of 0.7 (143% stocking density) suggested considerable alterations in occupancy time per stall, evidenced by maximum stall occupancy. Such alterations had already been detected from estimates of the predictably altered time of occupancy and were confirmed in practice. The effects of the 0.9 ratio (111% stocking density) were lower because maximum stall occupancy did not exceed 90% on the farms studied. Because the use of intermediate values, such as 0.8stalls/cow (125% stocking density), can cause trouble in barns with average occupancy levels above 50%, monitoring individual cows with special conditions such as heifers becomes essential. The theoretical determination of the time of occupancy predictably altered by the proposed reduction in the number of stalls is an indicator of the effect of such a reduction on stall use. The predictably altered time of occupancy can be determined for a specific herd, management system and barn based on a ratio of 1.00cow/stall. [Copyright &y& Elsevier]