Temporal Scheduling Research Articles

Synergistic Non-Intrusive Load Monitoring: Dual-Model Training and Inference for Improved Load Disaggregation Prediction

Load disaggregation is the process of identifying an individual appliance’s power demand within aggregate electrical load data. Virtually all recently proposed disaggregation methods are based on neural networks, thanks to their superior performance. Their achievable accuracy is, however, often limited by the quality of the data that have been used to train the underlying neural networks. In particular, if electrical appliances exhibit vastly differing temporal schedules and operational modes, their heterogeneous power consumption data can poison the disaggregation models and thus lead to a degraded overall accuracy. This paper presents a novel load disaggregation approach that relies on the use of multiple disaggregation models. We use a clustering method to partition training data by their power consumption patterns, allowing our approach to train separate models for appliance data exhibiting similar electricity consumption patterns. During disaggregation, our scheme selects the model that is closest to the appliance’s power consumption pattern, thereby leveraging the most fitting model for each specific instance. We demonstrate how our method surpasses the accuracy of the individual models designated for each cluster, while simultaneously improving upon the baseline model’s performance. When applied to the widely used ECO dataset, our approach achieves an average improvement of 13% in disaggregation accuracy over the use of a single baseline model.

Integrating Indigenous Knowledge into Sustainable Forest Management for Climate Change Adaptation and Mitigation Strategies: Case from Southern Slopes of Mount Slamet, Indonesia

Rural communities near forests are highly vulnerable to climate change due to frequent natural disasters and extreme weather. This study investigated local wisdom in forest management as a strategy to address climate challenges. Conducted in April–May 2024 on the southern slopes of Mount Slamet, Central Java (i.e., Gununglurah, Sokawera, and Sunyalangu Villages), the research used a participatory learning action approach and descriptive-quantitative analysis based on interpretive and critical sociology. Key findings highlight three aspects of community-based forest management: spatial categorization of forests into sacred conservation forests (tabet), protected forests in steep areas, and production forests managed through agroforestry; temporal scheduling based on the traditional pranatamangsa seasonal calendar; and plant selection for production, conservation, and spiritual purposes. These practices reflect a deep socio-cultural connection to forests and provide a foundation for integrating traditional wisdom with modern management methods. Legal recognition is essential to protect the rights of indigenous communities and their forest management systems, ensuring that these practices contribute to sustainable forest management and effective climate change adaptation and mitigation strategies.

Sequestration of gene products by decoys enhances precision in the timing of intracellular events

Expressed gene products often interact ubiquitously with binding sites at nucleic acids and macromolecular complexes, known as decoys. The binding of transcription factors (TFs) to decoys can be crucial in controlling the stochastic dynamics of gene expression. Here, we explore the impact of decoys on the timing of intracellular events, as captured by the time taken for the levels of a given TF to reach a critical threshold level, known as the first passage time (FPT). Although nonlinearity introduced by binding makes exact mathematical analysis challenging, employing suitable approximations and reformulating FPT in terms of an alternative variable, we analytically assess the impact of decoys. The stability of the decoy-bound TFs against degradation impacts FPT statistics crucially. Decoys reduce noise in FPT, and stable decoy-bound TFs offer greater timing precision with less expression cost than their unstable counterparts. Interestingly, when both bound and free TFs decay at the same rate, decoy binding does not directly alter FPT noise. We verify these results by performing exact stochastic simulations. These results have important implications for the precise temporal scheduling of events involved in the functioning of biomolecular clocks, development processes, cell-cycle control, and cell-size homeostasis.

Aligning extracurricular school activities with physical literacy: pilot evaluation through self-study of practice.

Although several important documents of education and health promotion on the international level favor practices geared toward physical literacy (PL), not all countries have yet gained experience with this holistic concept. Therefore, numerous stakeholders and practitioners who intend to align their interventional activities with PL will soon face the situation that there are no recommendations for their specific culture and language for how to design such programs. Given that such recommendations are also lacking for Germany, the goal of the present study within the uncontrolled pilot cycles of the PLACE study was (a) to describe the process of a female pedagogue (27 years old, previously unexperienced with PL) initially familiarizing herself with the PL concept and its implementation opportunities for the school setting, and (b) to retrace the process of developing and refining a PL-driven intervention for extracurricular physical education (60-90 min) of children in grades three and four at primary schools in Bremen. Adopting a self-study design, this endeavor emphasized continuous reflexivity involving: (a) session protocols; (b) biweekly discussions with another coach; (c) weekly discussions between scientists and stakeholders of youth development ("multi-perspective panel"); (d) weekly observations and impressions during field work; and (e) summative group interviews with children (n = 17, age range: 8-9 years, 17.6% boys). Written documents underwent qualitative content analysis with inductively generated categories. Despite explicit links between the theoretical PL domains and the intervention content, the character of how PL informed the intervention level was dominated by the stance and atmosphere implemented by the deliverer (e.g., participatory attitude, open mindset). Accordingly, the team revised the intervention primarily on the levels of organization (temporal schedule and sequences), instruction, and materials. After initial stages of didactically "surviving" within classes, the deliverer could increasingly integrate tasks of cognitive engagement and provide choice for students enabling individual autonomy for nurturing a person-centered approach. This study encourages teachers and stakeholders of physical education to seek exchange with scholars or other practitioners while simultaneously demonstrating patience in comprehensively internalizing PL and efficiently translating the concept into routines in line with individual's quality standards.

Nest site selection and fidelity of European pond turtle (Emys orbicularis) population of Babat Valley (Gödöllő, Hungary)

BackgroundThe conservation of aquatic and semiaquatic turtles requires knowledge of the area and vegetation structure of habitat used for nesting, and nesting migration route. We aimed to survey the effects of habitat features to the nest site selection, nesting success, and test the possibility of nest site fidelity. Our study was carried out at 10 different nesting areas, with special emphasis on data from returning females in a pond system in Hungary between 2014 and 2017.ResultsMost nesting attempts were found in closed sand steppes, uncharacteristic dry and semi-dry grasslands habitat patches. The principal component analysis (PCA) showed that increased sandy soil cover, sunlight and slope were important variables in nest site choice. The increasing PCA first axis score significantly increased the chance of an emergence. The degradation of open steppe vegetation, occurrence of weeds, invasive and disturbance tolerant species have a negative effect on the selection of nest sites. We observed that 96.55% of nests were located within 20 m south of a pine forest at preferred nest site at pond 5, which provided the right incubation temperature through partial shading. The returning females nested significantly closer to the northern pine forest than the single clutch females. Most probably the returning females already has the necessary experience to select the right nesting site. The individually marked females did not choose new nesting areas during the monitored years which suggests nesting area fidelity, but we did not find nest site fidelity.ConclusionThe maintenance of mosaic habitat structure, slowing down the succession process at the nesting area should be basic priorities in European pond turtle conservation programs. We suggested a spatial and temporal scheduling of land management and agricultural work to the local farmers. If the actual nest site is in an agricultural area, all work should be avoided throughout the year. Agricultural machinery should avoid the migration routes of adult turtles and emerged hatchlings during the concerned period. Under strong predation pressure, predator control should be carried out, and use nest protection.

Optimal Spatial–Temporal Scheduling for EVs With Stochastic Behaviors and Possible Inauthentic Information

This paper studies the spatial-temporal scheduling for electric vehicles with stochastic charging behaviors. A new spatial-temporal scheduling model is proposed to achieve both the spatial coordination and temporal coordination for EV users and is modeled as a non-convex optimization problem which can be solved by the coordinate descent method. Further, a new billing method of charging cost is proposed based on the Vickrey-Clarke-Groves to avoid possible inauthentic information from users. Specially, the individual rationality and incentive compatibility of the method are theoretically proved. Moreover, a stochastic optimization problem is proposed to describe the stochastic charging behavior of users, which can fit the real application better than the deterministic one. Consequently, an efficient charging strategy is obtained via chance constraint method. Simulation results and comparison studies show the effectiveness of both the model and the billing method.

Energy-aware temporal scheduling strategy of tasks for green data centers

Energy-aware temporal scheduling strategy of tasks for green data centers

Spatial–Temporal Scheduling of Electric Bus Fleet in Power-Transportation Coupled Network

With large passenger and battery capacity, electric buses can play dual roles – commuting tools in transportation networks and mobile energy storage units in power networks. To promote the beneficial synergy between power-transportation coupled networks, it is necessary to properly optimize the operation of electric buses in both networks. This paper proposes a spatial-temporal scheduling framework for the electric bus fleet in a power-transportation coupled network. By scheduling the plug-in locations and charging/discharging profiles of the electric buses, the model not only minimizes the total operational costs of the fleet (i.e., the electricity consumption and battery degradation costs), but also maximizes the revenue by providing flexibility to the power network. The coupled power and transportation constraints are explicitly described in the model. To address the uncertainty from fleet’s energy consumption, the chance-constrained programming is adopted that can be reformulated into second-order cones and efficiently solved by off-the-shelf solvers. Numerical experiments are conducted to validate the superiority of the proposed method based on the IEEE 33-bus distribution network and the Sioux Falls transportation network.

The Game of Timing: Circadian Rhythms Intersect with Changing Environments.

Recurring patterns are an integral part of life on Earth. Through evolution or breeding, plants have acquired systems that coordinate with the cyclic patterns driven by Earth's movement through space. The biosystem responses to these physical rhythms result in biological cycles of daily and seasonal activity that feed back into the physical cycles. Signaling networks to coordinate growth and molecular activities with these persistent cycles have been integrated into plant biochemistry. The plant circadian clock is the coordinator of this complex, multiscale, temporal schedule. However, we have detailed knowledge of the circadian clock components and functions in only a few species under controlled conditions. We are just beginning to understand how the clock functions in real-world conditions. This review examines what we know about the circadian clock in diverse plant species, the challenges with extrapolating data from controlled environments, and the need to anticipate how plants will respond to climate change.

Population processes in cyber system variability.

Variability is inherent to cyber systems. Here, we introduce ideas from stochastic population biology to describe the properties of two broad kinds of cyber systems. First, we assume that each of N0 components can be in only one of two states: functional or nonfunctional. We model this situation as a Markov process that describes the transitions between functional and nonfunctional states. We derive an equation for the probability that an individual cyber component is functional and use stochastic simulation to develop intuition about the dynamics of individual cyber components. We introduce a metric of performance of the system of N0 components that depends on the numbers of functional and nonfunctional components. We numerically solve the forward Kolmogorov (or Fokker-Planck) equation for the number of functional components at time t, given the initial number of functional components. We derive a Gaussian approximation for the solution of the forward equation so that the properties of the system with many components can be determined from the transition probabilities of an individual component, allowing scaling to very large systems. Second, we consider the situation in which the operating system (OS) of cyber components is updated in time. We motivate the question of OS in use as a function of the most recent OS release with data from a network of desktop computers. We begin the analysis by specifying a temporal schedule of OS updates and the probability of transitioning from the current OS to a more recent one. We use a stochastic simulation to capture the pattern of the motivating data, and derive the forward equation for the OS of an individual computer at any time. We then include compromise of OSs to compute that a cyber component has an unexploited OS at any time. We conclude that an interdisciplinary approach to the variability of cyber systems can shed new light on the properties of those systems and offers new and exciting ways to understand them.

Estimating flexibility preferences to resolve temporal scheduling conflicts in activity-based modelling

This paper presents a novel activity-based demand model that combines an optimisation framework for continuous temporal scheduling decisions (i.e. activity timings and durations) with traditional discrete choice models for non-temporal choice dimensions (i.e. activity participation, number and type of tours, and destinations). The central idea of our approach is that individuals resolve temporal scheduling conflicts that arise from overlapping activities, e.g. needing to work and desiring to shop at the same time, in order to maximise their daily utility. Flexibility parameters capture behavioural preferences that penalise deviations from desired timings. This framework has three advantages over existing activity-based modelling approaches: (i) the time conflicts between different temporal scheduling decisions including the activity sequence are treated jointly; (ii) flexibility parameters follow a utility maximisation approach; and (iii) the framework can be used to estimate and simulate a city-scale case study in reasonable time. We introduce an estimation routine that allows flexibility parameters to be estimated using real-world observations as well as a simulation routine to efficiently resolve temporal conflicts using an optimisation model. The framework is applied to the full-time workers of a synthetic population for the city of Lausanne, Switzerland. We validate the model results against reported schedules. The results demonstrate the capabilities of our approach to reproduce empirical observations in a real-world case study.

Dynamics of forced and unforced autophoretic particles

Chemically active, or autophoretic, particles that isotropically emit or absorb solute molecules undergo spontaneous self-propulsion when their activity is increased beyond a critical Péclet number ($Pe$). Here, we conduct numerical computations, using a spectral element based method, of a rigid, spherical autophoretic particle in unsteady rectilinear translation. The particle can be freely suspended (or ‘unforced’) or subject to an external force field (or ‘forced’). The motion of an unforced particle progresses through four regimes as$Pe$is increased: quiescent, steady, stirring and chaos. The particle is stationary in the quiescent regime, and the solute profile is isotropic about the particle. At$Pe=4$the fore–aft symmetry in the solute profile is broken, resulting in its steady self-propulsion. Our computations indicate that the self-propulsion speed scales linearly with$Pe-4$near the onset of self-propulsion, as has been predicted in previous studies. A further increase in$Pe$gives rise to the stirring regime at$Pe\approx 27$, where the fluid undergoes recirculation, while the particle remains essentially stationary. As$Pe$is increased even further, the particle dynamics is marked by chaotic oscillations at$Pe\approx 55$and higher, which we characterize in terms of the mean square displacement and velocity autocorrelation of the particle. Our results for an autophoretic particle under a weak external force are in good agreement with recent asymptotic predictions (Saha, Yariv & Schnitzer,J. Fluid Mech., vol. 916, 2021, p. A47). Additionally, we demonstrate that the strength and temporal scheduling of the external force may be tuned to modulate the chaotic dynamics at large$Pe$.

Is Self-Related Cognition Resistant to Time-Based Expectancy?

Abstract Individuals adapt to their environments by scheduling cognitive processing capacities selectively to the points in time where they are most likely required. This effect is known as time-based expectancy (TBE) and has been demonstrated for several cognitive capacities, like perceptual attention, task set activation, or response preparation. However, it has been argued that self-related cognition (i.e., processing of information linked to oneself) is universally prioritized, compared to non-self-related information in the cognitive system. Consequently, self-related cognition should be resistant to temporal scheduling by TBE, because individuals maintain a constantly high expectancy for self-related cognition, irrespective of its temporal likeliness. We tested this hypothesis in a task-switching paradigm where participants randomly switched between a self-related task and a neutral task. The tasks were preceded by a short or a long warning interval in each trial, and the interval duration predicted probabilistically the task type. We found that participants showed TBE for the neutral task but not for the self-related task. We conclude that the individual cannot benefit from time-based task expectancy when the to-be-expected task is constantly activated, due to its self-related nature.

Temporal-spatial scheduling of electric vehicles in AC/DC distribution networks

With the rapid development of power electronic technology, the AC/DC hybrid distribution network has become a new trend of distribution system architectures in recent years. And meanwhile, electric vehicles (EVs) equipped with both AC and DC charging interfaces tend to be utilized as ideally dispatchable resources in not only temporal but also spatial dimensions, whose charging/discharging schemes remarkably impact the secure and economical operation of distribution networks. In view of this background, this paper proposes a temporal-spatial scheduling model of EVs in AC/DC distribution networks, targeting at the optimum of the whole network losses as well as the inconveniences of EV users originated from following scheduling instructions. Besides the traditional constraints relevant to EVs’ temporal scheduling in just-AC distribution networks, the AC/DC hybrid topology and the restrictions for EVs’ spatial scheduling are properly involved in the proposed model. After a reasonable simplification procedure, the proposed model is represented as a Mixed Integer Quadratic Program (MIQP) problem that can be efficiently solved by off-the-shelf optimization algorithms. Through the analyses of a coupled electrical-geographical AC/DC distribution network, the proposed model can bring 6.3% and 6.8% cost savings respectively on two typical days.

Environmental Enrichment Factors Associated with the Activity Level of Bottlenose Dolphins under Professional Care

Environmental enrichment can be used to improve the welfare of dolphins in zoos and aquariums. Bottlenose dolphins under professional care are typically provided with a range of enrichment that has a variety of features and levels of complexity at various frequencies. In the present study, a subset of data from a larger study entitled “Towards understanding the welfare of cetaceans in zoos and aquariums” (colloquially called the Cetacean Welfare Study) was used to examine the relationship between activity level and enrichment buoyancy as well as enrichment provisioning schedules. Survey data were collected from accredited zoos and aquariums related to the types of enrichment provided to the dolphins and the frequency and duration they were supplied. Non-invasive bio-logging devices were used to record the dolphin kinematics one day per week over the course of two five-week data collection periods. Activity level related positively with the total duration of time non-stationary enrichment was provided. In addition, providing a larger number of enrichment types each between 26% and 50% of the days in a month (i.e., rotating many different types of enrichment across days on a moderate schedule) was positively related to activity level. Activity level was negatively related to the number of times sinking enrichment was provided. Understanding how the temporal schedule and features of various types of enrichment are related to activity levels will aid in developing progressively more effective enrichment programs.

Exploring Built Environment Influence on Taxi Vacant Time in Megacities: A Case Study of Chongqing, China

Driven by the emission peak and carbon neutrality targets, traditional cruising taxis are also under pressure to reduce emissions, and reducing taxi vacant time is an effective way to reduce emissions. This paper aims to examine the influence mechanism of built environment characteristics on vacant time of taxi trips from the supply-side perspective. To do so, the study uses a one week of taxi trajectory data of 2019 provided by a taxi company in Chongqing, a megacity in China. We take the divided square grid cell as the study units, calculate the taxi vacant time of 168 (7 × 24) hourly slots for each grid, and then calculate the built environment indicators of the grids with Point of Interest (POI) and vector road network data. A generalized additive model (GAM) based on panel data is constructed to investigate the influence mechanism of built environment on taxi vacant time. The results show that the time-varying trend of taxi vacant time is opposite to the time-varying trend of taxi trips and is similar to taxi trips in spatial distribution with significant spatial dependence. Taxi vacant time is negatively correlated with taxi trips. POI mixture, the number of enterprises, governmental agencies, and shopping services are negatively correlated with vacant time, while the number of sports and leisure services, hotel services, healthcare services, financial and insurance services, and living services are positively correlated with vacant time. An interesting finding is that metro stations significantly increase the vacant taxi time in the region. Average traffic flow speed, road density, the number of bus stops, catering services, residence communities, and motor vehicle services show strong nonlinear relationships with taxi vacant time, and their influence effects are alternately positive and negative. The study provides useful insights for understanding mechanisms of the role of built environment on taxi vacant time and has important implications for driver searching strategy improvement, taxi management measures development, spatial and temporal scheduling of taxi capacity resources, and urban transportation facilities layout planning.

A temporal-spatial cleaning optimization method for photovoltaic power plants

Cleaning is critical for photovoltaic (PV) systems, as it can remove dust deposition and keep the systems operating efficiently. Existing studies on PV cleaning focus predominately on determining optimal cleaning frequencies or intervals. In practice, however, route planning is also an indispensable decision to be made when cleaning large-scale PV plants. In this work, we study a temporal-spatial cleaning optimization problem for large-scale PV plants. A two-stage cleaning optimization policy that consists of a periodic planning stage and a dynamic adjustment stage is proposed. In the former stage, tentative cleaning intervals are determined; in the latter stage, the temporal scheduling and spatial routing problems are jointly optimized in a dynamic fashion, so as to minimize the total economic loss. We model the problem as an extended version of the classical traveling salesman problem (TSP), that is, a production-driven traveling salesman problem with time-dependent cost (PD-TSP-TC). Genetic algorithm is employed to solve the corresponding nonlinear 0–1 integer programming problem. A case study on a real PV plant is conducted to demonstrate the performance of the proposed method, which shows that the temporal-spatial cleaning optimization method results in a better performance than the one that ignores route planning and that models route planning as classical TSP.

A Coupled Sampling Design for Parameter Estimation in Microalgae Growth Experiment: Maximizing the Benefits of Uniform and Non-Uniform Sampling

As an important primary producer in aquatic ecosystems, the various parameters within the mathematical models are used to describe the growth of microalgae and need to be estimated by carefully designed experiments. Non-uniform sampling has proved to generate a deliberately optimized sampling temporal schedule that can benefit parameter estimation. However, the current non-uniform sampling method depends on prior knowledge of the nominal values of the model parameters. It also largely ignores the uncertainty associated with the nominal values, thus inducing unacceptable parameter estimates. This study focuses on the uncertainty problem and describes a new sampling design that couples the traditional uniform and non-uniform sampling schedules to benefit from the merits of both methods. Based on D-optimal design, we first derive the non-uniform optimal sampling points by maximizing the determinant of the Fisher information matrix. Then the confidence interval around the non-uniform sampling points is determined by Monte Carlo simulations based on the prior knowledge of parameter distribution. Finally, we wrap the non-uniform sampling points with the uniform sampling points within the confidence interval to obtain the ultimate optimal experimental design. Scenedesmus obliquus, whose growth curve follows a four-parameter model, was used as a case study. Compared with the traditional sampling design, the simulation results show that our proposed coupled sampling schedule can partly eliminate the uncertainty in parameter estimates caused by fixed systematic errors in observations. Our coupled sampling can also retain some advantages belonging to non-uniform sampling, in exploiting information maximization and managing the cost of sampling.

Reward-enhancing effects of d-amphetamine and its interactions with nicotine were greater in female rats and persisted across schedules of reinforcement.

Nicotine enhances the value of environmental stimuli and rewards, and reward enhancement can maintain nicotine consumption. Stimulants such as d-amphetamine are misused more by women and are commonly co-used with nicotine. d-Amphetamine potentiates nicotine's effects in human and animal research. To date, there are no published studies examining this interaction in a reward-enhancement task. The current study sought to investigate the reward-enhancing effects of nicotine alongside and coadministered with d-amphetamine. Further, we evaluated the persistence of reward enhancement across ratio and temporal schedules of reinforcement. We used 10 male and 10 female Sprague-Dawley rats. Enhancement was assessed within subjects by examining active lever pressing for a visual stimulus reinforcer on variable ratio 3, variable interval 30 s and variable time 30 s - variable ratio 3 schedules. Before 1-h sessions, rats received one injection of saline, 0.1 or 0.3 mg/kg d-amphetamine and one of saline or 0.4 mg/kg nicotine, making six possible drug combinations (saline + saline, saline + nicotine, 0.1 d-amphetamine + aline, 0.1 d-amphetamine + nicotine, 0.3 d-amphetamine + saline and 0.3 d-amphetamine + nicotine) experienced in a randomized order by each rat. When d-amphetamine was coadministered with nicotine, we found an interaction effect on reward enhancement that persisted across schedules of reinforcement. Males and females exhibited reward enhancement by 0.3 d-amphetamine, while only females showed reward enhancement by 0.1 d-amphetamine. Further, females responded more for the visual stimulus than males in all d-amphetamine conditions. Future studies should assess how reward enhancement is involved in high nicotine-amphetamine comorbidity rates and enhanced amphetamine misuse in women.

Heliconiini butterflies can learn time-dependent reward associations.

For many pollinators, flowers provide predictable temporal schedules of resource availability, meaning an ability to learn time-dependent information could be widely beneficial. However, this ability has only been demonstrated in a handful of species. Observations of Heliconius butterflies suggest that they may have an ability to form time-dependent foraging preferences. Heliconius are unique among butterflies in actively collecting pollen, a dietary behaviour linked to spatio-temporally faithful ‘trap-line' foraging. Time dependency of foraging preferences is hypothesized to allow Heliconius to exploit temporal predictability in alternative pollen resources. Here, we provide the first experimental evidence in support of this hypothesis, demonstrating that Heliconius hecale can learn opposing colour preferences in two time periods. This shift in preference is robust to the order of presentation, suggesting that preference is tied to the time of day and not due to ordinal or interval learning. However, this ability is not limited to Heliconius, as previously hypothesized, but also present in a related genus of non-pollen feeding butterflies. This demonstrates time learning likely pre-dates the origin of pollen feeding and may be prevalent across butterflies with less specialized foraging behaviours.