Search Results (44)

Construction projects still face persistent barriers to adopting whole life costing (WLC), such as fragmented data, a lack of standardization, and inadequate tools. This study addresses these limitations by proposing a core ontology for WLC, developed using an ontology design science research methodology. The ontology formalizes WLC knowledge based on ISO 15686-5 and incorporates professional insights from surveys and expert focus groups. Implemented in web ontology language (OWL), it models cost categories, temporal aspects, and discounting logic in a machine-interpretable format. The ontology’s interoperability and extensibility are validated through its integration with the building topology ontology (BOT). Results show that the ontology effectively supports cost breakdown, time-based projections, and calculation of discounted values, offering a reusable structure for different project contexts. Practical validation was conducted using SQWRL queries and Python scripts for cost computation. The solution enables structured data integration and can support decision-making throughout the building life cycle. This work lays the foundation for future semantic web applications such as knowledge graphs, bridging the current technological gap and facilitating more informed and collaborative use of WLC in construction. Full article

This study builds a bridge between the advancements from prospective life cycle assessments (pLCAs) and dynamic life cycle assessments (dLCAs) to improve the evaluation of circular economy (CE) strategies for long-lived products such as energy technologies. Based on a literature review of recent developments from pLCA and dLCA, an extended LCA methodology is proposed that provides guidance in the consideration and integration of technological and market dynamics across all major LCA steps of a dLCA, whose flows and impacts extend over a long period of time. This ensures a more accurate assessment of the impacts on global warming over time by explicitly incorporating temporal differentiation into goals and scopes, life cycle inventories, and interpretations. The methodology was applied to compare two CE measures for wind turbines: full repowering, including material recycling, and partial repowering. The analysis revealed that full repowering is the environmentally preferable option from the perspective of global warming potential, as the higher electricity output offsets the emissions associated with decommissioning and new construction. The findings were robust under various assumptions on future technological advancements, the underlying decarbonization scenario aligned with the Paris Agreement, and the application of discounting of future emissions. Ultimately, this work provides a practical yet adaptable approach for integrating future-oriented LCA methods into decision-making for more sustainable infrastructure and machinery. Full article

The differential toll policy has emerged as an effective method for regulating expressway traffic flow and has positively impacted the efficiency of vehicular movement, as well as balanced the spatial and temporal distribution of the road network. However, the acceptance of differentiated charging policies and the range of rates associated with these policies warrant further investigation. This study employs both revealed preference (RP) and stated preference (SP) survey methods to assess users’ willingness to accept the current differentiated toll scheme and to analyze the proportion of users opting for alternative travel routes and their behavioral characteristics in simulated scenarios. Additionally, we construct a Structural Equation Model-Latent Class Logistics (SEM-LCL) to explore the mechanisms influencing differentiated toll road alternative travel choices while considering user heterogeneity. The findings indicate that different tolling strategies and discount rates attract users variably. The existing differentiated tolling scheme—based on road sections, time periods, and payment methods—significantly affects users’ choices of alternative routes, with the impact of tolling based on vehicle type being especially pronounced for large trucks. The user population is heterogeneous and can be categorized into three distinct groups: rate-sensitive, information-promoting, and conservative-rejecting. Furthermore, the willingness to consider alternative travel routes is significantly influenced by factors such as gender, age, driving experience, vehicle type, travel time, travel distance, payment method, and past differential toll experiences. The results of this study provide valuable insights for highway managers to establish optimal toll rates and implement dynamic flow regulation strategies while also guiding users in selecting appropriate driving routes. Full article

The timing, cause, and magnitude of mammalian extinctions during the African Middle Pleistocene remain largely unresolved. The demise of Elephas/Palaeoloxodon recki, a lineage that had a great geographic and temporal span, represents a particularly enigmatic case of megafaunal extinction. Previous studies of Early Pleistocene fossil material have proposed that this lineage was a strict C₄-grazer, with its dietary specialization causing its extinction during a period of climatic instability that coincided with the Late Acheulean. Others have associated its disappearance with overhunting by hominins during the same period. We contribute to this debate by analyzing carbon and oxygen isotope data from the tooth enamel of late Early and Middle Pleistocene Palaeoloxodon specimens from various localities in the Afar Rift. To contextualize the isotopic data of Palaeoloxodon within its broader ecosystem, we also provide data from non-elephant species. Carbon isotope values indicate that while C₄ plants dominated diets, varying amounts of C₃ vegetation were also consumed throughout this period. Oxygen isotope values reflect an initial focus on stable water sources that were later broadened to include transient sources. Serially sampled teeth of P. cf. recki recki from Late Acheulean contexts in the Megenta research area show no significant seasonal shifts in δ¹³C or δ¹⁸O values, even during a period of heightened climatic instability regionally. Taken together, our results suggest that Palaeoloxodon was capable of flexibility in diet and drinking habits which belies its morphological specializations. Our results do not support the idea that an inability to adapt to climatic instability caused the extinction of P. recki recki during the Late Acheulean. There is also currently no solid evidence that hominin hunting activities were the cause. However, we cannot discount the potential cumulative impact of climatic-induced environmental pressures and advancements in hominin hunting technologies during the early Middle Stone Age on the eventual extinction of the Palaeoloxodon lineage during the Middle–Late Pleistocene interface. Full article

As residential adoption of renewable energy sources increases, optimizing rooftop photovoltaic systems (RTPVs) with Battery Energy Storage Systems (BESSs) is key for enhancing self-sufficiency and reducing dependence on the grid. This study introduces a novel methodology for sizing Home Energy Management Systems (HEMS), with the objective of minimizing the cost of imported energy while accounting for battery degradation. The battery model integrated nonlinear degradation effects and was evaluated in a real case study, considering different temporal data resolutions and various energy management strategies. For BESS capacities ranging from 1 to 5 kWh, the economic analysis demonstrated cost-effectiveness, with a Net Present Value (NPV) ranging from 54.53 € to 181.40 € and discounted payback periods (DPBs) between 6 and 10 years. The proposed HEMS extended battery lifespan by 22.47% and improved profitability by 21.29% compared to the current HEMS when applied to a 10 kWh BESS. Sensitivity analysis indicated that using a 5 min resolution could reduce NPV by up to 184.68% and increase DPB by up to 43.12% compared to a 60 min resolution for batteries between 1 and 5 kWh. This underscores the critical impact of temporal resolution on BESS sizing and highlights the need to balance accuracy with computational efficiency. Full article

Out-of-stock prediction refers to the activity of forecasting the time when a product will not be available for purchase because of an inventory deficiency. Due to difficulties, out-of-stock forecasting models now face certain challenges. Incorrect demand forecasting may result in a lack or excess of goods in stock, a factor that affects client satisfaction and the profitability of companies. Accordingly, the new approach BCHO-TCN LightGBM, which is based on Buzzard Coney Hawk Optimization with a deep temporal convolutional neural network and the Light Gradient-Boosting Machine framework, is developed to deal with all challenges in the existing models in the field of out-of-stock prediction. The role that BCHO plays in the LightGBM-based deep temporal CNNis rooted in modifying the classifier to improve both accuracy and speed. Integrating BCHO into the model training process allows us to optimize and adjust the hyperparameters and the weights of the CNN linked with the temporal DNN, which, in turn, makes the model perform better in the extraction of temporal features from time-series data. This optimization strategy, which derives from the cooperative behaviors and evasion tactics of BCHO, is a powerful source of information for the computational optimization agent. This leads to a faster convergence of the model towards optimal solutions and therefore improves the overall accuracy and predictive abilities of the temporal CNN with the LightGBM algorithm. The results indicate that when using data from Amazon India’s product listings, the model shows a high degree of accuracy, as well as excellent net present value (NPV), present discounted value (PDV), and threat scores, with values reaching 94.52%, 95.16%, 94.81%, and 95.76%, respectively. Likewise, in a k-fold 10 scenario, the model achieves values of 94.81%, 95.60%, 96.28%, and 95.86% for the same metrics. Full article

The efficiency and dynamics of hybrid electric vehicles are inherently linked to effective energy management strategies. However, complexity is heightened due to uncertainty and variations in real driving conditions. This article introduces an innovative strategy for extended-range electric vehicles, grounded in the optimization of driving cycles, prediction of driving conditions, and predictive control through neural networks. First, the challenges of the energy management system are addressed by merging deep reinforcement learning with strongly convex objective optimization, giving rise to a pioneering method called DQL-AMSGrad. Subsequently, the DQL algorithm has been implemented, allowing temporal difference-based updates to adjust Q values to maximize the expected cumulative reward. The loss function is calculated as the mean squared error between the current estimate and the calculated target. The AMSGrad optimization method has been applied to efficiently adjust the weights of the artificial neural network. Hyperparameters such as the learning rate and discount factor have been tuned using data collected during real-world driving tests. This strategy tackles the “curse of dimensionality” and demonstrates a 30% improvement in adaptability to changing environmental conditions. With a 20%-faster convergence speed and a 15%-superior effectiveness in updating neural network weights compared to conventional approaches, it also highlights an 18% reduction in fuel consumption in a case study with the Nissan Xtrail e-POWER system, validating its practical applicability. Full article

Environmental concerns inherently involve an intergenerational aspect, where today’s decisions can have far-reaching effects on future generations. Numerous national characteristics can forecast a nation’s commitment to investing in environmental sustainability. This study expands on previous research and offers evidence in support of Gott’s principle, which states that citizens may use their country’s age to forecast its remaining lifespan. Specifically, we show that a nation’s age positively relates to intergenerational solidarity—a country’s willingness to sacrifice for future generations. Furthermore, country age and other future-oriented variables, such as a country’s Long-Term Orientation and ability to overcome temporal discounting, are linked to sustainability-related indicators, indicating that countries concerned about the future also exhibit greater concern for the environment. These findings reinforce the value of framing a country as a long-standing entity and implementing intergenerational framing interventions to motivate pro-environmental engagement. Full article

Ecosystems provide humanity with a wide variety and high economic value-added services, from biomass structuring to genetic information, pollutants’ decomposition, water purification and climate regulation. The foundation of ecosystem services is the Eltonian Pyramid, where via prey–predator relationships, energy metabolism and biomass building take place. In the context of existing ecosystem services classification and valuation methods (e.g., CICES, MEA, TEEB), financial investments in ecosystem services essentially address the conservation of trophic pyramids. Our work’s main target is to investigate how trophic pyramids’ dynamics (stability or instability) impact the long-run discounting of financial investments on ecosystem services’ value. Specifically, a trophic pyramid with highly fluctuating populations generates higher risks for the production of ecosystem services, hence for ecological finance instruments coupled to them, due to higher temporal uncertainty or information entropy that should be incorporated into their discount rates. As this uncertainty affects negatively the net present value (NPV) of financial capital on ecosystem services, we argue that the minimization of biomass fluctuations in trophic pyramids via population control should be among the priorities of ecosystem management practices. To substantiate our hypothesis, we construct a logistic predation model, which is consistent with the Eltonian Pyramid’s ecological energetics. As the logistic predator model’s parameters determine the tropic pyramid’s dynamics and uncertainty, we develop an adjusted Shannon entropy index (H(N)_ADJ) to measure this effect as part of the discount rate. Indicatively, we perform a Monte Carlo simulation of a pyramid with intrinsic growth parameter values that yield oscillating population sizes. Finally, we discuss, from an ecological energetics standpoint, issues of competition and diversity in trophic pyramids, as special dimensions and extensions of our analytical framework. Full article

Climate change threatens economic and environmental stability and requires immediate action to prevent and counteract its impacts. As large investments are already going into mitigation efforts, it is crucial to know how to best allocate them in time and among the alternatives. In this work, we tackle this problem using optimal control methods to obtain the temporal profiles of investments and their allocation to either clean energy development or carbon removal technologies expansion. The optimal allocation aims to minimize both the abatement and damage costs for various scenarios and mitigation policies, considering the optimization time horizon. The results show that early investments and a larger share of demand satisfied by clean energy should be priorities for any economically successful mitigation plan. Moreover, less stringent constraints on abatement budgets and reduced discounting of future utility are needed for a more economically and environmentally sustainable mitigation pathway. Full article

Temporal discounting is a phenomenon where a reward loses its value as a function of time (e.g., a reward is more valuable immediately than when it delays in time). This is a type of intertemporal decision-making that has an association with impulsivity and self-control. Many pathologies exhibit higher discounting rates, meaning they discount more the values of rewards, such as addictive behaviors, bipolar disorder, attention-deficit/hyperactivity disorders, social anxiety disorders, and major depressive disorder, among others; thus, many studies look for the mechanism and neuromodulators of these decisions. This systematic review aims to investigate the association between pharmacological administration and changes in temporal discounting. A search was conducted in PubMed, Scopus, Web of Science, Science Direct and Cochrane. We used the PICO strategy: healthy humans (P-Participants) that received a pharmacological administration (I-Intervention) and the absence of a pharmacological administration or placebo (C-Comparison) to analyze the relationship between the pharmacological administration and the temporal discounting (O-outcome). Nineteen studies fulfilled the inclusion criteria. The most important findings were the involvement of dopamine modulation in a U-shape for choosing the delayed outcome (metoclopradime, haloperidol, and amisulpride). Furthermore, administration of tolcapone and high doses of d-amphetamine produced a preference for the delayed option. There was a time-dependent hydrocortisone effect in the preference for the immediate reward. Thus, it can be concluded that dopamine is a crucial modulator for temporal discounting, especially the D2 receptor, and cortisol also has an important time-dependent role in this type of decision. One of the limitations of this systematic review is the heterogeneity of the drugs used to assess the effect of temporal discounting. Full article

Attention deficit hyperactivity disorder (ADHD) results in significant functional impairment. Current treatments, particularly for adults, are limited. Previous research indicates that exercise may offer an alternative approach to managing ADHD, but research into different types of exercise and adult populations is limited. The aim of this study was to examine the effects of acute exercise (aerobic cycling vs mind-body yoga exercises) on symptoms of ADHD in adults. Adults with ADHD (N = 82) and controls (N = 77) were randomly allocated to 10 min of aerobic (cycling) or mind-body (Hatha yoga) exercise. Immediately before and after exercise, participants completed the Test of Variables of Attention task, Delay Discounting Task, and Iowa Gambling Task to measure attention and impulsivity. Actigraphy measured movement frequency and intensity. Both groups showed improved temporal impulsivity post-exercise, with cycling beneficial to all, whilst yoga only benefited those with ADHD. There were no effects of exercise on attention, cognitive or motor impulsivity, or movement in those with ADHD. Exercise reduced attention and increased movement in controls. Exercise can improve temporal impulsivity in adult ADHD but did not improve other symptoms and worsened some aspects of performance in controls. Exercise interventions should be further investigated. Full article

Prioritized experience replay (PER) is an important technique in deep reinforcement learning (DRL). It improves the sampling efficiency of data in various DRL algorithms and achieves great performance. PER uses temporal difference error (TD-error) to measure the value of experiences and adjusts the sampling probability of experiences. Although PER can sample valuable experiences according to the TD-error, freshness is also an important character of experiences. It implicitly reflects the potential value of experiences. Fresh experiences are produced by virtue of the current networks and they are more valuable for updating the current network parameters than the past. The sampling of fresh experiences to train the neural networks can increase the learning speed of the agent, but few algorithms can perform this job efficiently. To solve this issue, a novel experience replay method is proposed in this paper. We first define that the experience freshness is negatively correlated with the number of replays. A new hyper-parameter, the freshness discounted factor $\mu$, is introduced in PER to measure the experience freshness. Further, a novel experience replacement strategy in the replay buffer is proposed to increase the experience replacement efficiency. In our method, the sampling probability of fresh experiences is increased by raising its priority properly. So the algorithm is more likely to choose fresh experiences to train the neural networks during the learning process. We evaluated this method in both discrete control tasks and continuous control tasks via OpenAI Gym. The experimental results show that our method achieves better performance in both modes of operation. Full article

Scarcity goods have generally been perceived as high in value in real-world and empirical studies. However, few studies have investigated this value over time, such as performance in intertemporal decision making. This study’s chief objective was to determine how scarcity evaluation changes temporally. We used the electroencephalogram technique and an outcome evaluation task with the valuation of scarcity and ordinary rewards delivered at different times to explore the effect of scarcity on delay discounting. The feedback-related negativity (FRN) results show that ordinary goods were associated with a more negative amplitude than scarcity goods, and that rewards delivered in the future evoked more negative deflection compared to those delivered immediately. The prominent FRN effect was derived mainly from ordinary trials rather than scarcity trials in the immediate condition and in the future rather than only in the immediate condition. The Frontal Asymmetry Index (FAI) results show that the scarcity condition was associated with greater relative left frontal cortical activity than the ordinary condition when delivered immediately. The frontal asymmetry indicated greater approach motivation. Our electrophysiology data indicate that scarcity goods have a perceived high value, particularly when delivered immediately. Full article

The link between Attention-Deficit/Hyperactivity Disorder (ADHD) and steeper delay discounting has been established and incorporated into theories of ADHD. This study examines a novel interpretation according to which ADHD is linked to sub-optimal temporal decision-making and suggests inconsistency as a potential underlying mechanism. In two experiments, MTurk workers completed a self-report questionnaire on symptoms of ADHD and a temporal decision making task consisting of choices between smaller–immediate and larger–delayed options. The delayed option was better in some items, whereas the immediate option was better in others. The rate of choices of the delayed option and the consistency of choices were measured. The results of both studies show that high symptoms of ADHD were linked to fewer choices of the delayed option when it was better, but also to more choices of the delayed option when it was not better. In addition, ADHD was linked to higher inconsistency in both conditions. The findings suggest that ADHD is linked to sub-optimal temporal decision-making rather than steeper delay discounting, and provide further support to the phenomenon of inconsistency in ADHD. Full article