Search Results (163)

This paper presents a comparative analysis between classical maximum flow algorithms and modern deep Reinforcement Learning (RL) algorithms applied to traffic optimization in urban environments. Through SUMO simulations and statistical tests, algorithms such as Ford–Fulkerson, Edmonds–Karp, Dinitz, Preflow–Push, Boykov–Kolmogorov and Double $DQN$ are compared. Their efficiency and stability are evaluated in terms of metrics such as cumulative vehicle dispersion and the ratio of waiting time to vehicle number. The results show that classical algorithms such as Edmonds–Karp and Dinitz perform stably under deterministic conditions, while Double $DQN$ suffers from high variation. Recommendations are made regarding the selection of an appropriate algorithm based on the characteristics of the environment, and opportunities for improvement using DRL techniques such as PPO and A2C are indicated. Full article

The article presents a new method for evaluating investment projects in uncertain conditions, assuming that uncertainty may have two origins: aleatory (related to randomness) and epistemic (due to incomplete knowledge). Epistemic uncertainty is rarely considered in investment analysis, which can result in undervaluing the future opportunities and risks. Our contribution is built around a correlated random–fuzzy Geometric Brownian Motion, a hybrid Monte Carlo engine that propagates mixed uncertainty into a probability box, combined with three p-box-to-CDF transformations (pignistic, ambiguity-based and credibility-based) to reflect decision-maker attitudes. Our approach utilizes the Datar–Mathews method (DM method) to gather relevant information regarding the potential value of a real option. By combining probabilistic and possibilistic approaches, the proposed valuation model incorporates hybrid Monte Carlo simulation and a random–fuzzy Geometric Brownian Motion, considering the interdependence between parameters. The result of the hybrid simulation is a pair of upper and lower cumulative probability distributions, known as a p-box, which represents the uncertainty range of the Net Present Value (NPV). We propose three transformations of the p-box into a subjective probability distribution, which allow decision makers to incorporate their subjective beliefs and risk preferences when performing real option valuation. Thus, our approach allows the combination of objective available information about valuation of investment with the decision maker’s attitude in front of partial ignorance. To demonstrate the effectiveness of our approach in practical scenarios, we provide a numerical illustration that clearly showcases how our approach delivers a more precise valuation of real options. Full article

The quality of life in both urban and rural areas is highly dependent on the availability of services of general interest. This study examines the provision and accessibility of 41 types of point-specific services, divided into 10 categories, within the functional urban region of Zagreb, Croatia, characterized by a declining population, despite being the most populous area in Croatia. This study adopts a multi-service rather than a single-service research approach, providing a more comprehensive understanding of the phenomenon. Using GIS composite indices for service provision and accessibility by car (cumulative opportunities) were calculated. Cluster analysis (Ward method, quadratic Euclidean distance) revealed seven different geographical patterns. The results show different patterns of service provision and accessibility throughout the urban region. Two specific areas were highlighted: traditional and inner peripheral areas with low levels of both service provision and accessibility, and suburban areas with very good accessibility but very poor service provision. The results of this study are particularly valuable as they relate to a single functional urban area that includes both urban, suburban and rural settlements of different types, a spatial framework that has not been sufficiently analysed in the literature. Full article

Uncertainty is inevitable in real-world systems. If uncertainty is not effectively addressed, it may degrade the performance of model predictive control (MPC). This paper proposes a stochastic dual model predictive control (SDMPC) method for linear systems with parameter uncertainty and measurement noise. The method not only actively explores uncertainty while optimizing control but also introduces probabilistic output constraints to expand the set of feasible solutions. Specifically, Kalman filtering is employed to construct a real-time parameter estimator. The future output errors are incorporated into the nominal cost function as exploration signals to balance exploration and exploitation. Simulation results in the field of solar collectors show that SDMPC can effectively track the temperature by varying the inlet flow under changing environmental and opportunity constraints. The cumulative performance index of SDMPC is 125.3, compared to 316.4 obtained by conventional MPC, validating its effectiveness. Full article

Sugarcane products come from agro-industrial biomass that is increasingly used in the Brazilian energy matrix, which is important for the sustainability and diversification of renewable energy sources. This article examines the concentration and structure of the supply of sugarcane bioelectricity in Brazil from 1975 to 2023. It uses information on the quantity and cumulative licensed potential of sugarcane-based thermoelectric plants in operation, available from the National Electric Energy Agency (ANEEL) through its Generation Information System (SIGA). To measure regional concentration, the study considered geographical areas (large regions, states, intermediate regions and municipalities) using the following concentration indicators: the Concentration Ratio, Herfindahl–Hirschman Index, Theil Entropy, Comprehensive Concentration Index, and Hall–Tideman Index. The main results show a high concentration of sugarcane bioelectricity at regional and state levels, with a predominance in the Southeast-Central-West axis. During the period analyzed, the State of São Paulo remained the leader in terms of energy generated by sugarcane thermoelectric plants operating in Brazil. In the intermediate regions, the concentration was moderate, while at the municipal level, the concentration was low, indicating a highly competitive market. It can be concluded that the areas with the highest concentration are strategic for directing investments and guiding public policies for the sugarcane bioelectricity sector, which are priority locations for new opportunities. The identification of the most promising regions contributes to a more efficient development of the sector. Given that, a more equitable distribution of bioelectricity production across the country could enhance Brazil’s energy security, reduce regional vulnerabilities, and promote more resilient energy systems. Full article

Auricularia auricula (L.) is a widely cultivated edible mushroom, and the resource utilization of its residues offers significant opportunities for sustainable waste management and nutrient recovery. This study investigated the effects of substrate composition on nutrient dynamics and microbial diversity during the aerobic composting of Auricularia auricula (L.) residues. Two treatments were established: composting of Auricularia auricula (L.) residues alone (CR) and composting supplemented with green grass (CRG) over a 49-day period. The results showed that both treatments achieved compost maturity, characterized by a slightly alkaline pH, a germination index (GI) above 80%, and an electrical conductivity below 4 mS/cm. Both composts were odorless, insect-free, and dark brown. Compared to CR, the CRG treatment exhibited higher total organic carbon (TOC) degradation, cumulative total phosphorus (TP) and potassium (TK) levels, as well as enhanced urease, cellulase, and β-glucosidase activities. In contrast, CR retained higher total nitrogen (TN), humic carbon (HEC), fulvic acid carbon (FAC), humic acid carbon (HAC), and a greater humic-to-fulvic acid (HA/FA) ratio. Microbial community analysis revealed diverse bacterial and fungal taxa, with certain species positively correlated with nutrient cycling. Notably, specific substrate compositions promoted beneficial microbial proliferation, essential for efficient composting and nutrient mineralization. These findings not only provide a scientific basis for optimizing composting strategies of mushroom residues but also offer a practical pathway to convert agricultural waste into high-quality organic fertilizers. By enhancing soil fertility, reducing reliance on synthetic fertilizers, and promoting circular bioeconomy practices, this study contributes directly to sustainable agricultural development. CR and CRG treatments, respectively, support either nutrient retention or release, allowing tailored application based on crop demand and soil condition. This study underscores the potential of Auricularia auricula (L.) residues in composting systems, contributing to waste reduction and soil fertility enhancement through tailored substrate management, and offers practical insights into optimizing composting strategies for Auricularia farming by-products. Full article

Forest road networks are essential for forest operations but significantly contribute to carbon loss and landscape fragmentation in boreal ecosystems. This study evaluates the potential of reforesting unused forest roads to enhance carbon storage (CS) in Quebec’s boreal forests. Four reforestation scenarios were simulated using spatial data from AQréseau+ and the Ecoforestry Map of Quebec, combined with the CBM-CFS3 carbon model. These scenarios varied in site preparation conditions and species selection, including the use of fast-growing local species. Random forest (RF) models were applied to analyze the influence of key variables on CS dynamics, focusing on the road area and years to harvest. The study area covered approximately 294,000 km², and the temporal dimension was incorporated by estimating the construction dates of forest roads. Results show that scenarios integrating soil preparation and fast-growing species (S1I1) achieved the highest CS potential, with up to 6.8 million tons (Mt) of additional carbon stored over a 40–100 year period for medium-category roads, compared to 1.15 million tons in scenarios without intervention (S0I0). These findings underscore the role of reforestation in enhancing CS within managed forests. Future work should prioritize road segments for reforestation, considering ecological benefits, operational feasibility, and climate resilience. Full article

Background: Understanding the patterns of milk intake in the neonatal intensive care unit (NICU) will allow opportunities to intervene to improve mother’s milk supply. Objective: To quantify the type and volume of milk intake in premature infants throughout the NICU stay. Methods: This retrospective observational cohort study included infants born and admitted to the NICU at <33 weeks gestation from January 2014 to December 2017 who did not have contraindications for receiving mother’s own milk (MOM). Daily volume of MOM, pasteurized donor milk (PDM), and formula throughout the NICU stay were collected. Infants were categorized as exclusive human milk diet (EHM) if they consumed MOM and PDM or mixed diet if they consumed formula and MOM and/or PDM. Demographics, feeding outcomes, growth outcomes, and neonatal morbidities were collected. Results: Of 195 study infants, 133 (32%) received EHM. Cumulative volume and percent of MOM intake were greater in the EHM group compared to the mixed diet group. Age of first colostrum administration to infant was earlier in the EHM group than the mixed diet group (3.1 vs. 4.9, p = 0.013). By the second week of life, the EHM group received 100% of their feeds as MOM but the maximum MOM received in the mixed diet group was 63%. There was no difference in other feeding or neonatal outcomes between the groups. Conclusion: The EHM group received colostrum earlier than those who received a mixed diet with formula and reached full MOM by the second week of life. This represents the opportunity to address challenges of milk supply of mothers with premature infants in the NICU in the first two weeks after birth. Full article

Examining the energy consumer behavioral model is critical for national governments and academia. This endeavor seeks to uncover effective solutions amid the energy crisis and climate change challenges. This article delves into legislative developments within the energy sector, European Commission recommendations for reducing energy consumption, and existing constraints impacting individual consumers. By scrutinizing the relevant literature, we aimed to identify and analyze factors that can enhance individual benefits derived from energy savings. Then, a comprehensive set of variables was formulated to model the final consumers’ behavior. Data collection involved administering questionnaires to individual consumers, consumer associations, and energy micro-enterprises in Romania. The gathered data were meticulously analyzed using the Smart-Pls 4 statistical software. Building upon insights from specialized literature, this paper pinpoints the behavioral determinants influencing the reduction in energy consumption. These determinants serve as independent variables shaping the voluntary adoption of measures in lifestyle and behavior among various types of energy users. This study’s findings validate the assumptions presented in this article, highlighting that a reduction in energy consumption is a direct and intrinsic outcome achieved by cumulatively addressing several factors. These factors encompass investments in the energy sector, budget allocation for energy consumption expenditure, adherence to social behavior norms, access to pertinent information about the consequences of the energy crisis, and individual responsibility. Notably, the perception of energy-saving opportunities emerges as a mediator between the independent variables and energy savings with a significant effect. This aspect, developed for the first time in this article, draws inspiration from the prospect theory introduced by Kahneman and Tversky. Full article

The relationship between the service of public transport (PT) and its use is complex but can be simplified through the use of indicators. These indicators should be able to accurately reflect PT use so that improvements in the indicators lead to increases in PT use. Although researchers and planners use similar indicators to describe the access of PT stops, the indicators used to assess the accessibility of destinations differ. Researchers use specific location-based methods to analyze accessibility to spatially dispersed destinations, while practitioners often focus on connectivity to central (business) districts. This raises the question of which approach better reflects the use of PT. By combining the German National Household Travel Survey with nationwide timetable data, we examine the relationship between PT use and two indicators of PT service: (1) travel time to the nearest central district and (2) cumulative opportunity accessibility, both calculated as the ratio of PT to car travel. The results of our binary logit models indicate that the travel time ratio does not have a relevant influence on the choice of motorized transport mode, but the accessibility ratio does. Therefore, we suggest that practitioners should use location-based accessibility methods such as the cumulative opportunity ratio to evaluate and improve PT service planning. Full article

Purpose: This systematic literature review analyzes trends, key findings, and research opportunities in manufacturing sustainability from 2019 to 2024, with a focus on the integration of emerging technologies and socio-economic dimensions. Methodology: a systematic review of 181 publications was conducted, emphasizing technological advancements, research gaps, and the influence of global events on sustainable manufacturing. Findings: the review highlights: (1) a shift towards advanced technologies like AI-driven circular economy solutions, digital twins, and blockchain, which have demonstrated potential to reduce energy consumption by 30% and decrease material waste by 20%, significantly enhancing sustainability outcomes; (2) persistent gaps in addressing social, policy, and regulatory dimensions; (3) the role of the COVID-19 pandemic in accelerating digital transformation and reshaping sustainability priorities. Key findings also include PT Indocement achieving a cumulative 35% reduction in natural gas consumption through sustained optimization initiatives and a 12% increase in digital manufacturing adoption among SMEs in developing regions. Practical implications: strategic recommendations are provided for industry, policymakers, and academics to address regional disparities, ensuring a 50% increase in adoption rates of inclusive technologies within developing regions over the next five years, and align sustainability efforts with socio-economic contexts. Originality: this review presents a comprehensive analysis of current trends, actionable insights, and critical areas for future research, highlighting that organizations adopting AI and blockchain technologies report up to a 25% improvement in operational sustainability. Full article

Beekeeping is a crucial agricultural practice that significantly enhances environmental health and food production through effective pollination by honey bees. However, honey bees face numerous threats, including exotic parasites, large-scale transportation, and common agricultural practices that may increase the risk of parasite and pathogen transmission. A major threat is the Varroa destructor mite, which feeds on honey bee fat bodies and transmits viruses, leading to significant colony losses. Detecting the parasite and defining the intervention thresholds for effective treatment is a difficult and time-consuming task; different detection methods exist, but they are mainly based on human eye observations, resulting in low accuracy. This study introduces a digital portable scanner coupled with an AI algorithm (BeeVS) used to detect Varroa mites. The device works through image analysis of a sticky sheet previously placed under the beehive for some days, intercepting the Varroa mites that naturally fall. In this study, the scanner was tested for 17 weeks, receiving sheets from 5 beehives every week, and checking the accuracy, reliability, and speed of the method compared to conventional human visual inspection. The results highlighted the high repeatability of the measurements (R² ≥ 0.998) and the high accuracy of the BeeVS device; when at least 10 mites per sheet were present, the device showed a cumulative percentage error below 1%, compared to approximately 20% for human visual observation. Given its repeatability and reliability, the device can be considered a valid tool for beekeepers and scientists, offering the opportunity to monitor many beehives in a short time, unlike visual counting, which is done on a sample basis. Full article

The aim of this study is to construct and validate an expert system to predict the adaptation of irrigation technologies, water-saving strategies, and monitoring tools by small-scale farmers in Egypt. The research investigates the impact of economic, educational, environmental, and social factors on adaptation rates. To build the expert system, extensive knowledge was collected from experts, key concepts were identified, and production rules were created to generate tailored scenarios. These scenarios utilize the empirical cumulative distribution function (ECDF), selecting the scenario with the highest ECDF as the optimal irrigation technology. This approach ensures well-informed, data-driven decisions that are tailored to specific conditions. The expert system was evaluated under the conditions of ten small farms in Egypt. The results indicate that water cost and availability are significant drivers of technology adaptation. Specifically, subsurface drip irrigation (SDI) demonstrated an adaptation percentage of 75% at high water costs, with probabilities of 0.67 and 0.33, while soil mulching (SM) showed a 75% adaptation rate with a probability of 0.33 in high-cost scenarios. Conversely, when water availability was high, the adaptation percentage for all techniques was zero, but it reached 100% adaptation with a probability of 0.76 for SM and SDI and a probability of 1 for variable number of drippers (VND) and the use of sensors as monitoring tools during water shortages. Educational attainment and professional networks enhance the adaptation of advanced technologies and monitoring tools, emphasizing the role of knowledge and community engagement. Environmental conditions, including soil texture and salinity levels, directly affect the choice of irrigation methods and water-saving practices, highlighting the need for localized solutions. The source of irrigation water, whether groundwater or surface water, influences the preference for water-saving technologies. The study underscores the importance of tailored approaches to address the challenges and opportunities faced by small farmers in Egypt, promoting sustainable agriculture and efficient water management. The evaluation findings reveal that SDI is the most favored irrigation technology, with a probability of 0.55, followed by variable number of drippers (VND) at 0.38 and ultralow drip irrigation (ULDI) at 0.07 across various scenarios for small farmers. Regulated deficit irrigation (RDI) and SM are equally preferred water-saving strategies, each with a probability of 0.50. Sensors emerged as the preferred monitoring tool, boasting a high probability of 0.94. The analysis reveals the critical roles of economic pressures, educational levels, environmental conditions, and social networks in shaping the adaptation of sustainable agricultural practices. Full article

This study addresses the challenges of and opportunities for achieving the ambitious greenhouse gas emissions reduction target of the fishery sector of the Republic of Korea, set at 96% by 2030. We also focus on the current status of land-based aquaculture and underground seawater resource development, quantitatively compare energy inputs for land-based fish cultivation, and evaluate the potential of underground seawater to reduce CO₂ emissions. Since 2010, 762 underground seawater boreholes have been developed, yielding a cumulative daily pumpage of 125,780 m³. Jeollanam-do was found to have the highest daily pumpage, with an annual energy requirement of 131,205,613 Mcal. Despite the fact that the energy demands for underground seawater are higher in some months, it provides a 22.6% reduction in total annual energy consumption compared to surface water. The use of underground seawater for heating or cooling resulted in a 24.1% reduction in the required input energy. However, energy requirements increase due to the relatively high surface water temperature in some regions and seasons. This study also highlights the utilization of underground seawater in heating or cooling surface water via indirect applications using geothermal heat pumps. This innovative research broadens the methods of greenhouse gas mitigation, particularly in the agriculture, livestock, and fisheries industries. Full article

Fixed sleep schedules with an 8 h time in bed (TIB) are used to ensure participants are well-rested before laboratory studies. However, such schedules may lead to cumulative excess wakefulness in young individuals. Effects on older individuals are unknown. We combine modelling and experimental data to quantify the effects of sleep debt on sleep propensity in healthy younger and older participants. A model of arousal dynamics was fitted to sleep data from 22 young (20–31 y.o.) and 26 older (61–82 y.o.) individuals (25 male) undertaking 10 short sleep–wake cycles during a 40 h napping protocol, following >1 week of fixed 8 h TIB schedules. Homeostatic sleep drive at the study start was varied systematically to identify best fits between observed and predicted sleep profiles for individuals and group averages. Daytime sleep duration was the same on the two days of the protocol within the groups but different between the groups (young: 3.14 ± 0.98 h vs. 3.06 ± 0.75 h, older: 2.60 ± 0.98 h vs. 2.37 ± 0.64 h). The model predicted an initial homeostatic drive of 11.2 ± 3.5% (young) and 10.1 ± 3.5% (older) above well-rested. Individual variability in first-day, but not second-day, sleep patterns was explained by the differences in the initial homeostatic drive for both age groups. Our study suggests that both younger and older participants arrive at the laboratory with cumulative sleep debt, despite 8 h TiB schedules, which dissipates after the first four sleep opportunities on the protocol. This has implications for protocol design and the interpretation of laboratory studies. Full article