Search Results (75)

Central business district (CBD) hospitals must sustain reliable pressure relationships in critical rooms while reducing whole-facility carbon under tight space and disruption constraints. We developed an ontology-grounded semantic digital twin that normalizes building automation system (BAS) and building management system (BMS) telemetry into a unified semantic store consistent with Brick Schema, enabling portable asset discovery via query and thereby supporting forecasting, anomaly detection, and multi-objective optimization without dependence on vendor point naming conventions. Whole-facility impacts were verified using International Performance Measurement and Verification Protocol Option C–style measurement and verification with an S0-calibrated baseline model and residual-based savings attribution. Relative to the baseline (S0), the intervention (S3) produced a step increase in the critical-room pressure-compliance pass rate, tighter room-to-corridor differential-pressure (ΔP) control across airborne infection isolation and open room strata, and intent-aligned ventilation delivery (air changes per hour ratio distribution concentrated near unity; p < 0.05 where letter groups differ). Operational-state discrimination improved (AUC 0.649→0.696) and issue-resolution times shortened (left-shifted cumulative distribution function), indicating reduced service burden. Option C verification showed energy residuals shifting negative under S3, consistent with net savings versus baseline expectations. Across progressive maturity (S0→S3), time-to-value and burden fractions decreased, carbon intensity (tCO₂e m⁻²) decreased, long-tail exposure compressed (log-scale horizon), and composite performance indices increased (p < 0.05). These results demonstrate a verifiable pathway to pressure-reliable, decarbonized hospital operations at the whole-facility boundary while making the semantic layer’s utility explicit through query-driven, ontology-grounded asset discovery. We present an IPMVP Option-C–verifiable semantic digital-twin governance framework that links audited operational evidence (telemetry → actions → verification) to whole-facility energy and carbon outcomes while maintaining critical-room pressure-relationship reliability. Optimization benchmarking (including quantum annealing) is used as supporting decision-support evaluation, rather than as the central contribution. Full article

The widespread adoption of solar energy storage systems is transforming the global energy landscape, enabling more efficient use of renewable resources and enhancing energy resilience. The integration of residential batteries significantly enhances energy efficiency and sustainability by facilitating the storage of surplus renewable energy, providing reliable backup during power outages, and optimising energy consumption. This study explores the factors influencing end-user adoption of batteries, utilising the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as a guiding framework to analyse adoption behaviours and determinants. This study employs a qualitative approach using semi-structured interviews with stakeholders divided into three categories: regulatory authorities, industry experts, and end-users. This study highlights key factors influencing battery adoption, such as energy independence, grid reliability, and environmental impact, while addressing challenges like regulatory inconsistencies and installer training. Study extends UTAUT2 to residential battery adoption, emphasising performance expectancy, facilitating conditions, and price value in decision-making and makes a methodological contribution by validating deeper qualitative insights into renewable technology adoption. The practical implications emphasise the need for designing targeted policies, such as subsidies and net metering, alongside developing user-centric systems that enhance affordability, usability, and consumer awareness to facilitate residential battery adoption. Full article

Smart microgrids are localized energy systems that integrate distributed energy resources, such as photovoltaics (PVs) and battery storage, to optimize energy use, enhance reliability, and minimize environmental impacts. This paper investigates the operation of a smart microgrid installed at the Hellenic Mediterranean University (HMU) campus in Heraklion, Crete, Greece. The system, consisting of PVs and battery storage, operates under a zero feed-in scheme, which maximizes on-site self-consumption while preventing electricity exports to the main grid. With increasing PV penetration and growing grid congestion, this scheme is an increasingly relevant strategy for microgrid operations, including university campuses. A properly sized PV–battery microgrid operating under zero feed-in operation can remain financially viable over its lifetime, while additionally it can achieve significant environmental benefits. The study performed at the HMU Campus utilizes measured hourly data of load demand, solar irradiance, and ambient temperature, while PV and battery components were modeled based on real technical specifications. The study evaluates the system using financial and environmental performance metrics, specifically net present value (NPV) and annual greenhouse gas (GHG) emission reductions, complemented by sensitivity analyses for battery technology (lead–carbon and lithium-ion), load demand levels, varying electricity prices, and projected reductions in lithium-ion battery costs over the coming years. The findings indicate that the microgrid can substantially reduce grid electricity consumption, achieving annual GHG emission reductions exceeding 600 tons of CO₂. From a financial perspective, the optimal configuration consisting of a 760 kWp PV array paired with a 1250 kWh lead–carbon battery system provides a system autonomy of 46% and achieves an NPV of EUR 1.41 million over a 25-year horizon. Higher load demands and electricity prices increase the NPV of the optimal system, whereas lower load demands enhance the system’s autonomy. The anticipated reduction in lithium-ion battery costs over the next 5–10 years is expected to provide improved financial results compared to the base-case scenario. These results highlight the techno-economic viability of zero feed-in microgrids and provide valuable insights for the planning and deployment of similar systems in regions with increasing renewable penetration and grid constraints. Full article

Ecolabelling has emerged as a key instrument to communicate environmental performance to consumers, particularly in the agri-food sector where resource use and ecological pressures are highly interlinked. Conventional Life Cycle Assessment (LCA)-based ecolabels often suffer from methodological discretion, lack of territorial specificity, and limited consumer trust. This study investigates how the Water–Energy–Food–Ecosystem (WEFE) Nexus could be integrated into LCA-based ecolabelling, with a specific focus on pasta production as a representative case in the food industry. Indicators were collected from recent literature on LCA and Nexus applications, selected for simplicity and clear attribution to one WEFE dimension, and then evaluated by experts from COST Action CA20138 (NexusNet) through a two round Delphi protocol. The process yielded 23 indicators distributed across the four dimensions, which were subsequently compared with six Environmental Product Declarations to assess data availability and compatibility. The results suggest that many indicators can be computed with standard LCA inventories, while the Nexus perspective adds value by capturing multidimensional impacts and regional resource pressures. Further refinement and empirical testing are expected to enhance the framework’s applicability, but the findings already indicate that incorporating WEFE-based indicators into pasta ecolabelling could represent a promising pathway to improve analytical depth and consumer relevance, aligning circular economy principles with corporate assessment practices. Full article

An integrated field–analytical framework was applied to quantify the impact of two small-settlement treatment facilities (TF1 and TF2) on the Irtysh River (East Kazakhstan). The main objective of this study is to quantify effluent-driven dilution and non-conservative changes in key water-quality indicators downstream of TF1 and TF2 and to evaluate parsimonious models for predicting effluent-outlet BOD and COD from upstream measurements. Paired upstream–downstream control sections are sampled in 2024–2025 for 22 indicators, and plant influent–effluent records are compiled for key wastewater variables. Chloride-based conservative mixing indicated very strong dilution (approximately $D\approx 2.0\times {10}^3$ for TF1 and $D\approx 4.2\times {10}^2$ for TF2). Deviations from the mixing line were summarized using a transformation diagnostic $\theta$. At TF1, several constituents exceeded mixing expectations ($\theta \approx 13$ for COD, $\theta \approx 42$ for ammonium, and $\theta \approx 6$ for phosphates), while nitrate shows net attenuation $\theta <0$. At TF2, $\theta$ values cluster near unity, indicating modest deviations. Under a small-sample regime $\left(\mathrm{N}=10\right)$ and leave-one-out validation, regularized regression provided accurate forecasts of effluent-outlet BOD and COD. Lasso under LOOCV performed best (BOD_after: RMSE = 0.626, MAE = 0.459, and $R^2=0.976$; COD_after: RMSE = 0.795, MAE = 0.634, and $R^2=0.997$). The results reconcile strong reach-scale dilution with constituent-specific local departures and support targeted modernization and operational forecasting for water-quality management in small facilities. Full article

Taiwan has set an ambitious target of net-zero carbon emissions by 2050, relying heavily on offshore wind capacity of 13.1 GW by 2030 and 40–55 GW by 2050. Floating offshore wind (FOW) is expected to play a central role in meeting these targets, particularly in deep-water areas where fixed-bottom technology is technically constrained. This study combined S-curve modeling for capacity projections, learning curves for cost estimation, and input–output analysis to quantify economic and environmental impacts under three deployment scenarios. Our findings indicate that FOW development provides substantial economic benefits, particularly under the high-growth scenario. During the construction phase through 2040, total output is projected to exceed NTD 1.97 trillion, generating more than NTD 1 trillion in gross value added (GVA) and over 470,000 full-time equivalent (FTE) jobs. By 2050, operations and maintenance (O&M) output is expected to reach approximately NTD 50 billion, supporting roughly 14,200 jobs and about NTD 13.8 billion in income. Annual CO₂ reduction could reach up to 10.4 Mt by 2050 under the high-growth scenario, or about 6.86 Mt under the low-growth case, demonstrating the potential of FOW to drive industrial development while advancing national decarbonization. Full article

This article introduces an algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (UAV)-photogrammetry point clouds, offering an alternative to traditional ground filtering methods. Unlike conventional ground filters that rely on point cloud classification, the proposed approach employs heteroscedastic regression. The U-Net model predicts the conditional expected values of the elevation corrections, aiming to reduce the impact of vegetation on determined ground surface elevations. Concurrently, it estimates the logarithm of the elevation correction variance, allowing for direct quantification of the uncertainty associated with each elevation correction value. The algorithm was evaluated using three metrics: the root mean square error (RMSE) of vertical displacements, the percentage of nodes with determined displacement values, and the percentage of outliers among those values. Performance was assessed using the technique for order of preference by similarity to ideal solution (TOPSIS) method and compared against several ground-filter-based algorithms across four datasets, each including at least two time intervals. In most cases, the U-Net-based approach demonstrated a slight performance advantage over traditional ground filtering techniques. For example, for the U-Net-based algorithm, for one of the test datasets, the RMSE of the determined subsidences was 6.1 cm, the percentage of nodes with determined subsidences was 80.5%, and the percentage of outliers was 0.2%. For the same case, the algorithm based on the next best model (SMRF) allowed an RMSE of 7.7 cm to be obtained; for 77.3% of nodes, the subsidences were determined; and the percentage of outliers was 0.3%. Full article

In the context of global climate change, a comprehensive understanding of the spatiotemporal impacts of drought on vegetation productivity is essential for assessing terrestrial ecosystem stability. Utilizing outputs from six global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study systematically assessed historical and projected drought probability, the drought vulnerability of Net Primary Productivity (NPP), and overall drought risk across the Mongolian Plateau under three Shared Socioeconomic Pathway scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Results revealed that the Standardized Precipitation Evapotranspiration Index (SPEI) exhibited a declining trend, whereas NPP showed an overall increasing trend. These changes were most pronounced under the SSP5-8.5 scenario, with the SPEI decreasing at a rate of −0.39/10a and NPP increasing at 25.8/10a. Drought severity exhibited strong spatial heterogeneity, intensifying from northeast to southwest, whereas NPP demonstrated an inverse spatial pattern. The spatial distribution of high-drought-risk zones varied markedly across scenarios: the southwestern region was most affected under SSP1-2.6, the northwestern region under SSP2-4.5, and the southeastern region under SSP5-8.5. Based on 12-month SPEI values and NPP derived from the Carnegie–Ames–Stanford Approach (CASA) model, SSP2-4.5 presented the highest overall drought risk, despite lower emissions. The annual mean NPP drought vulnerability ranked as follows: SSP2-4.5 (0.60 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-2}{\mathrm{y}\mathrm{r}}^{-1}$) > SSP1-2.6 (−1.03 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-2}{\mathrm{y}\mathrm{r}}^{-1}$) > SSP5-8.5 (−1.24 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-2}{\mathrm{y}\mathrm{r}}^{-1}$). Projections indicated a substantial increase in drought occurrence probability during the period 2061–2100, particularly under SSP2-4.5 and SSP5-8.5. Under higher emissions, the spatial extent of areas with negative drought vulnerability values was expected to expand 68%. Wind speed was the dominant factor influencing drought risk under SSP1-2.6 and SSP2-4.5, whereas precipitation became the primary driver (45.34%) under SSP5-8.5. These findings offer critical insights for early drought warning systems and for strengthening ecosystem resilience across the Mongolian Plateau. Full article

Climate change may soon impact popcorn productivity. The aim was to assess physiological and growth traits in two popcorn genotypes with different water use efficiency under water-deficit stress. The plants were grown in a greenhouse under either water stress (WS) or non-water stress (WW) conditions. Gas exchange, chlorophyll fluorescence, and leaf temperature were assessed every three days, for a total of nine measurements. At the end of the assessment period, growth traits and the SPAD index were evaluated. Our hypotheses were as follows: (a) plants of the P7 genotype (water-efficient agronomic genotype) would take longer than L65 plants (water-inefficient agronomic genotype) to reduce photosynthetic rates under water stress conditions; (b) after re-irrigation, P7 plants would recover photosynthetic capacity with values similar to the period without water stress; and (c) P7 plants would recover photosynthetic capacity faster than L65 plants when subjected to the same period of water stress. The P7 genotype (agronomic water-efficient genotype) absorbed water more quickly due to higher root biomass, root length, and root volume. Yet, at 14 days after suspending irrigation (DASI), the P7 genotype had the lowest net CO₂ assimilation rate (A_net), stomatal conductance (g_s), and transpiration rates (E) values. However, L65 (agronomic water-inefficient genotype) had the lowest A_net, g_s, and E values only at 17 DASI. As a consequence of stomatal closure in both genotypes, the E rates were reduced, and there was an increase in leaf temperature for WS plants, while L65 had higher leaf temperature at maximum water stress. No photochemical damage was detected, indicating that the reduced A_net in WS was likely due to stomatal limitations and biochemical disturbances in both genotypes. Photosynthetic recovery occurred gradually, with full restoration of rates in both genotypes at the end of the experiment. Although our initial hypothesis expected the P7 genotype to maintain photosynthesis longer under water stress, our findings showed an earlier decline in A_net compared to L65. This result is likely due to the large root system of P7 exhausting the limited soil water more rapidly in pot conditions, accelerating the onset of stress. Full article

Model predictive control (MPC) is a commonly used online strategy for maximizing economic benefits in smart homes that integrate photovoltaic (PV) panels, electric vehicles (EVs), and battery energy storage systems (BESSs). However, prediction errors associated with PV power and load demand can lead to economic losses. Scenario-based MPC can mitigate the impact of prediction errors by computing the expected objective value of multiple stochastic scenarios. However, reducing the number of scenarios is often necessary to lower the computation burden, which in turn causes some economic loss. To achieve online operation and maximize economic benefits, this paper proposes utilizing the consensus alternating direction method of multipliers (C-ADMM) algorithm to quickly calculate the scenario-based MPC problem without reducing stochastic scenarios. First, the system layout and relevant component models of smart homes are established. Then, the stochastic scenarios of net load prediction error are generated through Monte Carlo simulation. A consensus constraint is designed about the first control action in different scenarios to decompose the scenario-based MPC problem into multiple sub-problems. This allows the original large-scale problem to be quickly solved by C-ADMM via parallel computing. The relevant results verify that increasing the number of stochastic scenarios leads to more economic benefits. Furthermore, compared with traditional MPC with or without prediction error, the results demonstrate that scenario-based MPC can effectively address the economic impact of prediction error. Full article

Refining the land use structure can boost land utilization efficiency and curtail regional carbon emissions. Nevertheless, prior research has predominantly concentrated on static linear planning analysis. It has failed to account for how future dynamic alterations in driving factors (such as GDP and population) affect simulation outcomes and how the land use spatial configuration impacts the attainment of the carbon-neutrality goal. In this research, 1 km spatial resolution LULC products were employed to meticulously simulate multiple land use scenarios across China at the national level from 2030 to 2060. This was performed by taking into account the dynamic changes in driving factors. Subsequently, an analysis was carried out on the low-carbon land use spatial structure required to reach the carbon-neutrality target. The findings are as follows: (1) When employing the PLUS (Patch—based Land Use Simulation) model to conduct simulations of various land use scenarios in China by taking into account the dynamic alterations in driving factors, a high degree of precision was attained across diverse scenarios. The sustainable development scenario demonstrated the best performance, with kappa, OA, and FoM values of 0.9101, 93.15%, and 0.3895, respectively. This implies that the simulation approach based on dynamic factors is highly suitable for national-scale applications. (2) The simulation accuracy of the PLUS and GeoSOS-FLUS (Systems for Geographical Modeling and Optimization, Simulation of Future Land Utilization) models was validated for six scenarios by extrapolating the trends of influencing factors. Moreover, a set of scenarios was added to each model as a control group without extrapolation. The present research demonstrated that projecting the trends of factors having an impact notably improved the simulation precision of both the PLUS and GeoSOS-FLUS models. When contrasted with the GeoSOS-FLUS model, the PLUS model attained superior simulation accuracy across all six scenarios. The highest precision indicators were observed in the sustainable development scenario, with kappa, OA, and FoM values reaching 0.9101, 93.15%, and 0.3895, respectively. The precise simulation method of the PLUS model, which considers the dynamic changes in influencing factors, is highly applicable at the national scale. (3) Under the sustainable development scenario, it is anticipated that China’s land use carbon emissions will reach their peak in 2030 and achieve the carbon-neutrality target by 2060. Net carbon emissions are expected to decline by 14.36% compared to the 2020 levels. From the perspective of dynamic changes in influencing factors, the PLUS model was used to accurately simulate China’s future land use. Based on these simulations, multi-scenario predictions of future carbon emissions were made, and the results uncover the spatiotemporal evolution characteristics of China’s carbon emissions. This study aims to offer a solid scientific basis for policy-making related to China’s low-carbon economy and high-quality development. It also intends to present Chinese solutions and key paths for achieving carbon peak and carbon neutrality. Full article

Forecast risk management is central to the financial management process. This study aims to apply Monte Carlo simulation to solve three classic probabilistic paradoxes and discuss their implementation in corporate financial management. The article presents Monte Carlo simulation as an advanced tool for risk management in financial management processes. This method allows for a comprehensive risk analysis of financial forecasts, making it possible to assess potential errors in cash flow forecasts and predict the value of corporate treasury growth under various future scenarios. In the investment decision-making process, Monte Carlo simulation supports the evaluation of the effectiveness of financial projects by calculating the expected net value and identifying the risks associated with investments, allowing more informed decisions to be made in project implementation. The method is used in reducing cash flow volatility, which contributes to lowering the cost of capital and increasing the value of a company. Simulation also enables more accurate liquidity planning, including forecasting cash availability and determining appropriate financial reserves based on probability distributions. Monte Carlo also supports the management of credit and interest rate risk, enabling the simulation of the impact of various economic scenarios on a company’s financial obligations. In the context of strategic planning, the method is an extension of decision tree analysis, where subsequent decisions are made based on the results of earlier ones. Creating probabilistic models based on Monte Carlo simulations makes it possible to take into account random variables and their impact on key financial management indicators, such as free cash flow (FCF). Compared to traditional methods, Monte Carlo simulation offers a more detailed and precise approach to risk analysis and decision-making, providing companies with vital information for financial management under uncertainty. This article emphasizes that the use of Monte Carlo simulation in financial management not only enhances the effectiveness of risk management, but also supports the long-term growth of corporate value. The entire process of financial management is able to move into the future based on predicting future free cash flows discounted at the cost of capital. We used both numerical and analytical methods to solve veridical paradoxes. Veridical paradoxes are a type of paradox in which the result of the analysis is counterintuitive, but turns out to be true after careful examination. This means that although the initial reasoning may lead to a wrong conclusion, a correct mathematical or logical analysis confirms the correctness of the results. An example is Monty Hall’s problem, where the intuitive answer suggests an equal probability of success, while probabilistic analysis shows that changing the decision increases the chances of winning. We used Monte Carlo simulation as the numerical method. The following analytical methods were used: conditional probability, Bayes’ rule and Bayes’ rule with multiple conditions. We solved truth-type paradoxes and discovered why the Monty Hall problem was so widely discussed in the 1990s. We differentiated Monty Hall problems using different numbers of doors and prizes. Full article

Climate change and related events such as temperature increase over time and more frequent extreme weather events constitute a risk to the population and wellbeing. This study contributes to the knowledge on this subject by analyzing changes in mortality in Portugal using the most recent historical and future climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6). A time-series distributed lag non-linear model (DLNM) was used to estimate the temperature-related mortality burdens in Portugal in the historical period (or reference, 1995–2014), the mid-century period (2046–2065), and the end of the century period (2081–2100) under moderate (SSP2-4.5) and extreme (SSP5-8.5) climate change scenarios. The findings show that winter periods of the contemporary climate (1995–2014) showed a significantly elevated risk of deaths from cold temperatures (RR = 2.23 (95% CI: 1.07, 4.64) at a minimum value of −3 °C), while at the maximum value (35.9 °C), the RR of 1.69 (95% CI: 1.01, 2.82) in the summer period indicated a moderate increase in risk. In terms of future projections, heat-related and extreme-heat-related mortality are higher under SSP5-8.5, while cold-related and extreme-cold-related mortality are generally higher under SSP2-4.5. Under the SSP2-4.5 scenario, the future periods of 2046–2065 and 2081–2100 showed a small net change in heat-related mortality. However, there is projected to be an increase in heat-related mortality due to increased heat, ranging from 0.13% to 0.14%. The impact of extreme heat is expected to result in a mortality increase of 0.03% to 0.04%, while extreme cold is expected to decrease mortality by −0.10%. Under the SSP5-8.5 scenario, the net change in mortality during the future period of 2046–2065 is estimated to decrease by −0.13%, with some uncertainty in the estimate. From 2081 to 2100, there is expected to be an estimated increase of 0.06% in mortality. The specific impact of increased heat shows an increase in heat-related mortality ranging from 0.15% to 0.17%, while extreme heat has an estimated increase of 0.04% to 0.05%. The developed framework provides a comprehensive assessment of excess mortality attributed to varying non-optimum temperatures for designing public health policies in Portugal. Full article

CO₂ is the main solvent used in enhanced oil recovery (EOR). However, its low density and viscosity compared to oil cause a decrease in sweep efficiency. Recently, dimethyl ether (DME), which is more efficient than CO₂, has been introduced into the process. DME improves oil recovery by reducing minimum miscible pressure (MMP), interfacial tension (IFT), and oil viscosity. Since DME is an expensive solvent, price reduction and appropriate injection scenarios are needed for economic feasibility. In this study, a compositional model was developed to inject DME with impure CO₂ streams, where the CO₂ was derived from one of these three purification methods: dehydration, double flash, and distillation. It was assumed that such a mixed solvent was injected into a heterogeneous reservoir where gravity override was maximized. As a result, lower oil recovery is achieved for the higher impurity content of the CO₂ stream, lower DME content, and more heterogeneous reservoir. When a high-purity CO₂ stream is used, the change in oil recovery according to DME content and heterogeneity of the reservoir is increased. When the lowest-purity CO₂ stream is used, the net present value (NPV) is the highest. For a homogeneous reservoir, the NPV is highest for all impure CO₂ streams. This optimization indicates a greater impact on revenue from reduced CO₂ purchase cost than on profit loss due to reduced oil recovery by impurities. Additional benefits can be expected when considering solvent reuse and carbon capture and storage (CCS) credits. Full article

The cotton mealybug, Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae), is an invasive polyphagous pest that has been reported in several tomato-producing Mediterranean countries. However, information regarding the impact of temperature variations on its potential damage and population dynamics on this crop is limited. The effect of four temperatures (20 ± 1 °C, 25 ± 1 °C, 30 ± 1 °C and 35 ± 1 °C) on the development, reproduction, and population growth parameters of P. solenopsis on tomatoes under controlled laboratory conditions was investigated using age-stage two-sex life tables. The increase in temperature caused a significant decrease in the developmental periods of all instars except eggs. The shortest durations of the life cycle (29.58 ± 0.28 days for females and 13.91 ± 0.25 days for males), the adult preoviposition period (APOP), and the total preoviposition period (TPOP) (APOP: 7.78 ± 0.09 days and TPOP: 18.33 ± 0.13 days) were obtained at 35 ± 1 °C. Fecundity varied with temperature, and the highest value was recorded at 30 ± 1 °C (183.29 ± 7.13 eggs/female). The highest average net reproduction rate (R₀) (154.24 ± 14.681 offspring/female), intrinsic rate of increase (r) (0.222 ± 0.0036 d⁻¹), and finite rate of increase (λ) (1.248 ± 0.00495 d⁻¹) were observed at 35 ± 1 °C. A simulation of population increase and structure under different temperatures over a period of 90 days revealed that the greatest expected population size was at 35 ± 1 °C, with the completion of four overlapping generations. The data from this study provide valuable information for adapted pest management approaches against P. solenopsis on tomato crops. Full article