Search Results (51)

Background: The aim of this study was to investigate the preliminary evidence supporting the validity of the Running Advisor Billat Training test (RABIT^®) in determining the three intensity domains in male adults with obesity. Methods: Thirteen male adults with obesity completed a graded (GRAD) and a RABIT^® test. The RABIT^® test consisted of three fixed levels of perceived exertion (RPE): (1) 10 min at RPE 13, (2) 5 min at RPE 16, and (3) 3 min at RPE 18. GRAD was composed of 1 min step, increasing speed by 0.5 km/h every minute until volitional exhaustion. Results: At RPE 18, maximal oxygen consumption ($\stackrel{.}{\mathrm{V}}$O₂max), minute ventilation ($\stackrel{.}{\mathrm{V}}$_E), maximal heart rate (HRmax), and running speed were not significantly different from the values measured during the GRAD. As well, oxygen consumption ($\stackrel{.}{\mathrm{V}}$O₂), $\stackrel{.}{\mathrm{V}}$_E, and HR measured during RPE 16 and RPE 13 of the RABIT^® test were not significantly different from the anaerobic threshold (AnT) and aerobic Threshold (AerT) values measured during GRAD. However, running speed at RPE 16 and RPE 13 of the RABIT^® test was lower by −5.03% (p = 0.041) and −7.00% (p < 0.001), compared to GRAD. Conclusions: The data obtained in our study provide preliminary evidence supporting the ability of the RABIT^® test to estimate maximal exercise parameters, as well as most parameters associated with AerT and AnT. Consequently, the test may be useful for identifying the three training intensity domains and for planning training sessions for adults with obesity. Full article

Building-Integrated Photovoltaics (BIPV) can contribute to decarbonisation, but its large-scale deployment requires accurate energy yield predictions that justify these systems during the decision-making process to ensure cost-effectiveness. In urban contexts, boundary conditions involve modelling strategies that can reliably represent the effect of shading from nearby elements. However, specific tools for proper modelling BIPV are not generally available and the workflow frequently requires the combination of different tools. Nowadays there is still no clear nor unique strategy for modelling BIPV, and expert groups are currently working on benchmarking analyses. This work compares energy yield estimations from two PV simulation software tools, System Advisor Model and PVsyst to seven years of experimental data (2017–2023) from five BIPV façade arrays distributed across three orientations (east, south and west). The main focus was twofold. Firstly, to analyse their management of shadows by following two different shading approaches: their built-in 3D modelling tools and a Digital Surface Model (DSM). Secondly, to evaluate the capability of these tools to simulate the performance of real BIPV systems. Results manifest that conventional and accessible PV software can be suitable for BIPV modelling as long as care is taken to properly assess the effect of shading, especially from urban tree canopies. The novel DSM strategy proposed is proven effective and can be a valid alternative in certain cases when the availability of in situ data is limited. Full article

This study examines the False Reality Bias in treasury management, a cognitive distortion through which small and medium-sized enterprises (SMEs) infer financial stability from salient bank balances while overlooking pending obligations and cash-flow timing. Using a firm-level dataset of 50 Spanish meat-processing SMEs, the analysis develops two behavioral-finance indicators: the Liquidity Misperception Index (PEL), capturing the divergence between salient liquidity cues and effective short-term obligations, and the Liquidity Misconfidence Index (ICEL), measuring managerial overconfidence in liquidity assessments. Results show that 41% of firms overestimate liquidity (average PEL = 1.21), while 40% exhibit excessive confidence (ICEL > 1.3), both significantly associated with liquidity distress. Econometric estimates indicate that firms with PEL values above 1.2 are 4.48 times more likely to experience liquidity crises, even after controlling for bank balance levels. Predictive models are used in an exploratory capacity, achieving classification accuracies above 80% and supporting the robustness of the behavioral signals identified. In addition, AI-assisted cash-flow simulations reduce liquidity misperception by 34.7% (p < 0.01). Overall, the findings provide micro-level evidence that cognitive biases systematically distort SME treasury decisions but can be partially corrected through targeted decision-support tools, offering practical insights for managers, advisors, and policymakers. Full article

Renewable energy systems are increasingly critical for achieving decarbonization and long-term energy security, particularly in rural regions with abundant local resources. While solar and wind technologies have become cost-competitive, their intermittency limits reliability when deployed independently. Biomass, by contrast, offers dispatchable renewable power but faces economic challenges related to feedstock logistics. This study evaluates a biomass-led hybrid renewable energy system (HRES) for Grenada County, Mississippi, integrating biomass, solar photovoltaic (PV), and wind resources to enhance system reliability and reduce environmental impacts. System performance and optimization were assessed using the System Advisor Model (SAM) and the Hybrid Optimization of Multiple Energy Resources (HOMER). The proposed configuration comprises approximately 80% biomass, 10% solar PV, and the remaining share from wind, producing a total annual electricity output of about 423 GWh, sufficient to meet regional demand. The subsystem-level levelized cost of energy (LCOE) was estimated at 12.10 cents/kWh for biomass, 4.07 cents/kWh for solar PV, and 8.62 cents/kWh for wind, with the overall hybrid cost influenced primarily by biomass feedstock transportation and storage. Environmental impact assessment based on U.S. EPA eGRID and IPCC factors indicates that the hybrid system achieves a weighted emission intensity of approximately 28.4 kg CO₂-eq/MWh, representing a reduction of over 94% compared to the regional grid. When scaled to annual generation, this corresponds to roughly 197,000 metric tons of avoided CO₂-equivalent emissions per year, alongside 80–95% reductions in acidification and eutrophication impacts. The results demonstrate that biomass-anchored hybrid systems can provide a reliable, low-carbon pathway for rural energy development, with further cost reductions achievable through targeted policy incentives and financing support. Full article

Tourist reviews provide essential insights into travellers experiences and public perceptions of destinations. In Kazakhstan, however, sentiment analysis, particularly using ensemble learning, remains underexplored for evaluating such reviews. This study proposes a hybrid stacking ensemble for sentiment analysis of English-language tourist reviews about Kazakhstan, integrating four complementary approaches: VADER, TextBlob, Stanza, and Local Context Focus Mechanism with Bidirectional Encoder Representations from Transformers (LCF-BERT). Each model contributes distinct analytical capabilities, including lexicon-based polarity detection, rule-based subjectivity evaluation, generalised star-rating estimation, and contextual aspect-oriented sentiment classification. The evaluation utilised a cleaned dataset of 11,454 TripAdvisor reviews collected between February 2022 and June 2025. The ensemble aggregates model outputs through majority and weighted voting strategies to enhance robustness. Experimental results (accuracy 0.891, precision 0.838, recall 0.891, and F1-score 0.852) demonstrate that the proposed method KazSATR outperforms individual models in overall classification accuracy and exhibits superior capacity for aspect-level sentiment detection. These findings underscore the potential of the hybrid ensemble as a practical and scalable tool for the tourism sector in Kazakhstan. By leveraging multiple analytical paradigms, the model enables tourism professionals and policymakers to better understand traveller preferences, identify service strengths and weaknesses, and inform strategic decision-making. The proposed approach contributes to advancing sentiment analysis applications in tourism research, particularly in underrepresented geographic contexts. Full article

The continued use of agrochemicals in Valle de Arista, SLP, Mexico, has generated loss of effectiveness of active ingredients and impacts on public health and the environment. To identify environmental and socioeconomic impacts, a quantification method was designed using the Kovach Environmental Impact Quotient and environmental accounting of pesticides (Leach and Mumford) that included agricultural diagnosis and identification of agrochemical impacts. Producers, technical advisors and agrochemicals dealers were surveyed as key agents of tomato (Solanum lycopersicum) and chili pepper crops (Capsicum annuum) due to their economic importance. Gower quotation coefficients were calculated to measure similarity of quantitative, qualitative and dichotomous variables with continuous, discrete and binary characteristics. The use of fungicides (carbendazim and chlorothalonil) showed the greatest environmental impact, followed by insecticides (endosulfan and thiametoxam) and herbicides. The negative externality averaged US$15.60 ha⁻¹ annually, corresponding to 50% of tomato, 31.25% of poblano pepper and 18.75% of serrano pepper. Estimated damages due to the use of greenhouses were 37.7% to the consumer, 21.2% to the worker, 14.8% to aquatic life, 3.6% to birds, 9.2% to bees and 3.3% to insects. Full article

Climate warming poses significant challenges for the sustainable management of natural water resources, making efficient planning and usage essential. This study evaluates the water requirements, irrigation demand, and rainfall deficits for two key vegetable crops, carrot and white cabbage, under projected climate scenarios RCP 4.5 and RCP 8.5 for the period 2031–2100. The analysis was conducted for Kraków and Rzeszów Counties in southern Poland using projected monthly temperature and precipitation data from the Klimada 2.0 portal. Potential evapotranspiration (ETp) during the growing season (May–October) was estimated using Treder’s empirical model and the crop coefficient method adapted for Polish conditions. The reference period for comparison was 1951–2020. The results reveal a significant upward trend in water demand for both crops, with the highest increases under the RCP 8.5 scenario–seasonal ETp values reaching up to 517 mm for cabbage and 497 mm for carrot. Rainfall deficits are projected to intensify, especially during July and August, with greater shortages in Rzeszów County compared to Kraków County. Irrigation demand varies depending on soil type and drought severity, becoming critical in medium and very dry years. These findings underscore the necessity of adapting irrigation strategies and water resource management to ensure sustainable vegetable production under changing climate conditions. The data provide valuable guidance for farmers, advisors, and policymakers in planning effective irrigation infrastructure and optimizing water-use efficiency in southern Poland. Full article

We explored the impact of high operating temperatures for monocrystalline silicon photovoltaic (PV) modules which dominate the market. Using nine years of hourly climate data with the System Advisor Model (SAM), we examined temperature impacts and cooling potential benefits across three climate zones in the United States. Assuming that cooling approaches can achieve a constant temperature decrease of ΔT independent of irradiance and environmental conditions, our simulations show that a ΔT = 10 °C temperature reduction could improve energy yield by almost 3% annually. Cooling technologies have the strongest impact during the hottest months, with even a 5 °C reduction raising efficiency by nearly 10%. When the minimum temperature of the cooled module is constrained to the ambient temperature, ΔT = 20 °C boosts the hottest month energy yield by over 25%. For economically viable cooling systems, the cooling cost should be much less than the break-even cost. We estimate break-even costs of USD 25–40/m² for 10 °C and USD 40–60/m² for 20 °C cooling for the locations simulated. For ΔT > 20 °C, the added energy yield shows diminishing returns with minimum increase in break-even costs. Full article

This article aims to evaluate the seasonal efficiency of natural gas boilers used in European households, highlighting the cost effectiveness, environmental benefits, and user comfort associated with higher-efficiency models, particularly those based on condensing technology. The study applies a standardized algorithm used in European energy labeling schemes to calculate the seasonal efficiency of household gas boilers. It further includes a comparative analysis of selected boiler models available on the Serbian market and outlines a step-by-step method for estimating gas savings when replacing older, less efficient boilers with modern units. Condensing boilers demonstrate significantly higher seasonal efficiency than standard models by recovering additional heat from exhaust gases. These improved boilers produce lower greenhouse gas emissions and offer annual fuel savings of approximately 10% to 30%, depending on the boiler’s age, system design, and usage patterns. The results also confirm the direct correlation between seasonal efficiency and annual fuel consumption, validating the use of efficiency-based cost comparisons. The analysis focuses on residential gas boilers available in the Serbian market, although the models examined are commonly distributed across Europe. The findings highlight the important role of energy efficiency labels—based on a standardized algorithm—in guiding boiler selection, helping consumers and policymakers make informed decisions that promote energy savings and reduce environmental impact. This article contributes to the theoretical and practical understanding of gas boiler efficiency by integrating algorithm-based evaluation with market data and user-centered considerations. It offers actionable insights for consumers, energy advisors, and policymakers in the context of Europe’s energy transition. Verifying the efficiency calculations of gas boilers requires a careful combination of theoretical methods, measured data, and adherence to standards. Full article

This study examined whether advice-taking differs when making subjective face trustworthiness estimates relative to objective face age estimates. Participants (N = 177) completed a judge–advisor system task to measure how much weight they would give to advice regarding the age of a face (i.e., an objective estimate with a single correct answer) versus the trustworthiness of a face (i.e., a subjective estimate without a correct answer). Measures of fluid intelligence and working memory assessed cognitive resources, and participants provided ratings of perceived difficulty and confidence for each type of estimate. The difference between initial estimates and advice provided a measure of actual difficulty for each type of estimate. We found that advice-taking was greater when estimating face age than face trustworthiness. In addition, perceived difficulty, confidence, and actual difficulty were greater for face trustworthiness than age estimates. However, greater advice-taking was associated with greater actual difficulty for face age estimates, and less actual difficulty for face trustworthiness estimates. While previous research suggests that advice-taking increases with task difficulty, the current data reveal that this may depend on type of estimate. Subjective trustworthiness estimates that were more difficult to estimate than objective age estimates were associated with less advice-taking, and this may be at least partly attributable to differing motivations underlying objective (age) vs. subjective (trustworthiness) estimations. Full article

The aim of this study was to assess the impact of the on-farm solar photovoltaic (PV) generation to offset grid electricity consumption on a commercial poultry farm in the US. Hourly electricity production by PV systems was estimated using the System Advisor Model (SAM) of the National Renewable Energy Lab (NREL) and compared with the estimated electric load demand of a broiler house. We analyzed the economic benefits of installing solar systems of three capacities under net energy metering and net energy billing pricing scenarios. Results suggested that a smaller PV installation of 35 kW_p, while substantially short of meeting energy needs, resulted in an 85% self-consumption ratio, whereas the larger 70 kW_p and 105 kW_p PV systems, led to 59% and 42% self-consumption ratios, respectively. This is important when analyzing pricing schemes as lesser amounts of PV energy created are sold to the grid with the smallest system, and, thereby, the least pricing effects on profitability occur across pricing scenarios. Although all scenarios lead to positive net present values (NPVs), under either type of the pricing scenarios, farm business owners realize more favorable energy sales with NEM, which would spur PV system adoption. Results of this study thus provide information to both poultry farmers, considering the size of system installations and policy makers interested in affordably increasing renewable energy supplies and/or rural development. Full article

This article presents the modeling of a solar photovoltaic system connected to the grid in rural low-income communities living in the semiarid region of Brazil. The model is based on the premise that enough electrical energy will be generated locally to meet the household demand for electricity and to produce a surplus that can be sold on the grid. The main idea is that the sun, usually associated with severe droughts in the Brazilian semiarid region, can be transformed into social income while fostering energy autonomy. To model the system, the computational tools Photovoltaic Software (PVSyst7.3.1) and System Advisor Model (SAM) were used. Several configurations (cases) of systems were analyzed and associated with the evaluation of three commercial electricity scenarios, considering the local electricity rate (USD/kWh). A case study was conducted in the rural settlement of Jacaré-Curituba in the Brazilian semiarid region, where different estimated sale prices for the energy surplus were compared with traditional monthly cash transfers to poor families from the social welfare program “Bolsa Família”. The results indicate the viability of this model as an income alternative for low-income communities. Full article

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow’s albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model’s performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production. Full article

Maryland is actively working towards doubling its Renewable Portfolio Standard (RPS) target, aiming to increase the share of renewable energy from 25% by 2020 to 50% by 2030. Furthermore, Maryland stands out as a state that strongly supports solar initiatives, offering incentives and specialized programs to assist residents in adopting solar energy solutions. The paper presents a multi-stage approach: Stage 1—Location Selection Process, Stage 2—Technical Feasibility Study, and Stage 3—Economical Feasibility Study. In Stage 1, the study focuses on three potential solar farm locations in Maryland: Westover, Princess Anne, and Eden. Stages 2 and 3 involve a feasibility assessment with detailed technical analysis using the NREL System Advisor Model (SAM) and PVWatts to determine monthly power to the grid and Energy Yield. Subsequently, economic feasibility is assessed using the NREL Clean Renewable Energy Estimation Simulation Tool (CREST), focusing on competitive levelized costs of energy (LCOE), payback time, and cumulative cash flows. Results indicate that all three locations exhibit promising solar irradiance levels, system outputs, and potential energy yields. Due to high solar irradiation, the Westover area has the highest energy yield at 1583.13 kWh/kW, while Princess Anne boasts the highest system output at 333.59 GWh. The economic evaluation suggests that all three locations become profitable within a two-year payback time, with competitive levelized costs of energy (LCOE). Westover emerges as the most cost-effective option at 5.99 cents/kWh, attributed to its higher solar irradiation values and energy yield compared to Princess Anne and Eden. Cumulative cash flows provide insights into long-term profitability, with Princess Anne, MD, having the highest Cumulative Cash Flow over 25 years at $183,383,304. By evaluating technical and economic aspects, this feasibility study offers quantitative insights to guide decision-making for the installation of Solar PV, considering both technological and economic feasibility. Full article

In the realm of Natural Gas Combined Cycle (NGCC) power plants, it is crucial to prioritize the mitigation of CO₂ emissions to ensure environmental sustainability. The integration of post-combustion carbon capture technologies plays a pivotal role in mitigating greenhouse gas emissions enhancing the NGCC’s environmental profile by minimizing its carbon footprint. This research paper presents a comprehensive investigation into the integration of solar thermal energy into the Besmaya Natural Gas Combined Cycle (NGCC) power plant, located in Baghdad, Iraq. Leveraging advanced process simulation and modeling techniques employing Aspen Plus software, the study aims to evaluate the performance and feasibility of augmenting the existing NGCC facility with solar assistance for post-carbon capture. The primary objective of this research is to conduct a thorough simulation of the Besmaya NGCC power plant under its current operational conditions, thereby establishing a baseline for subsequent analyses. Subsequently, a solar-assisted post-combustion capture (PCC) plant is simulated and seamlessly integrated into the existing power infrastructure. To accurately estimate solar thermal power potential at the Baghdad coordinates, the System Advisor Model (SAM) is employed. The integration of solar thermal energy into the NGCC power plant is meticulously examined, and the resulting hybrid system’s technical viability and performance metrics are rigorously evaluated. The paper contributes to the field by providing valuable insights into the technical feasibility and potential benefits of incorporating solar thermal energy into conventional natural gas power generation infrastructure, particularly in the context of the Besmaya NGCC plant in Baghdad. The power generation capacity of the plant was set at 750 MW. With this capacity, the annual CO₂ generation was estimated at 2,119,318 tonnes/year which was reduced to 18,064 tonnes/year (a 99% reduction). The findings aim to inform future decisions in the pursuit of sustainable and efficient energy solutions, addressing both environmental concerns and energy security in the region. Full article