Search Results (88)

Sweet potato is a valuable root due to its nutritional benefits, health-promoting properties, and technological applications. The peel, often discarded during food processing, can be employed in the food industry, supporting a circular economy. Purple sweet potato peel (PSPP) is rich in anthocyanins, which can be used as natural colourants and antioxidants. Optimising their extraction can enhance yield and reduce costs. The current work aimed to optimize anthocyanin and antioxidant recovery from PSPP using a Box-Behnken design and sonotrode ultrasound-assisted extraction (sonotrode-UAE). Three independent variables were analysed: extraction time (2–6 min), ethanol concentration (35–85%), and liquid-to-solid ratio (10–30 mL/g). The dependent variables included total monomeric anthocyanin content (TMAC), individual anthocyanins, and antioxidant activity. TMAC in 15 extracts ranged from 0.16 to 2.66 mg/g PSPP. Peonidin-3-caffeoyl-p-hydroxybenzoyl sophoroside-5-glucoside was the predominant anthocyanin. Among four antioxidant assays, Ferric-reducing antioxidant power (FRAP) showed the highest value. Ethanol concentration significantly influenced anthocyanin and antioxidant recovery (p < 0.05). The model demonstrated adequacy based on the coefficient of determination and variation. Optimal extraction conditions were 6 min with 60% ethanol at a 30 mL/g ratio. Predicted values were validated experimentally (coefficient of variation <10%). In conclusion, PSPP is a promising matrix for obtaining anthocyanin-rich extracts with antioxidant activity, offering potential applications in the food industry. Full article

Understanding and forecasting energy consumption patterns is crucial for improving energy efficiency and human well-being, especially in diverse infrastructures like Spain. This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. The primary aim is to identify the most effective model for predicting energy consumption based on historical data, contributing to the relationship between energy systems and urban well-being. The study emphasizes challenges in energy use and advocates for sustainable management practices. By forecasting energy demand over the next three years using linear regression, it provides actionable insights for energy providers, enhancing resilience in urban environments impacted by climate change. The findings deepen our understanding of energy dynamics across various building types and promote a sustainable energy future. Stakeholders will receive targeted recommendations for aligning energy production with consumption trends while meeting environmental responsibilities. Model performance is rigorously evaluated using metrics like Squared Mean Root Percentage Error (RMSPE) and Coefficient of Determination (R²), ensuring robust analysis. Training times for models in the LUCIA building ranged from 2 to 19 s, with the Decision Tree model showing the shortest times, highlighting the need to balance computational efficiency with model performance. Full article

Industry 5.0 is transforming energy demand by integrating sustainability into energy planning, ensuring market stability while minimizing environmental impact for future generations. There are several patterns for calculating energy consumption depending on whether it is measured daily, monthly, or annually through the integration of artificial intelligence approaches, particularly Artificial Neural Networks (ANNs) and Random Forests (RFs), and within the framework of Industry 5.0. This study employs machine learning techniques to analyze energy consumption data from two distinct buildings in Spain: the LUCIA facility in Valladolid and the FUHEM Building in Madrid. The implementation was conducted using custom MATLAB code developed in-house. Our approach systematically evaluates and compares the predictive performance of Artificial Neural Networks (ANNs) and Random Forests (RFs) for energy demand forecasting, leveraging each algorithm’s unique characteristics to assess their suitability for this application. The performances of both models are calculated using the Root Mean Square Percentage Error (RMSPE), Root Mean Square Relative Percentage Error (RMSRPE), Mean Absolute Percentage Error (MAPE), Mean Absolute Relative Percentage Error (MARPE), Kling–Gupta Efficiency (KGE), and also the coefficient of determination, R². Training times are validated using ANN and RF models. Lucia RF took 2.8 s, while Lucia ANN took 40 s; FUHEM RF took 0.3 s, compared to FUHEM ANN, which took 1.1 s. The performances of the two models are described in detail to show the effectiveness of each of them. Full article

This study investigates the significant energy optimization gaps in hotel retrofits in a subtropical climate, quantifying the missed energy-saving opportunities through advanced simulation techniques. Utilizing Design Builder software, the energy consumption of a hotel in Cienfuegos (Cuba) was assessed both before and after renovation, focusing on passive strategies (e.g., replacing single-glazed windows with double glazing) and active interventions (e.g., upgrading the air conditioning system). The results reveal that current retrofit strategies fail to reduce energy consumption substantially. Replacing single-glazed windows with double glazing could reduce annual energy use by 42%. Additionally, upgrading the existing chiller system or implementing a Variable Refrigerant Flow (VRF) system could result in 40% and 59.5% energy savings, respectively. The most significant energy reduction, 71%, is achieved when both interventions—upgrading the chiller and installing double-glazed windows—are implemented, reducing the energy consumption index (ECI) to a quarter of its current value. The life cycle cost (LCC) analysis demonstrates that energy-efficient investments offer considerable economic returns. For instance, an investment of USD 508,600 in a modern chiller system would generate net savings of USD 1,373,500 over its operational lifespan. This study underscores substantial economic and environmental losses from omitting energy efficiency considerations in hotel renovations. It calls for integrating comprehensive energy optimization strategies in retrofit planning, with each dollar invested in energy-saving measures potentially yielding USD 2.5 in life cycle savings. This approach is crucial for global hotel markets facing energy challenges. Full article

Background: The analysis of posture in the early stages of motor development has always been a subject of research and study. With the evolution of new technologies, the need arises to implement evaluation tools that allow an objective and effective assessment of postural control, which is intrinsically linked to motor development. Objectives: The objective was to analyze posture in babies from 0 to 6 months in ventral and dorsal decubitus using artificial intelligence to determine objective parameters of postural assessment. Methods: The study is an observational and cross-sectional study. The babies will be studied following a systematic kinesiological assessment, and the images of the babies will be taken, both in ventral and dorsal decubitus, on a glass platform, to analyze their posture by means of deep learning techniques. Results: Many authors have investigated posture in newborns. However, there is no method for assessing motor and postural development to determine the support area of typically developing babies. Artificial intelligence is postulated as an effective tool to objectively analyze the posture of babies and detect possible delays. Using deep learning techniques as a predictive tool, the support areas of each baby will be defined according to their age. Conclusions: Early detection of motor or postural developmental delays in babies to optimize effective treatment is of great importance. Artificial intelligence can help manage the complexity and growing volume of data in healthcare by knowing the correct postural control at each stage of a baby’s early months, while reducing the workload of healthcare professionals by facilitating decision-making. Full article

Background: The COVID-19 pandemic has affected mental health worldwide, exposing gaps in managing work-related disabilities. In Spain, Mutual Collaborators with Social Security play a pivotal role in managing temporary work disabilities associated with mental health conditions. Objectives: This study aimed to describe and analyze the prevalence, characteristics, and economic burden of mental health-related temporary work disabilities in Spain during the COVID-19 pandemic and recovery (2020–2022). Methods: A prospective longitudinal design was used, drawing on data from CHAMAN, a secure and nationally representative database managed by Asepeyo Mutual Insurance and maintained by Mutual Collaborators with Social Security (MCSSs). The study included workers who experienced temporary work disabilities due to mental health disorders diagnosed according to the International Classification of Diseases, Tenth Revision, during the COVID-19 state of alarm (14 March–21 June 2020) and in the following two years (2021–2022). The key variables were demographics, absence duration, cost per case, and relapse rates. The analyses included descriptive statistics and the Mann–Whitney U, Chi-square, and logistic regression tests. Results: In 2020, 5135 cases were recorded, with an aggregate cost of approximately EUR 44.8 million. Regional analysis showed that Catalonia, Castile–La Mancha, and Castile–León accounted for over half the expenditure, whereas costs were lower in other regions. Marked declines in incidence and costs were observed in 2021–2022, suggesting adaptation to post-pandemic conditions. However, significant gender disparities persisted, with women experiencing higher relapse rates and prolonged absences. Generalized Anxiety Disorder and Major Depressive Disorder were the most common diagnoses, particularly in sectors such as retail, elderly care, and call centers. Conclusions: Although the incidence and cost of temporary work disabilities declined, persistent gender inequalities, regional disparities, and sector-specific risks highlight the need for targeted mental health interventions. Enhancing psychosocial support, adopting gender-sensitive workplace policies, and improving regional healthcare infrastructure are essential to promote workforce well-being and ensure economic sustainability. Full article

The decarbonization of the building sector is essential to mitigate climate change, aligning with the EU’s Energy Performance of Buildings Directive (EPBD) and the transition from near-Zero-Energy Buildings (nZEBs) to Zero-Emission Buildings (ZEBs). This study introduces a novel and streamlined Life Cycle Assessment (LCA) methodology, in accordance with EN 15978, to holistically evaluate the Global Warming Potential (GWP) of buildings. Our approach integrates a calibrated dynamic simulation of operational energy use, performed with DesignBuilder, to determine precise operational CO₂ emissions. This is combined with a comprehensive assessment of embodied emissions, encompassing construction materials and transportation phases, using detailed Environmental Product Declarations (EPDs). Applied to the IndUVa nZEB case study, the findings reveal that embodied emissions dominate the life cycle GWP, accounting for 69%, while operational emissions contribute just 31% over 50 years. The building’s use of 63.8% recycled materials highlights the transformative role of circular economy strategies in reducing embodied impacts. A comparative analysis of three energy-efficiency scenarios demonstrates the IndUVa building’s exceptional performance, achieving energy demand reductions of 78.4% and 85.6% compared to the ASHRAE and CTE benchmarks, respectively. This study underscores the growing significance of embodied emissions as operational energy demand declines. Achieving ZEBs requires prioritizing embodied carbon reduction through sustainable material selection, recycling, and reuse, targeting a minimum of 70% recycled content. By advancing the LCA framework, this study presents a pathway for achieving ZEBs, driving a substantial reduction in global energy consumption and carbon emissions, and contributing to climate change mitigation. Full article

This study presents a comprehensive analysis of the energy efficiency and sustainability of Variable Refrigerant Flow (VRF) systems in university buildings during the winter season, offering significant contributions to the field. A novel methodology is introduced to accurately assess the real Seasonal Coefficient of Performance (SCOP) of VRF systems, benchmarked against conventional Heating, Ventilation, and Air Conditioning (HVAC) technologies, such as natural gas-fueled boiler systems. The findings demonstrate outstanding seasonal energy performance, with the VRF system achieving a SCOP of 5.349, resulting in substantial energy savings and enhanced sustainability. Key outcomes include a 67% reduction in primary energy consumption and a 79% decrease in greenhouse gas emissions per square meter when compared to traditional boiler systems. Furthermore, VRF systems meet 83% of the building’s energy demand through renewable energy sources, exceeding the regulatory SCOP threshold of 2.5. These results underscore the transformative potential of VRF systems in achieving nearly Zero-Energy Building (nZEB) objectives, illustrating their ability to exceed stringent sustainability standards. The research emphasizes the strategic importance of adopting advanced HVAC solutions, particularly in regions with high heating demands, such as those characterized by continental climates. VRF systems emerge as a superior alternative, optimizing energy consumption while significantly reducing the environmental footprint of buildings. By contributing to global sustainable development and climate change mitigation efforts, this study advocates for the widespread adoption of VRF systems, positioning them as a critical component in the transition toward a sustainable, zero-energy building future. Full article

Background: Chronic non-cancer pain (CNCP) is one of the leading causes of disability. The use of strong opioids (SOs) in the management of CNCP is increasing, although evidence supporting their use remains limited. Primary care (PC) plays a key role in this context. Objectives: Our objectives were to determine the prevalence and profile of patients using SOs for CNCP in PC consultations in Valladolid in 2022, and to describe the consumption of SO prescribed for CNCP from 2020 to 2023. Methods: We conducted a descriptive and retrospective study using data extracted from the Pharmaceutical Consumption Information System of Castilla y León. Patients in Valladolid with SO use for more than three months due to CNCP were analyzed. Sociodemographic and clinical characteristics of these patients in 2022 were described. The number of defined daily doses (nº DDDs) and costs from 2020 to 2023 were analyzed. Results: A total of 3642 patients were included (0.7% of the population of Valladolid), 71.8% of whom were women. Of the patients, 62.4% were aged 70 or older, 39.8% lived in rural areas, and 9.9% resided in nursing homes. The most frequently prescribed SOs in nº DDDs were fentanyl and tapentadol. The highest consumption in nº DDDs was in patients who lived in nursing homes, were over 70 years old and were resident in rural areas. The number of DDDs from 2020 to 2023 for SOs in DCNO increased by 41%. Conclusions: In total, 0.7% of the population of Valladolid consumes SOs for CNCP, mostly women and people over 70 years old. The consumption of strong opioids in DDDs grew by 41% from 2020 to 2023. Full article

In recent decades, the scientific community has faced a major challenge in the search for new therapies that can slow down or alleviate the process of neuronal death that accompanies neurodegenerative diseases. This study aimed to identify an effective therapy using neurotrophic factors to delay the rapid and aggressive cerebellar degeneration experienced by the Purkinje Cell Degeneration (PCD) mouse, a model of childhood-onset neurodegeneration with cerebellar atrophy (CONDCA). Initially, we analyzed the changes in the expression of several neurotrophic factors related to the degenerative process itself, identifying changes in insulin-like growth factor 1 (IGF-1) and Vascular Endothelial Growth Factor B (VEGF-B) in the affected animals. Then, we administered pharmacological treatments using human recombinant IGF-1 (rhIGF-1) or VEGF-B (rhVEGF-B) proteins, considering their temporal variations during the degenerative process. The effects of these treatments on motor, cognitive, and social behavior, as well as on cerebellar destructuration were analyzed. Whereas treatment with rhIGF-1 did not demonstrate any neuroprotective effect, rhVEGF-B administration at moderate dosages stopped the process of neuronal death and restored motor, cognitive, and social functions altered in PCD mice (and CONDCA patients). However, increasing the frequency of rhVEGF-B administration had a detrimental effect on Purkinje cell survival, suggesting an inverted U-shaped dose–response curve of this substance. Additionally, we demonstrate that this neuroprotective effect was achieved through a partial inhibition or delay of apoptosis. These findings provide strong evidence supporting the use of rhVEGF-B as a pharmacological agent to limit severe cerebellar neurodegenerative processes. Full article

The loss of functions in Spanish rural areas has triggered territorial inequalities and injustices in a highly complex geographical environment. After the COVID-19 pandemic and in a context of overpopulation in large cities, the rural area emerged as a space of opportunity for more sustainable territorial rebalancing. Despite the evident tendency towards their population emptying, they are places endowed with their own qualities and specific values, currently in danger, especially in border areas between the regions, which are far from centralised nuclei and generate conflicts due to the transfer of powers to them from the State. Among these values is cultural heritage, the safeguarding and enrichment of which depends on the balance between the landscape and the society that hosts it. This work focuses on access to archaeological sites—an important form of heritage prior to the establishment of actual regional divisions, in the depopulated frontier between Cantabria and Castile and Leon—whose potential is presented by their intrinsic relationship with the territory and their ability to identify historical landscapes, advocating for future sustainability on a territorial scale. With all of the above, a cultural landscape delimitation is proposed between both regions that share common characteristics and problems, promoting synergies and territorial readings by analysing the territorial assets of these interior areas so that their potential is not diminished. Full article

This study explores innovative methodologies for estimating the energy baseline (EnBL) of a university classroom building, emphasizing the critical roles of data quality and model selection in achieving accurate energy efficiency assessments. We compare time series models that are suitable for buildings with limited consumption data with univariate and multivariate regression models that incorporate additional variables, such as weather and occupancy. Furthermore, we investigate the advantages of dynamic simulation using the EnergyPlus engine (V5, USDOE United States) and Design Builder software v7, enabling scenario analysis for various operational conditions. Through a comprehensive case study at the UAO University Campus, we validate our models using daily monitoring data and statistical analysis in RStudio. Our findings reveal that model choice significantly influences energy consumption forecasts, leading to potential overestimations or underestimations of savings. By rigorously assessing statistical validation and error analysis results, we highlight the implications for decarbonization strategies in building design and operation. This research provides a valuable framework for selecting appropriate methodologies for energy baseline estimation, enhancing transparency and reliability in energy performance assessments. These contributions are particularly relevant for optimizing energy use and aligning with regulatory requirements in the pursuit of sustainable building practices. Full article

Primary dysmenorrhea is considered one of the main causes of pelvic pain during a woman’s childbearing years, resulting in poor quality of life. The objective was to evaluate the effectiveness of transcutaneous tibial nerve stimulation (TTNS) in painful symptomatology improvement and non-steroidal anti-inflammatory drug (NSAID) intake reduction in women with primary dysmenorrhea (PD) compared with a control group in the short, medium, and long terms. A single-blind, controlled clinical trial was developed. Participants were randomized to the experimental (TTNS) and control group (sham TTNS). Both groups received 12-weekly 30-min sessions with a NeuroTrac^TM PelviTone electrostimulation device. The intensity and severity of pain and non-steroidal anti-inflammatory drug (NSAID) intake were evaluated in the short-term (after treatment), medium-term (1–3 months), and long-term (6 months). A total of 61 participants were randomized, with a split of 31 (experimental group) and 30 (control group), but 55 participants completed the study and were analyzed. Statistically significant differences between both groups in the maximum pain intensity decrease (F = 4.88, p = 0.0043) measured with the visual analogue scale, as well as NSAID intake decrease (F = 4.68, p = 0.011) and days of their ingestion (F = 4.57, p = 0.012) occurred in the short term. Furthermore, significant decreases in the total number of NSAIDs ingested during the cycle (F = 3.82, p = 0.011) and the number of days on which patients ingested NSAIDs (F = 3.59, p = 0.015) in the medium–long term occurred. TTNS could be an effective and safe strategy to reduce pain caused by PD, which could reduce or complement the use of pharmacological techniques and other more invasive methods. Full article

This article aims to show how episcopal authority was built in the eastern part of the Kingdom of León (county of Castile), where a new kingdom and a vast diocese emerged in the mid-11th century. The monarchs of Castile empowered the strategic pre-urban town of Burgos in the northern Iberian Plateau as a single episcopal see, rather than the four that had existed in the area until then. The bishops were the agents that the monarchs needed in the long process that, from the destabilisation of the Visigothic diocese organisation caused by the Islamic invasion of the Iberian peninsula in the 8th century, led to the consolidation of episcopal power in the mid-13th century. The function and actions of the Burgalese bishops have been analysed in the three dimensions of their ecclesiastic authority and social significance: the patrimonial dimension (the bishop as the lord and owner of properties individually), the jurisdictional dimension and the pastoral dimension. This analysis has been able to establish three periods in the struggle of the Burgalese prelates: to define the territorial frame of their authority (the delimitation of the diocese boundaries), to recover the churches and jurisdictional rights (episcopal third and other ecclesiastic taxes) that were in the hands of the powerful Benedictine monasteries and lay people, and to affirm their hierarchical superiority over other diocese potestates. The study has identified the main strategies used by the bishops to reach those objectives: the signing of agreements to resolve disputes, the addition of abbots of collegial churches to the cathedral chapter to control key areas in the diocese, and the acquisition of properties in those areas. Full article

The aim of this study was to evaluate and compare hepatitis A outbreak-associated healthcare and epidemiological surveillance costs in Spain in two types of autonomous regions during 2010–2018: (1) regions with a prevention strategy based on universal hepatitis A vaccination of children and vaccination of high-risk population groups (Catalonia) and (2) regions with a prevention strategy based on vaccinating high-risk population groups (Castile and Leon, Murcia, Navarra, Community of Madrid, Community of Valencia). Healthcare costs were determined based on the resources used to treat hepatitis A outbreak-associated cases and hospitalizations. Epidemiological surveillance costs were calculated from the resources used during surveillance activities. The ratios for total, healthcare and epidemiological surveillance costs (regions without universal hepatitis A vaccination of children vs. Catalonia) were used to compare the two hepatitis A prevention strategies. From 2010 to 2018, the total, healthcare and epidemiological surveillance costs per million population were 1.75 times (EUR 101,671 vs. EUR 58,032), 1.96 times (EUR 75,500 vs. EUR 38,516) and 1.34 times greater (EUR 26,171 vs. EUR 19,515) in regions without universal hepatitis A vaccination of children than in Catalonia, respectively. The ratios tended to increase over time during 2010–2018. In 2015–2018, total, healthcare and epidemiological surveillance costs per million population were 2.68 times (EUR 69,993 vs. EUR 26,158), 2.86 times (EUR 53,807 vs. EUR 18,825) and 2.21 times greater (EUR 16,186 vs. EUR 7333) in regions without universal hepatitis A vaccination of children than in Catalonia, respectively. These findings suggest that universal hepatitis A vaccination of children could reduce hepatitis A outbreak-associated costs. Full article