Modeling Techniques Research Articles

Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization

Abstract The worldwide transition to cleaner, sustainable energy sources, prompted by population growth and industrialization, responds to uncertain fossil fuel prices and environmental concerns, highlighting the substantial benefits of renewable energy in reducing greenhouse gas emissions and addressing climate change. Derived from non-fossilized organic materials, biomass emerges as a significant and sustainable contributor to renewable energy. Its diverse nature is complemented by a range of conversion technologies, encompassing combustion, pyrolysis, gasification, and liquefaction, providing versatile avenues for biomass energy transformation. Gasification, the transformative process of converting organic matter into combustible gases under controlled oxygen levels, is accomplished through direct oxygen supply or pyrolysis. This method yields a dependable gaseous fuel versatile for heating, industrial processes, power generation, and liquid fuel production. Machine learning employs advanced statistical techniques for modeling across diverse industries, showcasing particular efficacy in optimizing thermochemical processes by precisely identifying the optimal operating conditions required for achieving desired product properties. These models utilize proximate biomass data to predict the elemental compositions of N2 and H2. Assessment of both single and two hybrid models indicated that the introduced optimizers significantly enhanced the estimation of N2 and H2 when combined with Decision Tree (DT), with Decision Tree Coupled with Artificial Hummingbird Algorithm (DTAH) proving particularly effective. Notably, DTAH demonstrated outstanding performance with remarkable R 2 values of 0.990 for N2 and 0.992 for H2. Additionally, the minimal Root Mean Square Error (RMSE) values of 1.291 and 1.550 for N2 and H2 predictions respectively underscore the precision of DTAH, establishing it as a suitable choice for practical real-world applications.

Predicting potential habitat distribution of the invasive species Rhynchophorus ferrugineus Olivier in China based on MaxEnt modelling technique and future climate change.

Changes in the distribution of species due to global climate change have a critically significant impact on the increase in the spread of invasive species. An in-depth study of the distribution patterns of invasive species and the factors influencing them can help to better predict and combat invasive alien species. Rhynchophorus ferrugineus Olivier is an invasive species that primarily harms plants of Trachycarpus H. Wendl. The pest invades trees in three main ways: by laying eggs and incubating them in the crown of the plant, on roots at the surface and at the base of the trunk or petiole. Most of the plants in the genus Trachycarpus are taller, and the damage is concentrated in the middle and upper parts of the plant, making control more difficult. In this paper, we combine 19 bioclimatic variables based on the MaxEnt model to project the current and future distributions of R. ferrugineus under three typical emission scenarios (2.6 W m-2 (SSP1-2.6), 4.5 W m-2 (SSP2-4.5) and 8.5 W m-2 (SSP5-8.5)) in the 2050s and 2090s. Among the 19 bioclimatic variables, five variables were screened out by contribution rates, namely annual mean temperature (BIO 1), precipitation of driest quarter (BIO 17), minimum temperature of coldest month (BIO 6), mean diurnal range (BIO 2) and precipitation of wettest quarter (BIO 16). These five variables are key environmental variables that influence habitat suitability for R. ferrugineus and are representative in reflecting its potential habitat. The results showed that R. ferrugineus is now widely distributed in the southeastern coastal area of China (high suitability zone), concentrating in the provinces of Hainan, Guangdong, Fujian, Guangxi and Taiwan. In the future, the area of high and low suitability zones will increase and the area of medium suitability zones will decrease. The area of low suitability zone will still be in the largest proportion. This study aims to provide a theoretical reference for the future control of R. ferrugineus from the perspective of geographic distribution.

Healthcare Data Analytics and Predictive Modelling: Enhancing Outcomes in Resource Allocation, Disease Prevalence and High-Risk Populations

Purpose: This study aims to explore the role of healthcare data analytics and predictive modeling in enhancing healthcare outcomes, specifically in resource allocation, disease forecasting, and identifying high-risk populations. Methodology: The research employs a comprehensive approach, utilizing various sources of healthcare data such as electronic health records (EHRs) and public health databases. Advanced analytical techniques, including machine learning, artificial intelligence, and big data analytics, are applied to derive actionable insights. Findings: The study reveals that predictive modeling significantly enhances resource optimization, enables accurate disease prevalence forecasting, and improves the identification of high-risk populations. Case studies demonstrate how these technologies lead to more efficient healthcare delivery, cost reduction, and better patient care outcomes. Unique Contribution to Theory, Policy, and Practice: This research contributes to the theoretical understanding of healthcare data analytics by integrating advanced predictive modeling techniques with real-world healthcare applications. It offers valuable insights for policymakers on the importance of investing in data infrastructure and promoting data-driven decision-making. Practically, the study provides healthcare organizations with actionable strategies to implement predictive analytics for improved resource allocation and patient care.

Species Distribution Models for Mesopelagic Mesozooplankton Community

ABSTRACTAimWe aimed to enhance our understanding of the distribution of mesopelagic mesozooplankton (MM) using species distribution models, assess the performance of various modelling techniques, identify key environmental predictors for MM distribution and compute their habitat suitability indices.LocationOur study focused on the mesopelagic zone globally, with data analysed from different oceans.TaxonOur focus was primarily on mesopelagic mesozooplankton, gathering data on 861 different species from the Mesopelagic Mesozooplankton and Micronekton (MMM) Database.MethodsWe used an ensemble of species distribution models, applying 10 different modelling algorithms and three multi‐model ensemble approaches. We explored two important factors that can affect model performance: subsampling and the choice of background points. We also estimated the relative importance of various environmental conditions such as mixed layer depth, temperature, salinity, net primary productivity, euphotic zone depth and dissolved nitrate concentration on the distribution of these species.ResultsEuphotic zone depth, salinity and dissolved nitrate concentration were identified as the most important variables for explaining the distribution of mesopelagic mesozooplankton. The ensemble modelling results were robust in areas with abundant observational records, but high uncertainty was observed in data‐limited regions. We found a patchy habitat suitability map for zooplankton when modelled within their native range, largely due to uneven sampling. Unrestricted range models yielded smoother patterns but could inaccurately project species in areas where they do not occur.Main ConclusionsOur study highlights the need for increased sampling effort in data‐limited regions to improve the accuracy of mesopelagic species distribution models. Despite some inaccuracies, unrestricted range models, assuming ecological equivalence (where different species occupying a similar ecological niche in different geographical regions or different ecosystems exhibit similar adaptations and behaviours), provide a reasonable comparison for habitat suitability maps and model performance. It also confirms the significant impact of certain environmental conditions on mesozooplankton distribution.

Os efeitos do aquecimento global na ampliação das áreas desertificadas: um estudo acerca da geografia econômica dos municípios do sertão da Bahia e possíveis mitigações da teoria da tríplice hélice

Global warming, caused by the burning of fossil fuels and deforestation, is triggering significant changes in the Earth's environment. Phenomena like El Niño and La Niña, analyzed through modeling techniques, reveal complex patterns affecting various time scales. In regions with fragile soils, the trend is rapid progression towards scenarios of desertification and abandonment in the coming years. The migration of rural populations from these areas to urban centers, abandoning subsistence economies, may increase poverty in cities, a movement comparable to the Industrial Revolution's exodus, already visible in economic and social data. This article theoretically reviews topics related to desertification resulting from climate change, aiming to identify the social and economic consequences of this phenomenon in the region. Additionally, it explores possible actions to mitigate problems caused by global warming. In conclusion, a solution based on the Triple Helix Theory, which promotes interaction between government, universities, and businesses, will be presented. This approach could be crucial in mitigating the impacts of desertification, fostering sustainable development and innovation in the affected region.

Health-Related Quality-of-Life Utility Values in Adults With Late-Onset Pompe Disease: Analyses of EQ-5D Data From the PROPEL Clinical Trial

Background: Pompe disease is a rare lysosomal storage disorder, leading to accumulation of glycogen characterized by muscle weakness, fatigue, pain, and, in the longer term, a requirement for ventilatory and ambulatory support, and early mortality if untreated. Clinical evidence suggests that enzyme replacement therapy improves health outcomes for adults with late-onset Pompe disease (LOPD). PROPEL was a Phase 3, double-blind, randomized controlled trial, which evaluated cipaglucosidase alfa plus miglustat, vs alglucosidase alfa plus placebo in 123 adult patients with LOPD (clinicaltrials.gov: NCT03729362). Objectives: To analyze EQ-5D health-related quality of life (HRQoL) utility data from PROPEL. Methods: Multilevel modeling techniques (mixed regression methods) were used to analyze PROPEL EQ-5D-3L estimates and predict utility values for 7 health states previously identified in an economic evaluation for LOPD. In PROPEL, EQ-5D-5L values were assessed at screening and at weeks 12, 26, 38, and 52. EQ-5D-5L utility values were mapped to EQ-5D-3L values using the van Hout algorithm as recommended by the EuroQoL and the National Institute of Health and Care Excellence position statement at time of analysis. UK population tariffs were applied for all EQ-5D utility valuations. Utility values were predicted according to 6-minute walk distance (6MWD) and percent predicted sitting forced vital capacity. Results: The mixed model predicted that EQ-5D-3L utility values for patients who could walk >75 m with LOPD ranged between 0.55 and 0.67 according to patient 6MWD and respiratory function. In this analysis, patients with a 6MWD ≤75 m, consistent with a health state requiring wheelchair support in the economic analysis, had a predicted utility value of 0.49. There were few patients in PROPEL who could walk ≤75 m at any time point in the study, hence, these utility estimates should be interpreted with caution. EQ-5D-3L utility estimates from PROPEL were consistent with previously reported EQ-5D-3L values in LOPD. Conclusions: Overall, the results from our analysis indicate that important HRQoL losses are associated with reductions in mobility and respiratory function for patients with Pompe disease. The study provides important evidence of HRQoL utility values for patients with advanced LOPD, a population for whom published data are limited.

Health-Related Quality-of-Life Utility Values in Adults With Late-Onset Pompe Disease: Analyses of EQ-5D Data From the PROPEL Clinical Trial

Background: Pompe disease is a rare lysosomal storage disorder, leading to accumulation of glycogen characterized by muscle weakness, fatigue, pain, and, in the longer term, a requirement for ventilatory and ambulatory support, and early mortality if untreated. Clinical evidence suggests that enzyme replacement therapy improves health outcomes for adults with late-onset Pompe disease (LOPD). PROPEL was a Phase 3, double-blind, randomized controlled trial, which evaluated cipaglucosidase alfa plus miglustat, vs alglucosidase alfa plus placebo in 123 adult patients with LOPD (clinicaltrials.gov: NCT03729362). Objectives: To analyze EQ-5D health-related quality of life (HRQoL) utility data from PROPEL. Methods: Multilevel modeling techniques (mixed regression methods) were used to analyze PROPEL EQ-5D-3L estimates and predict utility values for 7 health states previously identified in an economic evaluation for LOPD. In PROPEL, EQ-5D-5L values were assessed at screening and at weeks 12, 26, 38, and 52. EQ-5D-5L utility values were mapped to EQ-5D-3L values using the van Hout algorithm as recommended by the EuroQoL and the National Institute of Health and Care Excellence position statement at time of analysis. UK population tariffs were applied for all EQ-5D utility valuations. Utility values were predicted according to 6-minute walk distance (6MWD) and percent predicted sitting forced vital capacity. Results: The mixed model predicted that EQ-5D-3L utility values for patients who could walk >75 m with LOPD ranged between 0.55 and 0.67 according to patient 6MWD and respiratory function. In this analysis, patients with a 6MWD ≤75 m, consistent with a health state requiring wheelchair support in the economic analysis, had a predicted utility value of 0.49. There were few patients in PROPEL who could walk ≤75 m at any time point in the study, hence, these utility estimates should be interpreted with caution. EQ-5D-3L utility estimates from PROPEL were consistent with previously reported EQ-5D-3L values in LOPD. Conclusions: Overall, the results from our analysis indicate that important HRQoL losses are associated with reductions in mobility and respiratory function for patients with Pompe disease. The study provides important evidence of HRQoL utility values for patients with advanced LOPD, a population for whom published data are limited.

Requirements for the Development and Operation of a Freeze-Up Ice-Jam Flood Forecasting System

This article provides a comprehensive overview of ice-jam flood forecasting methodologies applicable to rivers during freezing. It emphasizes the importance of understanding river ice processes and fluvial geomorphology for developing a freeze-up ice-jam flood forecasting system. The article showcases a stochastic modelling approach, which involves simulating a deterministic river ice model multiple times with varying parameters and boundary conditions. This approach has been applied to the Exploits River at Badger in Newfoundland, Canada, a river that has experienced several freeze-up ice-jam floods. The forecasting involves two approaches: predicting the extent of the ice cover during river freezing and using an ensemble method to determine backwater flood level elevations. Other examples of current ice-jam flood forecasting systems for the Kokemäenjoki River (Pori, Finland), Saint John River (Edmundston, NB, Canada), and Churchill River (Mud Lake, NL, Canada) that are operational are also presented. The text provides a detailed explanation of the processes involved in river freeze-up and ice-jam formation, as well as the methodologies used for freeze-up ice-jam flood forecasting. Ice-jam flood forecasting systems used for freeze-up were compared to those employed for spring breakup. Spring breakup and freeze-up ice-jam flood forecasting systems differ in their driving factors and methodologies. Spring breakup, driven by snowmelt runoff, typically relies on deterministic and probabilistic approaches to predict peak flows. Freeze-up, driven by cold temperatures, focuses on the complex interactions between atmospheric conditions, river flow, and ice dynamics. Both systems require air temperature forecasts, but snowpack data are more crucial for spring breakup forecasting. To account for uncertainty, both approaches may employ ensemble forecasting techniques, generating multiple forecasts using slightly different initial conditions or model parameters. The objective of this review is to provide an overview of the current state-of-the-art in ice-jam flood forecasting systems and to identify gaps and areas for improvement in existing ice-jam flood forecasting approaches, with a focus on enhancing their accuracy, reliability, and decision-making potential. In conclusion, an effective freeze-up ice-jam flood forecasting system requires real-time data collection and analysis, historical data analysis, ice jam modeling, user interface design, alert systems, and integration with other relevant systems. This combination allows operators to better understand ice jam behavior and make informed decisions about potential risks or mitigation measures to protect people and property along rivers. The key findings of this review are as follows: (i) Ice-jam flood forecasting systems are often based on simple, empirical models that rely heavily on historical data and limited real-time monitoring information. (ii) There is a need for more sophisticated modeling techniques that can better capture the complex interactions between ice cover, water levels, and channel geometry. (iii) Combining data from multiple sources such as satellite imagery, ground-based sensors, numerical models, and machine learning algorithms can significantly improve the accuracy and reliability of ice-jam flood forecasts. (iv) Effective decision-support tools are crucial for integrating ice-jam flood forecasts into emergency response and mitigation strategies.

The K2 and TESS Synergy. III. Search and Rescue of the Lost Ephemeris for K2's First Planet

K2-2 b/HIP 116454 b, the first exoplanet discovery by K2 during its Two-Wheeled Concept Engineering Test, is a sub-Neptune (2.5 ± 0.1 R ⊕, 9.7 ± 1.2 M ⊕) orbiting a relatively bright (K S = 8.03) K-dwarf star on a 9.1 day period. Unfortunately, due to a spurious follow-up transit detection and ephemeris degradation, the transit ephemeris for this planet was lost. In this work, we recover and refine the transit ephemeris for K2-2 b, showing a ∼40σ discrepancy from the discovery results. To accurately measure the transit ephemeris and update the parameters of the system, we jointly fit space-based photometric observations from NASA’s K2, Transiting Exoplanet Survey Satellite, and Spitzer missions with new photometric observations from the ground, as well as radial velocities from HARPS-N that are corrected for stellar activity using a new modeling technique. Ephemerides becoming lost or significantly degraded, as is the case for most transiting planets, highlights the importance of systematically updating transit ephemerides with upcoming large efforts expected to characterize hundreds of exoplanet atmospheres. K2-2 b sits at the high-mass peak of the known radius valley for sub-Neptunes, and is now well-suited for transmission spectroscopy with current and future facilities. Our updated transit ephemeris will ensure no more than a 13 minute uncertainty through 2030.

Plasma power-to-X (PP2X): status and opportunities for non-thermal plasma technologies

Abstract This article discusses the ‘Power-to-X’ (P2X) concept, highlighting the integral role of non-thermal plasma (NTP) in P2X for the eco-friendly production of chemicals and valuable fuels. NTP with unique thermally non-equilibrium characteristics, enables exotic reactions to occur under ambient conditions. This review summarizes the plasma-based P2X systems, including plasma discharges, reactor configurations, catalytic or non-catalytic processes, and modelling techniques. Especially, the potential of NTP to directly convert stable molecules including CO2, CH4 and air/N2 is critically examined. Additionally, we further present and discuss hybrid technologies that integrate NTP with photocatalysis, electrocatalysis, and biocatalysis, broadening its applications in P2X. It concludes by identifying key challenges, such as high energy consumption, and calls for the outlook in plasma catalysis and complex reaction systems to generate valuable products efficiently and sustainably, and achieve the industrial viability of the proposed PP2X strategy.

Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.

Three-Dimensional Printing by Vat Photopolymerization on Textile Fabrics: Method and Mechanical Properties of the Textile/Polymer Composites

Composites of textile fabrics and 3D-printed layers have been investigated thoroughly during the last decade. Usually, material extrusion such as the fused deposition modeling (FDM) technique is used to build such composites, revealing challenges in preparing form-locking connections between both materials due to the highly viscous polymer melt, which can hardly be pressed into textile fabrics. Resins used for 3D printing by vat photopolymerization, i.e., for stereolithography (SLA), are less viscous and can thus penetrate deeper into textile fabrics; however, fixing a textile on the printing bed that is fully dipped into the resin is more complicated. Here, we present one possible solution to easily fix textile fabrics for SLA printing with consumer printers according to the digital light processing (DLP) sub-method. Also, we show the results of a study of the mechanical properties of the resulting textile/polymer composites, as revealed by three-point bending tests.

Efficient topic identification for urgent MOOC Forum posts using BERTopic and traditional topic modeling techniques

AbstractMOOC platforms provide a means of communication through forums, allowing learners to express their difficulties and challenges while studying various courses. Within these forums, some posts require urgent attention from instructors. Failing to respond promptly to these posts can contribute to higher dropout rates and lower course completion rates. While existing research primarily focuses on identifying urgent posts through various classification techniques, it has not adequately addressed the underlying reasons behind them. This research aims to delve into these reasons and assess the extent to which they vary. By understanding the root causes of urgency, instructors can effectively address these issues and provide appropriate support and solutions. BERTopic utilizes the advanced language capabilities of transformer models and represents an advanced approach in topic modeling. In this study, a comparison was conducted to evaluate the performance of BERTopic in topic modeling on MOOCs discussion forums, alongside traditional topic models such as LDA, LSI, and NMF. The experimental results revealed that the NMF and BERTopic models outperformed the other models. Specifically, the NMF model demonstrated superior performance when a lower number of topics was required, whereas the BERTopic model excelled in generating topics with higher coherence when a larger number of topics was needed.The results considering all urgent posts from the dataset were as follows: Optimal number of topics is 6 for NMF and 50 for BERTopic; coherence scores is 0.66 for NMF and 0.616 for BERTopic; and IRBO scores is 1 for both models. This highlights the BERTopic model capability to distinguish and extract diverse topics comprehensively and coherently, aiding in the identification of various reasons behind MOOC Forum posts.

Diverse decarbonization pathways under near cost-optimal futures

Energy system optimization models offer insights into energy and emissions futures through least-cost optimization. However, real-world energy systems often deviate from deterministic scenarios, necessitating rigorous uncertainty exploration in macro-energy system modeling. This study uses modeling techniques to generate diverse near cost-optimal net-zero CO2 pathways for the United States’ energy system. Our findings reveal consistent trends across these pathways, including rapid expansion of solar and wind power generation, substantial petroleum use reductions, near elimination of coal combustion, and increased end-use electrification. We also observe varying deployment levels for natural gas, hydrogen, direct air capture of CO2, and synthetic fuels. Notably, carbon-captured coal and synthetic fuels exhibit high adoption rates but only in select decarbonization pathways. By analyzing technology adoption correlations, we uncover interconnected technologies. These results demonstrate that diverse pathways for decarbonization exist at comparable system-level costs and provide insights into technology portfolios that enable near cost-optimal net-zero CO2 futures.

Cognitive ability, gender, and well-being in school contexts: longitudinal evidence from Sweden

While well-being does generally constitute a moderate predictor of school achievement, research on the predictive validity of cognitive ability for well-being in school contexts remains scant. The current study analyzed longitudinal relations between cognitive ability measured at age 13 (Grade 6) and well-being measured at age 18 (Grade 12, valid N = 2,705) in a Swedish sample, using several multivariate model techniques. The results indicate that cognitive ability was not a statistically significant predictor when several predictors were entered in a multiple regression model. However, gender was a significant covariate as girls and young women have a substantially lower degree of self-reported well-being. This casts light on the limitations of cognitive ability as a construct for some non-cognitive outcomes, at least in shorter and narrower spatial–temporal contexts.

Experimental Success in Marburg Virus Vaccination

The Marburg virus (MRV), classified within the Filoviridae family, was initially identified in 1967, precipitating Marburg virus disease (MARV), a severe and often fatal hemorrhagic fever. With its pronounced infectiousness, high mortality rate, and proclivity for epidemic outbreaks, MARV stands as a formidable public health menace. Despite extensive sporadic outbreaks and its designation as a priority disease, the quest for efficacious drugs or vaccines against MRV remains an ongoing challenge. Nonetheless, the relentless pursuit of scientific inquiry, augmented by the innovative application of immunoinformatics, is propelling forward vaccine development endeavors. Current vaccine candidates, spanning from VSV-based formulations to virus-like particle vaccines, exhibit encouraging outcomes in preclinical and clinical evaluations, boasting notable efficacy and safety profiles. Furthermore, the exploration of multivalent vaccine strategies, designed to target a spectrum of hemorrhagic fever viruses, holds promise in fortifying pandemic preparedness efforts. Immunoinformatics assumes a pivotal role in this context, offering predictive insights into vaccine candidate selection and optimization, thereby facilitating expedited development tailored to diverse demographic cohorts. The integration of computational modeling techniques into vaccine development paradigms represents a transformative avenue for effectively controlling MRV outbreaks on a global scale. Sustained collaboration and continued research endeavors are imperative to fully harness the potential of these advancements in confronting the formidable challenge posed by the lethal Marburg virus and safeguarding global public health.

Time Series Analysis to Estimate the Volume of Drinking Water Consumption in the City of Meoqui, Chihuahua, Mexico

Water is a vital resource for sustaining life and for numerous processes within the transformation industry. It is a finite resource, albeit one that can be renewed, and thus sustainable management is imperative. To achieve this objective, it is necessary to have the appropriate tools to assist with the planning policies for its management. This paper presents a time series analysis approach to measure and predict the pattern of water consumption by humans throughout subsectors (domestic, commercial, public sector, education, industry, and raw water) and total water consumption in Meoqui, Chihuahua, Mexico with data from 2011 to 2023, applying calibration model techniques to measure uncertainty in the forecasting. The municipality of Meoqui encompasses an area of 342 km2. The climate is semi-arid, with an average annual rainfall of 272 mm and average temperatures of 26.4 °C in summer and 9.7 °C in winter. The municipal seat, which has a population of 23,140, is supplied with water from ten wells, with an average consumption of 20 ± 579 m3 per user. The consumption of the general population indicates the existence of a seasonal autoregressive integrated moving average (SARIMA) (0,1,2)(0,0,2)12 model. (Sen’s Slope = 682.7, p < 0.001). The domestic sector exhibited the highest overall consumption, with a total volume of 17,169,009 m3 (13 ± 93). A SARIMA (2,1,0)(2,0,0)12 model was estimated, with a Sen’s slope of 221.65 and a p-value of less than 0.001. The second-largest consumer of total water was the “raw water” sector, which consumed 5,124,795 (30,146 ± 35,841) m3 and exhibited an SARIMA (0,1,1)(2,0,0)12 model with no statistically significant trend. The resulting models will facilitate the company’s ability to define water resource management strategies in a sustainable manner, in alignment with projected consumption trends.

Foreword to the special issue “Adjacent Interacting Masonry Structures”

AbstractThis special issue focuses on the seismic performance of adjacent interacting masonry structures, particularly in historical European city centers. The AIMS project provided unique data on interacting masonry structures through large-scale shake table tests on two adjacent half-scale stone masonry buildings. The experimental campaign was accomapgnied by a blind prediction study where participants modeled the aggregate’s seismic response. Findings highlight challenges in accurately predicting displacement demands and failure modes, providing critical insights for improving future modeling techniques for masonry buildings.

Morphometry of medial temporal lobe subregions using high-resolution T2-weighted MRI in ADNI3: Why, how, and what's next?

This paper for the 20th anniversary of the Alzheimer's Disease Neuroimaging Initiative (ADNI) provides an overview of magnetic resonance imaging (MRI) of medial temporal lobe (MTL) subregions in ADNI using a dedicated high-resolution T2-weighted sequence. A review of the work that supported the inclusion of this imaging modality into ADNI Phase 3 is followed by a brief description of the ADNI MTL imaging and analysis protocols and a summary of studies that have used these data. This review is supplemented by a new study that uses novel surface-based tools to characterize MTL neurodegeneration across biomarker-defined AD stages. This analysis reveals a pattern of spreading cortical thinning associated with increasing levels of tau pathology in the presence of elevated amyloid beta, with apparent epicenters in the transentorhinal region and inferior hippocampal subfields. The paper concludes with an outlook for high-resolution imaging of the MTL in ADNI Phase 4. HIGHLIGHTS: As of Phase 3, the Alzheimer's Disease Neuroimaging Initiative (ADNI) magnetic resonance imaging (MRI) protocol includes a high-resolution T2-weighted MRI scan optimized for imaging hippocampal subfields and medial temporal lobe (MTL) subregions. These scans are processed by the ADNI core to obtain automatic segmentations of MTL subregions and to derive morphologic measurements. More detailed granular examination of MTL neurodegeneration in response to disease progression is achieved by applying surface-based modeling techniques. Surface-based analysis of gray matter loss in MTL subregions reveals increasing and spatially expanding patterns of neurodegeneration with advancing stages of Alzheimer's disease (AD), as defined based on amyloid and tau positron emission tomography biomarkers in accordance with recently proposed criteria. These patterns closely align with post mortem literature on spread of pathological tau in AD, supporting the role of tau pathology in the presence of elevated levels of amyloid beta as the driver of neurodegeneration.

Exploring protein structural ensembles: Integration of sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling methods.

Under physiological conditions, proteins continuously undergo structural fluctuations on different timescales. Some conformations are only sparsely populated, but still play a key role in protein function. Thus, meaningful structure-function frameworks must include structural ensembles rather than only the most populated protein conformations. To detail protein plasticity, modern structural biology combines complementary experimental and computational approaches. In this review, we survey available computational approaches that integrate sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling techniques to derive all-atom structural models of rare protein conformations. We also propose strategies to increase the reliability and improve efficiency using deep learning approaches, thus advancing the field of integrative structural biology.