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Social commerce has emerged as a phenomenon that supports social interaction and user-generated content, allowing individuals to make informed purchasing decisions. This study aims to identify the antecedents of the sWOM behaviors present in social network sites (SNSs). Specifically, tie strength, homophily, trust, informational, and normative influence were included as sWOM antecedents. This study provides a deeper understanding of sWOM behavior in SNSs by identifying and analyzing separately three interrelated behaviors with distinct antecedents: opinion-giving, opinion-seeking, and opinion-passing. Additionally, flow experience is proposed as a less studied antecedent that interacts with all sWOM behaviors, and simultaneously, purchase intention. Our results suggest that flow experience, followed by opinion-seeking and opinion-passing, exerts the greatest influence on purchase intention. Additionally, our findings reveal that informative and normative influence are the analyzed antecedents that have the greatest impact on purchase intention. JEL Classification: M31. Plain language summary This study analyzes the sequential process of evaluation and use of social networks regarding the intention to purchase products recommended by other consumers. Three types of internet behaviors are differentiated: searching for product information, sharing this information with one’s contacts in social networks, and/or generating opinions and evaluations about the products. Several factors have been identified that could enhance both the three aforementioned behaviors and the final purchase intention of the product itself: tie strength, homophily, trust, informative influence, normative influence, and flow experience. The results of the study allow us to conclude that flow experience as well as opinion-seeking and opinion-passing behaviors are the factors that enhance product purchase intention the most. Of the remaining factors analyzed, the effect of informative and normative influences is most significant.

The concentration of ammonia (NH3) and the temperature of the air surrounding the rabbit habitat in the farm condition basic health processes such as breathing and feeding. The indoor climate in a rabbit farm is largely conditioned by the ventilation system (air conditioning). The objective of this study was to build a numerical model based on computational fluid dynamics (CFD) in order to evaluate, by numerical simulations, the air dynamics of a rustic farm. After the validation of the computational model, the thermal gradient and ammonia concentration were analyzed under three wind incidence angles (0°, 45°, and 90° with respect to the horizontal Z axis of the facility). The results of the simulations showed that, in the area occupied by the rabbits (AOR), the concentration of ammonia with respect to the source was reduced by 37.3% in the most favorable case (wind direction at 45°), and 21.2% in the least favorable case (wind direction at 0°), and the indoor temperature presented a maximum difference of 2 °C with respect to the outside temperature. Climate control is a more expensive cost in rabbit farm exploitation; dynamics modulation can serve as an auxiliary tool for reducing health risks in rabbits. The use of models based on fluid dynamics allowed us to understand the efficiency of the ventilation system, which must be increased to reduce the found temperature gradient. Through numerical simulation it will be possible to find alternatives to increase the ventilation rate.

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide and has had unprecedented effects in healthcare systems, economies and society. COVID-19 clinical presentation primarily affects the respiratory system causing bilateral pneumonia, but it is increasingly being recognized as a systemic disease, with neurologic manifestations reported in patients with mild symptoms but, most frequently, in those in a severe condition. Elderly individuals are at high risk of developing severe forms of COVID-19 due to factors associated with ageing and a higher prevalence of medical comorbidities and, therefore, they are more vulnerable to possible lasting neuropsychiatric and cognitive impairments. Several reports have described insomnia, depressed mood, anxiety, post-traumatic stress disorder and cognitive impairment in a proportion of patients after discharge from the hospital. The potential mechanisms underlying these symptoms are not fully understood but are probably multifactorial, involving direct neurotrophic effect of SARS-CoV-2, consequences of long intensive care unit stays, the use of mechanical ventilation and sedative drugs, brain hypoxia, systemic inflammation, secondary effects of medications used to treat COVID-19 and dysfunction of peripheral organs. Chronic diseases such as dementia are a particular concern not only because they are associated with higher rates of hospitalization and mortality but also because COVID-19 further exacerbates the vulnerability of those with cognitive impairment. In patients with dementia, COVID-19 frequently has an atypical presentation with mental status changes complicating the early identification of cases. COVID-19 has had a dramatical impact in long-term care facilities, where rates of infection and mortality have been very high. Community measures implemented to slow the spread of the virus have forced to social distancing and cancellation of cognitive stimulation programs, which may have contributed to generate loneliness, behavioral symptoms and worsening of cognition in patients with dementia. COVID-19 has impacted the functioning of Memory Clinics, research programs and clinical trials in the Alzheimer´s field, triggering the implementation of telemedicine. COVID-19 survivors should be periodically evaluated with comprehensive cognitive and neuropsychiatric assessments, and specific mental health and cognitive rehabilitation programs should be provided for those suffering long-term cognitive and psychiatric sequelae.

Thermal integrity profiling (TIP) is a nondestructive testing technique that takes advantage of the concrete heat of hydration (HoH) to detect inclusions during the casting process. This method is becoming more popular due to its ease of application, as it can be used to predict defects in most concrete foundation structures requiring only the monitoring of temperatures. Despite its advantages, challenges remain with regard to data interpretation and analysis, as temperature is only known at discrete points within a given cross-section. This study introduces a novel method for the interpretation of TIP readings using neural networks. Training data are obtained through numerical finite element simulation spanning an extensive range of soil, concrete, and geometrical parameters. The developed algorithm first classifies concrete piles, establishing the presence or absence of defects. This is followed by a regression algorithm that predicts the defect size and its location within the cross-section. In addition, the regression model provides reliable estimates for the reinforcement cage misalignment and concrete hydration parameters. To make these predictions, the proposed methodology only requires temperature data in the form standard in TIP, so it can be seamlessly incorporated within the TIP workflows. This work demonstrates the applicability and robustness of machine learning algorithms in enhancing nondestructive TIP testing of concrete foundations, thereby improving the safety and efficiency of civil engineering projects.

Background. We endeavored to construct a simple score based entirely on epidemiological and clinical variables that would stratify patients who require hospital admission because of community-acquired pneumonia into groups with a low or high risk of developing bacteremia. Methods. Derivation and internal validation cohorts were obtained by retrospective analysis of a database that included 3116 consecutive patients with community-acquired pneumonia from 2 university hospitals. Potential predictive factors were determined by means of a multivariate logistic regression equation applied to a cohort consisting of 60% of the patients. Points were assigned to significant parameters to generate the score. It was then internally validated with the remaining 40% of patients and was externally validated using an independent multicenter cohort of 1369 patients. Results. The overall rates of bacteremia were 12%-16% in the cohorts. The clinical probability estimate of developing bacteremia was based on 6 variables: liver disease, pleuritic pain, tachycardia, tachypnea, systolic hypotension, and absence of prior antibiotic treatment. For the score, 1 point was assigned to each predictive factor. In the derivation cohort, a cutoff score of 2 best identified the risk of bacteremia. In the validation cohorts, rates of bacteremia were <8% for patients with a score ⩽1 (43%-49% of patients), whereas blood culture results were positive in 14%-63% of cases for patients with a score ⩾2. Conclusions. This clinical score, based on readily available and objective variables, provides a useful tool to predict bacteremia. The score has been internally and externally validated and may be useful to guide diagnostic decisions for community-acquired pneumonia.

An ancestral inversion of 900 kb on chromosome 17q21, which includes the microtubule-associated protein tau ( ) gene, defines two haplotype clades in Caucasians (H1 and H2). The H1 haplotype has been linked inconsistently with AD. In a previous study, we showed that an SNP tagging this haplotype (rs1800547) was associated with AD risk in a large population from the Dementia Genetics Spanish Consortium (DEGESCO) including 4435 cases and 6147 controls. The association was mainly driven by individuals that were non-carriers of the ε4 allele. Our aim was to replicate our previous findings in an independent sample of 4124 AD cases and 3290 controls from Spain (GR@ACE project) and to analyze the effect of the H1 sub-haplotype structure on the risk of AD. The H1 haplotype was associated with AD risk (OR = 1.12; = 0.0025). Stratification analysis showed that this association was mainly driven by the ε4 non-carriers (OR = 1.15; = 0.0022). Pooled analysis of both Spanish datasets ( = 17,996) showed that the highest AD risk related to the H1/H2 haplotype was in those individuals that were the oldest [third tertile (>77 years)] and did not carry ε4 allele ( = 0.001). We did not find a significant association between H1 sub-haplotypes and AD. H1c was nominally associated but lost statistical significance after adjusting by population sub-structure. Our results are consistent with the hypothesis that genetic variants linked to the H1/H2 are tracking a genuine risk allele for AD. The fact that this association is stronger in ε4 non-carriers partially explains previous controversial results and might be related to a slower alternative causal pathway less dependent on brain amyloid load.

Background To facilitate population screening and clinical trials of disease-modifying therapies for Alzheimer’s disease, supportive biomarker information is necessary. This study was aimed to investigate the association of plasma amyloid-beta (Aβ) levels with the presence of pathological accumulation of Aβ in the brain measured by amyloid-PET. Both plasma Aβ42/40 ratio alone or combined with an FDG-PET-based biomarker of neurodegeneration were assessed as potential AD biomarkers. Methods We included 39 cognitively normal subjects and 20 patients with mild cognitive impairment from the AB255 Study who had undergone PiB-PET scans. Total Aβ40 and Aβ42 levels in plasma (TP42/40) were quantified using ABtest kits. Subjects were dichotomized as Aβ-PET positive or negative, and the ability of TP42/40 to detect Aβ-PET positivity was assessed by logistic regression and receiver operating characteristic analyses. Combination of plasma Aβ biomarkers and FDG-PET was further assessed as an improvement for brain amyloidosis detection and diagnosis classification. Results Eighteen (30.5%) subjects were Aβ-PET positive. TP42/40 ratio alone identified Aβ-PET status with an area under the curve (AUC) of 0.881 (95% confidence interval [CI] = 0.779–0.982). Discriminating performance of TP42/40 to detect Aβ-PET-positive subjects yielded sensitivity and specificity values at Youden’s cutoff of 77.8% and 87.5%, respectively, with a positive predictive value of 0.732 and negative predictive value of 0.900. All these parameters improved after adjusting the model for significant covariates. Applying TP42/40 as the first screening tool in a sequential diagnostic work-up would reduce the number of Aβ-PET scans by 64%. Combination of both FDG-PET scores and plasma Aβ biomarkers was found to be the most accurate Aβ-PET predictor, with an AUC of 0.965 (95% CI = 0.913–0.100). Conclusions Plasma TP42/40 ratio showed a relevant and significant potential as a screening tool to identify brain Aβ positivity in preclinical and prodromal stages of Alzheimer’s disease.

The HortSyst model is a new discrete time model for describing the dynamics of photo-thermal time (PTI), total dry matter production (DMP), N uptake ([N.sub.up]), leaf area index (LAI), and evapotranspiration ([ET.sub.c]) for greenhouse crops. The first three variables are considered as state variables and the latter two are conceptualized as output variables. This model was developed as a tool for decision support systems in Mexican greenhouses for the application of N and water in tomato (Solanum lycopersicum L.) production. The HortSyst has 13 parameters. It was used to calibrate the model and estimate the correct parameter values for the crop season. An experiment was carried out to test model predictions in a greenhouse during the autumn-winter season in Chapingo, Mexico. Tomato 'CID F1' was grown in a hydroponic system and plants were distributed with a density of 3.5 plants [m.sup.-2]. The tomato crop was transplanted on 21 August 2015. A weather station was installed inside the greenhouse to measure temperature, relative humidity, and global radiation. The HortSyst model provides an excellent predictive quality for DMP, [N.sub.up], LAI, and [ET.sub.c] according to the statistics. Values for bias (BIAS) were DMP (-3.897), [N.sub.up] (-0.071), LAI (0.026), and [ET.sub.c] (3.647), values for root mean square error (RMSE) were DMP (14.543), [N.sub.up] (0.500), LAI (0.100), and [ET.sub.c] (39.330), and values for modeling efficiency (EF)were DMP (0.996), [N.sub.up] (0.991), LAI (0.998), and [ET.sub.c] (0.815). The model proposed and described in this paper can be integrated as a decision support tool for N supply and irrigation management in greenhouse production systems.

Sensitivity analysis is the first step in elucidating how the uncertainties in model parameters affect the uncertainty in model outputs. Calibration of dynamic models is another issue of considerable interest, which is usually carried out by optimizing an objective function. The first aim of this research was to perform a global sensitivity analysis (GSA) with Sobol’s method for the 16 parameters of the new HORTSYST nonlinear model that simulates photo–thermal time (PTI), daily dry matter production (DMP), nitrogen uptake (Nup), leaf area index (LAI), and crop transpiration (ETc). The second objective was to carry out the calibration of the HORTSYST model by applying a differential evolution (DE) algorithm as the global optimization method. Two tomato (Solanum lycopersicum L.) crops were established during the autumn–winter and spring–summer seasons under greenhouse and soilless culture conditions. Plants were distributed with a density of 3.5 plants m−2. Air temperature and relative humidity were measured with an S-THB-M008 model sensor. Global solar radiation was measured with an S-LIB-M003 sensor connected to a U-30-NRC datalogger. In the sensitivity analysis run in the two growth stages, it was observed that a greater number of parameters were more important at the beginning of fructification than at the end of crop growth for 10% and 20% of the variation of the parameters. The sensitivity analysis came up with nine parameters (RUE, a, b, c1 , c2, A, Bd, Bn, and  PTIini) as the most important of the HORTSYST model, which were included in the calibration process with the DE algorithm. The best fit, according to RMSE, was for LAI, followed by Nup, DMP, and ETc for both crop seasons; the RMSE was close to zero, indicating a good prediction of the model’s performance.

Bean production is at risk due to climate change, declining water resources, and inadequate crop management. To address these challenges, dynamic models that predict crop growth and development can be used as fundamental tools to generate basic and applied knowledge such as production management and decision support. This study aimed to calibrate and evaluate the SIMPLE model under irrigation conditions for a semi-arid region in north-central Mexico and to simulate thermal time, biomass (Bio), and grain yield (GY) of common beans cv. ‘Pinto Saltillo’ using experimental data from four crop evapotranspiration treatments (ETct) (I50, I75, I100, and I125) applied during the 2020 and 2021 growing seasons. Both experiments were conducted in a randomized complete block design with three replicates. Model calibration was carried out by posing and solving an optimization problem with the differential-evolution algorithm with 2020 experimental data, while the evaluation was performed with 2021 experimental data. For Bio, calibration values had a root-mean-square error and Nash and Sutcliffe’s efficiency of <0.58 t ha−1 and >0.93, respectively, while the corresponding evaluation values were <1.80 t ha−1 and >0.89, respectively. The I50 and I100 ETct had better fit for calibration, while I50 and I75 had better fit in the evaluation. On average, the model fitted for the predicted GY values had estimation errors of 37% and 22% for the calibration and evaluation procedures, respectively. Therefore, an empirical model was proposed to estimate the harvest index (HI), which produced, on average, a relative error of 6.9% for the bean-GY estimation. The SIMPLE model was able to predict bean biomass under irrigated conditions for these semi-arid regions of Mexico. Also, the use of both crop Bio and transpiration simulated by the SIMPLE model to calculate the HI significantly improved GY prediction under ETct. However, the harvest index needs to be validated under other irrigation levels and field experiments in different locations to strengthen the proposed model and design different GY scenarios under water restrictions for irrigation due to climate change.