Three-level Hierarchical Model Research Articles

Crossnma: An R package to synthesize cross-design evidence and cross-format data using network meta-analysis and network meta-regression

BackgroundAlthough aggregate data (AD) from randomised clinical trials (RCTs) are used in the majority of network meta-analyses (NMAs), other study designs (e.g., cohort studies and other non-randomised studies, NRS) can be informative about relative treatment effects. The individual participant data (IPD) of the study, when available, are preferred to AD for adjusting for important participant characteristics and to better handle heterogeneity and inconsistency in the network.ResultsWe developed the R package crossnma to perform cross-format (IPD and AD) and cross-design (RCT and NRS) NMA and network meta-regression (NMR). The models are implemented as Bayesian three-level hierarchical models using Just Another Gibbs Sampler (JAGS) software within the R environment. The R package crossnma includes functions to automatically create the JAGS model, reformat the data (based on user input), assess convergence and summarize the results. We demonstrate the workflow within crossnma by using a network of six trials comparing four treatments.ConclusionsThe R package crossnma enables the user to perform NMA and NMR with different data types in a Bayesian framework and facilitates the inclusion of all types of evidence recognising differences in risk of bias.

Procedural volume and outcomes with atrial fibrillation ablation: A report from the NCDR AFib Ablation Registry

BackgroundThe association of hospital and physician procedure volume with outcome has not been well evaluated for atrial fibrillation (AF) ablation in contemporary practice. ObjectiveThis study aimed to determine the association between hospital and physician AF ablation volume and procedural success (isolation of all pulmonary veins) and major adverse events (MAEs). MethodsProcedures reported to the National Cardiovascular Data Registry AFib Ablation Registry between July 2019 and June 2022 were included. Hospital and physician procedural volumes were annualized and stratified into quartiles to compare outcomes. Three-level hierarchical (patient, hospital, and physician) models were used to assess the procedural volume–outcome relationship. ResultsA total of 70,296 first-time AF ablations at 186 US hospitals were included. Overall, procedural success and MAE rates were 98.5% and 1.0%, respectively. With hospital volume (Q4) as a reference, the likelihood of procedural success was lower for Q1 (odds ratio [OR], 0.44; 95% CI, 0.29–0.68), Q2 (OR, 0.50; 95% CI, 0.33–0.75), and Q3 (OR, 0.60; 95% CI, 0.40–0.89); the results were similarly significant for physician volume. With MAE for hospitals, there was an inverse procedural volume relationship for Q1 (OR, 1.78; 95% CI, 1.26–2.52) but not for Q2 (OR, 1.06; 95% CI, 0.77–1.46) or Q3 (OR, 1.19; 95% CI, 0.89–1.58) and similarly for physicians in Q1 and Q2 but not in Q3. An adjusted MAE ≤1% was predicted by an annual volume of approximately 190 for hospitals and 60 for physicians. ConclusionIn this national cohort, hospital and physician AF ablation procedural volumes were directly related to acute procedural success and inversely related to rates of MAE.

The leading role of personality in concerns about autonomous vehicles.

Development of autonomous vehicles (AVs) is growing in a rapid rate, however, the most dominant barriers in their adoption seem to be rather psychological than technical. The present online survey study aimed to investigate which demographical and personality dimensions predict attitudes towards AVs on a Hungarian sample (N = 328). Data was collected by convenience and snowball sampling. Three-level hierarchical regression models were applied: in the first level, demographical variables, then general personality traits and third, attitude-like personality factors were entered. We demonstrated that the predictive effect of age, gender and education disappeared when personality dimensions were included into the models. Importantly, more positive general attitudes towards technology and higher optimism regarding innovations predicted eagerness to adopt AVs. On the other hand, individuals with more negative attitudes and higher dependence on technology as well as those with lower level of Sensory Sensation Seeking and higher level of Conscientiousness were more concerned about AVs. Our results suggest that AV acceptance cannot be regarded as a one-dimensional construct and that certain personality traits might be stronger predictors of AV acceptance than demographical factors.

Social, Economic and Competitiveness aspects of Green Innovation

Objective: The purpose of this study is to analyze how the adoption of green innovation practices by companies can affect the economic (Gross Domestic Product - GDP), social (Human Development Index - HDI) and competitiveness (Global Competitiveness Index - GCI) Development of Developed Countries (DC) and Emerging Countries (EC). Method: For the analysis, a three-level hierarchical regression model was used with repeated measures and estimated by Maximum Likelihood from a sample of 4061 publicly traded companies, 80% of which from the G7 countries and 20% from the BRICS (Brazil, Russia, India, China, and South Africa). Results: The results showed that in relation to GDP, all green innovation indicators negatively influenced economic development in both DC and EC. With regard to the HDI, green innovation practices such as environmental management and environmental investments positively influence social development in DCs and ECs, respectively. As for competitiveness, the results revealed that only green innovation practices related to environmental policies showed statistically significant results demonstrating a negative relationship with the GCI both in the DC and in the EC. Contributions: The study contributes with possible ways for companies and governments to set goals to achieve Sustainable Development Goals, SDG-8 to promote sustainable and inclusive economic growth, SDG-9 to promote inclusive and sustainable industrialization and foster innovation, and SDG-10 to strengthen and revitalize the global partnership for sustainable development.

Идентификация параметров агент-ориентированной модели управления промышленным комплексом региона

Current challenges facing the Russian economy require models to optimise industrial management processes at the regional level. Therefore, a three-level hierarchical agent-based model for minimax control of the regional industrial complex is considered in this research. The study aims to develop an approach to solving a parameter identification problem of the agent-based model for managing the industrial complex of Sverdlovsk oblast. To this end, the paper presents a theoretical justification for the implemented approach, a formalisation of the aforementioned problem, and an algorithm for constructing and selecting models to assess management system parameters. The method of linear regression analysis was applied to solve the identification problem. The proposed approach was tested using data on 28 types of industrial activity in Sverdlovsk oblast for 2005–2021. The phase vector of statistical identification models was defined by the following parameters: the average annual number of employees of enterprises; fixed assets; gross value added; volume of shipped goods, performed works and services; balanced financial results of enterprises; investment in fixed capital; costs of implementing and using digital technologies. The control vector was determined by the factors of attracting budgetary and other (external) funds. As a result, 125 high-quality models were built to solve parameter identification problems of the agent-based model used for managing the industrial complex of Sverdlovsk oblast. The obtained statistical models can be used to establish communication between agents, clarify their specificity, calculate and assess management performance. The proposed approach can be applied to predict the development of regional industrial complexes in accordance with planned control actions, as well as to calculate control actions necessary to achieve target parameters.

The Use of Two-Piece Transmucosal Implants Designed with a Convergent Neck: A 6-Year Clinical Prospective Cohort Study Evaluating the Impact on Soft and Hard Tissues.

This prospective study evaluated the marginal bone level and periodontal parameters 6 years after placement of a convergent transmucosal neck implant. In total, 71 implants were placed in 60 patients. Of these, 52 implants were placed with a flapless technique and with the convergent neck exposed by 1 mm (early and delayed placement), while 19 implants were placed immediately after extraction (immediate placement). At least 3 months after insertion, customized abutments were applied and provisional resin crowns were cemented, while being careful to prevent any tissue compression. After 3 weeks, definitive metal-ceramic crowns were designed with the finish line at the soft tissue level and free from any compression. The implant survival rate at 6 years, marginal bone level changes (ΔMBL, mm), and Pink Esthetic Score (PES) were evaluated. Gingival phenotype was determined before the implant surgeries (thin/thick). A three-level hierarchical model was examined with a random intercept and random slope on follow-up time at the patient level and a random intercept at the implant level. In all, 57 patients with 67 implants reached the 6-year point; 3 patients (5.0%) dropped out. The implant survival rate was 100%. ΔMBL from baseline to 24 months was 0.58 mm (95% CI: 0.48, 0.68), while ΔMBL from 24 to 72 months was 0.33 mm (95% CI: 0.19, 0.47). PES improved steadily from 6 to 48 months (+0.96; 95% CI: 0.71, 1.20), with overall stability at 72 months. No correlation between MBL and PES was observed. A significant association was found between thick gingival phenotype and less MBL variation. Greater implant diameter offered more MBL stability after 3 years but resulted in lower PES values. The use of convergent transmucosal neck implants placed with a flapless transmucosal approach with the crown margin positioned at the gingival level preserved marginal bone and guided gingival morphology for up to 72 months. The gingival phenotype was the parameter that most affected the soft and hard tissues. In the long term, implant placement timing was not related to MBL.

Adherence to legislation limiting opioid prescription duration following musculoskeletal injury.

North Carolina had implemented legislation (Strengthen Opioid Misuse Prevention (STOP) Act) limiting opioid prescriptions to 5 days for acute pain and 7 days for post-operative pain. This study aimed to identify patient, prescriber, and facility characteristics associated with STOP Act adherence for patients with acute or post-surgical musculoskeletal (MSK) conditions. A three-level hierarchical logistic regression model was used to predict odds of adherence with STOP Act duration limits, accounting for fixed and random effects at the patient, prescriber, and facility levels. A large healthcare system in North Carolina. Patients (N = 6,849) presenting from 2018 to 2020 with a diagnosis of an acute MSK injury. The STOP Act limited the duration of opioid prescriptions in North Carolina. Prescriptions adhering to the STOP Act duration limits of 5 days (nonoperative) or 7 days (operative) were the primary outcome. Opioids were compliant with STOP Act duration limits in 69.3 percent of encounters, with 33 percent of variation accounted for by clinician and 29 percent by facility. Patients prescribed >1 opioid (odds ratio (OR) 0.46, 95 percent confidence interval (CI): 0.36, 0.58) had reduced odds of a compliant prescription; surgical patients had increased odds of a compliant prescription (outpatient surgery: OR 5.89, 95 percent CI: 2.43-14.29; inpatient surgery: OR 7.71, 95 percent CI: 3.04-19.56). Primary care sports medicine clinicians adhered to legislation less frequently than orthopedic surgeons (OR 0.38, 95 percent CI: 0.15, 0.97). Most prescriptions adhered to STOP Act legislation. Tailored interventions to improve adherence among targeted groups of prescribers, eg, those treating nonoperative injuries and sport medicine clinicians, could be useful.

Assessing multi-spatial driving factors of urban land use transformation in megacities: a case study of Guangdong–Hong Kong–Macao Greater Bay Area from 2000 to 2018

ABSTRACT Rapid morphological and socioeconomic changes have accelerated the urbanization process and urban land use transformation in China. Megacities comprise clusters of urban cities and exhibit both newly formed and well-developed urban land use development beyond administrative boundaries. It is necessary to distinguish the changing effects of spatial-varying driving factors on newly formed urban land uses from well-developed built-up areas in megacities. This study proposed a multi-spatial urbanization framework to quantify region-level socioeconomics, cluster-level ecological morphologies, and grid-level urban functional morphologies. A three-level Bayesian hierarchical model was developed to investigate the impacts of multi-spatial driving factors on urban land use transformation in megacities. The study period focused on the urbanization process between 2000 and 2018 in Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Results revealed that compared with well-developed urban built-up land, changing impacts of three-level driving factors in urban land use transformation could be captured based on the proposed Bayesian hierarchical model. The region-level total population was associated with increasing possibilities in forming new residential land than the well-developed ones in 35 districts/counties/cities in GBA. Cluster-level ecological attributes with higher proportion, lower edge density of urban built areas, and lower-degree ecological complexity showed increasing probability on newly formed industrial and public land. Grid-level urban functional factors including public transportation density and shopping/dining distribution exhibited significantly decreasing probability (coefficients: −2.12 to −0.51) on contributing newly formed land uses compared with the well-developed areas, whereas business/industry distribution represented higher (coefficients: 0.99 and 0.15) and lower probabilities (coefficient: −0.22) of forming industrial/public land and residential land separately. This research shows a new attempt to distinguish multi-spatial morphological and socioeconomic effects in urban land use transformation in megacities.

The Predictive Validity of Individualised Load-Velocity Relationships for Predicting 1RM: A Systematic Review and Individual Participant Data Meta-analysis.

Load-velocity relationships are commonly used to estimate one-repetition maximums (1RMs). Proponents suggest these estimates can be obtained at high frequencies and assist with manipulating loads according to session-by-session fluctuations. Given their increasing popularity and development of associated technologies, a range of load-velocity approaches have been investigated. This systematic review and individual participant data (IPD) meta-analysis sought to quantify the predictive validity of individualised load-velocity relationships for the purposes of 1RM prediction. In September 2022, a search of MEDLINE, SPORTDiscus, Web of Science and Scopus was conducted for published research, with Google Scholar, CORE and British Ethos also searched for unpublished research. Studies were eligible if they were written in English, and directly compared a measured and predicted 1RM using load-velocity relationships in the squat, bench press, deadlift, clean or snatch. IPD were obtained through requests to primary authors and through digitisation of in-text plots (e.g. Bland-Altman plots). Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the review conducted in accordance with PRISMA-IPD guidelines and an a priori protocol. Absolute and scaled standard error of the estimates (SEE/SEE%) were calculated for two-stage aggregate analyses, with bootstrapping performed for sampling variances. Estimates were pooled using three-level hierarchical models with robust 95% confidence intervals (CIs). One-stage analyses were conducted with random intercepts to account for systematic differences across studies and prediction residuals calculated in the absolute scale (kg) and as a percentage of the measured 1RM. Moderator analyses were conducted by including a priori defined categorical variables as fixed effects. One hundred and thirty-seven models from 26 studies were included with each identified as having low, unclear or high risk of bias. Twenty studies comprising 434 participants provided sufficient data for meta-analyses, with raw data obtained for 8 (32%) studies. Two-stage analyses identified moderate predictive validity [SEE% 9.8, 95% CI 7.4% to 12.2%, with moderator analyses demonstrating limited differences based on the number of loads (β2Loads:>2Loads = 0.006, 95% CI - 1.6 to 1.6%) or the use of individual or group data to determine 1RM velocity thresholds (βGroup:Individualised = - 0.4, 95% CI - 1.9 to 1.0%)]. One-stage analyses identified that predictions tended to be overestimations (4.5, 95% CI 1.5to 7.4kg), which expressed as a percentage of measured 1RM was equal to 3.7 (95% CI 0.5 to 6.9% 1RM). Moderator analyses were consistent with those conducted for two-stage analyses. Load-velocity relationships tend to overestimate 1RMs irrespective of the modelling approach selected. On the basis of the findings from this review, practitioners should incorporate direct assessment of 1RM wherever possible. However, load-velocity relationships may still prove useful for general monitoring purposes (e.g. assessing trends across a training cycle), by providing high-frequency estimates of 1RM when direct assessment may not be logistically feasible. Given limited differences in predictions across popular load-velocity approaches, it is recommended that practitioners opting to incorporate this practice select the modelling approach that best suits their practical requirements. https://osf.io/agpfm/ .

The impact of corporate sustainability performance on advertising efficiency

Over the years several studies have reported a significant waste of advertising budget, a finding which calls for strategies to increase advertising efficiency. While some factors, such as brand extensions or an optimal marketing mix, have already been identified as relevant determinants of advertising efficiency, changes in consumer psychographics have so far been neglected. The current study fills this gap by investigating how the emerging awareness and demand for corporate sustainability serve as a contextual factor leveraging or hindering advertising efficiency. Furthermore, we investigate how advertising efficiency has changed across various industry sectors from 2010 to 2019. A two-step procedure was applied to analyze the secondary data of 1950 observations from 195 US firms in five industry sectors over a period of 10 years. The resulting time series of firm-specific multi-directional efficiency scores confirms that advertising efficiency varies over time, justifying the relevance of a dynamic perspective for analyzing advertising efficiency. Furthermore, in support of our main claim, the investigation of the relationship between advertising efficiency and the environmental, social and governance performance of firms over time using a time-fixed effects panel regression and a three-level hierarchical regression model confirm the significant impact of corporate sustainability performance on advertising efficiency. Interestingly, this effect varies among different industry sectors and not all corporate sustainability activities impact advertising efficiency to the same extent. The results not only emphasize the relevance of corporate sustainability performance in increasing advertising efficiency, but also guide marketers on strategic marketing decisions related to the allocation of advertising budget.

Assessing the incidence and severity of drug adverse events: a Bayesian hierarchical cumulative logit model

ABSTRACT Detection of safety signals based on multiple comparisons of adverse events (AEs) between two treatments in a clinical trial involves evaluations requiring multiplicity adjustment. A Bayesian hierarchical mixture model is a good solution to this problem as it borrows information across AEs within the same System Organ Class (SOC) and modulates extremes due merely to chance. However, the hierarchical model compares only the incidence rates of AEs, regardless of severity. In this article, we propose a three-level Bayesian hierarchical non-proportional odds cumulative logit model. Our model allows for testing the equality of incidence rate and severity for AEs between the control arm and the treatment arm while addressing multiplicities. We conduct simulation study to investigate the operating characteristics of the proposed hierarchical model. The simulation study demonstrates that the proposed method could be implemented as an extension of the Bayesian hierarchical mixture model in detecting AEs with elevated incidence rate and/or elevated severity. To illustrate, we apply our proposed method using the safety data from a phase III, two-arm randomized trial.

Synthesizing cross-design evidence and cross-format data using network meta-regression.

In network meta-analysis (NMA), we synthesize all relevant evidence about health outcomes with competing treatments. The evidence may come from randomized clinical trials (RCT) or non-randomized studies (NRS) as individual participant data (IPD) or as aggregate data (AD). We present a suite of Bayesian NMA and network meta-regression (NMR) models allowing for cross-design and cross-format synthesis. The models integrate a three-level hierarchical model for synthesizing IPD and AD into four approaches. The four approaches account for differences in the design and risk of bias (RoB) in the RCT and NRS evidence. These four approaches variously ignoring differences in RoB, using NRS to construct penalized treatment effect priors and bias-adjustment models that control the contribution of information from high RoB studies in two different ways. We illustrate the methods in a network of three pharmacological interventions and placebo for patients with relapsing-remitting multiple sclerosis. The estimated relative treatment effects do not change much when we accounted for differences in design and RoB. Conducting network meta-regression showed that intervention efficacy decreases with increasing participant age. We also re-analysed a network of 431 RCT comparing 21 antidepressants, and we did not observe material changes in intervention efficacy when adjusting for studies' high RoB. We re-analysed both case studies accounting for different study RoB. In summary, the described suite of NMA/NMR models enables the inclusion of all relevant evidence while incorporating information on the within-study bias in both observational and experimental data and enabling estimation of individualized treatment effects through the inclusion of participant characteristics.

Methods for the Inclusion of Real-World Evidence in a Rare Events Meta-Analysis of Randomized Controlled Trials

Many rare events meta-analyses of randomized controlled trials (RCTs) have lower statistical power, and real-world evidence (RWE) is becoming widely recognized as a valuable source of evidence. The purpose of this study is to investigate methods for including RWE in a rare events meta-analysis of RCTs and the impact on the level of uncertainty around the estimates. Four methods for the inclusion of RWE in evidence synthesis were investigated by applying them to two previously published rare events meta-analyses: the naïve data synthesis (NDS), the design-adjusted synthesis (DAS), the use of RWE as prior information (RPI), and the three-level hierarchical models (THMs). We gauged the effect of the inclusion of RWE by varying the degree of confidence placed in RWE. This study showed that the inclusion of RWE in a rare events meta-analysis of RCTs could increase the precision of the estimates, but this depended on the method of inclusion and the level of confidence placed in RWE. NDS cannot consider the bias of RWE, and its results may be misleading. DAS resulted in stable estimates for the two examples, regardless of whether we placed high- or low-level confidence in RWE. The results of the RPI approach were sensitive to the confidence level placed in RWE. The THM was effective in allowing for accommodating differences between study types, while it had a conservative result compared with other methods. The inclusion of RWE in a rare events meta-analysis of RCTs could increase the level of certainty of the estimates and enhance the decision-making process. DAS might be appropriate for inclusion of RWE in a rare event meta-analysis of RCTs, but further evaluation in different scenarios of empirical or simulation studies is still warranted.

ВДОСКОНАЛЕННЯ ПРОЦЕСУ АДИТИВНОГО ВИРОБНИЦТВА АЛЮМІНІЄВОЇ ДЕТАЛІ З ВИКОРИСТАННЯМ ТЕХНОЛОГІЇ ЕЛЕКТРОДУГОВОГО ЗВАРЮВАННЯ

The article provides original findings of the improvement of the production of the wire arc additive manufacturing of aluminum. The simulation of the process of wire arc additive manufacturing of a three-level hierarchical model was carried out, while taking into account the requirements for manufacturability and quality of the obtained part, such as its geometric dimensions, residual stresses and maintaining of the optimal production speed. During the occurrence of problems with heat distribution, various trajectories of movement and topologies were used to solve them. The optimal trajectory and print pa-rameters are created in the form of temperature control for the specified part. Implemented the ability to use a telecommuni-cation feedback system using telecommunication devices such as pyro camera andinfrared temperature laser sensors to mon-itor temperature during problems detected by print simulation. The obtained data were used for the further possibility of generating an automated program for controlling the robot during the additive manufacturing process. Residual stresses and the possibility of defects in the manufactured part are determined. Based on the obtained data, the values of residual stresses and defects in the manufactured parts were determined.Improved print topology using a three-level hierarchical system. Introducing the use of laser sensors and thermal cameras to the telecommunications system of additive manufacturing using wire arc welding with the improvement of the direct printingprocess by means of controlling the spread of heat.

Attractiveness consumption, personality traits and sustainability: Construction and empirical application of evaluation indicators for attractive attributes of China-chic T-shirt products.

The rapid development of China's cultural and creative industries has given cultural fashion products, represented by China-chic () T-shirts, vitality. In recent years, with the arrival of the era of design-led emotional sensory, products with attractive and qualia characteristics have become an important tool to attract consumers and create a competitive brand. Therefore, this study aimed to build an evaluation index of the attractive attributes of China-chic T-shirt products from the perspective of attractive consumption to serve as an important basis for improving product development and thus promoting the sustainable development of China-chic T-shirt products. Firstly, a three-level hierarchical model was established for the sustainable development of China-chic T-shirt products based on literature research. Secondly, through the evaluation grid method and factor analysis method, we extracted the attractive elements and factors associated with China-chic T-shirt products and established an objective evaluation index for the attractive attributes of China-chic T-shirt products. Twenty-five participants with different decision-making personalities tested three products with expert validity. The weights of the evaluation indexes of the attractive attributes of China-chic T-shirt products were evaluated using the fuzzy analytic hierarchy process. Finally, a two-factor mixed design ANOVA was conducted to check the practical value of the evaluation index of the attractive attributes of China-chic T-shirt products. The results show that the evaluation indexes of the attractive attributes of China-chic T-shirt products include 7 attractive factors, including the brand image feature, perceptual association feature, beauty feature, delicacy feature, creativity feature, engineering feature, and green feature, as well as 32 attractive elements. Through an empirical application analysis, it was found that the index has an excellent ability to differentiate between the attractive characteristics of different styles of products and test participants with different decision-making personalities.

A light robust optimization model for a hierarchical healthcare network

PurposeA growing body of evidence points to the influence of location and allocation decisions on the structure of healthcare networks. The authors introduced a three-level hierarchical facility location model to minimize travel time in the healthcare system under uncertainty.Design/methodology/approachMost healthcare networks are hierarchical and, as a result, the linkage between their levels makes it difficult to specify the location of the facilities. In this article, the authors present a hybrid approach according to data envelopment analysis and robust programming to design a healthcare network. In the first phase, the efficiency of each potential location is calculated based on the non-radial range-adjusted measure considering desirable and undesirable outputs based on a number of criteria such as the target area's population, proximity to earthquake faults, quality of urban life, urban decrepitude, etc. The locations deemed suitable are then used as candidate locations in the mathematical model. In the second phase, based on the proposed robust optimization model, called light robustness, the location and allocation decisions are adopted.FindingsThe developed model is evaluated using an actual-world case study in District 1 of Tehran, Iran and relevant results and different sensitivity analyses were presented as well. When the percentage of referral parameters changes, the value of the robust model's objective function increases.Originality/valueThe contributions of this article are listed as follows: Considering desirable and undesirable criteria to selecting candidate locations, providing a robust programming model for building a service network and applying the developed model to an actual-world case study.

Predictive control based assessment of building demand flexibility in fixed time windows

This paper introduces a simple strategy for modular building energy management with explicit demand response based on a three-level hierarchical model predictive control that engages the entire building in optimal operation and financially viable flexibility provision. Financial viability of the assessed flexibility capacity is guaranteed regardless of the flexibility activation scenario, under the assumption that the entire building flexibility capacity is accepted. The strategy is verified on three pilot buildings. The selected pilot buildings have diverse configurations and are tested under different conditions to show the broad applicability of the developed approach. The analysis of the building flexibility is focused on particular characteristic days in the heating and cooling season and provides a comparison of the overall building operation costs for the following three control options applied to the building: hierarchical control with and without flexibility provision as well as conventional control. In this way, it is possible to quantify the benefits achievable exactly due to the advanced energy management system operation on the site. Flexibility sources and amounts within the building are analyzed in detail for each specific case.

The Moderating Effects of Board Independence and the Separation of Chairman–Chief Executive Officer Duality Roles on a Firm’s Value: Evidence from the Thai Listed Firms

Since the 1997 financial crisis, good corporate governance is one of the most contentious problems for Thai listed companies’ long-term viability. Unlike earlier research in Thailand, this article uses a three-level hierarchical regression model to examine the moderating effects of board independence and the separation of chairman–chief executive officer (CEO) duality roles on the firm’s value of the Thai listed firms between 2010 and 2019. This study confirms a negative relationship between family ownership and a firm’s value in Thailand. Nevertheless, it reveals that the association between family ownership and firm’s value becomes positive if the Thai listed firms have a high proportion of independent directors. Further evidence confirms that the separation of chairman–CEO duality roles can increase a firm’s value, but it cannot solely enhance the value in those firms with high family ownership. Finally, this study discusses empirically-based practical implications and recommendations.

The Bone Biomarker Response to an Acute Bout of Exercise: A Systematic Review with Meta-Analysis.

Circulating biomarkers are often used to investigate the bone response to an acute bout of exercise, but heterogeneity in factors such as study design, quality, selected biomarkers, and exercise and participant characteristics render it difficult to synthesize and evaluate available evidence. The aim of this study was to quantify the effects of an acute exercise bout on bone biomarkers, along with the influence of potential moderators such as participant, exercise, and design characteristics, using a systematic review and meta-analytic approach. The protocol was designed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) guidelines and prospectively published. Seven databases were systematically searched in accordance with predefined eligibility criteria. Bayesian three-level hierarchical meta-analysis models were used to explore the main effects of acute exercise on bone biomarkers, as well as potential moderating factors. Modelled effect sizes were interpreted according to three metrics, namely (1) evidence of an effect (defined by whether, or how much of, the credible interval [CrI] included zero); (b) the size of that effect (threshold values of 0.01, 0.2, 0.5 and 0.8 were used to describe effect sizes as very small, small, medium and large, respectively); and (c) the level of certainty in the estimated effect (defined using the GRADE framework). Pooling of outcomes across all designs and categories indicated that an acute bout of exercise increased bone resorption (ES0.50.10, 95% CrI 0.00-0.20) and formation (ES0.5 0.05, 95% CrI 0.01-0.08) markers but the effects were very smalland highly variable. Furthermore, moderator analyses revealed the source of some of this variability and indicated that exercise type and impact loading influenced the bone resorptive response. A moderate increase in C-terminal telopeptide of type 1 collagen (CTX-1) was observed in response to cycling (ES0.5 0.65, 95% CrI 0.20-0.99), with greater durations and more work leading to larger CTX-1 increases. CTX-1 response peaked within 15min and 2h after the exercise bout. Other exercise types did not influence CTX-1. Changes to all bone formation markers were very small and transient, with the very small increases returning to baseline within 15min of exercise cessation. No major trends for bone formation markers were identified across any of the moderating categories investigated. Certainty of evidence in most outcomes was deemed to be low or very low. The large influence of an acute bout of prolonged cycling on the bone resorption marker CTX-1, alongside the lack of a response of any biomarker to resistance or high-impact exercise types, indicate that these biomarkers may be more useful at investigating potentially osteolytic aspects of exercise, and raises questions about their suitability to investigate the osteogenic potential of different exercise types, at least in the short term and in response to a single exercise bout. Certainty in all outcomes was low or very low, due to factors including risk of bias, lack of non-exercise controls, inconsistency, imprecision and small-study effects. This investigation was prospectively registered on the Open Science Framework Registry ( https://osf.io/6f8dz ) and the full protocol underwent peer review prior to conducting the investigation.

Should I Signal Trust? Effect of Terrorism on Interpersonal Trust in Post-Conflict and Non-Post-Conflict Countries

ABSTRACT Generalized interpersonal trust is an essential component of a functioning society. While some studies have examined how the perception of terrorism affects trust, cross-national works investigating the impact of actual terrorist attacks on individual trust remain mixed. In this paper, I use insights from existing studies to disaggregate generalized interpersonal trust in response to terrorism in two distinct dimensions, prosocial motivation, and strategic signaling. While threat perception from terrorism lowers interpersonal trust in all contexts, I argue that actual events distinctively shape a person’s interpersonal trust. In a relatively stable and secure context of a non-post-conflict country, individuals living closer to terrorist incidents express increased interpersonal trust. But in post-conflict countries, those closer to terrorist incidents tend to show more distrust. To test the argument, I use the World Values Survey dataset of fifty-two states and create a terrorism scale for 717 survey regions within the countries, considering their spatial and temporal closeness to each terrorist incident. Results obtained from three-level hierarchical models (state, region, and individual) are robust and contribute to our understanding of how terrorism shapes interpersonal trust in different contexts.