Empirical Measures Research Articles

Differences in Total Factor Productivity and the Pattern of International Trade

In this work, we develop a trade model that explains the pattern of trade between countries based on differences in total factor productivity (TFP) while also accounting for differences in relative factor endowments. The novelty stems from the introduction of production functions derived by combining the Ricardian and Heckscher–Ohlin–Samuelson (H-O-S) theories, with TFP differences serving as the basis of comparative advantage. To this end, a testable hypothesis is derived. For the empirical measurement of the TFP in each industry and country, a constant elasticity of substitution (CES)-type production function was employed, and the TFP was calculated as the Solow residual from the production function’s fixed term. To offer a better understanding, the model was tested for the bilateral trade between Germany and Russia, and Germany and the Czech Republic. It was found that TFP differences can be used as a basis for explaining comparative advantages and, consequently, the bilateral pattern of trade between two countries.

Downside and Upside Uncertainty Shocks

Abstract An increase in uncertainty is not contractionary per se. What is contractionary is a widening of the left tail of the GDP growth forecast distribution, the downside uncertainty. On the contrary, an increase of the right tail, the upside uncertainty, is mildly expansionary. The reason why uncertainty shocks have been previously found to be contractionary is because movements in downside uncertainty dominate existing empirical measures of uncertainty. The results are obtained using a new econometric approach which combines quantile regressions and structural VARs.

Collective effects of rising average temperatures and heat events on oviparous embryos.

Survival of the immobile embryo in response to rising temperature is important to determine a species' vulnerability to climate change. However, the collective effects of 2 key thermal characteristics associated with climate change (i.e., rising average temperature and acute heat events) on embryonic survival remain largely unexplored. We used empirical measurements and niche modeling to investigate how chronic and acute heat stress independently and collectively influence the embryonic survival of lizards across latitudes. We collected and bred lizards from 5 latitudes and incubated their eggs across a range of temperatures to quantify population-specific responses to chronic and acute heat stress. Using an embryonic development model parameterized with measured embryonic heat tolerances, we further identified a collective impact of embryonic chronic and acute heat tolerances on embryonic survival. We also incorporated embryonic chronic and acute heat tolerance in hybrid species distribution models to determine species' range shifts under climate change. Embryos' tolerance of chronic heat (T-chronic) remained consistent across latitudes, whereas their tolerance of acute heat (T-acute) was higher at high latitudes than at low latitudes. Tolerance of acute heat exerted a more pronounced influence than tolerance of chronic heat. In species distribution models, climate change led to the most significant habitat loss for each population and species in its low-latitude distribution. Consequently, habitat for populations across all latitudes will shift toward high latitudes. Our study also highlights the importance of considering embryonic survival under chronic and acute heat stresses to predict species' vulnerability to climate change.

Adapting innovation capabilities in emerging economies: the role of learning and cultural and institutional distance in MNC subsidiaries in Korea

Purpose The rise of emerging economies in the innovation landscape has often been attributed to the positive spillovers of innovation capabilities from multinational corporations (MNCs). However, it is less certain that their innovative capabilities imported from the home country function effectively in the host country from the outset. This study examines the performance of the innovation capabilities of MNC subsidiaries in emerging economies over time by considering the gradual process of their learning about host countries.Design/methodology/approach We employed stochastic frontier analysis to measure innovation capabilities, our focal construct. For regression analysis, we applied the Mundlak estimator, a variant of the fixed-effects panel estimator, to a sample comprising subsidiaries of MNCs from technologically advanced nations operating in Korea between 2006 and 2016.Findings Our results indicate that the innovation capabilities of MNC subsidiaries initially underperform those of local firms but improve over time, eventually surpassing the capabilities of their local counterparts. Furthermore, our findings reveal that institutional distance amplifies the underperformance of the innovation capabilities of MNC subsidiaries.Originality/value This study contributes to the literature by extending both theoretical development and empirical measurement of innovation capabilities in cross-national settings. Additionally, it deepens our understanding of whether and how MNC subsidiaries adapt their innovation capabilities to the local market environment.

Consensus-based optimisation with truncated noise

Abstract Consensus-based optimisation (CBO) is a versatile multi-particle metaheuristic optimisation method suitable for performing non-convex and non-smooth global optimisations in high dimensions. It has proven effective in various applications while at the same time being amenable to a theoretical convergence analysis. In this paper, we explore a variant of CBO, which incorporates truncated noise in order to enhance the well-behavedness of the statistics of the law of the dynamics. By introducing this additional truncation in the noise term of the CBO dynamics, we achieve that, in contrast to the original version, higher moments of the law of the particle system can be effectively bounded. As a result, our proposed variant exhibits enhanced convergence performance, allowing in particular for wider flexibility in choosing the noise parameter of the method as we confirm experimentally. By analysing the time evolution of the Wasserstein- $2$ distance between the empirical measure of the interacting particle system and the global minimiser of the objective function, we rigorously prove convergence in expectation of the proposed CBO variant requiring only minimal assumptions on the objective function and on the initialisation. Numerical evidences demonstrate the benefit of truncating the noise in CBO.

Appraising LaQshya’s potential in measuring quality of care for mothers and newborns: a comprehensive review of India’s Labor Room Quality Improvement Initiative

BackgroundPoor intrapartum care in India contributes to high maternal and newborn mortality. India’s Labor Room Quality Improvement Initiative (LaQshya) launched in 2017, aims to improve intrapartum care by minimizing complications, enforcing protocols, and promoting respectful maternity care (RMC). However, limited studies pose a challenge to fully examine its potential to assess quality of maternal and newborn care. This study aims to bridge this knowledge gap and reviews LaQshya’s ability to assess maternal and newborn care quality. Findings will guide modifications for enhancing LaQshya’s effectiveness.MethodsWe reviewed LaQshya’s ability to assess the quality of care through a two-step approach: a comprehensive descriptive analysis using document reviews to highlight program attributes, enablers, and challenges affecting LaQshya’s quality assessment capability, and a comparison of its measurement parameters with the 352 quality measures outlined in the WHO Standards for Maternal and Newborn Care. Comparing LaQshya with WHO standards offers insights into how its measurement criteria align with global standards for assessing maternity and newborn care quality.ResultsLaQshya utilizes several proven catalysts to enhance and measure quality- institutional structures, empirical measures, external validation, certification, and performance incentives for high-quality care. The program also embodies contemporary methods like quality circles, rapid improvement cycles, ongoing facility training, and plan-do-check, and act (PDCA) strategies for sustained quality enhancement. Key drivers of LaQshya’s assessment are- leadership, staff mentoring, digital infrastructure and stakeholder engagement from certified facilities. However, governance issues, understaffing, unclear directives, competency gaps, staff reluctance towards new quality improvement approaches inhibit the program, and its capacity to enhance quality of care. LaQshya addresses 76% of WHO’s 352 quality measures for maternal and newborn care but lacks comprehensive assessment of crucial elements: harmful labor practices, mistreatment of mothers or newborns, childbirth support, and effective clinical leadership and supervision.ConclusionLaQshya is a powerful model for evaluating quality of care, surpassing other global assessment tools. To achieve its maximum potential, we suggest strengthening district governance structures and offering tailored training programs for RMC and other new quality processes. Furthermore, expanding its quality measurement metrics to effectively assess provider accountability, patient outcomes, rights, staff supervision, and health facility leadership will increase its ability to assess quality improvements.

The evolution of whole-brain turbulent dynamics during recovery from traumatic brain injury.

It has been previously shown that traumatic brain injury (TBI) is associated with reductions in metastability in large-scale networks in resting-state fMRI (rsfMRI). However, little is known about how TBI affects the local level of synchronization and how this evolves during the recovery trajectory. Here, we applied a novel turbulent dynamics framework to investigate whole-brain dynamics using an rsfMRI dataset from a cohort of moderate to severe TBI patients and healthy controls (HCs). We first examined how several measures related to turbulent dynamics differ between HCs and TBI patients at 3, 6, and 12 months post-injury. We found a significant reduction in these empirical measures after TBI, with the largest change at 6 months post-injury. Next, we built a Hopf whole-brain model with coupled oscillators and conducted in silico perturbations to investigate the mechanistic principles underlying the reduced turbulent dynamics found in the empirical data. A simulated attack was used to account for the effect of focal lesions. This revealed a shift to lower coupling parameters in the TBI dataset and, critically, decreased susceptibility and information-encoding capability. These findings confirm the potential of the turbulent framework to characterize longitudinal changes in whole-brain dynamics and in the reactivity to external perturbations after TBI.

A semantic segmentation algorithm for automated rapid melt pool identification from cross-sectional micrographs

Melt pool contours are an important empirical process measurement for metallic additive manufacturing. Melt pool boundaries indicate the maximal extent of the melting temperature contour. Melt pool boundaries characterize the effects of process parameters on melt pool size and variability and provide calibration and validation data for process simulations predicting thermal fields and microstructure. Serial cross sectioning of samples can produce numerous micrographs valuable for building robust melt pool contour datasets. However, the acquisition of contours from micrographs by hand is error prone and prohibitively time consuming for datasets large enough to capture variation within individual scan tracks and across a range of process conditions. In this work, we propose a semantic segmentation algorithm for automating melt pool contour acquisition from micrographs. The proposed algorithm is demonstrated on datasets from two different single scan track experiments conducted on a Powder Bed Fusion-Laser Beam platform using a bare Ti-6Al-4 V plate and an Inconel 718 plate with a 40 μm powder layer at different preheat temperatures. The algorithm was successfully trained and predicted the melt pool boundaries in the 300 image Ti-6Al-4 V dataset with 95% accuracy and an estimated reduction of 91% in processing time compared to data acquisition by hand. For the 1201 image Inconel 718 dataset, the algorithm achieved 89% accuracy and an estimated 99% time savings compared to hand measurements. The results for both datasets are demonstrated with a discussion about observed variability in the melt pool characteristics.

Modeling flow and deformation in porous media from pore-scale to the Darcy-scale

In this paper we address the connections between the computational models of coupled flow and mechanical deformation in soils at the Darcy-scale and pore-scale. At the Darcy scale the Biot model requires data including permeability which is traditionally provided by experiments and empirical measurements. At the pore-scale we consider the Discrete Element Method (DEM) to generate physically realistic assemblies of the particles, and we follow up with the Stokes flow model. Next we apply upscaling to obtain the permeabilities which we find dependent on the deformation. We outline the workflow with its challenges and methods, and present results which show, e.g., hysteretic dependence of the permeability and porosity on the load. We also show how to incorporate the deformation dependent permeability in a nonlinear Biot model, and illustrate with computational results.

Cumulative Blast Impulse Is Predictive for Changes in Chronic Neurobehavioral Symptoms Following Low Level Blast Exposure during Military Training.

Cumulative low-level blast exposure during military training may be a significant occupational hazard, increasing the risk of poor long-term outcomes in brain function. US Public Law 116-92 section717 mandates that US Department of Defense agencies document the blast exposure of each Service member to help inform later disability and health care decisions. However, which empirical measures of training blast exposure, such as the number of incidents, peak overpressure, or impulse, best inform changes in the neurobehavioral symptoms reflecting brain health have not been established. This study was approved by the US Army Special Operations Command, the University of North Carolina at Chapel Hill, and the VA Puget Sound Health Care System. Using methods easily deployable across different organizational structures, this study sought to identify and measure candidate risk factors related to career occupational blast exposure predictive of changes in neurobehavioral symptom burden. Blast dosimetry-symptom relationships were first evaluated in mice and then tested in a military training environment. In mice, the righting time neurobehavioral response was measured after exposure to a repetitive low-level blast paradigm modeled after Special Operations training. In the military training environment, 23 trainees enrolled in a 6-week explosive breaching training course, 13 instructors, and 10 Service member controls without blast exposure participated in the study (46 total). All participants provided weekly Neurobehavioral Symptom Inventory (NSI) surveys. Peak blast overpressure, impulse, total number of blasts, Time in Low-Level Blast Occupation, and Time in Service were analyzed by Bayesian analysis of regression modeling to determine their probability of influence on the post-training symptoms reported by participants. We tested the hypothesis that cumulative measures of low-level blast exposure were predictive of changes in neurobehavioral symptoms. In mice, repetitive blast resulted in reduced righting times correlated with cumulative blast impulse. In Service members, peak blast overpressure, impulse, total number of blasts, Time in Low-Level Blast Occupation, and Time in Service all showed strong evidence of influence on NSI scores after blast exposure. However, only models including baseline NSI scores and cumulative blast impulse provided significant predictive value following validation. These results indicate that measures of cumulative blast impulse may have utility in predicting changes in NSI scores. Such paired dosimetry-symptom measures are expected to be an important tool in safely guiding Service members' occupational exposure and optimizing force readiness and lethality.

Abstract 980: Plasma tumor fraction is an independent prognostic marker of response to front line chemotherapy in high-grade serous epithelial ovarian cancer: The MITO-16a/MANGO-OV2a/clinical trial experience

Abstract Background. Although 80% of high grade serous ovarian cancer (HGSOC) patients are initially responsive to platinum (Pt)-based chemotherapy, the majority of them experience relapse with a progressive Pt-resistant disease. One of the most challenging issue that hampers the possibility to effectively treat relapsed disease is the lack of biological information about tumor recurrence. To date, the Pt-free interval (PFI), an empirical measure of the time lagging between the end of front-line chemotherapy and relapse, is the only and widely accepted parameter to predict patient outcome and sensitivity to Pt second-line chemotherapy. We have previously demonstrated that analysis of circulating tumor DNA (ctDNA) by low pass whole genome sequencing (sWGS) is a suitable tool to predict patient outcome and to intercept early signs of relapsed disease (Paracchini et al. PMID:33323403). In the present study, we quantified the percentage of tumor fraction (TF) in plasma of patients enrolled within the frame of a phase III clinical trial (MITO-16A/MaNGO-OV2A-EUDRACT number: NCT01706120) to demonstrate, at baseline, the prognostic value of untargeted ctDNA analysis. Methods. 172 eligible patients enrolled with the frame of the MITO-16A/MaNGO-OV2A clinical trial, for whom plasma sample was collected at chemotherapy before chemotherapy, were selected for the study. For each plasma sample, circulating-free DNA (cfDNA) was purified and whole genome libraries (HyperPlus, Roche) sequenced (mean coverage 0.5X). ichorCN algorithm was used to infer the percentage of TF, as previously published (Paracchini et al. PMID:33323403). Results. In 168 out of 172 plasma samples analyzed, the TF was detectable and it ranged from 2.4% to 47%. Cox-model univariate analysis with patients’ overall survival (OS) and Progression-Free Survival (PFS), indicates association of TF levels with both OS and PFS (p<0.001). The prognostic relevance of TF was also confirmed in multivariate analysis, where, considering CA-125, residual tumor, stage of disease, histology and age as co-variables, TF maintained its statistical significance in PFS (HR = 2.1, CI 95%, p-value < 0.003). Conclusion. Retrospective analysis of baseline plasma samples collected within the frame of the MITO-16A/MaNGO-OV2A clinical trial, demonstrated that the percentage of TF is an independent prognostic marker of relapse. This finding confirms the importance of liquid biopsy analysis based on untargeted sWGS tool as an innovative approach to predict response to front line therapy and patient’s outcome Citation Format: Sergio Marchini, Lara Paracchini, Laura Arenare, Giovanni Scambia, Carmela Pisano, Riccardo Zadro, Laura Mannarino, Annamaria Ferrero, Paolo Chiodini, Daniela Califano, Sandro Pignata, Maurizio D'Incalci. Plasma tumor fraction is an independent prognostic marker of response to front line chemotherapy in high-grade serous epithelial ovarian cancer: The MITO-16a/MANGO-OV2a/clinical trial experience [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 980.

Thermodynamic property estimations for aqueous primary, secondary, and tertiary alkylamines, benzylamines, and their corresponding aminiums across temperature and pressure are validated by measurements from experiments

On Earth and beyond, organic chemistry often occurs in the presence of water within environments that deviate drastically from ambient conditions (25 °C and 1 bar). Accurately predicting aqueous organic reaction pathways is crucial toward understanding planetary scale processes such as the cycling of elements crucial for life (e.g., carbon and nitrogen). Advanced thermodynamic modeling can be utilized to determine the favorability of various organic reactions based on geologically relevant ranges of temperature and pressure, as well as compositional variables (e.g., pH). However, data that would otherwise allow for a diversity of organic compounds and environmental conditions to be modeled are sparse and rarely tested with experiments, particularly with regard to organic-nitrogen compounds. In this work, we develop a framework to estimate thermodynamic properties at ambient conditions that can then be extrapolated across ranges of temperature and pressure for aqueous primary, secondary, and tertiary amines and aminiums (protonated amines), specifically those structures containing linear alkyl chains and benzyl functional groups. We also performed hydrothermal experiments (250 °C, ∼40 bar) involving reactions of methylamines to test our resulting thermodynamic models, and we compare our models for other alkylamines and benzylamines to previous empirical measurements from the literature. Specifically, we use existing thermodynamic data along with our estimates at ambient conditions in combination with a variety of existing extrapolation methods related to the revised Helgeson-Kirkham-Flowers (HKF) equations of state to generate temperature- and pressure-dependent predictions of acid dissociation constants (i.e., pKa values) that strongly agree with previous empirical measurements. We use similar methods to predict product distributions for reactions involving primary, secondary, and tertiary amines/aminiums, as well as ammonia/ammonium and corresponding alcohols whose collective distributions depend on reversible substitution reactions. Our predictions are in good agreement with our experimental results involving the methylamine reaction system as well as previous experiments involving the benzylamine reaction system, for which we also produced thermodynamic estimates involving benzyl alcohol. The agreement between independent theoretical predictions and experimental measurements suggests that our estimated properties can be applied to modeling amine chemistry in other experimental and natural aqueous systems that range in temperature and pressure, providing new tools for planetary exploration.

Pump and sway: Wild primates use compliant supports as a tool to augment leaping in the canopy.

Despite qualitative observations of wild primates pumping branches before leaping across gaps in the canopy, most studies have suggested that support compliance increases the energetic cost of arboreal leaping, thus limiting leaping performance. In this study, we quantified branch pumping behavior and tree swaying in wild primates to test the hypothesis that these behaviors improve leaping performance. We recorded wild colobine monkeys crossing gaps in the canopy and quantitatively tracked the kinematics of both the monkey and the compliant support during behavioral sequences. We also empirically measured the compliance of a sample of locomotor supports in the monkeys' natural habitat, allowing us to quantify the resonant properties of substrates used during leaping. Analyses of three recordings show that adult red colobus monkeys (Piliocolobus tephrosceles) use branch compliance to their advantage by actively pumping branches before leaping, augmenting their vertical velocity at take-off. Quantitative modeling of branch resonance periods, based on empirical measurements of support compliance, suggests that monkeys specifically employed branch pumping on relatively thin branches with protracted periods of oscillation. Finally, an additional four recordings show that both red colobus and black and white colobus monkeys (Colobus guereza) utilize tree swaying to cross large gaps, augmenting horizontal velocity at take-off. This deliberate branch manipulation to produce a mechanical effect for stronger propulsion is consistent with the framework of instrumental problem-solving. To our knowledge, this is the first study of wild primates which quantitatively shows how compliant branches can be used advantageously to augment locomotor performance.

Strategic social value orientation and sustainability performance: A commensuration perspective

Firms increasingly express the strategic importance of creating social value in addition to financial and market value in their communications to investors. Yet, it is unclear what a strategic orientation towards social value creation entails and whether it differentiates firms in terms of their sustainability performance. This article provides a conceptualization of strategic social value orientation (SSVO) consisting of three behavioral components (leading the business with purpose, support of stakeholders, focus on consequences) and one shared belief (mutuality). Utilizing a novel linguistic, content-analytic measure of strategic social value orientation, we analyze annual letters to shareholders and sustainability ratings across 1580 firm-year observations, indicating that firms with a stronger strategic social value orientation show greater sustainability performance. The article explores commensurability of strategic attention towards social value creation, suggesting that future research prioritize more fine-grained assessments of social value, sustainability strategies and performance. Our study makes two main contributions. First, we advance theory at the intersection of sustainability and strategic orientation literatures by introducing a new concept of a firm’s strategic orientation toward social value creation (i.e. SSVO). Second, we develop and validate an empirical measure at the firm level for analyzing a firm’s strategic social value orientation making an important contribution to the commensuration of sustainability. Our findings offer guidance to scholars and implications for practitioners.

Temporal interference stimulation targets deep primate brain

Temporal interference (TI) stimulation, a novel non-invasive stimulation strategy, has recently been shown to modulate neural activity in deep brain regions of living mice. Yet, it is uncertain if this method is applicable to larger brains and whether the electric field produced under traditional safety currents can penetrate deep regions as observed in mice. Despite recent model-based simulation studies offering positive evidence at both macro- and micro-scale levels, the absence of electrophysiological data from actual brains hinders comprehensive understanding and potential application of TI. This study aims to directly measure the spatiotemporal properties of the interfered electric field in the rhesus monkey brain and to validate the effects of TI on the human brain. Two monkeys were involved in the measurement, with implantation of several stereo-electroencephalography (SEEG) depth electrodes. TI stimulation was applied to anesthetized monkeys using two pairs of surface electrodes at differing stimulation parameters. Model-based simulations were also conducted and subsequently compared with actual recordings. Additionally, TI stimulation was administered to patients with motor disorders to validate its effects on motor symptoms.Through the integration of computational electric field simulation with empirical measurements, it was determined that the temporally interfering electric fields in the deep central regions are capable of attaining a magnitude sufficient to induce a subthreshold modulation effect on neural signals. Additionally, an improvement in movement disorders was observed as a result of TI stimulation. This study is the first to systematically measure the TI electric field in living non-human primates, offering empirical evidence that TI holds promise as a more focal and precise method for modulating neural activities in deep regions of a large brain. This advancement paves the way for future applications of TI in treating neuropsychiatric disorders.

Critical dimensions in the empirical measurement of common shareholding

The debate on common shareholding and its potential antitrust effects is currently on the agenda of major institutions worldwide. Discussions point to the need for improved empirical quantification of this phenomena. This work presents a flexible, multifaceted statistical framework for a set of new common shareholding indicators, covering both firm and investor perspectives, which can be adopted under different economic models. Many indices currently used in the literature fall within this framework as special cases. Aggregation at market level yields suitable industry-level indicators, providing policymakers with tools to evaluate the extent of common ownership in strategic markets. The indices are tested using firm-level data for European Mobile Network Operators in 2007–2021, showing a sector with concentrated ownership under large corporate groups, but also the presence of institutional investors with extensive ownership across the major firms.

Is language change chiefly a social diffusion affair? The role of entrenchment in frequency increase and in the emergence of complex structural patterns

Complex systems research has chiefly investigated language change from a social dynamics perspective, with undeniable success. However, there is more to language change than social diffusion, i.e., a one-off adoption of an innovative variant by language users. Language use indeed factors in, besides prevalence (the percentage of adopters of the form in the community), lexical diversity (the number of different lexical items a conventionalized pattern combines with), and entrenchment (the average rate at which speakers choose the form in suitable pragmatic environments). Changes in token frequency may reflect changes in any of these three variables. To sort them out, we defined proxies to factor entrenchment out of empirical measures of prevalence and lexical diversity. From a French corpus, we analyzed 25 schematic constructions, featuring an open slot that hosts a variety of fillers. We show that their rise of token frequency across a change episode is mostly explained by entrenchment; however, the magnitude of the change is uniquely explained by the final extent of its lexical diversity. Furthermore, the fillers obey a construction-specific Zipf-Mandelbrot organization, that robustly holds throughout the change episode. We also show that in some cases, the fillers arise simultaneously, hinting at the possibility that such a complex organization emerges all at once, highlighting the role of structural features in language change.

Enhanced accuracy and high sensitivity in dielectric characterization through a compact and miniaturized metamaterial inspired microwave sensor

This academic paper introduces a novel sensor known as the Planar Microstrip-based Triple Ring Bridge Complementary Split Ring Resonator (TRB-CSRR) sensor. The main objective of this sensor is to determine the permittivity and thickness of solid dielectric substances. The TRB-CSRR sensor is meticulously designed to resonate precisely at a frequency of 4.86 GHz, achieving a significant notch depth of −33.5 dB. This configuration displays an enhanced average relative sensitivity of 20.2%. The study undertakes numerical assessments across various scenarios, encompassing situations where the sensor interacts with diverse dielectric materials. These assessments yield insights into alterations in resonant frequencies. By meticulously refining the design, the sensor's capability to confine electric fields precisely at the resonant frequency is amplified, ultimately resulting in heightened sensitivity towards dielectric characteristics. The sensor's efficacy is tested using materials featuring relative permittivity values spanning from 1.006 to 12.9, while consistently adhering to dimensions of 5 mm×5 mm×1.6 mm. To validate the conceptual frame-work, a physical sensor is fabricated, and its response is gauged through the utilization of a vector network analyzer (VNA-AV3672D). Employing curve fitting methodologies, alterations in resonance frequencies due to interactions with the tested materials are presented, underscoring the impact of permittivity and thickness. The outcomes derived from simulations, empirical measurements, and calculations display a robust alignment.

Territory, intangible heritage and value generation

This article focuses on the empirical measurement of the values generated by an intangible cultural heritage good in the territory that hosts it, understood as a set of positive externalities internalized by the territory and its inhabitants, both permanent and visitors. Three expressions of value generated by intangible heritage are measured and identified: cultural value (emotions and feelings that are positively correlated with individual well–being), social value (possibility of intercultural dialogue between individuals) and economic value (jobs and income in the territory). The article takes as a case study a local, popular and internationally recognized cultural festival: the Petronio Álvarez Pacific Music Festival, held in the city of Cali (Colombia). A mix of instruments is used to measure the values: a face-to-face survey of attendees (n = 1,030) and non-attendees (n = 227), interviews (n = 6) with managers/organizers and an input-output model adapted to the territory defined as the area of impact of the event. These instruments make it possible to estimate accurate measures of material (economic) and non–material (social, cultural) value. These measures can be used in current debates on the contribution of culture to the economy and society of the territories.

Full-Size Experimental Measurement of Combustion and Destruction Efficiency in Upstream Flares and the Implications for Control of Methane Emissions from Oil and Gas Production

Accurately measuring the combustion and destruction removal efficiency of flaring is important when accounting for methane emissions from oil and gas production. Despite this, the amount of experimental data from full-size flares is limited, especially for flares built without air or steam assistance. The use of a single destruction value of 98% is commonly applied. In this paper, we present new empirical measurements of flare efficiency using three common flare designs employed in upstream applications. Combustion products were analyzed using an extractive sampling method. The results demonstrate that whilst destruction efficiencies in excess of 98% are achievable, if the gas composition falls below a critical heating value of ~300 BTU/scf, the efficiency deteriorates leading to elevated methane emissions. This is further complicated by accurately measuring the flow of combustible gas and the impact of crosswinds. In an operational setting, continuous tracking of flare conditions is therefore a key resource in reducing methane emissions but further work is required to standardize how continuous performance tracking is evaluated if such measurements are to attain full traceability.