Dynamic Setting Research Articles

Environmentally Controlled Microfluidic System Enabling Immune Cell Flow and Activation in an Endothelialised Skin-On-Chip.

Integration of reconstructed human skin (RhS) into organ-on-chip (OoC) platforms addresses current limitations imposed by static culturing. This innovation, however, is not without challenges. Microfluidic devices, while powerful, often encounter usability, robustness, and gas bubble issues that hinder large-scale high-throughput setups. This study aims to develop a novel re-usable multi-well microfluidic adaptor (MMA) with the objective to provide a flexible tool for biologists implementing complex 3D biological models (e.g., skin) while enabling simultaneous user control over temperature, medium flow, oxigen (O2), nitrogen (N2), and carbon dioxide (CO2) without the need for an incubator. The presented MMA device is designed to be compatible with standard, commercially available 6-well multi-well plates (6MWPs) and 12-well transwells. This MMA-6MWP setup is employed to generate a skin-on-chip (SoC). RhS viability is maintained under flow for three days and their morphology closely resembles that of native human skin. A proof-of-concept study demonstrates the system's potential in toxicology applications by combining endothelialised RhS with flowing immune cells. This dynamic setting activates the monocyte-like MUTZ-3 cells (CD83 and CD86 upregulation) upon topical exposure of RhS to a sensitizer, revealing the MMA-6MWP's unique capabilities compared to static culturing, where such activation is absent.

Time for memorable consumption

A consumption event is memorable if the memory of the event affects well-being at times after the material consumption, as originally introduced by Gilboa et al. (2016). Our main contribution is to develop an axiomatic foundation of memorable consumption in a dynamic setting. Preferences are represented by the present value of the sum of utilities derived at each date from the current consumption and from recollecting the past. Our model accommodates well-known phenomena in psychology, such as the peak-end rule, duration neglect, and adaptation trends. We also provide foundations for a prominent special case of the representation with the Markovian property. The model is illustrated with applications in two different contexts: risk-taking behavior in a principal-agent problem and life-cycle consumption-savings decisions.

Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application

The mining industry is experiencing a transformative shift with the integration of automation, particularly through autonomous haul truck systems, and further advancements are anticipated with the application of swarm robotics. This study evaluates the performance of four swarm robot models, namely baseline, ant, firefly, and honeybee, in optimizing key mining operations such as ore detection, extraction, and transportation. Simulations replicating real-world mining environments were conducted to assess improvements in operational efficiency, scalability, reliability, selectivity, and energy consumption. The results demonstrate that these models can significantly enhance the precision and productivity of mining activities, especially in complex and dynamic settings. A case study of the Pilbara iron ore mine in Australia is presented to illustrate the practical applicability of these models in an actual mining context. The study also highlights specific enhancements in each model, including role specialization in the ant model, advanced communication in the firefly model, and improved localization combined with hybrid control in the honeybee model. While the honeybee model showed superior performance in high-precision tasks, its reliability was limited under high-error conditions, and it faced a computational resources bottleneck in large-scale operations, highlighting the need for further development. By evaluating these models against performance criteria, the study identifies the most suitable swarm models for various mining conditions, offering insights into achieving more sustainable, scalable, and efficient mining operations.

Photo-tunable hydrogels reveal cellular sensing of rapid rigidity changes through the accumulation of mechanical signaling molecules.

Cells use traction forces to sense mechanical cues in their environment. While the molecular clutch model effectively explains how cells exert more forces on stiffer substrates, it falls short in addressing their adaptation to dynamic mechanical fluctuations prevalent in tissues and organs. Here, using hydrogel with photo-responsive rigidity, we show that cells' response to rigidity changes is frequency dependent. Strikingly, at certain frequencies, cellular traction forces exceed those on static substrates 4-fold stiffer, challenging the established molecular clutch model. We discover that the discrepancy between the rapid adaptation of traction forces and the slower deactivation of mechanotransduction signaling proteins results in their accumulation, thereby enhancing long-term cellular traction in dynamic settings. Consequently, we propose a new model that melds immediate mechanosensing with extended mechanical signaling. Our study underscores the significance of dynamic rigidity in the development of synthetic biomaterials, emphasizing the importance of considering both immediate and prolonged cellular responses.

Characterisation of Fatty Acid Profiles in Tasmanian Atlantic Salmon Muscle, Oocytes and Eggs in Differing Fully Commercial Settings

ABSTRACTTasmanian Atlantic salmon broodstock may be conditioned in flow‐through (FT) systems where water quality fluctuates and temperatures approach their upper limit of thermal tolerance, or in recirculating aquaculture systems (RAS) where conditions are comparatively cool and stable. The impact of broad conditioning approach on the molecular cargo of salmon eggs has never been explored in dynamic commercial settings. Therefore, this study aimed to characterise the reproductive features of broodstock conditioned using different commercial approaches and determine whether the fatty acid (FA) profiles of muscle, oocytes and eggs varied between groups during vitellogenesis and at stripping. The collective conditioning approach had a marked impact on the somatic and reproductive dynamics of female salmon broodstock, and the eggs produced by each group of fish were fundamentally different in terms of their FA composition. Of particular note is the increased maternal investment of saturated FAs (mg.g−1) and monounsaturated FAs (percent and mg.g−1) by FT‐based eggs, and the higher n3 polyunsaturated FA (percent and mg.g−1), docosahexaenoic acid (DHA, percent and mg.g−1), arachidonic acid (ARA, percent), eicosapentaenoic acid (EPA, percent), n3:n6 ratio and ARA:EPA ratio of RAS‐based eggs. These metrics combined with the apparently higher prevalence of neural development and survival of RAS embryos suggest that RAS eggs were of higher quality. On the other hand, there is evidence to suggest that FT broodstock attempt to prepare their offspring for a (presumably) challenging early life environment by producing larger eggs that are proportionally rich in short‐chain FAs and contain a higher total quantity of FAs per egg.

Assessing The Impact Of Procrastination On Workplace Efficiency And Employee Performance

Background: Procrastination with self-regulation, motivation, and emotional control as its underlying causes is a psychological issue, in addition to being a mere time management problem (Metin et al., 2016). Procrastination at work takes many forms, like employees extending deadlines, postponing their assignments, or instead of their primary responsibilities, focussing on unimportant activities. Procrastination is a serious issue that can hinder organisational success aside from being a personal struggle for employees. Organisations can boost outcomes for performance, promote a more dynamic and productive work setting, and improve employee well-being by employing targeted strategies and interventions to address procrastination. This study's main goal is to assess procrastination and how it affects efficiency at work and the performance of an employee at work, with an emphasis on finding the critical elements that influence this habit and its effects in organisational contexts. Material and Method: This research is based on secondary data. The qualitative research is done using articles which are published in academic journals of repute. The care is taken to refer only latest articles unless the article is seminal or of great importance. Results: The results indicate that procrastination significantly hampers work performance and engagement, driven by psychological factors like anxiety, environmental distractions, and organizational shortcomings such as unclear goals. Addressing these factors through engagement strategies, clear objectives, and economic incentives can reduce procrastination, leading to improved productivity and a more motivated workforce. Conclusion: Procrastination negatively impacts performance, work engagement, and productivity. Addressing it requires understanding psychological, environmental, and organizational factors. Enhancing work engagement and clear goals, reducing distractions, and providing economic incentives can mitigate procrastination. By focusing on these aspects, organizations can boost productivity and foster a more efficient, motivated workforce.

The impact of a prehospital simulation on medical students' resourcefulness, personal growth initiative, and uncertainty tolerance.

Emergency medicine (EM) physicians often practice in dynamic, high-stress, and uncertain settings with limited resources. Although simulation has been shown to enhance various aspects of student development, its impact on medical students' personal growth initiative, resourcefulness, and tolerance of uncertainty-crucial traits for managing future crises as emergency physicians-remain unclear. The purpose of this study, therefore, was to determine a high-fidelity prehospital simulation's impact on medical students' resourcefulness, personal growth, and tolerance of uncertainty. We surveyed 107 fourth-year medical students before and after a multiday, high-fidelity prehospital simulation. The survey included items from the Intolerance of Uncertainty Scale-12 Item Form, the Personal Growth Initiative Scale, and the Resourcefulness Skills Scale. We compared students' pre- and post-simulation responses to investigate any change in their uncertainty intolerance, personal growth initiative, and resourcefulness following simulation participation. Students' scores significantly increased following the simulation for both resourcefulness (t(106) = -6.89, p < 0.001, d = -0.67) and personal growth initiative (t(106) = -6.22, p < 0.001, d = -0.60). Effect size calculations suggest that participating in the simulation had a medium to large effect on participants' resourcefulness and personal growth initiative. However, participants' tolerance of uncertainty scores prior to and following the simulation did not significantly differ (t(106) = 1.66, p = 0.100, d = 0.16), indicating that the simulation had little effect on participants' tolerance of uncertainty. Our results indicate that simulation is a promising educational tool for developing students' resourcefulness and personal growth initiative so they can navigate high-stress, low-resource environments. Follow-on research is needed to determine how to leverage simulation to enhance students' uncertainty tolerance in high-stress, low-resource environments.

Coastal Chronicles: Journey into the World of Coastal Systems

Coastal systems are complex and dynamic settings where the atmosphere, sea, and land combine to create a variety of landscapes, including coral reefs, beaches, and estuaries. These areas contribute to global sustainability initiatives delineated in the United Nations Sustainable Development Goals (SDGs), sustain biodiversity, and play crucial ecological and economic roles. For sustainable management and protection of coastal environments, it is imperative to possess an understanding of the physical attributes, biological constituents, and mechanisms that dictate their dynamics. The natural processes of tides, waves, and currents impact the geomorphology of coastal systems, resulting in the formation of features including dunes, beaches, estuaries, and deltas. Coastal vegetation that stabilizes sediments and supports a variety of habitats, like seagrasses, mangroves, and salt marshes, further enriches these landscapes. Fish, crabs, mollusks, and birds are among the coastal fauna that flourish in these habitats and add to the diversity of coastal life. However, human activities that accelerate natural processes like erosion, accretion, and sea-level riseuch as industrialization, pollution, and climate change pose an increasing threat to coastal systems. Thus, it is essential to use sustainable management techniques that strike a balance between development objectives and conservation initiatives. Coastal ecosystems must be protected and enhanced through the implementation of policies like Integrated Coastal Zone Management (ICZM), habitat restoration, and Marine Protected Areas (MPAs). A combination of mitigation and adaptation strategies, such as building coastal fortifications and promoting nature-based solutions, are also necessary for climate change adaption. We can ensure the resilience and vitality of coastal systems for present and future generations, as well as their sustained ecological and economic significance in the global environment, by tackling these intricate concerns cooperatively and comprehensively.

The Painful Double-Knock on Food Prices: 2008 Financial Crises and COVID-19 Pandemic

Food supply and demand chains are susceptible to global shocks. Unstable and sudden food price hikes cause serious malnutrition problems and increase the number of food-insecure people, especially in developing countries. Using the FAO Food Price Index (FFPI), this study makes one of the first attempts to utilize monthly observations of the FFPI in dynamic time series ARDL and ARX settings to identify the effects of food prices on COVID-19 infection rates and the 2008 global financial crisis. Our empirical findings confirm that the financial crisis significantly increased the FFPI, although its effects decreased as markets equilibrated between 2007 and 2009. The pandemic has had a mild impact on food prices in the short run compared to the 2008 crisis, but in the long run, the COVID-19 outbreak has a larger impact, with 1 million new COVID-19 infections associated with an increase of between 0.0464 and 0.0509 points in the FFPI. Food price volatility and hikes, even if short-term, increase poverty, malnutrition, and food insecurity, foster social unrest, and lower people’s living standards. This research implies that food prices are globally sensitive to both pandemics and financial crises, and the severity of the pandemic can drive global food prices higher, depending on the number of infections. Over the long run, the impact of the outbreak surpasses that of the financial crisis. The latter tends to have a major impact on food prices in the short run but subsequently declines as markets begin to equilibrate, reflecting asymmetries between the two phenomena in their effects on food prices. Overall, the results indicate that the financial crisis and the COVID-19 pandemic had a short-run, immediate, augmenting impact on food prices. However, while the 2008 crisis affected the supply side only, COVID-19 had impacts on both the demand and supply sides.

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP

BackgroundDerivative profiling is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations.ResultsThe results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines, significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the results. This comparison is repeated for transcriptomic and phosphoproteomic expression profiles previously characterized in Aspergillus nidulans. This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP). Gene Ontology (GO) term enrichment has been included in the app so that a user can automatically/programmatically describe the over/under-represented GO terms in the derivative profiling results using domain specific knowledge found in their organism’s specific GO database file. The advantage of the DPoP analysis is that it is computationally inexpensive, it does not require fold change calculations, it describes both instantaneous as well as overall behavior, and it achieves statistical confidence with signal trajectories of a single bio-replicate over four or more points.ConclusionsWhile we apply this method to time dynamic transcriptomic and phosphoproteomic datasets, it is a numerically generalizable technique that can be applied to any organism and any field interested in time series data analysis. The app described in this work enables omics researchers with no computer science background to apply derivative profiling to their data sets, while also allowing multidisciplined users to build on the nascent idea of profiling derivatives in omics.

Surfacing political clarity in farm and garden education

Farms and gardens are increasingly being recognized as dynamic educational settings through which learners can engage in a wide variety of activities and practices to connect with land, food, self, and community. Across these settings, teaching with political clarity represents a pathway for identifying, interrogating, and disrupting systems of oppression as they manifest in farm and garden education, as well as in land relations more broadly. Drawing on a series of interviews with farm and garden educators in California who identify as Black, Indigenous, and/or people of color, this study identifies key principles for sociopolitically conscious garden-based education by examining the ways in which study participants demonstrate political clarity with respect to their pedagogy. Findings center on educators’ work to build connections from learners’ cultural knowledge and practices, attend to intergenerational land-based traumas, take critical and holistic perspectives on health and well-being, engage land with intentionality and reverence, and flatten hierarchies and support collective decision-making. These pedagogical commitments offer an opportunity to more substantively attune the work of farm and garden education toward the goals of cultivating social and environmental justice.

Accelerating large-scale DEA computation using sequential categorization and dynamic reference set selection

Data envelopment analysis (DEA) is a well-known data-enabled analytic tool for evaluating relative efficiency of units with multiple inputs and multiple outputs. The DEA computation increases substantially in the presence of large samples. In this study, we first recall two lemmas to distinguish efficient units using arithmetic operations without solving linear programming (LP). Using the used lemmas, the total sample of units is partitioned into several sequential blocks, where units in the preceding blocks are relatively efficient to those in the subsequent blocks. A novel reference set selection procedure is then formulated. We implement the proposed approach into one of the fastest existing methods and demonstrate a significant improvement in elapsed time. We conduct simulation experiments and illustrate the outcomes across varying dimensions, cardinality, and density.

User-Centered Design and Usability of Voxe as a Pediatric Electronic Patient-Reported Outcome Measure Platform: Mixed Methods Evaluation Study.

Electronic patient-reported outcome measures (ePROMs) are standardized digital instruments integrated into clinical care to collect subjective data regarding patients' health-related quality of life, functional status, and symptoms. In documenting patient-reported progress, ePROMs can guide treatment decisions and encourage measurement-based care practices. Voxe is a pediatric and user-centered ePROM platform for patients with chronic health conditions. We aimed to describe the user-centered design approach involving feedback from end users and usability testing of Voxe's platform features to support implementation in a pediatric health care setting. Purposive sampling was used to recruit patients aged 8-17 years from 2 chronic illness populations in 2 pediatric hospitals in Canada. Patients' health care team members were also purposively recruited. One-on-one iterative testing sessions were conducted digitally by research team members with participants to obtain feedback on the appearance and functionalities of the Voxe platform prototype. Patients and health care providers (HCPs) completed Voxe-related task-based activities. International Organization for Standardization key performance indicators were tracked during HCP task-based activities. HCPs also completed the System Usability Scale. To test platform usability, the think-aloud technique was used by participants during the completion of structured tasks. After completing all task-based activities, patient participants selected 5 words from the Microsoft Desirability Toolkit to describe their overall impression and experience with the Voxe platform. Qualitative data about likes, dislikes, and ease of use were collected through semistructured interviews. Feedback testing sessions were conducted with patients and HCPs until Voxe was acceptable to participating end users, with no further refinements identified. Quantitative and qualitative data analysis were completed using descriptive statistics and content analysis. A total of 49 patients and 38 HCPs were recruited. Patients were positive about Voxe's child-centered design characteristics and notification settings. HCPs rated Voxe as user-friendly and efficient, with the time to complete tasks decreasing over time. HCPs were satisfied with the Voxe platform functionalities and identified the value of Voxe's system notifications, summarized display of ePROM results, and its capacity to integrate with electronic medical records. Patients' and HCPs' high satisfaction rates with the Voxe prototype highlight the importance of being responsive to user suggestions from the inception of eHealth platform developments to ensure their efficient and effective design. This paper describes the user-centered creation and usability testing of Voxe as an ePROM platform for implementation into clinical care for pediatric patients with chronic health conditions. As a patient-facing platform that can be integrated into electronic medical records, Voxe aligns with measurement-based care practices to foster quality patient-centered approaches to care. End users' positive feedback and evaluation of the platform's user-friendliness and efficiency suggest that Voxe represents a valuable and promising solution to systematically integrate patient-related outcome (PRO) data into complex and dynamic clinical health care settings. RR2-10.1136/bmjopen-2021-053119.

Impact of emotional intelligence on SME employees’ performance: Mediating role of organizational citizenship behavior

This study delves into the intricate correlation between emotional intelligence and employee performance within the dynamic setting of Saudi small and medium enterprises. Acknowledging the increasing importance of emotional intelligence in the workplace, this study examines how emotional intelligence influences employee performance, with particular attention to the mediating role of organizational citizenship behavior. Employing a comprehensive research framework, the study integrates quantitative methods to analyze data collected from 248 employees across various Saudi-based small and medium enterprises. The findings indicate that high emotional intelligence enhances employee performance, with organizational citizenship behavior serving as a crucial mediator, strengthening the positive impact of emotional intelligence on performance. The results demonstrated a robust positive correlation with organizational citizenship behavior (β = 0.719***) and a moderate positive correlation with employee performance (β = 0.314***). In addition, organizational citizenship behavior played a significant positive mediating role between employee intelligence and employee performance (β = 0.397***). This suggests a nuanced interplay among emotional intelligence, organizational citizenship behavior, and overall employee performance within the specific context of Saudi small and medium enterprises. Acknowledgment The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA, for funding this research work through project number NBU-FFR- 1502-03.

Backcalculation of elastic moduli of elements of layered halfspace on the basis of dynamic deformation analysis (by the example of highways)

The paper is devoted to the improvement of the method of inverse calculation of elasticity moduli of layers of motorway pavements in the dynamic setting, which involves the analysis of deformation characteristics in the time domain. To solve this problem, the mathematical model of a layered half-space is adapted to the calculation of amplitude-time characteristics of deformation on the surface of the layered medium, and the construction of the corresponding bowls of maximum values of vertical displacements. Adjustment of the calculated values of vertical displacements with respect to the registered experimental displacements in the field was performed. The correspondence between the final values of maximum vertical displacements, amplitude and time characteristics on the surface of the layered medium, and the shapes and areas of dynamic hysteresis loops on the surface of the investigated medium, achieved by adjusting the calculated characteristics relative to the experimental ones, has been demonstrated. For the first time in solving the problem of determining the mechanical parameters of a layered medium, it was proposed to consider dynamic hysteresis loops as one of the parameters characterising them and the correspondence of their calculated and experimental areas as a criterion of the adequacy of the achieved result.

Enhancing valuation of variable annuities in Lévy models with stochastic interest rate

This paper extends the valuation and optimal surrender framework for variable annuities with guaranteed minimum benefits in a Lévy equity market environment by incorporating a stochastic interest rate described by the Hull-White model. This approach frames a more dynamic and realistic financial setting compared to previous literature. We exploit a robust valuation mechanism employing a hybrid numerical method that merges tree methods for interest rate modeling with finite difference techniques for the underlying asset price. This method is particularly effective for addressing the complexities of variable annuities, where periodic fees and mortality risks are significant factors. Our findings reveal the influence of stochastic interest rates on the strategic decision-making process concerning the surrender of these financial instruments. Through comprehensive numerical experiments, and by comparing our results with those obtained through the Longstaff-Schwartz Monte Carlo method, we illustrate how our refined model can guide insurers in designing contracts that equitably balance the interests of both parties. This is particularly relevant in discouraging premature surrenders while adapting to the realistic fluctuations of financial markets. Lastly, a comparative statics analysis with varying interest rate parameters underscores the impact of interest rates on the cost of the optimal surrender strategy, emphasizing the importance of accurately modeling stochastic interest rates.

Understanding the drivers of innovative work behaviour among millennial employees in India’s IT sector: some exploratory research findings

Purpose The study aims to develop a hierarchical model for innovative work behaviour (IWB) that can capture the complex associations among the factors contributing to IWB within the information technology (IT) sector. To accomplish this, the authors rely on an abductive approach using a graph theoretic model, often called interpretive structural modelling (ISM). Design/methodology/approach After conducting an in-depth literature review and using the Delphi method, the authors identified 12 factors (11 enablers and IWB as an outcome). The authors collected data through the Delphi approach by sending the questionnaire to 11 experts from academia and the IT sector who have extensive experience and knowledge relevant to the study. The authors then used the ISM method to analyse the relationships among these factors and understand their driving forces. Findings Based on the ISM model and the Matrice d'Impacts Croisés Multiplication Appliquée à un Classement analysis, the authors have identified that inclusive leadership, proactive personality and knowledge creation ability are the variables with strong driving power but weak dependence. Conversely, IWB has strong dependence but weak driving power. These findings suggest that to foster IWB, the organisation should prioritise inclusive leadership, proactive personality and knowledge-creation ability to succeed in challenging times. The study’s findings contribute to the social exchange theory, which explains IWB in a dynamic setting. Additionally, the study helps address the significant concerns that most IT companies face during times of crisis. Practical implications The study provides valuable guidance for managers and policymakers who are grappling with the challenges of improving IWB in the IT sector. This study is particularly relevant as the industry is currently navigating an economic recession and facing intense competition from other tech companies launching new products and services. Originality/value This research holds great significance for top executives, line managers and policymakers in the IT industry. It sheds light on the relevance and importance of various factors facilitating millennials' IWB.

The Impact of Technology in Warehousing

The warehouse business has undergone a revolution with the integration of advanced technology, resulting in the transformation of traditional storage facilities into highly efficient and dynamic settings. The article delves into the ways in which warehousing operations have been transformed by a number of technological advancements, including automation, robotics, WMS, the IoT, AI, cloud computing, and AR. With increased efficiency, precision, and cost-effectiveness, these technologies have made it possible to manage inventories more effectively, fulfill orders more quickly, and make better decisions. These technology developments are expected to have an even greater impact on how supply chain management and logistics are developed in the future as the warehousing sector develops.

Shotgun DNA sequencing for human identification: Dynamic SNP selection and likelihood ratio calculations accounting for errors

Shotgun sequencing is a DNA analysis method that potentially determines the nucleotide sequence of every DNA fragment in a sample, unlike PCR-based genotyping methods that is widely used in forensic genetics and targets predefined short tandem repeats (STRs) or predefined single nucleotide polymorphisms (SNPs). Shotgun DNA sequencing is particularly useful for highly degraded low-quality DNA samples, such as ancient samples or those from crime scenes. Here, we developed a statistical model for human identification using shotgun sequencing data and developed formulas for calculating the evidential weight as a likelihood ratio (LR). The model uses a dynamic set of binary SNP loci and takes the error rate from shotgun sequencing into consideration in a probabilistic manner. To our knowledge, the method is the first to make this possible. Results from replicated shotgun sequencing of buccal swabs (high-quality samples) and hair samples (low-quality samples) were arranged in a genotype-call confusion matrix to estimate the calling error probability by maximum likelihood and Bayesian inference. Different genotype quality filters may be applied to account for genotyping errors. An error probability of zero resulted in the commonly used LR formula for the weight of evidence. Error probabilities above zero reduced the LR contribution of matching genotypes and increased the LR in the case of a mismatch between the genotypes of the trace and the person of interest. In the latter scenario, the LR increased from zero (occurring when the error probability was zero) to low positive values, which allow for the possibility that the mismatch may be due to genotyping errors. We developed an open-source R package, wgsLR, which implements the method, including estimation of the calling error probability and calculation of LR values. The R package includes all formulas used in this paper and the functionalities to generate the formulas.

Heat capacity and quantum compressibility of dynamical spacetimes with thermal particle creation

This work continues the investigation in two recent papers on the quantum thermodynamics of spacetimes, (1) placing what was studied in [] for thermal quantum fields in the context of early universe cosmology, and (2) extending the considerations of vacuum compressibility of dynamical spaces treated in [] to dynamical spacetimes with thermal quantum fields. We begin with a warning that thermal equilibrium condition is not guaranteed to exist or maintained in a dynamical setting and thus finite temperature quantum field theory in cosmological spacetimes needs more careful considerations than what is often described in textbooks. A full description requires nonequilibrium quantum field theory in dynamical spacetimes using “in-in” techniques. A more manageable subclass of dynamics is where thermal equilibrium conditions are established at both the beginning and the end of evolution, where the in-thermal state and the out-thermal state are both well defined. Particle creation in the full history can then be calculated in this in-out asymptotically-stationary setup via the S-matrix transition amplitudes. Here we shall assume an in-vacuum state. It has been shown that if the intervening dynamics has an initial period of exponential expansion, such as in inflationary cosmology, particles created from the parametric amplification of the vacuum fluctuations in the initial vacuum will have a thermal spectrum measured at the out-state. Under these conditions finite temperature field theory can be applied to calculate the quantum thermodynamic quantities. Here we consider a massive conformal scalar field in a closed four-dimensional Friedmann-Lemaître-Robertson-Walker universe based on the simple analytically solvable Bernard-Duncan model. We calculate the energy density of particles created from an in-vacuum and derive the partition function. From the free energy we then derive the heat capacity and the quantum compressibility of these spacetimes with thermal particle creation. We end with some discussions and suggestions for further work in this program of studies. Published by the American Physical Society 2024