Static Version Research Articles

A choice-based approach to dynamic capacitated multi-item lot sizing with demand uncertainty

With the purpose of planning and implementing pricing decisions on a tactical level as well as production decisions on an operational level, we consider – in an integrated form – the capacitated multi-item lot sizing problem with uncertain item demands and price-dependent discrete choice demand. The model is embedded into an overarching rolling horizon procedure allowing for adaptations to changes in demand and cost parameters. We first formulate the static problem version as a nonlinear mathematical program with underlying multinomial logit demand and subsequently linearize it to make it viable for mathematical programming solvers. Uncertainty of demands is taken into account by Monte Carlo simulation. More specifically, we generate random demand scenarios and utilize them as input data for the sample average approximation problem version. We further endow the problem setting with possibilities to incorporate pricing policy requirements such as restricting the number of price adaptations or defining periods without price adaptations. Overall, the developed approach yields a powerful tool for balancing item demands via pricing in a way favorable for adhering to available production capacities and thereby striking a balance between revenues and costs. Computational results confirm that adapting prices to time-dependent demand and cost parameters is exploited effectively to maintain a deliberately controlled production environment. Moreover, the integrated pricing and production setting allows to study the effect of pricing policy restrictions and demand uncertainties upon attainable profits.

Facial animacy in anthropomorphised designs: Insights from leveraging self-report and facial expression analysis for multimedia learning

Anthropomorphism is the act of attributing human-like characteristics to non-human objects and has played a key role in the field of emotional design in multimedia learning. Despite its significance, the relationship between animacy and anthropomorphism, particularly facial animacy, remains underexplored albeit its potential impact on learning engagement and emotional responses. Hence, this study aims to address this gap by examining the effects of facial animacy in anthropomorphised designs using a 3 × 2 design (none vs. static vs. animated) based on self-reported measure (SRM) and facial emotion recognition (FER) and how both measures are associated. The findings revealed discrepancies between both measures, with mostly moderate to weak correlations between hypothesised associations. SRM results indicated that face animacy decreased perceived boredom, while static and dynamic anthropomorphised designs increased curiosity. The FER results revealed notable similarities between designs without anthropomorphism and the static versions, highlighting that facial animacy led participants to express more joy and less neutral expressions. Additionally, neutral expressions were associated with lower enjoyment perception, while negative emotions, especially boredom, were linked to reduced attention.

AutoPeptideML: a study on how to build more trustworthy peptide bioactivity predictors.

Automated machine learning (AutoML) solutions can bridge the gap between new computational advances and their real-world applications by enabling experimental scientists to build their own custom models. We examine different steps in the development life-cycle of peptide bioactivity binary predictors and identify key steps where automation cannot only result in a more accessible method, but also more robust and interpretable evaluation leading to more trustworthy models. We present a new automated method for drawing negative peptides that achieves better balance between specificity and generalization than current alternatives. We study the effect of homology-based partitioning for generating the training and testing data subsets and demonstrate that model performance is overestimated when no such homology correction is used, which indicates that prior studies may have overestimated their performance when applied to new peptide sequences. We also conduct a systematic analysis of different protein language models as peptide representation methods and find that they can serve as better descriptors than a naive alternative, but that there is no significant difference across models with different sizes or algorithms. Finally, we demonstrate that an ensemble of optimized traditional machine learning algorithms can compete with more complex neural network models, while being more computationally efficient. We integrate these findings into AutoPeptideML, an easy-to-use AutoML tool to allow researchers without a computational background to build new predictive models for peptide bioactivity in a matter of minutes. Source code, documentation, and data are available at https://github.com/IBM/AutoPeptideML and a dedicated web-server at http://peptide.ucd.ie/AutoPeptideML. A static version of the software to ensure the reproduction of the results is available at https://zenodo.org/records/13363975.

Estimating the cost of young stock mortality in livestock systems-An application to sheep farming in Ethiopia.

In sub-Saharan Africa, pre-weaning young stock mortality (YSM) is in the order of 20 to 30% across most livestock species and production systems. High YSM has significant economic implications for livestock keepers, but few studies provide estimates of the "cost of YSM." This study explores a bio-economic herd modeling approach to estimate the "cost of YSM" at farming/livestock system level. The static zero-growth version of DYNMOD was used to calculate the annual physical and monetary output of a sheep flock consisting of 100 breeding females at different levels of lamb mortality. Production parameter values and prices were taken from recently published research. Calculations were carried out for values of lamb mortality decreasing from 30% to 0% in 5% intervals, with 20% representing the "baseline" YSM. Calculations were repeated for a "high" fertility scenario (100% vs. 59% parturition rate) to gauge the sensitivity of the cost of YSM to another parameter determining flock productivity. The relation of revenue per head and YSM is close to linear over the range of analyzed YSM with 1% decrease in YSM resulting in an increase in revenue per animal of approximately 1%. At the higher fertility rate, the absolute cost of YSM to sheep farmers is higher while the relative increase in revenue per animal resulting from YSM reduction is lower. The estimated difference in revenue of the 100-ewe flock between the 20% and 0% lamb mortality scenarios (at baseline fertility) amounts to approximately USD 90 per additionally surviving lamb, which is far above its market value. Reduced lamb mortality ultimately impacts flock revenue through increased sales of "mature" animals, which embody the value of a lamb plus the revenue/profit from raising it to marketable age/weight. The modeling results suggest that foregone profit is an important component of the systemic "cost of YSM." Consequently, expected profit per animal, in addition to its current market value, is essential for estimating the absolute cost of YSM at farming system level.

Sensorimotor Simulation's Influence on Stress: EEG and Autonomic Responses in Digital Interviews.

This study explored the role of sensorimotor simulation in modulating the stress response in individuals exposed to stressful digital simulated interviews. Participants were assigned to two different versions of a Digital Social Stress Test: a simulated version with a dynamic-realistic examining committee (Dyn-DSST) and a version with a static examining committee (Stat-DSST). During interview preparation, behavioral indices reflecting stress regulation and resistance, response times, and electroencephalographic (EEG) and autonomic indices were collected. Higher regulation scores were found for the Stat-DSST group compared to the Dyn-DSST group, probably induced by the presence of limited external sensory input in time and space, perceived as less stressful. The EEG results revealed a distinct contribution of the low- and high-frequency bands for both groups. Dyn-DSST required greater cognitive regulation effort due to the presence of a continuous flow of information, which can enhance sensory and motor activation in the brain. The SCR increased in the Dyn-DSST group compared to the Stat-DSST group, reflecting greater emotional involvement in the Dyn-DSST group and reduced sensory stimulation in the static version. In conclusion, the results suggest that sensorimotor simulation impacts the stress response differently in dynamic interviews compared to static ones, with distinct profiles based on behavioral, EEG, and autonomic measures.

The Impact of Asset Tangibility on the Speed of Adjusting Cash Holdings in the US non-financial companies

This study investigates the impact of a firm's asset tangibility on the speed of adjusting cash holdings. The Results show that the speed of adjusting cash holdings is 0.46, indicating that on average firms close around half of the deviation from target in one year. This value of CH-SOA provides evidence that US firms have a target (optimal) level of cash towards which they try to adjust their actual cash level. Moreover, such a result supports the dynamic version of the trade-off theory rather than the static version since the value of CH-SOA is less than one. Furthermore, the main result of this research shows that firms with lower asset tangibility adjust faster to the target level of cash.

Toward a Representation of Semantic Change in Linked Data

In this article, we introduce a new framework, the Intensional–Ontological Model (IOM), for representing meaning, and especially for representing semantic change, in linguistic linked data resources. This framework, which makes use of previous work in the literature on lexical semantics and ontologies, is intended to help clarify what we mean when we model semantic change and to assist in elaborating different ontology patterns for doing so. In this work, we assume a simple architecture, one which is at the basis of the well-known OntoLex-Lemon vocabulary and which consists of one or more lexicons linked to an ontology. Our model, which is based on this architecture and informed by previous work on word senses and ontologies, is intended to provide a clear interpretation for the modelling of both onomasiological and semiasological changes, in both static and dynamic versions. This article describes how the IOM framework represents word meaning as the relationship between a word and an ontological concepts in the ’static’ case, demonstrating that the IOM is compatible with OntoLex-Lemon (while at the same time providing a greater level of detail as to the meaning of the ’sense’ and ’reference’ relationships). It then goes on to detail how the IOM can help us understand how to model semantic shifts in linked data lexical resources with a focus on conceptual change and the addition of temporal information to semantic shift data.

Mitigation of Effect Modification by Psychological Status in Patients With Hearing Loss

Although patient-reported outcomes provide valuable insights, these subjective data may not align with objective test results. Hearing loss is a pervasive problem, such that concordance between subjective perceptions of hearing ability and objective audiogram assessments would be beneficial. To determine (1) whether psychological status is an effect modifier of the association between subjective patient reports of hearing ability and objective audiometry results, and (2) whether any effect modification observed in standard static questionnaires would be either mitigated or exacerbated by adaptive testing based on Item Response Theory analyses. This diagnostic study at a tertiary care center and community-based practice included consecutive adults who presented with queries related to hearing loss. Participants were recruited and enrolled and data analyses occurred from 2022 to 2024. Participants prospectively reported their hearing-specific abilities through either a standard static or adaptive version of the Inner Effectiveness of Auditory Rehabilitation (EAR) scale, alongside validated measures of their mental health and audiometry. Word recognition scores (WRS) and pure tone averages (PTA) were used to analyze audiometric testing. The association between subjective Inner EAR results and audiometry was evaluated. Stratified analyses were used to assess for effect modification by psychological status. The results of standard static and adaptive testing were compared. In this study of 395 patients (mean [range] age, 55.9 [18-89] years; 210 [53.2%] female), standard static Inner EAR mean scores were appropriately higher in patients with higher (better) WRS (50.7, 95% CI, 46.4-54.9), compared with patients with lower (worse) WRS (34.7, 95% CI, 24.3-45.1). However, among patients with worse mental health, there was no association between standard static Inner EAR scores and WRS. In contrast, adaptive Inner EAR mean scores were significantly higher for those with better WRS, regardless of mental health status. Thus, effect modification was observed in standard static assessments, whereas adaptive testing remained durably associated with audiometry, regardless of mental health. Psychological status was an effect modifier of the association between standard Inner EAR scale scores and audiometry, with a positive association observed only in those with better mental health. Adaptive testing scores, however, remained significantly associated with audiometry, even when mental status was worse. Adaptive testing may stabilize the association between subjective and objective hearing outcomes.

A stochastic programming approach to the antibiotics time machine problem

Antibiotics Time Machine is an important problem to understand antibiotic resistance and how it can be reversed. Mathematically, it can be modeled as follows: Consider a set of genotypes, each of which contain a set of mutated and unmutated genes. Suppose that a set of growth rate measurements of each genotype under a set of antibiotics is given. The transition probabilities of a ‘realization’ of a Markov chain associated with each arc under each antibiotic are computable via a predefined function given the growth rate realizations. The aim is to maximize the expected probability of reaching to the genotype with all unmutated genes given the initial genotype in a predetermined number of transitions, considering the following two sources of uncertainties: (i) the randomness in growth rates, (ii) the randomness in transition probabilities, which are functions of growth rates. We develop stochastic mixed-integer linear programming and dynamic programming approaches to solve static and dynamic versions of the Antibiotics Time Machine Problem under the aforementioned uncertainties. We adapt a Sample Average Approximation approach that exploits the special structure of the problem and provide accurate solutions that perform very well in an out-of-sample analysis.

Is it possible to make ‘living’ guidelines? An evaluation of the Australian Living Stroke Guidelines

BackgroundKeeping best practice guidelines up-to-date with rapidly emerging research evidence is challenging. ‘Living guidelines’ approaches enable continual incorporation of new research, assisting healthcare professionals to apply the latest evidence to their clinical practice. However, information about how living guidelines are developed, maintained and applied is limited. The Stroke Foundation in Australia was one of the first organisations to apply living guideline development methods for their Living Stroke Guidelines (LSGs), presenting a unique opportunity to evaluate the process and impact of this novel approach.MethodsA mixed-methods study was conducted to understand the experience of LSGs developers and end-users. We used thematic analysis of one-on-one semi-structured interview and online survey data to determine the feasibility, acceptability, and facilitators and barriers of the LSGs. Website analytics data were also reviewed to understand usage.ResultsOverall, the living guidelines approach was both feasible and acceptable to developers and users. Facilitators to use included collaboration with multidisciplinary clinicians and stroke survivors or carers. Increased workload for developers, workload unpredictability, and limited information sharing, and interoperability of technological platforms were identified as barriers. Users indicated increased trust in the LSGs (69%), likelihood of following the LSGs (66%), and frequency of access (58%), compared with previous static versions. Web analytics data showed individual access by 16,517 users in 2016 rising to 53,154 users in 2020, a threefold increase. There was also a fourfold increase in unique LSG pageviews from 2016 to 2020.ConclusionsThis study, the first evaluation of living guidelines, demonstrates that this approach to stroke guideline development is feasible and acceptable, that these approaches may add value to developers and users, and may increase guideline use. Future evaluations should be embedded along with guideline implementation to capture data prospectively.

Метод прогнозирования оттока клиентов банка на основе ансамблевой модели машинного обучения

The results of research are presented, the purpose of which was to develop a method for predicting the outflow of clients of a commercial bank based on the use of machine learning models (including deep artificial neural networks) for processing client data, as well as the creation of software tools that implement this method. The object of the study is a commercial bank, and the subject of the study is its activities in the B2C segment, which includes commercial interaction between businesses and individuals. The relevance of the chosen area of research is determined by the increased activity of banks in the field of introducing digital services to reduce non-operating costs associated, in particular, with retaining clients, since the costs of attracting new ones are much higher than maintaining existing clients. The scientific novelty of the research results is the developed method for predicting the outflow of commercial bank clients, as well as the algorithm underlying the software that implements the proposed method. The proposed ensemble forecasting model is based on three classification algorithms: k-means, random forest and multilayer perceptron. To aggregate the outputs of individual models, it is proposed to use a learning tree of fuzzy inference systems of the Mamdani type. Training of the ensemble model is carried out in two stages: first, the listed three classifiers are trained, and then, based on the data obtained from their outputs, a tree of fuzzy inference systems is trained. The ensemble model in the proposed method implements a static version of the forecast, the results of which are used in a dynamic forecast performed in two versions – based on the recurrent least squares method and based on a convolutional neural network. Model experiments carried out on a synthetic dataset taken from the Kaggle website showed that the ensemble model has a higher quality of binary classification than each model individually.

Predictive and incremental validity of the Static-99, Static-99R, and STABLE-2007 for sexual recidivism: A diagnostic test accuracy network meta-analysis (DTA-NMA).

The Static-99, Static-99R, and STABLE-2007 are internationally well-established instruments for predicting static and dynamic risks of sexual recidivism in individuals convicted of sexual offenses. Previous meta-analyses assessed their predictive and incremental validity, but none has yet compared the two Static versions and the Static-STABLE combinations. Here, we implemented diagnostic test accuracy network meta-analysis (DTA-NMA) to compare all tests and identify optimal cutoffs in one comprehensive analysis. The DTA-NMA included 32 samples comprising 45,224 adult male individuals. More information was available on the Static-99 (22 samples; 34,316 individuals) and the Static-99R (13 samples; 27,243 individuals), compared to the Static-99/STABLE-2007 (three samples; 762 individuals), the Static-99R/STABLE-2007 (two samples; 2,972 individuals), and the STABLE-2007 (three samples; 816 individuals). The primary outcome was the area under the receiver operating characteristic curve (AUC). The secondary outcomes were sensitivity and specificity. Optimal cutoffs were determined using the Youden index. The AUC suggested moderate predictive validity for Static-99 and Static-99R, whereas STABLE-2007 had no predictive value. The optimal cutoff of Static-99R was suggested to have higher specificity than that of Static-99, whereas sensitivity was comparable between instruments. The notion of incremental validity for STABLE-2007 could not be confirmed. This work represents the first meta-analysis to compare Static-99, Static-99R, STABLE-2007, and their combinations in one analysis. Static-99R demonstrated the highest specificity in predicting the risk of sexual recidivism, indicating a potential advantage in detecting true nonrecidivists. The findings are discussed, considering the current recommendations for assessing the risk of sexual recidivism in the criminal justice system. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Global fine-resolution data on springtail abundance and community structure

Springtails (Collembola) inhabit soils from the Arctic to the Antarctic and comprise an estimated ~32% of all terrestrial arthropods on Earth. Here, we present a global, spatially-explicit database on springtail communities that includes 249,912 occurrences from 44,999 samples and 2,990 sites. These data are mainly raw sample-level records at the species level collected predominantly from private archives of the authors that were quality-controlled and taxonomically-standardised. Despite covering all continents, most of the sample-level data come from the European continent (82.5% of all samples) and represent four habitats: woodlands (57.4%), grasslands (14.0%), agrosystems (13.7%) and scrublands (9.0%). We included sampling by soil layers, and across seasons and years, representing temporal and spatial within-site variation in springtail communities. We also provided data use and sharing guidelines and R code to facilitate the use of the database by other researchers. This data paper describes a static version of the database at the publication date, but the database will be further expanded to include underrepresented regions and linked with trait data.

Infographic summaries for clinical practice guidelines: results from user testing of the BMJ Rapid Recommendations in primary care

ObjectivesInfographics have the potential to enhance knowledge translation and implementation of clinical practice guidelines at the point of care. They can provide a synoptic view of recommendations, their rationale and...

The Generalized Thurstonian Unfolding Model (GTUM): Advancing the Modeling of Forced-Choice Data

Forced-choice (FC) measurement has become increasingly popular due to its robustness to various response biases and reduced susceptibility to faking. Although several current Item Response Theory (IRT) models can extract normative person scores from FC responses, each has its limitations. This study proposes the Generalized Thurstonian Unfolding Model (GTUM) as a more flexible IRT model for FC measures to overcome these limitations. The GTUM (1) adheres to the unfolding response process, (2) accommodates FC scales of any block size, and (3) manages both dichotomous and graded responses. Monte Carlo simulation studies consistently demonstrated that the GTUM exhibited good statistical properties under most realistic conditions. Particularly noteworthy findings include (1) the GTUM's ability to handle FC scales with or without intermediate statements, (2) the consistently superior performance of graded responses over dichotomous responses in person score recovery, and (3) the sufficiency of 10 mixed pairs to ensure robust psychometric performance. Two empirical examples, one with 1,033 responses to a static version of the Tailored Adaptive Personality Assessment System and the other with 759 responses to a graded version of the Forced-Choice Five-Factor Markers, demonstrated the feasibility of the GTUM to handle different types of FC scales. To aid in the practical use of the GTUM, we also developed the R package “ fcscoring.”

A mathematical model and metaheuristic approach to solve the real-time feeder vehicle routing problem

Unlike classic vehicle routing problems, real-time vehicle routing problems (RTVRPs) are real-world dynamic vehicle routing problems (DVRPs) in which customer requests are identified over the time horizon of operations without previous knowledge. In this problem, received requests are answered as quickly as possible and then dispatched to a service vehicle. Considering the RTVRP’s timing constraints, a solution must provide the best trade-off between the computation time required to find the solution and the proposed solution’s costs. A feeder vehicle routing problem (FVRP) consists of a heterogeneous fleet of vehicles, including trucks and motorcycles. In this problem, motorcycles pass easily in crowded areas, and the traffic of urban logistics is distributed easily. The feeder approach also lowers the number of times the vehicle returns to the central depot for loading, resulting in cost and time savings. The present article proposes a real-time feeder vehicle routing problem (RTFVRP) in a situation where every truck and every motorcycle can join during the freight delivery process. After modeling the RTFVRP through mixed-integer linear programming, a dynamic inertia weight particle swarm optimization (DIWPSO) algorithm is offered to solve the problem. The results of the static version of the FVRP in the small and the last time slices show that the GAMS outputs outperform those of the DIWPSO and differential evolution (DE), both indicating a similar performance. The results of running these algorithms for 21 DVRP test instances revealed that the PSO outperforms the DE in quality and solution runtime in both static and dynamic versions. Overall, the PSO and DE algorithms generated 34.33% and 30.37% cost savings in implementing RTFVRP, respectively, compared to the case that all requests were available at the beginning of the working day.

Static and dynamic orderings on Dungean argumentation frameworks - An overview

The article is devoted to the issue of comparing argumentation frameworks (AFs) which has received increasing attention in recent years. Knowing whether a given framework F possesses less information than an AF G or whether the latter is at least reachable from F in a certain future scenario is an important issue for several applications. The question what we precisely mean by information will depend on the particular context in which they are being used. Our analysis does not express any preferences here. However, if AFs are applied to fields like criminal justice, finance, and healthcare, where it is necessary to exercise caution, skeptical accepted arguments may be the most appropriate form of reasoning. The presented overview considers classical Dung semantics including the stable, admissible, complete, grounded and preferred one. The article is divided into two main axes: the distinction between extension-based and kernel-based orderings and the analysis of their static and dynamic versions. The results show that there are various relationships between these concepts, indicating that some orderings are stronger or weaker than others. The considered orderings are so-called preorders, i.e. they satisfy reflexivity and transitivity. We analyzed them w.r.t. bounds, meets and joins and also considered the induced equivalences. The latter often coincide with well-known equivalences, namely ordinary and strong equivalence.

Studying the Visual Representation of Microgestures

The representations of microgestures are essentials for researchers presenting their results through academic papers and system designers proposing tutorials to novice users. However, those representations remain disparate and inconsistent. As a first attempt to investigate how to best graphically represent microgestures, we created 21 designs, each depicting static and dynamic versions of 4 commonly used microgestures (tap, swipe, flex and hold). We first studied these designs in a quantitative online experiment with 45 participants. We then conducted a qualitative laboratory experiment in Augmented Reality with 16 participants. Based on the results, we provide design guidelines on which elements of a microgesture should be represented and how. In particular, it is recommended to represent the actuator and the trajectory of a microgesture. Also, although preferred by users, dynamic representations are not considered better than their static counterparts for depicting a microgesture and do not necessarily result in a better user recognition.

Surprising Decrease in the Martian He Bulge During PEDE‐2018 and Changes in Upper Atmospheric Circulation

AbstractUsing the Neutral Gas and Ion Mass Spectrometer (NGIMS) on the Mars Atmosphere Volatile and Evolution spacecraft (MAVEN), we analyzed data from Mars Year (MY) 32, 34, and 35 to examine the He bulge during the northern winter solstice (Ls ∼ 180–240), specifically focusing on the effects from the planet encircling dust event (PEDE‐2018). He collects on the dawn/nightside winter polar hemisphere of Mars. The seasonal migration of the He bulge has been observed and modeled (M. Elrod et al., 2017, https://doi.org/10.1002/2016JA023482; Gupta et al., 2021, https://doi.org/10.1029/2021JE006976). The MAVEN orbit precesses around Mars allowing for a variety of latitude and local time observations throughout the Martian year. MY 32, 34, and 35 had the best possible opportunities to observe the He bulge during northern winter (Ls ∼ 180–240). NGIMS observations during MY 32 and MY 35 revealed a He bulge from the nightside to dawn in alignment with modeling and previous publications. However, in MY 34, during the PEDE, the He bulge was not present, indicating that the PEDE directly impacted upper atmospheric circulation. Updates in modeling indicate changes in circulation and winds can cause He to shift further north than MAVEN was able to observe. While adding a simple static version of gravity waves to the Mars Global Ionosphere Thermosphere Model model may account for some of the variations in the global circulation during the dust event, other studies (e.g., Yiğit, 2023, https://doi.org/10.1038/s41561-022-01118-7) have posited that the gravity waves during the dust storm were more variable than the initial parameters we have included.

Mechanics of cell-cell junctions

Tissue cells in epithelial or endothelial monolayers are connected through cell-cell junctions, which are stabilized by transmembrane E-cadherin bonds and intracellular actin filaments. These bonds and junctions play a crucial role in maintaining the barrier function of epithelia and endothelia and are believed to transmit forces between cells. Additionally, E-cadherin bonds can impact the shape of cell-cell junctions. In this study, we develop a continuum mechanical model of the cell-cell junction by explicitly incorporating the cell membrane, distributions of E-cadherin bonds, cytoplasmic fluid pressure, and F-actin dynamics. The static force-balanced version of the model is able to analyze the influences of cell cortical tension, actin dynamics, and cytoplasmic pressure on the junction shape and E-cadherin bonds. Furthermore, an extended model that incorporates fluid flow, across the cell boundary as well as around the cell, is also examined. This model can couple cell-shape changes with cell cortical tension and fluid flow, and predicts the additional effect of fluid motion on cell-cell junction mechanics. Taken together, our models serve as an intermediate link between molecular-scale models of cell-junction molecules and cell-scale models of tissue and epithelia.