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Wheeler (Cladistics 2023, 39, 475) recently suggested that the issues with inapplicable characters in phylogenetic analysis can be dealt with directly by treating observed absences of a feature not in a separate absence/presence character but as insertion/deletion events in a complex character that describes the feature in all its variation; and that this dynamic homology view can be achieved by imposing a sequence or linear order on a set of characters and by analysing the resulting sequence character using custom alphabet tree alignment algorithms. As Wheeler observed, this approach can lead to considering inappropriate character states (such as a head state and a foot state) homologous. We show that it is also sensitive to the specific ordering assumption used and that such different character orders can lead to a preference for different trees. We present a simple four-taxon dataset with observations of absence, but no inapplicable characters or other kinds of character dependence, for which the dynamic homology framework gives different results to classic algorithms for independent characters, including an optimal tree with biologically impossible reconstructions at inner nodes (every terminal has a head but the inner nodes are headless). We show how these issues can be solved by removing the character ordering assumption that the approach requires. Doing so, the dynamic homology framework reduces in general to Maddison's (Syst. Biol. 1993, 42, 576) well-known proposal to deal with inapplicability using step matrix analysis of complex characters. If in addition costs are interpreted in terms of homology, it reduces to Goloboff et al.'s (Cladistics 2021, 37, 596) step matrix implementation for maximization of homology as applied to inapplicable characters. However, if used with homogeneous costs, as Wheeler suggested, it reduces to unordered analysis of such complex characters, which is known to treat tails that may share many observed features as irrelevant for establishing kinship when they differ in just one feature, e.g. colour., (© 2024 Willi Hennig Society.)

Objectives: The use of secondary databases has become popular for evaluating the effectiveness and safety of interventions in real-life settings. However, the absence of important confounders in these databases is challenging. To address this issue, the high-dimensional propensity score (hdPS) algorithm was developed in 2009. This algorithm uses proxy variables for mitigating confounding by combining information available across several healthcare dimensions. This study assessed the methodology and reporting of the hdPS in comparative effectiveness and safety research., Study Design and Setting: In this methodological review, we searched PubMed and Google Scholar from July 2009 to May 2022 for studies that used the hdPS for evaluating the effectiveness or safety of healthcare interventions. Two reviewers independently extracted study characteristics and assessed how the hdPS was applied and reported. Risk of bias was evaluated with the Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) tool., Results: In total, 136 studies met the inclusion criteria; the median publication year was 2018 (Q1-Q3 2016-2020). The studies included 192 datasets, mostly North American databases (n = 132, 69%). The hdPS was used in primary analysis in 120 studies (88%). Dimensions were defined in 101 studies (74%), with a median of 5 (Q1-Q3 4-6) dimensions included. A median of 500 (Q1-Q3 200-500) empirically identified covariates were selected. Regarding hdPS reporting, only 11 studies (8%) reported all recommended items. Most studies (n = 81, 60%) had a moderate overall risk of bias., Conclusion: There is room for improvement in the reporting of hdPS studies, especially regarding the transparency of methodological choices that underpin the construction of the hdPS., Competing Interests: Declaration of competing interest G.L.M. reports a relationship with Synapse Medicine that includes employment. C.P. reports a relationship with Parexel International that includes employment. L.L. reports a relationship with Synapse Medicine that includes board membership and employment. There are no competing interests for any other author., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Aim: To demonstrate the potential of fourth-order polynomials within a non-linear optimization framework for matching-adjusted indirect comparison (MAIC). Materials & methods: Simulated individual patient data were reweighted via fourth-order polynomials (polyMAIC) to match aggregate-level data across multiple baseline characteristics. The polyMAIC approach employed pre-specified matching tolerances and maximum allowable weights. Matching performance against aggregate-level targets was assessed, and also compared against the current industry-standard MAIC approach (Signorovitch). Results: The polyMAIC method matched aggregate-level targets within pre-specified tolerances. Effective sample sizes were either similar to or somewhat higher than those obtained from the Signorovitch method. Performance gains from polyMAIC tended to increase as matching complexity increased. Conclusion: PolyMAIC incorporates greater flexibility than the industry-standard MAIC approach and demonstrates matching potential.

Purpose: The authors developed and validated an algorithm using health administrative data to identify patients who are attached or uncertainly attached to a primary care provider (PCP) using patient responses to a survey conducted in Ontario, Canada., Design/methodology/approach: The authors conducted a validation study using as a reference standard respondents to a community-based survey who indicated they did or did not have a PCP. The authors developed and tested health administrative algorithms against this reference standard. The authors calculated the sensitivity, specificity positive predictive value (PPV) and negative predictive value (NPV) on the final patient attachment algorithm. The authors then applied the attachment algorithm to the 2017 Ontario population., Findings: The patient attachment algorithm had an excellent sensitivity (90.5%) and PPV (96.8%), though modest specificity (46.1%) and a low NPV (21.3%). This means that the algorithm assigned survey respondents as being attached to a PCP and when in fact they said they had a PCP, yet a significant proportion of those found to be uncertainly attached had indicated they did have a PCP. In 2017, most people in Ontario, Canada (85.4%) were attached to a PCP but 14.6% were uncertainly attached., Research Limitations/implications: Administrative data for nurse practitioner's encounters and other interprofessional care providers are not currently available. The authors also cannot separately identify primary care visits conducted in walk in clinics using our health administrative data. Finally, the definition of hospital-based healthcare use did not include outpatient specialty care., Practical Implications: Uncertain attachment to a primary health care provider is a recurrent problem that results in inequitable access in health services delivery. Providing annual reports on uncertainly attached patients can help evaluate primary care system changes developed to improve access. This algorithm can be used by health care planners and policy makers to examine the geographic variability and time trends of the uncertainly attached population to inform the development of programs to improve primary care access., Social Implications: As primary care is an essential component of a person's medical home, identifying regions or high need populations that have higher levels of uncertainly attached patients will help target programs to support their primary care access and needs. Furthermore, this approach will be useful in future research to determine the health impacts of uncertain attachment to primary care, especially in view of a growing body of the literature highlighting the importance of primary care continuity., Originality/value: This patient attachment algorithm is the first to use existing health administrative data validated with responses from a patient survey. Using patient surveys alone to assess attachment levels is expensive and time consuming to complete. They can also be subject to poor response rates and recall bias. Utilizing existing health administrative data provides more accurate, timely estimates of patient attachment for everyone in the population., (© Liisa Jaakkimainen, Imaan Bayoumi, Richard H. Glazier, Kamila Premji, Tara Kiran, Shahriar Khan, Eliot Frymire and Michael E. Green.)

The disposition index, calculated by multiplying measures of insulin secretion and insulin sensitivity, is widely applied as a sensitivity-adjusted measure of insulin secretion. We have recently shown that linearizing the underlying relationship uniquely permits identification of terms relating to maximal insulin secretion capacity and the secretion-coupling relationship, with both terms separately contributing to differences in the secretion-sensitivity relationship across gradations of glycemia. Here, we demonstrate the application of this linearized equation to the evaluation of treatment-induced changes in the insulin secretion-sensitivity relationship. We applied a combination of repeated-measures multivariate linear regression (evaluating treatment-induced changes in the joint relationship of insulin sensitivity and secretion) plus mixed-model repeated measures (evaluating treatment effects on maximal secretion capacity and on the secretion-sensitivity coupling slope) and compared against a usual application of the disposition index calculated from the same measurements. This novel approach allows a more informative description of treatment-induced changes compared with the usual disposition index, including isolating the source of change within the mutually adjusted relationship and identifying treatment-induced changes in the secretion-sensitivity coupling slope and in maximal insulin secretion. Application of this linearized approach provides an expanded understanding of treatment-induced changes in the insulin sensitivity-secretion relationship. NEW & NOTEWORTHY The linearized insulin secretion-sensitivity relationship allows separate evaluation of the secretion-sensitivity slope and of maximal insulin secretion. Here, we demonstrate the application of this methodology to the evaluation of clinical trial data, showing that it provides an expanded understanding of treatment-induced changes compared with the disposition index.

Introduction: The accuracy of blood pressure (BP) measurement is a prerequisite for the reliable diagnosis and management of hypertension., Objectives: This survey evaluated the use of office and out-of-office BP measurements and the antihypertensive pharmacological treatment in expert pediatric diabetes centers., Methods: A questionnaire was distributed in 78 reference pediatric diabetes centers of the SWEET international consortium. The methodology, devices, indications, and interpretation of office BP measurements (OBPM), 24-hour ambulatory BP monitoring (ABPM) and home BP monitoring (HBPM), and the preference for antihypertensive drug treatment was assessed. A grading score was developed to evaluate centers for overall BP measurement performance., Results: Fifty-two centers responded. The average score for OBPM methodology was 72.5%, for technology 77.5% and the overall center score was 74.75%.The majority of the centers used appropriate methodology and technology, however, there was heterogeneity among them. Manual auscultatory or automated devices specifically validated for children were used by 26/52 centers. ABPM was recommended by 35/52 centers (27/35 had health insurance coverage) and HBPM by 18/52 centers. The BP measurement methodology and devices used for ABPM and HBPM were frequently inadequate. Angiotensin converting enzyme inhibitors were the most frequently prescribed drugs for treating hypertension., Conclusions: The majority of SWEET pediatric diabetes centers use adequate methodology and devices for BP measurement. ABPM is recommended by two thirds of the centers, whereas HBPM is less widely used. Further improvement in the quality of office and out-of-office BP measurements and harmonization among centers is necessary according to current guidelines., (© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objectives: Treatment-resistant schizophrenia (TRS) is clinically defined as failure to respond to two antipsychotics of adequate dose and duration. An algorithm (registry TRS) was developed, for identifying patients with TRS in claim datasets from Sweden and the United States., Methods: Schizophrenia (SZ) patients aged ≥13 years were identified in both datasets and matched to controls. Patients were identified as having TRS by use of the registry TRS or ≥1 prescription for clozapine or use of other published criteria. The algorithm was compared for sensitivity, and patients with and without TRS were compared for psychiatric and hospital burden and Global Assessment of Functioning (GAF) scores. TRS prevalence was not assessed due to lack of clinically validated data to test the specificity of the algorithm., Results: Swedish registry TRS patients ≤45 years at first SZ diagnosis had significantly lower GAF scores and earlier disease onset than non-TRS patients. SZ patients with higher psychiatric comorbidity and hospital burden were more likely identified as TRS by all algorithms. The registry algorithm was significantly more sensitive to multiple inpatient stays and all psychiatric comorbidities at identifying TRS., Conclusion: The registry algorithm appeared more sensitive at identifying patients with TRS, who had greater psychiatric and hospital burden., (© 2019 John Wiley & Sons, Ltd.)

The present study aimed at identifying a suitable multiscale entropy (MSE) algorithm for assessment of complexity in a stride-to-stride time interval time series. Five different algorithms were included (the original MSE, refine composite multiscale entropy (RCMSE), multiscale fuzzy entropy, generalized multiscale entropy and intrinsic mode entropy) and applied to twenty iterations of white noise, pink noise, or a sine wave with added white noise. Based on their ability to differentiate the level of complexity in the three different generated signal types, and their sensitivity and parameter consistency, MSE and RCMSE were deemed most appropriate. These two algorithms were applied to stride-to-stride time interval time series recorded from fourteen healthy subjects during one hour of overground and treadmill walking. In general, acceptable sensitivity and good parameter consistency were observed for both algorithms; however, they were not able to differentiate the complexity of the stride-to-stride time interval time series between the two walking conditions. Thus, the present study recommends the use of either MSE or RCMSE for quantification of complexity in stride-to-stride time interval time series., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Nowadays, the use of eyetracking to determine 2-D gaze positions is common practice, and several approaches to the detection of 2-D fixations exist, but ready-to-use algorithms to determine eye movements in three dimensions are still missing. Here we present a dispersion-based algorithm with an ellipsoidal bounding volume that estimates 3D fixations. Therefore, 3D gaze points are obtained using a vector-based approach and are further processed with our algorithm. To evaluate the accuracy of our method, we performed experimental studies with real and virtual stimuli. We obtained good congruence between stimulus position and both the 3D gaze points and the 3D fixation locations within the tested range of 200-600 mm. The mean deviation of the 3D fixations from the stimulus positions was 17 mm for the real as well as for the virtual stimuli, with larger variances at increasing stimulus distances. The described algorithms are implemented in two dynamic linked libraries (Gaze3D.dll and Fixation3D.dll), and we provide a graphical user interface (Gaze3DFixGUI.exe) that is designed for importing 2-D binocular eyetracking data and calculating both 3D gaze points and 3D fixations using the libraries. The Gaze3DFix toolkit, including both libraries and the graphical user interface, is available as open-source software at https://github.com/applied-cognition-research/Gaze3DFix .

Ischaemic postconditioning (IPostC) was introduced for the first time by Zhao et al. as a feasible method for reduction of myocardial ischaemia-reperfusion (IR) injury. The cardioprotection by this protocol has been extensively evaluated in various species. Then, further research revealed that IPostC is a safe and convenient approach in limiting IR injury of non-myocardial tissues such as lung, liver, kidney, intestine, skeletal muscle, brain and spinal cord. IPostC has been conducted with different algorithms, resulting in diverse effects. The possible important factors leading to these differences are the difference in activation levels of signalling pathways and protective mediators by any algorithm, presence or absence of IPostC effectors in each tissue, or intrinsic characteristics of the tissues as well as the methodological biases. Also, the conflicting results have been shown with the application of the same algorithm of IPostC in certain tissues or animal species. The effectiveness of IPostC may depend upon various parameters including the species and the tissues characteristics. For example, different heart rates and metabolic rates of the species and unequal amounts of perfusion and blood flow of the tissues should be considered as the important determinants of IPostC effectiveness and should be thought about in designing IPostC algorithms for future studies. Due to these discrepancies, there is still no optimal single IPostC algorithm applicable to any tissue or any species. This issue is the main topic of the present article., (© 2017 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.)

Quantitative susceptibility mapping (QSM) is fast becoming a routine clinical tool in the evaluation and assessment of neurological diseases. Unfortunately, there is currently no established standard scanning protocol, and it is uncertain whether different acquisition strategies alter the derived estimates of magnetic susceptibility. Here, we compare some key deep grey matter susceptibility values in healthy adults acquired from various QSM sequences using either unipolar or bipolar readout gradients, accelerated imaging or not, and gradient-warp correction or not. Four healthy adult volunteers were scanned three times each within 4 days at 3 T. The eight different QSM combinations were acquired in different randomised order for each session, and then co-registered to an anatomical atlas. The average and standard deviations of magnetic susceptibilities in the caudate, putamen, red nucleus, internal and external globus pallidus were used in a linear mixed effects model to determine the influence of the various acquisition parameters. Gradient-warp correction was the only statistically significant fixed effect (p < 0.01), but its impact was small (~5% change) compared with the overall fixed effects. The random effects coefficients (i.e. the various tissues) were statistically significant. Based on our limited multiple observations in healthy adult volunteers, the susceptibilities in deep grey matter are statistically equivalent when QSM source data are acquired with or without accelerated imaging using either unipolar or bipolar readout gradients. There is, however, a statistically meaningful, but small, difference if gradient-warp correction is used or not. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

As accelerometers are commonly used for 24-h measurements of daily activity, methods for separating waking from sleeping time are necessary for correct estimations of total daily activity levels accumulated during the waking period. Therefore, an algorithm to determine wake and bed times in 24-h accelerometry data was developed and the agreement of this algorithm with self-report was examined. One hundred seventy-seven participants (aged 40-75 years) of The Maastricht Study who completed a diary and who wore the activPAL3™ 24 h/day, on average 6 consecutive days were included. Intraclass correlation coefficient (ICC) was calculated and the Bland-Altman method was used to examine associations between the self-reported and algorithm-calculated waking hours. Mean self-reported waking hours was 15.8 h/day, which was significantly correlated with the algorithm-calculated waking hours (15.8 h/day, ICC = 0.79, P = < 0.001). The Bland-Altman plot indicated good agreement in waking hours as the mean difference was 0.02 h (95% limits of agreement (LoA) = -1.1 to 1.2 h). The median of the absolute difference was 15.6 min (Q1-Q3 = 7.6-33.2 min), and 71% of absolute differences was less than 30 min. The newly developed automated algorithm to determine wake and bed times was highly associated with self-reported times, and can therefore be used to identify waking time in 24-h accelerometry data in large-scale epidemiological studies.

Objectives: One recommended use of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach is supporting quality assessment of evidence of comparisons included within a Cochrane overview of reviews. Within our overview, reviewers found that current GRADE guidance was insufficient to make reliable and consistent judgments. To support our ratings, we developed an algorithm to grade quality of evidence using concrete rules., Methods: Using a pragmatic, exploratory approach, we explored the challenges of applying GRADE levels of evidence and developed an algorithm to applying GRADE levels of evidence in a consistent and transparent approach. Our methods involved application of algorithms and formulas to samples of reviews, expert panel discussion, and iterative refinement and revision., Results: The developed algorithm incorporated four key criteria: number of participants, risk of bias of trials, heterogeneity, and methodological quality of the review. A formula for applying GRADE level of evidence from the number of downgrades assigned by the algorithm was agreed., Conclusion: Our algorithm which assigns GRADE levels of evidence using a set of concrete rules was successfully applied within our Cochrane overview. We propose that this methodological approach has implications for assessment of quality of evidence within future evidence syntheses., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Copyright of Cuadernos del Centro de Estudios de Diseño y Comunicación is the property of Cuadernos del Centro de Estudios de Diseno y Comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In response to the COVID‐19 pandemic shut‐down of in‐person research laboratories, remote data collection became mainstream in developmental psychology research. The aim of this study was to test the feasibility of two remote data collection methods for studying sleep and motor development in infancy and the relationship between the two. We asked 1371 parents of infants aged 4–17 months who used the Nanit baby monitor to take the online Survey of Well‐being of Young Children (SWYC) about their infants' motor experience. Nanit uses auto‐videosomnography and computer vision algorithms to calculate and report summary statistics of infants' sleep. Nanit replicated all expected developmental trends in sleep, while the SWYC only replicated some, appearing to underestimate infants at the transition between motor milestones. However, we replicated some well‐established findings regarding the relationship between sleep and motor development using both measures, suggesting that both Nanit and the SWYC can feasibly be used for remote data collection, especially if some modifications were made to how researchers explained using the SWYC to caregivers. Researchers should consider alternatives to traditional research methods not only when in‐person participation is difficult or impossible, but also when large samples are needed and/or simple measurement tools are sufficient. [ABSTRACT FROM AUTHOR]

The λ-exponential family has recently been proposed to generalize the exponential family. While the exponential family is well-understood and widely used, this is not the case yet for the λ-exponential family. However, many applications require models that are more general than the exponential family, and the λ-exponential family is often a good alternative. In this work, we propose a theoretical and algorithmic framework to solve variational inference and maximum likelihood estimation problems over the λ-exponential family. We give new sufficient optimality conditions for variational inference problems. Our conditions take the form of generalized moment-matching conditions and generalize existing similar results for the exponential family. We exhibit novel characterizations of the solutions of maximum likelihood estimation problems, that recover optimality conditions in the case of the exponential family. For the resolution of both problems, we propose novel proximal-like algorithms that exploit the geometry underlying the λ-exponential family. These new theoretical and methodological insights are tested on numerical examples, showcasing their usefulness and interest, especially on heavy-tailed target distributions. [ABSTRACT FROM AUTHOR]

Scholars worldwide have shown considerable interest in the industrial sector, mainly due to its abundant resources, which have facilitated the adoption of conveyor belt technologies like Robotic Arm-Based Conveyor Belts (RACBs). RACBs rely on four primary movements: (i.e., joint, motor, gear, and sensor), which can have a significant impact on the overall motions and motion estimation. To optimize these operations, an assistive algorithm has been developed to enhance the effectiveness of motion by achieving favorable gains. However, each motion requires specific criteria for Fractional Order Proportional Integral Derivative (FOPID) controller gains, leading to various challenges. These challenges include the existence of multiple evaluation and selection criteria, the significance of these criteria for each motion, the trade-off between criterion performance for each motion, and determining critical values for the criteria. As a result, the evaluation and selection of the Proposed Jaya optimization algorithm for RACB motion control becomes a complex problem. To address these challenges, this study proposes a novel integrated approach for selecting the Jaya optimization algorithm in different RACB motions. The approach incorporates two evaluation methods: the Nonlinear Autoregressive Moving Average with exogenous inputs (NARMA-L2) controller for Neural Network (NN) weighting of the criteria, and the Adaptive Neuro-Fuzzy Inference System (ANFIS) for selecting the Jaya optimization algorithm. The approach consists of three main phases: RACB-based NARMA-L2 Controller Identification and Pre-processing, Development of NARMA-L2 controller-based NARMA(L2)-FO(ANFIS)PD-I, and Evaluation of FOPID criteria based on JOA. The proposed approach is evaluated based on NARMA(L2)- FO(ANFIS)PD-I that given 0.4074, 0.3156, 0.3724, 0.1898 and 0.2135 for K_p_joint, K_i_motor, K_d_sensor, λ_gear, and µ_N respectively, which verifies the soundness of the proposed methodology. [ABSTRACT FROM AUTHOR]

In order to improve the performance of UAV’s autonomous maneuvering decision-making, this paper proposes a decision-making method based on situational continuity. The algorithm in this paper designs a situation evaluation function with strong guidance, then trains the Long Short-Term Memory (LSTM) under the framework of Deep Q Network (DQN) for air combat maneuvering decision-making. Considering the continuity between adjacent situations, the method takes multiple consecutive situations as one input of the neural network. To reflect the difference between adjacent situations, the method takes the difference of situation evaluation value as the reward of reinforcement learning. In different scenarios, the algorithm proposed in this paper is compared with the algorithm based on the Fully Neural Network (FNN) and the algorithm based on statistical principles respectively. The results show that, compared with the FNN algorithm, the algorithm proposed in this paper is more accurate and forwardlooking. Compared with the algorithm based on the statistical principles, the decision-making of the algorithm proposed in this paper is more efficient and its real-time performance is better. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study the difference gap function (for brevity, DG-function) and upper error bounds for an abstract class of variational-hemivariational inequalities with history-dependent operators (for brevity, HDVHIs). First, we propose a new concept of gap functions to the HDVHIs and consider the regularized gap function (for brevity, RG-function) for the HDVHIs using the optimality condition for the concerning minimization problem. Then, the DG-function for the HDVHIs depending on these RG-functions is constructed. Finally, we establish upper error bounds for the HDVHIs controlled by the RG-function and the DG-function under suitable conditions. [ABSTRACT FROM AUTHOR]

The superiorization methodology can be thought of as lying conceptually between feasibility-seeking and constrained minimization. It is not trying to solve the full-fledged constrained minimization problem composed from the modeling constraints and the chosen objective function. Rather, the task is to find a feasible point which is "superior" (in a well-defined manner) with respect to the objective function, to one returned by a feasibility-seeking only algorithm. We telegraphically review the superiorization methodology and where it stands today and propose a rigorous formulation of its, yet only partially resolved, guarantee problem. The real-world situation in an application field is commonly represented by constraints defined by the modeling process and the data, obtained from measurements or otherwise dictated by the model-user. The feasibility-seeking problem requires to find a point in the intersection of all constraints without using any objective function to aim at any specific feasible point. At the heart of the superiorization methodology lies the modeler desire to use an objective function, that is exogenous to the constraints, in order to seek a feasible solution that will have lower (not necessarily minimal) objective function value. This aim is less demanding than full-fledged constrained minimization but more demanding than plain feasibility-seeking. Putting emphasis on the need to satisfy the constraints, because they represent the real-world situation, one recognizes the "asymmetric roles of feasibility-seeking and objective function reduction", namely, that fulfilling the constraints is the main task while reduction of the exogenous objective function plays only a secondary role. There are two research directions in the superiorization methodology that nourish from this same general principle: Weak superiorization and strong superiorization. Since its inception in 2007, the superiorization methodology has evolved and gained ground, as can be seen from the, compiled and continuously updated, bibliography at: http://math.haifa.ac.il/yair/bib-superiorization-censor.html. [ABSTRACT FROM AUTHOR]

Copyright of Facta Universitatis: Law & Politics is the property of University of Nis, Serbia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Despite initial attempts to apply machine learning to archaeology dating back to the late 1990s, it was not until 2019 that its use began to become widespread. What advantages does this methodology have over previous methods? Can it be applied to all relevant fields of study? This article aims to answer these questions through an exhaustive review of archaeological studies that employ this methodology and by developing a model with a specific algorithm, based on Bayesian networks, to explore its benefits and limitations. [ABSTRACT FROM AUTHOR]

This paper uses phenomenological analysis to consider the phenomenon of becoming a follower. The data includes my field notes from mediated encounters with three of the most-subscribed YouTube content creators, as well as my personal recollections about these experiences, which are interspersed as event memories throughout my notes. Analyzing my event memories of these encounters enables me to follow my affective relationship to these YouTubers and their content, to untangle some of these knobby multiplicities and map the possibilities of following as they emerge. As I follow following around, as a researcher and (perhaps) as a follower myself, I reflect on the role of algorithms shaping who, what, and how we follow online media. Lastly, the methodological ramifications of a researcher becoming a follower-how following and researching bleed into each other, and why this matters-are also explored in this paper. [ABSTRACT FROM AUTHOR]

A method is proposed for calculating scalar products of pairs of seismic events that are closest in time to isolate sequences of epicenters of crustal earthquakes (chains) connected in space and time. To verify the developed methodology, the model and real catalogs of earthquakes of the Garm region in Tajikistan are compared. The efficiency of the proposed algorithm is shown. Based on the totality of the results of the allocation of chains, the issues that complicate the detection of the mentioned structures in the general case are considered. In the process of applying the created program to the real catalog of earthquakes of the Garm region, the existence of four pairwise orthogonal prevailing directions in their orientation was established. There were also some changes in the time of the predominant role of the selected strike of chains of earthquake epicenters. It is possible that these changes may reflect some elements of the dynamics of the seismic process in the Garm region. [ABSTRACT FROM AUTHOR]

Drawing on actor-network theory (ANT), this paper develops a ‘more-than-human’ conception of netnography to extend current thinking on the scope, focus and methods of netnographic research. The proposed approach seeks to account more clearly for the role of human and non-human actors in networked sociality and sets out to examine the interactions of people, technology and socio-material practices. The paper critiques reductive applications of netnography, bound by proceduralism, and advocates research that embraces the complex, multi-temporal, multi-spatial nature of internet and technology-mediated sociality. It challenges researchers to examine and account for the performative capacities of actors and their practices of enactment. By synthesising insights from ANT and emerging work in marketing and consumer research that adopts relational approaches, this paper outlines the challenges and opportunities in developing more-than-human netnographies as an approach to extend current netnography. [ABSTRACT FROM AUTHOR]

In the last few years, research studies and opinion pieces have tried to account for the new polarisation and dealignment of US politics after Trump and the post-Brexit UK politics. It is now well-established both by academic research and by Facebook's own research that Facebook leads to more polarisation in its users' political views, but rhetorical analysis has not yet accounted for the role played by algorithms in political communication and persuasion. What does social media do to rhetoric? The situation of speech in social media is often treated like in a public sphere when it should not be. This misconception prevents rhetorical studies to take into consideration the question of technology. By using the recent literature in critical algorithm studies, I develop a new approach in rhetorical criticism. I argue here that the increasing agency that algorithms have acquired in delivering and mediating rhetoric means that we must consider the role played by intermediaries when examining rhetorical situations. This paper sheds light on what I call the four conditionalities of algorithms on rhetoric: (1) programmed speech content, (2) the verticalisation of political communication, (3) the new biases produced by digital media, and (4) the rhetorical machine learning. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, the authors use a new methodology, called paper affiliation index, to create finance journal ranking using expert judgment and research impact, both of which are based on secondary, objective measures, thus making it possible to produce lists every year without human manipulation at virtually no cost. Design/methodology/approach: Bibliometrics. Python implementation. Findings: A new ranking with 65 finance journals. Research limitations/implications: This procedure helps to reduce bias and to deal with known problems associated with current methodologies. The data used in the methodology comes from public sources; the procedure is therefore easily replicable. This methodology is not subject-dependent and thus can be transferred to other realms of knowledge. Once the bibliometric institutional data has been gathered, the procedure is not computationally costly: a Python implementation of the algorithm executes the whole computation in a few seconds. Results seem to correct the pernicious Matthew effect which is so evident in citation-based methods. Originality/value: The institutional classification created includes all institutions that have contributed papers to the field of finance. The procedure helps to reduce bias and to deal with known problems associated with current methodologies. The data used in the methodology comes from public sources, the procedure is therefore easily replicable. The methodology is not subject-dependent and thus can be transferred to other realms of knowledge. Once the bibliometric institutional data has been gathered, the procedure is not computationally costly. [ABSTRACT FROM AUTHOR]

This article intervenes in contemporary discussions of critical algorithm studies about the meaning of the notion 'algorithm'. While many critical scholars as well as most public and private organisations understand this concept as a computational procedure instantiated by a programming code in a software stack, I argue that the algorithm is better understood as a 'figure'; a discursive short-hand pointing to diverse modes of procedural governance and not always digital ones. Since algorithmic figures are generated by a bundle of heterogeneous contexts, their emergence leads to conflicting visions about the reality, materiality and effects of algorithmisation. This article provides four ethnographic strategies to describe the contexts of production and circulation of algorithmic figures: observing the observers of algorithms; mapping and creating algorithmic figures; drawing relations across contexts of figuring; and analysing the transformative effects of algorithmic figures on the attempts to govern them. [ABSTRACT FROM AUTHOR]

Safe operation of the power grid requires a complete and robust control network to ensure full observability. However, redundancy measurements can create problems in expense, management, and control. The optimal phasor measurement unit (PMU) positioning problem (OPPP) is proposed to limit the number of PMUs deployed in the power grid and ensure the whole grid's observability in the meantime. A modified branch‐and‐bound algorithm (MBBA) to unravel the OPPP is presented. Original BBA, which uses a single search order to create a binary tree, gives only one solution to the OPPP, although more than one optimal solution exists. The proposed MBBA method consists of two different stages: the vertexes in the search tree are investigated by depth‐first search (DFS) in stage 1, and the search route continues as the breadth‐first search. In stage 1, the LP relaxing problems are solved by dual simplex, and in stage 2, the basic viable solution from stage 1 is used to configure the primary simplex until the optimum solution is found. OPPP is formulated as a binary decision variable MBBA model, minimizing linear objective function subject to linear matrix observability constraints. The MBBA model is unravelled using a linear integer‐based external approximation scheme. IEEE test systems are used to check the feasibility of the proposed approach. Matlab software performs simulation based on a number of graph theory‐based methods such as DFS, graph‐theoretical method, simulated annealing, and recursive N‐algorithms. These algorithms are compared to the algorithmic perspective of the proposed MBBA method. IEEE test network results confirm the validity of the proposed methodology. [ABSTRACT FROM AUTHOR]

Advances in implantable radio-telemetry or diverse biologging devices capable of acquiring high-resolution ambulatory electrocardiogram (ECG) or heart rate recordings facilitate comparative physiological investigations by enabling detailed analysis of cardiopulmonary phenotypes and responses in vivo. Two priorities guiding the meaningful adoption of such technologies are: (1) automation, to streamline and standardize large dataset analysis, and (2) flexibility in quality-control. The latter is especially relevant when considering the tendency of some fully automated software solutions to significantly underestimate heart rate when raw signals contain high-amplitude noise. We present herein moving average and standard deviation thresholding (MAST), a novel, open-access algorithm developed to perform automated, accurate, and noise-robust single-channel R-wave detection from ECG obtained in chronically instrumented mice. MAST additionally and automatically excludes and annotates segments where R-wave detection is not possible due to artefact levels exceeding signal levels. Customizable settings (e.g. window width of moving average) allow for MAST to be scaled for use in non-murine species. Two expert reviewers compared MAST's performance (true/false positive and false negative detections) with that of a commercial ECG analysis program. Both approaches were applied blindly to the same random selection of 270 3-min ECG recordings from a dataset containing varying amounts of signal artefact. MAST exhibited roughly one quarter the error rate of the commercial software and accurately detected R-waves with greater consistency and virtually no false positives (sensitivity, Se: 98.48% ± 4.32% vs. 94.59% ± 17.52%, positive predictivity, +P: 99.99% ± 0.06% vs. 99.57% ± 3.91%, P < 0.001 and P = 0.0274 respectively, Wilcoxon signed rank; values are mean ± SD). Our novel, open-access approach for automated single-channel R-wave detection enables investigators to study murine heart rate indices with greater accuracy and less effort. It also provides a foundational code for translation to other mammals, ectothermic vertebrates, and birds. [ABSTRACT FROM AUTHOR]

Goal of the research. In 2014 in Ukraine the massive decentralization reform has been initiated. It aimed to transform many single administrative units into much larger territorial communities. Spatial differentiation of the newly consolidated communities by values of their proximity to large cities and urban areas within the case of Zhytomyr region is the goal of the article. Methods of the research. Zhytomyr region is one of the most successful in Ukraine on implementation of the administrative reform, decentralization and best practices of the communities’ integration. The region had been selected for the in-depth study taking into account its central location, economic situation and the environment. Data of the State statistics agency of Ukraine, governmental and non-governmental institutions and Google Maps was applied to conduct the research and identify the territories’ groups. The research results. Aiming to perform spatial typology of the newly consolidated territorial communities we differentiated them depending on their proximity to cities and other urban centers firstly. Consequently three types of the communities have been defined: neighboring, relatively remote, and faraway. Distance of the communities to urban centers relates with their area and people size: the closer a territorial unit is located to the city, the smaller is its area and population. Other words, the proximity of a community to the city determines its economic capacity much more than its population and area. Statistical relations between duration of the communities’ consolidation and their spatial types prove the faraway and relatively remote communities have been united earlier than the neighboring. Taking into account the average number of settlements belonging to the one consolidated community it is revealed the closer to the regional center communities include less number of settlements. Two-dimensional classification of the communities by their proximity to regional and rayon centers confirms communities located close to cities and towns have better opportunities for the development. Novelty of the research results. The presented methodological approach to comprehensive differentiation of the territorial communities can be applied to identify their challenges, threats and prospects. For this purpose, the algorithm is proposed which combines indicators of spatial differentiation of rural areas, their economic status, social infrastructure, and the environment. Practical significance. The proposed methodological approach to spatial differentiation of rural territories is aimed assisting representatives of the communities, local self-government bodies and state authorities to develop and refine local strategies and programs, update coherent plans and identify target communities for certain projects. [ABSTRACT FROM AUTHOR]

The purpose of this study was to compare activPAL algorithm-estimated values for time in bed (TIB), wake time (WT) and bedtime (BT) against self-report and an algorithm developed by van der Berg and colleagues. Secondary analyses of baseline data from the Community Activity for Prevention Study (CAPs) were used in which adults ≥ 18 years wore the activPAL for seven days. Mixed-effects models compared differences between TIB, WT, and BT for all three methods. Bland-Altman plots examined agreement and the two-one-sided test examined equivalence. activPAL was not equivalent to self-report or van der Berg in estimating TIB, but was equivalent to self-report for estimating BT, and was equivalent to van der Berg for estimating WT. The activPAL algorithm requires adjustments before researchers can use it to estimate TIB. However, researchers can use activPAL's option to manually enter self-reported BT and WT to estimate TIB and better understand 24-hour movement patterns. [ABSTRACT FROM AUTHOR]

User focus groups can be a particularly effective way to design user-driven changes to library services. This paper explains how three faculty librarians designed and conducted virtual focus groups to improve the College of Charleston (CofC) Libraries' A-Z Database List; provides analysis of the data gathered via focus group sessions and a follow-up survey; details how this information has been used to make changes for the sake of student user experience (UX) and user interface (UI) design; and shares overall impressions and insights gained from the study. By inviting student participants to explore the list in a setting that was both structured and open-ended, librarians learned what mattered most to students using this resource. [ABSTRACT FROM AUTHOR]

Computational technologies have vastly replaced our prior modalities of information seeking. Social media platforms have become the first choice of many information seekers. Increasingly, these platforms are also becoming vehicles for coordinated, manipulative disinformation campaigns. These campaigns of computational propaganda have resulted in an information environment in which the assignment of authority and trust in information sources has become increasingly opaque. This epistemic process of information evaluation has increasingly become the purview of automated algorithmic systems that we as human beings tend to falsely implicitly trust to provide us with the most accurate information available. Propagandists have begun to exploit the algorithmic components of these systems as well as human cognitive deficits in order to manipulate public opinion, control the narrative of public discourse, and flood our information ecosystems in order to work towards the manufacture of false consensus on a wide range of political and cultural issues. This work aims to review and synthesize the literature on computational propaganda, how it manipulates our human cognitive deficits, and how information literacy can be utilized in order to correct for the resultant epistemic failure. [ABSTRACT FROM AUTHOR]

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In order to perform the control of a voltage source converter and enhance its capability of working, in this work, a new approach based on Fuzzy-Q-Learning algorithm is applied. The utilized method makes the fuzzy controller in dynamic mode that means this controller will be adapted in each variation in the system by changing the different values depends on the change in the system. By perform the DPC technique, the obtained results show that this new fuzzy reinforcement gives better performance to control a voltage source converter. [ABSTRACT FROM AUTHOR]

This study identifies and selects the most promising existing adaptive control algorithms for arterial streets, evaluates performance through simulation, and develops a plan for field testing of the most promising algorithms on a real-world arterial. A section of Highway 1 in the city of Lomita, California was selected as the test site for evaluation of adaptive signal control strategies. A methodology was developed to obtain a time-varying origin-destination (OD) matrix from the system loop detector data at the test site. The calibrated OD matrix was applied to a microscopic representation of the site created in PARAMICS. A plug-in for simulating signal control in PARAMICS was written to explicitly model pretimed, isolated actuated, coordinated actuated, traffic responsive, and critical intersection control, as well as adaptive strategies. The analysis of the simulation results shows that RHODES is the best control strategy at the selected arterial.

This paper presents an integrated computer simulation and Artificial Neural Network (ANN) algorithm for a stochastic Two-Stage Assembly Flow-Shop Scheduling Problem (TSAFSP) with setup times under a weighted sum of makespan and mean completion time (MCT) criteria, known as bi-criteria. Significantly, it should be noted that there is no mathematical model to analyse the stochastic model, therefore simulation is used to solve the problem. The simulation model enables decision makers to consider the influence of job scheduling on machines in order to examine both criteria simultaneously. Since it is not possible to evaluate all sequence combinations using the simulation model in a reasonable time, multilayered neural network meta-models have been trained and used to estimate objective function values composed of both makespan and mean completion time criteria for the stochastic TSAFSP. To the best of the authors’ knowledge, this is the first study that considers stochastic machine breakdown, processing times, setup times, makespan and mean completion time as objectives concurrently. The TSAFSP is modelled by Visual SLAM simulation software. The simulation output results are then given to the ANN as inputs to build the meta-model. This meta-model is then used to obtain the results with the optimum values. The advantage of these meta-model applications is a reduction in the number of simulation runs and consequently a reduced run time. Also, this is the first study that introduces an intelligent and flexible algorithm for handling stochastic TSAFSP. [ABSTRACT FROM PUBLISHER]

The authors present a virtual authoring environment for artistic creation in VR. It enables the effortless conversion of 2D images into volumetric 3D objects. Artistic elements in the input material are extracted with a convenient VR-based segmentation tool. Relief sculpting is then performed by interactively mixing different height maps. These are automatically generated from the input image structure and appearance. A prototype of the tool is showcased in an analog-virtual artistic workflow in collaboration with a traditional painter. It combines the expressiveness of analog painting and sculpting with the creative freedom of spatial arrangement in VR. [ABSTRACT FROM AUTHOR]

Using volumetric filmmaking as a medium for artists and designers requires the development of new methodologies and tools. We introduce an installation art project using the active volumetric filmmaking technology to investigate its possibilities in art practice. To do that, we developed a system to film volumetric video in real time, thereby allowing its users to capture large environments and objects without fixed placement or preinstallation of cameras. Active volumetric filmmaking helps us realize the digital reconstruction of physical space in real time and can be expected to ultimately facilitate the coexistence of real and virtual spaces. [ABSTRACT FROM AUTHOR]

Artificial intelligence generally and machine learning specifically have become deeply woven into the lives and technologies of modern life. Machine learning is dramatically changing scientific research and industry and may also hold promise for addressing limitations encountered in mental health care and psychotherapy. The current paper introduces machine learning and natural language processing as related methodologies that may prove valuable for automating the assessment of meaningful aspects of treatment. Prediction of therapeutic alliance from session recordings is used as a case in point. Recordings from 1,235 sessions of 386 clients seen by 40 therapists at a university counseling center were processed using automatic speech recognition software. Machine learning algorithms learned associations between client ratings of therapeutic alliance exclusively from session linguistic content. Using a portion of the data to train the model, machine learning algorithms modestly predicted alliance ratings from session content in an independent test set (Spearman's ρ =.15, p <.001). These results highlight the potential to harness natural language processing and machine learning to predict a key psychotherapy process variable that is relatively distal from linguistic content. Six practical suggestions for conducting psychotherapy research using machine learning are presented along with several directions for future research. Questions of dissemination and implementation may be particularly important to explore as machine learning improves in its ability to automate assessment of psychotherapy process and outcome. Public Significance Statement: Our study suggests that client-rated therapeutic alliance can be predicted using session content through machine learning models, albeit modestly. [ABSTRACT FROM AUTHOR]