General Setting Research Articles

Comparison of Homoclinic Bifurcations Between Grid-Following and Grid-Forming Converters

A second-order model is developed to describe the essential behavior of the grid-following converter (GFLC) and the grid-forming converter (GFMC) for studying the synchronization characteristics of grid-connected converter systems. In this paper, a general set of criteria in terms of the manifolds and saddle quantity is derived for studying the homoclinic bifurcation behavior of GFLCs and GFMCs. It is shown that three unstable periodic orbits and two homoclinic bifurcations may exist for the GFLC, while only one stable periodic orbit and one homoclinic bifurcation may exist for the GFMC. It is also found that after the onset of the first homoclinic bifurcation, the stable equilibrium point (SEP) is surrounded by the stable manifolds of the unstable equilibrium point (UEP), which forms the SEP's basin of attraction, for both GFLCs and GFMCs. In this case, the converters may lose their synchronization if the trajectory crosses over the UEP. Therefore, system parameters should be designed to avoid the onset of the aforementioned bifurcation so that the SEP's basin of attraction can be significantly enlarged to contain the UEP. As a result, the GFLC or GFMC can eventually resynchronize with the grid even when its trajectory passes the UEP. Finally, experimental results are provided to verify these theoretical findings.

EXPRESS: Cost Optimization in Cloud Computing: Capacity Reservation for Intermittent Random Demand Surges

The adoption of cloud computing has been accelerating over the last decade, while enterprise cloud users (“firms”) are struggling to manage their growing cloud expenditures in the face of intermittent digital demand surges caused by planned or random events. To deal with such challenges, a firm can employ reserved instances with the standard contract length (e.g., one year) to meet the stationary base demand, which we refer to as the base contracts, complemented by additional reserved instances with either standard or shorter contract lengths, which we refer to as the supplementary contracts, to cope with the demand surges. We first analyze a model whereby the surge and inter-surge durations are deterministic, demand magnitude is random, and cancellation of the reservations is allowed. We develop a capacity management plan for the firm including not only the optimal capacity levels, which follow a newsvendor-type solution, but also the optimal policy for managing the purchase, renewal, cancellation, or expiration of the supplementary contracts, which can be characterized as a two-threshold policy. Due to the complexity of the structure of the optimal policy, we also construct an effective heuristic policy by excluding the renewal option from the action space, which can be applied to a more general setting where the surge and inter-surge durations are random. We examine two model extensions: (1) when trades of reserved instances are allowed in a secondary marketplace; (2) when the firm does not have exact information about the distributions of the surge magnitude and duration while it can adjust the capacity levels as data unveils. Our analysis shows that the optimal policy for managing the supplementary contracts depends on the relative magnitude of the surge and inter-surge durations in relationship to the cancellation fee. Moreover, our numerical results show that cloud platforms that offer a secondary marketplace are more attractive to firms from a cost standpoint than those that allow cancellation only. The latter, without the secondary marketplace, however, can achieve parity with the former by offering a deeper discount rate for the reserved instances, thereby bypassing the cost of administering the secondary marketplace.

Preconception counselling in women of reproductive age attending cardiology clinics in Scotland

BackgroundGuidelines for the management of cardiovascular disease (CVD) recommend preconception risk stratification and counselling in all women of childbearing age. We assessed the provision of preconception counselling (PCC) among women...

Collective Intelligence Increases Diagnostic Accuracy in a General Practice Setting.

General practitioners (GPs) work in an ill-defined environment where diagnostic errors are prevalent. Previous research indicates that aggregating independent diagnoses can improve diagnostic accuracy in a range of settings. We examined whether aggregating independent diagnoses can also improve diagnostic accuracy for GP decision making. In addition, we investigated the potential benefit of such an approach in combination with a decision support system (DSS). We simulated virtual groups using data sets from 2 previously published studies. In study 1, 260 GPs independently diagnosed 9 patient cases in a vignette-based study. In study 2, 30 GPs independently diagnosed 12 patient actors in a patient-facing study. In both data sets, GPs provided diagnoses in a control condition and/or DSS condition(s). Each GP's diagnosis, confidence rating, and years of experience were entered into a computer simulation. Virtual groups of varying sizes (range: 3-9) were created, and different collective intelligence rules (plurality, confidence, and seniority) were applied to determine each group's final diagnosis. Diagnostic accuracy was used as the performance measure. Aggregating independent diagnoses by weighing them equally (i.e., the plurality rule) substantially outperformed average individual accuracy, and this effect increased with increasing group size. Selecting diagnoses based on confidence only led to marginal improvements, while selecting based on seniority reduced accuracy. Combining the plurality rule with a DSS further boosted performance. Combining independent diagnoses may substantially improve a GP's diagnostic accuracy and subsequent patient outcomes. This approach did, however, not improve accuracy in all patient cases. Therefore, future work should focus on uncovering the conditions under which collective intelligence is most beneficial in general practice. We examined whether aggregating independent diagnoses of GPs can improve diagnostic accuracy.Using data sets of 2 previously published studies, we composed virtual groups of GPs and combined their independent diagnoses using 3 collective intelligence rules (plurality, confidence, and seniority).Aggregating independent diagnoses by weighing them equally substantially outperformed average individual GP accuracy, and this effect increased with increasing group size.Combining independent diagnoses may substantially improve GP's diagnostic accuracy and subsequent patient outcomes.

Local conditions for global convergence of gradient flows and proximal point sequences in metric spaces

This paper deals with local criteria for the convergence to a global minimiser for gradient flow trajectories and their discretisations. To obtain quantitative estimates on the speed of convergence, we consider variations on the classical Kurdyka–Łojasiewicz inequality for a large class of parameter functions. Our assumptions are given in terms of the initial data, without any reference to an equilibrium point. The main results are convergence statements for gradient flow curves and proximal point sequences to a global minimiser, together with sharp quantitative estimates on the speed of convergence. These convergence results apply in the general setting of lower semicontinuous functionals on complete metric spaces, generalising recent results for smooth functionals on R n \mathbb {R}^n . While the non-smooth setting covers very general spaces, it is also useful for (non)-smooth functionals on R n \mathbb {R}^n .

Boolean proportions

The author has recently introduced an abstract algebraic framework of analogical proportions within the general setting of universal algebra. This paper studies analogical proportions in the boolean domain consisting of two elements 0 and 1 within his framework. It turns out that our notion of boolean proportions coincides with two prominent models from the literature in different settings. This means that we can capture two separate modellings of boolean proportions within a single framework which is mathematically appealing and provides further evidence for the robustness and applicability of the general framework.

The asbestos-asbestosis exposure-response relationship: a cohort study of the general working population.

The association between asbestos exposure and asbestosis in high-exposed industrial cohorts is well-known, but there is a lack of knowledge about the exposure-response relationship for asbestosis in a general working population setting. We examined the exposure-response relationship between occupational asbestos exposure and asbestosis in asbestos-exposed workers of the Danish general working population. We followed all asbestos-exposed workers from 1979 to 2015 and identified incident cases of asbestosis using the Danish National Patient Register. Individual asbestos exposure was estimated with a quantitative job exposure matrix (SYN-JEM) from 1976 onwards and back-extrapolated to age 16 for those exposed in 1976. Exposure-response relations for cumulative exposure and other exposure metrics were analyzed using a discrete time hazard model and adjusted for potential confounders. The range of cumulative exposure in the population was 0.001 to 18 fibers per milliliter-year (f/ml-year). We found increasing incidence rate ratios (IRR) of asbestosis with increasing cumulative asbestos exposure with a fully adjusted IRR per 1 f/ml-years of 1.18 [95% confidence interval (CI) 1.15- -1.22]. The IRR was 1.94 (95% CI 1.53-2.47) in the highest compared to the lowest exposure tertile. We similarly observed increasing risk with increasing cumulative exposure in the inception population. This study found exposure-response relations between cumulative asbestos exposure and incident asbestosis in the Danish general working population with mainly low-level exposed occupations, but there is some uncertainty regarding the exposure levels.

Spurious Local Minima are Common for Deep Neural Networks With Piecewise Linear Activations.

In this article, theoretically, it is shown that spurious local minima are common for deep fully connected networks and average-pooling convolutional neural networks (CNNs) with piecewise linear activations and datasets that cannot be fit by linear models. Motivating examples are given to explain why spurious local minima exist: each output neuron of deep fully connected networks and CNNs with piecewise linear activations produces a continuous piecewise linear (CPWL) function, and different pieces of the CPWL output can optimally fit disjoint groups of data samples when minimizing the empirical risk. Fitting data samples with different CPWL functions usually results in different levels of empirical risk, leading to the prevalence of spurious local minima. The results are proved in general settings with arbitrary continuous loss functions and general piecewise linear activations. The main proof technique is to represent a CPWL function as maximization over minimization of linear pieces. Deep networks with piecewise linear activations are then constructed to produce these linear pieces and implement the maximization over minimization operation.

Exploring Physical Educators' Self-Efficacy to Teach Students With Disabilities in General Physical Education.

The purpose of this study was to explore the ways in which in-service physical education teachers construct their self-efficacy beliefs toward teaching students with disabilities in general physical education classes. Using a qualitative descriptive approach situated within self-efficacy theory, data were collected via semistructured audio-recorded interviews with 16 in-service physical educators. Three interrelated themes were constructed: (a)The more I do it, the better I feel: the importance of professional experiences; (b)I've learned from others: the influence of colleagues; and (c)Being in the general educational setting is a challenge: the impact of contextual factors. Findings supported the influence of the four sources of self-efficacy (i.e.,mastery experience, vicarious experience, social persuasion, and affective and physiological state), in addition to potential contextual factors (i.e.,class sizes and availability of hands-on support), impacting participants' self-efficacy to teach students with disabilities in general physical education classes.

Paediatricians’ perspectives in treating lower urinary tract symptoms: a qualitative exploratory needs assessment study

BackgroundPaediatric lower urinary tract symptoms (LUTS) are common experiences among school-aged children, with prevalence rates reaching as high as 20%. Paediatricians are often first-line stakeholders in providing treatment for these...

Quantum Talagrand, KKL and Friedgut’s Theorems and the Learnability of Quantum Boolean Functions

We extend three related results from the analysis of influences of Boolean functions to the quantum setting, namely the KKL theorem, Friedgut’s Junta theorem and Talagrand’s variance inequality for geometric influences. Our results are derived by a joint use of recently studied hypercontractivity and gradient estimates. These generic tools also allow us to derive generalizations of these results in a general von Neumann algebraic setting beyond the case of the quantum hypercube, including examples in infinite dimensions relevant to quantum information theory such as continuous variables quantum systems. Finally, we comment on the implications of our results as regards to noncommutative extensions of isoperimetric type inequalities, quantum circuit complexity lower bounds and the learnability of quantum observables.

On α-z-Rényi divergence in the von Neumann algebra setting

We will investigate the α-z-Rényi divergence in the general von Neumann algebra setting based on Haagerup non-commutative Lp-spaces. In particular, we establish almost all its expected properties when 0 < α < 1 and some of them when α > 1. In an Appendix we also give an equality condition for generalized Hölder’s inequality in Haagerup non-commutative Lp-spaces.

ALGEBRA ENVIRONMENTS I.

Algebra environments capture properties of non–commutative conditional expectations in a general algebraic setting. Their study relies on algebraic geometry, topology, and differential geometry techniques. The structure algebraic and Banach manifolds of algebra environments and their Zariski and smooth tangent vector bundles are particular objects of interest. A description of derivations on algebra environments compatible with geometric structures is an additional issue. Grassmann and flag manifolds of unital involutive algebras and spaces of projective compact group representations in C∗–algebras are analyzed as structure manifolds of associated algebra environments.

Development and Evaluation of a Digital App for Patient Self-Management of Opioid Use Disorder: Usability, Acceptability, and Utility Study.

Self-management of opioid use disorder (OUD) is an important component of treatment. Many patients receiving opioid agonist treatment in methadone maintenance treatment settings benefit from counseling treatments to help them improve their recovery skills but have insufficient access to these treatments between clinic appointments. In addition, many addiction medicine clinicians treating patients with OUD in a general medical clinic setting do not have consistent access to counseling referrals for their patients. This can lead to decreases in both treatment retention and overall progress in the patient's recovery from substance misuse. Digital apps may help to bridge this gap by coaching, supporting, and reinforcing behavioral change that is initiated and directed by their psychosocial and medical providers. This study aimed to conduct an acceptability, usability, and utility pilot study of the KIOS app to address these clinical needs. We developed a unique, patient-centered computational software system (KIOS; Biomedical Development Corporation) to assist in managing OUD in an outpatient, methadone maintenance clinic setting. KIOS tracks interacting self-reported symptoms (craving, depressed mood, anxiety, irritability, pain, agitation or restlessness, difficulty sleeping, absenteeism, difficulty with usual activities, and conflicts with others) to determine changes in both the trajectory and severity of symptom patterns over time. KIOS then applies a proprietary algorithm to assess the individual's patterns of symptom interaction in accordance with models previously established by OUD experts. After this analysis, KIOS provides specific behavioral advice addressing the individual's changing trajectory of symptoms to help the person self-manage their symptoms. The KIOS software also provides analytics on the self-reported data that can be used by patients, clinicians, and researchers to track outcomes. In a 4-week acceptability, usability (mean System Usability Scale-Modified score 89.5, SD 9.2, maximum of 10.0), and utility (mean KIOS utility questionnaire score 6.32, SD 0.25, maximum of 7.0) pilot study of 15 methadone-maintained participants with OUD, user experience, usability, and software-generated advice received high and positive assessment scores. The KIOS clinical variables closely correlated with craving self-report measures. Therefore, managing these variables with advice generated by the KIOS software could have an impact on craving and ultimately substance use. KIOS tracks key clinical variables and generates advice specifically relevant to the patient's current and changing clinical state. Patients in this pilot study assigned high positive values to the KIOS user experience, ease of use, and the appropriateness, relevance, and usefulness of the specific behavioral guidance they received to match their evolving experiences. KIOS may therefore be useful to augment in-person treatment of opioid agonist patients and help fill treatment gaps that currently exist in the continuum of care. A National Institute on Drug Abuse-funded randomized controlled trial of KIOS to augment in-person treatment of patients with OUD is currently being conducted.

Orthogonal systems based stable estimator for non parametric regression problems

. In this work, we study a univariate as well as a multivariate Jacobi polynomial-based estimator for solving the fundamental problem of non parametric (NP) regression. The proposed NP regression estimator aims to approximate a possibly very low regular regression function from the knowledge of its noised values given at some n random sampling points. We first study the univariate version of the proposed estimator, then extend this study to the general multivariate case with random sampling vectors drawn according to an unknown distribution. Our proposed estimator has the advantage of being stable with reasonable time complexity. Moreover, it has an optimal convergence rate under the weak condition that the regression function has a certain low Sobolev smoothness property. Although the proposed estimators are studied under the hypothesis that the random sampling vectors are supported on the d dimensional unit hypercube, by applying trivial transformations, the results of this work are still valid in the more general setting of any d dimensional hyperrectangle. Finally, the theoretical results of this work are illustrated by some numerical simulations on synthetic as well as real datasets.

Total Cuntz semigroup, extension, and Elliott Conjecture with real rank zero

AbstractIn this paper, we exhibit two unital, separable, nuclear ‐algebras of stable rank one and real rank zero with the same ordered scaled total K‐theory, but they are not isomorphic with each other, which forms a counterexample to the Elliott Classification Conjecture for real rank zero setting. Thus, we introduce an additional normal condition and give a classification result in terms of the total K‐theory. For the general setting, with a new invariant, the total Cuntz semigroup [2], we classify a large class of ‐algebras obtained from extensions. The total Cuntz semigroup, which distinguishes the algebras of our counterexample, could possibly classify all the ‐algebras of stable rank one and real rank zero.

Out-of-Sample Tuning for Causal Discovery.

Causal discovery is continually being enriched with new algorithms for learning causal graphical probabilistic models. Each one of them requires a set of hyperparameters, creating a great number of combinations. Given that the true graph is unknown and the learning task is unsupervised, the challenge to a practitioner is how to tune these choices. We propose out-of-sample causal tuning (OCT) that aims to select an optimal combination. The method treats a causal model as a set of predictive models and uses out-of-sample protocols for supervised methods. This approach can handle general settings like latent confounders and nonlinear relationships. The method uses an information-theoretic approach to be able to generalize to mixed data types and a penalty for dense graphs to penalize for complexity. To evaluate OCT, we introduce a causal-based simulation method to create datasets that mimic the properties of real-world problems. We evaluate OCT against two other tuning approaches, based on stability and in-sample fitting. We show that OCT performs well in many experimental settings and it is an effective tuning method for causal discovery.

Water Quality of Tago River

Tago River in the municipality of San Miguel, Surigao del Sur province is the main irrigation source of rice fields. Water quality is an important factor to study a certain body of water by comparing physical, chemical and biological characteristics of a water sample with the Water Quality Guidelines and General Effluent Standard set by the DENR and APHA Standard Method for Examination of Water and Wastewater. The water quality of Tago River is determined by testing its physicochemical parameters: Biochemical Oxygen Demand (BOD), Chloride, Color, Dissolved Oxygen, Fecal Coliform, Nitrate, pH, Phosphate, Temperature and Total Suspended Solids. The sampling collection was taken at 3:28 pm from the upstream part of Tago River at Sagbayan. The samples were then brought to Science Resource Center. The quality of the river was assessed by comparing the values obtained with the standard set by DENR. The physicochemical properties of the water in Tago River were analyzed and found to have; Biochemical Oxygen Demand (4 mg/L), Chloride (8.90 mg/L), Color (5 TCU), Dissolved Oxygen (9.6 mg/L), Fecal Coliform (790 MPN/100 ml), Nitrate (<0.89 mg/L), water pH (7.7), Phosphate (0.420 mg/L), water temperature (12.6°C) and Total Suspended Solids (9 mg/L). The results revealed significant differences especially the Fecal Coliform which the test result exceeds from the obtained values and the standard limits imposed by the DENR 2016 it indicates contamination and the low Temperature result suggests hindrance for population growth. The overall results of the test further implies that the quality of the water in the Tago River contains a passable quality for propagation and growth of fish, agriculture, irrigation and livestock watering. Although, the result revealed a good water quality of the river, it is still advisable that the quality of the water in Tago river should be improved, maintained and preserved.

Interventions for challenging behavior of autistic students in general education settings: A systematic literature review

BackgroundAs the prevalence of autism spectrum disorder (ASD) increases, the number of students with ASD receiving their education in general education settings continues to rise. Yet, teachers are often underprepared to address the behavioral needs of autistic students in inclusive settings, which often leads to negative school outcomes. Currently, no review has systematically examined the characteristics and effects of behavioral interventions specifically for autistic students in general education settings. The purpose of this systematic literature review was to identify and synthesize empirical studies of interventions aimed at reducing the challenging behavior of students with ASD in general education settings. MethodA literature search was conducted across three academic databases, yielding 9561 articles that were screened for eligibility against a set of inclusion criteria. Additionally, an ancestral search of included articles and a pearling search of recent systematic reviews and meta-analyses on similar topics were conducted. Finally, 12 articles were descriptively synthesized. ResultsA lack of participant diversity in relation to disability, age, and race was found across the studies. Majority of the interventions were effective and interventions informed by functional behavioral assessments consistently yielded strong effects. Joint implementation of interventions was the most common form of researcher-practitioner collaboration, with a relative lack of studies involving practitioners in designing interventions. ConclusionsFindings of this review imply a broader issue of restrictive educational placement policies for autistic students who engage in challenging behavior and a critical need to support educators to implement more inclusive educational and behavior management practices.

Boyer–Lindquist Space-Times and Beyond: Metamaterial Analogues for Arbitrary Space-Times

Analogue space-times (and in particular metamaterial analogue space-times) have a long varied and rather complex history. Much of the previous related work to this field has focused on spherically symmetric models; however, axial symmetry is much more relevant for mimicking astrophysically interesting systems that are typically subject to rotation. Now it is well known that physically reasonable stationary axisymmetric space-times can, under very mild technical conditions, be put into Boyer–Lindquist form. Unfortunately, a metric presented in Boyer–Lindquist form is not well adapted to the “quasi-Cartesian” metamaterial analysis that we developed in our previous articles on “bespoke analogue space-times”. In the current article, we shall first focus specifically on various space-time metrics presented in Boyer–Lindquist form, and subsequently determine a suitable set of equivalent metamaterial susceptibility tensors in a laboratory setting. We shall then turn to analyzing generic space-times, not even necessarily stationary, again determining a suitable set of equivalent metamaterial susceptibility tensors. Perhaps surprisingly, we find that the well-known ADM formalism proves to be not particularly useful, and that it is instead the dual “threaded” (Kaluza–Klein–inspired) formalism that provides much more tractable results. While the background laboratory metric is (for mathematical simplicity and physical plausibility) always taken to be Riemann flat, we will allow for arbitrary curvilinear coordinate systems on the flat background space-time. Finally, for completeness, we shall reconsider spherically symmetric space-times, but now in general spherical polar coordinates rather than quasi-Cartesian coordinates. In summary, this article provides a set of general-purpose calculational tools that can readily be adapted for mimicking various interesting (curved) space-times by using nontrivial susceptibility tensors in general (background-flat) laboratory settings.