Standard Squares Research Articles

An adaptive harmonic polynomial cell method with immersed boundaries: Accuracy, stability, and applications

AbstractWe present a 2D high‐order and easily accessible immersed‐boundary adaptive harmonic polynomial cell (IB‐AHPC) method to solve fully nonlinear wave‐structure interaction problems in marine hydrodynamics using potential‐flow theory. To reduce the total number of cells without losing accuracy, adaptive quad‐tree cell refinements are employed close to the free‐surface and structure boundaries. The present method is simpler to implement than the existing IB‐HPC alternatives, in that it uses standard square cells both in the fluid domain and at the boundaries, thus without having to use the more complex and expensive overlapping grids or irregular cells. The spurious force oscillations on moving structures, which is a known issue for immersed boundary methods (IBMs), are eliminated in this study by solving a separate boundary value problem (BVP) for a Lagrangian acceleration potential. We also demonstrate that solving a similar BVP for the corresponding Eulerian acceleration potential is far less satisfactory due to the involved second derivatives of the velocity potential in the body‐boundary condition, which are very difficult to calculate accurately in an IBM‐based approach. In addition, we present, perhaps for the first time since the HPC method was developed, a linear matrix‐based stability analysis for the time‐domain IB‐AHPC method. The stability analysis is also used in this study as a general guide to design robust and stable numerical algorithms, in particular related to the treatment of boundary conditions close to the intersection between a Dirichlet and a Neumann boundary, which is essential in time‐domain wave‐structure interaction analyses using IBMs. We confirm theoretically through the stability analysis that square cells have the best stability properties. The present method has been verified and validated satisfactorily by various cases in marine hydrodynamics, including a moving structure in an infinite fluid, fully nonlinear wave generation and propagation, and fully nonlinear diffraction and radiation of a ship cross section.

Information system of economic and mathematical modelling of pricing in the residential sector of Ukraine

The article deals with the development of a web application on forecasting the dynamics of prices in the residential sector of Ukraine. The classification of economic forecasting methods according to which they are divided into formalized, extrapolated, intuitive methods of forecasting the financial state is presented. It has been established that forecasting methods should meet the following requirements: a combination of subjective value and objective significance of estimates; clear application of estimates, which does not allow different interpretations regarding the choice of methods; create the ability to accumulate statistical information and use it for forecasting. The model of standard square collocation for the real estate market is presented. A web-based application for forecasting the dynamics of prices in the Ukrainian housing sector was developed and tested.

Research on Noise Reduction and Enhancement Algorithm of Girth Weld Image

In order to eliminate the salt pepper and Gaussian mixed noise in X-ray weld image, the extreme value characteristics of salt and pepper noise are used to separate the mixed noise, and the non local mean filtering algorithm is used to denoise it. Because the smoothness of the exponential weighted kernel function is too large, it is easy to cause the image details fuzzy, so the cosine coefficient based on the function is adopted. An improved non local mean image denoising algorithm is designed by using weighted Gaussian kernel function. The experimental results show that the new algorithm reduces the noise and retains the details of the original image, and the peak signal-to-noise ratio is increased by 1.5 dB. An adaptive salt and pepper noise elimination algorithm is proposed, which can automatically adjust the filtering window to identify the noise probability. Firstly, the median filter is applied to the image, and the filtering results are compared with the pre filtering results to get the noise points. Then the weighted average of the middle three groups of data under each filtering window is used to estimate the image noise probability. Before filtering, the obvious noise points are removed by threshold method, and then the central pixel is estimated by the reciprocal square of the distance from the center pixel of the window. Finally, according to Takagi Sugeno (T-S) fuzzy rules, the output estimates of different models are fused by using noise probability. Experimental results show that the algorithm has the ability of automatic noise estimation and adaptive window adjustment. After filtering, the standard mean square deviation can be reduced by more than 20%, and the speed can be increased more than twice. In the enhancement part, a nonlinear image enhancement method is proposed, which can adjust the parameters adaptively and enhance the weld area automatically instead of the background area. The enhancement effect achieves the best personal visual effect. Compared with the traditional method, the enhancement effect is better and more in line with the needs of industrial field.

Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement

Modern bioprocess development employs statistically optimized design of experiments (DOE) and regression modeling to find optimal bioprocess set points. Using modeling software, such as JMP Pro, it is possible to leverage artificial neural networks (ANNs) to improve model accuracy beyond the capabilities of regression models. Herein, we bridge the gap between a DOE skill set and a machine learning skill set by demonstrating a novel use of DOE to systematically create and evaluate ANN architecture using JMP Pro software. Additionally, we run a mammalian cell culture process at historical, one factor at a time, standard least squares regression, and ANN-derived set points. This case study demonstrates the significant differences between one factor at a time bioprocess development, DOE bioprocess development and the relative power of linear regression versus an ANN-DOE hybrid modeling approach.

Association between Sour Taste SNP KCNJ2-rs236514, Diet Quality and Mild Cognitive Impairment in an Elderly Cohort.

Differences in sour-taste thresholds have been identified in cognition-related diseases. Diet is a modulator of cognitive health, and taste perception influences dietary preferences and habits. Heritable genetics and polymorphisms in the KCNJ2 gene involved in the transduction of sour taste have been linked to variations in sour taste and non-gustatory functions. However, relationships between sour taste genetics, mild cognitive impairment, and diet quality are yet to be elucidated. This study investigated the associations between the presence of the KCNJ2-rs236514 variant (A) allele, diet quality indices, and mild cognitive impairment evaluated by the Mini-Mental State Examination (MMSE), in a secondary cross-sectional analysis of data from the Retirement Health & Lifestyle Study. Data from 524 elderly Australians (≥65y) were analyzed, using standard least squares regression and nominal logistic regression modeling, with demographic adjustments applied. Results showed that the presence of the KCNJ2-A allele is associated with increased proportions of participants scoring in the range indicative of mild or more severe cognitive impairment (MMSE score of ≤26) in the total cohort, and males. These associations remained statistically significant after adjusting for age, sex, and diet quality indices. The absence of association between the KCNJ2-A allele and cognitive impairment in women may be related to their higher diet quality scores in all indices. The potential link between sour taste genotype and cognitive impairment scores may be due to both oral and extra-oral functions of sour taste receptors. Further studies are required on the role and relationship of neurotransmitters, sour taste genotypes and sour taste receptors in the brain, and dietary implications, to identify potential risk groups or avenues for therapeutic or prophylactic interventions.

Probabilistic Cellular Automata for Granular Media in Video Games

Granular materials are very common in the everyday world. Media such as sand, soil, gravel, food stuffs, pharmaceuticals, etc. all have similar irregular flow since they are composed of numerous small solid particles. In video games, simulating these materials increases immersion and can be used for various game mechanics. Computationally, full scale simulation is not typically feasible except on the most powerful hardware and tends to be reduced in priority to favor other, more integral, gameplay features. Here we study the computational and qualitative aspects of side profile flow of sand-like particles using cellular automata (CA). Our CA uses a standard square lattice that updates via a custom, modified Margolus neighborhood. Each update occurs using a set of probabilistic transitions that can be tuned to simulate friction between particles. We focus on the look of the sandpile structure created from an hourglass shape over time using different transition probabilities and the computational impact of such a simulation. The toppling behavior and final structure of the sandpiles are largely dependent on the chosen probabilities. As the probability to topple decreases, representing greater friction, elongated sandpiles begin to form, leading to longer settling times.

Asymptotic Analysis of Bifurcations in Feedforward Networks

A feedforward network is a unidirectionally coupled chain of dynamical systems in which the first cell is coupled to itself, and each successive cell is coupled to the next one. Feedforward networks have gained considerable interest because of their potential to enhance signal amplification and to manipulate the frequency of oscillations. Indeed, it has been shown that the growth rate of the bifurcation undergone by the final cell is much larger than the expected square root growth rate associated with the standard Hopf bifurcation. In this paper, we present a new approach to studying this growth rate phenomenon. We employ a two-time-scale analysis and asymptotic approximations to detect behavior associated with the growth rate phenomenon that has not been previously observed. In particular, we show that the Hopf bifurcation is not the only bifurcation capable of exhibiting this large growth rate behavior. Using asymptotic methods we show that it is not a special property of the Hopf bifurcation that allows for this accelerated growth rate; it is a combination of the unidirectional coupling and the higher-degree nonlinearities that cause this effect. Furthermore, we show that this large growth rate need not persist away from the bifurcation. In fact, the growth rate is asymptotic to the standard square root growth rate as the bifurcation parameter increases.

Caring Behaviors Inventory-24: translation, cross-cultural adaptation and psychometric testing for use in a Swedish context

BackgroundPatients’ health and wellbeing are promoted when nurses successfully conceptualize caring in clinical practice. Measuring caring behaviors can advance knowledge about caring and has potential to improve caring practices and the outcomes of care. The Caring Behaviors Inventory-24 (CBI-24) is an empirical instrument for measuring caring, developed to determine perceptions of caring among patients and nurses. Since the instrument was not available in Swedish, the aim of this study was therefore to translate into Swedish and cross-culturally adapt CBI-24 for a Swedish healthcare context, and to psychometrically analyze the Swedish version of CBI-24.MethodsThe study used a traditional forward and back translation process in six stages: (1) two simultaneous translations by bilingual experts; (2) expert review committee synthesis; (3) blind back translation; (4) expert review committee deliberations; (5) pre-testing with cognitive interviews, and (6) psychometric evaluations.ResultsThe translation process was systematically conducted and entailed discussions regarding semantic, idiomatic, experiential and conceptual equivalence. The cognitive interviews generated thoughts and reflections, which resulted in adjusting three items of the CBI-24 SWE. For psychometric analyses, 234 persons answered the questionnaire. Results indicated acceptable overall model fit in the χ2-value for the confirmatory factor analysis, while for the heuristic goodness-of-fit indices, the comparative fit index (CFI) and the standardized mean square residual (SMSR) indicated good model fits, and the root mean square error approximation (RMSEA) indicated an acceptable fit.ConclusionsCBI-24 SWE has been shown to be a psychometrically acceptable instrument for use in Swedish research contexts. Further studies regarding the clinical usefulness of the instrument may be in order. In particular, CBI-24 SWE should be evaluated among nurses in rural areas.

‘Welcome on board’: resource dependency and agency theoretic evidence from the South African life insurance market

PurposeThis paper examines the role of corporate governance on the financial performance of life insurers in South Africa. Specifically, the paper tests two competing hypotheses on the role of boards as effective monitors of opportunistic behaviour of executives, as prescribed by the agency theory or as an effective resource, as advocated by the resource dependency view.Design/methodology/approachThe paper estimates both static and dynamic panel data of 68 insurers from 2007 to 2014 using random effects, panel corrected standard error ordinary least squares and generalized method of moment’s estimation techniques. Board size, audit committee size, board independence and audit committee independence are used as the governance indicators while profitability is measured as returns on assets and equity.FindingsThe findings support both the resource dependency and agency theoretic views of boards. Specifically, the results indicate that large board and audit committees improve financial performance which supports the view of boards as effective resources for insurers. In addition, the role of non-executive directors in addressing agency conflict is reflected in the positive effect of board independence on financial performance. However, the long-run causal positive effect is only reported for audit committee size on return on assets. In addition, the paper also finds evidence of profitability persistence in the life insurance market. Finally, reinsurance usage, insurer size and market concentration were found to have a negative effect on financial performance.Practical implicationsThe findings re-enforce the important role of boards in their oversight responsibilities and as effective resources in the operations of highly specialized insurance businesses.Originality/valueAs far as the authors are concerned, this empirical analysis documents the first evidence of the linkages between governance mechanisms and financial performance of an insurance market in Africa.

Design and Validation of a Self-Aligning Index Finger Exoskeleton for Post-Stroke Rehabilitation.

Rehabilitation of hand functions is necessary to improve post-stroke patients' quality of life. There is initial evidence that hand exoskeletons should exercise flexion/extension (f/e) and abduction/adduction (a/a) of the fingers to rebuild hand functions. However, designing a self-alignment mechanism of the metacarpophalangeal (MCP) joint to improve its wearing comfort is still a challenge. In this paper, a novel index finger exoskeleton with three motors is proposed to help post-stroke patients perform finger a/a and f/e training. A spatial mechanism with passive degrees of freedom for the MCP joint is designed to realize human-robot axes self-alignment. The proposed mechanism's kinematic compatibility is analyzed to show its self-aligning capability, and the kineto-statics analysis is performed to present the exoskeleton's static characteristics. Finally, kinematic and static experiments have been conducted, and the results indicate that the standardized reaction forces square sum of the exoskeleton to the MCP joint can be reduced by 65.8% compared with the state-of-the-art exoskeleton. According to the experimental results, the exoskeleton can achieve the a/a and f/e training and human-robot axes self-alignment, and improve its comfortability. In the future, clinical trials will be further studied to test the exoskeleton.

TRES: An R Package for Tensor Regression and Envelope Algorithms

Recently, there has been a growing interest in tensor data analysis, where tensor regression is the cornerstone of statistical modeling for tensor data. The R package TRES provides the standard least squares estimators and the more efficient envelope estimators for the tensor response regression (TRR) and the tensor predictor regression (TPR) models. Envelope methodology provides a relatively new class of dimension reduction techniques that jointly models the regression mean and covariance parameters. Three types of widely applicable envelope estimation algorithms are implemented and applied to both TRR and TPR models.

Triple-Regularized Latent Subspace Discriminative Regression for Hyperspectral Image Classification

There are various types and distributions of noise in hyperspectral images. However, the existing classification models are vulnerable to noise in the data. To improve the robustness of the classification models, a novel hyperspectral image classification model is proposed, named triple-regularized latent subspace discriminative regression (TRLSDR). The core idea of TRLSDR is to add a latent subspace to the standard discriminative least squares regression model to extract high-order features from the visual space by undercomplete autoencoder, and then use clean data for classification. Three regularizers are introduced in this process: Tikhonov regularizer is used to avoid overfitting; Laplacian regularizer is used to capture the neighborhood relationship; and low-rank regularizer is used to alleviate the error of Laplacian matrix construction caused by noise in original samples. We designed experiments on five hyperspectral image datasets and the results show that the proposed model is superior to the existing regression models.

Systematic Design and Analysis of Wireless Power Transmission and Energy Distribution

The paper reports a systematic design and analysis of two independent public utilities-wireless power transmission and renewable energy distribution. Wireless Power transmission means the transmission of power from the power source to the load circuit wirelessly. It is a complex process but the demand for electricity is more than the amount being produced. The principle of the Project is based on Faraday’s Law of Electromagnetic induction. In this project, we run the High AC Frequency power transfer operation. The second design discusses renewable energy sources. Now a day's power is the most required office for the person. All the standard energy assets square measure draining step by step. Along these lines, we've to move from standard to non-traditional energy assets. During this, the blend of 2 energy assets is happening for example wind and elective energy. This technique castigates the property energy assets while not harming the character. We will give continuous force by exploiting the half-breed energy framework. Essentially this method involves the combination of 2 energy systems that may provide continuous power. Star panels square measure utilized for changing elective energy and wind turbines square measure utilized for changing breeze energy into power. This force will use for varied purposes. Age of power happens at a cheap price. This project manages the age of power by exploiting 2 sources blend that winds up in produce power at a cheap price while not harming the character balance.

Pricing Bermudan options using regression trees/random forests

The value of an American option is the maximized value of the discounted cash flows from the option. At each time step, one needs to compare the immediate exercise value with the continuation value and decide to exercise as soon as the exercise value is strictly greater than the continuation value. We can formulate this problem as a dynamic programming equation, where the main difficulty comes from the computation of the conditional expectations representing the continuation values at each time step. In (Longstaff and Schwartz, 2001), these conditional expectations were estimated using regressions on a finite-dimensional vector space (typically a polynomial basis). In this paper, we follow the same algorithm; only the conditional expectations are estimated using Regression trees or Random forests. We discuss the convergence of the LS algorithm when the standard least squares regression is replaced with regression trees. Finally, we expose some numerical results with regression trees and random forests. The random forest algorithm gives excellent results in high dimensions.

Impact of Socioeconomic Status on Concomitant Injuries and Access to Care Following ACL Injury

Time to ACL reconstruction has been shown to vary greatly in diverse patient populations, and delay has been associated with additional knee pathology. However, there is a paucity of literature examining the correlation between lower socioeconomic status, delay in treatment and increased concomitant pathologies following ACL injury. The purpose of this study was to identify socioeconomic factors in patients sustaining an ACL tear that may influence a delay in seeking treatment or lead to concomitant pathology at the time of surgery. All patients undergoing ACL reconstruction at our institution between November 2015 and August 2018 were identified. Patients were emailed a survey asking about income, highest level of education, primary language spoken at home and the number of re-injuries prior to surgery. Insurance status, date of injury, number of weeks from the initial injury to the first visit with orthopaedic surgery, and date of surgery were recorded from the patient’s medical record. Multiple linear regression with backwards elimination was used to determine statistically significant independent predictors for each outcome. A standard least squares model was used for continuous variables and a nominal regression model for nominal variables. Standard summary statistics are reported for demographic information. The predictive value of the time between the injury and seeing an orthopaedic surgeon on meniscus tears was assessed by establishing a cutoff value that maximized sensitivity and specificity. Then, the group that saw a surgeon faster was compared to the slower group by Chi-Square analysis or Fisher’s Exact Test, depending on the number of patients in each cell. P < .05 was considered significant. Of the 241 patients who met the inclusion criteria, 126 responded to the survey, with an average age of 26.5 ± 9.88 years. Insurance information from all 241 patients was used. Patients with commercial insurance visited an orthopaedic surgeon 39.4 weeks earlier (P=.012) and underwent surgery after being seen by an orthopaedic surgeon 5 weeks sooner (P=.016) as compared to patients with government insurance. English speaking patients independently predicted an earlier visit with an orthopaedic surgeon by 55.86 weeks compared to non-English-speaking patients (P=.027). Speaking English predicted earlier surgery after seeing an orthopaedic surgeon by 21 weeks compared to non-English speakers (P=.019). Additionally, non-English speakers had an odds ratio of 4.62 for having a bucket handle medial meniscus tear at the time of ACL reconstruction (P=.01; CI=1.67-21.3) compared to English speakers. With respect to education, having a college degree predicted earlier initial visit to an orthopaedic surgeon by 36 weeks (P=.023) and predicted earlier surgery by 32 weeks (P=.036) compared to those without a college degree. The level of education completed was not an independent predictor of a bucket handle meniscus tear at the time of surgery. Income level was not a significant independent predictor of number of weeks from initial injury to seeing an orthopaedic surgeon, or time between the initial visit with orthopaedics and surgery. However, having a household income less than $100,000/year independently predicted 0.25 more episodes of instability (P=.039), and an odds ratio of 7.37 (CI=1.20-53.39) for having a bucket handle medial meniscus tears at the time of surgery. Time from injury to seeing an orthopaedic surgeon longer than 13 weeks significantly increased the chance of any type of meniscus tear (P=.007) and specifically non-bucket tears (P=.022). Times longer than 30 weeks, 4 days significantly increased the chance of a bucket handle meniscus tear at the time of surgery compared to both no meniscus tear (P < .001) and a non-bucket type meniscus tear (P < .001). Patients with government insurance and who were non-English-speaking experienced later access to care and later surgery after orthopaedic surgery evaluation. Non-English-speaking patients also experienced higher rates of re-injury, instability, and bucket handle medial meniscus tears. Patients without a college degree also experienced later access to care. Patients with a household income less than $100,000/year experienced higher rates of instability and bucket handle medial meniscus tears. Delayed access to orthopaedic care longer than 13 weeks was associated with higher rates of meniscus tears, and after 30 weeks bucket handle meniscus tears were significantly increased. These data may inform the orthopaedic and broader medical communities of the impact of lower socioeconomic status on patients' access to care and higher rates of concomitant injuries.

Causal regression for online estimation of highly nonlinear parametrically varying models

This paper presents an online estimation method for highly nonlinear systems. This method fits the data to a nonlinear in the parameters regression model, where each parameter is an unknown function of known variables that we call causal regressors. We define this model as an Autoregressive with eXogenous inputs and Causal-regressors-Dependent Parameters (ARX-CDP) model. We are interested on modeling problems where the causal regressors can vary rapidly, as well as the parameters, to the point that the latter cannot be estimated by standard methods. Since the parameters causally depend on the causal regressor but do not depend directly on time, we propose to model each parameter evolution as a random walk process in its causal regressors domain, rather than in the time variable domain, as usual. Therefore, the parameter prediction depends on some past estimates at instants when the causal regressors are similar to the present one. The absolute difference between the current and past causal regressors is used to measure this similarity. We call this prediction method as causal prediction, which, together with a standard recursive least square algorithm, used to update the model, makes up the online causal estimator. Finally, a finite impulse response and a system with multi-dependent parameters are modeled using this proposal. We show experimentally that the causal regression method has a similar performance with other offline methods from literature, with the important advantage of operating in online mode.

Free-water DTI estimates from single b-value data might seem plausible but must be interpreted with care.

Free-water elimination DTI (FWE-DTI) has been used widely to distinguish increases of free-water partial-volume effects from tissue's diffusion in healthy aging and degenerative diseases. Because the FWE-DTI fitting is only well-posed for multishell acquisitions, a regularized gradient descent (RGD) method was proposed to enable application to single-shell data, more common in the clinic. However, the validity of the RGD method has been poorly assessed. This study aims to quantify the specificity of FWE-DTI procedures on single-shell and multishell data. Different FWE-DTI fitting procedures were tested on an open-source in vivo diffusion data set and single-shell and multishell synthetic signals, including the RGD and standard nonlinear least-squares methods. Single-voxel simulations were carried out to compare initialization approaches. A multivoxel phantom simulation was performed to evaluate the effect of spatial regularization when comparing between methods. To test the algorithms' specificity, phantoms with two different types of lesions were simulated: with altered mean diffusivity or with modified free water. Plausible parameter maps were obtained with RGD from single-shell in vivo data. The plausibility of these maps was shown to be determined by the initialization. Tests with simulated lesions inserted into the in vivo data revealed that the RGD approach cannot distinguish free water from tissue mean-diffusivity alterations, contrarily to the nonlinear least-squares algorithm. The RGD FWE-DTI method has limited specificity; thus, its results from single-shell data should be carefully interpreted. When possible, multishell acquisitions and the nonlinear least-squares approach should be preferred instead.

Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation

Fluorescence lifetime imaging microscopy (FLIM) is a key technology that provides direct insight into cell metabolism, cell dynamics and protein activity. However, determining the lifetimes of different fluorescent proteins requires the detection of a relatively large number of photons, hence slowing down total acquisition times. Moreover, there are many cases, for example in studies of cell collectives, where wide-field imaging is desired. We report scan-less wide-field FLIM based on a 0.5 MP resolution, time-gated Single Photon Avalanche Diode (SPAD) camera, with acquisition rates up to 1 Hz. Fluorescence lifetime estimation is performed via a pre-trained artificial neural network with 1000-fold improvement in processing times compared to standard least squares fitting techniques. We utilised our system to image HT1080—human fibrosarcoma cell line as well as Convallaria. The results show promise for real-time FLIM and a viable route towards multi-megapixel fluorescence lifetime images, with a proof-of-principle mosaic image shown with 3.6 MP.

Novel designs for square cyclone using rounded corner and double-inverted cones shapes

The main aim of the present study was to develop novel designs for conventional square cyclones in order to improve their separation efficiency using Computational Fluid Dynamics (CFD). Two novel design alterations were considered, and their performances were analyzed. First, the conical section of the conventional square cyclone was changed from single-cone to double inverted-cone. Second, the sharp corners of the main body were modified to rounded corners with different radiuses together with the use of the double inverted-cone. The results demonstrated that due to a smaller separation zone and higher particle concentration in the second lower cone, the square cyclone with rounded-corner and double-inverted cone produces higher separation efficiency among all simulated square cyclones under the same condition. Increasing the value of round corner (r/D) accelerated the flow inside the cyclone and led to a higher tangential velocity. Hence, a more intense centrifugal force field in the square cyclone was observed compared to single-cone square cyclone, and therefore, an improved collection efficiency was obtained. The performance of different cyclones for combined energy loss and collection efficiency was studied. A comparison of 50% cut-off diameters of different cyclones was presented, which revealed that the square cyclone with rounded-corner (r/D = 0.1), and double-inverted cone had the smallest 50% cut-off diameters for all considered pressure drops. The maximum 39.2% reduction of 50% cut-off diameter was obtained for this cyclone compared to the standard square cyclone.

Consistent inference for predictive regressions in persistent economic systems

This paper studies standard predictive regressions in economic systems governed by persistent vector autoregressive dynamics for the state variables. In particular, all – or a subset – of the variables may be fractionally integrated, which induces a spurious regression problem. We propose a new inference and testing procedure – the Local speCtruM (LCM) approach – for joint significance of the regressors, that is robust against the variables having different integration orders and remains valid regardless of whether predictors are significant and, if they are, whether they induce cointegration. Specifically, the LCM procedure is based on fractional filtering and band spectrum regression using a suitably selected set of frequency ordinates. Contrary to existing procedures, we establish a uniform Gaussian limit theory and a standard χ2-distributed test statistic. Using the LCM inference and testing techniques, we explore predictive regressions for the realized return variation. Standard least squares inference indicates that popular financial and macroeconomic variables convey valuable information about future return volatility. In contrast, we find no significant evidence using our robust LCM procedure. If anything, our tests support a reverse chain of causality, with rising financial volatility predating adverse innovations to key macroeconomic variables. Simulations are employed to illustrate the relevance of the theoretical arguments for finite-sample inference.