Loss Of Relevant Information Research Articles

Tales of twin cities: what are climate analogues good for?

This article provides an epistemological assessment of climate analogue methods, with specific reference to the use of spatial analogues in the study of the future climate of target locations. Our contention is that, due to formal and conceptual inadequacies of geometrical dissimilarity metrics and the loss of relevant information, especially when reasoning from the physical to the socio-economical level, purported inferences from climate analogues of the spatial kind we consider here prove limited in a number of ways. Indeed, we formulate five outstanding problems concerning the search for best analogues, which we call the problem of non-uniqueness of the source, problem of non-uniqueness of the target, problem of average, problem of non-causal correlations and problem of inferred properties, respectively. In the face of such problems, we then offer two positive recommendations for a fruitful application of this methodology to the assessment of impact, adaptation and vulnerability studies of climate change, especially in the context of what we may prosaically dub “twin cities”. Arguably, such recommendations help decision-makers constrain the set of plausible climate analogues by integrating local knowledge relevant to the locations of interest.

Assessing and auditing water transport systems by applying the energy equations

ABSTRACT In improving the energy efficiency of water transport systems, two critical stages are involved: assessment (to understand the system's operation and identify potential energy savings) and auditing (to locate and break down the energy losses). Both stages are based on energy balances, which can be conducted using either the extended Bernoulli equation or the energy integral equation. Both equations can be applied, but depending on the system, data availability, and the kind of study to be performed, one is preferable over the other. This paper analyses, applies and compares both equations, with a particular focus on the less commonly used energy integral equation in the hydraulic field. This more general equation includes thermal and transient effects and it is more suitable for analyzing complex systems. In contrast, the extended Bernoulli equation, while simpler to apply, can lead to the loss of relevant information, such as the evaluation of the topographic energy. The main objective of this work is to bridge the gap between these two fundamental energy equations and recommend the most appropriate one for hydraulic problems. Real examples are presented to show their differences and validate our recommendations.

Guiding student learning in video lectures: Effects of instructors’ emotional expressions and visual cues

In video lectures, instructors often use spontaneous emotional expressions (facial expressions, tone of voice) and visual cues (underlining, circling) to guide students' attention toward key instructional information. While previous research has confirmed the benefits of visual cues in guiding attention and processing specific information, there's a notable gap in understanding the role of emotional expression in this context. Moreover, there is a lack of comprehensive exploration regarding the specific design of both behaviors (whether they emphasize the same instructional information) and their effect on students. This study conducted two experiments. Experiment 1 first confirmed the guiding effect of an instructor's emotional expression on students, establishing the foundation for our research. Additionally, Experiment 1 explored the impact of the consistency/inconsistency of facial expressions and tone of voice, including student motivation, cognitive load, and learning performance. Results revealed the benefits of consistent positive emotional expressions on motivation and transfer performance, and the benefits of consistent negative emotional expressions on retention performance. Furthermore, we found that tone of voice was a key factor in guiding students, while facial expressions were associated with students' immediate memory. Building upon Experiment 1, Experiment 2 introduced visual cues to investigate the combined impact of these two guiding behaviors on students. Results regarding emotional expressions were replicated, confirming the positive effects of both. Moreover, we found that the irrelevance of visual cues weakened the guiding influence of emotional expression on students, leading to the loss of relevant information. Therefore, we suggest encouraging instructors to convey positive emotions to enhance the learning experience while emphasizing key information through negative emotional expressions accompanied by visual cues. Additionally, minimizing or concealing visual cues whenever possible is advisable when delivering content beyond visual representations.

An effective method for preparing histological sections of blow fly intra-puparial stages for minimum PMI estimations

Blow flies (Diptera: Calliphoridae) are generally early colonisers of fresh cadavers, enabling the estimation of a minimum post-mortem interval (minPMI) based on an accurate aging of the oldest immature stages associated with a cadaver. In blow flies, the pupal stage and the subsequent development of the adult take place inside a protective case, the puparium, formed from the hardened and darkened cuticle of the third instar larva. Because the puparium is an opaque structure that shows virtually no external changes, qualitative analyses of the internal tissues can be very informative for determining reliable age-specific morphological markers. Those analyses can be performed using either non-invasive but expensive and not widely accessible techniques, or traditional histological methods, which are invasive as they require the serial sectioning of the sample. Histological methods are often readily available for forensic researchers and practitioners; however, the histological study of blow fly intra-puparial stages has traditionally been hampered by the poor paraffin infiltration of tissues due to the abundance of fat bodies, resulting in usually fragmented sections and the subsequent loss of relevant information. We present here an effective method for the preparation of histological sections of blow fly intra-puparial stages, maximising the paraffin infiltration while enabling the production of clean and entire sections that allow for the use of reliable age-specific morphological markers, thus improving the accuracy of minPMI estimations when access to more costly techniques is not feasible.

Interhemispheric differences of electroencephalography signal characteristics in different sleep stages

ObjectiveThe current electroencephalography (EEG) measurement setup is complex, laborious to set up, and uncomfortable for patients. We hypothesize that differences in EEG signal characteristics for sleep staging between the left and right hemispheres are negligible; therefore, there is potential to simplify the current measurement setup. We aimed to investigate the technical hemispheric differences in EEG signal characteristics along with electrooculography (EOG) signals during different sleep stages. MethodsType II portable polysomnography (PSG) recordings of 50 patients were studied. Amplitudes and power spectral densities (PSDs) of the EEG and EOG signals were compared between the left (C3-M2, F3-M2, O1-M2, and E1-M2) and the right (C4-M1, F4-M1, O2-M1, and E2-M2) hemispheres. Regression analysis was performed to investigate the potential influence of sleep stages on the hemispheric differences in PSDs. Wilcoxon signed-rank tests were also employed to calculate the effect size of hemispheres across different frequency bands and sleep stages. ResultsThe results showed statistically significant differences in signal characteristics between hemispheres, but the absolute differences were minor. The median hemispheric differences in amplitudes were smaller than 3 μv with large interquartile ranges during all sleep stages. The absolute and relative PSD characteristics were highly similar between hemispheres in different sleep stages. Additionally, there were negligible differences in the effect size between hemispheres across all sleep stages. ConclusionsTechnical signal differences between hemispheres were minor across all sleep stages, indicating that both hemispheres contain similar information needed for sleep staging. A reduced measurement setup could be suitable for sleep staging without the loss of relevant information.

Developing Constrained Interval Operators for Fuzzy Logic with Interval Values

A well-known problem in the interval analysis literature is the overestimation and loss of information. In this article, we define new interval operators, called constrained interval operators, that preserve information and mitigate overestimation. These operators are investigated in terms of correction, algebraic properties, and orders. It is shown that a large part of the properties studied is preserved by this operator, while others remain preserved with the condition of continuity, as is the case of the exchange principle. In addition, a comparative study is carried out between this operator g¨ and the best interval representation: g^. Although g¨⊆g^ and g¨ do not preserve the Moore correction, we do not have a loss of relevant information since everything that is lost is irrelevant, mitigating the overestimation.

Contribution of lipids to the organelle differential profile of in vitro-produced bovine embryos

Each living organism is unique because of the lipid identity of its organelles. The diverse distribution of these molecules also contributes to the role of each organelle in cellular activity. The lipid profiles of whole embryos are well documented in the literature. However, this approach can often lead to the loss of relevant information at the subcellular and consequently, metabolic levels, hindering a deeper understanding of key physiological processes during preimplantation development. Therefore, we aimed to characterize four organelles in vitro-produced bovine embryos: lipid droplets (LD), endoplasmic reticulum (ER), mitochondria (MIT), and nuclear membrane (NUC), and evaluate the contribution of the lipid species to each organelle evaluated. Expanded blastocysts were subjected to cell organelle isolation. Thereafter, lipid extraction from cell organelles and lipid analysis using the Multiple Reaction Monitoring (MRM) profiling method were performed. The LD and ER displayed a greater number of lipids (Phosphatidylcholine - PC, Ceramide - Cer, and Sphingomielin - SM) with high signal-to-noise intensities. This result is due to the high rate of biosynthesis, lipid distribution, and ability to store and recycle lipid species of these organelles. The NUC had a more distinct lipid profile than the other three organelles, with high relative intensities of PC, SM, and triacylglycerols (TG), which is consistent with its high nuclear activity. MIT had an intermediate profile that was close to that of LD and ER, which aligns with its autonomous metabolism for some classes of phospholipids (PL). Our study revealed the lipid composition of each organelle studied, and the roles of these lipids could be associated with the characteristic organellar activity. Our findings highlight the lipid species and classes that are relevant for the homeostasis and function of each associated organelle and provide tentative biomarkers for the determination of in vitro embryonic development and quality.

Sparse Modeling Approach for Quasiclassical Theory of Superconductivity

We propose the sparse modeling approach for quasiclassical theory of superconductivity, which reduces the computational cost of solving the gap equations. The recently proposed sparse modeling approach is a consequence of the Green’s function having less information than its spectral function and hence is compressible without loss of relevant information. Using the intermediate representation of the Green’s function in the sparse modeling approach, the gap equation can be solved with only 10–100 sampled Matsubara Green’s functions, whereas the conventional quasiclassical theory requires 100–1000. The efficiency of the proposed method is demonstrated in bulk and vortex states, through self-consistently solve of the Eilenberger and gap equations. Consequently, the sparse modeling approach is concluded to be appropriate for all theoretical methods based on the Matsubara formalism in the quasiclassical theory of superconductivity.

Patient Safety Culture in a Tertiary Hospital: A Cross-Sectional Study.

Patient safety (PS) culture is the set of values and norms common to the individuals of an organization. Assessing the culture is a priority to improve the quality and PS of hospital services. This study was carried out in a tertiary hospital to analyze PS culture among the professionals and to determine the strengths and weaknesses that influence this perception. A cross-sectional descriptive study was carried out. The AHRQ Questionnaire on the Safety of Patients in Hospitals (SOPS) was used. A high perception of PS was found among the participants. In the strengths found, efficient teamwork, mutual help between colleagues and the support of the manager and head of the unit stood out. Among the weaknesses, floating professional templates, a perception of pressure and accelerated pace of work, and loss of relevant information on patient transfer between units and shift changes were observed. Among the areas for improvement detected were favoring feedback to front-line professionals, abandoning punitive measures and developing standardized tools that minimize the loss of information.

Proposal of a New Approach for Protected Deposit Area Registration in Public Administration Information Systems—A Case Study from Slovakia

Perception of the meaning and wider context in recording important information about objects that represent strategically valuable data is the basis for increasing their value and binding in order to strengthen their credibility. In recent years, emphasis has been placed on digitization and electronic data collection and their interpretation, which ensures the promotion of real-world objects. The protection of mineral wealth and the creation of protected deposit areas (PDAs) is often limited to an analogue form of documentation ensuring the raw material potential of that country. The often inefficient and insufficient way of managing data in public information systems (ISs) and their subsequent use in the customary procedural stages of other decision-making procedures of state authorities leads to the loss of relevant information in connection with such protected areas. This paper on specific studies emphasizes the need to use and follow procedures in strengthening the Slovak national concept based on data and technical compatibility supporting the exchange of information, which will support the expansion of the IS environment with data in connection with the protection of mineral wealth in the form of PDAs. As a result of the existing legislation and historical background, it is necessary to comprehensively evaluate the contexts that fundamentally enter into the content of data in the ISs of individual institutions recording fundamental information about objects in connection with the protection of deposit areas. The methods used and the analysis of input data on PDAs from the relevant information systems pointed to insufficient and incomplete records and presentation of data regarding PDAs. In this document, a solution is proposed which, as a result, consolidates the disparate way of registering PDAs and presents an organizationally more profitable way of exchanging data. It was found that only modern ISs and their filling with data, respecting the rules and principles of standardization, prioritizing the content of established, and validly reflecting data, are a high-quality basis for an interoperable environment containing the necessary information, for example, in the establishment of three-dimensional records.

Methylmercury Risk Assessment Based on European Human Biomonitoring Data.

A risk assessment (RA) was conducted to estimate the risk associated with methylmercury (MeHg) exposure of vulnerable European populations, using Human Biomonitoring (HBM) data. This RA was performed integrating published data from European HBM surveys and earlier EFSA approaches (EFSA 2012). Children/adolescents (3 to 17 years old) and women of childbearing age (18 to 50 years old) were selected as relevant study population groups for this RA. Two types of HBM datasets were selected: HBM studies (n = 18) with mercury (Hg) levels (blood and hair, total Hg and/or MeHg) in the general population in different EU countries and the DEMOCOPHES harmonized study in child–mother pairs (hair, total Hg) in 17 EU countries as a reference. Two approaches were included in the RA strategy: the first one was based on estimations of the fraction of children/adolescents and women of childbearing age, respectively, from the EU general population exceeding the HBM-I value established by the German Human Biomonitoring Commission, measured as Hazard Quotients (HQ); and the second approach was based on estimations of the fraction of the two population groups exceeding the Tolerable Weekly Intake (TWI) (or their equivalent to Tolerable Daily Intake (TDI)) defined by EFSA in 2012. The HQ approach showed that for both groups, the risk varies across EU countries and that some EU areas are close to or exceeding the exposure guidance values. This is the case of Spain and Portugal, which showed the highest HQ (GM and/or P95), probably due to their higher fish consumption. Results from the EFSA approach show that hair values of children/adolescents and women of childbearing age (both in selected HBM studies and in DEMOCOPHES study) are below the TDI of 1.9 µg/g; therefore, in general, the European population does not exceed the daily average/intake dose for MeHg and/or Hg. A possible risk underestimation was identified in our assessment since for many studies no data on P95 were available, causing loss of relevant information for risk characterization on the upper bound. In addition, data from other European countries also with high seafood consumption, such as France, Greece or Iceland, were not available. For this reason, further RA refinement is needed with harmonized and more widespread HBM data to account for differences in European exposure and associated risks, so that interventions to protect vulnerable citizens, can be applied.

Noise versus Resolution in Optical Chemical Imaging-How Reliable Are Our Measurements?

Optical chemical imaging has established itself as a valuable technique for visualizing analyte distributions in 2D, notably in medical, biological, and environmental applications. In particular for image acquisitions on small scales between few millimeter to the micrometer range, as well as in heterogeneous samples with steep analyte gradients, image resolution is essential. When individual pixels are inspected, however, image noise becomes a metric as relevant as image accuracy and precision, and denoising filters are applied to preserve relevant information. While denoising filters smooth the image noise, they can also lead to a loss of spatial resolution and thus to a loss of relevant information about analyte distributions. To investigate the trade-off between image resolution and noise reduction for information preservation, we studied the impact of random camera noise and noise due to incorrect camera settings on oxygen optodes using the ratiometric imaging technique. First, we estimated the noise amplification across the calibration process using a Monte Carlo simulation for nonlinear fit models. We demonstrated how initially marginal random camera noise results in a significant standard deviation (SD) for oxygen concentration of up to 2.73% air under anoxic conditions, although the measurement was conducted under ideal conditions and over 270 thousand sample pixels were considered during calibration. Second, we studied the effect of the Gaussian denoising filter on a steep oxygen gradient and investigated the impact when the smoothing filter is applied during data processing. Finally, we demonstrated the effectiveness of a Savitzky-Golay filter compared to the well-established Gaussian filter.

FUNDAMENTAL CONCEPTS AND RECENT APPLICATIONS OF FACTORIAL STATISTICAL DESIGNS

Factorial designs have been increasingly used in scientific investigations and technological development. The designs, through the use of matrices with all the treatment combinations, have been capable to effectively characterize the relationships between the variables of multi-factor experiments, assess the experimental variabilities, and derive mathematical functions that represent the behavior of the responses. Factorial designs were fractionalized, which substantially reduced the number of treatments without the loss of relevant information. The addition of central and star points to the factorial arrays has given them the orthogonality and rotatability characteristics, frequently used to fit models with curvature and identify critical regions of interest. Literature reports indicated that factorial designs, also called factorial experiments, were successfully applied in different types of investigations, including in cost evaluations and time-series studies. They were capable to estimate important features of the experiments, like the individual and combined effects of factors, the magnitude of residuals, additionally to express the relationships of the variables in polynomial equations, draw response surface and contour plots, and determine optimal combinations of parameters. In this review, the fundamental aspects of the Complete, Fractional, Central Composite Rotational and Asymmetrical factorial designs were conceptualized, and recent applications of these powerful tools were described.

PREDICTION OF CARDIORESPIRATORY FITNESS IN THE BRAZILIAN POPULATION AGED 20 TO 59 YEARS: A NON-EXERCISE MODEL APPROACH WITH SELF-REPORTED VARIABLES

ABSTRACT Unlike other more common health-related variables, cardiorespiratory fitness (CRF) is not frequently measured in the general population. This omission characterizes a loss of relevant information. Thus, the objectives of the present study were: a) to characterize the CRF of the Brazilian population aged 20 to 59 years and to develop normative values using a non-exercise equation for predicting maximal oxygen uptake (VO2max), and b) to verify the association between lower levels of CRF and the prevalence of chronic diseases. A total of 32,531 individuals from the National Health Survey (NHS-IBGE-2013) composed the sample. Only self-reported variables were included in the equation of Wier et al. (2006): sex, age, physical activity level, and body mass index. The mean predicted VO2max was 44.6, 39.3, 34.8 and 30.6 ml/kg/min for men, and 34.5, 29.6, 25.4 and 21.1 ml/kg/min for women aged 20-29, 30-39, 40-49 and 50-59 years, respectively. The 20th and 80th percentiles were established as the extremes (very low and very high CRF). Participants with low fitness had a 33% higher chance of cardiovascular disease, an 89% higher chance of diabetes mellitus, and a 67% higher chance of hypertension, regardless of sex, age and presence of obesity, which seem to corroborate the quality of the equation.

Large-scale tandem mass spectrum clustering using fast nearest neighbor searching.

Advanced algorithmic solutions are necessary to process the ever-increasing amounts of mass spectrometry data that are being generated. In this study, we describe the falcon spectrum clustering tool for efficient clustering of millions of MS/MS spectra. falcon succeeds in efficiently clustering large amounts of mass spectral data using advanced techniques for fast spectrum similarity searching. First, high-resolution spectra are binned and converted to low-dimensional vectors using feature hashing. Next, the spectrum vectors are used to construct nearest neighbor indexes for fast similarity searching. The nearest neighbor indexes are used to efficiently compute a sparse pairwise distance matrix without having to exhaustively perform all pairwise spectrum comparisons within the relevant precursor mass tolerance. Finally, density-based clustering is performed to group similar spectra into clusters. Several state-of-the-art spectrum clustering tools were evaluated using a large draft human proteome data set consisting of 25 million spectra, indicating that alternative tools produce clustering results with different characteristics. Notably, falcon generates larger highly pure clusters than alternative tools, leading to a larger reduction in data volume without the loss of relevant information for more efficient downstream processing. falcon is a highly efficient spectrum clustering tool, which is publicly available as an open source under the permissive BSD license at https://github.com/bittremieux/falcon.

Variationally scheduled quantum simulation

Eigenstate preparation is ubiquitous in quantum computing, and a standard approach for generating the lowest-energy states of a given system is by employing adiabatic state preparation (ASP). In the present work, we investigate a variational method for determining the optimal scheduling procedure within the context of ASP. In the absence of quantum error correction, running a quantum device for any meaningful amount of time causes a system to become susceptible to the loss of relevant information. Therefore, if accurate quantum states are to be successfully generated, it is crucial to find techniques that shorten the time of individual runs during iterations of annealing. We demonstrate our variational method toward this end by investigating the hydrogen and P4 molecules, as well as the Ising model problem on a two-dimensional triangular lattice. In both cases, the time required for one iteration to produce accurate results is reduced by several orders of magnitude in comparison to what is achievable via standard ASP. As a result, the required quantum coherence time to perform such a calculation on a quantum device becomes much less stringent with the implementation of this algorithm. In addition, our variational method is found to exhibit resilience against control errors, which are commonly encountered within the realm of quantum computing.

An Efficient Human Identification Through Iris Recognition System

As a part of a growing information society, nowadays the issue of security is more crucial than ever. In order to achieve high level of security, the potential of accurately recognize subjects based on their unique measurable physiological or behavioral characteristics has been receiving an increased concern by the research and development community. As biometrics has advanced, iris has been considered a preferred trait because unique pattern texture, lifetime stability, and regular shape contribute to good segmentation and recognition performance. The incredible uniqueness of iris patterns as well as the ability to capture iris images non-invasively has motivated us to develop automated system for iris recognition based on 2-D iris images. The 2DPCA (two-dimensional Principal Component Analysis) and GA (Genetic Algorithm) have been used as feature extraction and feature selection techniques for reducing the dimensionality of iris features without the loss of relevant Information. The Back Propagation Neural Network (BPNN) is implemented using Levenberg–Marquardt’s learning rule for iris recognition. The experimental results illustrated that the 2DPCA-GA achieved a high classification accuracy of 96.40 %.

Boosting computer vision performance by enhancing camera ISP

Traditional image signal processors (ISPs) are primarily designed and optimized to improve the image quality perceived by humans. However, optimal perceptual image quality does not always translate into optimal performance for computer vision applications. In [1], Wu et al. proposed a set of methods, termed VisionISP, to enhance and optimize the ISP for computer vision purposes. The blocks in VisionISP are simple, content-aware, and trainable using existing machine learning methods. VisionISP significantly reduces the data transmission and power consumption requirements by reducing image bit-depth and resolution, while mitigating the loss of relevant information. In this paper, we show that VisionISP boosts the performance of subsequent computer vision algorithms in the context of multiple tasks, including object detection, face recognition, and stereo disparity estimation. The results demonstrate the benefits of VisionISP for a variety of computer vision applications, CNN model sizes, and benchmark datasets.

Simultaneous Reconstruction of Conductivity, Boundary Shape, and Contact Impedances in Electrical Impedance Tomography

The objective of electrical impedance tomography (EIT) is to reconstruct the internal conductivity of a physical body based on current and voltage measurements at the boundary of the body. In many medical applications the exact shape of the domain boundary and contact impedances are not available. This is problematic as even small errors in the boundary shape of the computation domain or in the contact impedance values can produce large artifacts in the reconstructed images which results in a loss of relevant information. A method is proposed that simultaneously reconstructs the conductivity, the contact impedances and the boundary shape from EIT data. The approach consists of three steps: first, the unknown contact impedances and an anisotropic conductivity reproducing the measured EIT data in a model domain are computed. Second, using isothermal coordinates, a deformation is constructed that makes the conductivity isotropic. The final step minimizes the error of true and reconstructed known geometric properties (like the electrode lengths) using conformal deformations. The feasibility of the method is illustrated with experimental EIT data, with robust and accurate reconstructions of both conductivity and boundary shape.

Evaluation of pre-processing and variable selection on energy dispersive X-ray fluorescence spectral data with partial least square regression: A case of study for soil organic carbon prediction

Most studies which have reported soil fertility attributes employing Energy Dispersive X-ray Fluorescence (EDXRF) combined with multivariate calibration make use of elemental concentration data. This combination may cause relevant information loss contained in EDXRF spectra. However, a well-established soil EDXRF spectra data treatment procedure for multivariate calibration is not currently available. The objective of this study was to evaluate the influence of different pre-processing and variable selection methods in partial least square regression models using EDXRF spectral data. Measurements were obtained under two experimental conditions (15 kV and 50 kV at tube) for soil organic carbon determination. Poisson scaling + mean center proved to be the most suitable pre-processing for this data set. The variable selection by successive projection algorithm for interval selection in partial least squares improved the performance of all tested pre-processing (or at least kept constant in terms of the errors). The 15 kV condition models with Pareto scaling and Poisson scaling + mean center were the most accurate and precise. The ratio to performance of deviation values for these models was of 2.2. The figures of merit demonstrated the soil organic carbon determination feasibility using EDXRF spectral data with these pre-processing since the accuracy, precision and limits of detection were consistent with previous reports. Thus, this study contributes toward the establishment of an approach for soil EDXRF spectral data treatment for multivariate calibration. It also contributes to a better EDXRF variables interpretation which impacts soil organic carbon modeling, demonstrating the proposed methodology potential.