Standard Search Methods Research Articles

Implementing Swarm Robotics for Coordinated Multi-Agent Systems in Search and Rescue Operations to Improve Efficiency and Success Rates in Disaster Response

When there is a crisis, search and rescue efforts that are quick and successful can mean the difference between life and death. But standard search and rescue methods often have a hard time in emergency areas because they are so complicated and hard to plan for. This study suggests using swarm robots for organized multi-agent systems in search and rescue operations to deal with these problems. The objective is to form these operations more effective and effective. Swarm robots employments the thoughts of self-organization and decentralized control that came from studying social bug colonies to form frameworks with numerous free specialists that can act as a entirety. These bots, which are as a rule exceptionally basic and as it were have fundamental sense and talking capacities, work together without any issues to reach their objectives. These mechanical frameworks are solid, adaptable, and adaptable since they utilize the control of swarm insights. These are all imperative qualities for dealing with changing and perilous fiasco settings. One of the finest things almost utilizing swarm robots for look and protect is that it lets you rapidly cover huge zones. Conventional strategies that depend on a little bunch of organized robots or human labourers may not be able to look expansive calamity scenes rapidly. Swarm robots, on the other hand, lets you send out numerous units that can investigate the area at the same time, making it simpler to reach and quicker to reply. Swarm mechanical technology is additionally more strong when things go off-base or the world changes since it isn't controlled . The system can change and reform without centralized control if individual workers run into problems or run into problems. This keeps the search and recovery efforts going.

AI-PEDURO – Artificial intelligence in pediatric urology: Protocol for a living scoping review and online repository

BackgroundArtificial intelligence (AI) and machine learning (ML) methods are increasingly being applied in pediatric urology across a growing number of settings, with more extensive databases and wider interest for use in clinical practice. More than 30 ML models have been published in the pediatric urology literature, but many lack items required by contemporary reporting frameworks to be high quality. For example, most studies lack multi-institution validation, validation over time, and validation within the clinical environment, resulting in a large discrepancy between the number of models developed versus the number of models deployed in a clinical setting, a phenomenon known as the AI chasm. Furthermore, pediatric urology is a unique subspecialty of urology with low frequency conditions and complex phenotypes where clinical management can rely on a lower quality of evidence. ObjectiveTo establish the AI in PEDiatric UROlogy (AI-PEDURO) collaborative, which will carry out a living scoping review and create an online repository (www.aipeduro.com) for models in the field and facilitate an evidence synthesis of AI models in pediatric urology. Methods and AnalysisThe scoping review will follow PRISMA-ScR guidelines. We will include ML models identified through standardized search methods of four databases, hand-search papers, and user-submitted models. Retrieved records will be included if they involve ML algorithms for prediction, classification, or risk stratification for pediatric urology conditions. The results will be tabulated and assessed for trends within the literature. Based on data availability, models will be divided into clinical disease sections (e.g. hydronephrosis, hypospadias, vesicoureteral reflux). A risk assessment will be performed using the APPRAISE-AI tool. The retrieved model cards (brief summary model characteristics in table form) will be uploaded to the online repository for open access to clinicians, patients, and data scientists, and will be linked to the Digital Object Identifier (DOI) for each article. Discussion and ConclusionWe hope this living scoping review and online repository will offer a valuable reference for pediatric urologists to assess disease-specific ML models' scope, validity, and credibility to encourage opportunities for collaboration, external validation, clinical testing, and responsible deployment. In addition, the repository may aid in identifying areas in need of further research.

Shaping the future: Proposed bills to regulate new and emerging tobacco and nicotine products in Latin America and the Caribbean amidst public health concerns and industry interests.

To examine legislative efforts to regulate new and emerging tobacco and nicotine products (NETNPs) in Latin America and the Caribbean (LAC) between 2017 and 2022. Analysis of on-line national legislation databases to track NETNP bills using standard snowball search methods. Bills were identified in 15 out of the 33 LAC countries, accounting for a total of 91 bills targeting NETNPs (Table 1). Mexico had the highest number (n=38) of bills, followed by Brazil (n=16), Argentina (n=10), Colombia (n=8) and Chile (n=5). The remaining countries introduced two or fewer NETNP bills. Among the identified 91 bills, the majority (n=60) addressed both electronic cigarettes and HTPs, almost one third of bills (n=29) applied only to electronic cigarettes, and only two focused solely on HTPs. Six of the 15 countries with identified bills, enacted one NETNP law including Barbados, Bolivia, Costa Rica, Guyana, Mexico, and Panama. Since 2019, there has been a steady increase in the amount of NETNP bills introduced in LAC. In 2017 and 2018, 10 NETNP bills were introduced each year followed by 23 NETNP bills introduced in each 2019 and 2020, 19 in 2021, and 16 by August 2022. The substantial increase in the number of NETNP bills introduced since 2019 indicates the growing importance of regulating NETNPs in LAC. As NETNPs continue to evolve and grow it is important to continue monitoring and evaluating tobacco and nicotine regulatory initiatives while also staying ahead of the evolving market of NETNPs. Previous studies have analyzed the regulatory environment of new and emerging tobacco and nicotine products (NETNPs) but it is less known about legislative and regulatory efforts and attempts. This is the first known study to analyze legislative bills to regulate NETNPs in Latin America and the Caribbean (LAC). The substantial increase in the number of NETNP bills introduced in LAC since 2019 indicates the growing importance of regulating NETNPs in LAC. As NETNPs continue to evolve and grow it is important to continue monitoring and evaluating tobacco and nicotine regulatory initiatives while also staying ahead of the evolving market of NETNPs.

Halo sign on temporal artery ultrasound versus temporal artery biopsy for giant cell arteritis.

Halo sign on temporal artery ultrasound versus temporal artery biopsy for giant cell arteritis.

Ibuprofen for acute postoperative pain in children.

Children often require pain management following surgery to avoid suffering. Effective pain management has consequences for healing time and quality of life. Ibuprofen, a frequently used non-steroidal anti-inflammatory drug (NSAID) administered to children, is used to treat pain and inflammation in the postoperative period. 1) To assess the efficacy and safety of ibuprofen (any dose) for acute postoperative pain management in children compared with placebo or other active comparators. 2) To compare ibuprofen administered at different doses, routes (e.g. oral, intravenous, etc.), or strategies (e.g. as needed versus as scheduled). We used standard Cochrane search methods. We searched CENTRAL, MEDLINE, Embase, CINAHL and trials registries in August 2023. We included randomised controlled trials (RCTs) in children aged 17 years and younger, treated for acute postoperative or postprocedural pain, that compared ibuprofen to placebo or any active comparator. We included RCTs that compared different administration routes, doses of ibuprofen and schedules. We adhered to standard Cochrane methods for data collection and analysis. Our primary outcomes were pain relief reported by the child, pain intensity reported by the child, adverse events, and serious adverse events. We present results using risk ratios (RR) and standardised mean differences (SMD), with the associated confidence intervals (CI). We used GRADE to assess the certainty of the evidence. We included 43 RCTs that enroled 4265 children (3935 children included in this review). We rated the overall risk of bias at the study level as high or unclear for 37 studies that had one or several unclear or high risk of bias judgements across the domains. We judged six studies as having a low risk of bias across all domains. Ibuprofen versus placebo (35 RCTs) No studies reported pain relief reported by the child or a third party, or serious adverse events. Ibuprofen probably reduces child-reported pain intensity less than two hours postintervention compared to placebo (SMD -1.12, 95% CI -1.39 to -0.86; 3 studies, 259 children; moderate-certainty evidence). Ibuprofen may reduce child-reported pain intensity, two hours to less than 24 hours postintervention (SMD -1.01, 95% CI -1.24 to -0.78; 5 studies, 345 children; low-certainty evidence). Ibuprofen may result in little to no difference in adverse events compared to placebo (RR 0.79, 95% CI 0.51 to 1.23; 5 studies, 384 children; low-certainty evidence). Ibuprofen versus paracetamol (21 RCTs) No studies reported pain relief reported by the child or a third party, or serious adverse events. Ibuprofen likely reduces child-reported pain intensity less than two hours postintervention compared to paracetamol (SMD -0.42, 95% CI -0.82 to -0.02; 2 studies, 100 children; moderate-certainty evidence). Ibuprofen may slightly reduce child-reported pain intensity two hours to 24 hours postintervention (SMD -0.21, 95% CI -0.40 to -0.02; 6 studies, 422 children; low-certainty evidence). Ibuprofen may result in little to no difference in adverse events (0 events in each group; 1 study, 44 children; low-certainty evidence). Ibuprofen versus morphine (1 RCT) No studies reported pain relief or pain intensity reported by the child or a third party, or serious adverse events. Ibuprofen likely results in a reduction in adverse events compared to morphine (RR 0.58, 95% CI 0.40 to 0.83; risk difference (RD) -0.25, 95% CI -0.40 to -0.09; number needed to treat for an additional beneficial outcome (NNTB) 4; 1 study, 154 children; moderate-certainty evidence). Ibuprofen versus ketorolac (1 RCT) No studies reported pain relief or pain intensity reported by the child, or serious adverse events. Ibuprofen may result in a reduction in adverse events compared to ketorolac (RR 0.51, 95% CI 0.27 to 0.96; RD -0.29, 95% CI -0.53 to -0.04; NNTB 4; 1 study, 59 children; low-certainty evidence). Despite identifying 43 RCTs, we remain uncertain about the effect of ibuprofen compared to placebo or active comparators for some critical outcomes and in the comparisons between different doses, schedules and routes for ibuprofen administration. This is largely due to poor reporting on important outcomes such as serious adverse events, and poor study conduct or reporting that reduced our confidence in the results, along with small underpowered studies. Compared to placebo, ibuprofen likely results in pain reduction less than two hours postintervention, however, the efficacy might be lower at two hours to 24 hours. Compared to paracetamol, ibuprofen likely results in pain reduction up to 24 hours postintervention. We could not explore if there was a different effect in different kinds of surgeries or procedures. Ibuprofen likely results in a reduction in adverse events compared to morphine, and in little to no difference in bleeding when compared to paracetamol. We remain mostly uncertain about the safety of ibuprofen compared to other drugs.

New directions for Alzheimer's disease research from the Jackson Laboratory Center for Alzheimer's and Dementia Research 2022 workshop.

In September 2022, The Jackson Laboratory Center for Alzheimer's and Dementia Research (JAX CADR) hosted a workshop with leading researchers in the Alzheimer's disease and related dementias (ADRD) field. During the workshop, the participants brainstormed new directions to overcome current barriers to providing patients with effective ADRD therapeutics. The participants outlined specific areas of focus. Following the workshop, each group used standard literature search methods to provide background for each topic. The team of invited experts identified four key areas that can be collectively addressed to make a significant impact in the field: (1) Prioritize the diversification of disease targets, (2) enhance factors promoting resilience, (3) de-risk clinical pipeline, and (4) centralize data management. In this report, we review these four objectives and propose innovations to expedite ADRD therapeutic pipelines.

Handling Large-Scale Document Collections using Information Retrieval in the Age of Big Data

This paper's primary goal is to present an overview of big data and its analysis utilizing various methodologies, particularly evolutionary computing techniques, which improve information retrieval over standard search methods. is obtainable. Big data is defined as a huge, diverse collection of data that is difficult to handle with conventional computational methods and necessitates the use of more sophisticated statistical approaches in order to extract pertinent information from the data. Along with providing an overview of evolutionary computational approaches, this study also discusses some of the main models used for information retrieval.

The fossil fuel industry's suppression of public knowledge surrounding the toxic effects of benzene

Abstract Individuals working in industries that produce or use benzene, a chemical compound known to produce chronic health effects including leukemia and death, are occupationally exposed to the highest levels of benzene. It is much less known how corporations, and in particular Shell Oil Company, suppressed public health information and formed relationships to promote false data to influence legislation and standards regarding benzene. We conducted archival research in the UCSF Chemical Industry Documents Library by reviewing the Benzene collection which contains 4,025 Shell Oil Company internal documents (1920s-1990s). We conducted standard snowball keyword search methods and thematically coded for major events, individuals, and relationships regarding the suppression of benzene. In 1949, Shell executives privately admitted that there was no safe level of exposure to benzene. During the 1950s, Shell unsuccessfully attempted to find cheaper alternatives to replace benzene used in petroleum. As scientific evidence emerged regarding the health harms of benzene in the 1960s and 1970s, government regulatory agencies increasingly proposed regulations to minimize benzene exposure. In response, Shell executives funded research to downplay benzene exposure to workers. Shell also used these studies to suppress and misdirect emerging evidence. Meanwhile Shell developed internal employee handbooks and monitoring programs to minimize any threats of company liability instead of dropping the usage of benzene. Internal Shell documents reveal the company suppressed public knowledge surrounding the toxic Effects of benzene since the 1940s. Governments should ban the use of benzene to protect the health and safety of workers.

Evaluating the efficacy and safety of tumor treating fields versus laser interstitial thermal therapy in glioblastoma treatment: a comprehensive systematic review and meta-analysis

Abstract Tumor Treating Field (TTF) is considered a new treatment option for glioblastoma. Studies in newly diagnosed GBM (ndGBM) patients have demonstrated a good safety profile and improved efficacy. On the other hand, Laser Interstitial Thermal Therapy (LITT) is a minimally invasive method for treating intracranial tumors that are challenging to treat using conventional techniques. Laser Interstitial Thermal Therapy, a minimally invasive method, is the most indicated treatment technique. Its safety and effectiveness, however, have not been thoroughly substantiated in the literature. To create an accurate understanding of which therapy yields better outcomes to glioblastoma patients regarding safety and efficacy, we decided to run a systematic review and meta-analysis on their comparative safety and therapeutic effects in the treatment of glioblastoma. We used standard and extensive Cochrane search methods. The latest search date was June 2nd, 2023. The study period goes from inception to date. We included Randomized Controlled Trials (RCT) or quasi-RCT and all human-based observational studies assessing the comparative safety and therapeutic outcomes of tumor treating fields to laser interstitial thermal therapy in treating glioblastoma patients. We did not include case reports, abstract-only titles, letters to the editor, conference proceedings, articles with missing data, articles in other languages, and those reporting animal studies. Our study compares Tumor Treating Fields' safety and therapeutic effects on Laser Interstitial Thermal therapy in treating glioblastoma.

Fast Full-Search Algorithm of Fractal Image Compression for Acceleration Image Processing

A new processing algorithm based on fractal image compression is proposed for image processing efficiency. An image will partition into non-overlapping blocks called range blocks and overlapping blocks called domain blocks, with the domain blocks generally bigger than the range blocks, to achieve a rapid encoding time. This research introduced a new fast full-search algorithm approach that starts the search for the best matching domain in the range block from the closest points in the range blocks and expands the search until an acceptable match is found or the search is completed to save even more encoding time. The proposed fast full-search approach, despite its simplicity, is more efficient than the standard search method. The search reduction, peak signal to noise ratio, compression ratio, and encoding time of the suggested approach are all examined. The proposed method can encode a 512x512 grayscale Lena image in 0.36 seconds, with a total search reduction of 87% according to experimental results.

Competitive multiproduct contracting under multinomial logit demand

Despite the prevalence of multiproduct firms in many industries, the supply chain contracting literature has predominantly focused on problems where an upstream player offers only one product. This paper studies multiproduct contract design for competing manufacturers each with several products to sell via a common retailer. We consider two contracting schemes under multinomial logit demand (MNL): individual contracting, in which the manufacturers set a wholesale price for each of their products separately; and aggregate contracting, in which the manufacturers set the price as a function of the order quantities for their own products. We find that under individual contracting it is optimal for each manufacturer to offer the full range of his products to the retailer and set an equal wholesale margin for these products. This suggests that the classic result of an equal‐margin policy for the retailer also holds true for the manufacturers. Further, there is an equilibrium characterized by a system of nonlinear equations that can be solved using the standard bisection search method. Under aggregate contracting, a simple cost‐plus contract is optimal for the manufacturers. In equilibrium, each manufacturer charges a markup equal to his marginal contribution to the supply chain, and the supply chain profit is maximized. Comparing these two contracting schemes, we find among other results that the retailer prefers aggregate contracting when there is a large number of products from each manufacturer or when consumer heterogeneity is low, while the opposite is true for the manufacturers. In addition, both the retailer and the manufacturers prefer aggregate contracting when there are few manufacturers in the supply chain. It is also found that as the number of manufacturers or the number of products from each manufacturer increases, the supply chain efficiency loss induced by individual contracting is reduced. Finally, we extend our model to a hybrid setting in which one manufacturer adopts aggregate contracting and the other adopts individual contracting, and demonstrate to the manufacturers the value of aggregate contracting. These results provide useful guidance on optimal contract design for multiproduct firms.

A Hyperparameter Optimization Algorithm for the LSTM Temperature Prediction Model in Data Center

As the main tool to realize data mining and efficient knowledge acquisition in the era of big data, machine learning is widely used in data center energy-saving research. The temperature prediction model based on machine learning predicts the state of the data center according to the upcoming tasks. It can adjust the refrigeration equipment in advance to avoid temperature regulation lag and set the air conditioning temperature according to the actual demand to avoid excessive refrigeration. Task scheduling and migration algorithm based on temperature prediction can effectively avoid hot spots. However, the choice of hyperparameter of machine learning model has a great impact on its performance. In this study, a hyperparameter optimization algorithm based on MLP is proposed. On the basis of trying certain hyperparameters, the MLP model is used to predict the value of all hyperparameters’ space, and then, a certain number of high-quality hyperparameters are selected to train the model repeatedly. In each iteration, the amount of training data decreases gradually, while the accuracy of the model improves rapidly, and finally, the appropriate hyperparameter are obtained. We use the idea of mutation in the genetic algorithm to improve the probability of high-quality solutions and the loss function weighting method to select the solution with the best stability. Experiments are carried out on two representative machine learning models, LSTM and Random Forest, and compared with the standard Gaussian Bayes and Random Search method. The results show that the method proposed in this study can obtain high-precision and high-stability hyperparameter through one run and can greatly improve the operation efficiency. This algorithm is not only effective for LSTM but also suitable for other machine learning models.

First eROSITA study of nearby M dwarfs and the rotation-activity relation in combination with TESS

We present the first results with the ROentgen Survey with an Imaging Telescope Array (eROSITA) on board the Russian Spektrum-Roentgen-Gamma mission, and we combine the new X-ray data with observations with the Transiting Exoplanet Survey Satellite (TESS). We used theSUPERBLINKproper motion catalog of nearby M dwarfs as input sample to search for eROSITA and TESS data. We extractedGaiaDR2 data for the full M dwarf catalog, which comprises ~9000 stars, and we calculated the stellar parameters from empirical relations with optical/IR colors. Then we cross-matched this catalog with the eROSITA Final Equatorial Depth Survey (eFEDS) and the first eROSITA all-sky survey (eRASS1). After a meticulous source identification in which we associated the closestGaiasource with the eROSITA X-ray detections, our sample of M dwarfs is defined by 687 stars with SpT = K5..M7 (673 from eRASS1 and 14 from eFEDS). While for eRASSl we used the data from the source catalog provided by the eROSITA_DE consortium, for the much smaller eFEDS sample, we performed the data extraction, and we analyzed the X-ray spectra and light curves. This unprecedented data base for X-ray emitting M dwarfs allowed us to place a quantitative constraint on the mass dependence of the X-ray luminosity, and to determine the change in the activity level with respect to pre-main-sequence stars. TESS observations are available for 489 of 687 X-ray detected M dwarfs. By applying standard period search methods, we were able to determine the rotation period for 180 X-ray detected M dwarfs. This is about one-forth of the X-ray sample. With the joint eROSITA and TESS sample, and combining it with our compilation of historical X-ray and rotation data for M dwarfs, we examined the mass dependence of the saturated regime of the rotation-activity relation. A first comparison of eROSITA hardness ratios and spectra shows that 65% of the X-ray detected M dwarfs have coronal temperatures of ~0.5 keV. We performed a statistical investigation of the long-term X-ray variability of M dwarfs by comparing the eROSITA measurements to those obtained ~30 yr earlier during the ROSAT all-sky survey (RASS). Evidence for X-ray flares is found in various parts of our analysis: directly from an inspection of the eFEDS light curves, in the relation between RASS and eRASSl X-ray luminosities, and in a subset of stars that displays hotter X-ray emission than the bulk of the sample according to the hardness ratios. Finally, we point out the need to obtain X-ray spectroscopy for more M dwarfs to study the coronal temperature-luminosity relation, which is not well constrained by our eFEDS results.

Computational design synthesis for Fabrication-Aware assembly problems using building objects with dimensional variations

State-of-the-art computational design approaches for designing the assembly of a structure often rely on high-precision building objects, which can be difficult and costly to fabricate. To consider a wider range of construction materials and fabrication processes, e.g. low-cost 3D printing and brick making, this research introduces a novel computational design method for fabrication-aware assembly problems using existing building components with dimensional variations. Given a finite set of building objects, this study develops a statistical preprocessing approach and a modified pattern search with an efficient integer linear programming method to generate the assembly of a target structure, such that it is statically stable and optimized for geometric smoothness and space utilization. In two case studies, the proposed algorithm exhibits significantly better boundary fitting and space utilization compared with a genetic algorithm and a standard pattern search method and lower computational cost compared with a genetic algorithm.

Variable Definition and Independent Components

In the causal modeling literature, it is well known that ill-defined variables may give rise to ambiguous manipulations. Here, we illustrate how ill-defined variables may also induce mistakes in causal inference when standard causal search methods are applied. To address the problem, we introduce a representation framework, which exploits an independent component representation of the data, and demonstrate its potential for detecting ill-defined variables and avoiding mistaken causal inferences.

Remote homology clustering identifies lowly conserved families of effector proteins in plant-pathogenic fungi.

Plant diseases caused by fungal pathogens are typically initiated by molecular interactions between ‘effector’ molecules released by a pathogen and receptor molecules on or within the plant host cell. In many cases these effector-receptor interactions directly determine host resistance or susceptibility. The search for fungal effector proteins is a developing area in fungal-plant pathology, with more than 165 distinct confirmed fungal effector proteins in the public domain. For a small number of these, novel effectors can be rapidly discovered across multiple fungal species through the identification of known effector homologues. However, many have no detectable homology by standard sequence-based search methods. This study employs a novel comparison method (RemEff) that is capable of identifying protein families with greater sensitivity than traditional homology-inference methods, leveraging a growing pool of confirmed fungal effector data to enable the prediction of novel fungal effector candidates by protein family association. Resources relating to the RemEff method and data used in this study are available from https://figshare.com/projects/Effector_protein_remote_homology/87965.

Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise

In this paper, we develop convergence analysis of a modified line search method for objective functions whose value is computed with noise and whose gradient estimates are inexact and possibly random. The noise is assumed to be bounded in absolute value without any additional assumptions. We extend the framework based on stochastic methods from [C. Cartis and K. Scheinberg, Math. Program., 169 (2018), pp. 337--375] which was developed to provide analysis of a standard line search method with exact function values and random gradients to the case of noisy functions. We introduce two alternative conditions on the gradient which, when satisfied with some sufficiently large probability at each iteration, guarantees convergence properties of the line search method. We derive expected complexity bounds to reach a near optimal neighborhood for convex, strongly convex and nonconvex functions. The exact dependence of the convergence neighborhood on the noise is specified.

Visualisation tools for dependent peptide searches to support the exploration of in vitro protein modifications.

Dependent peptide searching is a method for discovering covalently-modified peptides-and therefore proteins-in mass-spectrometry-based proteomics experiments. Being more permissive than standard search methods, it has the potential to discover novel modifications (e.g., post-translational modifications occurring in vivo, or modifications introduced in vitro). However, few studies have explored dependent peptide search results in an untargeted way. In the present study, we sought to evaluate dependent peptide searching as a means of characterising proteins that have been modified in vitro. We generated a model data set by analysing N-ethylmaleimide-treated bovine serum albumin, and performed dependent peptide searches using the popular MaxQuant software. To facilitate interpretation of the search results (hundreds of dependent peptides), we developed a series of visualisation tools (R scripts). We used the tools to assess the diversity of putative modifications in the albumin, and to pinpoint hypothesised modifications. We went on to explore the tools' generality via analyses of public data from studies of rat and human proteomes. Of 19 expected sites of modification (one in rat cofilin-1 and 18 across six different human plasma proteins), eight were found and correctly localised. Apparently, some sites went undetected because chemical enrichment had depleted necessary analytes (potential 'base' peptides). Our results demonstrate (i) the ability of the tools to provide accurate and informative visualisations, and (ii) the usefulness of dependent peptide searching for characterising in vitro protein modifications. Our model data are available via PRIDE/ProteomeXchange (accession number PXD013040).

Deterministic Pilot Pattern Placement Optimization for OFDM Sparse Channel Estimation

Compressed sensing (CS) technologies have been widely adopted to pilot-assisted orthogonal frequency division multiplexing (OFDM) sparse channel estimation. However, only few works have focused on the location optimization of the pilot pattern. This paper investigates the pilot location optimization for the measurement matrix construction based on the minimum mutual coherence (MC) rule. We consider the design of the deterministic OFDM pilot pattern for solving a combinatorial optimization problem. The proposed approach utilizes the advantages of the Q-bit to update the location of the OFDM pilot pattern. The obtained results show that the proposed approach can form measurement matrix with a smaller MC and the estimated performance can be essentially improved compared with the standard genetic algorithms or random search method.

What's in a Definition? Holistic Nursing, Integrative Health Care, and Integrative Nursing: Report of an Integrated Literature Review.

Background: Nurses and others have used various terms to describe our caring/healing approach to practice. Because terms used can influence our image of ourselves and the image others have of us, we sought to clarify their meanings. Questions: How are the terms holistic nursing, integrative health care, and integrative nursing defined or described? Do we identify with these definitions/descriptions? Are the various terms the same or are they distinct? Method: We conducted an integrated review of peer-reviewed literature following the process described by Whittemore and Knafl. Using standard search methods, we reviewed full texts of 94 published papers and extracted data from 58 articles. Findings: Holistic describes "whole person care" often acknowledging body-mind-spirit. Holistic nursing defines a disciplinary practice specialty. The term integrative refers to practice that includes two or more disciplines or distinct approaches to care. Both terms, integrative and holistic, are associated with alternative/complementary modalities and have similar philosophical and/or theoretical underpinnings. Conclusions: There is considerable overlap between holistic nursing and integrative nursing. The relationship of integrative nursing to integrative health care is unclear based solely on definitions. Consideration of terms used provides opportunities for reflection, collaboration, and growth.