Exhaustive Search Research Articles

Tolerance to and postoperative outcomes with early oral feeding following elective bowel surgery: a systematic review with meta-analysis

Advancements in gastrointestinal surgery have directed attention toward optimizing recovery, including through the use of feeding methods that reduce prolonged postoperative hospital stays, complications, and mortality, among other undesirable outcomes. This study's primary goals were to identify current peer-reviewed literature reporting the postoperative outcomes of elective bowel surgery and to evaluate the clinical evidence of patients' tolerance to oral feeding following elective bowel surgery. An exhaustive literature search was conducted via PubMed and Scopus. The search results were screened for potential articles, and articles were assessed for eligibility based on prespecified eligibility criteria. The data were synthesized, and the results were reported and discussed thematically. The database search yielded 1,667 articles, from which 18 randomized controlled trials were chosen for inclusion in this study. This study included 874 early oral feeding (EOF) patients, 865 traditional oral feeding patients, and 91 patients whose postoperative care was unspecified. Data synthesis was done, and meta-analyses were conducted. The results showed that EOF patients required a significantly shorter time to tolerate a solid diet and had shorter hospital stays. In addition, bowel function was restored earlier in EOF groups. The results show good tolerance to EOF, shorter hospitalizations, and faster restoration of bowel function, suggesting that EOF after elective bowel surgery is relatively safe. However, further studies with similar baseline conditions should be conducted to verify these results.

Recent advances in crack detection technologies for structures: a survey of 2022-2023 literature

IntroductionCracks, as structural defects or fractures in materials like concrete, asphalt, and metal, pose significant challenges to the stability and safety of various structures. Addressing crack detection is of paramount importance due to its implications for public safety, infrastructure integrity, maintenance costs, asset longevity, preventive maintenance, economic impact, and environmental considerations.MethodsIn this survey paper, we present a comprehensive analysis of recent advancements and developments in crack detection technologies for structures, with a specific focus on articles published between 2022 and 2023. Our methodology involves an exhaustive search of the Scopus database using keywords related to crack detection and machine learning techniques. Among the 129 papers reviewed, 85 were closely aligned with our research focus.ResultsWe explore datasets that underpin crack detection research, categorizing them as public datasets, papers with their own datasets, and those using a hybrid approach. The prevalence and usage patterns of public datasets are presented, highlighting datasets like Crack500, Crack Forest Dataset (CFD), and Deep Crack. Furthermore, papers employing proprietary datasets and those combining public and proprietary sources are examined. The survey comprehensively investigates the algorithms and methods utilized, encompassing CNN, YOLO, UNet, ResNet, and others, elucidating their contributions to crack detection. Evaluation metrics such as accuracy, precision, recall, F1-score, and IoU are discussed in the context of assessing model performance. The results of the 85 papers are summarized, demonstrating advancements in crack detection accuracy, efficiency, and applicability.DiscussionNotably, we observe a trend towards using modern and novel algorithms, such as Vision Transformers (ViT), and a shift away from traditional methods. The conclusion encapsulates the current state of crack detection research, highlighting the integration of multiple algorithms, expert models, and innovative data collection techniques. As a future direction, the adoption of emerging algorithms like ViT is suggested. This survey paper serves as a valuable resource for researchers, practitioners, and engineers working in the field of crack detection, offering insights into the latest trends, methodologies, and challenges.

An Annotated Checklist of Invasive Species of the Phyla Arthropods and Chordates in Panama.

Invasive species are one of the five main causes of biodiversity loss, along with habitat destruction, overexploitation, pollution, and climate change. Numbers and species of invasive organisms represent one of the first barriers to overcome in ecological conservation programs since they are difficult to control and eradicate. Due to the lack of records of invasive exotic species in Panama, this study was necessary for identifying and registering the documented groups of invasive species of the Chordates and Arthropod groups in Panama. This exhaustive search for invasive species was carried out in different bibliographic databases, electronic portals, and scientific journals which addressed the topic at a global level. The results show that approximately 141 invasive exotic species of the Arthropoda and Chordata phyla have been reported in Panama. Of the 141 species, 50 species belonged to the Arthropoda phylum and 91 species belonged to the Chordate phylum. Panamanian economic activity could facilitate the introduction of alien species into the country. This study provides the first list of invasive exotic chordate and arthropod species reported for the Republic of Panama.

An online tool using clinical factors to estimate the probability of partial clinical remission of adult-onset Type 1 diabetes

A type 1 diabetes (T1D) diagnosis is often followed by a period of reduced exogenous insulin requirement, with acceptable glucose control, called partial clinical remission (pCR). Various criteria exist to define pCR, which is associated with better clinical outcomes.We aimed to develop formulae and a related online calculator to predict the probability of pCR at 3- and 12-months post-T1D diagnosis.We analysed data from 133 adults at their T1D diagnosis (mean ± SD age: 27 ± 6 yrs., HbA1c 11.1 ± 2.0 %, 98 ± 22 mmol/mol), 3- and 12-months later. All patients were enrolled in the prospective observational InLipoDiab1 study (NCT02306005). We compared four definitions of pCR: 1) stimulated C-peptide >300 pmol/l; 2) insulin dose-adjusted HbA1c ≤9 %; 3) insulin dose <0.3 IU/kg/24 h; and HbA1c ≤6.4 % (46 mmol/mol); and 4) insulin dose <0.5 IU/kg/24 h and HbA1c <7 % (53 mmol/mol). Using readily available demographics and clinical chemistry data exhaustive search methodology was used to model pCR probability.There was low concordance between pCR definitions (kappa 0.10). The combination of age, HbA1c, diastolic blood pressure, triglycerides and smoking at T1D onset predicted pCR at 12-months with an area under the curve (AUC) = 0.87. HbA1c, triglycerides and insulin dose 3-mths post-diagnosis had an AUC = 0.89. A related calculator for pCR in adult-onset T1D is available at http://www.bit.ly/T1D-partial-remission.

Risk factors for postpartum urinary retention: An updated systematic review and meta-analysis.

There are conflicting reports on the factors that increase the likelihood of postpartum urinary retention (PUR). We completed an updated systematic review and meta-analysis to identify the risk factors for PUR. An exhaustive search of the literature was undertaken using multiple databases, including PubMed, Web of Science, the Cochrane Library, and Embase to identify pertinent studies published up until November 4, 2023. Observational studies that provided outcomes to calculate the risk factors for PUR were included. Two investigators separately performed the extraction of pertinent data from the articles. The risk factors for PUR were identified by pooling adjusted and unadjusted odds ratios (ORs) and 95% confidence intervals (CIs). Heterogeneity test, sensitivity analysis, and publication bias assessment were performed. This meta-analysis included 21 studies with a total of 36 951 participants. Meta-analysis was performed for 14 risk factors, and eight of these were statistically significant. The risk factors that were identified in this review included instrumental delivery (OR, 2.96 [95% CI, 1.82-4.80]; 95% prediction interval [PI], 0.67-12.98), relatively long duration of labor (OR, 1.04 [95% CI, 1.02-1.06]; 95% PI, 1.00-1.08), episiotomy (OR, 1.56 [95% CI, 1.19-2.06] 95% PI, 0.64-3.83), nulliparity (OR, 1.55 [95% CI, 1.30-1.84]; 95% PI, 0.94-2.77), epidural analgesia (OR, 2.99 [95% CI, 1.78-5.03]; 95% PI, 0.53-16.76), labor augmentation (OR, 2.21 [95% CI, 1.49-3.28]; 95% PI, 0.12-39.26), labor induction (OR, 1.73 [95% CI, 1.12-2.66]; 95% PI, 0.40-7.39), and perineal injury (OR, 2.75 [95% CI, 1.95-3.89]; 95% PI, 1.10-6.92). Instrumental delivery, extended labor duration, episiotomy, nulliparity, epidural analgesia, labor augmentation/induction, and perineal injury are significant risk factors for PUR. The findings could help physicians identify patients at risk in the postpartum setting.

Dynamic Cluster Head Selection in WSN

A Wireless Sensor Network (WSN) comprises an ad-hoc network of nodes laden with sensors that are used to monitor a region mostly in the outdoors and often not easily accessible. Despite exceptions, several deployments of WSN continue to grapple with the limitation of finite energy derived through batteries. Thus, it is imperative that the energy of a WSN be conserved and its life prolonged. An important direction of work to this end is towards the transmission of data between nodes in a manner that minimum energy is expended. One approach to doing this is cluster-based routing, wherein nodes in a WSN are organised into clusters, and transmission of data from the node is through a representative node called a cluster-head. Forming optimal clusters and choosing an optimal cluster-head is an NP-Hard problem. Significant work is done towards devising mechanisms to form clusters and choosing cluster heads to reduce the transmission overhead to a minimum. In this article, an approach is proposed to create clusters and identify cluster heads that are near optimal. The approach involves two-stage clustering, with the clustering algorithm for each stage chosen through an exhaustive search. Furthermore, unlike existing approaches that choose a cluster-head on the basis of the residual energy of nodes, the proposed approach utilises three factors in addition to the residual energy, namely the distance of a node from the cluster centroid, the distance of a node from the final destination (base-station), and the connectivity of the node. The approach is shown to be effective and economical through extensive validation via simulations and through a real-world prototypical implementation.

Dynamic RIS partitioning in NOMA systems using deep reinforcement learning

The rapid evolution of wireless communication technologies necessitates innovative solutions to meet the increasing performance requirements of future networks, particularly in terms of spectral efficiency, energy efficiency, and computational efficiency. Reconfigurable Intelligent Surfaces (RIS) and Non-Orthogonal Multiple Access (NOMA) are emerging as promising technologies to enhance wireless communication systems. This paper explores the dynamic partitioning of RIS elements in NOMA systems using Deep Reinforcement Learning (DRL) to optimize resource allocation and overall system performance. We propose a novel DRL-based framework that dynamically adjusts the partitioning of RIS elements to maximize the achievable sum rate and ensure fair resource distribution among users. Our architecture leverages the flexibility of RIS to create an intelligent radio environment, while NOMA enhances spectral efficiency. The DRL model is trained online, adapting to real-time changes in the communication environment. Empirical results demonstrate that our approach closely approximates the performance of the optimal iterative algorithm (exhaustive search) while reducing computational time by up to 90 percent. Furthermore, our method eliminates the need for an offline training phase, providing a significant advantage in dynamic environments by removing the requirement for retraining with every environmental change. These findings highlight the potential of DRL-based dynamic partitioning as a viable solution for optimizing RIS-aided NOMA systems in future wireless networks.

Machine Learning Models for Predicting Mortality in Critically Ill Patients with Sepsis-Associated Acute Kidney Injury: A Systematic Review.

While machine learning (ML) models hold promise for enhancing the management of acute kidney injury (AKI) in sepsis patients, creating models that are equitable and unbiased is crucial for accurate patient stratification and timely interventions. This study aimed to systematically summarize existing evidence to determine the effectiveness of ML algorithms for predicting mortality in patients with sepsis-associated AKI. An exhaustive literature search was conducted across several electronic databases, including PubMed, Scopus, and Web of Science, employing specific search terms. This review included studies published from 1 January 2000 to 1 February 2024. Studies were included if they reported on the use of ML for predicting mortality in patients with sepsis-associated AKI. Studies not written in English or with insufficient data were excluded. Data extraction and quality assessment were performed independently by two reviewers. Five studies were included in the final analysis, reporting a male predominance (>50%) among patients with sepsis-associated AKI. Limited data on race and ethnicity were available across the studies, with White patients comprising the majority of the study cohorts. The predictive models demonstrated varying levels of performance, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.60 to 0.87. Algorithms such as extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) showed the best performance in terms of accuracy. The findings of this study show that ML models hold immense ability to identify high-risk patients, predict the progression of AKI early, and improve survival rates. However, the lack of fairness in ML models for predicting mortality in critically ill patients with sepsis-associated AKI could perpetuate existing healthcare disparities. Therefore, it is crucial to develop trustworthy ML models to ensure their widespread adoption and reliance by both healthcare professionals and patients.

Novel hybrid integrated Pix2Pix and WGAN model with Gradient Penalty for binary images denoising

This paper introduces a novel approach to image denoising that leverages the advantages of Generative Adversarial Networks (GANs). Specifically, we propose a model that combines elements of the Pix2Pix model and the Wasserstein GAN (WGAN) with Gradient Penalty (WGAN-GP). This hybrid framework seeks to capitalize on the denoising capabilities of conditional GANs, as demonstrated in the Pix2Pix model, while mitigating the need for an exhaustive search for optimal hyperparameters that could potentially ruin the stability of the learning process. In the proposed method, the GAN’s generator is employed to produce denoised images, harnessing the power of a conditional GAN for noise reduction. Simultaneously, the implementation of the Lipschitz continuity constraint during updates, as featured in WGAN-GP, aids in reducing susceptibility to mode collapse. This innovative design allows the proposed model to benefit from the strong points of both Pix2Pix and WGAN-GP, generating superior denoising results while ensuring training stability. Drawing on previous work on image-to-image translation and GAN stabilization techniques, the proposed research highlights the potential of GANs as a general-purpose solution for denoising. The paper details the development and testing of this model, showcasing its effectiveness through numerical experiments. The dataset was created by adding synthetic noise to clean images. Numerical results based on real-world dataset validation underscore the efficacy of this approach in image-denoising tasks, exhibiting significant enhancements over traditional techniques. Notably, the proposed model demonstrates strong generalization capabilities, performing effectively even when trained with synthetic noise.

Acute pericarditis: when is an exhaustive search of causes needed?

Acute pericarditis: when is an exhaustive search of causes needed?

Deployment Approach in WSNS Using Metaheuristic Algorithm: A Survey

Deploying wireless sensor networks (WSN) is one of the areas with the most significant research since the methods employed can have a big impact on the system's overall performance and the amount of energy the sensors in it consume.Thus, an effective deployment problem solution should not only "enhance its performance" but also save the energy needed to extend the lifetime of WSN. Finding an optimal solution for most deployment problems with limited computational resources is challenging, particularly for NP-hard optimization problems. By searching for a nearly optimal solution with constrained computational resources in a reasonable amount of time, metaheuristics present an alternative to exhaustive search and deterministic algorithms in solving these optimization problems. This paper classifies the deployment techniques of sensor networks and the metaheuristic algorithms.

Evaluating the efficacy of surgical and conservative approaches in mild autonomous cortisol secretion: a meta-analysis.

The management of Mild Autonomous Cortisol Secretion (MACS) remains a topic of debate among clinicians, with differing opinions on the effectiveness of surgical intervention compared to conservative treatment methods. This meta-analysis provides a comprehensive assessment of available literature to determine the most effective approach for treating this condition. On December 1, 2023, an exhaustive literature search of English databases Embase, PubMed, the Cochrane Library, Scopus, Web of Science, as well as the Chinese databases China HowNet, Wanfang Database, SinoMed Database, and Weipu Database using the keywords "Mild Autonomous Cortisol Secretion", "Subclinical Cushing's Syndrome", "Subclinical Hypercortisolism", "Mild Cortisol Autonomous Secretion", "Adrenal Incidentaloma", "Surgical Treatment" and "Adrenalectomy". The data were statistically analyzed using STATA version 15.0. In this comprehensive analysis involving 629 patients with MACS, the therapeutic efficacy of adrenalectomy was evident. The meta-analysis results indicate that compared to conservative treatment, surgical intervention more effectively improves obesity indicators in patients: waist circumference (SMD=-0.62, 95% CI: -1.06 to -0.18), BMI (SMD=-0.41, 95% CI: -0.62 to -0.20), enhances glycemic control: fasting blood glucose (SMD=-0.47, 95% CI: -0.68 to -0.26), glycated hemoglobin (SMD=-0.66, 95% CI: -0.95 to -0.38), improves lipid metabolism: triglycerides (SMD=-0.45, 95% CI: -0.73 to -0.16), lowers blood pressure: systolic blood pressure (SMD=-1.04, 95% CI: -1.25 to -0.83), diastolic blood pressure (SMD=-0.89, 95% CI: -1.12 to -0.65), and ameliorates hormonal metabolic disorder: 24h urinary free cortisol (SMD=-1.10, 95% CI: -1.33 to -0.87), ACTH (SMD=2.30, 95% CI: 1.63 to 2.97). All these differences are statistically significant. This meta-analysis shows that, compared to conservative treatment, surgical treatment is more effective in improving obesity indicators, glycemic control, lipid metabolism, reducing blood pressure, and ameliorating hormonal metabolic disorders in patients with MACS. These statistically significant results highlight the importance of considering surgical intervention in the management of patients with MACS. https://www.crd.york.ac.uk/prospero, identifier CRD42023492527.

A Systematic Review on the Path to Inclusive and Sustainable Energy Transitions

This paper surveys the energy literature and systematically reviews the path to an inclusive and sustainable energy transition by exploring factors that drive the current energy transitions, countries with advanced energy transition programs, and the roles of energy literacy and justice in energy transition. Utilizing an exhaustive literature search from 2001 to 2023 via the Scopus database, the study identifies strong policy frameworks, technological advancements, economic incentives, and international collaborations as pivotal factors in successful energy transitions. Case studies from the Nordic countries, Germany, and Poland highlight diverse approaches and significant progress, revealing valuable lessons for global application. Although energy literacy emerges as crucial for public acceptance and participation, fostering informed decision-making and supportive behaviors toward renewable energy initiatives, energy justice ensures equitable access to the benefits of energy transitions, addressing socio-economic impacts on marginalized communities. The study identifies a scarcity of research that articulates and integrates energy literacy and justice concurrently within the purview of energy transition. The paper recommends the adoption, integration, and institutionalization of frameworks that concurrently propagate energy literacy and guide fair and equitable energy transitions. The frameworks should encourage active community involvement, promote community ownership of renewable energy projects, ensure transparency and inclusivity, implement measures for equitable economic benefits, protect livelihoods, address historical distrust, and leverage social media to promote energy literacy and justice. Finally, the continuous monitoring and evaluation of energy transition initiatives are crucial to ensure that they meet evolving societal needs and environmental goals.

Priority-based subcarrier allocation algorithm for maximal network connectivity in 5G networks

With the widespread adoption of wireless technology, there has been a significant surge in the number of devices seeking wireless connectivity over the past decade. To meet the extensive demand for high-data-rate wireless connectivity, the fifth-generation (5G) cellular network plays a pivotal role. 5G cellular network aims to support a large number of applications with ultra-high data rates by maximizing device connectivity while satisfying quality of service (QoS) requirements. In this paper, we present an innovative priority-based subcarrier allocation (PSA) algorithm to address the challenge of maximizing connectivity in 5G new radio (5G NR) networks. Initially, we formulate the connectivity maximization problem as a subcarrier allocation problem by considering three key parameters: bandwidth requirement, waiting time, and energy level of user devices. The objective of the formulated problem is to optimally allocate subcarriers to multiple users in order to maximize connectivity while maintaining QoS requirements. To address the problem, we propose the PSA algorithm that prioritizes bandwidth, waiting time, and energy parameters using the R-method. To accommodate the network scenarios, we develop three variants of the PSA algorithm—PSA-1, PSA-2, and PSA-3. These variants allocate subcarriers based on the priority-based score of user. We carried out a simulation-based study to illustrate the effectiveness of our proposed algorithm in comparison to traditional methods. The simulation results reveal that our proposed algorithms outperform first come first serve (FCFS) and longest remaining time first (LRTF), and achieves comparable or superior results compared to priority and fairness-based resource allocation with 5G new radio numerology (PFRA-0N) in terms of the number of user allocations, average user allocation ratio, user drop ratios and average connectivity rate. Compared to the shortest job first (SJF) technique, our proposed PSA algorithm performance is slightly inferior in terms of the number of user allocations, average allocation ratio and average connectivity rate; however, it shows superior performance in drop ratios. Further, the proposed algorithms show significant improvements in execution time compared to the optimal exhaustive search solution.

Deep permutation type design for Chinese characters

With their intricate visual structure and cultural significance, Chinese characters present a unique realm for exploration in graphic design. However, the integration of graphic elements, such as two-dimensional primitives that are not limited to straight or curved strokes, into Chinese-type design relies mainly on the designer’s intuition. This paper investigates the application of deep permutation design methods to Chinese type design to explore the esthetic potential of graphic-based type design in a systematic and algorithmic manner. For this purpose, we propose a novel framework and present an implementation named ‘Permutype’, which operates in three phases: representation, generation, and evaluation. Through a series of visual experiments, we demonstrate how our framework promotes novel permutations and esthetic possibilities for Chinese characters. The methodology offers flexible graphics and exhaustive optimal scheme searches based on objective criteria and has the potential for extensive font libraries.

Associations between delay discounting and unhealthy behaviors in substance use recovery

BackgroundResearch on delay discounting (DD) is mixed on whether DD is a domain-specific component related to specific behaviors or a domain-general process that cuts across various behaviors. A pivotal group to test the associations between DD and unhealthy behaviors is individuals in recovery from substance use disorders (SUD), as they are moving away from a disorder toward a healthier state. MethodsIndividuals in SUD recovery (n = 317) completed the Temptation Scale, the Health Behaviors Questionnaire, and an Adjusting Delay Discounting Task. An exhaustive model space search was performed using linear regression to examine associations between DD with temptation, engagement in unhealthy behaviors, and the total number of unhealthy behaviors participants engage in. We also tested whether remission status is associated with the total number of unhealthy behaviors participants engage in. ResultsResults revealed that DD was positively associated with poor eating (p<.001), physical inactivity (p=.003), financial irresponsibility (p<.001), risky behaviors (p<.001), lack of personal development goals (p<.001), lack of household savings (p=.004), and lack of health behaviors (p=.003). DD was also positively associated with the total number of unhealthy behaviors participants engage in (p<.001). Participants who were not in remission engaged in more unhealthy behaviors compared to those who were in remission (p<.001). ConclusionIn a sample of individuals in recovery from SUD, DD is not domain-specific and undergirds engagement in several maladaptive health behaviors that can negatively impact recovery. Thus, DD can be a target for interventions aiming to reduce other maladaptive behaviors in SUD recovery.

Deep learning based bilateral filtering for edge-preserving denoising of respiratory-gated PET

BackgroundResidual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) — a locally adaptive image filter — allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by training a suitable network with the target of reproducing the results of manually adjusted case-specific bilateral filtering.MethodsAltogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ([18F]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[^{18}\ ext {F}]$$\\end{document}FDG, [18F]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[^{18}\ ext {F}]$$\\end{document}L-DOPA, [68Ga]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[^{68}\ ext {Ga}]$$\\end{document}DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the “optimal” filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs.ResultsThe automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal SUVmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {SUV}_\ ext {max}$$\\end{document} values of the unfiltered images with a low mean ± SD difference of δSUVmaxCNN,STD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta \ ext {SUV}_\ ext {max}^{\ ext {CNN},\ ext {STD}}$$\\end{document} = (−3.9 ± 5.2)% and δSUVmaxBF,STD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta \ ext {SUV}_\ ext {max}^{\ ext {BF},\ ext {STD}}$$\\end{document} = (−4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar SUVmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {SUV}_\ ext {max}$$\\end{document} values in the vast majority of cases with an overall average difference of δSUVmaxCNN,BF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta \ ext {SUV}_\ ext {max}^{\ ext {CNN},\ ext {BF}}$$\\end{document} = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with δNoiseCNN,BF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta \ ext {Noise}^{\ ext {CNN},\ ext {BF}}$$\\end{document} = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed.ConclusionsOur results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning.

New global multi-objective optimization of compact heat exchanger

This paper introduces a robust and global algorithm, the multi-objective version of the Set Trimming method (MOST). The application of MOST is demonstrated through optimization of a plate and fin heat exchanger. The results are compared with NSGA-II (Fast and elitism non-dominated sorting Genetic Algorithm) and an exhaustive search. The optimization focuses on effectiveness and annual cost, with seven selected design parameters and for six different cases. Results indicate that the Pareto optimal fronts obtained with NSGA-II are entirely dominated by those achieved using MOST in the all studied cases. Furthermore, MOST improves annual cost for the best economic point solution by up to 13.44% as compared to NSGA-II. Simultaneously, effectiveness is enhanced up to 0.04% when using MOST compared to NSGA-II. The computational time is also improved with the presented optimization algorithm compared to NSGA-II in the range of 78.6–88.8%.

Incorporation of artificial intelligence into nursing research: A scoping review.

The integration of artificial intelligence (AI) across different sectors, notably healthcare, is on the rise. However, a thorough exploration of AI's incorporation into nursing research, as well as its advantages and obstacles, is still lacking. The aim of this scoping review was to map the roles, benefits, challenges, and potentials for the future development and use of AI in the context of nursing research. An exhaustive search was conducted across seven databases: MEDLINE, PsycINFO, SCOPUS, Web of Science, CINAHL, Google Scholar, and ProQuest. Articles were additionally identified through manual examination of reference lists of the articles that were included in the study. The search criteria were restricted to articles published in English between 2010 and 2023. The Joanna Briggs Institute (JBI) approach for scoping reviews and the PRISMA-ScR guidelines guided the processes of source selection, data extraction, and data presentation. Twenty articles met the inclusion criteria, covering topics from ethical considerations to methodological issues and AI's capabilities in data analysis and predictive modeling. The review identified both the potentials and complexities of integrating AI into nursing research. Ethical and legal considerations warrant a coordinated approach from multiple stakeholders. The findings emphasized AI's potential to revolutionize nursing research, underscoring the need for ethical guidelines, equitable access, and AI literacy training to ensure its responsible and inclusive use.

Exploring Innovation Capabilities in Organizations through a Scientometric Approach in the Context of Manufacturing Industry

Over the last four decades, innovation capability has been studied to examine the innovation phenomenon based on the specific characteristics of organizations and systems under the theory of resources and capabilities. However, "capability" has diverse applications, and its correlation with innovation remains unclear. This study aims to clarify the definition of "innovation capability" in the context of the manufacturing industry and to comprehend how researchers presently investigate this concept. In order to meet this, an exhaustive search was conducted in the Scopus and Web of Science (WoS) databases, covering the period from 2018 to 2023. The selection of documents for analysis was determined by applying the scientometric method. The authors of this research identified 462 documents, and the bibliometric analysis, conducted using VOS viewer and Bibliometrix tools, revealed four main thematic areas within the research domain: sustainability, business performance, technology transfer, and knowledge management. As a result, this study identified the seminal authors of the concept. It provided a precise definition of "innovation capability", highlighting the significant interest that this research topic arouses in current academic literature.