Complete Search Research Articles

The effects of gaze-display feedback on medical students’ self-monitoring and learning in radiology

Self-monitoring is essential for effectively regulating learning, but difficult in visual diagnostic tasks such as radiograph interpretation. Eye-tracking technology can visualize viewing behavior in gaze displays, thereby providing information about visual search and decision-making. We hypothesized that individually adaptive gaze-display feedback improves posttest performance and self-monitoring of medical students who learn to detect nodules in radiographs. We investigated the effects of: (1) Search displays, showing which part of the image was searched by the participant; and (2) Decision displays, showing which parts of the image received prolonged attention in 78 medical students. After a pretest and instruction, participants practiced identifying nodules in 16 cases under search-display, decision-display, or no feedback conditions (n = 26 per condition). A 10-case posttest, without feedback, was administered to assess learning outcomes. After each case, participants provided self-monitoring and confidence judgments. Afterward, participants reported on self-efficacy, perceived competence, feedback use, and perceived usefulness of the feedback. Bayesian analyses showed no benefits of gaze displays for post-test performance, monitoring accuracy (absolute difference between participants’ estimated and their actual test performance), completeness of viewing behavior, self-efficacy, and perceived competence. Participants receiving search-displays reported greater feedback utilization than participants receiving decision-displays, and also found the feedback more useful when the gaze data displayed was precise and accurate. As the completeness of search was not related to posttest performance, search displays might not have been sufficiently informative to improve self-monitoring. Information from decision displays was rarely used to inform self-monitoring. Further research should address if and when gaze displays can support learning.

Efficacy of Intravenous Iron in Patients with Heart Failure with Reduced Ejection Fraction and Iron Deficiency: A Systematic Review and Meta-Analysis of Randomized Control Trials.

The European Society of Cardiology (ESC) provided a focused update to the 2021 Guideline for the Management of Heart Failure, now providing a 1A recommendation for intravenous iron in patients with heart failure with reduced ejection fraction (HFrEF) and iron deficiency (ID). However, the findings from randomized controlled trials (RCT) are mixed. This systematic review of RCTs aims to provide an update and synthesize the evidence addressing the association of intravenous iron with patient-based outcomes in patients with HFrEF and ID. Any RCT evaluating the effect of intravenous iron in patients with HFrEF and ID was eligible for inclusion. A complete search of the EMBASE and PubMed databases was conducted from inception until 15 September 2023. The primary outcome was the composite of the quality of life (QoL) questionnaires, while the secondary outcomes included first heart failure (HF) hospitalizations and all-cause mortality. Data extraction was performed independently by two reviewers. Data were pooled using a random-effects model. Of the 1035 references, 15 RCTs enrolling 6649 patients were included in this study. Intravenous iron was associated with significant improvement in the composite of QoL (standardized mean difference -1.36, 95% confidence interval [CI] -2.24 to -0.48; p=0.002), a significant reduction in first HF hospitalizations (hazard ratio [HR] 0.73, 95% CI 0.56-0.95; p=0.02), and with no change in all-cause mortality (HR 0.90, 95% CI 0.79-1.03; p=0.12). The certainty of the evidence ranged from moderate to very low. Intravenous iron is possibly associated with improved QoL and reduced HF hospitalizations, without impacting all-cause mortality. These findings not only support the use of intravenous iron in patients with HFrEF but also emphasize the need for well-designed and executed RCTs with granular outcome reporting and powered sufficiently to address the impact of intravenous iron on mortality in patients with HFrEF and ID. PROSPERO identifier number CRD42023389.

Search space division method for wrapper feature selection on high-dimensional data classification

Feature selection (FS) is an essential pre-processing technique for high-dimensional data. Wrapper-based FS techniques are known for their superior performance over filter FS. However, when the dimensionality of data is very high the wrapper techniques become computationally very expensive. To solve this problem of scalability, this paper proposes the concept of search space division (SSD) which leads to smaller search spaces and hence reduced computational cost. The proposed SSD approach is generic in nature and can be integrated with any wrapper-based FS technique. The SSD approach divides search space into multiple parts and the wrapper-based FS is independently applied to each part. To facilitate the interaction of features over the complete search space, all feature subsets obtained from each part are combined and the wrapper-based FS is again applied to get the final feature subset. Moreover, a new wrapper FS technique named Binary Rao (BRAO) Algorithm has been proposed. BRAO is based on the metaphor-less, parameter-less meta-heuristic optimization algorithm Rao-1. The proposed combination of SSD and BRAO algorithm named SSDR has produced better classification accuracies with a smaller number of features in a much shorter time as compared to other well-regarded wrapper FS techniques as well as classical FS techniques over thirteen benchmark high-dimensional datasets.

Better-than-classical Grover search via quantum error detection and suppression

We report better-than-classical success probabilities for a complete Grover quantum search algorithm on the largest scale demonstrated to date, of up to five qubits, using two different IBM platforms. This is enabled by error suppression via robust dynamical decoupling. Further improvements arise after the use of measurement error mitigation, but the latter is insufficient by itself for achieving better-than-classical performance. For two qubits, we demonstrate a 99.5% success probability via the use of the [[4, 2, 2]] quantum error-detection (QED) code. This constitutes a demonstration of quantum algorithmic breakeven via QED. Along the way, we introduce algorithmic error tomography (AET), a method that provides a holistic view of the errors accumulated throughout an entire quantum algorithm, filtered via the errors detected by the QED code used to encode the circuit. We demonstrate that AET provides a stringent test of an error model based on a combination of amplitude damping, dephasing, and depolarization.

A systematic review of oral wound healing indices.

A complete literature electronic search in 5 databases was conducted by two reviewers. A combination of keywords related to oral wound healing was used. A total of 11 articles were included in the evaluation of various procedures (conventional periodontal procedures, guided tissue regeneration, soft tissue reconstruction procedures, and tooth extractions), at different time points (1 day to 12 weeks), with a focus on diverse clinical signs and symptoms. Frequently evaluated parameters included wound dehiscence/epithelialization (91%), tissue color (redness) (73%), suppuration (55%), swelling/edema (55%), and hemostasis (55%). Other less commonly used parameters include esthetics-related and patient-centered outcomes. The available indices evaluate a diverse group of subjective clinical signs and symptoms to estimate the underlying biological healing events and assess the degree of clinical success. The majority of the included indices are not validated. Quantitative and objective subclinical parameters including blood perfusion, biomaterial stability, and completeness of epithelialization, are needed for customized wound healing care and better outcome prediction.

Clinical Efficacy of Ozone Therapy in Peri-implant Diseases: A Systematic Review

Introduction: With an increasing number of dental implants being placed each year, there has been a consequent rise in the frequency of peri-implant diseases. Ozone, a potent antibacterial agent, eliminates the microbes that cause infections around implants. Aim: To assess ozone therapy’s effectiveness of ozone therapy as an adjunct in the treatment of peri-implant diseases. Materials and Methods: A complete search was conducted in electronic databases such as PubMed, Google Scholar, and Cochrane, employing the eligibility criteria. After a thorough review, six studies published from 2013 to 2023 that evaluated the effectiveness of ozone therapy in clinical trials for the management of peri-implant diseases were included in this systematic review. Results: The six selected studies evaluated ozone therapy as a treatment for peri-implant diseases and evaluated relevant clinical indices. Four studies were in-vivo, out of which one evaluated peri-implantitis and three on peri-implant mucositis. Three studies showed a reduction in the Plaque Index (PI) during follow-up, while one study stated that it showed improvement at one month, with no significant further reduction seen. Two invitro studies stated that ozone therapy helped reduce bacterial adherence to the implant surface. Conclusion: Considering the findings of the previous studies, the present systematic review concludes that ozone therapy shows progressive improvement in the management of periimplant diseases.

Different Approaches to Islamic Medicine in Iran

Background: Subject and objectives: Using the capabilities of Islamic civilization is one of the topics that has attracted the attention of contemporary Islamic thinkers. In this context, various opinions have been presented about whether medicine is Islamic or not among Iranian thinkers. In this article, we seek to examine the negative and positive opinions about the nature of Islamic medicine. The purpose of this article is to find a precise definition of Islamic medicine and provide a correct solution in using this treatment model. Result: This research deals with the study of Islamic medicine through descriptive-analytical and documentary methods. In this research, a complete search of electronic banks, related books, and internet sites was conducted and all acceptable articles were reviewed completely and systematically. There are different views on the nature of Islamic medicine, each of which has looked at the issue from its own angle, some agree with it and some oppose it. Conclusion: Islamic medicine has very good capacities, although there are serious criticisms against it. Islamic medicine is a specialized science, and a common opinion should be reached about its limits and conditions, and a system should be designed and drawn for it. The revival of Islamic medicine will contribute to the development of the modern Islamic civilization.

Fixation of Mandibular Fractures with Lag Screw: Integrative Review

Introduction: The mandible is the only movable bone of the face and plays a crucial role in masticatory, swallowing, phonation, and aesthetic functions. Mandibular fractures have the potential to cause numerous important changes. Angle, parasymphysis, body and condyle are the anatomical regions most affected by trauma. Rigid Internal Fixation (RIF) facilitated the process of treatment and recovery of mandibular functions. The Lag Screw fixation technique is based on the use of screws that manage to compress the bone fragments without the use of plates.Objective: This integrative review aims to review the fixation of mandibular fractures using the Lag Screw technique.Methods: Through a complete search in PubMed, Web of Science, Embase, and Cochrane databases, nine articles that contemplated the method of fixation of mandibular fractures using the Lag Screw technique were included.Results: The studies showed that the technique was associated with a lower number of intra and postoperative complications, provided safe RIF, greater compression between the stumps, facilitating primary bone healing, and presented a shorter surgical time, economic advantage. On the other hand, the technique proved to be sensitive and limited only to specific cases.Conclusion: The application of the Lag Screw fixation technique is an effective and stable RIF method that can be performed in the treatment of mandibular fractures.

Multi-modal fusion architecture search for camera-based semantic scene completion

Camera-based Semantic scene completion (SSC) aims to infer the 3D volumetric occupancy and semantic categories of a scene simultaneously from a single RGB image. The main challenge of camera-based SSC is the lack of geometry information compared with RGB-D SSC. Although the estimated depth from RGB image will help SSC to some extent, the depth prediction quality is far from the demand of SSC. To solve this problem, we propose a NAS-based multi-modal fusion method to incorporate the semantic and geometry information from other intermediate representations (predicted depth and predicted 2D segmentation) to form a more robust 2D feature representation. A key idea of this design is that explicit 2D semantic information could alleviate the misleading information of 3D distortions introduced by estimated depth. Specifically, we propose the Confidence-Block to automatically learn an optimal architecture for routing and obtaining the depth prediction confidence. We propose the two-level fusion search space by decomposing the fusion search space into fusion stage search space and fusion operation search space. Moreover, we propose a confidence-aware 2D–3D projection module to alleviate the 3D projection error. Extensive experiments show that our method outperforms the state-of-the-art method by a large margin using a single RGB image on NYU and NYUCAD datasets.

On the Problem of Tension Forces Distribution in Cable System of Cable-Driven Parallel Robot

The paper proposes a method for controlling tension forces in statically indeterminable cable-driven systems based on the non-negative least squares method with control of singular or near-singular solutions and a complete search of all possible cable configurations. For cable-driven parallel robots the problem of controlling the cable tension forces is critical, because in the absence of control the cable tension forces are distributed unevenly, which leads to reduced robustness of the system, increased energy consumption and increased deterioration. And in special cases of cable system configuration the tension forces become so great that they lead to cable breaks. At the same time, correction of cable tension force distribution should not lead to significant deviations from the specified position of the mobile platform or, formulating the problem in terms of forces, to violation of kinetostatic equations. Thus, the problem of controlling the tension forces in the cable parallel robot system is solved as a problem of optimizing the tension forces of the cables according to the criteria of minimizing the norm of their vector in the configuration space and minimizing the norm of incoherence of the vector of forces and moments in the operational space of the robot. The developed algorithm is based on the solution of underdetermined systems of linear algebraic equations with finding the minimum least squares norms and subsequent zeroing of negative components of the solution vector. The paper considers examples of the solution of the set problem for the lower cable group of a construction 3D printer based on a cable-driven robot and for a 12-cable system

Contrast-associated acute kidney injury following intravenous contrast media computed tomography; new concept and future directions: A systematic review study on emergencies patients

Introduction: Computed tomography (CT) is a key method for various disorders. Image can be more quality with intravenous contrast media, however in some cases may be accompanied by a risk of kidney impairment. Objectives: This study aimed to investigate the association between acute kidney injury incidence and intravenous contrast media for CT in emergency patients. Methods and Materials: Search strategies were performed using standard keywords across international databases such as Web of Science, Scopus, Cochrane, PubMed, and Embase. Dimension, OpenGrey, DOAJ, CINAHL, and Google Scholar search engines were searched for a complete search. Additionally, manual searching was conducted using the references of related articles. Studies that reported the correlation between acute kidney injury incidence and intravenous contrast media were included in this systematic review. Results: First, 1185 studies were identified. After duplication, 533 studies remained and 417 were excluded. Out of 116 evaluated studies for retrieval, 49 were eliminated and 67 were assessed for eligibility. Ultimately, 28 studies with 48878 patients were included in the final review. Most of the studies were retrospective cohorts and have found no significant correlation between the incidence of acute kidney injury and intravenous contrast media administration (ICMA) for CT. Conclusion: Intravenous contrast media with a conventional dose for CT does not cause acute kidney injury unless in the presence of a particular condition. Registration: This study was compiled following the PRISMA checklist and its protocol was registered on the PROSPERO (CRD42023448461) and Research Registry (UIN: reviewregistry1690) websites.

Two-dimensional improved attribute reductions based on distance granulation and condition entropy in incomplete interval-valued decision systems

Attribute reductions rely on knowledge granulation and information measurement. Aiming at incomplete interval-valued decision systems (IIVDSs), an attribute reduction (with the FSR-AR/HS-AR algorithm) emerges by combining distance granulation and condition entropies; however, its distance measurement has defects for maximum and minimum fillings, while its condition entropy has promotion space on information enrichment. This paper utilizes FSR-AR/HS-AR to make the two-dimensional improvements of distance granulation and condition entropy to improve IIVDS-driven attribute reductions. Concretely, distance measures and similarity degrees are corrected via range completion and statistical enhancement, and the condition entropy is deepened by replacing credibility with coverage-credibility, so 2×2=4 reduction algorithms extend and improve FSR-AR/HS-AR. First, new maximal and minimal distances are defined via complete search and statistical optimization of missing values, so an amended fuzzy α-similarity relation induces knowledge granulation. Then, improved informational, conditional, joint entropies, and mutual information are established via coverage-credibility, and they obtain system equations and granulation nonmonotonicity. Furthermore, the two-dimensional improvements generate 2×2=4 attribute reductions, and the corresponding heuristic algorithms include FSR-AR/HS-AR and three improved algorithms. Finally, improvements in uncertainty measures and reduction algorithms are validated via table examples and data experiments, and the three novel algorithms outperform FSR-AR/HS-AR and contrastive algorithms for classification performances.

Blockchain-assisted verifiable certificate-based searchable encryption against untrusted cloud server for Industrial Internet of Things

The Industrial Internet of Things (IIoT) has brought practical application value to many industries, where significant amounts of IIoT data and resources are outsourced to cloud server (CS) via diverse networks for data fusion, monitoring, sharing, and calculation analysis. Considering privacy, there is a need to execute the encryption operation on the data before outsourcing, while how to retrieve the encrypted data from CS becomes a thorny issue. Furthermore, the untrusted CS in charge of storing and searching the ciphertexts may return incorrect or incomplete search results for some interest. Verifiable public key searchable encryption (VPKSE) provides the ability to encrypt data, retrieve ciphertext, and verify search results simultaneously. However, the malicious behavior of CS has not been sufficiently considered in most existing schemes, that is, their verifiability only ensures the correctness of search results, neglecting completeness. In this paper, the verifiability of VPKSE is re-examined, and three verifiability levels are defined detailedly. On this basis, a blockchain-assisted verifiable certificated-based searchable encryption (BVCBSE) scheme for IIoT is put forward. The integration of blockchain and cryptographic accumulator ensures that an untrusted CS must return correct and complete search results, achieving the highest level of verifiability. In addition, security analysis demonstrates that BVCBSE can resist keyword guessing attack. Performance evaluation illustrates that BVCBSE is efficient and practical.

Efficacy of Decision Aids in The Use of Left Ventricular Assist Device in Patients With Advanced Heart Failure: A Systematic Review and Meta-Analysis of Randomized Control Trials

Although left ventricular assist device (LVAD) implantation can improve survival in patients with end-stage heart failure, it is not without risk. Numerous complications are possible, and durable support requires substantial lifestyle changes. The use of various knowledge-assessment tools may allow for more informed patient decisions. To synthesize the totality of the evidence, we conducted a systematic review and meta-analysis to summarize the efficacy of decision aid (DA) use in patients with advanced heart failure who are eligible for LVAD. Any randomized controlled trial (RCT) evaluating the efficacy of DAs in patients considering LVAD was eligible for inclusion. A complete search of EMBASE and PubMed was conducted from the start until June 8, 2023. The primary outcome was patients' LVAD knowledge. Data extraction was performed independently by 2 reviewers. Data were pooled using a random-effects model. Of the 575 references, 2 RCTs randomizing 490 patients were included in this study. DAs were associated with no significant change in LVAD knowledge (standardized mean difference 0.07, 95% confidence interval -0.24 to 0.39, p=0.64) or decisional conflict (mean difference -1.48, 95% confidence interval -5.28 to 2.32, p=0.45). The certainty of the evidence ranged from moderate to very low. The use of DAs in LVAD-eligible patients with advanced heart failure resulted in no difference in patients' knowledge of LVAD after LVAD education. The findings from this study will aid in the power analysis of a well-designed RCT to evaluate and encourage further investigation into the efficacy and relevance of DAs in preparing patients for a life with LVAD.

Global rigorous coupled wave analysis for design of multilayer metasurface absorbers.

In this work, a novel Global NV-ETM RCWA method is proposed to accelerate the optimization of the periodic stepped radar absorbing structure. This method is based on the rigorous coupled-wave analysis (RCWA) utilizing the normal vector field (NV) and enhanced transmittance matrix (ETM) approach. The NV field dramatically improves the convergence rate for both dielectric and magnetic metasurfaces. The Global NV-ETM RCWA algorithm is developed to further accelerate the complete search calculations. Using the proposed method, the periodic stepped radar absorbing structures are efficiently optimized to realize the entire band absorption in 2-18 GHz. The optimization results demonstrate the Global NV-ETM RCWA method significantly increase the computational efficiency, with a 38-fold improvement over direct NV-ETM RCWA calculations when the truncation order N=3. This method provides a powerful tool for designing metasurface absorbers with various desired functionalities.

GAN-AE: an anomaly detection algorithm for New Physics search in LHC data

In recent years, interest has grown in alternative strategies for the search for New Physics beyond the Standard Model. One envisaged solution lies in the development of anomaly detection algorithms based on unsupervised machine learning techniques. In this paper, we propose a new Generative Adversarial Network-based auto-encoder model that allows both anomaly detection and model-independent background modeling. This algorithm can be integrated with other model-independent tools in a complete heavy resonance search strategy. The proposed strategy has been tested on the LHC Olympics 2020 dataset with promising results.

Review of Intraoral Vasculature and Implications on Incision Designs of Periodontal and Implant Surgeries.

Currently, the incision design for periodontal and implant surgeries is mainly based on the surgeon's personal preference. The primary aim of this study is to review the intricate periodontal microvascular system and to illustrate the potential impact of commonly applied flap designs on the integrity of this system. A complete literature electronic search resulted in 37 relevant articles. The maxillary, facial, and lingual arteries supply the microvasculature system, including the supraperiosteal, intraligamental, and intraosseous arterioles. These arterioles have their own territories yet are extensively interconnected. The impact of common papilla management techniques are discussed, including tunneling; papilla base, incision, and preservation; and flap-releasing incisions. Flap design can impact reperfusion and blood inflow in the early healing phase, which eventually influences wound closure probability, healing speed, surgical predictability, tissue volume change, and postoperative morbidity. Future studies on the three-dimensional distribution of microvasculature and clinical impact of various flap designs on tissue reperfusion can lead to evidence-based incision selection and improved wound-healing outcomes.

A machine learning-based approach for flames classification in industrial Heavy Oil-Fire Boilers

The burner combustion tuning is a complex problem that has been studied through flame monitoring and characterization. It has been observed that the flame electromagnetic spectrum and flickering contain specific flame information in combustion processes. This information is helpful for combustion stoichiometry tuning on burners. This paper described a method for selecting the best flame feature subset that can be computed from the scanner signal, in order to get the flame index and induce combustion stoichiometry on burners under specific combustion conditions. We propose a method for selecting a reduced subset with only the useful flame features for flame index classification. To extract the most relevant flame features we use a feature subset selection (FSS) algorithm and to determine the combustion state in burners, five flame indices were defined that represent the most common flame states in oil fuel-fired boilers. FSS includes complete, sequential, and random searches in order to eliminate redundant and noisy flame features to decrease the flame feature set dimension. A probabilistic neural network (PNN) algorithm was implemented for flame feature clustering. Signals from the actual flame scanner system and relevant variables from the boiler data acquisition system were used by the algorithms to calculate the burner flame index. A set of parametric tests was done in a heavy oil-fired boiler under well-known flame and index conditions to train and test the flame classifier. The results showed that only the four more relevant features are enough to classify flames with a good performance (92.3% accuracy), which is useful for burner combustion monitoring and optimization.

Optimizing Incident Detection Thresholds Using the A* Algorithm: An Enhanced Approach for the California Algorithm

This paper presents an improved version of the California Algorithm (CA), focusing on threshold selection criteria. The CA is a widely recognized incidence detection algorithm used as a benchmark for comparison with newly developed incident detection algorithms. This study proposes criteria for threshold selection in CA based on the A* algorithm, which aims to find optimal thresholds using a Performance Index (PI) as a cost function. Our proposed method reduces processing time by optimizing resource utilization and establishes a standard for threshold selection in CA for comparison and evaluation purposes. Experimental results from our proposed method demonstrate its effectiveness in reducing the complexity required to determine optimal thresholds. Optimization of the CA method using the A* algorithm results in a 98.68% reduction in the number of nodes searched compared to a Complete Search Tree (CST).

Conversational Context-sensitive Ad Generation with a Few Core-Queries

When people are talking together in front of digital signage, advertisements that are aware of the context of the dialogue will work the most effectively. However, it has been challenging for computer systems to retrieve the appropriate advertisement from among the many options presented in large databases. Our proposed system, the Conversational Context-sensitive Advertisement generator (CoCoA), is the first attempt to apply masked word prediction to web information retrieval that takes into account the dialogue context. The novelty of CoCoA is that advertisers simply need to prepare a few abstract phrases, called Core-Queries, and then CoCoA automatically generates a context-sensitive expression as a complete search query by utilizing a masked word prediction technique that adds a word related to the dialogue context to one of the prepared Core-Queries. This automatic generation frees the advertisers from having to come up with context-sensitive phrases to attract users’ attention. Another unique point is that the modified Core-Query offers users speaking in front of the CoCoA system a list of context-sensitive advertisements. CoCoA was evaluated by crowd workers regarding the context-sensitivity of the generated search queries against the dialogue text of multiple domains prepared in advance. The results indicated that CoCoA could present more contextual and practical advertisements than other web-retrieval systems. Moreover, CoCoA acquired a higher evaluation in a particular conversation that included many travel topics to which the Core-Queries were designated, implying that it succeeded in adapting the Core-Queries for the specific ongoing context better than the compared method without any effort on the part of the advertisers. In addition, case studies with users and advertisers revealed that the context-sensitive advertisements generated by CoCoA also had an effect on the content of the ongoing dialogue. Specifically, since pairs unfamiliar with each other more frequently referred to the advertisement CoCoA displayed, the advertisements had an effect on the topics about which the pairs spoke. Moreover, participants of an advertiser role recognized that some of the search queries generated by CoCoA fit the context of a conversation and that CoCoA improved the effect of the advertisement. In particular, they learned how to design of designing a good Core-Query at ease by observing the users’ response to the advertisements retrieved with the generated search queries.