Exhaustive Search Research Articles

Application of Submandibular Intubation as Alternative to Elective Short-Term Tracheotomy for Particular Cases of Oral and Maxillofacial Tumors Resection and Defects Reconstruction

Objective: To describe the cases of oral and maxillofacial tumors (OMFT) resection and defects reconstruction under submandibular intubation (SMI) performed in our institution; secondly, to systematically review and analyze the characteristics of studies about SMI in oral and maxillofacial surgery to estimate the incidence rate of complication. Method: Data related to all 6 patients included in this study were prospectively collected from November 2016 to November 2023. The tract for endotracheal tube was created by bluntly dissection from the submandibular area to the floor of mouth. We performed an exhaustive search for studies about SMI using Pubmed, Embase, Proquest, and Chinese Biomedical Literature Database. Meta-analysis of the incidence rate of complication and infection was performed using STATA 14.1. Results: There was a male predominance (87.3%); the mean age was 47.8±16.8 years; squamous cell carcinoma (SCC) was the most frequent diagnosis (33.3%); the mean time of the operation was 237.0±65.2 minutes; the SMI procedure mean time was 5.5±0.9 minutes; and the mean time to extubation 133.3±12.5 minutes. No accident or complication was observed intraoperatively and postoperatively. The systematic review included 22 out of 234 studies screened. The meta-analysis showed an overall complication rate and superficial infection rate of 5.7% and 3.7% in the last 15 years versus 20.2% and 10.6% in over 15 years ago. Conclusion: Submandibular intubation was found technically easy, safe, and effective alternative of elective short-term tracheotomy, orotracheal and nasotracheal intubations in our series of OMFT resection and defects reconstruction.

A Survey on Design Space Exploration Approaches for Approximate Computing Systems

Approximate Computing (AxC) has emerged as a promising paradigm to enhance performance and energy efficiency by allowing a controlled trade-off between accuracy and resource consumption. It is extensively adopted across various abstraction levels, from software to architecture and circuit levels, employing diverse methodologies. The primary objective of AxC is to reduce energy consumption for executing error-resilient applications, accepting controlled and inherently acceptable output quality degradation. However, harnessing AxC poses several challenges, including identifying segments within a design amenable to approximation and selecting suitable AxC techniques to fulfill accuracy and performance criteria. This survey provides a comprehensive review of recent methodologies proposed for performing Design Space Exploration (DSE) to find the most suitable AxC techniques, focusing on both hardware and software implementations. DSE is a crucial design process where system designs are modeled, evaluated, and optimized for various extra-functional system behaviors such as performance, power consumption, energy efficiency, and accuracy. A systematic literature review was conducted to identify papers that ascribe their DSE algorithms, excluding those relying on exhaustive search methods. This survey aims to detail the state-of-the-art DSE methodologies that efficiently select AxC techniques, offering insights into their applicability across different hardware platforms and use-case domains. For this purpose, papers were categorized based on the type of search algorithm used, with Machine Learning (ML) and Evolutionary Algorithms (EAs) being the predominant approaches. Further categorization is based on the target hardware, including Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), general-purpose Central Processing Units (CPUs), and Graphics Processing Units (GPUs). A notable observation was that most studies targeted image processing applications due to their tolerance for accuracy loss. By providing an overview of techniques and methods outlined in existing literature pertaining to the DSE of AxC designs, this survey elucidates the current trends and challenges in optimizing approximate designs.

Diagnosis and Treatment of Renal ANCA Vasculitis: A Summary of the Consensus Document of the Catalan Group for the Study of Glomerular Diseases (GLOMCAT)

The document provides a comprehensive overview of the diagnosis, monitoring, and treatment of anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) with renal involvement, focusing on granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA). It outlines the definitions, clinical presentation, histopathological classification, monitoring strategies, induction and maintenance treatments, as well as special considerations for relapsing, refractory, and frail patients with renal AAV. The document was prepared by the Catalan Group for the Study of Glomerular Diseases (GLOMCAT), which comprises nephrologists with extensive experience in the diagnosis and treatment of AAV patients. Several virtual and face-to-face meetings were held for coordination, section assignments, and content discussion. An exhaustive and systematic search of the literature was carried out, which included, among others, the following databases: PubMed, EMBASE, Cochrane Library, Google Scholar, and ClinicalTrials.gov, as well as the abstract books of national and international congresses. Overall, the document provides a comprehensive guide for clinicians managing patients with renal AAV, offering evidence-based recommendations for diagnosis, monitoring, and treatment across various clinical scenarios.

The holistic management of peripheral spondyloarthritis: focus on articular involvement in patients with inflammatory bowel disease.

To provide a comprehensive overview of peripheral spondyloarthritis (pSpA), focusing specifically on its occurrence and management in patients with inflammatory bowel disease (IBD). An exhaustive literature search was conducted in PubMed, Embase, Cochrane Database of Systematic Reviews, and Google Scholar to identify relevant studies on pSpA in IBD patients. Titles, abstracts, and full-text articles were screened for relevance. Data on study design, patient characteristics, diagnostic criteria, main findings, and conclusions were extracted from selected articles. Study quality was assessed using appropriate checklists. Information was synthesized narratively to summarize current understanding. pSpA is the most common extraintestinal manifestation in IBD, with a median prevalence of 16%. It worsens quality of life and requires collaboration between gastroenterologists and rheumatologists for optimal diagnosis and treatment. Several "red flags" guide appropriate specialist referral of IBD patients with suspected pSpA. Once the diagnosis is confirmed, the choice of therapy depends on IBD phenotype and patterns of articular/axial involvement. Anti-tumor necrosis factor (TNF) drugs are first-line biologics, with interleukin (IL)-12/23 and IL-23 inhibitors as alternatives for anti-TNF failure. Small molecules like apremilast and Janus kinase inhibitors also have utility. Recommended treatment algorithms exist, but more randomized controlled trials are needed. Early identification of pSpA is crucial in IBD patients to enable timely intervention, prevent structural damage, and minimize disability. A multidisciplinary, holistic approach addressing musculoskeletal and extra-musculoskeletal manifestations is key to optimal patient outcomes.

A Comparison of Battery Equivalent Circuit Model Parameter Extraction Approaches Based on Electrochemical Impedance Spectroscopy

This paper presents three approaches to estimating the battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). The ES approach is assisted by the LS method for the rough determination of the lower and upper bound of the ECM parameters, and the NLS approach is incorporated with the Monte Carlo run such that different initial guesses can be assigned to improve the goodness of EIS fitting. The proposed approaches are validated using both simulated and real EIS data. Compared to the LS approach, the ES and NLS approaches show better fitting accuracy at various noise levels, whereas in both the validation using simulated EIS data and actual EIS data collected from LG 18650 and Molicel 21700 batteries, the NLS approach shows better fitting accuracy than that of LS and ES approaches. In all cases, compared with the ES approach, the computational time of the NLS approach is significantly faster, and compared with the LS approach, the NLS approach shows a minimal difference in computational time and considerably better fitting performance.

Solving Paths Search Problems in Complex Graphs

The construction of models of various systems is associated with the enumeration of the values of the parameters of the elements of the structure and taking into account all the characteristics of operation and interaction of components to find a certain set of solutions that determine the configuration of the system. Such tasks belong to enumeration type tasks and imply that some of the next solutions from this set are obtained from the previous solution in a certain order. It is known that any problem of the enumeration type is solved only by methods of exhaustive search, and other methods for their enumeration do not exist yet. The paper presents a new method of searching paths in a graph – the method of node-graph transformation. The proposed method, unlike the existing ones, allows one to search all directed simple paths in an oriented graph of arbitrary structure much faster. In the known graph search methods (Breadth First Search and Depth First Search), the object of the search is a path. The total number of such paths in the graph determines the size of the search space. The main idea of the node-graph transformation method is to significantly reduce the size of the search space by enlarging the search objects. The enlargement of enumeration objects is performed by clustering paths into combinatorial objects that concentrate some set of paths of the same length according to certain rules. These combinatorial objects are called node-graphs. A node-graph refers to center-peripheral combinatorial objects, and specific node-graph transformation operations have been developed to enumerate all paths in the graph, which allow finding new paths based on previous paths. The method can be used as a basic toolkit to reduce the dimensionality of the search space for solutions to NP-complete problems while maintaining the universality and accuracy of the full search.

Measurements of F1‐ Region Ionosphere State Variables at Arecibo Through Quasi Height‐Independent Exhaustive Fittings of the Incoherent Scatter Ion‐Line Spectra

AbstractWe discuss an exhaustive search approach to fit the incoherent scatter spectrum (ISS) in the F1‐region for molecular ion fraction (fm), ion temperature (Ti), and electron temperature (Te). The commonly used “full profile” approach for F1‐region measurements parameterizes the molecular ion fraction as a function of altitude and fits all the related heights for the state variables. In our approach, we fit the ISS at each height for fm, Ti, Te, and ion velocity (Vi) independently. Our exhaustive search method finds all the major local minima at each altitude. Although a parameterized function is used to guide the algorithm in finding the best solution, the fitting parameters retain their local characteristics. Despite that fitting fm, Ti, and Te without constraints requires Doppler shift to be accurately determined and the ISS signal‐to‐noise ratio higher than the full‐profile method, simulations show that Ti, Te, and fm can be recovered within a few percent accuracy with a moderate signal‐to‐noise ratio. We apply the exhaustive search approach to the Arecibo high‐resolution incoherent scatter radar data taken on 13 September 2014. The derived ion and electron temperatures are sensitive enough to reveal thermosphere gravity waves commonly seen in the electron density previously. Our method is more robust than previous height‐independent fitting methods. Comparison with another Arecibo program indicates our results are likely more accurate. Simultaneous high‐resolution measurements of Ti, Te, fm, Vi, and electron concentration (Ne) in our approach open new opportunities for synergistic studies of the F1‐region dynamics and chemistry.

Extending battery lifetime of electric-hydraulic hybrid wheel loader through system parameter optimization

Parallel electric hydraulic hybrid powertrain (PEHH) is a promising substitute for electric direct drive to extend battery lifetime. The convex programming (CP) based simultaneous optimization method has been adopted in electric hybrid system parameter optimization, but it is difficult to extend it for PEHH due to its non-convexity. In this paper, a combined exhaustive search – convex programming (ES-CP) system parameter optimization method is proposed to address the non-convexity of PEHH system parameter optimization problem. In this method, the PEHH system parameter optimization problem is modeled and reformulated in convex form and the convex part is decoupled from the optimization problem. The CP method is used for the convex part and the ES method is used for the remained non-convex part. The ES-CP method is applied to the PEHH powertrain to extend its battery lifetime. Optimization results show that the battery lifetime of the ES-CP optimized PEHH system is 6.8% longer than that of the non-optimized system. The computing time of ES-CP is 97% lower than Latin hypercube sampling and 90% lower than genetic algorithm methods.

Optimal Intermediate Pressure Investigation in a CO₂ Transcritical Distributed Compression Refrigeration Cycle{fr}Recherche sur la Pression Intermédiaire Optimale dans un Cycle de Réfrigération à Compression Distribuée Transcritique au CO₂

The CO2 transcritical distributed compression cycle proposal provides a novel approach for refrigerant cooling in traditional transcritical cycles. This paper employed an exhaustive search method to derive the correlation formula for the optimal intermediate pressure. From the perspectives of thermodynamic performance and economics, a comparative analysis was conducted between the cycle established under the optimal intermediate pressure obtained in this study, the cycle based on equal compression ratios in traditional two-stage compression cycles, and the cycle established using the optimal secondary compression ratio method based on low-pressure stage discharge pressure mentioned in previous literature study. The research results indicate that the system's COP calculated using the obtained optimal intermediate pressure correlation method can be improved by up to 7.26% and 5.32%, respectively, compared to the traditional and literature-based methods. The exergy loss with the optimal intermediate pressure method is less than with the other two methods. The entransy dissipation rate of the system obtained using the optimal intermediate pressure correlation method is 24.61% and 50.14% lower than that of the traditional and literature-based methods, respectively. The investment cost of the main components using the optimal intermediate pressure correlation method is about 5% higher than that of the traditional method and about 1% higher than that of the optimal pressure ratio method. However, the total annual cost rate of the system is the lowest. The research enriches and improves the theory of the CO₂ transcritical distributed compression cycle, thereby facilitating the advancement of practical applications based on this cycle theory.

Reasons for Declining Participation in Inpatient Research Among Historically Minoritized Racial and Ethnic Communities: A Scoping Review

BackgroundTo promote equitable recruitment for studies conducted in the inpatient hospital setting, we sought to characterize reasons why individuals, both from historically minoritized racial and ethnic groups and the broader patient population, refuse participation in clinical trials within inpatient settings. MethodsAn exhaustive search of the literature was conducted in Cochrane Library, Google Scholar, Embase, MEDLINE, PubMed, Scopus, and Web of Science databases to find relevant articles published from the inception of each database to April 30, 2023. Studies recruiting patients during their inpatient stay and reporting reasons for refusing participation in clinical trials met the inclusion criteria. ResultsThe search resulted in 2264 citations, of which 22 were included. 14 did not report data related to race, while 19 reported no ethnicity data. Reasons for refusal across trials included study burden and inconvenience (n=16), transportation and logistical issues (n=13), lack of interest in research (n=12), and refusal to be randomized (n=10). Prominent concepts included the importance of incorporating social support systems in consenting processes, lack of efforts to include data or recruitment efforts for individuals from minoritized groups, and physician involvement in recruitment. DiscussionTo enhance participation among historically minoritized communities in clinical trials, greater efforts must be made to collect demographic information and document refusal reasons to inform future recruitment methods. Strategies include proactively accounting for culture and language differences in study design and recruitment and intentionally engaging social support networks. Limiting study burden and logistics and optimizing collaborations between clinical and research teams would promote accessibility and foster patient trust.

Multi-Objective Numerical Analysis of Horizontal Rectilinear Earth–Air Heat Exchangers with Elliptical Cross Section Using Constructal Design and TOPSIS

This study presents a numerical evaluation of a Horizontal Rectilinear Earth–air Heat Exchanger (EAHE), considering the climatic and soil conditions of Viamão, Brazil, a subtropical region. The Constructal Design method, combined with the Exhaustive Search, was utilized to define the system constraints, degree of freedom, and performance indicators. The degree of freedom was characterized by the aspect ratio between the vertical and horizontal lengths of the elliptical cross-section duct (H/L). The performance indicators for the EAHE configurations were assessed based on thermal potential (TP) and pressure drop (PD). The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied for multi-objective evaluation, and a methodology for EAHE is proposed. The problem was solved using FLUENT software (version 2024 R2), which employs the Finite Volume Method to solve the conservation equations for mass, momentum, and energy. The (H/L)T,o = 6.0 configuration showed a 16.4% increase in thermal performance for heating and 15.9% for cooling compared to the conventional circular duct. Conversely, the (H/L)F,o = 1.0 configuration reduced pressure loss by 65.33%. The integration of Constructal Design with TOPSIS facilitated the identification of optimized geometries that achieve a balance between performance indicators and those that specifically prioritize thermal or fluid dynamic aspects, being this approach an original scientific contribution of the present work.

Rapid Convolutional Algorithm for the Discovery of Blueberry Honey Authenticity Markers via Nontargeted LC-MS Analysis.

Bees produce honey through the collection and transformation of nectar, whose botanical origin impacts the taste, nutritional value, and, therefore, the market price of the resulting honey. This phenomenon has led some to mislabel their honey so that it can be sold at a higher price. Metabolomics has been gaining popularity in food authentication, but rapid data mining algorithms are needed to facilitate the discovery of new authenticity markers. A nontargeted high-resolution liquid chromatography-mass spectrometry (HR/LC-MS) analysis of 262 monofloral honey samples, of which 50 were blueberry honey, was performed. Data mining methods were demonstrated for the discovery of binary single-markers (compound was only detected in blueberry honey), threshold single-markers (compound had the highest concentration in blueberry honey), and interval ratio-markers (the ratio of two compounds was within a unique interval in blueberry honey). A novel convolutional algorithm was developed for the discovery of interval ratio-markers, which trained 14× faster and achieved a 0.2 Matthews correlation coefficient (MCC) units higher classification score than existing open-source implementations. The convolutional algorithm also had classification performance similar to that of a brute-force search but trained 1521× faster. A pipeline for shortlisting candidate authenticity markers from the LC-MS spectra that may be suitable for chemical structure identification was also demonstrated and led to the identification of niacin as a blueberry honey threshold single-marker. This work demonstrates an end-to-end approach to mine the honey metabolome for novel authenticity markers and can readily be applied to other types of food and analytical chemistry instruments.

Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles.

Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1≤C≤2.0 and 0≤α≤4, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C=2.0 and α=0.0736. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R2, greater than 0.97, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations.

Cost-Effective Power Management for Smart Homes: Innovative Scheduling Techniques and Integrating Battery Optimization in 6G Networks

This paper presents an Optimal Power Management System (OPMS) for smart homes in 6G environments, which are designed to enhance the sustainability of Green Internet of Everything (GIoT) applications. The system employs a brute-force search using an exact solution to identify the optimal decision for adapting power consumption to renewable power availability. Key techniques, including priority-based allocation, time-shifting, quality degradation, battery utilization and service rejection, will be adopted. Given the NP-hard nature of this problem, the brute-force approach is feasible for smaller scenarios but sets the stage for future heuristic methods in large-scale applications like smart cities. The OPMS, deployed on Multi-Access Edge Computing (MEC) nodes, integrates a novel demand response (DR) strategy to manage real-time power use effectively. Synthetic data tests achieved a 100% acceptance rate with zero reliance on non-renewable power, while real-world tests reduced non-renewable power consumption by over 90%, demonstrating the system’s flexibility. These results provide a foundation for further AI-based heuristics optimization techniques to improve scalability and power efficiency in broader smart city deployments.

COMPARATOR REGRESSION ANALYSIS IN CONDITIONS OF FUZZY INITIAL DATA

Abstract. The canonical task of regression analysis consists in studying the problem of estimation and mathematical description of the influence caused by one or more independent variables (predictors, explanatory variables) on the dependent (explained) variable. The subject of comparator identification problem is a specific modification of the standard regression analysis problem in the case when, for some reason, it is not possible to measure values of the dependent, explained variable. However, the problem of reconstructing a regression polynomial can be solved if it is possible to qualitatively compare the results of experiments and rank them, for example, in the order of increasing. Taking into account the results of ranking the values of the explained variable in different experiments, a corresponding system of inequalities is formed. The purpose of the research consists in developing a method for mathematical processing of the resulting system of inequalities for analytical assessment of the level of influence of explanatory variables on the explained variable. The method for solving the problem is based on transforming a system of inequalities into a system of linear algebraic equations. A significant drawback of the known approach to solving this problem consists in the fact that the resulting system of linear algebraic equations is an underdetermined one (the number of unknowns in this system exceeds the number of equations). At the same time, the system has an infinite number of solutions, among which there may be an uncontrollable number of unacceptable ones. The exhaustive search method for finding acceptable solutions is futile. In this regard, the problem of developing a correct method of comparator identification remains relevant. Another difficulty that accompanies the actual procedure of comparator identification when solving practical problems consists in the fact that measurements of values of the controlled parameters are not accurate. The uncertainty that arises in the conditions of a small sample of initial data can be removed using the tools of fuzzy mathematics. It is assumed that, based on preliminary research, a mathematical description of the corresponding membership function can be obtained for each of the controlled parameters. As a result, effective approaches to solving emerging problems have been proposed, based on the use of developed optimization procedures in the conditions of a small sample of fuzzy initial data.

Selective collaboration in distributed FxLMS active noise control systems

This paper addresses the selection of the collaboration configuration on distributed active noise control (ANC) systems. ANC systems aim to cancel out acoustic noise within a listening area. In distributed systems, the control task is delegated among multiple acoustic control nodes that generate the control signals by filtering a noise reference signal. The coefficients of each node filter are iteratively calculated using the filtered-X LMS algorithm. The stability is achieved when the adaptive filters computed in each node converge to finite values. However, acoustic coupling among nodes could lead to instability (i.e., divergence). Collaboration among selected nodes may avoid this phenomenon, although not just any collaboration configuration guarantees network stability. On the other hand, a collaborative distributed system presents two drawbacks: the stability assessment is computationally expensive, and communication requirements increase with the number of collaborations among nodes. In this paper, we propose and discuss several methods to establish a collaboration configuration that ensures system stability. The optimal configuration, which is characterized by the minimal number of necessary collaborations between nodes, can be identified through exhaustive search. However, this approach incurs a high computational cost, particularly in networks with many nodes. To address this challenge, we introduce several heuristic methods aimed at efficiently obtaining stable configurations.

OPTIMIZATION PROBLEM FOR NUMBER OF LOGIC GATES NEEDED TO IMPLEMENT MULTIPLE BOOLEAN FUNCTIONS USING DECODER

Abstract. Background. Computer circuits enable almost every piece of electronics to function. An opportunity for their optimization was found in one of the ways to implement a circuit, which involves a decoder. Decoders are basic circuit components whose behavior makes it easy to implement multiple Boolean functions. Functions are just rules by which informational signals are being processed in the circuit. To implement a function Logic gatEs (LEs) are used. LEs group multiple input signals and process them using binary logic operators like AND or NOR. Purpose. Develop a computer program that, based on provided functions, will find a solution associated with approximately minimal number of logic gates. Relevance. Prevalence of circuits increases the value of small optimizations, since any improvement gets multiplied by millions of circuits produced. Optimization at the logic level is being explored, which will retain value regardless of the physical properties of the circuit. Additionally, the theory of optimizing LE usage for circuits built on decoders is examined, which will alleviate future research. Methods. Development of the theory arose from an understanding that input signals can be grouped using LEs consciously. This way, LE outputs can be used in more than one function, which results in reduction of total number of LEs. It becomes evident that the problem can be restated in terms of sets. Similarity to known NP-hard problems emerges, which prompts usage of existing solution approaches. Among them, the greedy algorithm is chosen for its simplicity in program implementation. The connection between the problem and digital circuitry is weakened by the introduction of special evaluation functions that work with sets. So, the problem is reduced to several mathematical rules, which then are used as the appropriate parts of the greedy algorithm. Development of knowledge necessary to create the target program is thus concluded. Results. The target program is successfully implemented and can be seen in full at https://github.com/Gleb-05/MakeFuncForDC. A test is conducted on 1000 sets of four pseudorandom eight-term functions. It is estimated that the implementations proposed by the program require on average 0.75 of the initial number of LEs. The average running time is estimated at 2 milliseconds. In contrast, brute-force search for the same problem is theorized to run for years. Conclusions. A working program that finds an almost optimal solution is successfully created and tested. Its performance makes it potentially useful in the industrial scale of circuit manufacturing. Since the program essentially offers workload management, its possible applications go beyond the domain of digital electronics. Additionally, the theory of optimizing the use of LEs is presented for the first time. It can be used as the basis for future research on the implementation of multiple Boolean functions using decoder.

Suppressing Coda Events with a Bayesian Model of Global Scale Seismology

AbstractLarge seismic events often trigger a wave train of slow decaying energy known as the coda that can mislead signal detectors into forming coda detections that appear to look like regular phase detections. These coda detections can confuse event formation algorithms into building false events known as coda events. Naive solutions to this problem by dropping any detection that looks like a coda detection can have the negative consequence of missing real events. We propose to address this issue by extending an existing Bayesian approach, NET-VISA that has been designed to build event bulletins using a generative model of global-scale seismology. Our extensions significantly boost the existing work by reducing the total number of false events by nearly half and virtually eliminating coda events without changing the number of real events.

Challenges in Housing Accessibility Towards Universal Design

Housing is the most regulated type of building. Standards significantly influence the design and construction quality, often making universal design more expensive and challenging to implement. This study aims to review current Spanish regulations on accessibility in the housing sector, to promote non-discriminatory and independent access, while ensuring the safety of each resident. The research is exploratory and qualitative, involving a systematic bibliographic review through an exhaustive search of regulations in force until December 2023. In Spain, there are three laws (from 1999, 1960 and 2022), three decrees (from 2006, 2013 and 2015) and a national plan from the regulatory framework. The main regulation, the Technical Building Code, outlines the accessibility, safety and habitability requirements that buildings must adhere to. The most recent is Law 6/2022 on Cognitive Accessibility. Spain has robust legislation with innovative approaches and ambitious goals. However, there is a lack of societal awareness that housing is a fundamental right and that the regulations must be upheld. Several proposals for improvement have been made. These suggested changes address the specific challenges of each region and promote more inclusive and adaptable projects for all people from the early stages of design.

Optical circuit switched three-stage twisted-folded Clos-network design model guaranteeing admissible blocking probability

Some data center networks have already started to use optical circuit switching (OCS) with potential performance benefits, including high capacity, low latency, and energy efficiency. This paper addresses a switching network design to maximize the network radix, i.e., the number of terminals connected to the network under the condition that a specified number of identical switches with the size N×N and the maximum admissible blocking probability are given. Previous work presented a two-stage twisted and folded Clos network (TF-Clos) with a blocking probability guarantee for OCS, which has a larger network radix than TF-Clos with a strict-sense non-blocking condition. Expanding the number of stages allows for enhancing the network radix. This paper proposes a model designing an OCS three-stage TF-Clos structure with a blocking probability guarantee to increase the network radix compared to the two-stage TF-Clos. We formulate the problem of obtaining the network configuration that maximizes the network radix as an optimization problem. We conduct an algorithm based on an exhaustive search to obtain a feasible solution satisfying the constraints of the optimization problem. This algorithm identifies the structure with the largest network radix in non-increasing order to avoid unnecessary searches. Numerical results show that the proposed model achieves a larger network radix than the two-stage model.