Exhaustive Search Research Articles

Redundancy Allocation Problem for a Continuous‐State Series‐Parallel System With Degrading Components: Electric Vehicle Application

ABSTRACTSystem reliability is a critical factor in designing complex engineering systems. Redundancy allocation problem (RAP) has attracted more interest owing to its wide scope of industrial applications for system reliability optimization. RAPs for binary‐state and multistate systems have been extensively studied. However, the redundancy allocation for continuous‐state systems composed of degrading components has not yet been fully explored. This study attempts to build the RAP for the continuous‐state parallel‐series system consisting of identical, independent, and nonrepairable components under active and cold‐standby redundancy strategies. The degradation of individual components in a system is continuous and modeled by the gamma process. The degradation at the system level is derived according to a deterministic structure function that relates the system degradation to the degradation of its components. Genetic algorithm and exhaustive search method are used to find the cost‐optimal redundancy design while satisfying a system reliability requirement. Series‐parallel lithium‐ion battery pack systems for electric vehicles (EVs) are used as an illustrative example to validate the proposed RAP and optimization model.

High Throughput Screening of Disordered Rocksalt Type Overlithiated Anode

Rechargeable lithium-ion batteries with high energy density and fast-charging capability are vital for commercial application. Disordered rocksalt-type materials, characterized by overlithiation (cation/anion ratio > 1.0 due to additional lithium insertion), have emerged as promising candidates for high-rate and low-voltage anodes. Motivated by the already reported Li3+xV2O5 (0≤x≤2) anode, an exhausted search among all potential redox center as anodes have been performed with high throughput density functional theory (DFT) computations. A comprehensive search among all potential redox centers for anodes was conducted. In total, 23 redox centers from the periodic table were systematically evaluated for their stability, average voltage, electronic conductivity, and volumetric change during cycling. Potential overlithiated DRX anodes, comparable to the state-of-the-art Li3+xV2O5 (0≤x≤2) anode, will be showcased. Additionally, compositional design principles will be outlined, derived from data mining the high-throughput DFT data.

Human and Machine: A View through Kant's Use of Regulative Reason

This study examines a qualitative difference between human intelligence and artificial intelligence (AI) through the lens of Immanuel Kant's philosophy. This difference is based on the human mind's idea of unity and its intuitive ability to limit this unity. Kant argues that through a regulative use of reason, it is able to obtain conceptual wholes such as God, soul, and physics. We argue that this idea of totality, which Kant obtains through the use of regulative reason, has an important function in distinguishing the human mind from artificial intelligence. Based on Kant's idea of unity, our study determines that the human mind constructs a unity like a formal system. Artificial intelligence, which is made possible by this construction, is inadequate in solving many problems that have shown surprising developments in recent years. We attribute this inadequacy to the inability of computers to model the aforementioned idea of wholeness. We justify this claim through the problem of “narrowing the brute search space” in computational complexity, which is a problem area in computer science. This problem is based on the lack of a formal procedure for narrowing down a problem space with very large boundaries. When computers do not have an efficient procedure or an analytical solution to the problems they want to solve, they are forced to try all available solutions. In contrast, the human mind has cognitive abilities that allow it to intuitively narrow down these large problem spaces. Kant's use of regulative reason provides a framework for understanding this human faculty. According to Kant, the regulative function of reason provides the concepts of pure reason that orient scientific inquiry. For example, through a concept of pure reason, such as the cosmos, the human mind is able to limit the physical domain in such a way as to do physical science. Through Kant's use of regulative reason, we think that the human mind, by bringing together a set of formal signs, makes a limitation such as a formal system. Since we can conceptualize a unity such as a formal system, we can talk about algorithms that operate according to this system. The regulative use of reason, which enables the establishment of such wholes, creates a qualitative difference between AI when combined with the human's intuitive thinking ability. However, it is quite difficult to ground the connection between the idea of unity in Kant's philosophy and intuitive thinking solely on the basis of Kantian philosophy. Thus, we turn to the views of Henri Bergson and Nazif Muhtaroğlu to establish this connection. Bergson, while explaining the concept of motion, argues that the mind reaches such an idea of unity through an instinctive synthesis. In this respect, Bergson argues that movement is a mental synthesis insofar as it is a transition from one point to another. Similarly, Muhtaroğlu, after emphasizing that the intuitive cognition that accompanies reason is a direct, unmediated and rapid cognition, identifies the type of intuition that leads to the idea of unity as immediate intuition. Stating that this type of intuition is a cognitive intuition, Muhtaroğlu cites Archimedes' discovery of the laws of fluids as an example of this way of thinking. In our study, we use the example of Archimedes to show how intuition accompanies the narrowing of the field of brute force search. Thus, when the regulative use of reason and intuitive thinking come together, a difference emerges in the cognitive abilities of the human mind and artificial intelligence. Thanks to the regulatory use of reason, the human mind is able to have an awareness of the unity of the object field it is confronted with. The fact that this awareness is accompanied by intuitive thinking allows this field of unity to be narrowed. Since artificial intelligence cannot model both the use of regulative reasoning and intuitive thinking, it is subjected to the brute search method. We argue that such a deficiency underlies the lack of analytical solutions to problems of computational complexity. This deficiency reveals the difference between the human mind and artificial intelligence in problem solving and narrowing down large search spaces.

Exhaustive search for novel multicomponent alloys with brute force and machine learning

We present an algorithm for the high-throughput computational discovery of intermetallic compounds in systems with a large number of components. It is particularly important for high entropy alloys (HEAs), where multiple principal elements can form numerous potential intermetallic compounds during the condensation process, making it challenging to predict the dominant phase. Our algorithm is based on a brute-force evaluation of candidate structures with a fixed underlying lattice (FCC or BCC) accelerated by machine-learning interatomic potentials. The algorithm takes a set of chemical elements and a crystal lattice type as inputs and produces structures on and near the convex hull of thermodynamically stable structures. The candidate structures are evaluated using the low-rank potential (LRP), trained to reproduce energies of structures equilibrated with density functional theory (DFT). Thanks to extreme computational effectiveness of the LRP, it is feasible to evaluate hundreds of thousands of structures per second, per CPU core. Thus, our algorithm screens a complete set of candidate structures for a given system without missing any configurations. We validated our method on systems with BCC (Nb-W, Nb-Mo-W, V-Nb-Mo-Ta-W) and FCC (Cu-Pt, Cu-Pd-Pt, Cu-Pd-Ag-Pt-Au) lattices and discovered 268 new alloys not reported in the AFLOW database1, which we used as a benchmark.

Crystal Morphologic Design of Na-Layered Oxide Crystals by Automated High-Throughput Flux Method Experiments

Na x CoO2 (NCO), a type of sodium layered oxide, has attracted attention as a cathode material for sodium ion batteries (SIB). The P2-type NCO has a layered structure and is generally obtained as plate-like crystals. Crystal growth of high aspect ratio NCO would be desirable to improve output density. The flux method, which is a liquid-phase method using flux (molten salt) as a solvent, is a crystal growth method that can control the crystal plane. However, in the case of NCO crystal growth, the specific effects of the flux species have not been clarified. In addition, NCO has several phases (O3, P2, P'3, etc.), depending on the Na ratio. However, fluxes easily change the Na ratio in the reaction field. For this reason, experimental guidelines for crystal control cannot be established, and trial and error must be used. The experimental search space considered by the flux method is enormous, with at least 14,000 possible experimental search spaces (three levels of raw material species and heating temperatures, nine raw material Na:Co ratios, 18 flux species and even two-component mixtures are allowed), making an exhaustive search unrealistic. To overcome this issue, it would be necessary to develop a crystal phase diagram expressing the crystal polymorphism and aspect ratio. For this purpose, identification of fluxes effective for P2 phase formation and aspect ratio changes was carried out using a data-driven, wide-area experimental search.For the data-driven exploration, flux method experiments was conducted using the high-throughput flux method screening (HTFS) system, which was originally developed in our laboratory. In the experiments, the initial training data was collected using a grid search method, and applied to a Bayesian optimization system. In this time, regression model was constructed using the feature values extracted from X-ray diffraction (XRD) patterns as the objective variables and the experimental conditions as the explanatory variables.About 50 experimental data were collected in a grid search using the HTFS. The formation of P2-type NCO crystals was confirmed in more than half of the samples. However, diffraction lines originating from the impurity Co3O4 were observed in some samples, confirming the strong dependence of the product on the experimental conditions. Data-driven experiments were then undertaken. A total of about 30 experiments were carried out, and in many cases P2-type crystal samples were obtained using Na-based fluxes. In addition, the aspect ratio depended on the flux species and barrel-type crystal morphology was grown in some Na-based fluxes. In this presentation, the mechanism of flux contribution to crystal growth is discussed in terms of solute-solvent interaction and surface energy.AcknowledgementThis work was partly supported by the Strategic Innovation Program (SIP) of the Cabinet Office, and joint research with companies.

Impact of the deep squat on articular knee joint structures, friend or enemy? A scoping review.

The squat exercise has been shown to improve athletic performance. However, the use of the deep squat has been questioned due to claims that it may cause knee joint injuries. Therefore, the purpose of this scoping review was to synthesize existing literature concerning the impact of deep squats on knee osteoarticular health in resistance-trained individuals. This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) guidelines. The original protocol was prospectively registered in Figshare (https://doi.org/10.6084/m9.figshare.24945033.v1). A systematic and exhaustive search was conducted in different databases: PubMed, Scopus, Web of Science, and SPORTDiscus. Additional searches were performed in Google Scholar and PEDro. The main inclusion criteria were the following: (1) Articles of experimental, observational, or theoretical nature, including randomized controlled trials, longitudinal studies, case reports, integrative reviews, systematic reviews, and meta-analyses(Primary studies were required to have a minimum follow-up duration of 6 weeks, whereas secondary studies were expected to adhere to PRISMA or COCHRANE guidelines or be registered with PROSPERO; (2) Peer-reviewed articles published between 2000 and 2024; (3) Publications written in English, Spanish and Portuguese; (4) Studies reporting the effects of deep half, parallel or quarter squats on the knee or evaluating squats as a predictor of injury. The keyword search resulted in 2,274 studies, out of which 15 met all inclusion criteria. These 15 studies comprised 5 cohort studies, 3 randomized controlled trials, 4 literature or narrative reviews, 1 case study, and 2 systematic reviews, one including a meta-analysis. Overall, the risk of bias (ROB) across these studies was generally low. It is worth noting that only one study, a case study, associated deep squats with an increased risk of injury, the remaining 14 studies showed no negative impact of deep squats on knee joint health. The deep squat appears to be a safe exercise for knee joint health and could be included in resistance training programs without risk, provided that proper technique is maintained.

Computational Design of a Kit of Parts for Bending Active Structures

Bending-active structures are composed of elastic elements that deform to achieve a desired target shape. To support effective design, inverse algorithms have been proposed that optimize the geometry of each element specifically for each design. This makes it difficult to reuse elements across designs or gain efficiency in fabrication through mass production. We address this issue and propose a computational framework to rationalize bending-active structures into a sparse kit of parts. Our method solves for the optimal part geometry such that multiple input designs can be faithfully realized with the same kit of parts. Assigning parts to different assemblies leads to a combinatorial explosion that makes exhaustive search intractable. Instead, we propose a relaxed continuous optimization incorporating a physics-based simulation in its inner loop to model the elastic deformation of the bending-active structure accurately. Our algorithm allows analyzing different design trade-offs of a kit of parts to tune the balance between fabrication complexity and fidelity to the original designs. We demonstrate our method on three different classes of bending-active structures, showcasing the effectiveness of our approach for part reuse and sustainable practices in fabrication-driven design.

Impact of sensory-based therapy on problems with balance and posture in children with cerebral palsy: a systematic review and meta-analysis

ObjectiveThe present systematic review and meta-analysis was intended to evaluate the efficacy of Sensory Integration Therapy (SIT) in ameliorating postural and balance dysfunctions in pediatric populations with cerebral palsy (CP).MethodsAn exhaustive literature search was directed through several well-known systematic databases, including PubMed, Web of Science, Google Scholar, and Scopus, until July 2024. The inclusion criteria targeted studies on sensory integration therapy in children under 18 years diagnosed with CP. This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Physiotherapy Evidence Database (PEDro) scale and the Cochrane Risk of Bias-2 (RoB 2.0) were employed to evaluate the methodological rigor and potential bias of the included studies.ResultsSeven randomized controlled trials were selected, encompassing 203 children with CP. The interventions predominantly utilized SIT, focusing on enhancing sensorimotor integration. The findings reveal that sensory-based interventions significantly boost gross motor capabilities and skills, contributing to improved functional mobility and standards of living in CP children. These therapies also facilitate the postural control and active participation in day to day activities.ConclusionSensory-based interventions are efficacious in correcting postural and balance impairments in children with CP. Their integration into comprehensive rehabilitative frameworks is recommended to foster functional autonomy and augment overall developmental outcomes. Future studies should investigate the synergistic effects of these interventions with other therapeutic modalities to maximize rehabilitation efficacy.

Effectiveness of Probiotics, Prebiotics, and Synbiotics in Managing Insulin Resistance and Hormonal Imbalance in Women with Polycystic Ovary Syndrome (PCOS): A Systematic Review of Randomized Clinical Trials.

Background/Objectives: Polycystic ovary syndrome is a common endocrine disorder in women of reproductive age characterized by insulin resistance and hormonal imbalances. Recent research suggests that probiotics and synbiotics may improve these parameters by modulating the gut microbiota. This study systematically reviewed randomized clinical trials evaluating the impact of probiotic, prebiotic, and synbiotic supplementation on insulin resistance and hormonal parameters in women with PCOS. Methods: Exhaustive searches were conducted in PubMed, Cochrane CENTRAL, Scopus, Web of Science, and Embase, following PRISMA guidelines. Randomized trials assessing supplementation with probiotics, prebiotics, or synbiotics for at least 8 weeks in women diagnosed with PCOS according to the Rotterdam criteria were included. Data on participants, interventions, and outcomes related to insulin resistance and hormones were extracted. Results: Eleven studies from Iran involving overweight or obese women aged 15 to 48 were included. Probiotic and synbiotic supplementation showed significant improvements in insulin resistance (reductions in HOMA-IR, fasting glucose, and insulin), lipid profiles (decreased LDL and triglycerides; increased HDL), and hormonal balance (increased SHBG, decreased total testosterone). Synbiotics had more pronounced effects than probiotics or prebiotics alone. Adherence was high, and side effects were minimal. Conclusions: Despite promising results, limitations such as small sample sizes, homogeneous populations, and short intervention durations limit the generalization of the findings. Larger, longer, multicenter trials with diverse populations and standardized methodologies are needed to confirm the efficacy and safety of synbiotics in managing PCOS. Integrating these interventions could improve clinical management and quality of life for affected women, but additional evidence is required to support widespread use.

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.

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

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.

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.

Walter's Duiker (Philantomba walteri Colyn, 2010): A Literature Review on a Recently Identified and Potentially Vulnerable Species.

Based on recent taxonomic and molecular tools, the Walter's duiker (Philantomba walteri Colyn, 2010), endemic to the Dahomey Gap in West Africa, has been recognized as a new species in 2010. This species is largely hunted and may already be threatened by extinction. This review paper aims to synthesize the current knowledge on this species, covering its taxonomy, morphology, biology, ecology, diet, seed dispersal role, reproduction patterns, activity rate, parasitology, spatial distribution, habitats, population densities, and ongoing human pressures. We carried out an exhaustive literature search using nine databases, going through 1200 initial references to finally retain a total of only 11 research articles mentioning Walter's duiker. Very few publications exist on the species due to its recent discovery. Existing studies focus on feeding, parasitology, and hunting pressure. Walter's duiker distribution range extends over Togo, Benin, and Nigeria. The species is hunted in a large part of its range, including in the reserves that are supposed to protect it. We synthesize the biological information related to the Maxwell's duiker as well as the Walter's duiker, as historically, these two species have been confounded and are morphologically similar. Our synthesis also highlights the scientific gaps for a better understanding of the biology of this species, and it proposes priority themes for future research. Priority should be given to studying the diet composition of Walter's duiker by analyzing rumens and feces, its role in seed dispersal and forest regeneration, its home range and activity rate, and the estimation of its abundance. All these information together would allow to thoroughly assess the species status and contribute to its conservation.

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.