Exhaustive Search Research Articles

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.

The Importance of Argan Oil in Medicine and Cosmetology.

Argan oil, rich in unsaturated fatty acids and polyphenols, exerts beneficial effects on both the intestinal and skin microbiotas. In the gut, it promotes the growth of beneficial bacteria, such as lactobacilli, while reducing pathogenic bacteria, due to its anti-inflammatory properties that help maintain microbial balance. Additionally, it improves the integrity of the intestinal mucosa, reducing the risk of dysbiosis. On the skin, argan oil hydrates and balances the lipid environment, creating a favorable setting for beneficial microorganisms, while also possessing antimicrobial and anti-inflammatory properties that soothe conditions like eczema and acne. Thus, argan oil is valuable for overall health, supporting digestion and skin health. The objective of this review is to provide a summary of the benefits of argan oil for alternative and complementary medicine. An exhaustive search of the literature was carried out using targeted keywords. A set of 83 articles were selected and analyzed. As the mechanisms of action of argan oil are not completely understood, this work highlighted the benefits of this oil by analyzing its nutritional properties and its beneficial effects on the intestinal and skin microbiotas. Indeed, argan oil is valuable for overall health.

Do morphometric data improve phylogenetic reconstruction? A systematic review and assessment

BackgroundIsolating phylogenetic signal from morphological data is crucial for accurately merging fossils into the tree of life and for calibrating molecular dating. However, subjective character definition is a major limitation which can introduce biases that mislead phylogenetic inferences and divergence time estimation. The use of quantitative data, e.g., geometric morphometric (GMM; shape) data can allow for more objective integration of morphological data into phylogenetic inference. This systematic review describes the current state of the field in using continuous morphometric data (e.g., GMM data) for phylogenetic reconstruction and assesses the efficacy of these data compared to discrete characters using the PRISMA-EcoEvo v1.0. reporting guideline, and offers some pathways for approaching this task with GMM data. A comprehensive search string yielded 11,123 phylogenetic studies published in English up to Oct 2023 in the Web of Science database. Title and abstract screening removed 10,975 articles, and full-text screening was performed for 132 articles. Of these, a total of twelve articles met final inclusion criteria and were used for downstream analyses.ResultsPhylogenetic performance was compared between approaches that employed continuous morphometric and discrete morphological data. Overall, the reconstructed phylogenies did not show increased resolution or accuracy (i.e., benchmarked against molecular phylogenies) as continuous data alone or combined with discrete morphological datasets.ConclusionsAn exhaustive search of the literature for existing empirical continuous data resulted in a total of twelve articles for final inclusion following title/abstract, and full-text screening. Our study was performed under a rigorous framework for systematic reviews, which showed that the lack of available comparisons between discrete and continuous data hinders our understanding of the performance of continuous data. Our study demonstrates the problem surrounding the efficacy of continuous data as remaining relatively intractable despite an exhaustive search, due in part to the difficulty in obtaining relevant comparisons from the literature. Thus, we implore researchers to address this issue with studies that collect discrete and continuous data sets with directly comparable properties (i.e., describing shape, or size).

Machine learning prediction method for the interface bond strength between fiber reinforced polymer bars and concrete based on multi-feature driven analysis

In severe service conditions, corrosion of reinforcement poses a significant challenge for concrete structures. Fiber reinforced polymer (FRP) materials, characterized by their low weight, high strength, and superior corrosion resistance, are effective in enhancing the durability of these structures. The interfacial bond performance between FRP bars and concrete is crucial for their synergistic action and is pivotal for the design and safety evaluation of FRP-reinforced concrete structures. This paper conducts a comprehensive analysis of factors including the material and mechanical properties of FRP bars, concrete mechanical properties, and bond length. Utilizing an exhaustive search strategy, it identifies the optimal characteristic parameters for assessing the bond strength between FRP bars and concrete. Subsequently, these parameters are integrated with four machine learning (ML) algorithms—DT, SVM, RF and XGB—to train the nonlinear mapping relationship between influencing factors and bond strength, proposing a unified and interpretable prediction method for the FRP bar-concrete interface bond strength. The model's predictive accuracy is then validated using a test dataset, and a comparative analysis is performed among the predictive models generated by the four ML algorithms, as well as against empirical calculation formulas. The findings indicate that the ML models offer superior predictive performance and accuracy over empirical formulas, with the XGB algorithm demonstrating the highest overall accuracy and best performance. Moreover, to address the black-box nature of ML algorithms, the SHAP method is employed to enhance the interpretability of the bond strength prediction process. The newly developed hybrid ML model holds promise as a novel approach for accurately assessing the bond strength at the FRP bar-concrete interface.

Smooth Sigmoid Surrogate (SSS): An Alternative to Greedy Search in Decision Trees

Greedy search (GS) or exhaustive search plays a crucial role in decision trees and their various extensions. We introduce an alternative splitting method called smooth sigmoid surrogate (SSS) in which the indicator threshold function used in GS is approximated by a smooth sigmoid function. This approach allows for parametric smoothing or regularization of the erratic and discrete GS process, making it more effective in identifying the true cutoff point, particularly in the presence of weak signals, as well as less prone to the inherent end-cut preference problem. Additionally, SSS provides a convenient means of evaluating the best split by referencing a parametric nonlinear model. Moreover, in many variants of recursive partitioning, SSS can be reformulated as a one-dimensional smooth optimization problem, rendering it computationally more efficient than GS. Extensive simulation studies and real data examples are provided to evaluate and demonstrate its effectiveness.

Analysis of Handwriting for Recognition of Parkinson’s Disease: Current State and New Study

One of the symptoms of Parkinson’s disease (PD) is abnormal handwriting caused by motor dysfunction. The development of tablet technology opens up opportunities for an effective analysis of the writing process of people suffering from Parkinson’s disease, aimed at supporting medical diagnosis using machine learning methods. Several approaches have been used and presented in the literature that discuss the analysis and understanding of images created during the writing of single words or sentences. In this study, we propose an analysis based on a sequence of sentences, which allows us to assess the evolution of writing over time. The study material consisted of handwriting image samples acquired in a group of 24 patients with PD and 24 healthy controls. The parameterization of the handwriting image samples was carried out using domain knowledge. Using the exhaustive search method, we selected the relevant features for the SVM algorithm performing binary classification. The results obtained were assessed using quality measures, including overall accuracy, which was 91.67%. The results were compared with competitive works on the same subject and seem to be better (a higher level of accuracy with a much smaller number of features than those presented by others).

Summary of Best Evidence for Perioperative Management Practices in Hair Transplantation Patients : Facial Skeleton.

To integrate and summarize the best evidence on perioperative management practices for hair transplantation patients, providing an evidence-based reference for clinical. An exhaustive literature search was conducted to identify the best evidence for managing patients undergoing hair transplantation during the perioperative period. The databases searched included Up To Date, BMJ Best Practice, UK National Institute for Health and Care Excellence, National Guideline Clearing House, Scottish Intercollegiate Guidelines Network, Guidelines International Network, Cochrane Library, JBI Database of Systematic Reviews and Implementation Reports, PubMed, Web of Science, European Dermatology Forum, China National Knowledge Infrastructure, Wanfang Data, Medlive Guideline Network, and Sinomed. The search spanned publications from February 2013 to February 2024, focusing on clinical decisions, evidence summaries, guidelines, and expert consensus. We finally identified 22 articles with high-quality results (consisting of 9 clinical decisions, 6 guidelines, 7 expert consensuses), providing 41 pieces of evidence across seven categories: assessment of transplantation conditions, transplant planning and preoperative preparation, anesthetic preparations, surgical methods and operation skills, postoperative wound management, medication-related guidance, optimization of nursing and treatment strategies. Special emphasis has been placed on the sections covering anesthesia preparation, surgical methods, and operational techniques, with detailed explanations provided. The summarized best evidence on perioperative management practices for hair transplantation patients can serve as evidence-based guidelines for clinical. It is recommended that clinical staff adopt evidence-based recommendations to improve and optimize patient outcomes and promote postoperative recovery. As these evidences came from different countries, factors such as the clinical environment should be evaluated before application. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Artificial intelligence methods used in various aquaculture applications: A systematic literature review

AbstractThis research article presents a systematic literature review on the current state‐of‐the‐art artificial intelligence (AI) methodologies used in aquaculture applications. As the demand for seafood continues to grow, the aquaculture industry faces numerous challenges, including disease management, feeding optimization, water quality monitoring, and extraction of aquaculture area. To address these challenges effectively and sustainably, AI techniques have been increasingly applied in aquaculture systems over recent years. This review aims to analyze various AI methodologies utilized within different aspects of aquacultural practices. By examining existing studies and identifying trends and gaps in research areas related to AI integration into aquaculture practices, this paper provides valuable insights for further advancements. The purpose was to synthesize current knowledge on application and its challenges in implementing AI technologies within the commercial aquaculture industry. Specifically, the review is to identify and analyze peer‐reviewed studies reporting on applications of AI technologies in aquaculture industry, to classify and summarize the key findings from the selected studies in aquaculture operations through AI, and to evaluate and discuss any challenges reported regarding the implementation and adoption of AI solutions in commercial aquaculture. The overall goal was to comprehensively assess these via a systematic literature review process. Challenges of AI technologies and methods were identified in the research literature for applying AI to optimize commercial aquaculture practices and production. An exhaustive search of a scholarly database from Scopus, was performed and papers published in English between 2020 and 2024 were considered for inclusion. After a rigorous screening process involving over 116 studies, 57 highly relevant works were identified and analyzed according to key themes involving demonstrated AI applications, employed methodologies and challenges that are expected when applying such methods. The findings revealed that AI‐driven tools such as computer vision, machine learning, and predictive modeling hold much potential for enhancing sustainability, efficiency, and productivity within aquaculture operations through applications like disease monitoring, environmental management, and production optimization. However, the review also uncovered substantial challenges that will continue limiting widespread adoption, including restricted access to representative data, prohibitive expenses, technical complexities, lack of social acceptance, and data privacy and security concerns. This comprehensive synthesis of the current evidence available provides an empirical foundation upon which further progress can be built by identifying priority areas requiring additional research efforts to fully address challenges on the responsible integration of suitable solutions for the commercial aquaculture industry globally.

Identification of Pathogenic Missense Mutations of NF1 Using Computational Approaches

Neurofibromatosis type 1 (NF1) is a prevalent autosomal dominant disorder caused by mutations in the NF1 gene, leading to multisystem disorders. Given the critical role of cysteine residues in protein stability and function, we aimed to identify key NF1 mutations affecting cysteine residues that significantly contribute to neurofibromatosis pathology. To identify the most critical mutations in the NF1 gene that contribute to the pathology of neurofibromatosis, we employed a sophisticated computational pipeline specifically designed to detect significant mutations affecting the NF1 gene. Our approach involved an exhaustive search of databases such as the Human Gene Mutation Database (HGMD), UniProt, and ClinVar for information on missense mutations associated with NF1. Our search yielded a total of 204 unique cysteine missense mutations. We then employed in silico prediction tools, including PredictSNP, iStable, and Align GVGD, to assess the impact of these mutations. Among the mutations, C379R, R1000C, and C1016Y stood out due to their deleterious effects on the biophysical properties of the neurofibromin protein, significantly destabilizing its structure. These mutations were subjected to further phenotyping analysis using SNPeffect 4.0, which predicted disturbances in the protein’s chaperone binding sites and overall structural stability. Furthermore, to directly visualize the impact of these mutations on protein structure, we utilized AlphaFold3 to simulate both the wild-type and mutant NF1 structures, revealing the significant effects of the R1000C mutation on the protein’s conformation. In conclusion, the identification of these mutations can play a pivotal role in advancing the field of precision medicine and aid in the development of effective drugs for associated diseases.

An advanced symbol detection approach for MIMO-FBMC/OQAM scheme based on migrating birds optimization algorithm

Application of multiple-input multiple-output (MIMO) technology to the filter bank multicarrier/offset quadrate amplitude modulation (FBMC/OQAM) system provides great improvements on its robustness to channel fading effects. Nevertheless, the unique qualities of MIMO-FBMC/OQAM scheme do not make any sense without solving the symbol detection problem on its receiver part. To this end, an efficient symbol detecting strategy having the capability of recovering the transmitted symbol sequences with the highest accuracy is needed for the related system. Maximum likelihood (ML) detector is known with its excellent symbol detection performance in the literature. However, due to the usage of exhaustive search procedure, the computational complexity of the ML detector reaches extremely high levels with the increase of antenna size and modulation order. On the other hand, in the case that the exhaustive search procedure is substituted with the symbol optimization process, it becomes possible to achieve a large amount of complexity reduction in the conventional ML scheme without compromising too much on its symbol detection performance. In order to carry out an efficient symbol optimization in discrete space, we propose migrating birds optimization (MBO) algorithm based on cyclic bit flipping procedure in this paper. By virtue of employing the proposed MBO algorithm reinforced by the cyclic bit flipping mechanism for optimizing the symbol sequences, the computational complexity of the conventional ML is reduced to quite low levels. The percentages of complexity reduction achieved by the proposed scheme over the classical ML for 4 × 4, 6 × 6 and 8 × 8 MIMO configurations are equal to 29.688%, 87.188% and 98.299%, respectively. In addition to these huge complexity gains, an efficient symbol detection performance quite near to that of conventional ML is obtained thanks to the usage of aforementioned MBO-based symbol optimization procedure.

Evaluating the effects of dietary patterns on circulating C-reactive protein levels in the general adult population: an umbrella review of meta-analyses of interventional and observational studies.

Adopting a healthy dietary pattern may be an initial step in combating inflammation-related chronic diseases; however, a comprehensive synthesis evaluating current evidence is lacking. This umbrella review aimed to summarise the current evidence on the effects of dietary patterns on circulating C-reactive protein (CRP) levels in adults. We conducted an exhaustive search of the Pubmed, Scopus and Epistemonikos databases, spanning from their inception to November 2023, to identify systematic reviews and meta-analyses across all study designs. Subsequently, we employed a random-effects model to recompute the pooled mean difference. Methodological quality was assessed using the A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2) checklist, and evidence certainty was categorised as non-significant, weak, suggestive, highly suggestive or convincing (PROSPERO: CRD42023484917). We included twenty-seven articles with thirty meta-analyses of seven dietary patterns, fifteen of which (50 %) exhibited high methodological quality. The summary effects of randomised controlled trials (RCT) found that the Mediterranean diet was the most effective in reducing circulating CRP levels, followed by Vegetarian/Vegan and Energy-restricted diets, though the evidence was of weak quality. In contrast, Intermittent Fasting, Ketogenic, Nordic and Paleolithic diets did not show an inverse correlation with circulating CRP levels. Some results from combined interventional and observational studies, as well as solely observational studies, also agreed with these findings. These dietary patterns show the potential in reducing CRP levels in adults, yet the lack of high-quality evidence suggests future studies may alter the summary estimates. Therefore, further well-conducted studies are warranted.

Allocation of Microgrid-Forming resources for distribution systems reliability improvement

This paper proposes a novel method to allocate distributed generation and energy storage as Microgrid-Forming Resources (MFR) to improve the reliability of medium voltage (MV) grids. A graph-based approach is used, which simplifies the circuit analysis and reduces computational time when compared to exhaustive search methods. First, a medium voltage substation-level Nodal Fault-Effect Matrix (NFEM) is derived from the feeder’s topology, which is used for predictive reliability calculations, and calibrated with historical data. Second, a reduced set of scenarios of MFR allocation are generated using a graph community approach and evaluated in terms of reliability improvement and power and energy requirements of the distributed resources. Two case studies are presented based on real grids with different reliability characteristics and the results indicate that the proposed approach is an insightful tool for distribution system planners to evaluate the integration of microgrids to improve system reliability.

Numerical evidence for the existence of three different stable liquid water structures as indicated by local order parameter.

Structures of liquid water are controversial not only in supercooled polyamorphism but also in stable bulk liquids in the high temperature and pressure range. Several experimental studies in bulk liquid have assumed the existence of three different liquid water structures. If indeed the three liquid water structures are different, they should be clearly distinguished by some measure other than density that characterizes the difference in structural order. In this study, whether the three different bulk liquid water structures are real or not is numerically verified based on molecular simulations using a reliable water molecular model. Since these liquid water structures have been suggested to be related to three different crystal structures (i.e., ice Ih, III, and V), liquid structures are sampled from the vicinity of the ice Ih-liquid coexistence point, the ice III-V-liquid triple point, and the ice V-VI-liquid triple point, respectively. An attempt is made to introduce local order parameters (LOPs) as an indicator to distinguish these structures. A fast and exhaustive LOP search is performed by the molecular assembly structure learning package for Identifying order parameters. The selected LOP distinguishes the molecular structures of three different stable liquid waters with high accuracy, providing numerical evidence that these structural orders differ from each other. Furthermore, regions of the liquid water structures are drawn on a phase diagram using the LOP, demonstrating their consistency with experimental studies.

A Systematic Review of Synthesis and Application Base Nanomaterial using Pandanus Extract Plant

This systematic review investigates the production and utilization of nanomaterials using an extract from the Pandanus amaryllifolius plant. The introduction outlines the imperative role of nanomaterials in addressing the challenges faced by conventional materials, emphasizing the need for sustainable and efficient alternatives. Note that the problem statement underscores the existing gaps in the literature regarding the comprehensive exploration of Pandanus-based nanomaterials toward applications. The study conducted an extensive search on Scopus and Web of Science (WoS) databases, employing specific keywords. The exhaustive search produced 17 articles, which were classified into three primary themes: (1) Nanomaterials synthesis and utilization using extracts from Pandanus amaryllifolius, (2) Characteristics and uses of nanomaterials biosynthesized through biological processes, and (3) Bioactivity and practical applications of materials derived from plants. The review thoroughly examines the findings related to these specific topics, offering valuable information on the various uses and characteristics of nanomaterials produced with the help of Pandanus amaryllifolius extract. This paper enhances the comprehension of the potential applications of Pandanus-based nanomaterials in different fields by providing a detailed analysis of the current research. It serves as a significant reference for practitioners as well as researchers intrigued by the combination of plant extracts and nanotechnology.

Integrative taxonomy of Gymnobela and Pontiothauma (Conoidea: Raphitomidae) from Australian waters provides more evidence of transoceanic distribution in deep-sea gastropods

ABSTRACT The deep-sea raphitomid genus Gymnobela Verrill, 1884 (Neogastropoda: Conoidea) has historically been a container for a multitude of raphitomid species, assigned to this genus almost exclusively based on shell characters. Consequently, the genus has become taxonomically inflated. However, a 2021 molecular study redefined Gymnobela and taxonomically revised the genus. Herein, we present an integrative taxonomic study that, mainly based on samples from Australian waters, further defines this problematic genus, as well as the closely related genus Pontiothauma E.A. Smith, 1895. An automatic barcode gap discovery (ABGD) analysis of three mitochondrial genes resulted in six primary species hypotheses (four in Gymnobela and two in Pontiothauma). We recognised morpho-anatomical and bathymetric/geographical characters distinct to all six and found available names for four; we name one new species, Pontiothauma transregna sp. n., but abstain from naming one presumably unnamed Pontiothauma due to a lack of well-preserved adult specimen material. We provide clear synapomorphic characters unique to both Gymnobela and Pontiothauma, notably those of the radula. Furthermore, we present the first evidence supported by molecular data of a transoceanic distribution among the raphitomids, namely in Gymnobela angulosa Sysoev, 1988. Additionally, we recognise non-molecular evidence that might indicate similarly wide distributions in Gymnobela agasizzi (Verrill and Smith, 1880), Gymnobela engonia Verrill, 1884 and Gymnobela frielei Verrill, 1885. Consequently, and to avoid taxonomic splitting, we herein advocate for wide-ranging investigation of worldwide material when conducting taxonomic studies on deep-sea conoideans, due to growing evidence of transoceanic distributions in the group. Finally, through an exhaustive search of worldwide repositories and the literature, and in conjunction with our molecular, morpho-anatomical and bathymetric/geographical data, we have reassigned 16 species from Gymnobela to other genera within the family Raphitomidae. http://www.zoobank.org/urn:lsid:zoobank.org:pub:14347001-685D-4DF3-83D7-970E94D43815