Scientific Research Research Articles

Simple Recurrent Networks are Interactive.

There is disagreement among cognitive scientists as to whether a key computational framework - the Simple Recurrent Network (SRN; Elman, Machine Learning, 7(2), 195-225, 1991; Elman, Cognitive Science, 14(2), 179-211, 1990) - is a feedforward system. SRNs have been essential tools in advancing theories of learning, development, and processing in cognitive science for more than three decades. If SRNs were feedforward systems, there would be pervasive theoretical implications: Anything an SRN can do would therefore be explainable without interaction (feedback). However, despite claims that SRNs (and by extension recurrent neural networks more generally) are feedforward (Norris, 1993), this is not the case. Feedforward networks by definition are acyclic graphs - they contain no loops. SRNs contain loops - from hidden units back to hidden units with a time delay - and are therefore cyclic graphs. As we demonstrate, they are interactive in the sense normally implied for networks with feedback connections between layers: In an SRN, bottom-up inputs are inextricably mixed with previous model-internal computations. Inputs are transmitted to hidden units by multiplying them by input-to-hidden weights. However, hidden units simultaneously receive their own previous activations as input via hidden-to-hidden connections with a one-step time delay (typically via context units). These are added to the input-to-hidden values, and the sums are transformed by an activation function. Thus, bottom-up inputs are mixed with the products of potentially many preceding transformations of inputs and model-internal states. We discuss theoretical implications through a key example from psycholinguistics where the status of SRNs as feedforward or interactive has crucial ramifications.

Innovative Peptide-Based Plasmonic Optical Biosensor for the Determination of Cholesterol

Plasmonic-based biosensors have gained prominence as potent optical biosensing platforms in both scientific and medical research, attributable to their enhanced sensitivity and precision in detecting biomolecular and chemical interactions. However, the detection of low molecular weight analytes with high sensitivity and specificity remains a complex and unresolved issue, posing significant limitations for the advancement of clinical diagnostic tools and medical device technologies. Notably, abnormal cholesterol levels are a well-established indicator of various pathological conditions; yet, the quantitative detection of the free form of cholesterol is complicated by its small molecular size, pronounced hydrophobicity, and the necessity for mediator molecules to achieve efficient sensing. In the present study, a novel strategy for cholesterol quantification was developed, leveraging a plasmonic optical readout in conjunction with a highly specific cholesterol-binding peptide (C-pept) as a biorecognition element, anchored on a functionalized silica substrate. The resulting biosensor exhibited an exceptionally low detection limit of 21.95 µM and demonstrated a linear response in the 10–200 µM range. This peptide-integrated plasmonic sensor introduces a novel one-step competitive method for cholesterol quantification, positioning itself as a highly sensitive biosensing modality for implementation within the AVAC platform, which operates using reflective dark-field microscopy.

A comprehensive study of common and rare genetic variants in spermatogenesis-related loci identifies new risk factors for idiopathic severe spermatogenic failure

Abstract STUDY QUESTION Can genome-wide genotyping data be analysed using a hypothesis-driven approach to enhance the understanding of the genetic basis of severe spermatogenic failure (SPGF) in male infertility? SUMMARY ANSWER Our findings revealed a significant association between SPGF and the SHOC1 gene, and identified three novel genes (PCSK4, AP3B1, and DLK1) along with 32 potentially pathogenic rare variants in 30 genes that contribute to this condition. WHAT IS KNOWN ALREADY SPGF is a major cause of male infertility, often with an unknown aetiology. SPGF can be due to either multifactorial causes, including both common genetic variants in multiple genes and environmental factors, or highly damaging rare variants. Next-generation sequencing (NGS) methods are useful for identifying rare mutations that explain monogenic forms of SPGF. Genome-wide association studies (GWAS) have become essential approaches for deciphering the intricate genetic landscape of complex diseases, offering a cost-effective and rapid means to genotype millions of genetic variants. Novel methods have demonstrated that GWAS datasets can be used to infer rare coding variants that are causal for male infertility phenotypes. However, this approach has not been previously applied to characterize the genetic component of a whole case-control cohort. STUDY DESIGN, SIZE, DURATION We employed a hypothesis-driven approach focusing on all genetic variation identified, using a GWAS platform and subsequent genotype imputation, encompassing over 6 million polymorphisms and a total of 1571 SPGF patients and 2431 controls. Both common (minor allele frequency, MAF > 0.01) and rare (MAF < 0.01) variants were investigated within a total of 1797 loci with a reported role in spermatogenesis. This gene panel was meticulously assembled through comprehensive searches in the literature and various databases focused on male infertility genetics. PARTICIPANTS/MATERIALS, SETTING, METHODS This study involved a European cohort using previously and newly generated data. Our analysis consisted of three independent methods: (1) variant-wise association analyses using logistic regression models, (2) gene-wise association analyses using combined multivariate and collapsing (CMC) burden tests, and (3) identification and characterisation of highly damaging rare coding variants showing homozygosity only in SPGF patients. MAIN RESULTS AND THE ROLE OF CHANCE The variant-wise analyses revealed an association between SPGF and SHOC1-rs12347237 (p = 4.15E-06, OR = 2.66), which was likely explained by an altered binding affinity of key transcription factors in regulatory regions and the disruptive effect of coding variants within the gene. Three additional genes (PCSK4, AP3B1, and DLK1) were identified as novel relevant players in human male infertility using the gene-wise burden test approach (p < 5.56E-04). Furthermore, we linked a total of 32 potentially pathogenic and recessive coding variants of the selected genes to 35 different cases. LARGE SCALE DATA Publicly available via GWAS catalog (Accession number: GCST90239721). LIMITATIONS, REASONS FOR CAUTION The analysis of low-frequency variants presents challenges in achieving sufficient statistical power to detect genetic associations. Consequently, independent studies with larger sample sizes are essential to replicate our results. Additionally, the specific roles of the identified variants in the pathogenic mechanisms of SPGF should be assessed through functional experiments. WIDER IMPLICATIONS OF THE FINDINGS Our findings highlight the benefit of using GWAS genotyping to screen for both common and rare variants potentially implicated in idiopathic cases of SPGF, whether due to complex or monogenic causes. The discovery of novel genetic risk factors for SPGF and the elucidation of the underlying genetic causes provide new perspectives for personalized medicine and reproductive counselling. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the Spanish Ministry of Science and Innovation through the Spanish National Plan for Scientific and Technical Research and Innovation (PID2020-120157RB-I00) and the Andalusian Government through the research projects of “Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020)” (ref. PY20_00212) and “Proyectos de Investigación aplicada FEDER-UGR 2023” (ref. C-CTS-273-UGR23). SGM was funded by the previously mentioned projects (ref. PY20_00212 and PID2020-120157RB-I00). AGJ was funded by MCIN/AEI/10.13039/501100011033 and FSE “El FSE invierte en tu futuro” (grant ref. FPU20/02926). IPATIMUP integrates the i3S Research Unit, which is partially supported by the Portuguese Foundation for Science and Technology (FCT), financed by the European Social Funds (COMPETE-FEDER) and National Funds (projects PEstC/SAU/LA0003/2013 and POCI-01-0145-FEDER-007274). SS is supported by FCT funds (10.54499/DL57/2016/CP1363/CT0019), ToxOmics-Centre for Toxicogenomics and Human Health, Genetics, Oncology and Human Toxicology, and is also partially supported by the Portuguese Foundation for Science and Technology (UIDP/00009/2020 and UIDB/00009/2020). SLarriba received support from Instituto de Salud Carlos III (grant: DTS18/00101], co-funded by FEDER funds/European Regional Development Fund (ERDF)-a way to build Europe-), and from “Generalitat de Catalunya” (grant 2021SGR052). SLarriba is also sponsored by the “Researchers Consolidation Program” from the SNS-Dpt. Salut Generalitat de Catalunya (Exp. CES09/020). All authors declare no conflict of interest related to this study.

Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine

The improvements in collecting and processing data using machine learning algorithms have increased the interest in data mining. This trend has led to the development of real-life decision support systems (DSSs) in diverse areas such as biomedical informatics, fraud detection, natural language processing, face recognition, autonomous vehicles, image processing, and each part of the real production environment. The imbalanced datasets in some of these studies, which result in low performance measures, have highlighted the need for additional efforts to address this issue. The proposed method (HOUM) is used to address the issue of imbalanced datasets for classification problems in this study. The aim of the model is to prevent the overfitting problem caused by oversampling and valuable data loss caused by undersampling in imbalanced data and obtain successful classification results. The HOUM is a hybrid approach that tackles imbalanced class distribution challenges, refines datasets, and improves model robustness. In the first step, majority-class data points that are distant from the decision boundary obtained via SVM are reduced. If the data are not balanced, SLS is employed to augment the minority-class data. This loop continues until the dataset becomes balanced. The main contribution of the proposed method is reproducing informative minority data using SLS and diminishing non-informative majority data using the SVM before applying classification techniques. Firstly, the efficiency of the proposed method, the HOUM, is verified by comparison with the SMOTE, SMOTEENN, and SMOTETomek techniques using eight datasets. Then, the results of the W-SIMO and RusAda algorithms, which were developed for imbalanced datasets, are compared with those of the HOUM. The strength of the HOUM is revealed through this comparison. The proposed HOUM algorithm utilizes a real dataset obtained from a project endorsed by The Scientific and Technical Research Council of Turkey. The collected data include quality control and processing parameters of yarn data. The aim of this project is to prevent yarn breakage errors during the weaving process on looms. This study introduces a decision support system (DSS) designed to prevent yarn breakage during fabric weaving. The high performance of the algorithm may encourage producers to manage yarn flow and enhance the HOUM’s efficiency as a DSS.

Inverse Cherenkov dielectric laser accelerator with alternating phase focusing for subrelativistic particles

In this paper, we propose a dielectric laser acceleration scheme based on the inverse Cherenkov effect, which uses the principle of alternating phase focusing to accelerate, confine, and bunch the subrelativistic electron beam over an extended distance. In this scheme, the reference electron is always synchronized with the acceleration wave and is thus continuously accelerated. Electrons around it are alternately subjected to focusing and defocusing forces by alternately jumping its phase. Optimizing the focusing period can achieve stable beam confinement. Simulations show that a 100 keV electron bunch can be coherently accelerated with the average acceleration gradient of 220.8 MV/m, and it can travel nearly 200 μm without loss in a channel only 500 nm wide. This approach is scalable, potentially enabling the development of millimeter-scale MeV particle accelerators for ground-breaking use in medical, scientific, and materials research. Published by the American Physical Society 2024

Participatory science and course-based undergraduate research experience to improve research-informed teaching of evolution and palaeontology

AbstractThis study introduces a participatory science-inspired approach to teaching and curriculum design, involving undergraduate students directly in active research. Using a case study on dinosaur eye size, integrated into a first-year undergraduate course in Geology and Palaeontology at a UK university, this study presents the advantages and disadvantages of this approach for both teachers and learners. As part of the study, 22 undergraduate students were involved in data collection, analysis, and the subsequent publication processes, emphasizing active student participation in research. A subsequent survey demonstrated high student engagement and perceived relevance of this participatory science-style teaching approach. Results indicate that students found the approach appealing, engaging, and beneficial for understanding scientific concepts and methods. The case study shows that a participatory science approach through a course-based research experience can enhance student engagement and learning by providing meaningful, hands-on research experiences. This approach allowed students to apply theoretical knowledge in a realistic context, fostering their understanding of evolutionary theory through active data collection and analysis. However, care should be taken concerning data accuracy and ethical aspects, such as exploitation of labour and the recognition of knowledge creators and participants. Despite these challenges, the benefits of integrating such approaches into higher education curricula can be substantial, offering a valuable model for teaching evolutionary theory and related topics.

Harnessing full-text publications for deep insights into C. elegans and Drosophila biomaps.

In the rapidly expanding domain of scientific research, tracking and synthesizing information from the rapidly increasing volume of publications pose significant challenges. To address this, we introduce a novel high-throughput pipeline that employs ChatGPT to systematically extract and analyze connectivity information from the full-texts and abstracts of 24,237 and 150,538 research publications concerning Caenorhabditis elegans and Drosophila melanogaster, respectively. This approach has effectively identified 200,219 and 1,194,587 interactions within the C. elegans and Drosophila biomaps, respectively. Utilizing Cytoscape Web, we have developed a searchable online biomaps that link relevant keywords to their corresponding PubMed IDs, thus providing seamless access to an extensive knowledge network encompassing C. elegans and Drosophila. Our work highlights the transformative potential of integrating artificial intelligence with bioinformatics to deepen our understanding of complex biological systems. By revealing the intricate web of relationships among key entities in C. elegans and Drosophila, we offer invaluable insights that promise to propel advancements in genetics, developmental biology, neuroscience, longevity, and beyond. We also provide details and discuss significant nodes within both biomaps, including the insulin/IGF-1 signaling (IIS) and the notch pathways. Our innovative methodology sets a robust foundation for future research aimed at unravelling complex biological networks across diverse organisms. The two databases are available at worm.bio-map.com and drosophila.bio-map.com.

Does the COVID-19 Vaccines Effect on the Menstrual Cycle, Cross-Sectional Study Among Adult Females at King Saud University

COVID-19 vaccinations have been associated with irregularities in the menstrual cycle and symptoms occurring pre and post-menstruation, affecting women’s reproductive health. This study aimed to demonstrate the impact of COVID-19 vaccines on women’s reproductive health by examining the association between COVID-19 vaccines and disturbed menstrual cycles. This cross-sectional survey-based study was conducted among adult females at King Saud University, Saudi Arabia, from April 2022 to August 2022. A total of 380 participants were selected through a stratified random sampling technique. Ethical approval was obtained from the Deanship of Scientific Research at King Saud University. The data were analysed using descriptive statistics and appropriate statistical tests. The results revealed that 53.9% of the participants experienced disturbances in the menstrual cycle after receiving the COVID-19 vaccination. Adult females aged 18-25 showed the highest rate of disorders. Menstrual disruptions were substantially correlated with marital status (p = 0.05), with a significant association between the number of COVID-19 vaccine doses and the occurrence of menstrual disturbances (p = 0.001). The administration of the vaccination during menstruation was linked to a notable occurrence of menstrual disturbances, although the p-value showed no statistical significance. A significant correlation was examined (p < 0.05) between pre-existing menstrual problems and the occurrence of disorders after receiving the COVID-19 vaccine (p < 0.05). The study emphasised the importance of tailored healthcare treatments for women following COVID-19 vaccination and the need for regular evaluation of reproductive health outcomes post-COVID-19 vaccination.

The Nazi Bomb Project: A Case Study of Value Neutrality in Science and Technology

Scientists in Nazi Germany attempted but failed to build an atomic bomb or working nuclear reactor during the years of the Second World War, 1939-1945. Can philosophical meaning be drawn from this history? The history of the Nazi scientists and nuclear fission raises important issues concerning the alleged value-neutrality of scientific research and technological development. We can also ask whether there is an ethical dimension to the failure of the Nazi bomb project. Did the scientists who worked in the Third Reich make a moral decision not to complete the development of a working nuclear reactor and/or a usable nuclear explosive device? In this essay, I argue that Werner Heisenberg and other Nazi scientists fell victim to a value-neutral conception of science that led them to make unethical choices in support of the Nazi regime. The story of the Nazi bomb project exemplifies a more general philosophical point: if scientists fail to understand the social, political, and cultural context of their research they will be vulnerable to ethically wrong decisions.

Tritium determination in natural water samples in Fukushima from 2022 to 2023 using an ultra-low-level tritium counting system.

The Fukushima Daiichi Nuclear Power Plant (FDNPP) accident created large stockpiles of tritium containing cooling water, which is to be gradually released into the Pacific Ocean, gaining attention from surrounding countries, environmental groups, and local residents concerned with the possibility of increasing tritium concentrations in the water and food cycle. Establishing baseline concentration levels and monitoring tritium immission values are important for assuring public safety, providing data for scientific research and risk communication. Tritium concentrations in the environment are very low; therefore, tritium measurements require enrichment in order to estimate the radiation exposure from drinking water intake and provide information on the water cycle. Natural water samples were collected at Tomioka Town located south of the FDNPP. Samples were distilled, enriched by electrolysis, and re-distilled using an improved SPE method to preconcentrate tritium to measureable levels. Tritium concentrations were determined by a low-background liquid scintillation counter. The observed tritium concentrations were relatively low, rainwater had a mean and SD value of 0.40±0.13Bq/L, and freshwater samples showed similar concentrations, while brackish coastal water samples were below 0.13±0.02Bq/L. The observed tritium concentrations in this study are considered safe as effective doses based on annual drinking water intake; however, continous monitoring is necessary to assure public safety.

The public: an insider to scientific research

The public: an insider to scientific research

Environmental Occurrence and Biotic Concentrations of Ultrashort-Chain Perfluoroalkyl Acids: Overlooked Global Organofluorine Contaminants.

Per- and polyfluoroalkyl substances (PFASs) are a large group of anthropogenic fluorinated chemicals. Ultrashort-chain perfluoroalkyl acids (PFAAs) have recently gained attention due to their prevalence in the environment and increasing environmental concerns. In this review, we established a literature database from 1990 to 2024, encompassing environmental and biological concentrations (>3,500 concentration records) of five historically overlooked ultrashort-chain PFAAs (perfluoroalkyl carboxylic and sulfonic acids with less than 4 carbons): trifluoroacetic acid (TFA), perfluoropropanoic acid (PFPrA), trifluoromethanesulfonic acid (TFMS), perfluoroethanesulfonate (PFEtS), and perfluoropropanesulfonate (PFPrS). Our data mining and analysis reveal that (1) ultrashort-chain PFAAs are globally distributed in various environments including water bodies, solid matrices, and air, with concentrations usually higher than those of longer-chain compounds; (2) TFA, the most extensively studied ultrashort-chain PFAA, shows a consistent upward trend in concentrations in surface water, rainwater, and air over the past three decades; and (3) ultrashort-chain PFAAs are present in various organisms, including plants, wildlife, and human blood, serum, and urine, with concentrations sometimes similar to those of longer-chain compounds. The current state of knowledge regarding the sources and fate of TFA and other ultrashort-chain PFAAs is also reviewed. Amid the global urgency to regulate PFASs, particularly as countries worldwide have intensified such efforts, this critical review will inform scientific research and regulatory policies.

Hurdling obstructions on instructional management of science teachers in schools with challenging laboratory resources

Lacking the resources for hands-on scientific experimentation could impact both students' educational experiences and their long-term proficiency. Without access to laboratories, students miss out on crucial opportunities to engage in practical applications of theoretical concepts, hindering their ability to comprehend scientific principles fully. The purpose of this exploratory study was to analyze the experiences of science teachers (n=20) from last-mile schools in the Philippines. Narratives from science teachers were the primary data in this study being collected through one-on-one interview. Thematic analysis revealed that the science teachers were challenged by insufficient resources available in their laboratories while others conduct experiments only in their classrooms while having class. Adapting to their concerns, teachers were able to maintain their instructional competence through hands-on demonstrations, visual and audio-visual presentations, interactive activities, and printout instruction. For science teachers, addressing these challenges requires concerted efforts from policymakers, educational institutions, and stakeholders to ensure equitable access to resources and professional development opportunities for teachers. The study emphasized the need for sustainable solutions to bridge the gap between resource-rich and resource-poor schools, ultimately enhancing students' educational experiences and long-term proficiency in science.

Dynamic neuromuscular stabilization and its efficiency on sports performance

Neuromuscular stability is necessary for optimal sport performance. Stability is not achieved only by abdominal muscles, spine extensors and gluteals, but it is accomplished by precise coordination of these muscles and intra-abdominal pressure by the central nervous system. A primary role of the core muscles stability is to raise the intra-abdominal pressure and to increase the tension in the thoracolumbar fascia. Postural core instability is associated with poor dynamic balance control and with a high risk of injury or fall. Dynamic neuromuscular stabilization (DNS) is a rehabilitative approach to optimize the movement system based upon the scientific principles of developmental kinesiology and provides an effective tool in rehabilitation of sport injuries.

Soil test crop response nutrient prescription equations for improving soil health and yield sustainability—a long-term study under Alfisols of southern India

IntroductionEnhancing soil health and nutrient levels through fertilizers boosts agricultural productivity and global food security. However, careful fertilizer use is essential to prevent environmental damage and improve crop yields. The soil test crop response (STCR) is a scientific approach to fertilizer recommendation that ensures efficient use, supporting higher crop production while protecting the environment and preserving resources.MethodologyA long-term field experiment on the STCR approach was initiated in 2017 at the Zonal Agriculture Research Station, University of Agricultural Sciences, Bangalore, India. The experiment aimed to study the impact of STCR-based nutrient prescription along with farmyard manure (FYM) for a targeted yield of soybean (Glycine max), sunflower (Helianthus annuus), dry chili (Capsicum annuum), aerobic rice (Oryza sativa L.), foxtail millet (Setaria italica), okra (Abelmoschus esculentus), and kodo millet (Paspalum scrobiculatum) on yield and changes in soil health in comparison with other approaches of fertilizer recommendation.ResultsThe results showed a significant and positive impact of the integrated use of fertilizer with FYM based on the STCR approach on the productivity of all the crops and soil fertility. Significantly higher yields of soybean (23.91 q ha−1), sunflower (27.13 q ha−1), dry chili (16.67 q ha−1), aerobic rice (65.46 q ha−1), foxtail millet (14.07 q ha−1), okra (26.82 t ha−1), and kodo millet (17.10 q ha−1) were observed in the STCR NPK + FYM approach at yield level 1 compared to the general recommended dose and soil fertility rating approach. This approach outperformed the standard recommendations, enhancing nutrient uptake and efficiency across various crops. Utilizing the principal component analysis, the soil quality index effectively reflected the impact of nutrient management on soil properties, with the STCR NPK + FYM treatment at yield level 1 showing the highest correlation with improved soil physical and chemical parameters.DiscussionThe STCR approach led to improved yield, nutrient uptake, utilization efficiency, and soil health, thanks to a balanced fertilization strategy. This strategy was informed by soil tests and included factors like crop-induced nutrient depletion, baseline soil fertility, the efficiency of inherent and added nutrients through fertilizers and farmyard manure, and the success of yield-targeting techniques in meeting the nutritional needs of crops.

Dual Assay Validation of Rosmarinus officinalis Extract as an Inhibitor of SARS-CoV-2 Spike Protein: Combining Pseudovirus Testing, Yeast Two-Hybrid, and UPLC-Q Exactive Orbitrap-MS Profiling.

This study evaluates the effectiveness of Traditional Chinese Medicine (TCM) extracts in blocking the interaction between the SARS-CoV-2 Spike protein and human ACE2 receptor, utilizing a dual-method approach to explore the antiviral potential of natural compounds. This work aims to evaluate the capability of TCM extracts in inhibiting the SARS-CoV-2 Spike protein and ACE2 receptor interaction using advanced biochemical assays. A dual-method screening approach was utilized, beginning with a pseudovirus assay to assess the inhibition capabilities of TCM extracts invitro, followed by a split-ubiquitin yeast two-hybrid (Y2H) system to validate interactions in live cells. Active compounds were characterized and quantified using UPLC-Q-Exactive-Orbitrap-MS. Among the 91 TCM extracts tested, Rosmarinus officinalis exhibited the most potent inhibition in both pseudovirus and Y2H assays, significantly reducing viral entry and disrupting the Spike-ACE2 interaction. Comprehensive chemical profiling via UPLC-Q-Exactive-Orbitrap-MS identified 132 compounds, including phenolics, flavonoids, and terpenoids. This research validates the use of TCM extracts in viral inhibition strategies, demonstrating the utility of integrating traditional remedies with modern scientific approaches to discover new therapeutic agents.

Experimental Measurements of Explosion Effects Propagating in the Real Geological Environment—Correlation with Small-Scale Model

This research focuses on comparing small-scale and full-scale measurements of wave propagation from explosions by using scaling relationships to find significant correlations between the two. The study investigates how seismic waves generated by explosions behave in the geological environment. The research covers various aspects such as the development of the model, the explosive materials used, measurement methods, evaluation techniques, and relevant software. A scientific approach based on the principle of backward Fourier transform was used to process and evaluate the data, which helps to filter the frequencies. One of the important calculations discussed is the determination of the attenuation coefficient, which helps to describe how waves attenuate as they pass through a material. The research also deals with dynamic scaling, using the dynamic exponent as a scaling factor to provide a better understanding of the behavior of waves at different scales. By comparing real in situ data with results from small-scale models, the study provides a robust framework for predicting the effects of explosions in complex geological environments. The research results show a high correlation coherence of the statistical data files of up to 4.1%. For dynamic tasks and model scaling, an important result can be pointed out, namely the approximately fourfold decrease in the exponents of the dependence on the distance from the excitation source and the amplitudes between P-waves (0.4316) and R-waves (0.1219). Conclusions are targeted at the possibility of correlating three types of results: small-scale simulations, numerical simulations, and a real full-scale experiment.

A Machine-Learning Approach to Mitigate Ground Clutter Effects in the GPM Combined Radar-Radiometer Algorithm (CORRA) Precipitation Estimates

Abstract In this study, a machine-learning based methodology is developed to mitigate the effects of ground clutter on precipitation estimates from the Global Precipitation Mission Combined Radar-Radiometer Algorithm. Ground clutter can corrupt and obscure precipitation echo in radar observations, leading to inaccuracies in precipitation estimates. To improve upon previous work, this study introduces a general machine learning (ML) approach that enables a systematic investigation and a better understanding of uncertainties in clutter mitigation. To allow for a less restrictive exploration of conditional relations between precipitation above the lowest clutter-free bin and surface precipitation, reflectivity observations above the clutter are included in a fixed-size set of predictors along with the precipitation type, surface type, and freezing level to estimate surface precipitation rates, and several ML-based estimation methods are investigated. A Neural Network Model (NN) is ultimately identified as the best candidate for systematic evaluations, as it is computationally fast to apply while effective in applications. The NN provides unbiased estimates; however, it does not significantly outperform a simple bias correction approach in reducing random errors in the estimates. The similar performance of other ML approaches suggests that the NN’s limited improvement beyond bias removal is due to indeterminacies in the data rather than limitations in the ML approach itself.

The Current State of Leadership Research: Identifying Key Trends through Natural Language Processing and Machine Learning Analysis

The impact of leadership on organizational performance and success is a well-established theme, leading to a significant expansion in related scientific studies. This has made identifying and understanding the current state of literature challenging. Most studies focus on specific aspects of leadership, offering a fragmented view of the field. This study employs Natural Language Processing (NLP) techniques and a machine learning approach to analyze 19,429 research papers from Web of Science and Scopus. We aimed to identify and interpret current trends in leadership research, providing a comprehensive overview of the leadership domain, highlighting five key research trends: (1) Leadership and Digital Transformation Research (LDTR); (2) Leadership and Organizational Performance Research (LOPR); (3) Educational Leadership Research (ELR); (4) Leadership Practices and Development Research (LPDR); and (5) Gender and Diversity Leadership Research (GDLR). This overview not only delineates current trends but also aids researchers in identifying significant gaps in existing literature, thereby facilitating the development of innovative research projects and initiatives.

Zooplankton metacommunity knowledge over 20 years: a systematic review

Abstract The metacommunity theory aims to understand the species dynamics at different scales by considering the influence of local and regional processes. We conducted a systematic review of zooplankton metacommunity studies in global aquatic ecosystems (freshwater, marine and estuarine) looking for the distribution, time scale, type of system, type of environment, zooplankton group tested, ecological approach and metacommunity approach analyzed in the studies. We also present an overview of the observed patterns of the zooplankton metacommunity in different environmental conditions. We searched for scientific studies published until December 2022 on Scopus and Web of Science using the words: “zooplankton” and “metacomunnit*.” Our results show that studies on the zooplankton metacommunity have become recurrent over time (167 studies), especially for pond systems and for the groups of rotifers, cladocerans and copepods; however, several gaps remain related to studies distribution and the low number of studies in streams, reservoirs, with functional and phylogenetic diversity, and the elements of metacommunity structure. Our data improve the knowledge of the zooplankton metacommunity over time, help to identify the gaps that need to be explored in future studies and bring a general overview of the observed patterns for the zooplankton metacommunity.