Global Scale Research Articles

ENHANCING MILITARY TRAINING THROUGH VR APPLICATIONS

Virtual Reality (VR) technologies have ushered in a new era for military education and training, offering immersive learning environments that replicate real-world scenarios with unprecedented realism and detail. This paper delves into the multifaceted application of VR within military training, delving into its efficacy in bolstering soldier readiness, facilitating skill acquisition, and honing decision-making abilities crucial for operational success. Through a comprehensive review of recent literature and insightful case studies, the paper illuminates the myriad advantages VR brings to the forefront of military training methodologies. Notably, VR is lauded for its capacity to engender experiential learning, providing trainees with hands-on exposure to diverse combat situations without exposing them to physical harm. Moreover, the paper underscores the significant cost-saving potential inherent in VR-based training modalities, as they obviate the need for large-scale live exercises while still delivering unparalleled training value. Despite these remarkable benefits, the paper also addresses pertinent challenges impeding the widespread adoption of VR in military training contexts. Issues such as technological constraints, fidelity of simulation, and seamless integration into existing training frameworks warrant careful consideration to fully harness the transformative potential of VR. Nonetheless, by leveraging VR technologies judiciously, military organizations stand poised to optimize training effectiveness, enhance operational readiness, and adeptly navigate the ever-evolving landscape of security threats on a global scale. Key Words: Virtual Reality (VR), Military Training Experiential Learning, Operational Readiness, Technological Challenges

Impact of Indian Accounting Standards (IND AS) on Financial Statements

The adoption and implementation of accounting standards play a pivotal role in shaping the financial reporting landscape of a country, influencing how businesses communicate their financial performance and position to stakeholders. In the case of India, the transition from Generally Accepted Accounting Principles (GAAP) to Indian Accounting Standards (Ind-AS) marked a significant paradigm shift in the accounting framework. The move was aimed at aligning Indian accounting practices with global standards, fostering transparency, comparability, and international investment. Historically, India had been following its own set of accounting standards, which sometimes diverged from international norms, leading to challenges in cross-border financial analysis and investment decisions. Recognizing the need for convergence with International Financial Reporting Standards (IFRS), the Ministry of Corporate Affairs (MCA) in India mandated the adoption of Ind-AS for certain classes of companies, primarily listed entities and large unlisted companies. The adoption of Ind-AS was a phased process, with the first set of companies transitioning in the financial year 2016-17. The primary objective was to enhance the quality and reliability of financial reporting, making it more comparable and relevant on a global scale. Ind-AS aimed to bring about consistency in accounting treatments, disclosures, and presentation of financial statements, fostering a more transparent and investor-friendly environment. One of the key motivations behind the shift to Ind-AS was to facilitate easier access to global capital markets for Indian companies. By adhering to internationally recognized accounting standards, Indian businesses sought to attract foreign investment and broaden their investor base. This move was particularly crucial as India aimed to position itself as a global economic player, attracting multinational corporations and fostering cross-border investments. The implementation of Ind-AS introduced changes in various aspects of financial reporting, affecting balance sheets, income statements, and disclosure practices. Companies faced the challenge of adapting to new measurement criteria, recognition principles, and disclosure requirements. The impact of these changes extended beyond mere compliance; it influenced how companies assessed their assets, liabilities, and overall financial performance. Additionally, the adoption of Ind-AS prompted companies to reevaluate their internal control mechanisms, financial policies, and governance structures. The transition required a robust framework for training and educating financial professionals, auditors, and other stakeholders to ensure a smooth and accurate implementation of the new standards.

Novel off-Targeting Events Identified after Genome Wide Analysis of CRISPR-Cas Edited Pigs.

CRISPR-Cas technology has transformed our ability to introduce targeted modifications, allowing unconventional animal models such as pigs to model human diseases and improve its value for food production. The main concern with using the technology is the possibility of introducing unwanted modifications in the genome. In this study, we illustrate a pipeline to comprehensively identify off-targeting events on a global scale in the genome of three different gene-edited pig models. Whole genome sequencing paired with an off-targeting prediction software tool filtered off-targeting events amongst natural variations present in gene-edited pigs. This pipeline confirmed two known off-targeting events in IGH knockout pigs, AR and RBFOX1, and identified other presumably off-targeted loci. Independent validation of the off-targeting events using other gene-edited DNA confirmed two novel off-targeting events in RAG2/IL2RG knockout pig models. This unique strategy offers a novel tool to detect off-targeting events in genetically heterogeneous species after genome editing.

Estimating occupational disease burden: a way forward.

Estimates of occupational disease burden provide important information on which effective policy and regulations can be developed. However, there is no direct way that these data can be obtained, and most burden estimates are derived by merging different data from diverse sources to synthesize estimates of the number of people made ill or who have died from workplace exposures. In recent years, several research groups have published estimates of occupational health burden at national or global scales; these are not always consistent. The World Health Organisation and the International Labour Organisation have taken on the task of producing occupational disease burden estimates for several workplace agents, which we assume are to be seen as the definitive global, regional, and national data. In this commentary, we critique the WHO/ILO approach for their estimates of the non-melanoma skin cancer burden from solar ultraviolet radiation and some of their results for hazardous particulates. We provide recommendations for researchers undertaking occupational burden estimates that they should report along with their data.

Modern Armed Conflicts: Disinformation Campaigns Shaping the Digital Information Landscape

ABSTRACT The purpose of this paper is to reveal the influence and implications of information warfare during the current armed conflicts on the digital information landscape. Disinformation campaigns are not a new weapon on the battlefield, but the rapid development of technology, like artificial intelligence (AI), in recent history has contributed to the rapid transformation of the digital information landscape. This poses a threat to the future of digital information, blurring the lines between reality and fantasy, ultimately revealing the depth of disinformation as a contributor to psychological operations during these times. This paper will explore the history of disinformation during armed conflicts, review current literature on disinformation and the implications, analyze the disinformation campaigns utilized during current Russia-Ukraine and Israel-Hamas conflicts, and highlight the humanitarian direness of the transforming digital information landscape. It has been revealed that this issue is multifaceted-undetectably embedding itself within societies across the globe, influencing not only decisive military actions locally, but the minds of millions. Left unaddressed on a global scale, disinformation campaigns may leave the digital information landscape unrecognizable in the future while harming consumers everywhere.

A bibliometric analysis of interstitial cells of Cajal research

ObjectiveThe significance of interstitial cells of Cajal (ICC) in the gastrointestinal tract has garnered increasing attention. In recent years, approximately 80 articles on ICC have been published annually in various journals. However, no bibliometric study has specifically focused on the literature related to ICC. Therefore, we conducted a comprehensive bibliometric analysis of ICC to reveal dynamic scientific developments, assisting researchers in exploring hotspots and emerging trends while gaining a global perspective.MethodsWe conducted a literature search in the Web of Science Core Collection (WoSCC) from January 1, 2013, to December 31, 2023, to identify relevant literature on ICC. We employed bibliometric software, namely VOSviewer and CiteSpace, to analyze various aspects including annual publication output, collaborations, research hotspots, current status, and development trends in this domain.ResultsA total of 891 English papers were published in 359 journals by 928 institutions from 57 countries/regions. According to the keyword analysis of the literature, researchers mainly focused on “c-Kit,” “expression,” “smooth muscle,” and “nitric oxide” related to ICC over the past 11 years. However, with “SIP syncytium,” “ANO1,” “enteric neurons,” “gastrointestinal stromal tumors (GIST),” and “functional dyspepsia (FD),” there has been a growing interest in the relationship between ANO1, SIP syncytium, and ICC, as well as the role of ICC in the treatment of GIST and FD.ConclusionBibliometric analysis has revealed the current status of ICC research. The association between ANO1, SIP syncytium, enteric neurons and ICC, as well as the role of ICC in the treatment of GIST versus FD has become the focus of current research. However, further research and collaboration on a global scale are still needed. Our analysis is particularly valuable to researchers in gastroenterology, oncology, and cell biology, providing insights that can guide future research directions.

Spatio-temporal features based deep learning model for depression detection using two electrodes

Abstract Deep learning has made significant contributions to the medical field and has shown great potential in various applications. Its ability to process vast amounts of data and extraction of patterns has enabled breakthroughs in medical research, diagnosis, and treatment. The application of deep learning plays a vital role in depression detection. Depression is a neurological disorder characterized by persistent feelings of sadness, hopelessness, and a lack of interest. The prevalence of depression is a significant factor contributing to the rise in suicide cases on a global scale. The electroencephalogram (EEG) is a non-invasive technique used to detect depression. It records brain activity using multiple electrodes. The number of EEG electrodes used for measurement directly affects the instrumentation and measurement complexity of the experiment. The present manuscript proposes a deep learning model for depression detection, focusing on two electrodes named FP1 and FP2. The purpose of employing two electrodes is to enhance the system's portability while reducing data acquisition time and system cost. EEG is spatio-temporal data and possesses inherent spatial and temporal features. The present manuscript proposes a methodology for extracting temporal and spatial features. The temporal feature extraction module extracts temporal features in the time domain, and the spatial module extracts spatial features in the spatial domain. This manuscript presents a study on the applicability of two electrodes for depression detection. This research can enhance accessibility, user-friendliness, and easier data collection and analysis. The proposed deep learning model is evaluated on two benchmark datasets. It achieves 93.41% classification accuracy, 92.54% precision, 93.23% recall, 93.06% F1 score, and 97.80% AUC for HUSM dataset and for MODMA dataset it achieves 79.40% accuracy, 81.18% precision, 67.73% recall, 73.80% F1 score, and 85.66% AUC.

SUSTAINABLE DIGITAL TRANSFORMATION: THE NEXUS BETWEEN ICT AND GLOBAL GREEN ECONOMIC GROWTH

As a new engine to promote global economic and social development, information and communication technology (ICT) plays a key role in the field of modern economy. The aim of this paper is to investigate the nexus between ICT and green total factor productivity (GTFP) on a global scale. An extended data envelopment analysis model (DEA), named WINDOWS-US-SBM, was constructed for calculating the GTFP of 65 countries from 2007 to 2019. This paper empirically analyzes the spatial effect and the transmission mechanism of ICT development on GTFP in countries from different income groups. The results show a prominent imbalance between ICT development and GTFP in various countries. ICT can effectively improve GTFP and play a crucial role in lower middle-income countries. The development of ICT can improve GTFP through technological progress, energy intensity, and trade openness. This paper is helpful to provide policy guidance for the development of ICT and give a new perspective of global green development.

Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

Validation of a synthetic simulation model of endoscopic rectus sheath plication.

Literature reviews outline minimally invasive approaches for abdominal diastasis in patients without skin excess. However, few surgeons are trained in endoscopic rectus sheath plication, and no simulated training programs exist for this method. This study aimed to develop and validate a synthetic simulation model for the training of skills in this approach under the Messick validity framework. A cross-sectional study was carried out to assess the participants' previous level of laparoscopic/endoscopic skills by a questionnaire. Participants performed an endoscopic plication on the model and their performance was evaluated by one blinded observer using the global rating scale OSATS and a procedure specific checklist (PSC) scale. A 5-level Likert survey was applied to 5 experts and 4 plastic surgeons to assess Face and Content validity. Fifteen non-experts and 5 experts in abdominal wall endoscopic surgery were recruited. A median OSATS score [25 (range 24-25) vs 14 (range 5-22); p < 0.05 of maximum 25 points] and a median PSC score [11 (range 10-11) vs 8 (range 3-10); p < 0.05 of maximum 11 points] was significantly higher for experts compared with nonexperts. All experts agreed or strongly agreed that the model simulates a real scenario of endoscopic plication of the rectus sheath. Our simulation model met all validation criteria outlined in the Messick framework, demonstrating its ability to differentiate between experts and non-experts based on their baseline endoscopic surgical skills. This model stands as a valuable tool for evaluating skills in endoscopic rectus sheath plication.

Unravelling the role of oceanographic connectivity in the distribution of genetic diversity of marine forests at the global scale

AbstractAimGenetic diversity of marine forests results from complex interactions of eco‐evolutionary processes. Among them, oceanographic connectivity driven by dispersal through water transport is hypothesized to play a pivotal role, yet its relative contribution has not been addressed at the global scale. Here, we test how present‐day oceanographic connectivity is correlated with the distribution of genetic diversity of marine forests across the ocean.LocationGlobal.Time periodContemporary.Major taxa studiedMarine forests of brown macroalgae (order: Fucales, Ishigeales, Laminariales and Tilopteridales).MethodsThrough literature review, we compiled a comprehensive dataset of genetic differentiation, encompassing 699 populations of 30 species. A biophysical model coupled with network analyses estimated multigenerational oceanographic connectivity and centrality across the marine forest global distribution. This approach integrated propagule dispersive capacity and long‐distance dispersal events. Linear mixed models tested the relative contribution of site‐specific processes, connectivity and centrality in explaining genetic differentiation.ResultsWe show that spatiality‐dependent eco‐evolutionary processes, as described by our models, are prominent drivers of genetic differentiation in marine forests (significant models in 91.43% of the cases with an average R2 of 0.50 ± 0.07). Specifically, we reveal that 18.7% of genetic differentiation variance is explicitly induced by predicted contemporary connectivity and centrality. Moreover, we demonstrate that long‐distance dispersal is key in connecting populations of species distributed across large water masses and continents.Main conclusionsOur findings highlight the role of present‐day oceanographic connectivity in shaping the extant distribution of genetic diversity of marine forests on a global scale, with significant implications for biogeography and evolution. This understanding can pave the way for future research aimed at guiding conservation efforts, including the designation of well‐connected marine protected areas, which is particularly relevant for sessile ecosystems structuring species such as brown macroalgae.

The role of atmospheric CO2 in controlling sea surface temperature change during the Pliocene

Abstract. We present the role of CO2 forcing in controlling Late Pliocene sea surface temperature (SST) change using six models from Phase 2 of the Pliocene Model Intercomparison Project (PlioMIP2) and palaeoclimate proxy data from the PlioVAR working group. At a global scale, SST change in the Late Pliocene relative to the pre-industrial is predominantly driven by CO2 forcing in the low and mid-latitudes and non-CO2 forcing in the high latitudes. We find that CO2 is the dominant driver of SST change at the vast majority of proxy data sites assessed (17 out of 19), but the relative dominance of this forcing varies between all proxy sites, with CO2 forcing accounting for between 27 % and 82 % of the total change seen. The dearth of proxy data sites in the high latitudes means that only two sites assessed here are predominantly forced by non-CO2 forcing (such as changes to ice sheets and orography), both of which are in the North Atlantic Ocean. We extend the analysis to show the seasonal patterns of SST change and its drivers at a global scale and at a site-specific level for three chosen proxy data sites. We also present a new estimate of Late Pliocene climate sensitivity using site-specific proxy data values. This is the first assessment of site-specific drivers of SST change in the Late Pliocene and highlights the strengths of using palaeoclimate proxy data alongside model outputs to further develop our understanding of the Late Pliocene. We use the best available proxy and model data, but the sample sizes remain limited, and the confidence in our results would be improved with greater data availability.

A short bibliographic review concerning biomethane production from wastewater sludge.

Biomethane production by anaerobic digestion (AD) of sludge from municipal wastewater treatment is a viable practice to valorise the residues of these plants. However, although the relevant literature is abundant, no comprehensive reviews have been recently published on this topic. Detailed information concerning the factors influencing the AD process and values of biomethane production from the sludge from municipal wastewater treatment plants (MWWTPs) on the global scale may support technicians and researchers in both the planning and the design steps of an AD process. This study proposes a systematic review and a meta-analysis of the factors that noticeably influence biomethane yield deriving from AD of sludge from MWWTP. The reported values were systematically analysed compared to the main factors driving AD, including publication year, geographical area of each study, type of digested sludge, treatment in the water line of the MWWTP, possible sludge pre-treatments, type of digestion process, hydraulic retention time (HRT) and temperature regime of the AD process. A higher biomethane production was registered in North American plants compared to countries in other continents. Older studies published between 2001 and 2005 reported lower mean values compared to the more recent experiments. A gradient of 'primary sludge' > 'mixed sludge' > 'wastewater activated sludge' was found for the mean biomethane yield in relation to the digested sludge type. The mean biomethane yields for different types of sludge on a global scale are 0.425, 0.296 and 0.176 Nm3 kg VS-1 for primary sludge, mixed sludge and waste activated sludge, respectively. Overall, the study demonstrates: (i) the very large variability of biomethane yields from AD of the residues from MWWTPs (mainly due to the different characteristics of sludge) and (ii) the non-significance of some factors (i.e. treatment in the water line, pre-treatments, type of process, HRT and temperature regime) on energy yields from the AD process.

Responding to Covid-19 Pandemic: Policy and Governance Perspective to Reverse Migration in Uttarakhand

The coronavirus disease, commonly referred to as Covid-19, had engendered inadvertent and significant societal upheaval, widespread economic setbacks, and compelled the relocation of individuals on a global scale. Analogous repercussions have been witnessed in the context of India, notably exemplified by the mass exodus of migrant labourers from their designated areas of employment to their native regions, precipitated by a comprehensive governmental lockdown enacted to interrupt the transmission chain of Covid-19. This stringent lockdown exacerbated the plight of economically marginalised migrant workers, compelling them to endure financial precarity and minimal reserves while confined to their residences. Consequently, a substantial number of migrants, primarily employed in the informal sector, found themselves compelled to return to their ‘places of origin’ from their ‘places of destination’ as an imperative means of survival. The present article elucidates the characteristics and ramifications of this phenomenon of reverse migration, undertaking an evaluative analysis of India’s response to the challenges posed by reverse migration in the aftermath of the Covid-19 pandemic. Special attention is directed towards the policy and governance dynamics, with a particular focus on the hilly state of Uttarakhand, India.

Globally, songs and instrumental melodies are slower and higher and use more stable pitches than speech: A Registered Report.

Both music and language are found in all known human societies, yet no studies have compared similarities and differences between song, speech, and instrumental music on a global scale. In this Registered Report, we analyzed two global datasets: (i) 300 annotated audio recordings representing matched sets of traditional songs, recited lyrics, conversational speech, and instrumental melodies from our 75 coauthors speaking 55 languages; and (ii) 418 previously published adult-directed song and speech recordings from 209 individuals speaking 16 languages. Of our six preregistered predictions, five were strongly supported: Relative to speech, songs use (i) higher pitch, (ii) slower temporal rate, and (iii) more stable pitches, while both songs and speech used similar (iv) pitch interval size and (v) timbral brightness. Exploratory analyses suggest that features vary along a "musi-linguistic" continuum when including instrumental melodies and recited lyrics. Our study provides strong empirical evidence of cross-cultural regularities in music and speech.

Semi-quantitative group testing for efficient and accurate qPCR screening of pathogens with a wide range of loads

BackgroundPathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide. Efficient and timely testing plays a critical role in disease control and transmission prevention. Group testing is a well-established method for reducing the number of tests needed to screen large populations when the disease prevalence is low. However, it does not fully utilize the quantitative information provided by qPCR methods, nor is it able to accommodate a wide range of pathogen loads.ResultsTo address these issues, we introduce a novel adaptive semi-quantitative group testing (SQGT) scheme to efficiently screen populations via two-stage qPCR testing. The SQGT method quantizes cycle threshold (Ct) values into multiple bins, leveraging the information from the first stage of screening to improve the detection sensitivity. Dynamic Ct threshold adjustments mitigate dilution effects and enhance test accuracy. Comparisons with traditional binary outcome GT methods show that SQGT reduces the number of tests by 24% on the only complete real-world qPCR group testing dataset from Israel, while maintaining a negligible false negative rate.ConclusionIn conclusion, our adaptive SQGT approach, utilizing qPCR data and dynamic threshold adjustments, offers a promising solution for efficient population screening. With a reduction in the number of tests and minimal false negatives, SQGT holds potential to enhance disease control and testing strategies on a global scale.

Why Do CO2 Quadrupling Simulations Warm More Than Twice as Much as CO2 Doubling Simulations in CMIP6?

AbstractWe compare abrupt CO2‐quadrupling (abrupt‐4xCO2) and ‐doubling (abrupt‐2xCO2) simulations across 10 CMIP6 models. Two models (CESM2 and MRI‐ESM2‐0) warm substantially more than twice as much under abrupt‐4xCO2 than abrupt‐2xCO2, which cannot be explained by the non‐logarithmic scaling of CO2 forcing. Using an energy balance model, we show that increased warming rates within these two models are driven by both less‐negative radiative feedbacks and smaller global effective heat capacity under abrupt‐4xCO2. These differences are caused by a decrease in low cloud cover and shallower ocean heat storage, respectively; both are linked to smaller fractional declines in the Atlantic Meridional Overturning Circulation (AMOC) under abrupt‐4xCO2 (relative to abrupt‐2xCO2). On a global scale, higher climate sensitivity under larger forcing can be explained by a feedback‐temperature dependence; however, we find that forcing‐dependent spatial warming patterns due to AMOC decline are an important physical mechanism which reduces warming in a way that is not captured by a global‐mean framework.

Scanning Electrochemical Microscopy Meets Optical Microscopy: Probing the Local Paths of Charge Transfer Operando in Booster-Microparticles for Flow Batteries.

Understanding the oxidation/reduction dynamics of secondary microparticles formed from agglomerated nanoscale primary particles is crucial for advancing electrochemical energy storage technologies. In this study, the behavior of individual copper hexacyanoferrate (CuHCF) microparticles is explored at both global and local scales combining scanning electrochemical microscopy (SECM), for electrochemical interrogation of a single, but global-scale microparticle, and optical microscopy monitoring to obtain a higher resolution dynamic image of the local electrochemistry within the same particle. Chronoamperometric experiments unveil a multistep oxidation/reduction process with varying dynamics. On the one hand, the global SECM analysis enables quantifying the charge transfer as well as its dynamics at the single microparticle level during the oxidation/reduction cycles by a redox mediator in solution. These conditions allow mimicking the charge storage processes in these particles when they are used as solid boosters in redox flow batteries. On the other hand, optical imaging with sub-particle resolution allows the mapping of local conversion rates and state-of-charge within individual CuHCF particles. These maps reveal that regions of different material loadings exhibit varying charge storage capacities and conversion rates. The findings highlight the significance of porous nanostructures and provide valuable insights for designing more efficient energy storage materials.

DeepDive: estimating global biodiversity patterns through time using deep learning

Understanding how biodiversity has changed through time is a central goal of evolutionary biology. However, estimates of past biodiversity are challenged by the inherent incompleteness of the fossil record, even when state-of-the-art statistical methods are applied to adjust estimates while correcting for sampling biases. Here we develop an approach based on stochastic simulations of biodiversity and a deep learning model to infer richness at global or regional scales through time while incorporating spatial, temporal and taxonomic sampling variation. Our method outperforms alternative approaches across simulated datasets, especially at large spatial scales, providing robust palaeodiversity estimates under a wide range of preservation scenarios. We apply our method on two empirical datasets of different taxonomic and temporal scope: the Permian-Triassic record of marine animals and the Cenozoic evolution of proboscideans. Our estimates provide a revised quantitative assessment of two mass extinctions in the marine record and reveal rapid diversification of proboscideans following their expansion out of Africa and a >70% diversity drop in the Pleistocene.

Alien range size, habitat breadth, origin location, and domestication of alien species matter to their impact risks.

Invasive alien species are a major driver of biodiversity loss. Currently, the process of biological invasions is experiencing a constant acceleration, foreshadowing a future increase in the threat posed by invasive alien species to global biodiversity. Therefore, it is necessary to assess the impact risks of invasive alien species and related factors. Here, we constructed a dataset of negative environmental impact events to evaluate the impact risks of alien species. We collected information on 1071 established alien terrestrial vertebrates and then gathered negative environmental impacts for 108 of those species. Generalized linear mixed-effects model and phylogenetic generalized least-squares regression model were used to examine the characteristic (including life-history traits, characteristics related to distribution, and introduction event characteristics) correlates of species' impact risks at the global scale for 108 established alien terrestrial vertebrates (mammals, birds, reptiles and amphibians). Our results showed that a total of 3158 negative environmental impacts were reported for 108 harmful species across 71 countries worldwide. Factors associated with impact risks varied slightly among taxa, but alien range size, habitat breadth, origin location, and domestication were significantly correlated with impact risks. Our study aims to identify the characteristics of alien species with high-impact risks to facilitate urgent assessment of alien species and to protect the local ecological environment and biodiversity.