Global Location Research Articles

Transcriptional profiling identifies the early responses to Puccinia triticina infection in the adult plant leaf rust resistant wheat variety Toropi

AbstractLeaf rust, caused by Puccinia triticina (Pt), is a major disease of wheat and a significant problem for wheat production in Brazil. The Brazilian variety Toropi, released in 1965, has maintained high levels of field, adult plant resistance (APR) to leaf rust across global locations, while microscopic studies have indicated prehaustorial resistance mechanisms. Analyses of gene expression in flag leaves of Toropi, during the early stages of Pt infection, were undertaken to explore the mechanisms behind the APR in Toropi. Differential expression of wheat genes was undertaken, comparing Pt‐ to mock‐inoculated and Pt‐ to Pt‐inoculated time points. Analysis of gene expression indicated a strong response to Pt, which was fully active by 6 h after inoculation (hai). More genes were downregulated than upregulated, particularly at 6 and 12 hai. Gene Ontology enrichment analysis indicated a shutting down of RNA and protein synthesis and an early effect on photosynthesis, with disruption of the electron transfer chain. Analyses of upregulated genes identified genes involved in ATP‐binding and protein kinase activity at 6 hai, supporting a rapid metabolic response to Pt infection. A general upregulation of genes involved in transport and metabolism indicated the need to relocate protein and organic‐based resources. Alignment of differentially expressed genes with the genomic regions defining four leaf rust APR quantitative trait loci (QTLs) in Toropi identified candidate resistance genes, including a sugar transporter, a receptor kinase and a seven‐transmembrane MLO family protein. In addition, 60 Pt genes were identified, 11 being annotated as potential effector proteins.

Assessing the feasibility of sprinkler irrigation schemes and their adaptation to future climate change in groundwater over-exploitation regions

Promoting appropriate water-saving irrigation is an important pathway to alleviate groundwater overexploitation in irrigated agriculture. Whether an alternative irrigation method is feasible relies on quantitative assessments of its impacts on crop yield and hydrological cycle, especially in the groundwater overdraft area that faces a dilemma between water scarcity and food security. Using a groundwater-module-improved SWAT (Soil & Water Assessment Tool) model, five GCMs (General Circulation Models) projections of CMIP6 (Coupled Model Intercomparison Project 6), and a linear programming algorithm, sprinkler irrigation was simulated, evaluated, and optimized in a representative global groundwater mining location, the piedmont plain of the North China Plain. During the historical period (1993–2012), the results showed that, in general, sprinkler irrigation had advantages in benefiting crop transpiration of winter wheat and reducing percolation, hence improving water use efficiency with reference to traditional surface irrigation. Specifically, compared to traditional surface irrigation schemes, winter wheat yields increased by 12%− 14% with a 225 mm irrigation quota; they were stable with a 150 mm irrigation quota; but they decreased by 4% with a 75 mm irrigation quota. The shallow groundwater tables would decline at an average rate of 1.29 m/yr, 0.83 m/yr, and 0.35 m/yr, respectively, under sprinkler irrigation with 225 mm, 150 mm, and 75 mm irrigation quotas. Future climate changes (increased precipitation, air temperatures, and CO2 concentration) would bring effects of improving wheat yield by 6%− 7% and increasing groundwater vertical recharge by 58%− 122% in 2030–2049. The variations of predicted future shallow groundwater tables were − 0.37 m/yr, 0.01 m/yr, and 0.40 m/yr with the 225 mm, 150 mm, and 75 mm irrigation quotas, respectively, in the SSP2–4.5 scenario; and the corresponding results were − 0.45 m/yr, − 0.05 m/yr, and 0.34 m/yr in the SSP5–8.5 scenario. The optimization results showed that sprinkler irrigation could be implemented in approximately 50% of the study area to achieve shallow groundwater equilibrium and stable wheat production simultaneously in the near future. And the dominant sprinkler irrigation schedule after optimization was found to be scheme S3 (irrigation frequency for wheat was 5 times) with a limited irrigation quota of 150 mm. This study provides detailed data for planning sprinkler water-saving irrigation regimes and groundwater pumping strategies in China’s breadbasket, as well as a reference for global irrigated agriculture with over-drafted aquifers to balance groundwater conservation and grain production.

Sources of elevated organic acids in the mountainous background atmosphere of southern China

Formic acid (FA) and acetic acid (AA) are pivotal organic acids in the troposphere, significantly influencing atmospheric chemistry. However, their abundance and sources in the mountainous background atmosphere remain underexplored. We undertook continuous measurements of FA and AA in Nanling mountains, southern China, during autumn 2020 using a high-resolution time-of-flight chemical ionization mass spectrometer (HR-ToF-CIMS). Both acids registered higher concentrations than in other global high-altitude or forested locations, averaging at 0.89 (max: 3.91) and 0.95 (max: 3.52) ppbv for FA and AA, respectively. High concentrations of FA and AA in this forested background area arose from secondary formation and biomass burning, collectively contributing 71 % to 89 %. During episodes, FA and AA concentrations surged 2–3 times, owing to the enhanced atmospheric oxidation capacity. The secondary FA production was predominantly due to isoprene oxidation among the VOC precursors studied. However, observed inconsistencies between calculated and actual FA concentrations suggest overlooked precursors or mechanisms warranting further investigation. Our findings can enhance the understanding of organic acid characteristics and the interplay of biogenic and anthropogenic sources in the background atmosphere.

Globalizing research on global cities and international business

Living up to the expectations of the JIBS Decade Award, Goerzen, Asmussen, and Nielsen’s 2013 paper not only introduced the literature on global cities to the international business (IB) community but continues to be generative. In their “Retrospective and a Looking Forward” paper 10 years later, the authors highlight megatrends about people, places and things, and new contexts and alternative perspectives, and they encourage further new ways of thinking about global cities and IB. This commentary expands upon their framework of three overlapping circles of global issues, global organizations, and global locations, by drawing especially from recent experiences in the U.S. and research in economic geography and allied fields. Facing global issues of climate change, human rights, health, housing, and the impacts of digital technologies on work, cities offer prospects of responding to these challenges, a context for multinational enterprises (MNEs) to consider. Against the backdrop of large-scale global migrations of unskilled, mostly contract, workers to global cities in developed economies, recruitment agencies and advocacy groups for migrants are global organizations as important as MNEs. Finally, the fluidity of physical boundaries, as illustrated by city-regions, world regions beyond traditional Western-centric perspectives, and intra-national variations, is key to analyzing global locations.

TokenLink: a secure loyalty point exchange system powered by smart contracts

Loyalty initiatives refer to the rewards offered by a business to customers who make recurring purchases. Traditional loyalty programmes, on the other hand, have numerous disadvantages, including low redemption rates, expired points, high user acquisition costs, and the difficulty of administering multiple loyalty programmes. Several of these issues can be addressed by utilizing blockchain technology. This paper examines the shortcomings of current loyalty programs and the potential applications of blockchain technology to resolve them. In an effort to improve customer benefits and retention rates while making it simpler for businesses to operate their own loyalty programmes, author proposes a blockchain-powered global loyalty network. This platform intends to combine multiple loyalty programs into a single system to facilitate the transmission of loyalty points between users and to promote co-branding among numerous businesses with global locations.

The impact of global city location intensity on MNE performance: Leveraging learning and connectivity

Building on recent international business research that embraces insights from economic geography, we hypothesise that MNEs locating a high share of the total number of their subsidiaries in global cities (which we conceptualize as global city location intensity) exhibit higher financial performance. We further argue that knowledge-intensive firms benefit more from global city location intensity than less knowledge-intensive firms. We also posit that multinationality (international geographic dispersion) negatively moderates the relationship between global city location intensity and financial performance. We test our hypotheses on a sample of 497 MNEs and find strong support for our predictions. We contribute to the literature by introducing the notion of intensity into the relationship between global city locations and financial performance and call for considering scale effects when examining locational decisions and their consequences.

Characteristics of Aphasia in Ischemic Stroke Patients at Dr. Mahar Mardjono National Brain Center Hospital Indonesia in 2021

Highlights: 1. To author’s knowledge, this study is the first study done in National Brain Center Hospital In Jakarta2. No similar studies have been done during the pandemic era3. This study can add additional data to further studies about aphasia in comparison with post pandemic era Abstract Global aphasia, sensory transcortical aphasia, motor transcortical aphasia, motor aphasia, sensory aphasia, conduction aphasia and anomic aphasia can occur in ischemic stroke. There are different locations of lesions in each type of aphasia. There has been an increase in incidence and mortality of stroke patients during the COVID-19 pandemic. Before the pandemic, global aphasia was the most common type of aphasia. This study was conducted to determine the type of aphasia, clinical and radiological features, and also management of aphasia in ischemic stroke during the pandemic. This research is a descriptive study with a cross sectional approach. Sampling was done by total sampling technique. The research sample was ischemic stroke subjects with aphasia diagnosed from January 1, 2021 - December 31, 2021 at the National Brain Center Hospital Prof. Dr. dr. Mahar Mardjono Jakarta. Statistical analysis was done using IBM SPSS Statistics for Mac, Version 25.0 (IBM Corp., Armonk, N.Y., USA). There were 162 aphasic subjects with ischemic stroke. Age range was 34 – 87 years. Most subjects were male (59.9%), aged 55-65 years (37.0%). Hypertension (90.1%), diabetes mellitus (50.0%), and dyslipidemia (75.9%) were the dominant risk factors. The most common type of aphasia is global aphasia (43.8%) and motoric aphasia. (33,3%). The parietal lobe was the dominant location of global aphasia (38 subjects) and motoric aphasia (47 subjects). The therapy given to aphasic subjects with a history of ischemic stroke includes speech therapy (85.2%), antiplatelet therapy (98.1%), anticoagulants (19.1%), rTPA (1.2%), and neuroprotectors (3.0 %). In conclusion, in ischemic stroke subjects with aphasia at the National Brain Center Hospital during the pandemic, global aphasia was found to be the most common with the dominant global aphasia lesion location in the parietal lobe.

Deep Learning for Inversion of Tipper Data of a Certain Railway Tunnel in Tibet Area

The traditional inversion methods for tipper rely excessively on the selection of the initial model, whose global search capability is poor. Inspired by the significant approximation advantage of deep learning for nonlinear inverse problems with big data, we design a deep learning architecture called TipInv-net for the inversion of tipper. TipInv-net takes the improved U-net as the basic framework to obtain the tipper response characteristics of the abnormal body, and then, dense skip connection is applied among the nested standard convolution modules to alleviate the gradient disappearance problem and enhance feature propagation. It’s worth noting that we construct a feature pyramid of tipper response via average pooling to obtain multi-scale receptive fields, which, thus enhancing global and detail location of abnormal body. The theoretical model test indicates that the position and attitude of geological anomaly body can be distinguished via TipInv-net, even in the presence of a certain level of noise, the inversion accuracy will not be greatly affected. TipInv-net has strong generalization. Besides, after the training process of the network, we can obtain the inversion result immediately. In order to verify the effectiveness of the method in this paper, the inversion of the field tipper data of a section of a certain railway tunnel in Tibet area is carried out.

Smart wearable devices for real-time health monitoring

Smartwatches have emerged as powerful tools for health monitoring in recent years. These wearable devices combine advanced technology with portability, enabling individuals to track various aspects of their health and well-being conveniently. Heart rate monitoring has several advantages. Many smartwatches are equipped with optical heart rate sensors that continuously monitor a user’s heart rate. This feature provides real-time data on heart rate variations, which can be crucial for identifying irregularities and assessing overall cardiovascular health. Activity tracking: Smartwatches often include accelerometers and gyroscopes that track physical activities such as steps taken, distance traveled, and calories burned. This information can aid in setting and achieving fitness goals. Sleep tracking:Some smartwatches offer sleep tracking features, recording sleep duration, quality, and patterns. These data help users understand their sleep habits and make improvements for better rest. Global positioning system (GPS) and location tracking: Integrated GPS allows users to track their outdoor activities accurately, including running and cycling. This feature not only measures performance but also enhances safety during workouts. Stress monitoring: Certain smartwatches include tools that measure stress levels through heart rate variability analysis. Users can learn to manage stress more effectively based on these data. Electrocardiogram (ECG) and blood pressure monitoring:High-end smartwatches may include ECG and blood pressure monitoring capabilities. These features are particularly useful for individuals with heart conditions or hypertension. Notifications and alerts: Smartwatches can deliver notifications and alerts related to health goals, reminders to move, medication schedules, and emergencies. This functionality enhances user engagement and timely interventions. BENEFITS OF SMARTWATCHES IN HEALTH MONITORINGUnlike periodic doctor visits, smartwatches enable continuous health monitoring. Users can track their vital signs and activities throughout the day, providing a more comprehensive picture of their health. Smartwatches empower individuals to take control of their health. Users can make informed decisions about their lifestyle, exercise, and diet by providing real-time data. Smartwatches equipped with ECG and heart rate monitoring can detect irregularities and abnormalities early on. This early detection can be lifesaving for individuals at risk of heart-related conditions. Activity tracking and goal-setting features motivate users to engage in physical activities and maintain a healthier lifestyle. Achieving fitness goals can lead to increased motivation and adherence to healthier habits. Health data collected by smartwatches can be used to tailor fitness and wellness plans to an individual’s specific needs. This personalized approach can lead to more effective health improvements. LIMITATIONS AND CONSIDERATIONSAccuracy: While smartwatches offer valuable health data, their accuracy may vary. Factors such as skin type,device placement, and motion can affect the precision of measurements. Battery life: Continuous health monitoring can drain a smartwatch’s battery quickly. Users may need to recharge their devices frequently, potentially limiting the monitoring duration. Privacy and data security: Storing sensitive health data on a smartwatch raise concerns about privacy and data security. Manufacturers must implement robust security measures to protect users’ information. User reliance: There is a risk of over-reliance on smartwatches for health information. Users should be encouraged to consult health-care professionals for a comprehensive health assessment. CONCLUSIONSmartwatches have become versatile tools for health monitoring, offering a range of features that empower users to track and improve their well-being. While they have numerous benefits, users should know their limitations and use them as complementary tools in their health-care journey. Manufacturers and health-care providers play a critical role in ensuring the accuracy and security of the health data collected by these devices.

Location-Aware Web Service QoS Prediction via Deep Collaborative Filtering

Nowadays, there is a large number of web services with similar functions, from which users choose the best according to the quality of service (QoS). Hence, QoS prediction is a primary challenge in service recommendation. Most existing approaches model the user-service interaction relationship. However, the low-dimensional linear and the high-dimensional nonlinear relationships between users and services are seldom considered simultaneously. In addition, although location information, including the local location information of users and services, is incorporated to overcome data sparsity in most approaches. The global location information is seldom considered. Aiming at the above shortcomings, we propose a new QoS prediction model that fuses local and global location information of users and services in the interaction layer of the model. The proposed model uses a multilayer perceptron (MLP) to acquire high-dimensional nonlinear relationships of users and services, where the dot product is employed in complementing the learning of low-dimensional linear relationships. Experimental results on the real world dataset WS-Dream validate the prediction performance of the proposed model.

LSMCL: Long-term Static Mapping and Cloning Localization for autonomous robot navigation using 3D LiDAR in dynamic environments

One of the challenges in autonomous robot navigation applications is recognizing the exact location in a dynamic environment in which the location of surrounding objects changes frequently. This study proposes a long-term static mapping and cloning localization (LSMCL) method for estimating real-time accurate location using only natural landmarks even in a dynamic environment using a 3D LiDAR sensor. LSMCL comprises long-term static mapping (LSM) and cloning localization (CL). A LSM creates a 2D grid map and a 3D geometric feature map for objects whose positions do not change in space. A CL uses the generated 2D grid map and particle filter to estimate the 2D global position at the initial stage. After cloning the 2D global location to 3D space, the location is tracked through a 3D feature map and map matching. To verify the LSMCL’s usability in a real dynamic environment, a robot navigation experiment was conducted in a highly dynamic parking lot. The experimental results, analyzed in terms of initial localization success rate, location estimation accuracy and precision, processing time, and congested space application confirmed the LSMCL’s real-world applicability.

IoT based enabling home automation system for individuals with diverse disabilities

This paper addresses the challenges faced by individuals with impairments, focusing on their specific needs. Moreover, the insights from this study can be applied to individuals who have encountered accidents or various similar circumstances. The primary objective of this endeavor is to mitigate their discomfort. The project demonstrates the creation of an affordable machine control system utilizing IoT technology. This system incorporates functionalities such as speech control and hand gestures, making it beneficial for people with impairments. The Internet of Things (IoT) pertains to the interconnectedness of electronic devices, software, sensors, actuators, and network connectivity among machines, vehicles, structures, and other entities. This connectivity enables the gathering and sharing of data.The application of IoT technology in this undertaking facilitates wireless control of devices via internet connectivity. The project employs a Node MCU development board as the central computing module. Furthermore, the project envisions an interface that empowers users through voice commands and hand gestures, accessible through a dedicated app. The MIT Software Inventor employs an online speech-to-text infrastructure to enable speech control. The system listens for the user's vocal commands and recognizes hand angular positions. Upon detecting specific predefined phrases, corresponding actions are triggered to activate or deactivate the machines. The innovative aspect of this concept lies in its ability to empower differently-abled individuals to effortlessly control machines through voice commands and hand gestures. The proposed system extends its applications to the industrial sector, streamlining machinery management and enhancing personnel control. This app-based interface can be accessed from any global location, further enhancing its accessibility and usability.

Cascaded adaptive global localisation network for steel defect detection

Defect detection is crucial in ensuring the quality of steel products. This paper proposes a novel deep neural network, cascaded adaptive global location network (CAGLNet), for detecting steel surface defects. The main objective of this study is to address the challenges associated with the irregular shape and dense spatial distribution of defects on steel. To achieve this goal, CAGLNet integrates a feature extraction network that combines residual and feature pyramid networks, a cascade adaptive tree-structure region proposal network (CAT-RPN) that eliminates the need for prior knowledge, and a global localisation regression for steel defect detection. This paper evaluates the effectiveness of CAGLNet on the NEU-DET dataset and demonstrates that the proposed model achieves an average accuracy of 85.40% with a fast frames per second of 10.06, outperforming those state-of-the-art methods. These results suggest that CAGLNet has the potential to significantly improve the effectiveness of defect detection in industrial production processes, leading to increased production yield and cost savings. Abbreviations: AT-RPN, adaptive tree-structure region proposal network; CAGLNet, cascaded adaptive global location network; CAT-RPN, cascade adaptive tree-structure region proposal network; CNN, convolutional neural network; DNN, deep neural network; EPNet, edge proposal network; FPN, feature pyramid network; FCOS, fully convolutional one-stage detector; FPS, frames per second; GMM, Gaussian mixture model; IoU, intersection-over-union; ROIAlign, region of interest align; RPN, region proposal network; ResNet, residual network; ResNet50_FPN, residual network and feature pyramid network; SABL, side aware boundary localisation; SSD, single-shot multiBox detector; TPE, Tree-structured Parzen estimator

The Impact of Bariatric Surgery on Weight Loss and Glycemic Control in Patients With Obesity and Type 2 Diabetes: A Systematic Review.

Obesity and type 2 diabetes mellitus (T2DM) are global health challenges. Bariatric surgery has emerged as a potential intervention for managing these conditions, but its efficacy and impact need comprehensive evaluation.This systematic review aimed to assess the impact of bariatric surgery on weight loss and glycemic control in patients with obesity and T2DM. FollowingPreferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search was conducted in October 2023, primarily using PubMed. Studies were selected based on specific inclusion and exclusion criteria, focusing on bariatric surgery's relationship with weight loss and glycemic control. The quality of the included studies was assessed using the ROBINS-I (risk of bias in non-randomized studies of interventions) risk of bias assessment approach. Out of 272 initially identified studies, nine met the inclusion criteria. These studies, encompassing 10,445 participants from various global locations, predominantly targeted middle-aged participants. The findings consistently highlighted the benefits of bariatric surgery in weight reduction and improved glycemic control. However, the degree of benefits varied based on the type of surgical procedure, patient's BMI, and other individual factors. Bariatric surgery offers significant advantages in managing obesity and T2DM. While it consistently aids in weight reduction and glycemic control, individualized treatment approaches considering various patient and procedural factors are crucial for optimal outcomes. When applied to the right patient, bariatric surgery can offer significantly better glycemic control and weight reduction when compared to only medication control and lifestyle adjustments. However, future research should focus on long-term outcomes and the integration of surgical interventions with lifestyle and medical management.

A search-based geographic metadata curation pipeline to refine sequencing institution information and support public health.

The National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) has amassed a vast reservoir of genetic data since its inception in 2007. These public data hold immense potential for supporting pathogen surveillance and control. However, the lack of standardized metadata and inconsistent submission practices in SRA may impede the data's utility in public health. To address this issue, we introduce the Search-based Geographic Metadata Curation (SGMC) pipeline. SGMC utilized Python and web scraping to extract geographic data of sequencing institutions from NCBI SRA in the Cloud and its website. It then harnessed ChatGPT to refine the sequencing institution and location assignments. To illustrate the pipeline's utility, we examined the geographic distribution of the sequencing institutions and their countries relevant to polio eradication and categorized them. SGMC successfully identified 7,649 sequencing institutions and their global locations from a random selection of 2,321,044 SRA accessions. These institutions were distributed across 97 countries, with strong representation in the United States, the United Kingdom and China. However, there was a lack of data from African, Central Asian, and Central American countries, indicating potential disparities in sequencing capabilities. Comparison with manually curated data for U.S. institutions reveals SGMC's accuracy rates of 94.8% for institutions, 93.1% for countries, and 74.5% for geographic coordinates. SGMC may represent a novel approach using a generative AI model to enhance geographic data (country and institution assignments) for large numbers of samples within SRA datasets. This information can be utilized to bolster public health endeavors.

Evaluating the accessibility and equity of urban health resources based on multi-source big data in high-density city

Previous studies have focused more on the accessibility and equity of single-type facilities for urban health, limited in comprehensive assessment of multi-type facilities and ignored the complexity of urban system. This study takes the first attempt to explicitly defined and classified urban health resources considering the active and passive health demands by building a conceptual framework. An integrated approach framework, including the global collaborative location quotient (GCLQ), the Gaussian 2SFCA method and Gini coefficients, were constructed to evaluate the proximity, complementarity, accessibility and equity of multi-tiered healthy resources in Guangzhou. In addition, the accessibility associated with the socioeconomic characteristics were explored by the local correlation model and further explained by the Mann-Whitney U test. The results showed that significant differences in spatial access to various health resources. The diversity types could enhance the complementarity of health resources and promote comprehensive equity, but groups with low economic levels enjoyed less access to health resources. This study contributes to the international literature on health-seeking behaviors from the perspective of active and passive demands and employing advanced geospatialmodeling methods. Through accurate identification of spatial barriers for health, the findings could provide valuable suggestions for the healthy and sustainable development of high-density population cities.

Trace Metals in Global Air: First Results from the GAPS and GAPS Megacities Networks.

Trace metals, as constituents of ambient air, can have impacts on human and environmental health. The Global Atmospheric Passive Sampling (GAPS) and GAPS Megacities (GAPS-MC) networks investigated trace metals in the air at 51 global locations by deploying polyurethane foam disk passive air samplers (PUF-PAS) for periods of 3-12 months. Aluminum and iron exhibited the highest concentrations in air (x̅ = 3400 and 4630 ng/m3, respectively), with notably elevated values at a rural site in Argentina thought to be impacted by resuspended soil. Urban sites had the highest levels of toxic Pb and Cd, with enrichment factors suggesting primarily anthropogenic influences. High levels of As at rural sites were also observed. Elevated trace metal concentrations in cities are associated with local emissions and higher PM2.5 and PM10 concentrations. Brake and tire wear-associated metals Sb, Cu, and Zn are significantly correlated and elevated at urban locations relative to those at background sites. These data demonstrate the versatility of PUF-PAS for measuring trace metals and other particle-associated pollutants in ambient air in a cost-effective and simple manner. The data presented here will serve as a global baseline for assessing future changes in ambient air associated with industrialization, urbanization, and population growth.

Geo-aware graph-augmented self-attention network for individual mobility prediction

Even though some studies have found encouraging results, the sparsity of data and the complexity of mobility patterns remain significant challenges in predicting individual mobility. In order to effectively address the two challenges, we propose a novel framework called Geo-aware Graph-Augmented Self-Attention Network (GaGASAN). In GaGASAN, we construct a heterogeneous location graph consisting of a geospatial subgraph and a location transition subgraph to simultaneously model the impacts of geospatial distance and location transitions on all users. With the heterogeneous location graph, the impacts of geospatial distances and global location transitions of all users can be effectively merged, thereby mitigating the sparsity of individual mobility data. For the complex and variable mobility patterns of individuals, we employ a multi-scale time encoding technique and a self-attention mechanism to model different temporal patterns and capture long- and short-range contexts of sequence transitions. Extensive experiments shows that GaGASAN significantly outperforms eight baseline methods on all metrics. Our implementation code is available at https://github.com/mrc-wyh/GaGASAN.

Tracking endolithic microbiomes to support the sustainability and functioning of global drylands

AbstractDrylands cover almost half of the planet and support >25% the global population. In this era of global warming, they are expected to continue expanding by the end of the century as a consequence of predicted increases in aridity, which will affect multiple global locations that are already characterised by extreme temperatures, low and variable rainfall, and low soil fertility. In these fragile ecosystems, where microorganisms are integral to maintain functioning and primary productivity, endoliths (i.e., rock‐inhabiting microorganisms) play a key role in soil formation and dynamics and are and critical drivers of ecological succession. Here, we posit that endolithic microbes could also function as early alarm warning indicators for environmental changes in the most arid ecosystems. Nevertheless, studies on endoliths are still rather fragmentary and mainly focused in a few specific dry areas such as the Antarctic or Atacama deserts. A global appraisal of the structure and function of the endolithic microbiome is needed for the assessment of the current state of dryland biodiversity worldwide, and to identify the regions that are more vulnerable to global changes. Such an effort will provide new knowledge and will implement official and international initiatives to track and conserve biodiversity on global drylands.

Conditional normalizing flow for multivariate time series anomaly detection

Multivariate time series data is becoming increasingly ubiquitous in various fields such as servers, industrial applications, and healthcare. However, detecting anomalies in such data is challenging due to its complex time-dependent, high-dimensional, and label scarcity. Aiming at this problem, this paper proposes an Attention Factorization Normalizing Flow (AFNF) algorithm for unsupervised multivariate time series anomaly detection. Our hypothesis is that anomalies are in a low-density region of the distribution. To transform the complex density of high-dimensional time series into a simple evaluable conditional density, we propose a time series factorization strategy and parameterize the conditional information generated by factorization in the time and attribute dimensions using an attention mechanism. Moreover, to compensate for the lack of temporal information due to the permutation invariance attention mechanism, a adjacency contrasting approach is proposed to model the local invariance of the time series. To provide long-term location information, a learnable global location encoding is introduced. Conditional normalizing flows are applied to evaluate the conditional probability of the observations. Finally, through extensive experiments on three real data sets, our method yielded the best results and its effectiveness in density estimation and anomaly detection is demonstrated.