Vertical Domains Research Articles

Topological Polar Networks in Twisted Rhombohedral-Stacked Bilayer WSe2 Moiré Superlattices.

Sliding ferroelectricity enables materials with intrinsic centrosymmetric symmetry to generate spontaneous polarization via stacking engineering, extending the family of ferroelectric materials and enriching the field of low-dimensional ferroelectric physics. Vertical ferroelectric domains, where the polarization is perpendicular to atomic motion, have been discovered in twisted bilayers of inversion symmetry broken systems such as hexagonal boron nitride, graphene, and transition metal chalcogenides. In this study, we demonstrate that this symmetry breaking also induces lateral polar networks in twisted bilayer rhombohedral-stacked WSe2, as determined through symmetry considerations and vector piezoresponse force microscopy (V-PFM) results. Lateral polarization (LP) in saddle point (SP) regions forms head-to-tail triangular vortices, exhibiting elliptical domain shapes with widths up to 40 nm. The LP encloses the vertical polarization (VP), forming a network of Bloch-type merons and antimerons. Our work enhances the understanding of domain distribution and polarization orientation in moiré ferroelectrics.

Domain knowledge-powered attention for air traffic management hazardous events classification

The automated classification of hazardous events in Air Traffic Management (ATM) involves concise texts with specialized terminology. The problem is currently addressed by machine learning text classification methods; however, some methods overlook domain knowledge, while others require it excessively. This research aims to identify a process for integrating low-cost, text-based domain knowledge into classification to address the issue of domain shift. To achieve this, the Wide and Deep Bidirectional Encoder Representations from Transformers (WD-BERT) model is proposed, featuring a unique knowledge-powered attention mechanism. WD-BERT's Wide Attention module assesses the compatibility between input text and domain knowledge to determine class probabilities, while its Deep Attention module extracts contextual features guided by domain knowledge. Additionally, a text mining method is employed to extract domain knowledge from ATM regulation texts. The model is trained and evaluated using a dataset derived from the China ATM Hazard Source Database. It achieves a multi-label classification accuracy of 80.24% and an F1-micro score of 0.88, outperforming comparative models with state-of-the-art performance. Breakdown analyses of the model indicate that the carefully designed attention mechanism endows WD-BERT with the ability to integrate domain knowledge, allowing it to excel in contexts involving complex sentences, multiple terms, and domain shifts. The innovation of this study lies in the proposed domain knowledge-powered attention mechanism, which allows simply organized domain knowledge to effectively guide ATM hazardous events classification, alleviating the impact of domain shift. This method can also be applied to other vertical domains without knowledge graphs.

Chat2Data: An Interactive Data Analysis System with RAG, Vector Databases and LLMs

Traditional data analysis methods require users to write programming codes or issue SQL queries to analyze the data, which are inconvenient for ordinary users. Large language models (LLMs) can alleviate these limitations by enabling users to interact with the data with natural language (NL), e.g., result retrieval and summarization for unstructured data and transforming the NL text to SQL queries or codes for structured data. However, existing LLMs have three limitations: hallucination (due to lacking domain knowledge for vertical domains), high cost for LLM reasoning, and low accuracy for complicated tasks. To address these problems, we propose a prototype, Chat2Data, to interactively analyze the data with natural language. Chat2Data adopts a three-layer method, where the first layer uses Retrieval-Augmented Generation (RAG) to embed domain knowledge in order to address the hallucination problem, the second layer utilizes vector databases to reduce the number of interactions with LLMs so as to improve the performance, and the third layer designs a pipeline agent to decompose a complex task to multiple subtasks and use multiple round reasoning to generate the results in order to improve the accuracy of LLMs. We demonstrate Chat2Data with two real scenarios, unstructured data retrieval and summarization, and natural language-based structured data analysis. The online demo is available at http://vdemo.dbmind.cn.

Detecting the horizontal/vertical price relationship patterns in the global oil industry chain through network analysis

The investigation of price relationship patterns within the horizontal regional and vertical industry chain domains of oil market represents a crucial yet under-researched area. This study tackles the inherent complexities in identifying these patterns, arising from three key challenges: the presence of numerous downstream petrochemical products, the interconnectedness of oil product prices across two domains, and the existence of price influence loop. To address these challenges, this study selected 51 oil products from various regions and different segments of oil industry chain, and calculated and tested their price lead-lag relationships. Subsequently, we established and analyzed the price lead-lag relationship networks within two domains. Furthermore, we expanded network topological structure analysis from a single domain to encompass both domains. Finally, employing the network motif analysis method, we successfully detected the price relationship patterns, taking into consideration the intricate nature of price influence loops. Our findings illuminate the general characteristics of the oil market within two domains but also shed light on previous ignored price lead-lag relationships. The thorough analysis and conclusions drawn from this study offer detailed policy implications to downstream refineries and policy makers.

Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-Hoc Retrieval

With the development of deep learning and natural language processing techniques, pre-trained language models have been widely used to solve information retrieval (IR) problems. Benefiting from the pre-training and fine-tuning paradigm, these models achieve state-of-the-art performance. In previous works, plain texts in Wikipedia have been widely used in the pre-training stage. However, the rich structured information in Wikipedia, such as the titles, abstracts, hierarchical heading (multi-level title) structure, relationship between articles, references, hyperlink structures, and the writing organizations, has not been fully explored. In this paper, we devise four pre-training objectives tailored for IR tasks based on the structured knowledge of Wikipedia. Compared to existing pre-training methods, our approach can better capture the semantic knowledge in the training corpus by leveraging the human-edited structured data from Wikipedia. Experimental results on multiple IR benchmark datasets show the superior performance of our model in both zero-shot and fine-tuning settings compared to existing strong retrieval baselines. Besides, experimental results in biomedical and legal domains demonstrate that our approach achieves better performance in vertical domains compared to previous models, especially in scenarios where long text similarity matching is needed. The code is available at https://github.com/oneal2000/Wikiformer.

A novel infrared imager for studies of hydroxyl and oxygen nightglow emissions in the mesopause above northern Scandinavia

Abstract. The paper describes technical characteristics and presents the first scientific results of a novel infrared imaging system (imager) for studies of nightglow emissions coming from the hydroxyl (OH) and molecular oxygen (O2) layers in the mesopause region (80–100 km) above northern Scandinavia. The OH imager was put into operation in November 2022 at the Swedish Institute of Space Physics in Kiruna (67.86° N, 20.42° E; 400 m altitude). The OH imager records selected emission lines in the OH(3-1) band near 1500 nm to obtain intensity and temperature maps at around 87 km altitude. In addition, the OH imager registers infrared emissions coming from the O2 IR A-band airglow at 1268.7 nm in order to obtain O2 intensity maps at a slightly higher altitude, around 94 km. This technique allows the tracing of wave disturbances in both horizontal and vertical domains in the mesopause region. Validation and comparison of the OH(3-1) rotational temperature with collocated lidar and Aura Microwave Limb Sounder (MLS) satellite temperatures are performed. The first scientific results obtained from the OH imager for the first winter season (2022–2023) are discussed.

3‐D Ionospheric Imaging Over the South American Region With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS‐SA)

AbstractThis study has developed a new TEC‐based ionospheric data assimilation system for 3‐D regional ionospheric imaging over the South American sector (TIDAS‐SA) (45°S–15°N, 35°–85°W, and 100–800 km). The TIDAS‐SA data assimilation system utilizes a hybrid Ensemble‐Variational approach to incorporate a diverse set of ionospheric data sources, including dense ground‐based Global Navigation Satellite System (GNSS) line‐of‐sight Total Electron Content (TEC) data, radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate‐2 (COSMIC‐2), and altimeter TEC data from the JASON‐3 satellite. TIDAS‐SA can produce a reanalyzed three‐dimensional (3‐D) electron density spatial variation with a high time cadence, yielding spatial‐temporal resolution of 1° (latitude) × 1° (longitude) × 20 km (altitude) × 5 min. This allows us to reconstruct and study the 3‐D ionospheric morphology with multi‐scale structures. The performance of the data assimilation system is validated against independent ionosonde and in situ measurements through an experiment for a strong geomagnetic storm event on 03–04 November 2021. The results demonstrate that TIDAS‐SA can provide detailed and altitude‐resolved information that accurately characterizes the storm‐time ionospheric disturbances in vertical and horizontal domains over the equatorial and low‐latitude regions of South America.

New Subclass of Close-to-convex Functions Associated with the Vertical Strip Domains

New Subclass of Close-to-convex Functions Associated with the Vertical Strip Domains

Connected and Automated Mobility Services in 5G Cross-Border Environments: Challenges and Prospects

The next generation of mobile networks, namely 5G, promises significant qualitative and quantitative advances for multiple vertical domains. However, most studies and investigations assess these advances under the implicit assumption of a single network service provider, with typical national coverage. In this article, we take a close look at the automotive sector and highlight a series of challenges emerging in the context of its inherent (inter)national mobility and the corresponding importance of cross-border and/or multioperator environments. Our target is to pinpoint the key influential factors affecting the transition toward seamless (cooperative) connected and automated mobility services within and across national borders. To this end, we identify and analyze a series of challenges in the areas of, networking, application, security, and regulation. We further present and discuss a series of corresponding solutions investigated in the pragmatic context of our experimental activities.

Two assembly modes for SIN3 histone deacetylase complexes

The switch-independent 3 (SIN3)/histone deacetylase (HDAC) complexes play essential roles in regulating chromatin accessibility and gene expression. There are two major types of SIN3/HDAC complexes (named SIN3L and SIN3S) targeting different chromatin regions. Here we present the cryo-electron microscopy structures of the SIN3L and SIN3S complexes from Schizosaccharomyces pombe (S. pombe), revealing two distinct assembly modes. In the structure of SIN3L, each Sin3 isoform (Pst1 and Pst3) interacts with one histone deacetylase Clr6, and one WD40-containing protein Prw1, forming two lobes. These two lobes are bridged by two vertical coiled-coil domains from Sds3/Dep1 and Rxt2/Png2, respectively. In the structure of SIN3S, there is only one lobe organized by another Sin3 isoform Pst2; each of the Cph1 and Cph2 binds to an Eaf3 molecule, providing two modules for histone recognition and binding. Notably, the Pst1 Lobe in SIN3L and the Pst2 Lobe in SIN3S adopt similar conformation with their deacetylase active sites exposed to the space; however, the Pst3 Lobe in SIN3L is in a compact state with its active center buried inside and blocked. Our work reveals two classical organization mechanisms for the SIN3/HDAC complexes to achieve specific targeting and provides a framework for studying the histone deacetylase complexes.

Conditional Knowledge Extraction Using Contextual Information Enhancement

Conditional phrases provide fine-grained domain knowledge in various industries, including medicine, manufacturing, and others. Most existing knowledge extraction research focuses on mining triplets with entities and relations and treats that triplet knowledge as plain facts without considering the conditional modality of such facts. We argue that such approaches are insufficient in building knowledge-based decision support systems in vertical domains, where specific and professional instructions on what facts apply under given circumstances are indispensable. To address this issue, this paper proposes a condition-aware knowledge extraction method using contextual information. In particular, this paper first fine-tunes the pre-training model to leverage a local context enhancement to capture the positional context of conditional phrases; then, a sentence-level context enhancement is used to integrate sentence semantics; finally, the correspondences between conditional phrases and relation triplets are extracted using syntactic attention. Experimental results on public and proprietary datasets show that our model can successfully retrieve conditional phrases with relevant triplets while improving the accuracy of the matching task by 2.68%, compared to the baseline.

Fusion of IoT, AI, Edge–Fog–Cloud, and Blockchain: Challenges, Solutions, and a Case Study in Healthcare and Medicine

The digital transformation is characterized by the convergence of technologies—from the Internet of Things (IoT) to edge–fog–cloud computing, artificial intelligence (AI), and Blockchain—in multiple dimensions, blurring the lines between the physical and digital worlds. Although these innovations have evolved independently over time, they are increasingly becoming more intertwined, driving the development of new business models. With more adaptation, embracement, and development, we are witnessing a steady convergence and fusion of these technologies resulting in an unprecedented paradigm shift that is expected to disrupt and reshape the next-generation systems in vertical domains in a way that the capabilities of the technologies are aligned in the best possible way to complement each other. Despite the fact that the convergence of the four technologies can potentially tackle the main shortcomings of the existing systems, its adoption is still in its infancy phase, suffering from several issues, such as the absence of consensus toward any reference models or best practices. This article provides a comprehensive insight into the fusions of these paradigms by discussing a blend of topics addressing all the importation aspects from design to deployment. We will begin this article by providing an in-depth discussion on the main requirements, state-of-the-art reference architectures, applications, and challenges. Following this, we will present a reference architecture and a case study on privacy-preserving stress monitoring and management to better elaborate on the corresponding details and considerations.

The effect of Blockchain technology as a moderator on the relationship between big data and the risk of financial disclosure (analytical study in the Egyptian and Iraqi stock exchange)

This paper aims to explain the impact of Blockchain Technology as a moderator in the relationship between Big Data and The Risk of Financial Disclosure to address the Risk of Financial Disclosure. Therefore, this technique was used to reduce the risks of disclosing financial and accounting data for various companies. The estimated population size of accounting information systems and information technology professionals is more than 300 in the Egyptian and Iraqi Stock Exchange. The results indicate that all the direct hypotheses that reflect the relation between Big Data characteristics and FDR are supported which is less than 0.05. There is no impact of Volume on Financial Disclosure Risk with a p-value=0.074. the indirect effect of Blockchain Technology on the relationship between (VEL, VER and VOL) and Financial Disclosure Risk was significant with a p-value of 0.048,0.024,0.001 respectively, which is less than 0.05 and does not support the relationship between VAR and Financial Disclosure Risk a p-value of 0.735. Then, we recommend state-of-the-art studies on the use of blockchain for big data applications in different vertical domains such as smart cities, Financial transactions, smart transportation, and smart bank accounts. For a better understanding, some representative blockchain-big data projects are also presented and analysed. Finally, challenges and future directions are discussed to further drive research in the countries of the Middle East. This paper presents the novel solutions associated with Big Data with Financial Disclosure Risks that can be addressed by Blockchain technology. As Well as present the motivations behind the use of blockchain for big data. We show that blockchain has great potential for facilitating big data analytics such as control of dirty data, enhanced security and transparency, enhanced quality of data, the management of data sharing and, Addressing risk financial disclosure

Scalable and interoperable edge-based federated learning in IoT contexts

The analysis of data coming from massively deployed Internet of Things (IoT) devices pave the way to a myriad of intelligent applications in several vertical domains. Federated Learning (FL) has been recently proposed as a prominent solution to train Machine Learning (ML) models directly on top of devices (FL clients) generating data, instead of moving them to centralized servers in charge of training procedures. FL provides inherent benefits, mainly in terms of privacy preservation and reduction of network congestion for datasets exchange. Despite the huge recent research efforts, it still faces challenges for a practical implementation effectively targeting low communication footprint, robustness and interoperability. To fill this gap, in this work we propose a novel comprehensive framework built upon the Message Queue Telemetry Transport (MQTT) publish/subscribe messaging protocol and the Open Mobile Alliance (OMA) Lightweight Machine-to-Machine (LwM2M) semantics to facilitate FL operations and make them more suited to handle IoT devices acting as FL clients. The viability of the proposal as well as its communication efficiency compared to a literature solution are evaluated through a realistic Proof-of-Concept (PoC) under different link settings and for different datasets.

Can rats and ants exchange information between the horizontal and vertical domains?

Since traveling in nature involves encountering various vertical structures, integration of horizontal and vertical spatial information is required. One form of such integration is to use information acquired in one plane for spatial navigation in another plane. Here we tested whether rats and ants that learned a reward location in a horizontal maze could utilize this information when the maze was rotated to a vertical orientation and vice versa. Rats that were trained in a horizontal Y-maze required more time to reach the reward when the maze was vertically rotated, but they were more accurate in choosing the correct arm. In contrast, rats tested in a horizontal maze after being trained in a vertical maze were less accurate but reached the reward faster. Changes after maze rotation were moderate and non-significant in ants, perhaps since the number of ants arriving at the reward increased over trials, diminishing the effect of maze rotation in ants compared to rats. According to the notion that horizontal spatial information is encoded in more detail than vertical information, the slow performance of rats in the vertical domain could be due to a more physically demanding task whereas their accuracy was due to a preceding detailed horizontal encoding. In contrast, rats in the vertical maze could gather less detailed information and therefore were less accurate in subsequent horizontal trials, where the lower energy cost enabled them to swiftly correct wrong choices. Altogether, the present results provide an indication for transferring spatial information between horizontal and vertical dimensions.

Prototype Collision-Avoidance System for Small Multirotor Unmanned Aircraft at Low Altitude

The increasing number and type of unmanned aircraft system (UAS) operations provide a clear need for safe and efficient integration of UAS into the National Airspace System. The main obstacle in integration is the lack of certified collision avoidance solutions. Traffic Alert and Collision Avoidance System II has limitations to meet the requirements for UASs and has shown to lack the adaptability to future operational environments. The Airborne Collision Avoidance System X (ACAS X) has been developed to address these issues and increase surveillance, alerting, and resolution performance. This research investigates and assesses the performance of a prototype collision avoidance algorithm for low-level application tailored to the unique dynamic capabilities of multirotor small UASs (SUASs) based on the ACAS X framework. These unique capabilities of SUAS platforms allow them to hover, laterally deviate from the flight path, and use speed commands to resolve a conflict with potentially limited maneuvering space. The algorithm supports resolutions for both the horizontal and vertical domains. This paper describes the development and functioning of the prototype system, and it provides insight into the possible tailoring to specific operational and safety requirements for multirotor SUASs.

Guest Editorial Special Issue on AI-Driven IoT Data Monetization: A Transition From Value Islands to Value Ecosystems

As The trajectory of the Internet of Things (IoT) is moving at a rapid pace, most companies and organizations are awash and drowning in massive amounts of data. Determining how to profit from data deluge and unlock its value can give companies an edge in the market because data have the potential to add tremendous value to many aspects of a business [A1]. The market has already seen a level of monetization across vertical domains e.g., in the form of layering connected devices with a variety of Insights-as-a- Service options. Out of this arena, the data economy concept has been evolving, characterized by correctly monetizing data rather than simply hoarding it, which will provide a significant advantage in a competitive digital environment [A1]. The recent advent of technological advances in the fields of Big Data, Analytics, and Artificial Intelligence (AI) has opened new avenues of competition, where IoT data is considered a living and evolving entity that can unlock enormous opportunities for monetization. Such growth brought forth a slew of new tools, architectures, business models, platforms, and marketplaces, enabling organizations to monetize data successfully. In this context, emerging business models also strive to alter the power balance between users and companies that harvest information by utilizing usage policy enforcement and privacypreserving machine learning techniques. Monetizing data goes beyond just selling data. It is also possible to include steps that add value to data. Generally, organizations can monetize data by 1) utilizing it to make better business decisions or improve processes; 2) surrounding flagship services or products with data; or 3) selling information to current or new markets [A1].

Can the Surface Quasi‐Geostrophic (SQG) Theory Explain Upper Ocean Dynamics in the South Atlantic?

AbstractSatellite altimeters provide quasi‐global measurements of sea surface height, and from those the vertically integrated geostrophic velocity can be directly estimated, but not its vertical structure. This study discusses whether the mesoscale (30–400 km) dynamics of three regions in the South Atlantic can be described by the surface quasi‐geostrophic (SQG) theory, both at the surface and in depth, using outputs from an ocean general circulation model. At these scales, the model surface eddy kinetic energy (EKE) spectra show slopes close to k−5/3 (k−3) in winter (summer), characterizing the SQG and quasi‐geostrophic (QG) turbulence regimes. We use surface density and temperature to (a) reconstruct the stream function under the SQG theory, (b) assess its capability of reproducing mesoscale motions, and (c) identify the main parameters that improve such reconstruction. For mixed layers shallower than 100 m, the changes in the mixed‐layer depth contributes nine times more to the surface SQG reconstruction than the EKE, indicating the strong connection between the quality of the reconstruction and the seasonality of the mixed layer. To further explore the reconstruction vertical extension, we add the barotropic and first baroclinic QG modes to the surface solution. The SQG solutions reproduce the model density and geostrophic velocities in winter, whereas in summer, the interior QG modes prevail. Together, these solutions can improve surface correlations (>0.98) and can depict spatial patterns of mesoscale structures in both the horizontal and vertical domains. Improved spatial resolution from upcoming altimeter missions poses a motivating scenario to extend our findings into future observational studies.

Neutrons on Rails: Transregional Monitoring of Soil Moisture and Snow Water Equivalent

AbstractLarge‐scale measurements of the spatial distribution of water content in soils and snow are challenging for state‐of‐the‐art hydrogeophysical methods. Cosmic‐ray neutron sensing (CRNS) is a noninvasive technology that has the potential to bridge the scale gap between conventional in situ sensors and remote sensing products in both, horizontal and vertical domains. In this study, we explore the feasibility and potential of estimating water content in soils and snow with neutron detectors in moving trains. Theoretical considerations quantify the stochastic measurement uncertainty as a function of water content, altitude, resolution, and detector efficiency. Numerical experiments demonstrate that the sensitivity of measured water content is almost unperturbed by train materials. Finally, three distinct real‐world experiments provide a proof of concept on short and long‐range tracks. With our results a transregional observational soil moisture product becomes a realistic vision within the next years.

5G for Verticals: From Theory to Practice and Beyond [Guest Editorial

The 3rd Generation Partnership Project (3GPP) completed its Fifth Generation (5G) New Radio (NR) Release 16 and approved the OpenAPI specification in July 2020. The 5G NR technology is expected to support numerous future applications in one or a mix of three usage scenarios: enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and Ultra Reliable Low Latency Communications (URLLC). However, 5G is still lacking its killer applications. Which vertical applications will fully utilize the capabilities offered by 5G that cannot be provided by 4G? What is the unprecedented performance gain these applications can achieve with 5G in practice? To answer these and similar questions, the 3GPP started to focus on specific applications and field performance evaluations in Release 17, which includes communication service requirements for critical medical applications, enhancements for unmanned aerial vehicles, enhancements for cyber-physical control applications in vertical domains, enhanced relays for energy efficiency and extensive coverage.