Bidirectional Information Research Articles

MI-CAT: A transformer-based domain adaptation network for motor imagery classification

Due to its convenience and safety, electroencephalography (EEG) data is one of the most widely used signals in motor imagery (MI) brain–computer interfaces (BCIs). In recent years, methods based on deep learning have been widely applied to the field of BCIs, and some studies have gradually tried to apply Transformer to EEG signal decoding due to its superior global information focusing ability. However, EEG signals vary from subject to subject. Based on Transformer, how to effectively use data from other subjects (source domain) to improve the classification performance of a single subject (target domain) remains a challenge. To fill this gap, we propose a novel architecture called MI-CAT. The architecture innovatively utilizes Transformer’s self-attention and cross-attention mechanisms to interact features to resolve differential distribution between different domains. Specifically, we adopt a patch embedding layer for the extracted source and target features to divide the features into multiple patches. Then, we comprehensively focus on the intra-domain and inter-domain features by stacked multiple Cross-Transformer Blocks (CTBs), which can adaptively conduct bidirectional knowledge transfer and information exchange between domains. Furthermore, we also utilize two non-shared domain-based attention blocks to efficiently capture domain-dependent information, optimizing the features extracted from the source and target domains to assist in feature alignment. To evaluate our method, we conduct extensive experiments on two real public EEG datasets, Dataset IIb and Dataset IIa, achieving competitive performance with an average classification accuracy of 85.26% and 76.81%, respectively. Experimental results demonstrate that our method is a powerful model for decoding EEG signals and facilitates the development of the Transformer for brain–computer interfaces (BCIs).

Smartphone-Based Remote Monitoring for Chronic Heart Failure: Mixed Methods Analysis of User Experience From Patient and Nurse Perspectives.

Community-based management by heart failure specialist nurses (HFSNs) is key to improving self-care in heart failure with reduced ejection fraction. Remote monitoring (RM) can aid nurse-led management, but in the literature, user feedback evaluation is skewed in favor of the patient rather than nursing user experience. Furthermore, the ways in which different groups use the same RM platform at the same time are rarely directly compared in the literature. We present a balanced semantic analysis of user feedback from patient and nurse perspectives of Luscii, a smartphone-based RM strategy combining self-measurement of vital signs, instant messaging, and e-learning. This study aims to (1) evaluate how patients and nurses use this type of RM (usage type), (2) evaluate patients' and nurses' user feedback on this type of RM (user experience), and (3) directly compare the usage type and user experience of patients and nurses using the same type of RM platform at the same time. We performed a retrospective usage type and user experience evaluation of the RM platform from the perspective of both patients with heart failure with reduced ejection fraction and the HFSNs using the platform to manage them. We conducted semantic analysis of written patient feedback provided via the platform and a focus group of 6 HFSNs. Additionally, as an indirect measure of tablet adherence, self-measured vital signs (blood pressure, heart rate, and body mass) were extracted from the RM platform at onboarding and 3 months later. Paired 2-tailed t tests were used to evaluate differences between mean scores across the 2 timepoints. A total of 79 patients (mean age 62 years; 35%, 28/79 female) were included. Semantic analysis of usage type revealed extensive, bidirectional information exchange between patients and HFSNs using the platform. Semantic analysis of user experience demonstrates a range of positive and negative perspectives. Positive impacts included increased patient engagement, convenience for both user groups, and continuity of care. Negative impacts included information overload for patients and increased workload for nurses. After the patients used the platform for 3 months, they showed significant reductions in heart rate (P=.004) and blood pressure (P=.008) but not body mass (P=.97) compared with onboarding. Smartphone-based RM with messaging and e-learning facilitates bilateral information sharing between patients and nurses on a range of topics. Patient and nurse user experience is largely positive and symmetrical, but there are possible negative impacts on patient attention and nurse workload. We recommend RM providers involve patient and nurse users in platform development, including recognition of RM usage in nursing job plans.

Functional specialization and interaction in the amygdala-hippocampus circuit during working memory processing

Both the hippocampus and amygdala are involved in working memory (WM) processing. However, their specific role in WM is still an open question. Here, we simultaneously recorded intracranial EEG from the amygdala and hippocampus of epilepsy patients while performing a WM task, and compared their representation patterns during the encoding and maintenance periods. By combining multivariate representational analysis and connectivity analyses with machine learning methods, our results revealed a functional specialization of the amygdala-hippocampal circuit: The mnemonic representations in the amygdala were highly distinct and decreased from encoding to maintenance. The hippocampal representations, however, were more similar across different items but remained stable in the absence of the stimulus. WM encoding and maintenance were associated with bidirectional information flow between the amygdala and the hippocampus in low-frequency bands (1–40 Hz). Furthermore, the decoding accuracy on WM load was higher by using representational features in the amygdala during encoding and in the hippocampus during maintenance, and by using information flow from the amygdala during encoding and that from the hippocampus during maintenance, respectively. Taken together, our study reveals that WM processing is associated with functional specialization and interaction within the amygdala-hippocampus circuit.

Regulative development as a model for origin of life and artificial life studies

Using the formal framework of the Free Energy Principle, we show how generic thermodynamic requirements on bidirectional information exchange between a system and its environment can generate complexity. This leads to the emergence of hierarchical computational architectures in systems that operate sufficiently far from thermal equilibrium. In this setting, the environment of any system increases its ability to predict system behavior by “engineering” the system towards increased morphological complexity and hence larger-scale, more macroscopic behaviors. When seen in this light, regulative development becomes an environmentally-driven process in which “parts” are assembled to produce a system with predictable behavior. We suggest on this basis that life is thermodynamically favorable and that, when designing artificial living systems, human engineers are acting like a generic “environment”.

TCCL-Net: Transformer-Convolution Collaborative Learning Network for Omnidirectional Image Super-Resolution

As virtual reality and metaverse become more and more popular, the Omnidirectional Image (OI) has attracted extreme attention due to its immersive display characteristics. However, users only watch a portion of the content in a specific viewport extracted from a panoramic view, which will lead to a problem of resolution mismatch that requires High-Resolution (HR) for clear near-eye displays in viewports. Hence, it is necessary to exploit a Super Resolution (SR) solution for reconstructing Low-Resolution (LR) OIs. Different from 2D SR methods, the variation of pixel distributions along latitudes is a critical factor in designing an Omnidirectional Image Super-Resolution (OISR) scheme. In this paper, we put forward a novel end-to-end network with a Transformer and Convolution Collaborative Learning Network (TCCL-Net) for OISR. Firstly, Swin Transformer blocks and residual convolution blocks are employed to extract long-range and short-range dependencies, thereby digging into more rich and heterogeneous features from these two branches. Secondly, to better fuse these two features, cross-guided enhanced attention mechanisms are designed for bidirectional information enhancement onto both channel and spatial features. Thirdly, to alleviate nonuniformly pixel distributions across latitudes, we add an absolute positional encoding into Swin Transformer to represent patch weights at different positions and propose a tile-based panoramic reconstruction module to super-resolve various bands with different pixel sampling characteristics across latitudes. Experimental results on two available benchmark datasets demonstrate the superiority of the proposed approach over the state-of-the-art method in achieving OISR task.

Asymmetric Interoperability as a Strategy Among Provider Group Health Information Exchange: Directional Analysis

High levels of seamless, bidirectional health information exchange continue to be broadly limited among provider groups despite the vast array of benefits that interoperability entails for patient care and the many persistent efforts across the health care ecosystem directed at advancing interoperability. As provider groups seek to act in their strategic best interests, they are often interoperable and exchange information in certain directions but not others, leading to the formation of asymmetries. We aimed to examine the correlation at the provider group level between the distinct directions of interoperability with regard to sending health information and receiving health information, to describe how this correlation varies across provider group types and provider group sizes, and to analyze the symmetries and asymmetries that arise in the exchange of patient health information across the health care ecosystem as a result. We used data from the Centers for Medicare & Medicaid Services (CMS), which included interoperability performance information for 2033 provider groups within the Quality Payment Program Merit-based Incentive Payment System and maintained distinct performance measures for sending health information and receiving health information. In addition to compiling descriptive statistics, we also conducted a cluster analysis to identify differences among provider groups-particularly with respect to symmetric versus asymmetric interoperability. We found that the examined directions of interoperability-sending health information and receiving health information-have relatively low bivariate correlation (0.4147) with a significant number of observations exhibiting asymmetric interoperability (42.5%). Primary care providers are generally more likely to exchange information asymmetrically than specialty providers, being more inclined to receive health information than to send health information. Finally, we found that larger provider groups are significantly less likely to be bidirectionally interoperable than smaller groups, although both are asymmetrically interoperable at similar rates. The adoption of interoperability by provider groups is more nuanced than traditionally considered and should not be seen as a binary determination (ie, to be interoperable or not). Asymmetric interoperability-and its pervasive presence among provider groups-reiterates how the manner in which provider groups exchange patient health information is a strategic choice and may pose similar implications and potential harms as the practice of information blocking has in the past. Differences in the operational paradigms among provider groups of varying types and sizes may explain their varying extents of health information exchange for sending and receiving health information. There continues to remain substantial room for improvement on the path to achieving a fully interoperable health care ecosystem, and future policy efforts directed at advancing interoperability should consider the practice of being asymmetrically interoperable among provider groups.

An IOT based smart grid system for advanced cooperative transmission and communication

In the Internet of Things (IoT) ecosystem, devices will predominate, using it in a manner similar to how people used it. Devices will cooperating in a multicast network to collect, share, and forward information way while interacting with one another autonomously and without centralised supervision. The building of an intelligent environment the capability of real-time collection of data, which is crucial for maximising the value of the IoT, will make this possible. A typical electric grid is made up of many power plants that use various power generating units, such as coal-based units, gas-based units, hydro units, etc. The majority of the infrastructure and wires that make up the conventional electricity grid have been in existence for a long time, it should be mentioned. They require significant investments, so providing them could take years. As a result, many grid components are outmoded and must be maintained and monitored on a regular basis to keep power flowing. A sophisticated technology is the smart grid (SG) system that makes it easier to integrate green technology and environmental considerations. The SG cyber–physical system was implemented thanks to the advancement and use of communication technologies in the conventional power system. The Internet of Things (IoT) and essential devices are both present in the complicated architecture of the SG systems. The traditional electric grids are been transformed into smart and efficient grid known as “Smart grid”. The Internet of Things’ smart grid allows for two-way communications among connected devices and technology that can recognise and respond to human needs. The cost and reliability of a smart grid are superior to that of conventional power infrastructure. Through use and data maintenance, smart grid technology will assist in reducing energy use and costs. One of the primary contributions made to grids is the integration of IoT with producing facilities using sustainable energy at various levels. To enhance the smart grid for bidirectional information exchange, improve power quality, and increase reliability Internet of Things (IOT) devices are becoming an important part of smart electric grid. IOT Infrastructure (IOTI) provide a flexible, efficient and secure platform providing strategic management for monitoring and controlling of different operations under different working conditions. This paper discusses cyber security on IOT based infrastructure for electric power systems. A comprehensive study is highlighted which includes type of IOTs, architecture used for smart grid, and future challenges.

Integration of Agricultural Wireless Sensor Networks to Web-of-Things Through an Edge-Computing-Enriched WSNs/WoT Gateway

To connect agricultural wireless sensor networks (WSNs) to web services and applications, the agricultural WSNs/WoT gateway is empowered with local data management and network maintenance functions for downstream WSNs in addition to traditional upstream data collection. This work demonstrates a low-cost, highly scalable, rapidly deployable web of things (WoT) gateway with edge computing capabilities. First, an agricultural WSNs/WoT topology is architected, connecting a ZigBee WSNs to the Web for remote monitoring the local environmental and agronomical information, and simultaneously for managing the solar-powered WSNs for a prolonged lifespan according to the instant data scrawled from the cloud. Second, a WSNs/WoT gateway is designed with the hardware platform Raspberry Pi 3, which serves multiple needs for bidirectional information exchange and local WSNs management. Finally, experimentation demonstrates the proposed hardware platform and architecture can perform edge computing, and efficiently realize the up and down transmission and distribution of data stream.

The role of acupuncture on the autonomic nervous system in irritable bowel syndrome

Irritable bowel syndrome (IBS) is a functional bowel disorder characterized by recurrent abdominal pain, bloating, and irregular bowel movements with complex pathogenesis. Due to its high prevalence and reoccurring symptoms, it seriously impacts patients’ quality of life. One of the causes of IBS is turbulence of gastrointestinal motility and intestinal secretion due to dysfunction of the autonomic nervous system (ANS), which the includes the sympathetic nervous system, parasympathetic nervous system, and enteric nervous system. Current research has proven that acupuncture, a traditional Chinese therapy, is useful and effective for treating IBS. The ANS is a key pathway for bidirectional information transmission between the gastrointestinal tract and the central nervous system, which plays an important role in IBS treatment with acupuncture. Research on the mechanisms of acupuncture for IBS is receiving more attention, and the use of contemporary methods has made significant progress. Evidence suggests that acupuncture may mitigate the negative consequences of IBS, as seen by reduced inflammatory signaling, neurotransmitter levels in the colon and central neural tissues, and balanced gut flora. We review and discuss acupuncture’s possible mechanisms of action for IBS treatment via the ANS, including modulation of gastrointestinal motility, improvement of visceral hypersensitivity, and innervation of the gut-brain-microbiota axis. Our review demonstrates the available evidence for acupuncture treatment of IBS in clinical settings; however, we also observe that the relationship between the ANS and the gut-brain-microbiota axis is unclear, and further research is needed. Graphical abstract: http://links.lww.com/AHM/A57

Relationship between Dietary Polyphenols and Gut Microbiota: New Clues to Improve Cognitive Disorders, Mood Disorders and Circadian Rhythms.

Cognitive, mood and sleep disorders are common and intractable disorders of the central nervous system, causing great inconvenience to the lives of those affected. The gut-brain axis plays a vital role in studying neurological disorders such as neurodegenerative diseases by acting as a channel for a bidirectional information exchange between the gut microbiota and the nervous system. Dietary polyphenols have received widespread attention because of their excellent biological activity and their wide range of sources, structural diversity and low toxicity. Dietary intervention through the increased intake of dietary polyphenols is an emerging strategy for improving circadian rhythms and treating metabolic disorders. Dietary polyphenols have been shown to play an essential role in regulating intestinal flora, mainly by maintaining the balance of the intestinal flora and enhancing host immunity, thereby suppressing neurodegenerative pathologies. This paper reviewed the bidirectional interactions between the gut microbiota and the brain and their effects on the central nervous system, focusing on dietary polyphenols that regulate circadian rhythms and maintain the health of the central nervous system through the gut-brain axis.

Functional and mechanistic diversity in ant tandem communication

Communication is fundamental to the organization of animal societies, often resulting in the convergent evolution of similar social behavior across lineages. However, this similarity may conceal underlying functional and mechanistic differences. Here we combined network and information-theoretic analysis to quantify how tandem recruitment is distinguishable between two ant genera, Temnothorax and Diacamma. We show that Temnothorax uses tandem running to recruit additional recruiters, while Diacamma uses it principally to move the passive majority of their colony, a task that Temnothorax accomplishes with a different behavior, social carrying. Accordingly, the network structure of Diacamma tandems was dissimilar to that of Temnothorax, instead resembling the social-carrying networks in Temnothorax. Furthermore, our information-theoretical analysis on movement trajectories revealed that Diacamma tandem runs lack bidirectional information transfer, the signature of route learning in Temnothorax. By quantifying the diversity of similar communication systems, this study increases the resolution of our understanding of animal societies.

The gut microbiota metabolite capsiate regulate SLC2A1 expression by targeting HIF-1α to inhibit knee osteoarthritis-induced ferroptosis.

Ferroptosis is an iron-dependent cell death that has been found to aggravate the progression of osteoarthritis (OA) and gut microbiota- OA axis refers to the bidirectional information network between the gut microbiota and OA, which may provide a new way to protect the OA. However, the role of gut microbiota-derived metabolites in ferroptosis-relative osteoarthritis remains unclear. The objective of this study was to analyze the protective effect of gut microbiota and its metabolite capsiate (CAT) on ferroptosis-relative osteoarthritis in vivo and in vitro experiments. From June 2021 to February 2022, 78 patients were evaluated retrospectively and divided into two groups: The health group (n=39) and the OA group (n=40). Iron and oxidative stress indicators were determined in peripheral blood samples. And then in vivo and in vitro experiments, a surgically destabilized medial meniscus (DMM) mice model was established and treated with CAT or Ferric Inhibitor-1 (Fer-1). Solute Carrier Family 2 Member 1 (SLC2A1) short hairpin RNA (shRNA) was utilized to inhibit SLC2A1 expression. Serum iron was increased significantly but total iron binding capacity was decreased significantly in OA patients than healthy people (p < 0.0001). The least absolute shrinkage and selection operator clinical prediction model suggested that serum iron, total iron binding capacity, transferrin, and superoxide dismutase were all independent predictors of OA (p < 0.001). Bioinformatics results suggested that SLC2A1, Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1), and HIF-1α (Hypoxia Inducible Factor 1 Alpha)-related oxidative stress signaling pathways play an important role in iron homeostasis and OA. In addition, gut microbiota 16s RNA sequencing and untargeted metabolomics were used to find that gut microbiota metabolites CAT in mice with osteoarthritis were negatively correlated with Osteoarthritis Research Society International (OARSI) scores for chondrogenic degeneration (p=0.0017). Moreover, CAT reduced ferroptosis-dependent osteoarthritis in vivo and in vitro. However, the protective effect of CAT against ferroptosis-dependent osteoarthritis could be eliminated by silencing SLC2A1. SLC2A1 was upregulated but reduced the SLC2A1 and HIF-1α levels in the DMM group. HIF-1α, MALAT1, and apoptosis levels were increased after SLC2A1 knockout in chondrocyte cells (p=0.0017). Finally, downregulation of SLC2A1 expression by Adeno-associated Virus (AAV) -SLC2A1 shRNA improves osteoarthritis in vivo. Our findings indicated that CAT inhibited HIF-1a expression and reduced ferroptosis-relative osteoarthritis progression by activating SLC2A1.

Performance of SWIPT-Enabled FD TWR Network With Hardware Impairments and Imperfect CSI

Cooperative communication with energy harvesting capability increases network coverage and provides a green and sustainable solution for future wireless technologies. In this article, we analyze the performance of a two-way relay network, where bidirectional information exchange between two nodes takes place via simultaneous wireless information and power transfer-enabled relay node, operating in full-duplex (FD) mode over generalized Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> fading channels. Different nonlinear power amplifier (NLPA) models, such as solid-state power amplifier, traveling wave tube amplifier, and soft envelope limiter, are considered at the relay node. The effect of practical constraints, such as imperfect channel state information (CSI) and transceiver hardware impairments (HIs), is considered. Further, we consider the impact of residual self-interference (RSI) due to imperfect successive interference cancellation in the FD mode. For performance analysis, the closed-form expressions of outage probability (OP), asymptotic OP, system throughput, energy efficiency, ergodic capacity, and asymptotic ergodic capacity are derived. The diversity order of the considered network is evaluated. Further, the impact of imperfect CSI, NLPA, HIs, RSI, and other parameters are highlighted on the system performance. The performance of FD and half-duplex mode is also compared. Monte Carlo simulations confirm the accuracy of the derived closed-form expressions.

Design, Development, and Evaluation of an Automated Solution for Electronic Information Exchange Between Acute and Long-term Postacute Care Facilities: Design Science Research.

Information exchange is essential for transitioning high-quality care between care settings. Inadequate or delayed information exchange can result in medication errors, missed test results, considerable delays in care, and even readmissions. Unfortunately, long-term and postacute care facilities often lag behind other health care facilities in adopting health information technologies, increasing difficulty in facilitating care transitions through electronic information exchange. The research gap is most evident when considering the implications of the inability to electronically transfer patients' health records between these facilities. This study aimed to design and evaluate an open standards-based interoperability solution that facilitates seamless bidirectional information exchange between acute care and long-term and postacute care facilities using 2 vendor electronic health record (EHR) systems. Using the design science research methodology, we designed an interoperability solution that improves the bidirectional information exchange between acute care and long-term care (LTC) facilities using different EHR systems. Different approaches were applied in the study with a focus on the relevance cycle, including eliciting detailed requirements from stakeholders in the health system who understand the complex data formats, constraints, and workflows associated with transferring patient records between 2 different EHR systems. We performed literature reviews and sought experts in the health care industry from different organizations with a focus on the rigor cycle to identify the components relevant to the interoperability solution. The design cycle focused on iterating between the core activities of implementing and evaluating the proposed artifact. The artifact was evaluated at a health care organization with a combined footprint of acute and postacute care operations using 2 different EHR systems. The resulting interoperability solution offered integrations with source systems and was proven to facilitate bidirectional information exchange for patients transferring between an acute care facility using an Epic EHR system and an LTC facility using a PointClickCare EHR system. This solution serves as a proof of concept for bidirectional data exchange between Epic and PointClickCare for medications, yet the solution is designed to expand to additional data elements such as allergies, problem lists, and diagnoses. Historically, the interoperability topic has centered on hospital-to-hospital data exchange, making it more challenging to evaluate the efficacy of data exchange between other care settings. In acute and LTC settings, there are differences in patients' needs and delivery of care workflows that are distinctly unique. In addition, the health care system's components that offer long-term and acute care in the United States have evolved independently and separately. This study demonstrates that the interoperability solution improves the information exchange between acute and LTC facilities by simplifying data transfer, eliminating manual processes, and reducing data discrepancies using a design science research methodology.

Artificial Intelligence Technology-Based Semantic Sentiment Analysis on Network Public Opinion Texts

Considering that the current social network text analysis works poorly in accurate and effective sentiment prediction and management, a deep learning (D-L)-based text sentiment analysis method is proposed for the big data environment. First, the autoregressive language model mode XLNet is used to capture bidirectional text information and a sentiment analysis model XLNet-Multi-Attention-BiGRU. Then, considering the context information of social network texts, the defect of traditional GRU units only reading texts in order is overcome by introducing a BiGRU model to extract features in both directions. Finally, a multi-headed attention layer is added between the BiGRU and CRF layers to better capture the key information in the sentence by integrating multiple single-head attention. The results show that the precision, recall, and F1 value of the method proposed in this paper are the largest, with the highest reaching 92.64%, 92.32%, and 91.25%, respectively, which are 12.40%, 10.17%, and 9.63% higher than the maximum values of the other three methods, respectively.

CCIO: A Cross-Chain Interoperability Approach for Consortium Blockchains Based on Oracle.

Cross-chain interoperability can expand the ability of data interaction and value circulation between different blockchains, especially the value interaction and information sharing between industry consortium blockchains. However, some current public blockchain cross-chain technologies or data migration schemes between consortium blockchains need help to meet the consortium blockchain requirements for efficient two-way data interaction. The critical issue to solve in cross-chain technology is improving the efficiency of cross-chain exchange while ensuring the security of data transmission outside the consortium blockchain. In this article, we design a cross-chain architecture based on blockchain oracle technology. Then, we propose a bidirectional information cross-chain interaction approach (CCIO) based on the former architecture, we novelly improve three traditional blockchain oracle patterns, and we combine a mixture of symmetric and asymmetric keys to encrypt private information to ensure cross-chain data security. The experimental results demonstrate that the proposed CCIO approach can achieve efficient and secure two-way cross-chain data interactions and better meet the application needs of large-scale consortium blockchains.

Accurate and Real-Time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network

Efficiently and accurately detecting people from 3D point cloud data is of great importance in many robotic and autonomous driving applications. This fundamental perception task is still very challenging due to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(i)</i> significant deformations of human body pose and gesture over time and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(ii)</i> point cloud sparsity and scarcity for pedestrian objects. Recent efficient 3D object detection approaches rely on pillar features. However, these pillar features do not carry sufficient expressive representations to deal with all the aforementioned challenges in detecting people. To address this shortcoming, we first introduce a stackable Pillar Aware Attention (PAA) module to enhance pillar feature extraction while suppressing noises in point clouds. By integrating multi-point-channel-pooling, point-wise, channel-wise, and task-aware attention into a simple module, representation capabilities of pillar features are boosted while only requiring little additional computational resources. We also present Mini-BiFPN, a small yet effective feature network that creates bidirectional information flow and multi-level cross-scale feature fusion to better integrate multi-resolution features. Our proposed framework, namely PiFeNet, has been evaluated on three popular large-scale datasets for 3D pedestrian Detection, i.e. KITTI, JRDB, and nuScenes. It achieves state-of-the-art performance on KITTI Bird-eye-view (BEV) as well as JRDB, and competitive performance on nuScenes. Our approach is a real-time detector with 26 frame-per-second (FPS).

Modulating Brain Activity with Invasive Brain-Computer Interface: A Narrative Review.

Brain-computer interface (BCI) can be used as a real-time bidirectional information gateway between the brain and machines. In particular, rapid progress in invasive BCI, propelled by recent developments in electrode materials, miniature and power-efficient electronics, and neural signal decoding technologies has attracted wide attention. In this review, we first introduce the concepts of neuronal signal decoding and encoding that are fundamental for information exchanges in BCI. Then, we review the history and recent advances in invasive BCI, particularly through studies using neural signals for controlling external devices on one hand, and modulating brain activity on the other hand. Specifically, regarding modulating brain activity, we focus on two types of techniques, applying electrical stimulation to cortical and deep brain tissues, respectively. Finally, we discuss the related ethical issues concerning the clinical application of this emerging technology.

NRBdMF: A Recommendation Algorithm for Predicting Drug Effects Considering Directionality.

Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems, and various algorithms have been devised for it. A literature survey and summary of existing algorithms for predicting drug effects demonstrated that most such methods, including neighborhood regularized logistic matrix factorization, which was the best performer in benchmark tests, used a binary matrix that considers only the presence or absence of interactions. However, drug effects are known to have two opposite aspects, such as side effects and therapeutic effects. In the present study, we proposed using neighborhood regularized bidirectional matrix factorization (NRBdMF) to predict drug effects by incorporating bidirectionality, which is a characteristic property of drug effects. We used this proposed method for predicting side effects using a matrix that considered the bidirectionality of drug effects, in which known side effects were assigned a positive (+1) label and known treatment effects were assigned a negative (-1) label. The NRBdMF model, which utilizes drug bidirectional information, achieved enrichment of side effects at the top and indications at the bottom of the prediction list. This first attempt to consider the bidirectional nature of drug effects using NRBdMF showed that it reduced false positives and produced a highly interpretable output.

A State-of-the-Art Review of Smart Energy Systems and Their Management in a Smart Grid Environment

A smart grid (SG), considered as a future electricity grid, utilizes bidirectional electricity and information flow to establish automated and widely distributed power generation. The SG provides a delivery network that has distributed energy sources, real-time asset monitoring, increased power quality, increased stability and reliability, and two-way information sharing. Furthermore, SG provides many advantages, such as demand response, distribution automation, optimized use of electricity, economical energy, real-time grid status monitoring, voltage regulation or VAR control, and electricity storage. In this survey, we explore the literature on smart Grid enabling technologies until 2022. We dig out four major systems: (1) the smart grid’s prominent features and challenges; (2) the smart grid standard system and legislations; (3) smart grid energy subsystem; and (4) the smart grid management system and protection system for new researchers for their future projects. The research challenges and future recommendations are also presented in the conclusion section to explore the new paradigm.