Retrieval Technology Research Articles

A Look Back on the History of Cerebral Revascularization for Acute Ischemic Stroke: A Neurosurgeon's Perspective.

This review looks back on our experience with acute middle cerebral artery embolectomies in the 1990s, frowned upon by stroke experts at the time, and no match for the newly introduced and proven treatment of acute ischemic stroke with intravenous recombinant tissue plasminogen activator (alteplase). The past several decades have seen dramatic developments in acute cerebral revascularization, the major paradigm shift being in the form of endovascular thrombectomy. Mechanical thrombectomy has moved from the operating room, where we performed it, to the interventional angiography suite armed with ever-improving clot aspiration and retrieval technologies.

A temporal–spectral fusion transformer with subject-specific adapter for enhancing RSVP-BCI decoding

The Rapid Serial Visual Presentation (RSVP)-based Brain–Computer Interface (BCI) is an efficient technology for target retrieval using electroencephalography (EEG) signals. The performance improvement of traditional decoding methods relies on a substantial amount of training data from new test subjects, which increases preparation time for BCI systems. Several studies introduce data from existing subjects to reduce the dependence of performance improvement on data from new subjects, but their optimization strategy based on adversarial learning with extensive data increases training time during the preparation procedure. Moreover, most previous methods only focus on the single-view information of EEG signals, but ignore the information from other views which may further improve performance. To enhance decoding performance while reducing preparation time, we propose a Temporal-Spectral fusion transformer with Subject-specific Adapter (TSformer-SA). Specifically, a cross-view interaction module is proposed to facilitate information transfer and extract common representations across two-view features extracted from EEG temporal signals and spectrogram images. Then, an attention-based fusion module fuses the features of two views to obtain comprehensive discriminative features for classification. Furthermore, a multi-view consistency loss is proposed to maximize the feature similarity between two views of the same EEG signal. Finally, we propose a subject-specific adapter to rapidly transfer the knowledge of the model trained on data from existing subjects to decode data from new subjects. Experimental results show that TSformer-SA significantly outperforms comparison methods and achieves outstanding performance with limited training data from new subjects. This facilitates efficient decoding and rapid deployment of BCI systems in practical use.

Endovascular thrombectomy for distal medium vessel occlusions: A literature review

BackgroundThere is a lack of substantial evidence supporting the safety and effectiveness of endovascular thrombectomy in treating distal medium vessel occlusions (DMVOs). ObjectiveTo summarize the current evidence regarding endovascular thrombectomy for DMVOs. MethodsWe conducted a narrative review of key articles related to the diagnosis and management of DMVOs. We manually searched PubMed and Google Scholar from January 2010 to July 2023, and only included articles published in the English language. ResultsWhile diagnosing and treating DMVOs is tricky due to access limitations and potential limited benefit from mechanical clot removal, recent improvements in catheter and retrieval technology suggest that endovascular thrombectomy might be a potential treatment option. However, more high-quality research is needed to confirm its effectiveness for DMVOs. ConclusionExperts disagree on how to classify DMVOs and what the best mode of endovascular treatment is.

A Novel Method of Secured Data Distribution Using Sharding Zkp and Zero Trust Architecture in Blockchain Multi Cloud Environment

In the era of cloud computing, guaranteeing the safety and effectiveness of data management is of utmost importance. This investigation presents a novel approach that amalgamates the sharding concept, encryption, zero-knowledge proofs (zkp), and blockchain technology for secure data retrieval and data access control to improve data security, efficiency in cloud storage and migration. Further, we utilize user-specific digital wallets for secure encryption keys in order to encrypt the file before storing into the cloud. As Large files (greater than 50 MB) or Big data files (greater than 1 TB) require greater computational complexity, we leverage the sharding concept to enhance both space and time complexity in cloud storage. Hence, the large files are divided into shards and stored in different database servers. We also employ a blockchain smart contract to enhance secure retrieval of the file and also a secure access method, which ensures the privacy of the user. The zk-snark protocol is utilized to ensure the safe transfer of data between different cloud services. By utilizing this approach, data privacy is preserved, as only the proof of the data’s authenticity is shared with the verifier at the destination cloud, rather than the actual data themselves. The suggested method tackles important concerns related to data protection, privacy, and efficient resource utilization in cloud computing settings by ensuring it meets all the cloud policies required to store data. Since the environment maintains the privacy of the user data and the raw data of the user is not stored anywhere, the entire environment is set up as a Zero trust model.

A Study on the Integration of Big Data With Information Retrieval Technology in the Construction of Translation Talent Pools

This paper conducts a corresponding study on the construction of a translation talent pool internally through the context of information retrieval technology. It first discusses the current state of development of information retrieval technology in various countries in the present era and the current social development of information retrieval technology. A model of the system is constructed by basing it on information retrieval technology. The system model of information retrieval technology combined with the optimisation problems associated with translation talent pools is also investigated, incorporating a factorisation algorithm. The results of the study show that the system model of big data combined with information retrieval technology has good application in concrete practical applications after modelling and performing internal upgrading.

Fully Optical Control of Polarization Current Direction in a Cyanide-Bridged Trinuclear Complex.

As a remote and non-contact stimulus, light offers the potential for manipulating the polarization of ferroelectric materials without physical contact. However, in current research, the non-contact write-read (erase) process lacks direct observation through the stable current as output signal. To address this limitation, we investigated the photoinduced polarization switching capabilities of the cyanide-bridged compound [Fe2Co] using visible light, leading to the achievement of rewritable polarization. By subjecting [Fe2Co] crystals to alternating irradiation with 785 nm and 532 nm light, the polarization changes exhibited a distinct square wave pattern, confirming the reliability of the writing and erasing processes. Initialization involved exposing specific crystal units to 532 nm light for storing "1" or "0" information, while reading was accomplished by scanning the units with 785 nm light, resulting in brief current pulses for "1" states and no current signal for "0" states. This research unveils new possibilities for optical storage systems, paving the way for efficient and rewritable data storage and retrieval technologies, such as the next-generation memories.

Evaluation Method of Intelligent Teaching in Physical Training Classrooms Based on Multimedia Information Retrieval Technology

In different historical periods, people have different demands for Physical Education (PE), and the educational goal of school PE will also change, and the teaching mode and requirements of school PE will also be different. Multimedia information retrieval (IR) technology refers to searching in a large-scale multimedia database according to the content and context of media and media objects. In this paper, the Back Propagation instructional level of physical training class, and model is established. The BPNN is used to analyze the Multimedia IR data, and the simulation and experiment show the effectiveness of the assessment model, which provides a new method for comprehensively, fairly and scientifically evaluating the teaching work. This method is improved by 11.85% compared with the comparative assessment algorithms. The method of using BPNN to evaluate instructional level is easy to operate, which can overcome the shortcomings of traditional assessment methods, and avoid the complicated process of traditional assessment of instructional level.

Application of Interactive Object Model in Sports Teaching

Traditional physical education is just repeated training without quantitative teaching and training data results. Multimedia information analysis and retrieval technology can make repeated research on the content of competition or training, and increase the quantitative analysis of training indicators. Therefore, this paper proposes an interactive compressed domain key video object model to realize the design and production of physical education video, so as to quantify the effect of physical education. This paper discusses the interactive design and application of PE teaching video from three aspects: static, dynamic, and semantic. The interactive object model proposed in this paper can formulate corresponding strategies for different key sports video objects. The distortion rate of the complete key video object is 2.3%, which is 0.09% lower than the distortion rate of the Kim2002 scheme. From a usage perspective, through the scheme proposed in this article, coaches can obtain teaching videos with simple initial extreme values on an interactive interface, and the distortion rate of the video object is lower than that of traditional schemes.

Consumer Usage of Mobile Visual Search in China

As an emerging information retrieval technology, mobile visual search (MVS) has considerable development potential and broad market application prospects. Although many studies have explored user behaviors with various information technology artifacts, few have specifically investigated consumers' MVS intentions and behaviors. For filling the gap, this study is to integrate perceived contextual offer (PCO) and implementation intention into the theoretical model of UTAUT2, aiming to explore potential predictors of Chinese consumers' MVS usage intention and behavior. The results showed that consumers' perceptions of contextual offer, performance expectancy, hedonic motivation, and their using habit had significant effects on consumers' MVS usage intention. Habit, usage intention, and facilitating conditions showed significant predictive power for consumers' MVS implementation intention and their further usage behavior. Additionally, habit and usage intention indirectly influenced consumers' MVS usage through the complementary mediation of implementation intention.

The Evolution of Meteorological Satellite Cloud-Detection Methodologies for Atmospheric Parameter Retrievals

The accurate detection of clouds is an important first step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology, creating a new era in space observation exploration. Additionally, the initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, but more recently, cloud screening in satellite-acquired data is playing an increasingly critical role in the investigation of cloud-free data for the retrieval of soil moisture, vegetation cover, ocean color concentration and sea surface temperatures, as well as the environmental monitoring of a host of products, e.g., atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this review, the evolution of cloud-detection methodologies is highlighted with advancement in sensor hardware technology and machine learning algorithmic advances. The review takes into consideration the meteorological sensors usually used for atmospheric parameters estimation (thermodynamic profiles, aerosols, cloud microphysical parameters). Moreover, a discussion is presented on methods for obtaining the cloud-truth data needed to determine the accuracy of these cloud-detection approaches.

TSDSystem: a framework to collect, archive and share time series data at volcanological observatories

This paper presents a framework designed to collect, archive, and share time series data coming from sensor networks at Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (Italy), which we have developed and called Time Series Database management System (TSDSystem). The framework proposes a flexible database model for the standardization of sensor networks data and implements an optimized technology for storage and retrieval of acquired time series data. It is designed for the implementation of multiparametric databases and then suitable for development in volcanological observatories worldwide. The proposed framework provides a web service to perform writing and reading data via a standard web communication protocol, which easily enables interaction with other instruments or automatic systems. All results provided by the TSDSystem web service are represented using common data formats in the context of online services. In particular, the station networks metadata representation follows a schema inspired by the International Federation of Digital Seismograph Networks, widely known in seismology. A web GUI (graphical user interface) is provided to test and document the web service. Additionally, basic built-in web applications are supplied with the web GUI to perform joint and synchronized time series data visualization as well as representation of stations on a geographical map. The web GUI also offers administration tools for data access policy management, creation of monitoring dashboards and data publication through web pages. The framework implements an authorization system that can be used to restrict both writing or reading operations. The TSDSystem can also be a useful tool for engineering surveillance systems. The implementing code of the framework is available with an open source license on a public repository together with a user manual.

Fully Optical Control of Polarization Current Direction in a Cyanide‐Bridged Trinuclear Complex

As a remote and non‐contact stimulus, light offers the potential for manipulating the polarization of ferroelectric materials without physical contact. However, in current research, the non‐contact write‐read (erase) process lacks direct observation through the stable current as output signal. To address this limitation, we investigated the photoinduced polarization switching capabilities of the cyanide‐bridged compound [Fe2Co] using visible light, leading to the achievement of rewritable polarization. By subjecting [Fe2Co] crystals to alternating irradiation with 785 nm and 532 nm light, the polarization changes exhibited a distinct square wave pattern, confirming the reliability of the writing and erasing processes. Initialization involved exposing specific crystal units to 532 nm light for storing "1" or "0" information, while reading was accomplished by scanning the units with 785 nm light, resulting in brief current pulses for "1" states and no current signal for "0" states. This research unveils new possibilities for optical storage systems, paving the way for efficient and rewritable data storage and retrieval technologies, such as the next‐generation memories.

Development and Research of Chinese-English Slang Translation Retrieval System under Internet Environment

Slang plays an important role in cross-cultural communication. It is frequently used in spoken language and constantly updated. Under the drive of new needs, intelligent integrated retrieval system came into being. Based on information platforms such as the Internet, big data network and cloud computing, intelligent retrieval technology classifies and integrates English slang and develops an "English slang translation retrieval system", which greatly reduces the search cost of users and provides convenience for English learners and cross-cultural communicators.

Long-Term Stability of Low-Cost IoT System for Monitoring Water Quality in Urban Rivers

Monitoring water quality in urban rivers is crucial for water resource management since point and non-point source pollution remain a major challenge. However, traditional water quality monitoring methods are costly and limited in frequency and spatial coverage. To optimize the monitoring, techniques such as modeling have been proposed. These methods rely on networks of low-cost multiprobes integrated with IoT networks to offer continuous real-time monitoring, with sufficient spatial coverage. But challenges persist in terms of data quality. Here, we propose a framework to verify the reliability and stability of low-cost sensors, focusing on the implementation of multiparameter probes embedding six sensors. Various tests have been developed to validate these sensors. First of all, a calibration check was carried out, indicating good accuracy. We then analyzed the influence of temperature. This revealed that for the conductivity and the oxygen sensors, a temperature compensation was required, and correction coefficients were identified. Temporal stability was verified in the laboratory and in the field (from 3 h to 3 months), which helped identify the frequency of maintenance procedures. To compensate for the sensor drift, weekly calibration and cleaning were required. This paper also explores the feasibility of LoRa technology for real-time data retrieval. However, with the LoRa gateways tested, the communication distance with the sensing device did not exceed 200 m. Based on these results, we propose a validation method to verify and to assure the performance of the low-cost sensors for water quality monitoring.

Application Analysis of Music Video Retrieval Technology Based on Dynamic Programming in Piano Performance Teaching

Application Analysis of Music Video Retrieval Technology Based on Dynamic Programming in Piano Performance Teaching

Personalized recommendation of entertainment robots in fine arts education based on human–computer interaction and data mining

In the current digital entertainment environment, the art learning mode of human–computer interaction can effectively enhance students’ interest. Entertainment robots, as a novel personalized interaction tool, can provide a more intelligent and entertaining way for art teaching. This article analyzes the personalized recommendation of entertainment robots in art education based on human–computer interaction and data mining.On the basis of collecting a large number of fine art education resources and organizing and labeling them, data mining technology is used to extract user background information and interest preferences. Information retrieval technology is used to filter out content related to user interests from the massive art education resources based on the extracted user interests. We also use recommendation algorithms to sort and personalize the recommended content based on the user’s historical behavior and other user evaluations, in order to improve the accuracy of recommendation results and user satisfaction. The effectiveness and practicality of this personalized recommendation model were verified through user surveys and data analysis. The experimental results indicate that this model can quickly and accurately recommend suitable fine art education content for users, improving their learning effectiveness and experience.

Application of Innovations in Technical and Technological Updating of Ensuring the Security of Employees Databases of Engineering Enterprises: A Parallel-Vertical Approach to Optimizing Personnel Management

The main purpose of the article is to present a modern method of parallel-vertical data security, which, thanks to the counting of units in the i-th input bit slice and the parallel formation of the i-th bit slice of a sorted array of numbers, reduces the protection time. The object of the study is the personnel database of an engineering company. The research methodology involves the use of parallel sorting methods for the development of technical and technological means for ensuring the security of data arrays, methods for parallel search for maximum and minimum values for the development of hardware and software tools for searching for maximum and minimum values. The practical aspect of the obtained results lies in the development of technologies for parallel sorting and retrieval of data in real time with high efficiency in the use of equipment. A hardware structure is presented for simultaneous parallel-vertical search for maximum and minimum numbers to increase the speed of ensuring the security of personnel databases. The innovativeness of the results obtained lies in the schematic representation of the proposed approach to application. The diagrams themselves are the author's vision. The structure of a parallel-flow device for ensuring the security of a two-dimensional array of numbers using the displacement method is presented. In our opinion, such a technical-technological update will serve as an innovation in the context of the security of personnel databases of an engineering company.

Synergies and Challenges in the Integration of Cloud Computing and Deep Learning: Current Status, Interconnectedness, and Future Directions

This article reviews the status and recent developments in the integration of cloud computing and deep learning, as well as the interrelationship between these two technologies. The paper explores the intersection of cloud computing and deep learning in addressing cybersecurity challenges. Amidst the rapid expansion of the worldwide public cloud services market, the vulnerability to cyber-attacks and breaches in data management is on the rise. Different intrusion detection systems use different deep learning techniques to improve the effectiveness of intrusion detection in cloud computing environments. Additionally, the use of encryption technology and the corresponding deep learning retrieval technology further improves the security of cloud data. Moreover, the paper deeply studies how the scheduling mechanism of deep reinforcement learning can optimize the performance of cloud services by efficiently allocating resources and solving the problem of slow cloud service speed. It also derives the optimal energy strategy through deep neural networks to address the energy consumption challenges in cloud computing data centers. This article also reviews the five emerging architectures of cloud computing and explores the role of deep learning within these frameworks. Finally, it analyzes some of the challenges facing the future of cloud computing and deep learning, including the security and confidentiality of cloud computing, as well as low latency and high throughput optimization in the field of deep learning. In summary, this article provides insight into current trends, challenges, and future prospects for the evolving integration between cloud computing and deep learning.

Personalized music teaching service recommendation based on sensor and information retrieval technology

In order to fully utilize the existing resources of music information, people have found that music information retrieval technology has important research significance for personalized music teaching. Overall, music information retrieval technology is still in the experimental exploration stage and lacks technical and practical systems. In this context, this article proposes recommendation algorithms that can effectively adjust the content of information services based on user preferences. However, recommendation algorithms are still in an immature and complex stage, and there are still issues with their accuracy. In response to the issue of recommendation accuracy, this article utilizes information retrieval technology to optimize recommendation algorithms. Combine these two algorithms for recommendation. The main steps that affect recommendation results in recommendation algorithms include similarity calculation, nearest neighbor selection method, and score prediction method calculation. The experimental results indicate that the proposed algorithm can effectively solve the existing problems of users, and can also accurately retrieve the content of interest, greatly improving the diversity of the proposed content. We use utilizing algorithmic learning to create a personalized music teaching system that enables students to independently advance towards their self-development goals, ultimately achieving the goal of promoting the harmonious development of students' personalities.

Design And Development of Sketch Based Image Retrieval Using Deep Learning

In this cutting edge, the common wrong doing rate is expanding day-by-day and to manage up with this the criminal divisions as well ought to discover ways in which would speed up the by and large preparation and offer assistance in bringing one to justice. In response to rising crime rates, law enforcement agencies are turning to advanced algorithms capable of matching freehand sketches with images in databases. These algorithms, utilizing sophisticated feature extraction techniques and deep learning models, significantly enhance identification accuracy. By leveraging Sketch based image retrieval technology, investigations are expedited, leading to quicker suspect apprehensions and resolution of criminal cases. This results in improved public safety and justice outcomes, as well as more efficient law enforcement practices overall.