Time Information Research Articles

Research on ship trajectory compression based on a Dynamic Programming algorithm

The Automatic Identification System (AIS) is extensively used in monitoring vessel traffic, and ship navigation related information can be obtained from the AIS data. However, AIS data contain extensive redundant information, which leads to the general need to compress the data when applying it in practice or conducting research. In this paper, a three-dimensional compression of ship trajectories using the Dynamic Programming algorithm has been proposed. The AIS data near the ports of Long Beach and San Francisco in the United States were used to test and compare the Dynamic Programming algorithm with the Top-down Time-ratio algorithms. The experimental results show that the proposed algorithm can better retain the position and time information at low compression ratio such as 1%, 20% and 40%. Moreover, the algorithm is applicable to ship trajectories with different motion modes such as steering, mooring and straight ahead. The results show that the proposed algorithm can reasonably solve the problem of AIS data redundancy and ensure the quality of data, which is of practical significance for water transport, transport planning and other related research.

GeneXpert MTB/RIF Ultra in Pediatric Tuberculosis: How Disease Characteristics Modify Test Performances.

The diagnosis of pediatric tuberculosis (TB) is challenging, due to the lower sensitivity of microbiological tests, such as culture and microscopy, compared to their performance in adult cases. Guidelines have introduced molecular tests, including GeneXpert MTB/ RIF and GeneXpert MTB/RIF Ultra. These tests use a real-time polymerase chain reaction method and provide information on M. tuberculosis detection and drug-resistance-associated mutations in less than 2 hours. This retrospective single-center study aimed to evaluate the accuracy of GeneXpert and GeneXpert Ultra for the diagnosis of pediatric TB. This retrospective study was conducted on a total of 95 children diagnosed with probable or confirmed TB disease (74 diagnosed with pulmonary TB, 21 with extrapulmonary TB), who referred to the infectious disease unit at Meyer Children's Hospital in Florence, Italy, and tested with GeneXpert MTB/RIF or GeneXpert Ultra, from 2013 to 2023. GeneXpert and GeneXpert Ultra demonstrated a detection rate of 0.357 (95% CI 0.180 to 0.535) and 0.537 (95% CI 0.417-0.657), respectively. No child was tested with both tests. Patients' characteristics, including age and sex, did not significantly influence the test's performances. Notably, GeneXpert Ultra had a significantly higher detection rate in children with extrapulmonary TB (0.813, 95% CI 0.621 to 1.004) compared to that in children with pulmonary TB (p = 0.020). Gastric aspirate was the most tested specimen. Specimens that did not require invasive procedures for collection (including stool) yielded poor results. GeneXpert and GeneXpert Ultra permitted rapid evaluation of genotypic drug-sensitivity testing (DST), even though limited to rifampicin resistance, making necessary confirmation through phenotypic DST (performed on culture). The introduction of GeneXpert and GeneXpert Ultra improved TB diagnosis in children, by providing microbiological information in a short time, complementing results from culture, which remains the reference test in pediatric TB diagnosis.

Unsupervised health indicator fusing time and frequency domain information and its application to remaining useful life prediction

Unsupervised health indicator fusing time and frequency domain information and its application to remaining useful life prediction

AI 시대 땅과 공간의 풍수 환경에 관한 연구 - 공주대학교 신관 캠퍼스를 중심으로

The world is changing rapidly. Entering the era of the Fourth Industrial Revolution, China is rapidly evolving into a society of super-intelligence, super-convergence, and hyper-connection. Digital innovation technologies that drive changes in the new era, from big data and blockchain to the Internet of Things and AI, are already deeply embedded in the center of human life. This trend is expected to accelerate throughout society. Feng Shui has long been widely used in various fields to select important locations such as the capital of the country, the residential areas of individuals, and the graves of their ancestors. However, entering the digital age, classical knowledge of Feng S hui faces an inevitable historical turning point through convergence with AI. This represents a great innovation in convergence as well as a structuralist approach to longstanding Feng Shui thinking. Therefore, it is more urgent than ever to think about “new concept Feng Shui marketing,” which can open a new horizon for it by developing big data-based “customized Feng Shui content” that combines it and AI technology and provides related information in real time. To this end, it is necessary to build high-quality data for AI to learn the logic and thinking systems of Feng Shui. In this high school, we analyzed the location environment of the new campus of Gongju National University from a Feng Shui perspective and focused on quantifying the data values derived from it. This was to reduce the ambiguity of evaluation criteria, which is said to be a chronic problem of Feng Shui; and to input clearer and quantified prompts into AI. It is hoped that such research will provide an opportunity to enhance the status and existence value of Feng Shui as a traditional culture and ideology of the Korean people.

A Pilot Report on Extracting Symptom Onset Date and Time from Clinical Notes in Patients Presenting with Chest Pain.

Acute coronary syndrome (ACS) is an acute heart disease that often evolves rapidly. In ACS patients presenting with no-ST-segment elevation (NSTE-ACS), the timing of symptom onset pre-hospital may inform the disease stage and prognosis. We pilot-tested two off-the-shelf natural language processing (NLP) pipelines, namely parsedatetime and regular expression ( regex ), to extract date and time (DateTime) information of patient-reported chest pain symptoms from electronic health records (EHR) clinical notes. We included three types of clinical notes (N=71): History and Physical (n=49), Emergency Department Screening (n=3), and Triage Notes (n=19). All notes were manually annotated for the true DateTime of symptom onset. Parsedatetime returned matching DateTime outputs in 36 notes (50.7%), while regex returned zero matched outputs. Parsedatetime performed better than regex , although it was still suboptimal. Both pipelines require constant refinement and custom improvements. Methods for a large-scale, automated DateTime extraction from EHR clinical notes further investigation.

Lushio Parfume: Aplikasi Toko Parfum Berbasis Visual Studio. Net Solusi Pengelolaan Stok dan Transaksi yang Efisien

The problem in this study is that the perfume sales information system that is being used to manage sales data, inventory of goods, and payments is still conventional, namely using a manual bookkeeping system, where operational activities have not used available computer technology or devices. Therefore, the purpose of this study is to design and develop perfume sales applications using visual studio at Galaxy Parfum Shop. The method used in this research is a descriptive qualitative method with data collection techniques in the form of observation, interviews, and literature review. The result of the system created is a perfume sales application that can run well and be implemented. This application is able to provide stock information in real time, manage payments at the cashier, and print notes automatically after payment is made. In conclusion, this application can improve the efficiency of sales management, minimize recording errors, and speed up the transaction process at Toko Galaxy Parfum.

Lidija Ose (Osis) – Architect of Standard Residential Buildings and Furniture Designer

The article summarizes for the first time information about Lidija Ose (1922–1985), an architect of Soviet-era standard (typical) residential buildings and designer of built-in furniture, a graduate of the Faculty of Architecture of the Latvian State University (1952). It reflects the architect’s life and education, as well as her creative contribution during the period from the mid-1950s to the late 1970s when massive residential development took place and new standard multi-storey residential buildings were designed. The architect has contributed to the design, development, and construction of buildings of series M2, M3, 1-430, 1-316, 1-318, 103, 104, 119, and 467. L. Ose has also designed built-in kitchens, bathrooms, hallway furniture, building entrances, and interior and architectural elements (windows, balconies, doors, and their technical and structural details). Her work also includes cultural centres, clubs, cinemas, and a student dormitory. L. Ose has participated in several architectural design competitions, including for a sports centre on Lucavsala. Together with architects Marta Staņa (1913–1972), Imants Jākobsons (1934–1993), and Harolds Kanders (1927–2004), she participated in the design of the new building of the Daile Theatre at 75 Brīvības (then – Ļeņina) Street.

Penerapan Lelang Non Eksekusi Sukarela Online oleh Pejabat Lelang Kelas II (Studi Kasus Kantor Pejabat Lelang Kelas II Rosalinda, SH)

The purpose of this study is to find out the Mechanism of Implementing Online Voluntary Non-Execution Auctions and Analysis of the Application of Online Voluntary Non-Execution auctions Methods by Class II Auction Officials on movable objects. This study uses a normative juridical method with an analytical descriptive approach to analyze the implementation of online voluntary non-execution auctions at the Office of Class II Auction Officials Rosalinda, SH, through literature research, field, and interviews. The results of the study show that the implementation of voluntary non-execution auctions online reflects digital transformation in a more efficient, transparent, and inclusive auction system. Based on PMK Number 213/PMK.06/2020, this auction is carried out voluntarily with a closed bidding mechanism for privacy or open bidding for real-time competition. Key advantages include time efficiency, information disclosure, and wide access without geographical restrictions. Challenges such as data security and technological infrastructure remain a concern, but the implementation by Class II Auction Officials, such as in Rosalinda's Office, SH, is a successful example of the modernization of technology-based auction systems.

Utilization of Artificial Intelligence (AI) in Learning for College Students

The focus of education in the 4.0 era is not only on academic knowledge, but also on the development of important skills such as critical thinking, problem solving, creativity, collaboration, and digital literacy. These are skills needed to adapt to the changing world of work. The use of technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), and augmented reality (AR) has changed the way teachers and students interact. Learning can be done online with digital platforms, allowing access to wider information and flexibility of time. Artificial intelligence (AI) has changed many aspects of life, including education. This article aims to explore the use of AI in learning for students, with a focus on effectiveness, impact, and challenges faced. Through a literature review and case studies, this research identifies how AI can improve the learning experience, personalize education, and increase student engagement.

Multi-Granularity Temporal Embedding Transformer Network for Traffic Flow Forecasting.

Traffic flow forecasting is integral to transportation to avoid traffic accidents and congestion. Due to the heterogeneous and nonlinear nature of the data, traffic flow prediction is facing challenges. Existing models only utilize plain historical data for prediction. Inadequate use of temporal information has become a key problem in current forecasting. To address the problem, we must effectively analyze the influence of time periods while integrating the distinct characteristics of traffic flow across various time granularities. This paper proposed a multi-granularity temporal embedding Transformer network, namely MGTEFormer. An embedding input adeptly merges complex temporal embeddings, a temporal encoder to consolidate rich temporal information, and a spatial encoder to discern inherent spatial characteristics between different sensors. The combined embeddings are fed into the attention mechanism's encoder, culminating in prediction results obtained through a linear regression layer. Temporal embedding can help by fussing the period and various temporal granularities into plain historical traffic flow that can be learned adequately, reducing the loss of time information. Experimental analyses and ablation studies conducted on real traffic datasets consistently attest to the superior performance of the MGTEFormer. Our approach reduces the mean absolute error of the original models by less than 1.7%. Extensive experiments demonstrate the superiority of the proposed MGTEFormer over existing benchmarks.

Road side unit deployment optimization for the reliability of internet of vehicles based on information transmission model.

The Internet of Vehicles (IoV) makes it possible to transmit information in real time between vehicles, providing a modern approach for autonomous driving, traffic safety, and other applications. Roadside units (RSUs) contribute to the enhancement of IoV's reliability and transmission efficiency, while mitigating the impact of low IoV penetration. The objective of RSU deployment optimization is to minimize the total cost with the premise of ensuring IoV reliability. We construct a distance-based reliability measure for IoV, which is expressed as the proportion of information transmitted in online mode to the total transmission distance. The distance distribution of the online and offline transmissions is computed using the information transmission model. A bi-objective optimization model is established with the objectives of minimizing the cost of RSU and maximizing the reliability of IoV. Meanwhile, based on variable probabilities of crossover and mutation, a nondomination level-based NSGA-II (NNSGA-II) is designed to improve the solving efficiency. Numerical results show the advantage of the proposed model over the models evaluated with the objective of reducing transmission time can be up to 18% in different traffic scenarios, and NNSGA-II is significantly more computationally efficient.

Tense and agreement processing in native Spanish speakers with aphasia

Abstract Several cross-linguistic studies have reported an impairment of verbal morphosyntax in People with Aphasia (PWA), highlighting a greater impact on tense morphology in contrast to agreement-inflectional morphology (Benedet, Christiansen & Goodglass, 1998; Friedmann & Grodzinsky, 1997, 2000; Gavarrò & Martínez-Ferreiro, 2007; Kok et al., 2007; Wenzlaff & Clahsen, 2004). The Diacritical Encoding and Retrieval Hypothesis (DERH, Faroqi-Shah & Thompson, 2007) postulates that this deficit is due to a specific difficulty projecting the semantic time information to the morphosyntax of verbs, related to processing limitations in PWA. This study aimed to assess the processing of the tense and agreement inflection in Spanish-speaking PWA. A group of 9 PWA completed four tasks designed to study verbal inflection production and comprehension in Spanish: Sentence Completion, Sentence Elicitation, Grammaticality Judgements and Sentence-Picture Matching. As expected, PWA showed a significatively greater impairment in tense inflection compared to agreement inflection. Furthermore, the analysis showed that this differential impairment manifested when PWA were asked to encode semantic time information and retrieve the corresponding verb morphology. These findings are consistent with the postulates of the DERH.

Sustainable and Intelligent Parking System

Abstract—In public places like malls, hospitals, businesses, and market places, parking is a major problem. Finding parking lanes requires investment and manual labour, which adds time and congestion. This issue can be lessened with an automated parking system that uses infrared transmitter-receiver pairs in each lane and a display outside the gate. Additionally, the system uses RFID readers/tags, automatic billing, and entry- exit data logging to manage check-ins and check-outs. At neighbouring kiosks, users can register, create an account, and add money for recharging. Users are directed to the available parking spaces by video displays at the parking floor entrance. This system uses infrared transmitter-receiver pairs in each lane and displays outside the gate, providing users with real- time information about the availability of parking slots. Conventional parking systems lack intelligent monitoring, and parking lots are monitored by security guards. The proposed system uses a display unit to display a visual representation of parking, including empty and occupied slots, helping users decide where to park their car. The system also manages check-in and check-outs under RFID readers/tags, with additional features such as automatic billing and entry exit data logging. Users complete a one-time registration process, creating an account with personal information and money for recharge at nearby kiosks. Video displays at the entrance of the parking floor guide users to the vacant parking slots, showing green and red slots respectively. The sharp rise in vehicle density, especially during peak hours, makes it difficult for users to locate a parking space. Additionally, there is a smartphone app that lets customers check the availability of parking spaces and make the appropriate reservation. Smart parking can reduce pollution and fuel use in urban areas, increasing the economy. Parking management is the process of making better use of parking resources. To effectively manage parking, the first step is to identify the source of the problem. Finding enough parking is one of the industries with the fastest growth rates due to the increasing number of cars on the road. Usually, traffic has been a nightmare. clever parking options from Keywords—Vehicle density, Parking Space, Peak hours, Sharp Rise, Smart parking, Spaces, Parking, Increasing, Vehicles, Users, Urban, Traffic, Step, Source, RFID, Lane, Infrared, Gate

Charting The Path Of Formation: A Phenomenological Exploration Of The Acquired Values Of Former Seminarians

Seminary formation leaves an enduring impact on seminarians that extends far beyond the confines of the seminary walls. As seminarians journeyed through their formation, they are immersed in diverse values, teachings, and experiences that hone their character and worldview. This study explores the acquired values of the former seminarians during their time in seminary formation, exploring how these values continue to shape and influence their lives beyond the confines of the seminary walls. Employing a transcendental phenomenological approach, the researchers interviewed ten former seminarians who spent three to five years in the seminary. Through data analysis, three prominent themes surfaced on the acquired values: spiritual, moral, and personal competencies. These acquired values were not merely abstract concepts but tangible guiding principles that participants actively integrated into various aspects of human experience, including spiritual and ecclesial life, social interactions, and personal and professional development. By paying careful attention to the lived experiences of former seminarians, seminary programs can be crafted to nurture better the spiritual, moral, and personal development of future generations of clergy.

The serotonergic psychedelic N,N-dipropyltryptamine alters information-processing dynamics in in vitro cortical neural circuits.

Most of the recent work in psychedelic neuroscience has been done using noninvasive neuroimaging, with data recorded from the brains of adult volunteers under the influence of a variety of drugs. While these data provide holistic insights into the effects of psychedelics on whole-brain dynamics, the effects of psychedelics on the mesoscale dynamics of neuronal circuits remain much less explored. Here, we report the effects of the serotonergic psychedelic N,N-diproptyltryptamine (DPT) on information-processing dynamics in a sample of in vitro organotypic cultures of cortical tissue from postnatal rats. Three hours of spontaneous activity were recorded: an hour of predrug control, an hour of exposure to 10-μM DPT solution, and a final hour of washout, once again under control conditions. We found that DPT reversibly alters information dynamics in multiple ways: First, the DPT condition was associated with a higher entropy of spontaneous firing activity and reduced the amount of time information was stored in individual neurons. Second, DPT also reduced the reversibility of neural activity, increasing the entropy produced and suggesting a drive away from equilibrium. Third, DPT altered the structure of neuronal circuits, decreasing the overall information flow coming into each neuron, but increasing the number of weak connections, creating a dynamic that combines elements of integration and disintegration. Finally, DPT decreased the higher order statistical synergy present in sets of three neurons. Collectively, these results paint a complex picture of how psychedelics regulate information processing in mesoscale neuronal networks in cortical tissue. Implications for existing hypotheses of psychedelic action, such as the entropic brain hypothesis, are discussed.

Matese Mts. and Caserta District Karst Bauxites (Campania Region, Southern Italy): Insights on Geochemistry, Paleoclimate, Paleoenvironment, and Parental Affinity

In the Campania region (Southern Italy), in the Matese Mts. (Albian to Turonian/Coniacian) and Caserta district (Albian to Cenomanian), two karst bauxite deposits outcrop, consisting of flat lenses over shallow karst carbonate. Although the mineralogy and geochemistry of Campania bauxite deposits have been widely studied in recent years, new major and trace elements relationships were provided to highlight paleoclimatic and paleoenvironmental conditions that occurred during their formation. The purpose of this research is to provide for the first time information on the paleoclimatic and paleoenvironmental conditions that affected the bauxites of Campania. These deposits formed during different periods since the Matese deposit formed during intense weathering processes with more abundant precipitation while the Caserta district deposit experienced a more long-lasting exposure event. During the formation of the studied bauxites, the drier conditions favored the replacement of kaolinite by boehmite. R-mode factor analysis showed geochemical affinity among Al2O3, TiO2, and Nb. REEs minerals are mainly associated with the bauxite matrix while Zr, Hf, and V were mainly concentrated in detrital minerals during the later stages of bauxitization. Parental affinity indices (Eu/Eu* vs. Sm/Nd; Eu/Eu* vs. TiO2/Al2O3) assessed the origin of the protolith of the Campania bauxites by rejecting the hypothesis of the dissolution of the bedrock carbonate. The results confirmed the eolian transport of parental material with an Upper Continental Crust and an intermediate to mafic magmatic composition.

MLDM: A Multi Learning Domain Model for Fault Identification of Centrifugal Fan

Abstract In centrifugal fan fault diagnosis, the influence of multiple noise sources causes the collected fault signals to contain interference of different modes. Therefore, the difficulty of capturing fault characteristics greatly increases in this situation. In this article, a Multi Learning Domain Model (MLDM) scheme is proposed based on signal processing technology and artificial intelligence methods. The Haar Wavelet Convolution Extraction (HaarCE) module in the scheme is used to synchronously map features at different frequency scales, while the Lightweight Multi-Scale Feature Enhancement (LMFE) module enhances feature extraction at different time scales. Meanwhile, Upsampling is employed to enhance the expression of fault features. In addition, applying Information Rectification Learning (IRL) to feature maps extracted at each level allows for spatial representation and selection of extracted fault features, providing a basis for final decision-making. This scheme performs multi-level and multi-scale analysis on fault signals, jointly extracting time, frequency, and spatial information to improve the robustness and generalization ability of the model. Conduct experimental verification using data from the same type of centrifugal fan and rotor. The experimental results show that the recognition accuracy of the proposed model is about 93% for fan data at SNR=-4dB, which has certain competitiveness compared to other excellent models.

Research on the Application of the Internet of Things in Optimizing the Allocation of Ideological and Political Education Resources Using Machine Learning

This paper evaluates the application of the Internet of Things (IoT) in designing smart teaching systems by optimizing ideological and political education (IPE) resources using machine learning (ML). To increase educational reform’s efficacy, we also examine the issues with conventional IPE instruction. This study further develops a cutting-edge, smart framework for teaching ideological and political concepts in the educational setting and changes teaching according to real-life scenarios. Comprising the perception, network, and application stages, the IPE platform is a three-layer IoT architecture. The IoT and internet connections are used by the technology to receive effective IPE instructional activity information in real time. After that, the data are sent over the internet to the data center, where it is used as the initial information for applications, analysis of information, and modeling training. We are able to collect and acquire IPE teaching activity data sets on instructive and educational developments in courses by using IoT devices. The principal component analysis (PCA) approach is used to remove interface characteristics, date, and time elements from the data to increase the evaluation’s correctness. To remove noisy information from the data, the z-score normalization method is used. In addition, this paper suggests a novel efficient Cat Boost method inspired by hunter–prey optimization (AHPO-ECB) for evaluating IPE performance. In this case, the AHPO approach is employed to reduce the misprediction rate of the CB. The suggested model is then used to analyze the predicted performance using a Python program. The results of the experiments indicate that, in comparison to other models currently in use, the suggested AHPO-ECB model performs at the highest level when assessing IPE teaching performance.

Simplified Approach to Evaluate Cavitation Intensity Based on Time Information on Imposed Pressure in Liquid

Cavitation damage is an important research topic in fluid–structure interactions, such as those being studied using the mercury target for the pulsed neutron source at the Materials Life Science Experimental Facility/Japan Proton Accelerator Complex. Hence, the estimation of cavitation damage (cavitation intensity) is required from the perspective of structural integrity. The results of previous studies suggest that the maximum radii of cavitation bubbles immediately prior to collapse are related to cavitation intensity. Therefore, we propose a method for estimating the maximum radius from the time information by measuring the vibrations of structure walls that are induced by collapsing cavitation bubbles in a confined liquid. In this study, we used a magnetic impact testing machine to experimentally investigate the cavitation bubble dynamics, directly observe the bubble collapsing behavior, and measure the induced vibration. We experimentally confirmed that the time information is useful in the estimation of the maximum radii of bubbles. Moreover, we theoretically derived a simple evaluation formula to estimate the maximum radius from the time responses of the imposed pressure in a confined liquid in a structure.

Robust modelling of arterial blood pressure reconstruction from photoplethysmography

Blood pressure is a crucial indicator of cardiovascular disease, and arterial blood pressure (ABP) waveforms contain information that reflects the cardiovascular status. We propose a novel deep-learning method that converts photoplethysmogram (PPG) signals into ABP waveforms. We used -Net as a feature extractor and designed a Bi-block to capture individualised time information in encoder feature extraction. We further enhanced the prediction accuracy of the ABP waveforms by applying a combined loss function to each layer of deep supervision. We also propose a total error index (TEI) to measure overall performance. Furthermore, we extended our method from the UCI dataset to the VitalDB dataset, achieving mean absolute error ± standard deviation (MAE ± STD) values of 2.48 ± 1.95, 1.42 ± 1.42, and 1.48 ± 1.36 mmHg for systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) in UCI dataset, and 2.16 ± 1.53, 1.12 ± 0.59, and 1.35 ± 0.84 mmHg in VitalDB dataset, respectively. The mean ± STD values of the TEI index are 0.29 ± 0.10 in UCI dataset and 0.29 ± 0.15 in VitalDB dataset. These results demonstrate the superiority of the proposed method over existing methods and its robustness to different sampling frequencies and devices.