Aware System Research Articles

Flexible circuit-based spatially aware modular optical brain imaging system for high-density measurements in natural settings.

Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic conditions remains a significant challenge. The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide a real-time probe three-dimensional (3D) shape estimation to improve the use of fNIRS in everyday conditions. The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3D positions in real time to enable advanced tomographic data analysis and motion tracking. Optical characterization of the MOBI detector reports a noise equivalence power of 8.9 and at 735 and 850nm, respectively, with a dynamic range of 88dB. The 3D optode shape acquisition yields an average error of 4.2mm across 25 optodes in a phantom test compared with positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided. To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3D optode position acquisition, combined with lightweight modules ( ) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

Patterns of community antibiotic use with reference to the AWaRe classification of the World Health Organization.

Monitoring consumption through quality indicators and providing feedback is an important activity within programmes for improving antibiotic prescription. This study aims to describe the use of antibiotics for systemic use in community setting in Italy and to compare prescription patterns among regions based on quality prescription indicators to identify any inter-regional variability. The 2021 consumption (calculated as DDDs per 1000 inhabitants per day) of antibiotics for systemic use (reimbursed or purchased privately) in community setting were considered. The WHO AWaRe system was used to classify antibiotics into Access, Watch and Reserve groups, using the 60% of prescribed antibiotics belonging to the Access group out of the total DDDs as AWaRe target. The correlation between regional consumption and percentages of DDDs in the Access group was assessed through linear regression. A further indicator 'ratio of broad to narrow-spectrum antibacterials' was also calculated to compare the spectrum of prescribed antibiotics. Overall community antibiotic consumption in Italy in 2021 was 15.6 DDDs per 1000 inhabitants per day, with an increasing trend from north (13.0 DDDs) to south (19.5 DDDs) and the percentages of non-reimbursed consumption decreasing from north (33.1%) to south (21.5%). Only three regions out of 21 reached the AWaRe target (regional range 47.4%-62.9%), with a negative correlation between regional consumption and percentages of antibiotics belonging to the Access group (correlation coefficient -0.8; R 2 = 0.64; P value < 0.001). The 'ratio of broad to narrow-spectrum antibacterials' was 8.5 at national level, with the highest value in the centre (11.0) compared to the south (9.1) and north (7.0). The percentage of consumption of Access group antibiotics correlates very well with regional consumption in Italy, indicating that regions with higher antibiotics consumption also have worse prescribing patterns with a greater impact on bacterial resistance. Additional data are needed to better describe prescription patterns from an antimicrobial stewardship perspective.

Analisis Penyebab Rendahnya Kedisiplinan Pegawai Pada Unit Pelaksana Teknis Daerah (UPTD) Kesatuan Pengelolaan Hutan Produksi (KPHP) Dharmasraya Dinas Kehutanan Provinsi Sumatera Barat Menggunakan Grounded Theory

Employee discipline is an important factor in achieving organizational performance. Disciplined employees will comply with regulations, complete tasks on time, and show a professional attitude at work. Low employee discipline can have a negative impact on organizational performance, such as decreasing productivity, service quality and organizational image. Discipline is something that can be used as a benchmark to determine whether the functions of human resource management or leadership as a whole have been carried out well or not. This research aims to determine the factors that contribute to the low level of employee discipline at the UPTD KPHP Dharmasraya Forest Service of West Sumatra Province. The research method used is qualitative research with a Grounded Theory approach. The results of this research state that. factors that contribute to the low level of employee discipline at the UPTD KPHP Dharmasraya West Sumatra Province Forestry Service, ethical awareness, socio-economic impacts, reward and punishment systems, HRD policies and mental health.

Imaging sensor band comparison for situational awareness in wildfires

In recent decades, wildfires have become increasingly widespread and hazardous. Dryer, hotter weather combined with more frequent heat waves leave forest areas susceptible to sudden, intense, and fast-growing forest fires. To protect private property and mitigate the damage, Hotshot firefighters are deployed into these dangerous situations. Extensive satellite and aerial platforms possess optical techniques for monitoring wildfire risks and boundary tracking. Small unmanned aerial system (sUAS)-based EO/IR systems provide a solution for real-time, high resolution, targeted response to acquire information critical to the safety and efficacy of wildfire mitigation. Real-time imagery from a sUAS of the position of Hotshots and the progression of the fire boundary would be easily obtained and offer a method of ensuring safe deployment. An ideal sensor system for situational awareness in this environment would be able to image the ambient terrain and firefighters with good contrast while also detecting fire signatures and imaging through the smoke. Longer wavelength infrared bands have demonstrated imaging through the smoke of forest fires. However, near the wildfire where the Hotshots work, they also receive strong radiometric signals from the temperature of the smoke. The emitted signal of the smoke can obscure the line of sight similarly to the scattering effect of wildfire smoke in the visible spectrum. The reflective and emissive components of a wildfire scene are studied and compared in the visible (VIS, 0.4–0.7 µm), shortwave infrared (SWIR, 1.0–1.7 µm), extended SWIR (eSWIR, 2.0–2.5 µm), and longwave infrared (LWIR, 8–14 µm). Both a radiometric model and calibrated field measurements find a band that has the highest, to our knowledge, probability for a continuous line of sight for terrain, firefighters, and fire signatures in a wildfire scene.

Antibiotic prescription prevalence in Iranian outpatients: A focus on defined daily doses and the AWaRe classification system

The inappropriate use and overprescription of antibiotics pose a global health threat, particularly contributing to antimicrobial resistance. This study aims to evaluate antibiotic prescription prevalence in Iranian outpatients using the defined daily doses (DDD) and Access, Watch, and Reserve classification systems. This retrospective study analyzed electronic prescriptions for systemic antibiotics in Tehran, Iran, from March 2022 to March 2023. The data were obtained from the Iranian Health Insurance Organization and processed using the Cross-Industry Standard Process. Descriptive statistics and DDD per 1,000 inhabitants per day were calculated. A total of 817,178 antibiotic prescriptions were analyzed, with a sex distribution of 57.43% female and a median age of 48 years. On average, each patient received 1.89 antibiotics per prescription. Over 63% of antibiotics were classified in the "Watch" category, with Azithromycin being the most commonly prescribed (27.56%). The total DDD per 1,000 inhabitants per day was 4.99, with general practitioners accounting for 58.02% of the prescriptions, primarily prescribing Azithromycin. The study emphasizes the high use of Watch group antibiotics, indicating a need for improved prescribing practices. Education on antibiotic stewardship and stricter guidelines are necessary to combat antimicrobial resistance. Continuous monitoring is crucial to optimize antibiotic use in outpatient settings in Iran.

An Identification Method for Anomaly Types of Active Distribution Network Based on Data Mining

With the increasing penetration of distributed generators (DGs) and the growing demand for reliable power sources, it has become imperative to promptly identify anomalies in active distribution networks (ADNs). Additionally, anomaly identification is also crucial to assisting in targeting maintenance to handle failures after the action of relay protections. This study presents a highly accurate and rapidly responsive approach to identifying anomalies in ADNs. The proposed method uses three layers of intricately interconnected modules. In the first module, an iterative clustering layer is devised to process the original data. In the second module, an association rule layer is designed to identify hidden patterns based on the types of anomalies and the post-fault voltage. In the third module, a regression training layer is employed to train accurate identification models. The case study utilizes data samples collected by an IEEE 33-bus system with distributed generations as well as post-fault data from a practical distribution network. The results of comparative tests demonstrate that the proposed data-mining structure achieves reliable performance with high identification accuracy and short reaction times, indicating its ability to be applied to the state awareness system of ADNs.

User experiences of situational awareness systems in infrastructure construction

Infrastructure construction (IC) projects are dynamic, complex, and difficult to control and manage. Situational awareness (SA) systems have attracted growing interest in construction literature as an aid for human decision-making in order to forecast changes in project and operations situations. While technological advancements have been achieved in SA systems, very little empirical evidence exists on the actual experiences of IC professionals in relation to SA system usage. We interviewed 23 IC professionals to obtain data, which data we then analyzed by utilizing open coding. Based on our analysis, IC professionals adopt and integrate SA systems individually. On the other hand, often their SA exhibits a bias in favor of the subjective viewpoint of whoever is the dominant or responsible individual in their unit or team, and concealing facts by one or more people appears common. We thus conclude that SA systems can raise IC professionals’ awareness of a situation in ways that are objectively and easily visible and accessible to every individual. SA systems can also be used to conceal SA. This study contributes to earlier technology-focused research by revealing how the behavior of dominant individuals affects the user experience of SA systems.

Cyber risk assessment of cyber-enabled autonomous cargo vessel

The increasing interest in autonomous ships within the maritime industry is driven by the pursuit of revenue optimization, operational efficiency, safety improvement and going greener. However, the industry’s increasing reliance on emerging technologies for the development of autonomous ships extends the attack surface, leaving the underlying ship systems vulnerable to potential exploitation by malicious actors. In response to these emerging challenges, this research extends an existing cyber risk assessment approach called FMECA-ATT&CK based on failure modes, effects and criticality analysis (FMECA), and the MITRE ATT&CK framework. As a part of our work, we have expanded the FMECA-ATT&CK approach to assessing cyber risks related to systems with artificial intelligence components in cyber-enabled autonomous ships (e.g. autonomous engine monitoring and control). This new capability was developed using the information and semantics encoded in the MITRE ATLAS framework. FMECA-ATT&CK has been adopted due to its comprehensive and adaptable nature and its promising venue for supporting continuous cyber risk assessment. It helps evaluate the cyber risks associated with the complex and state-of-the-art operational technologies on board autonomous ships. The cyber risk assessment approach assists cybersecurity experts in aligning mitigation strategies for the cyber defence of autonomous ships. It also contributes towards advancing overall cybersecurity in the maritime industry and ensures the safe and secure sailing of autonomous ships. Our key findings after applying the proposed approach against a model of an autonomous cargo ship is the identification of the Navigation Situation Awareness System (NSAS) of the ship as being at the highest risk followed by the Autonomous Engine Monitoring and Control (AEMC) system. Additionally, we identified 3 high, 48 medium, and 5776 low risks across 29 components.

EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions

Abstract. The evaluation of streamflow predictions forms an essential part of most hydrological modelling studies published in the literature. The evaluation process typically involves the computation of some evaluation metrics, but it can also involve the preliminary processing of the predictions as well as the subsequent processing of the computed metrics. In order for published hydrological studies to be reproducible, these steps need to be carefully documented by the authors. The availability of a single tool performing all of these tasks would simplify not only the documentation by the authors but also the reproducibility by the readers. However, this requires such a tool to be polyglot (i.e. usable in a variety of programming languages) and openly accessible so that it can be used by everyone in the hydrological community. To this end, we developed a new tool named evalhyd that offers metrics and functionalities for the evaluation of deterministic and probabilistic streamflow predictions. It is open source, and it can be used in Python, in R, in C++, or as a command line tool. This article describes the tool and illustrates its functionalities using Global Flood Awareness System (GloFAS) reforecasts over France as an example data set.

Flexible-circuit-based 3-D aware modular optical brain imaging system for high-density measurements in natural settings.

Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic condition remains a significant challenge. The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide real-time probe 3-D shape estimation to improve the use of fNIRS in everyday conditions. The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3-D positions in real-time to enable advanced tomographic data analysis and motion tracking. Optical characterization of the MOBI detector reports a noise equivalence power (NEP) of 8.9 and 7.3 at 735 nm and 850 nm, respectively, with a dynamic range of 88 dB. The 3-D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared to positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided. To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3-D optode position acquisition, combined with lightweight modules (18 g/module) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

Resource-aware Federated Data Analytics in Edge-Enabled IoT Systems

In a resource constrained environment like Internet-of-Things (IoT) systems, it is critical to make optimal decisions on how much resources to allocate pre-processing and how much to allocate to model training, and which specific combination of preprocessing and learning should be selected. This talk first, provides an overview of some initial steps we took towards developing federated data pre-processing in IoT environments, and then a visionary overview of potential research problems related to developing an integrated resource-aware and Quality-of-Service (QoS)-aware data pre-processing and model training system is provided.

Propionate outperforms conventional acetate as electron donors for highly-sensitive electrochemical active biofilm sensors in water biotoxicity early-warning

With the ability to generate in situ real-time electric signals, electrochemically active biofilm (EAB) sensors have attracted wide attention as a promising water biotoxicity early-warning device. Organic matters serving as the electron donors potentially affect the electric signal's output and the sensitivity of the EAB sensor. To explore the influence of organic matters on EAB sensor's performance, this study tested six different organic matters during the sensor's inoculation. Besides the acetate, a conventional and widely used organic matter, propionate and lactate were also found capable of starting up the sensor. Moreover, the propionate-fed (PF) sensor delivered the highest sensitivity, which are respectively 1.4 times and 2.8 times of acetate-fed (AF) sensor and lactate-fed (LF) sensor. Further analysis revealed that EAB of PF sensor had more vulnerable intracellular metabolism than the others, which manifested as the most severe energy metabolic suppression and reactive oxygen species attack. Regarding the microbial function, a two-component system that was deemed as an environment awareness system was found in the EAB of PF, which also contributed to its high sensitivity. Finally, PF sensor was tested in real water environment to deliver early-warning signals.

Application Analysis of Security Situational Awareness System in Qinghai Provincial Meteorological Network

As the public's demand for the accuracy of meteorological services is increasing, the scale of meteorological network in Qinghai Province is expanding, the depth of the network level is extending, the topology is becoming more and more complex, and the security problems are becoming more and more prominent. Traditional security protection measures are unable to detect the problems in Qinghai Province meteorological network as a whole. Network Security Situational Awareness is an effective means to guarantee the security of meteorological network at the present stage by collecting comprehensive and macro security elements in the network environment and carrying out big data analysis and processing to have a macro and comprehensive judgment of the security situation of the network and to predict the security trend of the network system. This paper mainly focuses on the network security situational awareness system used in Qinghai meteorological network and gives a brief introduction to the deployment of the situational awareness platform and a brief overview of the supporting applications.

AI-Empowered Multimodal Hierarchical Graph-Based Learning for Situation Awareness on Enhancing Disaster Responses

Situational awareness (SA) is crucial in disaster response, enhancing the understanding of the environment. Social media, with its extensive user base, offers valuable real-time information for such scenarios. Although SA systems excel in extracting disaster-related details from user-generated content, a common limitation in prior approaches is their emphasis on single-modal extraction rather than embracing multi-modalities. This paper proposed a multimodal hierarchical graph-based situational awareness (MHGSA) system for comprehensive disaster event classification. Specifically, the proposed multimodal hierarchical graph contains nodes representing different disaster events and the features of the event nodes are extracted from the corresponding images and acoustic features. The proposed feature extraction modules with multi-branches for vision and audio features provide hierarchical node features for disaster events of different granularities, aiming to build a coarse-granularity classification task to constrain the model and enhance fine-granularity classification. The relationships between different disaster events in multi-modalities are learned by graph convolutional neural networks to enhance the system’s ability to recognize disaster events, thus enabling the system to fuse complex features of vision and audio. Experimental results illustrate the effectiveness of the proposed visual and audio feature extraction modules in single-modal scenarios. Furthermore, the MHGSA successfully fuses visual and audio features, yielding promising results in disaster event classification tasks.

Dynamic decision making for situational awareness using drones: Requirements, identification and comparison of decision support methods

Decision makers increasingly operate in real-time information-rich environments, where limited time is available for interpreting data to inform decisions. These environments are driven by static or mobile sensing devices that can provide numerous dynamic data points. A prominent approach in this space is to utilise drones, which can be deployed to gather targeted information. However, deciding how best to deploy available drones is nontrivial, and stands to benefit from decision support aids that plan routes. Such a system must operate under time constraints created by the changing attributes of routes as the situation unfolds. This study describes a dynamic decision support system (DSS) for situational awareness with drones. The system applies Multi-Criteria Decision Making (MCDM) methods within a dynamic genetic algorithm to provide a continuously revised ranking of routes. Five desiderata for dynamic decision support are presented. It is shown how a dynamic DSS can be equipped with declarative specification of preferences (Desiderata 1), dynamic revision of recommendations (Desiderata 2), and high diversity of options (Desiderata 3). The study then compares four MCDM methods, namely the Weighted Product Model (WPM), the Analytic Hierarchy Process (AHP), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), with regards to how consistently they trade-off between criteria (Desiderata 4) and the stability of results under small changes to criteria values (Desiderata 5). To evaluate the trade-offs between criteria we analyse the smoothness of change in criteria outcomes as criteria weightings increase for each algorithm. The outcomes are calculated by automating the selection of routes in a case study that applies drones to the task of harbour management. The stability of results for the different MCDM methods are compared. Perturbations were applied to sets of routes ranked by each algorithm then each algorithm was reapplied and the magnitude of the changes in ranking was assessed. Overall, TOPSIS was found to be the algorithm which made the most consistent trade-offs between criteria, only under-performing another algorithm with respect to a single criterion. AHP and WPM were the next most consistent algorithms and PROMETHEE was the least consistent algorithm. TOPSIS was also found to be the most stable method under small changes to criteria values. AHP was the second most stable, followed by PROMETHEE and WPM respectively. The results show that TOPSIS achieves the best result for both Desiderata 4 and 5 and consequently the study finds TOPSIS to be an appropriate MCDM method for dynamic decision support.

Proposal of Performance Indicators for Situational Awareness Systems from Viewpoint of STCW Convention

Proposal of Performance Indicators for Situational Awareness Systems from Viewpoint of STCW Convention

The limits of knowledge

The definition of knowledge encompasses various meanings, from the process by which we have the perception of the outside world, through the stimuli that we receive from this, when that perception becomes part of our system of awareness and experience. When awareness of ourselves and the outside world is acquired and recorded, we can define it as learning. In the broader, philosophical sense, knowledge consists in learning and retaining in the mind a notion, an information. In use, however, it has a more concrete semantic value, and can indicate the various degrees of knowledge, from the initial perception of the existence of a thing to the full cognition of its being, its modes and qualities. Whatever the subject, cell, organism, neuronal network, and so on, the process always takes place by the use of specific receptor systems, adapted to the reception of different stimuli from the environment. Because they are calibrated only on the stimuli they receive, the receptor systems have limited sensitivity. This limitation is extended to all living beings, who therefore have partial and incomplete knowledge of the world. In this work we are going to examine these limitations and invite scholars to become aware of them.

챗GPT를 활용한 대학 교양 글쓰기 수업 방안

In this study, writing methods suitable for the era of artificial intelligence and various practices using ChatGPT are conducted. Accordingly, everyone learns to easily receive the information they want, and how to combine commands to write the tool efficiently. First, in Chapter 2, suggestions for starting an artificial intelli gence writing class and functional parts related to artificial intelligence, such as the concept of artificial intelligence and the most easily used ChatGPT, are identified. Chapter 3 makes learners recognize the imperfections and errors of artificial intel ligence in the process of criticizing and reflecting while comparing problems with the text of artificial intelligence and the text of ChatGPT. It also lays the foundation for learners to establish an ethical awareness system in using ChatGPT while con sidering the nature of writing and the purpose of artificial intelligence. The last chapter presents practice and example questions using ChatGPT, and mentions the necessity. It focuses on increasing the use of ChatGPT effectively while conducting step-by-step practice, from writing mail to writing an outline for logical writing in everyday life. Through these activities, we can acquire an opportunity to gradually step on the rapidly arriving changes of the times and use artificial intelligence tech nology with a sense of ethics in the future, and learners can go as far as realizing the essence of writing through comparative learning.

Sparus Docking Station: A current aware docking station system for a non‐holonomic AUV

AbstractThis paper presents the design and development of a funnel‐shaped Sparus Docking Station intended for the non‐holonomic torpedo‐shaped Sparus II Autonomous Underwater Vehicle. The Sparus Docking Station is equipped with sensors and batteries, allowing for a stand‐alone long‐term deployment of the vehicle. An inverted Ultra Short Base‐Line system is used to locate the Docking Station as well as to provide long‐term drift‐less vehicle navigation. The Sparus Docking Station is able to observe the ocean currents using a Doppler Velocity Log, being motorized to allow its self‐alignment with the current. Moreover, a docking algorithm accounting for the current is used to guide the robot during the docking maneuver. The paper reports consecutive successful experimental results of the docking maneuver in sea trials in two different countries.

The freshwater discharge into the Adriatic Sea revisited

The present study reconstructs the river discharge climatology and its respective historical series for all rivers of the Adriatic Sea with averaged climatological daily river discharge above 1 m3s−1, to reach a better representation of the Adriatic rivers in hydrodynamic models and, consequently, to develop a more realistic freshwater balance in the different regions of the hydrographic basin. Based on the European Flood Awareness System (EFAS) data set, a careful method of identification and selection of the Adriatic rivers, followed by a rigorous assessment against observational data, was developed to evaluate the current state of the Adriatic river discharges and their respective trends throughout several climate indicators from 1991 to 2022. Observational data are limited to 85% of the identified rivers, totaling 98% of the overall freshwater input into the Adriatic Sea. The results confirm that the Shallow Northern Adriatic receives the largest freshwater inputs with a daily average exceeding 2,400 m3s−1, which amounts to 61% of the overall Adriatic discharges. Consequently, this region guides the freshwater seasonal cycle of the Adriatic Sea, which presents a well-defined pattern of two flood peaks in late autumn and late spring, separated by a minimum discharge period at mid-summer. From the Central to the Southern Adriatic subregions, the absence of snow-melting effects prevents the secondary flood peak during the spring, shaping the seasonal cycle of river discharges from a single flood peak in late autumn to a drought period in August. The 32 years of continuous river discharge data reveal a negligible trend in the overall Adriatic Sea but a negative trend for the last decade (2013–2022). This decadal decrease is driven by the extreme drought that drastically pounded the northern Adriatic in 2022.