Implementation Of Interface Research Articles

Design Application of Active Sea Service Based on The Web at KSOP Tanjung Emas

The process of processing the Active Sea Service Certificate at the Harbormaster and Port Authority (KSOP) of Tanjung Emas is still done manually, making it vulnerable to illegal levies and reliant on paper documents. Therefore, an application is needed to expedite services, enhance efficiency, and minimize the occurrence of illegal levies. This research aims to elucidate the steps for designing a SIMALA application at KSOP Tanjung Emas and to assess its effectiveness. This research employs the Research and Development method, incorporating Planning, Production, and Evaluation (PPE) steps. The effectiveness of the SIMALA application at KSOP Tanjung Emas is gauged using the N-gain value. The process of creating a SIMALA application design at KSOP Tanjung Emas begins with planning, which encompasses preparing tools and materials, conceptualizing the SIMALA application system model, designing the database, structuring database tables, and interface design. The subsequent step is production, involving program and interface implementation. The final step is evaluation, which includes black box testing, phase I improvements, phase II testing, and phase II improvements. The effectiveness of the SIMALA application at KSOP Tanjung Emas falls within the high category, as evidenced by the N-gain value of 0.764.

Adapting OGC's SensorThings API and data model to support data management and sharing for environmental sensors

Software is critical in managing environmental sensor data. The Open Geospatial Consortium (OGC) developed the “OGC SensorThings API” (STA) standard to address variability across sensors, observed variables, platforms, and protocols, facilitating development of sensing and Internet of Things applications. This paper details a Python/Django implementation of the STA application programming interface (API) and a PostgreSQL/Timescale implementation of the STA data model, enhancing availability of robust software for management and sharing of environmental sensor data. STA offers a RESTful interface with JSON data encoding, aligning with modern development patterns and facilitating interoperability. Integration of metadata from the Observations Data Model ensures data can be adequately described and interpreted. STA's flexibility allows lightweight query responses or comprehensive metadata inclusion, and a complementary data management API enhances use of STA within multi-user systems. Open-source code and deployment instructions in GitHub enable standalone or cloud deployments, enhancing accessibility and usability for researchers and practitioners.

REVEALS: an open-source multi-camera GUI for rodent behavior acquisition.

Deciphering the rich repertoire of mouse behavior is crucial for understanding the functions of both the healthy and diseased brain. However, the current landscape lacks effective, affordable, and accessible methods for acquiring such data, especially when employing multiple cameras simultaneously. We have developed REVEALS (Rodent Behavior Multi-Camera Laboratory Acquisition), a graphical user interface for acquiring rodent behavioral data via commonly used USB3 cameras. REVEALS allows for user-friendly control of recording from one or multiple cameras simultaneously while streamlining the data acquisition process, enabling researchers to collect and analyze large datasets efficiently. We release this software package as a stand-alone, open-source framework for researchers to use and modify according to their needs. We describe the details of the graphical user interface implementation, including the camera control software and the video recording functionality. We validate results demonstrating the graphical user interface's stability, reliability, and accuracy for capturing rodent behavior using DeepLabCut in various behavioral tasks. REVEALS can be incorporated into existing DeepLabCut, MoSeq, or other custom pipelines to analyze complex behavior. In summary, REVEALS offers an interface for collecting behavioral data from single or multiple perspectives, which, when combined with deep learning algorithms, enables the scientific community to identify and characterize complex behavioral phenotypes.

Augmented Reality Interface for Adverse-Visibility Conditions Validated by First Responders in Rescue Training Scenarios

Updating the equipment of the first responder (FR) by providing them with new capabilities and useful information will inevitably lead to better mission success rates and, therefore, more lives saved. This paper describes the design and implementation of a modular interface for augmented reality displays integrated into standard FR equipment that will provide support during the adverse-visibility situations that the rescuers find during their missions. This interface includes assistance based on the machine learning module denoted as Robust Vision Module, which detects relevant objects in a rescue scenario, particularly victims, using the feed from a thermal camera. This feed can be displayed directly alongside the detected objects, helping FRs to avoid missing anything during their operations. Additionally, the information exposition in the interface is organized according to the biometrical parameters of FRs during the operations. The main novelty of the project is its orientation towards useful solutions for FRs focusing on something occasionally ignored during research projects: the point of view of the final user. The functionalities have been designed after multiple iterations between researchers and FRs, involving testing and evaluation through realistic situations in training scenarios. Thanks to this feedback, the overall satisfaction according to the evaluations of 18 FRs is 3.84 out of 5 for the Robust Vision Module and 3.99 out of 5 for the complete AR interface. These functionalities and the different display modes available for the FRs to adapt to each situation are detailed in this paper.

Simona: a comprehensive R package for semantic similarity analysis on bio-ontologies

BackgroundBio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. Semantic similarity analysis on bio-ontologies quantitatively assesses the degree of similarity between biological concepts based on the semantics encoded in ontologies. It plays an important role in structured and meaningful interpretations and integration of complex data from multiple biological domains.ResultsWe present simona, a novel R package for semantic similarity analysis on general bio-ontologies. Simona implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. Moreover, it provides a robust toolbox supporting over 70 methods for semantic similarity analysis. With simona, we conducted a benchmark against current semantic similarity methods. The results demonstrate methods are clustered based on their mathematical methodologies, thus guiding researchers in the selection of appropriate methods. Additionally, we explored annotation-based versus topology-based methods, revealing that semantic similarities solely based on ontology topology can efficiently reveal semantic similarity structures, facilitating analysis on less-studied organisms and other ontologies.ConclusionsSimona offers a versatile interface and efficient implementation for processing, visualization, and semantic similarity analysis on bio-ontologies. We believe that simona will serve as a robust tool for uncovering relationships and enhancing the interoperability of biological knowledge systems.

Analyzing Riders’ Behavioral Adaptation to Driving Patterns of Advanced Autonomous Vehicles: A Virtual Reality Simulation Study

The necessity of human supervision and intervention during autonomous driving has long been a topic of controversial discussion. From a developer’s perspective, it is expected that users will readily adapt to well-calibrated autonomous driving systems (ADS) due to their superior performance in dynamic driving tasks (DDT) compared to conventional human-driven vehicles. However, when passengers experience an autonomous vehicle (AV), there may be an adjustment period during which they modify their behavior to accommodate the driving patterns of the ADS. Additionally, some passengers might not adapt to autonomous driving at all, highlighting potential limitations in the current ADS development strategy. This work studies the dynamics of human-automation interaction and introduces an “objective method”, which employs a Virtual Reality (VR)-enabled simulation approach for in-depth behavioral analysis concerning riders’ behavioral adaptation to autonomous driving. Specifically, we examined how participants interacted with and intervened in Level 4 ADS operating under conservative, moderate, and aggressive driving patterns in a fully autonomous environment. A realistic urban road network was recreated in VR, integrated with traffic microsimulation to generate various driving scenarios. Twenty-seven participants completed driving tasks across different AV modes, with their intervention behaviors analyzed in relation to traffic conditions and AV aggressiveness. Key findings include: (1) Participants showed higher intention to intervene but lower actual intervention rates under aggressive AV modes compared to moderate and conservative modes, suggesting quicker adaptation to more challenging driving scenarios. (2) Interventions generally proved unnecessary and sometimes detrimental to overall traffic performance in a full-AV environment. (3) Aggressive AV modes significantly improved traffic efficiency, with a 40% increase in average travel speed and a 53% reduction in waiting time. However, human interventions posed the greatest challenge to achieving optimal traffic conditions. This research provides insights into the complex dynamics of human-AV interaction and adaptation, offering valuable implications for AV interface design, implementation strategies, and public acceptance of autonomous driving technologies.

EHMI on the Vehicle or on the Infrastructure? A Driving Simulator Study

Automated vehicles (AVs) may require the implementation of an external human-machine interface (eHMI) to communicate their intentions to human-driven vehicles. The optimal placement of the eHMI, either on the AV itself or as part of the road infrastructure, remains undetermined. The current driving simulator study investigated the effect of eHMI positioning on human driving behavior, during the approach and execution of right turns at T-intersections. Forty-three participants drove under three conditions: absence of eHMI, eHMI on the AV (eHMIv), and eHMI integrated into the infrastructure (eHMIi). Participants encountered AVs that either yielded or did not yield to their vehicles. The results regarding the placement of the eHMI showed that both concepts are advantageous, but for different reasons. eHMIv was appreciated because implicit and explicit communication are congruent, although the AV must first be visually identified to respond to it. eHMIi was appreciated because a familiar cue is always at a known location in the environment; as a result, participants braked earlier for the intersection and came less close to the AV (which can be interpreted as a safety advantage or efficiency disadvantage). Although there are limitations to a driving simulator study like this, this research provides important insights into the fundamental question of how information placement affects drivers’ visual attention demands and driving behavior, topics that are important in view of the development of future cities.

Automating incidence and prevalence analysis in open cohorts

MotivationData is increasingly used for improvement and research in public health, especially administrative data such as that collected in electronic health records. Patients enter and exit these typically open-cohort datasets non-uniformly; this can render simple questions about incidence and prevalence time-consuming and with unnecessary variation between analyses. We therefore developed methods to automate analysis of incidence and prevalence in open cohort datasets, to improve transparency, productivity and reproducibility of analyses.ImplementationWe provide both a code-free set of rules for incidence and prevalence that can be applied to any open cohort, and a python Command Line Interface implementation of these rules requiring python 3.9 or later.General featuresThe Command Line Interface is used to calculate incidence and point prevalence time series from open cohort data. The ruleset can be used in developing other implementations or can be rearranged to form other analytical questions such as period prevalence.AvailabilityThe command line interface is freely available from https://github.com/THINKINGGroup/analogy_publication.

Pengembangan Sistem Informasi Perpustakaan Peminjaman dan Pengembalian Buku Berbasis Website Interaktif

Library SMA Negeri 1 Mlati still not accurate in data maintenance, so that impact on often happened mistake or not discipline in delay of return of book. In addition to obtaining information relating to the collection of books takes a long time. So that the System will run the System that can help librarians SMA Negeri 1 Mlati in the process of management of computerized data administration and System that can provide information to the librarian of SMA Negeri 1 Mlati for borrowers who have not returned the book. The purpose to facilitate officers in processing transactions and maintain data collection of books on the Library SMA Negeri 1 Mlati. The system development method used is the Prototype methodology which includes the identification of user needs, prototype creation, prototype testing, and prototype improvement. For data collection method using observation and interview method. For system model used is Context Diagram, Data Flow Diagram (DFD) and Entity Relationship Diagram (ERD). The software for the design and implementation of the program using Apache as a web server, mySQL as a database server, Sublime Text 3 as a writing editor programming language PHP and HTML. Results or output to be achieved that is generated library system that can handle data administration, data maintenance, and information borrower books on the library. The final stage is to implement an information system that includes the implementation of software, hardware, database and interface of the resulting system.

Design of an Android-Based Dam Flood and Drought Early Warning Information System (SI BANKER) in West Nusa Tenggara

Dam construction in Indonesia is currently very developed, namely 235 dams that have been built and West Nusa Tenggara Province contributes 30% of the number of dams in Indonesia, namely 70 dams spread across the islands of Lombok and Sumbawa, where 32 dams are in the Lombok River Basin and 38 dams in the Sumbawa River Basin. The current problem for the Europeanization and maintenance of existing dams in West Nusa Tenggara province is that there is no information system that can make it easier for officers at each dam to report the latest condition of the water level of each dam and provide early warning of the condition of the dam's water level whether it will be close to flood or dry conditions. Currently, to report water level conditions in each dam, officials at each dam can only report the results of water level monitoring under normal circumstances, floods or droughts only manually. The purpose of this study is to provide early warning information / notifications on flood and drought conditions in dams in West Nusa Tenggara province based on android. The research method used in the process of developing this system is the waterfall development method which consists of 5 stages, namely defining needs, designing, coding and implementation, testing and documentation. The results of this study are in accordance with the description in the previous chapter including interface implementation, system testing and evaluating the system. Evaluation is carried out to determine the shortcomings of the system, so that it can be better for future development. The conclusion of this study from the design and testing that has been carried out shows that all functions have run in accordance with the proposed system. For further research, it is recommended to create a similar Android mobile application but using a more complete framework and having many references to perfect some features in the application that are still not perfect.

Dynamic Landscape of Higher Education, The Intersection of Student Skills and Industry Demand

In today's dynamic job market, higher education students are confronted with the challenge of sifting through numerous career options that align with their skills, interests, and educational background. To tackle this hurdle, "Dynamic Landscape Of Higher Education, The Intersection Of Students Skills And Industry Demand" introduces a personalized job recommendation system tailored explicitly for higher education students. This system utilizes advanced recommendation algorithms and data analysis techniques to provide students with targeted job suggestions based on their academic qualifications, skills, interests, and career preferences. With a user-friendly interface, students can effortlessly input their information and receive tailored recommendations that evolve over time through continuous learning from user feedback. By offering data-driven insights and facilitating informed career decisions, this system empowers students to explore diverse career pathways and seamlessly transition from education to employment. The user interface of this system is designed to be intuitive and user-friendly, enabling students to easily input their information and preferences to receive tailored job recommendations. The recommendation system is adaptive, continuously learning from user feedback and interaction, ensuring that the recommendations provided evolve over time to better meet the changing needs and preferences of students. Recognizing the widening gap between industry requirements and graduating students' skills, this project proposes a comprehensive job recommendation system to bridge this divide and enhance placement opportunities. Leveraging advanced algorithms and machine learning techniques, the system evaluates students' skill sets and matches them with relevant job roles, facilitating a smooth transition from academia to the workforce. This multifaceted approach includes data collection, algorithm development, user interface design, and integration with existing campus placement systems. Collaborative efforts with academic institutions and industry partners aim to provide personalized job recommendations tailored to individual students' abilities and aspirations, with a focus on fairness and equity. Key components of the project include the design and implementation of an intuitive user interface, data-driven skill assessment methodologies, and continuous refinement of the recommendation algorithm based on user feedback and industry insights. Ethical considerations regarding data privacy and security are carefully addressed, reflecting a commitment to responsible use of student data. Ultimately, the success of the job recommendation system is measured by its ability to match students with suitable employment opportunities and its broader impact on career readiness and long-term professional success. By empowering students with the tools and resources needed to navigate the complexities of the modern job market, this project aims to catalyze positive change and foster a more inclusive and equitable future for all stakeholders involved.

Similar neural states, but dissimilar decoding patterns for motor control in parietal cortex.

Discrete neural states are associated with reaching movements across the fronto-parietal network. Here, the Hidden Markov Model (HMM) applied to spiking activity of the somato-motor parietal area PE revealed a sequence of states similar to those of the contiguous visuomotor areas PEc and V6A. Using a coupled clustering and decoding approach, we proved that these neural states carried spatiotemporal information regarding behaviour in all three posterior parietal areas. However, comparing decoding accuracy, PE was less informative than V6A and PEc. In addition, V6A outperformed PEc in target inference, indicating functional differences among the parietal areas. To check the consistency of these differences, we used both a supervised and an unsupervised variant of the HMM, and compared its performance with two more common classifiers, Support Vector Machine and Long-Short Term Memory. The differences in decoding between areas were invariant to the algorithm used, still showing the dissimilarities found with HMM, thus indicating that these dissimilarities are intrinsic in the information encoded by parietal neurons. These results highlight that, when decoding from the parietal cortex, for example, in brain machine interface implementations, attention should be paid in selecting the most suitable source of neural signals, given the great heterogeneity of this cortical sector.

Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG.

Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain-computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain-computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III-IV(A) and BCI competition IV-I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.

Fast Emulation of Fermionic Circuits with Matrix Product States.

We describe a matrix product state (MPS) extension for the Fermionic Quantum Emulator (FQE) software library. We discuss the theory behind symmetry-adapted MPSs for approximating many-body wave functions of spin-1/2 Fermions, and we present an open-source, MPS-enabled implementation of the FQE interface (MPS-FQE). The software uses the open-source pyblock3 and block2 libraries for most elementary tensor operations, and it can largely be used as a drop-in replacement for FQE that allows for more efficient but approximate emulation of larger Fermionic circuits. Finally, we show several applications relevant to both near-term and fault-tolerant quantum algorithms where approximate emulation of larger systems is expected to be useful: characterization of state preparation strategies for quantum phase estimation, the testing of different variational quantum eigensolver ansätze, the numerical evaluation of Trotter errors, and the simulation of general quantum dynamics problems. In all these examples, approximate emulation with MPS-FQE allows us to treat systems that are significantly larger than those accessible with a full statevector emulator.

Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence-Based Prognosis for Patients With Advanced Solid Tumors.

Use of artificial intelligence (AI) in cancer care is increasing. What remains unclear is how best to design patient-facing systems that communicate AI output. With oncologist input, we designed an interface that presents patient-specific, machine learning-based 6-month survival prognosis information designed to aid oncology providers in preparing for and discussing prognosis with patients with advanced solid tumors and their caregivers. The primary purpose of this study was to assess patient and caregiver perceptions and identify enhancements of the interface for communicating 6-month survival and other prognosis information when making treatment decisions concerning anticancer and supportive therapy. This qualitative study included interviews and focus groups conducted between November and December 2022. Purposive sampling was used to recruit former patients with cancer and/or former caregivers of patients with cancer who had participated in cancer treatment decisions from Utah or elsewhere in the United States. Categories and themes related to perceptions of the interface were identified. We received feedback from 20 participants during eight individual interviews and two focus groups, including four cancer survivors, 13 caregivers, and three representing both. Overall, most participants expressed positive perceptions about the tool and identified its value for supporting decision making, feeling less alone, and supporting communication among oncologists, patients, and their caregivers. Participants identified areas for improvement and implementation considerations, particularly that oncologists should share the tool and guide discussions about prognosis with patients who want to receive the information. This study revealed important patient and caregiver perceptions of and enhancements for the proposed interface. Originally designed with input from oncology providers, patient and caregiver participants identified additional interface design recommendations and implementation considerations to support communication about prognosis.

Transcutaneous Magnet Localizer for a Self-Contained Myokinetic Prosthetic Hand.

The search for a physiologically appropriate interface for the control of dexterous hand prostheses is an ongoing challenge in bioengineering. In this context, we proposed an interface, named myokinetic control interface, based on the localization of magnets implanted in the residual limb muscles, to monitor their contractions and send appropriate commands to the artificial hand. As part of such concept, this interface requires a transcutaneous magnet localizer that can be integrated in a self-contained limb prosthesis, a feature yet to be realized within the current state of the art. In an attempt to cover this gap, here we present a modular embedded system consisting of a computation unit able to acquire synchronized samples captured by up to eight acquisition units, so to localize multiple magnets. The system exhibits short computation times (<60ms) and power consumption (0.6-1.2W) which are suitable for use in a clinically viable prosthetic arm. The system proved able to localize magnets while moving at speeds in the range of physiological movements (<0.24m/s), with high accuracy (<1mm) and precision (<0.5mm). We demonstrated a system suitable for the implementation of a self-contained myokinetic prosthetic hand. These results pave the way towards the clinical implementation of the myokinetic interface, with amputees controlling an artificial arm by means of implanted magnets.

A verifiable EVM-based cross-language smart contract implementation scheme for matrix calculation

The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses, which involve the calculation of complex functions, such as matrix operations. However, complex functions such as matrix operations are difficult to implement on Ethereum Virtual Machine (EVM)-based smart contract platforms due to their distributed security environment limitations. Existing off-chain methods often result in a significant reduction in contract execution efficiency, thus a platform software development kit interface implementation method has become a feasible way to reduce overheads, but this method cannot verify operation correctness and may leak sensitive user data. To solve the above problems, we propose a verifiable EVM-based smart contract cross-language implementation scheme for complex operations, especially matrix operations, which can guarantee operation correctness and user privacy while ensuring computational efficiency. In this scheme, a verifiable interaction process is designed to verify the computation process and results, and a matrix blinding technology is introduced to protect sensitive user data in the calculation process. The security analysis and performance tests show that the proposed scheme can satisfy the correctness and privacy of the cross-language implementation of smart contracts at a small additional efficiency cost.

ERSN-OpenMC-Py: A python-based open-source software for OpenMC Monte Carlo code

The graphical user interface is a key element in facilitating the use of complex simulation software. This project describes the development of a graphical user interface called “ERSN-OpenMC-Py” for an existing neutron simulation code, OpenMC. The main goal is to make simulation more accessible to a wider audience by providing a user-friendly and intuitive user interface. The process of developing the graphical user interface is described in detail, including the different stages of development such as user interface design, user interface implementation, and user interface integration with the OpenMC simulation code. The development tools used, such as Python3 and PyQt5, are also explained. The user interface allows the user to control the simulation parameters and interact with the simulation results. Key features of the user interface include visualization of simulation results, modification of simulation parameters, saving and loading simulation configurations, as well as managing output files. The end result is a functional user interface that allows users to easily visualize simulation results and control simulation parameters in an intuitive manner. This user interface also provides a better user experience for non-programming experts who wish to use the simulation code for their own projects. Program summaryProgram Title: ERSN-OpenMC-Py, version 1.0CPC Library link to program files:https://doi.org/10.17632/83xrdht7mz.1Developer's repository link:https://github.com/mohamedlahdour/ERSN-OpenMC-PyLicensing provisions: GPLv2Programming language: Python 3External routines/libraries: NumPy, Matplotlib, PyQt5Nature of problem: The program is designed to be utilized in the everyday workflow for tasks such as preparing input files for the OpenMC code and analyzing the resulting calculations.Solution method: The graphical user interface of the OpenMC is created using PyQt5. Figures are plotted by means of matplotlib library.

Design and simulation implementation of SPI protocol interface based on Verilog

The SPI bus protocol can communicate with multiple peripherals without address bits. The transmission speed is fast and the configuration is simple and flexible, which solves the problem of serial communication between microprocessors and peripherals. In order to simply realize the function of the SPI bus and verify its stability, the SPI bus is designed and simulated verified using Verilog language and Modelsim software. The results show that the design realizes the full-dus-duserial synchronous communication function of SPI bus, with high data transmission stability and a data transmission speed of more than 2Mb/s. At the same time, during the test, it is found that the serial communication protocol can continue to be improved in terms of reliability, stability, and efficiency. Under the current social needs, the security of serial communication protocols is also put forward higher requirements and needs to be further adapted to the needs of the Internet of Things.

PyMIDA: A Graphical User Interface for Mass Isotopomer Distribution Analysis.

Mass isotopomer distribution analysis (MIDA) is an analytical technique that measures the synthesis rate of biological polymers using combinatorial probabilities and stable isotope labeling. Over the past few decades, this method has been developed and applied to a wide range of uses that have increased our understanding of metabolism and the etiology and monitoring of disease. There is currently no publicly available piece of software for performing MIDA calculations in a targeted manner without its functionality being limited to a specific use case. We present a cross-platform Python graphical user interface implementation for research to obtain kinetic parameters easily from stable-isotope labeling studies and provide the code and user manual on GitHub.