Intelligent Functions Research Articles

Implementasi Jurnalisme Era Baru dalam Konten Video Medcom.Id

The purpose of this study was to find out how Medcom.id's understanding of New Era Journalism was applied in the Video Content Division. This study uses The Theory of 8 functions of new era journalism by Bill Kovach and Tom Rosenstiel, as it’s primary theory. With qualitative interview method with internal staff of Medcom.id to find out the steps taken to implement the new era of journalism theory. The results showed that Medcom successfully implemented six of the eight functions of journalism in the new era. Two functions that have not been successfully implemented are the witnessing function and the intelligent aggregator function.

Design and modeling of an all-solid-state high power bidirectional DC switch

In order to improve the reliability of the aircraft ring network power distribution system for the future development needs of advanced multi-power and all-power aircraft, a design scheme of an all-solid-state high-power bidirectional DC switch is proposed to upgrade the aircraft power distribution system originally composed of traditional mechanical switches and some low-power solid-state power controllers to an all-solid-state aircraft power distribution system. The solid-state high-power bi-directional DC switch uses SiC MOSFETs as switching elements is modeled and simulated by Saber simulation software and uses reverse time overcurrent protection and short circuit protection design to realize high voltage control distribution, circuit protection, and fault isolation functions. The simulation verifies that the designed all-solid-state high-power bidirectional DC switch has short-circuit protection and overcurrent protection intelligent protection functions. Applying the design to the aircraft distribution loop network greatly enhances the reliability of the aircraft distribution system drives the development of aircraft multi-electricity-all-electricity technology, and also promotes the application of digital solid-state series modular switches in civil transportation and high-voltage power supply fields.

The Downside of Artificial Intelligence for Intellectuals in The Asian Society

The functionality of artificial intelligence (AI) is securing an effectuate role in contemporary era and solidifying its role in future. The entire world is astounded by the fascinating programs of AI in every field of human society weather its education or business, meanwhile, disregarding the perilous and the dark side of the AI on intellectuals such as academicians, scientists, teachers, and scholars. Doubtlessly, AI performs a crucial role in certain Asian countries such as Japan, China, South Korea, and Singapore. However, in developing countries of Asia such as India, Afghanistan, Pakistan, Sri Lanka etc., the AI possess several threats to the domain of intellectuals. This research paper analysis the side effects of AI in the bailiwick of academia in Asian countries. This research paper adopts transparency theory of perception as conceptual framework to evaluate and examine the negative impacts of AI on intellectuals. It utilizes secondary research data as a research methodology. The intent of this research paper also includes some precautionary measures for mitigating the adverse effects for rational minds.

Embodying Multifunctional Mechano-Intelligence in and Through Phononic Metastructures Harnessing Physical Reservoir Computing.

Recent advances in autonomous systems have prompted a strong demand for the next generation of adaptive structures and materials to possess built-in intelligence in their mechanical domain, the so-called mechano-intelligence (MI). Previous MI attempts mainly focused on specific case studies and lacked a systematic foundation in effectively and efficiently constructing and integrating different intelligent functions. Here, a new approach is uncovered to create multifunctional MI in adaptive structures using physical reservoir computing (PRC). That is, to concurrently embody computing power and the key elements of intelligence, namely perception, decision-making, and commanding, directly in the mechanical domain, advancing from conventional reliance on add-on computers and massive electronics. As an exemplar platform, a mechanically intelligent phononic metastructure is developed by harnessing its high-degree-of-freedom nonlinear dynamics as PRC power. Through analyses and experiments, multiple intelligent structural functions are demonstrated ranging from self-tuning wave controls to wave-based logic gates. This research provides the much-needed basis for creating future smart structures and materials that greatly surpass the state of the art-such as lower power consumption, more direct interactions, and better survivability in harsh environments or under cyberattacks. Moreover, it enables the addition of new functions and autonomy to systems without overburdening the onboard computers.

Convolutional neural network for substantiation of children's books, intelligent interactive communication and application analysis of voice question answering

At present, there are many children accompanying robots. A key indicator to measure their performance is convenient and efficient human–computer interaction. The implementation of voice question-answering technology needs to solve three problems: voice recognition, the establishment of a knowledge base, and answer matching. As the front-end data entry, speech recognition accuracy is directly related to the answer-matching effect of the back-end question-answering system. Based on convolutional neural network and intelligent communication technology, this paper analyzes the characteristics of children's reading robot and designs and develops intelligent voice interaction function on the Android platform. The focus of this paper is on Chinese word segmentation and keyword extraction and matching in the interaction function. Further, we designed a simple question-and-answer database for the children's reading robot. In this paper, convolutional neural network connection timing is used as the feature parameter. Through a large number of experiments, the structure of the CNN network is constantly adjusted to minimize the word error rate, optimize the model recognition rate, and better identify users' voice problems.

Planning And Evaluation of Technological System Development in Public Street Lighting to Support Sustainable Development of The Integrated City of Batam

Developing a smart public street lighting system involves the integration of advanced technologies to improve energy efficiency, reduce operational costs, enhance safety, and enable intelligent functionalities. This research discusses the planning and evaluation of the development of a smart public street lighting system in the Batam City area with the aim of increasing the sustainability of the smart city. The research method used is a case study to gain an in-depth understanding of existing lighting systems and involve relevant stakeholders, such as local governments, technical experts and local communities. The evaluation results show that the development of a smart public street lighting system has a positive impact on the sustainability of smart cities, increasing energy efficiency, safety and community participation. The integration of local wisdom and maritime aspects in planning intelligent public street lighting systems also makes an important contribution to the identity and sustainability of Batam City. This research recommendations for further development, strengthening local wisdom, collaboration and partnerships with the private sector, as well as expanding implementation to other regions.

Data Mining-Based Collision Scenarios of Vehicles and Two Wheelers for the Safety Assessment of Intelligent Driving Functions

The safety performance test of intelligent driving vehicles needs to rely on the collision scenarios in a real road traffic environment. In order to study the collision scenarios and accident characteristics of vehicles and two wheelers (TWs) in line with the complex traffic conditions in China, this paper proposes using clustering analysis to initially cluster traffic accident data to obtain the base scenarios and then applying the association rule algorithm to each base scenario to obtain the potential connection of its accident attributes and describe the collision scenarios in more detail. This study is based on data from 335 vehicle and two-wheeler crashes in the National Automobile Accident In-Depth Investigation System (NAIS). It used clustering analysis to cluster the crash data into different partitions to obtain eight clusters of vehicle and two-wheeler base scenarios and applied association rules to analyze the rest of the accident attributes, revealing common crash characteristics to describe the base scenarios in more detail. In the end, it constructed eleven types of detailed vehicle and two-wheeler collision scenarios covering straight roads, intersections, and T-junctions. The results provide richer and more suitable crash scenarios of vehicles and two wheelers in China’s complex traffic and is an important reference for the development of intelligent driving testing scenarios in the future.

Intelligent Soft Robotic Grippers for Agricultural and Food Product Handling: A Brief Review with a Focus on Design and Control

Advances in material sciences, control algorithms, and manufacturing techniques have facilitated rapid progress in soft grippers, propelling their adoption in various fields. In this review article, a comprehensive overview of the design and control aspects of intelligent soft robotic grippers tailored specifically for agricultural product handling is provided. Soft grippers have emerged as a promising solution for handling delicate and fragile objects. In this article, the recent progress in various gripper design, including fluidic and mechanical grippers, is elucidated and the role of advanced control approaches in enabling intelligent functions, such as object classification and grasping condition evaluation, is explored. Moreover, the challenges and opportunities pertaining to implementation of soft grippers in the agricultural industry are thoroughly discussed. While most demonstrations of soft grippers and their control strategies remain at the experimental stage, in this article, it is aimed to provide insights into the potential applications of soft grippers in agricultural product handling, thereby inspiring future research in this field.

Renewable Energy Impact on Distance Relay Power Swing Blocking and Fault Discrimination: A Review

The annual increase of the global load demand has led to higher penetration of inverter-based renewable energy resources like wind farms and solar PV into the modern grid system. Distance relay may mal-operate by incorrectly estimating line impedance as fault during swing scenarios considering the infeed contributions impact from renewable sources. The negative impact of these integrated power electronics-based devices on the power swing blocking (PSB) and out-of-steps tripping (OST) functions of the distance relay characteristics has not been extensively discussed in previous studies. This study divulges a comprehensive review of the various PSB and OST schemes studies conducted to prevent relay mal-operation during power swing (PS) and symmetrical faults. Also, the large-scale renewable resources penetrations impact the PS characteristic and trip decision operation of the distance relay divulged. The mining of distance relay event records for hidden useful knowledge deployment for intelligent PSB and OST functions is the future research direction. Using the distance relay divulged knowledge will assist in reducing the failure rate level of PSB and OST function distance relaying schemes, hence improving the degree of reliability/dependability of the power system under different operating conditions.

Large-Scale Service Function Chaining Management and Orchestration in Smart City

In the core networking of smart cities, mobile network operators need solutions to reflect service function chaining (SFC) orchestration policies while ensuring efficient resource utilization and preserving quality of service (QoS) in large-scale networking congestion states. To offer this solution, we observe the standardized QoS class identifiers of smart city scenarios. Then, we reflect the service criticalities via cloning virtual network function (VNF) with reserved resources for ensuring effective scheduling of request queue management. We employ graph neural networks (GNN) with a message-passing mechanism to iteratively update hidden states of VNF nodes with the objectives of enhancing allocation of resource blocks, accurate detection of availability statuses, and duplication of heavily congested instances. The deployment properties of smart city use cases are presented along with their intelligent service functions, and we aim to activate a modular architecture with multi-purpose VNFs and chaining isolation for generalizing global instances. Experimental simulation is conducted to illustrate how the proposed scheme performs under different congestion levels of SFC request rates, while capturing the key performance metrics of average delay, acceptance ratios, and completion ratios.

Unveiling the Potential of Artificial Intelligence and Machine Learning in the 5G Network Landscape: A Comprehensive Review

Exploring successful case studies, scholars and industry experts concur that artificial intelligence (AI) and 5G technology, as holistic solutions, exhibit remarkable efficiency. The integration of AI has notably resolved wireless communication dilemmas that defy traditional modeling approaches, substantially diminishing technological uncertainties. Furthermore, 5G technology is poised to amalgamate communication, computation, sensing, and control across diverse industries. However, the convergence of these capabilities also introduces complexity, a challenge that can be effectively addressed through the application of artificial intelligence and machine learning functionalities. These cutting-edge technologies not only ensure data security but also satisfy stringent latency requirements while minimizing the burden on both communication and computation resources. In this context, a thorough examination of machine learning and artificial intelligence applications geared towards optimizing communication, computation, and resource allocation within the realm of 5G technology holds paramount significance.
 In pursuit of this objective, the study endeavors to offer a comprehensive outlook on the current landscape of artificial intelligence research within the 5G domain. By scrutinizing recent studies, we aim to encapsulate the contributions and prevailing trends associated with these technologies. Ultimately, our aim is to empower researchers and industry practitioners with insights that will facilitate informed decision-making when selecting the most suitable machine learning and artificial intelligence approaches for their endeavors.

Moving water droplets induced electricity on an electret surface with a charge gradient

In solid-liquid systems, a variety of electrokinetic effects can generate electricity utilizing diverse forms of gradients, for instance, pressure and salinity gradients. Here we report a new electrokinetic effect that moving a water droplet on an electret surface with a charge gradient can induce electricity in circuit between the droplet and an electrode underneath the electret film. While the droplet moves along the charge gradient, electric double layers at the solid-liquid interfaces continue to be charged or discharged through the circuit due to the change in surface charge density, leading to a direct-current output that can be quantitatively described by a proposed model. Furthermore, the direct correlation between the electrical output and the surface charge gradient endows the droplet with intelligence to sense the surface charge distribution, and the read of binary digital information stored in a charge matrix using a tiny water droplet was achieved. The reported electrokinetic effect not only promises a new sustainable avenue to deliver application-friendly direct-current electricity, distinct from the instantaneous pulsed output of traditional devices, but also extends the intelligent functionality of water droplets.

Key technologies and applications of intelligent dispatching command for high-speed railway in China

PurposeThe intelligent Central Traffic Control (CTC) system plays a vital role in establishing an intelligent high-speed railway (HSR) system. As the core of HSR transportation command, the intelligent CTC system is a new HSR dispatching command system that integrates the widely used CTC in China with the practical service requirements of intelligent dispatching. This paper aims to propose key technologies and applications for intelligent dispatching command in HSR in China.Design/methodology/approachThis paper first briefly introduces the functions and configuration of the intelligent CTC system. Some new servers, terminals and interfaces are introduced, which are plan adjustment server/terminal, interface for automatic train operation (ATO), interface for Dynamic Monitoring System of Train Control Equipment (DMS), interface for Power Supervisory Control and Data Acquisition (PSCADA), interface for Disaster Monitoring, etc.FindingsThe key technologies applied in the intelligent CTC system include automatic adjustment of train operation plans, safety control of train routes and commands, traffic information data platform, integrated simulation of traffic dispatching and ATO function. These technologies have been applied in the Beijing-Zhangjiakou HSR, which commenced operations at the end of 2019. Implementing these key intelligent functions has improved the train dispatching command capacity, ensured the safe operation of intelligent HSR, reduced the labor intensity of dispatching operators and enhanced the intelligence level of China's dispatching system.Originality/valueThis paper provides further challenges and research directions for the intelligent dispatching command of HSR. To achieve the objectives, new measures need to be conducted, including the development of advanced technologies for intelligent dispatching command, coping with new requirements with the development of China's railway signaling system, the integration of traffic dispatching and train control and the application of AI and data-driven modeling and methods.

Tracing a new path in the field of AI and robotics: mimicking human intelligence through chemistry. Part I: molecular and supramolecular chemistry.

Chemical Artificial Intelligence (CAI) is a brand-new research line that exploits molecular, supramolecular, and systems chemistry in wetware (i.e., in fluid solutions) to imitate some performances of human intelligence and promote unconventional robotics based on molecular assemblies, which act in the microscopic world, otherwise tough to be accessed by humans. It is undoubtedly worth spreading the news that AI researchers can rely on the help of chemists and biotechnologists to reach the ambitious goals of building intelligent systems from scratch. This article reports the first attempt at building a Chemical Artificial Intelligence knowledge map and describes the basic intelligent functions that can be implemented through molecular and supramolecular chemistry. Chemical Artificial Intelligence provides new tools and concepts to mimic human intelligence because it shares, with biological intelligence, the same principles and materials. It enables peculiar dynamics, possibly not accessible in software and hardware domains. Moreover, the development of Chemical Artificial Intelligence will contribute to a deeper understanding of the strict link between intelligence and life, which are two of the most remarkable emergent properties shown by the Complex Systems we call biological organisms.

Biomimetic Macrophage–Fe3O4@PLGA Particle-Triggered Intelligent Catalysis for Killing Multidrug-Resistant Escherichia coli

Infections with multidrug-resistant (MDR) Gram-negative bacteria, such as MDR Escherichia coli (E. coli), remain a challenge due to the lack of safe antibiotics and high fatality rates under anti-infection therapies. This work presents a form of biomimetic intelligent catalysis inspired by the selective biocatalytic property of macrophages (MΦs), consisting of an intelligent controlling center (a living MΦ) and a Fenton reaction catalyst (Fe3O4@poly(lactic-co-glycolic acid) (PLGA) nanoparticles) for killing MDR E. coli without harming normal cells. The MΦ–Fe3O4@PLGA particles (i.e., the intelligent catalysis particles) exhibit selective biocatalysis activity toward MDR E. coli by producing H2O2 and lipid droplets (LDs). This process activates the lipid metabolism and glycan biosynthesis and metabolism pathways based on the result of RNA sequencing data analysis. The H2O2 further reacts with Fe3O4@PLGA to form highly toxic hydroxyl radicals (·OH), while the LDs contain antimicrobial peptides and can target MDR E. coli. The highly toxic ·OH and antimicrobial peptides are shown to combat with MDR E. coli, such that the antibacterial efficiency of the MΦ–Fe3O4@PLGA particles against MDR E. coli is 99.29% ± 0.31% in vitro. More importantly, after several passages, the intelligent catalysis function of the MΦ–Fe3O4@PLGA particles is well maintained. Hence, the concept of biomimetic intelligent catalysts displays potential for treating diseases other than infections.

Diversified caregiver input to upgrade the Young Children’s Participation and Environment Measure for equitable pediatric re/habilitation practice

BackgroundPractitioner and family experiences of pediatric re/habilitation can be inequitable. The Young Children’s Participation and Environment Measure (YC-PEM) is an evidence-based and promising electronic patient-reported outcome measure that was designed with and for caregivers for research and practice. This study examined historically minoritized caregivers’ responses to revised YC-PEM content modifications and their perspectives on core intelligent virtual agent functionality needed to improve its reach for equitable service design.MethodsCaregivers were recruited during a routine early intervention (EI) service visit and met five inclusion criteria: (1) were 18 + years old; (2) identified as the parent or legal guardian of a child 0–3 years old enrolled in EI services for 3 + months; (3) read, wrote, and spoke English; (4) had Internet and telephone access; and (5) identified as a parent or legal guardian of a Black, non-Hispanic child or as publicly insured. Three rounds of semi-structured cognitive interviews (55–90 min each) used videoconferencing to gather caregiver feedback on their responses to select content modifications while completing YC-PEM, and their ideas for core intelligent virtual agent functionality. Interviews were transcribed verbatim, cross-checked for accuracy, and deductively and inductively content analyzed by multiple staff in three rounds.ResultsEight Black, non-Hispanic caregivers from a single urban EI catchment and with diverse income levels (Mdn = $15,001–20,000) were enrolled, with children (M = 21.2 months, SD = 7.73) enrolled in EI. Caregivers proposed three ways to improve comprehension (clarify item wording, remove or simplify terms, add item examples). Environmental item edits prompted caregivers to share how they relate and respond to experiences with interpersonal and institutional discrimination impacting participation. Caregivers characterized three core functions of a virtual agent to strengthen YC-PEM navigation (read question aloud, visual and verbal prompts, more examples and/or definitions).ConclusionsResults indicate four ways that YC-PEM content will be modified to strengthen how providers screen for unmet participation needs and determinants to design pediatric re/habilitation services that are responsive to family priorities. Results also motivate the need for user-centered design of an intelligent virtual agent to strengthen user navigation, prior to undertaking a community-based pragmatic trial of its implementation for equitable practice.

(Invited) micro-Nanoscale Living Lasers for Healthcare Applications

Have you imagined of carrying a laser in your cell or body? Lasers are ubiquitous in our daily lives from industry, communication to medicine. The scale of lasers has also shrunk down to micron and nanoscales. As the scale of laser become smaller, the functions of lasers have also been redefined by transforming living biologicals into micro- and nanoscale lasers, so called living lasers. Such tiny lasers could therefore be used to detect or monitor critical chemical or physical signals in living cells or human body with distinctive sensitivity and intensity. In this talk, I will introduce the recent development of biological lasers and showcase how this interdisciplinary technology bridges laser physics and biology to tackle biomedical problems. In the first part, the talk will be focused on its application in detecting cancer and neurological diseases. In the second part, I will introduce how tiny lasers can be transformed with intelligent functions for information encoding and healthcare. This will cover applications including sensing at multiscales in biology, imaging in tissues, tracking and monitoring, as well as biomedical devices. Finally, discussion and outlook will be made on the strategies to pioneer novel on-chip and wearable laser devices for future medical diagnosis and bio-intelligent technology. Figure 1

Spiderweb-inspired fabrication of 3D MXene hybrid filler with multilayer and multiscale features for multifunctional polydimethylsiloxane composites

Polymer-based composite with high thermal conductivity and excellent fire safety is urgently desired and required in the field of thermal management of advanced electronic devices. In this study, inspired by the structure of a spiderweb, we reported a polymer composite with three-dimensional (3D) interconnected spiderweb-like structures by assembling adsorption of transition metal carbides/nitrides (MXene) and diethylenetriaminepenta(methylene-phosphonic acid) coordinated cobalt ions complex. In this composite, MXene combined with the coordination complex forms a 3D network structure with multi-layer/-scale features, which enables high cross-plane thermal conductivity (∼0.59 K w-1m−1) and excellent flame retardancy, especially including effective inhibition of toxic CO and asphyxiating CO2 production. Notably, the 3D MXene networks embedded in the polymer matrix impart the composite with an intelligent damage detection function that is able to detect the depth of cracks. This bioinspired strategy provides a promising approach to creating high-performance polymer-based composites.

Clinical Reasoning and Artificial Intelligence

Context: Artificial intelligence refers to a set of systems that are capable of performing functions similar to human intelligent functions. Today, artificial intelligence has been successfully incorporated into clinical decision support systems (CDSS). Evidence Acquisition: The current study aimed to briefly present a narrative mini-review on clinical reasoning and artificial intelligence. Data were gathered from Google Scholar, ScienceDirect, and PubMed databases using the "clinical decision support system, artificial intelligence, and clinical reasoning" keywords. Results: Clinical decision support systems are divided into two categories: Knowledge-based and data-driven. The first category is called the rule-based expert system, and the second category is also named the machine-learning system. The usefulness of the mentioned systems and artificial intelligence in interpreting algorithmic and statistical information, where the human element can easily make a mistake, is that they are much more efficient and work with fewer errors. However, when it comes to dealing with a patient and his complaints and symptoms, because of the requirement for clinical judgment, the human element works much better in obtaining a mental image of the patient’s condition. Artificial intelligence is specifically used in scenarios such as the diagnosis of electrolyte disorders, interpreting ECG findings, and recognizing the causes of myocardial hypertrophy. Nonetheless, artificial intelligence has challenges, such as a lack of responsibility for medical decisions and treatment errors. Conclusions: Referring to the above-mentioned benefits and challenges of artificial intelligence, artificial and human intelligence cannot be superior to each other, and both have an irreplaceable role in clinical decision-making. The new view is that the goal of CDSS is to help the physician make better decisions by processing vast pieces of information as a whole entity rather than individually.

A Comprehensive Evaluation of Vehicle Intelligent Barrier Avoidance Function under Special Roads Based on G1-CRITIC

As one of the core functions of the autonomous driving of vehicles under special roads, the intelligent barrier avoidance function plays an important role in improving traffic efficiency and ensuring driving safety. Scientific, reasonable, and comprehensive evaluation methods can provide the basis for intelligent vehicles before their use. The comprehensive evaluation index system is constructed for the avoidance ability and avoidance mode of intelligent vehicles against different barriers. The weights of qualitative indicators that are difficult to quantify are determined based on the order relation analysis (G1) method, and the weights of quantitative indicators are determined based on the CRITIC (criteria importance though intercriteria correlation) method. The overall system is comprehensively and quantitatively evaluated through the grey correlation degree method. The correctness of the evaluation method is verified via testing. According to the comprehensive evaluation method studied, the comprehensive evaluation result of the test vehicle is obtained. The intelligent barrier avoidance function of negative barriers is superior to that of positive barriers. The integrated evaluation method can obtain evaluation results of vehicle performance in different test scenarios.