Application Of Systems Research Articles

Challenges in service robot devices for elderly motion assistance

Abstract Motion assistance for elderly people is a field of application for service robotic systems that can be characterized by requirements and constraints of human–machine interaction and by the specificity of the user’s conditions. The main aspects of characterization and constraints are examined for the application of service systems that can be specifically conceived or adapted for elderly motion assistance by having to consider conditions of motion deficiency and muscular strength weakness as well as psychological aptitudes of users. The analysis is discussed in general terms with reference to elderly people who may not even suffer from specific pathologies. Therefore, the discussion focuses on the need for motion exercise in proper environments, including domestic ones and frame familiar to a user. The challenges of such applications oriented toward elderly users are discussed as requiring research and design of solutions in terms of specific portability, user-oriented operation, low costs, and clinical-physiotherapeutic functionality. Results of the author’s team experiences are presented as an example of problems and attempted solutions to meet the new challenges of service systems for motion assistance applications for elderly people.

THE SOFTWARE IMPLEMENTATION FOR AUTOMATIC GENERATION OF PETRI NETS

Context. The important task was solved during this scientific research related to specific development and verification of thefundamental suitability of the software application that provides visualization of the automatic synthesis of Petri nets while setting upthe multi-level control systems. This task is current because the for the first time the integration of means of discrete-continuous networksfrom the DC-Net environment in the Labview environment is realized through the implementation of automatic synthesis ofPetri nets. This makes it possible to automate the processes of synthesis for the control algorithms based on the development of appropriateintelligent systems. Objective. The purpose of the scientific work is to minimize the time and to automatize process in synthesis of the control algorithmsby integrating the means of discrete-continuous networks and implementing the principles of automatic synthesis of Petri nets. Method. This scientific article proposes the principle for automatic formation of Petri nets based on logical algorithm for classifyingvarious uncorrected algorithms. The multilayer neural network in the Labview 2009 software environment was implemented torealize the appropriate algorithm. This artificial neural network provides algorithm formation, automatic synthesis and operation ofPetri nets. The article is devoted to the study of operating principle of the software application implementing such automatic synthesisof Petri nets while setting up the multi-level control systems. Results. A number of experiments were performed on the classification of algorithms and formation of Petri nets based on theready-made software application. The control system was automatically set up based on the Labview 2009 environment applicationfor the determined object. As a result of these experiments we have determined the fundamental suitability of the software application for the synthesis ofsome multi-level automatic control systems. It was also shown during these experiments that all mismatch signals in the system anddeviations from the ratios of values controlled variables are reduced to zero. All parameters of the control systems settings werenoted after the multi-level system setting procedure on the front panel of the virtual stand. Conclusions. The task related to the software application development based on the Labview 2009 environment which providesthe automatic synthesis of Petri nets was solved in this scientific work. Thus the method of automatic synthesis of Petri nets andtechnology for developing certain algorithms based on the functioning of the artificial neural network was further developed.

A Comparative Study of Measurement Systems Using Machine Learning Techniques for Tool Fault Di-agnosis During Metal Removal in the Milling Process

Despite significant advancements in both measurement systems and machine learning techniques, the integration of these technologies for real-time tool fault diagnosis in milling processes remains under developed. Existing studies tend to focus on a comprehensive comparative analysis that bridges these two areas machine learning algorithms or the application of specific measurement sys-tems. There is also a gap in evaluating the cost-effectiveness and practicality of different measurement systems when integrated with machine learning models for industrial applications. This study addresses these gaps by conducting a detailed comparative analysis of multiple measurement sys-tems and their performance with machine learning techniques in a real-world milling context, aim-ing to provide practical recommendations for industry adoption. Using both traditional and Artificial Intelligence (AI) to define and exploit sensory systems in the milling process, as well as various (direct and indirect) monitoring approaches, are summarised in this study. Machine learning tech-niques SVM, KNN, DT performs better and provide higher accuracy and in feature extraction clas-sification techniques statistical features, wavelet transform with the Holder Exponent (HE) having higher accuracy for diagnosing the tool faults.

Additively Manufactured Structures for Precise and Robust Mounting of Optical Elements

The design freedom of Additive Manufacturing offers new opportunities for the design of mounting structures of optical systems, which are not feasible for conventional manufacturing approaches, thus opening up new areas of application for optical systems. We use this to develop a fully monolithic mounting structure for precise positioning of the optical elements, while simultaneously increasing their robustness against harsh environmental conditions. We additively manufacture such a mounting structure for an imaging lens and evaluate its optical performance with interferometric measurement of its wavefront, before and after applying a mechanical shock to the entire system. The results indicate a centering accuracy better than 6~µm, both in the initial positioning as well as the subsequent repositioning after a mechanical shock of 15G.

Exploring Applications of Flexible Vesicular Systems as Transdermal Drug Delivery.

Deformable lipidic-nano carriers are a category of advanced liposomal formulations. Deformable lipidic-nano carriers have a specific character to transform by rearranging the lipidic backbone to squeeze themself through a pore opening ten times smaller than their diameter when exposed to a variable condition like hydration gradient as these have potentially been used as a non-invasive delivery system to transdermally migrate various therapeutic agents for over three decades. Despite their vast application in transdermal drug delivery system, non-uniformity to express their chemical nature still exist and authors use various terms synonymously and interchangeably with each other. The present study delineates the terminologies used to express different derived deformable vesicular carriers to harmonize the terminological use. It also includes the effectiveness of deformable nanocarriers like Transferosomes, Ethosomes, Menthosomes, Invasomes, and Glycerosomes in skin conditions like basal cell carcinoma, fungal and viral infections, and hyperpigmentation disorders, along with others. Various review and research articles were selected from the 'Pubmed' database. The keywords like Transferosomes, Flexi-vesicular system, ultra-deformable vesicles, and nano-vesicular systems were used to extract the data. The data was reviewed and compiled to categorically classify different flexible vesicular systems. The composition of the different vesicular systems is identified and a report of various pathological conditions where the use of flexible lipid nanocarrier systems was implemented is compiled. The review also offers suggestive approaches where the applicability of these systems can be explored further.

Aptamer-guided Selective Delivery of Therapeutics to Breast Cancer Cells Expressing Specific Biomarkers

Abstract: Cancer biomarkers or tumor-associated antigens (TAA) are the focus area of current research in cancer biology for diagnosis, prognosis, screening, and targeted treatments. Breast cancer is the second most common type of cancer, affecting women more than men. Conventional methods and antibody-targeted therapies are less effective and suffer systemic cytotoxicity, poor tissue sensitivity, low penetration capacity, and reduced accumulation of the drug in tumor cells that limit its application and sometimes result in treatment failure. Opting for aptamer-mediated targeted delivery of various anti-cancer agents (drugs, siRNA, miRNA, shRNA and peptides) could possibly overcome these limitations by utilizing aptamer as a targeting ligand. The purpose of this article is to review the novel indicative biomarkers of breast cancer and also describe current applications of aptamer-guided active targeting systems in breast cancer therapy in vivo and in vitro.

Developing a Mobile Application for IoT-Powered Health Monitoring Systems

A multidisciplinary approach is typically required to address health issues effectively in medical emergencies. Traditional sensor-based diagnostics in healthcare, when applied on a large scale, demand numerous sensors and significant human intervention. To address these challenges, this research proposes an IoT-based healthcare application designed to streamline patient monitoring. The system is built around an online mobile application that enables continuous, wireless observation of patients' health. The primary objective is to develop a cost-effective solution that transmits vital patient parameters during emergencies. Sensors such as ECG monitors, heart rate sensors, and temperature sensors are employed to measure critical health indicators. The collected data is transmitted to the cloud via a microcontroller-connected module for storage on an IoT platform. In the cloud, the data is processed, and relevant feedback is generated, allowing doctors to assess the patient's condition remotely. This remote monitoring reduces the workload on healthcare professionals while ensuring accurate patient health assessments. In cases requiring urgent attention, alerts are sent to caregivers or doctors. This system aids in early intervention and enhances the quality of patient care. The proposed setup has been validated through simulations and various test experiments.

Разработка кроссплатформенного приложения для управления робототехническим комплексом

A brief analysis of the approaches currently used to develop management programs is provided, their application features, advantages and disadvantages are identified. For the study, the development of a cross-platform appli-cation of control systems for robotic complexes (RTC) was chosen, using the example of RTC with MP-11 robotic manipulators. The structure of the control system includes a controller on the Arduino Mega 2560 Pro Mini board with a loaded low-level program and an upper-level program. The development is carried out on the platform.NET MAUI, which allows you to create an application for Windows, Android, and IOS operating systems on a single code base, i.e. the same code is automatically compiled for all operating systems, taking into account their features and applied elements. The control computer is connected via USB, mobile devices via Bluetooth, while data is programmatically transmitted via a serial port. The description of the modes of operation of the developed application, its functioning algorithms, indicating the features of execution for this type of programs and the appearance of forms with elements located on them is given. The program provides manual and automatic modes of operation, as well as control of not only the RTC, but also its model. The key features of this application are the use of a single code base and design for the implementation of desktop and mobile applications for various operating systems without using network resources, unlike web applications.

Adaptive Method’s Application for the Identification Systems in Dynamic Weighing

Adaptive Method’s Application for the Identification Systems in Dynamic Weighing

Nanopipette dynamic microscopy unveils nano coffee ring

Liquid-phase electron microscopy (LP-EM) imaging has revolutionized our understanding of nanosynthesis and assembly. However, the current closed geometry limits its application for open systems. The ubiquitous physical process of the coffee-ring phenomenon that underpins materials and engineering science remains elusive at the nanoscale due to the lack of experimental tools. We introduce a quartz nanopipette liquid cell with a tunable dimension that requires only standard microscopes. Depending on the imaging condition, the open geometry of the nanopipette allows the imaging of evaporation-induced pattern formation, but it can also function as an ordinary closed-geometry liquid cell where evaporation is negligible despite the nano opening. The nano coffee-ring phenomenon was observed by tracking individual nanoparticles in an evaporating nanodroplet created from a thin liquid film by interfacial instability. Nanoflows drive the assembly and disruption of a ring pattern with the absence of particle-particle correlations. With surface effects, nanoflows override thermal fluctuations at tens of nanometers, in which nanoparticles displayed a "drunken man trajectory" and performed work at a value much smaller than kBT.

Adaptive Fault-Tolerant Tracking Control for Discrete-Time Nonstrict-Feedback Nonlinear Systems With Stochastic Noises

This paper discusses the problem of fault-tolerant tracking control for discrete-time nonstrict-feedback nonlinear systems in the presence of stochastic noises and actuator faults. The system is characterized by a discrete-time nonstrict-feedback structure and multiplicative stochastic noise related to the states, which poses a challenge for the design and analysis of the fault-tolerant controller. Moreover, the considered actuator faults include multiplicative and additive faults without prior information. By integrating the properties of the backstepping framework and neural networks, and by introducing an adaptive fault compensation term, a novel fault-tolerant tracking control strategy is proposed. The effects of faults are compensated and the difficulties caused by the system structure are overcome, avoiding the algebraic loop problem and overcoming the causal contradiction. Given specific parameters, the designed fault-tolerant controller can ensure that the tracking error converges to an adjustable region regarding the origin and that all system signals are uniformly bounded concerning the mean-square sense. The simulation examples illustrate the effectiveness of the designed fault-tolerant control method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —As system complexity has been increasing, practical systems are often affected by faults when performing tracking control. Fault-tolerant control can provide acceptable robustness and improve system safety. On the one hand, many practical systems are nonlinear, difficult to model mathematically and accurately, and affected by stochastic noises. On the other hand, with digital control, the object is a discrete-time system. In practical applications, the upper bounds of the faults and the noises are often unknown. In this paper, a novel adaptive fault-tolerant control method is proposed for the more general uncertain discrete-time nonlinear systems, i.e., nonstrict-feedback nonlinear systems, with stochastic noises and actuator faults. Neural networks and an adaptive fault compensation term are integrated into the fault-tolerant controller. By adjusting the controller parameters properly, it is ensured that the tracking error converges to a small neighborhood of zero in the event of a fault occurring. In future research, sensor faults, input constraints, optimal control design, and applications in multi-agent systems will be considered.

Advancing deep learning-based acoustic leak detection methods towards application for water distribution systems from a data-centric perspective

Against the backdrop of severe leakage issue in water distribution systems (WDSs), numerous researchers have focused on the development of deep learning-based acoustic leak detection technologies. However, these studies often prioritize model development while neglecting the importance of data. This research explores the impact of data augmentation techniques on enhancing deep learning-based acoustic leak detection methods. Five random transformation-based methods—jittering, scaling, warping, iterated amplitude adjusted Fourier transform (IAAFT), and masking—are proposed. Jittering, scaling, warping, and IAAFT directly process original signals, while masking operating on time-frequency spectrograms. Acoustic signals from a real-world WDS are augmented, and the efficacy is validated using convolutional neural network classifiers to identify the spectrograms of acoustic signals. Results indicate the importance of implementing data augmentation before data splitting to prevent data leakage and overly optimistic outcomes. Among the techniques, IAAFT stands out, significantly increasing data volume and diversity, improving recognition accuracy by over 7%. Masking enhances performance mainly by compelling the classifier to learn global features of the spectrograms. Sequential application of IAAFT and masking further strengthens leak detection performance. Furthermore, when applying a complex model to acoustic leakage detection through transfer learning, data augmentation can also enhance the effectiveness of transfer learning. These findings advance artificial intelligence-driven acoustic leak detection technology from a data-centric perspective towards more mature applications.

Solar Sail-Based Mars-Synchronous Displaced Orbits for Remote Sensing Applications

A solar sail is a propellantless propulsion system that allows a spacecraft to use solar radiation pressure as a propulsive source for planetary and deep space missions that would be difficult, or even unfeasible, to accomplish with more conventional thrusters, either chemical or electric. A challenging application for these fascinating propulsion systems is a heliocentric mission that requires a displaced non-Keplerian orbit (DNKO), that is, a solar sail-induced closed trajectory in which the orbital plane does not contain the Sun’s center of mass. In fact, thanks to the pioneering work of McInnes, it is known that a solar sail is able to reach and maintain a family of heliocentric DNKOs of given characteristics. The aim of this paper is to analyze the properties of Mars-synchronous circular DNKOs, which have an orbital period matching that of the planet for remote sensing applications. In fact, those specific displaced orbits allow a scientific probe to continuously observe the high-latitude regions of Mars from a quasi-stationary position relative to the planet. In this context, this paper also analyzes the optimal (i.e., the minimum-time) heliocentric transfer trajectory from the Earth to circular DNKOs in two special mission scenarios taken as a reference.

Ultrahigh energy density in dielectric nanocomposites by modulating nanofiller orientation and polymer crystallization behavior

High-energy density dielectrics for electrostatic capacitors are in urgent demand for advanced electronics and electrical power systems. Poly(vinylidene fluoride) (PVDF) based nanocomposites have attracted remarkable attention by intrinsic high polarization, flexibility, low density, and outstanding processability. However, it is still challenging to achieve significant improvement in energy density due to the common contradictions between electric polarization and breakdown strength. Here, we proposed a novel facile strategy that simultaneously achieves the construction of in-plane oriented BaTiO3 nanowires and crystallization modulation of PVDF matrix via an in-situ uniaxial stretch process. The polar phase transition and enhanced Young's modulus facilitate the synergetic improvement of electric polarization and voltage endurance capability for PVDF matrix. Additionally, the aligned distribution of nanowires could reduce the contact probability of nanowire tips, thus alleviating electric field concentration and hindering the conductive path. Finally, a record high energy density of 38.3 ​J/cm3 and 40.9 ​J/cm3 are achieved for single layer and optimized sandwich-structured nanocomposite, respectively. This work provides a unique structural design and universal method for dielectric nanocomposites with ultrahigh energy density, which presents a promising prospect of practical application for modern energy storage systems.

Multi-Device Security Application for Unmanned Surface and Aerial Systems

The use of autonomous and unmanned systems continues to increase, with uses spanning from package delivery to simple automation of tasks and from factory usage to defense industries and agricultural applications. With the proliferation of unmanned systems comes the question of how to secure the command-and-control communication links among such devices and their operators. In this work, we look at the use of the Messaging Layer Security (MLS) protocol, designed to support long-lived continuous sessions and group communication with a high degree of security. We build out MAUI—an MLS API for UxS Integration that provides an interface for the secure exchange of data between a ScanEagle unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) in a multi-domain ad-hoc network configuration, and experiment on system limits such as the ciphersuite set-up time and message handling rates. The experiments in this work were conducted in virtual and physical environments between the UAV, USV, and a controller device (all of different platforms). Our results demonstrate the viability of capitalizing on MLS’s capabilities to securely and efficiently transmit data for distributed communication among various unmanned system platforms.

Analysis and Design of Configurable Rectifier With Compensated Near-Zero Impedance Angle for Megahertz Wireless Power Transfer

Adapting to wide load range variations is always one of the main goals pursued by megahertz (MHz) wireless power transfer (WPT) systems. For this, the nonlinear phenomenon of the rectifier has become a problem that must be solved. Compared with the method of optimizing the efficiency around the maximum power point in the traditional design, this paper proposes a global load optimization method. Meanwhile, a novel configurable compensated near-zero impedance angle rectifier (NIAR) is proposed to take full advantage of the global optimization method. The NIAR not only realizes the compression of a wide range of impedance angles, but also solves the classic problems of Class E rectifiers such as large stress, voltage asymmetry, and low real part of input impedance. The configurable design allows the system to adjust the rectifier unit according to the output index, thereby fully improving the coil efficiency, and providing a solution for the application of high-voltage and high-power MHz WPT systems in the future. To verify the effectiveness of the proposed NIAR and design method, a 60 W prototype was built, and a traditional full-wave rectifier was used for comparison. The results show that the proposed NIAR system exhibits better characteristics in terms of system efficiency, voltage stress, and symmetry.

Semi-batch and batch low-salt-rejection reverse osmosis for brine concentration

Brine management strategies require efficient methods of desalinating high-salinity streams (> 7 wt%). Low-salt-rejection reverse osmosis (LSRRO) provides salt leakage to reduce the trans-membrane osmotic pressure, and has higher water recovery than reverse osmosis (RO). However, LSRRO has high energy consumption (e.g., >5kWh/m3 for seawater feed). In this work, we develop novel transient multistage processes (i.e., semi-batch and batch) that employ LSRRO membranes to desalinate high-saline streams. Herein, we use a simplified lumped model to capture the system performance under ideal and non-ideal conditions and optimize salt rejection per pass for the multistage system. The results show that the batch LSRRO system performs dewatering with fewer stages and more efficiently than the steady-state LSRRO. In particular, the multistage batch system significantly outperforms the steady-state LSRRO by reducing energy consumption between 38 % to 73 %. The ZLD application for 4-stage batch systems requires energy consumption between 1.07 and 6.96kWh/m3 for the feed salinity range between 0.1 and 1 mol/L. Conversely, semi-batch LSRRO is less efficient than steady-state and batch LSRRO. It is constrained by the loss due to the mixing of streams which results in more stages, tighter range for membrane selectivity, and higher energy consumption than batch LSRRO.

Problems of updating seismic norms in the conditions of application of new structural systems of residential and civil buildings

Problems of updating seismic norms in the conditions of application of new structural systems of residential and civil buildings

Fabrication of nanocrystal forms of ᴅ-cycloserine and their application for transdermal and enteric drug delivery systems.

ᴅ-cycloserine (DCS), an FDA-approved medicine for the treatment of tuberculosis, is also a partial agonist at the glycine recognition site of N-methyl-ᴅ-aspartate (NMDA) receptor and has shown significant treatment efficacy for central nervous system (CNS) disorders including depression, schizophrenia, Alzheimer's disease, and post-traumatic stress disorder. The physicochemical properties of DCS, however, limit the options of formulation and medicinal applications of DCS, and warrants further investigation for the development of CNS therapeutics. Nanocrystals play an important role in pharmaceutic design and development. The properties of nanocrystals are remarkably different from their bulk material counterpart, attributed to the large surface-area-to-volume ratio which can improve the bioavailability. In this study, for the first time, DCS, a highly water-soluble compound, has formed nanocrystals and this was confirmed by scanning electronic microscopy and X-ray powder diffraction. Furthermore, DCS nanocrystals were applied to several formulations to test their stability and then to the in vitro Franz diffusion test with reservoir patch formulation as well as in vivo pharmacokinetics study with enteric capsules. We tested these formulations regarding their nanocrystal physical properties, size effect, and dissolution rate, respectively. We found that DCS nanocrystals showed good performance in the Franz diffusion test and rodent pharmacokinetic studies due to the nanoparticle size and faster dissolution as compared with the commercial DCS powder. These DCS nanocrystal formulations could offer a new approach for the development of an advanced drug delivery system for the treatment of CNS disorders.

GAITBOOST: Boosting gait recognition performance with BAT-extreme learning machines algorithm

Gait analysis is a widely used technique for passive human identification and tracking, with potential applications in security and surveillance systems. However, existing gait recognition methods face challenges in handling changing angles and uncertain features. In this paper, we propose a novel gait recognition approach that leverages real-time spatio-temporal gait features, including step length, gait cycle, height, cadence, swing ratio, and foot length. We apply the Extreme Learning Machines (ELM) algorithm for classification, which has been shown to be effective in various applications due to its fast-learning speed and good generalization performance. To further enhance the recognition rate, we introduce an evolutionary BAT-optimized ELM algorithm that addresses the instability issue in ELM. The proposed BAT-ELM algorithm can optimize the hidden nodes and weights of ELM, which leads to improved efficiency in recognizing gait from multiple view angles ranging from 0° to 180°. Our comprehensive analysis of the proposed approach indicates that it outperforms other reported algorithms in terms of recognition rate and efficiency. Our work demonstrates the effectiveness of combining real-time spatio-temporal gait features with the BAT-ELM algorithm for gait recognition. The proposed approach has potential applications in various fields, including security and surveillance systems, healthcare, and robotics. Our findings highlight the importance of leveraging evolutionary algorithms to optimize machine learning models and achieve better performance in complex recognition tasks.