Application Of Systems Research Articles

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.

Fungicide Sensitivity of Phytophthora Isolates from the Washington Red Raspberry Industry.

Phytophthora rubi is an important pathogen causing Phytophthora root rot of red raspberries worldwide. Management of this disease is partially achieved with fungicides, but efficacy has been low, and growers are concerned about fungicide resistance. To determine whether fungicide resistance is developing, Phytophthora species were isolated from 26 raspberry fields with root rot, identified, and evaluated for sensitivity to four fungicides: mefenoxam, phosphorous acid, oxathiapiprolin, and dimethomorph. The majority of the recovered 152 Phytophthora isolates were P. rubi (143 isolates, 25 fields), with P. megasperma (8 isolates, 2 fields) and P. gonapodyides (1isolate, 1field) being found much less frequently. These results confirm P. rubi as the dominant species affecting the Washington red raspberry industry. Almost all tested isolates were sensitive to all four fungicide chemistries, although three isolates were less sensitive to mefenoxam, with effective concentration for 50% growth inhibition (EC50) values ranging from 3.53 to 100 μg active ingredient/ml. No resistance was detected against current fungicide label rates. However, other reasons were identified for why fungicides have been ineffective. Label rates vary widely by brand, and most fungicides are applied in the fall when P. rubi is inactive. In addition, some phosphorous acid products are only labeled for foliar applications, which have been shown to be less effective than soil applications in other agricultural systems. Efficacy trials are needed to compare foliar and soil fungicide applications at different times of the year for their ability to control Phytophthora root rot in red raspberry production fields.

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.

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.

Performance Evaluation of Single-Phase Boost-Type Cascaded H-Bridge Inverter in the Applications of Grid-Tied Photovoltaic Systems

In this paper, a new cascaded-H-bridge (CHB) inverter is proposed to enhance the input voltage of a renewable energy source to a desired level without any necessity of a dc-dc converter. The proposed topology consists of a pair of inductor and capacitor that serves as an input impedance network for the two full bridge inverters to boost the source voltage. An interleaved control scheme is proposed for the grid-tied inverter that independently supervises the duty cycle and the modulation index. The maximum power point tracking system commands the duty cycle whereas the dc-link voltage regulator maintains a constant voltage across the capacitors irrespective of the disturbances in the system. The proposed control scheme is primarily investigated in the MATLAB/SIMULINK platform. Different test cases are analysed to explore the performance of the proposed inverter in the applications of photovoltaic systems. An experimental prototype is designed in the laboratory to validate the simulation results.

Drought Responses in Three Apple Cultivars Using an Autonomous Sensor-based Irrigation System

Irrigation decision support systems evolving in the domestic temperate tree fruit production industry incorporate measures of soil moisture status, which diverges from classic physiological indicators of edaphic stress. This study used an autonomous sensor-based irrigation system to impose a water deficit (soil matric potential targets of –25, –40, –60, and –80 kPa) on ‘Autumn Gala’, ‘CrimsonCrisp’, and ‘Golden Delicious’ apple (Malus domestica) cultivars grafted to ‘Budagovsky 9’ rootstock in the greenhouse (n = 60). It was hypothesized that relationships between physiological plant function, assessed via infrared gas exchange and chlorophyll fluorescence, and the soil matric potential may be used to advance emerging irrigation decision support systems. Complications arising from defoliation by day 11 at –60 and –80 kPa indicate the generation of substrate-specific soil–water relationships in research applications of autonomous sensor-based irrigation systems. ‘Autumn Gala’ carbon assimilation rates at –80 kPa declined from day 0 to day 8 (9.93 and 5.86 μmol⋅m–2⋅s–1 carbon dioxide), whereas the transpiration rate was maintained, potentially reducing observed defoliation as other cultivars increased transpiration to maintain carbon assimilation. Correlation matrices revealed Pearson’s r ≤ |0.43| for all physiological metrics considered with soil matric potential. Nevertheless, exploratory regression analysis on predawn leaf water potential, carbon assimilation, transpiration, stomatal conductance, and nonphotochemical quenching exposed speculatively useful data and data shapes that warrant additional study. Nonlinear piecewise regression suggested soil matric potential may useful as a predictor for the rate of change in predawn leaf water potential upon exposure to a water deficit. The critical point bridging the linear spans, –30.6 kPa, could be useful for incorporating in emerging irrigation decision support systems.

Multiple Natural Polymers in Drug and Gene Delivery Systems.

Natural polymers are organic compounds produced by living organisms. In nature, they exist in three main forms, including proteins, polysaccharides, and nucleic acids. In recent years, with the continuous research on drug and gene delivery systems, scholars have found that natural polymers have promising applications in drug and gene delivery systems due to their excellent properties such as biocompatibility, biodegradability, low immunogenicity, and easy modification. However, since the structure, physicochemical properties, pharmacological properties and biological characteristics of biopolymer molecules have not yet been entirely understood, further studies are required before large-scale clinical application. This review focuses on recent advances in the representative natural polymers such as proteins (albumin, collagen, elastin), polysaccharides (chitosan, alginate, cellulose) and nucleic acids. We introduce the characteristics of various types of natural polymers, and further outline the characterization methods and delivery forms of these natural polymers. Finally, we discuss possible challenges for natural polymers in subsequent experimental studies and clinical applications. It provides an important strategy for the clinical application of natural polymers in drug and gene delivery systems.

Enhancing fall prediction in the elderly people using LBP features and transfer learning model

In an era where the detection and prevention of falls are crucial for the well-being of elderly and vulnerable individuals, achieving high accuracy in identifying such incidents is of utmost importance. This study introduces a modified NASNet, a novel transformer learning model designed for the classification of fall and not fall people based on local binary patterns (LBP) features. The modified NASNet model demonstrates high performance, achieving an accuracy, precision, recall, and F1 score of 99% with LBP features in differentiating fall from not fall. To assess the performance of NASNet, this research work conducted a comparative analysis with other prominent deep learning, and transfer learning models, as well as state-of-the-art machine learning frameworks. The findings indicate NASNet's enhanced performance in fall detection, highlighting its potential for application in real-time fall detection systems, contributing to a safer environment for individuals at risk. This research sets the stage for enhanced healthcare and assistance for vulnerable populations, addressing a critical concern in the healthcare sector.

INTELLIGENCE ANALYTICAL SUBSYSTEM OF CORPORATE MANAGEMENT (ON THE EXAMPLE OF AN OIL & GAS COMPANY)

The changes that have taken place in the Ukrainian economy in recent years due to the consistent impact of global macroeconomic, epidemiological and military factors of the current deep crisis - actualized for enterprises and corporations a complex adaptive reorganization of their analytical subsystem of their management [1, 2].
 In addition, in order to be consistently effective in today's global business environment (liberalization of markets, their globalization, increased competition, decreasing consumer loyalty, constant variation in oil and gas prices, etc.), oil and gas companies must have an innovative data and knowledge environment that allows seamless and effective joint leveraging knowledge inside the corporation and across the value chain [3]. The application of knowledge-based decision support systems as an important tool of innovative management policy enables effective and sustainable management of a complex of integrated processes of an oil and gas production company (mitigating the impact of crisis factors) [4].
 The scientific and practical results presented in the article regarding the methodology and design of innovative analytical subsystem of the corporation management information systems should be taken into account when reengineering corporate information systems not only in the oil and gas industry in Ukraine [5-6]. 
 Moreover, the obtained results are relevant and applicable not only for local companies, but also for international corporations on emerging markets in the context of global and regional macroeconomic and edipemical crisis phenomena.

Chiral Recognition of Chiral (Hetero)Cyclic Derivatives Probed by Tetraaza Macrocyclic Chiral Solvating Agents via 1H NMR Spectroscopy.

In the field of chiral recognition, chiral cyclic organic compounds, especially heterocyclic organic compounds, have attracted little attention and have been rarely studied as chiral substrates by means of 1H NMR spectroscopy. In this paper, enantiomers of thiohydantoin derivatives, representing typical five-membered N,N-heterocycles, have been synthesized and utilized for assignment of absolute configuration and analysis of enantiomeric excess. All enantiomers have been successfully differentiated with the assistance of novel tetraaza macrocyclic chiral solvating agents (TAMCSAs) by 1H NMR spectroscopy. Surprisingly, unprecedented nonequivalent chemical shift values (up to 2.052 ppm) of the NH proton of substrates have been observed, a new milestone in the evaluation of enantiomers. To better understand the intermolecular interactions between host and guest, Job plots and theoretical calculations of (S)-G1 and (R)-G1 with TAMCSA 1a were investigated and revealed significant geometric differentiation between the diastereomers. In order to evaluate practical applications of the present systems in analyzing optical purity of chiral substrates, enantiomeric excesses of a typical substrate (G1) with different optical compositions in the presence of a representative TAMCSA (1a) can be accurately calculated based on the integration of the NH proton's signal peaks. Importantly, this work provides a significant breakthrough in exploring and developing the chiral recognition of chiral heterocyclic organic compounds by 1H NMR spectroscopy.

A 3D virtual geographic environment for flood representation towards risk communication

Risk communication seeks to develop a shared understanding of disaster among stakeholders, thereby amplifying public awareness and empowering them to respond more effectively to emergencies. However, existing studies have overemphasized specialized numerical modelling, making the professional output challenging to understand and use by non-research stakeholders. In this context, this article proposes a 3D virtual geographic environment for flood representation towards risk communication, which integrates flood modelling, parallel computation, and 3D representation in a pipeline. Finally, a section of the Rhine River in Bonn, Germany, is selected for experiment analysis. The experimental results show that the proposed approach is capable of flood modelling and 3D representation within a few hours, the parallel speedup ratio reached 6.45. The intuitive flood scene with 3D city models is beneficial for promoting flood risk communication and is particularly helpful for participants without direct experience of floods to understand its spatiotemporal process. It also can be embedded in the Geospatial Infrastructure Management Ecosystem (GeoIME) cloud application for intelligent flood systems.

Self-doping mechanism for thermally activated delayed fluorescence molecules: a theoretical perspective

Interesting properties of dual conformations of thermally activated delayed fluorescence molecules show various novel applications in self-doping systems. However, the species and quantities of self-doping systems are not enough to satisfy the requirements and a theoretical investigation to reveal the self-doping mechanism is highly desired. By analysing the potential energy surface, two stable conformations of TP2P-PXZ-qa (QA) and TP2P-PXZ-qe (QE) are determined. The basic geometric and electronic structures are optimised, frontier orbital distributions and energies are obtained, transition properties are analysed and the energy consumption processes of excited states are analysed. Results indicate that two conformations (QA and QE) coexist in the solid phase, the QA conformation acts as host and the QE conformation acts as guest. Efficient intersystem crossing (ISC) and reverse ISC processes are determined for QE conformation. In addition, a substantial overlap exists between the emission spectrum of the QA conformation and the absorption spectrum of the QE conformation, indicating an effective energy transfer process from host to guest. Thus, relationships between molecular structures and photophysical properties are illustrated and a self-doping mechanism is theoretically revealed. Our work rationalises the experimental observations and offers a theoretical perspective for the self-doping mechanism, contributing to the development of efficient non-doped emitters.

Machine Learning for the Semi-Automatic 3D Decay Segmentation and Mapping of Heritage Assets

ABSTRACT In Architectural Heritage (AH), diagnostics represent a key topic, related to the assessment of the state of conservation, for a well-structured and integrated knowledge about the artefact. This is of paramount importance for buildings denoted by historic-artistic interest, often in widespread damaged conditions. Digital technologies could represent a valuable alternative, for the creation, processing, and analysis of 2D images or reality-capture 3D data. Indeed, image processing and artificial intelligence are spreading in the AH domain, for both documentation and conservation purposes. The present research proposes a novel methodological approach, based on the application of machine learning systems, for the assessment and mapping of decay evidence in an ancient artefact, within the diagnostic process. An unsupervised hierarchical clustering of point clouds has been validated and compared against a state-of-the-art supervised random forest. The presented pipeline has been tested on a plurality of case studies, representative of the territorial historic built heritage, qualified by a great variety, in terms of age, building type, surface, and lighting conditions. For most of the classes, the cloud-based unsupervised approach consents to accomplish better performances.

The problems and countermeasures of private education management based on fuzzy logic system

At present, private education plays an important role in China’s education system, but there are a series of problems and challenges in the management of private education. The purpose of this study is to discuss and solve the problems existing in the management of private education through the method of fuzzy logic system. Firstly, the application of fuzzy logic systems in related fields is reviewed to understand the potential and advantages of fuzzy logic systems. Then, through the analysis of the main problems in the management of private education, the root of these problems and their impact on the education system are revealed. By establishing a private education management model based on fuzzy logic system, the application and function of this model in problem analysis and decision making are demonstrated. In the analysis of the results, the accuracy and effectiveness of the fuzzy logic system in the management of private education are evaluated, and the targeted management strategies and countermeasures are put forward to solve the problems in the management of private education.

Amine-Carbamate Self-Immolative Spacers Counterintuitively Release 3° Alcohol at Much Faster Rates than 1° Alcohol Payloads.

Self-immolative (SI) spacers are degradable chemical connectors widely used in prodrugs and drug conjugates to release pharmaceutical ingredients in response to specific stimuli. Amine-carbamate SI spacers are particularly versatile, as they have been used to release different hydroxy cargos, ranging from 2° and 3° alcohols to phenols and oximes. In this work, we describe the ability of three amine-carbamate SI spacers to release three structurally similar imidazoquinoline payloads, bearing either a 1°, a 2° or a 3° alcohol as the leaving group. While the spacers showed comparable efficacy at releasing the 2° and 3° alcohols, the liberation of the 1° alcohol was much slower, unveiling a counterintuitive trend in nucleophilic acyl substitutions. The release of the 1° alcohol payload was only possible using a SI spacer bearing a pyrrolidine ring and a tertiary amine handle, which opens the way to future applications in drug delivery systems.