Application Of Systems Research Articles

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.

Nanozymes-Mediated Cascade Reaction System for Tumor-Specific Diagnosis and Targeted Therapy.

Cascade reactions are described as efficient and versatile tools, and organized catalytic cascades can significantly improve the efficiency of chemical interworking between nanozymes. They have attracted great interest in many fields such as chromogenic detection, biosensing, tumor diagnosis, and therapy. However, how to selectively kill tumor cells by enzymatic reactions without harming normal cells, as well as exploring two or more enzyme-engineered nanoreactors for cascading catalytic reactions, remain great challenges in the field of targeted and specific cancer diagnostics and therapy. The latest research advances in nanozyme-catalyzed cascade processes for cancer diagnosis and therapy are described in this article. Here, various sensing strategies are summarized, for tumor-specific diagnostics. Targeting mechanisms for tumor treatment using cascade nanozymes are classified and analyzed, "elements" and "dimensions" of cascade nanozymes, types, designs of structure, and assembly modes of highly active and specific cascade nanozymes, as well as a variety of new strategies of tumor targeting based on the cascade reaction of nanozymes. Finally, the integrated application of the cascade nanozymes systems in tumor-targeted and specific diagnostic therapy is summarized, which will lay the foundation for the design of more rational, efficient, and specific tumor diagnostic and therapeutic modalities in the future.

Fine-tuning DETR: Toward holistic process in plastic waste sorting system

Every year human discharges about 350 million tons of plastic waste into the environment and can be projected to triple in 2060 without any attempts to change situation. From 1970 to 2019, an estimation of 130 million tons of plastic waste was accumulated into the rivers, lakes and sea, while only 27 % is recycled and utilized. Moreover, waste treatment plants in most places around the world are using out-of-date technology, may pose a threat to the health of the workers. Therefore, it is essential to modernize these systems for protecting human health. This paper proposes fine-tuning DETR, which applies Artificial Intelligent in plastic waste sorting system. Consequently, this study analyzed the applicability of fine-tuning DETR in the domain of plastic waste categorization and its potential drawbacks. For fair experiment and evaluation, model candidates were trained and evaluated on an industrial plastic waste dataset. The fine-tuning DETR outperformed other candidates in the context of critical indicators, from accuracy (25.1 mAP), processing speed (28 FPS) to computational cost (GFLOPs 86). Furthermore, fine-tuning DETR possesses the capability of autonomous operation without requiring human intervention, distinguishing this candidate from other prevalent algorithms. Our research demonstrates that, fine-tuning DETR specifically and Transformer-based algorithms in general, are entirely suitable and hold significant potential for large-scale application in holistic plastic waste sorting systems.

Synthesis of Sorafenib-Ruthenium Complexes, Investigation of Biological Activities and Applications in Drug Delivery Systems as an Anticancer Agent.

Sorafenib, a multiple kinase inhibitor, is widely used as a first-line treatment for hepatocellular carcinoma. However, there is a need for more effective alternatives when sorafenib proves insufficient. In this study, we aimed to design a structure that surpasses sorafenib's efficacy, leading us to synthesize sorafenib-ruthenium complexes for the first time and investigate their properties. Our results indicate that the sorafenib-ruthenium complexes exhibit superior epidermal growth factor receptor (EGFR) inhibition compared to sorafenib alone. Interestingly, among these complexes, Ru3S demonstrated high activity against various cancer cell lines including sorafenib-resistant HepG2 cells while exhibiting significantly lower cytotoxicity than sorafenib in healthy cell lines. Further evaluation of cell cycle, cell apoptosis, and antiangiogenic effects, molecular docking, and molecular dynamics studies revealed that Ru3S holds great potential as a drug candidate. Additionally, when free Ru3S was encapsulated into polymeric micelles M1, enhanced cytotoxicity on HepG2 cells was observed. Collectively, these findings position Ru3S as a promising candidate for EGFR inhibition and warrant further exploration for drug development purposes.

Graph-based meta-learning for context-aware sensor management in nonlinear safety-critical environments

This study introduces a novel framework for optimizing energy efficiency and computational load in safety-critical robotic systems operating in nonlinear domains. Leveraging Graph Attention Networks for state awareness and decision-making, the framework employs adaptive sensor and filter toggling strategies to dynamically manage system resources through real-time inferential processes. Our framework maintains continuous robot operation in the presence of sensor noise and environmental disturbances by activating additional sensors, thus preventing system shutdowns or stalls. Few-shot meta-learning techniques further augment the model's adaptability, allowing it to generalize and make real-time decisions across varying operational conditions. An extensive evaluation reveals a reduced average energy consumption, compared to ‘always-on’ configurations, by 13.71% and CPU utilization by 29.07%, without compromising system performance and safety. We also introduce Matching Networks and Siamese Networks with different loss functions to assess the system's capability to adapt to different levels of criticality. Our experiments demonstrate that the system prioritizes performance and safety in high-critical scenarios while maximizing energy efficiency in less critical situations. The framework's real-time decision-making capability is particularly crucial in human–robot environments and holds significant implications for future applications in nonlinear control systems and resilient robotic systems.

Broadband directional filter in multilayer liquid crystal polymer films at W-band

In this work, a four-port directional filter (DF) with a broad passband and low reflection is proposed at the W-band, which comprises three unit filters at 92, 95, and 97 GHz, cascaded in series. Each unit consists of two microstrip lines in the top circuit layer for signal input and output, two pairs of apertures in the middle ground layer for directional coupling, and one square loop in the bottom layer as a selective resonator. By sweeping the working frequencies of the three units and optimizing the phase delays between them, the proposed filter achieves a 3-dB bandwidth as broad as 16%, an insertion loss of 2.5 dB at 95 GHz, and an out-of-band rejection of −28 and −23 dB at 80 and 110 GHz, respectively. The corresponding reflection attenuation is larger than 9.6 dB from 60 to 105 GHz. To verify our design, a prototype is fabricated and characterized, and its experimental data are consistent with the simulation. This work significantly expands the bandwidth of DFs and may find many applications in frequency division multiplexing and high-gain wireless systems.

Glucose-Driven Droplet Formation in Complexes of a Supramolecular Peptide and Therapeutic Protein.

Biology achieves remarkable function through processes arising from spontaneous or transient liquid-liquid phase separation (LLPS) of proteins and other biomolecules. While polymeric systems can achieve similar phenomena through simple or complex coacervation, LLPS with supramolecular materials has been less commonly shown. Functional applications for synthetic LLPS systems are an expanding area of emphasis, with particular focus on capturing the transient and dynamic state of these structures for use in biomedicine. Here, a net-cationic supramolecular peptide amphiphile building block with a glucose-binding motif is shown that forms LLPS structures when combined with a net-negatively charged therapeutic protein, dasiglucagon, in the presence of glucose. The droplets that arise are dynamic and coalesce quickly. However, the interface can be stabilized by addition of a 4-arm star PEG. When the stabilized droplets formed in glucose are transferred to a bulk phase containing different glucose concentrations, their stability and lifetime decrease according to bulk glucose concentration. This glucose-dependent formation translates into an accelerated release of dasiglucagon in the absence of glucose; this hormone analogue itself functions therapeutically to correct low blood glucose (hypoglycemia). These droplets also offer function in mitigating the most severe effects of hypoglycemia arising from an insulin overdose through delivery of dasiglucagon in a mouse model of hypoglycemic rescue. Accordingly, this approach to use complexation between a supramolecular peptide amphiphile and a therapeutic protein in the presence of glucose leads to droplets with functional potential to dissipate for the release of the therapeutic material in low blood glucose environments.

Ultrafast Charge Transfer Cascade in a Mixed-Dimensionality Nanoscale Trilayer.

Innovation in optoelectronic semiconductor devices is driven by a fundamental understanding of how to move charges and/or excitons (electron-hole pairs) in specified directions for doing useful work, e.g., for making fuels or electricity. The diverse and tunable electronic and optical properties of two-dimensional (2D) transition metal dichalcogenides (TMDCs) and one-dimensional (1D) semiconducting single-walled carbon nanotubes (s-SWCNTs) make them good quantum confined model systems for fundamental studies of charge and exciton transfer across heterointerfaces. Here we demonstrate a mixed-dimensionality 2D/1D/2D MoS2/SWCNT/WSe2 heterotrilayer that enables ultrafast photoinduced exciton dissociation, followed by charge diffusion and slow recombination. Importantly, the heterotrilayer serves to double charge carrier yield relative to a MoS2/SWCNT heterobilayer and also demonstrates the ability of the separated charges to overcome interlayer exciton binding energies to diffuse from one TMDC/SWCNT interface to the other 2D/1D interface, resulting in Coulombically unbound charges. Interestingly, the heterotrilayer also appears to enable efficient hole transfer from SWCNTs to WSe2, which is not observed in the identically prepared WSe2/SWCNT heterobilayer, suggesting that increasing the complexity of nanoscale trilayers may modify dynamic pathways. Our work suggests "mixed-dimensionality" TMDC/SWCNT based heterotrilayers as both interesting model systems for mechanistic studies of carrier dynamics at nanoscale heterointerfaces and for potential applications in advanced optoelectronic systems.

Recent Advances in Reversible Liquid Organic Hydrogen Carrier Systems: From Hydrogen Carriers to Catalysts.

Liquid organic hydrogen carriers (LOHCs) have gained significant attention for large-scale hydrogen storage due to their remarkable gravimetric hydrogen storage capacity (HSC) and compatibility with existing oil and gas transportation networks for long-distance transport. However, the practical application of reversible LOHC systems has been constrained by the intrinsic thermodynamic properties of hydrogen carriers and the performances of associated catalysts in the (de)hydrogenation cycles. To overcome these challenges, thermodynamically favored carriers, high-performance catalysts, and catalytic procedures need to be developed. Here, significant advances in recent years have been summarized, primarily centered on regular LOHC systems catalyzed by homogeneous and heterogeneous catalysts, including dehydrogenative aromatization of cycloalkanes to arenes and N-heterocyclics to N-heteroarenes, as well as reverse hydrogenation processes. Furthermore, with the development of metal complexes for dehydrogenative coupling, a new family of reversible LOHC systems based on alcohols is described that can release H2 under relatively mild conditions. Finally, views on the next steps and challenges in the field of LOHC technology are provided, emphasizing new resources for low-cost hydrogen carriers, high-performance catalysts, catalytic technologies, and application scenarios.

Synthesis Methodology for Discrete MIMO PID Controller with Loop Shaping on LTV Plant Model via Iterated LMI Restrictions

This paper presents a methodology for synthesizing discrete MIMO PID controllers through iterative solutions of LMIs. It justifies the necessity of direct synthesis of discrete controllers in digital control systems. The proposed methodology allows for synthesizing an LTI controller for an LTV plant model, ensuring the robust stability of the synthesized system. Robustness is further ensured by the small-gain theorem and a novel approach to loop shaping, enabling the use of arbitrary shape functions. As a result, this methodology provides a broad statement of the problem with performance criteria necessary for practical application in control systems. Numerical examples are used to illustrate the methodology, which is implemented using the MATLAB toolbox, freely available for use.

Modified pentagonal fractal antenna for ultra-wideband applications in communication systems

ABSTRACT Due to the recent expansion in the generations of the communications systems, it is utmost priority to transmit a large amount of data over a wideband frequency spectrum. For this single ultra-wideband (UWB) antennas are the prime choice as compared to the multiple narrow band antennas. A modified pentagonal fractal patch antenna with a modified ground plane covering ultra-wideband has been proposed. The antenna has a compact size of 25 mm × 25 mm × 1.6 mm with peak gain of 2.2 dB. The UWB coverage of the design is achieved by implementing modified co-planar ground, modified fractal geometry and coplanar feed. The proposed design possesses the significant resonance and radiation performance with very compact size on low-cost substrate while covering the whole UWB. Finally, the simulated resonating and radiation characteristics have been validated with the measured characteristics of the fabricated prototype and the results are quite promising.

Modeling skeletal dysplasia in Hurler syndrome using patient-derived bone marrow osteoprogenitor cells.

Dysostosis multiplex is a major cause of morbidity in Hurler syndrome (mucopolysaccharidosis type IH [MPS IH], OMIM #607014) because currently available therapies have limited success in its prevention and reversion. Unfortunately, the elucidation of skeletal pathogenesis in MPS IH is limited by difficulties in obtaining bone specimens from pediatric patients and poor reproducibility in animal models. Thus, the application of experimental systems that can be used to dissect cellular and molecular mechanisms underlying the skeletal phenotype of MPS IH patients and to identify effective therapies is highly needed. Here, we adopted in vitro/in vivo systems based on patient-derived bone marrow stromal cells to generate cartilaginous pellets and bone rudiments. Interestingly, we observed that heparan sulphate accumulation compromised the remodeling of MPS IH cartilage into other skeletal tissues and other critical aspects of the endochondral ossification process. We also noticed that MPS IH hypertrophic cartilage was characterized by dysregulation of signaling pathways controlling cartilage hypertrophy and fate, extracellular matrix organization, and glycosaminoglycan metabolism. Our study demonstrates that the cartilaginous pellet-based system is a valuable tool to study MPS IH dysostosis and to develop new therapeutic approaches for this hard-to-treat aspect of the disease. Finally, our approach may be applied for modeling other genetic skeletal disorders.