Complex Applications Research Articles

Expanding the Palette of SWIR Emitting Nanoparticles Based on Au Nanoclusters for Single-Particle Tracking Microscopy.

Single-molecule localization microscopy has proved promising to unravel the dynamics and molecular architecture of thin biological samples down to nanoscales. For applications in complex, thick biological tissues shifting single-particle emission wavelengths to the shortwave infrared (SWIR also called NIR II) region between 900 to 2100nm, where biological tissues are more transparent is key. To date, mainly single-walled carbon nanotubes (SWCNTs) enable such applications, but they are inherently 1D objects. Here, 0D ultra-small luminescent gold nanoclusters (AuNCs, <3nm) and ≈25nm AuNC-loaded-polymeric particles that can be detected at the single-particle level in the SWIR are presented. Thanks to high brightness and excellent photostability, it is shown that the dynamics of the spherical polymeric particles can be followed at the single-particle level in solution at video rates for minutes. We compared single particle tracking of AuNC-loaded-polymeric particles with that of SWCNT diffusing in agarose gels demonstrating the specificity and complementarity of diffusion properties of these SWIR-emitting nano-objects when exploring a complex environment. This extends the library of photostable SWIR emitting nanomaterials to 0D nano-objects of variable size for single-molecule localization microscopy in the second biological window, opening unprecedented possibilities for mapping the structure and dynamics of complex biological systems.

Advancing Fuzzy Logic: A Hierarchical Fuzzy System Approach

Fuzzy logic systems (FLS) are widely used in various engineering, medical, and scientific applications for modelling complex and uncertain systems. However, traditional FLS has limitations in handling complex and hierarchical structures due to their lack of scalability and interpretability. This paper proposes an approach to hierarchical fuzzy systems (HFS) that enhances the traditional FLS by providing a hierarchical structure with multiple levels of fuzzy rules. The main contribution of this paper is the proposal of HFS, which improves interpretability, scalability, and accuracy compared to traditional FLS, particularly for real-world applications. However, the question arises, "How can the FLS be converted into the HFS?" In this paper, the approach to HFS architecture will comprise two levels of FLS, where the first level determines the overall behaviour of the system, and the second level refines the output by considering the local behaviour. The proposed approach has been validated through experimental results on a case studies, such as the Iris flower classification. The results demonstrate that HFS provides more efficient and reliable solutions and can be applied to various complex and hierarchical systems in different domains, such as manufacturing, robotics, and decision-making.

Recognition of concrete microcrack images under fluorescent excitation based on attention mechanism deep recurrent neural networks

This study aims to recognize fluorescent excitation images of microcracks in concrete components through the adoption of an Attention Mechanism-based Deep Recurrent Neural Network (RNN) model, thereby enhancing the accuracy and efficiency of crack detection. Considering the significance of concrete crack detection and the limitations in efficiency and accuracy of existing methods, this paper proposes an innovative image processing technique that combines fluorescent excitation methods with deep learning models to achieve earlier and more accurate identification of concrete microcracks. C30 concrete specimens were prepared experimentally and treated with fluorescent solution spray. Fluorescent images of cracks under UV light excitation were collected and processed using a segmented attention mechanism deep RNN model. Various performance evaluation metrics, including mean Intersection over Union (mIoU), mean Image Intersection over Union (miIoU), mean Image Dice Coefficient (miDice), and F1 score, were employed to comprehensively assess the model's performance. The results demonstrate that the proposed model achieved significant effectiveness in concrete crack image recognition, showing higher mIoU, miIoU, miDice, and F1 scores compared to other representative deep learning models, thus proving its advantages in recognition accuracy and efficiency. Particularly, by introducing the segmented attention mechanism, the model could capture microcrack features more effectively, significantly improving the accuracy of crack identification. This method not only provides a new technical approach for the early detection of concrete cracks but also lays the foundation for further development of efficient and accurate crack detection technologies to accommodate more complex engineering application scenarios.

Aromatimide tetracarboxylate metal-framework materials: Fluorescence sensing application towards NACs and small drug molecules

Three multifunctional fluorescent Metal-Organic Framework (MOF) sensors are synthesized based on N, N’-bis(3, 5-dicarboxyphenyl)isophthalic diimide (H4BDCPPI) with Zn, Cu, and Cd ions. Structural analysis shows that although using the same organic conjugated aromatic imide tetracarboxylic acids ligand, their corresponding structures are different. Based on luminescence characteristics of the organic components, they exhibit good luminescent properties. They can sensitively detect nitro aromatic compounds (p-nitrophenol (PNP), 2,4-dinitrophenol (DNP) and 2,4,6-trinitrophenol (TNP)) and small drug molecules (colchicine and balsalazide disodium) with low limit of detection. The complex 1 is more sensitive for detection of TNP, compared to the other complexes. The mechanism of fluorescence sensing is proposed in detail based on analyses of PXRD, fluorescence lifetimes, and UV–vis spectra. This study explores the potential of MOFs based on H4BDCPPI in the field of fluorescence sensing for the first time, laying the theoretical foundation for application of complexes with conjugated aromatic imide tetracarboxylic acids.

Enhancing User Experience in Music Streaming: A React.js and Context API Approach for Spotify Clone

The evolution of web technologies has revolutionized the way music streaming platforms operate. This research paper delves into the development and implementation of a Spotify clone using React.js and Context API. The study aims to explore the efficacy of these technologies in replicating the core functionalities of the renowned music streaming service while evaluating their advantages and limitations in the context of a clone project. The research methodology involves an in-depth examination of React.js and its application in building user interfaces, along with an analysis of the Context API for state management within the Spotify clone. The paper outlines the step-by-step process of creating the clone, highlighting key architectural decisions, data handling strategies, and user experience design considerations. Additionally, this paper investigates the performance metrics, scalability, and maintenance aspects of the React.js and Context API-based Spotify clone in comparison to the original platform. The study incorporates user feedback and usability testing to evaluate the similarity in functionality and user experience between the clone and the original application. The findings of this research contribute to the understanding of leveraging React.js and Context API in replicating complex applications like Spotify. Moreover, the paper discusses the potential implications and future directions for utilizing these technologies in developing similar web-based projects. Key Word: React JS, Context API, Spotify Clone, User Interface, State Management, Web Development

Challenges and Outcomes of Combining Machine Learning with Software-Defined Networking for Network Security and management Purpose: A Review

Current research in data dissemination in Vehicular Ad Hoc Networks (VANETs) has examined different approaches and frameworks to enhance the effectiveness and dependability of information sharing between vehicles on the road. The integration of Machine Learning (ML) with Software-Defined Networking (SDN) has fundamentally transformed the field of network administration and security. This paper specifically addresses the challenges faced by traditional network architectures in effectively handling the increasing amount of data and complex applications. Software-Defined Networking (SDN), a cutting-edge framework, separates the control of network operations from the actual forwarding of data, offering a versatile and cost-effective solution. The combination of Software-Defined Networking (SDN) and Machine Learning (ML) allows for the extraction of valuable information from network data, leading to enhanced network management and the facilitation of predictive analytics. The aim of this study is to examine the feasibility and challenges of incorporating machine learning into software-defined networking (SDN), focusing particularly on practical applications. Integrating Machine Learning (ML) into Software-Defined Networking (SDN) presents challenges, including the requirement for robust algorithms to detect patterns and ensure security. It is crucial to carry out the tasks of developing and implementing machine learning models for real-time predictions and ensuring the robustness of the system. Research is essential to strike a balance between the transformative abilities of ML-SDN and the practical network environments. This helps to improve the resilience, security, and adaptability of network infrastructures in the digital age.

Two viologen-based complexes as persistent luminescent materials and their applications in inkless print and anticounterfeiting

Viologen-based complexes have garnered significant attention due to their multifunctional stimuli-responsive properties and tunable persistent luminescence. However, achieving ultralong afterglow emission and color change in these complexes remains a challenge, limiting their application in the field of persistent luminescence. Herein, two novel viologen-based complexes were successfully designed and synthesized using N,N′-bis(2-carboxyethyl)-4,4′-bipyridinium dichloride (H2bybpy) ligands and trimesic acid (H3BTC) with the transition metals Zn(II)/Cd(II). Complexes 1 and 2 display reversible photochromic and electrochromic properties. Interestingly, complexes 1 and 2 in their crystal state exhibit dual fluorescence and persistent luminescence, emitting a green and cyan afterglow when the UV lamp is turned off at 77 K. Incorporating phosphorescent molecules and changing the metal significantly extended the duration of persistent luminescence and altered the hue. The complexes were also demonstrated to have potential applications in inkless printing, color-changing films, data encryption, and anti-counterfeiting. This work enhances the application of viologen-based complexes in light-controlled persistent fluorescence and provides ideas for achieving ultra-long afterglow emission.

A review on the design and fabrication of polydimethylsiloxane thin-film composite membranes (TFCMs) for alcohol permselective pervaporation

Pervaporation-based membrane technology have gained increasing interest due to their energy efficiency and environmental sustainability. Polydimethylsiloxane (PDMS) thin-film composite membranes (TFCMs) have emerged as promising candidates for alcohol permselective applications. Compared to the development of pervaporation membrane for water purification, the optimization progress with the ethanol recovery membrane has been fairly slow and fails to meet requirements of bio-alcohol production. Despite the continuous efforts on membrane materials, the role of structural, physicochemical properties for the forming progress of selective layer on substrate is not a well-attended topic. This paper summarized the design and construction considerations of PDMS composite membranes for pervaporation from the viewpoint of the transport resistance model, including the effect of selective layer, support layer, and intermediate layer, to deeply understand and precisely regulate the preparation process. We high light the key role of crosslinking and spreading of PDMS on porous support layer in membrane fabrication. The structure-activity relationship between the separation performance of transport structure with the membrane solution properties, substrate pore structure, and the fabrication methods was discussed in detail. It was indicated that the challenges for the TFCMs applied on a pilot or industrial scale include the thickness control, coating and curing time optimization, and solvent issue. Finally, it was expected for further research to optimize the membrane fabrication process, develop novel advanced membrane materials and to explore complex and potential applications the new requirements in industry.

Extension of simple multi-attribute rating technique in uncertainty environment for 5G industry evaluation: Egyptian new administrative capital as a case study

As is well-known, multicriteria decision-making (MCDM) approaches can aid decision-makers in identifying the optimal alternative based on predetermined criteria. However, it is a big challenge to apply this approach in complex applications such as 5th generation (5G) industry assessment because criteria are challenging and trade-offs between them are hard. Also, assessment of the 5G industry involve strong uncertainty. So, this study is the first to evaluate the 5G industry using a new neutrosophic simple multi-attribute rating technique (N-SMART). Since neutrosophic set considers truth-degree, indeterminacy-degree, and falsity-degree, it is a more accurate instrument for evaluating uncertainty. The 5G assessment issue exemplifies the validity and great performance of our proposed method as: (1) its ability to deal with uncertainty phenomena; (2) its simplicity; and (3) its enhanced capacity to discern alternatives. Also, by considering the 5G service provided in the Egyptian New Administrative capital as a case study, the results showed that Ericsson 5G is the best choice and Nokia 5G is the worst choice.

ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning

Aiming at selecting a small subset of nodes with maximum influence on networks, the Influence Maximization (IM) problem has been extensively studied. Since it is #P-hard to compute the influence spread given a seed set, the state-of-the-art methods, including heuristic and approximation algorithms, faced with great difficulties such as theoretical guarantee, time efficiency, generalization, etc. This makes it unable to adapt to large-scale networks and more complex applications. On the other side, with the latest achievements of Deep Reinforcement Learning (DRL) in artificial intelligence and other fields, lots of works have been focused on exploiting DRL to solve combinatorial optimization problems. Inspired by this, we propose a novel end-to-end DRL framework, ToupleGDD, to address the IM problem in this paper, which incorporates three coupled graph neural networks for network embedding and double deep Q-networks for parameters learning. Previous efforts to solve IM problem with DRL trained their models on subgraphs of the whole network, and then tested on the whole graph, which makes the performance of their models unstable among different networks. However, our model is trained on several small randomly generated graphs with a small budget, and tested on completely different networks under various large budgets, which can obtain results very close to IMM and better results than OPIM-C on several datasets, and shows strong generalization ability. Finally, we conduct a large number of experiments on synthetic and realistic datasets, and experimental results prove the effectiveness and superiority of our model.

Non-covalent interaction of the plant protein composites-proanthocyanidins and the utilization in oil in water emulsions

The polyphenols and plant proteins are increasingly being utilized as functional food ingredients, it is critical to understand their interaction mechanism with each other. In this study, protein composites (RP-PP and RP-SPI) were fabricated using pH-driven technology by rice protein (RP) with pea protein (PP) and soy protein isolates (SPI), respectively. The interaction mechanism between the protein composites and proanthocyanidins (PA) was also explored using a combined approach of spectroscopy and thermodynamics. The analysis of fluorescence spectroscopy and turbidity confirmed the formation of the protein composites-PA colloidal complexes. The thermodynamic results calculated from fluorescence data and ITC analysis indicated the hydrogen bond was the main interaction force between the protein composites and PA. The secondary structures of the protein composites were changed with the decreased α-helix and β-turn due to the increasing PA. In addition, the existence of PA reduced the surface hydrophobicity, particle size, and the interfacial tension of proteins. Furthermore, the thermal stability and zeta potential of proteins increased after the binding of the PA. Ultimately, the PA showed a potential in reducing the dimension of droplet and enhancing storage stability of proteins-PA emulsion. This study provides useful information for the industrial application of the proteins-polyphenols complexes.

Interca: an R library implementing “automatic” interpretation of results of multiple correspondence analysis (MCA)

PurposeThe purpose of this paper is to develop a software-library in the R programming language that implements the concepts of the interpretive coordinate, interpretive axis and interpretive plane. This allows for the automatic and reliable interpretation of results from the multiple correspondence analysis (MCA) as previously proposed and published. Consequently, the users can seamlessly apply these concepts to their data, both via R commands and a corresponding graphical interface.Design/methodology/approachWithin the context of this study, and through extensive literature review, the advantages of developing software using the Shiny library were examined. This library allows for the development of full-stack applications for R users without the need for knowledge of the corresponding technologies required for the development of complex applications. Additionally, the structural components of a Shiny application were presented, leading ultimately to the proposed software application.FindingsSoftware utilizing the Shiny library enables nonexpert developers to rapidly develop specialized applications, either to present or to assist in the understanding of objects or concepts that are scientifically intriguing and complex. Specifically, with this proposed application, the users can promptly and effectively apply the scientific concepts addressed in this study to their data. Additionally, they can dynamically generate charts and reports that are readily available for download and sharing.Research limitations/implicationsThe proposed package is an implementation of the fundamental concepts of the exploratory MCA method. In the next step, discoveries from the geometric data analysis will be added as features to provide more comprehensive information to the users.Practical implicationsThe practical implications of this work include the dissemination of the method’s use to a broader audience. Additionally, the decision to implement it with open-source code will result in the integration of the package’s functions by other third-party user packages.Originality/valueThe proposed software introduces the initial implementation of concepts such as interpretive coordination, the interpretive axis and the interpretive plane. This package aims to broaden and simplify the application of these concepts to benefit stakeholders in scientific research. The software can be accessed for free in a code repository, the link to which is provided in the full text of the study.

Рекомендації щодо комплексного застосування гербіцидів

Рекомендації щодо комплексного застосування гербіцидів

Improving Access to Psilocybin-Assisted Therapy: Barriers, Challenges, and Recommendations

IntroductionPsilocybin-assisted therapy (PAT) has demonstrated significant potential in alleviating anxiety, depression, and psychological distress among individuals with terminal illnesses. However, numerous barriers prevent equitable access to this transformative treatment.ObjectivesThis study seeks to gather the perspectives of patients on the waitlist of PAT.MethodsSemi-structured interviews highlight the challenges faced by patients seeking PAT and their care providers and propose recommendations to enhance accessibility.ResultsThrough a case study of Roots to Thrive, a non-profit healthcare practice offering group-based PAT, obstacles such as complex application processes, fear of judgement, logistical and financial constraints, and systemic inequities are revealed. Moreover, Health Canada’s stringent control of PAT access via clinical trials and the Special Access Program (SAP) presents challenges for primary care providers and hinders the involvement of trained practitioners. The moral distress experienced by patients and providers due to delayed or denied access further emphasizes the urgency of addressing these barriers.ConclusionsAdvocates are calling for streamlined referral systems, expedited services for end-of-life patients, formal billing infrastructure, practitioner education, expanded coverage, legislative adjustments, post-therapy support, and collaboration with non-profit organizations and Indigenous Healers to promote equitable and effective PAT. By implementing these recommendations, barriers to PAT can be overcome, allowing more individuals to benefit from this therapy and find relief from the psychological distress associated with their conditions.Disclosure of InterestNone Declared

Pr3+-Doped Lithium Niobate and Sodium Niobate with Persistent Luminescence and Mechano-Luminescence Properties

In this research, a comprehensive series of Pr3+-doped lithium niobate and sodium niobate materials were obtained at different temperatures via solid-state sintering, and their structures and properties were compared. NaNbO3: 0.75% Pr3+ phosphors were synthesized by sintering at 1150 °C for 2 h and emitted red persistent luminescence for more than 1200 s, peaking at 612 nm under UV excitation, which was a typical long persistent luminescence phenomenon. Furthermore, the sample glowed when pressurized, and a red bright luminescence which lasted for several seconds was visible to the naked eye. This was a typical mechanical luminescence phenomenon of samples under mechanical stress, directly converting mechanical energy into light energy. It was determined that NaNbO3:Pr3+ and LiNbO3:Pr3+ both possess multimode luminescence. Owing to their red long persistent luminescence (LPL) and mechano-luminescence (ML) properties, Pr3+ phosphors can be employed in fields, such as display technologies, stress sensing, structural damage detection, and other complex applications.

Digital Marketing in the Age of Artificial Intelligence: Challenges, Opportunities, Trends

The article examines the main trends in marketing according to leading digital agencies in Bulgaria. Analysis of existing publications shows that artificial intelligence retains its dominant role in digital marketing. Without a doubt, AI provides marketers with new and effective ways to target audiences, personalize content and measure results. At the same time, already established and widely used marketing tools such as email marketing and content marketing continue to be a leading element in brand strategies for reaching relevant audiences. As a result of the content analysis, six key trends have been identified predetermining the development of marketing, developed as a priority in a digital environment: artificial intelligence, personalization, email marketing, the social network TikTok, video content and sustainability as a way of production, consumption and communication. In the context of the so-called never normal, reflecting the uncertainty and dynamics of the complex global reality, the knowledge and application of the challenges and opportunities reflected in the article in the era of artificial intelligence imply the achievement not only of competitiveness and significant success but also of higher sustainability for companies.

A deep reinforcement learning resource allocation strategy for integrated sensing, communication and computing

The growing number of sensor nodes requires higher computational and communication resources to meet the needs of more complex applications as increasingly sophisticated mobile communications and networks evolve. As energy and time are necessary to request these resources, Edge Computing(EC) has become a key enabler to solve energy and time consumption problems in the field of Integrated Sensing, Communication, and Computing (ISCC). Computation offloading is an important technology, and Edge devices (EDs) can be used to reduce latency and energy consumption by offloading tasks to EC servers. However, the transmission also causes latency and energy consumption, so this paper proposes a DDORS scheme to solve this problem by decomposing the original problem into two subproblems and using convex optimization and DRL to obtain optimal solutions to reduce the weighted total cost of completing tasks in terms of latency and energy consumption. The simulation results indicate that the proposed DDORS may drastically reduce costs when compared to various baseline schemes.

Recovery of cardiac electrophysiological alterations by heart rate complexity based on multiscale entropy following liver transplantation

Autonomic nervous dysfunction is a known cardiac sequalae in patients with end-stage liver disease and is associated with a poor prognosis. Heart rate analysis using nonlinear models such as multiscale entropy (MSE) or complexity may identify marked changes in these patients where conventional heart rate variability (HRV) measurements do not. To investigate the application of heart rate complexity (HRC) based on MSE in liver transplantation settings. Thirty adult recipients of elective living donor liver transplantation were enrolled. HRV parameters using conventional HRV analysis and HRC analysis were obtained at the following time points: (1) 1 day before surgery, (2) postoperative day (POD) 7, (3) POD 14, (4) POD 90, and (5) POD 180. Preoperatively, patients with MELD score ≥ 25 had significantly lower HRC compared to patients with lower MELD scores. This difference in HRC disappeared by POD 7 following liver transplantation and subsequent analyses at POD 90 and 180 continued to show no significant difference. Our results indicated a significant negative correlation between HRC based on MSE analysis and liver disease severity preoperatively, which may be more sensitive than conventional linear HRV analysis. HRC in patients with MELD score ≧ 25 improved over time and became comparable to those with MELD < 25 as early as in 7 days.

Сравнительная агротехнологическая оценка возделывания яровой пшеницы в южной лесостепи Западной Сибири

Abstract. Long-term (more than 20 years) studies were carried out on meadow – chernozem soil in the southern forest-steppe zone of Western Siberia. In a stationary grain-steam crop rotation (steam-wheat-wheat-wheat-barley), a comparative assessment of fertility, the state of agrophytocenosis, yield and quality of spring wheat grain, depending on the placement of the crop in the crop rotation, the tillage system, and complex chemicalization was carried out. The purpose of the research is to establish the influence and effectiveness of multi-level agricultural technologies of spring wheat on the elements of soil fertility, the state of agrophytocenosis, productivity and technological properties of grain in the southern forest-steppe of Western Siberia. Research methods. In the southern forest-steppe agricultural landscapes, the effectiveness of multi-level agricultural technologies of spring wheat cultivation has been established, with an increase in yield from 1.51 to 3.24 t/ha (2.1 times) and a decrease in technological parameters of grain. For the first time, the share contribution of chemicalization components in increasing the productivity of spring wheat cultivated by steam and in the grain-steam crop rotation has been established. Two-factor experience: factor A – tillage system (dump-plowing on the head of 20–22 cm, annually; combined-plowing in the fallow field and under the third wheat after steam for 20–22 cm and flat-cut on the head of 10–12 for the second wheat after steam barley; flat-cut on the head of 10–12 cm for all crops annually; minimal – in the fallow field cultivation on the 8–10 cm. in other fields without autumn treatment); factor B – means of intensification (control – without chemicals, the option of complex application of chemicals included the combined use of fertilizers (N24 P36 per 1 ha of arable land), recommended herbicides, fungicides and retardants). Results. It was found that intensive agrotechnology with the use of fertilizers and plant protection products makes the main contribution to increasing the yield and technological parameters of grain – 30–35 %, precursors – up to 20–25 %, weather conditions of the growing season – 18–20 %, the system of tillage in crop rotation – 10–12 %. The complex application of chemicals contributes to an increase in crop biomass to 2080 g/m2 (by 49 %), reduces water consumption to 73 mm per 1 t\ of grain (2.7 times), optimizes the nutrient regime of the soil and the phytosanitary state of agrophytocenosis, which ultimately increases yields from 1.51 to 3.27 t/ha while improving the technological properties of grain gluten content up to 26.4 – 29.4 %. Long-term rational use of chemicals does not lead to the accumulation of ecotoxicants in soil and grain. The scientific novelty was revealed for the conditions of Western Siberia with the long-term rational use of chemicals, the accumulation of ecotoxicants in the soil and grain was not observed.

A Trusted Authentication Scheme Using Semantic LSTM and Blockchain in IoT Access Control System

In edge computing scenarios, due to the wide distribution of devices, complex application environments, and limited computing and storage capabilities, their authentication and access control efficiency is low. To address the above issues, a secure trusted authentication scheme based on semantic Long Short-Term Memory (LSTM) and blockchain is proposed for IoT applications. The attribute-based access control model is optimized, combining blockchain technology with access control models, effectively improving the robustness and credibility of access control systems. Semantic LSTM is used to predict environmental attributes that can further restrict user access and dynamically meet the minimum permission granting requirements. Experiments show that when the number of certificates is 60, the computational overhead of the proposed method is only 203s, which is lower than other state-of-the-art methods. Therefore, the performance of the proposed schema in information security protection in IoT environments shows promise as a scalable authentication solution for IoT applications.