Current Approaches Research Articles

Virtual testbed for development and evaluation of power system digital twins and their applications

Digital twins (DTs) show considerable promise for development of applications and solutions across a diverse range of power system services. Ensuring these applications function as intended without causing disruptions or unexpected issues requires comprehensive testing prior to practical deployment. However, current approaches to power system digital twin (PSDT) application development and testing experience significant obstacles, including limitations and risks associated with accessing real-world power systems, models and data. This article introduces a virtual testbed based on the concept of a virtual physical twin (VPT), that replaces the need for access to a physical twin (PT), as a platform designed to remove the heavy reliance on online data flows and provide a close to real-world environment. A testbed enables simultaneous development and testing of DTs and their related applications in a realistic, interactive, and safe setting without impact on the operation of the actual power system. A complete workflow for PSDT testbed implementations is proposed, with all necessary steps from concept to the eventual connection to the physical system. To validate the practical implementation steps, utility, and value of the testbed, a real-time transient stability analysis with two artificial intelligence algorithms is provided. The proposed testbed is expandable and adaptable, promoting the development of advanced and intelligent PSDTs. The key benefit of the testbed is that it enables the development of DT and its application closely in a realistic environment without affecting the operation of PT.

Incremental schema integration for data wrangling via knowledge graphs

Virtual data integration is the current approach to go for data wrangling in data-driven decision-making. In this paper, we focus on automating schema integration, which extracts a homogenised representation of the data source schemata and integrates them into a global schema to enable virtual data integration. Schema integration requires a set of well-known constructs: the data source schemata and wrappers, a global integrated schema and the mappings between them. Based on them, virtual data integration systems enable fast and on-demand data exploration via query rewriting. Unfortunately, the generation of such constructs is currently performed in a largely manual manner, hindering its feasibility in real scenarios. This becomes aggravated when dealing with heterogeneous and evolving data sources. To overcome these issues, we propose a fully-fledged semi-automatic and incremental approach grounded on knowledge graphs to generate the required schema integration constructs in four main steps: bootstrapping, schema matching, schema integration, and generation of system-specific constructs. We also present Nextia DI , a tool implementing our approach. Finally, a comprehensive evaluation is presented to scrutinize our approach.

On-Water Surface Synthesis of Vinylene-Linked Cationic Two-Dimensional Polymer Films as the Anion-Selective Electrode Coating.

Vinylene-linked two-dimensional polymers (V-2DPs) and their layer-stacked covalent organic frameworks (V-2D COFs) featuring high in-plane π-conjugation and robust frameworks have emerged as promising candidates for energy-related applications. However, current synthetic approaches are restricted to producing V-2D COF powders that lack processability, impeding their integration into devices, particularly within membrane technologies reliant upon thin films. Herein, we report the novel on-water surface synthesis of vinylene-linked cationic 2DPs films (V-C2DP-1 and V-C2DP-2) via Knoevenagel polycondensation, which serve as the anion-selective electrode coating for highly-reversible and durable zinc-based dual-ion batteries (ZDIBs). Model reactions and theoretical modeling revealed the enhanced reactivity and reversibility of the Knoevenagel reaction on the water surface. On this basis, we demonstrated the on-water surface 2D polycondensation towards V-C2DPs films that show large lateral size, tunable thickness, and high chemical stability. Representatively, V-C2DP-1 presents as a fully crystalline and face-on oriented film with in-plane lattice parameters of a=b≈43.3 Å. Profiting from its well-defined cationic sites, oriented 1D channels, and stable frameworks, V-C2DP-1 film possesses superior bis(trifluoromethanesulfonyl)imide anion (TFSI-)-transport selectivity (transference, t_=0.85) for graphite cathode in high-voltage ZDIBs, thus triggering additional TFSI--intercalation stage and promoting its specific capacity (from ~83 to 124 mAh g-1) and cycling life (>1000 cycles, 95 % capacity retention).

Synthese an der Wasseroberfläche von Vinylen‐Verbundenen Kationischen Zwei‐Dimensionalen Polymerfilmen als Beschichtung für Anion‐Selektive Elektroden

AbstractVinylen verbrückte zwei‐dimensionale Polymere (V‐2DPs) und deren schichtgestapelten kovalent organische Gerüstverbindungen (V‐2D COFs) zeichnen sich durch ihre hohe π‐Konjugation in der Ebene und ihre robuste Struktur als vielversprechnende Kanidaten für energiebezogene Anwendungen aus. Allerdings sind derzeitige syntheisische Ansätze auf die Herstellung von V‐2D COF Pulver beschränkt, welche schlecht prozessierbar sind, was die Integration in Geräte, insbesondere für Membranthechnologien, welche auf dünnen Filmen beruhen erschwert. Im folgenden berichten wir über die neu‐artige Synthese von kationischen 2DPs‐Filmen an der Wasseroberfläche, die über Vinyleneinheiten verbrückt sind (V‐C2DP‐1 und V‐C2DP‐2) mittels Knoevenagel‐Polykondensation, diese dienen als anionenselektive Elektrodenbeschichtung für hochreversible und langlebige Zink‐basierte Dual‐Ionen‐Batterien (ZDIBs). Modellreaktionen und theoretische Modellierung zeigen eine erhöte Reaktivität und Reversibilität der Knoevenagel‐Reaktion an der Wasseroberfläche an. Basierend darauf zeigten wir die 2D Polykondensation hin zu V‐C2DP‐Filmen an der Wasseroberfläche, welche eine weitreichen‐de laterale Größe, anpassbare Dicke und hohe chemischer Stabilität besitzen. Repräsentativ zeigt sich V‐C2DP‐1 als ein vollständig crystalliner und flächen‐orientierter Film mit den in der Ebene liegenden Gitterparametern von a=b≈43.3 Å. Dank der gut definierten katinoischen Funktionalitäten, orientierten 1D‐Kanälen und des sabilen Gerüstes besitzt der V‐C2DP‐1 Film eine herausragende Transportselektivität von Bis(trifluormethansulfon)imid‐anion (TFSI−) (Transferrate, t =0.85) für die Graphitkathode in Hochspannungs‐ZDIBs. Die hohe Selektivität führt zu zusätzlichen TFSI−‐Intercalationsstufen wodurch eine höhere spezifischen Kapazität (von ~83 bis 124 mAh g−1), sowie Zyklusdauer (>1000 Zyklen, 95 % Kapazitätserhaltung) gefördert wird.

An Automated Analysis Framework for Epidemiological Survey on COVID-19.

For a long time, the prevention and control of COVID-19 has received significant attention. A crucial aspect of controlling the disease's spread is the epidemiological survey of patients and the subsequent analysis of epidemiological survey reports (case reports). However, current mainstream analysis approaches are all made manually. This manual method is time-consuming and manpower-intensive. This paper designs an automated visual epidemiological survey analysis (AVESA) framework for the epidemiological survey on COVID-19. AVESA designs a deep neural network for information extraction from case reports and automatically constructs an epidemiological knowledge graph based on predefined pattern. Moreover, a multi-dimensional knowledge reasoning model is developed for conducting knowledge reasoning in the complete COVID-19 epidemiological knowledge graph. In the entity extraction sub-task and multi-task extraction sub-task, AVESA achieved F1 scores of 85.12% and 92.29% respectively on the constructed dataset, significantly outperforming the standalone information extraction models. In full-graph computing, all three experiments align closely with manual analysis standards. In the risk analysis experiment, the weighted PageRank algorithm showed an average improvement of 11.21% in Top_Recall_n% over the standard PageRank algorithm. In the community detection experiment, the weighted Louvain algorithm showed a mere 4.34% community difference rate compared to manual analysis.

Targeted Treatment of Lung Cancer using Nanomaterials: Prospective and Advances

Background: Lung cancer is the second most lethal type of cancer, with a poor survival rate of 5 years. It is one of those malignant tumors that has grown most rapidly in the context of mortality and morbidity. Aim: This review article aims to provide insight into current nanotechnological approaches taken into consideration that provide advantages over conventional chemotherapy. Result and Discussion: After comparing conventional chemotherapy and nanotechnology-based therapies for lung cancer, the results showed that recent advances in nanomaterials proved to be more effective in lung cancer diagnosis, mitigation and treatment. Here, Surface-engineered smart nanocarrier- based inhalations, Bio-nanocarriers for lung cancer, gas plasma nanoparticles, and magnetic nanoparticles are discussed. Conclusion: After summarizing these nanomaterials, investigators concluded that the in-vivo and invitro effectiveness of recently developed nanoparticles was found to be better than that of conventional nanoparticles.

Physics-Constraint Variational Neural Network for Wear State Assessment of External Gear Pump.

Most current data-driven prognosis approaches suffer from their uncontrollable and unexplainable properties. To address this issue, this article proposes a physics-constraint variational neural network (PCVNN) for wear state assessment of the external gear pump. First, a response model of the pressure pulsation of the gear pump is constructed via a spectral method, and a compound neural network is utilized to extract features from the pressure pulsation signal. Then, the response model is formulated into an objective function to softly constrain the learning process of the neural network, forcing the learned features to have explicit physics meaning. Meanwhile, to characterize the system uncertainty, the variational inference is utilized to extend a Kullback-Leibler (KL) divergence into the objective function. Finally, the wear state is evaluated based on the distance of learned physics features. Experimental results on an external gear pump validate the merits of the proposed method in explainable representation learning and system uncertainty estimation. It also offers a controllable and explainable perspective to understand the dynamic behavior of the system.

Testing the role of associative learning in evidence-based treatments for anorexia nervosa.

Treatments for anorexia nervosa (AN) remain ineffective for many patients. Processes that can account for differential treatment outcomes remain mostly unknown. We propose that the field test the role of associative learning in current psychological treatments. We hold that this line of research could yield actionable information for understanding non-response and improving long-term outcomes. To make this argument, we define associative learning and outline its proposed role in understanding psychiatric disorders and their treatment. We then briefly review data exploring associative learning in AN. We argue that associative learning processes are implicitly implicated in existing treatments; by this rationale, baseline differences in learning may interfere with treatment response. Finally, we outline future research to test our hypotheses. Altogether, future research aimed at better understanding how associative learning may contribute to AN symptom persistence has the potential to inform novel directions in intervention research. PUBLIC SIGNIFICANCE: There is a pressing need to improve outcomes in treatments for anorexia nervosa (AN). We propose that individual differences in associative learning-the ability to form and update associations between cues, contexts, behaviors, and outcomes-may account for differential response to existing treatments. Undertaking this research could provide an understanding of how current treatments work and inform new approaches for those who may be at risk of poor outcomes.

Tensor-Based Least-Squares Solutions for Multirelational Signals and Applications.

The approach of least squares (LSs) has been quite popular and widely adopted for the common linear regression analysis, which can give rise to the solution to an arbitrary critically-, over-, or under-determined system. Such a linear regression analysis can be easily applied for linear estimation and equalization in signal processing for cybernetics. Nonetheless, the current LS approach for linear regression is unfortunately limited to the dimensionality of data, that is, the exact LS solution can involve only a data matrix. As the dimension of data increases and such data need to be represented by a tensor, the corresponding exact tensor-based LS (TLS) solution does not exist due to the lack of a pertinent mathematical framework. Lately, some alternatives such as tensor decomposition and tensor unfolding were proposed to approximate the TLS solutions to the linear regression problems involving tensor data, but these techniques cannot provide the exact or true TLS solution. In this work, we would like to make the first-ever attempt to present a new mathematical framework for facilitating the exact TLS solutions involving tensor data. To demonstrate the applicability of our proposed new scheme, numerical experiments regarding machine learning and robust speech recognition are illustrated and the associated memory and computational complexities are also studied.

Innovative Encapsulation Strategies for Food, Industrial, and Pharmaceutical Applications.

Bioactive metabolites obtained from fruits and vegetables as well as many drugs have various capacities to prevent or treat various ailments. Nevertheless, their efficiency, in vivo, encounter many challenges resulting in lower efficacy as well as different side effects when high doses are used resulting in many challenges for their application. Indeed, demand for effective treatments with no or less unfavorable side effects is rising. Delivering active molecules to a particular site of action within the human body is an example of targeted therapy which remains a challenging field. Developments of nanotechnology and polymer science have great promise for meeting the growing demands of efficient options. Encapsulation of active ingredients in nano-delivery systems has become as a vitally tool for protecting the integrity of critical biochemicals, improving their delivery, enabling their controlled release and maintaining their biological features. Here, we examine a wide range of nano-delivery techniques, such as niosomes, polymeric/solid lipid nanoparticles, nanostructured lipid carriers, and nano-emulsions. The advantages of encapsulation in targeted, synergistic, and supportive therapies are emphasized, along with current progress in its application. Additionally, a revised collection of studies was given, focusing on improving the effectiveness of anticancer medications and addressing the problem of antimicrobial resistance. To sum up, this paper conducted a thorough analysis to determine the efficacy of encapsulation technology in the field of drug discovery and development.

Organization of storage of various groups of medicines in pharmacy organizations

In the lecture, the significance of organizing the proper storage of various groups of pharmaceutical products in pharmacy institutions is discussed. The key principles and requirements for storing pharmaceutical products are illuminated, including maintaining the necessary temperature conditions, controlling humidity, tracking expiration dates, drug identification, as well as the peculiarities of storing light-sensitive and flammable medications. Special attention is given to the strict adherence to all established standards and procedures to ensure a high level of quality and safety in storing pharmaceutical products, in accordance with legislative requirements. Additionally, the lecture explores practical recommendations and current approaches to organizing the medication storage process to optimize the functioning of pharmacy organizations and to create conditions for safe and high-quality patient service.

Boundary-Aware Abstractive Summarization with Entity-Augmented Attention for Enhancing Faithfulness

With the successful application of deep learning, document summarization systems can produce more readable results. However, abstractive summarization still suffers from unfaithful outputs and factual errors, especially in named entities. Current approaches tend to employ external knowledge to improve model performance while neglecting the boundary information and the semantics of the entities. In this article, we propose an entity-augmented method (EAM) to encourage the model to make full use of the entity boundary information and pay more attention to the critical entities. Experimental results on three Chinese and English summarization datasets show that our method outperforms several strong baselines and achieves state-of-the-art performance on the CLTS dataset. Our method can also improve the faithfulness of the summary and generalize well to different pre-trained language models. Moreover, we propose a method to evaluate the integrity of generated entities. Besides, we adapt the data augmentation method in the FactCC model according to the difference between Chinese and English in grammar and train a new evaluation model for factual consistency evaluation in Chinese summarization.

Strategies for the Construction of Multicyclic Phage Display Libraries.

Peptide therapeutics have gained great interest due to their multiple advantages over small molecule and antibody-based drugs. Peptide drugs are easier to synthesize, have the potential for oral bioavailability, and are large enough to target protein-protein interactions that are undruggable by small molecules. However, two major limitations have made it difficult to develop novel peptide therapeutics not derived from natural products, including the metabolic instability of peptides and the difficulty of reaching antibody-like potencies and specificities. Compared to linear and disulfide-monocyclized peptides, multicyclic peptides can provide increased conformational rigidity, enhanced metabolic stability, and higher potency in inhibiting protein-protein interactions. The identification of novel multicyclic peptide binders can be difficult, however, recent advancements in the construction of multicyclic phage libraries have greatly advanced the process of identifying novel multicyclic peptide binders for therapeutically relevant protein targets. This review will describe the current approaches used to create multicyclic peptide libraries, highlighting the novel chemistries developed and the proof-of-concept work done on validating these libraries against different protein targets.

Current approach to pediatric differentiated thyroid cancer.

Differentiated thyroid cancers (DTC) is a rare cancer in children and adolescents, having features of different clinical presentation, biological behavior, and treatment from adult population. Most of the patient management guidelines are based on literature on adult population and the literature on children and adolescents still limited. There are still unsettled issues regarding both patient management and the therapy. However, the current approach for treatment of DTC includes thyroidectomy, lymph node dissection in patients with nodal metastases and possible use of Iodine-131 radiotherapy. The incidence of DTC is low in pediatric population, and the characteristics of the disease vary among different age groups within this population. Therefore, the literature depends on small cohorts and heterogeneous retrospective studies. This paper aims to review the current literature and give an overview to the approach in the management of DTC in pediatric population. DTC in pediatric population, has an aggressive nature, however the patient's overall survival is excellent. A multidisciplinary approach in the management of pediatric DTC patients would yield fewer side effects and a better life quality.

QbD Assisted Development and Validation of UV Spectroscopic Method in Estimation of Silymarin

Background: Silymarin is a flavonoid utilised in liver dysfunction for years; new studies have emphasised its potential utility as a therapy for nanoparticulate targeting in many other disorders. In order to establish the product quality of such compounds, researchers have been trying to develop a robust method of analysis, but the methods developed till now are too expensive and time-consuming. Here we proposed quality by design-assisted development and validation of the UV spectroscopic method using Design-Expert® software in the estimation of Silymarin for fabrication of nanoparticulate formulations, which is simple, accurate, cost-effective and non-tedious. Aim: The proposed method is a simple, new, robust, accurate, and precise UV visible spectroscopic technique for estimating silymarin produced in nano-formulations utilising an analytical quality by design (AQbD) approach. Methods: A UV spectrophotometric technique was established, as maximum absorption (287.7) was measured using a Shimadzu UV-1800 double-beam UV visible spectrophotometer. The characterization of silymarin was done by melting point, DSC and FTIR techniques. The two critical method variables chosen were scanning speed and sample interval to be analysed by the design of experiment methodology utilizing the central composite design principle, which shows robustness and optimized technique involved in this work. Results: The spectroscopy technique was developed and validated as per International Conference of Harmonization recommendations. The Beer's-Lambert rule was followed in a series of 2-12 μg/ml dilution increments, with a correlation value of R2 = 0.999. The method's linearity was shown to be excellent across the concentration range. The percent recovery of the current method approach was determined to be within the confidential limitations, i.e., less than 2% expressed as % RSD, and the methodology was proved to be precise at inter and intraday variations (% RSD). The LOD and LOQ were found to be 0.264 μg/ml and 0.801 μg/ml which were also determined correctly. During specificity testing, no interfering peaks were found. Conclusion: This UV approach has been used successfully to determine the quantity of silymarin present in the nanoparticulate formulation, which can be used for testing its other pharmaceutical dosage forms.

Drug Delivery System Approaches for Rheumatoid Arthritis Treatment: A Review.

Rheumatoid arthritis (RA) is a chronic autoimmune disease that has traditionally been treated using a variety of pharmacological compounds. However, the effectiveness of these treatments is often limited due to challenges associated with their administration. Oral and parenteral routes of drug delivery are often restricted due to issues such as low bioavailability, rapid metabolism, poor absorption, first-pass effect, and severe side effects. In recent years, nanocarrier-based delivery methods have emerged as a promising alternative for overcoming these challenges. Nanocarriers, including nanoparticles, dendrimers, micelles, nanoemulsions, and stimuli-sensitive carriers, possess unique properties that enable efficient drug delivery and targeted therapy. Using nanocarriers makes it possible to circumvent traditional administration routes' limitations. One of the key advantages of nanocarrier- based delivery is the ability to overcome resistance or intolerance to traditional antirheumatic therapies. Moreover, nanocarriers offer improved drug stability, controlled release kinetics, and enhanced solubility, optimizing the therapeutic effect. They can also protect the encapsulated drug, prolonging its circulation time and facilitating sustained release at the target site. This targeted delivery approach ensures a higher concentration of the therapeutic agent at the site of inflammation, leading to improved therapeutic outcomes. This article explores potential developments in nanotherapeutic regimens for RA while providing a comprehensive summary of current approaches based on novel drug delivery systems. In conclusion, nanocarrier-based drug delivery systems have emerged as a promising solution for improving the treatment of rheumatoid arthritis. Further advancements in nanotechnology hold promise for enhancing the efficacy and safety of RA therapies, offering new hope for patients suffering from this debilitating disease.

Перспективы применения нейронных сетей для процедурной генерации игрового контента в киберспорте

This article provides an overview of research and practices on using neural network technologies in the field of procedural content generation for esports games. The authors explore the history, current approaches to using artificial neural networks in creating game elements, including levels, characters and scenarios, with the aim of enhancing the gaming experience and increasing the uniqueness of the virtual game world. The article covers various aspects of using neural network methods, such as generative models, autoencoders, generative adversarial networks, deep learning, and recurrent neural networks, for creating dynamic and unpredictable content. Examples of the successful implementation of these technologies in popular esports games are discussed, and potential challenges and issues related to applying neural networks in this context are explored. The authors discuss the prospects for the further development of neural network technologies in esports and offer recommendations for their optimal implementation. Overall, the article presents an analysis of the current state and future opportunities for using neural network approaches for procedural content generation in esports scenarios.

Truthful mechanisms to maximize the social welfare in real-time ride-sharing

Ride-sharing contributes significantly to lowering trip expenses, easing traffic congestion and decreasing air pollution. However, current order pairing approaches in ride-sharing usually focus on minimizing total trip distances or maximizing platform profits, overlooking the drivers’ desire for increased earnings. As a result, drivers might provide dishonest information to gain higher profits, leading to inefficient order pairing for the ride-sharing platform and potential losses for both the platform and drivers. In this paper, we address this challenging issue by developing efficient order pairing mechanisms that maximize the social welfare of the platform and drivers. Specifically, we introduce two truthful auction-based order pairing mechanisms, SWMOM-VCG and SWMOM-GM, where drivers bid on platform-published orders to complete them and earn profits. We provide theoretical proof that both mechanisms fulfill the criteria of individual rationality, profitability, truthfulness and so on. Using real taxi order data from New York City, we assess the performance of both mechanisms and show that they achieve greater social welfare compared to existing methods. Additionally, we find that SWMOM-GM requires less computation time than SWMOM-VCG for order pairing, with only a minor reduction in social welfare.

Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment

Quality assessment of images and videos emphasizes both local details and global semantics, whereas general data sampling methods (e.g., resizing, cropping or grid-based fragment) fail to catch them simultaneously. To address the deficiency, current approaches have to adopt multi-branch models and take as input the multi-resolution data, which burdens the model complexity. In this work, instead of stacking up models, a more elegant data sampling method (named as SAMA, scaling and masking) is explored, which compacts both the local and global content in a regular input size. The basic idea is to scale the data into a pyramid first, and reduce the pyramid into a regular data dimension with a masking strategy. Benefiting from the spatial and temporal redundancy in images and videos, the processed data maintains the multi-scale characteristics with a regular input size, thus can be processed by a single-branch model. We verify the sampling method in image and video quality assessment. Experiments show that our sampling method can improve the performance of current single-branch models significantly, and achieves competitive performance to the multi-branch models without extra model complexity. The source code will be available at https://github.com/Sissuire/SAMA.

Operational performance and metal droplet formation in pulsed-shielded metal arc underwater welding

Underwater Shielded Metal Arc Welding (SMAW) is highly affected by the welding depth. The arc and molten metal in contact with the surrounding environment cause operational and metallurgical challenges regarding arc stability, metal transfer, gas formation, high-cooling rates, and diffusible hydrogen uptake. The hydrostatic pressure causes losses in consumable fusion efficiency, constricts the arc, increases the number of short-circuit events, and consequently decreases the welded joint’s process quality. In the present study, the novel approach of pulsed welding current is applied to wet shielded metal arc welding and is operational characteristics are evaluated in detail. Automated welding using an arc-voltage control system was used to obtain reproducible results. For the pulse conditions, two values of pulse current combinations, taking 140 A as the mean value, were set (ΔI of 40 A and 80 A). The same pulse and base duration were chosen, giving pulsing frequencies of 2.5 Hz and 25 Hz. The voltage and current signals were acquired and the short-circuit numbers and melting rates were calculated by processing the data stability factors. As a result, it was possible to weld with lower average welding currents through pulsed-current technology. This new approach can improve the stability of the wet SMAW process and contribute to obtaining better-quality welds without any changes for the underwater welder.