Aggregate System Research Articles

Characteristic stacked structures and luminescent properties of dinuclear lanthanide complexes with pyrene units.

A novel design strategy of stacked organic fluorophores using dinuclear lanthanide (Ln(III)) complexes is demonstrated for the formation of excimer. The dinuclear Ln(III) complexes are composed of two Ln(III) (Eu(III) or Gd(III)) ions, six hexafluoroacetylacetonate (hfa), and two pyrene-based phosphine oxide ligands. Single-crystal analysis revealed a rigid pyrene-stacked structure via CH-F (pyrene/hfa) intramolecular interactions. The rigid aggregation structures of the two-typed organic ligands around Ln(III) resulted in high thermal stability (decomposition temperature: 340°C). The aggregated ligands exhibited excimer-type green emission from the stacked pyrene-center. The change in the Ln(III) ion promotes effective shifts of excimer emissions (Gd(III):500nm, Eu(III):490nm). The organic aggregation system using red-luminescent Eu(III) also provides temperature-sensitive ratiometric emission composed of π-π* and 4f-4f transitions by energy migration between aggregated ligands and Eu(III).

A New Fast Control Strategy of Terminal Sliding Mode with Nonlinear Extended State Observer for Voltage Source Inverter.

To overcome the sensitivity of voltage source inverters (VSIs) to parameter perturbations and their susceptibility to load variations, a fast terminal sliding mode control (FTSMC) method is proposed as the core and combined with an improved nonlinear extended state observer (NLESO) to resist aggregate system perturbations. Firstly, a mathematical model of the dynamics of a single-phase voltage type inverter is constructed using a state-space averaging approach. Secondly, an NLESO is designed to estimate the lumped uncertainty using the saturation properties of hyperbolic tangent functions. Finally, a sliding mode control method with a fast terminal attractor is proposed to improve the dynamic tracking of the system. It is shown that the NLESO guarantees convergence of the estimation error and effectively preserves the initial derivative peak. The FTSMC enables the output voltage with high tracking accuracy and low total harmonic distortion and enhances the anti-disturbance ability.

Optimization of Aggregate Characteristic Parameters for Asphalt Binder—Aggregate System under Moisture Susceptibility Condition Based on Random Forest Analysis Model

Water damage to asphalt pavements is a common occurrence that lowers the quality of service they can offer and causes several traffic problems. The loss of adhesion characteristics in the system of the asphalt binder and aggregate is the primary source of the problem of water damage in asphalt mixes. A number of things, including the impact of aggregate characteristics on asphalt binder—aggregate systems’ adhesion characteristics, have been proven. Through the use of random forest analysis, this study seeks to maximize the screening of aggregate characteristic factors. In this research, the morphology characterization, chemical composition, and phase composition of the five aggregates were first studied, and their relevant characteristic parameters were calculated. A method of engineering evaluation of the resistance of asphalt mixtures to water damage was used to assess the water susceptibility of the five asphalt binder—aggregate systems. Next, utilizing the fuzzy comprehensive evaluation analysis approach, a thorough study of the water susceptibility of the five asphalt binder—aggregate systems was conducted. Finally, sensitivity analysis of the aggregate characteristic parameters was carried out by a random forest analysis model, so as to achieve the optimal screening of the aggregate characteristic parameters. The results showed that, during sensitivity analysis of each parameter of aggregate properties using random forest analysis, the SiO2 content of the aggregate had the highest importance, and the roughness had the highest importance among the morphology characterizations. The water susceptibility of the asphalt binder—aggregate system could be expressed by the SiO2 content and roughness of the aggregate characteristic parameters.

Smart Helmet

This project gives description of a smart helmet which is based on IOT system to avoid accidents during vehicle riding results due to recklessness of riders towards helmet as it won’t allow the vehicle to start until the riders wears the helmet. It consists of two modules one for the bike and other for the helmet. The bike module will act as a server and the helmet module will act as a client and the modules will act as an aggregated system when proper connectivity is established between the server and client module. As soon as the Wi-Fi connection is interrupted the bike will stop passing current to the ignition coil and hence stop it’s functioning.

Abstract 6554: Prediction of MET amplification from H&E images in non small cell lung cancer using self-supervised deep learning and its role in clinical trial enrollment

Abstract Objective: DNA amplifications of the cMET locus (METamp) are rare (1-6%) in non-small cell lung cancer (NSCLC). The ability to efficiently identify patients with METamp could accelerate development of cMET-targeting therapies by identifying patients for enrollment efficiently. We developed a self-supervised deep learning algorithm (SSDL) to predict METamp status using H&E stained whole slide images (WSI). Methods: Three datasets (CDtrain, CDNGS, CDFISH) consisting of data from 1,436 advanced NSCLC patients (1 slide/pt) were used to develop an SSDL algorithm to predict METamp status. The METamp+ status across datasets was standardized for CDtrain (n=788, METamp+ Prevalence=15%), CDNGS (n=186, 19 positive, 167 negative, Prev=10%) as harboring ≥5 copies of the gene using NGS(Next Generation Sequencing) and for CDFISH (n=462, 36 positive, 426 negative, Prev=8%) as MET CN gain to centromere 7 ratio (CEN) ≥2.5. The datasets were divided into a discovery set (CDtrain) for training and held-out test sets (CDNGS, CDFISH) for validation. To improve robustness of SSDL, we first pre-trained ResNet34 with Simple Contrastive Learning, using >25k WSIs from diverse sources. During the training phase, 4-fold cross validation was applied on CDtrain. The first 3-layers of ResNet34 were frozen while the final layer and an attention-based aggregation system were trained for METamp prediction. The performance was assessed using sensitivity, specificity, AUC, positive predictive value (PPV), and negative predictive value (NPV) on the test sets. Predictions from the model were binarized using two separate thresholds selected from CDtrain, resulting in a High-Sensitivity model (SSDLSens = 90% Sensitivity) and High-Specificity model (SSDLSpec = 90% Specificity). Each model’s potential impact on patient enrollment is presented below. Results: The trained SSDL models achieved an average cross-validation AUC of 0.77±0.04 across folds. The final trained model with the highest cross-validated AUC was chosen for METamp status prediction on the test sets. The selected model maintained predictive performance on CDNGS (AUC=0.82) and CDFISH (AUC=0.75) test sets. The SSDLSpec model had a PPV more than twice the baseline prevalence in both CDNGS (PPV=27%), and CDFISH (PPV=29%), providing a significant enrichment in METamp+ patients. The SSDLSens model correctly classified ~55% of METamp‒ in CDNGS and CDFISH with NPV of ≥97%. These results suggest a potential reduction in genetic screening by 50% while maintaining high sensitivity. Conclusions: The SSDL model robustly predicted METamp status in independent patient cohorts, regardless of METamp definitions (i.e., by NGS or FISH). We demonstrate the ability to classify patients with high sensitivity or specificity, which can help reduce genetic testing or surface potentially eligible patients for trials, respectively. Citation Format: Chaitanya Parmar, Oscar M. Carrasco-Zevallos, Joshua C. Curtin, Darshana Govind, Bing Xia, S. Martin Shreeve, Songbai Wang, Timothy Jatkoe, Patricia Raciti, Levon Demirdjian, Joel Greshock, Kristopher Standish, Stephen S.F. Yip. Prediction of MET amplification from H&E images in non small cell lung cancer using self-supervised deep learning and its role in clinical trial enrollment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6554.

Reliability-Based Optimal Integrated Microgrid Scheduling in Distribution Systems

This paper proposes a reliability-based optimal scheduling model of integrated microgrids. The proposed model is capable of delivering optimal scheduling of each individual microgrid and aggregated system. In addition, it is compatible with both grid-connected and islanded operation modes. The developed problem formulation of the proposed model takes into account the reliability indices SAIDI, SAIFI, and CAIDI to evaluate the reliability of the integrated system. Numerical simulations on a modified IEEE-33 bus test system, involving four integrated microgrids, were performed to investigate the advantages and the robustness of the developed model. Furthermore, the impact of merging Battery Energy Storage Systems (BESSs) on the optimization outcome is examined, which demonstrates its significant effectiveness in improving the power systems' reliability.

DEVELOPMENT OF CONSTRUCTION PROJECTS AS A COMPOND AGGREGATED SYSTEM

The article considers development as a modern form of investment activity in the real estate market. Features of development projects are outlined, and problematic issues of designing and implementation of development projects are identified from the point of view of an aggregated system approach, which is using the latest information technologies. Development is also considered within the framework of the urban development ecosystem, which is also a component of the principle of an aggregated system. The practical significance of the study lies in the fact that the main theoretical and methodological provisions can be used both for further theoretical research and in the practical activities of economic entities of the construction complex in order to improve project management and increase the efficiency of construction organizations and urban planning as whole.

Temporal Multimodal Data-Processing Algorithms Based on Algebraic System of Aggregates

In many tasks related to an object’s observation or real-time monitoring, the gathering of temporal multimodal data is required. Such data sets are semantically connected as they reflect different aspects of the same object. However, data sets of different modalities are usually stored and processed independently. This paper presents an approach based on the application of the Algebraic System of Aggregates (ASA) operations that enable the creation of an object’s complex representation, referred to as multi-image (MI). The representation of temporal multimodal data sets as the object’s MI yields simple data-processing procedures as it provides a solid semantic connection between data describing different features of the same object, process, or phenomenon. In terms of software development, the MI is a complex data structure used for data processing with ASA operations. This paper provides a detailed presentation of this concept.

Multi-Objective Taguchi Optimization of Cement Concrete Incorporating Recycled Mixed Plastic Fine Aggregate Using Modified Fuller’s Equation

Motivated by the multiple benefits of recycling plastic ingredients in cementitious materials, the present study focuses on the design of sustainable cement concrete incorporating recycled mixed plastic fine aggregate (MPFA) as a partial replacement of natural sand (NS). The MPFA produced in this work is composed of a combination of polymer types with similar concoctions to those observed in the postconsumer waste streams. This study approach is vastly different from past reported studies on the use of sorted, highly purified single-type recycled plastic aggregate in cement concrete. A multi-criteria decision-making technique, Best-Worst Method (BWM), was integrated with the Taguchi method to maximize the quality of MPFA concrete based on the Fuller–Thompson theory. More specifically, an L9 (34) Taguchi orthogonal array with four three-level design factors was adopted to optimize the fresh, durability, and mechanical properties of MPFA concrete. The results showed that MPFA concrete produced with 400 kg/m3 cement content, 0.43 water/cement ratio, 0.43 fine aggregate/total aggregate ratio, and 10 vol% MPFA content exhibited the highest quality. Findings from the present work also revealed that MPFA concrete produced with tailored particle size distribution of MPFA NS fine aggregate system achieved superior, if not comparable, qualities to those of conventional concrete.

Densification behavior of asphalt mixture and its relation with particle dynamic responses during gyratory compaction

Compaction of asphalt mixture is a critical step of the pavement construction, which determines the performance and durability for asphalt pavement. The densification behavior of asphalt mixture is complicated and its workability is prone to be affected by the material composition and construction conditions. Knowing the compaction process is of great significance for improving the pavement construction quality. This study aims to investigate the densification behavior of asphalt mixture in the compaction process and its correlation with dynamic responses of the built-in Smart Aggregate. Through Los Angeles abrasion tests and aggregate imaging measurement system, 40 kinds of coarse aggregates with different morphologies were prepared and employed to carry out gyratory compaction tests for asphalt mixtures. The height decline and density growth of the mixtures were concerned in different compaction stages. Meanwhile, the Smart Aggregate was embedded in the mixture to capture the particle dynamic responses, which were further correlated to the densification behavior of the mixture. The results show that material factors have the significant influence on the compaction process after the mixture has been compacted to the P1 at which the height change is less than 1 mm. The gyratory compaction process can be divided into different stages of 0∼P1, P1∼P0.1, P0.1∼P0 according to the change in mixture height, and every gyration would enable the percentage of maximum theoretical specific gravity (Gmm) increase with 1.46 %, 0.3 %, 0.11 % in the corresponding stage. After the threshold point P0, the increment of compaction degree after per gyration is less than 0.05 %. AC and SMA-13 mixtures would achieve the 95.5 %∼97.4 % of Gmm when they are compacted to locking point of P0. Asphalt mixture height is highly related to the relative rotation of the embedded Smart Aggregate. Change in the mixture height can be characterized by the variation of particle relative rotation, which could be further adopted to determine the locking state of asphalt mixture compaction in real-time.

Tracking COVID-19 in the United States With Surveillance of Aggregate Cases and Deaths.

Early during the COVID-19 pandemic, the Centers for Disease Control and Prevention (CDC) leveraged an existing surveillance system infrastructure to monitor COVID-19 cases and deaths in the United States. Given the time needed to report individual-level (also called line-level) COVID-19 case and death data containing detailed information from individual case reports, CDC designed and implemented a new aggregate case surveillance system to inform emergency response decisions more efficiently, with timelier indicators of emerging areas of concern. We describe the processes implemented by CDC to operationalize this novel, multifaceted aggregate surveillance system for collecting COVID-19 case and death data to track the spread and impact of the SARS-CoV-2 virus at national, state, and county levels. We also review the processes established to acquire, process, and validate the aggregate number of cases and deaths due to COVID-19 in the United States at the county and jurisdiction levels during the pandemic. These processes include time-saving tools and strategies implemented to collect and validate authoritative COVID-19 case and death data from jurisdictions, such as web scraping to automate data collection and algorithms to identify and correct data anomalies. This topical review highlights the need to prepare for future emergencies, such as novel disease outbreaks, by having an event-agnostic aggregate surveillance system infrastructure in place to supplement line-level case reporting for near-real-time situational awareness and timely data.

Promoting substitute aggregate system for the environmental-friendly geopolymer paver applications

Concerning the environmental imbalance caused by the over-exploitation of natural aggregates and the greenhouse emission of cementitious materials in the construction industry, in this paper, abundant laterite soil and natural coconut shells were used as partial substitutes for aggregate to make the cement-less geopolymer concrete a resource-efficient product. The effect of the alternative aggregate system in the geopolymer concrete was assessed in the aspects of strength and sustainability. The inclusion of laterite soil and coconut shell in the geopolymer resulted in 1.81 times improved toughness without significant change in the compressive strength. The use of laterite soil and coconut shell aggregates maintained the thermal stability up to 600 °C and reduces the consumption of 280 kg natural sand and 390 kg coarse aggregates while casting 1 m3 concrete, which supports to promote the alternative aggregate system for the efficient production of environmental-friendly geopolymer pavers.

Evaluating the Effectiveness of Commercially Developed Appraisal Instruments (CDAIs) Using Composite Indices to Assess, Compare, and Rank the Liveability, Quality of Living and Sustainability Performance of Cities and Communities

This manuscript presents an analysis of commercially developed appraisal instruments (CDAIs) using composite indices to assess, compare and rank the sustainability performance of cities and communities. A group of CDAIs using composite indices are commonly used to assess, compare, and rank the sustainability performance of cities and communities. As a sustainability assessment methodology, composite indices gather qualitative and quantitative information which is then used to calculate the overall performance of the principle (e.g., sustainability); the stand-alone number, commonly known as an index, is often used to compare and rank performance. Because of practicality and mistakenly perceived simplicity, the assessment methodology is often misunderstood and underestimated. Issues, skepticism, and criticism surrounding composite indices are rooted in the lack of structured and transparent methodological frameworks for the identification and selection of elements within each hierarchical level. Although scientifically-based methodologies and processes have been developed to assign relevance (i.e., weighting) and aggregate performance to calculate the stand-alone index, the effectiveness of the assessment methodology (i.e., composite indices) is still influenced by various degrees and types of subjectivity and uncertainty. To evaluate their effectiveness, the manuscript discusses three characteristics of CDAIs using composite indices: (1) the hierarchical structural organization (HSO) considers the aim of each hierarchical level in the assessment process, (2) the identification, selection and design of the elements (e.g., principle, sub-principles, criteria, indicators) included in each hierarchical level as a determinant factor in capturing the various facets of the sustainable development notion, and (3) the quantification methodology (i.e., weighting and aggregation system [W&AS]) implemented by the developer or proponent of the assessment tool. The analysis of CDAIs using composite indices effectiveness is partially assisted by three frameworks designed by consensus (FDC): (1) ISO 37130:2018 Sustainable development of communities—Indicators for city services and quality of life which is complemented with ISO 37122:2019 Sustainable cities and communities—Indicators for smart cities and ISO 37123:2019 Sustainable cities and communities—Indicators for resilient cities, (2) United Nations Sustainable Development Goals (UN SDGs) with emphasis on Goal 11, and (3) customized frameworks for sustainable cities (CFSS) with a focus on sustainability plans designed and implemented by the cities of Vancouver and Montreal which are used as case studies. While the findings support the applicability and usefulness of CDAIs using composite indices as assessment methodology, the appropriateness of comparing and ranking the sustainability performance of cities and communities is an unsettled debate with several areas for improvement and future research.

A new pattern to quantitatively evaluate the value of ecosystem services in the large-scale open-pit coal mining area

IntroductionOpen-pit coal mining could disrupt the ecosystem and lead to the loss of service values for the ecosystem through direct occupation or indirect impacts on adjacent ecosystems.MethodsIn this research, we combined a new accounting system, gross ecosystem product (GEP), with spatial–temporal analyses to quantify the ecological variation and explore its driving factors in Pingshuo, a large-scale open-pit coal mining area in China. GEP is an aggregate accounting system that can summarize the value of provisioning, regulating, and cultural ecosystem services (ES) in a single monetary metric. The spatial–temporal approaches used in our study were known as exploratory spatial data analyses and interpretable models in machine learning. Both spatial and non-spatial data, including remote sensing images, meteorological data, and official statistics, were applied in the research.ResultsThe results indicated the following: (i) From 1990 to 2020, the annual average growth rates of GEP decreased from 30.78 to 9.1%. Furthermore, the classified results of GEP revealed that the regions with rich ES quality rapidly reduced from 51.90 to 32.18%. (ii) Spatial correlation of GEP was significant, and the degree of spatial clustering was relatively high in the mining areas. Moreover, the mining areas also continually presented concentrated high-density and hot spot areas of GEP changes. (iii) The spatial–temporal effects were notable in the relationship between GEP and three socioeconomic factors, i.e., the mining effects, human activity intensity, and gross domestic product (GDP). (iv) The win–win development for both the economy and ecological environment in Pingshuo could be realized by restricting the annual growth rate of mining areas to between 4.56 and 5.03%.DiscussionThe accounting results and spatial–temporal analyses of GEP will contribute to the future regional sustainable development and ecosystem management in Pingshuo.

Rapid antenna optimization with restricted sensitivity updates by automated dominant direction identification

Meticulous tuning of geometry parameters turns pivotal in improving performance of antenna systems. It is more and more often realized using formal optimization methods, which is demonstrably the most efficient way of handling multiple design variables, objectives, and constraints. Although in some cases a need for launching global search arises, a typical design scenario only requires local optimization, especially when a decent initial design can be rendered using engineering experience or parametric studies. At the same time, antenna optimization is typically conducted using full-wave electromagnetic (EM) simulations, which entails considerable computational expenses. In this paper, we introduce a novel procedure for expedited antenna tuning. Its fundamental mechanism is to restrict the antenna response sensitivity updates to the selected dominant directions within the parameter space, determined based on the problem-specific knowledge, in particular, the estimated changes of antenna characteristics when moving across one-dimensional affine subspaces spanned by these directions. Thus, the said dominant directions affect the most the responses of the antenna structure under design as assessed using the introduced metrics. The decision making process concerning the number of directions to be used relies on quantification of the aggregated system response variability metrics. The proposed approach is demonstrated by means of several antenna structures and benchmarked against conventional trust-region algorithm, but also its accelerated versions. The results indicate considerable (up to over 60%) speedup over the reference procedure without noticeable quality degradation.

Comparative analysis of data using machine learning algorithms: A hydroponics system use case

This paper makes a comparison of machine learning algorithms for the analysis of four hydroponic datasets. Data have been gathered daily from hydroponic systems to predict the output of the hydroponic systems. This research compares the performance of the federated split Learning, Deep neural network, extreme Gradient Boosting (XGBoost), and Linear regression algorithms on four different hydroponic systems. These algorithms have been used to analyze the datasets of Nutrient Film Technic (NFT), Floating (FL), Aggregate (AG) and Aeroponic (AER) hydroponic systems. The results have indicated the performance of each model for each hydroponic system and how each algorithm have used the various multiple input features to make predictions of the onion bulb diameter and the errors encountered by each model. From the results obtained, it has been observed that the R square score is varied for each hydroponic system. This variation in the result has been also reflected in the Mean absolute errors obtained. This research determines which of the algorithms predict the optimal Onion bulb diameter (mm) using days after transplant (days), Temperature (°C), water consumption (Litres), Number of Leaves (NL), Nitrogen (mg/g), Phosphorus (mg/g), Potassium (mg/g), Calcium (mg/g), Magnesium (mg/g), Sulphur (mg/g), Sodium (mg/g) as independent variables. The results will be a guide in the choice of hydroponic system to adopt for food production based on the climatic parameters of the location, which is one of the numerous contributions of this research.

Effects of Terahertz Radiation on the Aggregation of Alzheimer’s Aβ42 Peptide

The pathophysiology of Alzheimer's disease is thought to be directly linked to the abnormal aggregation of β-amyloid (Aβ) in the nervous system as a common neurodegenerative disease. Consequently, researchers in many areas are actively looking for factors that affect Aβ aggregation. Numerous investigations have demonstrated that, in addition to chemical induction of Aβ aggregation, electromagnetic radiation may also affect Aβ aggregation. Terahertz waves are an emerging form of non-ionizing radiation that has the potential to affect the secondary bonding networks of biological systems, which in turn could affect the course of biochemical reactions by altering the conformation of biological macromolecules. As the primary radiation target in this investigation, the in vitro modeled Aβ42 aggregation system was examined using fluorescence spectrophotometry, supplemented by cellular simulations and transmission electron microscopy, to see how it responded to 3.1 THz radiation in various aggregation phases. The results demonstrated that in the nucleation aggregation stage, 3.1 THz electromagnetic waves promote Aβ42 monomer aggregation and that this promoting effect gradually diminishes with the exacerbation of the degree of aggregation. However, by the stage of oligomer aggregation into the original fiber, 3.1 THz electromagnetic waves exhibited an inhibitory effect. This leads us to the conclusion that terahertz radiation has an impact on the stability of the Aβ42 secondary structure, which in turn affects how Aβ42 molecules are recognized during the aggregation process and causes a seemingly aberrant biochemical response. Molecular dynamics simulation was employed to support the theory based on the aforementioned experimental observations and inferences.

INTRAOPERATIVE STATE OF THE HEMOCOAGULATION SYSTEM IN PATIENTS WITH OPEN AND ENDOVASCULAR REVASCULARIZATION OF INFRAINGUAL ARTERIAL SEGMENT IN THE PRESENCE OF STENOTIC-OCCLUSIVE PROCESS OF TIBIAL ARTERIES

Background. Among all postoperative complications associated with revascularization of atherosclerotic steno-occlusive process of the femoral-distal artery, thrombosis of the reconstruction segment is 6-32%. Objective. Prevention of thrombotic complications through vascular revascularization of the lower extremity by using a pathogenetically reasonable system of postoperative thromboprophylaxis. Methods. The study involved 97 patients with atherosclerotic stenotic-occlusive process of the infrainguinal artery in cases of stenotic-occlusive lesions of the tibial arteries. In order to study the state of the hemocoagulation system on patients, the methods for studying the indicators of coagulation, fibrinolytic and aggregation systems were used. Results. Development of hypercoagulable disorders in the patients who underwent endovascular revascularization of the arterial segment is more intense than in those who underwent open vascular revascularization methods. Hypercoagulation of blood at the intraoperative stage of revascularization takes place primarily due to the activity of factor IIa of the hemocoagulation cascade. Taking into account these circumstances, already at the surgery stage for thromboprophylaxis non-fractionated heparin (NFH) should be prescribed. At the same time for prevention of thromboembolic complications double anti-thrombocyte therapy: clopidogrel, acetylsalicylic acid should be prescribed. Conclusion. Hypercoagulant ability of the blood system, which develops after arterial reconstructive interventions, takes place against the background of low activity of the fibrinolytic blood system, nevertheless a gradual increase in the activity of the aggregation capacity of the blood. Development of hypercoagulable disorders at the intraoperative stage of surgery in the patients with endovascular methods of arterial reconstructions is more intense than in those treated with open methods of arterial revascularization. Taking into account these circumstances, already at the surgery stage for thromboprophylaxis an anticoagulant should be prescribed that has a targeted effect on factor IIa of the hemocoagulant cascade with simultaneous prevention of thromboembolic complications – double anti-thrombocyte therapy.

An ab initio method on large sized molecular aggregate system: Predicting absorption spectra of crystalline organic semiconducting films.

Theoretical description of electronically excited states of molecular aggregates at an ab initio level is computationally demanding. To reduce the computational cost, we propose a model Hamiltonian approach that approximates the electronically excited state wavefunction of the molecular aggregate. We benchmark our approach on a thiophene hexamer, as well as calculate the absorption spectra of several crystalline non-fullerene acceptors, including Y6 and ITIC, which are known for their high power conversion efficiency in organic solar cells. The method qualitatively predicts the experimentally measured spectral shape, which can be further linked to the molecular arrangement in the unit cell.

Revealing Mechanisms of Aging and Moisture on Thermodynamic Properties and Failure Patterns of Asphalt-Aggregate Interface from the Molecular Scale

The primary purpose of this study was to reveal the effect of aging and moisture on the failure behavior of asphalt binder–aggregate interfaces from thermodynamic properties and the molecular scale and to provide ideas for improving the service life and weather resistance of materials. A 12-component asphalt binder model, an oxidation-aged asphalt binder model, three types of aggregates (i.e., SiO2-, CaO-, and Al2O3-based) models, and corresponding confined asphalt-aggregate models in dry or wet conditions were developed using Materials Studio version 2019 software. Thermodynamic parameters that characterize interfacial failure behavior, including surface free energy, cohesive work, and adhesion work between asphalt binder (unaged and aged samples) and aggregates were obtained by molecular dynamics simulations. A pull-off test was simulated using molecular dynamics to analyze asphalt-aggregate failure patterns and mechanisms. Five tensile rates were applied in tensile simulations of asphalt-SiO2 aggregate, and bond strength at various temperatures was investigated. The findings suggested that oxidative aging reduces thermodynamic properties such as the surface free energy and cohesive energy of asphalt binders but enhances the potential and nonbond energy of asphalt binders. The ranking of the adhesion between unaged asphalt binder and various aggregates is as follows: SiO2>CaO>Al2O3. Oxidative aging has a distinct influence on the adhesion properties of the aforementioned asphalt binder–aggregate interfaces. Furthermore, outcomes for debonding energy and energy ratio (ER) value in the asphalt-CaO aggregate system exhibited the best water stability. Increasing tensile rate caused the failure patterns of the asphalt-aggregate mixes to change from adhesive failure damage between asphalt and aggregate to cohesive failure within the asphalt binder. Temperature had a nonnegligible impact on bond strength at the asphalt-aggregate interface.