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Photocatalytic technology is considered to be a sustainable strategy to convert H2 O and O2 into H2 O2. However, constructing photocatalytically active and stable organic photocatalyst remain a challenge. In this study, a new class of porous aromatic framework photocatalysts (BF-PAFs) were designed and synthesized, in which 9,9′-bifluorenylidene (99′-BF) and different alkynes are alternately connected. The BF-PAFs were constructed and served as photocatalysts for H2 O2 synthesis. Experimental results show that the introduction of different alkynes can effectively regulate the optical band gap and energy band structure, which may further determine their photocatalytic performance. Upon visible light irradiation, PAF-370 exhibits high efficiency for photosynthesis of H2 O2 with a production rate of 730 μmol g–1 h–1 in the presence of sacrificial reagent from water and oxygen via oxygen reduction reaction (ORR) pathway. Furthermore, up to 61 μmol H2 O2 could be generated from this photocatalytic system after 14 hours. [ABSTRACT FROM AUTHOR]

Metal-based catalysts play an essential role in organic chemistry and the chemical industry. This research designed and successfully synthesized a pillar-layered metal–organic framework (MOF) with the urea linkers, namely Basu-HDI, as a novel and efficient heterogeneous catalyst. Various techniques such as FT-IR, EDX, elemental mapping, SEM, XRD, BET, and TGA/DTA studied its structure and morphology. Then, we investigated the synthesis of new 1,8-naphthyridines utilizing Basu-HDI in mild conditions via a one‐pot, three‐component tandem Knoevenagel/Michael/ cyclization/anomeric-based oxidation reaction. Final products were achieved by anomeric-based oxidation without employing an oxidation agent. Remarkably, this tandem process gave a good range of new 1,8-naphthyridines with high yields in a short reaction time. The pure products were confirmed by FT-IR, 1H NMR, 13C NMR, and mass spectrometry techniques. Moreover, the introduced catalyst showed good efficiency and stability and can be reused four times without significantly reducing efficiency. [ABSTRACT FROM AUTHOR]

This work analyzes the effect of positioning small cells (SC) on an existing heterogeneously scattered macro cellular (MC) network. The impact of SC deployment based on the planned blend of K-means and user density (KUD) algorithms on an existing MC network is explored. With an application of the Cox process, heterogeneous and uneven distributions of 50 and 100 users were generated for investigation. The simulated outcomes and plotted graphs illustrate a gradual increment in the average user throughput, sum rate, and energy efficiency with the overlaying of 8 SCs using the proposed KUD algorithm in an existing MC. [ABSTRACT FROM AUTHOR]

Global industrialization has led to a pressing concern of water pollution from refractory reactive dyes. Among Advanced oxidation processes, the Fenton photocatalyst, particularly cobalt ferrite (CoFe2O4), is gaining attention due to its advantages, including stability, visible light utilization, recyclability, cost-effectiveness, and light corrosion resistance. This study investigates the synthesis and application of cobalt ferrite magnetic nanoparticles (CFNs) as a Fenton-like catalyst for degrading Reactive Blue 19 (RB19) dye under UV irradiation. CFNs were characterized using XRD, SEM, and VSM techniques. The photo degradation of RB19 was assessed under various conditions, including catalyst dosage, oxidant amount, RB19 concentration, and pH. The maximum removal efficiency of RB19 was achieved as 97.3% for synthetic wastewater under the optimal conditions of pH 3, CoFe2O4 dosage (1.5 g/L), reaction time = 60 min, and H2O2 (80 mM) in the presence of UV radiation (18 W) at the ambient room temperature of 22 °C. The results revealed that the CFNs exhibited high stability, reusability, and catalytic activity. Moreover, the mineralization studies indicated significant removal rates for both RB19 and chemical oxygen demand (COD). The findings suggest the promising application of CFNs in advanced wastewater treatment processes. [ABSTRACT FROM AUTHOR]

The large amount of refractory organic wastewater produced from industry and agriculture sectors poses a significant threat to both water ecosystems and human health, necessitating the exploration of cost-efficient and efficacious removal techniques. Persulfate, when activated by various catalysts, can produce oxidative species, demonstrating promising potential in remediating organic wastewater. In recent years, numerous studies have unveiled that persulfate can be readily decomposed into nonradicals, which exhibits high selectivity toward pollutants and robust performance in complex wastewater environments. However, the challenges in identifying non-radicals and the unclear catalytic mechanism hinder its further application. This paper critically reviews the research progress on non-radical oxidation in persulfate-based heterogeneous catalytic system. The main advancements and existing challenges in three non-radical oxidation pathways, i.e., singlet oxygen, electron transfer, and high-valent metal oxides, are summarized, and the key factors influencing the production of nonradicals are elaborated. The engineering aspects of non-radical oxidation system are further discussed, and the future prospects of this technology in wastewater treatment are envisaged. This review aims to bridge the knowledge gaps between current research and future requirements. [ABSTRACT FROM AUTHOR]

ABSTRACT The study focuses on the synthesis and characterization of a new mesoporous polysulfonamide stabilizer using condensation polymerization and the nanocasting technique. The resulting material, PGTSA, was modified with copper ions and used as a nanocatalyst in the reaction of benzaldehydes, 1H‐tetrazole‐5‐amine, and malononitrile. The performance of the mesoporous PGTSA/Cu (II) was compared with non‐porous PGTSA/Cu (II), highlighting the significance of support porosity in catalytic activity and loading of copper species. Based on the analysis of the ICP and catalytic performance, it was observed that mesoporous PGTSA/Cu (II) exhibited a higher level of catalytic activity compared to PGTSA/Fe3O4/Cu (II) and non‐porous PGTSA/Cu (II). Also, the catalytic activity of the aryl aldehyde was significantly influenced by its electrical characteristics. The recovery and reusability of the PGTSA/Cu (II) catalyst are noteworthy aspects, highlighting the practicality and sustainability of the catalytic system. The amount of copper in fresh catalyst and the recycled catalyst after six times recycling is 1.57% and 1.52%, respectively. Therefore, ICP‐OEIS analysis indicated that copper leaching of this catalyst is very low. The procedure offered high product yields, easy recovery of the nanocatalyst, remarkable reusability, short reaction times, and compatibility with various substrates. [ABSTRACT FROM AUTHOR]

The forward triage characteristic of telemedicine highlights its importance again in the COVID-19 pandemic. Telemedicine can provide timely emergency response in the case of environmental or biological hazards, and the patient's medical privacy data generated in this process can also accelerate the establishment of models for preventing and treating infectious diseases. However, the reuse process of telemedicine user privacy data based on federated learning also faces significant challenges. Differences in regions, economic levels, and grades lead to heterogeneous data and resource-constrained environments, seriously damaging the federated learning process. Besides, the weak password authentication of medical terminals and eavesdropping attacks on transmission channels may cause illegal access to terminals and platforms and leakage of sensitive data. This paper proposed a telemedicine data secure-sharing scheme based on heterogeneous federated learning. Specifically, we proposed a heterogeneous federated learning scheme with model alignment to guide telemedicine practice through the reuse of telemedicine data; in addition, we designed an SM9 threshold identity authentication scheme to guarantee that the patient's medical privacy data is protected from leakage during the federated learning process. We evaluated our scheme using two third-party medical datasets. The evaluation results indicate that this scheme can still assist the federated learning process in resisting data heterogeneity and resource constraints with almost no performance cost. [ABSTRACT FROM AUTHOR]

A biomass-based catalyst, CuxOy@CS-400, was employed as an excellent recyclable heterogeneous catalyst to realize the sulfonylation reaction of aniline derivatives with sodium sulfinates. Various substrates were compatible, giving the desired products moderate to good yields at room temperature. In addition, this heterogeneous copper catalyst was also easy to recover and was recyclable up to five times without considerably deteriorating in catalytic efficiency. Importantly, these sulfonylation products were readily converted to the corresponding 4-sulfonyl anilines via a hydrolysis step. The method offers a unique strategy for synthesizing arylsulfones and has the potential to create new possibilities for developing heterogeneous copper-catalyzed C-H functionalizations. [ABSTRACT FROM AUTHOR]

Finite-time consensus problem of hybrid-order heterogeneous multi-agent systems under a signed digraph topology is investigated in this paper. For heterogeneous multi-agent systems composed of first-order and second-order agents, a novel pinning event-triggered control protocol is devised to facilitate the attainment of the desired consensus state within a finite time. This control method overcomes communication barriers between first-order and second-order multi-agent systems, achieving effective control performance while reducing controller update frequency and communication costs. Based on graph theory and the Lyapunov stability method, several novel matrices are defined to address the finite-time consensus problem in hybrid-order multi-agent systems, and these matrices also facilitate the theoretical derivation process. Furthermore, it is demonstrated that the control protocol designed for hybrid-order systems is devoid of Zeno behavior. Finally, a detailed numerical example is supplied to illustrate the validity of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Summary: Energy consumption of multiprocessor's system is increasing day by day. The capability of multiprocessor systems and high compute‐intensive tasks play a major role in increasing energy consumption. Voltage frequency island (VFI) architecture partitioned the cores into groups for which voltage/frequency can be controlled by a single switch. VFI plays a major role in optimizing the energy consumption. We have generated the initial population by using the slot technique to VFI architecture. The genetic algorithm studied by many researchers to solve scheduling problems. So we combined the genetic algorithm with the VFI‐enabled architecture and slot approach called VFIGen. Then apply the VFIGen algorithm to optimize the energy consumption. When comparing the results of the proposed one with the existing state‐of‐art we achieved the performance gain by 28%$$ 28\% $$ to 39%$$ 39\% $$. [ABSTRACT FROM AUTHOR]

High‐performance rechargeable aluminum‐sulfur batteries (RASB) have great potential for various applications owing to their high theoretical capacity, abundant sulfur resources, and good safety. Nevertheless, the practical application of RASB still faces several challenges, including the polysulfide shuttle phenomenon and low sulfur utilization efficiency. Here, we first developed a synergistic copper heterogeneous metal oxide MoO2 derived from polymolybdate‐based metal‐organic framework as an efficient catalyst for mitigating polysulfide diffusion. This composite enhances sulfur utilization and electrical conductivity of the cathode. DFT calculations and experimental results reveal the catalyst Cu/MoO2@C not only effectively anchors aluminum polysulfides (AlPSs) to mitigate the shuttle effect, but also significantly promotes the catalytic conversion of AlPSs on the sulfur cathode side during charging and discharging. The unique nanostructure contains abundant electrocatalytic active sites of oxide nanoparticles and Cu clusters, resulting in excellent electrochemical performance. Consequently, the established RASB exhibits an initial capacity of 875 mAh g−1 at 500 mA g−1 and maintains a capacity of 967 mAh g−1 even at a high temperature of 50 °C. [ABSTRACT FROM AUTHOR]

A new sustainable heterogeneous catalyst for the effective synthesis of 4H-pyran derivatives under mild reaction conditions was investigated. This heterogeneous catalyst was prepared through the complexation reaction between the naturally obtained chitosan from Callinectes sapidus shells and zinc(II) ions (Zn(II)/Chitosan), and characterized using a variety of techniques, including thermogravimetric analysis (TGA), X-ray diffraction (XRD), scanning electron microscopy (SEM), and infrared spectroscopy (IR). The corresponding 4H-pyran derivatives were obtained in good to excellent yields (58–96%) in the presence of Zn(II)/chitosan catalyst using water as solvent at 60°C for only 20 min. The catalyst was easily separated from the reaction mixture by simple filtration, resulting in the desired products with excellent purity. In comparison to numerous other catalyst systems for the synthesis of 4H-pyran derivatives, Zn(II)/chitosan, a new sustainable heterogeneous catalyst, demonstrated remarkable efficacy in an environmentally benign solvent, allowing the desired products to be produced under more environmentally friendly reaction conditions. [ABSTRACT FROM AUTHOR]

Deseeded sunflower is a heterogeneous lignocellulosic biomass that has not been well utilized due to the incomplete understanding of its biological structure. In this study, deseeded sunflower was fractioned into the stalk rind, stalk pith, receptacle, bract, bractlet, leaf blade, and petiole, and their anatomy, cell morphology, and fiber dimension were studied using light microscope, environmental scanning electron microscope, and fiber quality analyzer. The results showed that the major fractions were the stalk rind and receptacle (49.4 wt% and 28.1 wt%, respectively) and each of the other fractions was less than 10 wt% of the total biomass. The pith was only composed of parenchyma tissue, and the other fractions were composed of epidermal, parenchyma, and vascular tissues. The arrangement and number of vascular tissues were different among fractions. The fiber length in the stalk rind was 0.823 mm, the width was 21.3 μm, and the aspect ratio was 38.6. The content of fiber fines in other fractions was higher than 50%, and these fractions should be developed for other uses. Fractionation was judged to be an effective way to achieve high value utilization of deseeded sunflower. [ABSTRACT FROM AUTHOR]

In this study, the strain field evolution mechanism of laser-repaired 304 stainless steel /In718 heterogeneous structure was studied using EBSD technique. The results show that the strain concentration in the repaired zone is much smaller than that in the substrate zone. As the laser power increases from 900 W to 1100 W, the strain concentration in the repaired zone and the substrate zone increases significantly. The different strain concentration in the repaired region and the substrate region is mainly related to the increase of lattice distortion, dislocation density and temperature gradient. In addition, because the grains with different orientations have different Taylor factor, and therefore, grain orientation is also key factor affecting the strain concentration. [ABSTRACT FROM AUTHOR]

Quantification of X‐ray photoelectron spectroscopy (XPS) data is often limited by the heterogeneous nature of the material surface. However, it is often the case that heterogeneous material contains areas within the analyzed area that are effectively homogeneous. In this Insight note, concepts, and methods used to analyze both XPS data are presented to extract both spatial and spectral information from heterogeneous surfaces. These concepts and methods are applied to a specific material surface that contains three chemical compounds separated spatially. The analysis entails converting XPS image data to spectral data and is designed to highlight the potential of XPS imaging in revealing compositional information correlation with spatial information. Properties of algorithms used to evaluate XPS images and spectra are described to outline their application to image data. A case study of an imaging XPS data set is presented that demonstrates how poor signal‐to‐noise images, where the signal is recorded for 4 s per image, are still open to analysis yielding useful information. Ultimately, the methods presented here will aid in interpreting complex XPS data obtained from spatially complex materials often obtained during extensive cycling, such as conventional or electrocatalysts. [ABSTRACT FROM AUTHOR]

In recent years, notable advancements have been achieved in the realm of identifying IP-based Internet of Things (IoT) devices and events. Nevertheless, the majority of methods rely on extracting fingerprints or features from plain text IP-based packets, which limits their ability to accommodate heterogeneous IoT devices such as ZigBee and Z-Wave, and fails to address the challenge of limited traffic samples. To tackle these issues, we propose a novel approach based on IoT communication characteristics and featuring module extensibility. This method is presented to effectively identify IoT devices and events from non-IP heterogeneous IoT network traffic. To shield the differences caused by the heterogeneous IoT protocol, a heterogeneous sample extraction platform with an extensible structure is created to extract raw sequence samples from ZigBee and Z-Wave traffic, with potential for expansion to other protocols. To address the challenges arising from the scarcity of samples, a sample identification framework based on IoT communication characteristics is devised to create synthetic samples from the raw sequence samples, enabling concurrent processing of the raw and synthetic samples using an identification model featuring two separate sequence networks. Comparative assessments of our method against baseline sequence models and the latest techniques demonstrate the advantages of our approach in identifying non-IP heterogeneous IoT traffic. The experimental results indicate that our method achieves an average accuracy improvement of 29.7% compared to baseline models using only raw samples. Furthermore, our method shows improvements of 22.1%, 21.5%, and 21.8% in macro precision, macro recall, and macro F1-score, respectively, over the latest method. [ABSTRACT FROM AUTHOR]

Electro‐organic synthesis, a method that uses electrical current to drive various organic transformations, has garnered significant attention, and so has heterogeneous electrosynthesis, a process that produces chemicals through electrochemical reactions at the interface between the electrode and an electrolyte. This concept highlights the synergistic potential of heterogeneous electrocatalysts, particularly in their use in cross‐coupling reactions. After thoroughly analyzing existing literature, key examples have been highlighted in this paper where heterogeneous electrocatalysts have facilitated the transformation of simple starting materials into useful products. This process lowers energy consumption and reduces waste. We hope this short review sparks the interest among those involved in organic electrosynthesis to explore and use heterogeneous electrocatalysis for cross‐coupling reactions. [ABSTRACT FROM AUTHOR]

This paper proposes a heterogeneous real-time baseband astronomical backend solution centered on RFSoC and combined GPU. The solution implemented the integration of the high-performance, completely configurable RF signal link to digitize, process, and transmit increasingly massive radio astronomy signals and provided a highly flexible and scalable scheme for the QTT backend. The RFSoC-based integration RF pre-processing front-end directly digitizes dual-polarization signals at the sampling rate of 2048 MHz and converts them to dual-16-channel data streams for parallel mixing and polyphase decimation filtering. The generated baseband data with 256 MHz center frequency and 100 MHz bandwidth is packaged and output to the GPU server through the 100GbE Interface. Complete processes of direct RF sampling, pre-processing, and high-speed transmission are implemented at the receiver. The GPU server executes real-time post-processing at the remote end. The dynamic configuration of two observation modes can be achieved with frequency resolutions of 3.051 and 0.763 kHz. The H2CO absorption line observations with the Nanshan 26-m radio telescope verified the system's performance and the design's rationality. This paper explores and confirms the RFSoC's applicability on the backend. The flexible and efficient QTT backend scheme was planned based on the proposed real-time baseband pattern by expanding multi-RFSoC and GPU computing nodes. [ABSTRACT FROM AUTHOR]

The preparation of a magnetically recoverable and reusable Fe3O4@benzothiazole-Cu(II) nanoparticles with their catalytic and electrochemical sensing properties are reported in this manuscript. The prepared Fe3O4@benzothiazole-Cu(II) nanoparticles are characterized using various analytical techniques, including XRD, FTIR, SEM, TEM, XPS, TGA and VSM analysis. The prepared system has shown good catalytic activity in the reduction of 2-NBA, achieving a reaction rate constant of 0.093 min−¹ in 2 min with a low catalyst loading. The synthesized catalyst also showed very high efficiency in the reduction of 4-nitro-phenol and methylene blue within 13 min and 4 min, respectively. The prepared material demonstrated good recovery yield and could be reused as a catalyst for multiple cycles without a significant loss in catalytic activity. It also exhibited a remarkable electrochemical sensing capability for the detection of ascorbic acid, H2O2, and paracetamol at various concentrations, as determined through cyclic voltammetry and impedance measurements. They exhibited clear redox transformations, indicating the electrochemical sensing ability of the prepared materials. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the Spark scalable distributed platform does not consider the computing capabilities of heterogeneous cluster nodes and load balance during job task scheduling, which affects the system performance, this paper constructs heterogeneous cluster nodes load balance scheduling policy under the Spark environment, Heterogeneous cluster node predicts the data distribution characteristics according to the sampling algorithm. divides the data into balancing partitions. According to the static load and dynamic load weight distribution, heterogeneous cluster node obtains the real-time load, and dynamically schedules job tasks. Finally. Wordcount, TeraSort, and K-means three benchmark tests were used to compare and analyze during heterogeneous cluster operation. Experimental results show that this algorithm can reduce the execution time significantly, and improve the performance of heterogeneous cluster. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles are increasingly frequently used in the fields of ocean observation, data collection, and tactical surveillance. Underwater navigation and near real-time communication are the most important factors limiting the performance of these vehicles. In this article, an underwater acoustic communication and positioning system (UACPS) based on heterogeneous gliders is presented. The system consists of an acoustic wave glider and an acoustic underwater glider, and includes an ultrashort baseline and an acoustic modem. The program and communication protocol of the communication and positioning algorithm are independently developed, and the hardware circuit design of the communication and ultra-short baseline positioning, as well as the embedded surface wave gliders and gliders are independently implemented. Sea trial results indicate that the communication distance of the system is more than 3 km and the positioning error is less than 5%. With the further improvement of system performance, the low cost, long time, long distance and near real-time communication characteristics of our UACPS can be used to integrate multiple autonomous unmanned platforms as part of intelligent surveillance robot networks. [ABSTRACT FROM AUTHOR]

The High Intensity Muon Beams (HIMB) project at the Paul Scherrer Institute (PSI) will deliver muon beams with unprecedented intensities of up to 10 10 muons / s for next-generation particle physics and material science experiments. This represents a hundredfold increase over the current state-of-the-art muon intensities, also provided by PSI. We performed beam dynamics optimisations and studies for the design of the HIMB beamlines MUH2 and MUH3 using Graphics Transport, Graphics Turtle, and G4beamline, the latter incorporating PSI's own measured π + cross-sections and variance reduction. We initially performed large-scale beamline optimisations using asynchronous Bayesian optimisation with DeepHyper. We are now developing an island-based evolutionary optimisation code glyfada based on the Paradiseo framework, where we implemented Message Passing Interface (MPI) islands with OpenMP parallelisation within each island. Furthermore, we implemented an island model that is also suitable for high-throughput computing (HTC) environments with asynchronous communication via a Redis database. The code interfaces with the codes COSY INFINITY and G4beamline. The code glyfada will provide heterogeneous island model optimisation using evolutionary optimisation and local search methods, as well as part-wise optimisation of the beamline with automatic advancement through stages. We will use the glyfada for a future large-scale optimisation of the HIMB beamlines. [ABSTRACT FROM AUTHOR]

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The suspension of nanoparticles into the conventional heat transfer fluids has been sampled as means of improving heat transfer efficacy of fluid. The current communication is aimed to discuss the unequal diffusion coefficients of reactant A (bulk fluid) and reactant B (catalyst at the wall) for the Eyring-Powell liquid motion over a surface with variable thickness. The effects such as variable fluid properties, stratification and gyrotactic microorganisms have been considered into account. The equations governing the fluid motion are transformed to the ordinary differential equations with the appropriate similarity transformations. The solutions of the system of transformed governing equations have been developed using the reliable numerical technique Runge-Kutta integration. It is concluded from the current study that, when gyrotactic microorganisms density and thermal stratifications considered as 0.1, the augmentation of Eyring-Powell fluid parameter enhances the velocity distribution and diminution of temperature distribution. Furthermore, when homogeneous reaction parameter rises from 0.1 to 1.0, a conspicuous augmentation is noticed in both concentration distribution of homogeneous and bulk distribution of heterogeneous at the wall. [ABSTRACT FROM AUTHOR]

Abstract Metal-based catalysts play an essential role in organic chemistry and the chemical industry. This research designed and successfully synthesized a pillar-layered metal–organic framework (MOF) with the urea linkers, namely Basu-HDI, as a novel and efficient heterogeneous catalyst. Various techniques such as FT-IR, EDX, elemental mapping, SEM, XRD, BET, and TGA/DTA studied its structure and morphology. Then, we investigated the synthesis of new 1,8-naphthyridines utilizing Basu-HDI in mild conditions via a one‐pot, three‐component tandem Knoevenagel/Michael/ cyclization/anomeric-based oxidation reaction. Final products were achieved by anomeric-based oxidation without employing an oxidation agent. Remarkably, this tandem process gave a good range of new 1,8-naphthyridines with high yields in a short reaction time. The pure products were confirmed by FT-IR, 1H NMR, 13C NMR, and mass spectrometry techniques. Moreover, the introduced catalyst showed good efficiency and stability and can be reused four times without significantly reducing efficiency.

Abstract The forward triage characteristic of telemedicine highlights its importance again in the COVID-19 pandemic. Telemedicine can provide timely emergency response in the case of environmental or biological hazards, and the patient’s medical privacy data generated in this process can also accelerate the establishment of models for preventing and treating infectious diseases. However, the reuse process of telemedicine user privacy data based on federated learning also faces significant challenges. Differences in regions, economic levels, and grades lead to heterogeneous data and resource-constrained environments, seriously damaging the federated learning process. Besides, the weak password authentication of medical terminals and eavesdropping attacks on transmission channels may cause illegal access to terminals and platforms and leakage of sensitive data. This paper proposed a telemedicine data secure-sharing scheme based on heterogeneous federated learning. Specifically, we proposed a heterogeneous federated learning scheme with model alignment to guide telemedicine practice through the reuse of telemedicine data; in addition, we designed an SM9 threshold identity authentication scheme to guarantee that the patient’s medical privacy data is protected from leakage during the federated learning process. We evaluated our scheme using two third-party medical datasets. The evaluation results indicate that this scheme can still assist the federated learning process in resisting data heterogeneity and resource constraints with almost no performance cost.

The Internet of Things (IoT) is an emerging technology incorporating various hardware devices and software applications to exchange, analyze, and process a huge amount of data. IoT uses cloud and fog infrastructures, comprising different hardware and software components like computing machines, networking components, storage, and virtualization elements. They can receive, process, store, and exchange data in real time. A cloud is a centralized system containing large data centres that are far from client devices. However, as IoT generates massive amounts of data, issues like latency, response time, execution of tasks within their deadline, and bandwidth arise when data is sent to the cloud for processing. Compared to the cloud, fog computing is vital as a distributed system consisting of millions of devices located at the minimum distance from the client devices. In addition, fog infrastructure reduces bandwidth and latency because it is closer to the end-user. However, maximizing utilization of resources, minimizing response time, and ensuring the completion of deadline-constrained tasks within their deadline are important research problems in fog computing. This research proposes a task scheduling technique called Resource Aware Prioritized Task Scheduling (RAPTS) in a heterogeneous fog computing environment. The aim is to execute deadline-constrained tasks within their deadlines, minimize response time and cost, as well as makespan, and maximize resource utilization of the fog layer. The RAPTS is implemented using iFogSim and its performance is evaluated regarding response time, resource utilization, task deadlines, cost, and makespan. The results have been compared with state-of-the-art fog schedulers like RACE (CFP) and RACE (FOP). The results reveal that the RAPTS have shown up to 29%, 53%, 15%, 11%, and 43% improvement in terms of resource utilization, response time, makespan, cost, and meeting task deadlines, respectively. [ABSTRACT FROM AUTHOR]

The High Intensity Muon Beams (HIMB) project at the Paul Scherrer Institute (PSI) will deliver muon beams with unprecedented intensities of up to 1010muons/s for next-generation particle physics and material science experiments. This represents a hundredfold increase over the current state-of-the-art muon intensities, also provided by PSI. We performed beam dynamics optimisations and studies for the design of the HIMB beamlines MUH2 and MUH3 using Graphics Transport, Graphics Turtle, and G4beamline, the latter incorporating PSI’s own measured π+ cross-sections and variance reduction. We initially performed large-scale beamline optimisations using asynchronous Bayesian optimisation with DeepHyper. We are now developing an island-based evolutionary optimisation code glyfada based on the Paradiseo framework, where we implemented Message Passing Interface (MPI) islands with OpenMP parallelisation within each island. Furthermore, we implemented an island model that is also suitable for high-throughput computing (HTC) environments with asynchronous communication via a Redis database. The code interfaces with the codes COSY INFINITY and G4beamline. The code glyfada will provide heterogeneous island model optimisation using evolutionary optimisation and local search methods, as well as part-wise optimisation of the beamline with automatic advancement through stages. We will use the glyfada for a future large-scale optimisation of the HIMB beamlines.

Abstract The synthesis of Octakis [3- (3-amino propyl triethoxysilane) propyl] octa-silsesquioxane (APTPOSS), a derivative of polyhedral oligomeric silsesquioxane, was utilized to produce an efficient nanocomposite. MNPs@Silica/APTPOSS was characterized through scanning electron microscopy, Fourier transform infrared spectroscopy, vibrating sample magnetometry, X-ray diffraction, and Thermogravimetric analysis. These magnetic nanoparticles, a combination of organic-inorganic hybrid polyhedral oligomeric silsesquioxane, were utilized as a proficient heterogeneous catalyst in the one-pot synthesis of spirooxindoles derivatives. Furthermore, they could be swiftly isolated and reused six times while maintaining their catalytic efficiency.

Abstract Background Compared with the time-consuming and labor-intensive for biological validation in vitro or in vivo, the computational models can provide high-quality and purposeful candidates in an instant. Existing computational models face limitations in effectively utilizing sparse local structural information for accurate predictions in circRNA-disease associations. This study addresses this challenge with a proposed method, CDA-DGRL (Prediction of CircRNA-Disease Association based on Double-line Graph Representation Learning), which employs a deep learning framework leveraging graph networks and a dual-line representation model integrating graph node features. Method CDA-DGRL comprises several key steps: initially, the integration of diverse biological information to compute integrated similarities among circRNAs and diseases, leading to the construction of a heterogeneous network specific to circRNA-disease associations. Subsequently, circRNA and disease node features are derived using sparse autoencoders. Thirdly, a graph convolutional neural network is employed to capture the local graph network structure by inputting the circRNA-disease heterogeneous network alongside node features. Fourthly, the utilization of node2vec facilitates depth-first sampling of the circRNA-disease heterogeneous network to grasp the global graph network structure, addressing issues associated with sparse raw data. Finally, the fusion of local and global graph network structures is inputted into an extra trees classifier to identify potential circRNA-disease associations. Results The results, obtained through a rigorous five-fold cross-validation on the circR2Disease dataset, demonstrate the superiority of CDA-DGRL with an AUC value of 0.9866 and an AUPR value of 0.9897 compared to existing state-of-the-art models. Notably, the hyper-random tree classifier employed in this model outperforms other machine learning classifiers. Conclusion Thus, CDA-DGRL stands as a promising methodology for reliably identifying circRNA-disease associations, offering potential avenues to alleviate the necessity for extensive traditional biological experiments. The source code and data for this study are available at https://github.com/zywait/CDA-DGRL .

The synthesis of Octakis [3- (3-amino propyl triethoxysilane) propyl] octa-silsesquioxane (APTPOSS), a derivative of polyhedral oligomeric silsesquioxane, was utilized to produce an efficient nanocomposite. MNPs@Silica/APTPOSS was characterized through scanning electron microscopy, Fourier transform infrared spectroscopy, vibrating sample magnetometry, X-ray diffraction, and Thermogravimetric analysis. These magnetic nanoparticles, a combination of organic-inorganic hybrid polyhedral oligomeric silsesquioxane, were utilized as a proficient heterogeneous catalyst in the one-pot synthesis of spirooxindoles derivatives. Furthermore, they could be swiftly isolated and reused six times while maintaining their catalytic efficiency. [ABSTRACT FROM AUTHOR]

Multilayer network models with heterogeneous nodes characterize complex network in the reality to the best effect, and bounded synchronization has more important practical significance in engineering applications. This paper aims at the bounded intra-layer synchronization of multilayer heterogeneous networks where nodal dynamics is different each other. Based on a newly-introduced vector norm, we derive the sufficient conditions for the bounded intra-layer synchronization in multiplex networks with one-to-one connections between layers by using a new effective approach. The condition integrates the intra-layer and inter-layer structure information, and can be as well used to the accurate intra-layer synchronization in multiplex networks with homogeneous nodes each layer. More interestingly, the theoretical results show the negative/positive coupling between layers has negative/positive effect for intra-layer synchronization, and the partial cutting of connections between layers increases the bound of intra-layer synchronization. Finally, numerical examples are provided to verify the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

A direct approach is developed using Streamline Upwind Petrov Galerkin (SUPG) concepts to determine the spatially varying property distribution in a nominally heterogeneous material. The approach is based on successful development of a SUPG-stabilized inverse finite element approach to solve the differential equations of equilibrium in terms of material properties, resulting in a matrix form [A] {E} = {R}, where [A] is a known function of measured axial strains (e.g., from StereoDIC) and axial positions, {R} is a known function of axial body forces, applied loads and reactions, and {E} is a vector of unknown material properties at discrete axial locations. Theoretical and computational developments for the SUPG-stabilized approach are described in detail for one-dimensional applications (e.g., heterogeneous tensile/compression specimens, tensile/compressive surfaces of beams). Property predictions using the SUPG method with analytic strains and additive Gaussian noise are shown to be in excellent agreement with known property values, whereas predictions using the classical Bubnov-Galerkin method exhibit large, spurious oscillations in the predicted material properties. To demonstrate the methodology using experimental measurements, a 3D-printed heterogeneous tensile specimen with independently measured material properties is tested and full-field strains measured at several load levels. Results confirm that SUPG finite element property predictions are in very good agreement with independently determined values at each load level along the specimen length, providing confidence that the SUPG FE analysis framework developed in this work is stable and extendable to multiple dimensions. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the morphology of ionic poly(dimethylsiloxane) silica nanocomposites of randomly grafted or chain-end-functionalized ionic PDMS melts using atomistic MD simulations. The localization of the charge alters the structure and dynamics of ionic PDMS chains near the nanosilica surface. The chain-end ionic PDMS obtains the largest dimensions, whereas the charge fraction of 10% of the random ionic copolymers leads to a contraction of PDMS chains. The charge fraction dramatically alters the dynamics of the ionic PDMS chains, although they reach the diffusive regime. An anisotropy of PDMS chain dynamics perpendicular and parallel to the nanosilica and an heterogeneity of PDMS dynamics from the nanosilica surface are observed for the longer randomly grafted and chain-end ionic PDMS chains. The longer randomly grafted ionic PDMS chains are strongly adsorbed in the vicinity of the nanosilica surface. [ABSTRACT FROM AUTHOR]

The transition from heterogeneous nucleation of nanoscale hardening S-phase precipitates on dislocation lines in cold-rolled aluminium alloy 2024 (Al–Cu–Mg) to a spatially homogeneous distribution during artificial ageing is studied by means of in situ and ex situ transmission electron microscopy (TEM). Three stages of precipitate evolution following nucleation on dislocation lines are established: (i) Growth and coalescence on dislocation lines, (ii) Ostwald ripening controlled by pipe diffusion, and (iii) Ostwald ripening controlled by volume diffusion. Whilst Ostwald ripening controlled by pipe diffusion is active (stage (ii) and transition from stage (ii) to stage (iii)) dislocation annihilation and/or re-arrangement can occur. [ABSTRACT FROM AUTHOR]

The concentration of anthocyanins in common beans indicates their nutritional value. Understanding this concentration makes it possible to identify the functional compounds present. Previous studies have presented color characterization as two-dimensional histograms, based on the probability mass function. In this work, we proposed a new type of color characterization represented by three two-dimensional histograms that consider chromaticity and luminosity channels in order to verify the robustness of the information. Using a neuroevolutionary approach, we also found a convolutional neural network (CNN) for the regression task. The results demonstrate that using three two-dimensional histograms increases the accuracy compared to the color characterization represented by one two-dimensional histogram. As a result, the precision was 93.00 ± 5.26 for the HSI color space and 94.30 ± 8.61 for CIE L*a*b*. Our procedure is suitable for estimating anthocyanins in homogeneous and heterogeneous colored bean landraces. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), clustering is employed to extend the network's lifespan. Each cluster has a designated cluster head. Pairing is another technique used within clustering to enhance network longevity. In this technique, nodes are grouped into pairs, with one node in an active state and the other in a sleep state to conserve energy. However, this pairing can lead to communication issues with the cluster head, as nodes in sleep mode cannot transmit data, potentially causing data loss. To address this issue, this study introduces an innovative approach called the "Awake Sleep Heterogeneous Nodes' Pairing" (ASHNP) algorithm. This algorithm aims to improve transmission efficiency in WSNs operating in heterogeneous environments. In contrast, Energy Efficient Sleep Awake Aware (EESAA) algorithm are customized for homogeneous environments (EESAA), while suitable for homogeneous settings, encounters challenges in handling data loss from sleep nodes. On the other hand, Energy and Traffic Aware Sleep Awake (ETASA) struggles with listening problems, limiting its efficiency in diverse environments. Through comprehensive comparative analysis, ASHNP demonstrates higher performance in data transmission efficiency, overcoming the shortcomings of EESAA and ETASA. Additionally, comparisons across various parameters, including energy consumption and the number of dead nodes, highlight ASHNP's effectiveness in enhancing network reliability and resource utilization. These findings underscore the significance of ASHNP as a promising solution for optimizing data transmission in WSNs, particularly in heterogeneous environments. The analysis discloses that ASHNP reliably outperforms EESAA in maintaining node energy, with differences ranging from 1.5% to 10% across various rounds. Specifically, ASHNP achieves a data transmission rate 5.23% higher than EESAA and 21.73% higher than ETASA. These findings underscore the strength of ASHNP in sustaining node activity levels, showcasing its superiority in preserving network integrity and ensuring efficient data transmission across multiple rounds. [ABSTRACT FROM AUTHOR]

With pervasive network access, cloud computing offers on-demand access to a shared pool of computing resources. To guarantee high quality cloud services, cloud task scheduling algorithms need to be dependable and efficient. Cloud infrastructure, however, are vulnerable to errors and malfunctions that may affect how tasks are carried out. In this research, a metaheuristic approach for adaptive fault tolerant job scheduling in cloud systems is proposed. The method efficiently schedules work while optimizing fault tolerance by adaptively applying numerous fault tolerance strategies and meta-heuristic based optimization. Additionally, a thorough analysis of modern Meta-heuristic based job scheduling algorithms is presented in this work, with an emphasis on the algorithm's fault tolerance and adaptability. Through simulation tests using CloudSim toolkit, adaptability, scalability, energy efficiency, and reliability matrices are used to compare the suggested algorithm against existing approaches. The suggested methodology works better than current techniques, according to the results, which indicate increased task throughput, scalability, energy efficiency, and fault tolerance across a range of cloud workloads. An efficient method for ensuring consistent task execution in unstable cloud environments is offered by the innovative scheduling architecture that incorporates six metaheuristics, redundancy, checkpointing and migration based fault tolerance in an adaptable manner. [ABSTRACT FROM AUTHOR]

In this paper, based on the spatial panel data of 30 provinces in China from 2008 to 2019, the relationship between environmental regulation (ER), environmental decentralization (ED) and the green total factor productivity of the construction industry (hereafter GTFPCI) was investigated through the spatial Dobbin model, and the heterogeneity of the relationship between three environmental regulations and the GTFPCI under different environmental decentralization intensities was studied through a threshold panel model. The results showed that the three environmental regulations had different degrees of impact on the GTFPCI, among which market-based environmental regulations (MERs) played the most obvious role. Environmental regulation and environmental decentralization have spillover effects on the GTFPCI in surrounding provinces. On the whole, with the increasing intensity of environmental decentralization, environmental regulation plays a stronger positive role in promoting the GTFPCI. However, through a threshold analysis, it was seen that the regulatory impact of different environmental regulations on the GTFPCI are heterogeneous in different ranges of environmental decentralization intensity. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the predictive value of echo features of radial probe endobronchial ultrasound (RP‐EBUS) in the differential diagnosis of malignant and benign 1esions. Methods: The clinical data of 336 patients with peripheral pulmonary lesions (PPLs) undergoing RP‐EBUS were analyzed in order to evaluate the predictive value of the three EBUS echo features including continuous margin, absence of a linear‐discrete air bronchogram, and heterogeneous in pulmonary lesions. The sensitivity and specificity for each echoic feature or in combination in diagnosing malignancy or benignity were determined. Results: 336 cases of PPLs including 216 cases of malignant lesions and 120 cases of benign lesions. The sensitivity and specificity of the continuous margin to the diagnosis of malignant lesions were 86.11% and 17.50%. The sensitivity and specificity of the absence of a linear‐discrete air bronchogram to the diagnosis of malignant lesions were 66.67% and 57.50%, and the positive predictive value was 73.85%. The sensitivity and specificity of heterogeneity to the diagnosis of malignant lesions were 65.28% and 72.50%, and the positive predictive value was 81.03%. The combination of heterogeneous and absence of a linear‐discrete air bronchogram could improve the diagnostic specificity to 87.50%, and the positive predictive value to 80.77%. Conclusion: The two EBUS echo features of heterogeneous and absence of a linear‐discrete air bronchogram have predictive value for PPLs, especially in the presence of two features the pulmonary lesions should be highly suspected malignant tumors. [ABSTRACT FROM AUTHOR]

Iron (Fe) is considered to be one of the most significant elements due to its wide applications. Recent years have witnessed a burgeoning interest in Fe catalysis as a sustainable and cost-effective alternative to noble metal catalysis in organic synthesis. The abundance and low toxicity of Fe, coupled with its competitive reactivity and selectivity, underscore its appeal for sustainable synthesis. A lot of catalytic reactions have been performed using heterogeneous catalysts of Fe oxide hybridized with support systems like aluminosilicates, clays, carbonized materials, metal oxides or polymeric matrices. This review provides a comprehensive overview of the latest advancements in Fe-catalyzed organic transformation reactions. Highlighted areas include cross-coupling reactions, C–H activation, asymmetric catalysis, and cascade processes, showcasing the versatility of Fe across a spectrum of synthetic methodologies. Emphasis is placed on mechanistic insights, elucidating the underlying principles governing iron-catalyzed reactions. Challenges and opportunities in the field are discussed, providing a roadmap for future research endeavors. Overall, this review illuminates the transformative potential of Fe catalysis in driving innovation and sustainability in organic chemistry, with implications for drug discovery, materials science, and beyond. [ABSTRACT FROM AUTHOR]

Manganese oxide octahedral molecular sieve (OMS-2) nano-rods were synthesized in the presence of HNO3 and then ion-exchanged with Ru3+ to obtain Ru-modified scaffolds abbreviated as Ru-OMS-2. Various techniques including X-ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), energy dispersive X-Ray spectroscopy (EDS), X-Ray photoelectron spectroscopy (XPS), and EDS mapping analysis were used to characterize the structure of the obtained product. Ru-OMS-2 was then used as a reusable heterogeneous catalyst in the selective oxidation of polycyclic aromatic hydrocarbons and heterocyclic compounds such as olanzapine and benzothiophene in an aqueous solution. Under mild reaction conditions, excellent yields of the desired products (10 examples, 55–99% yield) were obtained with high selectivity, and the catalyst retained activity for several consecutive cycles with a reasonable loss of activity (7.5% after 10 runs). [ABSTRACT FROM AUTHOR]

Low-carbon transformation of the economy is the inevitable orientation of socialism with Chinese characteristics to high-quality development in the new era, while the Chinese decentralized development model determines that the competition of local governments in China is an important factor influencing the green technological innovation on low-carbon transformation of the economy. How to achieve coordinated economic growth and ecological environment has become a problem for local governments. Data from a Chinese provincial panel covering the years 2007–2019 is used to investigate the effects of heterogeneous local government competition (Comp), namely, economic, ecological and service competitions on economic low-carbon transition, and moderating effects of heterogeneous government competition and green technology innovation (GTECH) on the low-carbon economic transition (LCT). The results reveal that there are substantial disparities in the consequences of heterogeneous government competition on low-carbon economic transition (LCT). Among them, economic competition significantly dampens economic low-carbon transition (LCT), and ecological competition and service competition significantly boost economic low-carbon transition (LCT). After performing robustness checks, these results continue to be strongly convincing. The study of moderating effects shows that economic competition can dampen the positive influence of green technology innovation (GTECH) to the economic low-carbon transition (LCT). However, ecological competition and service competition facilitate the promoting effect of green technology innovation on economic low-carbon transition (LCT). [ABSTRACT FROM AUTHOR]

Ozone simulated in Mars Global Climate Models (MGCMs) is used to assess the underlying chemistry occurring in the atmosphere. Currently, ozone total column abundance (TCA) is under‐predicted in MGCMs by up to 120%, implying missing or inaccurate chemistry in models. Heterogeneous reactions of hydroxyl radicals (HOX) have been offered as an explanation for some of this bias, because they cause ozone to increase at locations where it's currently under‐predicted. We use four simulations to compare modeled ozone TCA with observations from the UVIS spectrometer aboard the ExoMars Trace Gas Orbiter to improve the representation of heterogeneous processes and their impact on ozone. We use a gas‐phase only run, a dissociative scheme, an adsorbed HOX retention scheme, and a hybrid scheme that combines the dissociative mechanism with the retention of HOX on water ice. We find retention of HOX is dependent on water ice sublimation, and ozone abundance increases when water ice persists for longer periods (1–20 sols). Over time, the loss of HOX causes a depletion in H2O2 concentration (HOX reservoir), and thus allows ozone concentration to increase. When adsorbed HOX are desorbed and dissociate into other by‐products, HOX are not immediately available to destroy ozone. This results in larger ozone concentrations than if desorbed HOX are released directly back into their gaseous states. When using the hybrid scheme, ozone TCA is increased up to 50% where the ozone deficit is greatest, demonstrating the best agreement with observations, and implying that HOX radicals are both retained when adsorbed and dissociate. Plain Language Summary: Ozone is a trace gas in the martian atmosphere, sensitive to chemical and light‐induced reactions. This makes it ideal for assessing the underlying reactions for chemical species called hydroxyl radicals, which destroy ozone and would otherwise be too reactive and shortlived to be measured directly. There has been an under‐prediction of ozone in global climate models which implies missing or inaccurate reactions. Heterogeneous reactions (a reaction which includes two or more phases) have been suggested to fill the deficit in modeled ozone. This involves hydroxyl radicals adsorbing onto water ice, which causes ozone to increase. We look at different simulations to understand hydroxyl radical chemistry on the surface of water ice by comparing to observations from the ExoMars Trace Gas Orbiter. We run four climate model simulations to test different theories as to what is occurring at the surface. There are two theories (a) reactions happen at the surface which alter hydroxyl radicals into other chemicals, and (b) hydroxyl radicals stay adsorbed onto water ice for as long as the ice persists. When we combine these theories, we have a more accurate prediction of ozone, which we can then use to infer what happens at the surface. Key Points: Heterogeneous reactions can have a major impact on martian ozone abundance at locations where water ice clouds persist for several solsLifetime of adsorbed hydroxyl radicals depends on water ice sublimation and longer lifetimes result in a greater abundance of ozoneA combination of longer lifetimes and dissociation of adsorbed HOX suppresses gaseous HOX and improves simulated ozone total column abundance [ABSTRACT FROM AUTHOR]

A loan benchmark interest rate policy always becomes a challenging problem in the banking industry since it has a role in controlling bank loan expansion, especially when there is competition between two banks. This paper aims to assess the influence of the loan benchmark interest rate on the expansion of loans between two banks. We present a banking duopoly model in the form of two-dimensional difference equations which is constructed from heterogeneous expectation, where one of the banks sets its optimal loan volume based on the other bank’s rational expectation. The model‘s equilibrium is investigated, and its stability is analyzed using the Jury stability condition. Investigation indicates that to ensure the stability of the banking loan equilibrium, it is advisable to establish a loan benchmark interest rate that is lower than the flip bifurcation value. Some numerical simulations, such as the bifurcation diagram, Lyapunov exponent, and chaotic attractor, are presented to confirm the analytical findings. [ABSTRACT FROM AUTHOR]

The development of a low‐cost, reusable, and sustainable approach is a difficult and emerging demand in the coming arena. Here, we demonstrate the selective oxidation of primary alcohol, styrene to aldehyde, and alkyl benzene to appropriate ketone, under Blue LED irradiation. The developed approaches employ reusable catalysts (V2O5/TIO2) and green solvents (ACN, H2O) at room temperature. The substrate scope is widely investigated for various compounds of different functionalities. Organic transformations are supported by reaction mechanistic studies. The gram scale reaction and catalytic recyclability were also carried out to maximize the protocol's synthetic utility. [ABSTRACT FROM AUTHOR]

This review describes the advances of heterogenous phase‐transfer catalysts onium‐based and their importance from a sustainable point of view. Although heterogeneous phase‐transfer catalysts have shown incredible advantages in many organic reactions, examples of supported phase‐transfer catalysts onium‐based are still limited. In this review, we will examine the most common supports, including polymers, clays, and inorganic materials. Additionally, new recent developments in carbon materials application as efficient heterogeneous phase‐transfer catalysts in water will be reported, opening a new route toward green and sustainable procedures. Furthermore, considering the ever‐growing interest in asymmetric synthesis we wish to discuss the important achievements in the field of chiral heterogeneous phase‐transfer catalysts. [ABSTRACT FROM AUTHOR]

This study aims to compare the performance of ensembles according to their inherent diversity in the context of landslide susceptibility assessment. Heterogeneous and homogeneous ensemble types can be distinguished; four ensembles of each approach were implemented in the Djebahia region. The heterogeneous ensembles include stacking (ST), voting (VO), weighting (WE), and a new approach in landslide assessment called meta-dynamic ensemble selection (DES), while the homogeneous ensembles include AdaBoost (ADA), bagging (BG), random forest (RF), and random subspace (RSS). To ensure a consistent comparison, each ensemble was implemented using individual base learners. The heterogeneous ensembles were generated by combining eight different machine learning algorithms, while the homogeneous ensembles only used a single base learner, with diversity achieved through resampling the training dataset. The spatial dataset used in this study consisted of 115 landslide events and 12 conditioning factors, which were randomly divided into training and testing datasets. The models were evaluated through various aspects, including receiver operating characteristic (ROC) curves, root mean squared error (RMSE), landslide density distribution (LDD), threshold-dependent metrics (Kappa index, accuracy, and recall scores), and a global visual representation using the Taylor diagram. Additionally, a sensitivity analysis (SA) was conducted for the best performing models to assess the importance of the factors and the resilience of the ensembles. The results revealed that homogeneous ensembles outperformed heterogeneous ensembles in terms of AUC and threshold-dependent metrics, with AUC ranging from 0.962 to 0.971 for the test dataset. ADA was the best performing model for these metrics and the least in terms of RMSE (0.366). However, the heterogeneous ensemble ST provided a finer RMSE (0.272), and DES showed the best LDD, indicating a stronger potential to generalize the phenomenon. The Taylor diagram was consistent with the other results, indicating that ST was the best performing model, followed by RSS. The SA demonstrated that RSS was the most robust (mean AUC variation of − 0.022) and ADA was the least robust (mean AUC variation of − 0.038). [ABSTRACT FROM AUTHOR]

Background: Compared with the time-consuming and labor-intensive for biological validation in vitro or in vivo, the computational models can provide high-quality and purposeful candidates in an instant. Existing computational models face limitations in effectively utilizing sparse local structural information for accurate predictions in circRNA-disease associations. This study addresses this challenge with a proposed method, CDA-DGRL (Prediction of CircRNA-Disease Association based on Double-line Graph Representation Learning), which employs a deep learning framework leveraging graph networks and a dual-line representation model integrating graph node features. Method: CDA-DGRL comprises several key steps: initially, the integration of diverse biological information to compute integrated similarities among circRNAs and diseases, leading to the construction of a heterogeneous network specific to circRNA-disease associations. Subsequently, circRNA and disease node features are derived using sparse autoencoders. Thirdly, a graph convolutional neural network is employed to capture the local graph network structure by inputting the circRNA-disease heterogeneous network alongside node features. Fourthly, the utilization of node2vec facilitates depth-first sampling of the circRNA-disease heterogeneous network to grasp the global graph network structure, addressing issues associated with sparse raw data. Finally, the fusion of local and global graph network structures is inputted into an extra trees classifier to identify potential circRNA-disease associations. Results: The results, obtained through a rigorous five-fold cross-validation on the circR2Disease dataset, demonstrate the superiority of CDA-DGRL with an AUC value of 0.9866 and an AUPR value of 0.9897 compared to existing state-of-the-art models. Notably, the hyper-random tree classifier employed in this model outperforms other machine learning classifiers. Conclusion: Thus, CDA-DGRL stands as a promising methodology for reliably identifying circRNA-disease associations, offering potential avenues to alleviate the necessity for extensive traditional biological experiments. The source code and data for this study are available at https://github.com/zywait/CDA-DGRL. [ABSTRACT FROM AUTHOR]

AbstractFe3O4@propyl-semicarbazide-salicyl aldehyde-Cu (Fe3O4@sal-Cu) nanocatalyst was synthesized and characterized by several methods including XRD, SEM, EDX, VSM and FT-IR analyses. The Fe3O4@sal-Cu has been used as an efficient recyclable magnetic catalyst for the preparation of benzimidazoles. The catalyst was conveniently separated from the reaction mixture by an external magnet, and could be reused without any considerable change in catalytic activity. [ABSTRACT FROM AUTHOR]