The valorization of the large amount of crude glycerol formed from biodiesel industry, is of primordial necessity. One possible direction with high interest to the biorefiniery sector is the production of fuel additives as solketal, though the acetalization of glycerol with acetone. This is a chemical process that conciliates high sustainability and economic interest, since solketal contributes to the fullfilment of a Circular Economy Model through its use in biodiesel blends. The key to guarantee high efficiency and high sustainability for solketal production is the use of a recovery and recyclable heterogeneous catalyst. Some examples are already reported in the literature (first example in 2012), and this review intends to conciliate the information spreaded in this topic and established the most important reacional parameters and the main catalytic characteristics strategies to achieve high solketal production in short reaction time.

The prediction of tertiary RNA structures is significant to the field of medicine (e.g. mRNA vaccines, genome editing), and the exploration of viral transcripts. Though many RNA folding software exist, few studies have condensed their locus of attention solely to viral pseudoknotted RNA. These regulatory pseudoknots play a role in genome replication, gene expression, and protein synthesis. This study explores five RNA folding engines that compute either the minimum free energy (MFE) or the maximum expected accuracy (MEA). These folding engines were tested against 26 experimentally derived short pseudoknotted sequences (20-150nt) using metrics that are commonly applied to software prediction accuracy (e.g. F1 scoring, PPV). This paper reports higher accuracy RNA prediction engines (pKiss) when compared to previous iterations of the software, and when compared to older folding engines. They show that MEA folding software does not always outperform MFE folding software in prediction accuracy when assessed with metrics such as percent error, sensitivity, PPV, and F1 scoring when applied to viral pseudoknotted RNA. Moreover, the results suggest that thermodynamic model parameters will not ensure accuracy if auxiliary parameters such as Mg2+ binding, dangling end options, and H-type penalties are not applied. The observations reported in this paper highlight the quality between different ab initio prediction methods while enforcing the idea that a better understanding of intracellular thermodynamics is necessary for a more efficacious screening of RNAs.

As one of toxic Volatile Organic Pollutant (TVOCs), formaldehyde has a toxic effect on microorganisms, and then inhibits the biochemical process of formaldehyde wastewater treatment. Therefore, the selective degradation of formaldehyde is of great significance to achieve formaldehyde wastewater high-efficiency and low-cost treatment. This study constructed a heterogeneous Fe-ZSM-5/H2O2 Fenton system for selective degradation to target compounds. By bonding Fe3+ onto the surface of ZSM-5 molecular sieve, Fe-ZSM-5 was prepared successfully. XRD, BET and FT-IR spectral studies shown the prepared Fe-ZSM-5 was mainly composed of micropores. The influences of different variables on formaldehyde selective heterogeneous Fenton degradation performance were studied. The 93.72% formaldehyde degradation and the 98.15% selectivity of formaldehyde compared with glucose had demonstrated in the optimized Fenton system after 360 min. Notably, the resultant selective Fenton oxidation system had a wide range of pH suitability from 3.0 to 10.0. Also, the Fe-ZSM-5 was used in 5 consecutive cycles, while the degradation efficiency of formaldehyde did not drop dramatically. Using reactive oxygen species scavengers, hydroxyl radical was the main active substance to degrade formaldehyde. Furthermore, the great degradation performance of this system to high concentration of formaldehyde was acquired, and the degrading efficiency was more than 95%.

The use of alternative synthetic methods, in the face of traditional processes that do not conform to the principles of Green Chemistry, represent a problem in the pharmaceutical industry. The procedures for the synthesis of benzimidazole derivatives have become a focus in synthetic organic chemistry, as they are building blocks of strong interest for the synthesis of compounds with pharmacological activity. Various benzimidazole derivatives have found very strong application in medicine and their synthesis is reported in the literature. A simple and environmental Montmorillonite K10 (MK10) catalyzed method for the synthesis of benzimidazole derivatives has been developed. The use of MK10 as heterogeneous catalysis provides various advantages in terms of yields, in the work up procedure of the reaction, selectivity and the possible recycle of catalyst without waste formation. The reactions were carried out in solvent free condition and in short reaction time using an inexpensive and environmentally friendly heterogeneous catalysis and it has been shown that the reaction process is applicable in the industrial field.

Azo dyes are the most widely used synthetic dyes in the printing and dyeing process. However, the discharge of untreated azo dyes poses potential threat for human health and aqueous ecosystem. Herein, we fabricated a novel heterogenous catalyst - activated carbon fiber-supported ferric alginate (FeAlg-ACF) . Together with peroxymonosulfate (PMS) and visible light, this photocatalytic oxidation system was used to remove an azo dye - azophloxine. The results indicated that the proposed catalytic oxidation system can remove 100% azophloxine within 24 min, while under the same system, the removal rate was only 92 % and 84 % when ferric alginate was replaced with ferric citrate and ferric oxalate respectively, which showed the superiority of activated carbon fiber-supported ferric alginate. The degradation of azophloxine is achieved by the active radicals (SO4•− and •OH) released from PMS and persistent free radicals from activated carbon fiber. After treating for 24 min, the total organic carbon of azophloxine solution (50 μmol/L) decreased from 1.82 mg/L to 79.3 μg/L and the nitrate concentration of ions increased from 0.3 mg/L to 8.6 mg/L. That is, up to 93.5% azophloxine molecules were completely degraded into inorganic compounds. Consequently, potential secondary contamination by intermediate organic products during catalytic degradation was prohibited. The azophloxine removal ratio was kept almost constant after seven cycles, indicating the recyclability and longevity of this system. Furthermore, the azophloxine removal was still promising at high concentrations of Cl-, HCO3-, CO32-. Therefore, our proposed system is potentially effective to remove dye pollutants from seawater. It provides a feasible method for the development of efficient and environmental friendly PMS activation technology combined with FeAlg-ACF, has significant academic and application value.

This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

Background: Antibiotic resistance is an urgent issue everywhere in the globe. It is necessary to look into new sources of antibiotics to address this issue. Aim of the study: studying the purification of Corallopyronin B from various soil conditions in Egypt, as well as the antibacterial effectiveness of Corallopyronin B in preclinical animal testing and randomized human clinical trials phases 1/2. Type of the study: Screening experimental study. Methodology: Several soil conditions in Egypt were examined to create bacterial isolates that generated the antibiotic chemical Corallopyronin B. Using reversed-phase HPLC, Myxopyronin B was purified. The test antibiotic's minimum inhibitory concentration( MIC) and in vitro antibacterial activity were ascertained using the paper disc diffusion assay and the broth microdilution technique. Furthermore, in stages 1/2 of randomized clinical trials including human and animal models, pharmacokinetics, adverse drug reactions, and the in vivo antibacterial spectrum were discovered. Results: The soil bacterial isolate Corallococcus coralloides DSM 2259, grown on a Casein yeast peptone( CYP) plate, produced Corallopyronin B from its culture supernatant. At MICs more than 100 mcg/ml, the test antibiotic inhibited the growth of many Gram -ve bacteria, including Escherichia coli, while preventing the growth of numerous Gram +ve bacteria, with MICs ranging from 1 to 10 mcg/ml. Eukaryotic cells, on the other hand, including those in humans and fungi, were unharmed. The test antibiotic was shown to have a bactericidal effect by inhibiting bacterial DNA-dependent RNA polymerase( RNLP). In phases 1/2 of randomized human clinical trials, when 600 mg of the dose per 70 kg of body weight was administered SC, the Cmax was 8.6 mcg/ml at Tmax 1 hour; T1/2 reached 136 min following first-order kinetics of elimination. It stopped acting around 6-7 hours after SC was administered. In the preclinical and randomized human clinical trial phases 1/2, less than 6 percent of experimental candidates had uncommon toxicity, showing reduced bile flow. Protein binding with plasma albumin was detected which reached about 83%. Conclusion: The current work was noteworthy since it involved the production of the bactericidal antibiotic Corallopyronin B from Corallococcus coralloides DSM 2259 which was isolated from several soil environments in Egypt.

Nitrogen oxides (NOx), pivotal atmospheric pollutants, significantly imperil the environment and human health. The CH4-SCR process, leveraging the abundant and accessible methane (CH4), emerges as a promising avenue for NOx abatement. Previous studies have demonstrated that zeolite support with twelve-membered ring (12-MR) and five-membered ring (5-MR) structures are susceptible to framework collapse in the presence of water, leading to catalyst deactivation. Consequently, there is a necessity to explore novel zeolites with enhanced hydrothermal stability for application in CH4-SCR processes. This research introduced for the first time an investigation into a novel In-H-SSZ-39 catalyst, synthesized via ion exchange and meticulously optimized for preparation conditions, including calcination temperature, In ions concentraiton, and reaction conditions, including CH4/NO ratio, O2 concentration, water vapor content, and Gas hourly space velocity (GHSV). Notably, the In/H-SSZ-39 catalyst achieved superior performance of 42.1% at 605 °C, with a calcination temperature of 500 °C and an In ion concentration of 0.066 M, under CH4/NO ratio of 1.5:1, 10% O2, and GHSV of 21000 h-1. In addition, the catalytic performance was influenced by the concentration of water vapor and the GHSV value.

As companies increasingly undergo digital transformation, the role of talent management pro-cesses becomes pivotal in enhancing overall organizational performance. The research addresses the significance of talent management in corporate digital transformation, examining whether variations in the digitalization of these processes can be attributed to company size. A qualitative approach was employed, utilizing a questionnaire, collecting responses from 202 organizations across diverse sectors. Findings reveal disparities in digitalization throughout the talent man-agement process, with pronounced presence in the attracting, selecting, and rewarding phases, but diminishing in deployment and development, further declining in planning. A positive cor-relation between company size and adoption of specific digital platforms was observed. Larger enterprises exhibit greater utilization of digital platforms in talent deployment and development. Moreover, corporate communication tools are consistently utilized in the rewarding phase, irre-spective of company size. These findings offer practical insights for organizations to optimize their digitalization strategies based on their scale, thereby contributing to more effective and tailored digitalization endeavors. The uniqueness of this research lies in its exploration of the in-fluence of company size on the digitalization of talent management processes and its potential to explain variations across different stages of these processes.

Background: Antibiotic resistance is an urgent issue everywhere in the globe. It is necessary to look into new sources of antibiotics to address this issue. Aim of the study: studying the purification of Corallopyronin B from various soil conditions in Egypt, as well as the antibacterial effectiveness of Corallopyronin B in preclinical animal testing and randomized human clinical trials phases 1/2. Type of the study: Screening experimental study. Methodology: Several soil conditions in Egypt were examined to create bacterial isolates that generated the antibiotic chemical Corallopyronin B. Using reversed-phase HPLC, Myxopyronin B was purified. The test antibiotic's minimum inhibitory concentration( MIC) and in vitro antibacterial activity were ascertained using the paper disc diffusion assay and the broth microdilution technique. Furthermore, in stages 1/2 of randomized clinical trials including human and animal models, pharmacokinetics, adverse drug reactions, and the in vivo antibacterial spectrum were discovered. Results: The soil bacterial isolate Corallococcus coralloides DSM 2259, which was grown on a Casein yeast peptone( CYP) plate, produced Corallopyronin B from its culture supernatant. At MICs more than 100 mcg/ml, the test antibiotic inhibited the growth of many Gram -ve bacteria, including Escherichia coli, while also preventing the growth of numerous Gram +ve bacteria, with MICs ranging from 1 to 10 mcg/ml. Eukaryotic cells, on the other hand, including those in humans and fungi, were unharmed. The test antibiotic was shown to have a bactericidal effect by inhibiting bacterial DNA-dependent RNA polymerase( RNLP). In phases 1/2 of randomized human clinical trials, when 600 mg of the dose per 70 kg of body weight was administered SC, the Cmax was 8.6 mcg/ml at Tmax 1 hour; T1/2 reached 136 min following first-order kinetics of elimination. It stopped acting around 6-7 hours after SC was administered. In the preclinical and randomized human clinical trial phases 1/2, less than 6 percent of experimental candidates had uncommon toxicity, which showed up as reduced bile flow. Protein binding with plasma albumin was detected which reached about 83%. Conclusion: The current work was noteworthy since it involved the production of the bactericidal antibiotic Corallopyronin B from Corallococcus coralloides DSM 2259 which was isolated from several soil environments in Egypt.

Abstract: An efficient and environmentally Pumice as a new catalyst for designing of 3,4- dihydropyrimidin-2(1H) ones / thiones via one-pot multi component condensation of aromatic aldehydes, urea/ thiourea and ethyl acetoacetate or acetyl acetone in excellent yields (96-99 %). The advantages using of this new catalyst is very cheap, available, non-toxic, stable under thermal conditions, easy work-up, improved yields, the product of reaction is very pure without using chromatographic methods and solvent free conditions.

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

A comprehensive kinetic model of the Fischer-Tropsch synthesis (FTS) is developed in a fixed bed reactor under operating conditions (temperature, 230–235°C, pressure, 20–25 bar, gas hourly space velocity, 4000–5000 cm³(STP)/h/g_catalyst ,H₂/CO feed molar ratio, 2.1) over a Co based catalyst. Reaction rate equations based on Eley-Rideal (ER) type model for initiation step and Langmuir-Hinshelwood-Hougen-Watson (LHHW) type model for propagation and termination steps of the FTS reactions have been considered and the readsorption of olefins were taken into account. The model that was reported in the literature was modified in order to explain many significant deviations from the ASF distribution. Optimum parameters have been obtained by Genetic Algorithms (GA). The calculated activation energies to produce n-paraffins and 1-olefins were in the range of 82.24 to 90.68 kJ/mol and 100.66 to 105.24 kJ/mol, respectively. The hydrocarbon distribution in FTS reactions was satisfactorily predicted particularly for paraffins.

Fine chemicals are produced in small annual volume batch processes (often <10,000 tonnes per year), with a high associated price (usually > $10/kg). As a result of their usage in the production of speciality chemicals, in areas including agrochemicals, fragrances and pharmaceuticals, their necessity will remain high for the foreseeable future. This review article assesses current methods used to produce fine chemicals with heterogeneous catalysts, including both well-established methods as well as newer experimental methods. A wide range of methods utilising microporous and mesoporous catalysts has been explored, including their preparation and modification before use in industry. Their potential drawbacks, as well as benefits, have been analysed, with their feasibility compared to newer, recently emerging catalysts. The field of heterogeneous catalysis for fine chemical production is a dynamic and ever-changing area of research. This deeper insight into catalytic behaviour and material properties will produce more efficient, selective, and sustainable processes in the fine chemical industry. The findings from this article will provide an excellent foundation for further exploration and a critical review in this field of fine chemical production using micro- and mesoporous heterogeneous catalysts.

Demand for environmentally friendly types of energy is growing all over the world, which naturally increases the intensity of work on the production of hydrogen-containing fuel mixtures. Steam reforming of methanol is one of the most effective areas of research, as it is a process with low energy consumption. The results of steam reforming of methanol on the catalysts prepared by solution combustion synthesis (SCS) and impregnation by moisture capacity are presented. Investigation of the activity of Cu-Ce-Al catalysts of different composition was carried out for production of hydrogen and comparison of SCS with traditional impregnation method. Methanol conversion reaches 99%, while the selectivity of H2 was 88%, at 500 °C. It was found that CuAl2O4 spinel formation is due to substitution of Al3+ with Cu2+ Ce3+ cations. A consequence CuAl2O4 and CeAlO3 lattice parameters increase because of difference in ionic radius of Al3+ (0.53 Å) and Cu2+ (0.73 Å), Ce3+ (1.07 Å). Advantages of SCS catalysts in steam reforming of methanol were shown. The aim of this research is to develop a new catalyst for conversion of methanol into hydrogen containing fuel mixtures, which will further promote the organization of a new era of environmentally friendly energy-saving production methods.

A dioxomolybdenum catalyst [(MoO2Cl2-O-MoO2Cl2)(L)] (1) (with L (1S,2S)-N,N´-bis(2-pyridinecarboxamide)-1,2-cyclohexane) was prepared and used as catalyst for the desulfurization of a multicomponent model fuel containing the most refractory sulfur compounds in real fuels. This complex showed to have a high efficiency to oxidize the aromatic benzothiophene derivative compounds present in fuels, mainly using a biphasic 1:1 model fuel/MeOH system. This process conciliates catalytic oxidative and extractive desulfurization, occurring the oxidation of the sulfur compounds in the polar organic solvent. The oxidative catalytic performance of (1) showed to be influenced by the presence of water in the system. Using 50% aq. H2O2 it was possible to reuse the catalyst and the extraction solvent MeOH during ten consecutive cycles without loss of desulfurization efficiency.

The composites of heteropolyacids (H3PW12, H3PMo12) incorporated into amine-functionalized silica materials, were used for the first time as heterogeneous catalysts in the valorisation of glycerol (a large waste from biodiesel industry) by acetalization reaction with acetone. The polyoxotungstate catalyst H3PW12@AptesSBA-15 presented higher catalytic efficiency than the phosphomolybdate, with 97% of conversion with 97% of solketal selectivity, after 60 min at 25 °C, or 91% of glycerol conversion and the same selectivity, after 5 min, performing the reaction at 60 ºC. A relation of catalytic performance and acidity of the catalyst is here presented. Furthermore, the stability of the solid catalyst was investigated and discussed.

This paper proposed a three-layer distributed simulation network architecture: 1. Distributed virtual simulation network, 2. Perception and control sub-network, and 3. Collaborative communication service network. This simulation architecture runs on a distributed platform. The platform can provide a unique virtual scenario and several simulation services to verify the individual robot system's basic perception, control, and planning algorithms and the distributed collaborative algorithms of heterogeneous multi-robot systems. Furthermore, we designed a simulation experiment scenario for classical heterogeneous robot systems such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). At last, We gave a rough estimation model of the bandwidth.

The joint extraction of entities and relations is a critical task in information extraction, and its performance directly affects the performance of downstream tasks. However, existing joint extraction models based on deep learning exhibit weak processing capabilities for the phenomenon of multiple pronunciations of one character and multiple characters of one pronunciation when processing Chinese texts, resulting in performance loss. To address these issues, this paper introduces part-of-speech (POS) and pinyin features to aid the model in learning semantic features that are more contextually appropriate. A Chinese Joint Entity and Relation Extraction Model based on Progressive Contrastive Learning and Heterogeneous Feature Fusion is proposed (CJE-PCHF). During model training, an interactive fusion network based on progressive contrastive learning is employed to learn the dependencies between pinyin, POS, and semantic features. This guides the model in heterogeneous feature fusion, capturing higher-order semantic associations between heterogeneous features. On the commonly used DuIE evaluation dataset for joint extraction, this model achieved a significant improvement, with the F1 score increasing by 5.4% compared to the benchmark model CasRel.

Recently, methods to detect DoS and spoofing attacks that occur on in-vehicle networks using CAN Protocol are being studied through deep learning models such as CNN, RNN, and LSTM. These studies have produced significant results in the field of In-Vehicle Network attack detection using deep learning models. In addition, significant results are being achieved through research on applying time series-based deep learning models such as LSTM to detect DoS attacks and replay attacks occurring in in-drone networks by expanding them to drones using the UAVCAN protocol. In this paper, we conducted an experiment to detect in-drone network attacks through non-time series analysis using machine learning models and deep learning models, and through appropriate learning for each attack type, it can also be analyzed through non-time series analysis. The results showed that it was possible to detect attacks.

Ensuring access to water, sanitation, and hygiene (WASH) facilities, coupled with behavior change education in schools, is fundamental for fostering a conducive learning environment and promoting principles of inclusion, dignity, and equality. This study focuses on 12 primary schools in Anápolis, Brazil, encompassing 4,394 students and 248 teachers. WASH assessments were conducted through a questionnaire, observation of water and sanitation facilities and structures, and microbiological analysis. Over the period 2018 to 2020, the proportion of schools with safely managed drinking water surged from 42% to 92%. However, sanitation conditions in the schools were categorized as a basic level, with hygiene services rated as limited in 92% of the schools due to the absence of soap. While this study reveals that Anápolis schools generally align with WASH guidelines for basic/advanced drinking water services, there is a crucial need for enhancements in both hardware and software facilities to address sanitation and hygiene challenges.

This comprehensive review has systematically examined the use of heterogeneous solid acid catalysts in the production of biodiesel from wastewater-derived sludge by using the PRISMA methodology. The manuscript highlighted the composition and characteristics of wastewater-derived sludge, presenting both the opportunities and challenges associated with its use as a feedstock for biodiesel production. Various types of catalysts were dis-cussed, with a detailed exploration of heterogeneous solid acid catalysts, including zeolites, hetero-polyacid (HPA), mixed metal oxides, and sulphonic acid group catalysts. The advantages and limitations of these catalysts were critically analyzed, providing a balanced view of their potential in industrial applications. The application section delved into the catalytic transesterification reaction, mechanisms of biodiesel production, and the effects of catalyst loading on yield. Performance metrics such as catalytic activity, stability, recyclability, cost-effectiveness, and environ-mental impact were thoroughly evaluated, offering a clear understanding of the efficacy of these catalysts. Synthesis and characterization techniques were also reviewed, shedding light on the latest preparation methods and characterization techniques. Recent advances in catalyst development were presented, showcasing the innovative strides made in enhancing catalyst performance. The environmental and economic implications of using solid acid catalysts for bio-diesel production were assessed, emphasizing the importance of sustainability and economic viability.

This paper presents control algorithms enabling autonomous heterogeneous trucks to drive in platoons. Heterogeneous trucks imply that the hardware information (e.g., truck length, break, accelerator, or engine) of a truck may be distinct from that of another truck. We define a platoon as a collection of trucks where a manually driven truck (leader truck) is followed by several automatically controlled following trucks. The proposed approach is to make every autonomous truck keep following the leader's trajectory while maintaining a designated distance from its predecessor truck. As far as we know, this paper is unique in developing both lateral maneuver and speed control considering a platoon of heterogeneous trucks. The efficiency of the proposed approach is verified using simulations.

In this short review paper we have assembled the main characteristics of partial oxidation reactions (oxidative dehydrogenation and selective oxidation to olefins or oxygenates, as aldehydes and carboxylic acids and nitriles), as well as total oxidation, particularly for depollution, environmental issues and wastewater treatments. Both gas-solid and liquid-solid media have been considered with recent and representative examples within these fields. We have also discussed about their potential and prospective industrial applications. Particular attention has been brought to new raw materials stemming from biomass and to liquid-solid catalysts cases. This review paper also summarizes the progresses made in the use of unconventional activation methods for performing oxidation reactions, highlighting the synergy of these technologies with heterogeneous catalysis. Focus has been centered on usual catalysts activation methods but also on less usual ones, such as the use of ultrasounds, microwaves, grinding (mechanochemistry) and photo-activated processes, as well as their combined use.

Drug repositioning (also called drug repurposing) is a strategy for identifying new therapeutic targets for existing drugs. This approach is of great importance in pharmacology as it is a faster and cheaper way to develop new medical treatments. In this paper, we present, to our knowledge, the first application of multiplex-heterogeneous network embedding to drug repositioning. Network embedding learns the vector representations of nodes, opening the whole machine learning toolbox for a wide variety of applications including link prediction, node labelling or clustering. So far, the application of network embedding for drug repositioning focused on heterogeneous networks. Our approach for drug repositioning is based on multiplex-heterogeneous network embedding. Such method allows the richness and complexity of multiplex and heterogeneous networks to be projected in the same vector space. In other words, multiplex-heterogeneous networks aggregate different multi-omics data in the same network representation. We validate the approach on a task of link prediction and on a case study for SARS-CoV2 drug repositioning. Experimental results show that our approach is highly robust and effective for finding new drug-target associations.

In the last decades, we have witnessed a spectacular information explosion over the Internet. Millions of users are consuming the Internet through various services, such as mobile applications, and online games. The service providers, at the back-end side, are supported by state-of-art infrastructures. Targeting on providing the services at scale, virtualization is one of the emerging technologies used in data centers and cloud environments to improve the quality of services. In this project, we aim to develop a dynamic resource management scheme based on virtual containers. It collects the runtime job progress from the running tasks and allocates the resources dynamically to improve the overall system performance.

This study proposed the combination system consisting of biogas dry reforming reactor and solid oxide fuel cell (SOFC). Since a biogas dry reforming is an endothermic reaction, this study adopted a membrane reactor operated due to the non-equilibrium state with H2 separation from the reaction space. This study aimed to clarify the characteristics of Ni/Cr/Ru catalyst using for a biogas dry reforming membrane reactor. Additionally, this study also investigated the comparison of the characteristics of Ni/Cr/Ru catalyst with that of Ni/Cr catalyst. The impact of operation temperature, the molar ratio of CH4 : CO2, the differential pressure between the reaction chamber and the sweep chamber and the introduction of sweep gas on the characteristics of the biogas dry reforming membrane reactor using Pd/Cu membrane and Ni/Cr/Ru catalyst was examined. The concentration of H2 using Ni/Cr/Ru catalyst was larger than that using Ni/Cr catalyst by 2871 ppmV for the molar ratio of CH4 : CO2 = 1.5 : 1 at the reaction temperature of 600 ℃ and the differential pressure of 0 MPa without a sweep gas, especially. Under this condition, CH4 conversion, H2 yield and thermal efficiency were 67.4 %, 1.77×10-2 % and 0.241 %, respectively.

This study explores the stability of electrodeposited copper catalysts utilized in electrochemical CO2 reduction (ECR) across various amine media. The focus is on understanding the influence of different amine types, corrosion ramifications, and the efficacy of pulse ECR methodologies. Employing a suite of electrochemical techniques including potentiodynamic polarization, linear resistance polarization, cyclic voltammetry, and chronopotentiometry, the investigation reveals useful insights. The findings show that among the tested amines, CO2-rich monoethanolamine (MEA) exhibits the highest corrosion rate. However, in most cases the rates remain within tolerable limits for ECR operations. Primary amines, notably monoethanolamine (MEA), show enhanced compatibility with ECR processes, attributable to their resistance against carbonate salt precipitation and sustained stability in potential over extended durations. Conversely, tertiary amines such as methyldiethanolamine (MDEA) present challenges due to the formation of carbonate salts during ECR, impeding their effective utilization. The study highlights the effectiveness of pulse ECR strategies in stabilizing ECR. A noticeable shift in cathodic potential and reduced deposit formation on the catalyst surface through periodic oxidation underscores the efficacy of such strategies. These findings offer insights for optimizing ECR in amine media, thereby providing promising pathways for advancements in CO2 emission reduction technologies.

The purpose of this study is to investigate the perceived benefits of community gardening. A garden colony is a collection of adjacent plots of land primarily for the purpose of gardening. The methodology of this research consists of observations, interviews, questionnaires, and focus group. As a result of this research we found that gardening is a natural and safe way to meet the lifelong demands for a healthy life. We found specific mental and physical benefits as a result of this community gardening. We encourage communities to allow people to buy plots of land away from their homes to promote this healthy activity. The garden colony provides a place for individual renewal and restoration; this urban oasis offers a way to maintain and promote lifelong healthy living, and they are an important contribution to one’s life by adding a sense of meaning and purpose.

Academic researchers publish their work in various formats, such as papers, patents, and research reports, on different academic sites. When searching for a particular researcher's work, it can be challenging to pinpoint the right individual, especially when there are multiple researchers with the same name. In order to handle this issue, we propose a name disambiguation scheme of researchers with the same name based on heterogeneous academic sites. The proposed scheme collects and integrates research results from these varied academic sites, focusing on attributes crucial for disambiguation. It then employs clustering techniques to identify individuals who share the same name. Additionally, the proposed rule-based algorithm name disambiguation method and the existing deep learning-based identification method. This approach allows for the selection of the most accurate disambiguation scheme, taking into account the metadata available in the academic sites, using a multiclass classification approach. To demonstrate the effectiveness of the proposed method, we conduct various performance evaluations, measuring accuracy, recall, and the F1-measure, highlighting the scheme's superior performance in the name disambiguation.

Mesothelioma is a cancer arising from mesothelial cells lining the thoracic (pleura) or the abdominal (peritoneum) cavities, and more rarely from transformed mesothelial cells of pericardium or tunica vaginalis testis. The development of mesothelioma has been related to the exposure to carcinogenic mineral fibers, mainly asbestos. Malignant mesothelioma is an aggressive disease with diffuse nature, low median survival, and prolonged latency presenting difficulty in prognosis, diagnosis, and treatment. We here review all these aspects to underline the progress being made in investigation and to emphasise how much work remains to be done to improve prognosis and treatment.

Dynamics of human populations can be affected by various socio-economic factors through their influence on the natality and mortality rates, and on the migration intensity and directions. In this work we study an economic-demographic model which takes into account the dependence of the wealth production rate on the available resources. In the case of nonlocal consumption of resources the homogeneous in space wealth-population distribution is replaced by a periodic in space distribution for which the total wealth increases. For the global consumption of resources, if the wealth redistribution is small enough, then the homogeneous distribution is replaced by a heterogeneous one with a single wealth accumulation center. Thus, economic and demographic characteristics of nonlocal and global economies can be quite different in comparison with the local economy.

Clustering is an important task in data mining that has become more challenging due to the ever-increasing size of available datasets. To cope with these big data scenarios, a high-performance clustering approach is required. Sparse grid clustering is a density-based clustering method that uses a sparse grid density estimation as its central building block. The underlying density estimation approach enables the detection of clusters with non-convex shapes and without a predetermined number of clusters. In this work, we introduce a new distributed and performance-portable variant of the sparse grid clustering algorithm that is suited for big data settings. Our compute kernels were implemented in OpenCL to enable portability across a wide range of architectures. For distributed environments, we added a manager-worker scheme that was implemented using MPI. In experiments on two supercomputers, Piz Daint and Hazel Hen, with up to 100 million data points in a 10-dimensional dataset, we show the performance and scalability of our approach. The dataset with 100 million data points was clustered in 1198s using 128 nodes of Piz Daint. This translates to an overall performance of 352TFLOPS. On the node-level, we provide results for two GPUs, Nvidia's Tesla P100 and the AMD FirePro W8100, and one processor-based platform that uses Intel Xeon E5-2680v3 processors. In these experiments, we achieved between 43% and 66% of the peak performance across all compute kernels and devices, demonstrating the performance portability of our approach.

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition, which remains challenging for traditional methods due to the complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include: 1) The convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; 2) A large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and 3) Experimental results demonstrate that HDMF is super capable of copping with the AMC problem, and achieves much better performance when compared with the independent network. The source code and the database will be publically available.

Some global difficulties in the modern period include energy storage conversion. Researchers have looked to renewable energy resources to discover long-term answers to this dilemma. Fuel cells are one example of prospective energy producing technology. Although these technologies appear to be very enticing, they are known to be expensive due to high expenses. Catalyst costs, as well as concerns concerning energy density. Pt based Electrocatalysts have traditionally been used to drive ORR and offer efficient power outputs. However, cost constraints have limited the use of these electrocatalysts, rendering them inefficient. Apart from the high price, Pt-based Electrodes are prone to time-dependent drift and CO deactivation, both of which occur gradually. Electrocatalysts deteriorate over time, reducing the efficiency of fuel cells. This is why alternative electrocatalysts are in high demand for future energy applications. This review covers the comparative study of Platinium and Non-Platinium based electrocatalyst in fuel cells, their shortcomings and recent advancement for efficient and optimized electrocatalyst.

Abstract: Calcium-based catalysts are of a high interest for glycerol polymerization due to their high catalytic activity and large availability. However, their poor stability under reaction conditions is an issue. In the present study, we investigated the stability and catalytic activity of Ca-hydroxyapatites (HAps) as one of the most abundant Ca-source in nature. A stochiometric, a Ca-deficient and a Ca-rich HAps have been synthetized and tested as catalysts in the glycerol polymerization reaction. Deficient and stochiometric HAps exhibited a remarkable 100% selectivity to triglycerol at 15 % of glycerol conversion at 245 °C after 8 h of reaction in the presence 0.5 mol.% of catalyst. Moreover, under the same reaction conditions, Ca-rich HAp showed a high selectivity (88 %) to di- and triglycerol at a glycerol conversion of 27 %. Most importantly, these catalysts were unexpectedly stable towards leaching under the reaction conditions based on the ICP-OES results. However, based on the catalytic tests and characterization analysis performed by XRD, XPS, IR, TGA-DSC and ICP-OES, we found that HAps can be deactivated by the presence of the reaction products themselves, i.e., water and polymers.

In a constantly transforming retail financial sector, TCS has established a strategic alliance with Cencosud Scotiabank, expanding its role beyond consultancy to become an essential partner in the journey of growth and transformation. This collaboration reflects a shared commitment to advancing together in the competitive world of retail financial services, driving significant strategic change at Cencosud Scotiabank. As part of this effort, TCS has co-designed a series of practical workshops aimed at fostering and expanding the culture of innovation within the organization. These practical workshops are fundamental in the transformation process, providing deep and practical learning that serves as a solid foundation for the desired change. Each workshop session, carefully designed and executed according to an innovation playbook, aligns visual thinking tools with practical techniques to ensure smooth and effective execution of the work. The goal of these efforts is twofold: on the one hand, to strengthen the culture of innovation at Cencosud Scotiabank through collaboration; on the other, to firmly center the organization around the customer. The workshops are not just meetings; rather, they are collaborative spaces where creativity, deep user understanding, and agility converge to shape innovative solutions.

Electrocatalytic and photocatalytic hydrogen evolution reaction (HER) provides a promising approach to clean energy generation. Bimetallic single-atom catalysts raised and have been explored to be advanced catalysts for HER. It is urgent to review and summarize the recent advances in developing bimetallic single-atom HER catalysts. Firstly, the fundamentals of bimetallic single-atom catalysts are presented, highlighting their unique configuration of two isolated metal atoms on supports and resultant synergistic effects. Secondly, recent advances in bimetallic single-atom catalysts for electrocatalytic HER under acidic/alkaline conditions are then reviewed, including W-Mo, Ru-Bi, Ni-Fe, Co-Ag, and other dual-atom systems on graphene and transition metal dichalcogenides (TMDs) with enhanced HER activity versus monometallic analogs due to geometric and electronic synergies. Then, Photocatalytic bimetallic single-atom catalysts on semiconducting carbon nitrides for solar H2 production are also discussed. Finally, an outlook is provided on opportunities and challenges in precisely controlling bimetallic single-atom catalyst synthesis and gaining in-depth mechanistic insights into bimetallic interactions.

In the heterogeneous wireless networked control system (WNCS), age of information (AoI) of the actuation update and actuation update cost are important performance metrics. To reduce the monetary cost, the control system can wait for the availability of WiFi network of the actuator and then conduct the update by using WiFi network in an opportunistic manner, but it leads to the increased AoI of the actuation update. To assess this problem, this paper proposes a priority-aware actuation update scheme (PAUS) where the control system decides whether to deliver or delay the actuation update to the actuator by considering the control priority (i.e., robustness of AoI of the actuation update). For the optimal decision, we formulate a Markov decision process model and derive the optimal policy based on Q-learning. Simulation results demonstrate that PAUS outperforms the comparison schemes in terms of the expected reward.

We carry out in a thin heterogeneous porous layer, the multiscale analysis of Smoluchowski's discrete diffusion-coagulation equations describing the evolution density of diffusing particles that are subject to coagulate in pairs. Assuming that the thin heterogeneous layer is made of microstructures that are uniformly distributed inside, we obtain in the limit an upscaled model in lower space dimension. We also prove a corrector-type result very useful in numerical computations. In view of the thin structure of the domain, we appeal to a concept of two-scale convergence adapted to thin heterogeneous media to achieve our goal.

This study investigates the applications of reconfigurable intelligent surfaces (RIS) in heterogeneous downlink networks (HetNet). Due to the network densification, the small cell base station (SBS) interferes with the macrocell users (MUE). In this paper, we utilize RIS to mitigate cross-tier interference in an HetNet via directional beamforming by adjusting the phase shift of the RIS. We consider an RIS-assisted HetNet consisting of multiple SBS nodes and MUEs that utilize both direct paths and reflected paths. Therefore, the aim of this study is to maximize the sum rate of all MUEs by jointly optimizing the transmit beamforming of the macrocell base station (MBS) and the phase shift of the RIS. An efficient RIS reflecting coefficients-based optimization (RCO) is proposed based on a successive convex approximation approach. Simulation results are provided to show the effectiveness of the proposed scheme in terms of its sum rate in comparison with the scheme HetNet without RIS and the scheme HetNet with RIS but with random phase shifts.

The introduction of the Internet of Things (IoT), i.e. the interconnection of embedded devices over the Internet, has changed the world we live in from the way we measure, make calls, print information and even the way we get energy in our offices or homes. The convenience of IoT products, like CCTV cameras, IP phones, and oscilloscopes, is overwhelming for end-users. In parallel, however, security issues have emerged and it is essential for infrastructure providers to assess the associated security risks. In this paper, we propose a novel method to detect IoT devices and identify the manufacturer, device model, and the firmware version currently running on the device using the page source from the web user interface. We performed automatic scans of the large-scale network at the European Organization for Nuclear Research (CERN) to evaluate our approach. Our tools identified 233 IoT devices that fell into eleven distinct device categories and included 49 device models manufactured by 26 vendors from across the world.

{This study investigates the coordinated behaviors and the underlying collective intelligence in biological groups, particularly those led by informed leaders. By establishing new convergence criteria based on real biological group experiments, this research introduces the concept of a volitional term and heterogeneous networks, constructing a coupled force Cucker-Smale model with informed leaders. The inclusion of the informed leader aspect distinctively characterizes the dynamics of leaders and followers, enabling a more accurate representation of biological group behavior. The paper then proposes the Flock Leadership Hierarchy Network (FLH), a model reflecting real biological interactions. By employing time slicing and rolling time windows, the study methodically analyses group behavior stages, using volatility and convergence time as metrics to examine the relationship between group consistency and interactions. Comparative experiments show the FLH network's superior performance. The Kolmogorov-Smirnov test indicates that the FLH network follows a power-law distribution, highlighting the evolutionary intelligence of biological groups. This research offers new perspectives on the evolution of biological groups, contributing to our understanding of both natural and artificial systems, such as animal migration and autonomous drone operations.

Stress has been identified as a prominent contributor to the development and advancement of bowel cancer, however its specific involvement is intricate and diverse. This abstract tries to offer a concise summary of the correlation between stress and bowel cancer, consolidating significant discoveries from several research fields. The investigation commences by studying the molecular pathways by which chronic stress impacts the development of colon cancer. Chronic stress causes disruption in the functioning of neuroendocrine, immunological, and inflammatory pathways, leading to the development of a microenvironment within the gastrointestinal system that promotes the growth of tumours. Furthermore, changes in the composition of gut microbiota, variations in epigenetics, and alterations in immune function caused by stress also have a role in increasing the risk and advancing the advancement of cancer. Psychological aspects, including how one perceives stress, the tactics they use to cope with it, and the level of psychosocial support they receive, are crucial in determining a person's susceptibility to bowel cancer and impacting the effectiveness of treatment. Stress management therapies, such as cognitive-behavioral therapy and mindfulness-based stress reduction, have demonstrated potential in enhancing patient well-being and promoting adherence to treatment. Moreover, epidemiological studies and clinical trials offer vital knowledge regarding the correlation between stress and the risk, prognosis, and treatment results of bowel cancer. Although the evidence is inconclusive, it is essential to continue researching the intricate relationship between stress and bowel cancer. This will help us understand the connection better and create specific strategies to reduce its impact on patient outcomes.

This article systematically reviews the research achievements on the MnOx/TiO2 catalyst doped with transition metals or rare earth metals for the catalytic selective catalytic reduction of NH3 (SCR-NH3) reaction. The Methodi Ordinatio systematic literature analysis method was employed to rank relevant articles based on journal impact factors, publication years, and citation counts. The ranking data shows that the undoped MnOx/TiO2 catalysts exhibit a catalytic efficiency of up to approximately 90% for NOx reduction, while the doped MnOx/TiO2 catalysts with transition metals or rare earth metals can achieve a maximum reduction efficiency of 100%. Among all the doped metals, the catalyst doped with nickel demonstrates the best NOx reduction performance. The comprehensive literature research concludes that MnOx/TiO2-based SCR catalysts, especially those doped with transition metals or rare earth metals, have significant catalytic efficiency and development potential in reducing NOx emissions in SCR reactions.

This study presents a straightforward electrochemical and plasma deposition method for producing Cobalt-doped Blue-TiO2 nanotubes with enhanced catalytic properties. After a titanium plate has been anodized, specific procedures are carried out that cause oxygen vacancies to form inside the TiO2 nanostructures. The obtained catalysts were subjected to electrochemical tests (to identify charge transfer resistance and flat band potential), optical analysis (to determine the band gap and Urbach energy) and also characterized in terms of morphology, wettability and antibacterial effect in order to understand and analyze the impact of the Co doping method on the final catalyst characteristics. A hydrophilic film with star-shaped structures and with antibacterial effect was created when cobalt was electrochemically doped to Blue-TiO2 nanotubes. By using this electrochemical doping technique, the Urbach energy was raised from 1.171 to 3.836 eV while the band gap energy was decreased from 3.04 to 2.88 eV. Additionally, photodegradation experiments using artificial doxycycline (DOX) water were conducted to determine the practical relevance of the research findings. In areas like antimicrobial applications and photodegradation of DOX, these extra experiments aimed to show the practical applicability and potential advantages of the research findings.

We describe the direct formation of mixed-phase (1T and 2H) MoS2 layers on Si as a photocathode via atomic layer deposition (ALD) for application in the photoelectrochemical (PEC) reduction of water to hydrogen. Without typical series-metal interfaces between Si and MoS2, our p-Si/SiOx/MoS2 photocathode showed efficient and stable operation in hydrogen evolution reactions (HERs). The resulting performance could be explained by spatially genuine device architectures in three dimensions (that is, laterally homo and vertically heterojunction structures). The ALD-grown MoS2 overlayer with the mixed-phase 1T and 2H homojunction passivates light absorber and surface states and functions as a monolithic structure for effective charge transport within MoS2. It is also beneficial in the operation of p-i-n heterojunctions with inhomogeneous barrier heights due to the presence of mixed-phase cocatalysts. The effective barrier heights reached up to 0.8 eV with optimized MoS2 thicknesses, leading to a 670 mV photovoltage enhancement without employing buried Si p-n junctions. The fast-transient behaviors via light illumination show that the mixed-phase layered chalcogenides can serve as efficient cocatalysts by depinning the Fermi levels at the interfaces. A long-term operation of ~ 70 h was also demonstrated in a 0.5 M H2SO4 solution.

With the rapid development of Earth observation and information technology, people are increasingly able to access geospatial models. Geospatial models, based on principles of geography, utilize mathematical, statistical, as well as computer science methods to interpret and predict geographic phenomena. These models can be applied in the fields such as urban planning, environmental protection, traffic management to help decision-makers solve geography-related problems. However, integrating different geospatial models to collaboratively solve complex geographic problems still faces significant obstacles due to heterogeneity in model structure, dependencies, and running modes. In this study, we propose a containerized service-based integration framework for heterogeneous geospatial models (GeoCSIF). GeoCSIF consists of three main components: (1) Model encapsulation. It breaks down complicated geospatial models into independently manageable model units, and builds as unified service packages with a templated constraint method. (2) Model orchestration. It achieves an optimal combination of large-scale models with complex dependencies using a prioritization-based orchestration method. (3) Model publication. It incorporates heuristics into the model scheduling process, which can provide adaptive deployment for different model runs. Finally, a prototype system was developed to validate the effectiveness and progressiveness of GeoCSIF by the integrating process of heterogeneous flood disaster models.

Cloud computing nowadays is not an emerging topic, and virtualization is an indispensable technology to expedite cloud computing to become the next sign of the coming Internet revolution. In real life, scientists never stop at exploring the possibilities from such technology by investigating millions of experiments and applications to enhance the quality of virtual services. However, isolated construction for the virtual machine doesn’t save the technology from unwanted data volumes or insensitive processing time. Containers are created to address such problems, by distributing applications without initiating the entire virtual machine. Docker, as an important player in this game, is an open-source application of the container family. The management tool from Docker containers, Swamskit, does not take heterogeneities in either virtualized containers or physical nodes. There are different nodes in the cluster, and each node is different in configurations, resource availability, or concerning resource, etc. Furthermore, the requirements initiated by different services change all the time. The demand might be CPU-intensive (e.g. Clustering services) and also memory-intensive (e.g. Web services), or completely at the opposite. In this paper, we focus on exploring the Docker container cluster and designing, DRAPS, a resource-aware placement scheme, to improve the system performance in a heterogeneous cluster.

5G heterogeneous network overlaid by millimeter-wave (mmWave) access employs mmWave meshed backhauling as a promising cost-efficient backhaul architecture. Due to the nature of mobile traffic distribution in practice which is both time-variant and spatially non-uniform, dynamic construction of mmWave meshed backhaul is prerequisite to support the varying traffic distribution. Focusing on such scenario of outdoor dynamic crowd (ODC), this paper proposes a novel method to control mmWave meshed backhaul for efficient operation of mmWave overlay 5G HetNet through Software-Defined Network (SDN) technology. Our algorithm is featured by two functionalities, i.e., backhauling route multiplexing for overloaded mmWave small cell base stations (SC-BSs) and mmWave SC-BSs’ ON/OFF status switching for underloaded spot. In this paper, the effectiveness of the proposed meshed network is confirmed by both numerical analyses and experimental results. Simulations are conducted over a practical user distribution modeled from measured data in realistic environments. Numerical results show that the proposed algorithm can cope with the locally intensive traffic and reduce energy consumption. Furthermore, a WiGig (Wireless Gigabit Alliance certified) device based testbed is developed for Proof-of-Concept (PoC) and preliminary measurement results confirm the proposed dynamic formation of the meshed network’s efficiency.

In this paper, we propose a novel network representation learning model TransPath to encode heterogeneous information networks (HINs). Traditional network representation learning models aim to learn the embeddings of a homogeneous network. TransPath is able to capture the rich semantic and structure information of a HIN via meta-paths. We take advantage of the concept of translation mechanism in knowledge graph which regards a meta-path, instead of an edge, as a translating operation from the first node to the last node. Moreover, we propose a user-guided meta-path sampling strategy which takes users' preference as a guidance, which could explore the semantics of a path more precisely, and meanwhile improve model efficiency via the avoidance of other noisy and meaningless meta-paths. We evaluate our model on two large-scale real-world datasets DBLP and YELP, and two benchmark tasks similarity search and node classification. We observe that TransPath outperforms other state-of-the-art baselines consistently and significantly.

Due to their strong immersion and real-time interactivity, helmet-mounted VR devices are becoming increasingly popular. Based on these devices, an immersive virtual geographic environment (VGE) provides a promising method for research into crowd behavior in an emergency. However, the current cheaper helmet-mounted VR devices are not popular enough and will continue to coexist with PC-based systems for a long time. Therefore, a heterogeneous distributed virtual geographic environment (HDVGE) could be a feasible solution to solve the heterogeneous problems caused by various types of clients, and support implementation of virtual crowd evacuation experiments, with large numbers of concurrent participants. In this study, we developed an HDVGE framework and put forward a set of design principles to define the similarities between the real world and the VGE. We discussed the HDVGE architecture and proposed an abstract interaction layer, a protocol-based interaction algorithm and an adjusted dead reckoning algorithm to solve the heterogeneous distributed problems. We then implemented an HDVGE prototype system focusing on subway fire evacuation experiments. Two types of clients are considered in the system, PC and all-in-one VR. Finally, we evaluated the performances of the prototype system and the key algorithms. The results showed that in a low-latency LAN environment, the prototype system can smoothly support 90 concurrent users consisting of PC and all-in-one VR clients. HDVGE could serve as a new means of obtaining observational data about individual and group behavior in support of human geography research.

One important goal of research on heterogeneous catalysis for Fischer Tropsch synthesis (FTS) is to understand the connection linking catalyst morphology and its activity. Development of these effective catalysts highlights the potential for understanding the effectiveness of promoted supported catalysts and revealing their complex and dynamic effects in FTS. The metal-support interaction (MSI) is influenced by the physicochemical and textural features provided by the modified support and can directly influence the sizes of active metal crystallites, reactants and/or products mass transfer, dispersion, mechanical strength, stability and consequently the adsorption-desorption characteristics of the catalyst. Herein, this article explores the general strategies for modifying silica supports (achieved through doping, coating, or promoting the support or support precursor) and how these strategies can be manipulated to further alter catalyst performance and product selectivity by enhancing the amount of active metal species and improving the intrinsic turnover frequency (TOF). Furthermore, the review emphasises a new strategy (the inverse model of the silica support modification) aiming at overcoming the challenges posed by active metal coating or coverage, as well as pore blockages by promoters that occur during conventional preparation of promoted silica supported FT catalysts.

The oxidative coupling of methane (OCM) to produce ethane and ethylene (C2 compounds) as platform chemicals involves complex chemistry with reactions both in the gas phase and on the catalyst surface, resulting in a distribution of products at the expense of C2 selectivity. This work uses experimental data from a variety of mixed metal oxides on supports at different reaction conditions (temperature, contact time, and reactant flow rates) to train a random forest regressor that predicts methane conversion and C2 selectivity (key performance indicators (KPIs)), and deploys it to locate optimal conditions that maximize C2 yield for a catalyst. Investigating the regressor interpretability via feature importance reveals that the choice of metals and support are crucial to C2 selectivity predictions, while the predictions of methane conversion are driven by the reaction conditions. The machine learning (ML) regressor is used as a surrogate to develop performance curves for each of the catalysts via a multi-objective optimization routine that seeks to maximize the KPIs in the decision space of reaction conditions, is seen to locate optimal conditions at which the maximum C2 yields for catalysts are predicted to be 15%, higher on average. Analyzing the catalysts in the space of their performance curves with respect to a popular OCM catalyst, Mn-Na2WO4/SiO2, reveals distinct patterns based on intrinsic properties of metals and supports. Further, the decision space with catalyst descriptors and reaction conditions is optimized for high C2 yields using the ML surrogate, in a static multi-objective optimization routine, and an adaptive Bayesian routine, where the latter was found to have a wider field focus in proposing catalyst formulations and conditions. Transition metal oxides on a variety of supports were proposed but not their lanthanide oxide counterparts.

This study investigates the magnetization of Cobalt (Co) nanoparticles with two different crystal structures: a centered cubic (fcc) and an hexagonal close packed (hcp), under the magnetic field of the Earth. For that reason, the hysteresis loops of the two material samples are exported and compared with the corresponding value by experimental procedure in the laboratory under the Earth’s magnetization. For both samples, the nanoparticle number variation and the nearest neighbor distance effect, in the Co hysteresis loop are investigated, structural information that is useful for the sample preparation. For the simulations the Finite Element Method (FEM) was used, while experiments are conducted using a measurement set-up based on fluxgate magnetometers. From the simulation, it is proven that, for both samples, the nanoparticle number effect does not change the hysteresis loop. In contrast, when the distance between the nanoparticles increases, the magnetization obtains smaller value. This study aims to build a novel, simple and economical measurement arrangement enabling in situ magnetic measurements for ferromagnetic nano-material characterization. As the need for continuous knowledge of the condition and the phase of ferromagnetic nanoparticles that is crucial in many applications, like catalytic processes, this work aims to introduce an alternative approach that will add another dimension to conventional chemical reaction engineering this of the magnetic state of the material resolved in time and space and to contribute in the field of the monitoring and the early fault diagnosis of nanoparticle materials.

Organic compounds that contain poly-triazole are very important intermediates in pharmaceutical and chemical industry. Click chemistry is one of essential reactions that can form C-N bond with high atom economy. The research progress in metals catalyzed Click chemistry of azides and alkynes from the perspective of reaction mechanism is categorized and summarized.

Wireless Sensor Network (WSN) and the sensing devices are considered part of the core components of the Internet of Things (IoT). Hence, the modeling of IoT-WSNs is fundamental to having a better understanding, management, and deployment of this kind of technology. A fundamental issue in WSN is energy consumption due to the inherent limitations of this resource in the sensor devices. On the other hand, several issues arise in heterogeneous scenarios due to the coexistence of different sensor devices. Therefore, the modeling process becomes challenging and must consider the different transmission and operation cycles and modes that the transmission protocol implements to assure successful functioning in terms of transmission, synchronization, and energy savings. A duty-cycled MAC WSN protocol (PSA-MAC) has been modeled based on a pair of two-dimensional Discrete-Time Markov Chain (2D-DTMC), whose solution in terms of stationary probability distribution is used by expressions that have been derived to evaluate the energy consumption of sensor devices that could be used for WSN-based IoT applications. Analytical results of average energy consumption are obtained, where the synchronization, data, and sleep periods of a full transmission cycle are considered in a heterogeneous scenario with different node classes and priority assignments. Also, the normal and awake operation cycles are considered to assure synchronization and energy savings. Moreover, the transmission schemes of SPT (transmission of single packets) and APT (transmission of packets in aggregation) are also considered in the model. These results are validated through discrete event-based simulations, and the obtained results are accurate.

Heterogeneous systems of limited capacity have general applications in manufacturing, but also for logistic or service systems, due to the differences in server or workstation performance or work assignment, in close relationship with system flexibility, where saturation and blocking are ordinary situations of systems with high demand and limited capacity, so accurate loss quantification is essential for performance evaluation. Multi-class systems of limited capacity have been studied much less than parallel homogeneous systems (Erlang models). In this context, accurate models for parallel heterogeneous ordered-entry systems are developed: without any prior queue, M/Mi/c/c, and with a k capacity queue M/Mi/c/c+k. These new matrix models give an exact state formulation, and their accuracy is verified through discrete event simulation and comparison with literature results. Also, the effect of queue capacity is studied in relationship with the pattern of service rates. Next, the heterogeneous recirculating system model is also developed with good approximation results. Finally, the proposed models are applied to evaluate systems with non-exponential service times, through a new hybrid methodology by combining the Markovian model and the Monte Carlo Method (MCM) for normal or lognormal service times that also yield useful good approximations to the simulated system.

This article deals with a finite-source stock-dependent stochastic inventory system with heterogeneous servers and retrial facility. The system can store up to S number of items, and the lifetime of each item follows an exponential distribution. The primary customers arrive in a finite waiting hall from a finite source and get service from the multi-channel (c-heterogeneous servers). The customer’s arrival rate depends on the present stock level. Those customers who arrive while the waiting hall is full enter into the finite orbit. Also, customers in the waiting hall can become impatient and enter the orbit. The (s,Q) ordering policy is adopted for the replenishment of stocks. In the steady state, the joint probability distribution of a number of items in the inventory, busy servers, customers in the waiting hall, and orbit is computed. The comparative numerical analysis of heterogeneous and homogeneous servers on the expected total cost, the average number of customers in the waiting hall and orbit, the fraction of successful retrial rate, and the average impatient customer rate are also carried out.

Domain-specific systems-on-chip (DSSoCs) aim to narrow the gap between general-purpose processors and application-specific designs. CPU clusters enable programmability, while hardware accelerators tailored to the target domain minimize task execution times and power consumption. Traditional operating system (OS) schedulers can diminish the potential of DSSoCs as their execution times can be orders of magnitude larger than the task execution time. To address this problem, we propose a dynamic adaptive scheduling (DAS) framework that combines the advantages of a fast, low-overhead scheduler and a sophisticated, high-performance scheduler with a larger overhead. We present a novel runtime classifier that chooses the better scheduler type as a function of the system workload, leading to improved system performance and energy-delay product (EDP). Experiments with five real-world streaming applications indicate that DAS consistently outperforms fast, low-overhead, and slow, sophisticated schedulers. DAS achieves a 1.29x speedup and 45% lower EDP than the sophisticated scheduler under low data rates and a 1.28x speedup and 37% lower EDP than the fast scheduler when the workload complexity increases. Furthermore, we demonstrate that the superior performance of the DAS framework also applies to hardware platforms, with up to a 48% and 52% reduction in execution time and EDP, respectively.

Lipid mesophases are being intensively studied as potential candidates for drug-delivery purposes. Extensive experimental characterization has unveiled a wide palette of release features depending on the nature of the host lipids and of the guest molecule, as well as on the environmental conditions. However, only few simulation works have addressed the matter, which hampers a solid rationalization of the richness of outcomes observed in experiments. Particularly, to date there are no theoretical works addressing the impact of hydropathy on the transport of a molecule within lipid mesophases, despite the significant fraction of hydrophobic molecules among currently-available drugs. Similarly, the high heterogeneity of water mobility in the nanoscopic channels within lipid mesophases has also been neglected. To fill this gap, we introduce here a minimal model to account for these features in a lamellar geometry, and systematically study the role played by hydropathy and water-mobility heterogeneity by Brownian-dynamics simulations. We unveil a fine interplay between the presence of free-energy barriers, the affinity of the drug for the lipids, and the reduced mobility of water in determining the net molecular transport. More in general, our work is an instance of how multiscale simulations can be fruitfully employed to assist experiments in release systems based on lipid mesophases.

Fractional differential equations have become a fundamental modelling approach for understanding and simulating the many aspects of nonlocality and spatial heterogeneity of complex materials and systems. Yet, while real-order fractional operators are nowadays widely adopted, little progress has been made in extending such operators to complex-order counterparts. In this work, we introduce new definitions for the complex-order fractional Laplacian, fully consistent with the theory of averaging of smooth functions over fractal sets, and present tailored spectral methods for their numerical treatment. The proposed complex-order operators exhibit a dual particle-wave behaviour, with solutions manifesting wave-like features depending on the magnitude of the imaginary part of the fractional order. Reaction-diffusion systems driven by the complex-order fractional Laplacian exhibit unique spatio-temporal dynamics, such as equilibrium of diffusion in random materials by interference of scattered waves, conduction block and highly fractionated propagation, or the generation of completely novel Turing patterns. Taken together, our results support that the proposed complex-order operators hold unparalleled capabilities to advance the description of multiscale transport phenomena in physical and biological processes highly influenced by the heterogeneity of complex media.

This paper establishes methods that quantify the structure of statistical interactions within a given data set using the characterization of information theory in cohomology by finite methods, and provides their expression in terms of statistical physic and machine learning. Following [1–3], we show directly that k multivariate mutual-informations (I_k) are k-coboundaries. The k-cocycles are given by I_k = 0, which generalize statistical independence to arbitrary dimension k. The topological approach allows to investigate Shannon’s information in the multivariate case without the assumptions of independent identically distributed variables. We develop the computationally tractable subcase of simplicial information cohomology represented by entropy H_k and information I_k landscapes. The I₁ component defines a self-internal energy functional U_k, and (−1)^k I_k,k≥2 components define the contribution to a free energy functional G_k of the k-body interactions. The set of information paths in simplicial structures is in bijection with the symmetric group and random processes, provides a topological expression of the 2nd law and points toward a discrete Noether theorem (1st law). The local minima of free-energy, related to conditional information negativity and the non-Shannonian cone of Yeung [4], characterize a minimum free energy complex. This complex formalizes the minimum free-energy principle in topology, provides a definition of a complex system, and characterizes a multiplicity of local minima that quantifies the diversity observed in biology. Finite data size effects and estimation bias severely constrain the effective computation of the information topology on data, and we provide simple statistical tests for the undersampling bias and for the k-dependences following [5]. We give an example of application of these methods to genetic expression and cell-type classification. The maximal positive I_k identifies the variables that co-vary the most in the population, whereas the minimal negative I_k identifies clusters and the variables that differentiate-segregate the most. The methods unravel biologically relevant I₁₀ with a sample size of 41. It establishes generic methods to quantify the epigenetic information storage and a unified epigenetic unsupervised learning formalism.

Non-uniform grain boundary sliding can induce strain and rotation incompatibilities at perfectly planar interfaces. Explicit analytic expressions of stress and lattice rotation jumps are thus derived at a planar interface in the general framework of heterogeneous anisotropic thermo-elasticity with plasticity and grain boundary sliding. Both elastic fields are directly dependent on in-plane gradients of grain boundary sliding. It is also shown that grain boundary sliding is a mechanism that may relax incompatibility stresses of elastic, plastic and thermal origin although the latter are not resolved on the grain boundary plane. This relaxation may be a driving force for grain boundary sliding in addition to the traditionally considered local shears on the grain boundary plane. Moreover, the obtained analytic expressions are checked by different kinds of bicrystal shearing finite element simulations allowing grain boundary sliding and where a pinned line in the interface plane aims at representing the effect of a triple junction. A very good agreement is found between the analytic solutions and the finite element results. The performed simulations particularly emphasize the role of grain boundary sliding as a possible strong stress generator around the grain boundary close to the triple line because of the presence of pronounced gradients of sliding.

Linear mixed models are frequently used for analysing heterogeneous data in a broad range of applications. The restricted maximum likelihood method is often preferred to estimate co-variance parameters in such models due to its unbiased estimation of the underlying variance parameters. The restricted log-likelihood function involves log determinants of a complicated co-variance matrix. An efficient statistical estimate of the underlying model parameters and quantifying the accuracy of the estimation requires the first derivatives and the second derivatives of the restricted log-likelihood function, i.e., the observed information. Standard approaches to compute the observed information and its expectation, the Fisher information, is computationally prohibitive for linear mixed models with thousands random and fixed effects. Customized algorithms are of highly demand to keep mixed models analysis scalable for increasing high-throughput heterogeneous data sets. In this paper, we explore how to leverage an averaged information splitting technique and dedicate matrix transform to significantly reduce computations and to accelerate computing. Together with a fill-in reducing multi-frontal sparse direct solver, the averaged information splitting approach improves the performance of the computation process.

A novel type of phosphazene containing additive that act both as catalyst and as flame retardant for benzoxazine binders is presented in this study. The synthesis of a derivative of hexachlorocyclotriphosphazene (HCP) and meta-toluidine was carried out in the medium of the latter, which made it possible to achieve complete substitution of chlorine atoms in the initial HCP. Thermal and flammability characteristics of modified compositions are revealed. The modifier catalyzes the process of curing and shifts the beginning of reaction from 222.0 C for pure benzoxazine to 205.9 C for composition with 10 phr of modifier. The additive decreases the glass transition temperature of compositions. Achievement of the highest category of flame resistance (V-0 in accordance with UL-94) is ensured both by increasing the content of phenyl residues in the composition and by the synergistic effect of phosphorus and nitrogen. Brief research of the curing kinetics disclosed the complex nature of the reaction. An accurate two-step model is obtained using extended Prout-Tompkins equation for both steps.

The direct growth of single-walled carbon nanotubes (SWCNTs) with a narrow distribution of diameter or chirality remains elusive despite significant benefits in properties and applications. Nanoparticle catalysts are vital for SWCNT synthesis, but how to precisely manipulate their chemistry, size, concentration, and deposition remains difficult, especially within a continuous production process from the gas-phase. Here, we demonstrate the preparation of W₆Co₇ alloyed nanoparticle catalysts with precisely tunable stoichiometry using electrospray, which remain solid state during SWCNT growth. We also demonstrate continuous production of liquid iron nanoparticles with in-line size selection. With the precise size manipulation of catalysts in the range of 1-5 nm, and a nearly monodisperse distribution (σ_g < 1.2), an excellent size selection of SWCNT can be achieved. All of the presented techniques show great potential to facilitate the realization of single-chirality SWCNT production.

In this work, the solar light-induced redox photoactivity of the ZnO semiconductor material was used to prepare at room temperature CuxO-ZnO composite catalysts with a control of the chemical state of the copper oxide phase. The preparation of Cu2(I)O-ZnO and Cu(II)O-ZnO composite catalysts was achieved by using Cu(acac)2 in THF-water and Cu(NO3)2 in water as metallic precursor, respectively. Prior to the implementation of the photo-assisted synthesis method, the most efficient photoactive ZnO material was selected among different ZnO materials prepared by the low temperature polyol method and through the precipitation method with carbonates and carbamates as precipitation agent, by taking the photocatalytic degradation of the 4-chlorophenol compound in water under simulated solar light as model reaction. The ZnO support materials were characterized by XRD, BET, TGA, SEM and TEM, and the synthesis method was strongly influencing their photoactivity in terms of 4-chlorophenol degradation and of total organic carbon removal. The most photoactive ZnO material was prepared by precipitation with carbonates and calcined at 300°C, and was taking advantage of a high specific surface area and a small mean crystallite size for achieving a complete 4-chlorophenol mineralization within 70 min of reaction, with a minimum Zn2+ released to the solution due to surface photocorrosion. Beside thermal catalysis applications, this work opened a new route for the facile synthesis of Cu2O-ZnO heterojunction photocatalysts, that could take advantage under solar light of the heterojunction built between the p-type semi-conductor Cu2O with direct visible light band gap and the ZnO semiconductor phase.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence and structural knowledge of drugs, targets, and pockets using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model’s performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, the supremacy of S2DTA became strikingly apparent. It showcased a noteworthy 25.2% reduction in Mean Absolute Error (MAE) and an impressive 20.1% decrease in Root Mean Square Error (RMSE). Furthermore, S2DTA exhibited substantial advancements in other pivotal metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2. These metrics experienced remarkable increments of at least 19.6%, 17.5%, 8.1%, and a remarkable 49.4%, respectively. Finally, we conducted interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism, fully proving that S2DTA is a valuable and accurate tool for predicting DTA. For further exploration, the source data and code repository can be accessed at https://github.com/dldxzx/S2DTA.

Efficient resource allocation is crucial in clusters with frugal Single-Board Computers (SBCs) possessing limited computational resources. These clusters are increasingly being deployed in edge computing environments in resource-constrained settings where energy efficiency and cost-effectiveness are paramount. A major challenge in Hadoop YARN scheduling is load-balancing, as frugal nodes within the cluster can become overwhelmed, resulting in degraded performance and frequent occurrences of out-of-memory errors, ultimately leading to job failures. In this study, we introduce an Adaptive Multi-criteria Selection for Efficient Resource Allocation (AMS-ERA) in Frugal Heterogeneous Hadoop Clusters. Our criterion considers CPU, memory and disk requirements for jobs and aligns the requirements with available resources in the cluster for optimal resource allocation. To validate our approach, we deploy a heterogeneous SBC-based cluster consisting of 11 SBC nodes and conduct several experiments to evaluate the performance using Hadoop wordcount and terasort benchmark for various workload settings. The results are compared to the Hadoop-Fair, FOG and IDaPS scheduling strategies. Our results demonstrate a significant improvement in performance with the proposed AMS-ERA, reducing execution time by 27.2%, 17.4% and 7.6% respectively using terasort and wordcount benchmarks.

It is essential to deal with data scattered among heterogeneous information sources, which can be structured, semi-structured, or unstructured, to give consumers a cohesive perspective of the data. Information gathering is challenging as a result, and one of the major causes of this is that data sources are developed to support specific applications. A method to simplify this procedure is to use ontology representation as an intermediate step. An ontology represents a knowledge structure that reasonably reflects the real world's complexity and is ideally used in many industries today. This research aims to enhance the functionality of a global ontology system by implementing and integrating a natural language query system. Leveraging NLP-based approaches, we constructed SPARQL queries for our ontologies to facilitate natural language translation into a well-structured query format. A dedicated query component was developed to transform natural language questions into SPARQL queries, with the selection of the optimal query graph to generate the final queries. In the subsequent phase, we explored the integration of distinct sentence encoders to improve latent sentence representations during query construction. The generated SPARQL queries were executed on the global ontology output and then translated into source-specific questions. This innovative approach enables unified access to heterogeneous data sources through a user-friendly natural language querying interface.

The theory of heterogeneous nucleation was initially developed as a part of condensed matter physics and later it was used as an important engineering tool to design metallurgical processes. The success leads to wide applications of the theory in metallurgical practice. For example, engineering heterogeneous nucleation in ductile iron has been used to reduce shrinkage defects, suppress cementite formation, and modify size and shape of micro-structural constituencies. This demonstrates how theoretical knowledge could benefit industry practice. This overview aims to summarize the authors’ published studies in co-authorship with colleagues and students, which covered different aspects of engineering heterogeneous nucleation in multiphase cast alloys. Several approaches for engineering heterogeneous nucleation using thermodynamic simulation and practical methods for improving efficiency of nucleation using co-precipitation technique and a local transient melt supersaturation were suggested. Automated Scanning Electron Microscopy Energy-Dispersive-X-ray (SEM/EDX) analysis and a high-resolution Transmission Electron Microscopy (TEM) were used to verify simulation predictions. Practical examples of controlling micro-porosity shrinkage in cast irons with spheroidal graphite are presented to illustrate the power of engineering heterogenous nucleation. The authors appreciate the great efforts that the co-authors have made on the original, overviewed articles here.

Increasingly measured data in the context of smart cities can be used to develop new and innovative business models to increase efficiency and the value of life. A time-series classification algorithm can support to automatize many different processes such as forecasting services. In order to ensure data security and privacy, Federated Learning trains a global model collaboratively on multiple clients. Having different data-distributions and data-quantities across participating clients, neural networks suffer from slow convergence and overfitting. Based on different data-distributions, data-quantities and number of clients, we develop and evaluate different data-clustering strategies to update global model weights in comparison to the state of the art. We use public time-series data, generate various synthetic datasets and train a Relational-Regularized Autoencoder for classification purposes. Our results show an improvement of model performance concerning generalization.

In heterogeneous networks, the random walks based on meta-path requires prior knowledge and lacks flexibility. And the random walks based on non-meta-path only considers the number of node types, but does not consider the influence of schema and topology between node types in real networks. To solve the above problems, this paper proposes a novel model HNE-RWTIC (Heterogeneous Network Embedding Based on Random Walks of Type & Inner Constraint). Firstly, to realize the flexible walks, we design a Type strategy, which is the node type selection strategy based on the co-occurrence probability of node types. Secondly, to achieve the uniformity of node sampling, we design an Inner strategy, which is the node selection strategy based on the adjacency relationship between nodes. The Type & Inner strategy can realize the random walks based on meta-path, the flexibility of the walks, and can sample the node types and nodes uniformly in proportion. Thirdly, based on the above strategy, a transition probability model is constructed; then, we obtain the nodes embedding based on the random walks and Skip-Gram. Finally, in classification and clustering tasks, we conducted a thorough empirical evaluation of our method on three real heterogeneous networks. Experimental results shown that F1-Score and NMI of HNE-RWTIC outperform state-of-the-art approaches.

Speech emotion recognition remains a heavy lifting in natural language processing. It has strict requirements to the effectiveness of feature extraction and that of acoustic model. With that in mind, a Heterogeneous Parallel Convolution Bi-LSTM model is proposed to address these challenges. It consists of two heterogeneous branches: the left one contains two dense layers and a Bi-LSTM layer, while the right one contains a dense layer, a convolution layer, and a Bi-LSTM layer. It can exploit the spatiotemporal information more effectively, and achieves 84.65%, 79.67%, and 56.50% unweighted average recall on the benchmark databases EMODB, CASIA, and SAVEE, respectively. Compared with the previous research results, the proposed model achieves better performance stably.

Due to metal leaching and poor catalyst stability, the chemical industry’s fine chemical and pharmaceutical sectors have been historically reluctant to use supported transition metal catalysts to manufacture fine chemicals and active pharmaceutical ingredients. With the advent of new generation supported metal catalysts and flow chemistry, we argue in this study, this situation is poised to quickly change. Alongside heterogenized metal nanoparticles, both single-site molecular and single-atom catalyst will become ubiquitous. This study offers a critical outlook taking into account both technical and economic aspects.

The resource allocation for tasks in heterogeneous distributed system is a well known NP-hard problem. For the sake of making the makespan is minimized, it is hard to distribute the tasks to proper processors. The problem is even more complex and challenging when the processors have unavailable time and the tasks type are various. This paper investigates a resource allocation problem for hybrid tasks comprising both divisible and bag-of-tasks(BoT) in heterogeneous distributed system when the processors has unavailable time. First, the mathematical model, which minimizes the makespan of the hybrid tasks when the processors have unavailable time, is established. Second, we propose a scheduling algorithm referred to as bag-of-tasks allocate-pull and divisible task allocation (BoTAPDTA) algorithm for handling hybrid tasks on heterogeneous distributed systems. In addition, to solving the optimization model efficiently, a generic algorithm(GA) is proposed. For the sake of reducing the search space and solving the optimization model effectively, a two step scheduling algorithm(TSGA), which first allocate bag-of-tasks(BoT) using generic algorithm and then assign divisible task to processors like BoTAPDTA, is designed. Finally, numerical simulation experiments are conducted, and experimental results indicate the effectiveness of the proposed model and algorithm.

Textile wastewater containing dyes pose significant environmental hazards. Advanced oxidative processes, especially the heterogeneous photo-Fenton process, are effective in degrading a wide range of contaminants due to high conversion rates and ease of catalyst recovery. This study evaluates the photocatalytic degradation of azo dyes Acid Red 18 (AR18), Acid Red 66 (AR66) and Orange 2 (OR2) using magnetite as a catalyst. The magnetic catalyst was synthesized via a hydrothermal process at 150°C. Experiments were conducted at room temperature, investigating the effect of catalyst dosage, pH, and initial concentrations of H2O2 and AR18 dye. Kinetic and thermodynamic studies were performed at 25, 40 and 60°C for the three azo dyes (AR18, AR66 and OR2) and the effect of the dye structures on the photocatalytic efficiency was investigated. At 25 °C for 0.33 mmol.L-1 of dyes at pH 3.0, using 1.4 g.L-1 of catalyst and 60 mg.L-1 of H2O2 under a UV radiation of 16.7 mW.cm-2 the catalyst showed a 62,3% degradation for AR18, 79.6% for AR66, and 83.8% for OR2 in 180 minutes of reaction. The oxidation of azo dyes under these conditions is spontaneous and endothermic. The pseudo-first order kinetic constants indicated a strong temperature dependence with an order of reactivity of the type: OR2 &gt; AR66 &gt; AR18, which is associated to the characteristics of molecular complexity (size, steric hindrance, aromatic conjugation), electrostatic repulsion and the nature of the acid-base interactions on the catalytic surface.

The steady-state multiplicity of the porous, non-isothermal, catalyst pellet when two parallel and consecutive chemical reactions take place was analysed in this work. The geometry selected for the catalyst pellet is finite hollow cylinder. A numerical multigrid continuation technique with the preconditioned conjugate gradient squared as coarse grid solver was used. The continuation parameter is the dimensionless adiabatic heat rise (Prater number) for the first chemical reaction. The effect of the other governing parameters was analysed and the results are compared to those provided by the single chemical reaction.

The photoreduction of CO2 is an intriguing process, which allows the synthesis of fuels and chemicals. One of the limitations for CO2 photoreduction in the liquid phase is its low solubility in water. This point has been here addressed by designing a fully innovative concept of pressurized photoreactor, allowing operation up to 20 bar and applied to improve the productivity of this very challenging process. The photoreduction of CO2 in the liquid phase was performed using commercial TiO2 (Evonink P25), TiO2 obtained by flame spray pyrolysis (FSP) and gold doped P25 (0.2 wt% Au-P25) in the presence of Na2SO3 as hole scavenger (HS). The different reaction parameters (catalyst concentration, pH and amount of HS) have been addressed. The products in liquid phase were formic acid and formaldehyde. Moreover, for longer reaction time and with total consumption of HS, gas phase products formed (H2 and CO) after accumulation of significant amount of organic compounds in the liquid phase, due to their consecutive photoreforming. Enhanced CO2 solubility in water was achieved by adding a base (pH= 12-14). In basic environment, CO2 formed carbonates which further reduced to formaldehyde and formic acid and consequently formed CO/CO2+H2 in the gas phase through photoreforming. The deposition of small Au nanoparticles (3-5 nm) (NPs) onto TiO2 was found to quantitatively influence the products distribution and increase the selectivity towards gas phase products.

Permeability is a fundamental property of porous media, especially when studying resin transfer within a dense fibrous medium to manufacture composite materials. When the measurement of permeability tensor of composite reinforcements is difficult and time consuming, especially in thick, heterogeneous fibrous structures, a numerical approach based on the calculation of the tensor components on a 3D image of the material can be very advantageous. A new finite element based method proposed in this study allows solving very high-dimensional flow problems while limiting the biases associated with boundary conditions and the small size of the numerical samples addressed. The method is validated against academic test cases and against the results of a recent permeability benchmark exercise. The results emphasize that for heterogeneous and anisotropic microstructures, it is important to perform calculations on large samples using boundary conditions that do not suppress the transverse flows that occur when flow is forced out of the principal directions. Since these are not necessarily known in complex media, the permeability determination method must not introduce bias by generating non-physical flows.

Using annual panel data of 46 listed commercial banks in China from 2012 to 2021 and constructing a two-way fixed-effects model, this study empirically analyzes the competition and technology spillover effects of fintech on the profitability of commercial banks. The results show that (1) in the early stages of fintech development, the competition effect is larger than the technology spillover effect; thus, it is negatively correlated with commercial banks’ profitability. However, with the spread of innovative fintech, the technology spillover effects and commercial bank profitability will gradually improve. (2) The influence of fintech on the profitability of commercial banks differs. Compared to large commercial banks, fintech has more significant negative effects on small- and medium-sized commercial banks in the short run. However, the role of fintech for such banks will also grow in the future. The results of this study provide practical guidance for how commercial banks can respond to the fintech wave. To realize sustainable development of the banking industry, commercial banks should change their business philosophy and revenue model, vigorously improve their fintech innovation capability, differentiate their choice of fintech development routes, develop personalized customization with a focus on users, and ultimately realize digital transformation and upgrading.

Multiple sclerosis (MS) is a heterogeneous disease of the central nervous system that is governed by neural tissue loss and dystrophy during its progressive phase, with complex reactive pathological cellular changes. The immune-mediated mechanisms that promulgate the demyelinating lesions during the relapses of acute episodes are not characteristic of chronic lesions during progressive MS. This has limited our capacity to target the disease effectively as it evolves within the central nervous system white and gray matter, thereby leaving neurologists without effective options to manage individuals as they transition to a secondary progressive phase. The current review highlights the molecular and cellular sequelae that have been identified to cooperate and/or contribute to neurodegeneration that identifies individuals with progressive forms of MS. We emphasize the need for appropriate monitoring via known and novel molecular and imaging biomarkers that can accurately detect and predict progression for the purposes of newly designed clinical trials that may demonstrate efficacy of neuroprotection and potentially neurorepair. To achieve neurorepair, we focus on the modifications required in the reactive cellular and extracellular milieu, in order to enable endogenous cell growth as well as transplanted cells that can integrate and/or renew the degenerative MS plaque.

Retweet prediction is an important task related to different problems such as information spreading analysis, the automatic detection of fake news, social media monitoring, etc. In this study we explore the possibilities of retweet prediction based on heterogeneous data sources. In order to classify the tweet according to the amount of retweets, we combine features extracted from the multilayer network and the text. More specifically, we introduce a multilayer framework that proposes the multilayer network representation of Twitter. This formalism captures different users' actions and complex relationships as well as other key properties of communication on Twitter. We select a set of local network measures from each layer and construct a set of multilayer network features. In addition, we adopt a BERT-based language model, namely Cro-CoV-cseBERT to capture high-level semantics and structure of tweets as a set of text features. Then, we train six machine learning (ML) algorithms: random forest, multilayer perceptron, light gradient boosting machine, category embedding model, neural oblivious decision ensembles and attentive interpretable tabular learning model in the task of retweet prediction. We compare the performance of all six algorithms in three different setups (i) using only text features, (ii) using only multilayer network features and (iii) using both sets of features. We evaluate all setups in terms of standard evaluation measures i.e. precision, recall, F1-score and accuracy. For this task, we first prepare and use an empirical dataset of 199,431 tweets in the Croatian language posted during the period between January 1, 2020 and May 31, 2021. Our results indicate that by integrating multilayer network features with text features the prediction model would perform better than using just one set of features.

In this paper, industrial structure distortion, two-way FDI and carbon emission intensity are brought into a unified research framework, and based on China's panel data from 2011 to 2020, empirical tests are conducted employing Exploratory Spatial Data Analysis (ESDA), spatial econometric model and intermediary effect test. The results show the following. Firstly, China's industrial structure distortion index shows a downward trend. The industrial structure distortion index is the highest in the west, followed by the middle, and the lowest in the East. Secondly, the relationship between carbon emission intensity and economic development shows a "decoupling" effect and keeps decreasing year by year. The spatial disparity is remarkable, showing the pattern of "the east leading, the middle catching up and the west lagging ". At the provincial level, except in Xinjiang province, the carbon emission intensity of other provinces showed different degrees of decline. In terms of spatial distribution, the polarization characteristics of carbon emission intensity are significant, and the traditional spatial distribution pattern has been broken. Thirdly, there is a positive spatial correlation between China's industrial structure distortion, two-way FDI and carbon emission intensity. The distortion of industrial structure will not only lead to the increase of local carbon emission intensity but also produce reverse spillover to adjacent areas. IFDI and OFDI provide a strong driving force for the decline of carbon emission intensity. IFDI promotes the decline of carbon emission intensity in adjacent areas, while OFDI will increase the carbon emission intensity in surrounding areas. The interaction of IFDI and OFDI can significantly reduce the carbon emission intensity of local and adjacent areas. Fourthly, the results of intermediary effect analysis show that two-way FDI is the two channels of industrial structure distortion affecting carbon emission intensity. Industrial structure distortion can affect the transmission mechanism of carbon emission intensity by affecting two-way FDI.

Scattering of elastic waves in heterogeneous media has become one of the most important problems in the field of wave propagation due to its broad applications in seismology, natural resource exploration, ultrasonic nondestructive evaluation and biomedical ultrasound. Nevertheless, it is one of the most challenging problems because of the complicated medium inhomogeneity and the complexity of the elastodynamic equations. A widely accepted model for the propagation and scattering of elastic waves, which properly incorporates the multiple scattering phenomenon and the statistical information of the inhomogeneities is still missing. In this work, the author developed a multiple scattering model for heterogeneous elastic continua with strong property fluctuation and obtained the exact solution to the dispersion equation under the first-order smoothing approximation. The model establishes an accurate quantitative relation between the microstructural properties and the coherent wave propagation parameters and can be used for characterization or inversion of microstructures. Starting from the elastodynamic differential equations, a system of integral equation for the Green functions of the heterogeneous medium was developed by using Green’s functions of a homogeneous reference medium. After properly eliminating the singularity of the Green tensor and introducing a new set of renormalized field variables, the original integral equation is reformulated into a system of renormalized integral equations. Dyson’s equation and its first-order smoothing approximation, describing the ensemble averaged response of the heterogeneous system, are then derived with the aid of Feynman’s diagram technique. The dispersion equations for the longitudinal and transverse coherent waves are then obtained by applying Fourier transform to the Dyson equation. The exact solution to the dispersion equations are obtained numerically. To validate the new model, the results for weak-property-fluctuation materials are compared to the predictions given by an improved weak-fluctuation multiple scattering theory. It is shown that the new model is capable of giving a more robust and accurate prediction of the dispersion behavior of weak-property-fluctuation materials. Numerical results further show that the new model is still able to provide accurate results for strong-property-fluctuation materials while the weak-fluctuation model is completely failed. As applications of the new model, dispersion and attenuation curves for coherent waves in the Earth’s lithosphere, the porous and two-phase alloys, and human cortical bone are calculated. Detailed analysis shows the model can capture the major dispersion and attenuation characteristics, such as the longitudinal and transverse wave Q-factors and their ratios, existence of two propagation modes, anomalous negative dispersion, nonlinear attenuation-frequency relation, and even the disappearance of coherent waves. Additionally, it helps gain new insights into a series of longstanding problems, such as the dominant mechanism of seismic attenuation and the existence of the Mohorovičić discontinuity. This work provides a general and accurate theoretical framework for quantitative characterization of microstructures in a broad spectrum of heterogeneous materials and it is anticipated to have vital applications in seismology, ultrasonic nondestructive evaluation and biomedical ultrasound.

Floods are one of the most wide-spread, frequent, and devastating natural disasters that continue to increase in frequency and intensity. Remote sensing, specifically synthetic aperture radar (SAR), has been widely used to detect surface water inundation to provide retrospective and near-real time (NRT) information due to its high-spatial resolution, self-illumination, and low atmospheric attenuation. However, the efficacy of flood inundation mapping with SAR is susceptible to reflections and scattering from a variety of factors including dense vegetation and urban areas. In this study, the topographic dataset height above nearest drainage (HAND) was investigated as a potential supplement to Sentinel-1A C-Band SAR along with supervised machine learning to improve the detection of inundation in heterogeneous areas. Three machine learning classifiers were trained on two sets of features SAR only (VV & VH) and VV, VH & HAND to map inundated areas. Three study sites along the Neuse River in North Carolina, USA during the record flood of Hurricane Matthew in October 2016 were selected. The binary classification analysis (inundated as positive vs. non-inundated as negative) revealed significant improvements when incorporating HAND in several metrics including classification accuracy (ACC) (+37.1%), true positive rate (TPR) (+51.2%), and negative predictive value (NPV) (+23.7%), A marginal improvement of +1.4% was seen for positive predictive value (PPV), but true negative rate (TNR) fell -15.1%. By incorporating HAND, a significant number of areas with high SAR backscatter but low HAND values were detected as inundated which increased true positives. This in turn also increased the false positives detected but to a lesser extent as evident in the metrics. This study demonstrates that HAND could be considered a valuable feature to enhance SAR flood inundation mapping especially in areas with heterogeneous land covers with dense vegetation that interfere with SAR.

One of the research priorities in atmospheric chemistry is to advance our understanding of heterogeneous reactions and their effect on the composition of the troposphere. Chemistry on aqueous surfaces is particularly important in this regard because of their ubiquity and expanse. They range from the surfaces of oceans (360 million km²), cloud and aerosol drops (~10 trillion km²) to the fluids lining the human lung (~200 m²). Typically, ambient air contains reactive gases that may affect human health, influence climate and participate in biogeochemical cycles. Despite their importance, reactions between gases and solutes at air-aqueous interfaces are not well understood. New, surface-specific techniques are required that detect and identify the intermediates and products of such reactions as they happen on liquids. This is a tall order because genuine interfacial reactions are faster than mass diffusion into bulk liquids, and may produce novel species at low concentrations. Herein, we review evidence that validates online pneumatic ionization mass spectrometry of liquid microjets dosed by reactive gases as a technique meeting such requirements. Next, we call attention to results obtained by this approach on reactions of ozone, nitrogen dioxide and hydroxyl radicals with various solutes on aqueous surfaces. The overarching conclusion is that the outermost layers of aqueous solutions are unique media, where equilibria shift and reactions proceed faster than, in some cases along different pathways from the bulk liquids. The fact that the rates and mechanisms of reactions at air-aqueous interfaces may not be deduced from experiments in bulk liquids opens new conceptual frameworks and lines of research, and adds an overlooked dimension to atmospheric chemistry.

Due to the rapid development and popularity of the Internet, cloud computing has become an indispensable application service. However, how to assign various tasks to the appropriate service nodes is an important issue. Based on the reason above, an efficient scheduling algorithm is necessary to enhance the performance of system. Therefore, a Three-Layer Cloud Dispatching (TLCD) architecture is proposed to enhance the performance of task scheduling. In first layer, the tasks need to be distinguished to different types by their characters. Subsequently, the Cluster Selection Algorithm is proposed to dispatch the task to appropriately service cluster in the secondly layer. Besides, a new scheduling algorithm is proposed to dispatch the task to a suitable server in a server cluster to improve the dispatching efficiency in the thirdly layer. Basically, the TLCD architecture can obtain better task completion time than previous works. Besides, our algorithm and can achieve load-balancing and reliability in cloud computing network.

Pt-coated Ni layer supported on Ni foam catalyst (denoted PtNi/Nifoam) was investigated for the oxidation of the formic acid (FAO) in acidic media. The prepared PtNi/Ni foam catalyst was studied as a function of the formic acid (FA) concentration at bare Pt and PtNi/Nifoam catalysts. The catalytic activity of the PtNi/Nifoam catalysts, studied on the basis of the ratio of the direct and indirect current peaks (jd)/(jnd) for the FAO reaction, showed values about 10 times higher compared to those on bare Pt, particularly at a low formic acid concentrations, reflecting the superiority of the former catalysts for the oxidation of FA to CO2. Ni foams provide a large surface area for the FOR while synergistic effects between Pt nanoparticles and Ni-oxy species layer on Ni foams contribute significantly to the enhanced oxidation of FA via the direct pathway, making it almost equal to the indirect pathway, particularly at low formic acid concentrations.

The article describes synthesis of aminoorgano-functionalized silica as a perspective material for catalysis application. The amino groups have electron donor properties valuable for metal chemical state of palladium. So presence of electron donor groups is important for increasing of catalysts stability. The research is devoted to investigation of silica amino-modified support influence on activity and stability of palladium species in 4-nitroaniline hydrogenation process. A series of catalysts with different supports such as SiO2, SiO2-C3H6-NH2 (amino-functionalized silica), γ-Al2O3 and activated carbon were studied. The catalytic activity was studied in the hydrogenation of 4-nitroaniline to 1,4-phenylenediamine. The catalysts were characterized by scanning electron microscopy, transmission electron microscopy, x-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy and pulse chemisorption of hydrogen. The 5 wt. % Pd/SiO2-C3H6-NH2 catalyst exhibited the highest catalytic activity for 4-nitroaniline hydrogenation with 100% conversion and 99% selectivity with respect to 1,4-phenylenediamine.

Filamentous fungi such as Aspergillus spp. are opportunistic pathogens, which cause highly invasive infections, especially in immunocompromised individuals. Control of such fungal pathogens is increasingly problematic due to the small number of effective drugs available for treatment. Moreover, the increased incidence of fungal resistance to antifungal agents makes this problem a global human health issue. The cell wall integrity system of fungi is the target of antimycotic drugs echinocandins, such as caspofungin (CAS). However, echinocandins cannot completely inhibit the growth of filamentous fungal pathogens, which results in survival/escape of fungi during treatment. Chemosensitization was developed as an alternative intervention strategy, where co-application of CAS with the intervention catalyst octyl gallate (OG; chemosensitizer) greatly enhanced CAS efficacy, thus achieved ≥ 99.9% elimination of filamentous fungi in vitro. Based on hypersensitive responses of Aspergillus antioxidant mutants to OG, it is hypothesized that, besides destabilizing cell wall integrity, the redox-active characteristic of OG may further debilitate fungal antioxidant system.

The one pot hydrogenolysis/hydrogenation reaction of maltose to 2 moles of sorbitol has been carried out over different supported Cu catalysts in water at 180°C and 40 bar of H2. Only the catalysts supported on silicas were found to be effective in this reaction giving up to 86% yield in the desired product while the bare supports and the catalysts supported on alumina or silica alumina gave messy reactions. The peculiar activity of the two Cu/Silica systems tested was ascribed to high metal dispersion and suitable polarity of the catalyst surface. The reduced catalyst, exposing metallic Cu particles on the surface, showed unusual stability in the presence of water as a solvent and could be reused several times without any treatment.

Chronoamperometric curves of the oxidation of a ferrocenyl derivative by the potential step showed diffusion controlled-like currents less than the Cottrell's equation at a platinum wire electrode. The lower deviation cannot be explained by the Butler-Volmer heterogeneous kinetics, but was ascribed to the negatively capacitive current associated with the redox reaction. The deviation at the fully oxidized potential corresponds to the non-zero concentration at the electrode surface, which cannot be predicted from the Nernst equation. The Nernst equation expresses the relationship of the potential with the activity at the electrode surface rather than the concentration. The diffusion equation provides the relation of the current with the surface concentration rather than the activity. The negative capacitance or the non-zero concentration may arise from structure formation on the electrode owing to the dipole-dipole interaction, which is similar to the generation of the double layer capacitance including frequency dispersion. On this concept, we derive here expressions for the lowered diffusion-controlled current and the time-dependent surface concentration. The negatively capacitive current shows the time-dependence of t-0.9, which is similar to the decay of the double layer capacitive currents. The surface concentration decays with the t-0.4-dependence.

Nanoparticle (NP) transport is increasingly relevant to subsurface engineering applications such as aquifer characterization, fracture electromagnetic imaging and environmental remediation. An efficient field-scale simulation framework is critical for predicting NP performance and designing subsurface applications. In this work, for the first time, a streamline-based model is presented to simulate NP transport in field-scale subsurface systems. It considers a series of behaviors exhibited by engineered nanoparticles (NPs), including time-triggered encapsulation, retention, formation damage effects and variable nanofluid viscosity. The key methods employed by the algorithm are streamline-based simulation (SLS) and an operator-splitting (OS) technique for modeling NP transport. SLS has proven to be efficient for solving transport in large and heterogeneous systems, where the pressure and velocity fields are firstly solved on underlying grids using finite-difference (FD) methods. After tracing streamlines, one-dimensional (1D) NP transport is solved independently along each streamline. The adoption of OS enhances flexibility for the entire solution procedure by allowing different numerical schemes to solve different governing equations efficiently and accurately. For the NP transport model, an explicit FD scheme is used to solve the advection term, an implicit FD scheme is used for the diffusion term and an adaptive numerical integration is used to solve the retention terms. The model is implemented in an in-house streamline-based code, which is verified against analytical solutions, a commercial FD reservoir simulator (ECLIPSE) and an academic FD colloid transport code (MNMs). For a 1D homogeneous case, the effluent breakthrough curves (BTC) produced by the in-house simulator are in good agreement with the analytical solution and MNMs, respectively. For a two-dimensional (2D) heterogeneous case, the BTC and concentration pattern of the in-house simulator all match well with the solution produced by commercial simulator. Simulations on a synthetic three-dimensional (3D) nanocapsule application engineering design case, are performed to investigate the effect of fluid and NP properties on the displacement pattern of an existing subsurface fluid.

The unprecedented demand for services and applications, coupled with large network architecture calls for a radical melioration in evolving wireless networks. Massive multiple-input and multiple-out (massive MIMO) systems, small cell networks (SCN) and Heterogeneous networks (HetNet) are envisioned to meet the new quality of service (QoS) objectives of evolving networks. In this paper, we investigate massive MIMO with HetNet, where the intended macro BS signal follows Ricean fading and interfering Femto BS signals follow Rayleigh fading. Subject to the Ricean assumption, strong line-of-sight (LOS) channel exists in the coverage area, when the high power macro BS is mounted at a high altitude. Then, by exploiting matrix and stochastic geometric tools, we evaluate the signal-to-interference (SIR). And obtain QOS objectives: coverage and outage probabilities, and area spectral efficiency (ASE). Further, we investigate the role of multiple antenna system in improving the SIR. This involves massive MIMO beamforming coordination of the macro BSs through power control with max-min optimization for users at the cell-edge. Numerical results show that the coverage and outage performance converge for different user locations, pathloss and Ricean factor. Also, the monotonic increase in Ricean factor improves the SIR of a user located within coverage region. That is, a user is in an outage at a distance where the Ricean factor is very small. And, optimal macro BS antennas and Ricean factor that achieve the ASE performance guarantee a rate-fairness between the cooperating macrocells, and avoid strong Ricean channel correlation. The performance gain is dependent on Ricean factor and user location, but independent of cell size.

This review article explores the convergence of virtual reality (VR) and architectural design from an inclusive lens. It underscores the crucial role of virtual reality (VR) in serving as a catalyst for the incorporation of social and cultural sustainability principles in architectural projects. The exploration is guided by inclusive design principles, which encompass several aspects such as accessibility, cultural sensitivity, diversity and inclusivity, user-centered design, and empathy. These concepts serve as the basis for further investigation into the potential of virtual reality (VR) technology to enhance and heighten these features within architectural design, therefore promoting the progress of social and cultural sustainability. The review article highlights the profound impact that virtual reality (VR) may have, going beyond mere visualisation to generate architectural experiences that are immersive, interactive, and empathetic. These experiences are designed to take into account diverse perspectives and needs.

PdRe/Al2O3 catalysts are highly selective for hydrogenation of furfural to furfuryl alcohol (FAL). Moreover, synergy between the metals can result in greater specific activity (higher turnover frequency, TOF) than exhibited by either metal alone. Bimetallic catalyst structure depends strongly on the metal precursors employed and their addition sequence to the support. In this work, PdRe/Al2O3 catalysts were prepared by: (i) co impregnation (CI) and sequential impregnation (SI) of  Al2O3 using HReO4 and Pd(NO3)2, (ii) SI using NH4ReO4 and [Pd(NH3)4(NO3)2], (iii) HReO4 addition to a reduced and passivated Pd/Al2O3 catalyst, and (iv) impregnation with the double complex salt (DCS), [Pd(NH3)4(ReO4)2]. Raman spectroscopy and temperature-programmed reduction (TPR) evidence larger supported PdO crystallites in catalysts prepared using Pd(NO3)2 than [Pd(NH3)4(NO3)2]. Surface [ReO4]- species detected by Raman exhibit TPR peak temperatures from ranging 85 to 260°C (versus 375°C for Re/Al2O3). After H2 reduction at 400°C, the catalysts were characterized by chemisorption, temperature-programmed hydride decomposition (TPHD), CO diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), and scanning transmission electron microscopy (STEM) with energy-dispersive x-ray (EDX) spectroscopy. The CI catalyst containing supported Pd-Re alloy crystallites had a TOF similar to Pd/Al2O3 but higher (61%) FAL selectivity. In contrast, catalysts prepared by methods (ii-iv) containing supported Pd-Re nanoparticles exhibit higher TOFs and up to 78% FAL selectivity.

A series of supported CuO-based nanoparticle catalysts were prepared by impregnation method and used for the synthesis of glycerol carbonate from glycerol and CO2 in the presence of 2-cyanopyridine as a dehydrant and DMF as a solvent. The effects of supports (activated alumina, silicon dioxide, graphene oxide, graphene, and activated carbon), CuO loading amount, calcination temperature, and reaction parameters on the catalytic activity of catalyst were investigated in detail. XRD, FTIR, SEM, BET, and CO2-TPD were used for the characterization of the prepared catalysts. It is found that CuO/Al2O3 shows a higher catalytic activity, which depends on the CuO loading amount and calcination temperature. The surface area and amount of basic sites of the catalyst exhibit crucial effect on the catalytic activity of CuO/Al2O3. Furthermore, there is a synergistic effect between the catalyst and 2-cyanopyridine that the former has higher activation ability for glycerol and the latter acts not only as a dehydrant, but also as a promoter for CO2 activation. Recycling experiments reveal that this catalyst can be reused at least five cycles without any inactivation. Based on the experiment results and FTIR characterization, a possible reaction mechanism for the carbonylation of glycerol and CO2 is proposed.