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[Display omitted] • The selective amination of secondary alcohols with ammonia to form primary amines is demonstrated over Ru/SiO 2 in gas- and liquid-phases with and without a hydrogen cofeed. • Thermodynamic constraints are mapped for the overall reactions of alcohol amination, identifying conditions where near-quantitative yields of primary amines are feasible. • Operating conditions are correlated with rate and selectivity, facilitating optimization. • Amination is demonstrated for heterocylic secondary alcohols to produce multifunctional heterocycles (aminofurans, aminopyrans). • Amination rates are low for heterocylic alcohols, with product inhibition implicated in the apparent loss of activity. • Variations in amination rate are quantified upon perturbations to molecular structure, extending kinetic insights to a modest substrate scope (8 alcohols) This work considers the Ru-mediated amination of secondary alcohols with ammonia in vapor and liquid media. We map thermodynamic constraints, and we probe the impacts of species partial pressure, residence time, and reaction temperature on rate, selectivity, and catalyst stability. Alcohol amination consumes no H 2 , and H 2 has no significant impact on amination kinetics; however, operating under H 2 benefits Ru stability. Primary amine selectivity increases with ammonia pressure and alcohol conversion, and the latter observation is consistent with the formation of primary amines through a network of sequential reactions. Unfortunately, primary amines are susceptible to secondary deamination to form hydrocarbons. As is typical of processes that seek to isolate a reactive intermediate, the main selectivity challenge here is identifying residence times that are long enough to accumulate high substrate conversion but short enough to avoid secondary deamination. In general, moderate residence times will maximize the production of primary amines. Insights extend across a broad substrate scope, and we observe 70 – 90% yield of primary amines from linear, cyclic, and heterocyclic alcohols. That said, heterocyclic alcohols appear susceptible to product inhibition, so achieving high conversions requires longer residence times and/or increased ammonia pressures. [ABSTRACT FROM AUTHOR]

Swarm robotics system (SRS) is a type of artifact that employs multiple robots to work together in a coordinated way, inspired by the self-organizing behavior of social insects such as ants and bees. SRSs are known for their robustness, flexibility, and scalability. This study focuses on evolutionary robotics (ER) which uses artificial neural networks (ANNs) as controllers to operate autonomous robots. In traditional ER research, SRSs were often composed of teams of homogeneous robots, each of which is controlled by a single ANN. In contrast, this study focuses on the implementation of ER in a heterogeneous SRS. To evaluate our approach, we present the concept of employing multiple controllers for sub-teams in a swarm. Heterogeneity was achieved using different controllers for the same physical bodies. We simulated a cooperative transport task, in which the performance of heterogeneity was superior because the two ANN controllers were able to express a variety of behaviors as an entire swarm. Additionally, this study investigated how well the three types of parental selection methods of the heterogeneous approach, can help to optimize the performance of the swarm. [ABSTRACT FROM AUTHOR]

Background: Initial tumor enlargement (or pseudoprogression) instead of true tumor progression is a common phenomenon in patients with acoustic neuromas who are treated with stereotactic radiosurgery (SRS). This phenomenon can affect clinical decision-making and patient management. This study assessed the correlation between initial tumor enlargement and magnetic resonance imaging characteristics in patients with acoustic neuromas who were treated with linear accelerator (LINAC)-based SRS. The long-term tumor control outcomes were also analyzed. Materials and methods: In total, 330 patients with sporadic acoustic neuromas who were treated with LINAC SRS between March 2006 and March 2020 were retrospectively evaluated to assess their initial tumor enlargement. The tumors were divided into homogeneously enhanced, heterogeneously enhanced, and cystic types based on the morphological characteristics noted on magnetic resonance images. Tumor control was assessed in 275 patients with a follow-up duration of more than 2 years. Results: Initial enlargement was observed in 137 of 330 (41.5%) tumors as early as 3 months after LINAC SRS. Data analysis revealed that postoperative tumors with a residual volume lower than 2.5 cm3 had a lower incidence of initial enlargement (p = 0.039). No correlation was noted between the initial enlargement and morphological characteristics of tumors. In patients with a mean follow-up duration of 82.8 ± 37.2 months, heterogeneously enhanced tumors exhibited a lower control rate than homogeneously enhanced and cystic tumors (p = 0.045). No correlation was noted between initial enlargement and tumor control. Conclusion: Initial enlargement can occur as early as 3 months after SRS. Postoperative residual tumors with a volume lower than 2.5 cm3 exhibit a lower incidence of initial enlargement. Heterogeneously enhanced tumors have a lower local control rate. [ABSTRACT FROM AUTHOR]

We report the surface modification of ferrite nanoparticles (MFe2O4 NPs; M= Mn, Fe, Co, Ni, Cu, Zn) with chiral surfactants derived from cinchona alkaloids. The successful synthesis of chiral ferrites were characterized using powder X‐ray diffraction (PXRD), X‐ray photoelectron spectroscopy (XPS), Atomic force microscopy (AFM), Thermogravimetric analysis (TGA) and vibrating sample magnetometer (VSM). These chiral materials were highly active in the oxidative kinetic resolution (OKR) of racemic benzoins. Remarkably gemini surfactant modified Fe3O4 NPs were found to be a potential candidate in the OKR and (R)‐benzoin was isolated with 97% enantiomeric excess (ee). The sustainability of our catalyst has been demonstrated in the gram scale synthesis of (R)‐furoin (93% ee). The recyclability studies have also been performed and found that it could be recyclable with decrease in ee. [ABSTRACT FROM AUTHOR]

The overall photocatalytic CO2 reduction reaction (PCRR), which uses solar energy to convert CO2 and H2O into chemical feedstocks or fuels without sacrificial reagents, plays a momentous role in CO2 utilization and solar energy conversion. However, significant challenges remain in achieving efficient conversion. Researchers have explored various strategies to realize the overall PCRR efficiently. In this Review, we first explain the criteria for evaluating the overall PCRR and then summarize the following strategies developed over the past decade to promote it: self‐driving material development, Z‐scheme heterojunction construction, cocatalyst loading, heteroatom doping, surface vacancy creation, and carrier‐material matching. Finally, we discuss essential future research directions in the field. Through this comprehensive Review, we aim to provide strategic guidance for the development of efficient overall PCRR systems. [ABSTRACT FROM AUTHOR]

Detecting fraudulent users in social networks could reduce online fraud and telecommunication fraud cases, which is essential to protect the lives and properties of internet users and maintain social harmony and stability. We study how to detect fraudulent users by using heterogeneous graph representation learning and propose a heterogeneous graph representation learning algorithm to learn user node embeddings to reduce human intervention. The experimental results show promising results. This article investigates how to use better heterogeneous graph representation learning to detect fraudulent users in social networks and improve detection accuracy. [ABSTRACT FROM AUTHOR]

Heterogeneous multi-cloud environments make use of a collection of diverse performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications in the multi-cloud environment require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Scheduling tasks to distributed heterogeneous clouds is termed NP-complete which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of 'n' tasks in two groups among a set of 'm' clouds, three heuristicsPair-Task Threshold Limit (PTL), PTMax-Min, and PTMin-Max are proposed. Firstly, proposedheuristics calculate tasks threshold valuebased on the tasks attributes to determine the tasks scheduling order and then tasks are sorted in descending order of threshold value. Group 1 comprises ([n/2]) tasks ordered in descending value of threshold. Group 2 comprises remaining tasks ([n/2] − 1) ordered in ascending value of threshold. Secondly, tasks form group 1 are scheduled first based on minimum completion time, and then tasks in group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. The proposed approaches PTL, PTMax-Min, and PTMin-Max explicitly shows the better results in terms of reduced makespan, completion time, response time and more resource utilization compared to MCT, MET, and Min-min. [ABSTRACT FROM AUTHOR]

Novel tri-phase NiO–CdO-Nd 2 O 3 nanocomposite was prepared by coprecipitation method and its catalytic activity in transesterification reaction was studied. The synthesized nanocomposite was characterized by using FTIR, XRD, SEM and EDX to explore the functional, structural morphological and compositional properties respectively. In FTIR studies the vibrational bands for NiO, CdO and Nd 2 O 3 have been observed. The XRD pattern of nanocomposite revealed its crystalline nature and average particle size was calculated as 28nm. The SEM studies of nanocomposite revealed that the shape of nanocomposite is semi-circle and oval shaped. EDX analysis represented elemental composition of ternary composite. The newly prepared catalyst was used as heterogeneous catalyst in transesterification of triglycerides from mustard oil into fatty acid methyl esters as biodiesel fuel. The oval shaped nanoparticles showed good catalytic activity in biodiesel synthesis. The obtained biodiesel was thoroughly characterized using FTIR and 1H NMR spectroscopic techniques. FT-IR analysis was also used to find out percentage yield using absorption spectra of different oil & methanol molar ratio. The FAMEs composition of obtained biodiesel was determined by GC-MS analysis which showed presence of methyl palmitoleate (C16:1), methyl linoleate (C18:2), methyl oleate (C18:1), metyl erucate (C22:1) and methyl behenate (C22:0) at various retention time. The quality of fuel parameters such as density, viscosity, cloud, pour and flash points were determined using ASTM methods and found within limits. The effect of several parameters on yield of biodiesel such as oil to methanol ratio (1:6, 1:9, 1:12, 1:15 and 1:18) catalyst amount (0.1, 0.5 and 0.9g) time of reaction (1.5, 2.5, 5.0 and 7.5h) and effect of temperature (60–80 °C with interval of 5 °C) were studied in detail and optimized. The 80% yield of biodiesel was achieved at optimum conditions of 1:15 oil:methanol ratio, 0.5 g catalyst concentration, 5 h reaction time and 80 °C temperature. The reusability of catalyst was tested by using three times and the results were good with minor loss of catalytic activity. [ABSTRACT FROM AUTHOR]

fac-Re(2,2'-bipyridine)(CO)3Cl] (denoted as ReCC) is an efficient molecule-catalyst with high selectivity in the photoreduction of CO2 to CO in a homogeneous system. However, the two major drawbacks of Re(I) complexes in the homogeneous system, easy degradation and difficult separation, seriously hinder its development in the field of industrial applications. In this paper, we designed and prepared two different Re-complex fixation systems (denoted as ReCC@TiO2-5 wt% and ReCC-TiO2-5 wt%) based on TiO2 gel via the sensitization method and sol–gel method, respectively. Compared with a pure ReCC complex, both of them exhibited excellent photocatalytic reduction activity. In particular, the sol–gel hybrid system (ReCC-TiO2-5 wt%) displayed outstanding positive synergistic effects on the photocatalytic activity and the long durability of the photocatalytic process. A series of characterizations were carried out to explore the probable photocatalytic reduction process mechanism, which provides the theoretical basis and technical support for the Re complex fixation method. [ABSTRACT FROM AUTHOR]

This paper proposes a zone-based three-level heterogeneous clustering protocol (ZB-TLHCP) for heterogeneous WSNs. In ZB-TLHCP, the sensor field/region is divided into zones where super, advance, and normal nodes are deployed uniformly and randomly. The performance of the proposed ZB-TLHCP system is compared with that of zonal-stable election protocol (Z-SEP), distributed energy efficient clustering (DEEC), and threshold-based DEEC (TDEEC) protocol by varying the number of super and advance nodes, their energy levels for the fixed sensor field, and the total number of nodes. Matlab simulation results revealed that the proposed ZB-TLHCP solution performed better than Z-SEP, DEEC, and TDEEC protocols, as it increased the instability period, prolonged the network's lifetime, and achieved higher throughput values. [ABSTRACT FROM AUTHOR]

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Due to the advancement of sensor gadgets and telecommunication technology, wireless sensor networks (WSNs) have drawn a lot of observations in the recent period. Inaccessible terrain, disaster zones, or polluted conditions are typically where they are deployed at random, making battery replacement or recharge challenging or even impossible. Network lifespan is therefore extremely important to a WSN. An abundance of power-effective strategies in a diverse wireless sensor network are surveyed in this paper. We first provide an overview of the fundamental network radio representation and how it may be utilized to analyze different trade-offs between network deployment costs and an energy-efficient clustering approach. We also highlight a few protocols that can be utilized in heterogeneous networks that are energy efficient. [ABSTRACT FROM AUTHOR]

The protocol presents conversion of levulinic acid (LA) to γ-valerolactone (GVL), using polymer-supported carboxyl functionalized Zn–porphyrin (PSCFZnPP) as a photocatalyst under visible light at ambient conditions. The formation of GVL was achieved using 2-propanol as a hydrogen source over PSCFZnPP photocatalyst in a homemade photoreactor under 12-W LED light. The formation of GVL was achieved with 72% yield by transfer hydrogenation in 16 h at room temperature and confirmed by 1H-NMR and 13C-NMR at various intervals. The transformation of LA was confirmed by the presence of CH multiplet in 1H-NMR and deshielding of CH3 protons in GVL around 1 ppm, after 16 h with disappearance of OH of the substrate. It was further evidenced by the disappearance of ketonic carbonyl C peak height and reduction of LA in 13C-NMR. Finally, PSCFZnPP exhibited reusability for five times using optimized conditions without significant changes in the yield and structure. [ABSTRACT FROM AUTHOR]

This paper provides a theoretical model for the computation of the probability density function of multi-hop broadcast latency when using probabilistic broadcasting schemes in Wireless Sensor Networks. In this paper, a novel probabilistic approach is presented for directed data transmission without route discovery. In our model, we require each message can reach the BS successfully with a certain success probability and nodes which are located nearer to the base station relay messages with a certain relay probability. The relationship between the number of intermediate nodes and relay probability is analyzed and the condition for relay probability to guarantee a certain success probability of messages is obtained. This approach is robust, adaptive to change of topology of the sensor network and energy efficient. Implementation of this approach is discussed. Simulation helps to illustrate the main results obtained by analysis and shows that this approach is very energy efficient. [ABSTRACT FROM AUTHOR]

Incorporating a second metal into the monometallic bifunctional catalyst structure results in appearance of new catalytic characteristics and increase in the catalytic productivity. This type of catalyst is called bimetallic catalyst, which is better than a monometallic one in industrial processes, because it enhances some catalytic features like metal dispersion, activity, selectivity, stability, and lifespan. Different researches have asserted that the bimetallic catalyst increases the yield of the desired branched alkanes and decreases the undesired low-molecular-weight gaseous products. The enhancement of catalytic properties of the bimetallic catalyst is ascribed mainly to the electronic features and geometric structure of nanoparticles of two metals and increases the Lewis acidity and balance between metals and a supporter. In this paper, the theoretical and experimental fundamentals of bimetallic catalysis are reviewed based on many trusted experimental works of different researchers. [ABSTRACT FROM AUTHOR]

An important factor in tumor development and progression is the tumor microenvironment (TME), which is heterogeneous. Previous studies have mainly investigated the expression profile and prognostic values of genes in gastric cancer (GC) at the cell population level but neglected the interactions and heterogeneity between cells. The pattern of ligand–receptor (LR) interactions was delineated on a scRNA-seq dataset containing 44,953 cells from nine GC patients and a fourth bulk RNA-seq dataset including data from 1159 GC patients. We then constructed an LR.Score scoring model to comprehensively evaluate the influence of LR-pairs on the TME, overall survival, and immunotherapy response in GC patients from several cohorts. Cell communication network among 13 cell types was constructed based on the LR-pairs. We proposed a new molecular subtyping model for GC based on the LR-pairs and revealed the differences in prognosis, pathophysiologic features, mutation characteristics, function enrichment, and immunological characteristics among the three subtypes. Finally, an LR.Score model based on LR-pairs was developed and validated on several datasets. Based on the selected LR-pairs, we successfully constructed a novel prediction model and observed its well performance on molecular subtyping, target and pathway screening, prognosis judging, and immunotherapy response predicting. [ABSTRACT FROM AUTHOR]

Highly substituted pyrazoles have been prepared by condensing 1,3-diketones and various hydrazine derivatives using silica-phosphoric acid nanoparticles (nano-SPA). This nano solid catalyst has been prepared by the reaction of nano silica chloride with dry phosphoric acid. This green methodology has advantages such as short reaction times, simple work-up, high efficiency, reusability of the catalyst and no use of any solvents. [ABSTRACT FROM AUTHOR]

The fundamental principle of immune checkpoint blockade (ICB) is to protect tumor-infiltrating T cells from being exhausted. Despite the remarkable success achieved by ICB treatment, only a small group of patients benefit from it. Characterized by a hypofunctional state with the expression of multiple inhibitory receptors, exhausted T (Tex) cells are a major obstacle in improving ICB. T cell exhaustion is a progressive process which adapts to persistent antigen stimulation in chronic infections and cancers. In this review, we elucidate the heterogeneity of Tex cells and offer new insights into the hierarchical transcriptional regulation of T cell exhaustion. Factors and signaling pathways that induce and promote exhaustion are also summarized. Moreover, we review the epigenetic and metabolic alterations of Tex cells and discuss how PD-1 signaling affects the balance between T cell activation and exhaustion, aiming to provide more therapeutic targets for applications of combinational immunotherapies. [ABSTRACT FROM AUTHOR]

In the most popular distributed stream processing frameworks (DSPFs), programs are modeled as a directed acyclic graph. Using this model, a DSPF can benefit from the parallelism capabilities of distributed clusters. Choosing a reasonable number of vertices for each operator and mapping the vertices to the appropriate processing resources significantly affect the overall system performance. Due to the simplicity of the current DSPF schedulers, these frameworks perform poorly on large-scale clusters. In this paper, we present a heterogeneity-aware scheduling algorithm that finds the proper number of the vertices of an application graph and maps them to the most suitable cluster node. We begin with a pre-processing step which allocates the vertices to the given cluster nodes using profiling data. Then, we gradually increase the topology input rate in order to scale up the application graph. Finally, using a CPU utilization model which predicts the CPU workload based on the input rate to vertices and the processing node's CPU characteristics, we identify the bottlenecked vertices and allocate new instances derived from them to the least utilized processing resource. Our experimental results on Storm Micro-Benchmark show that (1) the prediction model estimate CPU utilization with 92% accuracy. (2) Compared to the default scheduler of Storm, our scheduler provides 7 to 44% throughput enhancement. (3) The proposed method can find the solution within 4% (worst case) of the optimal scheduler, which obtains the best scheduling scenario using an exhaustive search over problem design space. [ABSTRACT FROM AUTHOR]

Iterative stencil computations are widely used in numerical simulations. They present a high degree of parallelism, high locality and mostly-coalesced memory access patterns. Therefore, GPUs are good candidates to speed up their computation. However, the development of stencil programs that can work with huge grids in distributed systems with multiple GPUs is not straightforward, since it requires solving problems related to the partition of the grid across nodes and devices, and the synchronization and data movement across remote GPUs. In this work, we present EPSILOD, a high-productivity parallel programming skeleton for iterative stencil computations on distributed multi-GPUs, of the same or different vendors that supports any type of n-dimensional geometric stencils of any order. It uses an abstract specification of the stencil pattern (neighbors and weights) to internally derive the data partition, synchronizations and communications. Computation is split to better overlap with communications. This paper describes the underlying architecture of EPSILOD, its main components, and presents an experimental evaluation to show the benefits of our approach, including a comparison with another state-of-the-art solution. The experimental results show that EPSILOD is faster and shows good strong and weak scalability for platforms with both homogeneous and heterogeneous types of GPU. [ABSTRACT FROM AUTHOR]

To develop the criticality safety basis for any system, process, or package, the worst-case configuration of materials resulting in the maximum system reactivity must be determined. It is commonly accepted that in terms of the maximum system reactivity, at the lower enrichments used in current commercial practice (i.e., 5 wt% 235U), a heterogeneous configuration is bounding of a homogeneous mixture of fissile and moderating materials. However, a common assumption made is that with increasing enrichment, a homogeneous system can be bounding. With increased industry interest in utilizing higher enrichments for commercial applications with low-enriched uranium (LEU+) (≤10 wt% 235U), and high assay low-enriched uranium (HALEU) (≤20 wt% 235U) materials, it has become increasingly important to verify any assumptions and to have a better understanding of the expected system behavior at these higher enrichments. The SCALE code system was used to assess the effects of heterogeneity on system reactivity with varying enrichments and system configurations for a UO2 and water system, typical of a transportation package criticality analysis. The purpose of this assessment was to provide insight on the effect of material heterogeneity on system reactivity with increasing enrichment. The results of this study confirm that for systems with a higher hydrogen-to–fissile material (H/X) ratio, the homogeneous mixture of material may be bounding for HALEU materials. However, for systems with a lower hydrogen-to–fissile material ratio (H/X ≤ 200), a heterogeneous configuration of contents is expected to be bounding for most LEU materials. Overall, for any LEU system, including HALEU material, heterogeneous reactivity effects should always be considered. [ABSTRACT FROM AUTHOR]

Based on Vygotsky's concept of Zone of Proximal Development, dynamic assessment (DA) has at its core the integration of assessment and instruction through mediation. DA is practiced either individually or collectively. In group-dynamic assessment (G-DA), as Poehner (2009) introduced, learners are simultaneously exposed to mediation. The problem which has been ignored in G-DA is the homogeneity and heterogeneity of EFL leaners while being mediated. This study tried to investigate the effect of group-dynamic assessment on homogeneous and heterogeneous EFL learners' ability in listening comprehension. It also intended to find out if expert-novice and peer-to-peer mediation types in G-DA differed in their effects on homogeneous and heterogeneous EFL learners' command of listening comprehension. To this end, eighty intermediate EFL learners were chosen to be the participants. According to their language proficiency, they were assigned to two groups of forty homogeneous and forty heterogeneous learners. Then, each group was divided into two experimental groups. The participants in all groups attended three listening sessions. For the mediation, the participants were assisted through novice-expert and peer-to-peer mediation types. To find out the main and interaction effect of group type and mediation type in G-DA, the post-test scores were analysed through running a two-way ANOVA. The results indicated that both group type and mediation type had significant main and interaction effect on learners' listening comprehension. A post hoc test was also utilized to compare the mean differences between the groups.

Wireless sensor networks (WSNs) are widely used in agricultural greenhouses to monitor and control farming-related parameters. These networks are composed of multiple sensor nodes, usually deployed in an ad hoc fashion. But the nodes mostly run on batteries. So if a node fails or loses power, it creates areas in the network with no sensor coverage, consequently affecting the entire system. Therefore minimizing energy use is essential for extending the network lifetime. Researchers have proposed multiple energy-saving schemes in the past but the majority have not eliminated the sources of energy waste, and are not suitable for greenhouse applications. In light of this, we propose GS-MAC, a scalable and energy-efficient medium-access-control (MAC) protocol specialized for greenhouse monitoring and control: Unlike previous designs, our technique is applicable to both homogenous and heterogeneous settings. To minimize power use, nodes periodically sleep, but GS-MAC avoids periodic node synchronizations, unlike traditional duty cycling mechanisms. Instead, nodes use coordinated universal time (UTC) to maintain strict schedules to avoid energy waste and maintain constant low-duty cycles even with increasing node density. GS-MAC also uses short node addresses to reduce packet overheads. Finally, GS-MAC adopts a contention approach on reserved time slots scheduled between communication rounds to maintain scalability. Our work is evaluated on MATLAB, with simulation parameters obtained from actual hardware. The experiment results show that GS-MAC is more efficient by at least 2.7 times compared to previous research in terms of duty cycling, energy consumption, and network lifetime, in exchange for increased delays.

The Internet of Things (IoT) based smart city applications are the latest technology-driven solutions designed to collect and analyze data to enhance the quality of life for urban residents by creating more sustainable, efficient, and connected communities. Communication nodes are networked independently to monitor the circumstances, where they require being more energy-efficient and securing, to improve the performance of sustainable cities. Because, the enormous number of networked devices makes the smart cities application vulnerable to many security breaches, all of which have serious ramifications for a city and its residents’ safety, well-being, and economic development. Low-powered sensors have limitations in terms of battery life, short transmission range, and security considerations, despite the fact that the combination of edge computing and Green IoT considerably enhances network performance in terms of processing and data storage. Consequently, it is necessary to implement an advanced approach to provide energy resources with secure data transmission for sustainable cities. Therefore, this research proposes an Intelligent Buffalo-based Secure Edge-enabled Computing (IB-SEC) framework for smart cities by IoT platform, which aims to enhance communication efficiency, and reliability, with minimizing latency in terms of energy consumption and data security for data transmission. The developed IB-SEC platform utilizes a combination of the African Buffalo Optimization (ABO) algorithm and a Distributed Hash function-based security algorithm to enhance the efficiency, security, and reliability of data transmission in IoT-based smart city networks. This platform leverages the capabilities of secure edge computing and MAC protocols to achieve its goals, by implementing encryption, authentication, and access control mechanisms that ensure the wireless communication is secure, and the data is protected against unauthorized access. Overall, the IB-SEC framework provides an advanced approach to secure IoT networks and enable smart city applications. Moreover, the developed platform enables the integration of heterogeneous edge devices, sensors, and systems which is effectively managed and adapted by Median Access Control (MAC) protocols. The IB-SEC is implemented in MATLAB and validated through cutting-edge security algorithms in terms of improved security, reliability, throughput, and reduced latency and energy consumption.

Abstract Background The lack of interoperability between health information systems reduces the quality of care provided to patients and wastes resources. Accordingly, there is an urgent need to develop integration mechanisms among the various health information systems. The aim of this review was to investigate the interoperability requirements for heterogeneous health information systems and to summarize and present them. Methods In accordance with the PRISMA guideline, a broad electronic search of all literature was conducted on the topic through six databases, including PubMed, Web of science, Scopus, MEDLINE, Cochrane Library and Embase to 25 July 2022. The inclusion criteria were to select English-written articles available in full text with the closest objectives. 36 articles were selected for further analysis. Results Interoperability has been raised in the field of health information systems from 2003 and now it is one of the topics of interest to researchers. The projects done in this field are mostly in the national scope and to achieve the electronic health record. HL7 FHIR, CDA, HIPAA and SNOMED-CT, SOA, RIM, XML, API, JAVA and SQL are among the most important requirements for implementing interoperability. In order to guarantee the concept of data exchange, semantic interaction is the best choice because the systems can recognize and process semantically similar information homogeneously. Conclusions The health industry has become more complex and has new needs. Interoperability meets this needs by communicating between the output and input of processor systems and making easier to access the data in the required formats.

Abstract Recently, supervised speech separation has made great progress. However, limited by the nature of supervised training, most existing separation methods require ground-truth sources and are trained on synthetic datasets. This ground-truth reliance is problematic, because the ground-truth signals are usually unavailable in real conditions. Moreover, in many industry scenarios, the real acoustic characteristics deviate far from the ones in simulated datasets. Therefore, the performance usually degrades significantly when applying the supervised speech separation models to real applications. To address these problems, in this study, we propose a novel separation consistency training, termed SCT, to exploit the real-world unlabeled mixtures for improving cross-domain unsupervised speech separation in an iterative manner, by leveraging upon the complementary information obtained from heterogeneous (structurally distinct but behaviorally complementary) models. SCT follows a framework using two heterogeneous neural networks (HNNs) to produce high confidence pseudo labels of unlabeled real speech mixtures. These labels are then updated and used to refine the HNNs to produce more reliable consistent separation results for real mixture pseudo-labeling. To maximally utilize the large complementary information between different separation networks, a cross-knowledge adaptation is further proposed. Together with simulated dataset, those real mixtures with high confidence pseudo labels are then used to update the HNN separation models iteratively. In addition, we find that combing the heterogeneous separation outputs by a simple linear fusion can further slightly improve the final system performance. In this paper, we use cross-dataset to simulate the cross-domain situation in real-life. The term of “source domain” and “target domain” refer to the simulation set for model pre-training and the real unlabeled mixture for model adaptation. The proposed SCT is evaluated on both public reverberant English and anechoic Mandarin cross-domain separation tasks. Results show that, without any available ground-truth of target domain mixtures, the SCT can still significantly outperform our two strong baselines with up to 1.61 dB and 3.44 dB scale-invariant signal-to-noise ratio (SI-SNR) improvements, on the English and Mandarin cross-domain conditions, respectively.

Abstract Vegetation dynamics and land use information are significant for a better understanding of the ecological consequences of multiple mining activities. However, the high spatial heterogeneity of mine sites and diverse disturbance and recovery pathways make it a challenge to understand the dynamics of multiple mine sites over large areas. Here, we proposed a general framework for continuous monitoring of land use and vegetation dynamics in multiple mine sites and applied it to Pingxiang, China. First, annual land use and land cover (LULC) maps from 2000 to 2019 were generated using a modified Continuous Change Detection and Classification approach (CCDC). Second, the locations and extents of 86 mine sites on different scales were mapped individually and then aggregated into five groups according to the similarity and differences of vegetation change. Vegetation dynamics showed great heterogeneity across sites driven primarily by the spatial‐temporal variation in types and intensity of land use activities in and around the mine sites. We found the impact distance was typically 500–700 m in the region, but can be smaller than the potential impact distance in areas with land use activities. The long‐term slow recovery of vegetation conditions at some sites indicates that it might be a challenge to improve vegetation conditions naturally in a short time and human‐assisted restoration measures may be required. The systematic framework proposed in this study can be used to establish comprehensive and spatially‐explicit mine datasets at the regional scale, essential for understanding the dynamics and ecological consequences of multiple mining activities and coordinated management and restoration of heterogeneous mine sites.