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Background: Physical Activity 4 Everyone (PA4E1) is a whole-school physical activity program, with demonstrated efficacy (2012–2014). PA4E1 was adapted (scaled-up) and tested in a scale-up trial (2017–2020). This process evaluation study of the scale-up trial had 2 aims. First, to describe the acceptability, appropriateness, and feasibility of PA4E1 in the scale-up trial, from the perspective of school staff involved in the program management and delivery. Second, to generate themes that may explain school staff assessments of acceptability, appropriateness, and feasibility. Methods: Data were collected at various time points throughout the 2-year implementation phase. Online surveys were collected from In-School Champions, Head Physical Education teachers, Principals, and Physical Education teachers (quantitative data). Focus groups and interviews were conducted with In-School Champions, Principals, and Physical Education teachers (qualitative data). Existing published data on website engagement, adaptations, modifications, and the scale-up trial primary outcome (implementation of physical activity practices) were triangulated with the quantitative and qualitative during analysis, to generate themes. Results: School staff delivering PA4E1 reported it was highly acceptable, appropriate, and feasible. Seven themes were generated relating to acceptability, appropriateness, and feasibility. The themes related to how the program was funded, the delivery modes of implementation support, the identification of easy-wins, the recruitment of the right in-school champion, facilitating principal buy-in, mitigating the impact of school staff turnover, and engaging the whole school. Conclusions: Recommendations are made to inform future adaptations for PA4E1 and potentially school-based physical activity programs more generally. The findings may inform future scalability assessments of the suitability of programs for scale-up. [ABSTRACT FROM AUTHOR]

Purcell filters, which serve to suppress electromagnetic radiation and enhance the readout efficiency of qubit, are an indispensable component in superconducting quantum chips. With the increasing scale of quantum chips, the requirements for the performance and scalability of Purcell filters are becoming more stringent. In this report, a novel four-stage Purcell filter that enables fast measurement without exacerbating environmental damping of the qubits is presented. The design approach of the filter is derived from the serial and parallel configurations of λ/4 resonant cavities. The filter exhibits exceptional passband-to-stopband isolation, reaching up to 40 dB of isolation within the transition range of 400 MHz. Furthermore, the filter boasts a wide bandwidth for both the stopband and the passband, with the stopband ranging from 4 to 5.5 GHz and the passband extending from 5.8 to 6.5 GHz. In terms of spatial arrangement, each filter can be connected to over ten readout resonators for qubit readout. This innovative Purcell filter will significantly contribute to the development of high quality, scalable superconducting quantum chips. [ABSTRACT FROM AUTHOR]

To face market volatility, reconfigurable manufacturing systems (RMS) aim to efficiently and cost-effectively react to changes. We focus on one characteristic of RMS: the scalability (ability to adapt the volume of throughput). In the literature, the only few indicators for scalability are not always formally defined and usually only consider a partial view of scalability. Moreover, most of them are actually more suited for the configuration planning rather than for the design. However, the design of the RMS has a high impact on its scalability. We propose the first combinatorial definition of this problem and a new measure to fully assess the scalability of a system at the design phase. This measure, based on a multi-objective approach, can assess the scalability of single-product manufacturing systems, analysing all configurations that it can implement. We present numerical experiments to compare this indicator with a state-of-the-art scalability indicator and with some classical production line design indicators, and we show that future research should focus on scalability as a specific criterion to optimise during the design of an RMS. In addition, the results obtained allow us to infer some managerial insights on the best levers to use when performing a reconfiguration for scalability purpose. [ABSTRACT FROM AUTHOR]

Metal halide perovskite solar cells (PSCs) are infamous for their batch-to-batch and lab-to-lab irreproducibility in terms of stability and performance. Reproducible fabrication of PSCs is a critical requirement for market viability and practical commercialization. PSC irreproducibility plagues all levels of the community; from institutional research laboratories, start-up companies, to large established corporations. In this work, the critical function of atmospheric humidity to regulate the crystallization and stabilization of formamidinium lead triiodide (FAPbI3) perovskites is unraveled. It is demonstrated that the humidity content during processing induces profound variations in perovskite stoichiometry, thermodynamic stability, and optoelectronic quality. Almost counterintuitively, it is shown that the presence of humidity is perhaps indispensable to reproduce phase-stable and efficient FAPbI3-based PSCs.

In the contemporary era, blockchain technology has brought about a significant transformation in the realm of digital currency through innovations like Bitcoin. A blockchain serves as a decentralized ledger, ensuring an immutable record of transactions across a network. Recent observations indicate the pivotal role of blockchain technology not only in the financial sector but also in networking. This study considers blockchain as the essential link in establishing a genuinely decentralized, trustless, and secure environment for network nodes. The objective is to provide a systematic and comprehensive overview of futuristic endeavours in this domain. The exploration begins with an examination of the fundamental operational concepts of blockchains and how these systems achieve decentralization, security, and suitability. The focus then shifts towards addressing open research challenges within blockchain technologies, particularly in securing diverse communication networks such as Distributed Computing, Vehicular Ad-hoc Networks, Opportunistic Networks, and Delay Tolerant Networks. Simulation results underscore the superior security performance of blockchain, especially under conditions of attack. [ABSTRACT FROM AUTHOR]

Summary Deterministic communication plays a crucial role for real‐time distributed embedded systems by guaranteeing a low end‐to‐end delay and minimal jitter. Conflict‐aware iterated ILP‐based scheduling (IIS) is an incremental approach to enhance the scalability of single‐shot ILP computation to solve the computationally hard Qbv‐compliant offline scheduling problem for IEEE 802.1 Time‐Sensitive Networking (TSN). Taking into account the conflicting demands of the streams, IIS computes no‐wait schedules by dividing the set of streams into disjoint partitions, each of which is incrementally scheduled by an ILP solver. In this work, defining the no‐wait communication constraints for TSN scheduling, we propose a novel iterated scheduling approach, namely, O‐IIS, with a backtracking mechanism and partial solution support extending the conventional IIS procedure. O‐IIS relies on an ordering strategy to determine the order in which the constructed partitions will be processed by an ILP solver. We evaluate the performance of our approach in terms of schedulability success rate and schedule synthesis time using various network topologies and different partitioning schemes to construct the partitions. The evaluation results show that our approach provides significantly better scheduling performance compared to the existing work in the literature. [ABSTRACT FROM AUTHOR]

A novel microwave-mediated synthesis method was developed for the rapid and eco-friendly production of pyrimido[1,2-a]benzimidazole in high yields utilizing simple and cost-effective starting materials. This reaction was facilitated by the green organocatalyst guanidine hydrochloride. This protocol offers a metal-free and organocatalysed technique to afford a library of highly fluorescent pyrimido[1,2-a]benzimidazole derivatives. Key features of this synthetic method include its broad compatibility with functional groups, operational simplicity, and scalability, making it a promising technique for various applications. [ABSTRACT FROM AUTHOR]

Modifying network results is the most intuitive way to inject domain knowledge into network detection algorithms to improve their performance. While advances in computation scalability have made detecting large‐scale networks possible, the human ability to modify such networks has not scaled accordingly, resulting in a huge ‘interaction gap’. Most existing works only support navigating and modifying edges one by one in a graph visualization, which causes a significant interaction burden when faced with large‐scale networks. In this work, we propose a novel graph pattern mining algorithm based on the minimum description length (MDL) principle to partition and summarize multi‐feature and isomorphic sub‐graph matches. The mined sub‐graph patterns can be utilized as mediums for modifying large‐scale networks. Combining two traditional approaches, we introduce a new coarse‐middle‐fine graph modification paradigm (i.e. query graph‐based modification →$\rightarrow$ sub‐graph pattern‐based modification →$\rightarrow$ raw edge‐based modification). We further present a graph modification system that supports the graph modification paradigm for improving the scalability of modifying detected large‐scale networks. We evaluate the performance of our graph pattern mining algorithm through an experimental study, demonstrate the usefulness of our system through a case study, and illustrate the efficiency of our graph modification paradigm through a user study. [ABSTRACT FROM AUTHOR]

In this paper we consider the scalability of multi-angle QAOA with respect to the number of QAOA layers. We found that MA-QAOA is able to significantly reduce the depth of QAOA circuits, by a factor of up to 4 for the considered data sets. Moreover, MA-QAOA is less sensitive to system size, therefore we predict that this factor will be even larger for big graphs. However, MA-QAOA was found to be not optimal for minimization of the total QPU time. Different optimization initialization strategies are considered and compared for both QAOA and MA-QAOA. Among them, a new initialization strategy is suggested for MA-QAOA that is able to consistently and significantly outperform random initialization used in the previous studies. [ABSTRACT FROM AUTHOR]

To increase the ubiquity and merging of Olfactory Interfaces (OIs) into daily scenarios seamlessly and aesthetically, we propose OlfacKit, a fully integrated pipeline, which consists of (1) an interactive design tool that enables novice users to quickly model the physical forms of OIs and automatically generate fabrication-ready designs; (2) a construction workflow with a series of assembly kits and a video tutorial; (3) a WebUI for rapidly testing and prototyping smell control through Wi-Fi communication. We tested the atomization performance with different voltages and aroma liquids and further guided the implementation of the WebUI. Six applications have been explored, showing the flexibility and scalability of our toolkit. We conducted a workshop to evaluate the feasibility and usability of OlfacKit, involving 15 participants who proposed 108 creative ideas and implemented 13 working prototypes. We reported the detailed findings and discussed the insights and implications for future design. [ABSTRACT FROM AUTHOR]

A variety of 2-substituted 3-selanyl-chromenones are readily prepared through a cascade selanylation/cyclization of alkynyl aryl ketones with sulfonoselenoates. This transformation is performed under metal-free conditions and does not require an additional oxidant. It also has the advantage of broad functional group tolerance and scalability. [ABSTRACT FROM AUTHOR]

The growing interest in cost-effective and high-performing perovskite solar cells (PSCs) has driven extensive research. However, the challenge lies in upscaling PSCs while maintaining high performance. This study focuses on achieving uniform and compact perovskite films without pinholes and interfacial voids during upscaling from small PSCs to large-area modules. Competition in nucleation at concavities with various angles on rough-textured substrates during the gas-pumping drying process, coupled with different drying rates across the expansive film, aggravates these issues. Consequently, substrate roughness notably influences the deposition window of compact large-area perovskite films. We propose a supersaturation regulation approach aimed at achieving compact deposition of high-quality perovskite films over large areas. This involves introducing a rapid drying strategy to induce a high-supersaturation state, thereby equalizing nucleation across diverse concavities. This breakthrough enables the production of perovskite photovoltaics with high efficiencies of 25.58, 21.86, and 20.62% with aperture areas of 0.06, 29, and 1160 square centimeters, respectively. [ABSTRACT FROM AUTHOR]

Phosphorene, a 2D(two‐dimensional) material, holds immense promise in various applications due to its unique properties. Here, a comprehensive overview of their recent synthesis progresses, with a specific focus on three phosphorene varieties: black phosphorene, blue phosphorene, and violet phosphorene is provided. Black phosphorene, given its versatile properties, is extensively studied, but the challenges persist in achieving scalable production with high quality and controllable thicknesses. The synthesis of blue phosphorene involves epitaxial methods but results in relatively small structures, limiting its practical applications. Notably, the successful synthesis of violet phosphorene remains limited, achieved only through mechanical and liquid‐phase exfoliation (LPE) methods. Despite significant progression in phosphorene synthesis, a critical need is emphasized for a cost‐effective and easily controllable method capable of efficiently managing the thickness of phosphorene. The challenges, such as scalability issues and the presence of impurities, highlight the complexity of phosphorene preparation methods. The review emphasizes the importance of continued scientific engagement to overcome these obstacles and advance the research topic. In essence, while phosphorene shows great potential, unlocking its full range of applications requires further research and innovation in synthesis methodologies. [ABSTRACT FROM AUTHOR]

Neuromorphic devices constitute a novel approach to computing that takes inspiration from the brain to unify the processing and storage units. Memories based on phase‐change materials (PCMs) are potential candidates for such devices due to their non‐volatility and excellent scalability, however their use is hindered by their conductance variability and temporal drift in resistance. Recently, it has been shown that the utilization of phase‐change heterostructures consisting of nanolayers of the Sb2Te3 PCM interleaved with a transition‐metal dichalcogenide, acting as a confinement material, strongly mitigates these problems. In this work, superlattice heterostructures made of TiTe2 and two prototypical PCMs, respectively GeTe and Ge2Sb2Te5 are considered. By performing ab initio molecular dynamics simulations, it is shown that it is possible to switch the PCMs without destroying the superlattice structure and without diffusion of the atoms of the PCM across the TiTe2 nanolayers. In particular, the model containing Ge2Sb2Te5 shows weak coupling between the two materials during the switching process, which, combined with the high stability of the amorphous state of Ge2Sb2Te5, makes it a very promising candidate for neuromorphic computing applications. [ABSTRACT FROM AUTHOR]

This study proposed a machine learning approach to predict the scalability of broadband networks, which is crucial for ensuring fast and reliable internet connectivity. Scalability measures a network’s ability to handle increasing users, devices, and data traffic without compromising performance. The researchers leveraged the strengths of Random Forest (RF) and Support Vector Machine (SVM) algorithms to predict scalability. A large dataset of 40,000 data points was collected, focusing on six key metrics: Response Time, Bandwidth, Latency, Error Rate, Throughput, and Number of Users Connected. The data was preprocessed and divided into training and testing sets (80:20 ratio). Both RF and SVM algorithms were trained on the dataset, and a comparative analysis was conducted to determine which algorithm performed better. The results showed that the RF model outperformed the SVM model, achieving an accuracy of 95.0% compared to 91.0%. The RF model also exhibited higher precision, recall, and AUC scores. Feature importance analysis revealed that Response Time and Throughput were the most significant factors in determining network scalability. The study demonstrated the effectiveness of the RF model in predicting broadband network scalability, with a lower loss value (0.0133 for training and 0.0160 for validation) compared to the SVM model. This approach will help network operators and administrators predict and improve network scalability, ensuring reliable and fast internet connectivity. The study contributes to the development of machine learning-based solutions for broadband network performance evaluation and optimization. [ABSTRACT FROM AUTHOR]

Most of the existing MAPF methods based on communication learning have poor scalability or aggregate too much redundant information, resulting in inefficient communication. To solve these problems, this paper proposed disruptor identifiable communication (DIC), which learned concise communication excluding non-disruptors by judging whether the agent in the center of the field of view would change its decision-making due to the presence of neighbors, and successfully filtered out redundant information. At the same time, this paper further instantiated DIC and developed a new highly scalable distributed MAPF solver: disruptor identifiable communication based on reinforcement and imitation learning algorithm (DICRIA). Firstly, the disruptor discriminator and the policy output layer of DICRIA identified the disruptor. Secondly, the algorithm updated the information of the disruptor and the communication wish sender in two rounds of communication respectively. Finally, DICRIA output the final policy according to the coding results of each module. Experimental results show that DICRIA S performance is better than other similar solvers in almost all environment settings, and the algorithm increases the success rate by 5.2% on average compared to the baseline solver. Especially in dense problem instances with large-size maps, the algorithm even increases the success rate of DICRIA by 44.5% compared to the baseline solver. [ABSTRACT FROM AUTHOR]

The advent of federated learning has unveiled a collaborative approach to learning across data islands, yet its scalability faces challenges from Non-IID local data at federated nodes and insufficient oversight, accountability, and privacy in centralized frameworks. To tackle these issues, this paper proposed a blockchain-based trustworthy slice aggregation (BBTSA) and federated attribution optimization method (FedAom). FedAom adopted attribution thinking, using the integrated gradients method for attribution to identify parameters influencing model decisions. It considered parameter sensitivity in a tiered approach, preserving and enhancing key knowledge learned by the global model during local updates. This effectively utilized shared data to mitigate the Non-IID problem. Concurrently, BBTSA employed blockchain to establish a decentralized framework, facilitating noise exchange between nodes instead of direct parameters, with a cooperative tree structure for obfuscation to boost privacy. Validation on diverse distributions across two datasets indicates FedAom notably improves stability and convergence speed over baseline methods in most instances. Simultaneously, BBTSA ensures client privacy without compromising model accuracy, guaranteeing training oversight and privacy protection. [ABSTRACT FROM AUTHOR]

Boltzmann carrier statistics restrict the sub‐threshold swing (SS ≥ 60 mVdec–1) and, consequently, the supply voltage (≈ 1 V) of the thermionic transistor. This primary bottleneck results in rapidly increasing power consumption, heating, and associated reliability/scalability challenges as more devices are packed into a small footprint. Despite significant efforts from the device community, a suitable sub‐thermionic alternative is yet to emerge. In this work, an attempt is made to understand the physics of Schottky barrier FETs demonstrating SS < 60 mVdec–1, as evident in some experimental results. Employing the in‐house TCAD tool, the electrostatic potential in the device is accurately modeled with the sweeping gate bias. Next, the Tsu–Esaki model is used to compute the tunneling current. The analysis shows that the steep SS results from an abrupt change in the tunneling barrier profile, from a step‐like to a triangular‐like. However, in the ON regime, the drift component begins to dominate the transfer characteristics. Thus, the subtle interplay between the drift and tunneling components of transport leads to SS < 60 mVdec–1 in OFF‐state with improved Ion${\rm I}_{on}$ in ON‐state. Finally, using the calibrated model, the performance metrics of an all‐2D material‐based CMOS inverter operating at a low VDD ≈ 0.6 V are projected. [ABSTRACT FROM AUTHOR]

Large simulations with many independent sub-simulations are common in scientific computing. There are numerous challenges, however, associated with performing such simulations in shared computing environments. For example, sub-simulations may have wildly varying completion times or not complete at all, leading to unpredictable runtimes as well as unbalanced and inefficient use of human and computational resources. In this study, we use the actor model of concurrent computation to improve both the resource utilization and fault tolerance for large-scale scientific computing simulations. More specifically, we use actors in the SUMMA model to manage a large-scale hydrological simulation over the North American continent with over 500,000 independent sub-simulations. We find that the actors implementation outperforms a standard array job submission as well as the job submission tool GNU Parallel by better balancing the computational load across processors. The actors implementation also improves fault tolerance and can eliminate the user intervention required to detect and re-submit failed jobs. [ABSTRACT FROM AUTHOR]

This comprehensive review paper examines the challenges faced by blockchain technology in terms of scalability and proposes potential solutions and future research directions. Scalability poses a significant hurdle for Bitcoin and Ethereum, manifesting as low throughput, extended transaction delays, and excessive energy consumption, thereby compromising efficiency. The current state of blockchain scalability is analyzed, encompassing the limitations of existing solutions such as Sharding and off-chain scaling. Various proposed remedies, including layer 2 scaling solutions, consensus mechanisms, and alternative approaches, are investigated. The paper also explores the impact of scalability on diverse blockchain applications and identifies potential future research directions by integrating data science techniques with blockchain technology. Notably, nearly 110 primary research papers from reputable scientific databases like Scopus, IEEE Explore, Science Direct, and Web of Science were reviewed, demonstrating scalability in blockchain comprising several elements. Transaction throughput and network latency emerge as the most prominent concerns. Consequently, this review offers future research avenues to address scalability challenges by leveraging data science techniques like distributed computing and parallel processing to divide and process vast datasets across multiple machines. The synergy between data science and blockchain holds promise as an optimal solution. Overall, this up-to-date understanding of blockchain scalability is invaluable to researchers, practitioners, and policy makers engaged in this domain. [ABSTRACT FROM AUTHOR]

This paper deals with compatibility of space‐time kernels with (either) full, spatially dynamical, or space‐time compact support. We deal with the dilemma of statistical accuracy versus computational scalability, which are in a notorious trade‐off. Apparently, models with full support ensure maximal information but are computationally expensive, while compactly supported models achieve computational scalability at the expense of loss of information. Hence, an inspection of whether these models might be compatible is necessary. The criterion we use for such an inspection is based on equivalence of Gaussian measures. We provide sufficient conditions for space‐time compatibility. As a corollary, we deduce implications in terms of maximum likelihood estimation and misspecified kriging prediction under fixed domain asymptotics. Some results of independent interest relate about the space‐time spectrum associated with the classes of kernels proposed in the paper. [ABSTRACT FROM AUTHOR]

Software-defined perimeters (SDP) revolutionize network security by offering dynamic and adaptive access controls, focusing on user and device authentication to substantially reduce the attack surface. Despite their potential, traditional SDP models grapple with challenges in scalability and component-level security, limiting their effectiveness in complex digital environments. To overcome these limitations, this article introduces SecureSDP, a sophisticated evolution of the SDP framework designed to enhance scalability and bolster security for each network component. SecureSDP stands out for its seamless integration into varied organizational structures, delivering a robust and comprehensive security solution that strengthens the network's defenses across all layers. The key advancement of SecureSDP is evidenced by rigorous experimental evaluations, which demonstrate its superior performance in improving network security and scalability. Specifically, SecureSDP achieved substantial increases in the hardening scores across various tools: 65% in Lynis, 78% in Chef Inspec, and 30% in OpenSCAP for the SDP controller. These enhancements underscore SecureSDP's significant contributions to the field, marking a pivotal step forward in the development of more secure, scalable network infrastructures. [ABSTRACT FROM AUTHOR]

[Display omitted] • Retrieval of key mangrove traits based on Sentinel-2 surface reflectance data. • Gaussian Processes Regression (GPR) was trained on PROSAIL simulations and active learning intelligent sampling techniques. • GPR model gives insight in the most relevant bands and delivers per-pixel uncertainty interval. • Optimal GPR models were implemented in Google Earth Engine for end-user mapping over space and time. • Time series gap-filling via Whittaker smoothing enabled temporal decomposition to monitor mangrove trait dynamics. As mangrove forests are facing escalating threats from anthropogenic and natural stressors, Earth observation capabilities are providing an unprecedented view of these vital ecosystems. This study aimed to leverage the extensive archive and continuous stream of Sentinel-2 (S2) data and its integration into cloud-computing platforms to map key mangrove traits, indicative of ecosystem health and resilience. Specifically, a hybrid retrieval model implemented in Google Earth Engine (GEE) was operationalized for quantitative estimation of multiple mangrove traits from S2. For this purpose, the leaf-to-canopy radiative transfer model (RTM) PROSAIL in synergy with non-parametric Gaussian Processes Regression (GPR) were selected for hybrid retrieval models of effective Leaf Area Index (LAI e), Leaf Chlorophyll Content (C a b ), Leaf Water Thickness (C w), and Leaf Dry Matter (C m). GPR models were trained and optimized with a PROSAIL-based simulated dataset in combination with active learning sampling techniques to reduce the training dataset towards the most informative samples. Validation against in situ measurements showed high accuracy models achieved for LAI e ( R 2 = 0.693, NRMSE = 12.03 %) and Cab ( R 2 = 0.689, NRMSE = 13.29 %), while moderate performances were obtained for Cm ( R 2 = 0.597, NRMSE = 16.37 %) and Cw ( R 2 = 0.442, NRMSE = 16.66 %). The GPR models were subsequently implemented into GEE for processing of a time series data stream ranging from 2019 to 2023, together with the cloud-gap filling by applying a Whittaker smoother. Seamless maps of four key mangrove traits were generated simultaneously with per-pixel uncertainty estimation. The entire GEE-based workflow enables time series decomposition to uncover trends and seasonal dynamics. Supported by field validation, this study demonstrates a scalable approach for spatiotemporal monitoring of key mangrove traits using hybrid retrieval models via GEE implementation. These advances may contribute to national-scale monitoring in Vietnam and beyond to support mangrove ecosystem protection efforts and management thereof. [ABSTRACT FROM AUTHOR]

The transition from an error-prone, slower, and extremely high-volume legacy system like monolithic system to a faster, lighter, and error-free microservices based system is not always so simple. Microservices are independently deployable and allow for a better team autonomy. In this work, several migration efforts to migrate from a legacy based monolithic system to a pure distributed microservicesbased system has been tested and deployed in keeping two DevOps principles, the software code build and deployment time and latency in monolithic and microservices. Some real-time projects are considered to measure the performance and the time taken to execute the experiments. To measure the total build and deployment time and latency, Jenkins, Prometheus, and JMeter are installed which are industryrecommended softwares. It is observed that there is a total of 7 seconds taken to build and deploy at containers for 10 microservices whereas 10 monolith applications took almost 260 seconds to be built and deployed to the application server. While increasing more requests per second it is observed that upto 3000 requests per second, it impacted the response time of monolith applications but microservices stays the same. The main conclusion is that microservices are rarely impacted in response time with respect to requests per second. [ABSTRACT FROM AUTHOR]

This paper explores the factors that facilitate and impede blockchain technology adoption (BTA) within business organizations. Analysing 112 scholarly articles via thematic and bibliometric methods reveals a complex adoption landscape, highlighted by the proposition of an innovative conceptual framework that augments the conventional technology-organization-environment (TOE) framework. This augmented framework integrates enabling factors, such as enhanced security mechanisms via smart contracts, and barriers, including scalability challenges and regulatory limitations. The research offers significant insights into cultivating a conducive ecosystem for BTA within organizations and provides practical implications. [ABSTRACT FROM AUTHOR]

Pseudo-labeling methods are popular in semi-supervised learning (SSL). Their performance heavily relies on a proper threshold to generate hard labels for unlabeled data. To this end, most existing studies resort to a manually pre-specified function to adjust the threshold, which, however, requires prior knowledge and suffers from the scalability issue. In this paper, we propose a novel method named Meta-Threshold, which learns a dynamic confidence threshold for each unlabeled instance and does not require extra hyperparameters except a learning rate. Specifically, the instance-level confidence threshold is automatically learned by an extra network in a meta-learning manner. Considering limited labeled data as meta-data, the overall training objective of the classifier network and the meta-net can be formulated as a nested optimization problem that can be solved by a bi-level optimization scheme. Furthermore, by replacing the indicator function existed in the pseudo-labeling with a surrogate function, we theoretically provide the convergence of our training procedure, while discussing the training complexity and proposing a strategy to reduce its time cost. Extensive experiments and analyses demonstrate the effectiveness of our method on both typical and imbalanced SSL tasks. [ABSTRACT FROM AUTHOR]

Modular multilevel converters (MMCs) have gained widespread adoption in high-voltage direct current (HVDC) transmission due to their high voltage levels, low harmonic content, and high scalability. However, conventional control methods such as finite control set model predictive control (FCS-MPC) suffer from a heavy computational burden and sensitivity to system parameter variations, limiting the performance of MMCs. This paper proposes a data-driven approach based on model-free adaptive control with an event-triggered mechanism that demonstrates superior robustness against parameter mismatches and enhanced dynamic performance in response to sudden output changes. Moreover, the introduction of the event-triggered mechanism effectively reduces redundant operations, decreasing the computational burden and switching losses. Finally, the proposed strategy is validated through a MATLAB/Simulink simulation model. [ABSTRACT FROM AUTHOR]

Quantum hardware has the potential to efficiently solve computationally difficult problems in physics and chemistry to reap enormous practical rewards. Analogue quantum simulation accomplishes this by using the dynamics of a controlled many-body system to mimic those of another system; such a method is feasible on near-term devices. We show that previous theoretical approaches to analogue quantum simulation suffer from fundamental barriers which prohibit scalable experimental implementation. By introducing a new mathematical framework and going beyond the usual toolbox of Hamiltonian complexity theory with an additional resource of engineered dissipation, we show that these barriers can be overcome. This provides a powerful new perspective for the rigorous study of analogue quantum simulators. Analogue quantum simulation has looser experimental requirements than its digital counterpart, but rigorous theoretical results on the capabilities of realisable experiments are scarce. Here, the authors fill this gap by proposing an alternative mathematical framework, and showing how to overcome barriers to scalable implementations using additional resources such as engineered dissipation. [ABSTRACT FROM AUTHOR]

This article introduces an advanced solution for anonymizing large-scale sensitive data, addressing the limitations of traditional approaches when applied to vast datasets. By leveraging the Spark distributed computing framework, we propose a method that parallelizes the data anonymization process, enhancing efficiency and scalability. Utilizing Spark's resilient distributed datasets (RDD), the approach integrates two primary operations, Map_RDD and ReduceByKey_RDD, to execute the anonymization tasks. Our comprehensive experimental evaluation demonstrates our solution's effectiveness and improved performance in preserving data privacy while balancing data utility and confidentiality. A significant contribution of our study is the development of a wide array of solutions for data owners, particularly notable for a 500 MB dataset at an anonymity level of K=100, where our methodology produces 832 unique solutions. This study also opens avenues for future research in applying different privacy models within the Spark ecosystem, such as l-diversity and t-closeness. [ABSTRACT FROM AUTHOR]

Network-on-chip (NoC) technology is crucial for integrating multiple embedded computing cores onto a single chip. Consequently, this has led to the development of the wireless network-on-chip (WiNoC) concept, seen as a promising strategy to overcome scalability issues in communication systems within chips for future many-core architectures. This research analyses the impact of wireless transceiver subnet clustering on the hundred-core mesh-structured WiNoC architecture. The study aims to examine the effects of distance-based wireless transceiver placements on the transmission delay, network throughput, and energy consumption within a mesh wireless NoC architecture featuring a hundred cores, under specific routing strategies: X-Y, west-first, negative-first, and north-last. This research investigates the impact of positioning radio subnets at the farthest, farther, nearest, and closest positions within an architecture equipped with four wireless transceivers. The Noxim simulator was utilised to simulate the analysed wireless transceiver placements within the hundred-core mesh-structured WiNoC designs, with the objective of validating the results. The architecture with the wireless transceivers positioned at midway proximity (nearer and further) demonstrated the best performance, as evidenced by the lowest latencies for all evaluated deterministic routing algorithms, according to the simulation outcomes. [ABSTRACT FROM AUTHOR]

Recombinant adeno‐associated virus, serotype 9 (rAAV9) has shown promise as a gene therapy vector for muscle and central nervous diseases. High‐dose requirements of these therapies present critical safety considerations and biomanufacturing challenges. Notably, the reduction of empty capsids (ECs), which lack therapeutic transgene, from rAAV9 products is critical to maximize efficacy. Removal of rAAV ECs from full capsids is a major downstream challenge because of their highly similar biophysical characteristics. Ultracentrifugation (UC) reduces ECs but is laborious and difficult to scale. In this paper, to replace a poorly scalable UC process, we developed an anion exchange (AEX) chromatography for rAAV9 EC reduction from full capsids. AEX load preparation by dilution incurred major product loss. The addition of histidine and surfactants to dilution buffers increased yield and reduced aggregation. Elution salts were screened and sodium acetate was found to maximize yield and EC reduction. The most promising load dilution buffer and elution salt were used in combination to form an optimized AEX method. The process reduced ECs three‐fold, demonstrated robustness to a broad range of EC load challenges, and was scaled for large‐scale manufacture. Compared to UC, the AEX method simplified scale‐up, reduced ECs to comparable levels (20%), afforded similar purity and product quality, and increased yield by 14%. [ABSTRACT FROM AUTHOR]

Many countries struggle to effectively introduce Digital Education (DE) to all K-12 students as they lack adequately trained teachers. While cascade models of in-service teacher-professional development (PD) can rapidly deploy PD-programs through multiple levels of trainers to reach all teachers, they suffer from many limitations and are often ineffective. We therefore propose an adapted cascade model to deploy a primary school DE teacher-PD program throughout an administrative region. The model relies on teacher-trainers who (i) are active teachers in the region, (ii) have a prolonged trainer-PD with experts who piloted the teacher-PD program to acquire adult-trainer and DE-related competences, and (iii) are supported by the experts throughout the deployment. To validate the deployment model we used data from 14 teacher-trainers, the 700 teachers they trained, and 350 teachers trained by experts. The teacher-trainer findings demonstrate that the adapted cascade model effectively addresses most cascade models' limitations. The teacher-related findings further validate the adapted cascade model in terms of perception, motivation and adoption which are at least equivalent to those obtained with the experts. To conclude, the adapted cascade model is an effective means of spreading primary school DE PD-programs at a large scale and can be used in other DE reforms. [ABSTRACT FROM AUTHOR]

The recent introduction of full‐mesh virtual private network (VPN) solutions which offer near native performance, coupled with modern encryption algorithms and easy scalability as a result of a central control plane have a strong potential to enable the implementation of a seamless edge‐cloud continuum. To test the performance of existing solutions in this domain, we present a framework consisted of both essential and optional features that full‐mesh VPN solutions need to support before they can be used for interconnecting geographically dispersed compute nodes. We then apply this framework on existing offerings and select three VPN solutions for further tests: Headscale, Netbird, and ZeroTier. We evaluate their features in the context of establishing an underlay network on top of which a Kubernetes overlay network can be created. We test pod‐to‐pod TCP and UDP throughput as well as Kubernetes application programming interface (API) response times, in multiple scenarios, accounting for adverse network conditions such as packet loss or packet delay. Based on the obtained measurement results and through analysis of the underlying strengths and weaknesses of the individual implementations, we draw conclusions on the preferred VPN solution depending on the use‐case at hand, striking a balance between usability and performance. [ABSTRACT FROM AUTHOR]

Summary: Unsupervised person re‐identification based on video sequences can be applied to surveillance systems and is attracting much more attention. It aims to spot specific person in other scenes captured by different cameras. This work explores an innovative strategy, namely, learning to cluster unlabeled person in the videos through graph convolutional networks. In this article, we find that the possibility of inter‐frame linkage can be inferred from context. Therefore, a pose‐guided topology linkage clustering framework is proposed. Our framework consists of three modules: (i) a pose‐guided representation module; (ii) a pose‐guided embedding module; (iii) a link prediction module. First, the representation coding alone is performed at the level of relational induction bias, embedding the implicit pose structure information in image features. Then, based on the consideration of the topology relationship between adjacent and cross‐frame, graph convolutional network is introduced to infer the likelihood of linkage between frame nodes. Experiments show that the proposed method demonstrates excellent scalability in addition to being an effective response to person clustering in case of changes, and does not need the number of clusters as a prior. [ABSTRACT FROM AUTHOR]

Electrochromic (EC) devices represent an emerging energy-saving technology, exhibiting the capability to dynamically modulate light and heat transmittance. Despite their promising potential, the commercialization of EC devices faces substantial impediments such as high cost, intricate fabrication process, and low optical contrast inherent in conventional EC materials relying on the ion insertion/extraction mechanism. In this study, we introduce an innovative "electrode-free" electrochromic (EC) device, termed the EECD, which lacks an EC-layer on the electrodes during device assembling and in the bleached state. This device features a simplified fabrication process and delivers superior optical modulation. It achieves a high optical contrast ranging from 68-85% across the visible spectrum and boasts a rapid response time, reaching 90% coloring in just 17 seconds. In addition, EECD exhibits stable cycling for over 10,000 cycles without noticeable degradation and maintains functionality across a broad temperature range (0 °C to 50 °C). Furthermore, the fabricated large-area devices (40 cm × 40 cm) demonstrate excellent tinting uniformity, suggesting excellent scalability of this approach. Our study establishes a paradigmatic breakthrough for EC smart windows. Cheng Yang and co-workers develop an "electrode-free" electrochromic window, featuring a simplified fabrication process and delivering superior optical modulation capability. [ABSTRACT FROM AUTHOR]

Background: Complex health interventions (CHIs) are increasingly used in public health, clinical research and education to reduce the burden of disease worldwide. Numerous theories, models and frameworks (TMFs) have been developed to support implementation of CHIs. This systematic review aims to identify and critique theoretical frameworks concerned with three features of implementation; adaptability, scalability and sustainability (ASaS). By dismantling the constituent theories, analysing their component concepts and then exploring factors that influence each theory the review team hopes to offer an enhanced understanding of considerations when implementing CHIs. Methods: This review searched PubMed MEDLINE, CINAHL, Web of Science, and Google Scholar for research investigating the TMFs of complex health interventions. Narrative synthesis was employed to examine factors that may influence the adaptability, scalability and sustainability of complex health interventions. Results: A total of 9763 studies were retrieved from the five databases (PubMed, MEDLINE, CINAHL, Web of Science, and Google Scholar). Following removal of duplicates and application of the eligibility criteria, 35 papers were eligible for inclusion. Influencing factors can be grouped within outer context (socio-political context; leadership funding, inter-organisational networks), inner context; (client advocacy; organisational characteristics), intervention characteristics (supervision, monitoring and evaluation), and bridging factors (individual adopter or provider characteristics). Conclusion: This review confirms that identified TMFS do not typically include the three components of adaptability, scalability, and sustainability. Current approaches focus on high income countries or generic "whole world" approaches with few frameworks specific to low- and middle-income countries. The review offers a starting point for further exploration of adaptability, scalability and sustainability, within a low- and middle-income context. Trial registration: Not registered. [ABSTRACT FROM AUTHOR]

For wearable electronics, radial scalability is one of the key research areas for fibrous energy storage devices to be commercialized, but this field has been shelved for years due to the lack of effective methods and configuration arrangements. Here, the team presents a generalizable strategy to realize radial scalability by applying a synchronous-twisting method (STM) for synthesizing a coaxial-extensible configuration (CEC). As examples, aqueous fiber-shaped Zn-MnO2 batteries and MoS2-MnO2 supercapacitors with a diameter of ~500 μm and a length of 100 cm were made. Because of the radial scalability, uniform current distribution, and stable binding force in CEC, the devices not only have high energy densities (~316 Wh liter-1 for Zn-MnO2 batteries and ~107 Wh liter-1 for MoS2-MnO2 supercapacitors) but also maintain a stable operational state in textiles when external bending and tensile forces were applied. The fabricating method together with the radial scalability of the devices provides a reference for future fiber-shaped energy storage devices. [ABSTRACT FROM AUTHOR]

This paper propose a wide gain single-bridge interleaved three-level LLC resonant converter with current sharing capability. This novel converter offers numerous advantages, including low cost, low current ripple, reduced voltage and current stress, widely gain range, high efficiency, good current sharing capability and versatile application scalability. Utilizing the three-level inverter + full-wave rectifier LLC converter as a representative case, the paper conducts an in-depth analysis and research on the proposed method. Finality, a 600 W experimental prototype was constructed and tested. Experimental results reveal that the proposed converter exhibits lower current ripple and a broader gain range. Moreover, the converter shows good current sharing capability (with a resonant element tolerance of 10%, the current error between the two phases does not exceed 12%) and high efficiency (peaking at 95.8%). [ABSTRACT FROM AUTHOR]

Regime-switching models provide an efficient framework for capturing the dynamic behavior of data observed over time and are widely used in economic or financial time series analysis. In this paper, we propose a novel and robust hidden semi-Markovian regime-switching (rHSMS) method. This method uses a general ρ -based distribution to correct for data problems that contain atypical values, such as outliers, heavy-tailed or mixture distributions. Notably, the rHSMS method enhances not only the scalability of the distribution assumptions for all regimes, but also the scalability to accommodate arbitrary sojourn types. Furthermore, we develop a likelihood-based estimation procedure coupled with the use of the EM algorithm to facilitate practical implementation. To demonstrate the robust performance of the proposed rHSMS method, we conduct extensive simulations under different sojourns and scenarios involving atypical values. Finally, we validate the effectiveness of the rHSMS method using monthly returns of the S &P500 Index and the Hang Seng Index. These empirical applications demonstrate the utility of the rHSMS approach in capturing and understanding the complexity of financial market dynamics. [ABSTRACT FROM AUTHOR]

Background: Adverse childhood experiences can impact physical and mental health throughout the lifespan. To support families experiencing adversity and improve child health and developmental equity, an integrated, multi-sector response is required. Child and Family Hubs (Hubs) are a feasible and acceptable approach to providing such a response. In the Australian context, a number of federal and New South Wales (NSW) state policies support an integrated, multi-sector response using Hubs to support families experiencing adversity. This study examined NSW policy stakeholder and health service manager perspectives on the barriers and enablers to translating policy into practice in the implementation of Child and Family Hubs. Methods: Semi-structured interviews were conducted with 11 NSW government policy stakeholders and 13 community health service managers working in child and family policy and planning or child and family community-based services. Interviews were of 30–60 min duration and explored stakeholder knowledge, perspectives and experiences around childhood adversity, and barriers and enablers to operationalizing policies supporting Hubs. Analysis of barriers and facilitators to implementation of Hub models of care was undertaken using the Consolidated Framework for Implementation Research (CFIR). Results: Key barriers that emerged included short-term and inconsistent funding, lack of resourcing for a Hub co-ordinator, limited support for evaluation and insufficient time to plan for Hub implementation. Key enablers included flexibility and adaptability of Hub models to meet local needs, formal change management processes, strong governance structures and engagement among Hub practitioners. Key insights included the importance of targeted strategies to support sustained individual practice change and the need for organization-wide commitment to enable the successful adoption and maintenance of the Hub model of care. Conclusions: This study provides valuable insights and contributes evidence around what is needed to strengthen and support the operationalization and scalability of the Hub model of care. Key recommendations for Hub practitioners include the importance of formal change management processes and establishment of strong governance structures, while key recommendations for policymakers include the need for sustainable Hub funding and a standardized, evidence-based framework to support Hub implementation and evaluation. [ABSTRACT FROM AUTHOR]

The electrochemical reduction of nitroarenes allows direct access to manifold nitrogen containing heterocycles. This work reports the simple and direct electro-organic synthesis of 18 different examples of 2H,4H-4-hydroxy-1,4-benzoxazin-3-ones in up to 81% yield. The scalability of the method was demonstrated on a gram-scale. [ABSTRACT FROM AUTHOR]

A metal- and oxidant-free, radical C–H selenylative and tellurylative annulation of N-(2-alkynyl)anilines with diorganyl dichalcogenides is developed under electrochemical conditions for the sustainable synthesis of valuable 3-selanyl/tellanylquinolines up to 92% yield at room temperature. The developed protocol required only electricity as the green reagent and offers high atom economy, broad substrate scope, and efficient scalability. [ABSTRACT FROM AUTHOR]

The storage, transmission, and processing of data become significant problems when large digital data files or databases are involved, as in the case of decentralized online global databases such as blockchain. Here, we propose a novel method that allows for the scalability of digital assets, including blockchain databases in the download, validation, and confidentiality processes, by developing a lightweight blockchain technology called Entropic Blockchain. This is a computer-implemented mathematical method by which to generate an information-entropic numerical barcode representation of a digital asset. Using this technique, a 1–2 Mb block of digital data can be represented by a few bytes, significantly reducing the size of a blockchain. The entropic barcode file can be utilized on its own or as an optically machine-readable entropic barcode for secure data transmission, processing, labeling, identification, and one-way encryption, as well as for compression, validation, and digital tamper-proof checks. The mathematics of this process and all the steps involved in its implementation are discussed in detail in this article. [ABSTRACT FROM AUTHOR]

This study introduces a privacy-preserving approach for the real-time action detection in intelligent vehicles using a federated learning (FL)-based temporal recurrent network (TRN). This approach enables edge devices to independently train models, enhancing data privacy and scalability by eliminating central data consolidation. Our FL-based TRN effectively captures temporal dependencies, anticipating future actions with high precision. Extensive testing on the Honda HDD and TVSeries datasets demonstrated robust performance in centralized and decentralized settings, with competitive mean average precision (mAP) scores. The experimental results highlighted that our FL-based TRN achieved an mAP of 40.0% in decentralized settings, closely matching the 40.1% in centralized configurations. Notably, the model excelled in detecting complex driving maneuvers, with mAPs of 80.7% for intersection passing and 78.1% for right turns. These outcomes affirm the model's accuracy in action localization and identification. The system showed significant scalability and adaptability, maintaining robust performance across increased client device counts. The integration of a temporal decoder enabled predictions of future actions up to 2 s ahead, enhancing the responsiveness. Our research advances intelligent vehicle technology, promoting safety and efficiency while maintaining strict privacy standards. [ABSTRACT FROM AUTHOR]

River network routing's significance in reach-level flood forecasting over extensive domains is growing, requiring considerable computational resources for modeling networks comprising thousands to millions of reaches. Parallel computation plays a central role in timely forecasting in such cases. However, the sequentiality of upstream-to-downstream flow paths within river networks poses a significant challenge for parallelization. This study introduces a family of stream orders and an associated order-based parallel routing approach. We assign each reach an order that falls between one more than the maximum order of its upstream reaches and one less than the order of its downstream reach. This strategy enables the parallel simulation of reaches with identical orders while sequentially processing those with different orders, thus maintaining the crucial upstream-to-downstream dynamic. To further enhance parallel scalability, we strategically relax the upstream-to-downstream relationship along the longest flow paths, dividing the network into independent subnetworks and introducing halo reaches to mitigate the impact of inexact inflows. We validate our approach using China's Yangtze River basin, the country's largest river network with 53,600 fully connected reaches. Employing a conceptual parallel execution machine, we demonstrate that our method achieves 80% parallel efficiency with up to 25 processors. By strategically introducing breakpoints, we further enhance scalability, enabling efficient simulations on 77 processors while maintaining 80% efficiency. These results highlight the scalability and efficiency of our methods for large-scale, high-resolution river network modeling within Earth system models. Our study also lays a theoretical groundwork for optimizing stream orders and halo reach placements, crucial for advancing river network modeling. [ABSTRACT FROM AUTHOR]

Background: The concept of collaborative approaches involves community residents in joint decision-making processes to maintain or enhance their material and social conditions. During COVID-19, public services saw the benefits of actively collaborating with communities and involving residents in decision-making processes. As communities have resources and assets, they are well-placed to contribute to developing local health and wellbeing initiatives. An interdisciplinary and nationally funded three-phase research programme, "Mobilising community assets to tackle health inequalities", was established with the objective of utilising local, cultural, and natural assets to support health and wellbeing. The current study aimed to synthesise evidence collected by research teams awarded funding in phase one of the programme, comprising academic and non-academic, health and social care, voluntary and community partners. Methods: Ten online focus groups were conducted with research teams from across the UK exploring the successes and challenges of partnership working to tackle health inequalities using collaborative approaches to community-based research. Eight focus group questions were split between partnership working and health inequalities. Results: Thematic and content analysis produced 185 subthemes from which 12 themes were identified. Major themes representing an above average number of coded responses were research evidence; funding; relationships with partners; health inequalities and deprivation; community involvement; and health service and integrated care systems. Minor themes were link workers and social prescribing; training and support; place-based factors; longevity of programmes; setting up and scaling up programmes; and mental health. Conclusions: Successes included employing practice-based and arts-based methods, being part of a research project for those not normally involved in research, sharing funding democratically, building on established relationships, and the vital role that local assets play in involving communities. Challenges involved a lack of sustainable financial support, the short-term nature of funding, inconsistencies in reaching the poorest people, obtaining the right sort of research evidence, making sufficient research progress, building relationships with already over-burdened health care staff, and redressing the balance of power in favour of communities. Despite the challenges, participants were mainly optimistic that collective approaches and meaningful co-production would create opportunities for future research partnerships with communities. [ABSTRACT FROM AUTHOR]

This study addresses the critical need for securing corporate networks against anomalies, a pressing concern in ensuring the comprehensive security of these networks. It aims to develop and validate a new machine learning-based methodology for anomaly detection that is adaptable across various corporate network environments, highlighting the method’s potential practical applications. Employing a systematic approach, the research integrates system analysis of anomaly detection methodologies with an analytical review of machine learning techniques tailored for high-security measures and attack prevention in corporate networks. This dual approach ensures a robust framework for identifying and addressing network anomalies efficiently. The methodology demonstrated notable efficacy, with the proposed machine learning-based anomaly detection techniques achieving an efficiency rate upwards of 90% in identifying and categorizing network traffic types. This high level of precision allows for the effective tracking of network anomalies across diverse corporate networks and their respective devices and equipment. The findings underscore the substantial practical value of the developed methodology, offering a promising avenue for enhancing corporate network security. The implementation of this machine learning-based approach not only facilitates the timely detection of anomalies but also significantly contributes to the improvement of machine learning applications within the realm of network security. Future research could further refine these techniques, exploring scalability and real-time data analysis enhancements to bolster their effectiveness across various network configurations. [ABSTRACT FROM AUTHOR]