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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]

Semiconductor quantum dots are favorable candidates for quantum information processing due to their long coherence time and potential scalability. However, the calibration and characterization of interconnected quantum dot arrays have proven to be challenging tasks. One method to characterize the configuration of such an array involves using the Hubbard model. In this paper, we present an efficient characterization algorithm that efficiently extracts the Hubbard model parameters, including tunnel coupling and capacitive coupling energy, from experimental stability diagrams. Leveraging the dual-annealing optimizer, we determine the set of Hubbard parameters that best characterize the experimental data. We compare our method with an alternate, well-established measure of tunnel coupling and find good agreement within the investigated regime. Our extracted tunnel couplings range from 69 to 517 μ eV, and we discuss the limiting factors of our method. [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.

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]

In the exploration of recently developed Bayesian Generalized Nonlinear Models (BGNLM), this paper proposes a pragmatic scalable approximation for computing posterior distributions. Traditional Markov chain Monte Carlo within the populations of the Genetically Modified Mode Jumping Markov Chain Monte Carlo (GMJMCMC) algorithm is an NP-hard search problem. To linearize them, we suggest using instead variational Bayes, employing either mean-field approximation or normalizing flows for simplicity and scalability. This results in an evolutionary variational Bayes algorithm as a more scalable alternative to GMJMCMC. Through practical applications including inference on Bayesian linear models, Bayesian fractional polynomials, and full BGNLM, we demonstrate the effectiveness of our method, delivering accurate predictions, transparency and interpretations, and accessible measures of uncertainty, while improving the scalability of BGNLM inference through on the one hand using a novel variational Bayes method, but, on the other hand, enabling the use of GPUs for computations. [ABSTRACT FROM AUTHOR]

CoVID-19 has been linked to long-term consequences on several human body organs, including lung ailments, kidney malfunctions, heart dysrhythmia, alterations in brain nutrient levels, psychological difficulties, abrupt changes in blood pressure, and more. Because of the considerable variety in the impacts on different body parts, researchers find it challenging to create models that can incorporate these effects for treatment recommendations and future disease prevention scenarios. Thus, this article examines some of the most recently proposed models for identifying the impacts of CoVID19 on various human organs. This review examines the underlying theories in terms of clinical nuances, functional advantages, contextual limits, and potential empirical applications. Based on this discussion, researchers will be able to find the best models for detecting particular diseases on specific body parts. It was discovered that hybrid bioinspired models, when paired with deep learning-based classification algorithms, can effectively detect these impacts. This text also parametrically analyses these models in terms of accuracy, precision, and recall, allowing readers to select the best models for their performance-specific use cases. To expand on this discussion, this book evaluates a unique CoVID19 Classification Rank Metric (CCRM) that integrates these factors for thorough model identification. Based on this criteria, researchers will be able to develop appropriate models for clinical scenarios that have high accuracy, low delay, and scalability while costing less. [ABSTRACT FROM AUTHOR]

Persistent homology (PH) is a powerful mathematical method to automatically extract relevant insights from images, such as those obtained by high-resolution imaging devices like electron microscopes or new-generation telescopes. However, the application of this method comes at a very high computational cost that is bound to explode more because new imaging devices generate an ever-growing amount of data. In this paper, we present PixHomology, a novel algorithm for efficiently computing zero-dimensional PH on 2D images, optimizing memory and processing time. By leveraging the Apache Spark framework, we also present a distributed version of our algorithm with several optimized variants, able to concurrently process large batches of astronomical images. Finally, we present the results of an experimental analysis showing that our algorithm and its distributed version are efficient in terms of required memory, execution time, and scalability, consistently outperforming existing state-of-the-art PH computation tools when used to process large datasets. [ABSTRACT FROM AUTHOR]

Let r ≥ 0 be a fixed integer. If F is an edge set of a connected graph G satisfying the minimum degree of G − F being at least r , then F is a conditional faulty edge set of order r. The graph G is called F -strongly Menger-edge-connected if each pair of vertices u and v are connected by min { deg G − F (u) , deg G − F (v) } edge-disjoint paths in G − F , where deg G − F (u) and deg G − F (v) are the degrees of u and v in G − F , respectively. A graph G is m -strongly Menger-edge-connected of order r if G is F -strongly Menger-edge-connected of order r for every F ⊂ E (G) with | F | ≤ m and F is a conditional edge fault set of order r , and the maximum value of m is written as s m λ r (G). Hamming graph K L n has been widely concerned by researchers due to its excellent properties such as good connectivity, scalability, symmetry and iterativity. This paper considers various sufficient conditions of K L n to be F -strongly Menger-edge-connected of order r and determine the exact value of s m λ (L − 1) r (K L n) by studying the edge-disjoint paths in K L n with edge faults, s m λ (L − 1) r (K L n) = (L − 1) L r (n − r) − (L − 1) n , where 0 ≤ r ≤ n − 2 and n ≥ 3. [ABSTRACT FROM AUTHOR]

Accurate extraction of grassland sample coverage is crucial for regional ecological environment monitoring. Due to the strong feature learning capability, high flexibility, and scalability of deep learning methods, they have great potential in grassland sample extraction modelling. However, we still lack a model that can achieve both lightweight structure and effective performance for small object segmentation to considering the small target characteristics of grassland vegetation and the requirements for model deployment in later stages. Here, we combined the UNet model, which performs well in small target segmentation, with the lightweight network Shufflenetv2 model, proposing an improved UNet neural network, Shufflenetv2UNet, for grassland sample coverage extraction. The core of Shufflenetv2UNet is the removal of maximum pooling and double‐layer convolution modules from downsampling in the UNet neural network. In addition, the Inverted Residual Block structure module from Shufflenetv2 was added to achieve a lightweight model and improved extraction accuracy. The Shufflenetv2UNet achieves an accuracy of 98.23%, with a parameter size of 50.74 M, and a model inference speed of 0.004 s. Compared to existing extraction methods, this model has advantages in prediction accuracy, parameter size, and model inference speed. Moreover, Shufflenetv2UNet achieved different types of grassland sample coverage extractions, with good robustness, generalization, and universality, enabling investigators to quickly and accurately obtain grassland sample coverage. This allows more dynamic and accurate ground measurement data for regional grassland environmental monitoring. [ABSTRACT FROM AUTHOR]

Dual‐band electrochromic (DBEC) smart windows are an emerging energy‐saving technology, especially those constructed from single‐component DBEC materials as they offer an easy way to independently regulate visible (VIS) and near‐infrared (NIR) light. However, the current complicated organic ligand approach used to synthesize single‐component DBEC materials necessitates a post‐annealing treatment, which may severely compromise the scalability of DBEC smart windows. Herein, one organic ligand‐free process is developed to produce well‐controlled Nb‐doped anatase TiO2 nanocrystals (NTO NCs) followed by spraying on arbitrary substrates, giving NTO rigid or flexible films with good DBEC properties and a high optical modulation over 92%. A prototype of a 20 cm × 20 cm DBEC smart window is built. The simplicity and scalability of the synthesis and fabrication process make it a scalable approach to constructing large‐area electrochromic smart windows and foils, with promising applications in fields such as buildings and automobiles. [ABSTRACT FROM AUTHOR]

High-dimensional cell phenotyping is a powerful tool to study molecular and cellular changes in health and diseases. CyTOF enables high-dimensional cell phenotyping using tens of surface and intra-cellular markers. To utilize the full potential of CyTOF, we need advanced clustering and machine learning methodologies to enable automated gating of the complex data. Here we show that critical modifications to a machine learning based FlowSOM package and precise parameter optimization can enable us to reliably analyze the complex CyTOF data. We show the impact of key parameters on clustering outcomes while addressing bugs within the publicly available package. We modified the FlowSOM pipeline to fix the bugs, enable scalability to handle large datasets and perform parameter optimization. We further validated this modified pipeline on a substantial external immunological dataset demonstrating the need of data-specific tailored parameter optimization to ensure reliable definition and interrogation of immune cell populations associated with immune disorders. Optimized FlowSOM pipeline for reliable clustering of high-dimensional cytometry data. [ABSTRACT FROM AUTHOR]

We present a terahertz (THz) sensing chip capable of detecting trace biomolecules. Its operating bandwidth stretches from 0.2 to 1.35 THz, capable of signal strengths exceeding 38 dB above noise levels, achieved by the mitigation of mode competition issues. The high sensitivity of the sensing chip is used to observe the absorption fingerprints of α-lactose monohydrate, L-tyrosine, and L-histidine powders at their characteristic frequencies for sample masses as low as 0.3 mg. Using a solution titration technique, we demonstrate detection of the absorption fingerprint of α-lactose monohydrate for concentrations of 72 μg/μl, adding just 57.6 μg of the powder onto the chip after evaporation of the ethanol solvent, representing a twofold advancement over prior work by providing a significantly broader operating spectrum and higher sensitivity. The relatively easy manufacturability, low cost, and scalability of the sensing chip paves the way for studies of enhanced light–matter interaction between THz waves and trace analytes, while also unlocking innovative potential applications in biomolecular sensing, medical diagnostics, and environmental monitoring. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance. [ABSTRACT FROM AUTHOR]

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IOTA is an emerging decentralized computing paradigm for developing blockchain-based Internet of Things (IoT) applications. It has the advantages of zero transaction fees, incremental scalability, and high-performance transaction rates. Despite its well-understood benefits, IOTA nodes need to withstand considerable resource costs to generate the distributed ledger. The main reason for this is that IOTA abandons the original blockchain reward mechanism and does not charge transaction fees. Therefore, in this paper we address the cost optimization issue for IOTA based on Lyapunov optimization theory. We take the first step in investigating the cost optimization problem of IOTA and exploring a new optimization scheme using Lyapunov optimization theory. Our proposed scheme enables IOTA to minimize the total cost of IOTA nodes through a computational optimization algorithm. Then, an optimized transaction rate control algorithm can be designed based on the large deviation theory to reduce orphan tangles that waste computational costs. In addition, we define and deduce the effective width of the tangle to monitor the total throughput and reduce the time spent on cost optimization to avoid unnecessary waste of resources. Lastly, a comprehensive theoretical analysis and simulation experiments demonstrate that the proposed strategy is both efficient and practical. [ABSTRACT FROM AUTHOR]

Quantum computing and machine learning (ML) have received significant developments which have set the stage for the next frontier of creative work and usefulness. This paper aims at reviewing various data-encoding techniques in Quantum Machine Learning (QML) while highlighting their significance in transforming classical data into quantum systems. We analyze basis, amplitude, angle, and other high-level encodings in depth to demonstrate how various strategies affect encoding improvements in quantum algorithms. However, they identify major problems with encoding in the framework of QML, including scalability, computational burden, and noise. Future directions for research outline these challenges, aiming to enhance the excellence of encoding techniques in the constantly evolving quantum technology setting. This review shall enable the researcher to gain an enhanced understanding of data encoding in QML, and it also suggests solutions to the current limitations in this area. [ABSTRACT FROM AUTHOR]

The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is a widely used method for managing energy consumption in Wireless Sensor Networks (WSNs). However, it has limitations that affect network longevity and performance. This paper presents an improved version of the LEACH protocol, termed MFG-LEACH, which incorporates the Mean Field Game (MFG) theory to optimize energy efficiency and network lifetime. The proposed MFG-LEACH protocol addresses the imbalances in energy consumption by modeling the interactions among nodes as a game, where each node optimizes its transmission energy based on the collective state of the network. We conducted extensive simulations to compare MFG-LEACH with Enhanced Zonal Stable Election Protocol (EZ-SEP), Energy-Aware Multi-Hop Routing (EAMR), and Balanced Residual Energy routing (BRE) protocols. The results demonstrate that MFG-LEACH significantly reduces energy consumption and increases the number of packets received across different node densities, thereby validating the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The aim of the article was to design and develop new thermodynamically stable starch-based compounds, with scalable properties, that are melt-processable into finished products by classic or 3D printing methods. This is based on phenomena of de-structuring, entanglement compatibilization, and re-structuring of starch, along with the modification of the polymer, polyvinyl alcohol (PVA), by following an experimental sequence involving pre-treatment and melt compounding in two stages. The new compounds selection was made considering the dependence of viscoelastic properties on formulation and flowing conditions in both the melted and solid states. Starting from starch with 125 °C glass transition and PVA with a Tg at 85 °C, and following the mentioned experimental sequence, new starch-PVA compounds with a high macromolecular miscibility and proven thermodynamic stability for at least 10 years, with glass transitions ranging from −10 °C to 50 °C, optimal processability through both classical melt procedures (extrusion, injection) and 3D printing, as well as good scalability properties, were achieved. The results are connected to the approaches considering the relationship between miscibility and the lifetime of compounds with renewable-based polymer content. By deepening the understanding of the thermodynamic stability features characterizing these compounds, it can be possible to open the way for starch usage in medium-life compositions, not only for short-life applications, as until now. [ABSTRACT FROM AUTHOR]

This paper compares various approaches to the scalability and replicability analysis (SRA) of smart grid pilot projects, highlighting the need for a comprehensive SRA methodology as called for by the European Commission and International Energy Agency. This study addresses the need for a standardized SRA methodology and explores how three EU-funded projects—Platone, EUniversal, and IElectrix—adapted the general guidelines developed by the BRIDGE initiative. These guidelines provide recommendations for developing a comprehensive large-scale deployment analysis. The results show that while the guidelines are usable and flexible, project-specific conditions and data availability limitations—particularly in regulatory and technical analysis—can pose challenges. Some key recommendations to overcome these and facilitate future applications are identified. These include defining SRA methodologies and securing data-sharing agreements early. The lack of standardized approaches for presenting SRA results hampers cross-project comparison. Thus, creating an open-use case repository and updating the BRIDGE guidelines with more detailed examples, benchmarks, and reference networks is recommended. Additionally, linking SRA with cost–benefit analysis (CBA) is suggested in order to evaluate the commercial viability of smart grid solutions. The paper concludes that while the BRIDGE guidelines have proven to be fit for purpose, further developments are needed to facilitate their practical application in real-world projects. [ABSTRACT FROM AUTHOR]

Agile methods have emerged to overcome the obstacles of structured methodologies, such as the waterfall, prototype, spiral, and so on. There are studies showing the usefulness of agile approaches in software development. However, studies on Agile maintenance are very limited in number. Regardless of the chosen methodology, software maintenance can be carried out in either a local (on‐the‐premise) or global (distributed) environment. In a local environment, the software maintenance team is co‐located on the same premises, while in a global environment, the team is geographically dispersed from the customer. The main objective of this Systematic Mapping (SM) study is to identify the practices useful for software maintenance using the Agile approaches in the Cloud environment. We have conducted a comprehensive search in well‐known digital databases and examined the articles that map to the pre‐defined inclusion criteria. The study selected and analyzed 48 articles out of 320 published between 2000 and 2022. The findings of the mapping study reveal that Agile can resolve the major issues faced in traditional software maintenance, making the role of this approach significant in global/distributed software maintenance. Cloud computing plays a vital role in software maintenance. Most of the studies highlight the application of XP‐ and Scrum‐based Agile maintenance models. The study found a need for more Agile maintenance solutions in the cloud, highlighting the importance of agile in software maintenance, both locally and globally. Irrespective of the environment, Cloud computing provides a centralized platform for collaboration and communication, while also offering scalability and flexibility to adapt to diverse infrastructure needs. This allows agile maintenance practices to be implemented across both local and global environments, leveraging the cloud's capabilities to overcome geographical and infrastructural challenges. [ABSTRACT FROM AUTHOR]

Knowledge graphs have recently been introduced to the verification strategy field successfully representing the complexity of verification in real‐life applications. This format provides a scale‐free analysis of verification strategies compared to the more traditional verification artifacts such as requirement traceability matrices and verification matrices. Complexities can be observed visually and numerically both in terms of the problem scope and the entity interdependencies. In this paper, we retrieve verification strategy information patterns representing different aspects of verification. This is achieved by tapping into the network properties of knowledge graphs. They are dissected to detect knowledge patterns emerging from different parts of the verification artifacts. Similarities and differences between the two verification strategies are explained numerically and semantically. Seemingly unrelated requirements and verification activities are connected through indirect connections, and orthogonalities between independent requirements are analyzed. These findings validate the scalability of verification planning and assessment based on knowledge graphs. [ABSTRACT FROM AUTHOR]

The current pattern mining algorithms focus on discovering either frequent itemsets or high-utility itemsets. The goal of this research is to study the problem of mining frequent-utility itemsets. To solve this problem, two novel algorithms named FUIMTWU-Tree (Frequent-utility Itemset Mining based on TWU-Tree) and FUIMTF-Tree (Frequent-utility Itemset Mining based on TF-Tree) are presented based on the integration of IHUP and HUI-Miner. The TWU-tree and TF-Tree structures are utilised to avoid the unnecessary utility-list construction of itemsets that do not appear in a transaction dataset. The performance of the proposed algorithms is evaluated on various datasets. The results of the experiments demonstrate that FUIMTWU-Tree and FUIMTF-Tree perform efficiently in terms of speed, pruning performance and scalability. [ABSTRACT FROM AUTHOR]

• Though data on youth is limited, face-to-face CBT for BDD is effective across ages. • Digital treatments show efficacy, with sustained benefits and potential scalability. • Current evidence lacks diversity and may overlook underserved groups' experiences. • New therapies for visual deficits are being explored as potential treatment options. • Studies needed on long-term treatment effects and predictors of treatment response. Body dysmorphic disorder (BDD) is an underrecognized, challenging illness with severe comorbidities, demanding urgent advancements in treatment strategies. This state-of-the-science review describes current research on existing BDD treatments, beginning with a detailed discussion of cognitive-behavioral therapy (CBT), the primary psychosocial intervention for BDD, and its foundational theories. We emphasize the significant progress in the field, including the efficacy of face-to-face CBT, the promising outcomes of digital interventions for broadening access to care, and emerging treatments that warrant further exploration. The review also addresses the critical gap of targeted interventions for youth, considering the diseases' typical onset during adolescence. Our review also sheds light on the significant gap in research dedicated to testing these treatments in underserved communities, stressing the importance of including these populations in research and culturally informed and adapted, if necessary, care. The review concludes with recommendations for future directions, outlining areas for ongoing treatment development and research to expand the scope and efficacy of interventions for BDD. [ABSTRACT FROM AUTHOR]

In the autism field, there is increasing interest in translating evidence-based interventions (EBIs) into systems that serve young autistic children and their families. Public Early Intervention systems have been a focal point of research-based implementation efforts given that these systems are federally mandated to provide services to children birth to three years of age with developmental delays under Part C of the Individuals with Disabilities Education Act. Although a growing number of research studies are now training Early Intervention providers to deliver autism EBIs, this work has been conducted on a relatively small scale and has only just begun to consider the alignment of these models with Early Intervention systems and whether sufficient infrastructure exists to scale up these training efforts and to sustain their public health impact. This commentary aims to address this gap by reviewing factors that have been found to uniformly impact the scale-up of EBIs across diverse public systems (Fagan 20, 1147–1168, 2019), and to extend this framework to the implementation of EBIs within public Early Intervention systems. These factors include developer and funder capacity, the public's awareness of and support for EBIs, the system's leadership support for EBI use, the capacity for community engagement in implementation efforts, the availability of a skilled workforce capable of delivering EBIs, and the capacity for data monitoring and quality improvement. This commentary discusses how these factors may specifically impact the scale-up of autism EBIs within EI systems to support toddlers and young, autistic children, and implications for autism researchers. [ABSTRACT FROM AUTHOR]

Serverless computing is one of the latest technologies that has received much attention from researchers and companies in recent years since it provides dynamic scalability and a clear economic model. Serverless computing enables users to pay only for the time they use resources. This approach has several benefits, including optimizing costs and resource utilization; however, cold starts are a concern and challenge. Various studies have been conducted in the academic and industrial sectors to deal with this problem, which poses a significant research challenge. This paper comprehensively reviews recent cold start research in serverless computing. Hence, this paper presents a detailed taxonomy of several serverless computing strategies for dealing with cold start latency. We have considered two main approaches in the proposed classification: Optimizing Loading Times (OLT) and Optimizing Resource Usage (ORU), each including several subsets. The subsets of the primary approach OLT are divided into container-based and checkpoint-based. Also, the primary approach ORU is divided into machine learning (ML)-based, optimization-based, and heuristic-based approaches. After analyzing current methods, we have categorized and investigated them according to their characteristics and commonalities. Additionally, we examine potential challenges and directions for future research. [ABSTRACT FROM AUTHOR]

The growing popularity of blockchains highlights the need to improve their scalability. While previous research has focused on scaling transaction processing, the scalability of transaction creation remains unexplored. This issue is particularly important for organizations needing to send large volumes of transactions quickly or continuously. Scaling transaction creation is challenging, especially for blockchain platforms like Ethereum, which require transactions to include a sequence number. This paper proposes four different methods to scale transaction creation. Our experimental evaluation assesses the scalability and latency of these methods, identifying two as feasible for scaling transaction creation. Additionally, we provide an in-depth theoretical analysis of these two methods. [ABSTRACT FROM AUTHOR]

In recent years, containerization is becoming more and more popular for deploying applications and services and it has significantly contributed to the expansion of edge computing. The demand for effective and scalable container image management, however, increases as the number of containers deployed grows. One solution is to use a localized Docker registry at the edge, where the images are stored closer to the deployment site. This approach can considerably reduce the latency and bandwidth required to download images from a central registry. In addition, it acts as a proactive caching mechanism by optimizing the download delays and the network traffic. In this paper, we introduce an edge-enabled storage framework that incorporates a localized Docker registry. This framework aims to streamline the storage and distribution of container images, providing improved control, scalability, and optimized capabilities for edge deployment. Four demanding XR applications are employed as use cases to experiment with the proposed solution. [ABSTRACT FROM AUTHOR]

Blockchain is an emerging technology that improves efficiency, transparency, and security in applications such as fintech, smart cities, healthcare, etc. However, blockchain technology faces scalability issues as the volume of transactions grows. One solution to enhance the scalability is offloading transactions outside the main blockchain layer using the Optimistic Rollup. In this context, we propose the Early Fraud Detection (EFD) framework that utilizes Optimistic Rollups and incorporates early fraud proofs by applying Bloom–Merkle trees that aim to reduce the challenger's verification time and cost. The EFD framework has been tested using the Ethereum Mainnet Test Network and developed with Solidity. It demonstrates that the proposed EFD framework reduces the total cost to users by 25%. Moreover, it is robust against security threats, including Double Spending, Sybil, and Denial-of-Service (DOS) attacks. [ABSTRACT FROM AUTHOR]

The study focuses on the Vertical Gate-All-Around Multi-Bridge-Channel Field-Effect Transistor (VGAAMBCFET), which is a critical component in developing low-power applications. Several optimization strategies are being investigated to improve VGAA-MBCFET performance, with a particular focus on improving the on-off ratio, reducing leakage current, and fine-tuning I-V characteristics. The study emphasizes the distinctive vertical gate-all around structure and scalability benefits that distinguish VGAA- MBCFETs from horizontal counterparts such as Fin FETs and lateral GAAFETs. In-depth research on the scalability of VGAA-MBCFETs into smaller technological nodes is a significant priority, since it is recognized as crucial to influencing the future of sophisticated and compact integrated circuits. The paper acknowledges these innovations revolutionary impact on large-scale integration (LSI) improvements, as well as their influence on the trajectory of wearable technology within the larger context of integrated circuit miniaturization the issues provided by downsizing are openly discussed, with recognition of their complicated impact on wearable performance and functionality. As wearable technology aims to incorporate high performance computing systems, this study addresses the constraints of traditional techniques and investigates whether VGAA-MBCFETs can be a transformative technology, allowing for compact yet powerful designs in energy efficient electronic gadgets. The study uses robust modelling approaches to provide thorough insights into the capabilities and possible applications of VGAA-MBCFETs, providing a significant contribution to the ongoing progress of low-power electronic device technology. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of integrating multiple energy sources into a single-domain microgrid, commonly found in urban buildings, while also providing a platform for energy management. A Lyapunov stability analysis of a simple boost converter was used as a basis for designing the dual control loop of the grid. The versatility of the developed control structure allows for the incorporation of an arbitrary number of sources hence achieving scalability. Next, the energy in the microgrid was separated into exogenous energy and actuator energy. This yielded a description of the system that quantified the condition of stability independent of the decision made by a would-be energy management system. This, in turns, liberates the process of designing an optimized energy management system from stability concerns. The acquired theoretical findings were then translated to a simulation model, where multiple components of the grid were simulated under a typical scenario of operation. Once the simulation phase was concluded, a prototype of the designed grid was constructed to emulate the theoretical results. The prototype exhibited promising performance, matching the simulation predictions to a reasonable degree. [ABSTRACT FROM AUTHOR]

Event sequence data widely exists in real life, where each event is typically represented as a tuple, event type and occurrence time. Recently, neural point process (NPP), a probabilistic model that learns the next event distribution with events history given, has gained a lot of attention for event sequence modelling. Existing NPP models use one single vector to encode the whole events history. However, each type of event has its own historical events of concern, which should have led to a different encoding for events history. To this end, we propose Type-wise Neural Point Process (TNPP), with each type of event having a history vector to encode the historical events of its own interest. Type-wise encoding further leads to the realization of type-wise decoding, which together makes a more effective neural point process. Experimental results on six datasets show that TNPP outperforms existing models on the event type prediction task under both extrapolation and interpolation setting. Moreover, the results in terms of scalability and interpretability show that TNPP scales well to datasets with many event types and can provide high-quality event dependencies for interpretation. The code and data can be found at https://github.com/lbq8942/TNPP. [ABSTRACT FROM AUTHOR]

The payment channel network (PCN) is an effective off-chain scaling solution widely recognized for reducing operational costs on permissionless blockchains. However, it still faces challenges such as lack of flexibility, channel exhaustion, and poor sustainability. Currently, a separate deposit is required for each payment channel, which locks a significant amount of coins for a longer period. This restricts the ability to move locked coins across their channels off-chain. Additionally, unbalanced (unidirectional) transfers can lead to channel exhaustion, rendering the channel unsustainable. To address these issues, we propose a novel off-chain flexible PCN protocol called Flexichain. Unlike existing approaches, Flexichain allows users to deposit coins per user rather than per channel. This provides flexibility to move coins freely between channels without relying on the blockchain or disrupting the off-chain cycle. Flexichain is proven to be secure under the Universal Composability framework and resistant against channel exhaustion attacks. To assess the performance of Flexichain, we conduct experiments on both on-chain and off-chain operations using snapshots of the Lightning Network. We evaluate the on-chain gas costs, success ratio and success amount of off-chain payments under uniform and skewed payment demands, as well as the computational and communication overheads of the off-chain contracts. [ABSTRACT FROM AUTHOR]

Network reconnaissance and measurements play a central role in improving Internet security and are important for understanding the current deployments and trends. Such measurements often require coordination with the measured target. This limits the scalability and the coverage of the existing proposals. IP Identification (IPID) provides a side channel for remote measurements without requiring the targets to install agents or visit the measurement infrastructure. However, current IPID-based techniques have technical limitations due to their reliance on the idealistic assumption of stable IPID changes or prior knowledge, making them challenging to adopt for practical measurements. In this work, we aim to tackle the limitations of existing techniques by introducing a novel approach: predictive analysis of IPID counter behavior. This involves utilizing a machine learning (ML) model to understand the historical patterns of IPID counter changes and predict future IPID values. To validate our approach, we implement six ML models and evaluate them on realistic IPID data collected from 4,698 Internet sources. Our evaluations demonstrate that among the six models, the Gaussian Process (GP) model has superior accuracy in tracking and predicting IPID values. Using the GP-based predictive analysis, we implement a tool, called ZPredict, to infer various favorable information about target networks or servers. Our evaluation on a large dataset of public servers demonstrates its effectiveness in idle port scanning, measuring Russian censorship, and inferring Source Address Validation. Our study methodology is ethical and was developed to mitigate any potential harm, taking into account the concerns associated with measurements. [ABSTRACT FROM AUTHOR]

In this research, we use several machine learning methods and feature selection to process social media data, namely restaurant reviews. The selection feature used is a combination of information gain (IG) and adaptive boosting (AdaBoost) which is used to see its effect on the classification performance evaluation value of machine learning methods such as Naive Bayes (NB), K-nearest neighbor (KNN), and random forest (RF) which is the aim of this research. NB is very simple and efficient and very sensitive to feature selection. Meanwhile, KNN is known for its weaknesses such as biased k values, overly complex computation, memory limitations, and ignoring irrelevant attributes. Then RF has weaknesses, including that the evaluation value can change significantly with only small data changes. In text classification, feature selection can improve the scalability, efficiency and accuracy of text classification. Based on tests that have been carried out on several machine learning methods and a combination of the two selection features, it was found that the best classifier is the RF algorithm. RF produces a significant increase in value after using the IG and AdaBoost features. Increased accuracy by 10%, precision by 12.43%, recall by 8.14% and F1-score by 10.37%. RF also produces even accuracy, precision, recall, and F1-score values after using IG and AdaBoost with an accuracy value of 84.5%; precision of 85.58%; recall was 86.36%; and F1-score was 85.97%. [ABSTRACT FROM AUTHOR]

Clustering in vehicular ad-hoc networks (VANETs) plays a pivotal role in enhancing the reliability and efficiency of transmission among vehicles. VANET is a dynamic and highly mobile network where vehicles form clusters to enable effective data exchange, resource allocation, and cooperative actions. Clustering algorithm, helps vehicles self-organize into clusters based on connectivity and proximity, thus improving scalability and reducing transmission overhead. This cluster enables critical applications such as traffic management, collision avoidance, and data dissemination in VANET, which contribute to more efficient and safer transportation systems. Effective clustering strategy remains an active area of research to address the unique challenges posed by the diverse and rapidly changing environments of VANET. Therefore, this article presents a golden jackal optimization-based energy aware clustering scheme (GJO-EACS) approach for VANET. The presented GJO-EACS technique uses a dynamic clustering approach which adapts to the varying network topologies and traffic conditions, intending to extend the network lifetime and improve energy utilization. The results highlight the potential of the GJO-EACS technique to contribute to the sustainable operation of VANETs, making it a valuable contribution to the field of vehicular networking and smart transportation systems. [ABSTRACT FROM AUTHOR]