Scaling Solutions Research Articles

Challenges and solutions of scaling up

Challenges and solutions of scaling up

Re-evaluating the factor structure of the Tolerance of Ambiguity of Medical Students And Doctors (TAMSAD) scale in newly qualified doctors

Background Ambiguity and uncertainty are inherent within the practice of medicine. While theory suggests the construct may be multidimensional, scales such as the Tolerance of Ambiguity of Medical Students And Doctors (TAMSAD) act unidimensionally, at least in a local population. Therefore, the dimensionality of the Tolerance of Ambiguity (ToA) construct remains unclear. This study aims to explore the dimensionality of ToA in early postgraduate doctors using the TAMSAD scale in a UK national sample and consider the implications of this dimensionality for theory and practice. Methods We used data from 428 respondents in a national research project examining the experiences of newly qualified doctors in the UK (2020). We undertook an exploratory factor analysis (extracting one-factor to six-factor solutions) of the 29-item TAMSAD scale and compared findings to an existing integrative model of uncertainty tolerance. Results The analysis suggested that the ToA construct is multidimensional. The three-factor model and five-factor model provided clinically interpretable factors and had different merits. It appears that having an affinity for complexity is not simply the opposite of experiencing discomfort from uncertainty, and that a professional’s epistemological beliefs about the nature of medicine may influence their ToA. Conclusions These findings support an extension to a key integrative model of uncertainty tolerance, and support development of interventions to increase ToA in doctors. For example, through encouraging increased reflection on an individual’s own epistemological beliefs about medicine and the role of doctors. The potential impact of such interventions can be evaluated using scales such as the TAMSAD.

Patient-Centered Innovation: Involving Patients in Open Social Innovation.

Involving patients in the health-care delivery innovation has many benefits. Open social innovation (OSI) presents a fitting lens to examine and advance patient engagement in innovation. OSI offers a participatory approach to innovation, in which diverse groups of participants collaboratively generate ideas and scale solutions on complex social challenges. This study: (1) describes a pilot application of OSI, in which individuals serving on a hospital's patients and family advisory councils (PFACs) were invited to participate in an innovation contest; and (2) explores the extent to which patients' beliefs about their role in innovation relate to their participation in the contest. We conducted an innovation contest that invited PFAC members to share ideas that would improve patient experiences and then vote on and select the ideas that they wanted to see move forward. We measured patients' beliefs about their role in innovation in a survey before the contest. Twenty individuals submitted 27 ideas. Patients who expressed preference for more involvement in innovation were more likely to participate. Using OSI may help expand patient engagement in innovation, particularly among those who want to be more involved but do not feel authorized to voice ideas in traditional advisory committees. OSI spurred collaboration among patients, clinicians, quality improvement staff, hospital administrators, and other stakeholders in idea generation, elaboration, and implementation. More experimentation and research are needed to understand how OSI can be leveraged to capture patients' voice and incorporate them in care delivery innovation.

Veritas: Layer-2 Scaling Solution for Decentralized Oracles on Ethereum Blockchain with Reputation and Real-Time Considerations

Blockchainsand smart contracts are pivotal in transforming interactions between systems and individuals, offering secure, immutable, and transparent trust-building mechanisms without central oversight. However, Smart Contracts face limitations due to their reliance on blockchain-contained data, a gap addressed by ’Oracles’. These bridges to external data sources introduce the ’Oracle problem’, where maintaining blockchain-like security and transparency becomes vital to prevent data integrity issues. This paper presents Veritas, a novel decentralized oracle system leveraging a layer-2 scaling solution, enhancing smart contracts’ efficiency and security on Ethereum blockchains. The proposed architecture, explored through simulation and experimental analyses, significantly reduces operational costs while maintaining robust security protocols. An innovative node selection process is also introduced to minimize the risk of malicious data entry, thereby reinforcing network security. Veritas offers a solution to the Oracle problem by aligning with blockchain principles of security and transparency, and demonstrates advancements in reducing operational costs and bolstering network integrity. While the study provides a promising direction, it also highlights potential areas for further exploration in blockchain technology and oracle system optimization.

Investigation of 2020–2022 extreme floods and droughts in Sichuan Province of China based on joint inversion of GNSS and GRACE/GFO data

The Global Navigation Satellite System (GNSS) surface deformation measurement and Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-on (GFO) satellite gravimetry provide complementary strengths in estimating terrestrial water storage (TWS) changes, which helps quantify the evolutions of extreme floods and droughts. We implemented a joint inversion framework that only incorporates the contributions of GNSS and GRACE/GFO to recover the TWS changes for monitoring extreme floods and droughts in the Sichuan Province of China (SCP). The performance of TWS changes from the joint inversion is evaluated through closed-loop simulation and independent hydrometeorological data (i.e., precipitation (P), evapotranspiration (ET), and runoff (R)) based on the water balance equation (named P-ET-R) in the spatio-temporal domains. The simulation analysis results show that joint inversion presents better performance in recovering TWS changes than GNSS-only and GRACE/GFO-only solutions in the spatio-temporal scales. The measured joint inversion results reveal more spatial details of TWS changes than GNSS-only and GRACE/GFO-only solutions and present better correlations with GLDAS- and ERA5-derived P-ET-R (0.77 vs. 0.79) than GNSS-only (0.49 vs. 0.54) and GRACE/GFO-only (0.61 vs. 0.68) in the spatial domain. Meanwhile, the joint inversion results effectively characterize the spatio-temporal dynamics of extreme floods and droughts in SCP during 2020–2022, the average TWS surpluses are 33.61 and 32.86 mm in 2020 and 2022 flood events and the average TWS deficits are −36.78 and −63.87 mm in 2021 and 2022 drought events. In addition, the drought propagation time generally presents an increasing trend from southwestern to northeastern SCP. Furthermore, the average and minimum recovery time of drought and flood in the Sichuan basin is longer than those in the western mountains during 2020–2022. The proposed approach provides an effective estimation strategy for recovering reliable TWS changes for the investigation of extreme floods and droughts in regions where GNSS stations are distributed.

Contextualizing and generalizing drivers and barriers of urban living labs for climate resilience

AbstractUrban Living Labs are open innovation ecosystems that integrate research and innovation activities within urban communities. However, while solutions co‐created and tested in the Urban Living Labs must be contextualized and tailored to each city's uniqueness, broader impact requires generalization and systematic replication across geographical, institutional, and sectoral boundaries. This article examines nine Living Labs in European coastal cities, identifying several barriers and drivers for mainstreaming and upscaling solutions to increase climate resilience through the Living Lab Integrative Process. Our analysis focuses on three main categories. First, social and cultural aspects highlighted include stakeholder engagement and awareness, communication, and dissemination. Second, we assess institutional and political aspects, such as silos, bureaucracy, and resources. Last, we investigate technical factors as knowledge and experience, technical and internal capacity, data availability and accessibility, climate‐related policies and actions, and long‐term perspective. The results suggest that while some barriers and drivers are common across the cases, providing generalizable patterns, there are also specific differences requiring tailored solutions at the local scale. Nonetheless, the diversity in drivers indicates the potential for sharing knowledge across cases to translate, embed, and scale solutions, enhancing the transition toward climate resilience. Learning and innovation in real‐life contexts are fundamental in the Living Lab approach, and our findings demonstrate that cross‐case learning can enhance an iterative process of contextualizing and generalizing innovative climate solutions.

Unmanned aerial vehicle location systems. Antenna Arrays

Radars remain the only all–weather location systems, which is especially important in difficult conditions of their use. Antenna array technologies remain critical for the development of dynamic areas of technology and, first of all, UAVs location systems. The development of location systems and antenna arrays for UAVs has been going on for a long time. Radars and antenna arrays installed on medium– and large–class UAVs differ little from airplane and helicopter systems. Among UAVs, the maximum number of UAVs used are UAVs of minimal size, weight and cost. Due to their high maneuverability, minimal visibility and a large number of tasks to be solved. Therefore, radars are subject to minimum size, weight, dimensions and consumption requirements at maximum range. The widespread use of small UAVs has led to the fact that their radars and antenna arrays are on a par with automotive ones, when the possibilities for their placement are significantly limited, even more so than in cars. For the latter, the problem of miniaturization appeared earlier, but not as severely as in UAVs. Antenna arrays are subject to requirements for minimizing dimensions, weight and consumption. Nanotechnology provides this. It's safe to say that the time has come for large–scale solutions to complex problems in antenna systems. Depending on the type of a UAV, it is possible to use a number of methods for constructing antenna arrays: known and proven – linear and fractal; the most modern – metastructures; widely used – tiled; new: flexible and three–dimensional printed; a combination of several methods. Currently, the most attractive areas for the development of antenna arrays for UAVs are: scaling – growing a large number of similar structures to create large apertures; spacing antenna arrays across the surface of the carrier for viewing both forward, sideways and backward, as well as down and up. The development of silicon tiled antenna arrays has led to the realization of very high operating frequencies (90–100 GHz) on the most affordable process technologies (130–180 nm), and the antenna array is fabricated as a chip. The optimal option for constructing antenna arrays is to ensure the reception, conversion and preprocessing of input signals directly in the antenna system in real time, unloading the radar for solving problems that require powerful processing. Post–processing allows you to obtain a more accurate solution to the problem. The combination of pre–processing and post–processing, supported by advanced software and powerful artificial intelligence, expands the capabilities and improves the parameters of the location system. It is important that artificial intelligence ensures the autonomous movement of a UAV, although its use requires large consumption and memory resources. The integrated location system absorbed for the most part the capabilities of a large stationary radar. At the same time, minimum dimensions, weight and consumption are ensured to solve the necessary modern problems. Several radars with their own antenna arrays can be installed on a UAV, which operate in the internal network, complementing each other.

Innovative Strategies for Achieving Sustainable Development Goals Amidst Escalating Global Environmental and Social Challenges

In the face of escalating global environmental and social challenges, achieving the Sustainable Development Goals (SDGs) necessitates innovative strategies. This article conducts a comprehensive literature review to explore and synthesize diverse approaches towards sustainable development in an increasingly complex global landscape. By examining a broad range of scholarly publications, the study identifies key trends, challenges, and innovative solutions that have emerged in the pursuit of the SDGs. The analysis focuses on the integration of technological advancements, policy innovations, and community-driven approaches in addressing environmental degradation, social inequality, and economic challenges. The findings reveal that while progress has been made, significant gaps remain in effectively implementing and scaling sustainable solutions. The study highlights the critical role of interdisciplinary collaboration, policy integration, and inclusive governance in overcoming these challenges. It also emphasizes the need for aligning economic growth with environmental and social sustainability. The article concludes by proposing a framework for effective SDG implementation, calling for a holistic approach that integrates environmental conservation, social equity, and economic viability. This study contributes to the ongoing discourse on sustainable development, offering insights for policymakers, practitioners, and researchers in developing comprehensive strategies for achieving the SDGs.

Bitcoin Layer Two Scaling Solutions: Lightening Payment Channels Network Comprehensive Review, Mechanisms, Challenges, Open Issues and Future Research Directions

Blockchain technology era is triggered due to current advancement in the decentralized paradigms via facilitating secure collaboration among untrusted entities consequently superseding the necessity for the trusted third parties’ existence. Notwithstanding its potential, blockchain scalability faces severe limitations such as low throughput, high fees, and confirmation time latency. Several scaling solutions have been proposed, including layer one solutions that substantially require fundamentally amending blockchain underlying infrastructure resulted in further scalability complications. Layer two solutions, specifically Lightning Network, is a successful cryptographic layer built atop of the Bitcoin blockchain that aims to alleviate these implications by deporting transaction processing outside blockchain while ensuring security and consensus inherited from blockchain without compromising its infrastructure. Payment channels form the core of the lightning network, facilitating fast and secure transactions between participants. However, the network encounters substantial obstacles associated with transaction amounts that exceed the current channel capacity, leading to payment failures and limiting its effectiveness. Payment Channel Networks (PCN) have been risen as an evolved replacement that solves payment channel’s issues, seeking to enhance transaction throughput and reduce confirmation delays by solving limited capacities shortages. This paper thoroughly examines PCN efficiency impacting factors that hinder its current adoption growth and avoiding its widespread employment. Additionally, existing solutions are reviewed and categorized based on the PCN building architecture. The research concludes by outlining potential research avenues and future directions to improve the efficiency and practicality of the Lightning Network PCN. This research contribution aids the advent of blockchain layer two technology and participates in its integration into real-world applications.

No accelerating scaling cosmologies at string tree level?

We investigate the scalar potential in the parametric regime of string moduli space where string loops and higher derivative corrections to 10d supergravity can be ignored and where the fields are rolling down exponential slopes leading to powerlaw FLRW cosmologies, a.k.a. scaling solutions. We argue that these scaling solutions, if describing an accelerating expansion, are likely to be perturbatively unstable, for reasons identical to the perturbative instabilities in tree-level dS vacua.

Multi-Scale-Matching neural networks for thin plate bending problem

Physics-informed neural networks are a useful machine learning method for solving differential equations, but encounter challenges in effectively learning thin boundary layers within singular perturbation problems. To resolve this issue, multi-scale-matching neural networks are proposed to solve the singular perturbation problems. Inspired by matched asymptotic expansions, the solution is decomposed into inner solutions for small scales and outer solutions for large scales, corresponding to boundary layers and outer regions, respectively. Moreover, to conform neural networks, we introduce exponential stretched variables in the boundary layers to avoid semi-infinite region problems. Numerical results for the thin plate problem validate the proposed method.

Гидравлические исследования поливного модуля системы комбинированного орошения

Purpose: to state hydraulic characteristics and determine priority directions for improving the irrigation module design for combined drip and micro-sprinkler irrigation. Materials and methods. The study is based on the joint use of theoretical methods – a well-known mathematical model for calculating the hydraulic characteristics of the irrigation module and experiment, which allows assessing the problem state and scale solutions in any necessary proportions. Results. According to calculations, the uniformity of watering is maintained at an acceptable level at a lower head level in the range of 1.56–1.68 atm when installing six microsprinklers on a drip line. When installing eight sprinklers, the water head along the length of the drip pipeline decreased to an unacceptable 1.39–1.40 atm. Experimental studies have shown that there is only an option with two sprinklers installed on the pipeline above the level of 1.5 atm. Already with the installation of four sprinklers, the water head along the length of the pipeline decreased to 1.45–1.47 atm. At the same time, the variation in the actual microsprinkler performance reached 16 % or more. The discrepancy between experimental and model data is explained by the fact that the calculation model does not fully take into account local resistances that arise when connecting sprinklers. Conclusions. Two main reasons of the increased uneven distribution of irrigation water over the irrigated area have been identified. The first reason is a significant increase in water flow in the irrigation pipeline when emitters and sprinklers work together. The solution to this problem is to develop structures that make it possible to separate the water flow through emitters and through sprinklers in time. The second reason is an increase in local resistance in the water diversion units to the sprinklers. The solution to this problem is to develop special water diversion structures that would be characterized by minimal local resistance.

Unveiling Ethereum’s Future: LSTM-Based Price Prediction and a Systematic Blockchain Analysis

Cryptocurrency has emerged as a revolutionary innovation that has been replacing traditional finances and enthralling the worldwide technology landscape. This has gained a lot of popularity worldwide for its potential to enable peer-to-peer transactions and offer opportunities for investment and novelty. Nevertheless, it gives rise to issues concerning regulatory adherence, instability, and security apprehensions, turning them into a topic of continuous evaluation and investigation within the fields of finance and technology. This research paper presents a comprehensive exploration of the historical evolution of “Ethereum” as one of the leading blockchain platforms, with a primary focus on price prediction using a long-short-term memory (LSTM) machine learning model. The study includes various critical aspects of Ethereum, starting from its historical evolution to its potential future scope in scaling solutions and payments, and also covering the insights of Ethereum’s tokenomics, utility, and beyond. In addition, the methodology involves using the LSTM model to analyze data from Ethereum. The accuracy of price predictions is assessed by evaluating error metrics and further improved by visualizing the data through graphs that show indicators. This paper gives an in-depth perspective for anyone who is seeking a holistic understanding of cryptocurrencies, mainly concentrated on Ethereum, and also provides valuable guidance to investors, developers, and enthusiasts, encouraging them to make knowledgeable decisions in the ever-changing blockchain ecosystem.

Longitudinal Measurement Invariance of the DSM-5 Anxiety and Depression Severity Measures

Abstract: There is scarce evidence on the psychometric properties of the Severity Measures (SMs) of the DSM-5 longitudinally, especially among Spanish young adults. Thus, the main objective of the present study was to analyze the Longitudinal Measurement Invariance (LMI) of the SMs for Depression, Generalized Anxiety, Social Anxiety, Panic, Separation Anxiety, Agoraphobia, and Specific Phobia scales in four assessment waves at 6-month intervals. A total of 564 Spanish young adults ( Mage = 21.5 years; 67.9% females) participated in the study. Confirmatory Factor Analyses (CFAs) were carried out at each wave. Once the adequacy of the factor structure of the models was confirmed, three levels of LMI were examined (i.e., configural, metric, and scalar). Finally, we examined internal consistency (i.e., Cronbach’s α and ω) across assessment waves and gender groups. CFAs analyses supported a two-factor solution for the specific phobia scale and a one-factor solution for the rest of the SMs in baseline models. Evidence of LMI at the configural, metric, and scalar levels was observed for all seven SMs. Reliability indices ranged from .73 to .96. These results suggest that the DSM-5 SMs are useful assessment tools for longitudinal and follow-up studies in Spanish young adults.

THE EVOLUTION OF SMART CONTRACT PLATFORMS: A LOOK AT CURRENT TRENDS AND FUTURE DIRECTIONS

Blockchain-based smart contracts are self-running computer programs that can automate a variety of commercial activities. Currently, the majority of these decentralized applications are developed using smart contract platforms like Polkadot, Cardano, and Ethereum. In addition to analyzing current technology developments and prospective future applications, this article provides a historical review of smart contract platforms. The study emphasizes the significance of smart contract platforms for supporting blockchain-based applications and enabling decentralized finance (DeFi). It also looks at the emergence of layer-2 scaling solutions, the introduction of non-fungible tokens (NFTs), and the growing need of interoperability among different smart contract platforms. The article also looks at the potential for multi-chain smart contracts, the effects of quantum computing, the integration of AI and ML technologies with smart contract platforms, and the potential for smart contract platforms to support decentralized autonomous organizations (DAOs). The difficulties of expanding smart contract platforms, the requirement for uniformity in the creation of smart contracts, and the potential for smart contract platforms to revolutionize sectors like healthcare, real estate, and supply chain management are also covered. The paper emphasizes the significance of ongoing innovation and development in smart contract platforms for the expansion of the blockchain ecosystem as it draws to a close.

EFFICIENCY OF ACTIVATED NANO-HETEROJUNCTIONS ON SILICON AND SILICON CARBIDE SUBSTRATES

The concept, model, and examples of activated nanoscale heterojunctions on special silicon carbide and silicon substrates to ensure maximum power with combined geometric and quantitative scaling of semiconductor energy converter chips are considered. The issue of maximum efficiency of energy conversion and efficiency of separation of electron-hole pairs is investigated. A variant of optimizing the scaling solution is implemented by heterojunctions with variations in the sequences of layers with an increase in the concentration and direction of movement of nonequilibrium carriers for further increasing voltage conversion with charge pumping. Numerical simulation was performed to test the model with thin layers of GaN, GaP on SiC, SiC/Si. For the first time, definitions of activation of the heterojunction and activated nanoheterojunction as a fundamental structure are proposed. The accuracy of the proposed models is compared with the accuracy of known models, and it is shown that the results obtained are better than some solutions of these models known in the literature.

Auto Scaling Solutions for Cloud Applications

Auto Scaling Solutions for Cloud Applications

Asymptotic Matching between Weather and Climate Models

Abstract The Deep Numerical Analysis for Climate (DNA-Climate) is a pilot project to develop an Earth system model on a kilometer-scale horizontal mesh. The acronym “DNA” is based on the analogies between the hierarchical structures of atmospheric phenomena and living organisms. The multiscale structure of clouds and circulations may be analogous to the multiscale structure of cells and organs organized according to the blueprint, deoxyribonucleic acid (DNA). Whereas global cloud-resolving models (CRMs) can produce better solutions on shorter time scales that are decisively governed by the initial conditions, global climate models (GCMs) may generate reliable solutions on longer time scales that are largely determined to balance energy inputs and outputs. Our challenge is to build a physically valid model that consistently bridges the shorter- and longer-time-scale solutions in the intermediate time scales. Research topics of DNA-Climate are configured in consideration of the structural similarity between the climate modeling and the technique of matched asymptotic expansions in mathematics. The central question is whether a single modeling framework using only either global CRM or GCM will work adequately at all time scales of climate, or whether a multiscale modeling framework combining several models, of which each is only valid for limited time scales, will be needed. A multiscale modeling is an attractive framework for advancing climate modeling and would be an intriguing topic to be studied in parallel with global CRMs and GCMs.

Open social innovation: taking stock and moving forward*

ABSTRACT Open forms of organising innovation bear great potential to address societal challenges, such as the climate crisis. Existing approaches to open social innovation (OSI) draw on a corporate and organisation-centric open innovation model as a blueprint for addressing social and ecological problems. However, such problems are ‘wicked’ and ‘complex’ in nature and thus require concerted efforts from a diverse set of stakeholders, including businesses, government agencies, non-profits and communities. Based on a review of the open-, user- and social-innovation literature, this essay traces the evolution from an organisation-centric view (OSI 1.0) to a multi-stakeholder, cross-sectoral perspective (OSI 2.0). More specifically, we understand OSI as a concerted effort undertaken by multiple stakeholders from various sectors throughout the social innovation process, from diagnosing societal challenges, to developing ideas for how to solve problems, creating solutions, effectively scaling solutions and generating impact. We sharpen the terminology for OSI 2.0 and specify design dimensions for the effective orchestration of collaboration and coordination, and outline key areas for future research. Our objective is to foster dialogue between open- and user-innovation and social-innovation research.

Challenges and Experiences in Designing Interpretable KPI-diagnostics for Cloud Applications

Automated root cause analysis of performance problems in modern cloud computing infrastructures is of a high technology value in the self-driving context. Those systems are evolved into large scale and complex solutions which are core for running most of today’s business applications. Hence, cloud management providers realize their mission through a “total” monitoring of data center flows thus enabling a full visibility into the cloud. Appropriate machine learning methods and software products rely on such observation data for real-time identification and remediation of potential sources of performance degradations in cloud operations to minimize their impacts. We describe the existing technology challenges and our experiences while working on designing problem root cause analysis mechanisms which are automatic, application agnostic, and, at the same time, interpretable for human operators to gain their trust. The paper focuses on diagnosis of cloud ecosystems through their Key Performance Indicators (KPI). Those indicators are utilized to build automatically labeled data sets and train explainable AI models for identifying conditions and processes “responsible” for misbehaviors. Our experiments on a large time series data set from a cloud application demonstrate that those approaches are effective in obtaining models that explain unacceptable KPI behaviors and localize sources of issues.