Systematic Model Research Articles

A Study on the Role of Engineering Materials Innovation in Urban Infrastructure Development in Promoting Economic Growth

Accompanied by the high speed of infrastructure construction in China’s major cities, China’s construction industry has been growing rapidly, and the engineering materials industry in construction has begun to transform from weak to strong. The study constructs a systematic theoretical model of engineering materials innovation in the construction industry to realize a systematic understanding of the engineering materials innovation mechanism. Combined with the economic growth theory, the impact of engineering materials innovation on regional economic growth in major Chinese cities is explored by using the threshold regression model. Through the study, it is found that the current innovation of China’s engineering materials industry is mainly facing two major problems, namely the slowdown of industrial growth rate, and the reduction of fixed asset investment, while the diversification path of engineering materials technological innovation outputs is beginning to diverge, and the diversity and ubiquity indices are gradually increasing, but the growth rate is slowing down. Threshold regression shows that when engineering materials innovation every 1% growth, for regional economic growth (GDP) to promote the role of between 0.099%~0.112%. The research in this paper provides a new perspective and comprehensive explanation for the study of engineering materials innovation-driven economic development, enriches the theoretical study of regional economic growth, and also provides theoretical insights and practical guidance for the government’s policy making and enterprises’ innovation practice.

컨설턴트 역량에 관한 체계적 문헌고찰

The purpose of this study is to analyze previous research on consultant competencies, which has developed from various perspectives, and to suggest directions and implications for future research. Using a systematic literature review method, 42 domestic academic journal articles were selected for analysis, focusing on the publication period, journal type, keywords, and competencies required in different consulting fields. The findings are as follows: First, research on consultant competencies was minimal before 2010 but has increased significantly since. Second, journals show that consultant competencies are needed in diverse fields, with digital technology, talent development, and convergent thinking being key. University-affiliated institutions have contributed to strengthening the theoretical foundation through various methodologies. Third, keyword analysis highlighted relationship-building skills, service quality, and knowledge as essential competencies. Fourth, the analysis identified the need for a systematic competency model, emphasizing problem-solving, communication, customer orientation, change management, and adaptability. Based on these findings, this study highlights the need for systematic research on competencies within the consulting process from a macro perspective, while also emphasizing the importance of expanding research on consultants' intrinsic competencies from a micro perspective. Furthermore, it suggests that the application of methodologies combining various academic perspectives in a convergent approach should be expanded. This study is expected to serve as a foundational resource for future research on consultant competencies.

Systematic Modeling of Risk-Associated Copy Number Alterations in Cancer.

The determination of the cancer prognosis is paramount for patients and medical personnel so that they can devise treatment strategies. Transcriptional-based signatures and subtypes derived from cancer biopsy material have been used in clinical practice for several cancer types to aid in setting the patient prognosis and forming treatment strategies. Other genomic features in cancer biopsies, such as copy number alterations (CNAs), have been underused in clinical practice, and yet they represent a complementary source of molecular information that can add detail to the prognosis, which is supported by recent work in breast, ovarian, and lung cancers. Here, through a systematic strategy, we explored the prognostic power of CNAs in 37 cancer types. In this analysis, we defined two modes of informative features, deep and soft, depending on the number of alleles gained or lost. These informative modes were grouped by amplifications or deletions to form four single-data prognostic models. Finally, the single-data models were summed or combined to generate four additional multidata prognostic models. First, we show that the modes of features are cancer-type dependent, where deep alterations generate better models. Nevertheless, some cancers require soft alterations to generate a feasible model due to the lack of significant deep alterations. Then, we show that the models generated by summing coefficients from amplifications and deletions appear to be more practical for many but not all cancer types. We show that the CNA-derived risk group is independent of other clinical factors. Furthermore, overall, we show that CNA-derived models can define clinically relevant risk groups in 33 of the 37 (90%) cancer types analyzed. Our study highlights the use of CNAs as biomarkers that are potentially clinically relevant to survival in cancer patients.

Stability and dynamics of magnetic skyrmions in FM/AFM heterostructures

Magnetic skyrmions have garnered attention for their potential roles in spintronic applications, such as information carriers in computation, data storage, and nano-oscillators due to their small size, topological stability, and the requirement of small electric currents to manipulate them. Two key challenges in harnessing skyrmions are the stabilization requirement through a strong out-of-plane field, and the skyrmion Hall effect (SkHE). Here, we present a systematic model study of skyrmions in ferromagnetic/antiferromagnetic (FM/AFM) multilayer structures by employing both atomistic Monte Carlo and atomistic spin dynamics simulations. We demonstrate that skyrmions stabilized by exchange bias have superior stability to field-stabilized skyrmions due to the formation of a magnetic imprint within the AFM layer. Additionally, stacking two skyrmion hosting FM layers between two AFM layers suppresses the SkHE and enables the transport of AFM-coupled skyrmions with high velocity in the order of a few km/s. This proposed multilayer configuration could serve as a pathway to overcome existing limitations in the development of skyrmion-based devices, and the insights obtained through this study contribute significantly to the broader understanding of topological spin textures in magnetic materials. Published by the American Physical Society 2024

Global epidemiology of telogen effluvium after the COVID-19 pandemic: A systematic review and modeling study

Global epidemiology of telogen effluvium after the COVID-19 pandemic: A systematic review and modeling study

Systematic analysis and modeling of the FLASH sparing effect as a function of dose and dose rate

Systematic analysis and modeling of the FLASH sparing effect as a function of dose and dose rate

Decline in Atlantic Niño prediction skill in the North American multi-model ensemble

The Atlantic Niño has attracted considerable attention due to its profound climatic impacts. It has been reported that the strength of Atlantic Niño has been weakening since 2000, but it is not clear whether it would lead to a change in Atlantic Niño prediction skill. Here we find a dramatic decline in Atlantic Niño prediction skill since 2000 by evaluating the predictions of the North American Multi-Model Ensemble. The prediction skill decline is mainly associated with a climatic regime shift, which leads to a weakened El Niño-Southern Oscillation (ENSO) teleconnection to the sea surface temperature anomaly dipole mode over the South Atlantic. A systematic model deficiency may amplify the prediction skill decline. This study offers insights for understanding the Atlantic Niño predictability and for improving the simulation and prediction of Atlantic Niño events.

Antibacterial effect of phage cocktails and phage-antibiotic synergy against pathogenic Klebsiella pneumoniae.

The worldwide spread of antimicrobial resistance (AMR) has posed a great challenge to global public health. Phage therapy has become a promising alternative against difficult-to-treat pathogens. One important goal of this study was to optimize the therapeutic efficiency of phage-antibiotic combinations, known as phage-antibiotic synergy (PAS). Through comprehensive analysis of the phenotypic and genotypic characteristics of a large number of CRKp-specific phages, we developed a systematic model for phage cocktail combinations. Crucially, our finding demonstrated that PAS treatments not only enhance the bactericidal effects of colistin and tigecycline against multidrug-resistant (MDR) K. pneumoniae strains in in vitro and in vivo context but also provide a robust response when antibiotics fail. Overall, the optimized PAS therapy demonstrates considerable potential in combating diverse K. pneumoniae pathogens, highlighting its relevance as a strategy to mitigate antibiotic resistance threats effectively.

Estimating natural history of a treatment-eligible type 2 diabetes mellitus population in Scotland using linked primary and secondary care data: Scottish Diabetes Research Network - National Diabetes Dataset

Healthcare commissioners and those making reimbursement decisions require data on the “natural history” of diseases to estimate quantities such as life expectancy and quality of life. However, such data are commonly acquired from unrepresentative sources like trial placebo arms or case series. We used Scottish Diabetes Research Network-National Diabetes Dataset to characterise life expectancy for people with type 2 diabetes mellitus (T2DM). Data from >99% of people with diabetes in Scotland are available from primary and secondary care (including from clinical investigations) and can be linked to hospital admission and death records for research purposes. As part of an MRC-funded (MR/T017112/1) systematic review and modelling study (PROSPERO registration CRD42020184174) building on work funded by the Chief Scientific Office for Scotland (CSO HIPS_17_26), we used these linked data to define a treatment-eligible population for three drug classes — SGLT-2 inhibitors, GLP-1 agonists, and DPP-4 inhibitors. Fitting a Gompertz model, we estimated life expectancy for this ‘treatment-eligible’ group. The treatment-eligible population, compared to the entire Scottish T2DM population, was younger (63.8 (12.1) vs. 66.7 (12.7) year-old), with larger proportions of men (60.6% vs. 56.2%), higher HbA1c levels (67.4 (13.1) vs. 60.2 (15.1) mmol/L), larger proportions prescribed metformin (69.2% vs 55.2%) and higher life expectancy at age 65 (in women 21 vs 19 years and in men 20 vs 18 years). Using linked data to define and characterise treatment-eligible populations is feasible and may impact decision models. These findings will be useful to those making resourcing decisions and future economic analyses.

Quantitative evaluation of mantle flow traction on overlying tectonic plate: linear versus power-law mantle rheology

SUMMARY Sub-plate mantle flow traction (MFT) has been considered as a major driving force for plate motion; however, the force acting on the overlying plate is difficult to constrain. One of the reasons lies in the variable rheological flow laws of mantle rocks, for example, linear versus power-law rheology, applied in previous studies. Here, systematic numerical models are conducted to evaluate MFT under variable rheological, geometrical and kinematic conditions. The results indicate that MFT with power-law rheology is much lower than that with linear rheology under the same mantle/plate velocity contrast. In addition, existence of a lithospheric root in the overlying plate could enhance MFT, where integrated normal force acting on the walls of lithospheric root is much lower than the shear force in a large-scale domain. In the acting domain of several thousand kilometres, MFT with power-law rheology is comparable to the ridge push of about 3×1012 N m−1, whereas that with linear rheology is comparable to the slab pull of about 3×1013 N m−1. The roles of MFT in driving plate motion are further analysed for the Tethyan evolution. It indicates that MFT with power-law rheology could partially support the Wilson cycles experienced in the Tethyan system, whereas that with linear rheology could easily dominate any kinds of plate tectonic evolutions. The quantitative evaluation of MFT in this study clarifies the roles of rheological flow laws on MFT and could help us to better understand the contrasting results in previous numerical studies.

Work domain modeling of human-automation interaction for in-vehicle automation

Automated driving systems are deployed on public roads with little empirical support for the dominant justifications of enhanced safety and enhanced productivity. Furthermore, development of automated driving systems has been piecemeal rather than systematic while research on driver-automation interaction has relied on individual analysis of accidents and on observational studies of driving behavior in a simulator or on the road. In this paper, we apply Work Domain Analysis to develop a more systematic and comprehensive model of automated driving. We use a strategy of layering the driving automation onto the resulting ion-Decomposition Space for manual driving to mimic the existing design strategy of introducing automation to take over driving functions previously the responsibility of the human driver. Our analysis shows that automation does not unequivocally supports dominant driving values. Furthermore, our analysis revealed subtle interdependencies between human and technological functions. We conclude that an ion Decomposition Space offers a systematic view of driver-automation interaction that can suggest new insights for automation design.

Ethical AI in Financial Inclusion: The Role of Algorithmic Fairness on User Satisfaction and Recommendation

This study investigates the impact of artificial intelligence (AI) on financial inclusion satisfaction and recommendation, with a focus on the ethical dimensions and perceived algorithmic fairness. Drawing upon organizational justice theory and the heuristic–systematic model, we examine how algorithm transparency, accountability, and legitimacy influence users’ perceptions of fairness and, subsequently, their satisfaction with and likelihood to recommend AI-driven financial inclusion services. Through a survey-based quantitative analysis of 675 users in China, our results reveal that perceived algorithmic fairness acts as a significant mediating factor between the ethical attributes of AI systems and the user responses. Specifically, higher levels of transparency, accountability, and legitimacy enhance users’ perceptions of fairness, which, in turn, significantly increases both their satisfaction with AI-facilitated financial inclusion services and their likelihood to recommend them. This research contributes to the literature on AI ethics by empirically demonstrating the critical role of transparent, accountable, and legitimate AI practices in fostering positive user outcomes. Moreover, it addresses a significant gap in the understanding of the ethical implications of AI in financial inclusion contexts, offering valuable insights for both researchers and practitioners in this rapidly evolving field.

International trade of green hydrogen, ammonia and methanol: Opportunities of China's subregions

Global initiatives to reduce carbon emissions are stimulating the demand for green hydrogen, ammonia and methanol. By 2023, the total low-carbon hydrogen production capacity from announced projects worldwide is projected to reach 38 million tonnes. China is experiencing a surge in green hydrogen projects, with a growing number targeting international markets in addition to domestic demand. This study delves into a systematic modeling analysis to uncover the pivotal roles that China's subregions can play in shaping the future global trade of green hydrogen, ammonia, and methanol. Utilizing a dynamic programming model, complemented by an array of supply chain models for detailed cost estimations, the study highlights the unique advantages and vast potential of subregional engagement in both domestic and international trade. Furthermore, the research explores the essential infrastructure demands and assesses the impact of renewable energy costs and policy shifts on the evolving trade landscape.

Introducing a systematic and sustainable patient safety oversight function in a U.K. arms-length healthcare body

Background The healthcare landscape is experiencing a profound period of change with accelerating developments in medical research, new treatments, care delivery and the use of innovative technology to meet the challenges of delivering care. These changes offer opportunities but as with all innovation, require careful management to reduce the likelihood of harm and error. Objective The National Institute for Health and Care Excellence (NICE) has set out to introduce an adaptable, sustainable and systematic patient safety oversight group to ensure that patient safety is embedded at an organisation level supporting the healthcare system to overcome challenges and deliver high quality care, safely implementing the latest innovations in a timely manner. Method The patient safety oversight group was established through a scoping exercise, led by the senior responsible officer and a small project team. The article sets out the methods used, including extensive internal engagement through structured interviews with senior leadership teams, purposive sampling, data synthesis and reporting. An overview of the group is given and considerations for promoting patient safety information flows and optimising the use of safety signal intelligence are made. Conclusion Through reflection on changing needs within and surrounding the organisation, along with patient safety and complexity literature, NICE has developed an adaptable, systematic and sustainable model for managing patient safety. In addition to generating learning within NICE, the process highlights opportunities for improvement within the patient safety system. Result NICE has developed an adaptable, systematic and sustainable model for managing patient safety.

Unlocking bioremediation potential: harnessing an indigenous bacterial consortium from effluent treatment plants for industrial wastewater treatment

Common Effluent Treatment Plant (CETP) wastewater poses significant environmental and health risks, necessitating advanced treatment technologies to meet discharge standards. This study focuses on the collection and characterisation of wastewater from CETP Vatva, Ahmedabad, to evaluate physicochemical parameters heavy metal concentrations, and identify indigenous bacterial species. Using Taguchi’s systematic orthogonal array, an effective indigenous bacterial consortium (EIBC) was created for bioreactor-based CETP wastewater treatment. The 16S rDNA analysis revealed the presence of various bacterial strains, including the newly reclassified bacteria Stutzerimonas stutzeri. The analysis of the SI sample indicated substantial reductions in the concentrations of total dissolved solids (1090 mg L−1), biological oxygen demand (28 mg l−1), chemical oxygen demand (180 mg l−1), and total phosphorus (1.4 mg l−1) compared to their initial values of 7504 mg l−1, 29 6 mg l−1, 58 8 mg l−1, and 3.04 mg l−1, respectively, with a similar trend observed in samples SII and SIII. While turbidity was significantly reduced from initial concentrations ranging between 36–42 NTU to 4 NTU in SI, 5 NTU in SII, and 3 NTU in SIII samples, resulting in clear water, odour remained a persistent concern throughout the study. Heavy metal concentrations were within permissible discharge limits, with notable removal rates for Cu, Fe, and Cd. The study concludes that integrating systematic design modelling with bioreactor-based remediation effectively mitigates water pollution and safeguards human well-being.

A systematic multi-layer cognitive model for intelligent machine tool

A systematic multi-layer cognitive model for intelligent machine tool

Referring wisely: knowing when and how to make subspecialty consultations in hospital medicine.

Subspecialty consultations are becoming highly prevalent in hospital medicine, due to an ageing population with multimorbid conditions and increasingly complex care needs, as well as medicolegal fears that lead to widespread defensive medical practices. Although timely subspecialty consultations in the appropriate clinical context have been found to improve clinical outcomes, there remains a significant proportion of specialty referrals in hospital medicine which are inappropriate, excessive, or do not add value to patient care. In this article, we sought to provide an overview of the common problems pertaining to excessive quantity and suboptimal quality of inpatient subspecialty consultations made in real-world practice and highlight their implications for healthcare financing and patient care. In addition, we discuss the underlying contributing factors that predispose to inappropriate use of the specialist referral system. Finally, we offer a practical, multitiered approach to help rationalize subspecialty consultations, through (i) a systematic model ('WISE' template) for individual referral-making, (ii) development of standardized healthcare institutional referral guidelines with routine clinical audits for quality control, (iii) adopting an integrated generalist care model, and (iv) incorporating training on effective referral-making in medical education.

Multi-omics combined to investigate potential druggable therapeutic targets for stroke: A systematic Mendelian randomization study and transcriptome verification

ObjectiveStroke is a highly prevalent and disabling disease whose disease mechanisms are not fully understood. The discovery of disease-associated proteins with genetic evidence of pathogenicity provides an opportunity to identify new therapeutic targets. MethodWe examined the observed and causal associations of thousands of plasma and inflammatory proteins that were measured using affinity-based proteomic assays. First, we pooled >3000 relevant proteins using a fixed-effects meta-analysis of 2 population-based studies involving 48,383 participants, then investigated the causal effects of stroke and its subtype-associated proteins by forward Mendelian randomization using cis-protein quantitative locus genetic tools identified from genome-wide association studies of these >48,000 individuals. To improve the accuracy of causal estimation, we implemented a systematic Mendelian randomization model that accounts for cascading imbalances between instruments and tested the robustness of causal estimation through multi-method analyses. To further validate the hypothesis that ginsenoside Rg1 monomer acts on the five protein targets screened for drug-targeted regulation, we conducted a comparative analysis of the mRNA (gene) expression levels of a limited number of genes in the brain tissues of different groups of SD rats. The druggability of the candidate proteins was investigated and the mechanism of action and potential targeting side effects were explored by Phenome-wide MR. ResultsSix circulating proteins were identified to have a significant genetic association with stroke (PFDR < 0.05). For example, in patients with cardioembolic stroke, higher genetically predicted APRT was associated with a lower risk of cardioembolic stroke (ORivw [95 % CI] = 0.641 [0.517, 0.795]; P = 5.25 × 10−5, ORSMR [95 % CI] = 0.572, [0.397, 0.825], PSMR = 0.003). Mediation analyses suggested that atrial fibrillation, angina pectoris, and heart failure may mediate the association of CD40L, LIFR, and UPA with stroke. Molecular docking revealed promising interactions between the identified proteins and glycosides. Transcriptomic sequencing in animal models indicated that ginsenoside Rg1 may act through APRT, IL15RA, and VSIR pathways, with APRT showing significant variability in mRNA sequencing expression. Phenome-wide MR of the six target proteins showed an overwhelming predominance of PFDR > 0.05, indicating less toxicity. ConclusionsThe present study provides genetic evidence to support the potential efficacy of targeting the three druggable protein targets for the treatment of stroke. This is achieved by triangulating population genomic and proteomic data. Furthermore, the study validates the pathway mechanisms by which APRT, IL15RA, and VSIR dock ginsenoside Rg1 in animal models. This will help to prioritize stroke drug development.

Manipulating and measuring variation in deep neural network (DNN) representations of objects

We explore how DNNs can be used to develop a computational understanding of individual differences in high-level visual cognition given their ability to generate rich meaningful object representations informed by their architecture, experience, and training protocols. As a first step to quantifying individual differences in DNN representations, we systematically explored the robustness of a variety of representational similarity measures: Representational Similarity Analysis (RSA), Centered Kernel Alignment (CKA), and Projection-Weighted Canonical Correlation Analysis (PWCCA), with an eye to how these measures are used in cognitive science, cognitive neuroscience, and vision science. To manipulate object representations, we next created a large set of models varying in random initial weights and random training image order, training image frequencies, training category frequencies, and model size and architecture and measured the representational variation caused by each manipulation. We examined both small (All-CNN-C) and commonly-used large (VGG and ResNet) DNN architectures. To provide a comparison for the magnitude of representational differences, we established a baseline based on the representational variation caused by image-augmentation techniques used to train those DNNs. We found that variation in model randomization and model size never exceeded baseline. By contrast, differences in training image frequency and training category frequencies caused representational variation that exceeded baseline, with training category frequency manipulations exceeding baseline earlier in the networks. These findings provide insights into the magnitude of representational variations that can be expected with a range of manipulations and provide a springboard for further exploration of systematic model variations aimed at modeling individual differences in high-level visual cognition.

Enhancing Agriculture through AloT and Data Analytics Middleware

Middleware in Agriculture delves into the data analytics role of systematic Artificial Intelligence of Things (AIoT) in convergence agriculture with smart farming. AIoT devices, remote diagnostic data analytics platforms, data analytics, formal recognition, and vision learning have generated both the amount and nature of work in rural areas. The reason for the developing changes in the global population by age is the bias in the distribution of food resources, as well as changes in climate change and the soil condition of compost. Data scientists are soon attempting to dive convergence Internet of Things (IoT) advances in savvy cultivating to support ranchers in producing better seeds, crop assurance, and manures utilizing AIoT convergence technology[1]. This distinguishes consumer-provider-administrator as a service subscriber roleon the platform, which goes for earning the nation's economy and the profitability of ranchers. The central regions where AIoT begins to emerge are agricultural robots, scrutiny, and soil and yield observation. While middleware technology for data analysis is applied, there is a great advantage of analyzing regional observation data at a real-time level. Based on this analysis method, it will spread to high-tech industries connected to production, processing, and consumption, which are subdivided areas of each precision agriculture. In this sense, ranchers are utilizing sensors for data analysis and soil information in a detailed way to gather a model that the executives dynamic platforms might use for further examination. Through an outline of real-time data analytics AIoT applications in the farming convergence industry, this paper tries a trust data basement forecast to the farming protocols[2]. It starts with a roadmap to the AIoT and an investigation of real-time data strategies utilized in the horticultural industry data analytics using Universal Middleware, service platforms, vision learning, and dynamic statistics. This study advances a dynamic platform examination of the literature on data resources and how AIoT is utilized in farming convergence. This study looked at the unstructured systems of modern agriculture, divided these processes into devices and services, and presented a systematic formalization model. It should contribute to the integration of segmented data connectivity in the AIoT field. The enhanced research model contributes to the discovery of numerous untapped additional services between devices and services.