Refinement Of Model Research Articles

A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures.

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns.

Towards sustainable coastal management: a hybrid model for vulnerability and risk assessment

This paper presents the development of a Hybrid Model (HM) integrated with a Bayesian Network (BN) for comprehensive coastal vulnerability and risk assessment, with a focus on Konyaaltı Beach, Antalya, Turkey. The HM incorporates critical environmental parameters such as wind, waves, currents, and sediment transport to simulate conditions at vulnerable coastal areas and perform risk assessments for storm effects, flooding, and erosion. The model includes submodules for predicting coastal storms, quantifying sediment transport rates, assessing tsunami inundation severity, and categorizing storms based on beach typologies. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized for significant wave height predictions, enhancing the model's accuracy. The integration of hydrodynamic modeling, Bayesian networks, and ANFIS offers a robust framework for assessing coastal vulnerability and informing sustainable management practices. The study's results highlight the necessity for integrated risk management strategies, including adaptive infrastructure design, zoning and land use regulations, ecosystem-based management, and continuous monitoring and model refinement to enhance coastal resilience against dynamic environmental forces. This research provides valuable insights for mitigating the impacts of hazards on urban developments, contributing to the advancement of sustainable coastal management.

An outcome-driven threshold for pulse pressure amplification

Pulse pressure amplification (PPA) is the brachial-to-aortic pulse pressure ratio and decreases with age and cardiovascular risk factors. This individual-participant meta-analysis of population studies aimed to define an outcome-driven threshold for PPA. Incidence rates and standardized multivariable-adjusted hazard ratios (HRs) of cardiovascular and coronary endpoints associated with PPA, as assessed by the SphygmoCor software, were evaluated in the International Database of Central Arterial Properties for Risk Stratification (n = 5608). Model refinement was assessed by the integrated discrimination (IDI) and net reclassification (NRI) improvement. Age ranged from 30 to 96 years (median 53.6). Over 4.1 years (median), 255 and 109 participants experienced a cardiovascular or coronary endpoint. In a randomly defined discovery subset of 3945 individuals, the rounded risk-carrying PPA thresholds converged at 1.3. The HRs for cardiovascular and coronary endpoints contrasting PPA < 1.3 vs ≥1.3 were 1.54 (95% confidence interval [CI]: 1.00–2.36) and 2.45 (CI: 1.20–5.01), respectively. Models were well calibrated, findings were replicated in the remaining 1663 individuals analyzed as test dataset, and NRI was significant for both endpoints. The HRs associating cardiovascular and coronary endpoints per PPA threshold in individuals <60 vs ≥60 years were 3.86 vs 1.19 and 6.21 vs 1.77, respectively. The proportion of high-risk women (PPA < 1.3) was higher at younger age (<60 vs ≥60 years: 67.7% vs 61.5%; P < 0.001). In conclusion, over and beyond common risk factors, a brachial-to-central PP ratio of <1.3 is a forerunner of cardiovascular coronary complications and is an underestimated risk factor in women aged 30–60 years. Our study supports pulse wave analysis for risk stratification.

Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference.

Converting cryo-electron microscopy (cryo-EM) data into high-quality structural models is a challenging problem of outstanding importance. Current refinement methods often generate unbalanced models in which physico-chemical quality is sacrificed for excellent fit to the data. Furthermore, these techniques struggle to represent the conformational heterogeneity averaged out in low-resolution regions of density maps. Here we introduce EMMIVox, a Bayesian inference approach to determine single-structure models as well as structural ensembles from cryo-EM maps. EMMIVox automatically balances experimental information with accurate physico-chemical models of the system and the surrounding environment, including waters, lipids, and ions. Explicit treatment of data correlation and noise as well as inference of accurate B-factors enable determination of structural models and ensembles with both excellent fit to the data and high stereochemical quality, thus outperforming state-of-the-art refinement techniques. EMMIVox represents a flexible approach to determine high-quality structural models that will contribute to advancing our understanding of the molecular mechanisms underlying biological functions.

Hospital personnel perspectives on factors influencing acute care patient outcomes: a qualitative approach to model refinement

BackgroundHealth systems have long been interested in the best practices for staffing in the acute care setting. Studies on staffing often focus on registered nurses and nurse-to-patient staffing ratios. There were fewer studies on the relationship between interprofessional team members or contextual factors such as hospital and community characteristics and patient outcomes. This qualitative study aimed to refine an explanatory model by soliciting hospital personnel feedback on staffing and patient outcomes.MethodsWe conducted a qualitative study using semi-structured interviews and thematic analysis to understand hospital personnel’s perspectives and experiences of factors that affect acute care inpatient outcomes. Interviews were conducted in 2022 with 38 hospital personnel representing 19 hospitals across Washington state in the United States of America.ResultsFindings support a model of characteristics impacting patient outcomes to include the complex and interconnected relationships between community, hospital, patient, and staffing characteristics. Within the model, patient characteristics were positioned into hospital characteristics, and in turn these were positioned within community characteristics to highlight the importance of setting and context when evaluating outcomes. Together, these factors influenced both staff characteristics and patient outcomes, but these two categories also share a direct relationship.ConclusionFindings can be applied to hospitals and health systems in a variety of contexts to examine how external factors such as community resource availability impact care delivery. Future research should expand on this work with specific attention to how staffing changes and interprofessional team composition can improve patient outcomes.

A framework to analyse the probability of accidental hull girder failure considering advanced corrosion degradation for risk-based ship design

Ship’s hull girder failure could result from maritime accident that can cause human life loss, environmental disaster, and major economic impacts. In risk-based ship design paradigm, accounting for rare phenomena (e.g. ship-ship collision or grounding) is important to provide safe and durable structure. In-service corrosion-induced hull degradation should be considered at the design stage, as it can significantly affect structural strength. The current study presents a novel framework to estimate the probability of ship hull girder failure, accounting for novel corrosion modelling techniques and accidental damage. The associated uncertainties are considered using statistical sampling from evidence-based distributions. A state-of-the-art deterministic model for ultimate strength calculation is applied using Monte Carlo simulation approach, resulting in the probability of hull failure through a reliability assessment. Wave and still-water bending moments are considered random variables. Two case studies of tanker ships with varying sizes are executed to show the applicability of the proposed framework. The results indicate that proper consideration of corrosion is of high importance, as ageing can significantly increase the probability of failure if accidental damage happens. Therefore, whereas future research and model refinement are discussed, the presented framework can serve for risk-based ship design tool and assess existing structures’ safety.

Assessing the Accuracy of Artificial Intelligence Models in Scoliosis Classification and Suggested Therapeutic Approaches.

Background: Open-source artificial intelligence models (OSAIMs) are increasingly being applied in various fields, including IT and medicine, offering promising solutions for diagnostic and therapeutic interventions. In response to the growing interest in AI for clinical diagnostics, we evaluated several OSAIMs-such as ChatGPT 4, Microsoft Copilot, Gemini, PopAi, You Chat, Claude, and the specialized PMC-LLaMA 13B-assessing their abilities to classify scoliosis severity and recommend treatments based on radiological descriptions from AP radiographs. Methods: Our study employed a two-stage methodology, where descriptions of single-curve scoliosis were analyzed by AI models following their evaluation by two independent neurosurgeons. Statistical analysis involved the Shapiro-Wilk test for normality, with non-normal distributions described using medians and interquartile ranges. Inter-rater reliability was assessed using Fleiss' kappa, and performance metrics, like accuracy, sensitivity, specificity, and F1 scores, were used to evaluate the AI systems' classification accuracy. Results: The analysis indicated that although some AI systems, like ChatGPT 4, Copilot, and PopAi, accurately reflected the recommended Cobb angle ranges for disease severity and treatment, others, such as Gemini and Claude, required further calibration. Particularly, PMC-LLaMA 13B expanded the classification range for moderate scoliosis, potentially influencing clinical decisions and delaying interventions. Conclusions: These findings highlight the need for the continuous refinement of AI models to enhance their clinical applicability.

Prediction of post-hemorrhagic ventricular dilatation trajectory using a growth mixture model in preterm infants.

Early intervention for post-hemorrhagic ventricular dilatation (PHVD), guided by ventricular size measurements from cranial ultrasound (cUS), is associated with improved neurodevelopmental outcomes in preterm infants but benefits must be balanced against intervention risks. Anterior horn width (AHW) and ventricular index (VI) were measured from cUS for preterm infants (<29 weeks) with intraventricular hemorrhage admitted from 2010-2018. PHVD was defined as AHW > 6 mm or VI >97th percentile for postmenstrual age. Individual ventricular size trajectories were plotted, and a growth mixture model (GMM) used to identify latent trajectory classes and compare these to predetermined outcome of neurosurgical intervention. Measurements were obtained from 1543 cUS in 249 infants, of whom 39 had PHVD without and 17 PHVD with neurosurgical intervention based on signs of raised intracranial pressure. The GMM predicted trajectory identified: 93.3% of infants without PHVD, 88.2% and 30.8% of infants with PHVD with and without intervention using AHW; 100% of infants without PHVD, 52.9% and 59.0% of infants with PHVD with and without intervention using VI. The AHW GMM identified a significant proportion of infants with severe PHVD. Model refinement offers a promising approach for identifying differences in PHVD trajectory at an early stage to guide management. It is difficult to distinguish the trajectory of PHVD in the early stage of development, in particular PHVD that spontaneously arrests from slowly progressive PHVD which eventually requires intervention. We report the first modeling-based evaluation of PHVD trajectory for the prediction of short-term outcome of PHVD progression and neurosurgical intervention. With additional clinical validation and optimization to increase accuracy, predictive modeling has the potential to identify important differences in PHVD trajectory at an early stage in the clinical course, allowing for more individualized data-driven risk-benefit assessments to guide decisions on early intervention.

A Comparative Study of the Rheological Properties of a Fly Ash-Based Geopolymer Reinforced with PP Fiber for 3D Printing: An Experimental and Numerical Approach

The present study investigates the flow characteristics of fly ash-based (FA) geopolymers reinforced with polypropylene (PP) fibers during the extrusion process in three-dimensional printing. By applying the Herschel–Bulkley rheological model, this research provides a sound theoretical basis to understand the flow behavior of these materials under various conditions. The Herschel–Bulkley model describes the relationship between shear stress and the shear rate in non-Newtonian fluids, capturing yield stress and flow consistency. A combination of experimental and numerical techniques based on the Finite-Element Method (FEM) in COMSOL has been used in this study. The results of both experimental and simulation approaches are compared to examine the material behavior during extrusion. The experimental results indicate that PP fiber content significantly affects the rheological properties. Mixtures with high fiber content encountered issues such as high static yield. However, mixtures with moderate fiber content showed smoother extrusion processes, suggesting an optimal fiber addition range that balances mechanical properties and extrudability. The numerical simulations generally agreed with the experimental data up to a certain fiber content level, beyond which more complex interactions necessitate further model refinements. The investigation identified a 0.25% to 0.5% fiber content range that enhances performance without complicating the extrusion process, facilitating the production of properly printed structures.

Empowering Government Fiscal Efficiency: Usability Evaluation and E-Government Model Refinement

Empowering Government Fiscal Efficiency: Usability Evaluation and E-Government Model Refinement

Enhancing Scientific Argumentation Skills in High School Physics: A Blended Learning Approach with Project-based Instruction

This empirical investigation delves into the application of blended learning within project-based instruction (project-based blended learning), within the framework of physics education for eleventh-grade students in a high school setting. The primary aim is to unravel the repercussions of this teaching model on the students' proficiency in scientific argumentation, particularly in the domain of optical instruments. Employing the non-equivalent control group design, the study enlists two classes: the experimental and the control group at a senior high school in Bandar Lampung, Indonesia, encompassing a total of 60 students during the academic year 2022/2023. Quantitative evaluation of scientific argumentation skills was conducted through tests, while qualitative insights into student reactions were garnered via an open-ended questionnaire. The findings underscore a noteworthy enhancement in the scientific argumentation abilities of the experimental group (n-gain: 0.69) as opposed to the control group (n-gain: 0.40), with the test of mean differences yielding a significance level (2-tailed) of 0.00. Qualitative scrutiny of the data also brought to light a more pronounced positive reception from students in the experimental cohort, lending support to the potential refinement of the project-based blended learning model for the augmentation of students' scientific argumentation aptitude.

Plastid genome evolution in leafless members of the orchid subfamily Orchidoideae, with a focus on Degranvillea dermaptera.

Leafless, heterotrophic plants are prime examples of organismal modification, the genomic consequences of which have received considerable interest. In particular, plastid genomes (plastomes) are being sequenced at a high rate, allowing continual refinement of conceptual models of reductive evolution in heterotrophs. However, numerous sampling gaps exist, hindering the ability to conduct comprehensive phylogenomic analyses in these plants. Using floral tissue from an herbarium specimen, we sequenced and analyzed the plastome of Degranvillea dermaptera, a rarely collected, leafless orchid species from South America about which little is known, including its phylogenetic affinities. The plastome is the most reduced of those sequenced among the orchid subfamily Orchidoideae. In Degranvillea, it has lost the majority of genes found in leafy autotrophic species, is structurally rearranged, and has similar gene content to the most reduced plastomes among the orchids. We found strong evidence for the placement of Degranvillea within the subtribe Spiranthinae using models that explicitly account for heterotachy, or lineage-specific evolutionary rate variation over time. We further found evidence of relaxed selection on several genes and of correlations among substitution rates and several other "traits" of the plastome among leafless members of orchid subfamily Orchidoideae. Our findings advance knowledge on the phylogenetic relationships and paths of plastid genome evolution among the orchids, which have experienced more independent transitions to heterotrophy than any other plant family. This study demonstrates the importance of herbarium collections in comparative genomics of poorly known species of conservation concern.

Testing predictions from the memory and identity theory of ICD-11 complex posttraumatic stress disorder: Measurement development and initial findings

BackgroundThe ‘Memory and Identity Theory’ of ICD-11 Complex Posttraumatic Stress Disorder (CPTSD) was recently published but has not yet been subjected to empirical testing. The objective of this study was to evaluate newly developed measures of memory and identity disturbances and test hypothesized structural relations between these constructs and CPTSD symptoms. MethodsSelf-report data were collected from a nationwide sample of adults living in Ukraine (N = 2050) in September 2023. Exploratory factor analysis was used to assess the latent structure of the newly developed measures, and structural equation modeling was used to test the associations between memory and identity disturbances and CPTSD symptoms. Results90 % of participants experienced a lifetime trauma, and 9.2 % screened positive for ICD-11 CPTSD. Results indicated that the newly developed measures of memory and identity problems possessed satisfactory psychometric properties, and all but one of the model-implied structural associations were observed. ConclusionThis study provides initial empirical support for the Memory and Identity theory of ICD-11 CPTSD, indicating that the basis of this disorder lies in distinct memory and identity processes. Several effects not predicted by the theory were observed, and these can provide the basis for further model testing and refinement.

Creating and Validating a Digital Leadership Model for University Teachers, Liaoning Province

Background and Aims: In the era of digital transformation, digital leadership has emerged as a critical component of educators' ability to navigate and utilize data literacy in increasingly digitalized environments. University teachers, in particular, need digital leadership to adapt to advancements in technology and enhance their teaching and administrative skills. This study focuses on constructing and validating a digital leadership model specifically for university teachers in Liaoning Province. The research aims to identify the key components of digital leadership and develop a model that addresses the current and future needs of educators in this evolving landscape. Methodology: The study employs a grounded theory approach to build a theoretical structure of digital leadership for university teachers. Field interviews with a diverse group of university educators were conducted to gather qualitative data. This data was then analyzed through a series of open, axial, and selective coding processes, allowing the identification of six key categories that form the foundation of digital leadership. These categories were then subjected to quantitative analysis using structural equation modeling (SEM) to assess the fit of the proposed model. Validation was achieved through iterative model testing and refinement, ensuring that the final model accurately represents the digital leadership characteristics of university teachers. Results: The analysis revealed six key dimensions of digital leadership for university teachers: Digital Leading Insight: The ability to foresee digital trends and incorporate them into teaching strategies. Intelligent Teaching Organization: Effective integration of technology in teaching methods to enhance learning outcomes. Industry-Education Integration Promotion: Fostering collaborations between academia and industry to align educational practices with industry needs. Teacher Professional Development Learning: Ongoing self-improvement and learning to stay updated with technological advancements. Digital Cooperation and Communication Influence: Facilitating effective digital communication and collaboration within and across institutions. Digital Education Policy Decision-Making: Engaging in the formulation and implementation of digital policies in education. The model demonstrated a high degree of fit across all indices, confirming its validity as a framework for understanding digital leadership in the context of university educators. Conclusion: The study provides a validated model of digital leadership for university teachers, highlighting six core competencies that educators must develop to succeed in the digital era. Recommendations for fostering digital leadership include offering leadership development training, optimizing blended learning environments, enhancing collaboration, driving organizational change, establishing incentive mechanisms, and promoting an integrated culture of industry-education collaboration. The results suggest that addressing these areas will significantly enhance the digital leadership capacity of university teachers in Liaoning Province, thereby improving educational outcomes in the digital age.

Refinement of the train formation plan calculation model by considering the effect of switchyard stations' workload

The article is dedicated to improving the mathematical model for calculating the freight train formation plan (TFP) on the railway network of JSC «Ukrzaliznytsia». The system of organizing freight flows in rail transport is based on strategic planning through TFP, which allows increasing the efficiency of using cars and infrastructure, reducing delays in forming trains, and reducing their transportation time. At the same time, the existing TFP calculation models have certain shortcomings, in particular, the complexity of taking into account the numerous factors that influence the process of train formation. This study proposes a refinement of the TFP mathematical model by including the dependence of wagon-hour costs on the function of the variable utilization factor of technical stations. An automated calculation method developed by one of the authors, based on the application of the mathematical apparatus of genetic algorithms, was used for the calculations. A comparative analysis of the calculation results using the standard and improved models showed that taking into account the impact of station congestion allowed reducing costs by 405.7 wagon-hours, or 3.5% of the total costs for train accumulation and reformatting. The application of the proposed refinement of the TFP model allows the railway operator to obtain more accurate and justified planning results, which will contribute to improving the efficiency of wagon flow management. In addition, the implementation of this approach can serve as the basis for further improving the methods of strategic planning of the transportation process and minimizing the impact of uncertainty factors. The proposed refinement of the TFP calculation model has not only a practical focus, but also represents a theoretical example of the application of an approach aimed at reducing the level of epistemic uncertainty in the management system of the operational work of rail transport. The systematic development and implementation of such approaches creates the basis for mitigating the negative impact of factors that represent sources of other types of uncertainty

AMICO-COSMOS galaxy cluster and group catalogue up to z = 2: Sample properties and X-ray counterparts

Aims. We present the results of a new galaxy cluster search in the COSMOS field obtained using the Adaptive Matched Identifier of Clustered Objects (AMICO). Our aim was to produce a new cluster and group catalogue up to z = 2 by performing an innovative application of AMICO with respect to previous successful applications to wide-field surveys in terms of depth (down to r &lt; 26.7), small area covered (∼1.69 deg2 of unmasked effective area), and redshift extent. This sample and the comparative analysis we performed with the X-rays, allowed for the calibration of mass-proxy scaling relations up to z = 2 and down to less than 1013 M⊙, and constitutes the basis for the refinement of the cluster model for future applications of AMICO, such as the analysis of upcoming Euclid data. Methods. The AMICO algorithm is based on an optimal linear matched filter and detects clusters in photometric galaxy catalogues using galaxy location, photometric redshift and, in the simplest case, one galaxy property. We chose to use a single magnitude as the galaxy property, avoiding the explicit use of galaxy colour for the selection of clusters. We used three different magnitudes by performing three independent runs in the r-, Y-, and H-bands using both COSMOS2020 and COSMOS2015 galaxy catalogues. We created a composite visibility mask and cluster models for the signal to detect, and we estimated the noise directly from the data. Results. We performed a matching of the catalogues resulting from the three runs and merged them to produce a final catalogue that contains 1269 and 666 candidate clusters and groups with S/N &gt; 3.0 and &gt; 3.5, respectively. A total of 490 candidates are detected in all three runs. Most of the detections unmatched between runs have S/N &lt; 3.5, which can be set as a threshold for selecting a more robust sample. We assigned X-ray properties to our detections by matching the catalogue with a public X-ray selected group sample and by estimating, for unmatched detections, the X-ray properties at the location of AMICO candidates using Chandra+XMM-Newton data. There are in total 622 candidate clusters and groups with an X-ray flux estimate. This large sample of candidates with X-ray properties allowed the calibration of the scaling relations between two AMICO mass-proxies (richness and cluster amplitude) and X-ray mass and the study of their redshift dependence for the selection of the most stable photometric bands.

LBRT: Local-Information-Refined Transformer for Image Copy-Move Forgery Detection.

The current deep learning methods for copy-move forgery detection (CMFD) are mostly based on deep convolutional neural networks, which frequently discard a large amount of detail information throughout convolutional feature extraction and have poor long-range information extraction capabilities. The Transformer structure is adept at modeling global context information, but the patch-wise self-attention calculation still neglects the extraction of details in local regions that have been tampered with. A local-information-refined dual-branch network, LBRT (Local Branch Refinement Transformer), is designed in this study. It performs Transformer encoding on the global patches segmented from the image and local patches re-segmented from the global patches using a global modeling branch and a local refinement branch, respectively. The self-attention features from both branches are precisely fused, and the fused feature map is then up-sampled and decoded. Therefore, LBRT considers both global semantic information modeling and local detail information refinement. The experimental results show that LBRT outperforms several state-of-the-art CMFD methods on the USCISI dataset, CASIA CMFD dataset, and DEFACTO CMFD dataset.

Competitive strategy delivery in the service sector: a study of the behavioural model

PurposeOne of the most cited literature in SHRM is Schuler and Jackson’s (1987) behavioural model. This model proposes that organisational performance is dependent on the extent to which HRM practices can be effectively connected to competitive strategy and desired employee behaviours. Importantly, this model recognises the salient role of employee behaviour in performance outcomes and, moreover that different competitive strategies imply both promulgation and reinforcement of different sets of employee skills and behaviours. Surprisingly, despite its significant influence on SHRM, studies rarely examine this model in its entirety. Motivated by the need to better understand this model’s arguments in contemporary settings, our study uses a multi-actor design to explore the connections between competitive strategies (cost reduction and differentiation), employee behaviours, and HRM practices in service environments.Design/methodology/approachAdopting a multi-level, multi-actor survey design, our exploratory deductive study assesses the utility of strategic HRM’s (SHRM) behavioural model. Drawing on data from a sample of service organisations and using univariate analyses, we compare operationalised HRM practices and employee behaviours across different strategy types.FindingsResults lend provisional support for the behavioural model, particularly in the case of a differentiation strategy where notable differences in HRM practices and employee behaviours were observed. Findings suggest growing levels of memetic and competitive isomorphism may be occurring, with this likely attributable to the increased incidence of idea generation and information sharing about best practices occurring amongst practitioners, as well as a growing nuance in operating markets, managerial preferences, employee expectations, stakeholder objectives, and the like.Research limitations/implicationsOur study suggests refinements to the behavioural model are needed. Some support for the model’s key tenets is found, but these appear context specific. Thus, the merit in developing a priori typologies linking strategy type to HRM practices and employee behaviours where organisations operate in environments which are particularised and tumultuous appears debatable.Practical implicationsThis study highlights the behavioural model’s nuance to modern service organisations and, by doing so, practitioners are provided with a behavioural pathway for achieving competitive advantage through their HRM practices. Findings also suggest that increasingly competitive environments might be encouraging practitioners to engage in isomorphic behaviours.Originality/valueThe use of a comparative research design allowed our study to contribute much needed empiricism to the largely conceptually informed stylised typologies depicting the linkages between different competitive strategies, implied employee role behaviours and HRM practices, thereby supporting the need for model refinement.

Introducing a computer simulation to model flexural vibrations of sandwich structures reinforced by advanced nanocomposites surrounded by auxetic concrete foundation

This study introduces a computer simulation framework aimed at analyzing the flexural vibrations of sandwich structures comprising a polymer core sandwiched between two face sheets reinforced with graphene oxide powder (GOP) nanocomposites. The sandwich structures are situated on an auxetic concrete foundation, incorporating sophisticated theoretical formulations to approximate the three-dimensional displacement fields of GOP-reinforced rectangular plates under varying loading conditions. The research addresses the complex interplay of material properties and geometric configurations, crucial for optimizing the structural performance of advanced composite materials in practical applications. The computational model integrates analytical techniques to predict the dynamic behavior of the sandwich structure’s flexural vibrations. Key parameters such as the dispersion of GOP within the polymer matrix, and the auxetic properties of the concrete foundation are systematically varied and analyzed to understand their impact on the overall structural response. Results from the simulations provide insights into the natural frequencies of the GOP-reinforced sandwich structures, offering valuable data for design engineers and materials scientists. The study underscores the potential of graphene oxide powder nanocomposites in enhancing the mechanical performance of sandwich structures, particularly when supported by innovative foundation materials like auxetic concrete. Future research directions include validation and further refinement of the computational model to accommodate additional complexities and real-world conditions.

Federated Learning for Edge Computing in REST APIs: Investigate the Feasibility of Employing Federated Learning Techniques to Train Machine Learning Models Directly on Edge Devices Interacting with REST APIs, Minimizing Data Transfer and Enhancing Privacy

This research explores the feasibility of using federated learning techniques to train machine learning models directly on edge devices interacting through REST APIs. This article designs an architecture that facilitates federated learning using REST APIs for communication between a central server and edge nodes. The architecture ensures efficient model distribution, update aggregation, and iterative global model refinement. This research implements this system in a simulated environment to evaluate its performance. Initial results show that federated learning significantly reduces data transfer and enhances privacy without compromising model accuracy. The REST API-based communication model effectively coordinates updates between the central server and edge devices, and privacy-preserving techniques maintain data security throughout the process. This study confirms that federated learning is a viable solution for edge computing, addressing key challenges in data privacy and communication efficiency, and opens pathways for scalable and effective distributed learning systems in real-world IoT applications. Federated learning offers a decentralized alternative, enabling local model training on edge devices with only model updates shared with a central server. This approach minimizes data transfer and enhances privacy.