Simple Model Research Articles

Research on frequency shift signal detection algorithm for jointless track circuits based on improved relaxation algorithm

The accuracy of ZPW-2000 frequency shift signal demodulation is related to the safety and efficiency of high-speed trains. This paper proposed a kind of ZPW-2000 frequency-shift signal detection algorithm based on improved Relaxation algorithm (ZFSD-IR) to address the issue of low accuracy in detecting ZPW-2000 frequency shift signal under strong noise interference, as the traditional fast Fourier transform (FFT) spectrum analysis methods are susceptible to fence effects and spectrum leakage effects. Firstly, the principle of ZPW-2000 jointless track circuit was analyzed, and then the ideal and simplified models of ZPW-2000 frequency shift signal were established, respectively. Finally, the simulation experiments were conducted on ZPW-2000 frequency shift signal detection under different sampling durations and signal-to-noise ratios (SNR). The effectiveness of the proposed algorithm was verified through comparative analysis with the traditional algorithms. The research results indicated that the ZFSD-IR algorithm has better accuracy in carrier-frequency and low-frequency estimation than traditional algorithms, and can achieve accurate detection of ZPW-2000 frequency shift signal under the lower SNR conditions. It has high detection accuracy and good anti-interference ability, ensuring the safe operation of high-speed trains.

A codifiable methodology for estimating pallet sliding displacements on steel racking systems

AbstractTo facilitate the logistic processes within a modern warehouse, the contents of steel racking systems are not mechanically connected to the supporting beams, allowing a content‐sliding mechanism to develop when static friction is exceeded. Given that the mass of contents is dominant, this is beneficial for limiting the apparent inertia. On the other hand, pallet fall‐off may occur during strong seismic excitations, which is a failure mode that is not addressed by current seismic design processes and guidelines for racks. Along these lines, a codifiable methodology is proposed for estimating sliding displacements on steel racking systems, based on the statistical interpretation of a large set of response history analyses, using different rack configurations and ground motions. Firstly, a multi‐parametric analysis is conducted using simplified rack models to (i) select the intensity measure and engineering demand parameter that can best describe the problem of pallet sliding and (ii) identify the salient rack characteristics that dominate sliding behavior. Thereafter, a series of multi‐stripe analyses are performed using 180 rack realizations with different feature combinations to derive a so‐called, Empirical Sliding Prediction Equation (ESPE) by following a three‐step procedure: (a) perform regression on maximum sliding, (b) perform regression on the normalized sliding profile, and (c) combine steps a‐b to derive the denormalized profile at a given confidence level. The proposed empirical relationships are then validated through a comparison between the observed and fitted sliding displacements in three rack case studies.

Constraints on simplified dark matter models involving an s-channel mediator with the ATLAS detector in pp collisions at s=13\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{s} = 13$$\\end{document} TeV

This paper reports a summary of searches for a fermionic dark matter candidate in the context of theoretical models characterised by a mediator particle exchange in the s-channel. The data sample considered consists of pp collisions delivered by the Large Hadron Collider during its Run 2 at a centre-of-mass energy of s=13TeV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{s} = 13\\,\ extrm{TeV}$$\\end{document} and recorded by the ATLAS detector, corresponding to up to 140 fb-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}. The interpretations of the results are based on simplified models where the new mediator particles can be spin-0, with scalar or pseudo-scalar couplings to fermions, or spin-1, with vector or axial-vector couplings to fermions. Exclusion limits are obtained from various searches characterised by final states with resonant production of Standard Model particles, or production of Standard Model particles in association with large missing transverse momentum.

Validity of the Johns Hopkins Adjusted Clinical Groups system on the utilisation of healthcare services in Norway: a retrospective cross-sectional study

BackgroundThe Adjusted Clinical Groups (ACG) System is a validated electronic risk stratification system. However, there is a lack of studies on the association between different ACG risk scores and the utilisation of different healthcare services using different sources of input data. The aim of this study was therefore to assess the validity of the association between five different ACG risk scores and the utilisation of a range of different healthcare services using input data from either general practitioners (GPs) or hospitals.MethodsRegistry-based study of all adult inhabitants in four Norwegian municipalities that received somatic healthcare in one year (N = 168 285). The ACG risk scores resource utilisation band, unscaled ACG concurrent risk, unscaled concurrent risk, frailty flag and chronic condition count were calculated using age, sex and diagnosis codes from GPs and a hospital, respectively. Healthcare utilisation covered GP, municipal and hospital services. Areas under the receiver operating curve (AUC) were calculated and compared to the AUC of a model using only age and sex.ResultsUtilisation of all healthcare services increased with increasing scores in the “resource utilisation band” (RUB) and all other investigated ACG risk scores. The risk scores overall distinguished well between levels of utilisation of GP visits (AUC up to 0.84), hospitalisation (AUC up to 0.8) and specialist outpatient visits (AUC up to 0.72), but not out-of-hours GP visits (AUC up to 0.62). The score “unscaled ACG concurrent risk” overall performed best. Risk scores based on data from either GPs or hospitals performed better for the classification of healthcare services in their respective domains. The model based on age and sex performed better for distinguishing between levels of utilisation of municipal services (AUC 0.83–0.90 compared to 0.46–0.79).ConclusionsRisk scores from the ACG system is valid for classifying GP visits, hospitalisation and specialist outpatient visits. It does not outperform simpler models in the classification of utilisation of municipal services such as nursing homes and home services and outpatient emergency care in primary healthcare. The ACG system can be applied in Norway using administrative data from either GPs or hospitals.

Experimental characterization and 3D simulations of turbulent flames assisted by nanosecond plasma discharges

This paper presents quantitative experimental data generated for the validation of plasma-assisted combustion (PAC) simulations. These data are then used to validate the phenomenological model of Castela et al. They are also useful to test other PAC models. In the experiment presented here, Nanosecond Repetitively Pulsed (NRP) discharges are applied to a lean-premixed turbulent methane-air flame initially near the lean blow-off limit. The discharges significantly enhance the combustion and stabilize the flame after a few pulses. Electrical and optical diagnostics are employed to extensively quantify the transient and steady state of the plasma-stabilization process. The flame shape is characterized by OH* chemiluminescence imaging. In the discharge region, OH density profiles are obtained by 1D laser-induced fluorescence, and the gas temperature is measured by optical emission spectroscopy measurements. The local gas temperature increases by 1250 K, and the OH number density rises sevenfold when NRP discharges are applied. These results evidence the cumulative thermal and chemical effects of NRP discharges, which are especially challenging to replicate numerically. A Large Eddy Simulation (LES) of the experiment is performed. Combustion chemistry is modeled by an analytically reduced mechanism, while the plasma discharge is described by the low-CPU cost phenomenological model of Castela et al., which aims to capture the main thermal and chemical effects induced by the discharges. The model of Castela et al. is validated in the burnt gases by the remarkable agreement between the simulations and the experiments regarding the flame shape, the local gas temperature, and the OH number density. More generally, this work demonstrates the relevance of simplified plasma models in LES solvers to simulate complex plasma-assisted burners.

Contrasting behavior of urea in strengthening and weakening confinement effects on polymer collapse.

Biomolecules inhabit a crowded living cell that is packed with high concentrations of cosolutes and macromolecules that result in restricted, confined volumes for biomolecular dynamics. To understand the impact of crowding on the biomolecular structure, the combined effects of the cosolutes (such as urea) and confinement need to be accounted for. This study involves examining these effects on the collapse equilibria of three model 32-mer polymers, which are simplified models of hydrophobic, charge-neutral, and uncharged hydrophilic polymers, using molecular dynamics simulations. The introduction of confinement promotes the collapse of all three polymers. Interestingly, addition of urea weakens the collapse of the confined hydrophobic polymer, leading to non-additive effects, whereas for the hydrophilic polymers, urea enhances the confinement effects by enhancing polymer collapse (or decreasing the polymer unfolding), thereby exhibiting an additive effect. The unfavorable dehydration energy opposes collapse in the confined hydrophobic and charge-neutral polymers under the influence of urea. However, the collapse is driven mainly by the favorable change in polymer-solvent entropy. The confined hydrophilic polymer, which tends to unfold in bulk water, is seen to have reduced unfolding in the presence of urea due to the stabilizing of the collapsed state by urea via cohesive bridging interactions. Therefore, there is a complex balance of competing factors, such as polymer chemistry and polymer-water and polymer-cosolute interactions, beyond volume exclusion effects, which determine the collapse equilibria under confinement. The results have implications to understand the altering of the free energy landscape of proteins in the confined living cell environment.

Simplified calculation models for evaluating the post-earthquake axial load-carrying capacity of RC bridge piers

This study aims to develop simplified calculation models of the post-earthquake axial load-carrying capacity of reinforced concrete columns to determine the post-earthquake traffic flow capacity of the bridges for the evaluation and design process. To this end, the maximum and residual drift ratios are first determined as the engineering demand parameters for the simplified calculation model for the evaluation and design process, respectively. Then, the optimal models describing the relationships between the maximum/residual drift ratio and the post-earthquake axial load-carrying capacity are selected. The analysis results show the normal cumulative model in the coordinate system of ln(x)-y is the optimal relationship model. On this basis, the effects of high-sensitivity parameters (i.e. the axial compressive ratio and shear span ratio) on the post-earthquake axial load-carrying capacity are investigated. Results indicate that the axial compressive ratio negatively affects, while the shear span ratio positively affects the post-earthquake axial load-carrying capacity. Finally, the simplified calculation models of the post-earthquake axial load-carrying capacity are developed from the optimal model and the effects of the high-sensitivity parameters, and with validated high accuracy. The simplified calculation models provide engineers and policymakers with a tool to predict the post-earthquake traffic flow capacity of bridges.

Development of an intestinal mucosa ex vivo co-culture model to study viral infections.

Studying viral infections necessitates well-designed cell culture models to deepen our understanding of diseases and develop effective treatments. In this study, we present a readily available ex vivo 3D co-culture model replicating the human intestinal mucosa. The model combines fully differentiated human intestinal epithelium (HIE) with human monocyte-derived macrophages (hMDMs) and faithfully mirrors the in vivo structural and organizational properties of intestinal mucosal tissues. Specifically, it mimics the lamina propria, basement membrane, and the air-exposed epithelial layer, enabling the pioneering observation of macrophage migration through the tissue to the site of viral infection. In this study, we applied the HIE-hMDMs model for the first time in viral infection studies, infecting the model with two globally significant viruses: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and human norovirus GII.4. The results demonstrate the model's capability to support the replication of both viruses and show the antiviral role of macrophages, determined by their migration to the infection site and subsequent direct contact with infected epithelial cells. In addition, we evaluated the production of cytokines and chemokines in the intestinal niche, observing an increased interleukin-8 production during infection. A parallel comparison using a classical in vitro cell line model comprising Caco-2 and THP-1 cells for SARS-CoV-2 experiments confirmed the utility of the HIE-hMDMs model in viral infection studies. Our data show that the ex vivo tissue models hold important implications for advances in virology research.IMPORTANCEThe fabrication of intricate ex vivo tissue models holds important implications for advances in virology research. The co-culture model presented here provides distinct spatial and functional attributes not found in simplified models, enabling the evaluation of macrophage dynamics under severe acute respiratory syndrome coronavirus 2 and human norovirus (HuNoV) infections in the intestine. Moreover, these models, comprised solely of primary cells, facilitate the study of difficult-to-replicate viruses such as HuNoV, which cannot be studied in cell line models, and offer the opportunity for personalized treatment evaluations using patient cells. Similar co-cultures have been established for the study of bacterial infections and different characteristics of the intestinal tissue. However, to the best of our knowledge, a similar intestinal model for the study of viral infections has not been published before.

Transverse compressive behavior of plain and polypropylene fiber-reinforced concrete infilled steel tubes as hybrid steel-concrete nodes for discrete reticulated shell structures

This study investigates using concrete infilled steel tubes as hybrid nodes for discrete reticulated shell structures under compressive loading transverse to the tube axis. Three configurations were tested: empty, plain concrete infilled and polypropylene fiber-reinforced concrete infilled steel tubes. Displacement-controlled loading was applied through welded steel plates. Simplified beam theory models and nonlinear finite element analysis were used to evaluate the structural behavior. The concrete infilled steel tube specimens had higher stiffness and load-bearing capacity than the empty ones. The structural behavior of the concrete infilled specimens could be split into pre- and post-split-tensile cracking phases. No significant difference was found between the plain and fiber-reinforced concrete specimens under loads up to 1500 kN, which was the load limit of the testing machine. An additional fiber-reinforced concrete specimen, tested on a higher capacity machine, had a load-bearing capacity of 2400 kN and exhibited ductile failure.

Multifractality in monitored single-particle dynamics

We study multifractal properties in time evolution of a single particle subject to repeated measurements. For quantum systems, we consider circuit models consisting of local unitary gates and local projective measurements. For classical systems, we consider models for estimating the trajectory of a particle evolved under local transition processes by partially measuring particle occupations. In both cases, multifractal behaviors appear in the ensemble of wave functions or probability distributions conditioned on measurement outcomes after a sufficiently long time. While the nature of particle transport (diffusive or ballistic) qualitatively affects the multifractal properties, they are even quantitatively robust to the measurement rate or specific protocols. On the other hand, multifractality is generically lost by generalized measurements allowing erroneous outcomes or by postselection of the outcomes with no particle detection. We demonstrate these properties by numerical simulations and also propose several simplified models, which allow us to analytically obtain multifractal properties in the monitored single-particle systems. Published by the American Physical Society 2024

Comparative Analysis of Machine Learning Models for Predicting Student Success in Online Programming Courses: A Study Based on LMS Data and External Factors

Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Network (Multilayer Perceptron, MLP). We analyzed data from the Moodle Learning Management System (LMS) and external factors of 591 students enrolled in online object-oriented programming courses at the Universidad Estatal de Milagro (UNEMI) between 2022 and 2023. The data were preprocessed to address class imbalance using the synthetic minority oversampling technique (SMOTE), and relevant features were selected based on Random Forest importance rankings. The models were trained and optimized using Grid Search with cross-validation. Logistic Regression achieved the highest Area Under the Receiver Operating Characteristic Curve (AUC-ROC) on the test set (0.9354), indicating strong generalization capability. SVM and Neural Network models performed adequately but were slightly outperformed by the simpler models. These findings suggest that integrating LMS data with external factors enhances early prediction of student success. Logistic Regression is a practical and interpretable tool for educational institutions to identify at-risk students, and to implement personalized interventions.

De novo designed YK peptides forming reversible amyloid for synthetic protein condensates in mammalian cells

In mammalian cells, protein condensates underlie diverse cell functions. Intensive synthetic biological research has been devoted to fabricating liquid droplets using de novo peptides/proteins designed from scratch in test tubes or bacterial cells. However, the development of de novo sequences for synthetic droplets forming in eukaryotes is challenging. Here, we report YK peptides, comprising 9–15 residues of alternating repeats of tyrosine and lysine, which form reversible amyloid-like fibrils accompanied by binding with poly-anion species such as ATP. By genetically tagging the YK peptide, superfolder GFPs assemble into artificial liquid-like droplets in living cells. Rational design of the YK system allows fine-tuning of the fluidity and construction of multi-component droplets. The YK system not only facilitates intracellular reconstitution of simplified models for natural protein condensates, but it also provides a toolbox for the systematic creation of droplets with different dynamics and composition for in situ evaluation.

Selective Glycan Presentation in Liquid‐Ordered or ‐Disordered Membrane Phases and its Effect on Lectin Binding

Glycan‐protein interactions play a key role in various biological processes from fertilization to infections. Many of these interactions take place at the glycocalyx – a heavily glycosylated layer at the cell surface. Despite its significance, studying the glycocalyx remains challenging due to its complex, dynamic, and heterogeneous nature. This study introduces a glycocalyx model allowing for the first time to control spatial organization and heterogeneity of the glycan moieties. Glycan‐mimetics with lipid‐moieties that partition into either liquid‐ordered (Lo, lipid rafts) or liquid‐disordered (Ld) phases of giant unilamellar vesicles (GUVs), which serve as simplified cell membrane models micking lipid rafts, are developed. This phase‐specific allocation allows controlled placement of glycan motifs in distinct membrane environments, creating heteromultivalent systems that replicate the natural glycocalyx's complexity. We show that phase localization of glycan mimetics significantly influences recruitment of protein receptors to the membrane. Glycan‐conjugates in the ordered phase demonstrate enhanced lectin binding, supporting the idea that raft‐like domains facilitate stronger receptor interactions. This study provides a platform for systematically investigating spatial and dynamic presentation of glycans in biological systems and presents the first experimental evidence that glycan accumulation in lipid rafts enhances receptor binding affinity, offering deeper insights into the glycocalyx's functional mechanisms.

Selective Glycan Presentation in Liquid-Ordered or -Disordered Membrane Phases and its Effect on Lectin Binding.

Glycan-protein interactions play a key role in various biological processes from fertilization to infections. Many of these interactions take place at the glycocalyx - a heavily glycosylated layer at the cell surface. Despite its significance, studying the glycocalyx remains challenging due to its complex, dynamic, and heterogeneous nature. This study introduces a glycocalyx model allowing for the first time to control spatial organization and heterogeneity of the glycan moieties. Glycan-mimetics with lipid-moieties that partition into either liquid-ordered (Lo, lipid rafts) or liquid-disordered (Ld) phases of giant unilamellar vesicles (GUVs), which serve as simplified cell membrane models micking lipid rafts, are developed. This phase-specific allocation allows controlled placement of glycan motifs in distinct membrane environments, creating heteromultivalent systems that replicate the natural glycocalyx's complexity. We show that phase localization of glycan mimetics significantly influences recruitment of protein receptors to the membrane. Glycan-conjugates in the ordered phase demonstrate enhanced lectin binding, supporting the idea that raft-like domains facilitate stronger receptor interactions. This study provides a platform for systematically investigating spatial and dynamic presentation of glycans in biological systems and presents the first experimental evidence that glycan accumulation in lipid rafts enhances receptor binding affinity, offering deeper insights into the glycocalyx's functional mechanisms.

Default mode network tau predicts future clinical decline in atypical early Alzheimer's disease.

Identifying individuals with early-stage Alzheimer's disease (AD) at greater risk of steeper clinical decline would enable better-informed medical, support, and life planning decisions. Despite accumulating evidence on the clinical prognostic value of tau positron emission tomography (PET) in typical late-onset amnestic AD, its utility in predicting clinical decline in individuals with atypical forms of AD remains unclear. Across heterogeneous clinical phenotypes, patients with atypical AD consistently exhibit abnormal tau accumulation in the posterior nodes of the default mode network of the cerebral cortex. This evidence suggests that tau burden in this functional network could be a common imaging biomarker for prognostication across the syndromic spectrum of AD. Here, we examined the relationship between baseline tau PET signal and the rate of subsequent clinical decline in a sample of 48 A+/T+/N+ patients with mild cognitive impairment or mild dementia due to AD with atypical clinical phenotypes: Posterior Cortical Atrophy (n = 16), logopenic variant Primary Progressive Aphasia (n = 15), and amnestic syndrome with multi-domain impairment and young age of onset < 65 years (n = 17). All patients underwent magnetic resonance imaging (MRI), tau PET, and amyloid PET scans at baseline. Each patient's longitudinal clinical decline was assessed by calculating the annualized change in the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) scores from baseline to follow-up (mean time interval = 14.55 ± 3.97 months). Atypical early AD patients showed an increase in CDR-SB by 1.18 ± 1.25 points per year: t(47) = 6.56, p < .001, Cohen's d = 0.95. Across clinical phenotypes, baseline tau in the default mode network was the strongest predictor of clinical decline (R2 = .30), outperforming a simpler model with baseline clinical impairment and demographic variables (R2 = .10), tau in other functional networks (R2 = .11-.26), and the magnitude of cortical atrophy (R2 = .20) and amyloid burden (R2 = .09) in the default mode network. Overall, these findings point to the contribution of default mode network tau to predicting the magnitude of clinical decline in atypical early AD patients one year later. This simple measure could aid the development of a personalized prognostic, monitoring, and treatment plan, which would help clinicians not only predict the natural evolution of the disease but also estimate the effect of disease-modifying therapies on slowing subsequent clinical decline given the patient's tau burden while still early in the disease course.

Optimizing Energy Efficiency with a Cloud-Based Model Predictive Control: A Case Study of a Multi-Family Building

The Energy Performance of Buildings Directive (EPBD) has set a target to achieve carbon-neutral building stock and generate 80% of its electricity from renewable sources by 2050. While Model Predictive Control (MPC) can contribute significantly to energy flexibility in buildings, its remote implementation remains relatively unexplored, especially in the residential sector. The purpose of this research is to demonstrate the reliability, robustness, and computational efficiency of a cloud-based application of an MPC called Smart Energy Management (SEM) on a multi-family residential building. The SEM was tested on a virtual building model in TRNSYS using an open-source distributed event streaming platform for data exchange and synchronization. Simplified models for thermal behavior prediction, including an R3C3 model of the building, were developed in C++. The SEM was evaluated in eight scenarios with varying weather conditions, optimization criteria, and runtime periods. The results demonstrate that the SEM maintains stability and robustness over a 2-week period with a 15-minute planning resolution while ensuring thermal comfort. The C++ implementation of the optimization algorithm enables SEM deployment on low-spec servers, supporting cost-effective applications in real buildings with minimal intervention.

An Efficient Detection Mechanism of Network Intrusions in IoT Environments Using Autoencoder and Data Partitioning

In recent years, with the development of the Internet of Things and distributed computing, the “server-edge device” architecture has been widely deployed. This study focuses on leveraging autoencoder technology to address the binary classification problem in network intrusion detection, aiming to develop a lightweight model suitable for edge devices. Traditional intrusion detection models face two main challenges when directly ported to edge devices: inadequate computational resources to support large-scale models and the need to improve the accuracy of simpler models. To tackle these issues, this research utilizes the Extreme Learning Machine for its efficient training speed and compact model size to implement autoencoders. Two improvements over the latest related work are proposed: First, to improve data purity and ultimately enhance detection performance, the data are partitioned into multiple regions based on the prediction results of these autoencoders. Second, autoencoder characteristics are leveraged to further investigate the data within each region. We used the public dataset NSL-KDD to test the behavior of the proposed mechanism. The experimental results show that when dealing with multi-class attacks, the model’s performance was significantly improved, and the accuracy and F1-Score were improved by 3.5% and 2.9%, respectively, maintaining its lightweight nature.

Numerical evaluation of the impact of sandbars on cross-shore sediment transport and shoreline evolution

Nearshore nourishments are a common intervention used to mitigate sediment deficits in coastal areas experiencing erosion problems. This coastal intervention involves placing nourished sediments in the submerged zone of the beach profile to form an artificial submerged sandbar which are then distributed by natural forces occurring in the coastal zone. Coastal processes that control the morphological evolution of coastal zones operate on several spatial and temporal scales (seconds to years) and can be divided into cross-shore and longshore components. Usually, the numerical models that simulate the evolution of cross-shore profiles due to sediment transport within the profiles, are related with short to medium-term events (days to months), and shoreline evolution models, that allow for long-term analysis (years to decades), are typically only considering longshore sediment transport. Describing all the processes in a single numerical model is complex and computationally demanding, and therefore, the numerical models are typically focused on specific processes, categorized by their temporal and spatial scales. This article presents a numerical study focused on medium to long-term numerical modelling of coastal zone evolution, examining the combined effects of cross-shore and longshore sediment transport processes of sediment transport. The model incorporates two simplified numerical models: LTC - Long Term Configuration, for shoreline evolution and CS-Model, for cross-shore sediment exchanges. Applied to nine different sandbar scenarios, model results revealed that disturbances in sandbar volume tend to return to equilibrium through cross-shore sediment transport processes. In situations that considered a constant sandbar volume alongshore, the longshore effects are null, because the volume entering in the coastal domain is equal to the volume leaving, and the sediment balance is only dependent of the cross-shore processes. Variable sandbar volume alongshore leads to impacts on shoreline position due to longshore sediment transport gradients generated by the sandbar disturbance. The results demonstrate the potential of the proposed combined model for medium to long-term projections, allowing for the interpretation of how physical aspects of nearshore nourishments evolve. These aspects include sandbar volume, shoreline position, berm width, and shoreline displacements induced by cross-shore and longshore sediment transport processes. These parameters aid in understanding sandbar-beach sediment dynamics, which is valuable for supporting coastal management, particularly in the development and design of shoreface sand nourishments, enabling the optimization of sand resources.

Proposal of a revised 3-tier "2mm" risk classification model for postoperative pancreatic fistula in robotic pancreatoduodenectomy.

The recent International Study Group for Pancreatic Surgery (ISGPS) risk classification for postoperativepancreatic fistula (grade B/C) was developed based on data from open and mixed minimally invasive pancreatoduodenectomy. The ISGPS risk classification model has not been validated specifically for POPF after robotic pancreatoduodenectomy (RPD). We calculated the rate of POPF (ISGPS 2016 definition, grade B/C) by analyzing consecutive patients after RPD by surgeons after their learning curves (80 RPDs per surgeon). The validation of the ISGPS 4-tier and the simplified 3-tier risk classification was conducted using the area under the receiver operating curve (AUC). From 2019 to 2023, 187 patients after RPD were included. Neither the ISGPS 4-tier nor the simplified 3-tier classification model showed robust discrimination (AUC: 0.696 and 0.685, respectively). Moreover, both risk classifications failed to differentiate the rates of POPF and major complications among subgroups. Multivariate analysis suggested that soft pancreatic texture and pancreatic duct ≤ 2mm were independent risk factors for POPF after RPD. After adjusting the duct size's cutoff from 3 to 2mm, the revised 4-tier "2mm" classification model showed no significant difference between risk categories B and C (6.7% vs. 9.4%, P = 0.063). The revised 3-tier "2mm" classification model stratified patients into A (n = 54), B (n = 68), and C (n = 65) groups, with corresponding POPF rates of 0.0%, 8.8%, and 23.1% (P < 0.001), and major complication rates of 5.6, 14.7, and 24.6% (P = 0.014), respectively. Compared to the simplified 3-tier classification model, the revised 3-tier "2mm" classification model showed improved discrimination (AUC: 0.753 vs. 0.685, P = 0.034) and clinical utility. The current ISGPS 4-tier and the simplified 3-tier classification models lacked sufficient discrimination in patients after RPD. We propose a revised 3-tier "2mm" risk classification model for RPD with a robust discrimination, which requires further international validation with prospectively obtained data.

Deciphering Pathways and Thermodynamics of Protein Assembly Using Native Mass Spectrometry.

Protein oligomerization regulates many critical physiological processes, and its dysregulation can contribute to dysfunction and diseases. Elucidating the assembly pathways and quantifying their underlying thermodynamic and kinetic parameters are crucial for a comprehensive understanding of biological processes and for advancing therapeutics targeting abnormal protein oligomerization. Established binding assays, with limited mass precision, often rely on simplified models for data interpretation. In contrast, high-resolution native mass spectrometry (nMS) can directly determine the stoichiometry of biomolecular complexes in vitro. However, quantification is hindered by the fact that the relative abundances of gas-phase ions generally do not reflect solution concentrations due to nonuniform response factors. Recently, slow mixing mode (SLOMO)-nMS, which can quantify the relative response factors of interacting species, has been demonstrated to reliably measure the affinity (Kd) of binary biomolecular complexes. Here, we introduce an extended form of SLOMO-nMS that enables simultaneous quantification of the thermodynamics in multistep association reactions. Application of this method to homo-oligomerization of concanavalin A and insulin confirmed the reliability of the assay and uncovered details about the assembly processes that had previously resisted elucidation. Results acquired using SLOMO-nMS implemented with charge detection shed new light on the binding of recombinant human angiotensin-converting enzyme 2 and the SARS-CoV-2 spike protein. Importantly, new assembly pathways were uncovered, and the affinities of these interactions, which regulate host cell infection, were quantified. Together, these findings highlight the tremendous potential of SLOMO-nMS to accelerate the characterization of protein assembly pathways and thermodynamics and, in so doing, enhance fundamental biological understanding and facilitate therapeutic development. https://orcid.org/0000-0002-3389-7112.