Current Models Research Articles

Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models.

The World Health Organization has highlighted that cancer was the second-highest cause of death in 2019. This research aims to present the current forecasting techniques found in the literature, applied to predict time-series cancer incidence and then, compare these results with the current methodology adopted by the Instituto Nacional do Câncer (INCA) in Brazil. A set of univariate time-series approaches is proposed to aid decision-makers in monitoring and organizing cancer prevention and control actions. Additionally, this can guide oncological research towards more accurate estimates that align with the expected demand. Forecasting techniques were applied to real data from seven types of cancer in a Brazilian district. Each method was evaluated by comparing its fit with real data using the root mean square error, and we also assessed the quality of noise to identify biased models. Notably, three methods proposed in this research have never been applied to cancer prediction before. The data were collected from the INCA website, and the forecast methods were implemented using the R language. Conducting a literature review, it was possible to draw comparisons previous works worldwide to illustrate that cancer prediction is often focused on breast and lung cancers, typically utilizing a limited number of time-series models to find the best fit for each case. Additionally, in comparison to the current method applied in Brazil, it has been shown that employing more generalized forecast techniques can provide more reliable predictions. By evaluating the noise in the current method, this research shown that the existing prediction model is biased toward two of the studied cancers Comparing error results between the mentioned approaches and the current technique, it has been shown that the current method applied by INCA underperforms in six out of seven types of cancer tested. Moreover, this research identified that the current method can produce a biased prediction for two of the seven cancers evaluated. Therefore, it is suggested that the methods evaluated in this work should be integrated into the INCA cancer forecast methodology to provide reliable predictions for Brazilian healthcare professionals, decision-makers, and oncological researchers.

A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images

The current image generative models have achieved a remarkably realistic image quality, offering numerous academic and industrial applications. However, to ensure these models are used for benign purposes, it is essential to develop tools that definitively detect whether an image has been synthetically generated. Consequently, several detectors with excellent performance in computer vision applications have been developed. However, these detectors cannot be directly applied as they areto multi-spectral satellite images, necessitating the training of new models. While two-class classifiers generally achieve high detection accuracies, they struggle to generalize to image domains and generative architectures different from those encountered during training. In this paper, we propose a one-class classifier based on Vector Quantized Variational Autoencoder 2 (VQ-VAE 2) features to overcome the limitations of two-class classifiers. We start by highlighting the generalization problem faced by binary classifiers. This was demonstrated by training and testing an EfficientNet-B4 architecture on multiple multi-spectral datasets. We then illustrate that the VQ-VAE 2-based classifier, which was trained exclusively on pristine images, could detect images from different domains and generated by architectures not encountered during training. Finally, we conducted a head-to-head comparison between the two classifiers on the same generated datasets, emphasizing the superior generalization capabilities of the VQ-VAE 2-based detector, wherewe obtained a probability of detection at a 0.05 false alarm rate of 1 for the blue and red channels when using the VQ-VAE 2-based detector, and 0.72 when we used the EfficientNet-B4 classifier.

Model Selection Path and Construction of Model Confidence Set under High-Dimensional Variables

Model selection uncertainty has drawn a lot of attention from academics recently because it significantly affects parameter estimation and prediction. Scholars are currently addressing and quantifying uncertainty in model selection by concentrating on model combining and model confidence sets. In this paper, we present a new approach for building model confidence sets, which we call AMac. We provide a theoretical lower bound on the degree of confidence in the model confidence sets that AMac has built. Furthermore, we discuss how the implementation of current model confidence set construction methods becomes difficult when dealing with high-dimensional variables. To address this problem, we suggest building model selection paths (MSP) as a solution. We develop an algorithm for building MSP and show its effectiveness by utilizing the theories of adaptive lasso and lars. We perform an extensive set of simulation experiments to compare the performances of Mac and AMac methods. According to the results, AMac is more stable when there are fluctuations in noise levels. The model confidence sets built by AMac, in particular, achieve coverage rates that are closer to the desired confidence level, especially in the presence of high noise levels. To further confirm that MSP can successfully generate model confidence sets that maintain the given confidence level as the sample size increases, we conduct extensive simulation tests with high-dimensional variables. Ultimately, we hope that the strategies and concepts discussed in this work will improve results in subsequent research on the uncertainty of model selection.

Mathematical Modeling of Alpha-Tocopherol Early Degradation Kinetics to Predict the Shelf-Life of Bulk Oils.

The kinetics of lipid oxidation includes a lag phase followed by an exponential increase in oxidation products, which cause rancidity. Current models focus on the slope of this exponential curve for shelf-life estimation, which still requires the measurement of full oxidation kinetics. In this paper, we analyzed the formation of lipid oxidation products in stripped soybean oil containing different levels of α-tocopherol. The lag phases of lipid hydroperoxides and headspace hexanal formation were found to have a strong positive correlation with the α-tocopherol depletion time. We propose that the kinetics of antioxidant (α-tocopherol) depletion occur during the lag phase and could serve as an early shelf-life indicator. Our results showed that α-tocopherol degradation can be described by Weibull kinetics over a wide range of initial concentrations. Furthermore, we conducted in silico investigations using Monte Carlo simulations to critically evaluate the feasibility and sensitivity of the shelf-life prediction using early antioxidant degradation kinetics. Our results revealed that the shelf life of soybean oil may be accurately predicted as early as 20% of the overall shelf life. This innovative approach provides a more efficient and faster assessment of shelf life, ultimately reducing waste and enhancing product quality.

Guinea Pig Round Window Membrane Explantation for Ex Vivo Studies.

Efficient and minimally invasive drug delivery to the inner ear is a significant challenge. The round window membrane (RWM), being one of the few entry points to the inner ear, has become a vital focus of investigation. However, due to the complexities of isolating the RWM, our understanding of its pharmacokinetics remains limited. The RWM comprises three distinct layers: the outer epithelium, the middle connective tissue layer, and the inner epithelial layer, each potentially possessing unique delivery properties. Current models for investigating transport across the RWM utilize in vivo animal models or ex vivo RWM models which rely on cell cultures or membrane fragments. Guinea pigs serve as a validated preclinical model for the investigation of drug pharmacokinetics within the inner ear and are an important animal model for the translational development of delivery vehicles to the cochlea. In this study, we describe an approach for explantation of a guinea pig RWM with surrounding cochlear bone for benchtop drug delivery experiments. This method allows for preservation of native RWM architecture and may provide a more realistic representation of barriers to transport than current benchtop models.

Artificial intelligence and acute kidney injury

Digitalization is increasingly finding its way into intensive care units and with it artificial intelligence (AI) for critically ill patients. One promising area for the use of AI is in the field of acute kidney injury (AKI). The use of AI is primarily focused on the prediction of AKI, but further approaches are also being used to classify existing AKI into different phenotypes. Different AI models are used for prediction. The area under the receiver operating characteristic curve values (AUROC) achieved with these models vary and are influenced by several factors, such as the prediction time and the definition of AKI. Most models have an AUROC between 0.650 and 0.900, with lower values for predictions further into the future and when applying Acute Kidney Injury Network (AKIN) instead of KDIGO criteria. Classification into phenotypes already makes it possible to categorize patients into groups with different risks of mortality or requirement of renal replacement therapy (RRT), but the etiologies or therapeutic consequences derived from this are still lacking. However, all the models suffer from AI-specific shortcomings. The use of large databases does not make it possible to promptly include recent changes in therapy and the implementation of new biomarkers in arelevant proportion. For this reason, serum creatinine and urinary output, with their known limitations, dominate current AI models for prediction impairing the performance of the current models. On the other hand, the increasingly complex models no longer allow physicians to understand the basis on which the warning of athreatening AKI is calculated and subsequent initiation of therapy should take place. The successful use of AIs in routine clinical practice will be highly determined by the trust of the physicians in the systems and overcoming the aforementioned weaknesses. However, the clinician will remain irreplaceable as the decisive authority for critically ill patients by combining measurable and nonmeasurable parameters.

Large Language Models, Agency, and Why Speech Acts are Beyond Them (For Now) – A Kantian-Cum-Pragmatist Case

This article sets in with the question whether current or foreseeable transformer-based large language models (LLMs), such as the ones powering OpenAI’s ChatGPT, could be language users in a way comparable to humans. It answers the question negatively, presenting the following argument. Apart from niche uses, to use language means to act. But LLMs are unable to act because they lack intentions. This, in turn, is because they are the wrong kind of being: agents with intentions need to be autonomous organisms while LLMs are heteronomous mechanisms. To conclude, the article argues, based on structural aspects of transformer-based LLMs, that these LLMs have taken a first step away from mechanistic artificiality to autonomous self-constitution, which means that these models are (slowly) moving into a direction that someday might result in non-human, but equally non-artificial agents, thus subverting the time-honored Kantian distinction between organism and mechanism.

Next generation of computer vision for plant disease monitoring in precision agriculture: A contemporary survey, taxonomy, experiments, and future direction

Efficient and rational monitoring of plant health is an essential prerequisite for ensuring optimal crop production and resource management in the field of agriculture. Computer vision techniques have revolutionized visual disease monitoring with their exceptional visual recognition performance. However, despite the outstanding results, the widespread acceptance of these methods in agriculture practice is still in its early stages. This study presents a comprehensive survey of the next generation of computer vision models applied to plant disease monitoring in precision agriculture. Our study begins by tracing the evolution of agricultural computer vision research over the past decade, encompassing legacy methods such as convolutional neural networks (CNNs), progressing to newer techniques like vision transformers (ViTs), and culminating in cutting-edge vision multi-layer perceptrons (MLPs). Next, our study embraces both qualitative and quantitative approaches, supporting a profound review of literature and classifying methodologies and experimental approaches. A significant contribution lies in our comprehensive taxonomy, offering a fine-grained categorization of current computer vision models. This taxonomy meticulously highlights the potentials and limitations of these models while explaining their roles in improving plant disease management. Moreover, extensive experimental comparisons are conducted on PlantVillage dataset to evaluate the performance of state-of-the-art computer-vision models for plant recognition data. The obtained results are then utilized to draw insightful conclusions about the behavior of these models and provide guidance for selecting the most suitable one for specific tasks at hand. Additionally, we discuss open research avenues and future directions of computer-vision models in plant disease management including challenges related to the data scarcity, the computational efficiency, need for explainability, and multi-modal analysis.

Lost in the Middle: How Language Models Use Long Contexts

Abstract While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. We analyze the performance of language models on two tasks that require identifying relevant information in their input contexts: multi-document question answering and key-value retrieval. We find that performance can degrade significantly when changing the position of relevant information, indicating that current language models do not robustly make use of information in long input contexts. In particular, we observe that performance is often highest when relevant information occurs at the beginning or end of the input context, and significantly degrades when models must access relevant information in the middle of long contexts, even for explicitly long-context models. Our analysis provides a better understanding of how language models use their input context and provides new evaluation protocols for future long-context language models.

Two-stage robust optimization of unified power quality conditioner (UPQC) siting and sizing in active distribution networks considering uncertainty of loads and renewable generators

This paper proposes a robust optimization siting and sizing strategy for the unified power quality conditioner (UPQC) in the active distribution network (ADN) integrated with renewable energy generators. The optimization objectives including investment cost, line loss, and voltage deviation are integrated into an overall cost for both the planning and operating stages. A novel UPQC equivalent power injection model is proposed based on the auxiliary branch method. A set of linear and second-order conic (SOC) constraints are employed to accurately describe the steady-state operation of UPQC. An improved branch current flow model is developed to incorporate radial distribution networks with UPQCs. The UPQC siting and sizing planning is formulated as a mixed integer second-order conic programming (MISOCP) problem. Accounting for the uncertainty in loads and distributed generators, a two-stage robust optimization model is established to address worst-case operational scenarios. A solving strategy is developed based on the strong duality theory and the column-and-constraint generation (C&CG) algorithm, and then commercial solvers can solve the problem accurately and efficiently. Case studies on an IEEE 33-node distribution network demonstrate this strategy's robustness in dealing with loads and renewable energy uncertainties, making it applicable across different UPQC topologies and operation modes. The proposed method has superior performance over the representative meta-heuristic approaches in terms of global optimality as well as an over 7.5 times faster computation speed. This work has significance in enhancing the quality and efficiency of modern power systems highly integrated with renewable energy.

Classification of molecular subtypes for colorectal cancer and development of a prognostic model based on necroptosis-related genes

BackgroundNecroptosis could regulate immunity in cancers, and stratification of colorectal cancer (CRC) subtypes based on key genes related to necroptosis might be a novel strategy for CRC treatment. MethodThe RNA-sequencing data of CRC and other 31 types of cancers were obtained from The Cancer Genome Atlas (TCGA) database. Consensus clustering was performed based on protein-coding genes (PCGs) related to necroptosis score calculated by single sample gene set enrichment analysis (ssGSEA). Module genes showing a significant positive correlation with the necroptosis score were identified by weighted correlation network analysis (WGCNA) and further used to develop a risk stratification model applying least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. The risks score for each sample in CRC cohorts, immunotherapy cohorts and pan-cancer study cohorts was calculated. ResultTwo subgroups (C1 cluster and C2 cluster) of CRC were identified based on the necroptosis score. Compared with C1 cluster, the survival possibility of C2 cluster was greatly reduced, the levels of necroptosis score, immune cell infiltration, immune score and expression of immune checkpoint molecules were significantly increased and immunotherapy response was less active. Low-risk patients defined by the risk model had a significant survival advantage than high-risk counterparts in both CRC and the other 31 cancer types. Furthermore, the risk model was also more efficient than the Tumor Immune Dysfunction and Exclusion (TIDE) tool in predicting OS and immunotherapy response for the samples in the immunotherapy cohort. ConclusionCRC patients were classified by necroptosis score-related PCGs, and a risk model was designed to evaluate the immunotherapy and prognosis of patients with CRC. The current molecular subtype and prognostic model could help stratify patients with different risks and predict their prognosis and immunotherapy sensitivity.

Critical Review on the Sustainability of Electric Vehicles: Addressing Challenges without Interfering in Market Trends

The primary focus in electrifying the transportation sector should be sustainability. This can be effectively attained through the application of the seven eco-efficiency principles, which constitute the global standard for assessing the sustainability of products. Consequently, this framework should guide the development of current electric vehicle designs. The first section of the present article assesses the alignment of the automotive industry with these sustainability requirements. Results show that even though the electric vehicle promotes the use of cleaner energy resources, it falls short of adhering to the remaining principles. The implementation of advanced models in battery management systems holds great potential to enhance lithium-ion battery systems’ overall performance, increasing the durability of the batteries and their intensity of use. While many studies focus on improving current electric equivalent models, this research delves into the potential applicability of Reduced-Order Model techniques for physics-based models within a battery management systems context to determine the different health, charge, or other estimations. This study sets the baseline for further investigations aimed at enhancing the reduced-order physics-based modeling field. A research line should be aimed at developing advanced and improved cell-state indicators, with enhanced physical insight, for various lithium-ion battery applications.

Decolonizing refugeehood: The rise of climate refugees as a new legal subjectivity

This article examines the misrecognition of climate refugees as a form of climate coloniality, through the lens of decolonial environmental justice (EJ). I address two research questions: (1) Why is climate refugeehood a matter of decolonial EJ? (2) How can decolonial EJ contribute to overcoming the colonial impasse that prevents the expansion of the notion of a refugee in international law? This case of climate coloniality is examined through the tripartite notion of the coloniality of power, knowledge, and being to decolonize the concept of refugeehood while rethinking the current model of responsibility and the subjects entitled to it.

Hotspots of Floating Plastic Particles across the North Pacific Ocean.

The pollution of the marine environment with plastic debris is expected to increase, where ocean currents and winds cause their accumulation in convergence zones like the North Pacific Subtropical Gyre (NPSG). Surface-floating plastic (>330 μm) was collected in the North Pacific Ocean between Vancouver (Canada) and Singapore using a neuston catamaran and identified by Fourier-transform infrared spectroscopy (FT-IR). Baseline concentrations of 41,600-102,700 items km-2 were found, dominated by polyethylene and polypropylene. Higher concentrations (factors 4-10) of plastic items occurred not only in the NPSG (452,800 items km-2) but also in a second area, the Papaha̅naumokua̅kea Marine National Monument (PMNM, 285,200 items km-2). This second maximum was neither reported previously nor predicted by the applied ocean current model. Visual observations of floating debris (>5 cm; 8-2565 items km-2 and 34-4941 items km-2 including smaller "white bits") yielded similar patterns of baseline pollution (34-3265 items km-2) and elevated concentrations of plastic debris in the NPSG (67-4941 items km-2) and the PMNM (295-3748 items km-2). These findings suggest that ocean currents are not the only factor provoking plastic debris accumulation in the ocean. Visual observations may be useful to increase our knowledge of large-scale (micro)plastic pollution in the global oceans.

Combining metabolic flux analysis with proteomics to shed light on the metabolic flexibility: the case of Desulfovibrio vulgaris Hildenborough.

Desulfovibrio vulgaris Hildenborough is a gram-negative anaerobic bacterium belonging to the sulfate-reducing bacteria that exhibits highly versatile metabolism. By switching from one energy mode to another depending on nutrients availability in the environments" it plays a central role in shaping ecosystems. Despite intensive efforts to study D. vulgaris energy metabolism at the genomic, biochemical and ecological level, bioenergetics in this microorganism remain far from being fully understood. Alternatively, metabolic modeling is a powerful tool to understand bioenergetics. However, all the current models for D. vulgaris appeared to be not easily adaptable to various environmental conditions. To lift off these limitations, here we constructed a novel transparent and robust metabolic model to explain D. vulgaris bioenergetics by combining whole-cell proteomic analysis with modeling approaches (Flux Balance Analysis). The iDvu71 model showed over 0.95 correlation with experimental data. Further simulations allowed a detailed description of D. vulgaris metabolism in various conditions of growth. Altogether, the simulations run in this study highlighted the sulfate-to-lactate consumption ratio as a pivotal factor in D. vulgaris energy metabolism. In particular, the impact on the hydrogen/formate balance and biomass synthesis is discussed. Overall, this study provides a novel insight into D. vulgaris metabolic flexibility.

Numerical biomechanical finite element analysis of different trimming line designs of orthodontic aligners: An in silico study

A finite element model was used to investigate the effect of different designs and thicknesses of orthodontic aligner margins on their biomechanical behavior. A three-dimensional data set of an upper jaw was imported into the 3-matic software. The upper right central incisor tooth (Tooth 11) was separated from the remaining model, and its periodontal ligament and surrounding bone were designed. Aligners were designed with four different trimming lines (scalloped, straight, scalloped extended, straight extended), each with four different thicknesses (0.3, 0.4, 0.5, and 0.6 mm). The models were imported into a finite element package (Marc/Mentat). A linear elastic constitutive material model was applied. A facial 0.2 mm bodily malalignment of tooth 11 was simulated. The maximum resultant force was in the range of 1.0 N to 2.2 N. The straight trimming designs deliver higher resultant forces compared with scalloped trimming designs. Increasing the aligner thickness and/or extending the aligner edge beyond the gingival line leads to an increase in the resultant force. All designs showed an uneven distribution of the normal contact forces over the tooth surface with a predominant concentration toward the cervical third and distal third, particularly with the extended trimming designs. All designs showed uncontrolled tipping of the tooth. Based on the current model outcomes, the use of a straight extended trimming line design for aligners is favored because of its positive impact on force distribution and, consequently, the control of tooth movement. These findings provide aligner companies and orthodontists a valuable biomechanical evidence and guidance to enhance control over tooth movement and therefore optimize treatment outcomes. This can be achieved by trimming the edges of aligners with a straight extended design and selecting the appropriate aligner thickness.

Construction of a Clear‐Sky Three Dimensional Sub‐Grid Terrain Long‐Wave Radiative Effect Parameterization Scheme Under Isotropic Assumption

AbstractRugged topography considerably regulates the surface downwelling long‐wave radiation (SDLR) flux and further affects the surface radiation and energy balances. The three dimensional sub‐grid terrain long‐wave radiative effect (3DSTLRE) is absent in most current numerical models, which usually adopt plane‐parallel schemes to simulate the SDLR flux. This study has developed a clear‐sky 3DSTLRE parameterization scheme based on the isotropic assumption of SDLR at rugged terrains and systematically evaluated its ability over the Tibetan Plateau (TP). Results show that the 3DSTLRE scheme achieves good and stable performance regardless of the horizontal resolution, time of the year, and sub‐grid terrain complexity. At different model horizontal resolutions ranging from 0.025° to 0.8°, the normalized mean absolute errors (NMAE) of the daily SDLR flux simulated by the clear‐sky 3DSTLRE scheme over most of TP are less than 0.9%, and the NMAE of the daily SDLR flux produced by the clear‐sky 3DSTLRE scheme regionally averaged over the grids with different sub‐grid terrain complexity are less than 0.25% in different months. Neglecting the 3DSTLRE in the plane‐parallel schemes may lead to clearly underestimated SDLR flux over the rugged areas, and the underestimation increases with the horizontal resolution and sub‐grid terrain complexity. At different model horizontal resolutions, the mean underestimation of the clear‐sky daily SDLR flux simulated by the plane‐parallel scheme over most of TP ranges from 5 to 20 W · m−2 with a relative underestimation of 4∼10%. The 3DSTLRE scheme can clearly reduce the biases of plane‐parallel scheme and exhibits wide application prospects in various numerical models.

A new process-based and scale-aware desert dust emission scheme for global climate models – Part II: Evaluation in the Community Earth System Model version 2 (CESM2)

Abstract. Desert dust is an important atmospheric aerosol that affects the Earth's climate, biogeochemistry, and air quality. However, current Earth system models (ESMs) struggle to accurately capture the impact of dust on the Earth's climate and ecosystems, in part because these models lack several essential aeolian processes that couple dust with climate and land surface processes. In this study, we address this issue by implementing several new parameterizations of aeolian processes detailed in our companion paper in the Community Earth System Model version 2 (CESM2). These processes include (1) incorporating a simplified soil particle size representation to calculate the dust emission threshold friction velocity, (2) accounting for the drag partition effect of rocks and vegetation in reducing wind stress on erodible soils, (3) accounting for the intermittency of dust emissions due to unresolved turbulent wind fluctuations, and (4) correcting the spatial variability of simulated dust emissions from native to higher spatial resolutions on spatiotemporal dust variability. Our results show that the modified dust emission scheme significantly reduces the model bias against observations compared with the default scheme and improves the correlation against observations of multiple key dust variables such as dust aerosol optical depth (DAOD), surface particulate matter (PM) concentration, and deposition flux. Our scheme's dust also correlates strongly with various meteorological and land surface variables, implying higher sensitivity of dust to future climate change than other schemes' dust. These findings highlight the importance of including additional aeolian processes for improving the performance of ESM aerosol simulations and potentially enhancing model assessments of how dust impacts climate and ecosystem changes.

A pattern‐based tool to support companies in sustainability‐oriented business strategies: grounded theory research in agribusiness

AbstractSustainability is an issue that echoes in our daily lives, leading to a new post‐COVID‐19 crisis trend: sustainable business. Sustainability encompasses environmental, social, and economic aspects are reflected in corporate organizational practices and sustainable business models. Despite this, integrating sustainability into business practices presents a challenge. As indicated by the literature review, 90% of the existing sustainable business models are partially capable of covering the three dimensions of sustainability. This study proposes a pattern‐based tool to support companies in the implementation of sustainability‐oriented business strategies emerging from a grounded theory research in agribusiness. The proposed pattern‐based tool maps the current sustainable business models, the material topics, and Global Reporting Initiatives that a company can adopt to implement sustainability strategies. Sustainable business models came from literature review, material topics and GRIs were identified analyzing 107 European agribusiness companies. The proposed pattern‐based tool, can be explored through three patterns designed to implement economic, social, and environmental sustainability strategies. It was applied in a case study to better demonstrate how to use the proposed tool in the practice. Discussions and implications for academia and agribusinesses are provided.

BUSINESS MODEL INNOVATION AND VALUE CO- CREATION IN DIGITAL ENTREPRENEURIAL ECOSYSTEMS


 
 
 This study describes the process of value co-creation between platform-based start-ups and investors in terms of operant and operand resources exchange. A qualitative research is conducting throughout semi-structured interviews to Spanish start- ups that have been funded in the last three years, in order to identify the key resources, actors and interactions within the process of value co-creation within a B2B digital entrepreneurial ecosystem. Our preliminary findings demonstrate that offline platforms, personal interaction and negotiation ability enable and support the co- creation process between entrepreneurs and investors. Additionally, the exchange of operant resources between entrepreneurs, investors, and partners foster the process of business model innovation improving the current business models.