Classification Of Types Research Articles

Lithofacies Characteristics of the Lower Cretaceous Qing 1 Member in the Heiyupao Depression, Northern Binbei Area of the Songliao Basin

Strategic breakthroughs have been made in the exploration and evaluation of Gulong shale oil in the Songliao Basin. However, the Heiyupao Depression, located near the Gulong Depression, hosts a thick section of the Qingshankou Formation shale that has not been extensively studied. This paper presents novel insights into the lithofacies characteristics, depositional environment, and reservoir features of the Qingshankou Formation shale in the Heiyupao Depression, with a specific focus on the origin and maturation of organic-rich shale. Four core wells were drilled, and 152 core samples were analyzed through a variety of techniques, including rock type classification, mineral composition, TOC content, rare earth elements, rock pyrolysis, organic matter type determination, and CT scanning. Results indicate that the Qingshankou shale is dominated by felsic compositions and Type I kerogen, with organic maturity varying across the section. Based on lithology, sedimentary structures, mineralogy, and organic matter abundance, five distinct lithofacies are identified: high-organic mud-rich felsic shale, high-organic sand-rich felsic shale, medium-organic sand-rich felsic shale, medium-organic massive shale, and low-organic sand-rich felsic shale. Notably, the Type A lithofacies (high-organic mud-rich felsic shale) is identified as a primary source rock due to its intergranular and organic matter pores, albeit with low porosity and poor connectivity. In contrast, the Type E lithofacies (low-organic sand-rich felsic shale) have high porosity, well-developed micro- and nano-scale pores, and strong connectivity, marking them as the primary reservoirs. The characteristics of this region differ significantly from those of Gulong shale oil, requiring different extraction strategies. The mineral composition of such shale is predominantly felsic rather than mixed. The findings not only provide theoretical support for the exploration of complex lacustrine shale in the Songliao Basin but also offer valuable insights for the resource development of similar non-marine shale systems worldwide.

Mapping Forest Types along Ecological Gradient in Pakistan

Abstract Environmental variables influence the spatial distribution, pattern and structure of vegetation in complex mountainous landscape along varied geographical conditions. Present study explored the spatial distribution of four forest types across ecological gradient based on field data, climatic, topographic, and soil variables based on stepwise linear regression (SLR), decision trees (DT), random forests (RF), and Maxent modeling. Results showed that climatic variables. Results showed that climatic variables (particularly annual precipitation, precipitation of warmest and coldest quarter) have achieved the highest predictive capability for forest types mapping and outperformed other explanatory variables (topographic and edaphic). Among the rest of variables, elevation, sand contents and SOC have achieved good correlation and contained useful information in order explain forest type spatial distribution. Analysis of regression models revealed that RF has achieved the highest correlation (R2=0.923) and lowest RMSE 0.54, followed by the SLR model in which R2 value has been progressively increased from 0.41 (error 2.02) to 0.917 (0.77) with respect four different predictors models, each separate developed for topographic (n=5), soil (n-11), climatic (n=11) and combined of all datasets (n=27). CHAID DT showed that annual precipitation was the most important predictor for forest type classification with risk estimate of 0.412 (std error 0.31) and 0.478 (std error 0.52) for training and validation respectively. Maxent modeling showed impressive predictive performance of all forest types (STPF, MTF and DTF) along ecological gradient with average AUC values of 0.968, 0.918, and 0.940 respectively and climatic variables have highest gain compared to topographic and soil predictors. Present study suggests that mapping of forest types through machine learning algorithms may be improved by incorporating other explanatory variables such microclimate, soil types, nutrients, anthropogenic, demographic factors and spectral indices.

Diagnostic Challenge of Phenotypic Variability in COL2A1-Related Disorders: Four Novel Variants and Expanding the Clinical Spectrum.

Heterozygous COL2A1 gene mutations are associated with type 2 collagenopathies, characterized by a wide, diverse, and overlapping clinical spectrum in related diseases. Our goal is to describe the clinical, radiological, and molecular findings of patients with COL2A1-related dysplasia and investigate the phenotype-genotype correlation. We also aim to emphasize the challenge of categorizing COL2A1-related diseases with similar clinical and radiological phenotypes. Six patients from five unrelated families presented with short-trunk dwarfism, delayed motor milestones, waddling gait, normal intelligence, and similar radiological features, including delayed epiphyseal ossification, epimetaphyseal changes, scoliosis, lordosis, and platyspondyly, underwent whole exome sequencing. Demographic, clinical, laboratory, and radiological data were retrospectively obtained from hospital records. Segregation analysis was conducted using Sanger sequencing in all patients. Based on clinical, radiological, and molecular results, six patients were categorized into Kniest dysplasia, Spondyloepiphyseal dysplasia congenita, and Spondyloepimetaphyseal dysplasia Strudwick type. Four novel variants (c.1023+2T>C, p.Gly465Asp, p.Gly855Asp, p.Gly669Ala) were identified in the COL2A1 gene. Accurate classification of type 2 collagenopathies is essential for providing genetic counseling. Predicting extraskeletal manifestations and reducing morbidity through early diagnosis and treatment will significantly improve the quality of life for patients.

Comparison of Agricultural Crop Type Classifications with Different Machine Learning Algorithms (RF-SVM-ANN-XGBoost) by Generating Ground Truth Data from Farmer Declaration Parcels

In large-scale agricultural crop classification studies (Turkey, Adana, Çukurova Plain, 2500 km²), collecting sufficient and accurate ground truth data is costly, time-consuming, and unsustainable. This study utilized parcels registered in the Farmer Registration System (FRS) as ground truth data. By analyzing time series EVI curves, discrepancies were identified between declared and actual crops. Erroneous parcels were eliminated, and the corrected data were used in the classification process.Using multi-temporal Sentinel-2 images from 2021, this study compared the performance of Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost) algorithms for classifying crops like citrus, cotton, maize, peanut, sunflower, watermelon, wheat, and double-crop combinations (e.g., wheat-cotton, wheat-maize). The classification utilized 121 features (11 images × 10 Sentinel-2 bands + EVI). XGBoost achieved the highest overall accuracy (92.14%), followed by RF (89.15%), SVM (86.14%), and ANN (85.48%).The EVI index proved critical, particularly in separating spectral curves of double crops. While single crops like cotton, maize, and wheat yielded high classification accuracy, double crops with overlapping phenological stages had lower accuracy. The study highlighted that crops at distinct phenological stages performed well across algorithms, whereas crops with similar stages struggled to achieve high accuracy.This method of using corrected farmer-declared parcels (FDP) as ground truth data demonstrated high classification performance across all algorithms, proving its reliability. The findings emphasize that FDP can effectively replace traditional field data collection, reducing costs and improving efficiency. This classification approach supports agricultural production monitoring, yield estimation, water resource analysis, and sustainable policy-making, serving as a robust tool for agricultural evaluation

Representation Learning of Multi-Spectral Earth Observation Time Series and Evaluation for Crop Type Classification

Representation Learning of Multi-Spectral Earth Observation Time Series and Evaluation for Crop Type Classification

Describing seasonal mixtures of cloud regimes via “regimes of regimes”

Abstract We propose a new type of cloud classification, relevant to monthly or longer time scales, but which inherently still encompasses cloud subgrid variability information at ~100 km scales. Our proposed classification partitions frequencies of occurrence over these scales of previously defined cloud regimes (CRs). We call the resulting distinct cloud entities regimes of regimes (RORs). While the CRs have been previously shown to successfully classify daily mesoscale subgrid variability via distributions of cloud fraction within distinct combinations of cloud top pressure and cloud optical thickness, the RORs essentially represent the prevalent seasonal mixtures of these CRs. RORs thus embody the seasonal cloudiness of a mesoscale region. We show that each ROR can still be associated with more traditional cloud classifications via composites of coincident active (lidar and cloud radar) cloud views. In a first application that gauges the potential utility of RORs, we pair them with CERES EBAF radiative fluxes to gain insight into recent trends of the cloud radiative effect. The ROR corresponding to an environment of shallow convection stands out in this analysis largely because of its declining population. Our study demonstrates the potential of RORs to categorize globally mesoscale cloudiness at monthly/seasonal scales and to serve as proxies of different atmospheric states at these scales.

Machine learning recognition of hybrid lead halide perovskites and perovskite-related structures from X-ray diffraction patterns.

Identification of crystal structures is a crucial stage in the exploration of novel functional materials. This procedure is usually time-consuming and can be false-positive or false-negative. This necessitates a significant level of expert proficiency in the field of crystallography and, especially, requires deep experience in perovskite-related structures of hybrid perovskites. Our work is devoted to the machine learning classification of structure types of hybrid lead halides based on available X-ray diffraction data. Here, we proposed a simple approach for quickly identifying the dimensionality of inorganic substructures, types of connections of lead halide polyhedra and structure types using common powder XRD data and a ML-decision tree classification model. The average accuracy of our ML algorithm in predicting the dimensionality of inorganic substructures, the type of connection of lead halide and inorganic substructure topology based on theoretically calculated XRD patterns among 14 most common structure types reached 0.76 ± 0.07, 0.827 ± 0.028 and 0.71 ± 0.05, respectively. To test the transferability of the developed ML model, we expanded our dataset to 30 structure types. The average accuracy of our ML algorithm in predicting the dimensionality of inorganic substructures, the type of connection of lead halide and inorganic substructure topology based on theoretically calculated XRD patterns among 30 structure types reached 0.820 ± 0.022, 0.74 ± 0.05 and 0.633 ± 0.018, respectively. The validation of our decision tree classification ML model on experimental XRD data shows accuracies of 1.0 and 0.82 for dimension and structure type prediction. Thus, our approach can significantly simplify and accelerate the interpretation of highly complicated XRD data for hybrid lead halides.

ADVANTAGES AND DISADVANTAGES OF VECTOR SENSORS COMPARED TO CLASSICAL ACOUSTIC SENSORS AND ACOUSTIC ANTENNAS

The report examines the characteristics, advantages and disadvantages of vector acoustic sensors used in recent years in civil and military information systems for the detection, localization and classification of various types of targets and noise sources with high dynamics of emitted signals. A comparison with classical acoustic sensors and discrete acoustic antennas is given. Conclusions and recommendations are made. They are based on a review of publications and experiments.

Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data

Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data

Religious Syncretism in Ossetian Festive Rituals

The article studies one of the aspects of Ossetian ethnic religion as an interesting ideological complex that was formed at the intersection of a number of diverse cultures of Antiquity, the Middle Ages and the modern era. Its relevance is due to the growing tendency to study the Ossetian ethnic religion as a unique “unmixed” object, and therefore the category of “syncretism”, when applied to it, often raises objections. This study, therefore, can be considered as the author’s interpretation, as an outside perspective of the cult, of which the author herself is not an adherent. The central focus of the study is the Ossetian holiday of the Yuletide cycle Bynaty-khitsau, associated in folk beliefs with demonic forces. Within the paper, the main elements of its ritual are identified and compared with similar elements in Christianity, Judaism and Zoroastrianism. This is the first time this problem is addressed in this manner. The methodological foundation of the study is the classification of historical types of religious syncretism, as proposed by E.V. Belyaeva. In addition, as the research is synchronic, it is carried out within the framework of the structuralism of C. Lévi-Strauss, which views all cultural systems (language, mythology, religion, art, customs, traditions) as sign systems.

Correlation analysis of global sagittal alignment of the spine in cases of low-grade degenerative lumbar spondylolisthesis.

Disruptions in global sagittal spinal alignment can lead to changes in global sagittal spinal alignment, often manifesting as sagittal malalignment, where the trunk shifts forward. We proposed that these alignment changes are linked to degenerative lumbar spondylolisthesis (DS). The objective was to assess global spinal alignment in low-grade DS using sagittal vertical axis (SVA) classification. The patients with DS were categorized into three groups based on the adult spinal deformity classification: type I, defined by a SVA of less than 40mm; type II, with an SVA ranging from 40mm to 95mm; and type III, where the SVA is 95mm or greater. The study compared age and various sagittal parameters among these groups, including thoracic kyphosis (TK), lumbar lordosis (LL), sacral slope (SS), pelvic tilt (PT), and pelvic incidence (PI). Notable differences were observed between type I and type II, as well as between type I and type III, whereas no substantial variation was detected between type II and type III. Additionally, PI increased with advancing type classification, with a distinct contrast observed between type I and type III. A high PI is a key risk factor for worsening SVA in DS, and it may contribute to the initiation and progression of the condition.

Meta-analyses of mouse and human prostate single-cell transcriptomes reveal widespread epithelial plasticity in tissue regression, regeneration, and cancer

BackgroundDespite extensive analysis, the dynamic changes in prostate epithelial cell states during tissue homeostasis as well as tumor initiation and progression have been poorly characterized. However, recent advances in single-cell RNA-sequencing (scRNA-seq) technology have greatly facilitated studies of cell states and plasticity in tissue maintenance and cancer, including in the prostate.MethodsWe have performed meta-analyses of new and previously published scRNA-seq datasets for mouse and human prostate tissues to identify and compare cell populations across datasets in a uniform manner. Using random matrix theory to denoise datasets, we have established reference cell type classifications for the normal mouse and human prostate and have used optimal transport to compare the cross-species transcriptomic similarities of epithelial cell populations. In addition, we have integrated analyses of single-cell transcriptomic states with copy number variants to elucidate transcriptional programs in epithelial cells during human prostate cancer progression.ResultsOur analyses demonstrate transcriptomic similarities between epithelial cell states in the normal prostate, in the regressed prostate after androgen-deprivation, and in primary prostate tumors. During regression in the mouse prostate, all epithelial cells shift their expression profiles toward a proximal periurethral (PrU) state, demonstrating an androgen-dependent plasticity that is restored to normal during androgen restoration and gland regeneration. In the human prostate, we find substantial rewiring of transcriptional programs across epithelial cell types in benign prostate hyperplasia and treatment-naïve prostate cancer. Notably, we detect copy number variants predominantly within luminal acinar cells in prostate tumors, suggesting a bias in their cell type of origin, as well as a larger field of transcriptomic alterations in non-tumor cells. Finally, we observe that luminal acinar tumor cells in treatment-naïve prostate cancer display heterogeneous androgen receptor (AR) signaling activity, including a split between AR-positive and AR-low profiles with similarity to PrU-like states.ConclusionsTaken together, our analyses of cellular heterogeneity and plasticity provide important translational insights into the origin and treatment response of prostate cancer. In particular, the identification of AR-low tumor populations suggests that castration-resistance and predisposition to neuroendocrine differentiation may be pre-existing properties in treatment-naïve primary tumors that are selected for by androgen-deprivation therapies.

Evaluating the Efficacy and Safety of Halo-Femoral Traction and Halo-Gravity Traction Techniques in Severe Kyphoscoliosis With Spinal Cord Risk Classification (SCRC) Type 3 Over the Apex.

Retrospective cohort study. To analyze the efficacy and safety of Halo-femoral traction (HFT) following spinal release, and preoperative Halo-gravity traction (HGT) in patients with severe spinal kyphoscoliosis and spinal cord risk classification (SCRC) type 3 at the apex. A total of 73 patients (24 males, 49 females, mean age 22.4 ± 6.4 years) and 56 patients (15 males, 41 females, mean age 22.9 ± 10.4 years) were included in the HFT and HGT group, respectively. Radiographic parameters were measured at the initial assessment, post-traction, post-final surgery, and during each follow-up. Neurologic function was assessed using the Frankel score system. IONM alerts and all complications were documented. Quality-of-life was evaluated using the SF-36 questionnaire. In the HFT vs HGT group, the total correction rates were 39.9 ± 7.2% v.s. 41.3 ± 6.8% for the major Cobb and 36.6 ± 9.3% v.s. 44.4 ± 9.2% for global kyphosis (GK) after final surgery, respectively. The traction contributions were 57.6 ± 11.1% v.s. 52.3 ± 9.3% for major Cobb and 70.1 ± 10.5% v.s. 63.9 ± 11.1% for global kyphosis (GK), respectively. More than half of the total correction can be achieved gradually and safely through preoperative traction with patients in an awake state. No deterioration in neurological function was found post-final surgery. During the last follow-up, SF-36 questionnaire scores improved significantly in both groups (P < .05). Significant outcomes can be expected in patients with severe kyphoscoliosis, even with spinal cord risk classification (SCRC) type 3 at the apex undergoing HFT and HGT.

StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality

Road unevenness significantly impacts the safety and comfort of traffic participants, especially vulnerable groups such as cyclists and wheelchair users. To train models for comprehensive road surface assessments, we introduce StreetSurfaceVis, a novel dataset comprising 9,122 street-level images mostly from Germany collected from a crowdsourcing platform and manually annotated by road surface type and quality. By crafting a heterogeneous dataset, we aim to enable robust models that maintain high accuracy across diverse image sources. As the frequency distribution of road surface types and qualities is highly imbalanced, we propose a sampling strategy incorporating various external label prediction resources to ensure sufficient images per class while reducing manual annotation. More precisely, we estimate the impact of (1) enriching the image data with OpenStreetMap tags, (2) iterative training and application of a custom surface type classification model, (3) amplifying underrepresented classes through prompt-based classification with GPT-4o and (4) similarity search using image embeddings. Combining these strategies effectively reduces manual annotation workload while ensuring sufficient class representation.

SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data.

Rapid technological advancements have made it possible to generate single-cell data at a large scale. Several laboratories around the world can now generate single-cell transcriptomic data from different tissues. Unsupervised clustering, followed by annotation of the cell type of the identified clusters, is a crucial step in single-cell analyses. However, there is no consensus on the marker genes to use for annotation, and cell-type annotation is currently mostly done by manual inspection of marker genes, which is irreproducible, and poorly scalable. Additionally, patient-privacy is also a critical issue with human datasets. There is a critical need to standardize and automate cell-type annotation across datasets in a privacy-preserving manner. Here, we developed SwarmMAP that uses Swarm Learning to train machine learning models for cell-type classification based on single-cell sequencing data in a decentralized way. SwarmMAP does not require any exchange of raw data between data centers. SwarmMAP has a F1-score of 0.93, 0.98, and 0.88 for cell type classification in human heart, lung, and breast datasets, respectively. Swarm Learning-based models yield an average performance of 0.907 which is on par with the performance achieved by models trained on centralized data (p-val=0.937, Mann-Whitney U Test). We also find that increasing the number of datasets increases cell-type prediction accuracy and enables handling higher cell-type diversity. Together, these findings demonstrate that Swarm Learning is a viable approach to automate cell-type annotation. SwarmMAP is available at https://github.com/hayatlab/SwarmMAP.

Biological Activities and Mechanisms of Actions of Bioactive Compounds (BACS) Identified in Pure and Ternary Blends of Cocoyam, Soya Bean and Bambara Groundnut Flour Using Gas Chromatography-Mass Spectrometry (GC-MS) Technique: A Review

Background: functional foods contain bioactive compounds (BACS) and can be sourced from both animals and plants like cocoyam (CY), soya bean (SB) and bambara groundnut (BGN). previous studies have reported various bacs in each of these plants but literature is scanty on the types and amount, of bacs in such flour blends and formulations. when in combinations as blend, anti-oxidant, interactions occur, resulting in either potentiation, additive, synergistic or antagonistic effect, depending on several various variables. their in-vivo bioactivities which are due to various structural features of the BACS, can be antioxidant, anti-inflammatory, hypocholestrolaemic, antimicrobial and anti-diabetic activities. in this review the various bacs identified by gas chromotagraphy (GC)-mass spectrometry (MS) technique in the various formulations of CY-SB-BGN flour blends were researched for their biological activities and reported mechanisms of actions. Methodology: a google search of a study on bacs identified using gc-ms in cy-sb-bgn flour blend was conducted and one paper was identified. the bacs reported in the study was noted and literature search of the various biological activities of such compounds and their mechanisms of actions were conducted. the results were documented and discussed. Results and Discussion: the study revealed that thiadiazole, stilbene, aziridine, thiourea, amphetamine/phenylethylene, artemisinin, monoterpenoids, naphthalenes, i,4-diazpanes, phenols and flavanoids were the identified bacs in cy-sb-bgn flour blend formulations with both the pure and ternary blends showing different family classifications of compounds and different types of compounds in one family class. the biological activities and mechanisms of actions included antimicrobial, antioxidant, antiinflammatory and antidiabetic activities with reported mechanisms of actions for each of these invivo activities. Conclusion: there are bacs in cy-sb-bgn formulations which are reported from studies to possess anti-microbial, anti-inflammatory, anti-oxidant and anti-diabetic properties invivo, with very established mechanisms of actions, making the use of such plant foods in disease management scientific, hence the recommendation of such plant foods use as adjunct in chronic disease management. Keywords: Bioactive compounds, Plant foods, Cocoyam, Soya Bean, Bambara Groundnut

Investigating misclassification of type 1 diabetes in a population-based cohort of British Pakistanis and Bangladeshis using polygenic risk scores

Correct classification of type 1 (T1D) and type 2 diabetes (T2D) is challenging due to overlapping clinical features and the increasingly early onset of T2D, particularly in South Asians. Polygenic risk scores (PRSs) for T1D and T2D have been shown to work relatively well in South Asians, despite being derived from largely European-ancestry samples. Here we used PRSs to investigate the rate of potential misclassification of diabetes amongst British Bangladeshis and Pakistanis. Using linked health records from the Genes & Health cohort (n = 38,344) we defined two reference groups meeting stringent diagnostic criteria: 31 T1D cases, 1842 T2D cases, and after excluding these, two further groups: 839 insulin-treated diabetic individuals with ambiguous features and 5174 non-diabetic controls. Combining these with 307 confirmed T1D cases and 307 controls from India, we calculated ancestry-corrected PRSs for T1D and T2D, with which we estimated the proportion of T1D cases within the ambiguous group at ~ 6%, dropping to ~ 4.5% within the subset who had T2D codes in their health records (and are thus most likely to have been misclassified). We saw no significant association between the T1D or T2D PRS and BMI at diagnosis, time to insulin, or the presence of T1D or T2D diagnostic codes amongst the T2D or ambiguous cases, suggesting that these clinical features are not particularly helpful for aiding diagnosis in ambiguous cases. Our results emphasise that robust identification of T1D cases and appropriate clinical care may require routine measurement of diabetes autoantibodies and C-peptide.

Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types

Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of renal cell carcinoma subtypes. Nonetheless, the broader categorization of renal tissue into non-neoplastic normal tissue, benign tumor and malignant tumor remains understudied. This gap in research can primarily be attributed to the limited availability of extensive datasets including benign tumor and normal tissue in addition to specific type of renal cell carcinoma, which hampers the ability to conduct comprehensive studies in these broader categories. This research introduces a model aimed at classifying renal tissue into three primary categories: normal (non-neoplastic), benign tumor, and malignant tumor. Utilizing digital pathology while slide images (WSIs) from nephrectomy specimens of 2,535 patients from multiple institutions, the model provides a foundational approach for distinguishing these key tissue types. The study utilized a dataset of 12,223 WSIs comprising 1,300 WSIs of normal tissue, 700 WSIs of benign tumors, and 10,223 WSIs of malignant tumors. Employing the ResNet-18 architecture and a Multiple Instance Learning approach, the model demonstrated high accuracy, with F1-scores of 0.934 (CI: 0.933–0.934) for normal tissue, 0.684 (CI: 0.682–0.687) for benign tumors, and 0.878 (CI: 0.877–0.879) for malignant tumors. The overall performance was also notable, achieving a weighted average F1-score of 0.879 (CI: 0.879–0.880) and a weighted average area under the receiver operating characteristic curve of 0.969 (CI: 0.969–0.969). This model significantly aids in the swift and accurate diagnosis of renal tissue, encompassing non-neoplastic normal tissue, benign tumor, and malignant tumor.

Incidence of childhood and youth epilepsy: A population-based prospective cohort study utilizing current International League Against Epilepsy classifications for seizures, syndromes, and etiologies.

This study was undertaken to describe incidence and distribution of seizures, etiologies, and epilepsy syndromes in the general child and youth population, using the current International League Against Epilepsy (ILAE) classifications. The study platform is the Norwegian Mother, Father, and Child Cohort Study (MoBa). Epilepsy cases were identified through registry linkages facilitated by Norway's universal health care system and mandatory reporting to the Norwegian Patient Registry. A standardized protocol guided medical record review, leading to validation of diagnoses and classification of seizures, epilepsy types, syndromes, and etiologies based on the latest ILAE criteria. MoBa included 111 365 participants aged 12-21 years by the end of follow-up on December 31, 2020. We identified 1053 children and youth with epilepsy (CYE). A defined epilepsy syndrome and/or identified etiology was found in 76% of CYE in this population-based study. Seizure types exhibited variation by age at onset. Focal epilepsies were predominant, occurring in 61% of CYE, whereas generalized epilepsies were identified in 24% of CYE. Standard clinical assessment identified etiology in 30% of CYE and in 55% with onset age < 2 years. Structural and identified genetic etiologies constituted 21% and 10%, respectively. Including presumed genetic and rare etiologies, 53% exhibited known etiology. A defined ILAE epilepsy syndrome was found in 53% of CYE. The cumulative incidence per 1000 children of the following ILAE epilepsy syndrome groups were as follows: self-limited epilepsies, 2.25; idiopathic generalized epilepsies, 1.75; and developmental and/or epileptic encephalopathies, 2.62. Using the new ILAE classifications, this population-based childhood study provides incidences of seizures, epilepsies, and epilepsy syndromes. Half of epilepsy cases are classified as an ILAE epilepsy syndrome with its prognostic and therapeutic implications, but a substantial proportion of cases still have unknown etiology.

Vibration-Based Damage Prediction in Composite Concrete–Steel Structures Using Finite Elements

The prediction of structural damage through vibrational analysis is a critical task in the field of composite structures. Structural defects and damage can negatively influence the load-carrying capacity of the beam. Therefore, detecting structural damage early is essential to preventing catastrophic failures. This study addresses the challenge of predicting damage in composite concrete–steel beams using a vibration-based finite element approach. To tackle this complex task, a finite element model to a quasi-static analysis emulating a four-point pure bending experimental test was performed. Notably, the numerical model equations were carefully modified using the Newton–Raphson method to account for the stiffness degradation resulting from material strains. These modified equations were subsequently employed in a modal analysis to compute modal shapes and natural frequencies corresponding to the stressed state. The difference between initial and damaged modal shape curvatures served as the foundation for predicting a damage index. The approach effectively captured stiffness degradation in the model, leading to observable changes in modal responses, including a reduction in natural frequencies and variations in modal shapes. This enabled the accurate prediction of damage instances during construction, service, or accidental load scenarios, thereby enhancing the structural and operational safety of composite system designs. This research contributes to the advancement of vibration-based methods for damage detection, emphasizing the complexities in characterizing damage in composite structural geometries. Further exploration and refinement of this approach are essential for the precise classification of damage types.