University of Maine at Presque Isle

Educational policy documents are often treated as neutral administrative instruments. This paper argues that the Loi sur l’instruction publique (LIP) in Geneva functions as a "chrononormative" genre that enforces linear cognitive development as a prerequisite for institutional legitimacy. Drawing on Rhetorical Genre Studies and neurodiversity scholarship, the analysis demonstrates how the LIP’s reliance on age-based "cycles" and "retention" creates a hostile user experience for neurodivergent learners. The paper concludes by proposing Competency-Based Education (CBE) as a counter-genre that aligns institutional validity with cognitive diversity.

This paper discusses a method combining differential evolution algorithm and bidirectional long short-term memory neural network (BiLSTM) to optimize Transformer model, aiming to improve the accuracy of bank customer credit analysis and prediction. By optimizing the parameters of Transformer model through differential evolution algorithm and combining with the powerful time series analysis capability of BiLSTM, an efficient credit prediction model is constructed. In the process of model training, with the increase of the number of iterations, the correct rate of the model steadily improves and eventually becomes stable. Meanwhile, the value of the loss function also gradually decreases, indicating that the performance of the model on the training data is gradually enhanced. In addition, through confusion matrix analysis, we found that the prediction accuracy of the training set reached 86.15%, and that of the test set reached 82.91%, which showed that the model not only performed well on the training data, but also had strong generalization ability on the unseen data. In addition, the F1 score and DAUC values of the model are both positive, which further confirms the superiority of the model's prediction effect. The research results of this paper show that the Transformer model optimized by differential evolution algorithm and BiLSTM has high accuracy and good generalization ability in the analysis and prediction of bank customer credit. This optimization method not only improves the prediction performance of the model, but also enhances the stability and reliability of the model in practical application by reducing overfitting. Through this optimization strategy, banks can more accurately assess the credit risk of customers and make more informed credit decisions. This research provides a new technical means for credit risk assessment in the financial field, which is helpful to improve the efficiency and security of banking business.

A new species of branchiobdellidan annelid, Branchiobdella turkestanensis, is described having been removed from a specimen of Pontastacus kessleri (Schimkewitsch, 1886), reportedly from Turkestan but collected from the headwaters of the Syr Darya which is in the Fergana Valley, Uzbekistan, and deposited in the Natural History Museum Vienna, Austria. Specimens of its sister species, Branchiobdella kozarovi Subchev, 1978, from its type area were examined and key morphological features were reported to justify naming the new species. The size, length and shape of the spermatheca and male reproductive organs in Branchiobdella turkestanensis are approximately half the size of those in B. kozarovi. Specific differences are the spermathecal bulb is ovoid, with a terete glandular atrium in B. turkestanensis, while B. kozarovi has a terete spermathecal bulb and ental process, and a curved, wide tubular glandular atrium.

This study delves into the multifaceted challenges confronting children from vulnerable or fragile families, with a specific focus on learning disabilities, resilience (measured by grit), and material hardship—a factor intricately linked with children’s disabilities. Leveraging the predictive capabilities of machine learning (ML), our research aims to discern the determinants of these outcomes, thereby facilitating evidence-based policy formulation and targeted interventions for at-risk populations. The dataset underwent meticulous preprocessing, including the elimination of records with extensive missing values, the removal of features with minimal variance, and the imputation of medians for categorical data and means for numerical data. Advanced feature selection techniques, incorporating mutual information, the least absolute shrinkage and selection operator (LASSO), and tree-based methods, were employed to refine the dataset and mitigate overfitting. Additionally, we addressed the challenge of class imbalance through the implementation of the Synthetic Minority Over-sampling Technique (SMOTE) to enhance model generalization. Various ML models, encompassing Random Forest, Neural Networks [multilayer perceptron (MLP)], Gradient-Boosted Trees (XGBoost), and a Stacking Ensemble Model, were evaluated on the Future of Families and Child Wellbeing Study (FFCWS) dataset, with fine-tuning facilitated by Bayesian optimization techniques. The experimental findings highlighted the superior predictive performance of Random Forest and XGBoost models in classifying material hardship, while the Stacking Ensemble Model emerged as the most effective predictor of grade point average (GPA) and grit. Our research underscores the critical importance of tailored policy interventions grounded in empirical evidence to address childhood disabilities within fragile families, thus offering invaluable insights for policymakers and practitioners alike.

Per- and polyfluoroalkyl substances (PFAS) are a class of recalcitrant, highly toxic contaminants, with limited remediation options. Phytoremediation – removal of contaminants using plants – is an inexpensive, community-friendly strategy for reducing PFAS concentrations and exposures. This project is a collaboration between the Mi'kmaq Nation, Upland Grassroots, and researchers at several institutions who conducted phytoremediation field trials using hemp to remove PFAS from soil at the former Loring Air Force base, which has now been returned to the Mi'kmaq Nation. PFAS were analyzed in paired hemp and soil samples using targeted and non-targeted analytical approaches. Additionally, we used hydrothermal liquefaction (HTL) to degrade PFAS in the harvested hemp tissue. We identified 28 PFAS in soil and found hemp uptake of 10 of these PFAS. Consistent with previous studies, hemp exhibited greater bioconcentration for carboxylic acids compared to sulfonic acids, and for shorter-chain compounds compared to longer-chain. In total, approximately 1.4 mg of PFAS was removed from the soil via uptake into hemp stems and leaves, with an approximate maximum of 2% PFAS removed from soil in the most successful area. Degradation of PFAS by HTL was nearly 100% for carboxylic acids, but a portion of sulfonic acids remained. HTL also decreased precursor PFAS and extractable organic fluorine. In conclusion, while hemp phytoremediation does not currently offer a comprehensive solution for PFAS-contaminated soil, this project has effectively reduced PFAS levels at the Loring site and underscores the importance of involving community members in research aimed at remediating their lands.

Works that use point cloud avoid wasting time and cost of collection, using simulators and datasets available in the literature. In this way, there is access to an unlimited and organized amount of point clouds, an ideal setting for deep learning networks and Vehicular ad hoc networks (VANETs). However, models trained with synthetic data present problems when applied to real-world data.This work proposes the use of deep learning in the recognition of 3D objects captured with a Light Detection and Ranging (LIDAR), including a pre-processing stage. In addition, it is proposed two datasets, a real-world and a syntetic; each dataset includes three classes. A method of pre-processing is proposed to circumvent the distribution discrepancies of the proposed datasets and the existing datasets from literature, such as ModelNet. We use deep learning with the PointNet method, as it supports raw data from point clouds as input to the network. We performed three evaluation approaches: training and testing steps with the proposed datasets using <xref ref-type="disp-formula" rid="deqn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1)</xref> Lidar3DNetV1, which is a proposed network in this paper, <xref ref-type="disp-formula" rid="deqn2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(2)</xref> PointNet, and (3) classification of ModelNet datasets using Lidar3DNetV1. The proposed network achieved 98.33% of accuracy and a testing time of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$88~\mu \text{s}$ </tex-math></inline-formula> in the synthetic dataset, while in the real-world dataset, the network reached 98.48% and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$145~\mu \text{s}$ </tex-math></inline-formula> in accuracy and testing time, respectively.

Upper Miocene (~6 Ma) sediments from a north-south transect of Ocean Drilling Program Holes 848B, 849B, 850B, 851B across the equator in the Eastern Pacific Ocean record an episode of unusual skeletal variability associated with the silicoflagellate Bachmannocena diodon subsp. nodosa (Bukry) Bukry, 1987. Skeletons in this plexus persisted for between 55,000 and 75,000 years and include an arch across a 2-sided basal ring, a highly unusual morphology for the genus. Groups of skeletal morphotypes were common within the plexus episode, and occur in a systematic sequence that is consistent between sites. As a result of this study, silicoflagellates previously placed in the genus Neonaviculopsis Locker and Martini, 1986 are now considered part of the Bachmannocena diodon subsp. nodosa plexus.

Abstract Determining what caused the global Last Glaciation and last glacial termination, despite opposing orbital summer insolation signatures between the polar hemispheres, remains a puzzle of paleoclimatology. This problem can be addressed by comparing chronologies of glaciation from different latitudes and different climatic regimes in both hemispheres. Here, we present a10Be surface‐exposure chronology of glacial landforms constructed during and since the local Last Glaciation in the continental environment of Central Asia in the high Mongolian Altai (49°N, 88°E). Four belts of lateral moraines document maximal phases of the former Khoton glacier at 35,440 ± 980 years ago, 23,430 ± 850 years ago, 20,780 ± 610 years ago, and 19,520 ± 550 years ago. Our chronology indicates that deglaciation from these maximal positions began as early as 18,810 ± 510 years ago, was well underway by 17,680 ± 510 years ago, and was nearly completed by 16,040 ± 490 years ago. Overall, our chronology shows that glaciation in western Mongolia overlapped with the global Last Glacial Maximum and that extensive recession from glacial‐to‐interglacial limits took place early in the last glacial termination during Heinrich Stadial 1. Khoton Nuur deglaciation led the demise of large Northern Hemisphere ice sheets and increases in radiative forcing agents by several millennia. We suggest that this rapid switch in the mode of glaciation implies the involvement of an additional climatic factor that could have produced locally rapid warming and deglaciation ∼18,800–16,000 years ago.