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  In the generative tradition, the conditions on subextraction (i.e., the extraction from a phrase XP) have been captured by either one of the following incompatible formulations: (i) a formulation based on the notion of phase (the PIC, i.e., the Phase Impenetrability Condition); (ii) a formulation based on the notion of selection and on the configuration in which XP occurs (the subjacency condition). We provide two empirical case studies, both focused on Italian, to test the predictions of formulations (i)-(ii): inverse copular sentences and the complement of quality. Our main finding is that subextraction is naturally accounted for by the subjacency condition, not by the PIC. In this way, this study brings fresh empirical evidence to bear on the role of phases in the theory of locality.

A significant amount of research has indicated that students’ procrastination tendencies are an important factor influencing the performance of students in online learning. It is, therefore, vital for educators to be aware of the presence of such behavior trends as students with lower procrastination tendencies usually achieve better than those with higher procrastination. In the present study, we propose a novel algorithm—using student’s assignment submission behavior—to predict the performance of students with learning difficulties through procrastination behavior (called PPP). Unlike many existing works, PPP not only considers late or non-submissions, but also investigates students’ behavioral patterns before the due date of assignments. PPP firstly builds feature vectors representing the submission behavior of students for each assignment, then applies a clustering method to the feature vectors for labelling students as a procrastinator, procrastination candidate, or non-procrastinator, and finally employs and compares several classification methods to best classify students. To evaluate the effectiveness of PPP, we use a course including 242 students from the University of Tartu in Estonia. The results reveal that PPP could successfully predict students’ performance through their procrastination behaviors with an accuracy of 96%. Linear support vector machine appears to be the best classifier among others in terms of continuous features, and neural network in categorical features, where categorical features tend to perform slightly better than continuous. Finally, we found that the predictive power of all classification methods is lowered by an increment in class numbers formed by clustering.

Previous literature on the typology of gerund clauses in Portuguese has overlooked a peculiar type of clauses which are always introduced by como (‘as’) and display an array of characteristics that set them apart from all other gerund clauses (and from other, somehow similar, constructions in different languages). In this paper, we provide an in-depth syntactic and semantic characterisation of these como-gerund clauses and the contexts in which they arise, highlighting their similarities and differences regarding other constructions, namely resultative and depictive secondary predicates. We put forward a proposal to deal with their syntactic configurations and the restrictions they exhibit. We also propose that como is obligatory in these clauses because it marks a type-shift operation, which gives como gerund clauses a predicative interpretation, usually found in the nominal domain.

  In the generative tradition, the conditions on subextraction (i.e., the extraction from a phrase XP) have been captured by either one of the following incompatible formulations: (i) a formulation based on the notion of phase (the PIC, i.e., the Phase Impenetrability Condition); (ii) a formulation based on the notion of selection and on the configuration in which XP occurs (the subjacency condition). We provide two empirical case studies, both focused on Italian, to test the predictions of formulations (i)-(ii): inverse copular sentences and the complement of quality. Our main finding is that subextraction is naturally accounted for by the subjacency condition, not by the PIC. In this way, this study brings fresh empirical evidence to bear on the role of phases in the theory of locality.

This paper investigates Numberless Indefinite Definites (NIDs) in Italian, an understudied class of nominal expressions that, despite their morphologically definite and singular form, convey an indefinite and number-neutral interpretation (e.g., L’hotel ha la piscina lit. ‘the hotel has the pool’). The study addresses two main questions: (i) what grammatical factors license NIDs in Italian? And (ii) how can their indefinite and number-neutral readings be compositionally derived? First, I argue that NIDs are exclusively licensed in the object position of have-predicates, provided that the resulting V+NID combination expresses a characterizing property of the subject. Second, I propose a novel compositional analysis of NIDs. To account for number-neutrality, I argue that these constructions lack Number specification and do not project a Number Phrase. As a result, they denote definite kinds, which receive an existential interpretation via Derived Kind Predication when combining with the selecting have-predicate. Besides providing a compositional account of the existential and number-neutral interpretation of NIDs, this analysis also explains their obligatory narrow scope behavior and their restriction to taxonomic modification.

Anoikis, a mechanism of programmed apoptosis, plays an important role in growth and metastasis of tumors. However, there are still few available comprehensive reports on the impact of anoikis on colorectal cancer. A clustering analysis was done on 133 anoikis-related genes in GSE39582, and we compared clinical features between clusters, the tumor microenvironment was analyzed with algorithms such as \"Cibersort\" and \"ssGSEA\". We investigated risk scores of clinical feature groups and anoikis-associated gene mutations after creating a predictive model. We incorporated clinical traits to build a nomogram. Additionally, the quantitative real-time PCR was employed to investigate the mRNA expression of selected anoikis-associated genes. We identified two anoikis-related clusters with distinct prognoses, clinical characteristics, and biological functions. One of the clusters was associated with anoikis resistance, which activated multiple pathways encouraging tumor metastasis. In our prognostic model, oxaliplatin may be a sensitive drug for low-risk patients. The nomogram showed good ability to predict survival time. And SIRT3, PIK3CA, ITGA3, DAPK1, and CASP3 increased in CRC group through the PCR assay. Our study identified two distinct modes of anoikis in colorectal cancer, with active metastasis-promoting pathways inducing an anti-anoikis subtype, which has a stronger propensity for metastasis and a worse prognosis than an anoikis-activated subtype. Massive immune cell infiltration may be an indicator of anoikis resistance. Anoikis' role in the colorectal cancer remains to be investigated.

Despite new efficacy drugs and cell therapy have been used for multiple myeloma (MM) patients, some patients will relapse over time. We wonder the immune system play a vital role as well as MM cell during the development of disease. It is clear that the characteristic of myeloma cell is associated with the survival of MM patients. However, the link between the immune profiling and the prognosis of the disease is still not entirely clear. As more study focus on the role of immunity on multiple myeloma pathogenesis. There are plenty of study about the predictive role of immunity on the survival of multiple myeloma patients. Up to mow, the majority reviews published have focused on the immunotherapy and immune pathogenesis. It is indispensable to overlook the predictive role of immunity on multiple myeloma patients. Here, we give a review of vital previous works and recent progress related to the predictive role of immune profiling on multiple myeloma, such as absolute lymphocyte count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocytes and cytokines.

Notions like “nature” or “culture” do not denote a universal reality but a particular way, devised by the Moderns, of carving ontological domains in the texture of things. Other civilizations have devised different ways of detecting qualities among existents, resulting in other forms of organizing continuity and discontinuity between humans and nonhumans, of aggregating beings in collectives, of defining who or what is capable of agency and knowledge. The paper emphasizes that these processes of ontological predication are not “worldviews” but, properly speaking, styles of worlding. Ontology is taken here as designating a more elementary analytical level to study worlding than the one anthropology usually calls for. It is at this level, where basic inferences are made about the kinds of beings that exist and how they relate to each other, that anthropology can best fulfill its mission to account for how worlds are composed.

This study proposes an interpretable deep learning framework to address the high-dimensional and inherently unpredictable challenges associated with oil and gas drilling and completion operations. By comparing TabNet, Tab Transformer, Hopular, and TabDDPM through computational experiments under identical conditions, TabNet was selected as the optimal approach. The framework integrates Bayesian optimization (BO) with TabNet to model complex oilfield tabular datasets. Fair Cut Tree (FCT) and Synthetic Minority Over-sampling Technique (SMOTE) are incorporated to mitigate data missingness and imbalance, thereby enhancing dataset integrity and robustness. Empirical validation was conducted using 270 data entries collected from 15 distinct oil fields, specifically focusing on reservoir water sensitivity damage in natural core samples. The proposed framework exhibited superior predictive accuracy for the water sensitivity index on an independent test set, achieving a mean absolute percentage error (MAPE) of 4.4495% and a root mean square error (RMSE) of 4.05, underscoring its strong generalization capability. Moreover, this methodological approach enables a quantitative assessment of the influence of critical factors, including reservoir water salinity, initial permeability, and the mineralogical composition of rock formations, on water sensitivity predictions. This represents a significant advancement from traditional qualitative analyses to a more rigorous quantitative factor analysis, with the interpretability findings corroborating established mechanistic insights. The proposed framework offers a versatile and reliable solution for precise predictive modeling in complex drilling and completion scenarios reliant on tabular data, thereby providing a robust theoretical foundation and algorithmic support for accurate forecasting in the oil and gas industry.