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This article discusses a pilot project aimed at giving tertiary students a wider repertoire of resources to use in language learning, with a particular focus on Italian. This project responds to the exponential increase in and access to online data and the potential value such data represent for students studying additional languages at tertiary level. By examining whether current language students are aware of online resources, such as linguistic corpora and other potential applications of big data, we aim to provide an insight into the possible uses of corpus-assisted learning in the language classroom. In this paper, we detail a project undertaken in 2017 with undergraduate students of Italian in a major metropolitan university. Our project directed students to complete a translation task using corpora-based resources and assessed their experience through a post-assessment survey. Subsequently, we present our initial findings in relation to the possibilities of a corpus-based approach to language teaching and learning. While today's students are already predisposed to relying on online resources as part of their language studies, our results suggest students are not aware of emerging online resources such as corpora. Moreover, even when these resources are presented to students, the complex nature of the software programs used to interrogate corpora often results in their underutilisation.

The widespread utilization of direct current (DC) motors in real-life engineering applications has led to the need for precise speed control, making controllers a crucial aspect of DC motor systems. Proportional-integral-derivative (PID) controllers have been widely adopted due to their simplicity and effectiveness. However, recent advancements have introduced fractional order PID (FOPID) controllers that offer improved control performance for complex systems with nonlinear dynamics. To fully leverage FOPID controller’s benefits, an efficient tuning method is essential. In this study, we propose artificial rabbits optimization (ARO) algorithm with enhanced strategies, called IARO, to optimize the FOPID controller for DC motor speed regulation. The IARO algorithm incorporates an adaptive local search (ALS) mechanism and an experience-based perturbed learning (EPL) strategy, addressing the shortcomings of ARO and providing better exploration–exploitation balance. We validate the superiority of IARO over competitive algorithms on the CEC2020 benchmark functions, showcasing improved solution stability and consistency. The IARO algorithm is then applied to tune the FOPID controller for DC motor speed regulation. The problem is formulated as a constraint minimization task, optimizing the integral of time-weighted absolute error cost function while adhering to critical design requirements. Comparative simulations demonstrate the IARO algorithm’s ability to achieve superior cost function values and faster convergence compared to other algorithms' based FOPID controllers. The IARO-based FOPID controller exhibits enhanced stability, smoother speed response, larger gain margin, and wider bandwidth compared to other reported algorithms. Additionally, a hardware implementation is also conducted to further validate the practical applicability of IARO based design method. The IARO-based FOPID controller showed remarkable accuracy in tracking multi-step reference inputs and robustly rejected external disturbances, outperforming other recent optimization-based controllers. Additionally, the IARO-based PID controller achieved better performance in key time-domain metrics, including lower overshoot, faster rise time, shorter settling time, and minimized peak time.

Abstract Background The National Health Service in England aims to implement tobacco dependency treatment services in all hospitals by 2024. We aimed to assess the uptake of a new service, adapted from the Ottawa Model of Smoking Cessation, and its impact on 6-month quit rates and readmission or death at 1-year follow-up. Methods We conducted a pragmatic service evaluation of a tobacco dependency service implemented among 2067 patients who smoked who were admitted to 2 acute hospitals in London, England, over a 12-month period from July 2020. The intervention consisted of the systematic identification of smoking status, automatic referral to tobacco dependence specialists, provision of pharmacotherapy and behavioural support throughout the hospital stay, and telephone support for 6 months after discharge. The outcomes were (i) patient acceptance of the intervention during admission, (ii) quit success at 6 months after discharge, (iii) death, or (iv) readmission up to 1 year following discharge. Multivariable logistic regression was used to estimate the impact of a range of clinical and demographic variables on these outcomes. Results The majority (79.4%) of patients accepted support at the first assessment. Six months after discharge, 35.1% of successfully contacted patients reported having quit smoking. After adjustment, odds of accepting support were 51–61% higher among patients of all non-White ethnicity groups, relative to White patients, but patients of Mixed, Asian, or Other ethnicities had decreased odds of quit success (adjusted odds ratio (AOR) = 0.32, 95%CI = 0.15–0.66). Decreased odds of accepting support were associated with a diagnosis of cardiovascular disease or diabetes; however, diabetes was associated with increased odds of quit success (AOR = 1.88, 95%CI = 1.17–3.04). Intention to make a quit attempt was associated with a threefold increase in odds of quit success, and 60% lower odds of death, compared to patients who did not intend to quit. A mental health diagnosis was associated with an 84% increase in the odds of dying within 12 months. Conclusions The overall quit rates were similar to results from Ottawa models implemented elsewhere, although outcomes varied by site. Outcomes also varied according to patient demographics and diagnoses, suggesting personalised and culturally tailored interventions may be needed to optimise quit success.

Abstract Voltage-gated sodium channels (NaV) are pivotal proteins responsible for initiating and transmitting action potentials. Emerging evidence suggests that proteolytic cleavage of sodium channels by calpains is pivotal in diverse physiological scenarios, including ischemia, brain injury, and neuropathic pain associated with diabetes. Despite this significance, the precise mechanism by which calpains recognize sodium channels, especially given the multiple calpain isoforms expressed in neurons, remains elusive. In this work, we show the interaction of Calpain-10 with NaV's C-terminus through a yeast 2-hybrid assay screening of a mouse brain cDNA library and in vitro by GST-pulldown. Later, we also obtained a structural and dynamic hypothesis of this interaction by modeling, docking, and molecular dynamics simulation. These results indicate that Calpain-10 interacts differentially with the C-terminus of NaV1.2 and NaV1.6. Calpain-10 interacts with NaV1.2 through domains III and T in a stable manner. In contrast, its interaction with NaV1.6 involves domains II and III, which could promote proteolysis through the Cys-catalytic site and C2 motifs.

Summary: Background: Variants in GABRB2, encoding the β2 subunit of the γ-aminobutyric acid type A (GABAA) receptor, can result in a diverse range of conditions, ranging from febrile seizures to severe developmental and epileptic encephalopathies. However, the mechanisms underlying the risk of developing milder vs more severe forms of disorder remain unclear. In this study, we conducted a comprehensive genotype–phenotype correlation analysis in a cohort of individuals with GABRB2 variants. Methods: Genetic and electroclinical data of 42 individuals harbouring 26 different GABRB2 variants were collected and accompanied by electrophysiological analysis of the effects of the variants on receptor function. Findings: Electrophysiological assessments of α1β2γ2 receptors revealed that 25/26 variants caused dysfunction to core receptor properties such as GABA sensitivity. Of these, 17 resulted in gain-of-function (GOF) while eight yielded loss-of-function traits (LOF). Genotype-phenotype correlation analysis revealed that individuals harbouring GOF variants suffered from severe developmental delay/intellectual disability (DD/ID, 74%), movement disorders such as dystonia or dyskinesia (59%), microcephaly (50%) and high risk of early mortality (26%). Conversely, LOF variants were associated with milder disease manifestations. Individuals with these variants typically exhibited fever-triggered seizures (92%), milder degrees of DD/ID (85%), and maintained ambulatory function (85%). Notably, severe movement disorders or microcephaly were not reported in individuals with loss-of-function variants. Interpretation: The data reveals that genetic variants in GABRB2 can lead to both gain and loss-of-function, and this divergence is correlated with distinct disease manifestations. Utilising this information, we constructed a diagnostic flowchart that aids in predicting the pathogenicity of recently identified variants by considering clinical phenotypes. Funding: This work was funded by the Australian National Health & Medical Research Council, the Novo Nordisk Foundation and The Lundbeck Foundation.

Greenspaces and urban trees play an important role in moderating and improving urban environments to make them more comfortable and healthy for people. While trees provide important wellbeing benefits to people, urban planning and maintenance practices can create inhospitable and stressful environments where trees are unable to flourish or reach their potential. As the impacts of climate change intensify, the health of urban trees will become increasingly important. To explore how planning and maintenance practices are affecting urban trees, we draw on a case study of 31 street and park trees adjacent to high-use pedestrian areas in Wellington, New Zealand. We analysed these trees using six key factors that influence tree health to identify how planning and maintenance practices could be adjusted to support tree health and ecosystem function. We suggest that the six key factors have planning and maintenance implications for urban trees which are relevant to a variety of urban contexts, but particularly space-constrained urban streets requiring multiple functions and infrastructure. We argue that as climate impacts intensify, using these six key factors can help promote urban tree health and ecosystem function and avoid mal-adaptive planning and maintenance practices.

Summary: Soluble CD27 (sCD27) is a potential biomarker for diseases involving immune dysfunction. As there is currently little data on cerebrospinal fluid (CSF) sCD27 concentrations in the general population we measured CSF and plasma concentrations in 486 patients (age range 18–92 years, 57% male) undergoing spinal anesthesia for elective surgery. Across the complete cohort the median [range] sCD27 concentrations were 163 [400,000] pg/mL in plasma. Plasma sCD27, age and Qalb were the factors most strongly associated with CSF sCD27 levels. Reference sCD27 concentration intervals (central 95% of values) in a sub-group without the indication of neuropsychiatric, inflammatory or systemic disease (158 patients) were

This paper introduces a new metaheuristic technique known as the Greater Cane Rat Algorithm (GCRA) for addressing optimization problems. The optimization process of GCRA is inspired by the intelligent foraging behaviors of greater cane rats during and off mating season. Being highly nocturnal, they are intelligible enough to leave trails as they forage through reeds and grass. Such trails would subsequently lead to food and water sources and shelter. The exploration phase is achieved when they leave the different shelters scattered around their territory to forage and leave trails. It is presumed that the alpha male maintains knowledge about these routes, and as a result, other rats modify their location according to this information. Also, the males are aware of the breeding season and separate themselves from the group. The assumption is that once the group is separated during this season, the foraging activities are concentrated within areas of abundant food sources, which aids the exploitation. Hence, the smart foraging paths and behaviors during the mating season are mathematically represented to realize the design of the GCR algorithm and carry out the optimization tasks. The performance of GCRA is tested using twenty-two classical benchmark functions, ten CEC 2020 complex functions, and the CEC 2011 real-world continuous benchmark problems. To further test the performance of the proposed algorithm, six classic problems in the engineering domain were used. Furthermore, a thorough analysis of computational and convergence results is presented to shed light on the efficacy and stability levels of GCRA. The statistical significance of the results is compared with ten state-of-the-art algorithms using Friedman's and Wilcoxon's signed rank tests. These findings show that GCRA produced optimal or nearly optimal solutions and evaded the trap of local minima, distinguishing it from the rival optimization algorithms employed to tackle similar problems. The GCRA optimizer source code is publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/165241-greater-cane-rat-algorithm-gcra.

Lung adenocarcinoma (LUAD), a prevalent histological type of lung cancer and a subtype of non-small cell lung cancer (NSCLC) accounts for 45–55% of all lung cancer cases. Various factors, including environmental influences and genetics, have been identified as contributors to the initiation and progression of LUAD. Recent large-scale analyses have probed into RNASeq, miRNA, and DNA methylation alterations in LUAD. In this study, we devised an innovative deep-learning model for lung cancer detection by integrating markers from mRNA, miRNA, and DNA methylation. The initial phase involved meticulous data preparation, encompassing multiple steps, followed by a differential analysis aimed at identifying genes exhibiting differential expression across different lung cancer stages (Stages I, II, III, and IV). The DESeq2 technique was employed for RNASeq data, while the LIMMA package was utilized for miRNA and DNA methylation datasets during the differential analysis. Subsequently, integration of all prepared omics data types was achieved by selecting common samples, resulting in a consolidated dataset comprising 448 samples and 8228 features (genes). To streamline features, principal components analysis (PCA) was implemented, and the synthetic minority over-sampling technique (SMOTE) algorithm was applied to ensure class balance. The integrated and processed data were then input into the PCA-SMOTE-CNN model for the classification process. The deep learning model, specifically designed for classifying and predicting lung cancer using an integrated omics dataset, was evaluated using various metrics, including precision, recall, F1-score, and accuracy. Experimental results emphasized the superior predictive performance of the proposed model, attaining an accuracy, precision, recall, and F1-score of 0.97 each, surpassing recent competitive methods.

Artificial Intelligence (AI) in Smart Agricultural Facilities (SAF) often lacks explainability, hindering farmers from taking full advantage of their capabilities. This study tackles this gap by introducing a model that combines eXplainable Artificial Intelligence (XAI), with Predictive Maintenance (PdM). The model aims to provide both predictive insights and explanations across four key dimensions, namely data, model, outcome, and end-user. This approach marks a shift in agricultural AI, reshaping how these technologies are understood and applied. The model outperforms related studies, showing quantifiable improvements. Specifically, the Long-Short-Term Memory (LSTM) classifier shows a 5.81% rise in accuracy. The eXtreme Gradient Boosting (XGBoost) classifier exhibits a 7.09% higher F1 score, 10.66% increased accuracy, and a 4.29% increase in Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). These results could lead to more precise maintenance predictions in real-world settings. This study also provides insights into data purity, global and local explanations, and counterfactual scenarios for PdM in SAF. It advances AI by emphasising the importance of explainability beyond traditional accuracy metrics. The results confirm the superiority of the proposed model, marking a significant contribution to PdM in SAF. Moreover, this study promotes the understanding of AI in agriculture, emphasising explainability dimensions. Future research directions are advocated, including multi-modal data integration and implementing Human-in-the-Loop (HITL) systems aimed at improving the effectiveness of AI and addressing ethical concerns such as Fairness, Accountability, and Transparency (FAT) in agricultural AI applications.

Rising sea surface height is one of the world's vital issues in marine ecosystems because it greatly affects the ecosystems as well as the socio-economic life of the surrounding environment. Pekalongan is one area in Indonesia facing the effects of this phenomenon. This problem deserves to be explored further with complex approaches. One of them is a neural network to perform forecasting more accurately. In neural networks, the time series approach can be used with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). By adding the bidirectional method to each of these two approaches, we will find the best method to use to perform the analysis. The best results were obtained by forecasting for 960 days using Vanilla BiGRU. The results can be interpreted from multiple perspectives. The forecasting results showed a fluctuating pattern as in previous periods, so it can be said that the pattern is still quite normal, which indicates that the terminal can continue to operate normally. However, the forecasting results from this study are expected to be a reference for information for the government to prevent future dangers.

Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of metaheuristic optimization algorithms to address the unrelated parallel machine scheduling problem (UPMSP) through the lens of sustainable development goals (SDGs). The primary objective of this study is to explore how metaheuristic optimization algorithms can contribute to achieving sustainable development goals in the context of UPMSP. We examine a range of metaheuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and more, and assess their effectiveness in optimizing the scheduling problem at hand. The evaluation of the algorithms focuses on their ability to improve the optimization of job-to-machine assignments, enabling industries to efficiently minimize the overall makespan of scheduling tasks. This, in turn, leads to waste reduction and enhanced energy efficiency. To conduct a comprehensive analysis, we consider UPMSP instances that incorporate sustainability-related constraints and objectives. We assess the algorithms’ performance in terms of solution quality, convergence speed, robustness, and scalability, while also examining their implications for sustainable resource allocation and environmental stewardship. The findings of this study provide insights into the efficacy of metaheuristic optimization algorithms for addressing UPMSP with a focus on sustainable development goals. By leveraging these algorithms, industries can optimize scheduling decisions to minimize waste and enhance energy efficiency. The practical implications of this research are valuable for decision-makers, production planners, and researchers seeking to achieve sustainable development goals in the context of unrelated parallel machine scheduling. By embracing metaheuristic optimization algorithms, businesses can optimize their scheduling processes while aligning with sustainable principles, leading to improved operational efficiency, cost savings, and a positive contribution to the global sustainability agenda.