Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Privacy-Preserving Distributionally Robust Restoration of Electricity/Natural-Gas Systems Considering Coordination of Pump Storage Hydropower and Wind Farms

A swift power system restoration in a post-blackout event is one the most important challenges faced by the transmission system (TS) operators (TSO), which is particularly essential in the presence of wind farms, as their potential can be great in a fast restoration. In this study, we propose a bi-level decentralized approach to examine the influence of natural gas network (NGN) constraints on the bulk power system restoration process, taking into account the concurrent effects of pump storage hydropowers (PSHs) and wind farms. At the upper level of the problem, the transmission system (TS) operator (TSO) submits the amount of natural gas fuel consumed by the gas-fired units (GFUs) to the NGN by observing the restoration, operational, and topological constraints. The objective of the TSO is to maximize load servicing in the power grid restoration process. The equilibrium of the proposed bi-level problem is calculated by the analytical target cascading (ACT) algorithm, preserving the privacy of both electricity and NGNs. In the proposed study, an investigation has been conducted into the impact of the gas storage system (GSS) and linepack technology on enhancing the restoration process. Moreover, a moment-based distributionally robust optimization (DRO) approach has been deployed to model the uncertain behavior of wind farms in the restoration process. The proposed approach comprehensively examines the effects of the decentralized interconnection between electricity and NGNs in the restoration process. This facet holds great significance for the advancement of future sustainable energy systems. The results show that ignoring the NGN model leads to 11.92% higher level of unserviced loads.
FDViT: Improve the Hierarchical Architecture of Vision Transformer

Despite the fact that transformer-based models have yielded great success in computer vision tasks, they suffer from the challenge of high computational costs that limits their use on resource-constrained devices. One major reason is that vision transformers have redundant calculations since the self-attention operation generates patches with high similarity at a later stage in the network. Hierarchical architectures have been proposed for vision transformers to alleviate this challenge. However, by shrinking the spatial dimensions to half of the originals with downsampling layers, the challenge is actually overcompensated, as too much information is lost. In this paper, we propose FDViT to improve the hierarchical architecture of the vision transformer by using a flexible downsampling layer that is not limited to integer stride to smoothly reduce the sizes of the middle feature maps. Furthermore, a masked auto-encoder architecture is used to facilitate the training of the proposed flexible downsampling layer and produces informative outputs. Experimental results on benchmark datasets demonstrate that the proposed method can reduce computational costs while increasing classification performance and achieving state-of-the-art results. For example, the proposed FDViT-S model achieves a top-1 accuracy of 81.5%, which is 1.7 percent points higher than the ViT-S model and reduces 39% FLOPs.
Growth and yield response of wheat cultivars to hyper thermal stress

Eight promising wheat cultivars involving traditional tall and modern semi-tall were grown under normal ambient and hyper thermal (+3.0 °C) conditions to examine the growth and yield response of different wheat cultivars to hyper thermal stress. In general hyper thermal stress enhanced the stature of plant by increasing the length of leaf, internode and culm, and average leaf area mainly by increasing the area of flag leaf. It however, caused reduction in several growth and yield parameters i.e. number of tillers per m 2 , leaf area index, leafarea/tiller, leaf width, leaf weight ratio, number of grains/ spike, 1000 grain weight, biological and economic yields, harvest index, total and grain weight/tiller and grain spike ratio to varying extent in almost all the cultivars. As compared to reproductive growth, vegetative growth showed greater stability to hyper thermal stress. High temperature also hastened internode elongation, spike primordia initiation, flowering and finally maturity to varying degrees in all the cultivars. Among the cultivars tested, C 306 and Kundan which are recommended for dry areas and HD 2285 and HD 2643 identified for late sown condition showed greater stability in growth and yield attributes and eventually recorded higher grain yield than those of others under warmer condition.
Molecular Targeting of the Human Epidermal Growth Factor Receptor-2 (HER2) Genes across Various Cancers

Human epidermal growth factor receptor 2 (HER2) belongs to the ErbB family, a group of four transmembrane glycoproteins with tyrosine kinase activity, all structurally related to epidermal growth factor receptor (EGFR). These tyrosine kinases are involved in the transmission of cellular signals controlling normal cell growth and differentiation. If this transmission goes awry, it can lead to dysregulated growth of the cell. HER2 specifically can be implicated in the pathogenesis of at least eight malignancies. HER2 positivity quickly became a well-characterized indicator of aggressiveness and poor prognosis, with high rates of disease progression and mortality. After realizing the implication of HER2, it first became investigated as a target for treatment in breast cancer, and later expanded to areas of research in other cancer types. To this day, the most therapeutic advancements of anti-HER2 therapy have been in breast cancer; however, there have been strong advancements made in the incorporation of anti-HER2 therapy in other cancer types as well. This comprehensive review dissects HER2 to its core, incorporating the most up to date information. The topics touched upon are discussed in detail and up to 200 published sources from the most highly recognized journals have been integrated. The importance of knowing about HER2 is exemplified by the groundbreaking advancements that have been made, and the change in treatment plans it has brought to the oncological world in the last twenty years. Since its groundbreaking discovery there have been significant breakthroughs in knowledge regarding the actual receptor, the receptors biology, its mechanism of action, and advancements in tests to detect HER2 and significant strides on how to best incorporate targeted treatment. Due to the success of this field thus far, the review concludes by discussing the future of novel anti-HER2 therapy currently in development that everyone should be aware of.
Detection of Turkish Fake News From Tweets With BERT Models

As the number of people using social networks increases, more people are using social media platforms to meet their news needs. Users think that it is easier to follow the agenda by accessing news, especially on Twitter, rather than newspaper news pages. However, fake news is increasingly appearing on social media, and it is not always possible for people to obtain correct news from partial news pages or short Twitter posts. Understanding whether the news shared on Twitter is true or not is an important problem. Detecting fake tweets is of great importance in Turkish as well as in any language. In this study, fake news obtained from verification platforms on Twitter and real news obtained from the Twitter accounts of mainstream newspapers were labeled and, preprocessed using the Zemberek natural language processing tool developed for the Turkish language, and a dataset named TR_FaRe_News was created. Then, the TR_FaRe_News dataset was explored using ensemble methods and BoW, TF-IDF, and Word2Vec vectorization methods for fake news detection. Then a pre-trained BERT deep learning model was fine-tuned, and variations of the model extended with Bi-LSTM and Convolutional Neural Network (CNN) layers with the frozen and unfrozen parameters methods were explored. The performance evaluation was conducted using seven comparable datasets, namely BuzzFeedNews, GossipCop, ISOT, LIAR, Twitter15, and Twitter16, including an LLM-generated fake news dataset. Analyzing Turkish tweets and using fake news datasets generated by LLM is considered an important contribution. Accuracy values between 90 and 94% were obtained with the BERT and BERTurk + CNN models with 94% accuracy.
Prediction of genetic and selection parameters in pumpkin (Cucurbita moschata Duch.) progenies for morphoagronomic characteristics and pulp quality

The estimation of genetic parameters allows for the selection of superior individuals for important traits in plant breeding. The objectives of the present study were to estimate and predict the gains in genetic parameters, as well as to select superior individuals for morphoagronomic and pulp quality characteristics in C. moschata . Ten progenies derived from accessions collected in the northeast of Brazil were evaluated for 17 morphoagronomic characteristics and chemical quality of the pulp, including total carotenoids and β-carotene. The low values of additive genetic variance denote the need for additional selection cycles for the evaluated characteristics. Heritability greater than 30% was observed for 13 of the 17 variables analysed, indicating success in selection. Likewise, accuracy values between 74 and 93% were obtained for 15 of the evaluated variables, demonstrating the existence of high genetic variance. Individual ranking was conducted for the variables fruit weight (FRW), soluble solid content (SSC), titratable acidity (TA), total carotenoid content (CBT) and β-carotene (β-Car), identifying five individuals of progeny 10 that were promising for advancement in selection for SSC, CBT and β-car. In ranking the simultaneous evaluations for all the characteristics and taking into account the formats piriform and 'moranga', two individuals of progeny 10, which coincided with the top ranking for SSC, CBT and β-car, were identified. This identification of more than one promising individual enhances the potential of the progenies evaluated for the development of commercial and productive lines in semi-arid conditions.
Evaluation of the Rational Drug Use (RDU) literacy among undergraduate students

Background: Irrational drug usage is a global concern. WHO recommended a strategy for integrating education and awareness on the rational use of medicine into general education programs. Objective: To evaluate the rational drug use (RDU) literacy among the undergraduate students of Ubon Ratchathani University. Methods: This mixed-methods research consists of a quantitative cross-sectional study with a self-administered RDU literacy questionnaire and a qualitative in-depth interview study. Descriptive statistics and inferential statistics were used in the quantitative study. Thematic analysis was used in the qualitative study. Results: Students who participated in this study included 640 undergraduate students. Approximately half of the participants never studied a RDU-related course (50.94%). Although the findings revealed that most of the participants (73.13%) had good RDU literacy, many participants had less frequency of the right options on some questions (e.g., advertisement of health products). Health sciences students were 2.8 times more likely than non-health sciences students to have good RDU literacy (AOR=2.835, 95% CI: 1.752-4.587). Four main themes were derived from the qualitative study: 1. Definition of RDU; 2. Facilitators; 3. Concerns; 4. RDU country. Conclusion: While the majority of participants demonstrated good RDU literacy, some actually engaged in irrational drug use. Activities promoting RDU literacy among undergraduate students, particularly in faculties other than health sciences, are still required.
Approach to Thyroid Nodules: Diagnosis and Treatment

Thyroid nodules (TNs) are prevalent and found in up to 50% of individuals. While most TNs are benign, some can be malignant. The evaluation of TNs is crucial to rule out malignancy and identify those requiring surgical intervention. This study aimed to clarify the reported prevalence of TNs, focusing specifically on their various types, assessment and diagnostic processes, current evaluation methods, and evidence-based management. It also provides recommendations for follow-up. TNs are typically found during physical exams or incidentally during imaging procedures. Routine laboratory and clinical evaluations of TNs are common. Ultrasound is the preferred imaging method to determine if a TN needs a biopsy. Fine-needle aspiration (FNA) is crucial in deciding whether surgery or surveillance is necessary. TNs that show suspicious features on the ultrasound may require cytologic analysis to assess the risk of malignancy. The effectiveness of several supplementary molecular tests is still uncertain, although some studies report promising results. The management and treatment approach for TNs primarily depends on the results of FNA cytology and ultrasound characteristics. The optimal treatment strategy for TNs ranges from straightforward follow-ups for low-risk cases to surgical intervention for high-risk patients. Rather than adopting a uniform approach, clinicians should assess each patient on a case-by-case basis using current knowledge and a collaborative, multidisciplinary method.
Knowledge-Spreader: Learning Semi-Supervised Facial Action Dynamics by Consistifying Knowledge Granularity

Recent studies on dynamic facial action unit (AU) detection have extensively relied on dense annotations. However, manual annotations are difficult, time-consuming, and costly. The canonical semi-supervised learning (SSL) methods ignore the consistency, extensibility, and adaptability of structural knowledge across spatial-temporal domains. Furthermore, the reliance on offline design and excessive parameters hinder the efficiency of the learning process. To remedy these issues, we propose a lightweight and online semi-supervised framework, a so-called Knowledge-Spreader (KS), to learn AU dynamics with sparse annotations. By formulating SSL as a Progressive Knowledge Distillation (PKD) problem, we aim to infer cross-domain information, specifically from spatial to temporal domains, by consistifying knowledge granularity within Teacher-Students Network. Specifically, KS employs sparsely annotated key-frames to learn AU dependencies as the privileged knowledge. Then, the model spreads the learned knowledge to their unlabeled neighbours by jointly applying knowledge distillation and pseudo-labeling, and completes the temporal information as the expanded knowledge. We term the progressive knowledge distillation as "Knowledge Spreading", which allows our model to learn spatial-temporal knowledge from video clips with only one label allocated. Extensive experiments demonstrate that KS achieves competitive performance as compared to the state of the arts under the circumstances of using only 2% labels on BP4D and 5% labels on DISFA. In addition, we have tested it on our newly developed large-scale comprehensive emotion database BP4D++, which contains considerable samples across well-synchronized and aligned sensor modalities for alleviating the scarcity issue of annotations and identities.
Characterization and Cellular Toxicity Studies of Commercial Manganese Oxide Nanoparticles

Manganese oxide nanoparticles (MnOx NPs) are finding applications in several environmentally important areas such as farming and energy storage. MnOx NPs span a range of metal oxidation states that open up a wide range of applications in catalysis as well. As a result, it is important to understand how such materials can impact human health through incidental exposure. In this study, we examined a range of commercially available Mn2O3 NPs and compared our characterization data to those supplied by manufacturers. Discrepancies were noted and then measured values were used to assess the biological impact of these materials on three mammalian cell lines—A549, HepG2 and J774A.1 cells. Cell toxicity assays showed that all Mn2O3 particles exhibited cytotoxic effects that may be correlated, at least in part, to the production of reactive oxygen species. All eight nanoforms also activated caspase 3 but not caspase 1, although the magnitude of these changes varied greatly between materials.
Employee engagement among women in technical positions in the South Africa mining industry

The aims of this study were to investigate the factors that influence women's engagement in technical positions in the South African mining sector and to determine what could be done to promote their successful participation. A convergent parallel mixed-methods research design was used to ascertain the factors that facilitate, inhibit, and influence engagement. In the quantitative phase of the study, questionnaires were circulated to women employeees; and in the qualitative phase, semi-structured interviews were conducted with employer representatives, most of whom were human resource personnel. Confirmatory factor analysis showed that the three-factor structure (vigour, dedication, and absorption) of the Utrecht Work Engagement Scale fit the sample data reasonably well. Although there is room for improvement, respondents demonstrated acceptable levels of engagement in their work. On the other hand, the qualitative findings showed that employee engagement is impacted by unfavourable working conditions, work-life balance, and the mining industry's male-dominated work culture. The findings showed that employee engagement should be elevated to a core human resource function. To increase the participation of women in mining, human resource professionals are encouraged to collaborate with mine supervisors, managers, and employees to develop programmes that promote employees' absorption in and dedication to their work.
Considerazioni fitogeografiche sulla flora della Sicilia

La flore de la Sicile est dans l'ensemble assez riche et interessante (c. 3000 especes, dont 10% sont endemiques), ce qui est a imputer surtout aux particularites physiographiques de l'ile, ainsi qu'a ses vicissitudes paleogeographiques. Cette flore, tout en ayant ete l'objet de nombreuses etudes taxonomiques et floristiques des le siecle passe, merite tout de meme des recherches a l'heure actuelle, relatives a des especes et groupes critiques. Afin de mettre en evidence la diversite floristique et le considerable interet phytogeographique de l'ile, il est propose une subdivision du territoire, correspondant au domaine sicilien, s'individualisant, sur la base de la presence d'especes exclusives (endemiques ou non), une serie de districts reunis en sous-secteurs et secteurs. En outre, la carence des mesures de protection de la flore particulierement pour les especes rares et menacees, ainsi que les problemes relatifs a la conservation de cette flore, est mise en evidence.
Designing Conversational Agents for Emotional Self-Awareness

Emotional self-awareness is a crucial social-emotional skill for positive psychology and mental well-being. While prior works have used AI applications to foster emotion awareness, it is unclear what specific design features users prefer in the context of self-awareness skill practice. To better understand these features, we design, deploy, and evaluate Teddy, a mobile conversational agent that helps users practice verbalizing and reflecting on their emotions through experience sampling, affect labeling, and personal storytelling interactions. We conduct a user study with 18 days of interaction data from six participants to evaluate what aspects of Teddy contribute to emotion awareness. We find that users felt comfortable self-disclosing to the agent and found it helpful to verbalize their emotions, but desired more characteristics like social responsiveness and personalization. Based on our pilot study, we outline design insights for future work on AI agents that support practice of social-emotional skills such as emotional self-awareness.
Expresso-AI: An Explainable Video-Based Deep Learning Models for Depression Diagnosis

Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention. As a multidisciplinary topic, these objective measures should be interpretable and accessible to health care professionals, ensuring effective collaboration and treatment planning in the realm of mental health care. Even though current automated depression diagnosis approaches improved over the last decade, a critical gap exists as they often lack affect-specificity and interpretability, limiting their practical application and potential impact on mental health care. In particular, interpretability from temporal activities from videos when deep models are used is not fully explored. In this study, we present a novel framework for analyzing Deep Neural Networks’ decisions when trained on facial videos, specifically focusing on automatic depression severity diagnosis. By fine-tuning Deep Convolutional Neural Networks (DCNN) pre-trained on Action Recognition datasets on depression severity facial videos from AVEC depression dataset, our framework is able to interpret the model’s saliency maps by examining face regions and temporal expression semantics. Our approach generates both visual and quantitative explanations for the model’s decisions, providing greater insight into its reasoning. In addition to this interpretability, our video-based modeling has improved upon previous single-face benchmarks for visual depression diagnosis, resulting in enhanced predictive performance. Overall, our work demonstrates the successful development of a framework capable of generating hypotheses from a facial model’s decisions while simultaneously improving depression’s predictive capabilities.
An Automation Perception for Cotton Crop Disease Detection Using Machine Learning

Cotton crop disease detection and classification based on images of leaves is a significant objective in agriculture. Cotton crops play a vital role in India. Every year, due to the attack of diseases, cotton production is decreasing. The main reasons for disease in plants are the pests, insects, and pathogens used; if they are not controlled on time, they affect productivity. In advancing digital image processing technologies, machine learning and other techniques that identify early disease detection in plants are proposed. This paper focuses on improving disease detection in cotton crop plants and leaves. Machine learning is used to identify and predict cotton plant disease using images and leaves collected in an uncontrolled environment. This study uses machine learning to review the different problems identified regarding plant disease. Various experimental configurations are investigated to analyze the impacts of different leaf classes, plant disease combinations, and their categories. Cotton crops are detected to classify the various types of cotton plants. Cotton plant diseases have a wide range of illnesses, from bacterial deficiency to bacterial, fungal, viral, and vitamin and nutrient deficiency. The proposed approach indicates the cotton crop plant in the leaf disease by their implementation results.