mdpi-ai/citelens huggingface dataset:
Training Artificial Neural Networks Using a Global Optimization Method That Utilizes Neural Networks
Field: AI

Perhaps one of the best-known machine learning models is the artificial neural network, where a number of parameters must be adjusted to learn a wide range of practical problems from areas such as physics, chemistry, medicine, etc. Such problems can be reduced to pattern recognition problems and then modeled from artificial neural networks, whether these problems are classification problems or regression problems. To achieve the goal of neural networks, they must be trained by appropriately adjusting their parameters using some global optimization methods. In this work, the application of a recent global minimization technique is suggested for the adjustment of neural network parameters. In this technique, an approximation of the objective function to be minimized is created using artificial neural networks and then sampling is performed from the approximation function and not the original one. Therefore, in the present work, learning of the parameters of artificial neural networks is performed using other neural networks. The new training method was tested on a series of well-known problems, a comparative study was conducted against other neural network parameter tuning techniques, and the results were more than promising. From what was seen after performing the experiments and comparing the proposed technique with others that have been used for classification datasets as well as regression datasets, there was a significant difference in the performance of the proposed technique, starting with 30% for classification datasets and reaching 50% for regression problems. However, the proposed technique, because it presupposes the use of global optimization techniques involving artificial neural networks, may require significantly higher execution time than other techniques.
Impact of encorafenib on survival of patients with BRAFV600E-mutant metastatic colorectal cancer in a real-world setting
Field: ONCOLOGY

Purpose: Patients with BRAFV600E-mutant metastatic colorectal cancer (mCRC) have a dismal prognosis. The best strategies in these patients remain elusive. Against this background, we report the clinical course of patients with BRAFV600E-mutant mCRC to retrieve the best treatment strategy. Patients and methods: Clinico-pathological data were extracted from the electronic health records. Kaplan–Meier method was used to estimate overall (OS) and progression-free survival (PFS). Objective response rate (ORR) was assessed according to RECIST 1.1. Results: In total, 51 patients were enrolled. FOLFOXIRI was administered to 12 patients; 29 patients received FOLFOX or FOLFIRI as first-line treatment. Median OS was 17.6 months. Median PFS with FOLFOXIRI (13.0 months) was significantly prolonged (HR 0.325) as compared to FOLFOX/FOLFIRI (4.3 months). However, this failed to translate into an OS benefit (p = 0.433). Interestingly, addition of a monoclonal antibody to chemotherapy associated with superior OS (HR 0.523). A total of 64.7% patients received further-line therapy, which included a BRAF inhibitor in 17 patients. Targeted therapy associated with very favourable OS (25.1 months). Conclusion: Patients with BRAFV600E-mutated mCRC benefit from the addition of an antibody to first-line chemotherapy. Further-line treatment including a BRAF inhibitor has a dramatic impact on survival.
“The Work I Do Matters”: Cultivating a STEM Counterspace for Black Girls through Social-Emotional Development and Culturally Sustaining Pedagogies
Field: EDUCATION

Central to culturally sustaining pedagogy (CSP) is the notion that we sustain what we love by decentering the white gaze. Elevating CSP and the five core social-emotional learning competencies, we honed in on how Black and Brown girls developed knowledge and skills to manage their emotions, achieve goals, show empathy, and maintain healthy relationships within the context of a single-gender summer STEM program. These opportunities to engage in critical conversations to learn, unlearn, and relearn, while showing up as their full and authentic selves, are not often afforded in traditional STEM classes. This paper focuses on dialogue and interactions amongst four program participants—Samira, Rita, Brandy, and Joy. Critical discourse analysis was employed to challenge the dominance and reproduction of discourses by examining social contexts and systemic structures that they addressed in conversation. Findings revealed the importance of cultivating trusting and intentional learning spaces for Black and Brown girls to engage in open dialogue and critique oppressive discourses. It also displayed the significance of leaning into difficult conversations and pluralism to help adolescent girls realize the complexities of culture while also promoting joy and social-emotional development. Creating spaces that affirm Black and Brown girls matter; their contributions and work that they do matter.
A nomogram incorporating Ki67 to predict survival of acral melanoma
Field: ONCOLOGY

Background: The proliferation marker Ki67 is associated with the progression and prognosis of melanoma. However, its prognostic impact on acral melanoma (AM) remains unclear. Methods: A total of 314 AM patients were enrolled from a cohort of 5758 patients with melanoma at Peking University Cancer Hospital between 2006 and 2018. The patients were divided into Ki67 high- and low-expressing groups using a cut-off value of 30%. The associations between Ki67 and clinicopathologic characteristics as well as survival were analyzed. Cox proportional regression analysis was used to establish a nomogram to predict the survival probabilities of AM. Results: Among 314 patients, the Ki67-high group (Ki67 ≥ 30%) included 49.4% of patients at diagnosis. Patients in the Ki67-high group had lower median melanoma-specific survival (MSS) than those in the Ki67-low group (60.7 months vs. not reached, p < 0.001). In multivariate analyses, Ki67, lymph node metastasis and primary site were independent prognostic factors for MSS. The nomogram showed that Ki67 had the fourth greatest impact on survival, following Breslow thickness, lymph node metastasis and primary site. The C-index of the nomogram was 0.765 and 0.758 in the training and validation cohort, respectively. Area under the curve values were both near 0.8 in the training and validation cohorts. Net reclassification improvement and integrated discrimination improvement demonstrated that the predictive nomogram performed better than the traditional AJCC staging system. Conclusion: Ki67 expression is an independent prognostic factor for MSS in AM. A predictive model incorporating Ki67 and clinical factors was constructed to predict the prognosis of AM.
Using the DREAM Methodology for Course Assessment in the Field of ICT-Enabled Education for Sustainability
Field: PSYCHOLOGY

This study explores the application of the DREAM methodology for course assessment in three South East Asian universities aiming to embed sustainability and sustainable development goals (SDGs) in multiple academic disciplines enabled by information and communication technologies (ICTs). A mixing of content and thematic analysis was used, which aligns with the underpinning philosophy of the Diagnosing, Reviewing/Reflecting, Explaining, Assessing, Managing (DREAM) methodology. The DREAM methodology integrates five processes, starting from diagnosing, to reviewing/reflecting, explaining, assessing, and, finally, managing. Results show that merging semantic and latent themes has contributed to uncovering what messages students’ narratives convey and provided a space for focusing both on the surface and explicit meanings of the data as well as on theory building and policy making. They also show the effectiveness of the DREAM methodology in constructing new knowledge and generating meaningful interpretations and suggestions to teacher educators and other academic teaching staff, as well as higher education institutions’ policymakers and planners.
Federated Learning for IoT Intrusion Detection
Field: AI

The number of Internet of Things (IoT) devices has increased considerably in the past few years, resulting in a large growth of cyber attacks on IoT infrastructure. As part of a defense in depth approach to cybersecurity, intrusion detection systems (IDSs) have acquired a key role in attempting to detect malicious activities efficiently. Most modern approaches to IDS in IoT are based on machine learning (ML) techniques. The majority of these are centralized, which implies the sharing of data from source devices to a central server for classification. This presents potentially crucial issues related to privacy of user data as well as challenges in data transfers due to their volumes. In this article, we evaluate the use of federated learning (FL) as a method to implement intrusion detection in IoT environments. FL is an alternative, distributed method to centralized ML models, which has seen a surge of interest in IoT intrusion detection recently. In our implementation, we evaluate FL using a shallow artificial neural network (ANN) as the shared model and federated averaging (FedAvg) as the aggregation algorithm. The experiments are completed on the ToN_IoT and CICIDS2017 datasets in binary and multiclass classification. Classification is performed by the distributed devices using their own data. No sharing of data occurs among participants, maintaining data privacy. When compared against a centralized approach, results have shown that a collaborative FL IDS can be an efficient alternative, in terms of accuracy, precision, recall and F1-score, making it a viable option as an IoT IDS. Additionally, with these results as baseline, we have evaluated alternative aggregation algorithms, namely FedAvgM, FedAdam and FedAdagrad, in the same setting by using the Flower FL framework. The results from the evaluation show that, in our scenario, FedAvg and FedAvgM tend to perform better compared to the two adaptive algorithms, FedAdam and FedAdagrad.
Embracing a culture of talk: STEM teachers’ engagement in small-group discussions about photovoltaics
Field: EDUCATION

Background: Small-group discussions are well established as an effective pedagogical tool to promote student learning in STEM classrooms. However, there are a variety of factors that influence how and to what extent K-12 teachers use small-group discussions in their classrooms, including both their own STEM content knowledge and their perceived ability to facilitate discussions. We designed the present study to specifically target these two factors in the context of photovoltaics, an interdisciplinary field at the intersection of all STEM disciplines with potential to yield widespread benefits related to the use of solar technologies as a sustainable, renewable energy source. Teachers engaged in a series of small-group discussions based on photovoltaic source material (e.g., scientific articles) to build both their STEM content knowledge and capability with discussions, promoting their potential to design and deliver STEM instruction in their own classrooms using small-group discussion.Results: Overall, teachers productively engaged in rich STEM talk as they spent most of the time in the discussion asking authentic questions about photovoltaic topics in alignment with a variety of science and engineering disciplinary core ideas, responding to the questions with rich, elaborative talk, and taking on ownership of the discussions. Teachers also evidenced increases in their photovoltaic knowledge and their perceived capability to facilitate discussions. Finally, most teachers’ end-of-program lesson plans included the use of small-group discussions, and a subsample of teachers who completed a follow-up interview one year after the summer program reported greater enactment of discussion in their STEM classrooms.Conclusion: Our manuscript forwards an important contribution that draws from a practice-based approach to professional development in a way that not only better prepares teachers on what to teach (i.e., through enhanced PV content knowledge), but it also supports their ability to implement this instruction into their classrooms more effectively (i.e., though the use of small-group discussion). As such, this manuscript illustrates an innovative pedagogical approach for potential use in supporting teacher education and informs ways to enable teachers to build enhanced curricula for their STEM students.
Characterization of DoTc2 4510—Identifying HPV16 Presence in a Cervical Carcinoma Cell Line Previously Considered to Be HPV-Negative
Field: ONCOLOGY

Cervical cancer is the fourth leading cause of cancer deaths in women, with over 340,000 women dying from this disease in 2020. Almost all cases have an underlying persistent infection with an oncogenic high-risk type of human papillomavirus (HPV), mainly HPV16. While cervical squamous cell carcinoma is hardly ever HPV-negative, a small subset of adenocarcinoma exhibits absence of HPV, even after disproval of false-negative testing results due to low viral load. This proportion is evident in many cervical cancer studies and is reflected in the repertoire of model cell lines commonly used in research. As the viral origin of cervical cancer makes it a disease preventable and potentially treatable by immunotherapeutic approaches, it is the focus of many studies. For pertinent research, both a broad set of HPV-infected cervical carcinoma models are required, as well as stringent negative controls. A ubiquitously used HPV-negative cervical adenocarcinoma cell line is C-33A. Another cervical cancer cell line is available for purchase from the American Type Culture Collection (ATCC), namely DoTc2 4510, described to be HPV-negative and thus as a model for a rare gynecological malignancy. Here, we present findings proving that DoTc2 4510 is, in fact, an HPV16-positive cell line. This we assessed using a highly sensitive nested multiplex PCR protocol adapted for the identification of 12 carcinogenic HPV types and a second PCR targeting the HPV16 oncogenes E6 and E7. Subsequently, the protein expression of E6 and E7 was examined, as well as the expression of their target proteins p53, p21, and p16INK4a, to assess E6/E7 functionality. Finally, to attest to the survival dependence of DoTc2 4510 cells on HPV16, we performed an HPV16 E6/E7-targeted siRNA knock-down, which indeed led to senescence induction. Together, these findings demonstrate that DoTc2 4510 is an HPV16-transformed cell line.
High-Performance and Lightweight AI Model for Robot Vacuum Cleaners with Low Bitwidth Strong Non-Uniform Quantization
Field: AI

Artificial intelligence (AI) plays a critical role in the operation of robot vacuum cleaners, enabling them to intelligently navigate to clean and avoid indoor obstacles. Due to limited computational resources, manufacturers must balance performance and cost. This necessitates the development of lightweight AI models that can achieve high performance. Traditional uniform weight quantization assigns the same number of levels to all weights, regardless of their distribution or importance. Consequently, this lack of adaptability may lead to sub-optimal quantization results, as the quantization levels do not align with the statistical properties of the weights. To address this challenge, in this work, we propose a new technique called low bitwidth strong non-uniform quantization, which largely reduces the memory footprint of AI models while maintaining high accuracy. Our proposed non-uniform quantization method, as opposed to traditional uniform quantization, aims to align with the actual weight distribution of well-trained neural network models. The proposed quantization scheme builds upon the observation of weight distribution characteristics in AI models and aims to leverage this knowledge to enhance the efficiency of neural network implementations. Additionally, we adjust the input image size to reduce the computational and memory demands of AI models. The goal is to identify an appropriate image size and its corresponding AI models that can be used in resource-constrained robot vacuum cleaners while still achieving acceptable accuracy on the object classification task. Experimental results indicate that when compared to the state-of-the-art AI models in the literature, the proposed AI model achieves a 2-fold decrease in memory usage from 15.51 MB down to 7.68 MB while maintaining the same accuracy of around 93%. In addition, the proposed non-uniform quantization model reduces memory usage by 20 times (from 15.51 MB down to 0.78 MB) with a slight accuracy drop of 3.11% (the classification accuracy is still above 90%). Thus, our proposed high-performance and lightweight AI model strikes an excellent balance between model complexity, classification accuracy, and computational resources for robot vacuum cleaners.
Metastasis pattern and prognosis of large cell neuroendocrine carcinoma: a population-based study
Field: ONCOLOGY

Purpose: As a rare type of tumor, the metastasis pattern of large cell neuroendocrine carcinoma (LCNEC) is still unclear. Our aim was to investigate metastatic patterns and develop a predictive model of prognosis in patients with advanced LCNEC. Methods: Patients of LCNEC diagnosed between 2010–2015 from the Surveillance, Epidemiology and End Results (SEER) database were retrospectively included. Chi-square test was used for baseline characteristics analysis. Survival differences were assessed using Kaplan–Meier curves. Independent prognostic factors identified by multivariate Cox proportional risk model were used for the construction of nomogram. Results: 557 eligible patients with metastasis LCNEC (median (IQR), 64 (56 to 72) years; 323 males) were included in this research. Among patients with isolated metastases, brain metastases had the highest incidence (29.4%), and multisite metastases had worse OS (HR: 2.020: 95% CI 1.413–2.888; P < 0.001) and LCSS (HR: 2.144, 95% CI 1.480–3.104; P < 0.001) in all age groups. Independent prognostic indicators including age, race, T stage, N stage, chemotherapy, radiotherapy and metastatic site were used for the construction of nomogram. Concordance index (C-index) and decision-curve analyses (DCAs) showed higher accuracy and net clinical benefit of nomogram compared to the 7th TNM staging system (OS: 0.692 vs 0.555; P < 0.001; LCSS: 0.693 vs 0.555; P < 0.001). Conclusions: We firstly established a novel comprehensive nomogram to predict the prognosis of metastasis LCNEC. The prognostic model demonstrated excellent accuracy and predictive performance. Chemotherapy and metastasis pattern were the two strongest predictive variables. Close follow-up of patients with LCNEC is necessary to make individualized treatment decisions according to different metastasis patterns.
Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization
Field: AI

Convolutional Neural Networks (CNNs) have exhibited remarkable potential in effectively tackling the intricate task of classifying MRI images, specifically in Alzheimer’s disease detection and brain tumor identification. While CNNs optimize their parameters automatically through training processes, finding the optimal values for these parameters can still be a challenging task due to the complexity of the search space and the potential for suboptimal results. Consequently, researchers often encounter difficulties determining the ideal parameter settings for CNNs. This challenge necessitates using trial-and-error methods or expert judgment, as the search for the best combination of parameters involves exploring a vast space of possibilities. Despite the automatic optimization during training, the process does not guarantee finding the globally-optimal parameter values. Hence, researchers often rely on iterative experimentation and expert knowledge to fine-tune these parameters and maximize CNN performance. This poses a significant obstacle in developing real-world applications that leverage CNNs for MRI image analysis. This paper presents a new hybrid model that combines the Particle Swarm Optimization (PSO) algorithm with CNNs to enhance detection and classification capabilities. Our method utilizes the PSO algorithm to determine the optimal configuration of CNN hyper-parameters. Subsequently, these optimized parameters are applied to the CNN architectures for classification. As a result, our hybrid model exhibits improved prediction accuracy for brain diseases while reducing the loss of function value. To evaluate the performance of our proposed model, we conducted experiments using three benchmark datasets. Two datasets were utilized for Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and an international dataset from Kaggle. The third dataset focused on brain tumors. The experimental assessment demonstrated the superiority of our proposed model, achieving unprecedented accuracy rates of 98.50%, 98.83%, and 97.12% for the datasets mentioned earlier, respectively.
An Integrated Fermatean Fuzzy Multi-attribute Evaluation of Digital Technologies for Circular Public Sector Supply Chains
Field: AI

The barriers to implementing circular supply chains are well explored, but very little is provided to understand how these barriers play in public sector supply chains. Consequently, the role of digital technologies in addressing these barriers in the circularity of supply chains in the public sector remains a gap. Thus, this study bridges these gaps by evaluating digital technologies according to their relevance in addressing the identified barriers. In particular, eight domain experts who have sufficient knowledge and expertise in the domains of the public sector and circular economy were asked to elicit judgments in order to (1) set a threshold that defines the list of barriers that are significant to supply chains in the public sector, (2) obtain the priority weights of these barriers through the criteria importance through intercriteria correlation (CRITIC), and (3) rank the identified digital technologies based on their relevance in addressing the identified barriers in public sector supply chains using combinative distance-based assessment (CODAS) method, all under a Fermatean fuzzy set environment to account for epistemic uncertainties in judgment elicitation processes. This novel integration of the CRITIC and CODAS methods augmented by Fermatean fuzzy sets forms the methodological contribution of this work. Findings show that barriers associated with regulations restricting the collection of wastes, poor demand or acceptance for environmentally superior technologies, lack of expertise, technology, and information, operational risk, immature recycling technologies, and information sharing and communication were considered critical in managing circular public sector supply chains. The analysis also revealed that ripple effect modeling, simulation, and artificial intelligence are the priority digital technologies. These digital technologies offer efficiency and flexibility to decision-makers in analyzing complex and dynamic scenarios before the deployment of any circularity initiative, providing crucial information in its design and implementation. This paper outlines several managerial insights and offers possible agenda for future research.