Search Results (22)

Neuroendocrine tumors (NETs) are heterogeneous neoplasms with different molecular characteristics and prognosis. Although slow-growing, NETs are often diagnosed at an advanced stage. The treatment choice depends on primary site, extent, grade, growth rate, somatostatin receptor status, functional status, performance status, and comorbidities. Precise knowledge of the biological and molecular features of NETs has led to the development of novel therapies. Therapeutic options include somatostatin analogs, multi-targeted tyrosine kinase inhibitors (e.g., sunitinib), or mammalian targets of rapamycin (mTOR) inhibitors (e.g., everolimus), telotristat ethyl, chemotherapy, and peptide-receptor radionuclide therapy. Pivotal studies that led to approval, treatment-related adverse events, and safety concerns, as demonstrated in clinical trials and real-world clinical practice. Questions, such as the optimal timing, selection, and sequence of therapies, and biomarkers that predict response to the novel agents in an individual patient, remain to be answered. We propose a stepwise approach for the management of advanced Gastro-entero-pancreatic (GEP)-NETs that utilizes a multidisciplinary team of experts. Biomarkers may assist in both the diagnosis and post-treatment follow-up in patients with GEP-NETs. The next decade of research on GEP-NETs is promising and should provide new insights into the molecular underpinnings of this disease, therapy selection, and the sequencing of the available therapies, along with the potential role of AL in NET pharmacotherapy. Full article

The question generation system (QGS) for information technology (IT) education, designed to create, evaluate, and improve Multiple-Choice Questions (MCQs) using knowledge graphs (KGs) and large language models (LLMs), encounters three major needs: ensuring the generation of contextually relevant and accurate distractors, enhancing the diversity of generated questions, and balancing the higher-order thinking of questions to match various learning levels. To address these needs, we proposed a multi-agent system named Multi-Examiner, which integrates KGs, domain-specific search tools, and local knowledge bases, categorized according to Bloom’s taxonomy, to enhance the contextual relevance, diversity, and higher-order thinking of automatically generated information technology MCQs. Our methodology employed a mixed-methods approach combining system development with experimental evaluation. We first constructed a specialized architecture combining knowledge graphs with LLMs, then implemented a comparative study generating questions across six knowledge points from K-12 Computer Science Standard. We designed a multidimensional evaluation rubric to assess the semantic coherence, answer correctness, question validity, distractor relevance, question diversity, and higher-order thinking, and conducted a statistical analysis of ratings provided by 30 high school IT teachers. Results showed statistically significant improvements (p < 0.01) with Multi-Examiner outperforming GPT-4 by an average of 0.87 points (on a 5-point scale) for evaluation-level questions and 1.12 points for creation-level questions. The results demonstrated that: (i) overall, questions generated by the Multi-Examiner system outperformed those generated by GPT-4 across all dimensions and closely matched the quality of human-crafted questions in several dimensions; (ii) domain-specific search tools significantly enhanced the diversity of questions generated by Multi-Examiner; and (iii) GPT-4 generated better questions for knowledge points at the “remembering” and “understanding” levels, while Multi-Examiner significantly improved the higher-order thinking of questions for the “evaluating” and “creating” levels. This study contributes to the growing body of research on AI-supported educational assessment by demonstrating how specialized knowledge structures can enhance automated generation of higher-order thinking questions beyond what general-purpose language models can achieve. Full article

To combat global warming, many industrialized countries have announced plans to ban vehicles powered by fossil fuel in the near future. In alignment with this global initiative, many countries across the globe are committed to decarbonizing their public transportation sector, which significantly contributes to increased greenhouse gas emissions. A promising strategy to achieve this goal is the adoption of electric buses, specifically battery electric buses and fuel cell electric buses. Each technology offers distinct advantages and drawbacks, making the decision-making process complex. This research aims to answer two critical questions: What is the optimal choice for decarbonizing the bus transportation sector—electric battery buses or fuel cell electric buses? And what are the best energy carrier pathways for charging or refueling these buses? We propose a methodological framework based on multi-criteria decision-making to address these questions comprehensively. This framework utilizes the entropy weighting and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) methodologies to rank alternative bus technologies along with energy carrier pathways. The framework evaluates a range of criteria, including economic viability, energy demand, and environmental aspects. To illustrate the framework, we considered Qatar as a case study. Our results indicate that, with respect to economic viability and energy consumption, the operation of battery electric buses is favored over fuel cell electric buses, regardless of the energy pathway utilized during both the energy production and bus operation phases. However, from an environmental perspective, operating both bus alternatives using energy from green sources provides superior performance compared to when these buses are powered by natural gas sources. Full article

BPA has demonstrated enormous multisystem and multi-organ toxicity shown mainly in animal models. Meanwhile, the effects of its exposure in humans still require years of observation, research, and answers to many questions. Even minimal and short-term exposure contributes to disorders or various types of dysfunction. It is released directly or indirectly into the environment at every stage of the product life cycle, demonstrating its ease of penetration into the body. The ubiquity and general prevalence of BPA influenced the main objective of the study, which was to assess the toxicity and health effects of BPA and its derivatives based on the available literature. In addition, the guidelines of various international institutions or regions of the world in terms of its reduction in individual products were checked. Bisphenol A is the most widely known chemical and perhaps even the most studied by virtually all international or national organizations, but nonetheless, it is still controversial. In general, the level of BPA biomonitoring is still too high and poses a potential threat to public health. It is beginning to be widely argued that future toxicity studies should focus on molecular biology and the assessment of human exposure to BPA, as well as its substitutes. The effects of its exposure still require years of observation, extensive research, and answers to many questions. It is necessary to continue to deepen the knowledge and interest of many organizations, companies, and consumers around the world in order to make rational purchases as well as future choices, not only consumer ones. Full article

Unmanned Aerial Vehicles (UAVs), a subset of aerial robots, play crucial roles in various domains, such as disaster management, agriculture, and healthcare. Their application proves invaluable in situations where human intervention poses risks or involves high costs. However, traditional approaches to UAV path planning struggle in efficiently navigating complex and dynamic environments, often resulting in suboptimal routes and extended mission durations. This study seeks to investigate and improve the utilization of meta-heuristic algorithms for optimizing UAV path planning. Toward this aim, we carried out a systematic review of five major databases focusing on the period from 2018 to 2022. Following a rigorous two-stage screening process and a thorough quality appraisal, we selected 68 papers out of the initial 1500 to answer our research questions. Our findings reveal that hybrid algorithms are the dominant choice, surpassing evolutionary, physics-based, and swarm-based algorithms, indicating their superior performance and adaptability. Notably, time optimization takes precedence in mathematical models, reflecting the emphasis on CPU time efficiency. The prevalence of dynamic environmental types underscores the importance of real-time considerations in UAV path planning, with three-dimensional (3D) models receiving the most attention for accuracy in complex trajectories. Additionally, we highlight the trends and focuses of the UAV path planning optimization research community and several challenges in using meta-heuristic algorithms for the optimization of UAV path planning. Finally, our analysis further highlights a dual focus in UAV research, with a significant interest in optimizing single-UAV operations and a growing recognition of the challenges and potential synergies in multi-UAV systems, alongside a prevalent emphasis on single-target mission scenarios, but with a notable subset exploring the complexities of multi-target missions. Full article

Ηuman sustainability in dental enterprises, as in every workplace, is connected to air and water quality, eco-friendly and naturally designed working spaces, and the culture of the 4Rs. The purpose of this study was to assess pro-environmental behavior, as well as knowledge of preferences for circular economies and green building construction, among a sample of dental students and dentists in Greece. We further assessed the factors influencing their choices. Students (N1 = 93) and dentists (N2 = 126) filled in e-questionnaires from April to December 2022. The data revealed that both students and dentists lack knowledge about the circular economy (N1 = 67.74%, N2 = 68.25%), EU regulations on amalgam disposal (N1 = 64.51%, N2 = 58.73%), and plastic recycling (N1 = 76.34%, N2 = 76.98%); meanwhile, they do recycle at home (N1 = 80.64%, N2 = 82.54%) and have participated in voluntary environmental initiatives (N1 = 58.06%, N2 = 66.66%). Gender influences the importance of factors related to green dental practices, with women students being more likely to agree that increased costs for network changes (p = 0.02) and poor wastewater management (p = 0.01) are significant. Students from urban areas are more likely to give positive answers to questions related to the lack of state financial support (p = 0.02), low levels of green design in buildings (p = 0.03), the negligible direct financial benefits of green dental offices (p = 0.04), the negligible reputational benefits of green dental offices (p = 0.02), and the lack of continuing education training seminars on green dentistry (p = 0.05). For dentists, no significant relationships were observed, except for a weak positive relationship for the increases in costs due to changes related to utility networks (p = 0.08), while increases in waste energy (p = 0.12) and the waste of dental materials (p = 0.19) seemed significant only for dentists in urban areas. Women dentists were more likely to answer positively regarding wasting energy (p = 0.024) and the use of unapproved disinfection products (p = 0.036). The findings contribute ideas and solutions for green dental practice buildings and sustainable behaviors through educational activities and regarding the social aspects of factors such as age, experience in dentistry, gender, and urbanism. This study also provides a basis for future multi-disciplinary research on dental quality assurance, the psychology of environmentalism, economics, and behavioral science in dentistry. Full article

An automatic story generation system continuously generates stories with a natural plot. The major challenge of automatic story generation is to maintain coherence between consecutive generated stories without the need for human intervention. To address this, we propose a novel multi-modal story generation framework that includes automated storyline decision-making capabilities. Our framework consists of three independent models: a transformer encoder-based storyline guidance model, which predicts a storyline using a multiple-choice question-answering problem; a transformer decoder-based story generation model that creates a story that describes the storyline determined by the guidance model; and a diffusion-based story visualization model that generates a representative image visually describing a scene to help readers better understand the story flow. Our proposed framework was extensively evaluated through both automatic and human evaluations, which demonstrate that our model outperforms the previous approach, suggesting the effectiveness of our storyline guidance model in making proper plans. Full article

The initial hype around Automated Vehicle (AV) technologies has subsided, and it is now being realized that near-term deployment of AV technologies will be in the form of low-speed shared automated shuttles in geofenced districts with a high density of trip demand. A concept labeled ‘Automated Mobility Districts’ (AMD) has been coined to define such deployments. A modeling and simulation toolkit that can act as a decision support tool for early-stage AMD deployments is desired for answering the questions such as (i) for a series of given conditions, such as the amount of travel demand and automated shuttle fleet configuration, what is the expected mode split for shared automated vehicle (SAV) services? (ii) for that mode share of SAVs, what level-of-service and network performance can be anticipated? To answer these research questions, an innovative and integrated framework of multi-mode choice and microscopic traffic simulation model is presented to obtain the equilibrium of mode split for various modes in AMDs, based on real-time traffic simulation data. The proposed framework was tested using travel demand and road network data from Greenville, South Carolina, considering a car, walk, and two SAV on-demand ridesharing modes in a proposed AMD. Results from the study demonstrated the efficacy of the proposed framework for solving the mode split equilibrium in an AMD. In addition, sensitivity analyses were conducted to understand the impact of factors such as waiting times and fleet resources on mode share equilibrium for SAVs. Full article

Mediterranean towns and their surroundings show specific characteristics, such as urban structure, presence of complex stratification of heritage, and often seasonality, which makes the choice of spatial organization and construction technology for building construction of high importance in relation to sustainable development. For such purpose, the SOnCT model, based on multi-criteria decision analysis, has been developed which takes into account optimal building interventions in Mediterranean towns from a sustainable development perspective, highlighting their spatial-technical aspects. The presented research answers the questions of how sustainable development goals can be implemented in the case of construction interventions in Mediterranean areas, especially in smaller settlements that present very fragile status and specific characteristics not comparable to northern towns. This paper presents the construction and verification of the evaluation and prioritization model for selecting the optimal spatial organization and construction technology based on the criteria of sustainability, spatial characteristics, and the United Nations’ Sustainable development goals. Full article

Background: Healthcare students that refuse to get vaccinated may expose themselves and their patients to several vaccine-preventable diseases, especially during outbreaks or at peak epidemic activity, becoming a threat to themselves and their patients. This study aimed to assess their attitudes towards and perception of vaccines and vaccination. Methods: An anonymous questionnaire was shared with medical students, pharmacy students and medical residents in Hygiene and Preventive Medicine at the University of Florence (Italy), in February 2021. The questionnaire contained 39 questions with open, multi-choice, yes–no, Likert scale answers. A Vaccine Hesitancy Index (VHI) was then calculated. A descriptive statistical analysis was performed. Results: A total of 473 students participated in this study. All students were in favour of vaccination (99.2%) but a relatively low number of participants judged their level of knowledge about vaccinations as “good” (21.8%) or “excellent” (0.6%). About half of students declared that they are not adequately trained during their academic courses. The VHI showed low levels of vaccine hesitancy (mean ± SD 0.38 ± 0.16); moreover, the students were willing to get vaccinated against SARS-CoV-2 when recommended (88.2%) and thought that these vaccines are generally safe. Few students considered the development of SARS-CoV-2 vaccines (13.1%) and the procedures for evaluating clinical trials for marketing authorisation of these vaccines (12.9%) too fast to guarantee their efficacy and safety. Conclusions: Since vaccination and vaccine hesitancy and acceptance topics are being paid increasing attention by the population, new strategies to increase future healthcare professionals’ willingness to promote vaccination and get vaccinated, as well as knowledge on vaccines and vaccination, will be of the utmost importance to fight vaccine preventable diseases. Full article

While research on traditional media brands has increased in recent years, few studies examine news media brands and their brand strategies, particularly distinctive brand associations unrelated to media brand content and their impact on audience media brand choice and attention. Numerous studies highlight the significance of content as an element of the media brand and its vital role in audience selection. In a market where news and information are oversaturated and comparable, the dilemma for news media companies is what distinguishes them when the news content may be the same across all channels. Multi-platform consumption deludes and decreases brand associations, thus providing media brands with even more challenging brand differentiation and strong brand association management. The younger Generation Z prefers and uses more global and social media platforms than national media from the media audience perspective and future audiences. This audience consumes less national media than global and social media platforms. This is especially true of younger viewers, who are more focused on platforms and experiences. In a setting where cross-platform distribution stresses the significance of media brand associations and content experiences, the capacity of media brands to maintain brand preference and choice in a highly competitive market becomes increasingly crucial. According to the authors’ analysis, data reveals that younger audiences consume less national media and prefer international media, which raises the spectrum of future domestic media audiences. Examining the unique characteristics of media brand associations that positively influence audience preference and media brand choices among younger audiences would not only answer some difficult questions for national media brands concerning how to attract younger audiences, but it would also lay the groundwork for meeting the needs of audiences for a unified media brand experience across numerous platforms, without sacrificing strong and unique media brand associations. This study focuses on national news media brands and analyses the attributes of news media brands, as well as their significance for the Generation Z audience in media brand choice and engagement. The study highlights the importance of content experience in defining the uniqueness of media brands and its effect on brand selection and audience consumption. The authors used linear regression analyses and the decision tree approach to predict the most significant correlations between media brand attributes and brand uniqueness. Full article

Unlike previous dialogue-based question-answering (QA) datasets, DREAM, multiple-choice Dialogue-based REAding comprehension exaMination dataset, requires a deep understanding of dialogue. Many problems require multi-sentence reasoning, whereas some require commonsense reasoning. However, most pre-trained language models (PTLMs) do not consider commonsense. In addition, because the maximum number of tokens that a language model (LM) can deal with is limited, the entire dialogue history cannot be included. The resulting information loss has an adverse effect on performance. To address these problems, we propose a Dialogue-based QA model with Common-sense Reasoning (DQACR), a language model that exploits Semantic Search and continual learning. We used Semantic Search to complement information loss from truncated dialogue. In addition, we used Semantic Search and continual learning to improve the PTLM’s commonsense reasoning. Our model achieves an improvement of approximately 1.5% over the baseline method and can thus facilitate QA-related tasks. It contributes toward not only dialogue-based QA tasks but also another form of QA datasets for future tasks. Full article

Answering different multi-choice machine reading comprehension (MRC) questions generally requires different information due to the abundant diversity of the questions, options and passages. Recently, pre-trained language models which provide rich information have been widely used to address MRC tasks. Most of the existing work only focuses on the output representation at the top layer of the models; the subtle and beneficial information provided by the intermediate layers is ignored. This paper therefore proposes a multi-decision based transformer model that builds multiple decision modules by utilizing the outputs at different layers to confront the various questions and passages. To avoid the information diversity in different layers being damaged during fine-tuning, we also propose a learning rate decaying method to control the updating speed of the parameters in different blocks. Experimental results on multiple publicly available datasets show that our model can answer different questions by utilizing the representation in different layers and speed up the inference procedure with considerable accuracy. Full article

Background: Alzheimer’s disease (AD) is the major cause of age-associated cognitive decline, and in the absence of effective therapeutics is progressive and ultimately fatal, creating a dire need for successful prevention and treatment strategies. We recently reported results of a successful proof-of-concept trial, using a personalized, precision medicine protocol, but whether such an approach is readily scalable is unknown. Objective: In the case of AD, there is not a single therapeutic that exerts anything beyond a marginal, unsustained, symptomatic effect. This suggests that the monotherapeutic approach of drug development for AD may not be an optimal one, at least when used alone. Using a novel, comprehensive, and personalized therapeutic system called ReCODE (reversal of cognitive decline), which proved successful in a small, proof-of-concept trial, we sought to determine whether the program could be scaled to improve cognitive and metabolic function in individuals diagnosed with subjective cognitive impairment, mild cognitive impairment, and early-stage AD. Methods: 255 individuals submitted blood samples, took the Montreal Cognitive Assessment (MoCA) test, and answered intake questions. Individuals who enrolled in the ReCODE program had consultations with clinical practitioners, and explanations of the program were provided. Participants had follow-up visits that included education regarding diet, lifestyle choices, medications, supplements, repeat blood sample analysis, and MoCA testing between 2 and 12 months after participating in the ReCODE program. Pre- and post-treatment measures were compared using the non-parametric Wilcoxon signed rank test. Results and Conclusions: By comparing baseline to follow-up testing, we observed that MoCA scores either significantly improved or stabilized in the entire participant pool—results that were not as successful as those in the proof-of-concept trial, but more successful than anti-amyloid therapies—and other risk factors including blood glucose, high-sensitivity C-reactive protein, HOMA-IR, and vitamin D significantly improved in the participant pool. Our findings provide evidence that a multi-factorial, comprehensive, and personalized therapeutic program designed to mitigate AD risk factors can improve risk factor scores and stabilize or reverse the decline in cognitive function. Since superior results were obtained in the proof-of-concept trial, which was conducted by a small group of highly trained and experienced physicians, it is possible that results from the use of this personalized approach would be enhanced by further training and experience of the practicing physicians. Nonetheless, the current results provide further support indicating the potential of such an approach for the prevention and reversal of cognitive decline. Full article

To address the problem of poor semantic reasoning of models in multiple-choice Chinese machine reading comprehension (MRC), this paper proposes an MRC model incorporating multi-granularity semantic reasoning. In this work, we firstly encode articles, questions and candidates to extract global reasoning information; secondly, we use multiple convolution kernels of different sizes to convolve and maximize pooling of the BERT-encoded articles, questions and candidates to extract local semantic reasoning information of different granularities; we then fuse the global information with the local multi-granularity information and use it to make an answer selection. The proposed model can combine the learned multi-granularity semantic information for reasoning, solving the problem of poor semantic reasoning ability of the model, and thus can improve the reasoning ability of machine reading comprehension. The experiments show that the proposed model achieves better performance on the $C^3$ dataset than the benchmark model in semantic reasoning, which verifies the effectiveness of the proposed model in semantic reasoning. Full article