Search Results (5,226)

Controlling protein crystallization processes is essential for improving downstream processing in biotechnology. This study investigates the combination of machine learning-based image analysis and in situ microscopy for real-time monitoring of protein crystallization kinetics. The experimental research is focused on the batch crystallization of an alcohol dehydrogenase from Lactobacillus brevis (LbADH) and two selected rational crystal contact mutants. Technical protein crystallization experiments were performed in a 1 L stirred crystallizer by adding polyethyleneglycol 550 monomethyl ether (PEG 550 MME). The estimated crystal volumes from online microscopy correlated well with the offline measured protein concentrations in solution. In addition, in situ microscopy was superior to offline data if amorphous protein precipitation occurred. Real-time image analysis provides the data basis for online estimation of important batch crystallization performance indicators like yield, crystallization kinetics, crystal size distributions, and number of protein crystals. Surprisingly, one of the LbADH mutants, which should theoretically crystallize more slowly than the wild type based on molecular dynamics (MD) simulations, showed better crystallization performance except for the yield. Thus, online monitoring of scalable protein crystallization processes with in situ microscopy and real-time image analysis improves the precision of crystallization studies for industrial settings by providing comprehensive data, reducing the limitations of traditional analytical techniques, and enabling new insights into protein crystallization process dynamics. Full article

Online medical forums have emerged as vital platforms for patients to share their experiences and seek advice, providing a valuable, cost-effective source of feedback for medical service management. This feedback not only measures patient satisfaction and improves health service quality but also offers crucial insights into the effectiveness of medical treatments, pain management strategies, and alternative therapies. This study systematically identifies and categorizes key aspects of patient experiences, emphasizing both positive and negative sentiments expressed in their narratives. We collected a dataset of approximately 15,000 entries from various sections of the widely used medical forum, patient.info. Our innovative approach integrates content analysis with aspect-based sentiment analysis, deep learning techniques, and a large language model (LLM) to analyze these data. Our methodology is designed to uncover a wide range of aspect types reflected in patient feedback. The analysis revealed seven distinct aspect types prevalent in the feedback, demonstrating that deep learning models can effectively predict these aspect types and their corresponding sentiment values. Notably, the LLM with few-shot learning outperformed other models. Our findings enhance the understanding of patient experiences in online forums and underscore the utility of advanced analytical techniques in extracting meaningful insights from unstructured patient feedback, offering valuable implications for healthcare providers and medical service management. Full article

Sentiment analysis is a well-known task that has been used to analyse customer feedback reviews and media headlines to detect the sentimental personality or polarisation of a given text. With the growth of social media and other online platforms, like Twitter (now branded as X), Facebook, blogs, and others, it has been used in the investment community to monitor customer feedback, reviews, and news headlines about financial institutions’ products and services to ensure business success and prioritise aspects of customer relationship management. Supervised learning algorithms have been popularly employed for this task, but the performance of these models has been compromised due to the brevity of the content and the presence of idiomatic expressions, sound imitations, and abbreviations. Additionally, the pre-training of a larger language model (PTLM) struggles to capture bidirectional contextual knowledge learnt through word dependency because the sentence-level representation fails to take broad features into account. We develop a novel structure called language feature extraction and adaptation for reviews (LFEAR), an advanced natural language model that amalgamates retrieval-augmented generation (RAG) with a conversation format for an auto-regressive fine-tuning model (ARFT). This helps to overcome the limitations of lexicon-based tools and the reliance on pre-defined sentiment lexicons, which may not fully capture the range of sentiments in natural language and address questions on various topics and tasks. LFEAR is fine-tuned on Hellopeter reviews that incorporate industry-specific contextual information retrieval to show resilience and flexibility for various tasks, including analysing sentiments in reviews of restaurants, movies, politics, and financial products. The proposed model achieved an average precision score of 98.45%, answer correctness of 93.85%, and context precision of 97.69% based on Retrieval-Augmented Generation Assessment (RAGAS) metrics. The LFEAR model is effective in conducting sentiment analysis across various domains due to its adaptability and scalable inference mechanism. It considers unique language characteristics and patterns in specific domains to ensure accurate sentiment annotation. This is particularly beneficial for individuals in the financial sector, such as investors and institutions, including those listed on the Johannesburg Stock Exchange (JSE), which is the primary stock exchange in South Africa and plays a significant role in the country’s financial market. Future initiatives will focus on incorporating a wider range of data sources and improving the system’s ability to express nuanced sentiments effectively, enhancing its usefulness in diverse real-world scenarios. Full article

Active learning techniques are taking the classroom by storm. Numerous research articles have highlighted the benefits of active learning techniques on student understanding, knowledge retention, problem solving, and teamwork. One avenue to introduce active learning into the classroom is the gamification of course learning content. Educational escape rooms are one such example in which students solve a series of puzzles related to course content to “escape” within a set time frame. Escape games play an interesting role in motivating students, building communication skills and allowing for multimodal learning, having been shown to increase students’ test results and enjoyment of the course content. In lieu of the traditional escape room format, a fully immersive room(s) with classical escape room puzzles (finding items, riddles, alternative locking mechanisms) is used alongside learning activities, and educators have begun to develop truncated activities for easier applications in larger classrooms. In this review, we explore several escape room activities: immersive, paper-based, Battle Boxes, condensed escape activities, and online/virtual, providing examples of the types of puzzles included therein. We similarly discuss the creation of escape room materials and recommendations for the interested educator, as well as the learning benefits of engaging in puzzle development. Finally, we provide an overview on methods to assess active learning through escape rooms, establishing an overview of empirical evidence towards their effectiveness as a learning tool. Full article

Energy efficiency constitutes a pivotal performance indicator for 5G New Radio (NR) networks and beyond, and achieving optimal efficiency necessitates the meticulous consideration of trade-offs against other performance parameters, including latency, throughput, connection densities, and reliability. Energy efficiency assumes it is of paramount importance for both User Equipment (UE) to achieve battery prologue and base stations to achieve savings in power and operation cost. This paper presents an exhaustive review of power-saving research conducted for 5G and beyond 5G networks in recent years, elucidating the advantages, disadvantages, and key characteristics of each technique. Reinforcement learning, heuristic algorithms, genetic algorithms, Markov Decision Processes, and the hybridization of various standard algorithms inherent to 5G and 5G NR represent a subset of the available solutions that shall undergo scrutiny. In the final chapters, this work identifies key limitations, namely, computational expense, deployment complexity, and scalability constraints, and proposes a future research direction by theoretically exploring online learning, the clustering of the network base station, and hard HO to lower the consumption of networks like 2G or 4G. In lowering carbon emissions and lowering OPEX, these three additional features could help mobile network operators achieve their targets. Full article

Studies comparing student outcomes for online vs. in-person classes have reported mixed results, though with a majority finding that lower-performing students, on average, fare worse in online classes, attributed to the lack of built-in structure provided by in-person instruction. The online/in-person outcome disparity was normative for non-major geology classes at the University of Mississippi prior to COVID-19, but the difference disappeared in the years after 2020. Previously distinct trendlines of GPA-based predictions of earned-grade for online and in-person classes merged. Of particular concern, outcomes for in-person classes declined to match pre-COVID-19 online expectations, with lower-GPA students disproportionally impacted. Objective evidence of continued decline in student learning, masked by sliding grading scales, is also presented with a long-term record of exam scores drawing from the same question pool for over a decade. Average scores remained relatively constant until COVID-19. Scores then declined in each successive year, attributed to an increase over time in the percentage of enrolled students who had been in high school during the pandemic shutdowns. At the close of 2023, exam scores showed no signs of returning to pre-COVID-19 outcomes. The negative impacts of the shutdowns, with greater impact on those who were in high school during the pandemic, appear to be due to a loss in the developmental life-skills (e.g., self-motivation, focus, critical thinking, social development) needed to thrive in college, not just reduced exposure to preparatory subject material. These results provide a global cautionary message for the management of future pandemics. Full article

In this work, we describe the implementation of an infrastructure of conversational chatbots by using natural language processing and training within the Rasa framework. We use this infrastructure to create a chatbot assistant for the users of a bioinformatics suite. This suite provides a customized interface solution for omic pipelines and workflows, and it is named GPRO. The infrastructure has also been used to build another chatbot for a Laboratory Information Management System (LIMS). The two chatbots (namely, Genie and Abu) have been built on an open framework that uses natural language understanding (NLU) and machine learning techniques to understand user queries and respond to them. Users can seamlessly interact with the chatbot to receive support on navigating the GPRO pipelines and workflows. The chatbot provides a bridge between users and the wealth of bioinformatics knowledge available online. Full article

Moving target detection is one of the most important tasks of radar systems. The clutter echo received by radar is usually strong and heterogeneous when the radar works in a complex terrain environment, resulting in performance degradation in moving target detection. Utilizing prior knowledge of the clutter distribution in the space–time domain, this paper proposes a novel moving target detection network based on small-sample transfer learning and attention mechanism. The proposed network first utilizes offline data to train the feature extraction network and reduce the online training time. Meanwhile, the attention mechanism used for feature extraction is applied in the beam-Doppler domain to improve classification accuracy of targets. Then, a small amount of real-time data are applied to a small-sample transfer network to fine-tune the feature extraction network. Finally, the target detection can be realized by the fine-tuned network. Simulation experiments show that the proposed network can eliminate the influence of heterogeneous clutter on moving target detection, and the attention mechanism can improve clutter suppression under a low signal-to-noise ratio regime. The proposed network has a lower computational load compared to conventional neural networks, enabling its use in real-time applications on space-borne/airborne radars. Full article

The interdisciplinary teaching of contemporary physics has become increasingly common in physics education, especially for high school students and teachers. This approach, which integrates content and methodologies from various disciplines, fosters scientific reasoning, enhances creativity, and increases student motivation and interest in physics. The use of digital technologies, such as social media platforms, supports these educational goals by facilitating the inclusive and cost-effective dissemination of scientific knowledge and the development of soft skills. This paper introduces the “Gravitas” project, an initiative that employs an interdisciplinary approach to present contemporary physics topics to high school students through social media. Coordinated by the Cagliari Division of the National Institute of Nuclear Physics (INFN) in Italy, the “Gravitas” project offers a non-traditional learning environment where students explore modern physics and philosophy and the history of science. Through the creation of educational materials, such as social media posts, students actively engage in their learning. In 2022, around 250 students from 16 high schools across Sardinia, Italy, participated in this project. This paper discusses the learning outcomes, highlighting the potential of integrating formal high school curricula with innovative educational and digital tools. Full article

In the past decade, Unmanned Aerial Vehicles (UAVs) have garnered significant attention across diverse applications, including surveillance, cargo shipping, and agricultural spraying. Despite their widespread deployment, concerns about maintaining stability and safety, particularly when carrying payloads, persist. The development of such UAV platforms necessitates the implementation of robust control mechanisms to ensure stable and precise maneuvering capabilities. Numerous UAV operations require the integration of payloads, which introduces substantial stability challenges. Notably, operations involving unstable payloads such as liquid or slung payloads pose a considerable challenge in this regard, falling into the category of mismatched uncertain systems. This study focuses on establishing stability for slung payload-carrying systems. Our approach involves a combination of various algorithms: the incremental backstepping control algorithm (IBKS), integrator backstepping (IBS), Proportional–Integral–Derivative (PID), and the Sparse Online Gaussian Process (SOGP), a machine learning technique that identifies and mitigates disturbances. With a comparison of linear and nonlinear methodologies through different scenarios, an investigation for an effective solution has been performed. Implementation of the machine learning component, employing SOGP, effectively detects and counteracts disturbances. Insights are discussed within the remit of rejecting liquid sloshing disturbance. Full article

This study investigated the value of simulation workshops designed to enhance motivational discourse between mathematics teachers and struggling students who have difficulty keeping up with the curriculum, especially in advanced mathematics. Grounded in the self-determination theory, we examined teachers’ motivational discourse by having them participate in simulated individual dialogues with students, with a focus on the differences in the motivational discourse with male and female students. Twenty-nine middle school mathematics teachers (89.6% female; mean experience = 9.4 years, SD = 8.7) participated in the online simulations, each of which presented a scenario where an actor portrayed a struggling student contemplating dropping out of math class. Based on the observational measures of motivational discourse, the findings reveal significant gender disparities in that teachers tended to provide more support and autonomy to male students. Moreover, they tend to direct more frequent and intense autonomy-suppressing behaviors toward female students. The results highlight the efficacy of simulation-based workshops in uncovering teachers’ hidden behavioral patterns. It also highlights the importance of simulation-based learning to tailor professional development issues and for addressing unconscious gender biases in mathematics education. Full article

Background: The purpose of this study was to compare the opinions of biomedical students from Russia and Central Serbia about learning methods in the time of the COVID-19 pandemic. Methods: This is a comparative questionnaire study that used the validated questionnaire tool eMedQ, conducted via the online platform Anketolog.ru from February to May 2022 at Sechenov University and the University of Kragujevac in the same period. At Sechenov University, 694 students took part in the survey, while at the University of Kragujevac, the total number of participants was 209. The eMedQ questionnaire, in Russian and Serbian, consists of 45 closed-ended questions with 7 domains: demographic characteristics, experience with online teaching, education process (teaching organization), aspects of mental functioning, clinical skills, technical aspects, and quality of life. Results: During the COVID-19 lockdown, in Serbian and Russian biomedical faculties, we observed the high flexibility of Russian students with greater experience when it comes to online education before the pandemic compared to students from Serbia. Also, the Russian students declared that they were strongly motivated to achieve clinical skills and to learn, while a larger number of Serbian students reported disrupted mental functioning and learning problems. Conclusions: At the time of isolation, at Serbian and Russian biomedical faculties, we noticed the higher flexibility of Russian students with more experience than students from Serbia. Also, the Russian students declared that they were strongly motivated both to acquire clinical skills and to learn, while a larger number of Serbian students reported reduced mental functioning and learning problems. Full article

In the post-COVID-19 era, tourism and cultural industries have begun to bounce back, and their “revenge tourism” desire has forced “Special Forces-style tourism”, which was popularized throughout Chinese social media in 2023. This study explores a current knowledge gap in understanding how social media inspires Generation Z’s engagement in Special Forces tourism and implications for practical sustainable tourism. By leaning on the SOR model, Social Identity Theory, and Social Learning Theory, this study investigates how supportive and interactive social media environments shape the sustainable engagement intentions of Generation Z. We experimentally find that perceived supportive communication about sustainability issues significantly inflates sustainable engagement intentions in high-interactivity settings, by encouraging flow experiences and vicarious reinforcement that facilitate socially responsible travel decisions. Key findings include identification of supportive online interactions that can facilitate sustainable tourism among born-digital travelers, who enable informed, socially and environmentally responsible tourism behaviors. Full article

During the COVID-19 pandemic, the New Jersey Safe Schools Program (NJSS) surveyed a subset of newer NJ high school (HS) teachers who completed NJSS work-based learning supervisory trainings from October 2021 to June 2023. The purpose of this study was to gain insight on NJ HS teacher perceptions of school provided mental health services, and well-being supports received during the COVID-19 pandemic. Via online surveys, teachers anonymously identified who should be responsible for supporting mental well-being in schools, satisfaction with existing mental health services, and self-care practices implemented during the COVID-19 pandemic. Of the 114 HS teachers surveyed, nearly 70% would recommend existing school mental health services to colleagues, 53% would like an increase in mental health and counseling services available at their school, and 44% would like their schools to improve mental health literacy. This study presents insight into the needs teachers expressed for appropriate school mental health support and services. Data will inform guidance for how to better address identified needs, including employee wellness, and creating positive social and emotional school environments. School districts should prioritize the implementation of suitable and equitable school-based mental health services to teachers and students alike to promote healthy and productive school environments. Full article

Visual object tracking is a fundamental task in computer vision, with applications ranging from video surveillance to autonomous driving. Despite recent advances in transformer-based one-stream trackers, unrestricted feature interactions between the template and the search region often introduce background noise into the template, degrading the tracking performance. To address this issue, we propose FETrack, a feature-enhanced transformer-based network for visual object tracking. Specifically, we incorporate an independent template stream in the encoder of the one-stream tracker to acquire the high-quality template features while suppressing the harmful background noise effectively. Then, we employ a sequence-learning-based causal transformer in the decoder to generate the bounding box autoregressively, simplifying the prediction head network. Further, we present a dynamic threshold-based online template-updating strategy and a template-filtering approach to boost tracking robustness and reduce redundant computations. Extensive experiments demonstrate that our FETrack achieves a superior performance over state-of-the-art trackers. Specifically, the proposed FETrack achieves a 75.1% AO on GOT-10k, 81.2% AUC on LaSOT, and 89.3% $P_{norm}$ on TrackingNet. Full article