A Study on Natural Lighting Design Strategies for Teaching Buildings in Hot-summer and Cold-winter Zone of China—A case of the Arts and Sciences Building of Xinyang Normal University

The natural lighting of buildings plays an important role in creating a comfortable indoor light environment and reducing the energy consumption of artificial lighting. Teaching buildings have special requirements for the indoor light environment. Classroom glare, corridor backlit, and low natural illumination in corridor are light pollution problems easily appear in teaching building, which cannot be ignored in the design of teaching building. Regarding the issues above, the paper took the Arts and Sciences Building of Xinyang Normal University as an example, through the architectural modeling, space forms, facade effects and other features, used VELUX simulation software to simulate the illuminance and daylighting parameters of different sunroofs and provided solutions for classroom glare and corridor lighting. Ultimately, the paper analyzed the building lighting energy saving schemes based on regional climate and environment, and found out the best balance point for the energy saving design of lighting and thermal environment, meanwhile, provided valuable and practical reference for lighting design of corridor skylights in the region

Analysis of an enhanced random forest algorithm for identifying encrypted network traffic

The focus of this paper is to apply an improved machine learning algorithm to realize the efficient and reliable identification and classification of network communication encrypted traffic, and to solve the challenges faced by traditional algorithms in analyzing encrypted traffic after adding encryption protocols. In this study, an enhanced random forest (ERF) algorithm is introduced to optimize the accuracy and efficiency of the identification and classification of encrypted network traffic. Compared with traditional methods, it aims to improve the identification ability of encrypted traffic and fill the knowledge gap in this field. Using the publicly available datasets and preprocessing the original PCAP format packets, the optimal combination of the relevant parameters of the tree was determined by grid search cross-validation, and the experimental results were evaluated in terms of performance using accuracy, precision, recall and F1 score, which showed that the average precision was more than 98 %, and that compared with the traditional algorithm, the error rate of the traffic test set was reduced, and the data of each performance evaluation index were better, which It shows that the advantages of the improved algorithm are obvious. In the experiment, the enhanced random forest and traditional random forest models were trained and tested on a series of data sets and the corresponding test errors were listed as the basis for judging the model quality. The experimental results show that the enhanced algorithm has good competitiveness. These findings have implications for cybersecurity professionals, researchers, and organizations, providing a practical solution to enhance threat detection and data privacy in the face of evolving encryption technologies. This study provides valuable insights for practitioners and decision-makers in the cybersecurity fiel

Insight into improving antidepressant adherence and symptoms by pharmacist intervention: A review

Purpose: To assess the effectiveness of antidepressant medication adherence-improving intervention by a pharmacist and its impact on clinical symptoms of depression among outdoor depressive patients.Methods: Various databases such as PubMed, Embase, and Scopus were used sources for the literature published during the last 20 years. Pharmacist intervention studies involving adult depressed patients (≥ 17 years old) and treated with antidepressants were included. Twelve studies met the inclusion criteria.Results: These studies depicted various levels of interventions in which pharmacist counseled and educated the patients to support medication adherence. In only one of the studies, pharmacist intervention exercised significant effect on the depression features of patients.Conclusion: The findings suggest that the implication of antidepressant medication adherenceimproving intervention by pharmacist leads to the improved adherence of adult depressive patients to antidepressants. However, pharmacist intervention did not show any significant influence on depression symptomology, necessitating further studies on the topic.Keywords: Pharmacist care, Depression, Antidepressants, Intervention, Medication adherenc

Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting

Electric load forecasting is an indispensable component of electric power system planning and management. Inaccurate load forecasting may lead to the threat of outages or a waste of energy. Accurate electric load forecasting is challenging when there is limited data or even no data, such as load forecasting in holiday, or under extreme weather conditions. As high-stakes decision-making usually follows after load forecasting, model interpretability is crucial for the adoption of forecasting models. In this paper, we propose an interactive GAM which is not only interpretable but also can incorporate specific domain knowledge in electric power industry for improved performance. This boosting-based GAM leverages piecewise linear functions and can be learned through our efficient algorithm. In both public benchmark and electricity datasets, our interactive GAM outperforms current state-of-the-art methods and demonstrates good generalization ability in the cases of extreme weather events. We launched a user-friendly web-based tool based on interactive GAM and already incorporated it into our eForecaster product, a unified AI platform for electricity forecasting

In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT

The way users acquire information is undergoing a paradigm shift with the advent of ChatGPT. Unlike conventional search engines, ChatGPT retrieves knowledge from the model itself and generates answers for users. ChatGPT's impressive question-answering (QA) capability has attracted more than 100 million users within a short period of time but has also raised concerns regarding its reliability. In this paper, we perform the first large-scale measurement of ChatGPT's reliability in the generic QA scenario with a carefully curated set of 5,695 questions across ten datasets and eight domains. We find that ChatGPT's reliability varies across different domains, especially underperforming in law and science questions. We also demonstrate that system roles, originally designed by OpenAI to allow users to steer ChatGPT's behavior, can impact ChatGPT's reliability. We further show that ChatGPT is vulnerable to adversarial examples, and even a single character change can negatively affect its reliability in certain cases. We believe that our study provides valuable insights into ChatGPT's reliability and underscores the need for strengthening the reliability and security of large language models (LLMs)

Understanding Public Online Donations on Social Media during the Pandemic: A Social Presence Theory Perspective

The COVID-19 pandemic has had a huge impact on the global economy and health care, but online donations from the public on social media have increased significantly. However, the role of social presence in motivating people to donate online during the pandemic has been largely unexplored. This study examines the relationship between social presence on social media and online donation behavior during the pandemic using social presence theory. We explore the interplay between social presence, perceived threat, social properties of social media, and donation intentions. The results showed that social presence based on social media, perception of others and social interaction significantly affected social media online donation participation, and the perceived threat of COVID-19 significantly moderated online donation participation. Our research contributes to the understanding of online donation behavior during a pandemic crisis and provides insights into how social media can be leveraged for effective donation campaigns

MGTBench: Benchmarking Machine-Generated Text Detection

Nowadays large language models (LLMs) have shown revolutionary power in a variety of natural language processing (NLP) tasks such as text classification, sentiment analysis, language translation, and question-answering. In this way, detecting machine-generated texts (MGTs) is becoming increasingly important as LLMs become more advanced and prevalent. These models can generate human-like language that can be difficult to distinguish from text written by a human, which raises concerns about authenticity, accountability, and potential bias. However, existing detection methods against MGTs are evaluated under different model architectures, datasets, and experimental settings, resulting in a lack of a comprehensive evaluation framework across different methodologies In this paper, we fill this gap by proposing the first benchmark framework for MGT detection, named MGTBench. Extensive evaluations on public datasets with curated answers generated by ChatGPT (the most representative and powerful LLMs thus far) show that most of the current detection methods perform less satisfactorily against MGTs. An exceptional case is ChatGPT Detector, which is trained with ChatGPT-generated texts and shows great performance in detecting MGTs. Nonetheless, we note that only a small fraction of adversarial-crafted perturbations on MGTs can evade the ChatGPT Detector, thus highlighting the need for more robust MGT detection methods. We envision that MGTBench will serve as a benchmark tool to accelerate future investigations involving the evaluation of state-of-the-art MGT detection methods on their respective datasets and the development of more advanced MGT detection methods. Our source code and datasets are available at https://github.com/xinleihe/MGTBench