903 research outputs found
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
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
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
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
Sequencing-enabled Hierarchical Cooperative CAV On-ramp Merging Control with Enhanced Stability and Feasibility
This paper develops a sequencing-enabled hierarchical connected automated
vehicle (CAV) cooperative on-ramp merging control framework. The proposed
framework consists of a two-layer design: the upper level control sequences the
vehicles to harmonize the traffic density across mainline and on-ramp segments
while enhancing lower-level control efficiency through a mixed-integer linear
programming formulation. Subsequently, the lower-level control employs a
longitudinal distributed model predictive control (MPC) supplemented by a
virtual car-following (CF) concept to ensure asymptotic local stability, l_2
norm string stability, and safety. Proofs of asymptotic local stability and l_2
norm string stability are mathematically derived. Compared to other prevalent
asymptotic local-stable MPC controllers, the proposed distributed MPC
controller greatly expands the initial feasible set. Additionally, an auxiliary
lateral control is developed to maintain lane-keeping and merging smoothness
while accommodating ramp geometric curvature. To validate the proposed
framework, multiple numerical experiments are conducted. Results indicate a
notable outperformance of our upper-level controller against a distance-based
sequencing method. Furthermore, the lower-level control effectively ensures
smooth acceleration, safe merging with adequate spacing, adherence to proven
longitudinal local and string stability, and rapid regulation of lateral
deviations
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