The indoor lighting environment is a key factor affecting human health and safety. In particular, people have been forced to study or work more for long periods of time at home due to the COVID-19 pandemic. In this study, we investigate the influence of physical indoor environmental factors, correlated color temperature (CCT), and illumination on computer work fatigue. We conducted a within-subject experiment consisting of a 10 min-long task test under two different illumination settings (300 lx and 500 lx) and two CCTs (3000 K and 4000 K). Physiological signals, such as electroencephalogram (EEG), electrocardiograph (ECG), and eye movement, were monitored during the test to objectively measure fatigue. The subjective fatigue of eight participants was evaluated based on a questionnaire conducted after completing the test. The error rate of the task test was taken as the key factor representing the working performance. Through the analysis of the subjective and objective results, computer work fatigue was found to be significantly impacted by changes in the lighting environment, where human fatigue was negatively correlated with illumination and CCT. Improving the illumination and CCT of the work environment, within the scope of this study, helped to decrease the fatigue degree—that is, the fatigue degree was the lowest under the 4000 K + 500 lx environment, while it was relatively high at 3000 K + 300 lx. Under indoor environment conditions, the CCT factor was found to have the greatest effect on computer work fatigue, followed by illumination. The presented results are expected to be a valuable reference for improving the satisfaction associated with the lighting environment and to serve as guidance for researchers and reviewers conducting similar research.

A novel liquid-infiltrated photonic crystal fiber model applicable in liquid sensing for different test liquids—water, ethanol and benzene—has been proposed. One core hole and three air hole rings have been designed and a full vector finite element method has been used for numerical investigation to give the best results in terms of relative sensitivity, confinement loss, power fraction, dispersion, effective area, nonlinear coefficient, numerical aperture and V-Parameter. Specially, the assessed relative sensitivities of the proposed fiber with water, ethanol and benzene are 94.26%, 95.82% and 99.58%, respectively, and low confinement losses of 1.52 × 10⁻¹¹ dB/m with water, 1.21 × 10⁻¹² dB/m with ethanol and 6.01 × 10⁻¹⁶ dB/m with benzene, at 1.0 μm operating wavelength. This novel PCF design is considered simple and can be easily fabricated for practical use, and the assessed waveguide properties has determined the potential applicability in real liquid sensing applications.

The slanted edge method is a basic approach for measuring the modulation transfer function (MTF) of imaging systems; however, its measurement accuracy is limited in practice. Theoretical analysis of the slanted edge MTF measurement method performed in this paper reveals that inappropriate edge angles and random noise reduce this accuracy. The error caused by edge angles is analyzed using sampling and reconstruction theory. Furthermore, an error model combining noise and edge angles is proposed. We verify the analyses and model with respect to (i) the edge angle, (ii) a statistical analysis of the measurement error, (iii) the full width at half-maximum of a point spread function, and (iv) the error model. The experimental results verify the theoretical findings. This research can be referential for applications of the slanted edge MTF measurement method.

This paper mainly studies the effect of artificial lighting environmental factors on the psychological emotions of observers in the large and practical space of the museums. The purpose is to reveal the relationship between the observers’ response and the artificial lighting condition in the actual art museum space. Field research regarding three art museums in Japan was carried out and the optical environment parameters applied in those museums were quantified. The innovation method is to define the artificial lighting environment space in the way of classified lighting design. Thirty one observers were invited to evaluate the three art museum’s lighting environment. In addition, this paper analyzes and discusses the influence of the actual spatial lighting parameters of museum buildings on observers’ psychological emotions (comfort, clarity, preference and warmth), under three modes of illuminance and correlated colour temperature (CCT) combination. Using one-way analysis of variance and correlation analysis, through analysis get the correlation of the four evaluation and three lighting environments indexes are less than 0.05, the observer in an environment with high illuminance and a high CCT had higher psychological evaluation of the art museum.

With the rapid development of urbanization, ambient air pollution is becoming increasingly serious. Out of many pollutants, fine particulate matter (PM_2.5) is the pollutant that affects the urban atmospheric environment to the greatest extent. Fine particulate matter (PM_2.5) concentration prediction is of great significance to human health and environmental protection. This paper proposes a CNN-SSA-DBiLSTM-attention deep learning fusion model. This paper took the meteorological observation data and pollutant data from eight stations in Bijie from 1 January 2015 to 31 December 2022 as the sample data for training and testing. For the obtained data, the missing values and the data obtained from the correlation analysis performed were first processed. Secondly, a convolutional neural network (CNN) was used for the feature selection. DBILSTM was then used to establish a network model for the relationship between the input and actual output sequences, and an attention mechanism was added to enhance the impact of the relevant information. The number of units in the DBILSTM and the epoch of the whole network were optimized using the sparrow search algorithm (SSA), and the predicted value was the output after optimization. This paper predicts the concentration of PM_2.5 in different time spans and seasons, and makes a comparison with the CNN-DBILSTM, BILSTM, and LSTM models. The results show that the CNN-SSA-DBiLSTM-attention model had the best prediction effect, and its accuracy improved with the increasing prediction time span. The coefficient of determination (R²) is stable at about 0.95. The results revealed that the proposed CNN-SSA-DBiLSTM-attention ensemble framework is a reliable and accurate method, and verifies the research results of this paper in regard to the prediction of PM_2.5 concentration. This research has important implications for human health and environmental protection. The proposed method could inspire researchers to develop even more effective methods for atmospheric environment pollution modeling.

This paper presents a UAV-based road illumination measurement system and evaluates its performance through experiments. The system employs a HUBSAN Zino 2+ UAV, STM32F103RCT6 microcontroller, BH1750 illuminance sensor, and GPS and integrates flight, processing, measurement, cloud platform, obstacle avoidance, communication, and power supply units via the OneNET cloud platform. Both hardware and software designs were implemented, using the Z-score algorithm to handle outliers in illumination data. The system showed a single-point measurement error rate of 1.14% and a MAPE of 5.08% for multi-point measurements. In experiments, the system’s horizontal and vertical illuminance RMSE were 1.92 lx and 1.75 lx, respectively. The real-time visualization interface improved operational efficiency, cutting labor costs by half and time costs by nearly four-fifths. UAV control and monitoring from the roadside ensured safety during measurements. The system’s efficiency and wide measurement range enabled extended experiments, collecting illuminance data across multiple horizontal and vertical planes. This resulted in the creation of both horizontal and innovative vertical-plane illuminance distribution maps. These findings provide valuable data for evaluating road lighting quality, enhancing road traffic safety, and improving road illumination design.