Suffering from environmental distress like carbon emissions, traffic restrictions have been enforced extensively in distribution logistics. Reasonable arrangement of urban freight transportation can effectively improve distribution efficiency, reduce distribution costs, and alleviate the impact of traffic restrictions in distribution logistics. In response to increasingly stringent traffic restrictions, we establish a multi-objective optimization model, including the minimum distributions and the minimum carbon emissions. Given that the limits of battery capacity and cargo capacity, we build a green vehicle routing problem with soft time windows (GVRPTW) model with heterogenous fleets. In this study, three different factors, that is restricted area, travel time of vehicles, and carbon tax prices, are discussed in details. In order to solve the NP-hard model, we propose an improved ant colony optimization algorithm (IACO) by optimizing the state transition probability, and verifies the worth of the algorithm. The experimental results can explore the impacts of traffic restriction policies on the formulation of distribution scheme and offer reference opinions for the government to formulate reasonable restriction policies and better guide logistics enterprises to reduce carbon emissions.

Optical electronic nose (e-nose) are drawing more and more attention from the academia and industry in Internet of Things applications. Since the CCD-based portable spectrometer is commonly used in gas sensing system of e-nose, the baseline drift interference is easy to be introduced, which reduces the accuracy and sensitivity of the system and makes the sensing performance worse. For this issue, firstly, the baseline drift data were analyzed and the characteristics of the baseline drift interference brought by the portable spectrometer were found. Then, according to the characteristics of the drift, a baseline drift suppression technique for portable spectrometer was proposed. Specifically, it mainly includes four steps: data preprocessing, dimension reduction using principal component analysis (PCA), sources separation using independent component analysis (ICA) and reconstruction after interference suppression. At last, experimental results show that, the proposed technique suppressed the interference effectively, and even better compared with classical existing methods.

The feature extraction technique for an electronic nose (e-nose) applied in tobacco smell detection in an open country/outdoor environment with periodic background strong interference is studied in this paper. Principal component analysis (PCA), Independent component analysis (ICA), re-filtering and a priori knowledge are combined to separate and suppress background interference on the e-nose. By the coefficient of multiple correlation (CMC), it can be verified that a better separation of environmental temperature, humidity, and atmospheric pressure variation related background interference factors can be obtained with ICA. By re-filtering according to the on-site interference characteristics a composite smell curve was obtained which is more related to true smell information based on the tobacco curer’s experience.

Purpose: This paper aims to propose an odor sensing system based on wide spectrum for e-nose, based on comprehensive analysis on the merits and drawbacks of current e-nose. Design/methodology/approach: The wide spectral light is used as the sensing medium in the e-nose system based on continuous wide spectrum (CWS) odor sensing, and the sensing response of each sensing element is the change of light intensity distribution. Findings: Experimental results not only verify the feasibility and effectiveness of the proposed system but also show the effectiveness of least square support vector machine (LSSVM) in eliminating system errors. Practical implications: Theoretical model of the system was constructed, and experimental tests were carried out by using NO₂ and SO₂. System errors in the test data were eliminated using the LSSVM, and the preprocessed data were classified by euclidean distance to centroids (EDC), k-nearest neighbor (KNN), support vector machine (SVM), LSSVM, respectively. Originality/value: The system not only has the advantages of current e-nose but also realizes expansion of sensing array by means of light source and the spectrometer with their wide spectrum, high resolution characteristics which improve the detection accuracy and realize real-time detection.

WiFi-based gesture recognition using domain-independent Body-coordinate Velocity Profile (BVP) features has demonstrated strong cross-domain generalization, but existing methods generally rely on closed-set assumptions, limiting their use in open-world scenarios. In this paper, we present a systematic empirical study of open-set gesture recognition on WiFi BVP features. On the CNN–GRU backbone, we evaluate seven representative OSR methods—MSP, OpenMax, Energy, ODIN, K-NN, prototype, and rejection head—under two backbone strategies (frozen and full fine-tuning), across six unknown gestures and three random seeds. Our experiments show that closed-set Softmax classifiers consistently misclassify unknown gestures with high confidence (49.7–74.3% across six gestures), and that the closed-set high-confidence misclassification rate is strongly associated with open-set AUROC ($R^2$ = 0.918, p = 0.0026), whereas embedding-space geometric distance is not ($R^2$ = 0.018, p = 0.80). This suggests that open-set difficulty may stem from classifier decision-boundary confusion rather than geometric separation. We further find that no single OSR method consistently outperforms all others across unknown gestures, and that frozen and fine-tuned backbones yield comparable open-set performance (typically within ±0.02 AUROC). Finally, we provide multi-objective evaluation and deployment observations, including Pareto frontiers and multiple operating points. These results offer a systematic benchmark for open-set WiFi gesture recognition and highlight the importance of classifier-level factors in determining open-set difficulty.