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In many cases, the first step in large-carnivore management is to obtain objective, reliable, and cost-effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical-site-occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost-effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well-coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population-parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters. En muchos casos, el primer paso en el manejo de carnívoros grandes es la obtención de parámetros objetivos, confiables y rentables de estimaciones poblacionales por medio de procedimientos que sean reproducibles en el tiempo. Sin embargo, el monitoreo de depredadores en áreas extensas es complicado y los datos tienen un nivel alto de incertidumbre. Diseñamos una estrategia multimétodo y multiestado basada en modelos bayesianos jerárquicos de ocupación de sitio que combinan múltiples métodos de seguimiento de censo para estimar los estados de diferentes poblaciones para su uso en el monitoreo de grandes depredadores a una escala regional. Usamos a los lobos (Canis lupus) como especie objetivo y generamos estimaciones confiables del número de sitios con reproducción de lobos (presencia de cachorros). Utilizamos dos conjuntos de datos sobre lobos de España (Galicia Occidental en 2013 y Asturias en 2014) para probar el modelo. Con base a los muestreos de aullidos, la estimación ingenua (es decir, la estimación basada sólo en las observaciones) del número de sitios con reproducción fue de 9 y 25 sitios en Galicia Occidental y Asturias, respectivamente. Nuestro modelo mostró 33.4 (DS 9.6) y 34.4 (3.9) sitios con reproducción de lobos, respectivamente. El número de sitios ocupados con reproducción de lobos fue de 0.67 (DS 0.19) y 0.76 (0.11), respectivamente. Este modelo puede usarse para diseñar programas de monitoreo más rentables (es decir, para definir el esfuerzo de muestreo necesario por sitio). Nuestro enfoque podría ser utilizado para seguimientos bien coordinados a través de múltiples fronteras administrativas y poblaciones, y apoyaría una mejor toma de decisiones para el manejo de grandes carnívoros a escala de paisaje. El uso de este marco de trabajo bayesiano proporciona una manera simple de visualizar el grado de incertidumbre alrededor de los parámetros de estimaciones de población, y así proporciona a los administradores y a los sectores implicados una estrategia intuitiva para interpretar los resultados de los monitoreos. Nuestra estrategia puede aplicarse extensamente a escalas espaciales grandes en el monitoreo de vida silvestre en los casos en que las probabilidades de detección difieren entre los estados poblacionales cuando se están utilizando varios métodos para estimar diferentes parámetros poblacionales.

Although student engagement has been a widely researched area known to improve student learning and a topic of scholarly debate for many decades now, this has yet to be the case in Azerbaijan. Data from the National Survey of Student Engagement, conducted among 433 undergraduate students of the 18-23 age range (M = 21.37, SD = 1.43) at eight universities in Azerbaijan, allowed us to examine variations in the conditions meant to foster student engagement, as well as students’ perspectives on improving their educational experiences. Specifically, we looked at differences related to academic challenges, learning with peers, teacher experiences, and campus environment. Student engagement varied across disciplines. Small universities in the capital city provided better collaborative learning conditions. However, students at regional universities were more satisfied with the quality of student-faculty interactions. Nonetheless, students saw a strong need for fundamental changes in higher education in Azerbaijan, focusing on improving the quality of teachers, teaching and the curriculum. The study provided an overview of student engagement variations across institutions and disciplines and how students conceptualise necessary improvements in student experiences. Institutional leaders must understand the variations for seeking essential changes in the HE system to effectively accommodate students’ needs and expectations.

With autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a mainstream solution for accurate obstacle detection. This article presents a detailed survey on mmWave radar and vision fusion based obstacle detection methods. First, we introduce the tasks, evaluation criteria, and datasets of object detection for autonomous driving. The process of mmWave radar and vision fusion is then divided into three parts: sensor deployment, sensor calibration, and sensor fusion, which are reviewed comprehensively. Specifically, we classify the fusion methods into data level, decision level, and feature level fusion methods. In addition, we introduce three-dimensional(3D) object detection, the fusion of lidar and vision in autonomous driving and multimodal information fusion, which are promising for the future. Finally, we summarize this article.

Sensors in many applications must select the largest element in a sequence of currents. This can be performed in an analog way by the Winner-Take-All (WTA) circuit. This paper considers the classic version of the WTA Lazzaro circuit, working with MOS devices in a subthreshold regime. Since the separation of the gainer by analytically computable “decision levels” has recently been introduced, this paper aims to numerically verify and discuss these levels and their dependence on circuit and device parameters. For VT, the threshold voltage of MOS devices, which is primarily responsible for differences between components (mismatch), its relationship with the output voltages is theoretically demonstrated and numerically checked.

In real-world applications, the parameters used to describe the decision-making problem are imprecise and vague. Fuzzy modeling of the problem using fuzzy numbers (FNs) can address the inherent vagueness of the parameters effectively. A crucial aspect of decision-making, particularly when uncertainty exists at lower levels, involves ranking FNs. This paper introduces a novel defuzzification technique for ranking Generalized Trapezoidal Fuzzy Numbers with Left and Right Heights (GTFNLRH). For the purpose of ranking, the proposed approach derives a representative value of GTFNLRH. This involves finding a Triangular Fuzzy Quantity (TFQ) using centroids with vagueness at lower decision levels, calculating the value (VAL), representing the ill-defined magnitude, determining the ambiguity (AMB), quantifying the vagueness within its ill-defined magnitude of the GTFNLRH, and applying VAL and AMB to the TFQ. Based on these representative values, a novel ranking criterion is established that overcomes the limitations of ranking different FNs observed in some existing fuzzy ranking methods. An application of the novel defuzzification technique is also investigated to assess the fuzzy risk associated with product manufacturing by different companies.

This paper presents the simultaneous utilization of video images and inertial signals that are captured at the same time via a video camera and a wearable inertial sensor within a fusion framework in order to achieve a more robust human action recognition compared to the situations when each sensing modality is used individually. The data captured by these sensors are turned into 3D video images and 2D inertial images that are then fed as inputs into a 3D convolutional neural network and a 2D convolutional neural network, respectively, for recognizing actions. Two types of fusion are considered—Decision-level fusion and feature-level fusion. Experiments are conducted using the publicly available dataset UTD-MHAD in which simultaneous video images and inertial signals are captured for a total of 27 actions. The results obtained indicate that both the decision-level and feature-level fusion approaches generate higher recognition accuracies compared to the approaches when each sensing modality is used individually. The highest accuracy of 95.6% is obtained for the decision-level fusion approach.

In cases with a large number of sensors and complex spatial distribution, correctly learning the spatial characteristics of the sensors is vital for structural damage identification. Graph convolutional neural networks (GCNs), unlike other methods, have the ability to learn the spatial characteristics of the sensors, which is targeted at the above problems in structural damage identification. However, under the influence of environmental interference, sensor instability, and other factors, part of the vibration signal can easily change its fundamental characteristics, and there is a possibility of misjudging structural damage. Therefore, on the basis of building a high-performance graphical convolutional deep learning model, this paper considers the integration of data fusion technology in the model decision-making layer and proposes a single-model decision-making fusion neural network (S_DFNN) model. Through experiments involving the frame model and the self-designed cable-stayed bridge model, it is concluded that this method has a better performance of damage recognition for different structures, and the accuracy is improved based on a single model and has good damage recognition performance. The method has better damage identification performance in different structures, and the accuracy rate is improved based on the single model, which has a very good damage identification effect. It proves that the structural damage diagnosis method proposed in this paper with data fusion technology combined with deep learning has a strong generalization ability and has great potential in structural damage diagnosis.

Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value.

This paper presents a novel approach to substantially improve the detection accuracy of structural damage via a one-dimensional convolutional neural network (1-D CNN) and a decision-level fusion strategy. As structural damage usually induces changes in the dynamic responses of a structure, a CNN can effectively extract structural damage information from the vibration signals and classify them into the corresponding damage categories. However, it is difficult to build a large-scale sensor system in practical engineering; the collected vibration signals are usually non-synchronous and contain incomplete structure information, resulting in some evident errors in the decision stage of the CNN. In this study, the acceleration signals of multiple acquisition points were obtained, and the signals of each acquisition point were used to train a 1-D CNN, and their performances were evaluated by using the corresponding testing samples. Subsequently, the prediction results of all CNNs were fused (decision-level fusion) to obtain the integrated detection results. This method was validated using both numerical and experimental models and compared with a control experiment (data-level fusion) in which all the acceleration signals were used to train a CNN. The results confirmed that: by fusing the prediction results of multiple CNN models, the detection accuracy was significantly improved; for the numerical and experimental models, the detection accuracy was 10% and 16–30%, respectively, higher than that of the control experiment. It was demonstrated that: training a CNN using the acceleration signals of each acquisition point and making its own decision (the CNN output) and then fusing these decisions could effectively improve the accuracy of damage detection of the CNN.

Aiming at the problem of poor effect of object detection with visible images under low light conditions, the decision-level fusion detection method of visible and infrared images is studied. Taking YOLOX as the object detection network based on deep learning, a decision-level fusion detection algorithm of visible and infrared images based on light sensing is proposed. Experiments are carried out on LLVIP dataset, which is a visible-infrared paired dataset for low light vision. Through comparative analysis, it is found that the decision-level fusion algorithm based on Soft-NMS and light sensing obtained the optimal AP value of 69.0%, which is 11.4% higher than the object detection with visible images and 1.1% higher than the object detection with infrared images. The experimental results show that the decision-level fusion algorithm based on Soft-NMS and light sensing can effectively fuse the complementary information of visible and infrared images, and improve the object detection effect under low light conditions.