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Current camera traps use passive infrared triggers; therefore, they only capture images when animals have a substantially different surface body temperature than the background. Endothermic animals, such as mammals and birds, provide adequate temperature contrast to trigger cameras, while ectothermic animals, such as amphibians, reptiles, and invertebrates, do not. Therefore, a camera trap that is capable of monitoring ectotherms can expand the capacity of ecological research on ectothermic animals. This study presents the design, development, and evaluation of a solar-powered and artificial-intelligence-assisted camera trap system with the ability to monitor both endothermic and ectothermic animals. The system is developed using a central processing unit, integrated graphics processing unit, camera, infrared light, flash drive, printed circuit board, solar panel, battery, microphone, GPS receiver, temperature/humidity sensor, light sensor, and other customized circuitry. It continuously monitors image frames using a motion detection algorithm and commences recording when a moving animal is detected during the day or night. Field trials demonstrate that this system successfully recorded a high number of animals. Lab testing using artificially generated motion demonstrated that the system successfully recorded within video frames at a high accuracy of 0.99, providing an optimized peak power consumption of 5.208 W. No water or dust entered the cases during field trials. A total of 27 cameras saved 85,870 video segments during field trials, of which 423 video segments successfully recorded ectothermic animals (reptiles, amphibians, and arthropods). This newly developed camera trap will benefit wildlife biologists, as it successfully monitors both endothermic and ectothermic animals.

To examine stress redistribution phenomena in bridges subjected to varying operational conditions, this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail steel arch bridge. An initial statistical comparison of sensor data distributions reveals clear temporal variations in stress redistribution patterns. XGBoost (eXtreme Gradient Boosting), a gradient-boosting machine learning (ML) algorithm, was employed not only for predictive modeling but also to uncover the underlying mechanisms of stress evolution. Unlike traditional numerical models that rely on extensive assumptions and idealizations, XGBoost effectively captures nonlinear and time-varying relationships between stress states and operational/environmental factors, such as temperature, traffic load, and structural geometry. This approach allows for the identification of critical periods and conditions under which stress redistribution becomes significant. Results indicate a clear shift of stress concentrations from beam ends toward mid-span regions following the commencement of metro operations, reflecting both structural adaptation and localized overstress near arch ribs. Furthermore, the model generates robust predictions of stress evolution, demonstrating potential applications in early warning systems and fatigue risk assessment. This work represents the first application of interpretable gradient-boosting techniques to stress redistribution modeling in double-deck bridges. In addition, a Stress Redistribution Index (SRI) is proposed, derived from this monitoring study and finite-element-based transverse load distributions, to quantify temporal stress shifts between midspan and edge beams. The results provide both theoretical contributions and practical guidance for the design, inspection, and maintenance of complex bridge structures.

A goal of runtime software-fault monitoring is to observe software behavior to determine whether it complies with its intended behavior. Monitoring allows one to analyze and recover from detected faults, providing additional defense against catastrophic failure. Although runtime monitoring has been in use for over 30 years, there is renewed interest in its application to fault detection and recovery, largely because of the increasing complexity and ubiquitous nature of software systems. We present taxonomy that developers and researchers can use to analyze and differentiate recent developments in runtime software fault-monitoring approaches. The taxonomy categorizes the various runtime monitoring research by classifying the elements that are considered essential for building a monitoring system, i.e., the specification language used to define properties; the monitoring mechanism that oversees the program's execution; and the event handler that captures and communicates monitoring results. After describing the taxonomy, the paper presents the classification of the software-fault monitoring systems described in the literature.

Structural health monitoring (SHM) is an active area of research devoted to systems that can autonomously and proactively assess the structural integrity of bridges, buildings, and aerospace vehicles. Recent technological advances promise the eventual ability to cover a large civil structure with low-cost wireless sensors that can continuously monitor a building's structural health, but researchers face several obstacles to reaching this goal, including high data-rate, data-fidelity, and time-synchronization requirements. This article describes two systems the authors recently deployed in real-world structures.

An inherent requirement of teaching using online learning platforms is that the teacher must analyze student activity and performance in relation to course learning objectives. Therefore, all e-learning environments implement a module to collect such information. Nevertheless, these raw data must be processed to perform e-learning analysis and to help teachers arrive at relevant decisions for the teaching process. In this paper, UBUMonitor is presented, an open-source desktop application that downloads Moodle (Modular Object-Oriented Dynamic Learning Environment) platform data, so that student activity and performance can be monitored. The application organizes and summarizes these data in various customizable charts for visual analysis. The general features and uses of UBUMonitor are described, as are some approaches to e-teaching improvements, through real case studies. These include the analysis of accesses per e-learning object, statistical analysis of grading e-activities, detection of e-learning object configuration errors, checking of teacher activity, and comparisons between online and blended learning profiles. As an open-source application, UBUMonitor was institutionally adopted as an official tool and validated with several groups of teachers at the Teacher Training Institute of the University of Burgos.

In this paper, we introduce a new form of behavioral biometrics based on mouse dynamics, which can be used in different security applications. We develop a technique that can be used to model the behavioral characteristics from the captured data using artificial neural networks. In addition, we present an architecture and implementation for the detector, which cover all the phases of the biometric data flow including the detection process. Experimental data illustrating the experiments conducted to evaluate the accuracy of the proposed detection technique are presented and analyzed. Specifically, three series of experiments are conducted. The main experiment, in which 22 participants are involved, reproduces real operating conditions in computing systems by giving participants an individual choice of operating environments and applications; 284 hours of raw mouse data are collected over 998 sessions, with an average of 45 sessions per user. The two other experiments, involving seven participants, provided a basis for studying the confounding factors arising from the main experiment by fixing the environment variables. In the main experiment, the performance results presented using receiver operating characteristic (ROC) curves and a confusion matrix yield at the crossover point (that is, the threshold set for an equal error rate) a false acceptance rate (FAR) of 2.4649 percent and a false rejection rate (FRR) of 2.4614 percent.

Integrating wireless sensor networks with the traditional wired grid poses several challenges. The technical challenges center on the development of sensors and sensor network infrastructure, including the need to comply with emerging APIs for grid and Web services. Process-driven challenges, which center on the development and adoption of new business models and applications, are driving this technology. We describe two widely different sensor network applications - emergency medical services and supply chain management - and describe how they fit into a new data-collection network based on the Hourglass publish-subscribe paradigm.

Eye-tracking is an important approach to collect evidence regarding some participants’ driving fatigue. In this contribution, the authors present a non-intrusive system for evaluating driver fatigue by tracking eye movement behaviours. A real-time eye-tracker was used to monitor participants’ eye state for collecting eye-movement data. These data are useful to get insights into assessing participants’ fatigue state during monotonous driving. Ten healthy subjects performed continuous simulated driving for 1–2 h with eye state monitoring on a driving simulator in this study, and these measured features of the fixation time and the pupil area were recorded via using eye movement tracking device. For achieving a good cost-performance ratio and fast computation time, the fuzzy K-nearest neighbour was employed to evaluate and analyse the influence of different participants on the variations in the fixation duration and pupil area of drivers. The findings of this study indicated that there are significant differences in domain value distribution of the pupil area under the condition with normal and fatigue driving state. Result also suggests that the recognition accuracy by jackknife validation reaches to about 89% in average, implying that show a significant potential of real-time applicability of the proposed approach and is capable of detecting driver fatigue.

This paper presents the results of an experiment in security evaluation. The system is modeled as a privilege graph that exhibits its security vulnerabilities. Quantitative measures that estimate the effort an attacker might expend to exploit these vulnerabilities to defeat the system security objectives are proposed. A set of tools has been developed to compute such measures and has been used in an experiment to monitor a large real system for nearly two years. The experimental results are presented and the validity of the measures is discussed. Finally, the practical usefulness of such tools for operational security monitoring is shown and a comparison with other existing approaches is given.

For Web services to expand across the Internet, users need to be able to efficiently access and share Web services. The authors present a query infrastructure that treats Web services as first-class objects. It evaluates queries through the invocations of different Web service operations. Because efficiency plays a central role in such evaluations, the authors propose a query optimization model based on aggregating the quality of Web service (QoWS) parameters of different Web services. The model adjusts QoWS through a dynamic rating scheme and multilevel matching in which the rating provides an assessment of Web services' behavior. Multilevel matching allows the expansion of the solution space by enabling similar and partial answers.