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Thunder God Vine has multiple effects such as anti-inflammatory, anti-tumor, and anti-fertility. One of its main active ingredients, triptriolide, can exert anti-tumor effects by targeting transcription factors, kinases, or inhibiting the ubiquitin/proteasome pathway to inhibit cell proliferation and promote tumor cell apoptosis. However, its high toxicity and poor water solubility limit its application. This study aims to develop a novel photoelectrochemical (PEC) sensor based on MoS₂/Bi₂MoO₆ nanohybrids for the rapid and accurate detection of triptriolide. The PEC sensor is a fundamental tool for extracting and identifying valuable bioactive compounds from this plant. Under optimal conditions, 0.01–10ngmL${}^{-1}$ of triptriolide could be identified, with the detection limit being as low as 0.005ngmL${}^{-1}$. With advantages like rapid response, high sensitivity and preferable stability, the developed sensor is suitable for real-time monitoring of triptriolide levels in actual samples.

Drinking water systems are vulnerable to attack. While homeland security initiatives commonly focus on aerial CW/BW attacks, they have tended to ignore the far more inexpensive and easy to orchestrate attacks on drinking water distribution networks. An approach that utilizes off-the-shelf broad-spectrum analytical instruments coupled with advanced interpretive algorithms to provide detection-response networks for water is described. This report summarizes the development of interpretive algorithms applied to drinking water instrumentation and the implications for immediately engineering and deploying distribution based detection-protection systems. Data obtained from in house testing along with testing at ECBC was used to produce fingerprint response data from traditional as well as non-military threat agents. Data obtained from a Battelle/EPA ETV study addresses issues such as long-term deployment and ability to detect and characterize contaminants. Loop testing at ECBC and Battelle demonstrates extrapolation of beaker testing fingerprints to flowing systems. Real world deployment data is used to demonstrate recognition and classification of actual events and heuristic capabilities of the system along with its potential role in enhancing water quality above and beyond its obvious security aspects. The system is shown to be a practical measure to detect and characterize backflow events involving both chemicals and bioagents.

The DELPHI Data Acquisition and Control System uses the Error and Message handling Utility (EMU) as a standard distributed system which deals with messages generated by the controlling and monitoring processes. A tool which allows one to look at EMU messages as they are produced and to retrieve past messages is described. This utility, being based on a configuration file, can be used both by the central and individual detector operators. Versions exist with both MOTIF (X-windows) and MHI (“VT100”) user interfaces.

Since there is a general perception that the defence industry is more susceptible to corruption compared to other sectors, using a unique database provided by Transparency International (TI), we examine the role of firm level antecedents on firm level corruption risk in the defence industry. We find that larger firms have lower levels of firm level corruption risk. Managerial shareholding is associated with higher levels of corruption risk. Firms that voluntarily disclose more information regarding their corruption control systems tend to have lower levels of corruption risk. Finally, listed firms also have lower levels of firm level corruption risk. We find that the “listing effect” is stronger among firms in financially developed countries ostensibly due to the better scrutiny and monitoring by market participants. In our analysis, we control for country level variables such as a composite index of government effectiveness in controlling defence industry corruption.

In this paper, we present a new approach for monitoring power consumption in several processes. The generalization of the envSOM algorithm, a variant of Self-Organizing Map (SOM), is used to build an electrical model and visualize the information. The envSOM extended to n hierarchical phases allows us to obtain a more accurate model from real past data. The model is conditioned hierarchically on environmental variables. In this way, time variables can be used to consider seasonality and weekday/hour periodicity. Time variable maps and electrical component planes make it possible to visualize and analyze power consumption. The representation of the Best Matching Unit (BMU) or its trajectory on these maps enables the on-line monitoring.

Intelligent control has become an important research direction of a combine harvester. However, the impact of cleaning control parameters in rice–wheat combine harvesters on cleaning loss rate and impurity rate often tends to be contradictory. In this paper, an intelligent controlling model based on multi-objective optimization particle swarm (MOPSO) was constructed to solve this problem. The control model can real-time monitor the cleaning performance such as the cleaning loss rate and impurity rate and regulate the cleaning operation conditions such as the angle of the air distributor plate, the opening of the upper sieve and the fan speed. The field operation experiment of 10kg feeding rice–wheat combine harvester proves that the control model based on MOPSO is more effective than the model based on fuzzy control.

Online monitoring is essential to enhance the reliability for various systems including cyber-physical systems and Web services. During online monitoring, the system traces are checked against monitoring rules in real time to detect deviations from normal behaviors. In general, the rules are defined as boundary conditions by the experts of the monitored system. This work studies the problem of synthesizing online monitoring rules in the form of temporal logic formulas in an automated way. The monitoring rules are described as past-time signal temporal logic (ptSTL) formulas and an algorithm to synthesize such formulas from a given set of labeled system traces is proposed. The algorithm searches the formula space using genetic algorithms and produces the best formula representing a monitoring rule. In addition, online STL monitoring algorithm is improved to efficiently compute a quantitative valuation for piecewise-constant signals from ptSTL formulas, thus, to reduce the overhead of the real-time computation. The effectiveness of the results is shown on two illustrative examples inspired from online monitoring of Web services.

Monitoring is widely applied in problem diagnosis, fault localization, and system maintenance. And since the cloud infrastructure is complex, the applications on the cloud are therefore complex, which makes monitoring in cloud more difficult. Rich monitors that contain composite and heterogeneous probes are often used in service-oriented system monitoring. These rich monitors often involve multiple entities, and the interpretation may require expert opinions from multiple domains. This paper proposes a knowledge-based collaborative monitoring approach to find out minimal cost monitor deployment in a cloud environment. The approach contains two main phases. In the knowledge acquisition phase, three acquisition tables, monitor-probe relationship matrix, cost of monitoring, and probe-problem dependence matrix, are generated according to diagnosis ontology and monitor ontology acquired from domain experts. And then based upon the three acquisition tables and three consensus building strategies, we formulate the problem of optimizing the cost of monitoring as an Integer Linear Programming (ILP) problem, which is NP-Complete. In the monitor deployment phase, the proposed algorithm applies two heuristic rules to address the problem. Three experiments are conducted to evaluate the performance of the proposed approach. The results from the experiments show that our approach is effective and produce quality approximate solutions in monitor deployment.

We address the problem of diagnosing complex discrete-event systems such as telecommunication networks. Given a flow of observations from the system, the goal is to explain those observations by identifying and localizing possible faults. Several model-based diagnosis approaches deal with this problem but they need the computation of a global model which is not feasible for complex systems like telecommunication networks. Our contribution is the proposal of a decentralized approach which permits to carry out an on-line diagnosis without computing the global model. This paper describes the implementation of a tool based on this approach. Given a decentralized model of the system and a flow of observations, the program analyzes the flow and computes the diagnosis in a decentralized way. The impact of the merging strategy on the global efficiency is demonstrated and illustrated by experimental results on a real application.

Envelopes are a form of decision rule for monitoring plan execution. We describe one type, the DP envelope, that draws its decisions from a look-up table computed off-line by dynamic programming. Based on an abstract model of agent progress, DP envelopes let a developer approach execution monitoring as a problem independent of issues in agent design. We discuss the application of DP envelopes to a small transportation planning simulation, and discuss the issues that arise in an empirical analysis of the results.

Flaring activity of Active Galactic Nuclei (AGN) in VHE γ-ray astronomy is observed on timescales from minutes to years and can be explained either by the interaction of relativistic jets with the surrounding material or by imprints of the central engine, like temporal modulation caused by binary systems of supermassive black holes. The key to answer those questions lies in combining 24/7 monitoring with short high sensitivity exposures as provided by the third generation γ-ray astronomy instruments like MAGIC, VERITAS and H.E.S.S. The long-term observations can be provided by a global network of small robotic Cherenkov telescopes.¹ As a first step, we are currently setting up a dedicated Cherenkov telescope, which will carry out joint observations with the Whipple 10 m telescope for AGN monitoring. The new telescope will be designed for low costs but high performance by upgrading one of the former HEGRA telescopes, still located at the MAGIC site on the Canary Island of La Palma (Spain). The main novelties will be its robotic operation and a novel camera type, resulting in a greatly improved sensitivity and a lower energy threshold.

Public transport via trams has become increasingly important in daily life to meet the growing demand for economic and environmental considerations. In order to ensure the safety of the railway system and reduce service costs, it is necessary to understand the tribological behavior of the system. There are various types of damage such as fatigue, wear, and cracking that can harm the wheels and rails. To determine the wear mechanisms and identify the situations in which wear movements occur, it is crucial to monitor factors such as temperature, surface roughness, and contact micro-hardness between the wheel and the rail. The objective of this study is to analyze the tribological behavior and the impact of climatic conditions on the wheel–rail contact system in the desert area of the Ouargla tramway in Algeria. To achieve this, an intelligent sensor programmed by a microcontroller was utilized to measure the temperature of the wheel–rail contact. Additionally, a hardness tester was used to measure the micro-hardness of the rail, and a roughness tester was utilized to monitor the surface condition of the rail. The measured temperature values during the passage of the tramway varied between ambient temperature and a temperature of 450^∘C. The micro-hardness values ranged from 270 Hv to 440 Hv, and the roughness values varied between 0.4$\mu$m and 2.7$\mu$m.

If e-business contracts are to be widely used, they need to be supported by the IT infrastructure of the organizations concerned. This implies that the interactions between systems in different organizations must be guided by the contract and there must be sufficiently strong checks and balances to ensure that the contract is in fact obeyed. This includes facilities for the unbiased monitoring of correct behaviour and the reporting of exceptions.

One of the ways to provide this support is to generate it directly from the agreed contract. This paper considers the steps necessary to provide sufficient automation in the support and checking of e-Business contracts for them to offer efficiency gains and so to become widely used. It focuses on the role of models, taking a model-driven approach to development and discussing both the source and target models and the transformational pathways needed to support the contract-based business processes.

Weak internal controls should increase risk perception among various contracting parties, e.g., institutional investors. This study examines whether the penalty firms pay for weak internal controls is associated with ownership decisions made by institutional investors in Taiwan and whether such decisions differ from those made by qualified foreign institutional investors (denoted as QFIIs) and local institutional investors. Empirical results indicate that weak internal controls are negatively associated with changes to institutional investor ownership, particularly for QFIIs. Further evidence shows that this negative association is more pronounced for firms with high divergence of control and cash-flow rights. This suggests that, faced with weak internal controls, institutions passively vote with their feet rather than actively monitor their portfolio firms. We demonstrate several diagnostic tests and show that the results are robust in various specifications.

Users are increasingly relying on smartphones, hence concerns such as mobile app security, privacy, and correctness have become increasingly pressing. Software analysis has been successful in tackling many such concerns, albeit on other platforms, such as desktop and server. To fill this gap, he have developed infrastructural tools that permit a wide range of software analyses for the Android smartphone platform. Developing these tools has required surmounting many challenges unique to the smartphone platform: dealing with input non-determinism in sensor-oriented apps, non-standard control ow, low-overhead yet high-fidelity record-and-replay. Our tools can analyze substantial, widely-popular apps running directly on smartphones, and do not require access to the app’s source code. We will first present two tools (automated exploration, record-and-replay) that increase Android app reliability by allowing apps to be explored automatically, and bugs replayed or isolated. Next, we present several security applications of our infrastructure: a permission evolution study on the Android ecosystem; understanding and quantifying the risk posed by URL accesses in benign and malicious apps; app profiling to summarize app behavior; and Moving Target Defense for thwarting attacks.

Nowadays, it is essential to capture and evaluate student action in the physical education classroom to assess their behavior. Every student’s performance is unique in physical activity. Every time, the staff or trainer cannot watch and evaluate the students individually. At the university level, the use of classroom capture systems is becoming more widespread. However, due to technology’s recent growth and application, the research on classroom capture systems’ efficacy in university classrooms has been minimal. This paper is proposed for the student action capture and evaluation system. Image preprocessing is the process of preparing pictures for use in model training and inference. This covers resizing, orienting, and color adjustments, among other things. As a result, a change that can be an augmentation in certain cases can be better served as a pretreatment step in others. The DL-IF uses cloud technology for data storage and evaluation. DL-IF uses the imaging technology to monitor students’ actions and responses in the classroom. The image data are evaluated based on the trained set of data provided in DL-IF’s Artificial Neural Network (ANN). The evaluation of unique individuality in every student’s performance is reported to the respective trainer. The simulation analysis of the proposed method DL-IF proves that it can monitor, capture and evaluate every student’s action in all physical activity classrooms. Hence, it proved that this framework could work with high accuracy and minimized mean square error rate.

This paper presents field tests performed on a slab-on-girder pre-stressed concrete bridge. The bridge was tested under static loading, crawling loading, and dynamic loading. A full three-dimensional finite element prediction under both static and dynamic loadings was carried out and the results were compared with the field measurements. While acoustic emission (AE) monitoring of bridge structures is not a new vista, the method has not been fully exploited in bridge monitoring. Though numerous quantitative methods have been proposed, they have not yet developed to be useful for actual field tests of bridges. Therefore, in this study, an attempt was made to use the intensity analysis technique for damage quantification using the AE method.

This paper presents an experimental investigation of the performances of concrete-filled glass fiber-reinforced polymer (GFRP) and GFRP externally wound steel circular tubes, subjected to freeze-thaw cycles ranging from -18°C to 18°C. The variation in hoop strains of the tubes during the freeze-thaw cycles was monitored by embedded fiber Bragg Grating (FBG) strain sensors in GFRP layers or between GFRP and steel tube. The residual hoop strain after each freeze-thaw cycle indicates the possible degradation of GFRP materials, such as cracks, debonding of GFRP-concrete or GFRP-steel due to mismatch of the coefficient of thermal expansion, as well as water immersion. A synergistic effect of FRP and steel tubes on the confinement of inside concrete was revealed, resulting in well-improved ductility. After 56 freeze-thaw cycles, remarkable degradation were found in the axial strength, modulus, and strain for concrete-filled GFRP tubes. However, the GFRP-steel tube system showed a negligible reduction in the ultimate axial strain by the freeze-thaw cycles with less degradation in the axial strength and modulus.

The cables of the long-span bridge are usually featured as ultra-low frequency, hence making the acceleration unable to accurately capture the information, e.g. damping ratios, for assessing the cable state assessment and mitigating the excessive structural vibration. The displacement was approved to be more sensitive to the low-frequency vibration than the acceleration. However, there is still a lack of effective method to accurately monitor the long-term displacements of bridge cables using reference-free methods. To address this issue, this paper develops a novel acceleration-based approach for monitoring the long-term displacements of the cables of long-span bridges. In the monitoring scheme, recursive least squares method is utilized to conduct baseline correction in the time domain integration of acceleration. An adaptive band-pass filtering method considering cable vibration characteristics is used to eliminate noise, thus avoiding the difficulty of selecting the cut-off frequency by experience in traditional methods. A numerical test of an analytical cable model and a field experiment of the hanger of a full-scale suspension bridge are applied to the applicability and robustness of the developed method. Result shows that adaptive band-pass filter considering the vibration characteristics is suitable for estimating the displacements of the cables. The estimated displacements using the developed method agree well with the background truth in both time and frequency domains.

Surface roughness prediction based solely on cutting parameters provides a quantified value regardless of tool condition, making it suitable for initial parameter selection. However, to achieve accurate surface roughness prediction, the current tool condition must be incorporated into the model. This can be accomplished by utilizing vibration or cutting force signals. In this study, we develop a hybrid response surface methodology-artificial neural network (RSM-ANN) model for predicting surface roughness by combining cutting parameters and vibration data. Four different RSM models were developed, and the best-performing model was selected as input for the hybrid RSM-ANN model. A comparison was made between the hybrid model, a basic ANN model with four inputs (three cutting parameters and one mean vibration in the $Z$-direction), and other ANN models with all possible combinations of these four variables. The hybrid model demonstrated the highest accuracy with the mean square error of 0.00332 with the highest coefficient of regression value of 0.99802 when compared to the other models.