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With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.

The environment changes constantly at various time scales and, in order to survive, species need to keep adapting. Whether these species succeed in avoiding extinction is a major evolutionary question. Using a multilocus evolutionary model of a mutation-limited population adapting under strong selection, we investigate the effects of the frequency of environmental fluctuations on adaptation. Our results rely on an “adaptive-walk” approximation and use mathematical methods from evolutionary computation theory to investigate the interplay between fluctuation frequency, the similarity of environments, and the number of loci contributing to adaptation. First, we assume a linear additive fitness function, but later generalize our results to include several types of epistasis. We show that frequent environmental changes prevent populations from reaching a fitness peak, but they may also prevent the large fitness loss that occurs after a single environmental change. Thus, the population can survive, although not thrive, in a wide range of conditions. Furthermore, we show that in a frequently changing environment, the similarity of threats that a population faces affects the level of adaptation that it is able to achieve. We check and supplement our analytical results with simulations.

Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.

INTRODUCTION: This paper presents a comprehensive structural monitoring framework for wind turbine blades based on MEMS-FBG (micro-electro-mechanical systems—fiber Bragg grating) sensor fusion technology.OBJECTIVES: The system integrates high-resolution strain and vibration sensing across multiple blade segments, combined with real-time data processing, fault detection, and SCADA-level visualization.METHODS: A multilayered load consistency model is introduced, incorporating thermal compensation, strain-to-load calibration, and a novel consistency index (η) to quantify inter-blade aerodynamic symmetry.RESULTS: Experimental validation was conducted on a 2.0 MW wind turbine over a 60-day continuous monitoring campaign. Static calibration demonstrated a load reconstruction accuracy within ±3%, while dynamic data revealed a strong correlation between load and blade vibration (R ≥ 0.86).CONCLUSION: Fault simulation through pitch angle manipulation confirmed the system’s rapid alarm response within 3 seconds for major asymmetry events. Additionally, signal drift testing showed MEMS-FBG sensors exhibited 80–95% lower drift than conventional resistance strain gauges under rotating and EMI-intensive conditions.

For large space dimensions, the log-linear convergence of the elitist evolution strategy with a 1/5 success rule on the sphere fitness function has been observed, experimentally, from the very beginning. Finding a mathematical proof took considerably more time. This paper presents a review and comparison of the most consistent theories developed so far, in the critical interpretation of the author, concerning both global convergence and the estimation of convergence rates. I discuss the local theory of the one-step expected progress and success probability for the (1+1) ES with a normal/uniform distribution inside the sphere mutation, thereby minimizing the SPHERE function, but also the adjacent global convergence and convergence rate theory, essentially based on the 1/5 rule. Small digressions into complementary theories (martingale, irreducible Markov chain, drift analysis) and different types of algorithms (population based, recombination, covariance matrix adaptation and self-adaptive ES) complete the review.

As an essential factor of evolutionary algorithms (EAs), mutation operator plays an important role in exploring the search space, maintaining the diversity of individuals and breaking away local optimums. In most standard evolutionary algorithms, the mutation operator is independent from the recombination operator. Nevertheless, in biogeography-based optimization (BBO), the mutation operator is affected not only by predefined constants but also by recombination models, namely the migration operator. However to date, the relationship between the mutation and migration has never been investigated. To reveal the relationship and evaluate the mutation models, we utilize drift analysis to investigate the expected first hitting time of BBO with different migration models. The analysis compares three different kinds of mutation models in a mathematical way and the conclusion is helpful for designing migration models of BBO. The simulation results are also in agreement with our analysis.

Electronic warfare (EW) is an important capability that provides advantage to defence forces over their adversaries. Defence forces gather tactical intelligence through EW sensors, which provide the means to counter hostile actions of enemy forces. Functions of an EW system is threat detection and the area surveillance so as to determine the identity of surrounding emitters. Emitter classification system identifies possible threats by analysing intercepted signals. Problem of identifying emitters based on its intercepted signal characteristics is a challenging problem in electronic warfare studies. Major issues and challenges for emitter classification such as drifting of emitter parameters due to aging, operational characteristic of an emitter, i.e., same emitter can operate on multiple bands and multiple pulse repetition frequencies (PRFs) are highlighted. A novel approach based on some well-known statistical methods, e.g., regression analysis, hypothesis testing, and discriminent analysis is proposed. The effectiveness of the proposed approach has been tested over ELINT (Electronic Intelligence) data and illustrated using simulation data. The proposed approach can play a solution for wide variety of problems in emitter classification in electronic warfare studies. [PUBLICATION ABSTRACT]

Photocatalytic oxidation of propane using hydrothermally synthesized TiO2 samples with similar primary crystal size containing different ratios of anatase, brookite and rutile phases has been studied by measuring light-induced propane conversion and in situ DRIFTS (diffuse reflectance Fourier transform infrared spectroscopy). Propane was found to adsorb on the photocatalysts, both in the absence and presence of light. The extent of adsorption depends on the phase composition of synthesized titania powders and, in general, it decreases with increasing rutile and brookite content. Still, the intrinsic activity for photocatalytic decomposition of propane is higher for photocatalysts with lower ability for propane adsorption, suggesting this is not the rate-limiting step. In situ DRIFTS analysis shows that bands related to adsorbed acetone, formate and bicarbonate species appear on the surface of the photocatalysts during illumination. Correlation of propane conversion and infrared (IR) data shows that the presence of formate and bicarbonate species, in excess with respect to acetone, is composition dependent, and results in relatively low activity of the respective TiO2. This study highlights the need for precise control of the phase composition to optimize rates in the photocatalytic oxidation of propane and a high rutile content seems to be favorable.

In this study a new method for nonlinear static analysis based on the relative displacements of stories is proposed that is able to be implemented in a single stage analysis and considers the effects of an arbitrary number of higher modes. The method is called the extended drift pushover analysis procedure ( EDPA ). To define the lateral load pattern, values of the relative displacements of stories are calculated using the elastic modal analysis and the modal combination factors introduced. For determining the combination factors, six different approaches are examined. Buildings evaluated in this study consist of four special steel moment-resisting frames with 10–30 stories. Responses including relative displacements of stories, story shear forces and rotation of plastic hinges in each story are calculated using the proposed approaches in addition to modal pushover analysis and nonlinear dynamic time history analyses. The nonlinear dynamic analysis is implemented using ten consistent earthquake records that have been scaled with regard to ASCE7 - 10. Distribution of response errors of story shears and plastic hinge rotations show that a major part of error corresponds to the second half of the buildings studied. Thus, the mentioned responses are corrected systematically. The final results of this study show that implementing the EDPA procedure using the third approach of this research is able to effectively overcome the limitations of both the traditional and the modal pushover analyses methods and predict the seismic demands of tall buildings with good accuracy.

In this work, the results of gas phase cyclohexane photocatalytic oxidative dehydrogenation on MoOx/SO4/TiO2 catalysts with DRIFTS analysis are presented. Analysis of products in the gas-phase discharge of a fixed bed photoreactor was coupled with in situ monitoring of the photocatalyst surface during irradiation with an IR probe. An interaction between cyclohexane and surface sulfates was found by DRIFTS analysis in the absence of UV irradiation, showing evidence of the formation of an organo-sulfur compound. In particular, in the absence of irradiation, sulfate species initiate a redox reaction through hydrogen abstraction of cyclohexane and formation of sulfate (IV) species. In previous studies, it was concluded that reduction of the sulfate (IV) species via hydrogen abstraction during UV irradiation may produce gas phase SO2 and thereby loss of surface sulfur species. Gas phase analysis showed that the presence of MoOx species, at same sulfate loading, changes the selectivity of the photoreaction, promoting the formation of benzene. The amount of surface sulfate influenced benzene yield, which decreases when the sulfate coverage is lower. During irradiation, a strong deactivation was observed due to the poisoning of the surface by carbon deposits strongly adsorbed on catalyst surface.