Cadmium and lead soil contamination is a widespread environmental problem that requires profound and sustainable solutions. These toxic elements can be naturally occurring on the Earth’s crust or from man-made origins. Cadmium and lead could accumulate and translocate in soil over the long term. Thus, their risk of entering the food chain is extremely elevated and their effects on the living organisms in the food web are of great concern. The main purpose of this review study is to emphasize the risk to human health of cadmium and lead as an environmental contaminant in soil and plants. Human exposure to cadmium and lead can cause severe illness; for instance, long-term exposure to cadmium can alter kidney health and cause dysfunction. Additionally, lead threatens the nervous system and causes countless diseases. Hence, the remediation of cadmium and lead from soil before they enter the food chain remains essential, and regular monitoring of their principal sources is crucially needed for a sustainable soil ecosystem.

Most conventional Fuzzy Logic Controller (FLC) rules are based on the knowledge and experience of expert operators: given a specific input, FLCs produce the same output. However, FLCs do not perform very well when dealing with complex problems that comprise several input variables. Hence, an optimization tool is highly desirable to reduce the number of inputs and consequently maximize the controller performance, leading to easier maintenance and implementation. This paper, presents an enhanced fuzzy logic controller applied to a photovoltaic system. Specifically, both inputs and membership functions are reduced, resulting in a Highly Reduced Fuzzy Logic Controller (HRFLC), to model a 100kW gridconnected Photovoltaic Panel (PV) as part of a Maximum Power Point Tracking (MPPT) scheme. A DC to DC boost converter is included to transfer the total energy to the grid over a three-level Voltage Source Converter (VSC), which is controlled by varying its duty cycle. FLC generates control parameters to simulate different weather conditions. In this study, only one input representing the current variation (ΔI) of the FLC is used to provide an effective and accurate solution. This reduction in simulation inputs results in a novel HRFLC which simplifies the solar electric system design with output Membership Functions (MFs). Both are achieved by grouping two rules instead of using an existing state-of-the-art method with twenty-five MFs. To the best of our knowledge, this is the first FLC able to provide such rules compression. Finally, a comparison with different techniques such as Perturb and Observe (P&O) shows that HRFLC can improve the dynamic and the steady state performance of the PV system. Notably, experimental results report a steady state error of 0.119%, a transient time of 0.28s and an MPPT tracking accuracy of 0.009s.

The switch and crossing (S&C) is one of the most important parts of the railway infrastructure network due to its significant influence on traffic delays and maintenance costs. Two central questions were investigated in this paper: (I) the first question is related to the feasibility of exploring the vibration data for wear size estimation of railway S&C and (II) the second one is how to take advantage of the Artificial Intelligence (AI)-based framework to design an effective early-warning system at early stage of S&C wear development. The aim of the study was to predict the amount of wear in the entire S&C, using medium-range accelerometer sensors. Vibration data were collected, processed, and used for developing accurate data-driven models. Within this study, AI-based methods and signal-processing techniques were applied and tested in a full-scale S&C test rig at Lulea University of Technology to investigate the effectiveness of the proposed method. A real-scale railway wagon bogie was used to study different relevant types of wear on the switchblades, support rail, middle rail, and crossing part. All the sensors were housed inside the point machine as an optimal location for protection of the data acquisition system from harsh weather conditions such as ice and snow and from the ballast. The vibration data resulting from the measurements were used to feed two different deep-learning architectures, to make it possible to achieve an acceptable correlation between the measured vibration data and the actual amount of wear. The first model is based on the ResNet architecture where the input data are converted to spectrograms. The second model was based on a long short-term memory (LSTM) architecture. The proposed model was tested in terms of its accuracy in wear severity classification. The results show that this machine learning method accurately estimates the amount of wear in different locations in the S&C.

Railway infrastructure plays a major role in providing the most cost-effective way to transport freight and passengers. The increase in train speed, traffic growth, heavier axles, and harsh environments make railway assets susceptible to degradation and failure. Railway switches and crossings (S&C) are a key element in any railway network, providing flexible traffic for trains to switch between tracks (through or turnout direction). S&C systems have complex structures, with many components, such as crossing parts, frogs, switchblades, and point machines. Many technologies (e.g., electrical, mechanical, and electronic devices) are used to operate and control S&C. These S&C systems are subject to failures and malfunctions that can cause delays, traffic disruptions, and even deadly accidents. Suitable field-based monitoring techniques to deal with fault detection in railway S&C systems are sought after. Wear is the major cause of S&C system failures. A novel measuring method to monitor excessive wear on the frog, as part of S&C, based on fiber Bragg grating (FBG) optical fiber sensors, is discussed in this paper. The developed solution is based on FBG sensors measuring the strain profile of the frog of S&C to determine wear size. A numerical model of a 3D prototype was developed through the finite element method, to define loading testing conditions, as well as for comparison with experimental tests. The sensors were examined under periodic and controlled loading tests. Results of this pilot study, based on simulation and laboratory tests, have shown a correlation for the static load. It was shown that the results of the experimental and the numerical studies were in good agreement.

Waterborne pathogens affect all waters globally and proceed to be an ongoing concern. Previous methods for detection of pathogens consist of a high test time and a high sample consumption, but they are very expensive and require specialist operators. This study aims to develop a monitoring system capable of identifying waterborne pathogens with particular characteristics using a microfluidic device, optical imaging and a classification algorithm to provide low-cost and portable solutions. This paper investigates the detection of small size microbeads (1–5 µm) from a measured water sample by using a cost-effective microscopic camera and computational algorithms. Results provide areas of opportunities to decrease sample consumption, reduce testing time and minimize the use of expensive equipment.

Scour and slope instability are a major issue in keeping the railway structures safe and operational. Conventional measurement techniques do not allow real-time monitoring of slope movement and/or are time/resource consuming. Novel fiber optic technology could be utilized to continuously monitor variations in movement in the slopes along embankments adjacent to railway tracks so that early detection and warnings can be issued to avoid catastrophic events, as well as to enhance maintenance operations. To this end, a fiber optic Bragg grating-based sensor network embedded into a steel bar has been utilized to evaluate its feasibility to be installed in a railway embankment for slope movement monitoring. The paper highlights the initial laboratory results followed by a discussion on the practical implementation of the sensor system.