Global environmental pollutants are becoming intense because of the increasing human population, urbanisation, and industrialisation. Human health and the ecosystem are affected by soil and water contamination. Therefore, creating strategies is essential to tackle this persistent issue. In the process, the health and environmental risk associated with these pollutants can be signifi-cantly reduced. Previously, traditional remediation techniques have been employed in combating these environmental pollutants, proving ineffective. Mycoremediation, which uses fungi or their compounds to remediate environmental pollutants, has shown to be a cost-efficient, environmen-tally friendly, and effective method of environmental remediation that includes organic, inorganic, and emerging contaminants (antibiotics, pharmaceuticals). This review provides an overview of various mycoremediation approaches through fungi for biosorption, precipitation, biotransfor-mation, and sequestration of environmental pollutants. In addition, the removal of metals, persis-tent organic pollutants, and other emerging contaminants by mycoremediation was highlighted. For example, fungi such as Pleurotusdryinus, Trameteshirsuta MK640786, and Aspergillusniger shows 91%, 94%, and 98.4% degradation of pollutants ranging from pesticides to azo dyes, respectively. Furthermore, prospects of mycoremediation to remove heavy metals and emerging pollutants from waters and soils were discussed. It was elucidated that fungi have great potential for the mycoremediation of emerging pollutants such as heavy metals, pharmaceuticals, polycyclic aromatic hydrocarbons (PAHs), pesticides, and weedicides. The findings suggested a knowledge gap exists to enhance the rate of the mycoremediation process. Therefore, a possible framework of mycoremediation was proposed to facilitate this promising technology for rectifying global environmental problems. For mycoremediation procedures to be as effective as possible, further studies are needed on fungal enzymes’ role, activities, and regulation. Full article

Trace lines on the outcrop of a rock mass are usually the primary data source for the estimation of rock structure. It is important to obtain the data of trace lines precisely. Photogrammetry is well suited to finish this task. However, this is mainly conducted by commercial software, and not every researcher has easy access to the method of digital photogrammetry. This study aims to provide researchers with a low-cost method of building a photogrammetry-based textured 3D point cloud model (FMBPM) and display the applicability of the method to estimating the rock structure of rock masses. In the FMBPM, a digital single-lens reflex camera with a prime lens and a total station are the necessary hardware employed to capture images and measure the coordinates of feature points. A coordinate transformation means of converting model coordinates to physical coordinates was introduced. A program for calculating a joint orientation based on the coordinates of inflection points on the trace line of the joint was developed. A section of a rock slope was selected as a case to show the procedures and the practicability of the FMBPM. The textured 3D point cloud model of the rock slope was successfully built, and the rock structure of the rock slope was analyzed using the joint disk model generated based on the trace lines extracted from the point cloud model. The results show that: (1) the precision of the point coordinates of the textured 3D point cloud model could achieve 3.96 mm, taking the data of the total station as the reference; (2) the rock structure of the slope is good, according to the value of the rock quality designation; (3) the new method is applicable in engineering practices. Full article

Transformers have become increasingly prevalent in computer vision research, especially for object detection. To accurately and efficiently distinguish the stem end of pomelo from its black spots, we propose a hierarchical feature detector, which reconfigures the self-attention model, with high detection accuracy. We designed the combination attention module and the hierarchical feature fusion module that utilize multi-scale features to improve detection performance. We created a dataset in COCO format and annotated two types of detection targets: the stem end and the black spot. Experimental results on our pomelo dataset confirm that HFD’s results are comparable to those of state-of-the-art one-stage detectors such as YOLO v4 and YOLO v5 and transformer-based detectors such as DETR, Deformable DETR, and YOLOS. It achieves 89.65% mAP at 70.92 FPS with 100.34 M parameters. Full article

The Italian “Guidelines for risk classification and management, security assessment and monitoring of existing bridges”, published in 2020 after the collapse of the Polcevera viaduct in Northern Italy, present a multilevel methodology that involves on-site operators and universities/research centers carrying out and validating a management process from on-site survey to the bridges’ condition assessment. The main goals of this process are to acquire appropriate knowledge of the current state and its evolution over time of the overall buildings that compose the infrastructures, with the aim to support the managing companies in a decision-making process and the purpose of guaranteeing service in full safety. In particular, the guidelines propose the use of engineered software platforms for data digitalization of the structures with the aim to create a Building Management System (BMS) in which the main historical and current state information is collected and can then be uploaded continually. In 2020, the CUGRI (Inter-University Research Center for the Prediction and Prevention of Major Hazards) and the SAM (Southern Highways Company) launched an innovative surveillance management model established on a multidisciplinary approach based on Geography Markup Language (GML), BIM tools, on-site interdisciplinary inspections, and multi-hazard analysis. The experimented methodology provides the on-site training of inspectors, the elaboration of suitable BIM models according to the above guidelines, and AINOP (National Archive of Public Infrastructures) requirements, and an expert judgement process for preliminary bridge assessment and data validation to support the maintenance managing process. The study presents an innovative operative model for the surveillance process, which integrates on-site expeditious inspections and multidisciplinary expert judgements by using an appropriate digitalization of the bridges with BIM and GIS technologies. The paper illustrates the experimental methodology performed on the A3 highway, which connects Naples to Salerno in Southern Italy, highlighting issues and opportunities, moreover in a first interdisciplinary contribution of object-oriented landslide mapping modelling. Full article

Neurodegenerative disorders, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), are common age-related diseases responsible for high disability. Disease-modifying treatments for AD and PD are still lacking, but symptomatic therapies are available, although limited by difficult administration and patients’ scarce compliance at later disease stages. Transbuccal Drug Delivery Systems (TDDSs) include chemical-physics biotechnologies and mechatronic approaches, allowing drug delivery via the transbuccal route, a strategy that may theoretically overcome the limitations imposed by conventional oral administration. In this review, we provided a snapshot of TDDSs, their mechanism of action, the existing subtypes, and their potential application in PD and AD patients. We found a variety of TDDSs, including tablets, solutions, sprays, patches, and the more sophisticated “mechatronic” IntelliDrug and OraFuse devices using a system of pumps and valves for continuous drug release. Several trials have been conducted either on models or patients to test the safety and efficacy of the antidementia and antiparkinsonian agents delivered by TDDSs, which produced encouraging results that suggest future application on a larger scale. Moreover, oral health has emerged as a fundamental prerequisite for the successful use of TDDSs. Accordingly, greater attention to oral hygiene is now due in patients with neurodegenerative disease. Full article

Despite great technical capabilities, the theory of non-contact temperature measurement is usually not fully applicable to the use of measuring instruments in practice. While black body calibrations and black body radiation thermometry (BBRT) are in practice well established and easy to accomplish, this calibration protocol is never fully applicable to measurements of real objects under real conditions. Currently, the best approximation to real-world radiation thermometry is grey body radiation thermometry (GBRT), which is supported by most measuring instruments to date. Nevertheless, the metrological requirements necessitate traceability; therefore, real body radiation thermometry (RBRT) method is required for temperature measurements of real bodies. This article documents the current state of temperature calculation algorithms for radiation thermometers and the creation of a traceable model for radiation thermometry of real bodies that uses an inverse model of the system of measurement to compensate for the loss of data caused by spectral integration, which occurs when thermal radiation is absorbed on the active surface of the sensor. To solve this problem, a hybrid model is proposed in which the spectral input parameters are converted to scalar inputs of a traditional scalar inverse model for GBRT. The method for calculating effective parameters, which corresponds to a system of measurement, is proposed and verified with the theoretical simulation model of non-contact thermometry. The sum of effective instrumental parameters is presented for different temperatures to show that the rule of GBRT, according to which the sum of instrumental emissivity and instrumental reflectivity is equal to 1, does not apply to RBRT. Using the derived models of radiation thermometry, the uncertainty of radiation thermometry due to the uncertainty of spectral emissivity was analysed by simulated worst-case measurements through temperature ranges of various radiation thermometers. This newly developed model for RBRT with known uncertainty of measurement enables traceable measurements using radiation thermometry under any conditions. Full article

Efficient and accurate predictions of creep rupture life are essential for ensuring the integrity of high-temperature components. In this work, a machine learning-based framework is developed for the quick screening of crucial features and accurate prediction of the creep rupture life of modified 9Cr-1Mo steels. A feature screening protocol based on correlation filtering and sequential feature selection techniques is established for identifying critical features that significantly affect the prediction performance from a set of numerous descriptors. Moreover, several machine learning algorithms are employed for model training to examine their ability to map the complex nonlinear interactions between multivariate features and creep life. The results show that the test stress, test temperature, tempering time, and the contents of S and Cr are identified as the crucial features that greatly influence the life prediction performance of modified 9Cr-1Mo steels. Moreover, the Gaussian process regression (GPR) model with these five selected crucial features exhibits the highest prediction accuracy among various machine learning strategies. Finally, an additional dataset out of model training and testing is used to further validate the efficacy of the constructed GPR model. The validated results demonstrate that most creep data are distributed inside the two-factor band lines. Results from this work show that the developed machine learning framework can offer high accuracy and excellent adaptability in predicting the creep life of modified 9Cr-1Mo steels under various environmental conditions. Full article

Maintenance of production equipment has a key role in ensuring business continuity and productivity. Determining the implementation time and the appropriate selection of the scope of maintenance activities are necessary not only for the operation of industrial equipment but also for effective planning of the demand for own maintenance resources (spare parts, people, finances). A number of studies have been conducted in the last decade and many attempts have been made to use artificial intelligence (AI) techniques to model and manage maintenance. The aim of the article is to discuss the possibility of using AI methods and techniques to anticipate possible failures and respond to them in advance by carrying out maintenance activities in an appropriate and timely manner. The indirect aim of these studies is to achieve more effective management of maintenance activities. The main method applied is computational analysis and simulation based on the real industrial data set. The main results show that the effective use of preventive maintenance requires large amounts of reliable annotated sensor data and well-trained machine-learning algorithms. Scientific and technical development of the above-mentioned group of solutions should be implemented in such a way that they can be used by companies of equal size and with different production profiles. Even relatively simple solutions as presented in the article can be helpful here, offering high efficiency at low implementation costs. Full article

The goal of the present study was to use machine learning to identify how gender, age, ethnicity, screen time, internalizing problems, self-regulation, and FoMO were related to problematic smartphone use in a sample of Canadian adolescents during the COVID-19 pandemic. Participants were N = 2527 (1269 boys; M_age = 15.17 years, SD = 1.48 years) high school students from the Lower Mainland of British Columbia, Canada. Data on problematic smartphone use, screen time, internalizing problems (e.g., depression, anxiety, and stress), self-regulation, and FoMO were collected via an online questionnaire. Several different machine learning algorithms were used to train the statistical model of predictive variables in predicting problematic smartphone use. The results indicated that Shrinkage algorithms (lasso, ridge, and elastic net regression) performed better than other algorithms. Moreover, FoMO, emotional, and cognitive self-regulation made the largest relative contribution to predicting problematic smartphone use. These findings highlight the importance of FoMO and self-regulation in understanding problematic smartphone use. Full article

A satellite formation operating in low-altitude orbits is subject to perturbations associated to the higher-order harmonics of the gravitational field, which cause a degradation of the formation configurations designed based on the unperturbed model of the Hill–Clohessy–Wiltshire equations. To compensate for these effects, periodic reconfiguration maneuvers are necessary, requiring the prior allocation of a propellant mass budget and, eventually, the use of resources from the ground segment, having a non-negligible impact on the complexity and cost of the mission. Using the Hamiltonian formalism and canonical transformations, a model is developed that allows designing configurations for formation flying invariant with respect to the zonal harmonic perturbation. $J_n$ invariant configurations can be characterized, selecting the drift rate (or boundedness condition) and the amplitude of the oscillations, based on four parameters which can be easily converted in position and velocity components for the satellites of the formation. From this model, a guidance strategy is developed to inject a satellite approaching another spacecraft into a bounded relative trajectory about it and the optimal time for the maneuver, minimizing the total $\mathrm{\Delta }V$, is identified. The effectiveness of the model and of the guidance strategy is verified on some scenarios of interest for formations operating in a sun-synchronous and a medium-inclination low Earth orbit and a medium-inclination lunar orbit. Full article

In precision engineering, the use of compliant mechanisms (CMs) in positioning devices has recently bloomed. However, during the course of their development, beginning from conceptual design through to the finished instrument based on a regular optimization process, many obstacles still need to be overcome, since the optimal solutions often lie on constrained boundaries or at the margin of safe/unsafe domains. Accordingly, if uncertainty occurs during the fabrication or operation of the mechanism, it might lose its functions, rendering the design infeasible. This paper proposes a universal design process for positioning CMs, consisting of two steps: optimal design of the pseudo-rigid-body model, and reliability-integrated multi-objective optimization design using NSGA-II algorithms. This optimization algorithm is applied in the design of a feed drive mechanism for micromachining. The optimal design is also fabricated and tested. The results calculated for the displacement amplification ratio, natural frequency, and input/output stiffness using different approaches, including analytical methods, simulations, and experiments, were compared to evaluate the efficiency of the proposed synthesis method, and show discrepancies of less than 5%. Thus, the results convincingly support the applicability of the proposed optimization algorithm for the design of other precision-positioning CMs prone to failure in vulnerable conditions. Full article

Wearable devices have been introduced for research purposes and especially for environmental monitoring, with the aim of collecting large amounts of data. In a previous study, we addressed the measurement reliability of low-cost thermohygrometers. In this study, we aim to find out how human thermal plume could affect the measurement performance of thermohygrometers. For this purpose, we used a Do-It-Yourself device that can be easily replicated. It consists of 10 iButtons with 3D-printed brackets to position them at different distances from the body. The device was attached to the user’s belt in a seated position. We considered two scenarios: a summer scenario with an air temperature of 28 °C and a clothing thermal resistance of 0.5 clo and an autumn scenario with an air temperature of 21 °C and a clothing thermal resistance of 1.0 clo. The results show that the proximity of the measurement station to the body significantly affects the accuracy of the measurements and should be considered when developing new wearable devices to assess thermal comfort. Therefore, we recommend that at least two thermohygrometers be considered in the development of a new wearable device if it is to be worn on a belt, with one positioned as close to the body as possible and the other at least 8 cm away, to determine if and how the standard thermal comfort assessment differs from the user’s personal perception and whether spatial proximity might also play a role. Full article

To satisfy the continuously high energy consumption and high computational capacity requirements for IoT applications, such as video monitoring, we integrate solar harvesting and multi-access edge computing (MEC) technologies to develop a solar-powered MEC system. Considering the stochastic nature of solar arrivals and channel conditions, we formulate a stochastic optimization problem to maximize network energy efficiency under the constraints of energy queue stability, task queue stability, peak transmission power, and maximum CPU frequency of each sensor. To solve the long-term stochastic optimization problem, we propose a Lyapunov-based online joint computational offloading and resource scheduling optimization algorithm, transforming the long-term stochastic problem into a series of deterministic subproblems in each time slot. Simulation results show that the proposed algorithm can find the optimal solution to tradeoff long-term energy efficiency and queueing backlog without requiring a priori knowledge of the channel state and energy arrival, which is a more realistic solution for practical solar-powered MEC systems. Full article

In this paper, a nonlinear controller for tracking a desired trajectory for an underactuated hovercraft is considered. It is a modification of a method known from the literature. However, the control algorithm considered here has two important features that differ from the mentioned control strategy. First, it concerns the case when the center of mass does not coincide with the geometric center, which results in additional forces and moments of force. The lack of symmetry causes the original trajectory tracking method not to take this fact into account, while the proposed approach is a generalization of the known concept. Here, a diagonalization of the inertia matrix has been applied, by means of a velocity transformation, which made it possible to reduce the symmetric matrix to a diagonal form. Secondly, the transformed quasi-velocity equations of motion allow some insight into the dynamics of the vehicle as it moves, which was not shown in the source work. The offered approach was verified by numerical tests for a hovercraft model with three DOF and for two desired trajectories. The method can be useful in preliminary simulation studies at the controller selection stage without experimental validation. Full article

Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average $F_1$ score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities. Full article