A paddle wheel aerator is developed in this research, which includes a wireless sensor network for measuring the water quality in the aquaculture of marine shrimp ponds. It can also move to the desired position to fill oxygen and measure water quality. A basic requirement is good standard water quality to prevent shrimp from epidemics and improve production. The water quality paddle wheel aerator applies a microcontroller and sensors to measure eight parameters of water quality. These water quality parameters are observed on the web application via an IoT module. The movement system of the water-quality paddle wheel aerator consists of a LiDAR, GPS, and remote RF signal. Each item of data is recorded immediately on the cloud server while the water-quality paddle wheel moves in the marine shrimp farm. The controlled paddle wheel aerator is harnessed automatically to enhance precisely the spatial monitoring resolution of the measurement system installed, which is needless for a multiple measurement system with high cost of investment. Also, farmers can access real data through the Line application. Hence, they are able to plan and control a good environment for aquaculture, preventing the occurrence of various epidemics and decomposing organic matter in the pond.

In high-traffic harbor waters, marine radar frequently encounters signal interference stemming from various obstructive elements, thereby presenting formidable obstacles in the precise identification of ships. To achieve precise pixel-level ship identification in the complex environments, a customized neural network-based ship segmentation algorithm named MrisNet is proposed. MrisNet employs a lightweight and efficient FasterYOLO network to extract features from radar images at different levels, capturing fine-grained edge information and deep semantic features of ship pixels. To address the limitation of deep features in the backbone network lacking detailed shape and structured information, an adaptive attention mechanism is introduced after the FasterYOLO network to enhance crucial ship features. To fully utilize the multi-dimensional feature outputs, MrisNet incorporates a Transformer structure to reconstruct the PANet feature fusion network, allowing for the fusion of contextual information and capturing more essential ship information and semantic correlations. In the prediction stage, MrisNet optimizes the target position loss using the EIoU function, enabling the algorithm to adapt to ship position deviations and size variations, thereby improving segmentation accuracy and convergence speed. Experimental results demonstrate that MrisNet achieves high recall and precision rates of 94.8% and 95.2%, respectively, in ship instance segmentation, outperforming various YOLO and other single-stage algorithms. Moreover, MrisNet has a model parameter size of 13.8M and real-time computational cost of 23.5G, demonstrating notable advantages in terms of convolutional efficiency. In conclusion, MrisNet accurately segments ships with different spot features and under diverse environmental conditions in marine radar images. It exhibits outstanding performance, particularly in extreme scenarios and challenging interference conditions, showcasing robustness and applicability.

While Neural Radiance Fields (NeRF) are gaining increasing interest in various domains as innovative methods for novel view synthesis and image-based reconstruction, their potential application in the realm of Cultural Heritage remains unexplored. Purpose of this paper is to assess the effectiveness of applying NeRF to sets of images of digital heritage objects and sites. The study’s findings demonstrate that NeRF could be valuable when used in combination with or as a comparison to other well-established techniques such as photogrammetry, to expand the possibilities of preserving and presenting heritage assets with enhanced visual fidelity and accuracy. Particularly, NeRF show promising results in improving the rendering of translucent and reflective surfaces, objects with homogeneous textures, and elements with intricate details. In addition, we demonstrate that, when considering the same set of input images (with known camera poses), reducing the image quality or the number of images results in significantly less information loss with NeRF compared to photogrammetry. This suggests that NeRF is preferentially suited for scenarios involving sparse information or low-quality photos or videos, which could be especially valuable in risky or challenging situations.

Smelting used to be less efficient in the past, so wastes obtained from historical processing at smelter plants usually contain certain quantities of valuable metals. A case study was conducted, which included an investigation of the physicochemical, mineralogical, and microstructural properties of raw material found at the historic slag pond near the Topilnica Veles smelter in North Macedonia. The Pb-Zn slag was sampled using traditional geophysical methods such as drill holes and pitholes. The mineralogical and microstructural analysis revealed that the Pb-Zn slag is a very complex and inhomogeneous alternative raw material with utilizable levels of metals, specifically Pb (2.24 wt%), Zn (7.10 wt%), and Ag (27.53 ppm). Amorphous phase, lead alloys, zinc alloys, wurtzite, sphalerite, galena, cerussite, elemental silver, elemental copper, elemental iron, magnetite, spinel, rutile, hematite, and troilite were also identified through qualitative mineralogical analysis. The glassy matrix composed of spinel, silicates, and mixed spinel-silicate is the most abundant constituent in slag composition. The various potential applications of the slag are possible based on its mineralogical and geochemical properties. Because the extraction of Pb, Zn, and Ag has certain economic potential, the next step, which will include gravity concentration and magnetic separation procedures, will be to form metal concentrates into their collective concentrate, from which the maximum amount of silver can be extracted. Slag's amorphous spinel, silicate, and mixed spinel-silicate phases, which contain high concentrations of SiO2, Al2O3, CaO, and Fe2O3, are suitable for use in building materials such as cement clinker, filler, or aggregate for concrete or mortar. The goal of this research is to close the circle of slag's reutilization potential in terms of Net Zero and Zero Waste principles, so it is critical to thoroughly investigate the material and establish methods and preparation processes, as well as ways of concentrating useful components into commercial products.

Jacobian-free Newton Krylov (JFNK) is an attractive method to solve nonlinear equations in nuclear engineering systems, and has been successfully applied to steady-state neutron diffusion (k-eigenvalue) problems and multi-physics coupling problems. Preconditioning technique plays an important role in JFNK algorithm, significantly affecting computational efficiency. The key point is how to automatically construct a high-quality preconditioning matrix that can improve the convergence rate, and perform the preconditioning matrix factorization efficiently. An efficient reordering-based ILU(k) preconditioner is proposed to achieve the above objectives. In detail, the finite difference technique combined with the coloring algorithm is utilized to construct an efficient preconditioning matrix with low computational cost automatically. Furthermore, the reordering algorithm is employed for the ILU(k) to reduce the additional non-zero elements and pursue robust computational performance. A two-dimensional LRA neutron steady-state benchmark problem is used to evaluate the performance of the proposed preconditioning technique, and steady-state neutron diffusion problem with thermal-hydraulic feedback is also utilized as a supplement. The results show that coloring algorithms can efficiently and automatically construct the preconditioning matrix. The reordering-based ILU(k) preconditioner shows excellent robustness, avoiding the effect of fill-in levels k choice in incomplete LU factorization.

Construction safety is especially important because the construction industry is so important to a country's development. Significant research and practice have been conducted to mitigate potential risks during construction and improve worker efficiency. With the rapid advancement of cognitive neuroscience and the incorporation of medical technology in recent years, various wearable monitoring devices have been widely used in the construction field for real-time monitoring of workers' physical and mental status. Among these, the use of EEG (Electroencephalogram) in construction environment research allows researchers to gain insight into the physical and mental states of construction workers while performing construction tasks. This review introduces EEG technology and portable EEG devices, followed by an overview of their use in both monitoring workers' adverse reactions and identifying hazards on construction sites, providing an effective guide for EEG research in the construction field and on-site safety management.

Modelling and presenting mathematical relationships for human behaviour is one of the most complex issues that researchers have always dealt with. In this article, a bottom-up framework for calculating agricultural needs is presented using the socioeconomic characteristics of farmers (such as education level, age, and dependence on income on agriculture) and how their lands are located concerning each other (interactions between neighbours). The objective function of this framework is to maximize the profit of individual farmers based on the amount of water received. Two scenarios, ABM1 (not considering neighbourhood effects) and ABM2 (all cases of farmers' placement and feeling neighbourhood effects), were investigated. In the first scenario (ABM1), there was a noteworthy reduction in water deficit volumes by approximately 35%, accompanied by a 20% increment in farmers' profits. Interestingly, higher risk-taking tendencies correlated with reduced profit margins. The second scenario (ABM2) underscored the significant role of neighborhood dynamics in cultivating diverse behavioral patterns among farmers, subsequently affecting their profitability. A granular examination revealed that farmers with a higher propensity for risk-taking generally accrued lower profits. Additionally, the study facilitated the calculation of total annual profits and average water consumption for each farmer, offering valuable insights for optimizing water resource management and allocation strategies. These findings are instrumental for planners and water resource managers aiming to promote sustainable agricultural practices and efficient water use.

Active magnetic bearings have higher efficiency than other bearings because there is no physical contact. However, this benefit is mitigated by the addition of electrical power consumption. It is therefore important for magnetic bearings to have efficient power electronics. Gallium nitride-based transistors are a relatively new form of transistor which have shown to be more efficient than MOSFETs. There is an insufficient body of literature in the area of application of these transistors for magnetic levitation. This work presents a simple 1 degree-of-freedom voltage controlled levitation experiment in which levitation is achieved with both a gallium nitride transistor and a MOSFET.
Voltage losses and current consumption are measured when using each transistor during levitation. In particular, transients during open-to-closed and closed-to-open states are measured for PWM pulsing. It is found that the
gallium nitride transistor is superior in both switching time and efficiency in situ for magnetic levitation.

In this paper, corticosteroid–β-cyclodextrin (β-CD) inclusion complexes have been prepared by Supercritical AntiSolvent (SAS) precipitation to enhance the dissolution rate of dexamethasone (DEX) and prednisolone (PRED), which are poorly water-soluble drugs. The processing of the active principles in the absence of a carrier led to the almost complete extraction of the active principle (the small amount of obtained material precipitates in the form of crystals). The coprecipitation of the ingredients in the presence of β-CD was investigated at different concentrations, pressures, and molar ratios. For both the corticosteroids, the optimized operating conditions were 40 °C, 120 bar, an equimolar ratio, and a concentration in DMSO of 20 mg/mL; these conditions led to the attainment of microparticles with a mean diameter equal to 0.197±0.180 μm and 0.131±0.070 μm in the case of DEX and PRED, respectively. The Job’s method confirmed the formation of inclusion complexes in correspondence with the 1/1 mol/mol ratio. Compared to the pure ingredients, the obtained powders have an improved release rate, which is about 3 times faster in both cases.

The paper illustrates a general framework in which a neural network application can be easily integrated and proposes a traffic forecasting approach that uses neural networks based on graphs. The method minimizes the communication network (between vehicles and the database servers) load and represents a reasonable trade-off between communication network load and forecasting accuracy. Traffic prediction leads to the choice of less congested routes and therefore to the reduction of energy consumption. The traffic is forecasting using a LTSM neural network with a regression layer. The inputs of the neural network are sequences - obtained from graph that represent the road network - at specific moments of time that are read from traffic sensors or the outputs of neural network (forecasting sequences). The input sequences can be filtered to improve the forecasting accuracy. This general framework is based on Contiki IoT operating system that ensure support for wireless communication and efficient implementation of processes in a resource constrained system and it is particularized to implement a graph neural network. Two cases are studied: one case in which the traffic sensors are periodically read and the other case in which the traffic sensors are read when their values changes are detected. A comparison between the cases is made and the influence of filtering is evaluated. The obtained accuracy is very good, very close to the accuracy obtained in infinite precision simulation, and the computation time is low enough and the system can work in real time.

With the global warming and the frequent occurrences of harsh environments, mooring line failures are frequent. Response analysis of the platform structure and its residual mooring system under multiple mooring line failures is needed. Therefore, it is necessary to investigate the change law of platform motion response after mooring line failure to find out the reasons for the change. Studies for different failure combinations of mooring lines show that the variation of the steady-state equilibrium position of the platform is closely related to the stiffness, with a clear functional. The duration and amplitude of the transient response are mainly influenced by the Morison drag force. And it was found that the suspended mooring line reduced the motion responses. It is also found that the mooring line failure increased the effective tension and suspension length of the riser. Based on the improved Dezert-Smarandache theory (DSmT), it is calculated that mooring line failure has little effect on the failure probability in different zones of the riser.

This paper addresses a mode-dependent state-feedback H∞ control for descriptor hybrid systems, considering both the absence and presence of actuator saturation. Firstly, the necessary and sufficient conditions for the stochastic admissibility criterion with H∞ performance ? of the closed-loop system is proposed. Given the proposed non-convex condition, the author reformulates it into linear matrix inequalities (LMIs). Then, to extend the result to the systems with actuator saturation, the actuator saturated control input is expressed as a linear combination of given state-feedback control input and a virtual control input that remains under the saturation level. To verify this expression, the set invariant condition is also suggested by using the singular mode-dependent Lyapunov function candidate. Therefore, the LMI conditions for the existence of the mode-dependent state-feedback H∞ control and the ellipsoidal shape of invariant set are successfully derived. Two numerical examples demonstrate the effectiveness of the proposed approach in optimizing H∞ performance $\gamma$ and identifying the largest invariant set.

Estimating the Remaining Useful Life (RUL) of aircraft engines holds a pivotal role in enhancing safety, optimizing operations, and promoting sustainability, thus being a crucial component of modern aviation management. Precise RUL predictions offer valuable insights into an engine’s condition, enabling informed decisions regarding maintenance and crew scheduling. In this context, we propose a novel RUL prediction approach in this paper, harnessing the power of Bi-directional LSTM and Transformer architectures, known for their success in sequence modeling, such as natural languages. We adopt the encoder part of the full Transformer as the backbone of our framework, integrating it with a self-supervised denoising autoencoder that utilizes Bidirectional LSTM for improved feature extraction. Within our framework, a sequence of multivariate time series sensor measurements serves as the input, initially processed by the Bidirectional LSTM autoencoder to extract essential features. Subsequently, these feature values are fed into our Transformer encoder backbone for RUL prediction. Notably, our approach simultaneously trains the autoencoder and Transformer encoder, different from the naive sequential training method. Through a series of numerical experiments carried out on the C-MAPSS datasets, we demonstrate that the efficacy of our proposed models either surpasses or stands on par with that of other existing methods.

Electric mobility is one of the ways to contain greenhouse gas and local pollutants emissions in urban areas. Nevertheless, the massive introduction of battery-powered electric vehicles (EVs) brings some concerns related to their energy demand. Modelling vehicle usage and charging behavior is essential for charge demand forecasting and energy consumption estimation. Therefore, it is crucial to understand how the charging decisions of EV owners are influenced by different factors, ranging from the charging infrastructure characteristics to the users’ profiles. This review intends to investigate the approaches used to investigate on charging behavior and highlight trends and differences between the results, remarking on any gaps worthy of further investigation.

To estimate the progressive collapse resistance capacity of a multi-column frame tube structure with the assembled truss beam composite floor (ATBCF), the pushdown analysis, and the nonlinear dynamic analysis as well, are conducted for such a structure by using the alternate load path (ALP) method. The bearing capacities of the remaining structures at three different work conditions, which are the side middle column failure, the edge middle column failure, and the corner column failure, are individually studied, and the collapse mechanism for the remaining structures is analyzed from the aspects of the internal force redistribution and the failure mode of the second defense line. Simultaneously, the influence of the column failure time on the dynamic response of the remaining structure and the dynamic amplification coefficient are discussed. The results show that the residual bearing capacity of the remaining structure with the bottom corner column failure is higher than that of the one with the side or edge middle column failure, while the latter has a stronger plastic deformation capacity. When the ALP method is adopted to operate the progressive collapse analysis, it is reasonable to take the column failure time as 0.1 times of the first-order vertical vibration period of the remaining structure, and it is suitable to set the dynamic amplification coefficient as of 2.0, which is the ratio of the maximum dynamic displacement to the static displacement of the remaining structure under the transient loading condition.

In this paper, the overall performance of Cu-(In,Ga)-Se₂ (CIGSe) solar cells was improved in the ultraviolet (UV)–visible wavelength region by two-segment process. In which, adjusting DC sputtering powers (20–40 W) for absorbers (segment I), and metal-alloy compositions (CdS, ZnO/CdS, ZnMgO/CdS and ZnMgO) for buffers (segment II) were explored and characterized. Upon choosing the optimal-30-W CIGSe-absorber (with 0.95-CGI ratio) sputtered by the segment-I process, the Cu-rich film can distinctly boost grain growth, thus reducing the trap state density. After segment-II process as the toxic-CdS alternative, the optimal Zn_0.9Mg_0.1O-alloy buffer can reach the best conversion efficiency (η = 8.70%) was attained for the environmental protection. Meanwhile, and the overall internal/external quantum efficiencies (IQE/EQE) were improved by 13.15%, respectively for the 2.48–3.62-eV bandgap (short wavelength) range. The developed photovoltaic (PV) module (with 9 optimum-CIGSe cells) exhibits acceptable stability with a variance within ±5% over the 60-day experiment. This discovery in PV-device research contributes to a new scientific understanding of renewable energy. Furthermore, this study undeniably enhances the progress of practical applications for PV-modules in alignment with sustainable development goals. It also actively supports the development of eco-friendly communities.

Real-time multi-axis distributed tactile sensing is a critical capability if robots are to perform stable gripping and dexterous manipulation, as it provides crucial information about the sensor-object interface. In this paper, we present an optical-based 6-axis tactile sensor designed in a fingertip shape for robotic dexterous manipulation. The distributed sensor can precisely estimate local XYZ force and displacement at ten distinct locations, and provide global XYZ force and torque measurements. Its compact size, comparable to that of a human thumb, and minimal thickness allow seamless integration onto existing robotic fingers, eliminating the need for complex modifications to the gripper. The proposed sensor design uses a simple, low-cost fabrication method. Moreover, the optical transduction approach uses light angle and intensity sensing to infer force and displacement from deformations of individual sensing units that form the overall sensor, providing distributed 6-axis sensing. The local force precision at each sensing unit in the X, Y, and Z axes is 20.89 mN, 19.19 mN, and 43.22 mN, respectively, over an local force range of approximately ±1.5 N in X and Y and 0 to -2 N in Z. The local displacement precision in the X, Y and Z axes is 56.70 μm, 50.18 μm and 13.83 μm, respectively, over a local displacement range of ±2 mm in the XY directions and 0 to -1.5mm in Z (i.e., compression). Additionally, the sensor can measure global torques, Tx, Ty and Tz, with a precision of of 1.90 N-mm, 1.54 N-mm and 1.26 N-mm, respectively. The fabricated design is showcased by integrating it with an OnRobot RG2 gripper and illustrating real-time measurements during in simple demonstration task which generate changing global forces and torques.

Hard coatings like Titanium diboride (TiB2) on silicon substrates are used for interconnects. micro-electro-mechanical devices and metallurgical protective coatings were analyzed based on shallow depths of nanoindentation penetration which involves a change in the nature of the contact from Hertzian to Pyramidal both in loading as well as unloading modes. Features like ductile fracture, linear unloading, and the creation of electrical conduction paths were analyzed. These high-precision features are influenced by the thickness and crystalline nature of the films and are significant for device fabrication.

This study explores the dynamics of liquid holdup in a lab-scale co-current two-phase upflow moving packed bed reactor, specifically examining how superficial gas velocity influences the line average external liquid holdup at a fixed superficial liquid velocity. Utilizing Gamma-ray Densitometry (GRD) for precise measurements, this research extends to determining line average internal porosity within catalyst particles and the overall external bed porosity. Conducted with an air-water system within a bed packed with 3 mm extrudate porous particles, the study presents a novel methodology to calculate liquid holdup, grounded in Beer–Lambert's law. Findings reveal a decrease in liquid holdup corresponding with increased superficial gas velocity across axial and radial locations, with a notable transition from bubbly to pulse flow regime at a critical velocity of 3.8 cm/sec. Additionally, the lower sections of the packed bed exhibited higher external liquid holdup compared to the middle sections at varied gas velocities. The liquid holdup distribution appeared uniform at lower flow rates, whereas higher flow rates favored the middle sections.

The International Maritime Organization (IMO) is enforcing increasingly stringent regulations on ship carbon emissions, The International Maritime Organization (IMO) has been implementing progressively strict regulations on ship carbon emissions, leading to the adoption of the virtual arrival (VA) method by many vessels to reduce their carbon footprint. However, the effectiveness of the traditional VA method often varies in busy ports with complex traffic organization scenarios. To address this, our study presents a novel, comprehensive model that integrates vessel scheduling with the VA approach. This model is designed to achieve a dual objective: reducing carbon emissions through Virtual Arrival and simultaneously minimizing vessel waiting times. In addition to these goals, it incorporates essential aspects of safety, efficiency, and fairness in port management, utilizing the NSGA-2 algorithm to find optimal solutions. This model has been tested and validated through a case study at Ningbo-Zhoushan port, employing its dataset. The results demonstrate that our innovative model and algorithm significantly outperform traditional scheduling methods, such as First-Come-First-Serve (FCFS) and Virtual-Arrival Last-Serve (VALS), particularly in terms of operational efficiency and reduction in vessel carbon emissions.

This research aims to delve into the intricacies of combustion processes, specifically focusing on heating oil and a blend of heating oil with TPO (Tire Pirolysis Oil) in a self-developed evaporative combustion chamber featuring steam injection. The primary objective is to scrutinize the impact of steam injection on the combustion dynamics. Conducting a series of tests, the investigation involved the meticulous manipulation of stoichiometric ratios while introducing ambient air through gravity fuel flow. Subsequent iterations of these tests incorporated the introduction of steam into the ambient air stream. The examination encompassed the combustion of both heating oil and the TPO blend within the combustion chamber. The evaluation criteria comprised an in-depth analysis of flame characteristics, temperature distribution within the combustion chamber, and the quantification of emissions such as particulate matter (PM), nitrogen oxides (NOx), carbon dioxide (CO2), carbon monoxide (CO), and water vapor (H2O). Throughout the experimentation phase, commercially available diesel fuel served as the primary fuel source. To facilitate the tests, the combustion chamber under scrutiny was seamlessly integrated into an AVL engine test bench system. Essential parameters, including fuel consumption, were meticulously gauged using an AVL 735 fuel flow meter, while fuel temperature was monitored using the AVL 745 fuel temperature conditioning system. The intake air, a crucial element in the combustion process, was quantified with precision using an AVL Flowsonix sensor. Emission measurements were conducted meticulously using state-of-the-art equipment, with gaseous emissions analyzed using an AVL FTIR AMA i60 exhaust gas analyzer. Simultaneously, soot emissions were quantified through employment of an AVL Micro Soot sensor. This comprehensive approach not only delves into the fundamental aspects of combustion but also extends its reach to the exploration of innovative techniques, such as steam injection, to enhance combustion efficiency and reduce emissions. The integration of advanced measurement tools ensures a robust and thorough analysis of the combustion process and its environmental implications.

When the formation flying consists of satellites that are independent from each other and forms a virtual synthetic aperture telescope that replaces a single large telescope with all of its optical elements it is required mutual coordination between the satellites with high accuracy in order to achieve good optical observations. In this paper we consider the problem of developing a control system for a tetrahedral satellite formation flying representing a synthetic aperture telescope in the geostationary orbit for high resolution monitoring of fire in the forest area in infrared spectrum. Synthesizing the image with good quality requires to keep the configuration of formation with µm-class accuracy. Study of dynamics of passive uncontrolled motion of formation flying showed a significant deviation of the configuration from the required shape with a high frequency due to the action of gravitational forces. To keep the configuration in the required form with µm accuracy the analysis of efficiency of various controllers was carried out in the process of numerical simulation. The simulation results made it possible to highlight the features of using various approaches to the development of control system for the satellite formation flying representing a synthetic aperture telescope.

This study explores the application of numerical analysis and material models to predict ice impact loads on ships and offshore structures operating in polar regions. An explicit finite element analysis (FEA) approach was employed to simulate an ice and steel plate collision experiment conducted in a cold chamber. The pressure and strain history during the ice collision were calculated and compared with the experimental results. Various material model configurations were applied to the FEA to account for the versatile behavior of ice—whether ductile or brittle—its elastic-plastic yield criteria, and its dynamic strain rate dependency. In addition to the standard linear elastic perfectly plastic and linear elastic-plastic relationships, this study incorporated the crushable foam and Drucker-Prager models, based on the specific ice yield criteria. Considering the ice’s strain rate dependency, collision simulations were conducted for each yield criteria model to compute the strain and reaction force of the plate specimens. By comparing the predicted pressures for each material model combination with the pressures from ice collision experiments, our study proposes material models that consider the yielding, damage, and behavioral characteristics of ice. Lastly, our study proposes a combination of ice material properties that can accurately predict collision force.

Airworthiness certification requires proof of structure strength, which is performed generally through building block approach. To achieve this, representative intermediate scale experiments generated by test benches are in general needed, additionally to material characterization at coupons scale and structure testing at large scale. The VERTEX test bench can generate combined loading of tension/compression-shear-pressure on structural elements and was modelled with Finite Elements to perform virtual testing representative of its intermediate-scale specificity. The numerous bolted joints of the bench were modelled and their behavior identified in previous tests, so the model could quantitatively estimate the transfer function of the bench, which is the relation between the displacements imposed by the jacks and the resulting loads on a given sample. The VERTEX model was identified to represent load shapes and amplitudes on a training set, and was later confronted to a validation set of tests of tension and shear. A model with ideal boundary conditions was also developed for comparison but it failed to predict some load shape specificities and did not give any indication of loading amplitude. Application cases of the developed model are shown to assess a range of virtual testing possibilities.

The aeroelastic response of lightweight low-speed aircraft with slender wings under extreme flow turbulence intensity is not well-understood. Experiments on a commmercial unmanned aerial vehicle (UAV) with a 3-meter wingspan and aspect ratio of 13.6 were performed in a large open-return wind tunnel with extreme flow turbulence intensity of T∞≈10%. The structural dynamics of the wing in the bending mode and the flow beneath the wing to capture the effect of aileron deflection was measured using laser displacement sensors and tomographic particle image velocimetry (PIV) techniques. The unsteady lift produced by the wing was also measured using a high-capacity load cell at an angle of attack, α of 2 degrees for three freestream velocities U∞ of 13.4 m/s, 17.9 m/s, and 26.8 m/s representing the UAV’s stall through cruise speed. It was found that high flow turbulence intensity with large integral length scales relative to the wing chord plays a dominant role in the large unsteady lift and wing displacements measured. The power spectral density (PSD) of wing structural vibration shows that flow shedding from the wing and the integral length scales have a significant impact on the overall power inherent in the bending vibration of the wing. Computation of vorticity iso-surfaces in the flow measurement volume surrounding the aileron reveal a striking observation; aileron deflection, δa of 10∘becomes less effective in producing additional spanwise vorticity, which is proportional to circulation and lift, at U∞ of 26.8 m/s because the freestream already has elevated levels of vorticity. A paradigm shift in design is suggested for light aircraft structures with slender wings operating in highly turbulent flow, which is to employ multiple control surfaces in order to respond to this flow and mitigate large bending or torsion displacements and the probability of structural failure.

In view of the problem of charging and hydrogen filling facilities construction in the transition from fuel vehicles to electric vehicles and hydrogen fuel cell vehicles, in order to meet the electric energy demand of electric vehicles and hydrogen energy demand of hydrogen fuel cell vehicles at the same time, this paper puts forward a method of siting and capacity determination of electric-hydrogen refueling integrated station (EHRIS) based on Voronoi diagram and particle swarm algorithm based on calculating the demand of charging and filling of hydrogen in vehicles. Firstly, OD pair(Origin-Destination) is used to represent the starting point and end point of the car to portray the travel demand of the car, and on the basis of the traffic network model, the shortest driving path of the new energy car is determined by Dijkstra algorithm, and Monte Carlo simulation is used to get the electric-hydrogen energy demand of the car; secondly, the Voronoi diagram is introduced to classify the service scope of the electric-hydrogen charging station to determine the equipment capacity of the electric-hydrogen charging station, while taking into account the electric-hydrogen charging and refueling capacity. Secondly, a Voronoi diagram is introduced to divide the service scope of the EHRIS, determine the capacity of the equipment in the EHRIS, and consider the distance constraints between the sites of EHRIS, so as to make the division of the site and service scope more reasonable. Finally, a dynamic optimal current model framework for distribution networks based on second-order cone relaxation is established, and each element of the active distribution network is planned, so that the distribution network can operate safely and stably after being connected to the EHRIS. With the objective of minimizing the total social cost of EHRIS and considering the constraints of the charging equipment and hydrogen production and injection equipment of the EHRIS, a siting and capacity model to meet the electric-hydrogen energy demand of electric and hydrogen fuel cell vehicles is developed, and solved by a particle swarm algorithm. Finally, the simulation planning is carried out with Sioux Falls city and IEEE33 network, which ensures the stable operation of the power grid while meeting the energy demand of automobiles, and the results verify the effectiveness and feasibility of the proposed method.

Bifacial PV modules have become the pervasive choice for utility-scale solar photovoltaic (PV) installations. Understanding bifacial PV performance for commercial deployments is, therefore, critically important. Although there have been studies on the modeling of Bifacial PV performance, to the best of our knowledge, the comprehensive integration of racking and mounting geometry for Bifacial PV performance simulation is yet to be reported. In this work, we modeled the racking and mounting geometry for a field-deployed operational PV array. We then applied optical ray-trace simulation to study the impact of those structures on array performance. The study reveals that the support structures have a considerable impact on the rear surface irradiance and overall bifacial PV performance, amounting to as much as 30% shading in certain situations. The work analyzes the impact of installation parameters such as tilt, height, and row spacing on the bifacial PV array’s performance. Thus, a novel holistic approach to studying the sensitivity of various installation parameters’ impact on bifacial PV array performance, with the racking and mounting structure’s geometry integrated, has been demonstrated through the work. This knowledge and its practice enables site-specific design optimization for utility-scale bifacial solar PV arrays and synergistic PV applications such as agrivoltaics and carports, where maximizing power generation is not necessarily the sole objective.

This paper proposes a radial alternating materials phononic crystal (RAM-PnC). By simulating the band gap structure of the phononic crystal, it is verified that there is a complete acoustic band gap at the resonant frequency of 175.14 MHz, which can prevent the propagation of elastic waves in a specific direction. The proposed alternately arranged radial phononic crystal structure is applied to the thin-film piezoelectric-on-silicon (TPOPS) MEMS resonator. The finite element simulation method increases the anchor quality factor (Qanchor) from 60596 to 659536011 at the operating frequency of 175.14 MHz, which is about 10,000 times higher. The motion resistance of the RAM-PnC resonator is reduced from 156.25 Ω to 48.31 Ω compared with the traditional resonator. At the same time, the insertion loss of the RAM-PnC resonator is reduced by 1.1 dB compared with the traditional resonator.

Intelligent ship technology is currently an international research hotspot, and model predictive control is widely used in the path-tracking control of intelligent vehicles. To construct an intelligent ship anti-disturbance path-tracking control method, firstly, an environmental disturbance model was constructed with the actual meteorological data of the target sea area. Secondly, the Fossen ship equation of motion is linearized and discretized as the ship motion model. Thirdly, the expression of the prediction equation is derived from the ship motion model. Fourthly, the cost function is constructed by using the polar diameter and polar angle values of the ship. Fifth, the power function in the cost function is replaced with an exponential function to obtain an improved cost function. Sixthly, according to the Lyapunov theory and the MPC terminal constraint theory, the stability of the improved cost function is verified. Seventh, different test paths are set up, the environmental disturbance model is taken as the external disturbance, the ship motion model, the prediction equation, and the improved cost function are used to design the anti-disturbance path-tracking control algorithm according to the model prediction control idea for simulation experiments. Finally, different MATLAB simulation results show that the improved cost function can resist disturbance of the external wave, current, and wind, and effectively track the target path. Therefore, this study provides a reference for improving the navigation safety of ship path-tracking.

Renewable energy sources are increasingly crucial for meeting global energy demands in an eco-friendly manner, particularly as the world shifts away from fossil fuels and faces higher volatility of the market prices. Portugal, a country with limited fossil fuel reserves and high energy dependency, has heavily invested in renewables in recent years. However, renewables like sunlight, wind, and water are inherently unsteady due to climate influences, resulting in energy production fluctuations. To mitigate this issue, energy storage systems are being deployed to stabilize renewable energy supply during production lulls. This project aims to assess an energy storage system and define technical specification in order to maximize profitability at the Finerge-owned Alto Douro wind farm. To achieve this, we've conducted an energy demand analysis, followed by simulations to assess system performance in relation to energy use and profit, while varying parameters such as power and efficiency. We've also compared commercial proposals from different battery manufacturers utilizing various technologies. Our findings suggest that high-power, quick charge-discharge lithium-ion batteries are the most suitable solution, given their efficiency and modularity. These attributes are vital for achieving current and future profitability.

This paper describes RTMN 2.0 - an extension of the modeling language RTMN. RTMN combines process modeling and robot execution. The reusability and intuitive robot programming allows people without programming expertise to plan and control robots. While RTMN focuses on the ease of use and flexibility of robot programming by providing an intuitive modelling language, RTMN 2.0 concentrates on Human Robot Collaboration (HRC), which represents the current trend of the industry shift from ‘mass-production’ to ‘mass-customization’. While traditional robot systems provide high payload capabilities and repeatability, they suffer from limited flexibility and dexterity. Therefore, human-robot collaboration is a suitable arrangement to leverage the unique capabilities of both humans and robots for increased efficiency and quality in industrial scenarios. HRC has made a great impact on the robotic industry: increased efficiency, reduced costs and improved productivity, which can be adopted to make up for the skill gap of a shortage of workers in the manufacturing industry. The extension in RTMN 2.0 includes the following notations: HRC tasks, requirement, Key Performance Indicator (KPI), condition check and decision making, join/split and data association. With all these additional elements RTMN 2.0 covers the full scale of agile manufacturing – light out and collaborative.

With the continuous breakthrough of natural language processing, the application of intelligent question answering technology in electric power system has attracted wide attention. However, at present, the traditional question answering system has poor performance and is difficult to be applied in engineering practice. This paper proposes an improved BERTserini algorithm for intelligent answering of electric power regulations based on a BERT model. The proposed algorithm is implemented in two stages. The first stage is the text segmentation stage, where a multi-document long text preprocessing technique is utilized that accommodates the rules and regulations text, and then Anserini is used to extract paragraphs with high relevance to the given question. The second stage is answer generation and source retrieval stage, where a two-step fine-tuning based on the Chinese BERT model is applied to generate precise answers based on given questions, while the information regarding documents, chapters, and page numbers of these answers are also output simultaneously. The algorithm proposed in this paper eliminates the necessity for manual organization of professional question-answer pairs, thereby effectively reducing the manual labor cost compared to traditional question answering systems. Additionally, this algorithm exhibits a higher degree of exact match rate and a faster response time for providing answers.

Building Information Modelling (BIM) is increasingly adopted in supporting Facility Management (FM). However, in the future perspective of efficiently encouraging the management of building estates, e.g. owned by public authorities or institutions, providing an effective integration between Building Information Models and Geographic Information Systems (GIS) becomes also essential. This contribution is therefore aimed at presenting a methodological framework integrating BIM and GIS environments by mainly taking advantage of open-source tools (e.g. QGIS and Dynamo) and non-proprietary data exchange formats like the COBie (Construction-Operations Building Information Exchange) international standard. Unlike what has been mainly proposed in the literature concerning BIM-GIS integration so far, the methodology presented in this paper does not provide for an integral transfer of BIM data to the GIS platform. The work led instead to the development of an effective bidirectional integration between the two informative systems, by properly relating them and allowing for an easy switch from one system database to the other. This integrated framework is intended to enable facility managers to easily visualize in the GIS platform, through queryable 2D and 3D maps, some relevant information from BIM to efficiently manage workspaces. At the same time, this hybrid informative system allows BIM practitioners to access and manage more detailed information stored in Building Information Models. The findings of experimental applications of the proposed methodological approach to the ex-Macciotta pediatric hospital, a healthcare building owned by the University of Cagliari, are finally presented and discussed.

Smart, reliable, and connected multi-modal mobility has been a long-standing goal of transit services. This paper focuses on the smart, seamless, and multi-modal mobility service in the context of “Mobility as a Service” (MaaS). Intelligent mobility is the smarter, greener, and more efficient movement of passengers around the world. Increasingly, mobility is approached as a service. This study first conducts an extensive literature review on mobility behavior and demand pattern of MaaS end-users. It then extends the mechanism of supply chain, MaaS, synergy (i.e., vertical cooperation synergy, horizontal competition synergy), and coopetition to develop the multi-tier closed-loop intelligent mobility service supply chain network. This paper explains the intelligent mobility service supply chain network from following perspectives: (i) mobility service taxonomy of MaaS; (ii) aims of intelligent mobility service supply chain network; (iii) urban rail transit (URT)-centered alternatives for integrated multimodal journey planning, i.e. access + URT + egress, and both access and egress can be served by Mobility-On-Demand (MOD) transport; (iv) node member imperatives. From a synthesis of insights from the ‘during’ journey, this study puts forward the synergetic design of intelligent mobility service supply chain network, including: (i) multi-tier closed-loop structure; (ii) key nodes identification for the physical multimodal transport network in the supply chain; (iii) hybrid synergy mechanisms among the partners, i.e., synergy principle, temporal splitting approach for coopetition synergy; (iv) index systems and evaluation methods for synergy measurement. This study also contributes to the integrated multimodal journey planning. In concluding, the paper highlights the important implications of the proposed intelligent mobility service supply chain network for MaaS bundle design and adverse effects reduction, resulting from 1 + 1 > 2 synergy effects.

The paper deals with an electrothermal model of a thermoelectric converter dedicated to performing simulations of coupled thermal and electrical phenomena taking place in harvesting processes. The proposed model is used to estimate the electrical energy gain from waste heat that would be sufficient to supply electronic circuits, in particular autonomous battery-less nodes of wireless sensor networks (WSN) and Internet of Things (IoT) devices. The developed model is not limited to low power electronic solutions such as WSN or IoT; it can also be scaled up and applied to simulations of considerably higher thermal power conversion. In the paper, a few practical case studies are presented that show the feasibility and suitability of the proposed model for complex simultaneous simulation processes, in both electrical and thermal domains. The first example deals with a combined simulation of the electrothermal model of a thermoelectric generator (TEG) and an electronic harvester circuit based on Analog Devices’ power management integrated circuit LTC3108. The second example relates to the thermalization effect in heat sink-less harvesting applications that could be mitigated by a pulse mode operation.

The pursuit of an environmentally sustainable manufacturing process advocates for the substitution of harmful reagents for less damaging and recyclable solutions. This study aims to assess the effectiveness of using cellulose microfibrils synthesized via different hydrolysis reactions as reinforcing agents in polyvinyl alcohol (PVA) at varying con-centrations. The investigation explores the morphology, thermal properties, and chemi-cal behavior of the cellulose particles. The cellulose microfibrils (CMF) produced using citric acid exhibited the highest yield and aspect ratio. Notably, particles from organic acids demonstrated greater thermal stability, with oxalic acid-derived particles displaying the maximum thermal degrada-tion temperature. Subsequently, cast films of PVA reinforced with the cellulose microfibrils underwent comprehensive analyses, including Fourier transfer infrared (FTIR) spectroscopy, thermal degradation temperature (Td), differential scanning calorimetry (DSC), and tensile strength tests. The thermal behavior of cast films experienced notable changes with the addition of cellulose particles, evidenced by increased melting and crystallinity temperatures, along with a rise in the degree of crystallinity. The incorporation of cellulose particles led to a substantial improvement in mechanical properties. Films containing CMF dis-played higher Young’s modulus, and the sample incorporating 5% CMF derived from citric acid exhibited the most significant increase in modulus.

This article investigates the source of booming noise emanating from the tailgates of electric vehicles, along with proposed strategies to mitigate it. This study involved the measurement of booming noises during on-road vehicle tests to pinpoint their origins. Additionally, operational deflection shapes (ODS) were extracted from the tailgate vibration signals to gain insight into its dynamic behavior. Modal tests were conducted on the tailgate to determine its dynamic characteristics and compared with driving test results to reveal the mechanism responsible for tailgate-induced booming noise. It was established that such noise is primarily due to the tailgate modes resulting from a combination of rigid body motion in the fore-aft direction and deformation in the central section of the panel. An analytical model of the tailgate was developed using commercial finite element analysis software to propose measures for reducing booming noise. Experimental findings validated the model accuracy. Structural enhancements were implemented to enhance the panel stiffness and improve the connection between the vehicle and tailgate via bushings to dampen the booming noise resulting from tailgate motion. Under random force inputs, analytical results demonstrated a 13.8% reduction in maximum deformation in the tailgate model in the improved structural configuration with increased panel stiffness.

The results of a study of the processes of formation of macro- and micro-inhomogeneity of ingots and rolled sheets are presented. To study the nature and sources of surface defects in rolled sheets, a comparative method of structural-concentration analysis of metal at the end-to-end metallurgical process of ingot - slab - sheet metal has been developed, based on the metallographic method of studying the structure and qualitative analysis of non-metallic inclusions. The research results showed that the quality of the surface of rolled sheets is determined mainly by the physical heterogeneity of the crust zone of the ingots, and the internal defects of thin rolled sheets largely depend on the contamination of the metal with non-metallic inclusions and off-axis segregation. A mechanism for transforming defects in the casting cortical zone into surface and internal defects in rolled sheets is proposed.

Experimental measurement of porous polypropylene (PP) using the DTC-25 TA laboratory equipment and hot box test has been compared. The thermal conductivity of materials indicates the insulation capability of building materials. Excellent building materials will have a lower thermal conductivities value as well as other building insulator performance metrics. While results show that increasing the volume fraction of fluid in the porous PP has an inverse association with the thermal conductivity of the material as predicted by porous media theories, there is a marked difference in the measured values of the thermal conductivity using the two methods. The thermal conductivity values of porous from DTC-25 and hot box test were 0.21 and 0.0033 W/mK, respectively. The difference in the thermal conductivity values was due to the misapplication of the Fourier’s guarded heat flow model in the DTC-25 device to a convective fluid porous medium material.

Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a dominant approach due to its capacity to capture intricate data patterns and relationships. However, a prevalent challenge lies in the black-box nature of many deep learning algorithms, which often operate without adhering to the underlying physical characteristics intrinsic to the studied phenomena. To address this limitation and enhance the fusion of data-driven methodologies with the fundamental physics of the system under study, this paper leverages physics-informed neural networks (PINNs). Specifically, a simple but realistic numerical case study of an extended Jeffcott rotor model, encompassing damping effects and anisotropic supports for a more comprehensive modelling, is considered. PINNs are used for the estimation of five parameters that characterize the health state of the system. These parameters encompass the radial and angular position of the static unbalance due to the disk installed on the shaft, the stiffness along the principal axes of elasticity, and the non-rotating damping coefficient. The estimation is conducted solely by exploiting the displacement signals from the centre of the disk and, to showcase the efficacy and precision provided by this novel methodology, various scenarios involving different constant rotational speeds are examined. Additionally, the impact of noisy input data is also taken into account within the analysis.

This paper conducts an extensive literature review focusing on the utilisation of crack tip plasticity as a crucial parameter in determining and enhancing crack growth models. The review encompasses a comprehensive analysis of various methodologies, predominantly emphasising numerical simulations of crack growth models while also considering analytical approaches. Although experimental investigations are not the focus of this review, their relevance and interplay with numerical and analytical methods are acknowledged. The paper critically examines these methodologies, providing insights into their advantages and limitations. Ultimately, this review aims to offer a holistic understanding of the role of crack tip plasticity in the development of effective crack growth models, highlighting the synergies and gaps between theoretical, experimental, and simulation-based approaches.

Bandsaw blades are typical band-shaped cutting tools characterized by a low stiffness and micro-level cutting depth, resulting in distinct wear mechanisms compared to rigid cutting tools. In this study, the evolution of bandsaw tooth wear mechanisms during the bandsawing of Cr12MoV cold-working steel was investigated. The tool life was divided into two stages according to the tooth wear mechanism: a rapid wear stage (Stage I) and a homogeneous wear stage (Stage II). In Stage I, the wear mechanism was dominated by chipping, although multiple wear mechanisms were found because of the relatively poor manufacturing tolerance compared to rigid cutting tools, resulting in remarkable differences between the cutting depths of the individual teeth. In Stage II, abrasive and adhesive wear were the primary wear mechanisms instead of chipping, due to the decreased width and setting magnitude tolerance after Stage I. Furthermore, methods for increasing the bandsaw performance were proposed based on the tooth wear mechanisms.

The integration of robotics and image processing has led to the realization of robot autonomy in dynamic environments through the provision of visual feedback. This paper presents the application of parallel and open-link robots in palletizing and shape drawing tasks as enhanced by visual feedback from image processing. In determining the set of joint angles that could be used to reach the desired position and orientation of the end effector, the geometric approach in which the spatial geometry of the robotic arms was decomposed into several plane geometry problems was employed. Image processing techniques were used to enhance the performance of the robotic manipulators. In one approach, Color-based segmentation was used to distinguish between different objects in the workspace by using predefined color markers as references in the L*a*b color space. Classification of each pixel in the workspace image was then done by calculating the Euclidean distance between that pixel and a predefined color marker. A second approach employed Edge detection to identify the boundaries of objects within the workspace image by employing the Hough Transform mathematical model to detect the abrupt changes in the image brightness pixel-wise. The pixel locations from Hough were then sorted sequentially to outline the detected object. The integration of image processing with the robotic tasks was expected to improve the precise detection of the position of objects as well as the outline of geometric shapes. The incorporation of visual feedback allowed for dynamic robot manipulation in which prior knowledge of the workspace was not requisite. This led to improved pick and place as well as shape detection as applied in palletizing and shape drawing tasks actuated by the parallel and serial link manipulators, respectively.

This proposed research explores a novel approach to image classification by deploying a complex-valued neural network (CVNN) on a field-programmable gate array (FPGA), specifically for classifying 2D images transformed into polar form. The aim of this research is to address the limitations of existing neural network models in terms of energy and resource efficiency, by exploring the potential of FPGA-based hardware acceleration in conjunction with advanced neural network architectures like CVNNs. The methodological innovation of this research lies in the Cartesian to polar transformation of 2D images, effectively reducing the input data volume required for neural network processing. Subsequent efforts focused on constructing a CVNN model optimized for FPGA implementation, emphasizing the enhancement of computational efficiency and overall performance. The experimental findings provide empirical evidence supporting the efficacy of the image classification system developed in this study. One of the developed models, CVNN_128, achieves an accuracy of 88.3% with an inference time of just 1.6ms and a power consumption of 4.66mW for the classification of the MNIST test dataset consists of 10,000 frames. While there is a slight concession in accuracy compared to recent FPGA implementations that achieve 94.43%, our model significantly excels in classification speed and power efficiency—surpassing existing models by more than a factor of 100. In conclusion, the paper demonstrates the substantial advantages of FPGA-implementation of CVNNs for image classification tasks, particularly in scenarios where speed, resource, and power consumption are critical. The study’s reproducible results and corresponding code are available on GitHub at the following link: https://github.com/mahmad2005/CVNNonFPGA

It is a critical issue to allocate redundancy to critical smart grid infrastructure for disaster recovery planning. In this study, we present a framework to combine statistical prediction methods and optimization models for the optimal redundancy allocation problem. First, we develop statistical simulation methods to identify critical nodes of very large-scale smart grid infrastructure based on the topological features of embedding networks, and then present a linear integer programming model based on generalized assignment problem (GAP) for redundancy allocation of critical nodes in smart grid infrastructure. The model is specifically implemented in the context of smart grid infrastructure. The findings demonstrate that the combined approach of statistical simulation and optimization effectively addresses the size limitations inherent in a sole optimization approach. Notably, the optimal solutions for redundancy allocation in very large grid systems highlight that the cost of redundancy is only a fraction of the economic losses incurred due to weather-related outages.

Synthetic fiber ropes are frequently used as mooring lines in floaters supporting Floating Offshore Wind Turbines (FOWTs) due to their various advantages. Damages in these lines can lead to the disruption of the FOWTs normal operation and thus their early detection is crucial. Currently, this damage detection problem has been investigated exclusively for FOWT chain mooring lines. This study focuses on the damage detection in FOWT synthetic mooring lines under varying environmental-operational conditions (EOCs) via two types of vibration-based Structural Health Monitoring methods: those based on Multiple Models and those based on a single Functional Model. The methods are based on multivariate AutoRegressive or on scalar Transmittance Function (TF) AutoRegressive with eXogenous input models. All methods are evaluated through a Monte Carlo study involving 390 simulations with a 10MW wind turbine mounted on the semi-submersible OO-Star wind floater and two damage scenarios (10%, 14% stiffness reduction) to a single mooring line. The results using acceleration signals from only two measuring positions on the mooring line, indicate the great potential for robust SHM in FOWTs synthetic mooring lines with the TF model-based methods successfully detecting the considered early stage damage scenarios, even for EOCs not encountered in the methods training.

Enhancing traffic safety is one of the fundamental objectives of Intelligent Transport Systems (ITS), and it aligns closely with the principles of sustainable transport. Due to specific differences in infrastructure, vehicles, and users’ behaviour, places where different modes of traffic intersect are recognized as critical points of the traffic system, making them crucial aspects of Sustainable Urban Mobility Plans (SUMPs) implementation. The SUMPs aim to create urban mobility that is not only environmentally friendly and efficient but also safe for all users. The continuous development and widespread adoption of innovative ITS technologies have paved the way for a system that could provide drivers with real-time information about both immediate and potential dangers at these critical points. This paper provides an overview of previous research in the field, investigating the impact of information systems on drivers’ behaviour, various detection and communication solutions that can be effectively integrated into such a system, as well as a brief overview of models and solutions that have been developed to warn drivers in a similar context. The reviewed literature offered valuable insights on which a novel driver information system architecture framework is proposed. This framework can contribute to the ongoing safety improvement in multimodal transport networks within the context of sustainable transport.

Micro/Nano Robot is an intelligent and efficient microrobot that can perform specific tasks under the influence of external stimuli. Depending on the application scenarios, the microrobot can adaptively transform into appropriate functional forms under different external stimuli, thus perfectly matching the needs. To date, microbots have been widely used in targeted therapy, drug delivery, tissue engineering, environmental remediation, and other fields. Although the applications of microrobots are promising, there are only a few reviews that can focus on the preparation methods and driving mechanisms. Therefore, it is necessary to outline the current status of the development of these microrobots in order to provide some new insights for the further development of the field. Therefore, this paper reviews the research progress of microrobots in terms of preparation methods, stimulus response mechanisms and applications, and highlights the applicability of different preparation methods and stimulus types. Finally, the current challenges faced by microrobots are highlighted and possible solutions are proposed to facilitate the practical application of microrobots.

Small submersible drainage pumps discharge leaking water and rainwater in buildings. In an emergency (e.g., heavy rain or accident), monitoring the flow rate in advance is necessary to enable optimal operation, considering the point where the pump operates abnormally when the water level increases rapidly. In another, pump performance optimization is crucial for energy saving policy. Therefore, it is necessary to meet the challenges of submersible pump systems, including sustainability and pump efficiency. The final goal of this study is to develop an energy-saving, high-efficiency submersible drainage pump capable of responding to emergencies. In particular, this paper targeted the hydraulic performance improvement of a submersible drainage pump model. Before the development of driving mode-related technology capable of emergency response, we first tried to find a way to improve the performance characteristics of the existing submersible drainage pump. Rather than designing a new pump, we disassembled the current pump and reverse engineering. Then, numerical simulation was performed to analyze the flow characteristics and efficiency of the pump. Then, the pump test was carried out to obtain the performance and validated with numerical results. Results revealed that changing the cross-sectional shape of the impeller reduced the flow separation and enhanced velocity and pressure distributions. Also, it reduced the power and increased efficiency. Results also showed that the pump's efficiency increased to 5.56% at a discharge rate of 0.17 m3/min, and overall average efficiency increased to 6.53%. In addition, the submersible pump design method could be suitable for the optimized pump's impeller and casing numerically. This paper can provide insight and information on the design optimization of pumps.

In order to address the issues of weight, cost and sustainability issues associated with the application of glass fibre in structural composites, plant fibres are proposed as an alternative to reduce and replace the usage of glass fibres. Despite this, there is still a gap in comprehensive research on plant fibre composites, especially with regard to their durability in real structural applications. This research investigates the fatigue and impact properties of pultruded hybrid polyester composites reinforced with glass fibre and kenaf, tailored for structural applications. Utilising kenaf fibres in mat form, unidirectional glass fibre direct roving yarns and unsaturated polyester resin as key components, pultruded kenaf/glass hybrid profiles were meticulously fabricated. Comparative cost and weight analyses were conducted between pultruded hybrid and non-hybrid profiles. The study showed that the pultruded kenaf/glass alternative laminates exhibit commendable fatigue and impact resistance, making them suitable for use in moderately loaded structural applications. Moreover, the use of kenaf as a hybrid alternative shows significant cost and weight reductions. This research provides a basis for the development of sustainable and efficient structural materials and highlights the significant role of material design in shaping the future of engineering applications.

This paper introduces the implementation of a genetic programming (GP)-based procedure to the automatic design and tuning of process controllers. The proposed approach makes a significant contribution to the field of artificial intelligence (AI) in control engineering. Unlike other controller design methods, the GP-based program handles the entire design in the time domain, including differential operations like derivatives and integrals, without the need for intermediate inverse Laplace transformation. This approach not only simplifies the design process but also ensures that all generated controllers are implementable in physical systems. Furthermore, GP’s functions set includes various mathematical operations beyond basic arithmetic operators, such as trigonometric, exponential, and logarithmic operators. The performance and validity of the resulting controllers generated by the proposed GP-based approach are evaluated by verifying whether the generator can replicate the structure and performance of those produced by traditional controller design methods and, in some cases, achieve even better results. As a result, the GP-based approach presents a promising solution for automating the controller design process and addressing control problems in various engineering applications.

It remains unclear in the literature what is the cause of the so-called alkali-carbonate reaction (ACR) damage to concrete yet, a consensus is reached when considering dedolimitization as non-expansive. However, expansion and cracks as distress features are often attributed to alkali-silica reaction (ASR) when evidence of a reactive mineral is found in the aggregate. Evidently, different types of aggregates may produce different types and degrees of damage. Therefore, this work aims to assess the damage to concrete generated and propagated by the so-called ACR susceptible reactive Kingston coarse aggregate through mechanical testing (i.e., direct shear test), microscopy (the damage rating index – DRI), and other techniques. Distinct induced expansion levels (i.e., 0%, 0.05%, 0.12%, and 0.20%) were selected to compare the distress caused by ACR to concrete affected by ASR. Result show that the behaviour of ACR, namely as captured through the DRI, is inconsistent with that of ASR thus attesting to ACR being a distinct distress mechanism.

Vibrations of plate structures placed in a supersonic flow is considered. The undisturbed fluid flow is parallel to the plate. Two specific problems are treated: in the first one the plate is in the form of an infinite strip and the flow is in the direction of its finite length. Rigid walls extend from the sides of the plate indefinitely. In the second problem, the plate is a finite rectangle and the flow is parallel to one of its sides. The first problem is a limiting case of the second problem. The flow is modeled by piston theory which assumes that the fluid pressure on the plate is proportional to local slope. This approximation is widely used at high speeds, and reduces the interaction between the fluid flow and the vibrations of the plate to an additional term in the vibration equation. The resulting problem can be solved by assumed mode methods. In this study, the solution is also carried out by using the collocation method. The main result is the flutter velocity of the free fluid flow under which the plate vibrations become unstable. Finally, simple expressions are proposed between the various non-dimensional parameters that allows quick estimation of flutter velocity.

Finnish government's carbon neutrality goal by 2035 requires integration of renewable energy sources into the power grid. Considering the stochasticity of these resources, additional sources of flexibility are necessary to balance supply and demand in the power grid. District heating network (DHN) operators in Finland plan to shut down fossil-fuel-based combined heat and power plants and electrify heating systems by deploying heat pumps (HPs) and electric boilers. Techno-economic analysis and optimal operation of DHN-connected HPs and electric boilers in providing ancillary balancing services considering the 15-minute granularity in the balancing markets in Finland were investigated. The objective was to maximize the potential revenue for DHN operators gained from the day-ahead electricity market and frequency containment reserve (FCR) balancing markets. Three inter-connected DHNs in the Helsinki metropolitan area were optimized considering the reference year 2019 and each operator's decarbonization strategies for 2025. HPs could gain the highest profit from FCR-D up-regulation market, while the electric boiler may gain considerable profits from FCR-D down-regulation market. Compared to other balancing markets studied, the FCR-N market had a limited profit margin. Sensitivity analysis indicated that spot electricity prices and CO2 emission allowance prices have a significant impact on the profit from balancing markets.

Alternative food sources are essential in both low-resource settings or during emergencies like abrupt sunlight reduction scenarios. Seaweed presents a promising option but requires investigation into the viability of unconventionally sourced ropes for harvesting. As such, a low-cost reliable method to test the tensile strength of rope is needed to validate alternative materials for use in harvesting seaweed. Unfortunately, commercial rope testing jigs alone range in price from several thousand to tens of thousands of dollars. To fill this need, this article reports on an open-source design for tensile strength rope testing hardware. The hardware design focuses on using readily available parts that can be both sourced from a hardware store and manufactured with simple tools to provide for the greatest geographic accessibility. The jig design, which can be fabricated for CAD$20 is two to three orders of magnitude less expensive than commercially available solutions. The jig was built and tested using a case study example investigating denim materials (of 1 5/8”, 3 1/4", 4 7/8”, 6 1/2", and 8 1/8” widths) as a potential alternative rope material for seaweed farming. Denim demonstrated strength up to 1.65kN for the widest sample and the jig demonstrated sufficient strength and stiffness for operating at forces below 4kN. The results are discussed and areas for future improvements are outlined to adapt the device to other circumstances and increase the strength of materials that can be tested.

The paper presents the basic structural parts, a 3D model and the overall design of a laboratory machine, which was created to detect vibrations generated by the casing of a conveyor roller ro-tating at different speeds. The intention of the authors was to verify whether plastic brackets in-serted into the structurally modified trestles of a fixed conveyor idler can reduce the vibration values transmitted from the rotating conveyor roller to the trestle of a fixed idler. Experimental vibration measurements taken on the non-rotating parts of conveyor rollers, performed on a laboratory machine according to ISO 10816, are suitable for characterizing their operating condi-tions with regard to trouble-free operation. The aim of this paper is to detect the vibrations of a rotating conveyor roller on a laboratory machine in the defined places of a fixed conveyor idler and also on the steel frame of a laboratory machine that represents the supporting track of a belt conveyor. Vibrations detected by piezoelectric acceleration sensors were recorded by a measur-ing apparatus and displayed in the environment of Dewesoft X software. The measurements show that the vibration values grow with the increasing speed of the conveyor roller rotation. Experimental measurements have proven the correctness of the assumption that the vibrations transmitted to the trestle of a fixed conveyor idler are lower by up to 40% when using plastic brackets into which the axle of the conveyor roller is attached, compared to the solution where the axle of the conveyor roller is inserted into the notches of a steel trestle.

Capsizing accidents are regarded as marine accidents with a high rate of casualties per accident. Approximately 89% of all such accidents occur in small ships (vessels with gross tonnage less than 10 tons). Stability calculations are critical for assessing the risk of capsizing incidents and evaluating a ship's seaworthiness. Despite the high frequency of capsizing accidents involving small ships, they are generally exempt from adhering to stability regulations, thus remaining systemically exposed to the risk of capsizing. Moreover, the absence of essential design documents complicates direct ship stability calculations. This study utilizes hull form feature data—obtained from the general arrangement of small ships—as input for a deep learning model. The model is structured as a multilayer neural network and aims to infer hydrostatic curves, which is required data for stability calculations.

With growing regulations on ship emissions, specifically carbon dioxide and nitrogen oxides, this study emphasizes the critical need for improving the performance of existing ship diesel engines. Focusing on easily modifiable injection conditions within the fuel system, the research aims to enhance combustion characteristics for operational ships. The study provides a concise analysis of the combustion performance improvements achievable in the field. The study employed a range of calculation conditions, spanning from fuel injection pressures of 20 MPa to 160 MPa, injection timing from 15 degrees before top dead center (BTDC) to 5 degrees after top dead center (ATDC), and spray angles varying between 10 degrees and 50 degrees. A comprehensive analysis was conducted, focusing on fuel distribution behavior, combustion dynamics, carbon monoxide (CO) production and extinction, as well as nitrogen oxide (NO) production. This thorough examination aimed to provide insights into the complex interplay of these factors in order to optimize combustion performance in ship diesel engines. The analysis indicated that substantial unburned fuel occurred at injection pressures below 40 MPa and spray angles less than 20 degrees. This issue was significantly mitigated when injection pressures exceeded 80 MPa or a spray angle of 40 degrees was employed. High flame propagation speeds were observed at injection pressures over 80 MPa and injection timings before BTDC 10. Carbon monoxide emissions were notably low when injection pressures exceeded 80 MPa and a 40-degree spray angle was used. Nitrogen oxide emissions were minimized under conditions of injection pressure below 40 MPa, injection timing after BTDC 5 degrees, and a spray angle between 30 and 40 degrees. The study concludes that the most effective injection conditions for concurrently reducing fuel consumption, minimizing incomplete combustion products, and lowering nitrogen oxide emissions in the target engine are optimal when the injection pressure is set at 80 MPa, the injection timing is positioned at 5 degrees before top dead center (BTDC), and the spray angle is maintained at 40 degrees.

In this work, a modeling technique utilizing the P-Graph framework was used for a case 1 study involving biomass-based local energy production. In recent years, distributed energy systems 2 gained attention. These systems aim at satisfying energy supply demands, supporting the local 3 economy, decreasing transportation needs and dependence on imports, and in general obtaining a 4 more sustainable energy production process. Designing such systems is a challenge, for which novel 5 optimization approaches were developed to help decision-making. Previous work used the P-Graph 6 framework to optimize energy production in a small rural area, involving manure, intercrops, grass, 7 and corn silage as inputs, and fermenters. Biogas is produced in fermenters, and Combined Heat 8 and Power (CHP) plants provide heat and electricity. A more recent result introduced the concept of 9 operations with flexible inputs in the P-Graph framework. In this work, the concept of flexible inputs 10 was applied to model fermenters in the original case study. A new implementation of the original 11 decision problem was made both as a Mixed-Integer Linear Programming (MILP) model and as a 12 purely P-Graph model by using the flexible input technique. Both approaches provided the same 13 optimal solution, with a 31% larger profit than originally reported.

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores into cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

Availability and continuous operation under critical conditions are very important in electric machine drive systems. However, such systems, are suffering from several type of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnosis multiple open-circuit fault in three phase two level voltage source inverter for induction motor drive applications. The proposed approach uses fault diagnosis variables obtained from sigmoid transformation of the motor stator currents. The open-circuit fault diagnosis variables are then introduced to a Bidirectional Long Short-Term Memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault diagnosis algorithm effectiveness and robustness.

Doubly-fed induction generators (DFIG) find extensive application in variable-speed wind power plants, providing notable advantages such as cost-effectiveness, operational flexibility across varying speeds, and enhanced power quality. This research focuses on the control of DFIGs employed in variable-speed wind turbine configurations. A suitable mathematical model is chosen for representative systems following a comprehensive review of contemporary research. Subsequent analysis reveals the instability of the open-loop time response of the system. To address this instability, the initial approach involves the implementation of the conventional Model Predictive Controller (MPC). However, the outcomes indicate that this controller falls short of delivering satisfactory performance despite the enhanced stability. In the subsequent phase, efforts are made to mitigate the impact of wind input variability by utilizing the Kalman filter, given its effectiveness in handling high variability. Following this, a novel methodology is introduced, which combines nonlinear MPC with the Lyapunov function. This method is based on the nonlinear model of the system. By using the Lyapunov function in the nonlinear MPC structure, the stability of the designed controller is guaranteed. The simulation results conducted using MATLAB software show that the output variables of the modeled DFIG system achieve stability within a reasonable timeframe applying the input.

This study was conducted to recover valuable metals (silicon and silver) using acid leaching and substitution reactions with copper powder from the pulverized solar cell separated from the PV ribbon. Leaching experiments were conducted according to nitric acid concentration by molar solution as 1M, 2M, and 3M using an ultrasonic cleaner at a reaction temperature of 60 °C for the leaching times of 30 to 120 minutes. Si phases were detected at all leaching reaction times, it was confirmed that the aluminium was removed effectively if the nitric acid leaching time was more than 30 minutes, and silicon with a purity above 99% could be recovered. The silver was precipitated by adding copper, which has greater reducibility than Cu, Al, Si, Pb, and Sn. When the addition of copper powder was 0.75 and 1.0 g, precipitate was produced 0.23 and 0.55 g, respectively. The optimal condition for the recovery of silver by the addition of copper powder was 1 g, where is recovery rate reached 93.3%.

Lithium-ion batteries with improved energy densities have made understanding the Solid Electrolyte Interphase (SEI) generation mechanisms that cause mechanical, thermal, and chemical failures more complicated. SEI processes reduce battery capacity and power. Thus, a review of this area's understanding is important. It is essential to know how batteries degrade in EVs to estimate battery lifespan as it goes, predict, and minimize losses, and determine the ideal time for a replacement. Lithium-ion batteries used in EVs mainly suffer two types of degradation: calendar degradation and cycling degradation. Despite the existence of several existing works in the literature, several aspects of battery degradation remain unclear or have not been analyzed in detail. This work presents a systematic review of existing works in the literature. The results of the present investigation provide insight into the complex relationships among various factors affecting battery degradation mechanisms. Specifically, this systematic review examined the effects of time, side reactions, temperature fluctuations, high charge/discharge rates, depth of discharge, mechanical stress, thermal stress, and the voltage relationship on battery performance and longevity. The results revealed that these factors interact in complex ways to influence the degradation mechanisms of batteries. For example, high charge currents and deep discharges were found to accelerate degradation, while low temperatures and moderate discharge depths were shown to be beneficial for battery longevity. Additionally, the results showed that the relationship between cell voltage and State-of-Charge (SOC) plays a critical role in determining the rate of degradation. Overall, these findings have important implications for the design and operation of battery systems, as they highlight the need to carefully manage a range of factors to maximize battery performance and longevity. The result is an analysis of the main articles published in this field in recent years. This work aims to present new knowledge about fault detection, diagnosis, and management of lithium-ion batteries based on battery degradation concepts. The new knowledge is presented and discussed in a structured and comprehensive way.

This paper presents a Modified Gate Diffusion Input (MGDI) design with four inputs for Quantum-dot Cellular Automata (QCA) technology. QCA is a promising nanotechnology that offers benefits such as low power consumption, and high density, making it a potential alternative to CMOS technology. The proposed planar cell design has advantages over previous designs, including a 19% reduction in the number of cells, a 29% reduction in total energy consumption, and a 44% reduction in average energy loss at the same occupancy level. The proposed block is capable of implementing 21 basic combinational functions and various sequential functions. The simulation of functions in 18nm technology and energy consumption calculation indicates that the resulting circuits have improvements of 41%, 27%, 27%, and 20% compared to multiplexers, comparators, four-bit parity circuits, and single-bit memory, respectively. Additionally, the number of cells in the proposed circuits has improved by 10%, 7%, 22%, and 19%, respectively.

Keywords: friction stir processing; additive manufacturing; wire; titanium alloy; material transfer; microstructure; microhardness

The paper presents the numerical and experimental results of research aimed at determining the influence of hardness in the range of 50-90 Shore A of layered tools made of elastomeric materials on the possibility of forming Inconel 625 nickel-based alloy sheets. A stamping die made of 90MnCrV8 steel (hardness 60HRC) was designed for forming embosses in drawpieces, ensuring various stress states on the cross-section of the formed element. The principle of operating the stamping die was based on the Guerin method. Finite element-based numerical modelling of the forming process for various configurations of polyurethane inserts was also carried out. The drawpieces obtained by sheet forming were subjected to geometry tests using optical 3D scanning. The results confirm that in the case of forming difficult-to-deform Inconel 625 Ni-based alloy sheets, the hardness of the polyurethane inserts significantly affects the geometric quality of the obtained drawpieces. The assumptions made in numerical simulations were verified in experimental studies. Based on the test results, it was concluded that the selection of polyurethane hardness should be determined by the shape of the formed element. Significant non-uniform sheet metal deformation was also found, which may pose a problem in the process of designing forming tools and technology of plastic forming of Inconel 625 Ni-based alloy sheets.

The BP neural network and PSO algorithm are applied into analyzing the reliability of long-span Concrete Filled Steel Tubular (CFST) arch bridges. Firstly, using BP neural network to fit the structural performance function. And then the PSO method was used to calculate the reliability index. A long-span Concrete Filled Steel Tubular arch bridges reliability indices were calculated whether considering the geometric nonlinearity or not. The calculation and analysis results showed that the BP neural network and PSO algorithm compensated the deficiency of the traditional reliability analysis methods, improved the calculation accuracy, provided a new thought and means for the research on the reliability of long-span bridge structure, and well applied to the reliability analysis of long-span Concrete Filled Steel Tubular arch bridges. Additionally, the reliability analysis of long-span Concrete Filled Steel Tubular arch bridges on service limit state must consider the geometric nonlinearity effect, or the results will tend to be unsafe.

In recent years, vision-centric perception has played a crucial role in autonomous driving tasks, encompassing functions such as 3D detection, map construction, and motion forecasting. However, the deployment of vision-centric approaches in practical scenarios is hindered by substantial latency, often deviating significantly from the outcomes achieved through offline training. This disparity arises from the fact that conventional benchmarks for autonomous driving perception predominantly conduct offline evaluations, thereby largely overlooking the latency concerns prevalent in real-world deployment. While a few benchmarks have been proposed to address this limitation by introducing effective evaluation methods for online perception, they do not adequately consider the intricacies introduced by the complexity of input information streams. To address this gap, we propose the Autonomous-driving Streaming I/O (ASIO) benchmark, aiming to assess the streaming inputs characteristics and online performance of vision-centric perception in autonomous driving. To facilitate this evaluation across diverse streaming inputs, we initially establish a dataset based on the CARLA Leaderboard. In alignment with real-world deployment considerations, we further develop evaluation metrics based on information complexity specifically tailored for streaming inputs and streaming performance. Experimental results indicate significant variations in model performance and ranking under different major camera deployments, underscoring the necessity of thoroughly accounting for the influences of model latency and streaming inputs characteristics during real-world deployment. To enhance streaming performance consistently across distinct streaming inputs features, we introduce a backbone switcher based on the identified streaming inputs characteristics. Experimental validation demonstrates its efficacy in perpetually improving streaming performance across varying streaming inputs features.

This paper proposes a circuit model of Cross-Coupled CMOS AC – DC Charge Pump (XC – CP) operating in subthreshold region. The aim is improving the efficiency to design XC – CPs with variety of specifications, e.g., input and output voltages and AC input frequency. First, it is shown that the output resistance (Ro) of XC – CP is much higher than those of CPs with single diodes (SD – CP) and ultra-low-power diodes (ULPD – CP) as charge transfer switches (CTSs). Second, the reason behind the above feature of XC – CP is identified by a simple model that the gate-to-source voltages of CTS MOSFETs are independent of the output voltage of the CP. Third, high but finite Ro of XC – CP is explainable with a more accurate model that includes the dependence of the saturation current of MOSFETs operating in subthreshold region on the drain-to-source voltage which is a function of the output voltage of CP. The model was in good agreement with measured and simulated results of XC – , SD – and ULPD – CPs fabricated in 250 nm CMOS.

In order to enhance the comfort of cycling, it is imperative to investigate the effects of vibration on non-motorized bicycle riding from the perspectives of road characteristics and traffic features. Through an analysis of the mechanisms by which road and traffic conditions influence cycling vibrations, 13 influencing factors were identified. Subsequently, the non-motorized bicycle lanes in Wuhan city were selected as the subject of empirical research, where three-axis accelerometers attached to the rider's torso were employed to measure and categorize vibration comfort levels. The experimental road segments were found to exhibit comfort levels falling between slightly uncomfortable and relatively uncomfortable. Further analysis of the influencing factors was conducted using the Random Forest algorithm and Logistic Regression. The results revealed that six factors significantly impact the comfort of cycling: the presence of dedicated non-motorized bicycle lanes, the absence of physical separation between non-motorized and motorized traffic, cycling speed, the number of road surface irregularities, the presence of parking areas within the non-motorized bicycle lane, and the type of non-motorized bicycle. This study provides valuable insights into the factors affecting non-motorized bicycle lane usage and contributes to the refined design of urban non-motorized bicycle infrastructure, thereby facilitating better support for sustainable urban transportation.

The growing adoption of distributed energy production technologies, and the potential of energy underutilization when it is produced by non-connected groups, has raised interest in ‘sharing economy’ concepts in the electricity sector. We suggest that mechanisms, such as peer-to-peer (P2P) energy trading, will allow users to exchange their surplus energy for mutual benefits, stimulate the adoption of renewable energy, encourage communities to ‘democratically’ control their own energy supplies for local development, and improved energy efficiency, and create many other benefits This approach is receiving increasing attention across the world, particularly in Germany, the Netherlands and Australia. Nevertheless, the actual development and implementation of these platforms is slow and mostly limited to trial activities. This study investigates the challenges and barriers facing P2P energy trading developments based on previous academic and industry studies. We provide a comprehensive multidimensional barrier analysis through a PESTLE approach to assess the barriers from a variety of perspectives, including the political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) aspects. This approach clarifies the many intersecting problem fields of P2P trading in renewable energy, and the paper identifies a list of such barriers, and discusses the prospects for addressing these issues. We also elaborate on the importance of incentive-based P2P market design.

In this study, the efficiency of coating (alginate and carrageenan) and osmotic dehydration pre-treatment (NaCl and sucrose) on decreasing oil uptake of potato strips was assessed. It was examined how various pre-treatments affected the sample’s texture, morphology, color and density properties as well as their moisture content and effective moisture diffusion coefficient (Deff). Results showed that NaCl osmotic dehydration pre-treatment and sucrose solution decreased oil uptake by 30.76% and 41.75%. This is attributed to moisture removal increasing caused by improving dielectric properties. Osmotic dehydration and coating pre-treatment frequently enhanced product shrinkage, browning index, a* and ∆E. Conversely, density, lightness, breaking force, Deff and b* decreased. In control samples, SEM revealed a severely damaged internal structure in comparison to a tissue homogenous layer produced by coating. For moisture loss and oil uptake, the coefficient of determination (R2) and root mean square error (RMSE) of eight empirical models were compared. Finally, the Dehghannya and Krokida model (moisture loss) and the Movahhed and Naghavi model (oil uptake) were found the best fit to the experimental data. Although coating pre-treatment and osmotic dehydration can be used as an alternate de-oiling approach for potato chips, the mechanism of oil uptake reduction differ.

Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRU) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system’s performance. The lateral distance between the right side of the commercial vehicle and the VRU as well as obstacles between them and the speed difference can take great influence on the reliability of the assistance system. Depending on the concrete time of the system’s warning signal the accident can be avoided or not by the driver when reacting on this signal.

This article presents a proposed system that enables energy transfer from the dc grid to a single-phase onboard grid, operating at an hi frequency. In addition to the energy transfer function, it additionally enables compensation of reactive power in the ac grid when the converter operates at less than rated power. To operate in compensator mode, it is necessary to decouple the current from the grid into active and reactive components and control them independently. To achieve good dynamics, synchronizers that operate in the dq system are used. In the case of a single-phase grid, it requires such a synchronizer to generate virtual quadrature signals. The used systems based on second-order generalized integrator are good, but to improve dynamics in the work a quadrature generator based on trigonometric calculations was proposed. The developed system was implemented in a proportional resonant current control system. Tests performed in steady state and in dynamic states related to typical grid disturbances showed better dynamic properties than the standard integrator-based system.Keywords: power transfer; compensator; P+R controller

Industries are required to utilize treatment technologies to reduce contaminants in wastewater prior discharge and valorize by-products to increase sustainability and competitiveness. Most acid leaching gypsum purification studies have obviated the treatment of the highly acidic wastewater produced. In this work, acidic wastewater from acid leaching purification of post-consumer gypsum was treated to recover a valuable solid product and reusable water. The main aims of this work were to determine the impact of recirculating acidic and treated wastewaters on the efficiency of the acid leaching purification process and to valorize the impurities in the wastewater. Samples were characterized through X-ray fluorescence and X-ray diffraction. SimaPro 9.5 and ReCiPe 2016 midpoint method were used for the life cycle assessment of three wastewater management approaches. The reuse of the acidic wastewater did not improve the chemical purity of gypsum. Soluble impurities were precipitated at pH 10.5 as a magnesium-rich gypsum that could be commercialized as fertilizer or soil ameliorant. The alkaline treated water was reused for 6 acid leaching purification cycles without impacting the efficiency of the purification process. An acid leaching-neutralization-filtration-precipitation approach demonstrated superior overall environmental performance. Barriers and enabling measures for the implementation of an in-house wastewater treatment were identified.

Aircraft heat exchangers play a crucial role in maintaining thermal balance and ensuring that essential components operate efficiently and safely. In this context, it is highly relevant to gain knowledge about the deterioration and the predominant damage mechanisms of these components. In this study, two different non-destructive methods are employed to analyze the damage initiation of a heat exchanger part during fatigue test. On the one hand, Acoustic Emission technique is employed using membrane-free microphones capable of capturing a broad bandwidth. The acoustic events were classified by a machine learning algorithm to determine their source and damage mechanisms. On the other side, Digital Image Correlation (DIC) allowed the measurement of the strain evolution along the test and more precisely, redistributions were considered as damage indicators. As a result, it was identified that AE served as early damage indicator as the cumulative number of events was in good agreement with the severity of the damage. With respect to classification two clear clusters ascribed to different type of events were identified. In the case of DIC, strain redistributions gave clear indications of damage or deterioration but at a later stage compared to AE

This study aimed to investigate the impact of incorporating Refuse Derived Fuel (RDF) into a production brick mixture in order to mitigate difficulties encountered in the production environment. Three distinct tests were conducted to assess the preparation process. The first test served as a reference, utilizing 100% clay material without RDF. The second test involved the inclusion of 10% RDF, obtained from a waste factory, with a grain size less than 20mm. In the third test, the RDF was mixed with silica sand after undergoing hammer milling with a 2mm sieve, followed by a 10% incorporation into the mixture. The primary objective was to comprehend the behavior of these mixtures in two critical stages: extrusion and drying. Extrusion performance was evaluated by examining the shaping process from the mold, vacuum conditions within the extruder, and the cutting of wet sample bricks. Furthermore, the drying procedure was assessed in terms of shrinkage, drying sensitivity, and appearance. Comparisons were made among the three test scenarios to identify the influence of RDF on these key parameters. Preliminary results indicate that the inclusion of RDF impacted the extrusion process. The addition of RDF resulted in variations in the flow characteristics of the mixture, affecting the shape formation from the mold and the vacuum conditions within the extruder. Additionally, the cutting of wet sample bricks exhibited some differences compared to the reference test. Concerning the drying phase, the presence of RDF influenced shrinkage behavior and drying sensitivity. The mixture with RDF showed different shrinkage patterns compared to the reference, and its drying sensitivity was altered. Moreover, the appearance of the bricks after drying exhibited notable variations due to the incorporation of RDF. In conclusion, incorporating RDF in the brick mixture introduces changes in both the extrusion and drying stages. The alteration in flow characteristics and vacuum conditions during extrusion, as well as the variations in shrinkage, drying sensitivity, and appearance during drying, suggest that further investigation is necessary to optimize the incorporation of RDF for improved brick production. These findings contribute to the understanding of the challenges and opportunities associated with the utilization of RDF in brick manufacturing processes.

This study presents the design and implementation of a PLC microprocessor adhering to the IEC-61131-3 standard, executed on a Cyclone-V FPGA using a DE10-NANO development board. Our microprocessor optimizes the central processing unit by streamlining the data path, achieving a remarkable simulated response time of approximately 60 ns, equivalent to three clock cycles at a 50MHz frequency for Boolean operations. To substantiate our approach, we conducted practical experiments utilizing a FESTO conveyor station, employing relays as actuators, and incorporating optical and inductive sensors. The results underscore the feasibility of our proposed approach and serve as practical validation of its efficacy. This work introduces a promising avenue for the development of cost-effective PLCs employing SoC FPGA variants. Additionally, a thorough comparison of execution times with other early reported architectures. Our microprocessor outperforms even well-established PLCs like the S7-312, with substantial reductions in execution times of 94.54% for floating-point operations, 71.42% to 93.33% for word operations, and up to 78.57% for bit operations.

This paper analyzes low-Earth orbit (LEO) satellite downlinks when an airborne interference source moves parallel to the satellite trajectory by considering the relative angle differences be-tween the satellites and the interference sources. To make the experimental interference situations more like actual environments, the LEO trajectories are obtained from two-line element set (TLE) data. Airborne interference sources with various altitudes move parallel to the LEO trajectories, and the jamming to signal (J/S) ratio is calculated based on the relative angle differences between the ground station, the LEO satellite, and the interference source. In order to calculate the relative angle difference psi, the coordinates of the satellite and the interference source are converted from the World Geodetic System 1984 (WGS84) to the ground station-centered east-north-up (ENU) system. By applying the relative angle difference psi, we can obtain the sidelobe gain of the ground station antenna in the direction from which the interference comes. The results of the study confirm that, from a J/S ratio perspective, the distances between the ground station, LEO satellite, and airborne interference source are important, and in particular, the relative angle difference psi between the interference source and the satellite is more critical factor.

The impact of climate change on water resources is a global issue with significant regional implications. The Ghba subbasin has experienced a higher socioeconomic change. In this paper, we assess the potential impact of climate change and human activity on water supply and demand in the Ghba subbasin of Ethiopia. We then evaluate the effectiveness of regional adaptation strategies for water supply management under these conditions. To do this, we use the statistical downscaling model (SDSM) and the water assessment and planning tool (WEAP) to simulate and analyse future water conditions. After running the model through a calibration and validation process, minimum (Tmin) and maximum (Tmax) temperatures, water demand and unmet water demand were projected for 2100 horizon, using different climate change scenarios. The results showed that the model's performance, calibration, and validation were all satisfactory. The analysis showed that, under A2 and B2 emission scenarios of the Intergovernmental Panel on Climate Change (IPCC), the mean precipitation will increase by 31% and 26%, respectively. The projected mean Tmax and Tmin will be warmer than the baseline period, with Tmax increasing by 1.5°C (A2) and 1.3°C (B2), and Tmin by 1.7°C (A2) and 1.5°C (B2) by 2100. The results also illustrate that in all scenarios, water consumption would rise and that this need will not be satisfied, and the pressure on water resources will intensify, resulting in a water shortage. The findings show that future water demand under the influence of climate change is projected to exceed 210 million cubic meters (MCM) by 2100. The findings show that while the evaluations of the recommended adaption techniques are effective, they are not enough to guarantee the Ghba subbasin's water sustainability.

There has been controversy around the mechanical properties of Mg-alloys such as AZ91D and the large variation of these have been seen. The current paper addresses this controversy through specially fabricated samples combined with tensile testing and advanced metallography, including 3D reconstruction of the phases. The results show that despite a more brittle nature of the fracture, the equiaxed microstructure displays a better elongation as compared to a dendritic microstructure. The main conclusion is that this is primarily caused by the nature, or tortuosity, of the Mg17Al12-network in the material.

In this study, an experiment was conducted on hoop pine (Araucaria cunninghamii), which is a common tree species in Taiwan. Holes with diameters of 4, 5, 7, 10, 12, and 14 cm were drilled in dry hoop pine timber with a diameter of 39 cm and a height of 57 cm, and an 800 MHz Ground Penetrating Radar (GPR) antenna were used to determine the minimum image resolution and the size of the holes in the wood. GPR images of the timber were subjected to a filtering process to obtain characteristic reflection curves of the damage to the timber. Curves with clear waveforms were observed in the GPR profiles of eccentric holes with a diameter of 4 cm. Profiles of the damage were obtained and profile images with the trunk radius at the centre were transformed into polar coordinate images. From these polar coordinate images, the location, size and diameter of the eccentric holes in the wood were identified, and the reflection curves were characterised by waveforms and irregular fluctuations. The risk levels of tree trunks were determined on the basis of their t/R (minimum normal wall thickness of the cross-section of the trunk/radius of the trunk) values and hole area ratios. A comparison between the GPR-based results and the actual diameters of the holes revealed that the filtered GPR profiles could be used for the efficient and accurate assessment of damage to hoop pine timber (average error rate ≤ 4%).

Concrete cracks and local damage can affect the bond performance between concrete and steel bars, thereby reducing the durability of reinforced concrete structures. Compared with general concrete crack repair methods, biomineralization repair not only has effective bonding capabilities but is also particularly environmentally friendly. In view of this, this study aimed to apply biomineralization technology to repair damaged fiber-reinforced lightweight aggregate concrete (LWAC). Two groups of LWAC specimens were prepared. The control group used lightweight aggregates (LWAs) without bacterial spores and nutrient sources, and the experimental group used LWAs containing bacterial spores and nutrient sources. These specimens were first subjected to compression tests and pull-out tests, respectively, and thus were damaged. After the damaged specimen healed itself in different ways for 28 days, secondary compression and pull-out tests were conducted. The self-healing method of the control group involved placing the specimens in an incubator. The experimental group was further divided into experimental group I and experimental group II. The self-healing method of experimental group I was the same as that of the control group. The self-healing method of experimental group II involved soaking the specimen in a mixed solution of urea and calcium acetate for two days, and then taking it out and placing it in an incubator for two days, with a cycle of four days. The test results showed that, compared with the control group specimens, the relative bond strength ratio of the experimental group II specimens increased by 17.9%. Moreover, the EDS and XRD analysis results confirmed that the precipitate formed at the crack was calcium carbonate, which improved the compressive strength and bond strength after self-healing. This indicates that the biomineralization maintenance method used in experimental group II is more effective.

In recent years, strain sensors have penetrated various fields. The capability of sensors to convert physical signals into electrical signals is of great importance in healthcare. However, it is still challenging to obtain sensors with high sensitivity, large operating range and low cost. In this paper, a stretchable strain sensor made of a double-layer conductive network, including a biomimetic multilayer graphene-Ecoflex (MLG-Ecoflex) substrate and a multilayer graphene-carbon nanotubes (MLG-CNTs) composite up-layer was developed. The combined action of the two layers led to an excellent performance with an operating range of up to 580% as well as a high sensitivity (gauge factor (GFmax) of 1517.94). In addition, a pressure sensor was further designed using the bionic vein-like structure with a multi-layer stacking of MLG-Ecoflex/MLG-CNTs/MLG-Ecoflex to obtain a relatively high deformation along the direction of thickness. The device presented a high sensing performance (up to sensitivity of 0.433 kPa-1), capable of monitoring small movements of the human body such as vocalisations and gestures. The good performance of the sensors together with a simple fabrication procedure (flip-molding) make it potential for some applications, for example, human health monitoring and other areas of human interaction.

Automatic generation control (AGC) plays a vital role in creating an equilibrium between generated output power and load demand, in order to maintain frequency at desired value. This study focuses on performance analysis of simple-multi attribute rating technique (SMART) assisted proportional-integral-derivative (PID) controller design for AGC of two interconnected power systems. PID controller is designed by minimizing frequency variations, area control errors for area-1 and area-2, and tie-line power deviation. An objective function is framed considering error minimization of aforementioned factors as sub-objectives. The construction of objective function involves using the integral of time-multiplied absolute error (ITAE) for frequency deviations for area-1 and area-2, ITAE for deviation in tie-line power, and ITAEs for area control errors for area-1 and area-2 as sub-objective functions. By assigning appropriate weights to these sub-objective functions, an overall objective function is formed. This study determines these weights in an organized manner using SMART method, rather than randomly/equally assigning them. Overall objective function is minimized using Jaya algorithm. To demonstrate effectiveness of the proposed Jaya-based PID controller, its performance is analysed and compared with controllers tuned using other optimization algorithms, including sine cosine, Luus-Jaakola, teacher-learner based optimization, Nelder-Mead simplex, and elephant herding optimization. Considering six different case studies that consider a range of load variations, responses for fluctuations in frequency and tie-line exchange are plotted. Statistical and non-parametric analysis performed further provide additional insights into the performance of Jaya-based PID controller.

In the evolving landscape of pharmaceutical manufacturing, a comprehensive continuous production process is being crafted for small-scale active pharmaceutical ingredient production. This study focuses on continuous crystallization with separate nucleation and crystal growth units, as well as continuous downstream processing, encompassing filtration, washing, and drying until the formation of free-flowing particles. We introduce a novel continuous nucleator designed based on solubility data and produced via 3D printing, enabling a fast and precise small-scale manufacturing of a nucleator meeting the requirements for nucleation and for the further growth processes. The nucleator was evaluated with regard to its suitability for continuous long-term operation across various coupled crystallizers. As a practical application example it is connected to a slug flow crystallizer to enable high-quality continuous crystallization. Additionally, the full integration of downstream processes using the continuous vacuum screw filter to achieve free-flowing product particles is realized. Even under non-optimized process conditions, with the help of in-situ generation of nuclei free-flowing product particles were successfully obtained. This is particularly useful during drug development when no material is available for seed addition and quickly obtain product for further characterization.

The classification of Trojans in modern analog microcircuits is considered. Using high-speed operational amplifiers (Op-Amps) as an example, the relationship between Op-Amp circuit design and the formation of undesirable states in typical switching circuits is shown. Circuit solutions are proposed to minimize the Trojan states of high-speed Op-Amps. These include limiting the maximum output currents of the input stages of the Op-Amp during the transient process and the use of differentiating transient correction circuits in the Op-Amp.

The H2-ICE project aims at developing, through numerical simulation, a new generation of hybrid powertrains featuring a hydrogen fueled Internal Combustion Engine (ICE) suitable for 12-meter urban buses, in order to provide a reliable and cost-effective solution for the abatement of both CO2 and criteria pollutant emissions. The full exploitation of the potential of such a traction system requires a substantial enhancement of the state of the art since several issues have to be addressed. In particular, the choice of the more suitable fuel injection system, as well as the control of the combustion process, are extremely challenging. Firstly, a high-fidelity 3D-CFD model will be exploited to analyze the in-cylinder H2 fuel injection through supersonic flows. Then, after the optimization of the injection and combustion process, a 1D model of the whole engine system will be built and calibrated allowing the identification of a “sweet spot”, in the ultra-lean combustion region, characterized by extremely low NOx emissions and, at the same time, high combustion efficiencies. Moreover, to further enhance the engine efficiency well above 40 %, different Waste Heat Recovery (WHR) systems will be carefully scrutinized, including both Organic Rankine Cycle (ORC)-based recovery units as well as electric turbo-compounding. A Selective Catalytic Reduction (SCR) aftertreatment system will be developed to further reduce NOx emissions to near-zero levels. Finally, a dedicated torque-based control strategy for the ICE coupled with the Energy Management Systems (EMS) of the hybrid powertrain, both optimized by exploiting Vehicle-To-Everything (V2X) connection, allow targeting an H2 consumption of 0.1 kg/km. Technologies developed in the H2-ICE project will enhance the know-how necessary to design and build engines and after-treatment systems for the efficient exploitation of H2 as a fuel, as well as for their integration into hybrid powertrains.

Since sensor-based perception systems are used in autonomous vehicle applications, validating such systems is imperative to guarantee the robustness of the systems before they are being put to use. In this study, a comprehensive corruption-related simulation-based robustness verification and enhancement process for sensor-based perception systems is proposed. Firstly, we present a methodology and scenario-based corruption generation tools for creating diverse simulated test scenarios that can analogously represent real-world traffic environments, especially considering corruption types related to safety concern. Then, an effective corruption similarity filtering algorithm is proposed to remove corruption types with high similarity and identify the representative corruption types to represent all considered corruption types. As a result, we can generate efficient corruption-related robustness test scenarios with less testing time and good scenario coverage. Subsequently, we perform the vulnerability analysis of object detection models to identify model weaknesses and construct an effective training dataset for model vulnerability enhancement. This enhances the tolerance of object detection models to weather and noise-related corruptions, ultimately improving the robustness of the perception system. We employ case studies to demonstrate the feasibility and effectiveness of the proposed robustness verification and enhancement procedures. Additionally, we explore the impact of different "similarity overlap threshold" parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

Keywords: Wall-like RC columns; Strengthening; FRP; NSM rebars; Steel plates; Testing; FE modeling; Preloading.

Fabry-Pérto (FP) cavity is the essential component of ultra-stable laser (USL) for gravitational wave detection, which couples multi-physic (Optical/Thermal/Mechanics) fields and requires ultra-high precision. To satisfy the requirements of precise and efficient design, a multi-physic coupling optimization method for fixing cubic FP cavity based on data learning is proposed. A multi-physic model for the cubic FP cavity is established and the performance is obtained by finite element analysis. The key performance indices (V, wF, wF) and key design variables (d, l, F) are determined considering the features of the FP cavity. The 49 sets of data by orthogonal experiment are acquired for the establishment and comparison of different data learning models (NN, RSF, KRG). The result turns out that the neural network has the best performance. Based on NSGA-II, the Pareto optimal front is obtained and the optimal combination of design variables is finally determined as {5,32,250}. The performance after optimization has proven to be a great improvement, of which the displacement under the fixing force and vibration test are decreased by more than 60%. The optimization strategy can not only help the design of the FP cavity but also enlighten other optimization fields.

A novel concrete beam-column connection utilizing L-shaped steel bars is proposed to address the growing demand for prefabricated buildings and to ensure good seismic performance in such beam-column structures. After positioning two prefabricated beams with L-shaped tendons into the designated connection points at the top and bottom of the columns, concrete is poured into the post-cast section of the joint and the composite beam area, realizing a connection between the beams and columns. Quasi-static tests were performed on four combined nodes and one cast-in-place node to investigate their failure modes and stress mechanisms. Seismic performance indices, including hysteretic curves, skeleton curves, ductility performance, and energy dissipation capacity, were analyzed. Concurrently, the finite element method (FEM) was employed for parameter analysis. By integrating the test and FEM results, an equation for calculating the shear capacity of the connection was derived. The findings demonstrate that the hysteresis curve of the newly developed prefabricated joints is relatively full, with their overall performance index comparable to that of cast-in-place joints. Additionally, enhancing the post-casting area of concrete, the length of the L-shaped bars, the concrete strength in the composite beam region, the axial compression ratio, or the steel tube dimensions can effectively improve overall performance. The derived equation for the shear-bearing capacity of the connection satisfies design and application requirements.

Temper embrittlement is a major challenge encountered during the heat treatment of high-performance steels for large forgings. This study investigates the microstructural evolution and mechanical properties of Cr-Ni-Mo-V thick-walled steel, designed for new large forgings with a tensile strength of 1500 MPa, under different tempering cooling rates. Optical microscopy (OM), scanning electron microscopy (SEM), and electron backscatter diffraction (EBSD) were employed to analyze the microstructural features. The results demonstrate that the embrittlement occurring during air cooling after tempering is attributed to the concentration of impurities near Fe3C at the grain boundaries. The low-temperature impact toughness at -40°C after water quenching reaches 29 J due to the accelerated cooling rate during tempering, which slows down the diffusion of impurity elements towards the grain boundaries, resulting in reduced concentration and dislocation density, and increased stability of the grain boundaries, thereby enhancing toughness. the bainite content decreases and the interface between martensite and bainite undergoes changes after water quenching during tempering. These alterations influence the crack propagation direction within the two-phase microstructure, further modifying the toughness. These findings contribute to the understanding of temper embrittlement and provide valuable guidance for optimizing heat treatment processes to enhance the performance of high-performance steels in large forgings.

The increase in online orders following the COVID-19 pandemic has heightened consumer ex-pectations regarding prompt deliveries. However, this phenomenon has subsequently triggered a substantial increase in the number of vehicles navigating through urban areas, highly affecting the overall quality of life within cities. Capitalizing on the complexities posed by urban logistics, this paper proposes an innovative digital business model for logistics services. In this context, we introduce “Urban Logistics as a Service (ULaaS), platform”, a concept rooted in the principals of circular economy and harnessed through the application of the Canvas methodology. The con-ceptual framework outlines the ULaaS ecosystem and explores potential user-functionality com-binations. The proposed platform serves as both an on-demand service provider and a control hub for stakeholders such as traffic authorities, policymakers, logistics providers, carriers, and shippers. This platform streamlines citywide logistics to enhance movement efficiency, ease congestion, and reduce emissions. The ULaaS concept was validated through analysis of data collected from interviews in the unique social context of Campinas City, Brazil. The key value-generating mod-ules within the ULaaS ecosystem include parking reservations, shared warehousing, e-commerce deliveries, and comprehensive logistics solutions. However, implementing Urban Logistics as a Service has challenges. These include defining public and private sector roles, creating effective pricing models, and fairly distributing respon-sibilities among service providers. The paper also elucidates various monetization models and required investments, crucial for fostering potential private sector involvement.

Since the energy sector is the main source of greenhouse gases, it has the highest potential for improvement. Improvements can be achieved by generating energy from renewable sources. Combined with thermal energy storage, concentrating solar power plants represent a promising technology for dispatchable renewable energy, ensuring a stable energy supply even in remote areas without contributing to greenhouse gas emissions during operation. However, it is important to note that greenhouse gases and other polluting emissions may occur during the manufacturing process of concentrating solar power plant components. This work analyses the design of the receiver to produce thermal energy for the existing solar dish CSP plant at the Energy Center of the Politecnico di Torino. The objective of this plant is to produce hot water for a small case study. Considering the average solar irradiance for Turin equal to 800 W/m2, the surface heat flux was obtained from the first part of the analysis, which was used to obtain the maximum internal temperature in the receiver equal to 873.7 °C. The maximum temperature obtained was a constraint for the selection of the material for the solar receiver. Of the various possibilities, copper was chosen. In the second part of the work, a Life Cycle Assessment was carried out to compare the emissions generated during the production of the main components of the CSP plant with the emissions generated by the methane-fuelled water heater that produces the same amount of water as the CSP plant. It can be concluded that the manufacture of the main components of the CSP plant results in lower greenhouse gas emissions than the operational phase of a conventional natural gas-fired water heater (1.6 kg CO2/day).

To ensure sustainability of the future power grid, the rate of expansion of distributed renewable energy resources (DRER) has introduced operational challenges. These includes managing transmission constraints with DRER power injection, dispatching DRER efficiently, managing system frequency and ensuring sufficient reactive power for voltage support. Coupled with the intensification of wildfires, power infrastructure across the United States faces challenges to minimize the impact to maintain system reliability and resiliency. This research embarks on a comprehensive evaluation, beginning with an in-depth historical analysis to delineate regions most susceptible to wildfires. Utilizing a multidimensional approach, the study assesses wildfire-induced risks to power grids, by integrating historical wildfire occurrences, real-time wildfire proximities, Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation metrics, and system parameters, by employing Principal Component Analysis (PCA) based optimal weights, leading to the formulation of a novel risk factor model. This risk factor has the potential to be the key to ensuring the resilience of a renewable-rich smart grid when faced with a severe weather event. Our model’s applicability was further verified through an empirical assessment, selecting representative networks from diverse regions, providing insights into the geographical variability of risk factors. Ultimately, this study offers stakeholders and policymakers a comprehensive tool-set, empowering decisions regarding infrastructure investments, grid reinforcements, and strategic power rerouting to ensure consistent energy delivery using risk assessment against wildfire.

Bacteriotherapy is emerging as a strategic and effective approach to treat infections by providing putatively harmless bacteria (i.e., probiotics) as antagonists to pathogens. A proper delivery of probiotics or their metabolites (i.e., post-biotics) can avail itself of biomaterial encapsulation by innovative manufacturing technologies. This review paper aims at providing the most recent biomaterial-assisted strategies proposed to treat infections or disbiosis using bacteriotherapy. We revised the encapsulation processes across multiscale biomaterial approaches, which could be ideal to target different tissues and suit diverse therapeutic opportunities. Hydrogels, and in particular polysaccharides, are the focus of this review, as they have been reported to better sustain vitality of the live cells incorporated. Specifically, the approaches for fabricating hydrogel-based devices with increasing dimensionality (D), namely, 0D (i.e., particles), 1D (i.e., fibers), 2D (i.e., fiber meshes), and 3D (i.e., scaffolds) endowed with probiotics, were detailed by describing their advantages and challenges, along with a future overlook in the field. Electrospinning, electrospray and 3D bioprinting were investigated as new biofabrication methods for probiotic encapsulation within multidimensional matrices. Finally, examples of biomaterial-based systems for cell and post-biotic release were reported.

The Internet of Things (IoT) is experiencing exponential growth, revolutionizing various industries with its ability to connect devices and enable advanced applications. In the realm of energy production, IoT offers significant potential for optimizing processes and improving efficiency. This study presents a comprehensive monitoring and energy management strategy for the University of Cuenca's microgrid photovoltaic (PV) system, utilizing IoT and ThingSpeak as key technologies. The proposed strategy leverages intelligent environments and utilizes ThingSpeak as a robust platform for data presentation and analysis. By integrating IoT devices and sensors, real-time monitoring of PV system parameters such as solar radiation and temperature is achieved. The collected data is then analyzed using advanced algorithms and visualized through ThingSpeak, enabling effective energy management and decision-making. The developed monitoring system was rigorously tested in a laboratory microgrid setting, where the PV system is interconnected with other generation and storage systems, as well as the electrical grid. Through the seamless integration of these components, the monitoring system provides enhanced visibility and control over the microgrid's energy production. The results demonstrate the successful implementation of the monitoring system, showcasing its ability to improve the supervision, automation, and analysis of daily energy production. By leveraging IoT technologies and ThingSpeak, stakeholders can access real-time data, analyze performance trends, and optimize energy resources.

Multiple entities use unmanned underwater drones for various functions, but how can they navigate underwater at such depths without a pilot? It is essential to understand how different unmanned underwater drones, such as remotely operated and autonomous underwater vehicles, function with underwater sensing technology. As each unmanned underwater drone has its function, the combination or chief use of the sensors is equally essential to recognize. Different underwater drone designs can be more cost-effective when a human is not on board as it can lead to a smaller, streamlined design in which oxygen and pressure control need not be a factor. Without considering oxygen limitations, these underwater drones can remain submerged for extended periods. These underwater vehicles operate autonomously or in conditions that may leave the individuals controlling the trajectory relying upon these sensors. It is also necessary to review how these sensors work, how they are implemented, and how they can help the public. This review was performed to address these research questions and provide an overview of sensors employed by unmanned underwater vehicles.