Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) and grasshopper optimization algorithm (GOA), are also considered as benchmark models to be compared with ALO. Considering seven slump effective factors, namely cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA), the mentioned algorithms are synthesized with a neural network to determine the best-fitted neural parameters. The most appropriate complexity of each ensemble is also found by a population-based sensitivity analysis. The findings revealed that the proposed ALO-NN model acquires a good approximation of concrete slump, regarding the calculated root mean square error (RMSE = 3.7788) and mean absolute error (MAE = 3.0286). It also outperformed both BBO-NN (RMSE = 4.1859 and MAE = 3.3465) and GOA-NN (RMSE = 4.9553 and MAE = 3.8576) ensembles. Full article

We have proposed a flexible sensor that can sense shear and normal forces, and can be fabricated through a simple process using only one layer of polyvinylidene fluoride (PVDF) film. For the measurement of shear and normal forces, one layer of PVDF film was sealed in a three-dimensionally structured polydimethylsiloxane (PDMS). In the structure, the sensor produced voltage signals corresponding to the shear and normal forces. Using this property, we aimed to demonstrate how to sense the magnitude and direction of the force applied to the sensor from its output voltages. Furthermore, the proposed sensor with a 2 × 2 array was able to measure the applied force in real time. Full article

The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R²), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R², MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R², MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings. Full article

There is a growing market for electric vehicles (EVs) in recent years. Due to this, many studies on electric vehicles are in progress and research on charging operations for EVs are especially active. Recent research trends on electric vehicle routes rely on the stochastic modelling of various factors such as convenience of a user’s point of view, charging station (CS), location, destination, and so on. In this paper, a charging control scheme for electric vehicles is proposed from the point of view of the system operators rather than the user. From a user’s point of view, the EV route can be set up directly, but it is difficult for the system operator to directly participate in the route of the EV. In this paper, a method is proposed to indirectly change the route of the EV by changing the charging cost through real-time dynamic pricing, in order to prevent risks in the system operation due to voltage fluctuations in the system. With dynamic pricing, the voltage of the system is kept within a stable range, and the EV user sets the route with an economic benefit. The proposed scheme is verified through Dijkstra’s algorithm and a control strategy via a simulation model using MATLAB. Full article

This paper aims to manufacture recycled concrete hollow block (RCHB) which can be used for the masonry structure with seismic requirements. Five RCHB masonry walls were tested under cyclic loading to evaluate the effect of the axial compression stress, aspect ratio, and the materials of structural columns on the seismic performance. Based on the test results, the failure pattern, hysteresis curves, lateral drift, ductility, stiffness degradation, and the energy dissipation of the specimens were analyzed in detail. The results showed that with the increase of aspect ratios, the ductility of RCHB masonry walls increased, but the horizontal bearing capacity and energy dissipation of RCHB masonry walls decreased. With the increase of compressive stress, the bearing capacity and energy dissipation performance of RCHB masonry walls were improved, and the stiffness degraded slowly. The results also demonstrated that the RCHB masonry walls with structural columns, depending on whether the structural columns were prepared by ordinary concrete or recycled concrete, could increase the bearing capacity, ductility, and energy dissipation of specimens. The research confirmed that RCHB masonry walls could meet the seismic requirements through thoughtful design. Therefore, this study provided a new cleaner production for the utilization of construction waste resources. Full article

Two medium-low temperature coal tars (MLCTs) derived from the pyrolysis of low-grade bituminous coal were separated into 11 narrow fractions by true boiling distillation. The primary property and chemical composition analysis of MLCTs and their distillate narrow fractions were investigated at the macroscopic and molecular level by gas chromatography-mass spectrometer (GC-MS) and proton nuclear magnetic resonance (¹H NMR). The two MLCTs show obvious characteristics of medium-low temperature coal tar, including a high H/C, high-oxygen and nitrogen, low-sulfur, low-density, and low viscosity. As the boiling point increases, the molecular weight of each distillate fraction increases continuously. Meanwhile, the yield of each distillate fraction increases gradually, except for the 270–300 °C distillate fractions. The oxygen content in the 170–230 °C distillate fractions is much higher than that of the other distillate fractions. The dominant groups of compounds in the MLCTs were saturates, aromatics, and resins, and the resin content was above 24.5 wt%. The molecular composition of the below 170 °C fractions mainly consists of benzene, toluene, and xylene, and the main phenolic compounds in the 170–230 °C distillate fraction are low-rank phenols, such as phenol, cresol, and xylenol. Although the macroscopic properties of the MLCT-Z and MLCT-S were quite similar, the molecular composition, the group composition and hydrogen distribution in each MLCT and its narrow distillate fractions are still different. The present work has contributed to our present understanding of the composition of MLCTs and to the guiding of the efficient processing of MLCTs. Full article

Phase change materials (PCMs) are classified according to their phase change process, temperature, and composition. The utilization of PCMs lies mainly in the field of solar energy and building applications as well as in industrial processes. The main advantage of such materials is the use of latent heat, which allows the storage of a large amount of thermal energy with small temperature variation, improving the energy efficiency of the system. The study of PCMs using computational fluid dynamics (CFD) is widespread and has been documented in several papers, following the tendency that CFD nowadays tends to become increasingly widespread. Numerical studies of solidification and melting processes use a combination of formulations to describe the physical phenomena related to such processes, these being mainly the latent heat and the velocity transition between the liquid and the solid phases. The methods used to describe the latent heat are divided into three main groups: source term methods (E-STM), enthalpy methods (E-EM), and temperature-transforming models (E-TTM). The description of the velocity transition is, in turn, divided into three main groups: switch-off methods (SOM), source term methods (STM), and variable viscosity methods (VVM). Since a full numerical model uses a combination of at least one of the methods for each phenomenon, several combinations are possible. The main objective of the present paper was to review the numerical approaches used to describe solidification and melting processes in fixed grid models. In the first part of the present review, we focus on the PCM classification and applications, as well as analyze the main features of solidification and melting processes in different container shapes and boundary conditions. Regarding numerical models adopted in phase-change processes, the review is focused on the fixed grid methods used to describe both latent heat and velocity transition between the phases. Additionally, we discuss the most common simplifications and boundary conditions used when studying solidification and melting processes, as well as the impact of such simplifications on computational cost. Afterwards, we compare the combinations of formulations used in numerical studies of solidification and melting processes, concluding that “enthalpy–porosity” is the most widespread numerical model used in PCM studies. Moreover, several combinations of formulations are barely explored. Regarding the simulation performance, we also show a new basic method that can be employed to evaluate the computing performance in transient numerical simulations. Full article

Global warming is now considered to be one of the greatest challenges worldwide. International environmental agreements have been developed in response to climate change since the 1970s. The construction industry is considered one of the main contributors to global warming. In order to mitigate global warming effects, the construction industry has been exploring various approaches to mitigate the impacts of carbon dioxide emissions over the entire life cycle of buildings. The application of different structural systems is considered a means of reducing the carbon dioxide emissions from building construction. The purpose of this research is to assess the environmental performance of three different slab systems during the construction phase. In this study, a process-based life cycle assessment (LCA) method was applied in order to evaluate the level of performance of the three slab systems. The results showed total CO₂ emissions of 3,275,712, 3,157,260, and 2,943,695 kg CO₂ eq. for the ordinary reinforced concrete slab, flat plate slab, and voided slab systems, respectively. The manufacturing of building materials is by far the main contributor to CO₂ emissions, which indicate 3,230,945, 3,117,203, and 2,905,564 kg CO₂ eq., respectively. Comparing the building materials in the three slab systems, reinforcing bars and forms were significant building materials to reduce the CO₂ emissions in the flat plate slab and voided slab systems. In this study, reinforcing bars were the main contributor to lowering the carbon dioxide emissions in the flat plate slab and voided slab systems. The results of this study show that amongst all the three different slab systems, the voided slab system shows the greatest reduction potential. Moreover, replacing the ordinary reinforced concrete slab system by alternative methods would make it possible to reduce the carbon dioxide emissions in building projects. Full article

In this paper, a machine learning-based tunable optical-digital signal processor is demonstrated for a short-reach optical communication system. The effect of fiber chromatic dispersion after square-law detection is mitigated using a hybrid structure, which shares the complexity between the optical and the digital domain. The optical part mitigates the chromatic dispersion by slicing the signal into small sub-bands and delaying them accordingly, before regrouping the signal again. The optimal delay is calculated in each scenario to minimize the bit error rate. The digital part is a nonlinear equalizer based on a neural network. The results are analyzed in terms of signal-to-noise penalty at the KP4 forward error correction threshold. The penalty is calculated with respect to a back-to-back transmission without equalization. Considering 32 GBd transmission and 0 dB penalty, the proposed hybrid solution shows chromatic dispersion mitigation up to 200 ps/nm (12 km of equivalent standard single-mode fiber length) for stage 1 of the hybrid module and roughly double for the second stage. A simplified version of the optical module is demonstrated with an approximated 1.5 dB penalty compared to the complete two-stage hybrid module. Chromatic dispersion tolerance for a fixed optical structure and a simpler configuration of the nonlinear equalizer is also investigated. Full article

To deal with increasingly competitive challenges, today’s companies consider supplier performance as a crucial factor to their competitive advantage. Supplier development is one of the recent approaches to supplier performance enhancement and consistently requires relationship-specific investments. It is important to invest money, experts and/or machines in a supplier to minimize the risk of an inefficient supply chain while maximizing the level of profitability. This paper provides the number of optimization models to confront this issue utilizing Model Predictive Control. We consider a centralized and distributed setting with two manufacturers and one supplier, which enables us to simulate more realistic scenarios. We implement cooperative and non-cooperative scenarios to assess their impact on the manufacturers’ revenue. Results reveal that the cooperative setting between manufacturers pays off better than non-cooperative and collaborative settings in long-term investments. However, for short-term investments, the non-cooperative setting performs better than the others. We can conclude that, in short-term supplier development investments, an added value is generated since both the manufacturers and the supplier gain flexibility, therefore, investing separately can end up with higher profit for both manufacturers. Full article

Combining graphene with other novel layered materials is a possible way for engineering the band structure of charge carriers. Strong spin-orbit coupling in BiTeX compounds and the recent fabrication of a single layer of BiTeI points towards a feasible experimental realization of a Kane–Mele phase in graphene-based heterostructures. Here, we theoretically demonstrate the tunability of the topological phase of hybrid systems built from graphene and BiTeX (X = I, Br, Cl) layers by uniaxial in-plane tensile and out-of plane compressive strain. We show that structural stress inherently present in fabricated samples could induce a topological phase transition, thus turning the sample in a novel experimental realization of a time reversal invariant topological insulator. Full article

Vibrotactile interfaces are an inexpensive and non-invasive way to provide performance feedback to body-machine interface users. Interfaces for the upper extremity have utilized a multi-channel approach using an array of vibration motors placed on the upper extremity. However, for successful perception of multi-channel vibrotactile feedback on the arm, we need to account for vibration propagation across the skin. If two stimuli are delivered within a small distance, mechanical propagation of vibration can lead to inaccurate perception of the distinct vibrotactile stimuli. This study sought to characterize vibration propagation across the hairy skin of the forearm. We characterized vibration propagation by measuring accelerations at various distances from a source vibration of variable intensities (100–240 Hz). Our results showed that acceleration from the source vibration was present at a distance of 4 cm at intensities >150 Hz. At distances greater than 8 cm from the source, accelerations were reduced to values substantially below vibrotactile discrimination thresholds for all vibration intensities. We conclude that in future applications of vibrotactile interfaces, stimulation sites should be separated by a distance of at least 8 cm to avoid potential interference in vibration perception caused by propagating vibrations. Full article

The advances in information and communication technology are consistently beneficial for the healthcare sector. A trend in the healthcare sector is the progressive shift in how data are acquired and the storage of such data in different facilities, such as in the cloud, due to the efficiency and effectiveness offered. Digital images related to healthcare are sensitive in nature and require maximum security and privacy. A malicious entity can tamper with such stored digital images to mislead healthcare personnel and the consequences of wrong diagnosis are harmful for both parties. A new type of cyber attack, a false image injection attack (FIIA) is introduced in this paper. Existing image tampering detection measures are unable to guarantee tamper-proof medical data in real time. Inspired by the effectiveness of emerging blockchain technology, a security framework, image chain (iChain) is proposed in this paper to ensure the security and privacy of the sensitive healthcare images. The practical challenges associated with the proposed framework and further research that is required are also highlighted. Full article

Cloud computing services that provide computing resources to users through the Internet also provide computing resources in a virtual machine form based on virtualization techniques. In general, supercomputing and grid computing have mainly been used to process large-scale jobs occurring in scientific, technical, and engineering application domains. However, services that process large-scale jobs in parallel using idle virtual machines are not provided in cloud computing at present. Generally, users do not use virtual machines anymore, or they do not use them for a long period of time, because existing cloud computing assigns all of the use rights of virtual machines to users, resulting in the low use of computing resources. This study proposes a scheme to process large-scale jobs in parallel, using idle virtual machines and increasing the resource utilization of idle virtual machines. Idle virtual machines are basically identified through specific determination criteria out of virtual machines created using OpenStack, and then they are used in computing services. This is called the idle virtual machine–resource utilization (IVM–ReU), which is proposed in this study. Full article

Bioenergy crops are a promising option for integrating fossil fuels and achieving European environmental targets. Among these, Short Rotation Forestry (SRF) crops and biogas plants have been considered an opportunity for sustainable agricultural development due to their environmental benefits. In this case study, an N balance was performed by comparing an SRF Platanus hispanica plantation with a permanent meadow, both located in an area with highly permeable soils, using two different amounts of organic fertilization (digestate) for each system (0, 170 and 340 kg-N ha⁻¹ y⁻¹). The results obtained indicate that, in the presence of highly permeable soils, the SRF is not effective in retaining N during the initial stage of growth, despite the use of a suitable application rate of digestate. Higher N leaching rates occurred in SRF crops compared to permanent meadows. Moreover, the N potential removal rate did not vary proportionally with the applied dose of digestate. To avoid N leaching excess, the annual applied N should be not only within 170 kg-N ha⁻¹ y⁻¹ (Nitrate Directive legal limits for nitrate vulnerable zone) but should also follow precise and accurate distribution practices, like: controlled grassing between the tree rows and soil’s minimum tillage immediately after the digestate spreading. Full article