Appl. Sci., Volume 11, Issue 22 (November-2 2021) – 574 articles

One of the causes of mortality in bees is varroosis, a bee disease caused by the Varroa destructor mite. Varroa destructor mites may occur suddenly in beehives, spread across them, and impair bee colonies, which finally die. Edge IoT (Internet of Things) devices capable of processing video streams in real-time, such as the one we propose, may allow for the monitoring of beehives for the presence of Varroa destructor. Additionally, centralization of monitoring in the Cloud data center enables the prevention of the spread of this disease and reduces bee mortality through monitoring entire apiaries. Although there are various IoT or non-IoT systems for bee-related issues, such comprehensive and technically advanced solutions for beekeeping and Varroa detection barely exist or perform mite detection after sending the data to the data center. The latter, in turn, increases communication and storage needs, which we try to limit in our approach. In the paper, we show an innovative Edge-based IoT solution for Varroa destructor detection. The solution relies on Tensor Processing Unit (TPU) acceleration for machine learning-based models pre-trained in the hybrid Cloud environment for bee identification and Varroa destructor infection detection. Our experiments were performed in order to investigate the effectiveness and the time performance of both steps, and the study of the impact of the image resolution on the quality of detection and classification processes prove that we can effectively detect the presence of varroosis in beehives in real-time with the use of Edge artificial intelligence invoked for the analysis of video streams. Full article

Most regulations only allow the use of the coarse fraction of recycled concrete aggregate (RCA) for the manufacture of new concrete, although the heterogeneity of RCA makes it difficult to predict the compressive strength of concrete, which is an obstacle to the incorporation of RCA in concrete production. The compressive strength of recycled aggregate concrete is closely related to the dosage of its constituents. This article proposes a novel artificial neural network (ANN) model to predict the 28-day compressive strength of recycled aggregate concrete. The ANN used in this work has 11 neurons in the input layer: the mass of cement, fly ash, water, superplasticizer, fine natural aggregate, coarse natural or recycled aggregate, and their properties, such as: sand fineness modulus of sand, water absorption capacity, saturated surface dry density of the coarse aggregate mix and the maximum particle size. Two training methods were used for the ANN combining 15 and 20 hidden layers: Levenberg–Marquardt (LM) and Bayesian Regularization (BR). A database with 177 mixes selected from 15 studies incorporating RCA were selected, with the aim of having an underlying set of data heterogeneous enough to demonstrate the efficiency of the proposed approach, even when data are heterogeneous and noisy, which is the main finding of this work. Full article

The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, with the commercial and scientific fields being the most notorious ones. In particle physics, ML has been proven a useful resource to make the most of projects such as the Large Hadron Collider (LHC). The main advantage provided by ML is a reduction in the time and effort required for the measurements carried out by experiments, and improvements in the performance. With this work we aim to encourage scientists working with particle colliders to use ML and to try the different alternatives that are available, focusing on the separation of signal and background. We assess some of the most-used libraries in the field, such as Toolkit for Multivariate Data Analysis with ROOT, and also newer and more sophisticated options such as PyTorch and Keras. We also assess the suitability of some of the most common algorithms for signal-background discrimination, such as Boosted Decision Trees, and propose the use of others, namely Neural Networks. We compare the overall performance of different algorithms and libraries in simulated LHC data and produce some guidelines to help analysts deal with different situations. Examples include the use of low or high-level features from particle detectors or the amount of statistics that are available for training the algorithms. Our main conclusion is that the algorithms and libraries used more frequently at LHC collaborations might not always be those that provide the best results for the classification of signal candidates, and fully connected Neural Networks trained with Keras can improve the performance scores in most of the cases we formulate. Full article

The interconnection of nanotechnology and medicine could lead to improved materials, offering a better quality of life and new opportunities for biomedical applications, moving from research to clinical applications. Magnetite nanoparticles are interesting magnetic nanomaterials because of the property-depending methods chosen for their synthesis. Magnetite nanoparticles can be coated with various materials, resulting in “core/shell” magnetic structures with tunable properties. To synthesize promising materials with promising implications for biomedical applications, the researchers functionalized magnetite nanoparticles with silica and, thanks to the presence of silanol groups, the functionality, biocompatibility, and hydrophilicity were improved. This review highlights the most important synthesis methods for silica-coated with magnetite nanoparticles. From the presented methods, the most used was the Stöber method; there are also other syntheses presented in the review, such as co-precipitation, sol-gel, thermal decomposition, and the hydrothermal method. The second part of the review presents the main applications of magnetite-silica core/shell nanostructures. Magnetite-silica core/shell nanostructures have promising biomedical applications in magnetic resonance imaging (MRI) as a contrast agent, hyperthermia, drug delivery systems, and selective cancer therapy but also in developing magnetic micro devices. Full article

With the density of wireless networks increasing rapidly, one of the major goals in next-generation wireless LANs (Local Area Networks) is to support a very dense network with a large number of closely deployed APs (Access Points) and crowded users. However, the CSMA (Carrier-Sense Multiple Access)-based medium access control of current wireless network systems suffers from significantly degraded performance when the network becomes dense. Recent WLAN (Wireless Local Area Networks) standards include measures for increasing spatial reuse such as BSS (Basic Service Set) coloring, but the schemes based on BSS coloring such as OBSS/PD (Overlapping BSS/Preamble Detection) have limitations in improving spatial reuse. In this paper, we propose a spatial reuse method for uplink which can utilize BSS color and proximity information to improve the efficiency of carrier sensing and thus spatial reuse. Specifically, through the BSS color and the proximity information, a node receiving a preamble can figure out how far the receiver of the ongoing traffic is located. This information is used to determine whether the node should aggressively start transmitting or defer its transmission to protect the ongoing transmission. Simulation results show that the proposed method outperforms existing methods in terms of throughput and fairness. Full article

On-device artificial intelligence has attracted attention globally, and attempts to combine the internet of things and TinyML (machine learning) applications are increasing. Although most edge devices have limited resources, time and energy costs are important when running TinyML applications. In this paper, we propose a structure in which the part that preprocesses externally input data in the TinyML application is distributed to the hardware. These processes are performed using software in the microcontroller unit of an edge device. Furthermore, resistor–transistor logic, which perform not only windowing using the Hann function, but also acquire audio raw data, is added to the inter-integrated circuit sound module that collects audio data in the voice-recognition application. As a result of the experiment, the windowing function was excluded from the TinyML application of the embedded board. When the length of the hardware-implemented Hann window is 80 and the quantization degree is $2^{-5}$, the exclusion causes a decrease in the execution time of the front-end function and energy consumption by 8.06% and 3.27%, respectively. Full article

Local modal vibration could adversely affect the dynamical environment, which should be considered in the structural design. For the mode switching phenomena, the traditional structural optimization method for problems with specific order of modal frequency constraints could not be directly applied to solve problems with local frequency constraints. In the present work, a novel approximation technique without mode tracking is proposed. According to the structural character, three reasonable assumptions, unchanged mass matrix, accordant modal shape, and reversible stiffness matrix, have been used to transform the optimization problem with local frequency constraints into a problem with nodal displacement constraints in the local area. The static load case is created with the modal shape equilibrium forces, then the displacement constrained optimization is relatively easily solved to obtain the optimal design, which satisfies the local frequency constraints as well. A numerical example is used to verify the feasibility of the proposed approximation method. Then, the method is further applied in a satellite structure optimization problem. Full article

This study experimentally explores the thermofluidic performance of a cryogenic micro-pin fin cooler with two-phase liquid nitrogen flows. The liquid nitrogen cooling system is introduced to investigate the performance of the micro-pin cooler in a cryogenic condition. The result reveals that the nominal value of the base heat transfer coefficients of the micro-pin fin cooler with liquid nitrogen flows, 240 kW/m²-K at a mass flow rate of 2.23 g/s, is an order of magnitude greater than that with FC-72 flows. The result also demonstrates that the base heat transfer coefficient of the micro-pin fin cooler is nearly three times greater than that of the micro-gap cooler, not containing any fins. This study shows the feasibility of the cryogenic micro-pin fin cooler for thermally controlling very high heat density devices such as high-power laser diode bars, of which the heat density can reach 2000 kW/m². Full article

The main purpose of the article is to describe the methodology used for multi-criteria optimization of the geometric features of the orthopedic implant used for the reconstruction of the anterior cruciate ligament located in the knee joint. The methodology includes: 1. Method of development of the bones of the knee joint model; 2. Method of multi-criteria optimization of the geometric features of the orthopedic implant using an artificial immune system, the objective function and the Pareto front; 3. Expert evaluation method based on forms. The work confirmed that the assumed thesis, a multi-criteria optimization using an artificial immune system, which is a specially defined objective function, and the Pareto method, which allows to determine the geometrical features of the implant, will lead to fulfill optimal blood perfusion and sufficient strength properties of the implant simultaneously. We conclude that the described methodology allowed to achieve the optimal geometrical features of the orthopedic implant used for reconstruction of the anterior cruciate ligament located in the knee joint. Full article

Purpose: Computer-aided methods for mandibular reconstruction have improved both functional and morphological results in patients who underwent segmental mandibular resection. The purpose of this study is to evaluate the overlaying of virtual planning in terms of measures of the Computer Assisted Design/Computer Assisted Manufacturing CAD/CAM plate for mandibular reconstruction in patients who are ineligible for the insertion of reconstructing the titanium plate supported by fibular free flap, due to their poor health status, or in the presence of specific contraindications to autologous bone flap harvest. Materials and methods: The retrospective study performed analyzed the results of nine patients. The patients were treated at the Maxillofacial Surgery Unit of Policlinico S. Orsola of Bologna, Italy, and Policlinico San Marco, Catania, Italy, from April 2016 to June 2021. Superimposition between planning and post operative Computed Tomography CT scan was performed to assess the accuracy. Results: All reconstructive procedures were carried out successfully. No microsurgery-related complications occurred. In two cases, we had plate misplacement, and in one case, plate exposure that led to plate removal. The average accuracy of the series assessed after CT superimposition, as previously described, was 0.95 mm. Conclusions: Considering that microvascular bone transfer is a high-risk procedure in BRONJ patients, we can conclude that the positioning of a customized bridging mandibular prosthesis (CBMP), whether or not it is associated with a microvascular soft tissue transfer, is a safe technique in terms of surgical outcome and feasibility. Full article

The comparison of output powers between self-Raman Nd:YVO₄ lasers and Nd:YVO₄/KGW Raman lasers operating at lime and orange wavelengths is presented. We exploit the LBO crystal with cutting angle θ = 90° and φ = 8° for the lime wavelengths, and then we change the angle to θ = 90° and φ = 3.9° for the orange wavelengths. In self-Raman Nd:YVO₄ lasers, experimental results reveal that thermal loading can impact on the output performances, especially at the high pump power. However, by using a KGW crystal as Raman medium can remarkably share the thermal loading from gain medium. Besides, the designed coating for high reflectively at the Stokes field on the surface of KGW also improved the beam quality and reduced the lasing threshold. For self-Raman Nd:YVO₄ lasers, we have achieved the output powers of 6.54 W and 5.12 W at 559 nm and 588 nm, respectively. For Nd:YVO₄/KGW Raman lasers, the output powers at 559 nm and 589 nm have been increased to 9.1 W and 7.54 W, respectively. All lasers operate at a quasi-CW regime with the repetition rate 50 Hz and the duty cycle 50%. Full article

In order to improve the geo-location accuracy of the airborne optoelectronic platform and eliminate the influence of assembly systematic error on the accuracy, a systematic geo-location error correction method is proposed. First, based on the kinematic characteristics of the airborne optoelectronic platform, the geo-location model was established. Then, the error items that affect the geo-location accuracy were analyzed. The installation error between the platform and the POS was considered, and the installation error of platform’s pitch and azimuth was introduced. After ignoring higher-order infinitesimals, the least square form of systematic error is obtained. Therefore, the systematic error can be obtained through a series of measurements. Both Monte Carlo simulation analysis and in-flight experiment results show that this method can effectively obtain the systematic error. Through correction, the root-mean-square value of the geo-location error have reduced from 45.65 m to 12.62 m, and the mean error from 16.60 m to 1.24 m. This method can be widely used in systematic error correction of relevant photoelectric equipment. Full article

The aim of this paper is to present the optimal design process and an optimized model for a discontinuous armature arrangement permanent magnet linear synchronous motor (PMLSM). The stator tooth shapes are optimized to reduce detent force. When the shape of the stator is changed to reduce the detent force, the saturation magnetic flux density and the back electromotive force characteristics change. Multi-objective optimization is used to search for the local lowest point that can improve the detent force, saturation magnetic flux density, and back EMF characteristics. To reduce the detent force generated at the outlet edge, a trapezoidal auxiliary tooth was installed and the performance was analyzed. The experiment’s response surface methodology is used as an optimization method and all the experimental samples are obtained from finite-element analysis. The validity of this method is verified by comparing the optimized FEA model to the initial FEA model. Full article

Omnidirectional mobile wall-climbing robots have better motion performance than traditional wall-climbing robots. However, there are still challenges in designing and controlling omnidirectional mobile wall-climbing robots, which can attach to non-ferromagnetic surfaces. In this paper, we design a novel wall-climbing robot, establish the robot’s dynamics model, and propose a nonlinear model predictive control (NMPC)-based trajectory tracking control algorithm. Compared against state-of-the-art, the contribution is threefold: First, the combination of three-wheeled omnidirectional locomotion and non-contact negative pressure air chamber adhesion achieves omnidirectional locomotion on non-ferromagnetic vertical surfaces. Second, the critical slip state has been employed as an acceleration constraint condition, which could improve the maximum linear acceleration and the angular acceleration by $164.71\%$ and $22.07\%$ on average, respectively. Last, an NMPC-based trajectory tracking control algorithm is proposed. According to the simulation experiment results, the tracking accuracy is higher than the traditional PID controller. Full article

Radon gas is a harmful pollutant with a well-documented adverse influence on public health. In poorly ventilated environments, that are often prone to significant radon levels, studies indicate a known relationship between human radon exposure and lung cancer. Recent technology advances, notably on the Internet of Things (IoT) ecosystem, allow the integration of sensors, computing, and communication capabilities into low-cost and small-scale devices that can be used for implementing specific cyber-physical systems (CPS) for online and real-time radon management. These technologies are crucial for improving the overall building indoor air quality (IAQ), contributing toward the so-called cognitive buildings, where human-based control is tending to decline, and building management systems (BMS) are focused on balancing critical factors, such as energy efficiency, human radon exposure management, and user experience, to achieve a more transparent and harmonious integration between technology and the built environment. This work surveys recent IoT technologies for indoor radon exposure management (monitoring, assessment and mitigation), and discusses its main challenges and opportunities, by focusing on methods, techniques, and technologies to answer the following questions: (i) What technologies have been recently in use for radon exposure management; (ii) how they operate; (iii) what type of radon detection mechanisms do they use; and (iv) what type of system architectures, components, and communication technologies have been used to assist the referred technologies. This contribution is relevant to pave the way for designing more intelligent and sustainable systems that rely on IoT and Information and Communications Technology (ICT), to achieve an optimal balance between these two critical factors: human radon exposure management and building energy efficiency. Full article

Introduction The use of scaffolds in tissue engineering is becoming increasingly important as solutions need to be found to preserve human tissues such as bone or cartilage. Various factors, including cells, biomaterials, cell and tissue culture conditions, play a crucial role in tissue engineering. The in vivo environment of the cells exerts complex stimuli on the cells, thereby directly influencing cell behavior, including proliferation and differentiation. Therefore, to create suitable replacement or regeneration procedures for human tissues, the conditions of the cells’ natural environment should be well mimicked. Therefore, current research is trying to develop 3-dimensional scaffolds (scaffolds) that can elicit appropriate cellular responses and thus help the body regenerate or replace tissues. In this work, scaffolds were printed from the biomaterial polycaprolactone (PCL) on a 3D bioplotter. Biocompatibility testing was used to determine whether the printed scaffolds were suitable for use in tissue engineering. Material and Methods An Envisiontec 3D bioplotter was used to fabricate the scaffolds. For better cell-scaffold interaction, the printed polycaprolactone scaffolds were coated with type-I collagen. Three different cell types were then cultured on the scaffolds and various tests were used to investigate the biocompatibility of the scaffolds. Results Reproducible scaffolds could be printed from polycaprolactone. In addition, a coating process with collagen was developed, which significantly improved the cell-scaffold interaction. Biocompatibility tests showed that the PCL-collagen scaffolds are suitable for use with cells. The cells adhered to the surface of the scaffolds and as a result extensive cell growth was observed on the scaffolds. The inner part of the scaffolds, however, remained largely uninhabited. In the cytotoxicity studies, it was found that toxicity below 20% was present in some experimental runs. The determination of the compressive strength by means of the universal testing machine Z005 by ZWICK according to DIN EN ISO 604 of the scaffolds resulted in a value of 68.49 ± 0.47 MPa. Full article

The web and its recent advancements represent a great opportunity to build universal, rich, multi-user and immersive Web3D/WebXR applications targeting Cultural Heritage field—including 3D presenters, inspection tools, applied VR games, collaborative teaching tools and much more. Such opportunity although, introduces additional challenges besides common issues and limitations typically encountered in this context. The “ideal” Web3D application should be able to reach every device, automatically adapting its interface, rendering and interaction models—resulting in a single, liquid product that can be consumed on mobile devices, PCs, Museum kiosks and immersive AR/VR devices, without any installation required for final users. The open-source ATON framework is the result of research and development activities carried out during the last 5 years through national and international projects: it is designed around modern and robust web standards, open specifications and large open-source ecosystems. This paper describes the framework architecture and its components, assessed and validated through different case studies. ATON offers institutions, researchers, professionals a scalable, flexible and modular solution to craft and deploy liquid web-applications, providing novel and advanced features targeting Cultural Heritage field in terms of 3D presentation, annotation, immersive interaction and real-time collaboration. Full article

Tourist traceability is the analysis of the set of actions, procedures, and technical measures that allows us to identify and record the space–time causality of the tourist’s touring, from the beginning to the end of the chain of the tourist product. Besides, the traceability of tourists has implications for infrastructure, transport, products, marketing, the commercial viability of the industry, and the management of the destination’s social, environmental, and cultural impact. To this end, a tourist traceability system requires a knowledge base for processing elements, such as functions, objects, events, and logical connectors among them. A knowledge base provides us with information on the preparation, planning, and implementation or operation stages. In this regard, unifying tourism terminology in a traceability system is a challenge because we need a central repository that promotes standards for tourists and suppliers in forming a formal body of knowledge representation. Some studies are related to the construction of ontologies in tourism, but none focus on tourist traceability systems. For the above, we propose OntoTouTra, an ontology that uses formal specifications to represent knowledge of tourist traceability systems. This paper outlines the development of the OntoTouTra ontology and how we gathered and processed data from ubiquitous computing using Big Data analysis techniques. Full article

Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact and plan the restoration process, a rapid intervention by authorities is needed, which can be enhanced by the use of satellite imagery and automatic burned area delineation methodologies, accelerating the response and the decision-making processes. In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework. Experimental results compare different model architectures and loss functions on a very large real-world Sentinel2 satellite dataset. Furthermore, a novel multi-channel attention-based analysis is presented to uncover the prediction behaviour and provide model interpretability. A perturbation mechanism is applied to an attention-based DS-UNet to evaluate the contribution of different domain-driven groups of channels to the severity estimation problem. Full article

In this work, chemical parameters such as sugar (glucose and fructose) content, organic acid (total acids, malic and tartaric acids), total phenolic content and the antioxidant activity of 12 white wines (chardonnay, pinot blanc and pinot gris) from various wine regions in Slovakia were studied in order to identify differences among the varieties and wine-growing regions. The wine samples were examined by Fourier-transform infrared spectroscopy (FTIR) and UV-VIS spectrophotometry (for determination of total polyphenolic content (TPC) and total antioxidant activity (TAA)) methods. Content of alcohol ranged between 11.50% and 13.80% with the mean value 12.52%. Mean content of total acids varied between 4.63 ± 0.09 and 6.63 ± 0.05 g.L⁻¹, tartaric acid varied between 1.62 ± 0.09 and 2.93 ± 0.03 g L⁻¹, malic acid was found in the concentrations ranged from 0.07 ± 0.05 and 2.50 ± 0.08 g L⁻¹ and lactic acid was present between 1.53 and 0.01 g L⁻¹. The content of fructose was, in general, higher in the samples from the Južnoslovenská and Nitrianska wine regions and glucose was higher in the Malokarpatská wine region. Chardonnay wines showed the highest content of total polyphenols and the antioxidant activity in the samples ranged from 51.06 ± 027 to 72.53 ± 0.35% inhibition of DPPH. The PCA analysis based on chemical descriptors distinguished the Nitrianska and Stredoslovenská wine regions. According to similarities among the wine samples, four main classes were formed by cluster analysis. Full article

In order to better assess the seismic hazard in the northern region of Algeria, the shear-wave velocity structure in the Middle-Chelif Basin is estimated using ambient vibration single-station and array measurements. The Middle-Chelif Basin is located in the central part of the Chelif Basin, the largest of the Neogene sedimentary basins in northern Algeria. This basin hosts the El-Asnam fault, one of the most important active faults in the Mediterranean area. In this seismically active region, most towns and villages are built on large unconsolidated sedimentary covers. Application of the horizontal-to-vertical spectral ratio (HVSR) technique at 164 sites, and frequency–wavenumber (F–K) analysis at 7 other sites, allowed for the estimation of the ground resonance frequencies, shear-wave velocity profiles, and sedimentary cover thicknesses. The electrical resistivity tomography method was used at some sites to further constrain the thickness of the superficial sedimentary layers. The soil resonance frequencies range from 0.75 Hz to 12 Hz and the maximum frequency peak amplitude is 6.2. The structure of the estimated shear-wave velocities is presented in some places as 2D profiles to help interpret the existing faults. The ambient vibration data allowed us to estimate the maximum depth in the Middle-Chelif Basin, which is 760 m near the city of El-Abadia. Full article

Every year the world loses about 50% of fruits and vegetables post-harvest and in the supply chain. The use of biodegradable coatings and films with antioxidant properties has been considered an excellent alternative to extend the shelf life of food. Therefore, the objective of this work was to develop a coating based on yam (Dioscorea rotundata L.) starch-containing lime, fennel, and lavender essential oils to extend the shelf life of strawberries (Fragaria vesca l.). The tensile properties, barrier properties (water vapour permeability (WVP) and oxygen permeability (OP)), moisture content, water-solubility, absorption capacity, water contact angle, optical properties, the antioxidant activity of the resultant starch-based coatings were evaluated. After that, the active properties of the coatings were assessed on strawberries inoculated with Aspergillus niger during 14 days of storage at 25 °C. The results showed that the incorporation of essential oils improved the elongation and WVP and provided antioxidant capacity and antimicrobial activity in the films. In particular, the essential oil of lime showed higher antioxidant activity. This fact caused the unwanted modification of other properties, such as the decrease in tensile strength, elastic modulus and increase in OP. The present study revealed the potential use of lime, fennel, and lavender essential oils incorporated into a polymeric yam starch matrix to produce biodegradable active films (antioxidant and antimicrobial). Obtained films showed to be a viable alternative to increase the shelf life of strawberries and protect them against Aspergillus niger. Full article

This study evaluated the axial capacity of cold-formed racking upright sections strengthened with an innovative reinforcement method by finite element modelling and artificial intelligence techniques. At the first stage, several specimens with different lengths, thicknesses and reinforcement spacings were modelled in ABAQUS. The finite element method (FEM) was employed to increase the available datasets and evaluate the proposed reinforcement method in different geometrical types of sections. The most influential factors on the axial strength were investigated using a feature-selection (FS) method within a multi-layer perceptron (MLP) algorithm. The MLP algorithm was developed by particle swarm optimization (PSO) and FEM results as input. In terms of accuracy evaluation, some of the rolling criteria including results showed that geometrical parameters have almost the same contribution in compression capacity and displacement of the specimens. According to the performance evaluation indexes, the best model was detected and specified in the paper and optimised by tuning other parameters of the algorithm. As a result, the normalised ultimate load and displacement were predicted successfully. Full article

This article presents a procedure for designing footwear production plants with a Decision Support System combined with an expert system and a simulation approach. The footwear industry has many operations and is labour intensive. Optimisation of plant layout, machinery, and human resources is very important to design the footwear manufacturing system, making adequate investment in space and equipment. In the industry it is essential to reduce the process time, so the research is based on a Decision Support System combined with an expert system and simulation to improve the design of the manufacturing plan. This work contains two case studies, direct injection manufacturing and assembly and carburising methods, which are compared to analyse all the necessary resources to have the best cost–benefit ratio. In each case, a precise knowledge of the type and quantity of machinery and human resources is needed to estimate the production. This comparison has been done through simulations and using a knowledge base of an expert system. The conclusions are presented in which an improvement in production time is obtained by applying the methodology developed in the study. Full article

Palestinian healthcare institutions face difficulties in providing effective service delivery, particularly in times of crisis. Problems arising from inadequate healthcare service delivery are traceable to issues such as spatial coverage, emergency response time, infrastructure, and manpower. In the Gaza Strip, specifically, there is inadequate spatial distribution and accessibility to healthcare facilities due to decades of conflicts. This study focuses on identifying hospital site suitability areas within the Gaza Strip in Palestine. The study aims to find an optimal solution for a suitable hospital location through suitability mapping using relevant environmental, topographic, and geodemographic parameters and their variable criteria. To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. Identification of the most significant parameters (conditioning factors) that influence a suitable hospital location was achieved by employing correlation-based feature selection (CFS) with the search algorithm (greedy stepwise). Thus, the suitability map of potential hospital sites was modeled using a support vector machine (SVM), multilayer perceptron (MLP), and linear regression (LR) models. The results of the predicted sites were validated using CFS cross-validation and the receiver operating characteristic (ROC) curve metrics. The CFS analysis shows very high correlations with R2 values of 0.94, 0. 93, and 0.75 for the SVM, MLP, and LR models, respectively. Moreover, based on areas under the ROC curve, the MLP model produced a prediction accuracy of 84.90%, SVM of 75.60%, and LR of 64.40%. The findings demonstrate that the machine learning techniques used in this study are reliable, and therefore are a promising approach for assessing a suitable location for hospital sites for effective health delivery planning and implementation. Full article

Today, surgical operations are less invasive than they were a few decades ago and, in medicine, there is a growing trend towards precision surgery. Among many technological advancements, augmented reality (AR) can be a powerful tool for improving the surgery practice through its ability to superimpose the 3D geometrical information of the pre-planned operation over the surgical field as well as medical and instrumental information gathered from operating room equipment. AR is fundamental to reach new standards in maxillofacial surgery. The surgeons will be able to not shift their focus from the patients while looking to the monitors. Osteotomies will not require physical tools to be fixed on patient bones as guides to make resections. Handling grafts and 3D models directly in the operating room will permit a fine tuning of the procedure before harvesting the implant. This article aims to study the application of AR head-mounted displays (HMD) in three operative scenarios (oncological and reconstructive surgery, orthognathic surgery, and maxillofacial trauma surgery) by the means of quantitative logic using the Quality Function Deployment (QFD) tool to determine their requirements. The article provides an evaluation of the readiness degree of HMD currently on market and highlights the lacking features. Full article

Atomic layer deposition (ALD) of high-κ dielectrics on two-dimensional (2D) materials (including graphene and transition metal dichalcogenides) still represents a challenge due to the lack of out-of-plane bonds on the pristine surfaces of 2D materials, thus making the nucleation process highly disadvantaged. The typical methods to promote the nucleation (i.e., the predeposition of seed layers or the surface activation via chemical treatments) certainly improve the ALD growth but can affect, to some extent, the electronic properties of 2D materials and the interface with high-κ dielectrics. Hence, direct ALD on 2D materials without seed and functionalization layers remains highly desirable. In this context, a crucial role can be played by the interaction with the substrate supporting the 2D membrane. In particular, metallic substrates such as copper or gold have been found to enhance the ALD nucleation of Al₂O₃ and HfO₂ both on monolayer (1 L) graphene and MoS₂. Similarly, uniform ALD growth of Al₂O₃ on the surface of 1 L epitaxial graphene (EG) on SiC (0001) has been ascribed to the peculiar EG/SiC interface properties. This review provides a detailed discussion of the substrate-driven ALD growth of high-κ dielectrics on 2D materials, mainly on graphene and MoS₂. The nucleation mechanism and the influence of the ALD parameters (namely the ALD temperature and cycle number) on the coverage as well as the structural and electrical properties of the deposited high-κ thin films are described. Finally, the open challenges for applications are discussed. Full article

A reverse vending machine motivates citizens to bring recyclable waste by rewarding them, which is a viable solution to increase the recycling rate. Reverse vending machines generally use near-infrared sensors, barcode sensors, or cameras to classify recycling resources. However, sensor-based reverse vending machines suffer from a high configuration cost and the limited scope of target objects, and conventional single image-based reverse vending machines usually make erroneous predictions about intentional fraud objects. This paper proposes a dual image-based convolutional neural network ensemble model to address these problems. For this purpose, we first created a prototype reverse vending machine and constructed an image dataset containing two cross-sections of objects, top and front view. Then, we chose convolutional neural network models widely used in image classification as the candidates for building an accurate and lightweight ensemble model. Considering the size and classification performance of candidates, we constructed the best-fit ensemble combination and evaluated its classification performance. The final ensemble model showed a classification accuracy higher than 95% for all target classes, including fraud objects. This result proves that our approach achieves better robustness against intentional fraud objects than single image-based models and thus can broaden the scope for target resources. The measurement results on lightweight embedded platforms also demonstrated that our model provides a short inference time that is enough to facilitate the real-time execution of reverse vending machines based on low-cost edge artificial intelligence devices. Full article

Dogs and cats tend to show their conditions and desires through their behaviors. In companion animal behavior recognition, behavior data obtained by attaching a wearable device or sensor to a dog’s body are mostly used. However, differences occur in the output values of the sensor when the dog moves violently. A tightly coupled RGB time tensor network (TRT-Net) is proposed that minimizes the loss of spatiotemporal information by reflecting the three components (x-, y-, and z-axes) of the skeleton sequences in the corresponding three channels (red, green, and blue) for the behavioral classification of dogs. This paper introduces the YouTube-C7B dataset consisting of dog behaviors in various environments. Based on a method that visualizes the Conv-layer filters in analyzable feature maps, we add reliability to the results derived by the model. We can identify the joint parts, i.e., those represented as rows of input images showing behaviors, learned by the proposed model mainly for making decisions. Finally, the performance of the proposed method is compared to those of the LSTM, GRU, and RNN models. The experimental results demonstrate that the proposed TRT-Net method classifies dog behaviors more effectively, with improved accuracy and F1 scores of 7.9% and 7.3% over conventional models. Full article

We aimed to evaluate potential new treatments for knee osteoarthritis (OA). The National Institute of Health ClinicalTrials.gov database was searched for “Osteoarthritis, Knee”. We found 565 ongoing interventional studies with a total planned enrollment of 111,276 subjects. Ongoing studies for knee OA represent a very small fraction of the registered clinical trials, but they are over a quarter of all knee trials and over two thirds of all OA studies. The most researched topic was arthroplasty, with aspects such as implant design changes, cementless fixation, robotic guidance, pain management, and fast track recovery. Intraarticular injections focused on cell therapies with mesenchymal stem cells sourced from adipose tissue, bone marrow, or umbilical cord. We could see the introduction of the first disease modifying drugs with an impact on knee OA, as well as new procedures such as geniculate artery embolization and geniculate nerve ablation. Full article