ALBERT

Description: At present, inspection systems process visual data captured by cameras, with deep learning approaches applied to detect defects. Defect detection results usually have an accuracy higher than 94%. Real-life applications, however, are not very common. In this paper, we describe the development of a tire inspection system for the tire industry. We provide methods for processing tire sidewall data obtained from a camera and a laser sensor. The captured data comprise visual and geometric data characterizing the tire surface, providing a real representation of the captured tire sidewall. We use an unfolding process, that is, a polar transform, to further process the camera-obtained data. The principles and automation of the designed polar transform, based on polynomial regression (i.e., supervised learning), are presented. Based on the data from the laser sensor, the detection of abnormalities is performed using an unsupervised clustering method, followed by the classification of defects using the VGG-16 neural network. The inspection system aims to detect trained and untrained abnormalities, namely defects, as opposed to using only supervised learning methods.

Description: Synthetic polypeptides prepared from N-carboxyanhydrides (NCAs) of α-amino acids are useful for elucidating the relationship between the primary structure of natural peptides and their immunogenicity. In this study, complex copolypeptide sequences were prepared using a recently developed technique; specifically, the random copolymerization of l-alanine NCA with NCAs of l-glutamic acid 5-benzylester (Bn-Glu NCA), S-benzyl-cysteine (Bn-Cys NCA), O-benzyl-l-serine (Bn-Ser NCA), and l-phenylalanine (Phe NCA) was performed using N-heterocyclic carbene (NHC) catalysts. The NHC-initiated Ala NCA/Bn-Glu NCA and Ala NCA/Bn-Cys NCA copolymerization reactions achieved 90% conversion within 30 min. The reactivity ratio values estimated using the Kelen and Tüdos method show that poly(Bn-Glu-co-Ala) and poly(Bn-Cys-co-Ala) have random repeating units with rich alternating sequences, whereas poly(Bn-Ser-co-Ala) and poly(Phe-co-Ala) contain a larger proportion of Ala-repeating units than Bn-Ser and Phe in random placement.

Description: For an industry to succeed in a competitive market, it should continuously take care of not only its stakeholders but also its technical efficiency and productivity. In this paper, data envelopment analysis was combined with Malmquist productivity analysis to investigate the pattern of multifactor productivity changes in the European energy industry over the period from 2005–2016. The results showed that the whole industry was technically inefficient and had large potential for improvement. A slight average increase in productivity that was observed over the studied period proved to be sensitive to the financial and economic situation and equally sensitive to technological and efficiency advances. As for efficiency gains, they reflected the nature of the energy industry, implying that they were due to scale efficiencies rather than human resource improvements. Although technological innovation and the optimal scale of production increased productivity, the slow pace at which this occurred and the negative outlook highlighted by the observed trends call for more serious consideration of the future productivity deployment of the European energy industry, particularly in the context of its decarbonisation, diversification, and modernisation.

Description: This paper presents the results of an experimental program aimed at the assessment of the freeze–thaw (F–T) resistance of concrete based on the evaluation of fracture tests accompanied by acoustic emission measurements. Two concretes of similar mechanical characteristics were manufactured for the experiment. The main difference between the C1 and C2 concrete was in the total number of air voids and in the A300 parameter, where both parameters were higher for C1 by about 35% and 52%, respectively. The evaluation of the fracture characteristics was performed on the basis of experimentally recorded load–deflection and load–crack mouth opening displacement diagrams using two different approaches: linear fracture mechanics completed with the effective crack model and the double-K model. The results show that both approaches gave similar results, especially if the nonlinear behavior before the peak load was considered. According to the results, it can be stated that continuous AE measurement is beneficial for the assessment of the extent of concrete deterioration, and it suitably supplements the fracture test evaluation. A comparison of the results of fracture tests with the resonance method and splitting tensile strength test shows that all testing methods led to the same conclusion, i.e., the C1 concrete was more F–T-resistant than C2. However, the fracture test evaluation provided more detailed information about the internal structure deterioration due to the F–T exposure.

Description: The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which evaluates contemporary intelligent-based research directions to address the gap that still exists in the field. The main components of any intelligent-based system are the training datasets, the algorithms, and the evaluation metrics; these were the main benchmark criteria used to assess the intelligent-based systems included in this research article. This research provides a rich source of references for scholars seeking to determine their scope of research in this field. Furthermore, although the paper does present a set of suggestions about future inductive directions, it leaves the reader free to derive additional insights about how to develop intelligent-based systems to counter current and future network attacks.

Description: The Y Balance Test (YBT) is a dynamic balance assessment typically used in sports medicine. This work proposes a deep learning approach to automatically score this YBT by estimating the normalized reach distance (NRD) using a wearable sensor to register inertial signals during the movement. This paper evaluates several signal processing techniques to extract relevant information to feed the deep neural network. This evaluation was performed using a state-of-the-art human activity recognition system based on recurrent neural networks (RNNs). This deep neural network includes long short-term memory (LSTM) layers to learn features from time series by modeling temporal patterns and an additional fully connected layer to estimate the NRD (normalized by the leg length). All analyses were carried out using a dataset with YBT assessments from 407 subjects, including young and middle-aged volunteers and athletes from different sports. This dataset allowed developing a global and robust solution for scoring the YBT in a wide range of applications. The experimentation setup considered a 10-fold subject-wise cross-validation using training, validation, and testing subsets. The mean absolute percentage error (MAPE) obtained was 7.88 ± 0.20%. Moreover, this work proposes specific regression systems to estimate the NRD for each direction separately, obtaining an average MAPE of 7.33 ± 0.26%. This deep learning approach was compared to a previous work using dynamic time warping and k-NN algorithms, obtaining a relative MAPE reduction of 10%.

Description: The influence of four naturally occurring mineral additives (zeolite, diatomite, trass and bentonite) on the hydration and properties of cement pastes and mortars was investigated. The materials change the phase composition, heat of hydration (determined by calorimetry) and mechanical properties of composites. After 28 days, the amount of Ca(OH)2 was reduced by up to 23% and up to 35% more C-S-H was formed, as proved by TG measurements. Differences were observed in the kinetics of heat release, especially for 25% of the addition. In the calorimetric curves, an additional exothermic effect is observed, related to the alteration in the hydration of C3A in cement. From the point of view of beneficial influence on mechanical properties of mortars, the additives could be ranked as follows: bentonite 〈 diatomite, zeolite 〈 trass after 2 days and bentonite 〈 diatomite 〈 trass 〈 zeolite after 28 days of curing. The highest compressive strength (58.5 MPa) was observed for the sample with a 10% addition of zeolite. Zeolite, trass, bentonite and diatomite are all pozzolanic materials; however, their activity varies to an extent due to the differences in their specific surface area and the content of the amorphous phase, responsible for the pozzolanic reaction.

Description: Traffic congestion and the occurrence of traffic accidents are problems that can be mitigated by applying cooperative adaptive cruise control (CACC). In this work, we used deep reinforcement learning for CACC and assessed its potential to outperform model-based methods. The trade-off between distance-error minimization and energy consumption minimization whilst still ensuring operational safety was investigated. Alongside a string stability condition, robustness against burst errors in communication also was incorporated, and the effect of preview information was assessed. The controllers were trained using the proximal policy optimization algorithm. A validation by comparison with a model-based controller was performed. The performance of the trained controllers was verified with respect to the mean energy consumption and the root mean squared distance error. In our evaluation scenarios, the learning-based controllers reduced energy consumption in comparison to the model-based controller by 17.9% on average.

Description: In this paper, a robust observer-based control strategy for n-DOF uncertain robot manipulators with fixed-time stability was developed. The novel fixed-time nonsingular sliding mode surface enables control errors to converge to the equilibrium point quickly within fixed time without singularity. The development of the novel fixed-time disturbance observer based on a uniform robust exact differentiator also allows uncertain terms and exterior disturbances to be proactively addressed. The designed observer can accurately approximate uncertain terms within a fixed time and contribute to significant chattering reduction in the traditional sliding mode control. A robust observer-based control strategy was formulated, according to a combination of the fixed-time nonsingular terminal sliding mode control method and the designed observer, to yield global fixed time stability for n-DOF uncertain robot manipulators. The proposed controller proved definitively that it was able to obtain global stabilization in fixed time. The approximation capability of the proposed observer, the convergence of the proposed sliding surface, and the effectiveness of the proposed control strategy in fixed time were fully confirmed by simulation performance on an industrial robot manipulator.

Description: The simple act of walking can occasionally cause ankle sprains. Traditionally, the rehabilitation of a sprained ankle involves physical therapy. Physical therapy is one of the health professions that help regain mobility through manual exercises. Aquatic therapy is one of the most potent water-based anti-inflammatory methods currently employed that increases local blood circulation, decreases pain and swelling, and promotes speedy healing. Several studies have demonstrated that ankle rehabilitation robots have immense potential in patients’ rehabilitation and recovery; however, these robots cannot be used underwater. This paper introduces the design, development, and control of a therapeutic robot incorporating aquatic therapy for ankle rehabilitation. Its primary objective is to design and control a one degree of freedom ankle rehabilitation robot that can be used in water and can recirculate hot water to simultaneously perform physical therapy and aquatic therapy. To conduct this study, an ankle rehabilitation robot was designed, modeled, developed, and controlled. The design and control techniques were evaluated by means of simulation and experimental results.

Description: (1) Background: Obesity (OB) is a frequent co-morbidity in Binge Eating Disorder (BED), suggesting that both conditions share phenotypical features along a spectrum of eating-related behaviors. However, the evidence is inconsistent. This study aimed to comprehensively compare OB-BED patients against OB individuals without BED and healthy, normal-weight controls in general psychopathological features, eating-related phenotypes, and early life experiences. (2) Methods: OB-BED patients (n = 37), OB individuals (n = 50), and controls (n = 44) completed a battery of standardized questionnaires. Responses were analyzed using univariate comparisons and dimensionality reduction techniques (linear discriminant analysis, LDA). (3) Results: OB-BED patients showed the highest scores across assessments (e.g., depression, emotional and stress eating, food cravings, food addiction). OB-BED patients did not differ from OB individuals in terms of childhood traumatization or attachment styles. The LDA revealed a two-dimensional solution that distinguished controls from OB and OB-BED in terms of increasing problematic eating behaviors and attitudes, depression, and childhood adversities, as well as OB-BED from OB groups in terms of emotional eating tendencies and self-regulation impairments. (4) Conclusions: Findings support the idea of a shared spectrum of eating-related disorders but also highlight important distinctions relevant to identifying and treating BED in obese patients.

Description: Community-based rehabilitation (CBR) is a multi-sectorial community strategy for guaranteeing that people with disabilities enjoy the same rights and opportunities as all other community members. CBR is organized in a five-component matrix—namely, health, education, social, livelihood, and empowerment. To measure the effectiveness of CBR, the World Health Organization (WHO) has developed standardized indicators. The objective of the present study is to translate and validate the CBR indicators (CBR-Is), providing preliminary evidence of their use for disability in Italy. After obtaining permission from the WHO, the CBR-Is followed a process of translation and cross-cultural adaptation according to international guidelines. An examination of internal consistency and reliability was than performed. The intra-rater reliability was estimated using the Intraclass Correlation Coefficient with a 95% confidence interval. In order to measures the differences between people with and without disabilities, an independent sample t-test was used for quantitative indicators. The Italian version of the CBR-Is (IT-CBR-Is) was administered to 234 people. The internal consistency showed a good value, with a Cronbach’s alpha coefficient of 0.862, and the intra-rater reliability analysis showed solid values for each domain (range: 0.723–0.882). Statistically significant differences between people with and without disabilities were found for each domain of the CBR matrix—namely, health, social, education, livelihood, and empowerment. The IT-CBR-Is are consistent and reliable measures when used to investigate disability in a community-based inclusive development perspective. National stakeholders can now have specific indicators to implement services and actions for people with disabilities.

Description: Strong evidence from studies on primates and rodents shows that changes in pupil diameter may reflect neural activity in the locus coeruleus (LC). Pupillometry is the only available non-invasive technique that could be used as a reliable and easily accessible real-time biomarker of changes in the in vivo activity of the LC. However, the application of pupillometry to preclinical research in rodents is not yet fully standardized. A lack of consensus on the technical specifications of some of the components used for image recording or positioning of the animal and cameras have been recorded in recent scientific literature. In this study, a novel pupillometry system to indirectly assess, in real-time, the function of the LC in anesthetized rodents is presented. The system comprises a deep learning SOLOv2 instance-based fast segmentation framework and a platform designed to place the experimental subject, the video cameras for data acquisition, and the light source. The performance of the proposed setup was assessed and compared to other baseline methods using a validation and an external test set. In the latter, the calculated intersection over the union was 0.93 and the mean absolute percentage error was 1.89% for the selected method. The Bland–Altman analysis depicted an excellent agreement. The results confirmed a high accuracy that makes the system suitable for real-time pupil size tracking, regardless of the pupil’s size, light intensity, or any features typical of the recording process in sedated mice. The framework could be used in any neurophysiological study with sedated or fixed-head animals.

Description: Smoke-free pregnancies have long-term health benefits for mothers and babies. This paper quantitatively examines factors associated with smoke-free pregnancies among Aboriginal and Torres Strait Islander women (hereafter Aboriginal women) and qualitatively explores their smoking cessation (SC) experiences during pregnancy. An Aboriginal-led online cross-sectional study on SC was conducted with Aboriginal women and in partnership with Aboriginal communities, between July and October 2020. The present analysis includes participants who made a pregnancy-related quit attempt (N = 103). Chi-squared tests, logistic regression models, and thematic analysis of free-form text responses were performed. The adjusted odds of having smoke-free pregnancies were 4.54 times higher among participants who used Aboriginal Health Services (AHS) (AOR = 4.54, p-value 0.018). Participants living in urban settings had 67% lower odds of having smoke-free pregnancies compared to their regional/remote counterparts (AOR = 0.33, p-value 0.020). Qualitative data revealed strong motivations to reduce tobacco-related harms to the fetus and variability in quitting experiences at different stages of and across pregnancies. Smoking cessation care (SCC) can support Aboriginal women meaningfully if their quitting experiences are considered in SCC development and implementation. Consistent funding for AHS-led SCC is needed to garner health benefits for Aboriginal peoples. More research into urban versus regional/remote differences in maternal SC is recommended.

Description: During the last few years, attention has overwhelmingly focused on the integrated management of urban services and the demand of customers for locally-based supply. The rapid growth in developing smart measuring devices has made the underlying systems more observable and controllable. This exclusive feature has led the system designers to pursue the implementation of complex protocols to provide faster services based on data exchanges. On the other hand, the demands of consumers for locally-based supply could cause a disjunction and islanding behavior that demands to be dealt with by precise action. At first, keeping a centralization scheme was the main priority. However, the advent of distributed systems opened up new solutions. The operation of distributed systems requires the implementation of strong communication links to boost the existing infrastructure via smart control and supervision, which requires a foundation and effective investigations. Hence, necessary actions need to be taken to frustrate any disruptive penetrations into the system while simultaneously benefiting from the advantages of the proposed smart platform. This research addresses the detection of false data injection attacks (FDIA) in energy hub systems. Initially, a multi-hub system both in the presence of a microgrid (the interconnected smart energy hub-based microgrid system) and without it has been modeled for energy management in a way that allows them to cooperate toward providing energy with each other. Afterward, an FDIA is separately exerted to all three parts of the energy carrier including the thermal, water, and electric systems. In the absence of FDIA detection, the impact of FDIA is thoroughly illustrated on energy management, which considerably contributes to non-optimal operation. In the same vein, the intelligent priority selection based reinforcement learning (IPS-RL) method is proposed for FDIA detection. In order to model the uncertainty effects, the unscented transformation (UT) is applied in a stochastic framework. The results on the IEEE standard test system validate the system’s performance.