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The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.

Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human–computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this paper, we conduct a systematic review of facial and pose emotion recognition using DL and computer vision, analyzing and evaluating 77 papers from different sources under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our review covers several topics, including the scope and purpose of the studies, the methods employed, and the used datasets. The scope of this work is to conduct a systematic review of facial and pose emotion recognition using DL methods and computer vision. The studies were categorized based on a proposed taxonomy that describes the type of expressions used for emotion detection, the testing environment, the currently relevant DL methods, and the datasets used. The taxonomy of methods in our review includes Convolutional Neural Network (CNN), Faster Region-based Convolutional Neural Network (R-CNN), Vision Transformer (ViT), and “Other NNs”, which are the most commonly used models in the analyzed studies, indicating their trendiness in the field. Hybrid and augmented models are not explicitly categorized within this taxonomy, but they are still important to the field. This review offers an understanding of state-of-the-art computer vision algorithms and datasets for emotion recognition through facial expressions and body poses, allowing researchers to understand its fundamental components and trends.

Metal additive manufacturing (AM) is a layer-by-layer process that makes the direct manufacturing of various industrial parts possible. This method facilitates the design and fabrication of complex industrial, advanced, and fine parts that are used in different industry sectors, such as aerospace, medicine, turbines, and jewelry, where the utilization of other fabrication techniques is difficult or impossible. This method is advantageous in terms of dimensional accuracy and fabrication speed. However, the parts fabricated by this method may suffer from faults such as anisotropy, micro-porosity, and defective joints. Metals like titanium, aluminum, stainless steels, superalloys, etc., have been used—in the form of powder or wire—as feed materials in the additive manufacturing of various parts. The main criterion that distinguishes different additive manufacturing processes from each other is the deposition method. With regard to this criterion, AM processes can be divided into four classes: local melting, sintering, sheet forming, and electrochemical methods. Parameters affecting the properties of the additive-manufactured part and the defects associated with an AM process determine the method by which a certain part should be manufactured. This study is a survey of different additive manufacturing processes, their mechanisms, capabilities, shortcomings, and the general properties of the parts manufactured by them.

ObjectivesTo derive and validate a new disease activity measure for systemic lupus erythematosus (SLE), the SLE Disease Activity Score (SLE-DAS), with improved sensitivity to change as compared with SLE Disease Activity Index (SLEDAI), while maintaining high specificity and easiness of use.MethodsWe studied 520 patients with SLE from two tertiary care centres (derivation and validation cohorts). At each visit, disease activity was scored using the Physician Global Assessment (PGA) and SLEDAI 2000 (SLEDAI-2K). To construct the SLE-DAS, we applied multivariate linear regression analysis in the derivation cohort, with PGA as dependent variable. The formula was validated in a different cohort through the study of: (1) correlations between SLE-DAS, PGA and SLEDAI-2K; (2) performance of SLEDAI-2K and SLE-DAS in identifying a clinically meaningful change in disease activity (ΔPGA≥0.3); and (3) accuracy of SLEDAI-2K and SLE-DAS time-adjusted means in predicting damage accrual.ResultsThe final SLE-DAS instrument included 17 items. SLE-DAS was highly correlated with PGA (r=0.875, p<0.0005) and SLEDAI-2K (r=0.943, p<0.0005) in the validation cohort. The optimal discriminative ΔSLE-DAS cut-off to detect a clinically meaningful change was 1.72. In the validation cohort, SLE-DAS showed a higher sensitivity than SLEDAI-2K (change ≥4) to detect a clinically meaningful improvement (89.5% vs 47.4%, p=0.008) or worsening (95.5% vs 59.1%, p=0.008), while maintaining similar specificities. SLE-DAS performed better in predicting damage accrual than SLEDAI-2K.ConclusionSLE-DAS has a good construct validity and has better performance than SLEDAI-2K in identifying clinically significant changes in disease activity and in predicting damage accrual.

Real-time monitoring of torque in a rotating shaft is not easy to implement with technologies such as optic fiber sensors or strain gages. Surface acoustic wave (SAW) sensors are wireless and passive and can be used to monitor strain in moving parts. Commercial solutions (sensors, antennas and interrogation unit) can easily be purchased from some companies; however, they are not customized and may not meet the specificity of the measurements. In order to evaluate the adequacy of commercial off-the-shelf (COTS) solutions, temperature and strain sensors fabricated by SENSeOR (Besançon, France) were mounted on a load cell. The sensors were calibrated using a thermal chamber and a universal testing machine. The load cell was then assembled together with a steel shaft that rotated at different speeds inside an oven. The commercial antennas were replaced with an RF (radio frequency) coupler and the sensors were interrogated with the commercial interrogation unit. The influence of rotation in the accuracy on the measurements, as well as the adequacy of the sensors structure, was evaluated. It can be concluded that SAW sensors can be used to measure temperature or torque in a rotating environment; however, some customization of the components is required in order to overcome the limitations posed by COTS sensing solutions.

Currently, welding processes have become one of the most used methods for joining materials in all kinds of industries, thanks to properties such as high speed and high tensile strength. However, despite these advantages, this type of connection method has some drawbacks, for example, residual stress and structural distortion, mainly due to the process thermal cycles. Structural distortion is one of the major concerns of industrial joining practice. In order to decrease distortion, the variation of welding sequence, direction, and clamping conditions, have been applied through several years, by trial and error tests. However, numerical simulation enables virtual examination of the welding, mainly due to the progress on the numerical methods, which stimulated the research on welding simulation models. These models can cover a wide spectrum of physical and thermal processes occurring during, and after welding. The aim of this paper is to provide wider information about types of finite element method (FEM) in fusion welding processes and to demonstrate the accuracy of FEM models results compared to experimental.

Mucosal healing (MH) is currently accepted as one of the best treatment targets in Crohn's disease. However, even in patients with sustained MH, residual bowel wall inflammation can still be detected by cross-sectional imaging. The long-term benefits of obtaining transmural healing (TH) have not been previously assessed.MethodsWe performed an observational study including 214 patients with Crohn's disease with a magnetic resonance enterography (MRE) and colonoscopy performed within a 6-month interval. Patients were classified as having TH (inactive MRE and colonoscopy), MH (active MRE with inactive colonoscopy), or no healing (active colonoscopy). Need for surgery, hospital admission, and therapy escalation were evaluated at 12 months of follow-up.ResultsPatients with TH presented lower rates of hospital admission, therapy escalation, and surgery than patients with MH or no healing. In logistic regression analysis, endoscopic remission (odds ratio 0.331 95% confidence interval [0.178–0.614], P < 0.001) and MRE remission (odds ratio 0.270 95% confidence interval [0.130–0.564], P < 0.001) were independently associated with a lower likelihood of reaching any unfavorable outcome.ConclusionsTH is associated with improved long-term outcomes in Crohn's disease and may be a more suitable target than MH.

The rise of robotics and the Internet of Things (IoT) could potentially represent a significant shift towards a more integrated and automated future, where the physical and digital domains may merge. However, the integration of these technologies presents certain challenges, including compatibility issues with existing systems and the need for greater interoperability between different devices. It would seem that the rigidity of traditional robotic designs may inadvertently make these difficulties worse, which in turn highlights the potential benefits of modular solutions. Furthermore, the mastery of new technologies may introduce additional complexity due to the varying approaches taken by robot manufacturers. In order to address these issues, this research proposes a Robot Operating System (ROS2)-based middleware, called the “ROS2-based gateway”, which aims to simplify the integration of robots in different environments. By focusing on the payload layer and enabling external communication, this middleware has the potential to enhance modularity and interoperability, thus accelerating the integration process. It offers users the option of selecting payloads and communication methods via a shell interface, which the middleware then configures, ensuring adaptability. The solution proposed in this article, based on the gateway concept, offers users and programmers the flexibility to specify which payloads they want to activate depending on the task at hand and the high-level protocols they wish to use to interact with the activated payloads. This approach allows for the optimisation of hardware resources (only the necessary payloads are activated), as well as enabling the programmer/user to utilise high-level communication protocols (such as RESTful, Kafka, etc.) to interact with the activated payloads, rather than low-level programming.

This systematic review aimed to provide a comprehensive view of (1) the purposes of research studies using smart city infrastructures to promote citizen participation in the cities’ management and governance, (2) the characteristics of the proposed solutions in terms of data sources, data quality, and data security and privacy mechanisms, as well, as strategies to incentivize citizen participation, and (3) the development stages of the applications being reported. An electronic search was conducted combining relevant databases and keywords, and 76 studies were included after a selection process. The results show a current interest in developing applications to promote citizen participation to identify urban problems and contribute to decision-making processes. Most of the included studies considered citizens as agents able to report issues (e.g., issues related to the maintenance of urban infrastructures or the mobility in urban spaces), monitor certain environmental parameters (e.g., air or acoustic pollution), and share opinions (e.g., opinions about the performance of local authorities) to support city management. Moreover, a minority of the included studies developed collaborative applications to involve citizens in decision-making processes in urban planning, the selection of development projects, and deepening democratic values. It is possible to conclude about the existence of significant research related to the topic of this systematic review, but also about the need to deepen mechanisms to guarantee data quality and data security and privacy, to develop strategies to incentivize citizen participation, and to implement robust experimental set-ups to evaluate the impact of the developed applications in daily contexts.

Defect detection is a key element of quality control in today’s industries, and the process requires the incorporation of automated methods, including image sensors, to detect any potential defects that may occur during the manufacturing process. While there are various methods that can be used for inspecting surfaces, such as those of metal and building materials, there are only a limited number of techniques that are specifically designed to analyze specialized surfaces, such as ceramics, which can potentially reveal distinctive anomalies or characteristics that require a more precise and focused approach. This article describes a study and proposes an extended solution for defect detection on ceramic pieces within an industrial environment, utilizing a computer vision system with deep learning models. The solution includes an image acquisition process and a labeling platform to create training datasets, as well as an image preprocessing technique, to feed a machine learning algorithm based on convolutional neural networks (CNNs) capable of running in real time within a manufacturing environment. The developed solution was implemented and evaluated at a leading Portuguese company that specializes in the manufacturing of tableware and fine stoneware. The collaboration between the research team and the company resulted in the development of an automated and effective system for detecting defects in ceramic pieces, achieving an accuracy of 98.00% and an F1-Score of 97.29%.