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Nowadays, H13 hot work steel is a commonly used hot work die material in the industry; however, its creep behavior for additively manufactured H13 steel parts has not been widely investigated. This research paper examines the impact of volumetric energy density (VED), a critical parameter in additive manufacturing (AM), and the effect of post heat-treatment nitrification on the creep behavior of H13 hot work tool steel, which is constructed through selective laser melting (SLM), which is a powder bed fusion process according to ISO/ASTM 52900:2021. The study utilizes nanoindentation tests to investigate the creep response and the associated parameters such as the steady-state creep strain rate. Measurements and observations taken during the holding phase offer a valuable understanding of the behavior of the studied material. The findings of this study highlight a substantial influence of both VED and nitrification on several factors including hardness, modulus of elasticity, indentation depth, and creep displacement. Interestingly, the creep strain rate appears to be largely unaltered by these parameters. The study concludes with the observation that the creep stress exponent ( n ) shows a decreasing trend with an increase in VED and the application of nitrification treatment.

The elevator industry is constantly expanding creating an increased demand for the integration of high technological tools to increase elevator efficiency and safety. Towards this direction, Additive Manufacturing (AM), and especially metal AM, is one of the technologies that could offer numerous competitive advantages in the production of industrial parts, such as integration of complex geometry, high manufacturability of high-strength metal alloys, etc. In this context, the present study has 3D designed, 3D printing manufactured, and evaluated novel bioinspired structures for elevator safety gear friction pads with the aim of enhancing their dynamic friction performance and eliminating the undesired behavior properties observed in conventional pads. Four different friction pads with embedded bioinspired surface lattice structures were formed on the template of the friction surface of the conventional pads and 3D printed by the Selective Laser Melting (SLM) process utilizing tool steel H13 powder as feedstock material. Each safety gear friction pad underwent tribological tests to evaluate its dynamic coefficient of friction (CoF). The results indicated that pads with a high contact surface area, such as those with car-tire-like and extended honeycomb structures, exhibit high CoF of 0.549 and 0.459, respectively. Based on the acquired CoFs, Finite Element Models (FEM) were developed to access the performance of braking pads under realistic operation conditions, highlighting the lower stress concentration for the aforementioned designs. The 3D-printed safety gear friction pads were assembled in an existing emergency progressive safety gear system of KLEEMANN Group, providing sufficient functionality.

Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.

This paper explores the energy-intensive cement industry, focusing on a plant in Greece and its mill and kiln unit. The data utilized include manipulated, non-manipulated, and uncontrolled variables. The non-manipulated variables are computed based on the machine learning (ML) models and selected by the minimum value of the normalized root mean square error ( NRMSE ) across nine (9) methods. In case the distribution of the data displayed in the user interface changes, the user should trigger the retrain of the AI models to ensure their accuracy and robustness. To form the objective function, the expert user should define the desired weight for each manipulated or non-manipulated variable through the user interface (UI), along with its corresponding constraints or target value. The user selects the variables involved in the objective function based on the optimization strategy, and the evaluation is based on the comparison of the optimized and the active value of the objective function. The differential evolution (DE) method optimizes the objective function that is formed by the linear combination of the selected variables. The results indicate that using DE improves the operation of both the cement mill and kiln, yielding a lower objective function value compared to the current values.

Composite 3D printing filaments integrating antimicrobial nanoparticles offer inherent microbial resistance, mitigating contamination and infections. Developing antimicrobial 3D-printed plastics is crucial for tailoring medical solutions, such as implants, and cutting costs when compared with metal options. Furthermore, hospital sustainability can be enhanced via on-demand 3D printing of medical tools. A PLA-based filament incorporating 5% TiO 2 nanoparticles and 2% Joncryl as a chain extender was formulated to offer antimicrobial properties. Comparative analysis encompassed PLA 2% Joncryl filament and a TiO 2 coating for 3D-printed specimens, evaluating mechanical and thermal properties, as well as wettability and antimicrobial characteristics. The antibacterial capability of the filaments was explored after 3D printing against Gram-positive Staphylococcus aureus ( S. aureus , ATCC 25923), as well as Gram-negative Escherichia coli ( E. coli , ATCC 25922), and the filaments with 5 wt.% embedded TiO 2 were found to reduce the viability of both bacteria. This research aims to provide the optimal approach for antimicrobial and medical 3D printing outcomes.

In the last decade, there has been a notable advancement in diverse bioreactor types catering to various applications. However, conventional bioreactors often exhibit bulkiness and high costs, making them less accessible to many researchers and laboratory facilities. In light of these challenges, this article aims to introduce and evaluate the development of a do-it-yourself (DIY) 3D printed smart bioreactor, offering a cost-effective and user-friendly solution for the proliferation of various bioentities, including bacteria and human organoids, among others. The customized bioreactor was fabricated under an ergonomic design and assembled with 3D printed mechanical parts combined with electronic components, under 3D printed housing. The 3D printed parts were designed using SOLIDWORKS ® CAD Software (2022 SP2.0 Professional version) and fabricated via the fused filament fabrication (FFF) technique. All parts were 3D printed with acrylonitrile butadiene styrene (ABS) in order for the bioreactor to be used under sterile conditions. The printed low-cost bioreactor integrates Internet-of-things (IoT) functionalities, since it provides the operator with the ability to change its operational parameters (sampling frequency, rotor speed, and duty cycle) remotely, via a user-friendly developed mobile application and to save the user history locally on the device. Using this bioreactor, which is adjusted to a standard commercial 12-well plate, proof of concept of a successful operation of the bioreactor during a 2-day culture of Escherichia coli bacteria (Mach1 strain) is presented. This study paves the way for more in-depth investigation of bacterial and various biological-entity growth cultures, utilizing 3D printing technology to create customized low-cost bioreactors.

The agricultural sector faces numerous challenges in ensuring optimal soil health and environmental conditions for sustainable crop production. Traditional soil analysis methods are often time-consuming and labor-intensive, and provide limited real-time data, making it challenging for farmers to make informed decisions. In recent years, Internet of Things (IoT) technology has emerged as a promising solution to address these challenges by enabling efficient and automated soil analysis and environmental monitoring. This paper presents a 3D-printed IoT-based Agro-toolbox, designed for comprehensive soil analysis and environmental monitoring in the agricultural domain. The toolbox integrates various sensors for both soil and environmental measurements. By deploying this tool across fields, farmers can continuously monitor key soil parameters, including pH levels, moisture content, and temperature. Additionally, environmental factors such as ambient temperature, humidity, intensity of visible light, and barometric pressure can be monitored to assess the overall health of agricultural ecosystems. To evaluate the effectiveness of the Agro-toolbox, a case study was conducted in an aquaponics floating system with rocket, and benchmarking was performed using commercial tools that integrate sensors for soil temperature, moisture, and pH levels, as well as for air temperature, humidity, and intensity of visible light. The results showed that the Agro-toolbox had an acceptable error percentage, and it can be useful for agricultural applications.

Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson's disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient's physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness.

This paper proposes a generic algorithm for industries with degrading and/or failing equipment with significant consequences. Based on the specifications and the real-time status of the production line, the algorithm provides decision support to machinery operators and manufacturers about the appropriate lifetime extension strategies to apply, the optimal time-frame for the implementation of each and the relevant machine components. The relevant recommendations of the algorithm are selected by comparing smartly chosen alternatives after simulation-based life cycle evaluation of Key Performance Indicators (KPIs), considering the short-term and long-term impact of decisions on these economic and environmental KPIs. This algorithm requires various inputs, some of which may be calculated by third-party algorithms, so it may be viewed as the ultimate algorithm of an overall Decision Support Framework (DSF). Thus, it is called "DSF Core". The algorithm was applied successfully to three heterogeneous industrial pilots. The results indicate that compared to the lightest possible corrective strategy application policy, following the optimal preventive strategy application policy proposed by this algorithm can reduce the KPI penalties due to stops (i.e., failures and strategies) and production inefficiency by 30-40%.

Selective laser melting (SLM) is one of the most reliable and efficient procedures for Metal Additive Manufacturing (AM) due to the capability to produce components with high standards in terms of dimensional accuracy, surface finish, and mechanical behavior. In the past years, the SLM process has been utilized for direct manufacturing of fully functional mechanical parts in various industries, such as aeronautics and automotive. Hence, it is essential to investigate the SLM procedure for the most commonly used metals and alloys. The current paper focuses on the impact of crucial process-related parameters on the final quality of parts constructed with the Inconel 718 superalloy. Utilizing the SLM process and the Inconel 718 powder, several samples were fabricated using various values on critical AM parameters, and their mechanical behavior as well as their surface finish were examined. The investigated parameters were the laser power, the scan speed, the spot size, and their output Volumetric Energy Density (VED), which were applied on each specimen. The feedstock material was inspected using Scanning Electron Microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDX) analysis, and Particle-size distribution (PSD) measurements in order to classify the quality of the raw material. The surface roughness of each specimen was evaluated via multi-focus imaging, and the mechanical performance was quantified utilizing quasi-static uniaxial tensile and nanoindentation experiments. Finally, regression-based models were developed in order to interpret the behavior of the AM part's quality depending on the process-related parameters.

Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality.

Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers' abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions.

Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat's identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.

Indoor localization systems have already wide applications mainly for providing localized information and directions. The majority of them focus on commercial applications providing information such us advertisements, guidance and asset tracking. Medical oriented localization systems are uncommon. Given the fact that an individual's indoor movements can be indicative of his/her clinical status, in this paper we present a low-cost indoor localization system with room-level accuracy used to assess the frailty of older people. We focused on designing a system with easy installation and low cost to be used by non technical staff. The system was installed in older people houses in order to collect data about their indoor localization habits. The collected data were examined in combination with their frailty status, showing a correlation between them. The indoor localization system is based on the processing of Received Signal Strength Indicator (RSSI) measurements by a tracking device, from Bluetooth Beacons, using a fingerprint-based procedure. The system has been tested in realistic settings achieving accuracy above 93% in room estimation. The proposed system was used in 271 houses collecting data for 1⁻7-day sessions. The evaluation of the collected data using ten-fold cross-validation showed an accuracy of 83% in the classification of a monitored person regarding his/her frailty status (Frail, Pre-frail, Non-frail).