Search

Reduced pyramids, including in particular pyramids without analysis filters, are known to produce excellent results when used for lossless signal and image compression. The present paper presents a methodology for the optimal construction of such pyramids by selecting the interpolation synthesis post-filters so as to minimize the error variance at each level of the pyramid. This establishes optimally efficient interpolative pyramidal lossless compression. It also has the added advantage of producing lossy replicas of the original which, at lower resolutions, retain as much similarity to the original as possible. This is highly useful for the progressive coding of signals or images needed for many applications such as fast browsing through image databases or hybrid lossless/lossy medical image coding. The general optimization methodology is developed first, for a general family of reduced pyramids. Subsequently, this is applied to the optimization of pyramids in this family formed using separable, two-dimensional (2-D) quincunx and three-dimensional (3-D) face-centered orthorhombic lattice sampling matrices. It is shown that this family includes in particular the well known 2-D and 3-D 'hierarchical interpolation' (HINT) techniques which have been particularly popular for the lossless compression of medical records. Optimal versions of these techniques are determined for 2-D and 3-D images characterized by separable or isotropic correlation functions. The advantages of the developed methods are demonstrated by experimental evaluation. It is shown that the method outperforms the HINT method for the lossless compression of 3-D images. It is also shown to outperform all other known interpolative coders and to produce results comparable to the best predictive lossless coder of 2-D images. Index Terms--Lossless image compression, minimum variance filters, progressive image transmission, reduced pyramids.

Summary: The early detection of potential malfunctions at process systems can significantly reduce downtime and improve their overall operability. In that context, this paper demonstrates the behavior and response, through a comparative analysis, of novel data‐driven diagnosis methods for interdependent time series. The proposed real‐time slope statistic profile method utilizes a self‐adaptive sliding window based on a real‐time classification technique of linear trend profiles of both interdependent time series and internal condition so as to avoid misdetections. The calculation of the linear trend profile is based on a standard parametric linear trend test, and the selection of possible incidents is based on its two‐level cross‐checking. All possible combinations for the calculation of the trend test and cross‐checking are created to explore their efficiency. The proposed methods are tested against real data sets from a chemical process system of the Centre for Research and Technology Hellas/Chemical Process Energy and Resources Institute derived from specific scenarios during nominal operating conditions. [ABSTRACT FROM AUTHOR]

BACKGROUND: Accessibility evaluation of Graphical User Interfaces (GUIs) is a field of ongoing research. Towards an evaluation framework for automatic accessibility assessment, a novel methodology using Virtual User Models (VUMs) is presented. OBJECTIVE: To establish a new framework for GUI accessibility assessment using an automated hierarchical task analysis method which uses virtual user models for cognitive disability to replicate real testers. METHODS: A two-phase process which involves the creation of custom GUI interaction scenario sequences in an implementation-agnostic manner and then the simulated reproduction of these interactions by virtual impaired users. Emphasis is given on the cognitive disabilities, after the development of an advanced cognitive model to be applied over well-established VUM architectures. RESULTS: The results of the actual testers were found correlated to the results extracted from the VUMs performing the same tasks. CONCLUSIONS: The proposed approach is a holistic accessibility assessment of GUIs using Virtual User Models (VUM) in task simulations which can describe cognitive declined functionality as parameterized by VUM. [ABSTRACT FROM AUTHOR]

The present study proposes a novel multimodal biometrics framework for identity recognition and verification following the concept of the so called 'on-the-move' biometry, which sets as the final objective the non-stop authentication in an unobtrusive manner. Gait, that forms the major modality of the scheme, is complemented by new dynamic biometric signatures extracted from several activities performed by the user. Gait recognition is performed through a robust scheme that is based on geometric descriptors of gait energy images and is able to compensate for undesired gait behaviour like walking direction variations and stops. On the other hand, the biometric signatures, based on the user activities, are extracted by tracking of three points of interest and are seen to provide a powerful auxiliary biometric trait. Finally, score level fusion is performed and the experimental results illustrate that the proposed multimodal biometric scheme provides very promising results in realistic application scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy expert system (FES) for the detection of the physiological manifestations of extreme hypovigilance is presented. A large number of features that describe the eyelid activity of drivers is examined, and fuzzy logic is used for the fusion of the most prominent features to not only increase the accident prediction accuracy but also provide a reliable system that generates a small number of false warnings. For the development and testing of the system, driving simulator data from 35 drowsy subjects were used. In addition, a secondary control group of 13 alert drivers was used for the estimation of the trained system's false alarm ratio. The results show that a fuzzy combination of eyelid activity parameters may lead to a system with high sensitivity and specificity in predicting sleep onset and related accidents. [ABSTRACT FROM PUBLISHER]

This paper presents a novel framework for 3-D object content-based search and retrieval, appropriate for both partial and global matching applications. The framework is based on a graph representation of a 3-D object which is enhanced by local geometric features. The 3-D object is decomposed into meaningful parts and an attributed graph is constructed based on the connectivity of the parts. Every 3-D part is approximated with a suitable superellipsoid and a novel 3-D shape descriptor, called a 3-D distance field descriptor, is computed and associated to the corresponding graph nodes. The matching process used is based on attributed graph matching algorithm appropriate for this application. The proposed method not only provides successful retrieval results in terms of geometric similarity but also is invariant to rotation, translation and scaling of an object as well as to the different poses of articulated objects. Finally, it can be effectively used for partial and global 3-D object retrieval. [ABSTRACT FROM PUBLISHER]

This paper proposes an innovative gait identification and authentication method based on the use of novel 2-D and 3-D features. Depth-related data are assigned to the binary image silhouette sequences using two new transforms: the 3-D radial silhouette distribution transform and the 3-D geodesic silhouette distribution transform. Furthermore, the use of a genetic algorithm is presented for fusing information from different feature extractors. Specifically, three new feature extraction techniques are proposed: the two of them are based on the generalized radon transform, namely the radial integration transform and the circular integration transform, and the third is based on the weighted Krawtchouk moments. Extensive experiments carried out on USF ldquoGait Challengerdquo and proprietary HUMABIO gait database demonstrate the validity of the proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, a 3D shape-based approach is presented for the efficient search, retrieval, and classification of protein molecules. The method relies primarily on the geometric 3D structure of the proteins, which is produced from the corresponding PDB files and secondarily on their primary and secondary structure. After proper positioning of the 3D structures, in terms of translation and scaling, the spherical trace transform is applied to them so as to produce geometry-based descriptor vectors, which are completely rotation invariant and perfectly describe their 3D shape. Additionally, characteristic attributes of the primary and secondary structure of the protein molecules are extracted, forming attribute-based descriptor vectors. The descriptor vectors are weighted and an integrated descriptor vector is produced. Three classification methods are tested. A part of the FSSP/DALI database, which provides a structural classification of the proteins, is used as the ground truth in order to evaluate the classification accuracy of the proposed method. The experimental results show that the proposed method achieves more than 99 percent classification accuracy while remaining much simpler and faster than the DALI method [ABSTRACT FROM PUBLISHER]

Measuring the similarity between three-dimensional (3-D) objects is a challenging problem, with applications in computer vision, molecular biology, computer graphics, and many other areas. This paper describes a novel method for 3-D model content-based search based on the 3-D Generalized Radon Transform and a querying by-3-D-model approach. A set of descriptor vectors is extracted using the Radial Integration Transform (RIT) and the Spherical Integration Transform (SIT), which represent significant shape characteristics. After the proper alignment of the models, descriptor vectors are produced which are invariant in terms of translation, scaling and rotation. Experiments were performed using three different databases and comparing the proposed method with those most commonly cited in the literature. Experimental results show that the proposed method is adequately satisfactory in terms of both precision versus recall and time needed for retrieval, and that it can be used for 3-D model search and retrieval in a highly efficient manner. [ABSTRACT FROM PUBLISHER]

The present paper proposes a novel method for content-based search in a database of VRML three-dimensional (3-D) models. The proposed technique is based on a querying-by-3-D-model approach. A set of shape-based descriptors are extracted from the reference 3-D model and compared to the corresponding descriptors of the VRML models contained in the database. The descriptors used vary from simple geometric measurements such as the aspect ratio or a binary 3-D shape mask to more complex and sophisticated shape-based criteria such as the edge paths of each 3-D model. Similarity measures are then introduced for the specific descriptors and introduced into a 3-D model-matching algorithm. Experimental results are presented, evaluating the performance of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper presents a novel methodology for content-based search and retrieval of 3D objects. After proper positioning of the 3D objects using translation and scaling, a set of functionals is applied to the 3D model producing a new domain of concentric spheres. In this new domain, a new set of functionals is applied, resulting in a descriptor vector which is completely rotation invariant and thus suitable for 3D model matching. Further, weights are assigned to each descriptor, so as to significantly improve the retrieval results. Experiments on two different databases of 3D objects are performed so as to evaluate the proposed method in comparison with those most commonly cited in the literature. The experimental results show that the proposed method is superior in terms of precision versus recall and can be used for 3D model search and retrieval in a highly efficient manner.

This paper reviews the state of the art in the field of assistive devices for sight-handicapped people. It concentrates in particular on systems that use image and video processing for converting visual data into an alternate rendering modality that will be appropriate for a blind user. Such alternate modalities can be auditory, haptic, or a combination of both. There is thus the need for modality conversion, from the visual modality to another one; this is where image and video processing plays a crucial role. The possible alternate sensory channels are examined with the purpose of using them to present visual information to totally blind persons. Aids that are either already existing or still under development are then presented, where a distinction is made according to the final output channel. Haptic encoding is the most often used by means of either tactile or combined tactile/kinesthetic encoding of the visual data. Auditory encoding may lead to low-cost devices, but there is need to handle high information loss incurred when transforming visual data to auditory one. Despite a higher technical complexity, audio/haptic encoding has the advantage of making use of all available user's sensory channels.

Stereoscopic image sequence processing has been the focus of considerable attention in recent literature for videoconference applications. A novel Bayesian scheme is proposed in this paper, for the segmentation of a noisy stereoscopic image sequence. More specifically, occlusions and visible foreground and background regions are detected between the left and the right frame while the uncovered-background areas are identified between two successive frames of the sequence. Combined hypotheses are used for the formulation of the Bayes decision rule which employs a single intensity-difference measurement at each pixel. Experimental results illustrating the performance of the proposed technique are presented and evaluated in videoconference applications.

A novel combined frequency and spatial domain method is presented in this paper for blockiness reduction for low bit rate compressed images. The method consists of two stages: in the first, better estimates of the reconstructed DCT coefficients are obtained based on their observed probability distribution. In the second, an efficient postprocessing scheme consisting of a region classification algorithm and a spatial adaptive filtering is applied for blockiness removal. The type of filtering is decided on the basis of an estimation of the local characteristics of the coded image. The main advantage of the proposed method is the efficient combination and design of these two stages which are acting complementarily for the reduction of blocking artifacts. This approach is shown to produce excellent results in removing blocking artifacts. The efficient performance of the proposed algorithm is due, firstly, to the proposition that the shape and the position of the filter kernel are adjusted according to the characteristics of the local image region and secondly, to the employment of the modified improved DCT coefficients by the postprocessing filter. Experimental results illustrating the performance of the proposed method are presented and evaluated.

Multiview image sequence processing has been the focus of considerable attention in recent literature. This paper presents an efficient technique for object-based rigid and non-rigid 3D motion estimation, applicable to problems occurring in multiview image sequence coding applications. More specifically, a neural network is formed for the estimation of the rigid 3D motion of each object in the scene, using initially estimated 2D motion vectors corresponding to each camera view. Non-linear error minimization techniques are adopted for neural network weight update. Furthermore, a novel technique is also proposed for the estimation of the local non-rigid deformations, based on the multiview camera geometry. Experimental results using both stereoscopic and trinocular camera setups illustrate and evaluate the proposed scheme. [Copyright &y& Elsevier]

Despite its growing age, the JPEG is among the most popular choices as a standard compression scheme for continuous-tone still images. In this paper, a novel technique is proposed to alleviate the blocking artifacts that usually occur in JPEG coded images especially at low bit rates. The proposed algorithm consists of two stages: firstly, the AC coefficients are estimated based on their observed probability distribution and secondly, a postprocessing scheme is applied for blockiness removal, consisting of a region classification algorithm and a spatial adaptive filtering. Experimental results demonstrate the efficiency of the proposed method. [Copyright &y& Elsevier]

Objective: The use of smartwatches has attracted considerable interest in developing smart digital health interventions and improving health and well-being during the past few years. This work presents a systematic review of the literature on smartwatch interventions in healthcare. The main characteristics and individual health-related outcomes of smartwatch interventions within research studies are illustrated, in order to acquire evidence of their benefit and value in patient care., Methods: A literature search in the bibliographic databases of PubMed and Scopus was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, in order to identify research studies incorporating smartwatch interventions. The studies were grouped according to the intervention's target disease, main smartwatch features, study design, target age and number of participants, follow-up duration, and outcome measures., Results: The literature search identified 13 interventions incorporating smartwatches within research studies with people of middle and older age. The interventions targeted different conditions: cardiovascular diseases, diabetes, depression, stress and anxiety, metastatic gastrointestinal cancer and breast cancer, knee arthroplasty, chronic stroke, and allergic rhinitis. The majority of the studies (76%) were randomized controlled trials. The most used smartwatch was the Apple Watch utilized in 4 interventions (31%). Positive outcomes for smartwatch interventions concerned foot ulcer recurrence, severity of symptoms of depression, utilization of healthcare resources, lifestyle changes, functional assessment and shoulder range of motion, medication adherence, unplanned hospital readmissions, atrial fibrillation diagnosis, adherence to self-monitoring, and goal attainment for emotion regulation. Challenges in using smartwatches included frequency of charging, availability of Internet and synchronization with a mobile app, the burden of using a smartphone in addition to a patient's regular phone, and data quality., Conclusion: The results of this review indicate the potential of smartwatches to bring positive health-related outcomes for patients. Considering the low number of studies identified in this review along with their moderate quality, we implore the research community to carry out additional studies in intervention settings to show the utility of smartwatches in clinical contexts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Although eHealth interventions are increasingly recognized as a useful tool to support healthcare, relatively few studies focus on the physician-end's usability. This study aims to evaluate the Healthcare Professional's (HCP) platform of the Take-A-Breath project, a Greek initiative for personalized respiratory disease monitoring, training and self-management. The pre-pilot usability study, involving 10 participants, combines qualitative methods, behavioral observations, and standardized measures of user experience and usability. While relatively high scores indicate overall acceptance, concerns are also discussed, particularly related with the volume of information provided and actions available to the users, hindering the usability of the system due to an overload effect. Findings emphasize also the need for more tailored in-app wordings as well as the integration of similar systems with the already set up electronic health record systems. This study contributes to understanding digital intervention success among HCPs in respiratory healthcare.

This study aims to evaluate the patient's platform of the Take-A-Breath project-a Greek project for personalized respiratory disease support. The pre-pilot usability study, involving 11 participants, employed a mixed-methods approach. While calculated scores indicate overall acceptance, concerns are identified, particularly regarding the learning curve needed for the guided inhalation feature, the application's core functionality. Users appreciate the feature's utility and design after repeated use. Findings recommend user manuals and healthcare professional training, providing essential insights for the upcoming RCT.

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.

Energy-related occupant behaviour in the built environment is considered crucial when aiming towards Energy Efficiency (EE), especially given the notion that people are most often unaware and disengaged regarding the impacts of energy-consuming habits. In order to affect such energy-related behaviour, various approaches have been employed, being the most common the provision of recommendations towards more energy-efficient actions. In this work, the authors extend prior research findings in an effort to automatically identify the optimal Persuasion Strategy (PS), out of ten pre-selected by experts, tailored to a user (i.e., the context to trigger a message, allocate a task or providing cues to enact an action). This process aims to successfully influence the employees' decisions about EE in tertiary buildings. The framework presented in this study utilizes cultural traits and socio-economic information. It is based on one of the largest survey datasets on this subject, comprising responses from 743 users collected through an online survey in four countries across Europe (Spain, Greece, Austria and the UK). The resulting framework was designed as a cascade of sequential data-driven prediction models. The first step employs a particular case of matrix factorisation to rank the ten PP in terms of preference for each user, followed by a random forest regression model that uses these rankings as a filtering step to compute scores for each PP and conclude with the best selection for each user. An ex-post assessment of the individual steps and the combined ensemble revealed increased accuracy over baseline non-personalised methods. Furthermore, the analysis also sheds light on important user characteristics to take into account for future interventions related to EE and the most effective persuasion strategies to adopt based on user data. Discussion and implications of the reported results are provided in the text regarding the flourishing field of personalisation to motivate pro-environmental behaviour change in tertiary buildings., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Kalamaras et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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.

The importance of construction automation has grown worldwide, aiming to deliver new machineries for the automation of roads, tunnels, bridges, buildings and earth-work construction. This need is mainly driven by (i) the shortage and rising costs of skilled workers, (ii) the tremendous increased needs for new infrastructures to serve the daily activities and (iii) the immense demand for maintenance of ageing infrastructure. Shotcrete (sprayed concrete) is increasingly becoming popular technology among contractors and builders, as its application is extremely economical and flexible as the growth in construction repairs in developed countries demand excessive automation of concrete placement. Even if shotcrete technology is heavily mechanized, the actual application is still performed manually at a large extend. RoBétArméEuropean project targets the Construction 4.0 transformation of the construction with shotcrete with the adoption of breakthrough technologies such as sensors, augmented reality systems, high-performance computing, additive manufacturing, advanced materials, autonomous robots and simulation systems, technologies that have already been studied and applied so far in Industry 4.0. The paper at hand showcases the development of a novel robotic system with advanced perception, cognition and digitization capabilities for the automation of all phases of shotcrete application. In particular, the challenges and barriers in shotcrete automation are presented and the RoBétArmésuggested solutions are outlined. We introduce a basic conceptual architecture of the system to be developed and we demonstrate the four application scenarios on which the system is designated to operate., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Kostavelis I et al.)

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.

In the last decade, the development of customized biodegradable scaffolds and implants has attracted increased scientific interest due to the fact that additive manufacturing technologies allow for the rapid production of implants with high geometric complexity constructed via commercial biodegradable polymers. In this study, innovative designs of tibial scaffold in form of bone-brick configuration were developed to fill the bone gap utilizing advanced architected materials and bio-inspired diffusion canals. The architected materials and canals provide high porosity, as well as a high surface area to volume ratio in the scaffold facilitating that way in the tissue regeneration process and in withstanding the applied external loads. The cellular structures applied in this work were the Schwarz Diamond (SD) and a hybrid SD&FCC hybrid cellular material, which is a completely new architected material that derived from the combination of SD and Face Centered Cubic (FCC) structures. These designs were additively manufactured utilizing two biodegradable materials namely Polylactic acid (PLA) and Polycaprolactone (PCL), using the Fused Filament Fabrication (FFF) technique, in order to avoid the surgery, for the scaffold's removal after the bone regeneration. Furthermore, the additively manufactured scaffolds were examined in terms of compatibility and assembly with the bone's physical model, as well as, in terms of mechanical behavior under realistic static loads. In addition, non-linear finite element models (FEMs) were developed based on the experimental data to accurately simulate the mechanical response of the examined scaffolds. The Finite Element Analysis (FEA) results were compared with the experimental response and afterwards the stress concentration regions were observed and identified. Τhe proposed design of scaffold with SD&FCC lattice structure made of PLA material with a relative density of 20% revealed the best overall performance, showing that it is the most suitable candidate for further investigation (in-vivo test, clinical trials, etc.) and commercialization., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective: Long term behavioural disturbances and interventions in healthy habits (mainly eating and physical activity) are the primary cause of childhood obesity. Current approaches for obesity prevention based on health information extraction lack the integration of multi-modal datasets and the provision of a dedicated Decision Support System (DSS) for health behaviour assessment and coaching of children., Methods: Continuous co-creation process has been applied in the frame of the Design Thinking Methodology, involving children, educators and healthcare professional in the whole process. Such considerations were used to derive the user needs and the technical requirements needed for the conception of the Internet of Things (IoT) platform based on microservices., Results: To promote the adoption of healthy habits and the prevention of the obesity onset for children (9-12 years old), the proposed solution empowers children -including families and educators- in taking control of their health by collecting and following-up real-time information about nutrition, physical activity data coming from IoT devices, and interconnecting healthcare professionals to provide a personalised coaching solution. The validation has two phases involving +400 children (control/intervention group), on four schools in three countries: Spain, Greece and Brazil. The prevalence of obesity decreased in 75.5% from baseline levels in the intervention group. The proposed solution created a positive impression and satisfaction from the technology acceptance perspective., Conclusions: Main findings confirm that this ecosystem can assess behaviours of children, motivating and guiding them towards achieving personal goals. Clinical and Translational Impact Statement-This study presents Early Research on the adoption of a smart childhood obesity caring solution adopting a multidisciplinary approach; it involves researchers from biomedical engineering, medicine, computer science, ethics and education. The solution has the potential to decrease the obesity rates in children aiming to impact to get a better global health.

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%.

This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks. Regarding robotic harvesting, apples, strawberries, tomatoes and sweet peppers are mainly the crops considered in publications, research projects and commercial products. The reported harvesting agricultural robotic solutions, typically consist of a mobile platform, a single robotic arm/manipulator and various navigation/vision systems. This paper reviews reported development of specific functionalities and hardware, typically required by an operating agricultural robot harvester; they include (a) vision systems, (b) motion planning/navigation methodologies (for the robotic platform and/or arm), (c) Human-Robot-Interaction (HRI) strategies with 3D visualization, (d) system operation planning & grasping strategies and (e) robotic end-effector/gripper design. Clearly, automated agriculture and specifically autonomous harvesting via robotic systems is a research area that remains wide open, offering several challenges where new contributions can be made., Competing Interests: Conflict of InterestsThe authors declare that they have no conflict of interest., (© The Author(s) 2023.)