Search Results (203)

The importance of the electronics sector in the modern world is unquestionable, as it demonstrates clean technology, dry processes, and efficient design, which favor Industry 4.0 and sustainability. Nonetheless, the large number of instruments developed, and their correspondent quick obsolescence, imply an increment in electronic waste. Therefore, in this work, with the aim of diminishing obsolescence, we developed and customized one application that runs independently of systems and takes advantage of the existing computing structures. The application is a new edge computing structure (the AIFC) that is based on an enterprise service bus (ESB) developed in decentralized microservices. In this study, we conducted action research involving the collaboration of researchers and practitioners, and the tests involved six different scenarios; they used existing low-cost, basic computing environments and ranged from the proof of concept, prototype, minimum viable product, and scalability to the roadmap for the structure implementation. The six scenarios emulated sections of a small and medium-sized enterprise (SME), and all the developed microservices communicate with each other to perform data filtering, processing, storage, query, and sensor data acquisition. The results show that it is possible to carry out these functions with low latency and without any decrement or even increase in performance when compared with more conventional cloud computing structures, and it is also possible to manipulate different products that do not have single, consolidated structures. Moreover, there is no need to update machines or communication structures, which are the main factors of rapid obsolescence. Therefore, following the steps of the AIFC development, the results from the proof of concept to the minimum viable product and scalability tests correspond to a roadmap for a sustainable solution and are an important tool for both Industry 4.0 and SMEs. Full article

In recent years, the pervasive presence of visual disinformation in the media and visual culture, propelled by technological advancements, has become an escalating concern. This article asserts the urgent need to revise the current conceptual framework for addressing this challenge. A significant hurdle is the ambiguity surrounding the very concept of manipulation. Two distinct concepts of manipulation coexist—one with moral implications and the other without. This article examines this conceptual discrepancy across academic cultures, identifying them as anchored, respectively, in the social sciences and humanities and in the natural sciences and medicine. It then analyzes how these two concepts are used in white papers and other policy documents that guide responses to visual disinformation from 2018 to 2021. The article further investigates the complexities of these manipulation concepts within photography and visual expression. By elucidating and questioning them, the article aims to enhance the framework for addressing visual manipulation, foster interdisciplinary collaboration, and enrich theories of camera-based imaging across various fields. Overall, this article highlights deficiencies in the current framework and strives to improve it, thereby aiding in tackling visual disinformation and fostering effective collaboration among stakeholders. Full article

Domain adaptation (DA) is essential for developing robust machine learning models capable of operating across different domains with minimal retraining. This study explores the application of domain adaptation techniques to 3D datasets for industrial object detection, with a focus on short-range and long-range scenarios. While 3D data provide superior spatial information for detecting industrial parts, challenges arise due to domain shifts between training data (often clean or synthetic) and real-world conditions (noisy and occluded environments). Using the MVTec ITODD dataset, we propose a multi-level adaptation approach that leverages local and global feature alignment through PointNet-based architectures. We address sensor variability by aligning data from high-precision, long-range sensors with noisier short-range alternatives. Our results demonstrate an 85% accuracy with a minimal 0.02% performance drop, highlighting the resilience of the proposed methods. This work contributes to the emerging needs of Industry 5.0 by ensuring adaptable and scalable automation in manufacturing processes, empowering robotic systems to perform precise, reliable object detection and manipulation under challenging, real-world conditions, and supporting seamless human–robot collaboration. Full article

In order to address environmental and climate change in a sustainable manner, it is necessary for corporations to make commitments in accordance with sustainable principles, which can be communicated through the use of indicators. Many indicator-based sustainability assessments neglect the supply chain stages by reporting results with incomplete and misleading information. Indicators are sometimes manipulated to the benefit of companies, a strategy known as “greenwashing”. One method of misleading is to transfer the non-sustainable activities of one company to other related companies. With such practices, the question of how to measure sustainability remains unclear. This paper proposes and defines the sustainability meta-indicator ($SmI$) which can serve as a tool for the assessment of misleading sustainability claims. Its value depends on the sustainability indicators of the other collaborating companies where the company in question pays money, and the corresponding amount of paid money. The results of using $SmI$ on synthetic data demonstrate that the proposed meta-indicator facilitates the evaluation of misleading sustainability claims. The aim of the $SmI$ is to satisfy consumers’ need for information about the sustainability of a company’s products or services. In addition, it can be used as a communication mechanism to enhance the value of companies and support new partnerships and business strategies. For policymakers, this meta-indicator serves as a tool to support measures against misinformation and misleading consumers. Full article

In recent years, technologies for human–robot interaction (HRI) have undergone substantial advancements, facilitating more intuitive, secure, and efficient collaborations between humans and machines. This paper presents a decentralized HRI platform, specifically designed for printed circuit board manufacturing. The proposal incorporates many input devices, including gesture recognition via Leap Motion and Tap Strap, and speech recognition. The gesture recognition system achieved an average accuracy of 95.42% and 97.58% for each device, respectively. The speech control system, called Cellya, exhibited a markedly reduced Word Error Rate of 22.22% and a Character Error Rate of 11.90%. Furthermore, a scalable user management framework, the decentralized multimodal control server, employs biometric security to facilitate the efficient handling of multiple users, regulating permissions and control privileges. The platform’s flexibility and real-time responsiveness are achieved through advanced sensor integration and signal processing techniques, which facilitate intelligent decision-making and enable accurate manipulation of manufacturing cells. The results demonstrate the system’s potential to improve operational efficiency and adaptability in smart manufacturing environments. Full article

The monitoring and control of an assembly/disassembly/replacement (A/D/R) multifunctional robotic cell (MRC) with the ABB 120 Industrial Robotic Manipulator (IRM), based on IoT (Internet of Things)-cloud, VPN (Virtual Private Network), and digital twin (DT) technology, are presented in this paper. The approach integrates modern principles of smart manufacturing as outlined in Industry/Education 4.0 (automation, data exchange, smart systems, machine learning, and predictive maintenance) and Industry/Education 5.0 (human–robot collaboration, customization, robustness, and sustainability). Artificial intelligence (AI), based on machine learning (ML), enhances system flexibility, productivity, and user-centered collaboration. Several IoT edge devices are engaged, connected to local networks, LAN-Profinet, and LAN-Ethernet and to the Internet via WAN-Ethernet and OPC-UA, for remote and local processing and data acquisition. The system is connected to the Internet via Wireless Area Network (WAN) and allows remote control via the cloud and VPN. IoT dashboards, as human–machine interfaces (HMIs), SCADA (Supervisory Control and Data Acquisition), and OPC-UA (Open Platform Communication-Unified Architecture), facilitate remote monitoring and control of the MRC, as well as the planning and management of A/D/R tasks. The assignment, planning, and execution of A/D/R tasks were carried out using an augmented reality (AR) tool. Synchronized timed Petri nets (STPN) were used as a digital twin akin to a virtual reality (VR) representation of A/D/R MRC operations. This integration of advanced technology into a laboratory mechatronic system, where the devices are organized in a decentralized, multilevel architecture, creates a smart, flexible, and scalable environment that caters to both industrial applications and educational frameworks. Full article

Titanium additive manufacturing using powder bed fusion technologies has seen notable growth since 2015, particularly in high-performance sectors such as aerospace, biomedical, and automotive industries. This study focuses on key areas like metallic powder manipulation, laser optimization, and process control, with selective laser melting emerging as the dominant technique over electron beam melting. Advancements in powder materials and laser systems have been crucial to improving the efficiency and quality of the process, particularly in enhancing microstructure and porosity control. The bibliometric analysis reveals significant global interest, driven mainly by collaborations among institutions in Germany, the United States, and China, where further international cooperation is required to scale titanium additive manufacturing. However, additional research is essential to address challenges in scalability, sustainability, and post-processing, thus expanding the applications of PBF technology across industries. In conclusion, titanium processing via powder bed fusion is poised to make substantial contributions to the future of manufacturing, provided current challenges are addressed through innovation and enhanced global collaboration. Full article

The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems. Full article

Force control is one of the core modules for surface finishing such as grinding, polishing and sanding. However, the current force control methods based on human skills lack in-depth analysis of data patterns or are only applicable to flat surfaces. In addition, surface finishing is mainly performed by hand, resulting in low processing efficiency and poor product consistency. Therefore, this paper proposes a force control method that incorporates human skills to achieve relatively accurate force skill transfer and complex surface finishing. Firstly, human skills consisting of the force skill and the motion skill are learned. The force skill is used to generate the desired force. Then, a series of discrete poses are obtained based on human demonstration and combined with the motion skill to generate the desired trajectory. Finally, a computed-torque impedance control method is proposed to achieve relatively accurate force skill transfer and complex surface finishing by incorporating the desired trajectory and the desired force. The experiments are conducted on a platform composed of a 7-DOF collaborative robot manipulator from Franka Emika and a complex violin surface. The results demonstrate that the proposed force control method can achieve relatively accurate force skill transfer and improve the surface quality of the workpiece. Full article

The food industry increasingly depends on technological assets to improve the efficiency and accuracy of fruit processing and quality control. This article enhances the application of computer vision with collaborative robotics to create a non-destructive system. The system can automate the detection and handling of fruits, particularly tomatoes, reducing the reliance on manual labor and minimizing damage during processing. This system was developed with a Raspberry Pi 5 to capture images of the fruit using a PiCamera module 3. After detecting the object, a command is sent to a Universal Robotics UR3e robotic arm via Ethernet cable, using Python code that integrates company functions and functions developed specifically for this application. Four object detection models were developed using the TensorFlow Object Detection API, converted to TensorFlow Lite, to detect two types of fruit (tomatoes) using deep learning techniques. Each fruit had two versions of the models. The models obtained 67.54% mAP for four classes and 64.66% mAP for two classes, A rectangular work area was created for the robotic arm and computer vision to work together. After 640 manipulation tests, a reliable area of 262 × 250 mm was determined for operating the system. In fruit sorting facilities, this system can be employed to automatically classify fruits based on size, ripeness, and quality. This ensures consistent product standards and reduces waste by sorting fruits according to pre-defined criteria. The system’s ability to detect multiple fruit types with high accuracy enables it to integrate into existing workflows, thereby increasing productivity and profitability for food processing companies. Additionally, the non-destructive nature of this technology allows for the inspection of fruits without causing any damage, ensuring that only the highest-quality produce is selected for further processing. This application can enhance the speed and precision of quality control processes, leading to improved product quality and customer satisfaction. Full article

Nominal tense is a cross-linguistically rare and understudied phenomenon, with past vs. non-past being the minimal distinction. In some languages, past nominal tense implies the reading ‘deceased’, while in others, lifetime effects (i.e., implicatures about whether an individual is dead or alive) restrict its use with permanent properties (as with kinship terms). In our study, we investigate for the first time the online processing of lifetime effects in sentences with past nominal tense and kinship terms following contextual information about an individual’s lifetime status (dead or alive). An end-of-sentence acceptability judgment task completes the study. Evidence comes from 25 speakers of Pomak (Slavic, Greece) who use the realis past suffix to form, among others, definite articles, contrasting with a generic suffix for the future, habitual, and irrealis. In collaboration with a local Pomak research assistant, we prepared 80 experimental sentences with past nominal tense in four conditions, manipulating Lifetime Status (dead × alive) and Tense Concord between nominal and verbal tense/aspect (congruent × incongruent). Our results suggest that past nominal tense with kinship terms triggers a lifetime effect which is apparent during online processing. In the sentence final region, dead referents with future verbal tense are read faster, possibly due to the overtness and severity of the violation. Reading disruptions for dead referents while processing nominal tense are also discussed. In the acceptability task, participants rated sentences only based on the agreement of the lifetime status with verbal tense/aspect as the violation is overt and severe. The present study therefore offers support to previous reports of lifetime effects in other languages with nominal tense, highlighting another similarity between nominal and verbal tense. Full article

Small and medium-scale enterprises (SMEs) need a platform that actively enables collaboration with research institutions and consultants as SMEs lack the financial resources to conduct independent research. Such a platform will require a verifiable manipulation-free system to enable, execute, and record collaboration activities and to track reputations among the organizations and individuals that use the platform. Blockchain provides an opportunity to build such a collaborative platform by enabling the verifiable recording of the results of the collaborations, aggregating the resulting reputation of the collaborating parties, and offering tokenized incentives to reward positive contributions to the platform. Cryptocurrencies from which blockchain tokens are derived are volatile, thereby reducing business organizations’ interest in blockchain applications. Hence, there is a need to design a self-sustaining valuable token model that incentivizes user behaviours that positively contribute to the platform. This paper explores the application of game theory in analyzing token-based economic interactions between various groups of users in an implemented blockchain-based collaboration platform to design and simulate a token distribution system that provides a fair reward mechanism for users while also providing a dynamic pricing model for the utility value provided by platform tokens. To achieve this objective, we adopted the design science research method, a running case of a blockchain collaboration platform that enables research collaboration, and extensive form games in game theory, first to analyze and simulate token outcomes of users of the collaboration platform. Secondly, the research used a logarithmic model to show the dynamic utility pricing property of the developed token model where the self-sustainability of the token is backed by the availability of an internal resource within the platform. Thirdly, we applied a qualitative approach to analyze potential risks in the designed token model and proposed risk mitigation strategies. Thus, the resulting models and their simulations, such as token distribution models and a dynamic token utility model, as well as the identified token risks and their mitigation strategies, represent the main contributions of this work. Full article

Introduction: Rabies remains a significant global threat, yet accurate estimations of its impact are hindered by the lack of confirmatory diagnoses. Postmortem diagnosis of rabies traditionally involves invasive brain tissue testing, a process met with resistance from deceased patients’ families, impeding consent. This paper presents and evaluates an innovative yet unpublished transnasal approach for postmortem brain tissue collection, offering a minimally invasive, easier, faster, and safer method. This method preserves the cadaver’s integrity, potentially easing family reluctance towards autopsies. The limited testing of both human and animal rabies in Ghana highlights the challenges in diagnosing this fatal disease. Scarce diagnostic resources and the complexity of obtaining brain tissue samples exacerbate the issue. Cultural and religious beliefs surrounding autopsies contribute to familial hesitation, as families view these procedures as disruptive and disfiguring, further complicating consent. Methodology: The transnasal technique involves approaching the brain tissue through the nostrils and cribriform plate without any superficial manipulation of the patient’s head and face, thereby preserving the aesthetics and natural features of the person. Results: Technological advancements and seamless One Health collaboration among governmental, non-governmental, and research entities locally and globally have culminated in Ghana’s first confirmed rabies diagnosis using this method of brain tissue collection. This success emphasizes the efficiency and feasibility of the transnasal brain collection approach and the invaluable role of the One Health approach and collaborative efforts in overcoming diagnostic challenges in rabies control. Full article

In financial markets, irrational behaviors such as hyperbolic discounting and panic selling are prevalent. However, their widespread empirical associations remain unexplored. Numerous behavioral theories discuss how cognitive biases exacerbate panic selling through the lens of immediate loss aversion, a phenomenon in which individuals exhibit impulsive decision-making tendencies due to an intense fear of financial loss during market upheaval. Despite the theoretical elucidation, empirical investigations of these dynamics are lacking. Using a robust dataset comprising 121,293 active investors sourced from a collaborative effort between Hiroshima University and Rakuten Securities Inc., this study used mean comparison tests and probit regression to analyze hyperbolic discounting’s role in panic selling behavior on the global COVID-19 financial crisis. The findings reveal that hyperbolic discounting plays a central role in triggering investors’ impulsive panic selling behavior, which is driven primarily by fear of potential losses. Other factors that influence panic selling behavior include age, male gender, low education level, financial literacy, household income, household assets, risk aversion, and overconfidence in financial knowledge. Our study explicates the need to address cognitive biases in financial decision making during market crises through strategies such as targeted financial education, regulatory interventions against market manipulation, and the provision of professional advice to investors. Full article

The combination of different aerodynamic configurations and propulsion systems, namely, aero-propulsion, affects flight performance differently. These effects are closely related to multidisciplinary collaborative aspects (aerodynamic configuration, propulsion, energy, control systems, etc.) and determine the overall energy consumption of an aircraft. The potential benefits of distributed propulsion (DP) involve propulsive efficiency, energy-saving, and emissions reduction. In particular, wake filling is maximized when the trailing edge of a blended wing body (BWB) is fully covered by propulsion systems that employ boundary layer ingestion (BLI). Nonetheless, the thrust–drag imbalance that frequently arises at the trailing edge, excessive energy consumption, and flow distortions during propulsion remain unsolved challenges. These after-effects imply the complexity of DP systems in multidisciplinary optimization (MDO). To coordinate the different functions of the aero-propulsive configuration, the application of MDO is essential for intellectualized modulate layout, thrust manipulation, and energy efficiency. This paper presents the research challenges of ultra-high-dimensional optimization objectives and design variables in the current literature in aerodynamic configuration integrated DP. The benefits and defects of various coupled conditions and feasible proposals have been listed. Contemporary advanced energy systems, propulsion control, and influential technologies that are energy-saving are discussed. Based on the proposed technical benchmarks and the algorithm of MDO, the propulsive configuration that might affect energy efficiency is summarized. Moreover, suggestions are drawn for forthcoming exploitation and studies. Full article