Search Results (30)

This paper presents an open-architecture mechatronic drive platform for operating a three-phase BLDC servomotor in an industrial robotic axis. A sequential and iterative mechatronic design methodology is adopted, integrating electronic design, digital control, mechanical development, and experimental prototyping, with emphasis on open-loop operation. The electronic circuit was designed using schematics and a PCB and validated in Proteus Design Suite 8.15 (Labcenter Electronics Ltd., London, UK) to verify switching sequences and inverter behavior. The power stage is based on a six-switch insulated-gate bipolar transistor (IGBT) inverter module, complemented by an independent snubber protection board and a dedicated digital gate-drive control board. The mechanical enclosure was designed using computer-aided design (CAD), CAD software tools (Shapr3D, version 5.911.0 (9224), Shapr3D Zrt., Budapest, Hungary), and fabricated via 3D printing. Switching behavior was simulated in Octave using parameters from a real industrial BLDC servomotor (Yaskawa SGMAH series) extracted from a Motoman robotic axis. The contribution is design-oriented in a mechatronic engineering sense, emphasizing accessibility, openness, and experimental enablement of industrial drive hardware rather than control-performance optimization. An industrial Yaskawa BLDC servomotor from the Motoman robot is used to determine switching sequences and safe operating parameters. Experimental open-loop tests were conducted by directly commanding the six inverter switching sectors, resulting in the stable synchronous rotation of the motor on the developed electromechanical platform. Full article

Amid growing sustainability and ethical concerns, digital innovation increasingly requires integrating social, environmental, and governance responsibility into technological development. However, little is known about how organizations in emerging economies—particularly in North Africa—operationalize these principles in practice. This study addresses this gap by exploring how institutional and economic actors in Morocco incorporate responsibility principles into their digital innovation strategies. Adopting an exploratory qualitative design, we conducted 27 semi-structured interviews with digital managers from public organizations (ministries, institutions, and local authorities) and private firms (technology companies, start-ups, and large corporations). The central research question guiding this study is: How do Moroccan organizations perceive and implement the principles of responsible digital innovation (RDI)? Data were analyzed using NVivo 14 software through thematic coding and triangulated with policy documents to enhance validity. The results reveal growing awareness of digital sustainability issues—particularly energy efficiency, accessibility, and data protection—yet the degree of responsible practice varies by sector, firm size, and regulatory environment. Key obstacles include limited expertise, absence of ethical performance metrics, and competitive pressures constraining investment in RDI. Conversely, ethical charters, frugal design, and stakeholder engagement emerge as key drivers. The study concludes that embedding responsibility in digital innovation requires shared governance frameworks, supportive public policies, and cross-sector collaboration to promote inclusive and sustainable technological progress. While context-specific, this research opens avenues for comparative and quantitative studies on RDI across emerging economies. Full article

Spiking neural networks (SNNs) provide a biologically inspired, event-driven alternative to artificial neural networks (ANNs), potentially delivering competitive accuracy at substantially lower energy. This tutorial-study offers a unified, practice-oriented assessment that combines critical review and standardized experiments. We benchmark a shallow fully connected network (FCN) on MNIST and a deeper VGG7 architecture on CIFAR-10 across multiple neuron models (leaky integrate-and-fire (LIF), sigma–delta, etc.) and input encodings (direct, rate, temporal, etc.), using supervised surrogate-gradient training implemented in Intel Lava, SLAYER, SpikingJelly, Norse, and PyTorch. Empirically, we observe a consistent but tunable trade-off between accuracy and energy. On MNIST, sigma–delta neurons with rate or sigma–delta encodings achieve 98.1% accuracy (ANN baseline: 98.23%). On CIFAR-10, sigma–delta neurons with direct input reach 83.0% accuracy at just two time steps (ANN baseline: 83.6%). A GPU-based operation-count energy proxy indicates that many SNN configurations operate below the ANN energy baseline; some frugal codes minimize energy at the cost of accuracy, whereas accuracy-oriented settings (e.g., sigma–delta with direct or rate coding) narrow the performance gap while remaining energy-conscious—yielding up to threefold efficiency compared with matched ANNs in our setup. Thresholds and the number of time steps are decisive factors: intermediate thresholds and the minimal time window that still meets accuracy targets typically maximize efficiency per joule. We distill actionable design rules—choose the neuron–encoding pair according to the application goal (accuracy-critical vs. energy-constrained) and co-tune thresholds and time steps. Finally, we outline how event-driven neuromorphic hardware can amplify these savings through sparse, local, asynchronous computation, providing a practical playbook for embedded, real-time, and sustainable AI deployments. Full article

Quantum machine learning (QML) integrates quantum computing with classical machine learning. Within this domain, QML-CQ classification tasks, where classical data is processed by quantum circuits, have attracted particular interest for their potential to exploit high-dimensional feature maps, entanglement-enabled correlations, and non-classical priors. Yet, practical realizations remain constrained by the Noisy Intermediate-Scale Quantum (NISQ) era, where limited qubit counts, gate errors, and coherence losses necessitate frugal, noise-aware strategies. The Data Re-Uploading (DRU) algorithm has emerged as a strong NISQ-compatible candidate, offering universal classification capabilities with minimal qubit requirements. While DRU has been experimentally demonstrated on ion-trap, photonic, and superconducting platforms, no implementations exist for spin-based quantum processing units (QPU-SBs), despite their scalability potential via CMOS-compatible fabrication and recent demonstrations of multi-qubit processors. Here, we present a pulse-level, noise-aware DRU framework for spin-based QPUs, designed to bridge the gap between gate-level models and realistic spin-qubit execution. Our approach includes (i) compiling DRU circuits into hardware-proximate, time-domain controls derived from the Loss–DiVincenzo Hamiltonian, (ii) explicitly incorporating coherent and incoherent noise sources through pulse perturbations and Lindblad channels, (iii) enabling systematic noise-sensitivity studies across one-, two-, and four-spin configurations via continuous-time simulation, and (iv) developing a noise-aware training pipeline that benchmarks gate-level baselines against spin-level dynamics using information-theoretic loss functions. Numerical experiments show that our simulations reproduce gate-level dynamics with fidelities near unity while providing a richer error characterization under realistic noise. Moreover, divergence-based losses significantly enhance classification accuracy and robustness compared to fidelity-based metrics. Together, these results establish the proposed framework as a practical route for advancing DRU on spin-based platforms and motivate future work on error-attentive training and spin–quantum-dot noise modeling. Full article

Severe neonatal hyperbilirubinemia (SNH) (jaundice) is responsible for over 114,000 preventable neonatal deaths annually, as the technology that can treat the condition is cost-prohibitive for low- and middle-income countries. In this study an open-source neonatal phototherapy device (NPTD) to treat SNH was designed, built, and validated against the phototherapy technical specifications set by the American Academy of Pediatrics and UNICEF. The open-source device can be built for a tenth of the cost of the least expensive proprietary one on the market, with treatment metrics equivalent to or exceeding commercial devices available in developed nations. This device, whose material costs are USD 93.00, was shown to deliver an irradiance up to 80 µW/cm²/nm, within the acceptable wavelength range of 420–500 nm. It was further demonstrated that the unit could deliver a uniform distribution of irradiance (34.5 ± 4.3 µW/cm²/nm) over a surface area exceeding 3200 cm². These findings show that the open-source NPTD is capable of delivering accurate, consistent, and reliable irradiances for the management of SNH. By releasing full documentation in an open-source manner, the device may be broadly used to ensure affordable and consistent low-cost means of improving the quality of care for SNH. Full article

The increasing complexity of the Edge–Cloud Continuum (ECC), driven by the rapid expansion of the Internet of Things (IoT) and data-intensive applications, necessitates implementing innovative methods for automated and efficient system management. In this context, recent studies focused on the utilization of self-* capabilities that can be used to enhance system autonomy and increase operational proactiveness. Separately, anomaly detection and adaptive sampling techniques have been explored to optimize data transmission and improve systems’ reliability. The integration of those techniques within a single, lightweight, and extendable self-optimization module is the main subject of this contribution. The module was designed to be well suited for distributed systems, composed of highly resource-constrained operational devices (e.g., wearable health monitors, IoT sensors in vehicles, etc.), where it can be utilized to self-adjust data monitoring and enhance the resilience of critical processes. The focus is put on the implementation of two core mechanisms, derived from the current state-of-the-art: (1) density-based anomaly detection in real-time resource utilization data streams, and (2) a dynamic adaptive sampling technique, which employs Probabilistic Exponential Weighted Moving Average. The performance of the proposed module was validated using both synthetic and real-world datasets, which included a sample collected from the target infrastructure. The main goal of the experiments was to showcase the effectiveness of the implemented techniques in different, close to real-life scenarios. Moreover, the results of the performed experiments were compared with other state-of-the-art algorithms in order to examine their advantages and inherent limitations. With the emphasis put on frugality and real-time operation, this contribution offers a novel perspective on resource-aware autonomic optimization for next-generation ECC. Full article

Short-form video platforms have shifted advertising from standalone, time-bounded spots to feed-embedded, swipeable stimuli, creating a high-velocity processing context that can penalize casting complexity. We ask whether a “one face, many roles” casting strategy (a single actor playing multiple characters) outperforms multi-actor executions, and why. A two-phase pretest (N = 3500) calibrated a realistic ceiling for “multi-actor” casts, then four experiments (total N = 4513) tested mechanisms, boundary conditions, and alternatives. Study 1 (online and offline replications) shows that single-actor ads lower cognitive load and boost account evaluations and purchase intention. Study 2, a field experiment, demonstrates that Need for Closure amplifies these gains via reduced cognitive load. Study 3 documents brand-type congruence: one actor performs better for entertaining/exciting brands, whereas multi-actor suits professional/competence-oriented brands. Study 4 rules out cost-frugality and sympathy using a budget cue and a sequential alternative path (perceived cost constraint → sympathy). Across studies, a chain mediation holds: single-actor casting reduces cognitive load, which elevates brand authenticity and increases purchase intention; a simple mediation links cognitive load to account evaluations. Effects are robust across settings and participant gender. We theorize short-form advertising as a context-embedded persuasion episode that connects information-processing efficiency to authenticity inferences, and we derive practical guidance for talent selection and script design in short-form campaigns. Full article

Interactions between human behavior, legal regulations, and monitoring technology in road traffic systems provide an everyday example of complex biosocial–technical systems. In this paper, a study is reported that investigated the potential for a thrifty world model to predict consequences from choices about road traffic system design. Colloquially, the term thrifty means economical. In physics, the term thrifty is related to the principle of least action. Predictions were made with algebraic machine learning, which combines predefined embeddings with ongoing learning from data. The thrifty world model comprises three categories that encompass a total of only eight system design choice options. Results indicate that the thrifty world model is sufficient to encompass biosocial–technical complexity in predictions of where and when it is most likely that accidents will occur. Overall, it is argued that thrifty world models can provide a practical alternative to large photo-realistic world models, which can contribute to explainable artificial intelligence (AI) and to frugal AI. Full article

Optical sensors have emerged as a popular technology for sensing biological and chemical analytes in various fields, including environmental monitoring, toxicology, disease/infection screening, and food processing, due to their ease of use, high sensitivity, and specificity. In this study, we introduce ColorX, an ultra-portable and smart spectrophotometric device based on a commercially available fitness tracker. ColorX exploits the in-built LEDs and photodiodes of a fitness tracker for wavelength-specific absorption measurements and can be controlled wirelessly using a companion smartphone app. The device’s raw data are transmitted via Bluetooth and stored on the app for analysis and data visualisation. We validated the performance of ColorX against a standard benchtop spectrophotometer by experimentally testing five different measurements related to water quality: nitrite (>0.07 mg/L, %avgCV: 1.06)), sulphate (>18 mg/L, %avgCV: 0.39), chromium (>0.002 mg/L, %avgCV: 0.51), free chlorine (>0.005 mg/L, %avgCV: 0.68), and turbidity (>2.97 NTU, %avgCV: 1.04). Our results showed that ColorX had comparable performance to the benchmark spectrophotometer (R² values > 0.9 in all cases). Due to its ultra-portability, water-proof design, wireless control, and smartphone-aided data analysis, we believe ColorX will be highly beneficial for a wide range of on-field spectrophotometric applications. Our work demonstrates the potential of frugal science to develop affordable and accessible technology for optical sensing. Full article

In this paper, we deal with the problem of monitoring network latency. Indeed, latency is a key network metric related to both network performance and quality of service, since it directly impacts on the overall user’s experience. High latency leads to unacceptably slow response times of network services, and may increase network congestion and reduce the throughput, in turn disrupting communications and the user’s experience. A common approach to monitoring network latency takes into account the frequently skewed distribution of latency values, and therefore specific quantiles are monitored, such as the 95th, 98th, and 99th percentiles. We present a comparative analysis of the speed of convergence of the sequential FRUGAL-1U, FRUGAL-2U, and EASYQUANTILE algorithms and the design and analysis of parallel, message-passing-based versions of these algorithms that can be used for monitoring network latency quickly and accurately. Distributed versions are also discussed. Extensive experimental results are provided and discussed as well. Full article

The global population growth and the imperative to achieve decarbonization goals suggest a re-evaluation of conventional building design approaches. A shift towards sustainability, guided by the Triple Bottom Line principle, becomes essential. In this context, a design paradigm rooted in frugality and creativity can be a promising alternative. This study delves into the role of creative frugality within affordable housing. Through case studies, we showcase technological solutions designed following frugality and creativity. These solutions demonstrate key research findings, such as the relationship between sustainability, circular practices, and frugal, low-technology buildings. These findings are enabled by the concepts of flexibility, adaptability, and disassemblability. Full article

With the continuous advancement of digital globalization, enterprises need to seek development opportunities in the context of diversification and resource shortage. Frugal innovation provides a new way for enterprises to realize social value and create a win-win situation. Based on 113 enterprises’ survey data, fuzzy set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA) are used to explore the multiple concurrency factors and causality mechanisms of digital transformation, organizational resilience, learning from failure and design thinking for frugal innovation. It is found that: (1) Digital transformation, organizational resilience, internal learning from failure, external learning from failure and design thinking cannot effectively stimulate frugal innovation alone, and linkage matching of multiple antecedents is needed to promote frugal innovation. (2) There are three paths to producing high frugal innovation, namely, external learning from failure and design thinking driving under digital transformation learning from failure driving under design thinking guidance, organizational resilience driving under digital transformation. (3) There are two paths to produce non-high frugal innovation, which are asymmetrical with high frugal innovation path. Our results are helpful for the study of digital transformation and frugal innovation at the enterprise level. In addition, our research results also provide practical solutions and a theoretical basis for enterprises to carry out frugal innovation activities under the new economic normal. Full article

Most government schools and colleges run on low budgets in India, yet they cater to a large population of students. In government settings, biology labs lack basic equipment such as simple microscopes or compound microscopes or sometimes possess just one instrument. In the absence of compound or simple microscopes, the students lose interest in science. To solve this issue, the Department of Biotechnology, Ministry of Science and Technology, Government of India, introduced the origami microscope, the foldscope, into the Indian educational system. In this article, we describe the design of a sustainable, inclusive, and equitable curricula for teaching biological concepts using the foldscope. We describe the implementation of foldscope-adapted curricula in post-secondary settings to teach natural selection, developmental biology, parasitology, and economic Zoology via individual, small-group, and large-group field trips, and project-based learning that involves experiential learning. We found that these culturally sensitive pedagogies involving translingual instructions had the potential to retain students and make science education accessible for the linguistically and culturally diverse population of India. We could successfully implement our project as per the five priority action areas outlined by UNESCO. Therefore, we propose the adoption of the foldscope-adapted curricula under SDG4 to make STEM education accessible in budget-frugal settings. Full article

The presented article aims to design an educational test bench setup for smart grids and renewable energies with multiple features and techniques used in a microgrid. The test bench is designed for students, laboratory engineers, and researchers, which enables electrical microgrid system studies and testing of new, advanced control algorithms to optimize the energy efficiency. The idea behind this work is to design hybrid energy sources, such as wind power, solar photovoltaic power, hydroelectric power, hydrogen energy, and different types of energy storage systems such as batteries, pumped storage, and flywheel, integrating different electrical loads. The user can visualize the state of the components of each emulated scenario through an open-source software that interacts and communicates using OPC Unified Architecture protocol. The researchers can test and validate new solutions to manage the energy behavior in the grid using machine learning and optimization algorithms integrated in the software in form of blocks that can be modified and improved, and then simulate the results. A model-based system of engineering is provided, which describes the different requirements and case studies of the designed test bench, respecting the open-source software and the frugal innovation features in which there is use of low-cost hardware and open-source software. The users obtain the opportunity to add new sources and new loads, change software platforms, and communicate with other simulators and equipment. The students can understand the different features of smart grids, such as defect classification, energy forecasting, energy optimization, and basics of production, transmission, and consumption. Full article

The present research aims to find how firms achieve innovation performance through innovation networks. This study also explores the mediating role of digital innovation and the moderating role of frugal innovation. Quantitative research design is used for data collection and analysis. To analyze the study’s hypotheses we select the energy firms which aim to attain innovation performance. An indirect effect with Soble test was used to check mediation analysis. The results proved that the innovation performance of energy firms is predicted by innovation networks. Our findings proved that digital innovation acts as a mediator between innovation networks and innovation performance links. Our results also show that frugal innovation strengthens the interplay between innovation networks and innovation performance links. This study highlights how energy firms can stabilize innovation performance through the combined influence of innovation networks, digital innovation and frugal innovation. The managers of energy firms should prefer innovation networks to update their knowledge about the upcoming/latest procedures to achieve innovation performance. Moreover, the role of digital innovation in the current digital world is also very important, and the present study used it as mediator. Full article