Search Results (145)

Sustainable computing is now a mainstream expectation of the profession, yet its treatment in higher education remains uneven, and often reflects assumptions of stable power, affordable connectivity, and frequent hardware refresh. This conceptual paper offers a critical synthesis of the misalignment between globally promoted sustainability competencies and the infrastructural realities of African higher education. We argue that when curricula designed for resource-abundant settings are adopted without adaptation in contexts shaped by energy volatility, high data costs, and complex device ecologies, a design–reality gap emerges: students may learn the language of sustainability but lack the practical competence to engineer resilient, resource-aware systems. Employing an explanatory synthesis of two evidence pools, i.e., global work on sustainable computing education and Africa-focused scholarship on infrastructure constraints, we propose the Context-Aware Sustainable Computing Education Framework. The framework integrates three dimensions of reform: pedagogy that shifts from awareness to context-aware action competence through constraint-led challenges, curriculum reform that embeds frugal computing and lifecycle stewardship as technical rigour within core modules, and an infrastructure-as-driver stance that treats the campus energy and device environment as a living laboratory for responsible trade-offs. We conclude with tiered implementation pathways, showing how departments can progress from minimum viable changes to institutional approaches. The synthesis positions African universities as credible contributors to global thinking on resilient computing under tightening resource constraints. Full article

The accelerating transition to low carbon energy systems has intensified the demand for nickel and cobalt from low-grade (<1.5 wt.%) refractory lateritic ores. These low-grade laterites are however not amenable to conventional beneficiation due to their complex mineralogy, eclectic physicochemical properties, and fine Ni–Co dissemination. This review examines recent advances made in the extraction of nickel and cobalt from complex low-grade lateritic ores, emphasizing the interplay between ore mineralogy, chemistry, beneficiation, pretreatment, and processing route selection. Developments in selective ore comminution–classification have led to the generation of Ni-rich fine fractions (undersize) and Co-rich coarse fractions (oversize), enabling differentiated extraction strategies that improve resource utilization, frugal energy use, and process efficiency. Mechanical activation via stirred media milling, thermal calcination-induced structural disorder, and dehydroxylate goethite products, are shown to significantly enhance Ni–Co leaching kinetics under both atmospheric and heap leaching conditions. A critical comparison of pyrometallurgical (rotary-kiln electric furnace) and hydrometallurgical (HPAL, EPAL, heap, atmospheric, bioleaching) routes demonstrates that ore-specific optimization is essential to balance recovery, acid consumption, and greenhouse gas emissions. The novel resin in moist mix (RIMM) process, which integrates ambient leaching and in situ ion exchange selective recovery, is shown to offer potential for sustainable values extraction from sub-economic resources. Furthermore, the review highlights the key innovation challenges and concomitant opportunities for enhanced critical battery metal recovery from complex laterite ores. Full article

Medical question answering (QA) systems and conversational agents have attracted growing interest as tools that can assist clinicians, support medical students, and help patients navigate complex information sources. However, existing evaluations of retrieval strategies largely overlook the cost–benefit balance—here referred to as frugality, under realistic computational constraints. This work introduces a frugality-based evaluation framework that jointly assesses accuracy improvements and computational cost to determine when retrieval-augmented generation is beneficial in medical question answering, rather than evaluating retrieval effectiveness through accuracy alone. This study addresses these gaps through a systematic comparative framework that evaluates retrieval relevance, computational efficiency, and knowledge base composition across multiple biomedical QA tasks. We employ open-source LLMs (LlaMA-3-8B-Instruct, Mistral-7B-Instruct-v0.3, and DeepSeek-7B-Chat) across three benchmark medical QA datasets, including MedMCQA, MedQA-USMLE, and PubMedQA. In addition to that, we evaluate a dataset with larger contexts to simulate model distraction across the CliniQG4QA dataset using additional models (Meditron-7B, Qwen2.5-7B-Medical, Medgemma-4B, Phi-3-mini-4k-Instruct, and GPT4o-Mini). We examine how retrieval design choices alter the accuracy–latency trade-off, examining how relevance, corpus design, and hardware constraints interact in medical retrieval-augmented generation (RAG) systems. Our comprehensive results demonstrate when retrieval is genuinely beneficial versus when it imposes unnecessary computational costs, highlighting interactions between relevance and corpus designs in QA. Full article

Professors, students, and researchers from universities around the world use software distributed under licenses for numerical simulation purposes, which requires a computer with considerable hardware capabilities. This implies a high cost of simulations in engineering applications that require dynamic modeling using numerical methods, particularly in robotics and nonlinear control. This article compares and analyzes the performance of a frugal simulation scheme based on the use of low-cost, free, and open-source technology, specifically a low-power, single-board minicomputer (Raspberry Pi) in conjunction with GNU-Octave software. The benchmark is a numerical simulation of trajectory tracking control in the joint space of a Selective Conformal Assembly Robot Arm (SCARA). To perform this task, a system of coupled nonlinear differential equations is solved in matrix form using a numerical method known as an ODE solver. This solution includes the control law and the dynamic system model derived from Euler–Lagrange formalism. The time complexity and accuracy are analyzed to compare the performance of the frugal simulation tool with that of a conventional simulation setup consisting of a personal computer and MATLAB^TM running the same simulation code. The analysis shows minimal deviations in the numerical solutions and reasonable time complexity. Moreover, the frugality score of this approach and the low acquisition cost of the simulation tool enable the creation of simulation laboratories at universities with limited budgets for education and research. Full article

Frugal innovation is an important area of academic research for organizations seeking to create value with limited resources. However, one important theoretical issue remains unclear: the available literature does not clarify how environmental orientations produce frugal innovation outcomes or how leadership capabilities determine this relationship within the resource-based view. Specifically, the processes by which both external and internal environmental orientations contribute to frugal innovation in resource-constrained environments, and the way transformational leadership is viewed as an intangible strategic resource, are not developed in theory. Therefore, the objective of this study is to explain the importance of frugal innovation in telecom companies in Saudi Arabia and to examine its theoretical link to the resource-based view. Furthermore, this study aims to investigate the relationship between environmental orientation and frugal innovation. Environmental orientation is estimated using both internal and external orientations. Furthermore, this study examines the moderating role of transformational leadership in the relationship between external and internal environmental orientation and frugal innovation in telecom companies in Saudi Arabia. For the empirical analysis, data were collected via a well-developed questionnaire sent to employees of three leading telecom companies: Zain, Mobily, and Saudi Telecom Company (STC). The sample size consisted of 436 employees. Furthermore, SmartPLS 4 software was used for PLS-SEM modeling to test hypotheses and assess model validity. After the data analysis, it was found that external environmental orientation has a positive and significant relationship with frugal innovation, and internal environmental orientation also shows a positive relationship. Furthermore, it was found that transformational leadership positively moderates the relationship between external and internal environmental orientations with frugal innovation in the telecommunications company. Furthermore, this study concluded that the telecommunications sector makes a significant contribution to economic development and that executives’ transformational leadership skills are an important moderator in analyzing environmental orientation and in enhancing employees’ abilities to utilize limited resources to achieve maximum benefits. This study is highly relevant to policymakers and researchers in the context of frugal innovation, in support of the organization’s objective. Full article

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

Integrating emerging Industry 4.0 technologies into smart factories has been widely discussed, particularly challenges regarding the practical use of a blockchain; one remaining challenge is the role of a blockchain beyond logistics and traceability, as well as its ability to support explicit trust measurement in real industrial environments. Existing studies often treat trust as a conceptual or cloud-oriented construction, without linking it to measurable production events. This study proposes a blockchain service-level agreement (SLA) to measure trust at an open-source frugal smart factory (SF). Trust is defined as a dynamic quantitative score derived from measurable process events, including estimated and response times, assembly correctness, and transaction outcomes; all of this is calculated through a smart contract implemented on a blockchain network. The approach is implemented in a tangram puzzle assembly process that integrates cyber-physical systems, edge computing, artificial intelligence, cloud computing, data analytics, cybersecurity, and the blockchain within a unified SF architecture. The framework was experimentally validated across four representative assembly scenarios: (i) the SF delivered the puzzle in time and was correctly assembled (${\lambda }_s$ = 0.1734), (ii) the puzzle was completed within tolerance time (${\lambda }_s$ = 0.0649), (iii) the puzzle was delivered on time and was incorrectly assembled (${\lambda }_s$ = 0.0005), and (iv) the puzzle was completed outside the tolerance time and was correctly assembled (${\lambda }_s$ = 4.91 × ${10}^{-5}$); demonstrating that the model accurately estimates expected assembly times and updates trust without manual intervention during a physical manufacturing task, addressing the limitations of prior conceptual and cloud-based approaches. The main research contributions include an operational SLA-based trust model, the demonstration of the feasibility of applying blockchain-based SLAs in a physical SF environment, and evidence that a blockchain can be justified as a mechanism for managing and measuring trust in SF, rather than solely for traceability or logistics. Full article

Between 2018 and 2025, alternative finance expanded while micro-, small- and medium-sized enterprises in emerging economies continued to face a substantial funding gap. This study examines how entrepreneurial frugality articulates frugal ecosystems, access to alternative finance, resilience and SME performance within a single explanatory framework. Following PRISMA 2020 and PRISMA-S, we conduct a systematic review of Scopus, Web of Science and Cairn; out of 1483 records, 106 peer-reviewed studies are retained and assessed using the Mixed Methods Appraisal Tool and a narrative synthesis approach. The findings show that frugal ecosystems characterized by pooled assets, norms of repair and modularity, and lightweight digital tools reduce experimentation costs and develop frugal innovation as an organizational capability. This capability enhances access to alternative finance by generating readable quality signals, while non-bank channels provide a financial buffer that aligns liquidity with operating cycles and strengthens entrepreneurial resilience. The article proposes an operationalized conceptual model, measurement guidelines for future quantitative surveys, and public policy and managerial implications to support frugal and inclusive innovation trajectories in emerging contexts. 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

Ship detection plays a pivotal role in safeguarding maritime security, regulating vessel traffic, and bolstering national maritime defense. While contemporary lightweight models predominantly emphasize parameter reduction, efforts to curtail computational demands remain underexplored. In this study, we propose a lightweight multi-feature channel convolution module (MFC-Conv) to create an efficient backbone network. This module adeptly propagates multi-scale feature information, yielding a holistic representation while approximating residual architectures in a computationally frugal manner, thereby promoting seamless gradient flow during optimization. Notably, MFC-Conv can be re-parameterized into a streamlined two-layer convolutional structure devoid of branching or partitioning, streamlining deployment on resource-constrained edge devices. Complementing this, a multi-feature attention module (MFA) is proposed to augment localization and classification efficacy with negligible overhead. Furthermore, leveraging the inherent resolution traits of satellite SAR imagery, the decoder is refined to minimize redundant computations. Empirical evaluations across diverse datasets reveal that our framework outperforms the baseline by slashing parameters by 57.8% and FLOPs by 42.7%. Relative to two leading lightweight state-of-the-art (SOTA) models, it achieves computational reductions of 51.4% and 25.0%, respectively, thereby enabling viable onboard satellite deployment for ship detection. Full article

This integrative systematic review addresses the existing gap in understanding how frugal innovation can scale sustainably in emerging economies. Through a mixed-methods analysis based on the PRISMA 2020 protocol, 142 documents published between 2019 and 2025 in Scopus and Web of Science were examined. Scientific and grey literature. The methodology combined bibliometric mapping using VOSviewer, qualitative analysis with NVivo, and a Delphi panel of 15 experts, allowing for the triangulation of theoretical, empirical, and prospective evidence. The findings reveal a transition from a phase of contextual adaptation (2019–2021) to one of systematization and governance (2022–2025), highlighting that the so-called «Frugal Scalability Paradox»—the tension between hyper-contextualization and standardization—constitutes the main obstacle to the sustainability and expansion of frugal models. This study contributes both theoretically and practically by offering a taxonomy of value mechanisms, proposing a hybrid governance framework, and outlining a research agenda focused on inclusion, technological modularity, and impact funding. Frugal innovation is redefined here as a paradigm of intelligent tension management, capable of balancing local relevance and global replicability. Full article

Considering sustainable consumption not just as an exercise of individual choice but a shared and collective activity, this study explores the role of conscious and responsible consumption initiatives (CRCIs) driving citizens’ adoption of sustainable lifestyles. This research followed a qualitative approach, combining documentary research and twenty-six in-depth interviews with practitioners in eight grassroots consumer initiatives located in Galicia (Spain). The results show that CRCIs favor members’ consumption of organic, seasonal, fair, and locally produced food. The findings also reveal that engagement in these initiatives nurtures three interconnected types of learning—cognitive, attitudinal, and behavioral—which contribute to wider adoption of sustainable practices related to shifts in dietary habits, energy use, mobility, and frugality. CRCIs facilitate gradual transitions toward reduced meat consumption, favoring the intake of plant-based foods, and greater self-efficacy in preparing sustainable meals. These behavioral changes are incremental, motivated by inner reflection, practical experience, and consciousness around alternative economic models. However, the consistent adoption of sustainable eating habits is hindered by cultural and psychological barriers like cultural traditions, entrenched habits, and time constraints. In conclusion, these grassroots initiatives are interesting entry points for engaging citizens in sustainable lifestyles, becoming also gateways to the broader social and solidarity economy movement. Full article

Sericulture sustains rural livelihoods in Asia, Africa, and Latin America, where it provides income for women, elderly workers, and smallholder households. Yet this sector faces a critical technological divide: traditional reeling methods remain labor-intensive and uncompetitive, while industrial innovations advance along trajectories that are poorly suited to low-resource contexts. This article presents a patent landscape of silk-reeling technologies retrieved from Espacenet and PATENTSCOPE (2000–2024), comprising 212 unique records. Patents were evaluated against six criteria: resource efficiency, knowledge accessibility, durability and reparability, context adaptability, equity and inclusion, and by-product valorization. This review reveals a strong industrial bias, with most patents emphasizing energy-intensive steaming, mechanized feeding, and digital control, while only a small fraction addresses rural conditions or social inclusion. Current innovations therefore tend to marginalize traditional producers from emerging bio-based value chains. This study contributes to discussions on how technological design can support rural sericulture, highlighting the need for resource-efficient, modular, and socially inclusive solutions. Future research should extend patent analysis to mulberry cultivation, silkworm breeding, and by-product recovery to fully integrate sericulture into the circular bioeconomy. 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