Search Results (6,251)

Speedcubing, the competitive practice of fast solving the Rubik’s Cube, has gained global popularity both as a sporting and an educational activity. Aside from its recreational value, speedcubing may contribute to broader social and developmental outcomes. This study aims to examine the potential of speedcubing as a tool for sustainable social development, concentrating on its educational, psychological, and social implications and its relationship to selected United Nations Sustainable Development Goals (SDGs). An anonymous online survey consisting of 26 items (22 used for the main analysis and 4 demographic items) was conducted among 112 participants associated with the speedcubing community, including active competitors, coaches, and parents. The questionnaire addressed accessibility, cognitive and social competencies, and perceived educational and social benefits, as well as user preferences regarding digital tools supporting learning. The results indicate that participation in speedcubing supports the development of analytical thinking, problem-solving skills, perseverance, and self-control. Respondents also emphasized its educational value, accessibility, and role in fostering fair play and social integration. These findings suggest that speedcubing may contribute to several Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), and SDG 11 and SDG 12 (Sustainable Cities and Communities; Responsible Consumption and Production). Full article

Proportional, fair scheduling in OFDM-based vehicle-to-everything (V2X) uplink causes the resource-block allocation of each vehicle to vary from slot to slot, yet conventional semantic encoders produce a fixed number of output tokens regardless of the instantaneous channel capacity. When the encoder output exceeds the slot budget, transmitted features are truncated and the resulting federated learning gradient is corrupted—a problem that affected 23% of training rounds for non-line-of-sight vehicles in our experiments. The difficulty is worsened by a spatial pattern common in urban deployments: vehicles at congested intersections suffer the poorest propagation conditions while carrying the training data most relevant to safety, and throughput-driven client selection excludes them in favor of vehicles with strong channels but uninformative scenes. We address both issues within a single framework for OFDM-based V2X federated learning. On the transmission side, a Sensing-Guided Adaptive Modulation (SGAM) module derives a per-slot token budget from the current resource-block allocation and selects tokens through differentiable Gumbel-TopK pruning with a hard capacity clip, so the transmitted token count stays within the slot budget. On the scheduling side, a Channel-Decoupled Federated Learning (CDFL) module partitions clients independently by channel quality and data complexity, selects diverse representatives per partition via facility location optimization, and corrects for partition-size imbalance through inverse propensity weighting during model aggregation. Experiments on NuScenes with 20 non-IID vehicular clients under realistic OFDM channel simulation demonstrate a Macro-F1 of 0.710 (+8.7 points over the Oort-adapted baseline), zero budget violations throughout training, and a 75% reduction in training variance; the worst-class F1 more than doubles relative to FedAvg. Full article

Single-player role-playing games (RPGs) combine two promises that do not always align: delivering a compelling narrative experience (world, characters, choices, and consequences) while sustaining a demanding ludic trajectory in which players face obstacles, master systems, and progress over time. This Systematic Literature Review (SLR) synthesizes existing evidence on the evolution of narrative and challenge in single-player RPGs from a player-centered perspective, with particular attention paid to immersion, engagement, flow, and perceived agency. A multi-database search strategy was conducted across Google Scholar, Scopus, IEEE Xplore, and the ACM Digital Library using query strings targeting narrative/agency, challenge and dynamic difficulty adjustment (DDA), adaptive difficulty, and the historical evolution of RPG narrative design, following a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-reported selection flow and Rayyan-supported screening. From 423 identified records, duplicates and non-eligible records were removed through staged screening, yielding 43 reports sought for retrieval; because six were not accessible in full text at consolidation, the synthesis was conducted on 37 full-text articles. The findings indicate (i) a predominance of work on narrative and agency, where agency is framed as a design effect rather than merely the presence of explicit branching choices; (ii) a recent rise in challenge/adaptation research, frequently tied to flow, fairness, and differentiated player profiles; and (iii) the emergence of artificial intelligence (AI)-driven approaches, including non-player character (NPC) systems, combat AI, reinforcement learning, and large language model (LLM)-based narrative control, which amplify core design trade-offs between narrative coherence and perceived agency. Beyond synthesizing a dispersed body of literature, the review contributes an integrated player-centered analytical framework that brings together narrative, challenge, and player experience, while also highlighting the need for more consistent measurement practices, stronger comparative designs, and longer-term empirical work in single-player RPG research. Full article

Minimum wage policies have become a central instrument for promoting social and economic sustainability by ensuring sufficient income to cover basic needs and reduce inequalities. They align with recent predistribution approaches in the literature and with goal 10.4 of the United Nations 2030 Agenda. In the European context, these policies are explicitly embedded within the sustainable development and just transition agenda, where the European Union emphasises that securing fair wages is a necessary condition for inclusive, balanced and equality-enhancing growth. At the same time, the methodological debate has evolved from early time-series-based approaches to a new generation of quasi-experimental studies, which provide more rigorous and less biased evidence. Within this framework, Spain represents a relevant case due to the scale and persistence of its minimum wage reforms since 2019, yet the Spanish case has lacked a systematic synthesis comparable to those available for other advanced economies (e.g., Germany, the UK, the USA). This article offers the first systematic synthesis of empirical evidence on the effects of the minimum wage in Spain from the 1990s to 2025, following the PRISMA 2020 methodology. This process yielded a large number of articles, from which an initial selection of 249 was made. Following the full screening and eligibility assessment, 34 articles were retained. The results allow for an analysis of the current state of research on the effects of the minimum wage across multiple dimensions, especially on employment and inequality. Other aspects, such as productivity, prices, other business adjustments, administrative obstacles, and public finances, are still poorly addressed in the available literature. In any case, this is a valuable exercise in understanding how wage policies can help to clarify the relationship between minimum wage policies and the transformation of labour markets. Full article

Multi-link terrestrial backhauls create a shared transport environment in which heterogeneous multipath protocols compete for the same forwarding resources while reacting to congestion with different control logics. In this paper, we investigate this problem in a 5G Integrated Access and Backhaul (IAB) scenario where an IAB node aggregates traffic from multiple User Equipments (UEs) and forwards it toward the core network over two terrestrial backhaul paths. We focus on the coexistence of Multipath TCP (MPTCP) and Multipath QUIC (MPQUIC), evaluating how cross-protocol Congestion Control (CC) pairings affect performance. Specifically, all feasible BBR, CUBIC, and Reno cross-pairings are assessed under symmetric and asymmetric dual-backhaul conditions, considering Enhanced Mobile Broadband (eMBB) and dense low-rate traffic regimes representative of mMTC-like operation. The analysis considers throughput, Jain’s fairness index, jitter , and packet loss to identify the trade-offs of each CC pairing. Results show that CC selection is a first-order design factor in MPTCP/MPQUIC coexistence over shared backhauls. No single pairing is uniformly optimal across all metrics: some configurations provide more balanced throughput sharing, others improve fairness, while the most favorable solutions for jitter do not necessarily maximize transport efficiency. These findings identify CC pairing as a tuning dimension for multi-link backhaul systems based on heterogeneous multipath transports. Full article

The integration of artificial intelligence with unobtrusive sensing technologies is transforming occupational health monitoring by enabling continuous, objective assessment of worker activities in real industrial environments. This study focuses on the accurate recognition of subtle motion actions within logistics workflows using multichannel optical motion-capture data. We investigate several deep learning architectures commonly employed for temporal motion analysis, including tCNN, Transformer, CNN–LSTM, and ConvLSTM. To enhance robustness and fairness across workers with varying movement styles, a subject-independent evaluation protocol is adopted, and a multiscale temporal learning strategy is explored to better capture fine-grained and low-saliency actions. Experimental results show that the proposed multiscale tCNN achieves the highest accuracy, obtaining per-class recall range between 73% and 83% and an overall accuracy of approximately 79%, consistently outperforming recurrent and attention-based architectures. These findings demonstrate the effectiveness of multiscale convolution-based temporal modeling for recognizing subtle motion actions and highlight the potential of combining optical motion capture with AI analytics to support unobtrusive, reliable occupational health monitoring in smart industry environments. Full article

The inherent redundancy in vehicle sensor data, coupled with constrained onboard resources and stringent latency requirements, renders traditional bit-oriented transmission paradigms inefficient for autonomous-driving perception tasks. Semantic communication offers a promising direction by shifting the focus from bit-level fidelity to task-level information delivery. In this paper, we propose a unified framework that integrates hierarchical transmission and online scheduling for Internet of Vehicles (IoV)-oriented collaborative perception. The proposed hierarchy separates information into two complementary layers: a coarse metadata layer (object bounding boxes) for latency-critical awareness, and fine-grained visual semantics (multi-scale region-of-interest (ROI) patches) for perception-intensive tasks. We formulate an online scheduling problem that jointly exploits Age of Information (AoI) and Channel State Information (CSI) to dynamically decide what to transmit and at what fidelity under per-frame budget constraints. To address cross-scheme fairness, we report resource utilization under a fixed kbps/fps physical budget and evaluate robustness using a combination of a lightweight task-proxy metric and COCO-style Average Recall (AR100) under ROI-only evaluation. The hierarchical transmission architecture, combined with AoI awareness, reduces global semantic staleness by approximately 78%. The Lyapunov-based online scheduler enables intelligent, signal-to-noise ratio (SNR)-adaptive switching between coarse and fine semantic levels, ensuring robust perception under varying channel quality. Under strict physical-budget constraints and unreliable channel conditions, joint source-channel coding (JSCC) exhibits significantly stronger task robustness than conventional schemes: at 0 dB SNR, the task-proxy detection rate improves by nearly 47 percentage points over the uncoded baseline. Full article

Model-Agnostic Meta-Learning (MAML) and Prototypical Networks (ProtoNet) establish the foundational paradigms for few-shot classification. However, MAML suffers from optimization instability caused by reconstructing classification boundaries for every new task. Conversely, ProtoNet lacks the internal mathematical capacity necessary for task-specific parameter adaptation under domain shifts. To reconcile these structural limitations, we introduce Metric-based Model-Agnostic Meta-Learning (M²AML). By completely excising the parameterized classification layer from the episodic adaptation sequence, our framework replaces traditional inner-loop classification with a dynamic self-exclusive geometric similarity metric. Substituting functional mappings with spatial distance optimizations efficiently resolves evaluation conflicts, thereby establishing perfectly synchronized inner and outer learning rates alongside substantially accelerated adaptation steps. Extensive experiments across mini-ImageNet, tiered-ImageNet, and CIFAR-FS validate our approach against a comprehensive array of established algorithms. To ensure strictly fair comparative evaluations, we meticulously reproduce the MAML, ProtoNet, and Proto-MAML baselines. Empirical results demonstrate that M²AML achieves state-of-the-art performance across most evaluation settings, delivering absolute accuracy improvements ranging from 0.1% to 2.1% over existing leading models. Full article

In contemporary debates on the United Nations Sustainable Development Goals, there is growing recognition that artificial intelligence (AI) may contribute meaningfully to SDG 2 (Zero Hunger), particularly by enhancing the efficiency of food aid distribution and resource allocation. However, such optimism must be critically situated within the broader institutional and ethical contexts in which AI operates. This study argues that the effectiveness of AI in conflict-affected settings is contingent not only on technical capacity but also on governance structures, ethical safeguards, and institutional trust, dimensions closely aligned with SDG 16 (Peace, Justice, and Strong Institutions). Using the Gaza Strip as a case study, this article demonstrates that AI-driven food assistance mechanisms may inadvertently reinforce structural vulnerabilities. Specifically, algorithmic targeting of aid risks deepening dependency, exacerbating digital exclusion, and weakening already fragile governance systems. The absence of robust data accountability frameworks further complicates these dynamics, raising concerns regarding transparency, fairness, and long-term sustainability. The findings caution against privileging technical efficiency at the expense of socio-political stability. Rather, they highlight that the sustainability of AI interventions in humanitarian contexts fundamentally depends on the credibility and legitimacy of institutions. Accordingly, this study proposes a conceptual model for AI in hunger relief and digital humanitarianism that integrates technical innovation with institutional accountability and social trust. This study presents a narrative review informed by structural searching that examines the influence of AI on food security interventions in fragile contexts. This analysis applies a combined ethical governance and sustainability lens to assess current applications and risks. This research advances a broader analytical framework that moves beyond purely technical interpretations of AI, emphasizing its role as a socio-political tool, through identifying five key pillars for sustainable AI governance: data sovereignty, algorithmic accountability, inclusive system design, community-led governance, and market integrity. Full article

Emerging technologies (ETs), such as AI and reality techniques, are reshaping educational measurement. However, existing studies remain dispersed and are rarely synthesized in ways that clarify how ETs participate in the evidentiary work of educational measurement. Guided by PRISMA 2020, we systematically reviewed 933 empirical studies published between 2016 and 2025 in formal educational settings. We coded studies by (a) grain size (micro, meso, macro), (b) Constructing Measures Theory building blocks (construct map, item design, outcome space, measurement model), and (c) ET category. Results showed a strong concentration at the micro level (88.88%) and in outcome space and measurement model work (86.80% combined), indicating that ET-enabled innovation has focused primarily on transforming performances into indicators and modeling those indicators for interpretation and decision-making. Learning analytics and educational data mining, machine learning and deep learning, and automated scoring and feedback systems were the dominant ET clusters. These findings point to an uneven development of ET-enabled educational measurement. Included studies also indicating recurring concerns about transparency, fairness, and governance are linked to the field’s main areas of ET-enabled concentration. We therefore argue for closer alignment among construct claims, evidence, modeling, and intended use, and offer implications for developers, researchers, and education practitioners. Full article

Recent research by Yucel and colleagues suggests that children perceive equality-based fairness violations (resources being distributed unequally) as less serious than prototypical moral harms, but that making the harmful consequences of unfairness salient shifts these judgments toward the moral domain. We examined whether merit-based fairness violations (someone receiving less than they earned) would similarly shift judgments toward the moral domain by making the injustice more salient. Replicating prior work, 4-year-old children (N = 62) rated prototypical moral violations as significantly more severe than equality-based fairness violations, which were rated as similar in severity to conventional violations. Contrary to predictions, merit-based fairness violations also showed this pattern: They were judged as less severe than prototypical moral violations and similarly severe as both equality-based fairness violations and conventional violations. Children also did not consistently group either type of fairness violation with moral or conventional violations. These findings contribute to a growing body of evidence that children’s (and adults’) perceptions of fairness—whether equality-based or merit-based—are more nuanced than previously thought and that unfairness may not spontaneously be treated like other, more prototypical moral norm violations. Full article

Objective: The aim was to evaluate the diagnostic accuracy and temporal reproducibility of multimodal large language models (LLMs) in the image-based diagnosis of oral mucosal lesions. Materials and Methods: The study included 100 anonymized clinical photographs of oral mucosal conditions obtained from the archive of the Department of Oral Medicine, School of Dental Medicine, University of Zagreb. Images were categorized into four subgroups: physiological variations, benign mucosal lesions, oral potentially malignant disorders, and oral cancer (25 images each). Three multimodal LLMs (ChatGPT-5.1 Plus, Gemini 3 Pro, and Perplexity Pro) analyzed each image using an identical prompt and were required to provide a single most probable diagnosis based solely on visual features. To evaluate temporal reproducibility, the entire evaluation was repeated in three independent testing cycles conducted at one-month intervals. Diagnostic accuracy was compared using chi-square tests, while intra-model agreement across cycles was assessed using Fleiss’ kappa. Results: Gemini demonstrated the highest diagnostic accuracy, reaching 78% correct responses in cycles 2 and 3, significantly outperforming ChatGPT (55–57%) and Perplexity (28–31%) (p < 0.00001). Subgroup analyses showed similar trends, with Gemini achieving the highest accuracy across most lesion categories. Intra-model agreement across cycles was moderate for ChatGPT (κ = 0.525), fair for Gemini (κ = 0.338) and Perplexity (κ = 0.409). Gemini also showed the highest proportion of responses that remained correct across all three cycles (51%). Conclusions: Multimodal LLMs demonstrate promising diagnostic capabilities in the image-based assessment of oral mucosal lesions; however, variability in reproducibility highlights the need for cautious clinical implementation and further validation. Full article

The Mobile Reliable Opportunistic Routing (MROR) protocol improves data-forwarding reliability in Cognitive Radio Sensor Networks (CRSNs) through mobility-aware virtual contention groups and handover zoning. However, its heuristic decision logic is difficult to optimize under highly dynamic spectrum access and random node mobility. To address this limitation, we present DRL-MROR, a refined routing framework that incorporates deep reinforcement learning (DRL) to enable intelligent and adaptive forwarding decisions. In DRL-MROR, the secondary users (SUs) act as autonomous agents that observe local state information, including primary-user activity, link quality, residual energy, and neighbor-mobility patterns. Each agent learns a forwarding policy through a Deep Q-Network (DQN) optimized for long-term network utility in terms of throughput, delay, and energy efficiency. We formulate routing as a Markov Decision Process (MDP) and use experience replay with prioritized sampling to improve learning stability and convergence. The DQN used at each node is intentionally lightweight, requiring 5514 trainable parameters, about 21.5 kB of weight storage in 32-bit precision, and approximately 5.4k multiply-accumulate operations per inference, which supports practical deployment on edge-capable CRSN nodes. Extensive simulations show that DRL-MROR outperforms the original MROR protocol and representative AI-based routing baselines such as AIRoute under diverse operating conditions. The results indicate gains of up to 38% in throughput, 42% in goodput, a 29% reduction in energy consumed per packet, and an approximately 18% improvement in network lifetime, while maintaining high route stability and fairness. DRL-MROR also reduces control overhead by about 30% and average end-to-end delay by up to 32%, maintaining strong performance even under elevated PU activity and higher node mobility. These results show that augmenting opportunistic routing with lightweight DRL can substantially improve adaptability and efficiency in next-generation IoT-oriented CRSNs. Full article

Fully decoupled radio access networks (FD-RANs) achieve spectral efficiency and coverage flexibility for 6G via independent uplink (UL) and downlink (DL) base station operation, yet dynamic user mobility brings critical challenges to joint user association and resource allocation. Asymmetric interference and heterogeneous base station capacities cause persistent network unfairness, while uncoordinated mobility management triggers ping-pong handovers and heavy handover overheads. To resolve these intertwined problems, we propose a fully decoupled, mobility-resilient and fairness-guaranteed framework, which integrates short-term congestion pricing with the long-term Jain fairness index for equitable resource distribution and introduces a composite handover penalty with a strict physical hysteresis margin to block invalid handovers. We formulate the optimization problem as a novel Sliding-Window Hysteresis-Integrated Fairness Two-Layer Multi-Armed Bandit (SHIFT-MAB) model, embedding an exponentially weighted moving average (EWMA) sliding-window mechanism to track real-time channel fluctuations efficiently. Theoretical analysis confirms the model’s decoupling optimality, sublinear regret bound and fairness convergence. Extensive simulations show that SHIFT-MAB effectively suppresses invalid handovers, ensures high network fairness, optimizes system utility and achieves a superior handover–throughput trade-off. Full article

Geriatric pharmacotherapy is usually challenged by physiological senescence. For instance, progressive declines in organ function and alterations in body composition can complicate drug disposition. However, conventional pharmacometrics models commonly have limited capacity to map these high-dimensional, nonlinear relationships. In this review, we are examining the recent shift toward integrating machine learning (ML) with mechanistic pharmacokinetic (PK)/pharmacodynamic (PD) models to improve the accuracy and precision of dosing. Machine learning approaches like Random Forest and XGBoost consistently provided more accurate exposure predictions and significantly more efficient computational workflows than conventional methods. Nevertheless, concerns such as “black box” transparency and the potential of algorithmic bias toward specific patient demographics are challenging. It is important to incorporate explainability tools like SHAP, and adopting FAIR data principles is crucial for achieving professional trust and ensuring site-specific generalizability. Full article