Search Results (577)

Medium-term 5G/NB-IoT planning is made difficult by simultaneous uncertainty in device adoption and per-device traffic behavior because deterministic point forecasts do not quantify overload risk or support reliability-based capacity decisions. A diffusion-to-dimensioning workflow is proposed in which S-curve adoption modeling, bounded usage priors, scenario stress testing, and Monte Carlo uncertainty propagation are combined to generate predictive demand distributions, exceedance curves, and quantile-based capacity rules. The framework is applied to a Great Britain case study for 2025–2029 using smart meter deployment data and an M2M-based proxy for asset-tracking adoption. Analysis shows that planning-year upper-tail outcomes are driven primarily by asset-tracking usage uncertainty rather than by proxy scale alone. A ±30% perturbation of the AT adoption anchor changes $Q_{0.95}$ by approximately ±29.8%, whereas stressed AT usage increases $Q_{0.95}$ by 74.4%. Plausible positive dependence among key AT operational inputs further raises $Q_{0.95}$ by 18.3–22.5%. Limited hold-out evaluation provides strong out-of-sample support for the smart meter adoption stage and plausibility-only support for the shorter AT proxy. The framework is intended for medium-term, data-lean planning settings and is designed to support transparent risk-based capacity decisions rather than deterministic point sizing. Full article

The rising global incidence of kidney failure is increasing pressure on hemodialysis unit operations, with operational vulnerabilities further exposed by the COVID-19 pandemic. This scoping review mapped evidence on Lean management, discrete event simulation (DES), and virtual reality (VR) in hemodialysis units; compared reported outcome domains and performance indicators; identified barriers to Lean implementation; and assessed the empirical basis for a combined Lean–DES–VR framework. English-language peer-reviewed articles, conference papers, and book chapters addressing Lean, DES, VR, or their combination in dialysis settings were searched in Scopus, PubMed, SpringerLink, IEEE Xplore, ACM Digital Library, and Google Scholar to 30 June 2024; grey literature and opinion pieces were excluded. Structured data extraction and thematic narrative synthesis were applied. Twenty-seven studies were included (Lean n = 4, DES n = 9, VR n = 13, DES + VR n = 1). DES studies mainly reported operational outcomes, whereas VR studies focused predominantly on patient-centered rehabilitation and experience. Most studies examined methods in isolation, and integrated Lean–DES–VR applications were almost entirely absent. The literature suggests complementarity among these approaches but provides no robust empirical basis for a fully integrated framework. No protocol was prospectively registered. Full article

Experimental Governance (EG) has emerged as a strategic framework for managing complexity in high-uncertainty environments. However, its application in the private sector remains fragmented, often conflated with purely operational tools. This study addresses this gap by performing a conceptual transfer of EG principles into the domain of business processes. Through an expanded Systematic Literature Review (SLR) of 41 peer-reviewed articles (covering the period 2004–2026), we identify what we term the ‘Internalization Paradox’: while firms rapidly adopt experimental methodologies like Agile or Lean, they often fail to embed them into formal governance structures that ensure long-term accountability and institutional learning. This updated review incorporates cutting-edge discussions on Artificial Intelligence (AI) governance, experimentalist metagovernance, and the strategic regulation of uncertainty. Our findings suggest that organizational resilience is not merely a byproduct of technological readiness, but an emergence of ‘Institutionalized Experimentalism’. We propose a Conceptual Framework that operationalizes EG through iterative feedback loops, corporate sandboxes, and adaptive decision rights, providing a robust roadmap for future empirical research in management and organizational theory. Full article

Integrating Lean Construction (LC), the Circular Economy (CE), and Construction 5.0 (C5.0) remains challenging in emerging delivery contexts. This difficulty increases when procurement routines determine which practices become enforceable across tendering, contracting, and site execution. This study prioritized barriers to LCCE5.0 implementation in Morocco and translated expert judgments into actionable recommendations. A structured literature review informed the barrier inventory and conceptual framing. The study proposed a three-layer, life-cycle LCCE5.0 framework that links governance, operational routines, and digital enablers. It operationalized 40 critical barrier factors across six dimensions and five life-cycle macro-phases. A two-round Delphi study was conducted with 22 Moroccan experts using a 7-point Likert scale. Barriers were ranked using Round 2 (T2) medians with ties resolved using the interquartile range. Top-box agreement (ratings of 6–7) and consensus tiers were reported. The ranking showed strong stability across rounds, with 92.5% of barrier factors remaining stable. Kendall’s W at T2 equaled 0.817 (p < 0.001), indicating high panel consensus. Results indicated that constraints clustered in upstream governance. Three procurement-centered regulatory and contractual barriers topped the ranking (Mdn_T2 = 7). These barriers reflected missing CE procurement guidelines, limited weighting of environmental criteria, and the absence of circularity and digital requirements in tenders. Six additional barriers reinforced this procurement bottleneck. They included limited owner commitment, weak enforcement authority, limited top-management commitment, and regulatory instability. They also included low interorganizational trust, limited risk-sharing contracts, and tool-centered deployment of LCCE5.0 practices. These findings support procurement-focused recommendations to institutionalize auditable circular requirements and data-enabled verification in tendering and contracting routines. The proposed LCCE5.0 mechanism and the resulting recommendations require empirical validation beyond this Delphi-based prioritization. Full article

The topology identification (TI) of low-voltage distribution networks (LVDNs) is the foundation for their intelligent operation and lean management. However, the existing identification methods may produce inconsistent results under measurement noise, missing data, and heterogeneous load behaviors. Without principled multiple method fusion and meter-level confidence quantification, the reliability of the identification results is questionable in the absence of ground-truth topology. To address these challenges, a confidence-aware TI (Ca-TI) method for the LVDN based on weakly supervised learning (WSL) and Dempster–Shafer (D-S) evidence theory is proposed, aiming to infer each meter’s latent topology connectivity label and quantify the meter-level confidence without ground truth by fusing different identification methods. Specifically, within the framework of data programming (DP) in WSL, different TI methods were modeled as labeling functions (LFs), and a weakly supervised label model (WSLM) was adopted to learn each method’s error pattern and each meter’s posterior responsibility; within the framework of D-S evidence theory, an uncertainty-aware basic probability assignment (BPA) was constructed from each meter’s posterior responsibility, with posterior uncertainty allocated to ignorance, and was further discounted according to the missing data rate; subsequently, a consensus-calibrated conflict-gated (CCCG)-enhanced D-S fusion rule was proposed to aggregate the TI results of multiple methods, producing the final TI decisions with meter-level confidence. Finally, the test was carried out in both simulated and actual low-voltage distribution transformer areas (LVDTAs), and the robustness of the proposed method under various measurement noise and missing data was tested. The results indicate that the proposed method can effectively integrate the performances of various TI methods, is not adversely affected by extreme bias from any single method, and provides the meter-level confidence for targeted on-site verification. Further, an engineering deployment scheme with cloud–edge collaboration is further discussed to support scalable implementation in utility environments. Full article

Energy thriftiness and metabolic adaptations have had a crucial role in the emergence and spreading of the Homo lineage in the world. A higher-energy demand was required not only for the growing body mass, encephalization and human proliferation, but also for the survival adaptations to the environmental stresses. Because lean body mass lacks the energy-storage capacity required to supply the body’s demands, dedicated fat-storing cells originated. To feed such fat stores, the hominid evolution developed “meat-adaptive” genes to detect, digest and metabolize higher fat diets, and body-fat stores can be affected by lifestyle through hormonal-controlled daily energy balance. In energy surplus conditions, hypertrophy and hyperplasia of adipocytes can occur, with hypertrophic adipocyte signaling both a neo-adipocyte differentiation (leading to hyperplasia) and a local macrophage density (resident + infiltrated macrophages) for fat surplus scavenging. Adiposity-induced inflammation is caused by fat-overstored (hypertrophied) adipocytes that may operate as an overactive endocrine organ secreting an array of pro-inflammatory adipokines that, in combination with resident-macrophage activity and infiltrated blood-recruited, monocyte-derived macrophages, amplify the inflammatory process by spurting pro-inflammatory cytokines into the bloodstream. From an evolutionary perspective, obese humans represent a natural selection overexpressing the “thrifty” genes evolved for efficient food collection and fat deposition intended to help in survival in prolonged periods of famine. However, genetically speaking, obesity is a polygenic multifactorial disorder. Considering the rapidity of obesity-epidemic growth worldwide, epigenetic sets forth the key assumption of the mismatch between our human genome molded over thousands of generations, coping with the unprecedented dietary and physical conditions. Consequently, obesity would be due to our evolutionary-adapted polygenic-charge expressed by a deteriorated lifestyle characterized by high energy-dense food intake coupled with a reduction in caloric expenditure stemming from new mobility-reducing technologies. As a model of lifestyle change (LiSM), our 28-year on-going longitudinal study (“Moving for Health”) has shown effectiveness in the reduction not only of obesity but especially of its comorbidities, in a (10 week to 3 year) length-dependent LiSM. However, a disappointing progressive decrease in compliance with the study has been observed and attributed to the resistance of people to change their actual “obesogenic” lifestyle, basically represented by the individuals’ demand for labor-saving technologies and convenient, affordable, palatable foods. Full article

The intensification of Global Warming and Urban Heat Island phenomena necessitates advanced, computationally effective tools for evaluating outdoor thermal comfort and microclimatic dynamics by means of Mean Radiant Temperature assessment. However, existing high-resolution physical models often suffer from prohibitive computational costs. This research proposes LUCIDiT (Lean Urban Comfort Intelligent Digital Twin), a physically based modeling framework implemented for a quick mean radiant temperature assessment inside complex urban morphologies. The method integrates a simplified balance of mutual radiative heat exchanges with recursive time-series filtering to account for the thermal inertia of different urban materials, alongside greenery heat exchange due to evapotranspiration. This architecture creates an operational urban comfort digital twin that reduces computational times by orders of magnitude for large-scale mappings, without sacrificing physical accuracy. Validation against drone-acquired thermographic data and the established Urban Multi-scale Environmental Predictor model demonstrates high reliability and coherence with the real physical phenomena and context. The application to an urban pilot site in Florence reveals that strategic interventions, such as substituting impervious surfaces with irrigated greenery and arboreal canopies, can mitigate radiant loads by up to 20 °C. Findings show that the proposed urban comfort digital twin can be a robust, scalable instrument for designing evidence-based climate adaptation strategies and quick testing mitigation scenarios to enhance urban resilience. Full article

This study proposes a real-time data-based Digital Value Stream Mapping (Digital VSM) framework that integrates Artificial Intelligence (AI) feature selection and discrete-event simulation validation to enhance production system performance. Unlike conventional VSM approaches that rely on static, manually aggregated data, the proposed framework uses real-time operational data to dynamically quantify Value Added (VA), Non-Value Added (NVA), and Necessary Non-Value Added (NNVA) activities. To improve decision accuracy, an Artificial Neural Network (ANN) combined with Genetic Algorithm (GA) feature selection is employed to identify dominant production variables influencing lead time and line imbalance. Furthermore, Ranked Positional Weight (RPW) optimization results are validated through Tecnomatix Plant Simulation to ensure robustness before physical implementation. The proposed framework was applied to a discrete manufacturing line, resulting in a reduction of total lead time from 8755 s to 6400 s and an increase in process ratio from 33.64% to 45.91%, with line efficiency reaching 91.7%. The findings demonstrate that integrating Digital VSM with AI-driven feature selection and simulation validation transforms Lean analysis from a descriptive tool into a predictive and validated decision-support system suitable for Industry 4.0 environments. Full article

Groundwater management increasingly relies on participatory governance, yet most existing participatory frameworks lack mechanisms for iterative learning and continuous improvement and further lack structured operational indicators, systematic monitoring–feedback integration, and institutionalized mechanisms that embed participation within measurable governance cycles rather than treating it as a one-time procedural input. Conversely, Lean thinking, particularly the Plan–Do–Check–Act (PDCA)-based continuous improvement principles, offers systematic methods for feedback and adaptation, but remains underexplored in environmental governance contexts. This paper bridges these traditions by conceptualizing participatory groundwater governance as a continuous improvement system, thus aligning community participation with PDCA logic in order to enhance adaptive management and sustainability outcomes. This study introduces a novel conceptual synthesis that integrates Lean management principles into participatory groundwater governance. In the current research, a methodological framework is proposed for integrating Lean thinking, particularly the Plan–Do–Check–Act cycle, with participatory groundwater governance, thus producing a Lean–participatory groundwater governance (Lean–PGG) framework. To conceptualize the framework, a set of eight rubric-based indicators was developed from a literature matrix of 54 peer-reviewed case studies selected through predefined inclusion criteria and multi-stage screening procedures, in order to evaluate participation, governance readiness, tool application, data use, monitoring, learning, and institutionalization. Each variable indicator was then scored on a three-point scale and categorized into the PDCA maturity levels The findings suggest a consistent heuristic trend across cases, characterized by comparatively stronger performance in the planning and implementation stages. A clear majority of studies scored in the moderate-to-high range (≥2.5/3) for the Plan and Do dimensions, whereas only a limited proportion demonstrated structured Check mechanisms and fewer still exhibited institutionalized Act processes. This asymmetry indicates persistent gaps in the consolidation of evaluation and feedback within participatory groundwater governance systems. This Lean–PGG framework thus demonstrates how continuous improvement mechanisms, i.e., feedback loops, reflection, and adaptive standardization, can strengthen participatory groundwater governance. The proposed framework offers a replicable and practical model for integrating continuous improvement into environmental and groundwater governance, fostering adaptive management, resource efficiency, and sustainability outcomes. Full article

Due to the significant challenges faced by healthcare systems, medical establishments strive to set the tone by integrating new concepts to bridge this gap. Here, Lean Healthcare (LH) has been inspired by Lean Management (LM). Utilizing LM to optimize industrial processes and reduce waste presented a real opportunity to enhance the quality of medical services. For more improvement, healthcare systems pushed themselves to keep up with progress by implementing Industry 4.0 (I4.0) tools, such as IoT, Big Data analytics, and AI with LH and sustainability practices. The results promised better quality of care. Although this concept offers significant potential for more efficient workflows and optimizing medical processes, studies examining their combined implementation are still scarce. This research fills the gap via a literature review (LR) of peer-reviewed articles published between 2015 and 2025. The review investigates the impact of integrating smart technologies into LH frameworks and highlights how LH contributes to sustainability across multiple dimensions: economic, social, technological and environmental. Key findings show the impact of combining advanced tools with lean principles by reducing waiting times (25%) and length of stay while also improving satisfaction. Sustainability-centered adaptations of LH incorporate social and environmental comparative parameters such as resource consumption, for instance, reducing operational costs by up to 30–40%. Many challenges were faced with this implementation, such as cultural, technical challenges (e.g., complexity of integration with digital systems), and sustainability barriers. However, to overcome these barriers, this paper proposes a holistic implementation that aligns lean processes with organizational change and sustainability goals. Full article

Background/Objectives: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is a global health crisis, but current diagnostics are limited. Liver biopsy is invasive, magnetic resonance imaging-proton density fat fraction (MRI-PDFF) is expensive, and quantitative ultrasound methods are low-accuracy, especially in patients with a high body mass index (BMI). This study introduces a novel thermo-acoustic (TA) method that generates ultrasound signals based on tissue electrical conductivity, where lean tissue (high in water and electrolytes) absorbs more radio-frequency (RF) energy than fatty tissue, providing a direct molecular contrast for fat. Methods: A prospective, cross-sectional feasibility study compared a new thermo-acoustic fat fraction (TAFF) score with the reference standard MRI-PDFF in 40 subjects with suspected fatty liver disease. Bland–Altman analysis, Deming regression, and Binary classification performance were tested. To establish system stability, a dedicated Repeatability and Reproducibility (R&R) study (N = 14) evaluated inter-operator and intra-operator consistency using an Intraclass Correlation Coefficient (ICC) derived from a two-way random-effects ANOVA model. Results: TAFF estimates demonstrated a substantial correlation (r = 0.89) with MRI-PDFF and an average absolute error of 3.04% fat fraction. Classification performance was high, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.92 at the 12% fat fraction threshold and 0.99 at the 20% fat fraction threshold. The R&R study confirmed robust stability (intraclass correlation = 0.89) and a negligible mean inter-operator difference of 0.36%. Estimation errors showed no statistically significant correlation with BMI or other body habitus measurements. Conclusions: These findings support thermoacoustics’ potential as an accurate, non-invasive, point-of-care solution that can serve as a new imaging biomarker. By providing predictive values closely aligned with MRI-PDFF across the full MASLD spectrum, TAFF can complement currently available ultrasound methods to address the cost and access constraints of MRI for the assessment, diagnosis, and monitoring of MASLD. Full article

Background/Objectives: Hajj is a major annual mass gathering. It requires prolonged walking under conditions of fatigue, heat stress, and crowd density, which increases mobility difficulties and fall risk, particularly among older adults and individuals with chronic diseases. Therefore, rapid operational mobility screening is required to identify risk and plan mobility. To support an operational mobility-classification workflow in a pre-Hajj setting, this study evaluated whether Timed Up and Go (TUG)-based stratification, combined with spatiotemporal gait and plantar pressure measurements, differentiates fall-risk categories. Methods: We conducted a cross-sectional study at a seasonal medical center near Al-Haram in Madinah Al-Munawwarah (21 May–3 June 2025) within the “I Lean On It” screening initiative. Participants completed the TUG and dynamic baropodometric gait assessments. We stratified the risk of falling as low (≤10 s), moderate (10.1–13.5 s), and high (>13.5 s) according to the TUG performance. We performed between-group comparisons using the Kruskal–Wallis test and evaluated the associations using Spearman’s correlation analysis. Results: Participants were classified as having low (n = 103), moderate (n = 24), or high (n = 29) fall risk. TUG performance significantly increased across the fall-risk groups. Significant between-group differences were observed in cadence, half-step length, walking speed, test duration, and functional mobility, whereas plantar pressure magnitude and gait symmetry did not differ significantly. Spearman correlation analysis showed significant negative correlations between TUG time and sex (rs = −0.357), half-step length (rs = −0.617), walking speed (rs = −0.577), and cadence (rs = −0.420). Significant positive correlations were observed with weight-bearing time (right: rs = 0.584; left: rs = 0.461), test duration (rs = 0.376), and number of steps acquired (rs = 0.356) (all p ≤ 0.003). Overall, TUG performance was primarily associated with dynamic gait and functional mobility. Conclusions: Integrated functional mobility and spatiotemporal gait screening significantly differentiate fall risk and provide clinically actionable mobility-support guidance in a mass-gathering pre-Hajj clinical workflow. Full article

Metabolic dysfunction-associated fatty liver disease (MAFLD) comprises phenotypic subgroups, including type-2 diabetes-associated MAFLD (T2D-MAFLD), obesity-associated MAFLD (OB-MAFLD), and lean MAFLD (L-MAFLD). Emerging evidence indicates that dysregulation of miRNAs plays a key role in MAFLD pathogenesis and progression. This study evaluated the diagnostic accuracy of a plasma miRNA-based signature as a non-invasive biomarker for early detection and phenotypic stratification of MAFLD. A total of 393 MAFLD patients and 109 healthy controls were enrolled. Plasma expression of miR-122, miR-103a, miR-222, miR-15a, miR-34a, miR-192, miR-197, and miR-99a was quantified using Reverse transcription polymerase chain reaction. Compared to controls, MAFLD patients exhibited significant upregulation of miR-122, miR-103a, miR-222, miR-15a, and miR-34a, alongside downregulation of miR-197 and miR-99a. Multinomial logistic regression revealed phenotype-specific associations: miR-103a, miR-34a, and miR-197 with T2D-MAFLD; miR-122, miR-222, and miR-99a with OB-MAFLD; and miR-15a with L-MAFLD. Receiver operating characteristic analysis demonstrated highest individual diagnostic accuracy for miR-197 in T2D-MAFLD (AUC = 0.784), miR-99a in OB-MAFLD (AUC = 0.869), and miR-15a in L-MAFLD (AUC = 0.776). Integrating combined miRNA panels with biochemical markers further improved diagnostic performance and clinical utility, achieving high positive and negative predictive values. In conclusion, plasma miRNA signatures enable phenotype-specific discrimination of MAFLD subtypes and may serve as promising non-invasive tools pending multi-center validation. Full article

Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive topological interception. To bridge this gap, this study proposes a proactive bi-level scheduling framework that mathematically integrates strategic quality inspection planning with operational low-carbon project execution. Specifically, a Generalized Total Cost (GTC) model is formulated to internalize multi-objective trade-offs—including time, cost, and carbon emissions—into a unified financial metric through market-based shadow prices. This framework is operationalized through a novel bi-level Hybrid Evolutionary Algorithm (H-TS-CDBO). By combining the global exploration capabilities of Chaotic Dung Beetle Optimization with the local refinement mechanisms of Tabu Search, the proposed solver is specifically engineered to navigate the topological ruggedness induced by proactive inspection interventions. Empirical benchmarking validates the computational robustness of the solver, while an illustrative case study substantiates a critical managerial paradigm shift from “passive remediation” to “active prevention”: compared to traditional methods, a marginal preventive investment of 5.4% functions as an effective containment mechanism, yielding a 40.8% net reduction in the GTC. Furthermore, a sensitivity analysis regarding varying static carbon tax rates simulates algorithmic adaptation under diverse regulatory intensity thresholds, delineating an actionable pathway for project managers to achieve lean, low-carbon synergy amidst evolving regulatory pressures. Full article

This study addresses a critical gap in understanding how Lean Manufacturing (LM) practices, particularly people-centered approaches, can enhance operational performance within the unique industrial context of Saudi Arabia’s Vision 2030 economic transformation. The concept of Lean Manufacturing involves a systematic approach and principles aimed at enhancing efficiency, minimizing inefficiencies, and boosting output in manufacturing operations. While LM principles are well-established globally, their application in Gulf Cooperation Council (GCC) economies remains understudied, particularly regarding the central role of workforce engagement in successful implementation. The main objective of the study is to investigate the implications of LM on the productivity of the industry sector. Specifically, this research examines how the integration of people-centered practices with traditional LM constructs (Just-in-Time, Jidoka, Stability and Standardization) influences operational outcomes in Saudi manufacturing firms. A survey was conducted among specific private and public enterprises to collect data, yielding a 55.8% response rate and 67 valuable responses from a pool of 120 contacted companies. The sample encompassed small, medium, and large enterprises across seven manufacturing sectors. SmartPLS 3 and SPSS were used to assess the structural and measurement models. Common method bias was evaluated using Harman’s single-factor test. The findings demonstrate that implementing the recommended LM structural model significantly enhances operational performance. Notably, people integration exhibited the strongest influence on operational performance (β = 0.361), suggesting that human-centered approaches may be particularly salient in the Saudi context. These findings offer practical guidance for manufacturing firms seeking to align lean initiatives with Vision 2030 objectives. Full article