Search Results (1,730)

Nepal’s decline of public industrial enterprises was not always followed by comparable domestic productive capacity. This study introduces the industrial replacement gap (IRG) as an initial conceptual and diagnostic framework for assessing such replacement. We triangulate World Bank indicators (1980–2023), UN Comtrade product data (1998–2022), Ministry of Finance enterprise records, and Department of Industry registrations (FY 1990/91–2022/23). Results show persistent manufacturing stagnation, import-dominated trade in several product groups linked to public enterprises, and mixed sectoral proxy signals. Average employment per newly registered manufacturing firm fell from 142 workers during early liberalization to 88 in recent years. In the secondary exploratory analysis, SHAP places foreign direct investment inflows and trade openness highest in predictive importance within the fitted Random Forest, but the small sample and modest performance do not support causal interpretation. For sustainable industrialization, the IRG complements aggregate SDG 9.2 indicators by linking enterprise decline with sector-level capacity, trade, and employment evidence, while direct production and environmental data remain necessary for validation. For public managers, the revive, restructure, repurpose, and replace framework supports asset audits and feasibility screening. For the government, the findings support sector-specific capacity strategies that combine employment and regional resilience with resource efficiency, environmental safeguards, and monitoring, rather than uniform revival or market-led replacement. Full article

The macroalga Kappaphycus alvarezii is a renewable source of bioactive compounds with potential in nanobiotechnology, particularly the green synthesis of metallic nanoparticles, where its extracts act as reducing and stabilizing agents. The aim of this study was to investigate the green synthesis of silver nanoparticles (AgNPs) using aqueous extracts of K. alvarezii, evaluating the influence of pH, silver nitrate (AgNO₃) and extract concentration, temperature, and reaction time, through two 2³ factorial designs with central points, as well as characterizing the obtained AgNPs by ultraviolet–visible spectroscopy (UV-Vis), dynamic light scattering (DLS), zeta potential, nanoparticle tracking analysis (NTA), and transmission electron microscopy (TEM). The selected synthesis conditions were identified as pH 13, 5.0 mg mL⁻¹ extract, and 2.5 mM AgNO₃, with two effective combinations of temperature and reaction time: 50 °C for 30 min or 60 °C for 15 min. The formation of AgNPs was confirmed by a surface plasmon resonance band at ~420 nm, showing spherical morphology, a negative zeta potential, and particle sizes ranging from 93–100 nm (NTA) to 174–192 nm (DLS), the latter reflecting the presence of aggregates in suspension. Colloidal behavior indicated partial stability, with a tendency toward aggregation during prolonged storage. Overall, these findings demonstrate the potential of K. alvarezii as a sustainable source of bioactive compounds for the synthesis of AgNPs, supporting its applicability as an antioxidant nanomaterial platform. Further biological validation and comprehensive sustainability assessments are required to evaluate its potential applications in sectors such as health, cosmetics, and agriculture. This approach reinforces the principles of Green Chemistry and aligns with several Sustainable Development Goals (SDGs), including SDG 3, 9, 12, and 14, particularly regarding the sustainable sourcing of the biomass used. Full article

Official statistical indicators are essential for monitoring progress toward the Sustainable Development Goals (SDGs). However, indicator data, metadata, source datasets, producing agencies, and global SDG references are often managed and disseminated in separate structures, limiting semantic integration, traceability, and machine-readability. This study proposes an indicator-centric knowledge graph model for integrating and tracing SDG indicators in Indonesia. The model positions StatisticalIndicator as the central entity that connects SDGs, SDG targets, global indicator references, national and proxy indicator metadata, source datasets, publishing agencies, controlled codelists, and statistical observations. Following a reuse-first design principle, the model was implemented using established semantic web vocabularies, extended with only seven custom properties. The model is instantiated and evaluated through a purposively selected case of 16 child-related SDG indicators in Indonesia, chosen to maximize structural heterogeneity across producing agencies, data sources, and indicator–framework relationships, including nationally adapted and proxy indicators. The knowledge graph was evaluated using SPARQL-based competency questions with predefined ground truth and query execution time measurement, SHACL structural validation complemented by mutation-based negative testing, traceability coverage analysis with source verification, and supplementary expert judgement. The findings demonstrate that the proposed indicator-centric knowledge graph model can support semantic integration, metadata access, and end-to-end traceability of official statistical indicators with a minimal, interoperable vocabulary extension. While the empirical evidence is limited to a single-country, single-domain instantiation, the model itself is domain-agnostic; its layers and relations are defined independently of the thematic content of the indicators. Full article

As the 2030 deadline approaches with most Sustainable Development Goal (SDG) targets off-track, the global development community faces a critical choice: continue with technocratic indicator-focused implementation or embrace the fundamental transformation the SDGs originally promised. This paper argues that the convergence of planetary health, the wellbeing economy, and systems thinking offers a pathway to restore the SDGs’ transformative potential through community-governed, power-redistributive approaches. Much of current SDG implementation has become a technocratic exercise that obscures power dynamics, ignores Indigenous knowledge systems, and perpetuates growth-oriented economic logic that conflicts with planetary boundaries. This approach treats symptoms rather than root causes while maintaining consultation models that create an illusion of community engagement without redistributing decision-making authority. The wellbeing economy movement and planetary health framework offer complementary lenses for recalibration: the wellbeing economy challenges GDP-focused development by prioritizing collective flourishing within ecological limits, while planetary health reframes development within Earth’s life-support systems. However, translating these frameworks into concrete policy mechanisms requires systems thinking that includes explicit power analysis and centers relational accountability to communities most affected by current systemic failures. A post-2030 agenda must resist fixing the SDGs through more indicators and instead institutionalize community governance, redistribute decision-making power, and co-create implementation tools that communities own and adapt. This requires breaking silos between health, environmental, and economic sectors, while centering Indigenous voices as leaders rather than beneficiaries. The convergence of crises demands courage to chart a new path guided by community wisdom, planetary boundaries, and relational accountability; one that restores not only ecosystems but decision-making power to those who understand that health, economy, and ecology are inseparable. Full article

Synthetic fibre composites deliver high mechanical performance at a substantial energy and carbon cost, motivating a shift to renewable reinforcements. Flax and hemp are credible candidates, but their uptake is constrained by thermal stability and inherent combustibility, and their performance depends strongly on how completely the laminate is consolidated during manufacture. Vacuum-Assisted Resin Infusion Moulding (VARIM) yields low-void, well-consolidated laminates, yet the thermal and acoustic behaviour of VARIM-processed flax and hemp composites has not been compared directly. This study evaluates unidirectional flax/epoxy and hemp/epoxy laminates, each consisting of five plies produced under identical VARIM conditions, using TGA, DSC, limiting oxygen index (LOI) and UL 94 HB testing, SEM, and four-microphone impedance tube transmission loss (TL) measurements (ASTM E2611). Both laminates remained thermally stable to approximately 200–220 °C. Hemp/epoxy recorded a higher 5 wt.% degradation temperature (222 versus 207 °C), char residue (10.22 versus 9.29 wt.%) and average TL (13.22 versus 11.56 dB, 250–2000 Hz), while both laminates returned an identical LOI of 21.54% and a UL 94 HB rating. The fibre-governed properties therefore differed between the two laminates whereas the flammability response, governed by the shared epoxy matrix, did not. The glass transition temperature also differed (71.9 versus 65.3 °C) but is attributed to a small difference in degree of cure rather than to the reinforcement. The results provide baseline data for selecting VARIM-processed bio-composites for thermal management and passive noise control. The findings support Sustainable Development Goals (SDGs) 9, 12, and 13 through the development of sustainable, low-carbon bio-composite materials. Full article

Ensuring that artificial intelligence contributes to sustainable, equitable education requires more than aggregate accuracy—it requires verifying that predictive systems serve all learners fairly, including across geographic regions. We audit a dropout-prediction pipeline built on the Open University Learning Analytics Dataset (OULAD) for disparities across gender, disability, and geographic region, using a student-level train/test partition to prevent the same student’s records from contaminating both sets. Logistic regression and random forest classifiers attain approximately 0.85 accuracy and 0.91–0.92 AUC overall, yet region-stratified recall (true-positive rate) ranges from 0.55 in Wales to 0.80 in the West Midlands Region, an equal-opportunity gap of 0.25 that is corroborated by region-specific AUC, by a random forest classifier, and, for actual withdrawals, by a likelihood-ratio test showing region predicts being missed by the classifier beyond what the Index of Multiple Deprivation (IMD) explains. A region-isolation test shows that excluding region as a model predictor is associated with a significantly narrower gap in both model families (0.13–0.19 without region versus 0.25–0.28 with region), an association not explained by IMD band alone; because the bootstrap 95% confidence interval on this difference ($[-0.003,0.173]$) narrowly includes zero, we treat the attribution to region specifically as suggestive rather than conclusively established. A naive region-specific decision-threshold mitigation, evaluated correctly on a held-out validation set, does not improve the gap; a shrinkage-regularized version recovers a modest, observed reduction (0.24 to 0.17) on the held-out test set, without a formal uncertainty interval for this difference, at the cost of a near-doubling of regional false-positive rates. Because our analysis is retrospective and several predictors are computed over the full module presentation, these findings support methodological lessons for the design and auditing of future systems rather than direct claims about the performance or fairness of an operational, real-time early-warning intervention; we report them, including the mitigation failure, as evidence that dropout-prediction systems audited only for aggregate accuracy, without a properly validated regional fairness assessment, risk under-serving or unevenly burdening students in specific regions, working against rather than for the aims of SDG 4. Full article

Digital labor platforms have expanded employment in urban service work while shifting much of the operational and occupational risk of that work onto workers. Whether such employment can support decent work—the central labor commitment of SDG 8—remains unresolved. Algorithmic dispatching, real-time monitoring, and continuous performance evaluation have raised delivery efficiency, but they have also intensified the tension between operational speed and the conditions under which that work is performed. The prior literature has largely examined algorithmic control as a relationship between platforms and workers, yet has paid less attention to how intermediary organizations translate platform rules into enforceable—and at times protective—practices. What remains under-theorized is not the existence of intermediaries but the mechanisms through which they absorb, translate, and redistribute algorithmic pressure. Drawing upon a 16-week ethnographic investigation at Subcontracted Station J in Nanjing, China, this study integrates approximately 120 h of participant observation, semi-structured interviews with 24 relevant actors, and 827 digital trace data entries from three workplace WeChat groups to examine how subcontracted stations mediate algorithmic pressure and its impact on decent work. We find that station managers selectively absorb, translate, and redistribute algorithmic pressure through four practices we term penalty buffering, risk buffering, resource buffering, and knowledge buffering. We conceptualize this meso-level governance mechanism as “managerial buffering.” Our findings demonstrate that achieving the decent work championed by SDG 8 cannot rely solely on informal repairs by grassroots organizations; rather, protective practices within these mechanisms must be institutionalized into explicit, universal, and enforceable framework agreements. This study also acknowledges that the applicability of the proposed “managerial buffering” framework may depend on local institutional and socioeconomic contexts. Constrained by a single-case design, future research could conduct comparative studies across cities, platforms, and stations to further test the empirical manifestations of managerial buffering under diverse institutional environments. Full article

Liquefied Petroleum Gas (LPG) subsidies have become an important policy instrument for improving energy affordability, reducing energy poverty, and supporting economic activities among vulnerable populations in Indonesia. However, the long-term sustainability of LPG subsidy policies remains a subject of debate due to concerns regarding fiscal burdens, governance challenges, and environmental impacts. This study aims to assess the sustainability of LPG subsidy policies through a case study in Cirebon City, West Java, Indonesia, using the Triple Bottom Line framework that integrates social, economic, and environmental dimensions. A qualitative case study approach was employed, utilizing semi-structured interviews, Focus Group Discussions (FGDs), field observations, and document analysis involving key stakeholders, including government agencies, Pertamina, LPG distributors, micro-enterprises, and household beneficiaries. Data were analyzed using thematic analysis. The findings indicate that LPG subsidies contribute positively to social sustainability by improving energy access, reducing household energy expenditures, and enhancing social welfare. The policy also supports economic sustainability through increased purchasing power, improved productivity of micro, small, and medium enterprises (MSMEs), and local economic resilience. From an environmental perspective, LPG subsidies help reduce dependence on traditional fuels such as firewood and kerosene, thereby contributing to cleaner household energy use and indirectly supporting forest conservation. The study identifies significant challenges related to mistargeting, distribution leakages, fiscal pressures, and continued dependence on fossil fuels, which may hinder long-term sustainability and energy transition efforts. The findings suggest that LPG should be positioned as a transition fuel rather than a permanent energy solution. Strengthening subsidy-targeting mechanisms, improving digital governance systems, enhancing fiscal efficiency, and gradually promoting renewable energy adoption are essential to support sustainable energy governance and the achievement of Sustainable Development Goals (SDGs). This study contributes to the literature by providing an integrated sustainability assessment of LPG subsidy policies within the context of developing countries. Full article

A key aspect of sustainability is regenerating inhabited urban areas without demolishing them, since this helps retain the embodied carbon and spatial capital built up in existing buildings while preventing the displacement that results from clearing land. In this approach, which prioritises retention, a major issue when reviving a district is where the first intervention should take place. The usual method is to map out ‘readiness’—a combination of accessibility, public transport facilities, and current levels of activity—and then focus interventions on areas where this criterion is highest. Yet because readiness is evaluated in terms of existing assets, it tends to identify streets that are already working well, and these are also the ones with the least potential for further improvement. This study measures the part of a district which has so far been left unused. The latent field of a district consists of streets that are highly accessible within the larger city network but are not recruited by routes connecting the district’s specified supra-local destinations. The shape of this latent field, whether in the form of long lines or scattered patches, remained stable across the tested threshold and generator-inventory perturbations, whereas its size mainly reflects the modelling assumptions. The practical consequences of this approach are evident in the ISKRA creative-district project, situated in the historic part of Toruń, Poland. Placed at the point of peak readiness, the insertion model brings 11% of the target rib onto the route skeleton; the same generator 500 m along the rib brings 70%. A similar intervention carried out in a different location has no effect. Activation is not related to distance, but it is limited by distance. When applying the method to Toruń’s Old Town, the resulting latent field comprises areas rather than lines, showing that a different intervention strategy is needed. The method requires only an open street network, a list of the main district destinations, and a commitment to preserving the existing urban structure; it generates siting predictions that can be tested using pedestrian counts. Full article

Human induced pluripotent stem cells (hiPSCs) have revolutionized cardiovascular research by providing a renewable source of patient-specific cardiomyocytes for disease modeling, drug discovery, precision medicine, and regenerative therapies. Temporal modulation of canonical Wnt/β-catenin signaling has established the current gold standard for efficient and reproducible cardiac differentiation under chemically defined conditions. However, conventional Wnt-based protocols consistently generate cardiomyocytes with fetal-like structural, electrophysiological, metabolic, and contractile characteristics, highlighting that lineage specification alone is insufficient to achieve functional maturation. This review discusses recent advances in engineering the cardiac developmental niche by integrating extracellular matrix remodeling, biomaterials, biomechanical and bioelectrical stimulation, metabolic regulation, multicellular interactions, and microfluidic technologies to better recapitulate the dynamic microenvironment of human cardiogenesis. We further examine how emerging bioengineered platforms, including engineered heart tissues, cardiac organoids, and heart-on-chip systems, enhance the physiological relevance of hiPSC-derived cardiac models. Finally, we discuss future perspectives arising from the convergence of developmental biology, tissue engineering, biomaterials, artificial intelligence, and microphysiological systems, proposing that the next generation of cardiac differentiation platforms will depend on integrating canonical Wnt signaling within biomimetic developmental microenvironments to generate mature human cardiac tissues with enhanced translational potential. By advancing physiologically relevant human cardiac models for disease modeling, drug discovery, and regenerative medicine, this work also supports the research and innovation priorities underlying Sustainable Development Goal 3 (SDG 3), particularly those related to reducing the burden of non-communicable diseases and strengthening health-related research and development. Full article

Accurate estimation of regional forest carbon storage is essential for assessing forest carbon sink capacity and supporting climate change mitigation. This study developed a UAV-LiDAR-assisted multi-source remote sensing approach to estimate forest AGB and carbon storage in Fuzhou City. UAV-LiDAR data acquired close to satellite overpasses were used to generate temporally matched plot-scale AGB samples through individual-tree segmentation, tree height–DBH estimation, and allometric equations. After screening, 204 samples were retained. Four predictor combinations (Landsat, SAR, Landsat + SAR, and Landsat + SAR + other factors) were combined with SWR and CatBoost to build eight AGB models after RF-RFE feature selection. The optimal model was applied to analyze forest carbon storage changes from 2015 to 2023. CatBoost generally outperformed SWR, with lower RMSE, MAE, and rRMSE. The CatBoost model integrating Landsat, SAR, and other factors performed best (RMSE = 19.83 t·hm⁻², MAE = 17.02 t·hm⁻², rRMSE = 21.73%). Forest carbon storage increased from 32.43 × 10⁶ t in 2015 to 36.76 × 10⁶ t in 2023, alongside increases in forest area and carbon density. These findings suggest the potential of UAV-LiDAR for temporally matched AGB sampling and regional forest carbon storage estimation in subtropical areas, contributing to relevant SDGs. Full article

The rapid integration of digital technologies into education has transformed electronic textbooks (e-textbooks) from static replicas of printed books into interactive, multimedia-rich learning environments. This bibliometric literature review synthesizes findings from 89 peer-reviewed studies published between 2000 and 2024, retrieved from IEEE Xplore, ACM Digital Library, ScienceDirect, and SpringerLink. Following the Kitchenham and Charters methodology and complemented by bibliometric analyses, this review is guided by the overarching research question (RQ): How are e-textbooks conceptualized, designed, and deployed to support learning outcomes while promoting accessibility and equity? This guiding question is complemented by three additional RQs: (1) How has the field of e-textbooks developed over time? (2) Which countries are leading research related to interactive textbooks, considering quantitative indicators of publications/country? (3) What evidence exists regarding the contribution of e-textbooks to learning outcomes? The analysis reveals a clear trajectory from early static digitization toward intelligent platforms incorporating adaptive feedback, personalization, and analytics. Well-designed e-textbooks using multimedia learning principles demonstrate significant potential to enhance conceptual understanding, learner engagement, and motivation—especially in K–12 contexts. However, the field remains challenged by definitional inconsistencies, the uneven application of accessibility standards, and the geographic concentration of research output in North America and East Asia. These disparities highlight an urgent need to align digital textbook development with broader global education goals. By foregrounding accessibility, inclusion, and equitable access, e-textbooks can play a transformative role in advancing Sustainable Development Goal 4 (Quality Education) by ensuring inclusive, engaging learning opportunities, and SDG 10 (Reduced Inequalities) by reducing educational disparities across diverse learner populations. This review concludes by calling for interdisciplinary frameworks that integrate cognitive science, human–computer interaction, and artificial intelligence to guide the design of the next generation of interactive, accessible, and pedagogically robust e-textbooks—tools that are not only technologically innovative, but also aligned with global commitments to equitable and sustainable education for all. Full article

Achieving the Sustainable Development Goals (SDGs) requires education that extends beyond formal schooling and fosters inclusion, lifelong learning, and social participation. Informal learning communities remain underexplored in this respect. This study examines how members of the speedcubing community perceive its educational and social potential in relation to SDG 4, 5, 10, and 11. The case was chosen for its open participation, intergenerational interaction, and the absence of age and gender categories in competitions. Anonymous questionnaires concerning 112 speedcubers, recruited in Poland, were completed by the speedcubers, by their parents or guardians, or jointly. Responses were analysed using descriptive statistics, non-parametric tests with effect sizes, and correspondence analysis by experience, gender, and respondent role. The community was rated as easy to enter (71.4% positive on five-point scales) and as accessible through free online resources (94.6%), with strong affirmation of its intergenerational character (93.8%) and culture of mutual respect (89.3%). Perceived equality of opportunity did not differ between female and male speedcubers, in contrast to patterns reported for other mind sports. Respondents also saw potential for engaging older adults socially. In formal education, potential was recognised above all in extracurricular clubs, without significant subgroup differences. These perceptions suggest that inclusive informal learning communities may complement formal education in supporting the SDGs. Full article

Financial institutions face an escalating volume of cybersecurity threats, yet existing decision frameworks rarely link predictive analytics to operational security priorities. Drawing on Task-Technology Fit theory, this study develops a machine learning-based decision-support framework to classify cybersecurity breach types in financial institutions and to identify the organizational risk factors that determine them. Analyzing 935 publicly disclosed incidents from the VERIS Community Database (VCDB, NAICS 52), we compare XGBoost against Random Forest, Logistic Regression, and Decision Tree. XGBoost achieves the most balanced performance (accuracy: 95.19%; weighted F1: 0.9519; 5-fold CV: 96.68% ± 0.21%). Feature importance analysis reveals ATM/kiosk infrastructure and breach pattern as the strongest predictors, translating into concrete SOC monitoring priorities. This framework supports UN/SDG 9 (Industry, Innovation and Infrastructure) and UN/SDG 16 (Peace, Justice and Strong Institutions) by strengthening the cyber resilience of financial institutions through open, replicable, data-driven methods. The open-data framework is replicable without commercial threat intelligence licenses. Full article

Digitalization may be associated with environmental innovation, but this relationship can depend on governance mechanisms that protect data and support trustworthy information use. This study examines Digital Governance Readiness (DGR)—a formative composite of standardized data-privacy and cybersecurity indicators—and a Consumer-Oriented Green Innovation (COGI) Index combining Environmental Innovation, Product Responsibility, and alignment with SDGs 9 and 12. Using LSEG Refinitiv Eikon data for an unbalanced panel of 225 European listed industrial firms (1833 firm-year observations over ten fiscal years), the preferred firm- and year-fixed-effects model reports a positive DGR–COGI association (β = 0.195, p < 0.01). Based on the reported sample standard deviations, a one-standard-deviation increase in DGR corresponds to approximately 0.244 standard deviations of COGI. The association remains positive in the reported lagged, PCA-based, winsorized sensitivity specifications. Because the design is observational, time-varying firm-level controls are not included, and the historical missing-component rule for the archived composites cannot be reconstructed from the available analytical record, the results are interpreted as conditional descriptive associations rather than causal or independently identified effects. The findings are consistent with privacy and cybersecurity governance being organizational capabilities linked to broader environmental and consumer-facing sustainability outcomes, but they do not isolate an explanatory effect of digital governance. Full article