Search Results (43,569)

Integrated biomass valorization platforms can improve resource efficiency, but coupling solvent extraction with thermochemical conversion changes the inherent hazard profile of the overall system. This study assesses an integrated avocado oil–biochar platform based on Creole-Antillean avocado from northern Colombia, where edible oil is recovered from fruit pulp by hexane extraction and seed residues are converted to biochar by slow pyrolysis. The Heikkilä Inherent Safety Index (ISI) was applied to steady-state Aspen Plus^® design data using the worst-case scoring rules. Exothermic secondary-reaction screening identified CO methanation as the controlling secondary reaction (I_rs = 4), while the as-modeled coexistence of H₂, CO, CH₄, and free O₂ in the 400 °C pyrolysis vapor was conservatively classified as a fire/explosion interaction (I_int = 4). Carbon monoxide controlled the substance-specific hazard term with max(I_fl + I_ex + I_tox) = 10. Classification of the cyclone-based equipment and process structure against the original criteria gave I_EQ = 2 and I_ST = 2. The resulting chemical and process subindices were I_CH = 18 and I_PS = 9, respectively, giving a total ISI of 27. An elemental closure reconstructed from the R-YIELD product specification closes the C, H, and O bookkeeping, but free molecular O₂ is explicitly treated as a yield-model artifact rather than as a physically validated pyrolysis product. The result is therefore interpreted as a conceptual-stage hazard screen rather than a safe/unsafe classification. Solvent containment, chemically consistent pyrolysis modeling, vapor-treatment design, thermal moderation, and process simplification are identified as the principal safety-oriented design priorities. Full article

The accelerating digital transformation of finance is reshaping the institutional and analytical environment in which central banks address climate-related financial risks while maintaining price and financial stability. This paper examines the interplay between Artificial Intelligence (AI), Sustainable Development Goals (SDGs), and the green transition, with particular attention to their implications for monetary policy and climate-risk assessment in central banking. The study does not apply AI or machine-learning techniques empirically; rather, it adopts a qualitative and institutional perspective to examine the potential role of AI and broader digital capabilities in strengthening the identification, assessment, and monitoring of physical and transition risks within central banks’ mandates. The study adopts a conceptual and policy-oriented analytical approach based on evidence from Eurostat Sustainable Development Goal indicators, the Network for Greening the Financial System (NGFS) climate scenarios, European Central Bank (ECB) reports, and European Systemic Risk Board (ESRB) analyses. These institutional frameworks are comparatively examined to assess how climate scenarios, stress-testing methodologies, sustainability indicators, and digital analytical capabilities can support macro-financial risk assessment and climate-aware monetary policy. Particular attention is given to the institutional mechanisms through which these approaches can contribute to understanding the transmission of climate-related risks to inflation, financial stability, and banking-sector resilience. The findings suggest that the integration of SDG indicators, climate scenarios, climate-risk assessment frameworks, and emerging digital capabilities provides a more comprehensive basis for understanding the implications of climate-related risks for monetary and financial stability. The ECB’s Climate and Nature Plan 2024–2025 illustrates how digital technologies, enhanced data capabilities, and climate-risk modelling are increasingly incorporated into monetary-policy implementation, prudential supervision, and portfolio management. Within this broader digital transformation, AI represents an emerging analytical capability that may support the processing of complex datasets and the monitoring of climate-related risks. However, its effective use requires appropriate governance, data quality, transparency, institutional coordination, and regulatory safeguards. The analysis also highlights potential trade-offs between sustainability objectives and traditional central-bank mandates. This paper contributes to the emerging literature on sustainable finance, climate-aware monetary policy, and the digital transformation of central banking by developing an integrated institutional framework linking AI, SDG indicators, climate scenarios, and climate-risk assessment. The findings provide policy-relevant insights into how central banks can enhance their analytical and institutional capacities to address climate-related financial risks and support the green transition while maintaining price and financial stability. Full article

Comparisons of path-tracking controllers based mainly on tracking error may overlook differences in steering activity, vehicle response, and lane containment. This study compares Pure Pursuit, Stanley, and Model Predictive Control using a common virtual vehicle model and multiple safety-related performance indicators. The virtual vehicle model was evaluated against two separate CAN recordings from the same vehicle without recalibration between evaluations. Vehicle-speed and front-wheel-speed predictions showed R² values above 0.97 in both recordings; yaw-rate R² values were 0.957 and 0.930, whereas lateral-acceleration agreement was lower, particularly in the second recording. A Common Path Manager provided consistent reference-path information and steering constraints for all three controllers. They were evaluated at 5 and 10 km/h on idealized hairpin and spiral paths using tracking errors, steering-command-rate RMS, kinematic-response measures, and center-of-gravity-based and sampled vehicle-body lane-containment measures. MPC achieved the lowest cross-track-error RMSE in both scenarios, whereas Pure Pursuit produced the lowest steering-command-rate, lateral-acceleration, and yaw-rate RMS values. Stanley achieved the lowest heading-error RMSE in the spiral scenario. In the hairpin tests, the vehicle center of gravity remained within the lane for all controllers, although the body was partially outside the modeled lane corridor in 37.048–53.352% of evaluated samples. These findings show that lower tracking error does not necessarily coincide with lower steering-command activity or improved CG-based lane keeping. The evaluated quantities are safety-related performance indicators for the investigated simulation conditions and do not constitute a comprehensive assessment of vehicle safety. Full article

Atmospheric particulate-matter variability reflects the combined effects of emissions, meteorology, and regional transport, and its interpretation benefits from the integration of complementary observations. This study combines two years (2023–2024) of aerosol light absorption at 880 nm (Abs880), PM2.5, PM10, and meteorological observations from ACTRIS/EBAS and ARPA Puglia to characterize the urban-scale covariation in Lecce, southern Italy. Since the aerosol-absorption and ARPA measurements were obtained at sites separated by approximately 6.3 km, the resulting relationships are interpreted as urban-scale associations rather than as co-located source-attribution measurements. After quality assurance/quality control, UTC time normalization, daily aggregation (>75% completeness), temporal alignment, and z-score standardization for PCA, the integrated observations were examined using seasonal comparisons, linear regression, principal component analysis, and HYSPLIT backward trajectories. PM2.5/PM10 ratio showed seasonal variability in both study years, with higher mean values during the winter. The PM2.5 was positively associated with the Abs880 (R² = 0.68 in 2023 and 0.59 in 2024), and the season-stratified regressions remained positive in all the seasonal subsets, although their strength varied. The meteorology-adjusted sensitivity models also retained a positive Abs880 coefficient after an HAC correction for serial dependence. The wind speed showed a weaker inverse association with PM2.5, whereas the temperature and the relative humidity showed weak pairwise associations. A PCA identified a common covariance direction among PM2.5, PM10, and Abs880, with additional meteorological structure in the six-variable sensitivity analysis. Two representative episodes contrasted an enhanced Abs880 under low-wind conditions with a spring Saharan-dust intrusion. Overall, this study characterizes the relationships among particulate matter, aerosol light absorption, meteorology, and regional transport over the 2023–2024 observation period. FAIR-oriented data practices and interoperable monitoring infrastructures provide the methodological framework that enables the integration, traceability, and reproducibility of these analyses. Full article

The relatively slow pace of progress in achieving most of the objectives set forth in the 2030 Agenda for Sustainable Development (2030 Agenda) indicates a rather pessimistic outlook for their timely implementation. This, in turn, calls into question not only the attainment of the declared targets but also the sustainability of global society amid intensifying social, economic, and environmental challenges. These circumstances necessitate a reconsideration of existing approaches and mechanisms for achieving these goals, along with the more active engagement of all relevant stakeholders and a broadening of the range of potential solutions. The study demonstrates that progress toward the Sustainable Development Goals (SDGs) can be positively influenced by the implementation of contemporary paradigms such as Industry 4.0, Industry 5.0, Society 5.0, the green and circular economy, and open innovation. However, these paradigms primarily address only specific dimensions of sustainable development. This results in the current imbalance in progress toward the SDGs. The authors argue that addressing this imbalance requires intensifying the implementation of these contemporary paradigms through the formation and development of human resources as key drivers of progress. This should be achieved by ensuring the complementarity of knowledge, skills, and competencies in core domains with sustainability-oriented perspectives. In this context, artificial intelligence technologies are considered a key enabling instrument, as their rapidly evolving capabilities open up new opportunities for enhancing the efficiency and effectiveness of human resources. Accordingly, the purpose of this study is to substantiate the role of artificial intelligence technologies in shaping and developing human resources within the framework of priority societal development paradigms. The successful implementation of these paradigms can contribute to accelerating sustainable development. This article is conceptual in nature and constitutes a systematic literature review. The research methodology is based on a comprehensive review of scholarly literature on sustainable development, contemporary economic and societal paradigms, and digital technologies, available in the Google Scholar database. As a result of the research, 89 studies were selected based on the PRISMA methodology. The study also draws upon analytical reports on the implementation of the SDGs, as well as reviews of sustainable development practices and artificial intelligence adoption. General scientific and specialized research methods were employed, including theoretical and historical analysis, induction and deduction, and comparative and gap analysis. The findings provide a theoretical substantiation of the role of artificial intelligence in generating a synergistic effect from the implementation of modern development paradigms in the economy and society, which can have a positive impact on the dynamics of the SDGs. Full article

Open-pit metal mines require carbon-management tools that connect heterogeneous operational data with process-level accounting and prospective production-plan assessment. This study developed an integrated carbon-emission assessment and decarbonization decision-support system combining a MySQL database, standardized data interfaces and batch imports, a process-level accounting model, and a production-plan-driven scenario model. The boundary covers drilling, blasting, loading, haulage, crushing, beneficiation, and tailings handling, with land-disturbance-induced carbon-stock loss assessed separately. The deployed system was evaluated at the Julong open-pit copper mine using 2025 historical records for computational verification and the 2027 monthly production plan for conditional scenario assessment. Absolute relative differences for electricity, diesel, explosives, and mine-wide emissions were below 3%, with a mean absolute relative difference of 0.99%. Under the 2027 baseline, annual emissions were 1762.53 kt CO₂ eq. The combined mitigation scenario reduced emissions by 21.65% and lowered emission intensity from 8.49 to 6.65 t CO₂ eq/t concentrate. These results show that the system integrates historical carbon-emission assessment with production-plan-driven decarbonization screening and converts process data into traceable evidence for comparing mitigation options. It therefore serves as a carbon-focused decision-support tool for sustainability-oriented mine planning, although broader applicability requires multi-mine validation and explicit uncertainty analysis. Full article

This paper presents a scenario-based, Gaborone-oriented surrogate-feeder study using a modified IEEE 69-bus radial test feeder remapped onto representative Gaborone urban functional zones, to test whether coordinated siting and control of EV charging stations (EVCS), photovoltaic (PV) generation, and battery energy storage (BESS) can increase the EV charging load a feeder accepts without further degrading its already non-compliant evening-peak voltage (below the ANSI C84.1 0.95 p.u. limit even before any EVCS, PV, or BESS is added). The capacities reported are therefore incremental no-further-degradation capacities rather than standard feasible hosting capacities and should not be read as utility-specific EV connection limits. Four scenarios are examined: an uncoordinated baseline, unmanaged PV/BESS addition, Adaptive Dandelion Optimizer (ADBO)-based joint siting/sizing of EVCS, PV, and BESS with smart-inverter Volt-VAR/Volt-Watt control, and stochastic/high-penetration stress testing. Uncoordinated PV/BESS addition is found to introduce a separate midday overvoltage and reverse-flow risk without resolving the pre-existing undervoltage. Coordinated ADBO-based siting adds substantial incremental EVCS capacity without further lowering the minimum voltage, and Volt-VAR control adds further support at the evening peak by using otherwise-idle nighttime PV inverter capacity; neither intervention achieves full statutory compliance. A stochastic cloud-transient test also reveals a rapid substation power ramp that a voltage-compliance check alone would miss. ADBO is benchmarked against PSO, a real-coded GA, and GWO: it reaches a comparable best-case objective value but is less consistent across runs and converges more slowly, so it is presented as a tested candidate rather than a demonstrated superior method. The principal limitation of this study is that the feeder, load, irradiance, and algorithm-comparison data are a transparent surrogate and sensitivity analysis rather than measured utility data; the results should accordingly be interpreted as a methodological demonstration of coordinated EVCS-PV-BESS planning rather than a validated assessment of Botswana Power Corporation’s Gaborone network. Full article

Previous studies have often focused on individual supply parameters or limited operating conditions, leaving the combined effects of supply air temperature, velocity, and angle insufficiently characterized in compact, high-density dormitory environments. To address this gap, this study evaluated the combined effects of air conditioner set point temperature, supply air velocity, and supply air angle on thermal conditions, local carbon dioxide (CO₂) exposure, and electricity use in a four-person university dormitory in Wuhan, China. A three-factor full-factorial field experiment covered 36 operating conditions, and seven representative conditions were further analyzed using transient computational fluid dynamics (CFD). Increasing the set point temperature from 18 to 26 °C reduced mean measured electricity use from 0.751 to 0.552 kW. At the marginal-mean level, increasing the supply air velocity from 2 to 5 m/s was associated with a decrease in measured CO₂ concentration at the monitoring points from 764.17 to 603.92 ppm, while mean electrical power increased from 0.547 to 0.729 kW. Factorial analysis further identified significant interaction effects among the operating parameters, particularly for CO₂ concentration, indicating that these marginal trends varied across parameter combinations. PMV-PPD evaluation identified nine conditions satisfying the thermal-comfort criteria, while low set points and high local air speeds were associated with cool discomfort. Normalized CFD fields showed that velocity mainly governed jet strength and mixing intensity, whereas angle redirected cold-air and CO₂ transport paths. By combining full-factorial field measurements with mechanism-oriented CFD analysis, this study provides an integrated evaluation of thermal comfort, local CO₂ exposure, and electrical power demand and identifies a feasible operating window within the investigated dormitory configuration and operating range. Full article

The climate crisis and environmental degradation have intensified ecological, social, and economic challenges in Iran, increasing the need for environmental communication that supports public engagement, policy development, and long-term environmental governance. However, environmental reporting remains largely episodic and constrained by political, legal, organizational, and professional limitations. This study develops an empirically grounded conceptual model of sustainable media using a grounded theory approach based on interviews with Iranian journalists, media managers, documentary filmmakers, communication scholars, and environmental experts. The findings conceptualize sustainable media as a model comprising causal, contextual, intervening, strategic, and consequential components. The model proposes that sustainable media can be maintained through the interaction of institutional capacities, including legal protection, economic independence, organizational resilience, scientific expertise, and professional development, with communicative capacities such as agenda-setting, audience orientation, transparency, editorial independence, and collaboration among journalists, scientists, and environmental stakeholders. These capacities support continuous, context-sensitive, and solution-oriented environmental journalism. The study contributes to environmental communication scholarship by integrating perspectives on environmental communication, mediatization, and hybrid media systems into a unified framework in a West Asian context and offers practical insights for strengthening environmental journalism and long-term environmental governance in an ecologically vulnerable and politically constrained environment. Full article

Skeleton-based human action recognition is attractive for practical vision systems because it represents motion compactly, but occlusion and pose-estimation failures can remove spatially adjacent joints for extended periods and degrade graph-based recognition. We propose a Topology-Aware Graph Learning (TAGL) framework that couples three mechanisms: Neighborhood Expansion Masking generates spatially correlated, sequence-level joint loss during training; Adaptive Multi-Hop Graph Learning reweights information from different graph distances when local neighborhoods become unreliable; and Holistic Topological Mapping provides complementary global structural cues through persistent topology. TAGL achieves 93.3%/97.9% top-1 accuracy on NTU RGB+D 60, 90.4%/91.6% on NTU RGB+D 120, 97.0% on Northwestern-UCLA, and 47.2% on UAV-Human. Across six heterogeneous skeleton corruptions, its average accuracy reaches 85.8%, compared with 79.3% without NEM and 84.5% with random masking, while requiring 1.78 M parameters and 2.58 GFLOPs. These results show that TAGL improves tolerance to incomplete and structurally degraded skeleton observations without excessive computational overhead. Its compact and corruption-aware design is therefore promising for skeleton-based monitoring systems operating under imperfect pose observations, including safety surveillance, human–robot interaction, and rehabilitation-oriented motion analysis. Full article

Cybersecurity investments are critical for organizational resilience, yet their business value remains difficult to quantify, validate, and communicate to executive stakeholders. This study addresses this challenge by combining an expanded Systematic Literature Review (SLR) with a Design Science Research (DSR) approach. The SLR identifies and synthesizes 132 full-text studies and analyzes existing approaches across financial, technical, governance-related, risk-based, and temporal evaluation dimensions. The findings reveal a structural validation gap in cybersecurity investment evaluation: while many approaches support ex-ante prediction, investment justification, or risk-based decision-making, comparatively few provide mechanisms for ex-post validation of whether financial assumptions are realized through operational outcomes. This disconnect limits the ability of organizations to assess whether cybersecurity investments deliver the expected value over time. To address this gap, the paper proposes the CISO Investment Validation Loop (CIVL), a cyclical design artifact that links financial assumptions with operational proxy indicators across the cybersecurity investment lifecycle. CIVL is conceptually illustrated through a scenario involving the consolidation of legacy firewall infrastructure into a Secure Access Service Edge (SASE) architecture. The study contributes a lifecycle-oriented framework for connecting ex-ante cybersecurity investment assumptions with ex-post operational validation mechanisms and highlights the need for further empirical and practitioner-based evaluation of CIVL in real organizational settings. Full article

This article examines whether support for religious freedom among university students in Croatia and Italy forms distinct profiles and which factors are associated with profile membership. Drawing on questionnaire data from university students in Croatia (N = 603) and Italy (N = 714), with 1170 complete cases included in the cluster analysis, K-means cluster analysis identified three profiles: Consistent Supporters (36.0%), Conditional Supporters (39.2%), and Restrictive Supporters (24.8%). Consistent Supporters combined strong endorsement of religious freedom with support for pluralism, recognition, and accommodation. Conditional Supporters endorsed religious freedom in positive absolute terms but combined this with the strongest assimilation orientation, weaker pluralism and recognition, and more selective institutional accommodation. Restrictive Supporters displayed the weakest support across most dimensions. Multinomial logistic regression showed that dominant Church favouritism and institutional religious privileging were associated with higher odds of both Conditional and Restrictive rather than Consistent membership. Croatian respondents, those with more right-wing political orientations, and those with weaker family religious socialization were also more likely to belong to both less-consistent profiles, whereas religious affiliation and self-rated religiosity were not independently significant in the final model. The findings demonstrate that support for religious freedom is multidimensional and may remain positive while becoming conditional in its public and institutional application. Full article

This study proposes a genetic algorithm-enhanced dhole optimization algorithm coupled with a long short-term memory network (GA-DOA-LSTM) for dynamic identification of a high bypass ratio separate-flow turbofan engine during acceleration. A component-level JT9D model was implemented in T-MATS, and a dataset was generated at 54 flight operating points under step, ramp, and parabolic throttle excitations. Low- and high-pressure spool speeds, high-pressure compressor outlet total pressure, combustor exit temperature, and fuel flow were used as inputs, while low-pressure turbine exit temperature (T5) and thrust were predicted. GA-DOA combines dual-population coevolution, a global optimum pool, periodic elite exchange, greedy selection, and a final DOA refinement to jointly optimize the LSTM architecture and output loss weights. Compared with LSTM, PSO-LSTM, SSA-LSTM, DOA-LSTM, and GA-LSTM, the proposed model achieved RMSE, MAE, and MAPE values of 1.6121 K, 1.0136 K, and 0.1512% for T5 and 2430.10 N, 1832.24 N, and 1.2576% for thrust, respectively. The model maintained consistent transient tracking under different altitudes, Mach numbers, and throttle profiles, demonstrating its potential for aero-engine performance assessment, health monitoring, and control-oriented modeling. The main limitation is that the present validation remains simulation-based and engine-specific; experimental and cross-engine validation will therefore be required before practical deployment. Full article

To address overhang sagging in laser powder bed fusion (L-PBF) of meso-scale film-cooling holes, this study proposes a 777TD hybrid geometry combining the nominal 7° diffuser features of a 777 hole with a pointed, self-supporting roof. Ideal smooth-wall computational fluid dynamics (CFD) calculations compared cylindrical, teardrop, 777, and 777TD holes with diameters of 0.5–0.8 mm at a blowing ratio of 1.0 and a density ratio of 1.5. At D = 0.8 mm, the predicted area-averaged adiabatic film-cooling effectiveness was 0.3308 for 777TD and 0.3208 for 777, a nominal relative increase of 3.1%. Separately, Haynes 230 specimens were fabricated at build orientations of 30° and 60° and evaluated by X-ray computed tomography (CT). At D = 0.8 mm and a 30° build orientation, the in-tolerance proportion in the selected outlet region was 93% for 777TD and 76% for 777, an increase of 17 percentage points under a ±0.1D deviation criterion. These results support further evaluation of the hybrid geometry for meso-scale cooling hole manufacture. The CFD comparison and CT conformity assessment provide complementary evidence; the cooling performance of the as-built holes has not been experimentally validated. Full article

Freezing cold injuries (FCIs) remain a recurrent challenge in cold-weather military operations despite preventive routines and extensive institutional experience in the Norwegian Armed Forces (NAF). A previous published qualitative study examined the causes and consequences of FCIs and the possible influence of military culture. The present secondary analysis examined the same interviews to address a prevention-oriented research question: what did injured soldiers believe could have been done differently to reduce the incidence of FCIs? Three themes emerged from the analysis: (1) translating FCI education into practice, (2) communication and preventive routines, and (3) participant suggestions for preventive actions. Despite receiving formal training, participants described difficulties with applying FCI knowledge and protocols under operational pressure. Their accounts suggest that prevention depends not only on general awareness but also on whether preventive knowledge and routines can be effectively applied under operational conditions. Full article