Complex engineering systems often involve interacting transport fields in which diffusion, field-driven fluxes, production or decay, and nonlinear feedback jointly control stability, transport, and localization. Although originating in mathematical biology, the Keller–Segel system is a canonical coupled-field transport model with this structure and provides a stringent benchmark for computational methodologies aimed at complex and multiphysics transport problems. A multidimensional hybrid numerical–analytical formulation based on the Generalized Integral Transform Technique (GITT) is developed for a parabolic–parabolic Keller–Segel system with homogeneous Neumann boundary conditions. Spatial averages are treated analytically, zero-mean fields are expanded in normalized Neumann eigenfunctions, and exact cosine-product selection rules generate a sparse nonlinear modal system. The transformed equations are advanced using CVODE, a variable-step, variable-order ordinary differential equation (ODE) solver from the SUNDIALS (Suite of Nonlinear and Differential/Algebraic Equation Solvers) package, with variable-order backward differentiation formulas (BDF), together with a matrix-free scaled preconditioned generalized minimal residual (SPGMR) solver in two dimensions. Systematic one- and two-dimensional convergence studies are performed for chemotactic sensitivities $\chi$ = 1–5 and are complemented by independent conservative finite-volume/method-of-lines solutions in physical space. The finite-volume discretizations exhibit second-order self-convergence and closely reproduce the converged GITT fields. The parametric study quantifies the transition from weak relaxation to strong aggregation and the growth of genuinely two-dimensional mixed modes. For $\chi$ = 5, successive truncations up to 7,920 spatial modes (15,840 coupled ODEs) resolve a strongly localized regime. Physical and spectral localization scales contract consistently, while normalized profiles collapse onto an approximately invariant shape. Independent late-time extrapolations yield characteristic times clustered near $t$ $\approx$ 2.184. The results demonstrate a high-resolution hybrid framework for nonlinear coupled-field partial differential equation (PDE) systems with cross-field transport, evolving localization, and demanding multiscale resolution, while providing numerical evidence compatible with possible finite-time singularization without constituting a mathematical proof of blow-up.
The rapid diffusion of artificial intelligence (AI) is adding a fast-growing workload to the data centres that host it, with consequences for electricity systems, household energy costs, and the environment. This article examines the sustainability implications of AI-driven data-centre expansion in Europe along three connected dimensions—environmental sustainability, energy and social equity, and the sustainability of the digital transition—and asks who gains and who pays. Doctrinal analysis of European Union (EU) law and policy was combined with a comparative, descriptive assessment of published data. The study synthesised evidence from international agencies, EU institutions, national statistical offices, regulators, and peer-reviewed research, and traced household electricity costs and data-centre development from 2023 onward in three purposively selected cases: Ireland, the United Kingdom, and Spain. Data-centre electricity demand, of which AI workloads are an increasingly important component, is growing far faster than overall demand and is highly concentrated geographically. In exposed regions this concentration is associated with grid-capacity constraints and may contribute to upward pressure on network and system costs. Household prices in the period were, however, shaped predominantly by gas prices, taxation, subsidies, renewable output, and network charges, and the study does not estimate causal effects. The expansion also generates environmental externalities, notably carbon emissions and a growing stream of electronic waste. Because energy costs weigh most heavily on low-income households, these pressures carry a distributional risk: a potential “double burden” in which households already in energy poverty benefit least from AI while bearing part of its infrastructure costs. The article maps the EU response by legal status—measures in force, formally proposed, and under discussion—and argues that assessments of AI’s social value should internalise its energy, climate, resource, and distributional costs. It closes with actor-specific recommendations on clean-supply additionality, cost-reflective network-cost allocation, safeguards for vulnerable consumers, hardware circularity, and sustainability metrics.
This study develops and validates a data-driven model based on an artificial neural network (ANN) to predict the effective shear strength parameters (effective angle of internal friction and effective cohesion) of granular soils from basic index properties. A database of 1,105 secondary records, obtained from standardized geotechnical tests performed at Peruvian universities, was used for training and internal validation. External verification was carried out with 39 independent samples tested by consolidated-drained direct shear in accordance with ASTM International D3080/D3080M and Peruvian Technical Standard (Norma Técnica Peruana, NTP) 339.171. The network, with three hidden layers, dropout, and batch normalization, uses particle size distribution, Atterberg limits, dry density, and moisture content as input variables. In the external test, the model achieved a coefficient of determination ($R^2$) of 0.86 for the effective angle of internal friction (mean absolute error [MAE] = 1.21°; root mean square error [RMSE] = 1.75°) and 0.77 for the effective cohesion (MAE = 1.09 kPa; RMSE = 2.14 kPa). Unlike previous studies based on limited or heterogeneous datasets, this work uses a broad database of Peruvian soils, which facilitates regional calibration. Gradient and sensitivity analyses indicated that particle size distribution is the most influential factor on shear strength, whereas moisture content has a comparatively smaller influence. The results suggest that the proposed network may serve as a preliminary estimation tool for the shear strength of Peruvian granular soils when basic index properties are available. However, given the small external test set and the regional scope of the data, its extension to other soil types or regions requires further validation.
The strength assessment of thick-walled composite cylinders under internal pressure requires consideration of the nonuniform stress distribution and the interaction of stress components arising from material orthotropy and axial restraint. Conventional assessments based primarily on circumferential stress may not adequately represent the multiaxial stress state governing failure initiation. This study investigates the elastic stress response and initial failure of an internally pressurized thick-walled cylinder made of Hexcel AS4/3501-6 carbon fiber/epoxy composite, with particular attention to the effects of wall thickness and three-dimensional stress interaction. An analytical formulation was developed using orthotropic elasticity under plane-strain conditions, and the radial, circumferential, and axial normal stresses were determined across the cylinder wall. The three-dimensional Tsai–Wu criterion was then applied to evaluate the combined influence of these stresses on failure initiation and to determine the allowable internal pressure. The calculations showed a nonuniform stress distribution, with the circumferential stress reaching its maximum at the inner surface and decreasing toward the outer wall. For the representative configuration, the maximum circumferential stress was approximately 188 MPa, while the axial stress remained nonzero because of the imposed axial constraint. Variations in wall thickness produced substantial changes in the stress distribution and the predicted failure response. The analysis also established a procedure for identifying the pressure at which the Tsai–Wu failure threshold is first reached. These findings clarify the role of orthotropic elasticity and multiaxial stress interaction in the strength assessment of pressurized composite cylinders. The analytical framework provides a basis for evaluating initial failure margins and examining the influence of wall thickness on structural pressure capacity.
The integration of artificial intelligence (AI) into engineering practice is bringing new dimensions to the role of a leader, including professional judgment, collaboration, and responsibility for decisions made with AI’s support. However, desirable personal leadership orientations do not necessarily correlate with desirable work conditions. This study examines this perceived capability–enablement divide among engineers in Iraq. Survey data were collected from 144 engineers working in Iraq using 39 Likert-scale questions. The primary within-respondent comparison used bootstrap estimation, the Wilcoxon signed-rank test, and a cumulative ordinal fixed-effects model covering 5,616 item responses. Adjusted associations were examined using ordinary least squares regression with heteroskedasticity-consistent type 3 (HC3) standard errors and complete-profile permutation tests. Individual Leadership Orientation (ILO) was rated more favorably ($M$ = 3.789, standard deviation [SD] = 0.435) than Organizational Enablement (OE) ($M$ = 3.479, SD = 0.848). The mean paired difference was 0.309 points (95% bootstrap confidence interval [CI] [0.187, 0.443], $p$ $<$ .001; Cohen’s d$_z$ = 0.394). Using the ordinal model, and controlling for respondent and item differences, the same directionality was found; that is, endorsement of Organizational Enablement was associated with lower odds (odds ratio [OR] = 0.450, 95% CI [0.380, 0.534], $p$ $<$ .001). AI-use frequency was associated with ILO, but its association did not differ significantly from that with OE ($p$ = .242). Education was associated much more strongly with individual orientation than enablement. The results suggest that there is a perceived gap in capability and enablement among the engineers participating, rather than any broad opposition to AI. The results indicate that leadership orientation and perceived organizational support are separate factors to consider when designing responsible AI implementation for engineering organizations.
Accurate identification of meditative states using electroencephalography (EEG) is crucial for objective neural assessment and brain-computer interface applications. However, reliable classification remains challenging because of signal nonstationarity, artifacts, inter-individual variability, and differences across recording sessions. To address these challenges, NeuroState, a dual-path classification framework integrating time-frequency analysis, conventional machine learning, and attention-enhanced deep learning, was developed for automated classification of meditation-related cognitive states. Initially, EEG signals were transformed into time-frequency representations using the Morlet continuous wavelet transform (CWT), allowing temporal variations in spectral activity to be characterized. Two complementary classification pathways were subsequently implemented. In the conventional machine-learning pathway, wavelet-derived scalograms were vectorized, and dimensionality was reduced using principal component analysis (PCA) before classification with support vector machine and random forest classifiers. In the deep-learning pathway, the two-dimensional scalograms were directly processed using a wide squeeze-and-excitation residual network, in which residual feature extraction and channel-wise attention were integrated to facilitate the adaptive learning of discriminative time-frequency representations. The framework was evaluated using the publicly available OpenNeuro ds001787 dataset, comprising EEG recordings from 24 participants, including 12 experienced meditators and 12 individuals with limited meditation experience. The wide squeeze-and-excitation residual network achieved a test accuracy of 98.29%, outperforming the support vector machine and random forest classifiers, which achieved accuracies of 88.59% and 81.65%, respectively. These findings demonstrate the superior classification performance of attention-enhanced deep learning compared with the evaluated conventional machine-learning approaches. These findings suggest that attention-enhanced deep learning applied to structured EEG time-frequency representations may provide more accurate discrimination of meditation-related cognitive states than conventional machine-learning approaches based on PCA-reduced features. NeuroState thus provides a promising computational framework for EEG-based meditative state classification, with potential applications in meditation monitoring, neurofeedback, and brain-computer interface systems.
Cotton fiber-reinforced polymer composites have emerged as promising sustainable structural materials. However, the combined influence of fiber volume fraction ($V_f$) and fiber orientation on their mechanical performance has not been fully quantified. In this study, the mechanical behavior of cotton fiber-reinforced epoxy composites containing fiber volume fractions ($V_f$) of 20 vol.%, 40 vol.%, and 50 vol.% was systematically investigated through finite element analysis using ANSYS. Two representative reinforcement architectures, namely unidirectional and tri-directional fiber orientations, were modeled to evaluate their responses under tensile, flexural, and compressive loading conditions. The mechanical performance was assessed to elucidate the reinforcing mechanisms associated with different fiber configurations. It was found that the mechanical properties were consistently enhanced as the fiber volume fraction increased. Among all configurations, the composite containing 50 vol.% fiber volume fractions with a tri-directional orientation exhibited the highest mechanical performance, achieving tensile, flexural, and compressive strengths of 57.67, 71.03, and 68.31 MPa, respectively, corresponding to improvements of 46.1%, 17.5%, and 16.1% over the 20 vol.% tri-directional composites. Similarly, the 50 vol.% unidirectional composite demonstrated increases of 43.4% in tensile strength, 15.2% in flexural strength, and 17.3% in compressive strength relative to the corresponding 20 vol.% unidirectional configuration. Superior mechanical performance was consistently achieved by the tri-directional architecture compared with the unidirectional counterpart, which was attributed to a more uniform stress transfer mechanism, reduced stress concentration, and improved load redistribution, as confirmed by the von Mises stress distributions obtained from the finite element simulations. These findings demonstrate that both fiber volume fraction and fiber orientation play critical roles in governing the mechanical behavior of cotton fiber-reinforced epoxy composites. The numerical results further indicate that optimizing reinforcement architecture can substantially improve structural efficiency while maintaining the inherent sustainability and lightweight characteristics of natural fiber composites, thereby supporting their broader application in environmentally responsible engineering structures. 20%, 40%, and 50%.
Background: Reconfigurable manufacturing systems (RMSs) enable rapid adaptation of machines, tools, and control software to produce different part families. Objectives: This paper addressed a Multiple Part Family Production (MPFP) problem selecting the best reconfiguration policy (RP) when switching production between part families. RPs are represented as sequences of reconfiguration steps and evaluated by number of setups, total reconfiguration cost, and total reconfiguration time. This work presented a conceptual digital twin (DT) based framework for forming RP applicable to reconfigurable flow line (RFL). Method: This framework describes how a DT can model candidate reconfiguration sequences by using real or historical data to estimate time and cost, and support virtually in what if analyses for policy comparison prior to physical changes. The manuscript focuses on framework design, required data flows, and decision logic rather than on implementation or experimental deployment. Results: This work provides a structured DT architecture which acts as reconfiguration decision making tool for engineering managers and a formal representation of reconfiguration policies and evaluation metrics, and a roadmap for integrating data sources, simulation components, and validation procedures. A worked example is also presented to demonstrate how the proposed framework supports a management decision. Illustrative results show that RP$_1$ and RP$_3$ require only one reconfiguration, whereas RP$_2$ incurs an additional change at Stage 3. Consequently, RP$_1$ is the most efficient policy, followed by RP$_3$, while RP$_2$ imposes the highest reconfiguration burden. Conclusion: This work proposes a novel DT-driven RP for MPFP, applicable to an RFL, integrating real-time data analytics into RMS to manage reconfigurable machine tool (RMT) configurations across four production stages. The selected RP is intended to improve delivery performance, capacity utilization, production continuity, and system availability by minimizing setups and reconfiguration time/cost, thereby reducing downtime and maintaining a smoother production flow.
This study explores students’ perceptions, awareness, and engagement with sustainability and energy-efficiency initiatives in higher education institutions, with particular attention to the visibility of campus sustainability practices. An exploratory cross-sectional survey was conducted among 149 students from five universities. Data were collected through a structured questionnaire and analyzed using descriptive statistical techniques, including frequencies, percentages and mean scores. Students demonstrated strong environmental concern and positive attitudes towards sustainability. However, awareness of institutional sustainability initiatives, environmental management systems (EMS) and energy-efficiency measures was generally low. Limited communication and visibility of sustainability actions were identified as potential barriers to student engagement. The findings should be interpreted in light of the convenience sampling approach and reliance on self-reported perceptions. Future studies could employ broader samples and more advanced analytical methods. Higher education institutions can strengthen sustainability engagement by improving communication, increasing the visibility of sustainability initiatives and creating more opportunities for student participation. Greater student involvement in sustainability initiatives may contribute to the development of environmentally responsible attitudes, behaviors and sustainability competencies. The study provides exploratory evidence on sustainability visibility and student engagement in higher education and highlights the importance of communication and institutional transparency in fostering sustainability awareness and participation.
The financial sustainability of non-governmental organizations (NGOs) is potentially vulnerable to fluctuations in external financing, particularly in resource-constrained settings where continued access to funding may be uncertain. This study examined the relationship between external financing fluctuations and the financial sustainability of NGOs in Kakamega County, Kenya, drawing on Resource Dependence Theory (RDT). An explanatory research design was adopted, and a census approach was used to target all 69 registered NGOs in the county, of which 67 participated, yielding a response rate of 97.1%. Primary data were collected using semi-structured questionnaires and analyzed using Pearson’s correlation and linear regression in the Statistical Package for the Social Sciences (SPSS). A positive and statistically significant correlation was reported between external financing fluctuations and financial sustainability (r = 0.619, p < 0.05). Regression analysis also indicated a positive association, with a reported unstandardized regression coefficient of B = 0.321. The regression model was reported to yield R = 0.836, R² = 0.699 and an adjusted R² = 0.680, suggesting that a substantial proportion of the observed variation in financial sustainability was accounted for by the predictors included in the model. These findings suggest that external financing dynamics are relevant to the financial sustainability of NGOs in Kakamega County. However, the direction and magnitude of this relationship should be interpreted in accordance with the operational definition of financing fluctuations and the specification of the regression model. The findings provide a basis for further investigation into how changes in external funding affect the financial resilience of NGOs and underscore the importance of examining financing patterns when assessing organizational sustainability.
Hard point-of-interconnection (POI) export limits can strand otherwise-available photovoltaic (PV) energy unless battery storage is dispatched with sufficient anticipation and under physically consistent constraints. This study evaluates a constraint-audited receding-horizon dispatch framework for curtailment recovery at an anonymized 230 MW utility-scale PV plant with a modeled 195.5 MW export cap and a nominal 30 MW/140 MWh battery energy storage system (BESS). The mixed-integer model predictive controller enforces BESS power and energy limits, charge–discharge exclusivity, no grid charging, forecast-dependent discharge headroom, first-action receding-horizon execution, and a common 50%-to-50% terminal-energy protocol. Twelve rolling monthly folds from September 2024 to August 2025 are evaluated against cap-only operation and a protocol-matched perfect-information Oracle. Cap-only operation curtails 26,279 MWh (4.9872% of evaluated PV energy). A transparent 72 h Diurnal model predictive control (MPC) reduces curtailment to 11,719.454 MWh, recovers 14,559.546 MWh, and closes 98.450% of the modeled cap-only-to-Oracle recoverable-energy opportunity; the matched Oracle curtails 11,490.175 MWh. All 3 h closed-loop trajectories satisfy the modeled export cap, BESS limits, terminal equality, charge–discharge exclusivity, discharge-headroom rule, and no-grid-import condition. A matched information-sensitivity analysis then compares History-CatBoost and Weather-CatBoost under the same controller. Weather-CatBoost lowers daylight mean absolute error (MAE) by 2.225 MW at 24 h and 1.175 MW at 72 h relative to History-CatBoost, while annual curtailment is 288.601 MWh higher at 24 h and 99.699 MWh lower at 72 h; the monthly block-bootstrap intervals include zero in both cases. Histogram gradient boosting (HGB) robustness and BESS-sensitivity results further show that the incremental control value of weather information is model- and configuration-dependent. Under the tested plant, cap, BESS, 3 h resolution, horizon, and terminal protocol, physically constrained anticipatory dispatch captures most of the modeled recoverable curtailed energy, leaving comparatively limited residual opportunity for additional forecast sophistication.
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