Search Results (1,368)

This study offers a descriptive analysis of complex thinking as a form of cognitive competency among a group of 52 public servants holding local leadership positions in the Philippines. By extending the empirical examination of complex thinking beyond educational contexts and into local public leadership, the study contributes to an emerging line of research on the cognitive competencies associated with decision making in decentralized governance environments. Drawing on complexity theory applied to public decision making, it assumes that local governance requires the capacity to integrate heterogeneous information, anticipate interdependencies, and act under conditions of uncertainty. The assessment employed the eComplexity instrument using an adapted 21-item version structured into four dimensions: systemic, scientific, critical, and innovative thinking. Scores were rescaled to a 0–100 metric and, after confirming non-normality (Shapiro–Wilk), non-parametric tests were applied (Mann–Whitney, Kruskal–Wallis, and Dunn’s post hoc test with Bonferroni correction), along with Spearman’s rho correlations to examine dimensional coherence. No significant differences were observed by gender or income. Age showed overall variation across several dimensions, but robust pairwise differences were concentrated between the 31–40 and 41–50 age groups in systemic thinking and in the global score. Employment status differentiated only scientific thinking, with higher medians among permanent staff than contractual/project personnel. Correlations among dimensions were positive and significant, with particularly strong associations between systemic, critical, and innovative thinking, supporting the interpretation of complex thinking as an integrated competency in local public leadership. The findings should be interpreted considering the study’s descriptive design, localized convenience sample, and reliance on self-reported measures, which limit statistical generalizability beyond the analyzed context. Beyond its descriptive findings, the study offers initial empirical evidence relevant to governance research on the cognitive competencies associated with decision making among grassroots public leaders operating in decentralized institutional contexts. Examining complex thinking at this level helps illuminate how public actors interpret interdependencies, evaluate information, and navigate uncertainty in everyday governance practice. Full article

Hyperspectral imaging (HSI) combined with chemometrics has emerged as a rapid and non-destructive technique for fruit quality evaluation, enabling efficient monitoring of biochemical changes during postharvest storage. Among quality indicators, antioxidant activity is closely associated with nutritional value and physiological stability. This study aimed to develop an HSI-based approach for assessing the antioxidant capacity of strawberries subjected to different pre-cooling treatments during storage. Strawberries were treated with five pre-cooling methods and stored for up to 41 days. Antioxidant activity was measured using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical-scavenging assay. Hyperspectral data were collected and preprocessed using multiplicative scatter correction (MSC), followed by partial least squares regression (PLSR) to construct predictive models. Among the treatments, immersion vacuum cooling combined with one-cycle pulsing (IVCWP1) exhibited significantly higher DPPH scavenging activity (61.17 ± 12.31%) than immersion vacuum cooling with water (IVCW, 52.89 ± 18.30%) (p < 0.05). The PLSR model developed using MSC-corrected average reflectance spectra showed superior predictive performance and a higher coefficient of determination (R²) than models based on raw spectra. The results demonstrate that HSI coupled with chemometrics is an effective and practical tool for non-destructive evaluation of antioxidant activity and comparison of pre-cooling strategies in strawberries. Full article

Cognitive stress, also known as mental workload, constitutes a central topic within the field of psychophysiology due to its role in modulating attention, autonomic regulation, and stress reactivity. Furthermore, it bears direct relevance to practical monitoring systems that employ non-invasive sensing techniques. This study investigates whether a multimodal, non-invasive measurement setup can detect systematic physiological differences between Resting periods and short episodes of cognitive load within the same individuals. Additionally, it explores the capacity of such a system to differentiate tasks characterized by varying cognitive demands. A sequential, within-subject protocol was employed, comprising five consecutive phases (rest 1, Stroop, rest 12, subtraction, rest 3), during which five modalities were recorded concurrently: EEG, heart rate (HR), galvanic skin response (GSR), facial surface temperature, and oxygen saturation (SpO₂). Beyond phase-wise inspection of time-series data, an exploratory assessment of similarity across participants was conducted using correlation coefficients. The maximum cross-participant correlations observed were 0.88 (HR), 0.90 (GSR), 0.83 (facial temperature), and 0.77 (SpO₂); however, these correlations were used only as exploratory descriptors of inter-individual similarity and did not imply a significant phase effect. For inferential analysis, phase-wise epoch means were evaluated through one-factor repeated-measures ANOVA. The heart rate exhibited a robust main effect of phase (F(4, 32) = 10.5862, p_GG = 0.01044, ηp² = 0.5696), with higher HR observed during cognitive load epochs (e.g., 77.841 ± 11.777 bpm at rest 1 versus 83.926 ± 14.532 bpm during subtraction). The relatively large standard deviation reflects variability between subjects rather than variability within epochs. Regarding processed baseline-referenced GSR, the omnibus phase effect was not statistically significant under the conservative Greenhouse–Geisser correction; therefore, GSR was interpreted as exploratory in this dataset. Facial temperature and SpO₂ likewise did not show statistically significant omnibus phase effects under Greenhouse–Geisser correction (e.g., SpO₂: p_GG = 0.1209). EEG-derived measures provide supplementary central evidence of task engagement; entropy variations within an approximate dynamic range of 0.2 to 0.8 were observed, and the α/θ ratios demonstrated nearly a twofold distinction between rest and cognitive load epochs across different leads. Full article

The energy transition represents a complex, multi-level system subject to profound uncertainty and recurrent shocks. Current policy design approaches predominantly rely on static optimization frameworks (centralized, calculative models that presume stable conditions and predictable technological trajectories). Yet evidence from the 2021–2023 energy crisis in Europe, coupled with structural challenges in market liberalization and renewable integration, demonstrates persistent challenges in policy implementation. Price interventions affect competitive dynamics; subsidies influence technology selection; capacity mechanisms create coordination tensions; and rigid tariff structures create misalignments with evolving grid needs. This paper argues that these recurrent policy tensions stem not from implementation gaps, but from an inadequate theoretical foundation: the treatment of energy systems as optimizable rather than as complex, adaptive systems operating under Knight–Mises uncertainty and Huerta de Soto dynamic efficiency. This work explores an alternative framework grounded in dynamic efficiency, complex–uncertain systems, decentralized incentives, and adaptive governance (international–domestic, public–private, etc.). This review uses the theoretical and methodological framework of the Heterodox Synthesis, an alternative to the Neoclassical Synthesis. There is a reinterpretation of some insights from Knight and Mises (uncertainty), Hayek (distributed knowledge), Huerta de Soto (dynamic efficiency) and contemporary complexity economics into operational criteria applicable to energy policy design: (1) robustness to deep uncertainty; (2) preservation of price signals and risk-bearing mechanisms; (3) alignment of incentives across distributed actors; (4) institutional adaptability; and (5) minimization of ex post policy corrections. Through illustrative application to four critical policy instruments (price caps, renewable subsidies, capacity mechanisms, and network tariff design), it is shown how this framework identifies systematic tensions and consequences that conventional analysis overlooks. The contribution is exploratory in a bootstrap way: theoretical, by integrating classical and contemporary economics into energy governance; methodological, by operationalizing dynamic efficiency into evaluable criteria distinct from existing adaptive governance frameworks; and sectorial, by providing policymakers and regulators with diagnostic tools for assessing design robustness in conditions of deep uncertainty and rapid transition. According to this review, improved energy policy design under uncertainty is not achieved through more sophisticated optimization (in a calculative way), but through institutional architectures that preserve creative and adaptive learning, maintain distributed decision-making capacity, and remain functional when assumptions prove incorrect or not well-known. Full article

This study investigates whether external terms of trade (TOT) and mining-sector GDP in Peru share a stable long-run relationship. Although mining has played a central role in the country’s growth trajectory, its performance remains highly exposed to international price cycles, raising questions about its structural sustainability under persistent external shocks. Using quarterly data for 2001–2024, the analysis applies Johansen cointegration techniques and estimates a bivariate Vector Error Correction Model (VECM) to evaluate long-run co-movement and short-run adjustment dynamics. The results identify a single cointegrating relationship in which mining GDP acts as the primary adjustment variable, gradually correcting deviations from long-run equilibrium, while short-run TOT shocks do not exert direct contemporaneous effects on mining growth. The estimated speed of adjustment is low, suggesting a prolonged convergence process consistent with the capital-intensive and rigid structure of the mining sector. Robustness exercises—including estimation with heteroskedasticity and autocorrelation consistent (HAC) standard errors and an extended specification incorporating gross fixed capital formation—confirm the stability of the long-run relationship. These findings indicate that the structural sustainability of mining output depends on the interaction between external price dynamics and the sector’s capacity to adjust to persistent international shocks. The study concludes that, in the Peruvian case, structural sustainability in the mining sector is not determined solely by global price trends, but is also conditioned by domestic productive and institutional factors that govern the speed of adjustment in the presence of sustained external volatility. Full article

The paper examines the energy transition using Poland as a case study. The model was estimated based on annual data for Poland for the period of 1990–2024 (n = 35). The estimation was carried out using the OLS method with HAC correction, and the statistical significance of parameters was assessed using statistical tests. Based on econometric analysis, the impact was examined throughout the entire research period, with additional analysis of the structural break dummy for 2015. It was verified whether this impact had changed since 2015 compared to the earlier period. The data were used to calculate indicators, arranged in three groups: (1) capacity availability indicators (for the availability of the overall power system and for the renewable energy sources (RES)); (2) indicator of emission intensity (the indicator was defined as the ratio of total greenhouse gases emission to real GDP); (3) indicator of the economy’s energy intensity (the indicator was defined as primary energy consumption per unit of GDP). Annual summaries of these indicators constituted the input data for econometric modelling. The aim of the empirical analysis was to deepen the identification of mechanisms shaping greenhouse gas emission intensity by incorporating into the model indicators of generation capacity availability and measures of the economy’s energy intensity. The data collection based on constructed greenhouse gas emission intensity and energy intensity indicators of the economy enables the analysis of the increase in emission intensity regardless of the scale of the economy, in the system of power availability for the entire energy system, as well as for renewable energy sources. This approach makes it possible to move away from the analysis of absolute volumes toward a structural perspective that better reflects the real production capabilities of the power system as well as the efficiency of energy use in the economy. The results indicate that economic energy intensity is the dominant determinant of greenhouse gas emission intensity in Poland during the research period. The econometric analysis estimates show a positive and statistically significant relationship between energy intensity and emissions intensity, whereas generation capacity availability indicators—both for the total power system and for renewable energy sources—do not exhibit statistically significant effects. However, it was found that this impact was not constant throughout the entire period (β is 0.455 for pre-2015 and 0.325 for post-2015). Sensitivity analysis based on point elasticities reveals that a 1% increase in energy intensity of GDP leads to an increase in greenhouse gas emission intensity (by approximately 1.18% pre-2015 and 0.85% post-2015), whereas analogous changes in total capacity availability and RES availability are associated with substantially smaller effects (0.10% and 0.20%, respectively). These findings suggest that improvements in economy-wide energy efficiency played a more decisive role in reducing emissions intensity than short-term variations in generation capacity availability. Full article

Accurate inflow wind conditions are essential for operational wind farms. However, wind conditions from the Supervisory Control and Data Acquisition (SCADA) system are significantly affected by rotor-induced disturbances and thus cannot reliably represent the true inflow. Although LiDAR can directly measure inflow wind conditions, its data availability is highly sensitive to environmental conditions, frequently leading to insufficient valid samples. Existing studies generally apply the Nacelle Transfer Function (NTF) to empirically correct SCADA wind speed, yet its accuracy remains limited. Consequently, this study proposes a deep learning model for LiDAR-referenced inflow wind condition estimation from SCADA data. First, variations in LiDAR data availability and their influencing factors are systematically analyzed. The deviations and correlations between SCADA data and LiDAR measurements are quantitatively characterized. Subsequently, a deep learning model is developed, employing a time–frequency dual-branch residual network to extract features from SCADA data, while incorporating the Gram matrix as an additional input to provide auxiliary information. Finally, the proposed method is validated using measurements from two offshore turbines with different rated capacities. The results demonstrate that the proposed approach outperforms comparative methods, enabling more accurate estimation of inflow wind speed and direction. Full article

To enhance the mechanical properties of bamboo-reinforced concrete slabs, 1%, 1.5%, and 2% of steel fibers (SF) were added to C30 bamboo-reinforced concrete slabs to produce two test groups, each containing 12 slabs. One group was tested under static loads, and the other under impact loads. In each group, the slab thickness was set to 50 mm, 65 mm, and 80 mm, and the steel fiber dosages were 0%, 1%, 1.5%, and 2%. While existing studies on bamboo-reinforced concrete slabs (BRCS) have primarily focused on static flexural behavior, and research on steel fiber-reinforced concrete (SFRC) has mainly addressed fiber network effects in plain or steel-reinforced matrices, the synergistic mechanism between bamboo and SF in steel fiber-reinforced bamboo-reinforced concrete slabs (SFRBCS) under dynamic impact loading remains unexplored. This study innovatively combines bamboo’s elastic energy absorption with SF’s plastic energy dissipation. Static load and drop hammer impact tests were carried out in each group to study the mechanical properties of SFRBCS under static and dynamic loads. The test results show that: under static load, adding SF transforms the failure mode of the slab from brittle shear failure to ductile bending failure, increases the ultimate load, and delays the development of the main crack. Under the action of impact loads, bamboo absorbs impact energy through elastic deformation, while SF dissipates energy through plastic deformation. The combined effect of the two significantly slows down the development speed of cracks. The slab with 80 mm thick and 2% SF dosage exhibits excellent impact ductility. Based on theoretical analysis and tests, the corresponding correction coefficients are introduced to establish the bearing capacity calculation model of SFRBCS under uniformly distributed loads, considering the synergistic effect of the mechanical properties of bamboo and the reinforcing effect of SF. The combination of 1.5% SF dosage and 80 mm slab thickness can effectively enhance the material utilization rate (defined as the ratio of the increment in ultimate bearing capacity to the increment in steel fiber dosage). Test and calculation models provide a theoretical basis for the design and application of SFRBCS, which is applicable to engineering fields such as low-rise buildings and temporary structures. Full article

Background/Objectives: Astrocytes play a critical role in maintaining brain homeostasis and are increasingly recognized as active contributors to neurodegenerative processes. Metabolic dysfunction in astrocytes has been implicated in the onset and progression of Alzheimer’s disease (AD), yet the underlying metabolic alterations remain poorly characterized. Methods: We used an optimized protocol for untargeted metabolomic profiling of both intracellular and extracellular compartments of primary human astrocytes derived from AD patients and healthy subjects (HS) using ¹H nuclear magnetic resonance (NMR) spectroscopy. Cells were treated with oligomeric Aβ_1-42 to model pathological conditions. Results: Aβ_1-42 treatment induced intracellular metabolic alterations in both AD and HS astrocytes, including a consistent reduction in phosphocreatine, potentially indicating impaired energy-buffering capacity. Notably, a decrease in β-alanine was observed only in AD astrocytes, suggesting alterations in carnosine-related antioxidant defence. Analysis of conditioned media revealed differential responses between groups: AD astrocytes showed increased extracellular levels of 2-oxoglutarate, citrate, and glycine, whereas HS astrocytes exhibited reduced extracellular levels of leucine and isoleucine, suggesting distinct adaptive metabolic responses to Aβ-induced stress. However, none of these differences remained statistically significant after correction for multiple testing. Conclusions: These findings suggest that NMR-based metabolomics can detect subtle metabolic shifts in human astrocyte models of AD and HS exposed to amiloidogenic challenge. Given the limited sample size and the exploratory design adopted, the results should be interpreted as preliminary and require validation in larger, better-matched cohorts. Nevertheless, this study provides a methodological framework and generates biologically plausible hypotheses regarding astrocyte metabolic responses relevant to AD pathophysiology. Full article

This study developed and validated a Machine Learning (ML) ecosystem for the early screening of school victimization among Ecuadorian adolescents, a phenomenon that poses a critical barrier to educational equity. Addressing previous methodological limitations, this research intentionally eliminated circular reasoning by excluding all internal psychometric items from the feature set, focusing strictly on sixteen socio-environmental and demographic predictors. A quantitative study was conducted with 1413 students in the province of Tungurahua, utilizing the Synthetic Minority Over-sampling Technique (SMOTE) to correct class imbalance. Supervised classification algorithms, including SVM, Random Forest, and XGBoost, were compared. The results demonstrated that the Random Forest model achieved the most balanced performance, reaching an Accuracy of 60.3% and a Macro F1-score of 0.382. Feature importance analysis identified household structure (Living_With_Monoparental) and Family_Coping_Capacity as the most significant predictors of high-risk profiles. These findings provided a statistically honest and ecologically valid tool for Student Counseling Departments (DECE), enabling a transition toward proactive risk identification grounded in observable social vulnerability rather than reactive symptom reporting. Full article

The pile–bucket foundation, comprising a bucket attached to a single pile, represents an innovative offshore solution for supporting wind turbines. Previous studies on laterally loaded foundations have primarily focused on single piles and pile–bucket systems; however, the effects of bucket size and loading eccentricity on lateral capacity and soil failure mechanisms remain insufficiently understood. This study investigates the lateral performance of pile–bucket foundations in silty sand under static loading conditions. Seven three-dimensional numerical simulations were conducted to evaluate the influence of bucket diameter, embedment depth, and loading eccentricity. Results indicate that pile–bucket foundations achieve 37–60% higher lateral capacity than single piles and 3–4 times the capacity of standalone buckets. Increasing bucket diameter produces more significant improvements than increasing embedment depth, whereas higher loading eccentricity reduces lateral capacity, ranging from an 8% increase to a 10% decrease relative to a single pile. Increases in loading eccentricity, bucket diameter, and embedment depth shift the rotation center upward by approximately 3–9%, compared with a single pile. At the mudline, the bucket resists up to 75% of the lateral load, while the pile carries up to 92% of the moment. Failure mechanisms are dominated by excessive rotation, including wedge-type failure near the mudline and deep rotational soil flow. Increasing bucket diameter or embedment depth raises bending moments by 5–9%, while higher eccentricity amplifies them by 32–50%. A modified analytical formulation incorporating a correction factor of 1.16 improves the prediction of the rotation center position. These findings provide quantitative guidance for the design and optimization of pile–bucket foundations supporting offshore wind turbines. Full article

This study examines the False Reality Bias in treasury management, a cognitive distortion through which small and medium-sized enterprises (SMEs) infer financial stability from salient bank balances while overlooking pending obligations and cash-flow timing. Using a firm-level dataset of 50 Spanish meat-processing SMEs, the analysis develops two behavioral-finance indicators: the Liquidity Misperception Index (PEL), capturing the divergence between salient liquidity cues and effective short-term obligations, and the Liquidity Misconfidence Index (ICEL), measuring managerial overconfidence in liquidity assessments. Results show that 41% of firms overestimate liquidity (average PEL = 1.21), while 40% exhibit excessive confidence (ICEL > 1.3), both significantly associated with liquidity distress. Econometric estimates indicate that firms with PEL values above 1.2 are 4.48 times more likely to experience liquidity crises, even after controlling for bank balance levels. Predictive models are used in an exploratory capacity, achieving classification accuracies above 80% and supporting the robustness of the behavioral signals identified. In addition, AI-assisted cash-flow simulations reduce liquidity misperception by 34.7% (p < 0.01). Overall, the findings provide micro-level evidence that cognitive biases systematically distort SME treasury decisions but can be partially corrected through targeted decision-support tools, offering practical insights for managers, advisors, and policymakers. Full article

Populations in areas with limited hydrological data face ongoing challenges related to water supply and management, with climate change increasing the risks of floods and droughts. New remote sensing and modeling tools can improve land and water management in these regions, especially when combined with limited ground measurements and local knowledge of extreme events. This study examined hydrological extremes and land cover change impacts in the Grande Rivière du Nord watershed, Haiti, using satellite and model-based data. Precipitation extremes were obtained from the Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM (GPM IMERG; 2000–2025), and streamflow data were sourced from the Group on Earth Observation Global Water Sustainability (GEOGLOWS) system and bias-corrected with a small historical hydrologic database. Annual maximum series were created and fitted with Gumbel, Lognormal, and Generalized Extreme Value (GEV) distributions using the L-moment method. Goodness-of-fit tests identified the best models, and precipitation amounts for return periods of 2–100 years were estimated. The precipitation maxima aligned with locally reported extreme events, and GEV provided the best overall fit. Using the bias-corrected streamflow, a hydrologic model was calibrated and validated and then applied to land cover change scenarios. Simulations suggest that moderate land-use change can increase peak flows beyond channel capacity, raising flood risk and informing adaptation planning in northern Haiti, which has limited data. Full article

Non-orthogonal multiple access (NOMA) has been identified as one of the key technologies for 6G capacity and latency gains. However, existing implementation challenges of the NOMA technique, related to carrier, timing, and phase offsets, successive interference cancellation (SIC) error propagation, packet loss dynamics, and host to software defined radios processing jitter, create obstacles in the practical implementation of NOMA. This paper bridges the gap between theory and hardware by introducing a complete two-user NOMA transmit–receive chain on a low-cost ADALM-Pluto software defined radio (SDR) platform. The proposed implementation integrates matched filtering, offset estimation and correction, SIC with waveform reconstruction and subtraction, and reliability reinforcement via rate-1/2 convolutional coding with Viterbi decoding. We have performed a complete validation of the proposed design in both downlink and uplink modes. We collected data regarding the packet-level and system-related metrics, such as end-to-end latency, bit error rate (BER), and success rate. Moreover, we demonstrate the implementation of the uplink NOMA without need for expensive GPS-disciplined oscillators by leveraging the Pluto Rev-C dual-transmit channels that share a common oscillator. We present detailed experimental results at 915 MHz with BPSK modulation for the downlink performance, and also show a full implementation of the uplink NOMA. We observe excellent reliability for the downlink setup and good reliability for the uplink system. Full article