Search Results (14,482)

We present a unifying thesis which posits that the biological resolution of uncertainty is a fundamental adaptation to entropy’s negative impact on the highly ordered molecular structures required to maintain the living state. These molecular biological adaptations are highly conserved and play a critical role in the survival of the earliest multicellular organisms and the vertebrates thereafter. The imperative to reduce cognitive uncertainty is effected through the dopaminergic Medial Forebrain Bundle (MFB) Reward Prediction Error (RPE) mechanism, or its homologous equivalents, to compute a biological valuation of information. This hypothesis is supported by the central role of the MFB seeking system as the neural substrate of exploratory behavior that leads to the reduction of uncertainty when information is apprehended and cognitively assimilated. We define the human experience of intellectual beauty as the subjective emotional reward that is activated by the MFB seeking system. Accordingly, humans experience intellectual beauty when a high-entropy state of confusion is suddenly resolved into a low-entropy state of insight. In humans, the neuroanatomical substrates of inductive reasoning, inquiry, and the intellectual beauty to which they lead are present at birth. What develops postnatally is synaptic plasticity in the connections among these neurons that is activated in the loving didactic relationship that is established between mother and child during infancy. This dynamic is critically dependent on observational learning on the part of the child. It is supported by the joyful engagement and emotional support of the mother. This provides a paradigm of joy in learning that we propose is the developmental origin of intellectual beauty. This is the reinforcement that maintains inquiring behavior in the search for information that is needed to resist the adverse effects of entropy on life. This paper traces the continuous thread of uncertainty resolution from its phylogenetic origins in associative learning to the intuitive science of early childhood, and ultimately to the highest levels of human inquiry in science, as well as literary, musical and visual arts. The intuitive scientific method gives rise to the collective intelligence of groups, an evolved trait that likely contributed to the success of our hominin ancestors. At the societal level, this collective intelligence scales into the institutional working of markets, driving the macroeconomic price discovery of new information to counter entropy. Importantly, we compare the cost of information across the disparate domains of pharmaceutical drug discovery and the contemporary art market to demonstrate that the imperative to reduce uncertainty manifests as a universal, falsifiable mechanism for the “price discovery” of information. Full article

This article presents the Asymmetric Cross-Market Dynamics Network (ACMD-Net), a forecasting framework built on the premise that commodity markets are fundamentally asymmetric. Three symmetry assumptions are statistically tested and rejected: volatility symmetry via the GJR-GARCH leverage test $\gamma >0$, $p<0.001$, coupling symmetry via the directional Granger causality DM statistic, 3.18–3.67, $p<0.001$, and cointegration symmetry via a likelihood ratio test, $p<0.01$. Each rejected hypothesis motivates a corresponding architectural component, yielding causally interpretable forecasts unavailable in black-box alternatives. The model is evaluated on daily CBOT futures for corn, wheat, and soybeans from January 2010 to December 2023, $T=3508$. ACMD-Net achieves RMSE reductions of 37–42% over ARIMA and 15–17% over standard LSTM. At short horizons ($h=1$), TFT achieves marginally lower point RMSE (3–$4\%$, not statistically significant; DM $p>0.05$); at long horizons ($h=22$), TFT continues to report the lowest point RMSE across all commodities; differences versus ACMD-Net are not statistically significant DM $<1.96$ for all commodities. The architecture’s predictive value lies in economically grounded interpretability and superior directional accuracy rather than universal RMSE dominance. Directional accuracy ranges from 60 to 62% $p<0.001$, and net-positive trading returns are obtained for wheat and soybeans at 8–12-basis-point transaction costs. Ablation analysis identifies temporal attention as the primary performance driver, RMSE $+22.2\%$, upon removal, with econometric features contributing an additional 24.9% gain. Full article

The co-pyrolysis of waste sawdust and non-recyclable polypropylene at 500 °C was investigated for low-cost adsorbent production and solid waste valorization in rural Thailand. Sustainability was evaluated through techno-economic analysis, gate-to-gate environmental screening, CO₂ emission accounting, adsorption cost analysis, and social return on investment assessment. The sawdust–polypropylene biochar produced at 500 °C production system requires a total capital expenditure of 46,000 THB and achieves a unit production cost of 316 THB kg⁻¹ at a 35% w/w biochar yield, 7–14 times lower than commercial granular-activated carbon and powdered-activated carbon. Based on a hypothetical community-scale deployment scenario, the estimated capital expenditure payback period under in-house granular-activated carbon substitution falls below six months. All annual techno-economic and SROI results presented in this study represent scenario-based screening estimates and should not be interpreted as demonstrated community-scale performance. Gate-to-gate environmental screening estimated gross production emissions of 8.771 kg CO₂e kg⁻¹ SPB-500. A consequential waste-diversion scenario incorporating carbon sequestration and avoided-disposal credits yielded a hybrid scenario-based net greenhouse-gas balance of +3.082 kg CO₂e kg⁻¹ SPB-500, supporting its potential application under the evaluated scenario of approximately 0.4–5.9 kg CO₂e kg⁻¹ relative to commercial-activated carbon benchmarks. Social return on investment analysis yields a base–case ratio of 1.39:1 (five-year total present value: 6,025,841 THB; minimum across sensitivity scenarios: 1.13:1), with water quality improvement (SDG 6; 46.1%) and health risk reduction (20.7%) jointly accounting for 66.8% of monetized outcomes, confirming investment justification from public health benefits alone. Quantifiable alignment is demonstrated across five UN Sustainable Development Goals. Collectively, these findings suggest that SPB-500 co-pyrolysis has the potential to be an economically accessible and socially beneficial waste-valorization technology under the evaluated scenario, supporting its potential application in decentralized heavy-metal remediation in resource-constrained communities. Full article

Expanding university campuses face a dual challenge: meeting higher internal travel demand while reducing congestion and emissions. Using stated-preference data (412 respondents; 9 choice tasks per person), this study quantifies the expected adoption of two low-emission micromobility options—bicycle and electric scooter—relative to walking and car for intra-campus trips at an Ecuadorian university. Under Random Utility Theory, we estimate a multinomial logit (MNL) and a panel mixed logit (MIXL) model, treating MIXL as the preferred specification. Simulated maximum likelihood with 12,000 draws shows MIXL substantially improves fit and reveals marked heterogeneity in time sensitivity. In out-of-sample prediction (TEST), the two prospective modes achieve comparable average choice probabilities (bicycle = 0.289; electric scooter = 0.286), with a joint acceptance of 0.575, supporting the feasibility of a micromobility program under the evaluated scenarios. Environmental impact (gCO₂/km) exhibits limited aggregate influence compared with operational factors (time and cost). A complementary WTP-space MIXL provides VOT estimates and confirms that 10–20% time reductions increase expected acceptance. Overall, results indicate that adoption is most likely when sustainability objectives are translated into tangible service improvements in effective speed, reliability, and affordability. Full article

Physical access to robotic manipulators remains constrained by cost, safety requirements, and limited laboratory availability, creating barriers to both research and education. This paper presents a computational framework that combines stochastic error modeling with Digital Twin technology to characterize positioning uncertainty in a six-degree-of-freedom manipulator without requiring physical hardware. Four independent noise sources—joint encoder noise, thermal drift, elastic link deformation, and geometric parameter tolerances—are modeled as stochastic processes and propagated through the manipulator kinematics using Monte Carlo simulation with N = 10,000 trials across 50 workspace configurations. The results reveal that elastic deformation dominates the combined positioning error by a factor of 45.94 over encoder noise, contributing 99.97% of the total root-mean-square (RMS) uncertainty. A probabilistic workspace map constructed from 3000 sampled configurations quantifies accuracy and manipulability across the reachable space, exposing a counterintuitive trade-off: configurations with higher manipulability indices tend to exhibit larger positioning errors due to gravitational loading on extended links. Two control algorithms—a reverse process-based control law (RPBCL) and sliding mode control (SMC)—are evaluated under stochastic conditions over 200 trials. SMC achieves a mean steady-state error of 0.0029 mm, representing a 48.2% reduction compared to RPBCL (0.0056 mm), with the difference confirmed statistically significant by a two-sample t-test (t = 5.066, p = 0.000002). All results are visualized through a Unity3D Digital Twin interface that renders probabilistic workspace maps, three-dimensional error ellipsoids, and a real-time sliding surface monitor. The proposed framework provides a foundation for safe, hardware-free evaluation of manipulator control strategies in engineering education and research. Full article

High electricity prices remain a substantial barrier to electric vehicle (EV) diffusion. To address this challenge, we propose a bundled sales model that integrates EVs with distributed, operationally off-grid photovoltaic (PV) systems for self-consumption. Using a sequential game-theoretic framework and scenario analysis calibrated to U.S. and German data, we show that, within the calibrated scenarios and declared system boundaries, bundling accelerates EV adoption and reduces modeled oil dependency, measured as the physical volume of fossil fuel displaced by the bundled fleet. In Germany, bundling increases oil-dependency reduction by 7.8 percentage points, to 34.5%, relative to the traditional unbundled model. The bundled model also delivers stronger decarbonization, yielding incremental lifecycle emission reductions of 11% in the U.S. and 29% in Germany under the declared system boundary. Three insights follow. First, bundling is especially advantageous in markets with high grid tariffs, strong solar irradiance, or falling PV costs. Second, decoupling EV charging from carbon-intensive grids promotes household energy self-sufficiency and helps households become more resilient energy prosumers. Third, the threshold analysis indicates that the model is already viable in high-tariff markets such as Germany, while declining battery costs are likely to trigger a tipping point in lower-tariff markets such as the U.S., supporting a gradual diffusion pattern from suburbs to cities. These findings identify a viable pathway for low-carbon transport transitions through synergistic EV–solar integration. Full article

Nowadays, alternative cost-effective and environmentally friendly waste materials have gained popularity over conventional chemical additives for the stabilization of weak soil in seasonal freeze–thaw (FT) areas. Molasses, a byproduct of sugar production, is utilized in various sectors; however, there are no studies in the literature concerning the use of molasses in the stabilization of clayey soil under FT cycles. To address this aim, in this study, a series of unconfined compressive strength (UCS) tests were conducted on both unstabilized and stabilized samples with molasses ratios ranging from 4% to 14%, subjected to 0, 5, 10, and 15 FT cycles following 7 and 28 days of curing. The assessment focused on the stress–strain (σ-ε) responses, unconfined compressive strength (q_u), failure strain (ε_f), secant modulus (E₅₀), and failure mode. The results demonstrated that maximum performances appeared with 10% molasses ratios, showing a 1.39–2.62-fold increase in the q_u values. As the curing period increased, the q_u and E₅₀ values increased significantly, while the ε_f values exhibited a diminishing trend. It was also observed that the FT cycles caused a dramatic reduction in the shear strength of unstabilized samples, ranging from approximately 42% to 69%; however, molasses-stabilized samples demonstrated higher resistance. Furthermore, XRD, SEM, and EDX analyses were carried out to investigate the mineralogical, microstructural, and chemical behaviors. Finally, an empirical equation was proposed to predict the q_u of molasses-stabilized soil subjected to FT cycles. Full article

Over a decade of research on media for cultured meat and seafood production has resulted in multiple highly efficient serum-free and chemically defined formulations for some species, but it has also identified challenges yet to be solved—especially for aquatic cell lines. Depending on the product and cell type, the approach to develop highly efficient, sustainable, and low-priced media can diverge greatly. In this review, we provide an in-depth overview of this complex research area to facilitate strategic decision-making for stakeholders. We evaluate the advantages and limitations of utilizing hydrolysates, growth factor mutants, growth factor alternatives, and stabilizers in serum-free media formulations published for cultured meat production, as well as ongoing research efforts on developing adequate media for cultured seafood. We critically analyze strategies aimed at reducing medium costs and enhancing sustainability of cultured meat and seafood production, including their food-compatibility assessment. We summarize topics that require further exploration, such as identification of species-specific growth factors—particularly for aquatic species; exploration of hydrolysates as a substitute for basal medium; waste medium recycling strategies; and the potential application of artificial intelligence (AI) and machine learning (ML) technologies to enhance these areas. Additionally, we consider possible emerging regulatory issues and their impact on media formulation development. Finally, key performance indicators for media formulations are proposed to guide future strategic and operational improvements regarding an economical and sustainable production process. Full article

Large-scale breeder chicken farms generate high-moisture manure, and all-in/all-out management can constrain continuous manure handling, especially during cold northern winters. This study proposed and evaluated a staged resource-recovery pathway for breeder chicken manure under all-in/all-out farm management. The pathway consisted of an implemented on-farm primary aerobic fermentation stage for rapid reduction and sanitization, an implemented centralized secondary aerobic fermentation stage for standardized organic fertilizer production, and a proposed solar-greenhouse-assisted low-temperature module for seasonal continuity support. System performance was assessed through a practice-based case evaluation using enterprise operational records, field investigations, and routine monitoring data on manure generation, process parameters, product quality, and logistics/cost indicators. The primary stage showed a relatively stable operational window across case farms, with fresh manure moisture contents of 85–90%, compost temperatures increasing from approximately 30 °C to 60 °C before declining to about 40 °C, and pH values ranging from 7.0 to 9.5, while batch duration and moisture-control pathways varied among farms. The secondary stage demonstrated standardized downstream processing capacity; the tested organic fertilizer complied with NY/T 525-2021, while the bio-organic fertilizer specifications met the benchmark requirements of NY 884-2012. The conceptual winter continuity-support module was discussed as a conceptual engineering supplement requiring future operational validation. Overall, the evaluated pathway may provide a practice-based reference for sustainable manure management, standardized fertilizer production, and circular agricultural resource recovery under intensive breeder chicken production conditions. Full article

Although CFM is a promising non-thermal stabilization technology for blackberry juice, its industrial application is limited by permeate flux decline during long-term operation, while most previous studies have focused on short processing times. This study evaluated the effect of high-shear homogenization prior to enzymatic depectination on flux decline, product quality, and techno-economic feasibility during CFM. Juice processed by conventional grinding, high-shear homogenization, and enzymatic treatment was filtered through a 0.2-µm ceramic membrane at 150 kPa using feed volumes of 100–400 L. Homogenization reduced particle size and suspended insoluble solids, resulting in higher permeate flux, improved flux stability, and greater productivity. Flux decline analysis showed that high-shear homogenization extended the stable filtration regime and delayed severe fouling, sustaining an average J_px of 65.3 L h⁻¹ m⁻² at VCR ~30 with feed volumes up to 400 L. Product quality was preserved, ensuring microbial reduction while improving anthocyanin and ellagitannin recovery (95% and 80%, respectively) and enhancing blackberry aroma. In addition, HS3+E reduced energy consumption and beverage production cost while achieving a positive NPV and a 21% IRR. Overall, homogenization improved the industrial feasibility of long-term CFM processing of Andean blackberry juice. Full article

Real-time wideband radio frequency (RF) spectrum monitoring is increasingly important for unmanned aerial vehicle (UAV) detection and RF surveillance. Low-cost software-defined radio (SDR) networks are attractive but constrained by limited instantaneous bandwidth per node, I/Q data transfer bottlenecks over USB 2.0, and multi-node computational overhead. This paper proposes a bandwidth-efficient FPGA-GPU heterogeneous architecture addressing these limitations. A hardware-efficient cell-averaging constant false alarm rate (CA-CFAR) IP core is deployed on the edge FPGA of each SDR node, forwarding only signal-containing intervals to reduce data transfer volume proportionally to the target duty cycle. Spectra from multiple nodes are stitched into a wideband view and processed in real time via a GPU-accelerated pipeline. The CA-CFAR IP occupies 16.3% of available LUTs with no BRAM and a fixed 10-cycle latency at 100 MHz. Experiments on a five-SDR testbed demonstrate an 88% data transfer reduction at a 10% duty cycle, 376 μs latency from signal acquisition to display-buffer preparation, 96.26% detection probability at −83.16 dBm (SNR ≈ 13 dB), and a 4.5× to 6.0× GPU speedup over CPU processing. These results support real-time wideband RF monitoring on resource-constrained SDR platforms. Full article

The growing demand for sustainable and environmentally friendly materials has accelerated interest in the valorization of industrial wastes within the framework of the circular economy. In this study, porous wollastonite-based ceramics were successfully fabricated using cement kiln bypass dust (CKD) and waste glass cullet as low-cost and sustainable secondary raw materials. The proposed approach aims to mitigate environmental pollution, reduce landfill disposal, conserve natural resources, and promote the recycling of industrial by-products into value-added ceramic products. Different batch compositions containing varying proportions of CKD and glass cullet were prepared, compacted, and subsequently sintered under controlled conditions to induce crystallization. The crystallization behavior and phase development were characterized by X-ray diffraction (XRD), while the microstructural features were examined using scanning electron microscopy (SEM). Physical and dielectric properties, including bulk density, open porosity, dielectric constant (ε′), dielectric loss (ε″), and electrical conductivity (σ), were also evaluated. The results confirmed the successful formation of parawollastonite as the predominant crystalline phase, accompanied by a relatively homogeneous porous microstructure. The prepared ceramics exhibited high open porosity values ranging from 52.55 to 63.63% and low bulk densities between 1.050 and 1.318 g cm⁻³, making them suitable for lightweight construction and insulation applications. Dielectric measurements performed over the frequency range of 50 Hz–8 MHz revealed that both dielectric constant (ε′) and dielectric loss (ε″) decreased with increasing frequency. At 50 Hz, ε′ and ε″ ranged from 8.44–9.39 and 0.709–0.733, respectively. The electrical conductivity values (~10⁻² μS cm⁻¹) at low frequencies indicated insulating behavior, whereas poor-to-fair semiconducting characteristics were observed at higher frequencies. The incorporation of large amounts of recycled CKD and waste glass significantly reduced dependence on virgin raw materials while providing a sustainable route for waste utilization. Consequently, this work demonstrated an environmentally responsible and economically viable strategy for producing porous wollastonite-based ceramics with potential applications in both the construction and electrical sectors, thereby contributing to resource efficiency, waste valorization, carbon-emission reduction, and sustainable industrial development. Full article

The increasing complexity of scientific research projects and growing competition for public funding require data-driven approaches to support evidence-based decision-making in research management. This paper presents an integrated machine learning-based analytical pipeline for scientific project analysis, positioning the contribution as methodological integration and empirical insight rather than the development of new algorithms. Using open data from the CORDIS database on projects funded under the Sixth Framework Programme (FP6), the study combines three complementary analytical tasks—structure identification, budget prediction, and funding-completeness classification—within a single empirical decision-support workflow. Unsupervised analysis based on dimensionality reduction and clustering reveals distinct structural patterns in project characteristics and identifies atypical large-scale projects characterized by substantially higher budgets and consortium sizes. Regression models predict total project costs, achieving R² ≈ 0.50 on unseen data; the gap between explained and unexplained variance is interpreted in relation to latent contextual factors not available in administrative records. Classification models distinguish between fully and partially funded projects (threshold r ≥ 0.70); the high discriminative performance (AUC > 0.97) is interpreted cautiously in light of target-variable construction and potential feature dependence. Association rule mining identifies interpretable funding patterns (strongest rule lift = 6.30). The study contributes a reproducible example of how mature machine learning techniques can be systematically integrated to generate domain-specific evidence for research funding governance. Full article

With the advancement of global energy conservation and emission reduction, hydrogen-powered ships (HPSs) have received great attention. However, the current drainage volume of HPSs is generally small, and its operating load fluctuates greatly due to the influence of hydrological and meteorological conditions in the waterway. Therefore, a reasonable energy management strategy (EMS) is needed to allocate the output of hydrogen fuel cells (HFCs) and lithium batteries (LBs). This article proposes a two-stage EMS framework for HPSs based on dynamic empirical modeling and model predictive control (DEM-MPC) to achieve optimal operational energy efficiency of the HFC-LB energy supply system. Firstly, a DEM probabilistic load forecasting (PLF) model was established by combining the operational status data of an HPS system with the meteorological data of waterway water level. The DEM model was constructed using delay coordinate embedding (DCE) and nearest neighbor prediction (NNP) methods to obtain future multi-step PLF sequences as important reference information for the EMS. Subsequently, the PLF sequence is used as input for MPC to optimize the output allocation of the EMS. In the first stage of MPC, the efficiency of HFCs and LBs is optimized, and in the second stage, the comprehensive cost is optimized. Finally, the method was validated using actual data from an HPS in the Yangtze River waterway. The results indicate that the proposed DEM-MPC framework significantly improves the overall operational energy efficiency of HPSs. Full article

The deep coalbed methane in the SSL block is characterized by strong in situ stresses, elevated thermal conditions, and over-pressured reservoirs. These conditions lead to poor adaptability of vertical-well fracturing technology for CBM extraction. Due to the high investment cost of staged fracturing of horizontal wells, it is necessary to optimize the well type and fracturing process parameters before the experiment, in order to find the optimal horizontal section length and fracturing process parameters. On the basis of in-depth analysis of the characteristics of deep coalbed methane reservoirs, numerical simulation technology was used to carry out optimization research on segmented fracturing horizontal well technology from the aspects of horizontal section length, number of fracturing fractures, fracturing conductivity, fracturing length, and fracturing pattern. Research has shown that segmented fracturing of horizontal wells can communicate the coal seam cleavage fracture system, with a large drainage area and good pressure reduction and gas production effects. The optimal wellbore structure parameters for segmented fracturing of horizontal wells are a horizontal section length of 1000–1200 m, divided into 4–6 sections for fracturing, with a spacing of 120–150 m and uniform distribution of fractures. This well type has been applied in adjacent blocks and has shown good development benefits. Full article