Search Results (984)

This study addresses the growing complexity and heterogeneity of global decarbonization processes, which limit the effectiveness of traditional linear forecasting and policy approaches, and aims to develop an integrated model for coordinated forecasting and management at the macroeconomic level. The proposed D-CPFR (Decarbonization—Collaborative Planning, Forecasting and Replenishment) framework combines country clustering based on self-organizing maps, nonlinear forecasting using multilayer perceptrons, and scenario-based multi-criteria optimization. The results demonstrate that clustering serves not only as an analytical tool but also as a structural basis for forming network interactions among countries with similar decarbonization characteristics, enabling coordinated decision-making and policy alignment. The model provides a mechanism for integrating forecasting outputs with joint management processes, including information exchange, scenario coordination, and investment planning within and across clusters. The findings confirm that the hybrid approach improves the representation of nonlinear relationships and supports more accurate and differentiated modeling of decarbonization trajectories. The proposed framework can be applied to the development of adaptive climate strategies, enhancement of resource allocation efficiency, and support of sustainable economic development across countries with varying levels of economic and energy development. Full article

Vertical shear of the zonal wind across the equatorial tropopause over the Indian Ocean to the Maritime Continent is caused by a combination of the quasi-biennial oscillation (QBO) of the stratosphere and the seasonal cycle and interannual variability of the upper troposphere. The Madden–Julian Oscillation (MJO) has been previously observed to be more active during the easterly than the westerly phase of the QBO. Kelvin waves interacting with the background flow explain most of the propagation characteristics of the MJO in the equatorial upper troposphere. This work assesses the hypothesis that Kelvin wave propagation under conditions of easterly wind in both the upper troposphere and stratosphere maintains the upper tropospheric MJO circulation, but that this signal is disrupted with westerly wind shear that likely includes critical layers that would prevent Kelvin wave energy from passing. Linear regression of reanalysis data against an MJO index shows more coherent downward propagating Kelvin waves during easterly shear and no Kelvin-wave-like signal near the tropopause during conditions expected to include critical layers there. Historical analysis of this vertical shear shows that it is the primary focus of enhanced MJO variance with the easterly QBO, yielding the seasonally enhanced signal December through February and the erratic variability from year to year due to tropospheric contributions to shear. A wavenumber frequency spectrum analysis of lower stratospheric zonal wind shows that power shifts from high to low frequency between QBO westerly to easterly phases, consistent with Kelvin waves propagating at the phase speed range of the MJO during easterly QBO. Full article

Anaerobic digestion (AD) plays a central role in renewable energy generation and sustainable waste management. However, the operation of AD systems remains challenging due to the complex interactions among microbial communities, substrate variability, and non-linear process dynamics. Traditional monitoring and control approaches often fail to anticipate disturbances or maintain optimal conditions. Recent advances in artificial intelligence (AI) and machine learning (ML) provide new opportunities to model, predict, and control AD processes by leveraging high-resolution sensor data and data-driven algorithms. This review synthesizes current progress in ML- and AI-based approaches for prediction, optimization, and intelligent control of AD, with particular emphasis on data processing pipelines, neural network architectures, soft sensors, digital twins, and explainable AI. Full article

In grid-connected smart integrated energy systems with high shares of renewable generation, source-side variability and inadequate coordination among battery storage, other energy carriers, and the external grid limit local renewable-electricity utilization and impede deep decarbonization. This study proposes a machine-learning-assisted, renewable-driven framework for multi-energy coupling and scenario-based multi-objective optimization of electricity–heat–hydrogen–storage systems. Historical meteorological and load data are processed using K-means clustering and Latin hypercube sampling to construct representative operating scenarios across multiple volatility regimes and characterize source–load uncertainty. The equipment model includes photovoltaic arrays, wind turbines, heat pumps, electrolyzers, fuel cells, grid-interactive battery energy storage, thermal storage, and hydrogen storage; cross-carrier conversion dynamics and emissions from purchased electricity and natural gas are embedded in the energy-balance constraints. A mixed-integer linear programming formulation then co-optimizes battery charging and discharging, grid exchange, and other multi-energy flows with respect to operating cost, carbon emissions, and renewable-energy curtailment. At 95% renewable-energy penetration, the proposed method achieves a renewable-energy absorption rate of 91.6% and a curtailment rate of 8.4%. Across the carbon-price cases, annualized operating cost ranges from 126.5 × 10⁴ to 141.2 × 10⁴ USD yr⁻¹, while carbon-emission intensity ranges from 26.4 to 38.5 gCO₂/kWh_eq. Under the specified high-risk grid disturbances, the coordinated strategy limits load shedding to 1.8–73% below deterministic scheduling and 79% below the heuristic benchmark—and maintains 92.6% hydrogen self-sufficiency. These results provide a data-driven modeling and decision framework for battery–grid coordination and deep decarbonization in smart integrated energy systems. Full article

Understanding cow-level factors associated with enteric methane (CH₄) is important for precision-based methane mitigation in dairy systems. This study evaluated associations between laser methane detector (LMD)-based CH₄ concentration, relative CH₄ concentration-based indices, biochemical markers, sensor-derived variables, and milk traits in clinically healthy Holstein cows, with an emphasis on parity. Ninety-one cows within 100 days in milk (DIM) were examined: 46 primiparous and 45 multiparous cows. Methane concentration was measured using a portable LMD. Data were evaluated using parity comparisons, principal component analysis (PCA), DIM-adjusted multiple linear regression with parity interaction terms, and DIM-adjusted partial correlation analyses with Benjamini–Hochberg false discovery rate (FDR) correction. Mean CH₄ concentration did not differ between primiparous and multiparous cows (370.83 vs. 361.16 ppm; p = 0.765), despite higher estimated group-level dry matter intake (DMI; +11.2%) and milk yield (+23.1%) in multiparous cows. The milk-yield-adjusted CH₄ concentration index was higher in primiparous cows (11.26 vs. 8.91 ppm CH₄/kg milk; p = 0.022). In the primary PCA-based regression model, the energy metabolism/lipid mobilisation component showed a positive but non-significant association with CH₄ concentration (p = 0.077), while the overall model’s explanatory capacity was limited (R² = 0.105; adjusted R² = 0.018). Exploratory DIM-adjusted subgroup analyses identified associations involving non-esterified fatty acids (NEFA), triglycerides, serum iron, and milk lactose that remained significant after FDR correction. However, formal predictor × parity interaction analyses did not confirm effect modification by parity. These findings suggest that LMD-based CH₄ concentration is best interpreted within a broader metabolic, behavioural, and production context and warrants validation in larger longitudinal studies. Full article

New energy vehicle (NEV) manufacturers must reduce operational waste while adapting to technological change and preserving long-term value. This study tests an integrated model linking Lean Practices, Process Innovativeness, Digital Transformation, and Value Impact. Of 500 survey invitations distributed across China’s NEV supply chain, 400 complete responses passed a broad screen; 49 cases reporting less than one year of lean or digital implementation were subsequently excluded, yielding a duration-consistent primary sample of 351. The constructs were specified as first-order composites and evaluated using component-based partial least squares. Lean Practices were positively associated with Value Impact (β = 0.366) and Process Innovativeness (β = 0.288), and Process Innovativeness was positively associated with Value Impact (β = 0.409). At mean Digital Transformation, the indirect association through Process Innovativeness was 0.118. Digital Transformation positively moderated the Lean Practices–Process Innovativeness association (β = 0.118, p = 0.013), although the interaction was small (f² = 0.020) and less robust after excluding low-variation responses. All endogenous indicators had positive Q²predict values, and PLS-SEM produced lower RMSE than the linear benchmark for all 14 indicators. The findings identify organizational mechanisms relevant to resource optimization and sustainable manufacturing, but not direct environmental or social sustainability effects. Full article

Background: Although dietary fat and carbohydrates are major determinants of gut microbiota composition, their interactive effects across changing dietary monosaccharide-to-lard energy ratios remain incompletely understood. This study descriptively examined treatment-level cecal microbiome profiles across dietary gradients in which lard (L) was progressively replaced with glucose (G) or fructose (F). Methods: A carbohydrate-free, lard-rich control diet was formulated, and lard was progressively replaced with glucose or fructose while maintaining a constant protein-to-energy ratio. Cecal contents from eight rats per dietary group were pooled in equal amounts, yielding one composite microbiome sample per treatment. Pooled samples were characterized by shotgun metagenomic sequencing. Sequencing/classified read counts (CRs) and relative abundance (RA) were treated as complementary sequencing-derived representations rather than measures of absolute bacterial abundance. Microbiome outcomes were interpreted descriptively at the treatment level. Results: Across the pooled treatment profiles, CRs and RA showed non-linear patterns and did not consistently change in parallel, providing complementary descriptions of treatment-level taxonomic responses. CR patterns indicated a combined effect of L and monosaccharide content, with several mixed L–monosaccharide diets showing lower CRs than both the L_6.03 reference and the lard-free endpoints. Differences between the G and F series were most apparent at low L and high monosaccharide levels, particularly under lard-free conditions, although their magnitude and direction varied among taxa. Hierarchical clustering and exploratory correlation networks provided complementary descriptions of treatment-level community organization. Conclusions: The pooled treatment-level microbiome profiles revealed non-linear responses to changing dietary L–monosaccharide composition, with CRs and RA providing partly different information on taxonomic patterns. Differences between the G and F series were most apparent at low L and high monosaccharide levels, particularly under lard-free conditions. Full article

Rugged terrain dominates the Qinghai–Tibet Plateau, where complex landforms profoundly modulate solar energy availability. The reasonable distribution and development of solar energy resources in this region should be based on the accurate assessment of the effects of complex topography. This study proposes a high-resolution methodological framework to quantify the topographic–climatic interactions within Lhasa. Based on GIS technology (ArcGIS 10.8), this study employs a topographic decomposition method to extract micro-topographic variables, which are integrated into a distributed solar radiation model with 30 m resolution to simulate spatial–temporal radiation dynamics. Furthermore, we utilize the geographical detector (GD) model to attribute the spatial variance of radiation to specific topographic and surface drivers. The results show that: (1) The seasonal distribution of solar radiation is highly heterogeneous, with the monthly average extraterrestrial solar radiation (ESR) peaking in July (1190 MJ/m²) and reaching its lowest in December (469 MJ/m²). (2) Factor detection using the GD model indicates that slope aspect (q = 0.49) and Sky View Factor (SVF, q = 0.38) are the primary individual drivers of direct solar radiation, followed by surface albedo (q = 0.26) and DEM (q = 0.12). (3) Interaction detection reveals strong non-linear synergies, where the combined interaction of aspect and SVF yields the highest explanatory power (q = 0.889), followed by slope and albedo (q = 0.732). The outcomes of this research provide both a theoretical foundation and high-resolution spatial datasets to support photovoltaic site selection, enhance passive solar building design, and advance refined sustainable energy planning in complex alpine terrains globally. Full article

The central challenge related to the development of electric vehicles (EVs) involves the effective integration of multiple input sources to create a robust and efficient power system. Recent advancements have focused on optimizing the power distribution within hybrid systems that combine batteries, supercapacitors, and renewable sources like solar PV. Conventionally, energy management systems (EMSs) have relied on rule-based algorithms or deterministic optimization methods. However, these techniques often lack adaptability under real-world driving conditions and face significant challenges regarding their generalizability and computational complexity when applied to dynamic driving cycles. Machine learning approaches are capable of modeling the complex, non-linear interactions between multiple energy sources to ensure intelligent power coordination. This paper proposes a novel Extreme Gradient Boosting (XGBoost)-based intelligent EMS for a BLDC motor-driven electric vehicle (e-bike) utilizing a hybrid battery–solar configuration with a supercapacitor for regenerative braking. The proposed system integrates a super-boost converter for efficient multi-source power delivery. The results show accurate energy source identification, effective multi-source coordination, improved energy utilization, reduced battery stress, and a rapid decision-making capability, which prove the feasibility of the proposed method for real-time electric bicycle energy management. The simulation was executed using the MATLAB/Simulink platform, and the obtained results are outlined. Full article

The initiation of electrochemical corrosion on steel surfaces begins with water molecule aggregation, though the atomic-scale mechanisms from adsorption and wetting to corrosive microdroplet formation remain unclear. Using molecular dynamics simulations, this work investigates the formation of corrosive aqueous micro-environments on iron-based surfaces during early condensation. It focuses on the regulatory effects of surface roughness and local hydrophilic sites on condensation nucleation, droplet growth, and wetting. Results show a linear correlation between droplet contact angle and solid–liquid interaction energy, with temperature dependence controlled by the substrate’s intrinsic wettability. For fence-type rough surfaces, we clarify the transition from a critical to a mixed (Cassie–Wenzel) wetting state, confirming that roughness enhances intrinsic wettability. Condensation analysis reveals that stronger solid–liquid interaction promotes water adsorption and induces a shift from dropwise to filmwise condensation, with interphase temperature difference driving heat transfer. On hydrophobic surfaces with local hydrophilic sites, these sites serve as preferential nucleation points. Their size effect can pin the three-phase contact line, leading to droplet growth in a high-contact-angle mode. This study offers an atomic-scale view of how condensation creates the initial aqueous environment required for electrochemical corrosion, providing theoretical insight into phase-change heat transfer and interfacial behaviour on complex surfaces. The findings guide the design of surfaces resistant to condensation-induced corrosion. Full article

Despite the growing importance of intercultural marketing in global consumer markets, limited research has examined how intercultural marketing factors interact and contribute to corporate competitiveness within internationally recognised consumer brands. Therefore, this case study investigates these relationships using the example of a globally recognised consumer brand. Drawing on Hofstede’s Cultural Dimensions Theory and intercultural marketing literature, this study investigates the relationships between event marketing, consumer behaviour and sales factors using the case of Red Bull in the global energy drink industry. Data obtained from 50 respondents were analysed using affinity diagrams, relationship diagrams, linear regression and the Analytic Hierarchy Process (AHP). The results confirmed positive relationships between event marketing and consumer behaviour (r = 0.401) and between consumer behaviour and sales factors (r = 0.400). The study contributes to international marketing literature by proposing an integrated framework linking intercultural marketing activities with corporate competitiveness and identifying the most influential marketing sub-criteria. The study further focuses on the identification and comparison of sub-criteria using the Analytic Hierarchy Process (AHP) method. Full article

Background: Improving meat quality while maintaining growth efficiency remains a major challenge in poultry production. However, the molecular mechanisms underlying breed-specific meat quality variation remain unclear. This study aimed to investigate how breed-specific growth patterns influence meat quality and elucidate metabolic and transcriptional mechanisms involved. Methods: Pectoralis major meat quality traits and multi-omics profiles were characterized in three genetically distinct chicken breeds—the fast-growing Small White-Feathered chicken (XBJ), the slow-growing Huiyang Bearded chicken (HXJ), and the layer-type Hy-Line Brown chicken (HLH)—at 50, 180, and 300 days of age. Twelve birds per breed per age were used for phenotypic measurement (n = 108 in total), and eight birds per breed per age were subjected to metabolomic and transcriptomic profiling. Phenotypes were analyzed using linear mixed-effects models with breed, age, and their interaction as fixed effects and pen nested within breed as a random effect, followed by Tukey-adjusted pairwise comparisons (p < 0.05). Differential metabolites were screened by OPLS-DA (VIP > 1, p < 0.05), and differentially expressed genes were identified using DESeq2 (|log2FC| ≥ 1, FDR < 0.05). Integrative analyses were performed to identify key genes, metabolites, and pathways associated with meat quality. Results: Phenotypic evaluation revealed a breed-dependent growth–meat quality trade-off, with XBJ exhibiting superior growth but poorer water-holding capacity and meat color, whereas HXJ and HLH showed better tenderness and color at the expense of growth. Metabolomic analysis revealed lipid metabolism as a major contributor to breed-specific divergence, with triglyceride-driven divergence predominating at early and middle stages, whereas later-stage differences involved glycerophospholipid and amino acid metabolism. Transcriptomic analysis revealed significant breed-specific differences in expressed genes at 50 and 180 days, enriched in pathways related to muscle structure, ECM remodeling, and energy metabolism, consistent with metabolic and phenotypic divergence. Integrated analyses identified 28 candidate genes and 71 core metabolites associated with meat quality traits, with PLIN1 and SLC1A6 emerging as key regulators associated with TG species, drip loss, shear force, and BMW. Conclusions: These findings reveal molecular mechanisms underlying the growth–meat quality trade-off and highlight lipid metabolic regulation as a key contributor to meat quality variation. The identified gene–metabolite networks provide insights for molecular breeding to improve chicken meat quality. Full article

High-precision two-dimensional intermolecular potential energy surfaces (PESs) for Rg–CuF (Rg = Ar, Kr, Xe) were constructed at the coupled-cluster singles and doubles with non-iterative triples [CCSD(T)] level by employing aug-cc-pVXZ (X = D, T, Q) basis sets, and the energies were extrapolated to the complete basis set (CBS) limit. All three complexes exhibit a consistent topological pattern: the global minimum corresponds to a collinear Rg–Cu–F configuration, and the local minimum corresponds to an anti-linear Rg–F–Cu configuration. As the atomic number of noble gas increases, the Rg–Cu equilibrium distance lengthens while the binding strength remarkably enhances. Bound state calculations were performed based on these PESs to yield rotational levels, which can be used to derive the intermolecular vibrational frequencies, molecular structures and spectroscopic parameters for all primary isotopologues. The predicted rotational constants B are in excellent agreement with the experimental observations, attaining a sub-MHz accuracy at the AVTZ level for Kr–CuF and at the CBS limit for Ar–CuF and Xe–CuF. Vibrational wavefunction analysis reveals that the intermolecular vibrational modes of Kr–CuF and Xe–CuF are highly localized, consistent with the pronounced molecular rigidity observed experimentally. Isotopic effect analysis reveals a well-defined linear relationship between the changes in the rotational constant B and the intermolecular vibrational frequency in relation to the reduced mass of the complex, which provides a reliable basis for predicting spectroscopic parameters of unobserved isotopologues. Symmetry-adapted perturbation theory (SAPT) energy decomposition further demonstrates that the Rg–Cu interaction is dominated by induction forces, with significant contributions from dispersion and electrostatics, and exhibits notable charge transfer character. This polarization and orbital overlap transcend the conventional van der Waals picture and reveal a partially covalent nature in noble gas transition metal interactions. Full article

Background: Whether routinely available laboratory measures are associated with longitudinal bone mineral density (BMD) change in clinical practice remains uncertain, particularly when treatment exposure and other major skeletal determinants are incompletely recorded. This study examined BMD trajectories and incident osteoporosis in a Saudi dual-energy X-ray absorptiometry (DXA) cohort. Anti-osteoporosis therapy, glucocorticoid exposure and menopausal status were unavailable, limiting causal interpretation. Methods: This retrospective longitudinal cohort included DXA examinations performed at King Saud University Medical City (KSUMC), Riyadh, from 2016 to 2021. The trajectory analysis comprised 2147 patients with at least two scans and a minimum one-year interval. Diagnostic category was based on the lowest T-score across the lumbar spine, bilateral femoral necks and distal radius. Twenty linear mixed-effects (LME) models evaluated time-by-biomarker interactions with Benjamini–Hochberg correction. Cox proportional hazards (PH) regression was the primary analysis of incident osteoporosis among 789 patients without osteoporosis at baseline. Results: During 2045 person-years of observation, 107 patients developed osteoporosis (5.2 events per 100 person-years). No event occurred among 124 patients with normal baseline BMD, compared with 107 events among 665 patients with baseline osteopenia (log-rank p < 0.001). In the complete-case Cox model (n = 384; 53 events; C-index = 0.679), baseline osteopenia had a hazard ratio (HR) of 3.12 (95% Confidence interval (CI) 0.89–10.97; p = 0.076); no modelled covariate reached statistical significance. None of the 20 time-by-biomarker interactions remained significant after multiplicity correction (smallest q = 0.214). Four nominal terms with raw p < 0.10 were below the measurement-based clinical threshold and were compatible with chance variation. Conclusions: No routinely measured biomarker demonstrated clinical utility for identifying BMD trajectory in this cohort. Baseline DXA category separated the observed progression pattern, although this partly reflects the distance from the diagnostic threshold and should not be interpreted as a validated prediction model. The principal contribution is a carefully characterised null result. Prospective studies with explicit treatment tracking and repeated bone-turnover measurements are required for confirmation. Full article

Three-dimensional Dirac semimetal (3D DSM), characterized by linear band dispersion and strong terahertz nonlinear optical responses, has attracted increasing interest as promising materials for compact nonlinear photonic devices. Optical bistability (OB), which enables two stable output states under the same input condition, is of particular importance to all-optical switching, optical logic gates, and optical memory. However, achieving OB with a sufficiently low switching threshold remains a key challenge. Here, we propose a prism-coupled multilayer structure incorporating 3D DSMs to realize low-threshold, tunable OB by exciting the surface plasmon polaritons (SPPs). The prism-coupling configuration enables efficient excitation of SPPs, producing strong local-field enhancement around the nonlinear 3D DSM layer. This enhanced light–matter interaction, together with the large nonlinear refractive index of the 3D DSM, substantially reduces the electric-field threshold required for bistable switching. Numerical results show that OB can be achieved with an incident electric-field threshold on the order of 10⁵ V/m through optimizing the material and structural parameters. Moreover, the switching threshold and hysteresis loop width can be flexibly controlled by varying the Fermi energy, relaxation time, and geometric parameters of the 3D DSM multilayer structure. These results suggest that SPP-assisted 3D DSM structures provide an effective platform for low-threshold, actively tunable optical bistable devices in integrated terahertz photonic systems. Full article