Search Results (494)

Neural metric learning is often assessed by retrieval accuracy, but a learned dissimilarity can rank examples well while failing to have norm-like algebraic structure. This paper studies a precise finite question: within a Euclidean-anchored residual family of neural dissimilarities, can one reduce sampled defects of homogeneity, subadditivity, and dyadic reconstruction on latent differences without destroying retrieval performance? The construction is inspired by asymptotically metrically normable (AMN) vector spaces, but its claims are finite, sampled, and latent: it does not prove global AMN rigidity or certify a metric on the input space. The framework is motivated by the observation that many learned similarities have the form $K=exp(-E/\tau )$ and therefore encode an unbounded distance-like quantity or squared distance-like quantity behind a bounded affinity. The AMN-relevant object is this cost, not the bounded kernel value. We formalize bounded-perturbation stability of the large-scale specific energy $E(nv,0)/n$, the conversion of subadditivity into multiplicative affinity consistency, and the quotient interpretation in which directions of zero large-scale cost are collapsed. The mathematical development then introduces finite dyadic diagnostics, learned-gauge and convex-unit-ball interpretations, finite norm-envelope witnesses, dyadic stability bounds, and refinement towers of witness norms. The empirical part reports full official Fashion-MNIST experiments with supervised-contrastive and proxy-anchor-style Euclidean baselines, post hoc audits for shrinkage, residual flexibility, off-training scales, and latent extrapolation, and a ten-seed full-query/full-gallery UCI Human Activity Recognition benchmark. The results show that Euclidean objectives can be stronger for Recall@1, whereas AMN-inspired residual regularization substantially reduces finite norm-like defects inside the residual family. The contribution is therefore a finite diagnostic and regularization framework for learned latent dissimilarities, not a state-of-the-art retrieval objective. Full article

The lower heating value (LHV) is one of the most important fuel properties in thermochemical conversion systems, directly affecting energy balances, process efficiency, and fuel selection. Although several empirical correlations have been proposed in the literature, most existing models are limited to specific fuel classes or focused primarily on higher heating value (HHV) prediction. In this work, generalized regression models were developed to estimate the LHV of solid, liquid, and gaseous fuels from ultimate analysis data. A comprehensive database containing 520 fuels was used to construct both a full polynomial model and a simplified polynomial formulation. The predictive performance of the models was evaluated using multiple statistical metrics, while robustness and cross-validated predictive performance were assessed through K-fold cross-validation, residual analysis, Bland–Altman analysis, and variance inflation factor diagnostics (VIF). The full polynomial model achieved the highest fitting accuracy, whereas the simplified formulation demonstrated improved statistical stability and reduced multicollinearity with only minor reductions in predictive performance. Compared with representative literature correlations, the proposed models showed competitive or superior predictive capability, demonstrating their applicability as practical preliminary correlations for practical engineering and energy-related applications. Full article

Microbial reduction of soluble selenium oxyanions is a sustainable strategy for remediating selenium-contaminated environments, yet the molecular mechanisms underlying selenite tolerance in the genus Rhodococcus remain poorly understood. In this study, we investigated the proteomic adaptation of the highly tolerant strain Rhodococcus qingshengii PM1 under high-concentration selenite stress (50 mM Na₂SeO₃) using a data-independent acquisition (DIA)-based quantitative proteomics approach. A total of 3335 proteins were identified, and 3310 proteins were retained for downstream analysis. Comparative proteomics revealed 1411 differentially expressed proteins, including 972 upregulated and 439 downregulated proteins in the selenite-treated group. These changes indicate extensive systems-level proteomic reprogramming and support a growth–defense trade-off strategy. Strain PM1 strongly upregulated ferredoxin and multiple respiratory-chain- and oxidoreductase-associated proteins, suggesting a ferredoxin-associated electron-transfer network that may contribute to Se(IV) transformation and intracellular redox adjustment. In parallel, proteins involved in sulfur assimilation, cysteine/methionine and selenocompound metabolism, ergothioneine biosynthesis, GSH-associated metabolism, Trx/MSH thiol-redox systems, peroxidase/Ohr-Prx detoxification, metalloid/oxyanion resistance, urease-associated pH adaptation, DNA repair, and cell-envelope remodeling were induced, indicating activation of multilayered defense and homeostasis mechanisms. Conversely, proteins associated with central carbon metabolism, carbohydrate uptake, and ribosome-dependent translation were repressed, suggesting reduced growth investment and energy conservation under severe selenite pressure. Overall, this study provides a systems-level proteomic framework for understanding Se(IV) resistance in R. qingshengii PM1 and identifies candidate targets for future functional validation, strain engineering, and selenium/metal(loid) bioremediation. Full article

Background/Objectives: Dysphagia may adversely affect nutritional status in patients with Multiple Sclerosis (MS). This study aimed to investigate the associations between dysphagia severity and (i) nutritional status, assessed by the Malnutrition Universal Screening Tool (MUST) and Global Leadership Initiative on Malnutrition (GLIM) criteria, and (ii) secondary sarcopenia indicators according to the European Working Group on Sarcopenia in Older People-2 (EWGSOP2) framework. Materials and Methods: This cross-sectional study enrolled 32 consecutive adult outpatients with confirmed MS and self-reported dysphagia (DYMUS ≥ 1). Dysphagia severity was evaluated using the Dysphagia in Multiple Sclerosis (DYMUS) questionnaire, the Eating Assessment Tool-10 (EAT-10), and the Yale Swallow Protocol. Nutritional assessment included MUST screening and GLIM-based malnutrition diagnosis. Muscle function was evaluated via handgrip strength, calf circumference, and 4-metre gait speed. Results: GLIM-defined malnutrition was identified in 12 (37.5%) patients. Dysphagia severity was significantly associated with MUST score (ρ = 0.596, p < 0.001) and the presence of GLIM-defined malnutrition (median DYMUS 6.5 vs. 4.0; p = 0.012). In exploratory logistic regression, higher DYMUS scores were associated with GLIM-defined malnutrition. Conversely, no significant associations were found between dysphagia severity and handgrip strength, calf circumference, or sarcopenia classification (p > 0.30 for all). The categorical severe-sarcopenia rate was not considered reliably interpretable because of a pronounced gait speed floor effect. Conclusions: In ambulatory MS patients with dysphagia, dysphagia severity was associated with nutritional risk indicators and GLIM-defined malnutrition, but not with the primary muscle strength and mass indicators evaluated. Because MUST and GLIM reflect composite nutritional risk rather than confirmed protein–energy deficiency, these findings should be regarded as exploratory and hypothesis-generating. The present data did not permit a reliable estimate of sarcopenia prevalence because of a pronounced gait speed floor effect and the absence of body composition measurement. As a preliminary practical consideration, these findings may support combined dysphagia and nutritional screening in multidisciplinary MS outpatient care, pending confirmation in larger prospective cohorts. Full article

Integrating semi-transparent photovoltaics (STPV) into greenhouse structures offers an effective approach to optimizing the Food–Energy Nexus and maximizing sustainable land-use efficiency. However, a knowledge gap remains regarding how specific STPV spectral signatures drive plant morpho-physiological acclimation across multiple cultivation cycles. This study presents a 19-month multi-cycle, proof-of-concept evaluation of the structural growth dynamics and physiological responses of generative (tomato) and vegetative (lettuce) crops under greenhouse prototypes with two distinct thin-film STPV technologies: Cadmium Telluride (CdTe) and amorphous Silicon (a-Si), compared to an unshaded transparent control. Biometric monitoring revealed that morphological acclimation (Shade-Avoidance Syndrome) was highly plastic, driven by the interplay between spectral filtering and seasonal irradiance limits. While structural adaptations, such as foliar expansion and stem elongation under the a-Si spectrum, were pronounced during specific transitional seasons (e.g., early spring), these morphological differences largely homogenized across treatments during periods of extreme high or low natural irradiance. Despite the shading penalty, this morphological acclimation successfully sustained agronomic fresh mass. Systemic efficiency, quantified by the Land Equivalent Ratio (LER) as a relative biophysical synergy index, demonstrated notably crop-specific synergies. Under an extended single fruiting cycle, the CdTe prototype showed potential to improve yield, achieving a maximum LER of 1.66 for the high-light-demanding tomato (Y_crop = 1.40). Conversely, the a-Si module excelled with the shade-tolerant lettuce during early vegetative stages in high-radiation periods, achieving peak LERs up to 1.55. These findings provide a biophysical baseline to help guide future scalability assessments prior to full-scale commercial agrivoltaic (APV) implementation for sustainable food systems. Full article

In the era of digital transformation, the operational reliability of hydraulic energy conversion systems is paramount for the overall efficiency of sustainable integrated energy infrastructures. This study evaluates the robustness of machine learning-based fault diagnosis for positive displacement pumps, which are critical components in energy-intensive industrial applications. The research addresses a key challenge: the instability of diagnostic features under varying operational regimes. Using vibration signals from units at three distinct wear levels, we evaluated multiple machine learning architectures, including SVM, KNN, and ensemble trees. Our findings reveal that traditional data-driven models suffer a performance degradation of over 21% when subjected to domain shifts caused by load variability. To mitigate this, we implemented a frequency-domain signal conditioning layer that aligns extracted descriptors with physically meaningful wear phenomena. This enhanced feature representation improved classification accuracy to 93.5% under variable load conditions. The results demonstrate that improving the robustness of diagnostic models is essential for reliable operation, maintenance planning, and energy efficiency of hydraulic energy conversion systems within modern industrial energy infrastructures. Full article

Tumor metastasis constitutes a frequent contributor to high mortality rates, with EMT intimately implicated in this disseminative process. Accumulating evidence in recent years indicates that neoplastic cells undergoing EMT frequently exhibit concurrent metabolic reprogramming. Multiple modalities—including glycolysis, mitochondrial oxidative phosphorylation, lipid metabolism, as well as amino acid metabolism—cooperatively supply energy, facilitate membrane remodeling, and sustain redox homeostasis. Specifically, glycolytic flux, oxidative phosphorylation, lipid turnover, and amino acid catabolism/anabolism function in a concerted manner to meet the bioenergetic demands, support biogenesis of cellular membranes, and preserve the intracellular redox equilibrium during phenotypic conversion. Notably, intermediate metabolites can in turn modulate the trajectory of EMT through signal transduction cascades or epigenetic modifications. This review systematically delineates the bidirectional regulatory circuitry interconnecting EMT and metabolic reprogramming; furthermore, it examines the implications of this crosstalk for neoplastic disease progression. Finally, therapeutic strategies targeting the nexus of metabolic reprogramming and EMT are summarized. Full article

This paper presents the design, implementation, and experimental validation of a portable photovoltaic charging station with IoT-based monitoring for autonomous low-power applications. The system integrates a 120 W photovoltaic module, LiFePO₄ battery storage, MPPT regulation, DC/AC conversion, and an ESP32-S3-based acquisition unit connected to a cloud platform for real-time telemetry. Electrical and environmental variables were recorded to evaluate energy balance, conversion losses, State of Charge evolution, and load compatibility under different seasonal operating conditions. Field tests showed that under high-irradiance summer conditions, the prototype supplied multiple laptop loads for approximately 4.5 h with stable operation. In contrast, winter trials revealed a structural energy deficit equivalent to 120% of the initial 24 Ah storage capacity, mainly due to reduced irradiance and cumulative conversion losses ranging from 15% to 25%. Based on the experimental data and deterministic energy-balance modelling, an optimized configuration using a 100 Ah LiFePO₄ battery bank and MPPT regulation was assessed through deterministic energy-balance modelling, thus reducing the required State of Charge to 28.8% under the analyzed operating profile. The results demonstrate the feasibility of a low-cost, IoT-enabled photovoltaic platform for renewable energy harvesting, autonomous power supply, and real-time performance assessment. Full article

The energy recovery of biomass is frequently implemented through single-output systems or passive management schemes, resulting in underutilization of its thermodynamic potential and losses in economic value, climate benefits, and useful co-products. This study formalizes the concept of biopolygeneration as a diagnostic principle aimed at maximizing biomass utilization through the simultaneous production of multiple energy services and the valorization of secondary streams. A dimensionless metric, the Biopolygeneration Diagnostic Index (BDI), is proposed to quantify this concept. The index is bounded within $[0,1]$ and integrates five sub-indices: energy efficiency ($I_E$), thermal integration ($I_T$), energy self-sufficiency ($I_A$), exergetic quality of outputs ($I_Q$), and co-product valorization ($I_V$). Weights were determined using the Analytic Hierarchy Process ($w_1=0.40$, $w_2=0.24$, $w_3=w_4=0.14$, $w_5=0.08$; $\mathrm{CR}=0.007$). The BDI was evaluated using six cases, including five operating plants and one validated computational model representing five biomass conversion technologies in four countries. Results ranged from 0.453 for an engine without combined heat and power (CHP) to 0.733 for a cascade trigeneration system. Under identical feed conditions, the incorporation of CHP ($C1\to C2$) increased the BDI from 0.453 to 0.715, representing a 57.7% improvement attributable solely to heat recovery. Current limitations include the small validation sample ($n=6$) and the reconstruction of $I_A$ and $I_V$ from technological characteristics due to the absence of standardized reporting in the literature. Although these sub-indices account for only 22% of the total weighting ($w_{I_A}+w_{I_V}=0.22$), the present results should be considered a proof of concept rather than a fully empirical validation. The BDI provides a thermodynamically consistent framework for comparing bioenergy systems across technologies and supports technical, regulatory, and investment decision making. Broader validation using larger measurement-based datasets is required before claims of universality can be established. Full article

Background: Cancer-associated cachexia (CAC) is a multifactorial syndrome driven by a profound metabolic and inflammatory dysregulation. Due to the central role of the insulin-like growth factor 1 (IGF-1) pathway in regulating muscle mass, energy metabolism, and inflammation, this study evaluated the relevance of five IGF-1 axis-related single-nucleotide polymorphisms (SNPs), namely IGF1 rs6220, insulin-like growth factor 1 receptor (IGF1R) rs2016347 and rs2684788, growth hormone receptor (GHR) rs6873545, and insulin receptor substrate 1 (IRS1) rs1801278. Methods: The impact of these variants on CAC onset and overall survival (OS) was assessed in a cohort of 140 cancer patients. Results: While overall-cohort analyses did not reach statistical significance, exploratory analyses suggested potential associations between the IGF1 rs6220 GG and GHR rs6873545 CC genotypes and increased CAC risk in male patients. A trend for higher CAC prevalence was also noted in younger patients (<63 years) with the rs6873545 CC genotype. For pre-CAC and CAC patients, exploratory subgroup analyses on patients’ OS were conducted following no significant results in the overall cohort. Among older patients and those with high prognostic nutritional index (PNI; >44.2), the IGF1 rs6220 G allele was associated with longer OS. Conversely, the IGF1R rs2016347 G allele and rs2684788 T allele were linked to poorer OS across multiple pre-CAC and CAC subgroups. The effects of GHR rs6873545 varied across subgroups, suggesting context-dependent activity. Conclusions: This study highlights the functional heterogeneity of IGF-1 axis-related genetic variants, indicating potential to serve as predictors of CAC. Given the exploratory nature of these findings, validation in larger cohorts is required to confirm the associations found. Full article

Power electronic DC–DC conversion stages play a pivotal role in photovoltaic (PV) energy conversion. Here, maximum power point tracking (MPPT) is necessary to regulate the operating point of the converter with high bandwidth and robustness in the presence of irradiance and temperature disturbances. This paper proposes an MPPT scheme based on an Intelligent Wave Algorithm (IWA) for a PV source connected to a flyback DC–DC converter. The proposed IWA is formulated as a population-based metaheuristic that updates the converter’s duty cycle to maximize PV power while reducing the oscillations commonly observed in classical methods. A unified MATLAB/Simulink test bench has been developed in which multiple MPPT algorithms—Perturb and Observe (P&O), Incremental Conductance (InC), Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO) and the proposed IWA—are implemented in parallel flyback subsystems that share the same PV module and converter parameters. The simulation results show that the IWA method achieved consistent convergence to the maximum power point more rapidly than both classical and advanced meta-heuristic methods, obtaining 12.5% better response time and 8.9% better steady-state output power than the method closest to it. Overall, the findings suggest that combining a flyback converter with IWA-based maximum power point tracking (MPPT) improves the efficiency and stability of energy harvesting, making this approach suitable for low- to medium-power photovoltaic (PV) applications within modern power electronics conversion systems. Full article

In this paper, we study energy bounded solutions $u_{\epsilon }$ converging weakly to 0 of the subcritical problem ${\displaystyle -\Delta u+gu=h{u}^{\frac{n+2}{n-2}-\epsilon },u>0\mathrm{in}\mathsf{\Omega },u=0\mathrm{on}\partial \mathsf{\Omega },}$ where $\mathsf{\Omega }$ is a $C^2$ bounded domain in ${\mathbb{R}}^n$ with $n\ge 4$, g is a $C^1$ positive function on $\overline{\mathsf{\Omega }}$, h is a $C^3$ positive function on $\overline{\mathsf{\Omega }}$, and $\epsilon$ is a small positive parameter. Assuming that the normal derivative of h is negative on the boundary, we prove that $u_{\epsilon }$ must blow up in the interior of the domain. Moreover, we determine the precise location of the blow-up points and the corresponding blow-up rates. Conversely, for sufficiently small $\epsilon$, we construct blowing-up solutions that converge weakly to zero, which allows us to obtain a multiplicity result for the problem. In contrast, when the normal derivative of h is positive at a boundary point b, we show that it is possible to construct solutions converging to zero and blowing up precisely at b. Full article

Land surface temperature (LST) is a core physical parameter that characterizes land surface processes and surface-atmosphere energy exchange. As the demand for high-accuracy LST products intensifies across diverse research domains—including climate science, hydrology, and ecosystem modeling—the systematic quantification of pixel-level retrieval uncertainties has become essential for generating long-term, consistent Climate Data Records (CDRs). However, existing studies predominantly emphasize algorithmic development or localized validation, with limited attention to systematic cross-site and long-term uncertainty assessments. This gap impedes a comprehensive understanding of the compositional structure and spatiotemporal variability of LST retrieval uncertainties under heterogeneous surface and atmospheric conditions. In this study, based on the improved generalized split-window (GSW) algorithm and error propagation theory, the total uncertainty (U_total) and its four primary components—algorithm uncertainty (U_a), land surface emissivity uncertainty (U_e), noise equivalent delta temperature uncertainty (U_n), and atmospheric water vapor uncertainty (U_w)—at the pixel level over long time series and across multiple sites are quantified. Our analysis spans a 21-year period (2003–2023) and encompasses multiple geographically distributed sites, utilizing high-quality Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data—specifically MYD11_L2 and MOD11_L2 products—collocated at the locations of 15 globally distributed ground-based reference sites. These sites are used to represent diverse climatic regimes and land-cover conditions, rather than to provide point-scale “true” LST values for residual-based validation. Results show that the interquartile range (IQR) of U_total is consistently concentrated between 1.0 and 1.2 K, demonstrating long-term stability. Systematic differences in U_total are identified across sensor platforms and diurnal cycles: U_total for Aqua/MYD data (1.13–1.25 K) is marginally higher than that for Terra/MOD data (1.05–1.17 K); similarly, daytime U_total (1.08–1.23 K) is generally slightly elevated relative to nighttime U_total (1.05–1.18 K). The contributions of individual uncertainty components to U_total exhibit substantial variation, with mean relative contributions of 81.97%, 11.32%, 4.46%, and 2.25% for U_e, U_a, U_n, and U_w, respectively. The dominant drivers of U_total differ markedly across climatic regions: in arid regions, U_total is predominantly governed by U_e, termed “emissivity-dominated,” accounting for over 85% of the total; conversely, humid tropical regions exhibit a “surface-atmosphere co-influenced” regime, characterized by a reduced contribution from U_e and correspondingly enhanced contributions from U_a and U_w. Furthermore, U_total decreases with increasing total column water vapor (TCWV) (Pearson correlation coefficient r = −0.498; linear slope k = −0.0425 K/(g/cm²)), and increases with increasing viewing zenith angle (VZA) (r = 0.208; k = 0.0022 K/degree). While U_a, U_n, and U_w all increase with TCWV, U_e decreases. Full article

A comprehensive and systematic study was conducted to address a series of key technical challenges encountered in the grinding process at a copper mine. These issues included the complex mechanical properties of the feed ore, which led to low grinding efficiency, difficulty in achieving the required grinding fineness for flotation, uneven particle size distribution in the grinding products, and severe occurrences of overgrinding and undergrinding. Based on the semi-theoretical ball diameter formula, the optimal initial ball size distribution for the ball mill was precisely calculated as Φ70:Φ50:Φ40:Φ30 = 15:25:35:25. Through laboratory-scale grinding tests and Discrete Element Method (DEM) simulations, a systematic analysis of multiple indicators under three different ball loading schemes was performed, including the motion state of particles inside the mill, the collision behavior of the grinding media, and the energy distribution. This analysis confirmed the rationality and effectiveness of the literature scheme. Industrial trial results showed the following: the yield of the +0.20 mm fraction decreased by 4.15 percentage points, and the yield of the −0.010 mm fraction and its proportion relative to the −0.074 mm fraction decreased by 10.17 and 19.10 percentage points, respectively. Conversely, the yields of the intermediate separated fraction (−0.20 + 0.010 mm), the easily separated fraction (−0.074 + 0.018 mm) and the −0.074 mm qualified fraction increased by 14.32, 14.13, and 7.29 percentage points, respectively. The grinding technical efficiency improved by 19.55 percentage points. Furthermore, the specific steel ball consumption decreased by 46 g/t, a reduction of 5.07%. The copper concentrate recovery increased by 0.65 percentage points, resulting in an annual increase of 40.51 tons of copper metal, additional revenue of CNY 3.2483 million, and steel ball cost savings of CNY 603,500. Collectively, this optimization generated a total economic benefit of CNY 3.8518 million. By optimizing the ball size distribution, the particle size composition of the grinding products was significantly improved, the flotation indicators were enhanced, and the grinding media consumption cost was reduced, achieving quality improvement and efficiency increase in the mineral processing. This study provides a valuable reference for solving similar grinding problems. Full article

A lightweight and high-efficiency microwave-absorbing material was developed via an in situ solvothermal pyrolysis strategy by anchoring sphere-like Fe₃O₄ nanostructures onto bamboo-derived porous carbon (BPC). The resulting composites preserve the intrinsic anisotropic honeycomb architecture of bamboo while introducing uniformly distributed magnetic nanoparticles, enabling synergistic dielectric–magnetic loss. Electromagnetic parameters, alongside impedance matching, were successfully modulated through the optimization of precursor concentrations. Of the evaluated materials, BPC-0.9 stood out for its intense attenuation, recording an RL_min of −45.17 dB at a 1.8 mm thickness. Furthermore, a significant effective absorption bandwidth of 6.65 GHz was attained by the BPC-0.6 sample at only 2.2 mm. Several factors contribute to the boosted efficiency, starting with conductive and interfacial polarization losses paired with multiple scattering events. Furthermore, magnetic loss components, encompassing eddy current effects as well as natural and exchange resonances, play a pivotal role in optimizing the material’s response. Furthermore, radar cross-section (RCS) modeling reveals a substantial reduction of 19.9 dB·m², verifying the material’s viability for real-world stealth technologies. Our findings offer a straightforward methodology for fabricating magnetic carbon structures from biomass with adjustable dielectric responses, underscoring their potential in high-performance energy conversion and low-density microwave absorption. Full article