Search Results (1,415)

The paradigm-shift idea of murburn concept is no hypothesis but developed directly from fundamental facts of cellular/ecological existence. Murburn involves spontaneous and stochastic interactions (mediated by murzymes) amongst the molecules and unbound ions of cells. It leads to effective charge separation (ECS) and formation/recruitment of diffusible reactive species (DRS, like radicals whose reactions enable ATP-synthesis and thermogenesis) and emission of radiations (UV/Vis to ELF). These processes also lead to a chemo-electromagnetic matrix (CEM), ascertaining that living cell/organism react/function as a coherent unit. Murburn concept propounds the true utility of oxygen: generating DRS (with catalytic and electrical properties) on the way to becoming water, the life solvent, and ultimately also leading to phase-based macroscopic homeostatic outcomes. Such a layout enables cells to become simple chemical engines (SCEs) with powering, coherence, homeostasis, electro-mechanical and sensing–response (PCHEMS; life’s short-term “intelligence”) abilities. In the current review, we discuss the coacervate nature of cells and dwell upon the ways and contexts in which various radiations (either incident or endogenously generated) could interact in the new scheme of cellular function. Presenting comparative evidence/arguments and listing of systems with murburn models, we argue that the new perceptions explain life processes better and urge the community to urgently adopt murburn bioenergetics and adapt to its views. Further, we touch upon some distinct scientific and sociological contexts with respect to the outreach of murburn concept. It is envisaged that greater awareness of murburn could enhance the longevity and quality of life and afford better approaches to therapies. Full article

To address the issues in existing 3D point cloud facet generation networks, specifically, the tendency to produce a large number of empty facets and the uncertainty in facet count, this paper proposes a novel deep learning framework for robust facet segmentation. Based on the generated facet set, two exploratory applications are further developed. First, to overcome the bottleneck where inaccurate empty-facet detection impairs the downsampling performance, a facet-abstracted downsampling method is introduced. By using a learned facet classifier to filter out and discard empty facets, retaining only non-empty surface facets, and fusing point coordinates and local features within each facet, the method achieves significant compression of point cloud data while preserving essential geometric information. Second, to solve the insufficient precision in organ segmentation within crop point clouds, a facet growth-based segmentation algorithm is designed. The network first predicts the edge scores for the facets to determine the seed facets. The facets are then iteratively expanded according to adjacent-facet similarity until a complete organ region is enclosed, thereby enhancing the accuracy of segmentation across semantic boundaries. Finally, the proposed facet segmentation network is trained and validated using a synthetic dataset. Experiments show that, compared with traditional methods, the proposed approach significantly outperforms both downsampling accuracy and instance segmentation performance. In various crop scenarios, it demonstrates excellent geometric fidelity and semantic consistency, as well as strong generalization ability and practical application potential, providing new ideas for in-depth applications of facet-level features in 3D point cloud analysis. Full article

Kombucha, a traditional fermented beverage, has become an important topic in global health beverage research due to its potential health benefits. The aim of this review is to integrate the existing literature and analyze the interactions among microbial communities during the fermentation process of kombucha, especially how Saccharomyces, Acetobacter, and Lactobacillus generate bioactive components with health benefits through the cascade reaction in sugar metabolism–ethanol oxidation–organic acid accumulation. We also focus on the effects of fermentation conditions (e.g., time, temperature, and strain) on the microbial community structure and metabolic pathways, as well as their effects on the bioactive components and quality of kombucha microbiota (the microbial community in kombucha). By combing and analyzing the existing studies, this review provides an important theoretical basis for the optimization of the fermentation process, enhancement of health benefits, and development of functional beverages of kombucha microbiota, as well as new ideas for future research directions. Full article

The general framework of Entropic Dynamics (ED) is used to construct non-relativistic models of relational Quantum Mechanics from well-known inference principles—probability, entropy and information geometry. Although only partially relational—the absolute structures of simultaneity and Euclidean geometry are still retained—these models provide a useful testing ground for ideas that will prove useful in the context of more realistic relativistic theories. The fact that in ED the positions of particles have definite values, just as in classical mechanics, has allowed us to adapt to the quantum case some intuitions from Barbour and Bertotti’s classical framework. Here, however, we propose a new measure of the mismatch between successive states that is adapted to the information metric and the symplectic structures of the quantum phase space. We make explicit that ED is temporally relational and we construct non-relativistic quantum models that are spatially relational with respect to rigid translations and rotations. The ED approach settles the longstanding question of what form the constraints of a classical theory should take after quantization: the quantum constraints that express relationality are to be imposed on expectation values. To highlight the potential impact of these developments, the non-relativistic quantum model is parametrized into a generally covariant form and we show that the ED approach evades the analogue of what in quantum gravity has been called the problem of time. Full article

Background/Objectives: BRIGHTLIGHT was the national evaluation of adolescent and young adult (AYA) cancer services in England. BRIGHTLIGHT results were not available when the most recent healthcare policy (NHSE service specifications for AYA Cancer) for AYA was drafted and therefore did not consider BRIGHTLIGHT findings and recommendations. We describe the co-development and delivery of a Policy Lab to expedite the implementation of the new service specification in the context of BRIGHTLIGHT results, examining the roles of multi-stakeholders to ensure service delivery is optimised to benefit AYA patients. We address the key question, “What is the roadmap for empowering different stakeholders to shape how the AYA service specifications are implemented?”. Methods: A 1-day face-to-face policy lab was facilitated, utilising a unique, user-centric engagement approach by bringing diverse AYA stakeholders together to co-design strategies to translate BRIGHTLIGHT evidence into policy and impact. This was accompanied by an online workshop and prioritisation survey, individual interviews, and an AYA patient workshop. Workshop outputs were analysed thematically and survey data quantitatively. Results: Eighteen professionals and five AYAs attended the face-to-face Policy Lab, 16 surveys were completed, 13 attended the online workshop, three professionals were interviewed, and three AYAs attended the patient workshop. The Policy Lab generated eight national and six local recommendations, which were prioritised into three national priorities: 1. Launching the service specification supported by compelling communication; 2. Harnessing the ideas of young people; and 3. Evaluation of AYA patient outcomes/experiences and establishing a national dashboard of AYA cancer network performance. An animation was created by AYAs to inform local hospitals what matters to them most in the service specification. Conclusions: Policy and research evidence are not always aligned, so when emerging evidence does not support current guidance, further exploration is required. We have shown through multi-stakeholder involvement including young people that it was possible to gain a different interpretation based on current knowledge and context. This additional insight enabled practical recommendations to be identified to support the implementation of the service specification. Full article

Accurate wellbore trajectory prediction is of great significance for enhancing the efficiency and safety of directional drilling in coal mines. However, traditional mechanical analysis methods have high computational complexity, and the existing data-driven models cannot fully integrate non-sequential features such as stratum lithology. To solve these problems, this study proposes a parallel input gated recurrent unit (Pi-GRU) model based on the TensorFlow framework. The GRU network captures the temporal dependencies of sequence data (such as dip angle and azimuth angle), while the BP neural network extracts deep correlations from non-sequence features (such as stratum lithology), thereby achieving multi-source data fusion modeling. Orthogonal experimental design was adopted to optimize the model hyperparameters, and the ablation experiment confirmed the necessity of the parallel architecture. The experimental results obtained based on the data of a certain coal mine in Shanxi Province show that the mean square errors (MSE) of the azimuth and dip angle angles of the Pi-GRU model are 0.06° and 0.01°, respectively. Compared with the emerging CNN-BiLSTM model, they are reduced by 66.67% and 76.92%, respectively. To evaluate the generalization performance of the model, we conducted cross-scenario validation on the dataset of the Dehong Coal Mine. The results showed that even under unknown geological conditions, the Pi-GRU model could still maintain high-precision predictions. The Pi-GRU model not only outperforms existing methods in terms of prediction accuracy, with an inference delay of only 0.21 milliseconds, but also requires much less computing power for training and inference than the maximum computing power of the Jetson TX2 hardware. This proves that the model has good practicability and deployability in the engineering field. It provides a new idea for real-time wellbore trajectory correction in intelligent drilling systems and shows strong application potential in engineering applications. Full article

For transmission systems with regular order excitation, the order vibration will be conducted to each component of the system and affect the stability and service life of the system. A method with a regular non-circular active pulley is proposed in this paper, which is used to counteract the regular order excitation and the regular load excitation. A toothed belt drive system with second-order excitation is taken as an example. According to the existing analytical model of the tooth belt drive system, the modeling process and analytical solution algorithm of the system are derived. Based on the coordinate transformation, the algorithms for any position of an elliptical pulley and the common tangent of the circular pulley are given. And the algorithm for the arc length of the elliptical pulley at any arc degree is proposed. The influence of the phase and eccentricity in the elliptical pulley on the dynamic performance of the system is analyzed. Then the experimental verification is carried out. This shows that this system can generate excitation opposite to the main order rotational vibration of the driving pulley and opposite to the load of the driven pulley. Under the combined effect of other load pulleys in the system, there will be an amplification phenomenon in its vibration response. Considering the decrease in the belt span tension and the decline in the performance of energy-absorbing components after long operation, the presented method can better maintain the stability of system performance. This method can provide new ideas for the vibration reduction optimization process of systems with first-order wave excitation. Full article

Memory is a fundamental neurological function, essential for animal survival. Over the course of vertebrate evolution, elaborations in the forebrain telencephalon create new memory mechanisms, meaning basal vertebrates such as amphibians must have a less sophisticated system of memory acquisition, storage, and retrieval than the well-known hippocampal-based circuitry of mammals. Personality also appears to be a fundamental vertebrate trait and is generally defined as consistent individual behavior over time and across life history stages. In anuran amphibians (frogs), personality studies generally ask whether adult frogs retain the personality of their tadpole stage or whether personality shifts with metamorphosis, an idea behavioral ecologists term adaptive decoupling. Using a multidisciplinary perspective and recognizing there are ~7843 species of frogs, each with some molecular, morphological, physiological, or behavioral feature that makes it unique, we review, clarify, and provide perspective on what we collectively know about memory and personality and their mechanisms in anuran amphibians. We propose four working hypotheses: (1) as tadpoles grow, new telencephalic neurons become integrated into functional networks, producing behaviors that become more sophisticated with age; (2) since carnivores tend to be more bold/aggressive than herbivores, carnivorous anuran adults will be more aggressive than herbivorous tadpoles; (3) each amphibian species, and perhaps life history stage, will have a set point on the Shy–Bold Continuum; and (4) around this set point there will be a range of individual responses. We also suggest that several factors are slowing our understanding of the variety and depth of memory and personality possibilities in anurans. These include the scala natura approach to comparative studies (i.e., the idea that one frog represents all frogs); the assumption that amphibians are no more than simple reflex machines; that study species tend to be chosen more for convenience than taxonomic representation; and that studies are designed to prove or disprove a construct. This latter factor is a particular hindrance because what we are really seeking as scientists is not the confirmation or refutation of ideas, but rather what those ideas are intended to produce, which is understanding. Full article

The production of spirits generates significant amounts of waste in the form of fusel oils-previously treated mainly as an environmental problem. This paper presents an innovative installation designed to recover valuable components from this difficult waste. The key achievement is the effective separation and recovery of pyrazine derivatives-natural aromatic compounds with high utility value in the food, cosmetics and pharmaceutical industries. The designed system allows for the recovery of as much as 98% of pyrazines and isoamyl alcohol and isobutanol fractions with a purity above 96%, which is a significant advance compared to previous disposal methods. The installation was designed to be consistent with the idea of a circular economy, maximizing the use of by-products and minimizing losses. The results of the work indicate that fusel oils, previously perceived as waste, can become a source of valuable secondary raw materials, and the presented solution opens up new possibilities for the sustainable development of the alcohol industry. Full article

The efficient depolymerization of lignin has become a key challenge in the preparation of high-value-added chemicals. Graphitic carbon nitride (g-C₃N₄)-based photocatalytic system shows potential due to its mild and green characteristics over other depolymerization methods. However, its inherent defects, such as a wide band gap and rapid carrier recombination, severely limit its catalytic performance. In this paper, a g-C₃N₄ modification strategy of K⁺ doping and surface alkalinization is proposed, which is firstly applied to the photocatalytic depolymerization of the lignin β-O-4 model compound (2-phenoxy-1-phenylethanol). K⁺ doping is achieved by introducing KCl in the precursor thermal polymerization stage to weaken the edge structure strength of g-C₃N₄, and post-treatment with KOH solution is combined to optimize the surface basic groups. The structural/compositional evolution of the materials was analyzed by XRD, FTIR, and XPS. The morphology/element distribution was visualized by SEM-EDS, and the optoelectronic properties were evaluated by UV–vis DRS, PL, EIS, and transient photocurrent (TPC). K⁺ doping and surface alkalinization synergistically regulate the layered structure of the material, significantly increase the specific surface area, introduce nitrogen vacancies and hydroxyl functional groups, effectively narrow the band gap (optimized to 2.35 eV), and inhibit the recombination of photogenerated carriers by forming electron capture centers. Photocatalytic experiments show that the alkalinized g-C₃N₄ can completely depolymerize 2-phenoxy-1-phenylethanol with tunable product selectivity. By adjusting reaction time and catalyst dosage, the dominant product can be shifted from benzaldehyde (up to 77.28% selectivity) to benzoic acid, demonstrating precise control over oxidation degree. Mechanistic analysis shows that the surface alkaline sites synergistically optimize the C_β-O bond breakage path by enhancing substrate adsorption and promoting the generation of active oxygen species (·OH, ·O₂⁻). This study provides a new idea for the efficient photocatalytic depolymerization of lignin and lays an experimental foundation for the interface engineering and band regulation strategies of g-C₃N₄-based catalysts. Full article

Urban vegetation shows significant spatial differences due to the combined effects of natural and human factors, yet fine-scale evolutionary patterns and their cross-scale feedback mechanisms remain limited. This study focuses on the Yangtze River Delta (YRD), the top economic area in China. By integrating data from multiple Landsat sensors, we built a high—resolution framework to track vegetation dynamics from 1990 to 2020. It generates annual 30-m Enhanced Vegetation Index (EVI) data and uses a new Vegetation Green—Brown Balance Index (VBI) to measure changes between greening and browning. We combined Mann-Kendall trend analysis with machine—learning based attribution analysis to look into vegetation changes across different city types and urban—rural gradients. Over 30 years, the YRD’s annual EVI increased by 0.015/10 a, with greening areas 3.07 times larger than browning. Spatially, urban centers show strong greening, while peri—urban areas experience remarkable browning. Vegetation changes showed a city-size effect: larger cities had higher browning proportions but stronger urban cores’ greening trends. Cluster analysis finds four main evolution types, showing imbalances in grey—green infrastructure allocation. Vegetation baseline in 1990 is the main factor driving the long-term trend of vegetation greenness, while socioeconomic and climate drivers have different impacts depending on city size and position on the urban—rural continuum. In areas with low urbanization levels, climate factors matter more than human factors. These multi-scale patterns challenge traditional urban greening ideas, highlighting the need for vegetation governance that adapts to specific spatial conditions and city—unique evolution paths. Full article

This study presents a neural-network-based method for on-demand design of terahertz metasurface sensors, aimed at detecting plant endogenous and exogenous molecules. The approach uses target performance indicators (constructed via fingerprint peaks) as inputs and structural parameters as outputs, employing a neural network to map the complex relationship between them. Two single-resonant-peak metasurface sensors were developed to detect abscisic acid and gibberellic acid. The abscisic acid metasurface sensor achieved an average MSE of 5.66 × 10⁻⁶ and R_ER of 0.167%, while the gibberellic acid metasurface sensor had an average MSE of 8 × 10⁻⁷ and R_ER of 0.086%. Their resonant peaks highly matched the substance fingerprint peaks, enabling specific detection. Metasurface sensors’ sensitivities were effectively controlled using correlation analysis and neural networks, achieving remarkable levels of 156.7 and 150.1 GHz/RIU, allowing trace detection. Three dual-resonant-peak metasurface sensors were designed to improve the detection specificity for chlorophyll and folpet and to detect chlorophyll and folpet simultaneously. These metasurface sensors exhibited average MSEs of 1.4 × 10⁻⁵, 1.6 × 10⁻⁶, 1.35 × 10⁻⁵ and R_ERs of 0.27%, 0.088%, 0.20%. The model also worked for four other plant-related molecules, proving its strong generalization ability. Overall, for different application scenarios of exogenous and endogenous molecules in plants, the on-demand design methodology offers a whole new set of ideas for quickly designing and widely applying metasurface sensors with suitable performance indicators. Full article

Non-unique fixed-point theorems play a pivotal role in the mathematical modeling to solve certain typical equations, which admit more than one solution. In such situations, traditional outcomes fail due to uniqueness of fixed points. The primary aim of the present article is to investigate a non-unique fixed-point theorem in the framework of a metric space endowed with a local class of transitive binary relations. To obtain our main objective, we introduce a new nonlinear contraction-inequality that subsumes the ideas involved in four noted contraction conditions, namely: almost contraction, Boyd–Wong contraction, Pant contraction and relational contraction. We also establish the corresponding uniqueness theorem for the proposed contraction under some additional hypotheses. Several examples are furnished to illustrate the legitimacy of our newly proved results. In particular, we deduce a fixed-point theorem for almost Boyd–Wong contractions in the setting of abstract metric space. Our results generalize, enhance, expand, consolidate and develop a number of known results existing in the literature. The practical relevance of the theoretical findings is demonstrated by applying to study the existence and uniqueness of solution of a specific periodic boundary value problem. Full article

This study comprehensively evaluated nitrogen (N) management in 27 European countries from 1990 to 2021, utilizing the FAO and LUCAS databases. The EU countries were categorized into four groups based on their agricultural production intensities: low (L), medium–low (ML), medium–high (MH), and high (H). Additionally, a new Sustainable Nitrogen Management Indicator (SNMI) has been introduced to measure the sustainability of agricultural production. The analyses reveal significant variation in nitrogen fertilization intensity among EU countries, which correlates with differences in yield levels. Generally, higher fertilization leads to higher nutrient loss; however, the nitrogen losses per unit of yield show only minor differences between high- and low-intensity countries. From 1990 to 2021, a general improvement was observed in nitrogen management across all groups, as evidenced by a significant decline in the SNMI, indicating that agricultural production has become more sustainable. Notably, low-intensity countries showed the most significant improvement, with increased nitrogen input per hectare since the 1990s, demonstrating that moderate fertilization can enhance N use efficiency. In contrast, high-intensity countries saw decreased nitrogen inputs but still improved SNMI. These trends support the idea of sustainable intensification. The multidimensional SNMI comprehensively assesses eco-efficiency by highlighting environmental threats and production benefits. This paper demonstrates that SNMI is robust and easy to calculate using available datasets, and it can be implemented to assess nitrogen management efficiency at various scales. Full article

Technological advances are prompting mining companies to explore new options to enhance the efficiency of activities such as drilling, blasting, ventilation, and the loading and hauling of ore and waste. The emergence of digital environments, such as the Metaverse, allows companies in mining and other industrial sectors to simulate or predict scenarios in real time, generate ideas, and propose solutions before implementing them in the real world. There are various risks associated with the Metaverse and virtual worlds; however, there is insufficient information about the potential threats that could impact the Mining Metaverse. This investigation aims to establish a preliminary model for the efficient integration of the Metaverse into the mining industry. It highlights its potential by referencing previously adopted technologies such as virtual reality (VR), augmented reality (AR), and the Internet of Things (IoT) in mining and other sectors. It also seeks to identify and explain the risks associated with using a Mining Metaverse, considering constraints that will be valuable not only to the Australian mining industry but also on a global scale. Full article