Search Results (213)

China’s economy is shifting from an era of rapid expansion to one focused on high-quality development, making it imperative to tackle environmental degradation linked to energy use. Understanding how New Quality Productive Forces (NQPF) interact with energy efficiency, along with the mechanisms driving this relationship, is essential for economic transformation and long-term sustainability. This study establishes an evaluation framework for NQPF, integrating technological, green, and digital dimensions. We apply fixed-effects models, the spatial Durbin model (SDM), a moderation model, and a threshold model to analyze the influence of NQPF on Green Total Factor Energy Efficiency (GTFEE) and its spatial implications. This underscores the necessity of distinguishing it from traditional productivity frameworks and adopting a new analytical perspective. Furthermore, by considering dimensions such as input, application, innovation capability, and market efficiency, we reveal the moderating role and heterogeneous effects of artificial intelligence (AI). The findings are as follows: The development of NQPF significantly enhances GTFEE, and the conclusion remains robust after tail reduction and endogeneity tests. NQPF has a positive spatial spillover effect on GTFEE; that is, while improving the local GTFEE, it also improves neighboring regions GTFEE. The advancement of AI significantly strengthens the positive impact of NQPF on GTFEE. AI exhibits a significant U-shaped threshold effect: as AI levels increase, its moderating effect transitions from suppression to facilitation, with marginal benefits gradually increasing over time. Full article

As economically important sympatric species in the Northwest Pacific, the Japanese sardine (Sardinops melanostictus) and Chub mackerel (Scomber japonicus) exhibit significant biological interactions. Understanding the impact of interspecies competition on their habitat dynamics can provide crucial insights for the sustainable development and management of these interconnected species resources. This study utilizes fisheries data of S. melanostictus and S. japonicus from the Northwest Pacific, collected from June to November between 2017 and 2020. We integrated various environmental parameters, including temperature at different depths (0, 50, 100, 150, and 200 m), eddy kinetic energy (EKE), sea surface height (SSH), chlorophyll-a concentration (Chl-a), and the oceanic Niño index (ONI), to construct interspecific competition species distribution model (icSDM) for both species. We validated these models by overlaying the predicted habitats with fisheries data from 2021 and performing cross-validation to assess the models’ reliability. Furthermore, we conducted correlation analyses of the habitats of these two species to evaluate the impact of interspecies relationships on their habitat dynamics. The results indicate that, compared to single-species habitat models, the interspecific competition species distribution model (icSDM) for these two species exhibit a significantly higher explanatory power, with R² values increasing by up to 0.29; interspecific competition significantly influences the habitat dynamics of S. melanostictus and S. japonicus, strengthening the correlation between their habitat changes. This relationship exhibits a positive correlation at specific stages, with the highest correlations observed in June, July, and October, at 0.81, 0.80, and 0.88, respectively; interspecific competition also demonstrates stage-specific differences in its impact on the habitat dynamics of S. melanostictus and S. japonicus, with the most pronounced differences occurring in August and November. Compared to S. melanostictus, interspecific competition is more beneficial for the expansion of the optimal habitat (HIS ≥ 0.6) for S. japonicus and, to some extent, inhibits the habitat expansion of S. melanostictus. The variation in migratory routes and predatory interactions (with larger individuals of S. japonicus preying on smaller individuals of S. melanostictus) likely constitutes the primary factors contributing to these observed differences. Full article

The present study aimed to optimize amaranth microgreen production by evaluating key factors such as the seeding density (SD), substrate type (ST), electrical conductivity (EC), and the application intervals of the nutrient solution. A split-plot experimental design was employed, with three EC levels (tap water at 0.3 dS m⁻¹) and nutrient solutions at 1.0 (50% half-strength) and 2.0 dS m⁻¹ (100% full-strength) assigned to the main plots. The subplots combined two ST (coconut fiber and phenolic foam) with four SD (25, 50, 75, and 100 g m⁻²). Two experiments were conducted using this setup, varying the application intervals of water or nutrient solutions for either two or four hours. Asteca amaranth microgreens were cultivated for eight days (a total of 10 days from sowing). The traits analyzed were seedling height (SH), seedling fresh matter (SFM), SFM yield (SFMY), seedling dry matter (SDM), SDM yield (SDMY), water content in seedling, and water productivity of SFM. The results showed that using a half-strength nutrient solution was sufficient for amaranth production compared to using water alone. Coconut fiber outperformed phenolic foam across all evaluated parameters. Based on these findings, we recommend cultivating amaranth microgreens at a SD of 80 g m⁻² on coconut fiber substrate using a nutrient solution of 1.0 dS m⁻¹ EC applied at 2 h intervals. Full article

Copper pyrithione (CuPT), an emerging biocide used in ship antifouling coatings, may accumulate in marine sediments and pose risks to non-target organisms. However, current research on CuPT toxicity remains limited. Litopenaeus vannamei, one of the world’s most important aquaculture shrimp species, relies heavily on its hepatopancreas for energy metabolism, detoxification, and immune responses. Due to their benthic habitat, these shrimps are highly vulnerable to contamination in sediment environments. This study investigated the toxicological response in the hepatopancreas of L. vannamei exposed to CuPT (128 μg/L) for 3 and 48 h. Terminal deoxynucleotidyl transferase dUTP Nick-End Labeling (TUNEL) fluorescence staining revealed increased apoptosis, deformation of hepatic tubule lumens, and the loss of stellate structures in the hepatopancreas after CuPT 48 h exposure. A large number of differentially expressed genes (DEGs) were identified by transcriptomics analysis at 3 and 48 h, respectively. Most of these DEGs were related to detoxification, glucose transport, and immunity. Metabolomic analysis identified numerous significantly different metabolites (SDMs) at both 3 and 48 h post-exposure, with most SDMs associated with energy metabolism, fatty acid metabolism, and related pathways. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of metabolomics and transcriptome revealed that both DEGs and SDMs were enriched in arachidonic acid metabolism, fatty acid biosynthesis, and glycolysis/gluconeogenesis pathways at 3 h, while at 48 h they were enriched in the starch and sucrose metabolism, amino sugar and nucleotide sugar metabolism, and galactose metabolism pathways. These results suggested that CuPT disrupts the energy and lipid homeostasis of L. vannamei. This disruption compelled L. vannamei to allocate additional energy toward sustaining basal physiological functions and consequently caused the accumulation of large amounts of reactive oxygen species (ROS) in the body, leading to apoptosis and subsequent tissue damage, and ultimately suppressed the immune system and impaired the health of L. vannamei. Our study elucidates the molecular mechanisms of CuPT-induced metabolic disruption and immunotoxicity in L. vannamei through integrated multi-omics analyses, providing new insights for ecological risk assessment of this emerging antifoulant. Full article

Understanding the geographic distribution of mammal species is essential for informed conservation planning, maintaining local ecosystem stability, and addressing research gaps, particularly in data-deficient regions. This study investigated the distribution and richness of 20 mammal species within Nyerere National Park (NNP), a large and understudied protected area in Southern Tanzania. We applied species distribution models (SDMs) using presence data collected through ground surveys between 2022 and 2024, combined with environmental variables derived from remote sensing, including land surface temperature, vegetation indices, soil moisture, elevation, and proximity to water sources and human infrastructure. Models were constructed using the Maximum Entropy (MaxEnt) algorithm, and performance was evaluated using the Area Under the Curve (AUC) metric, yielding high accuracy ranging from 0.81 to 0.97. Temperature (32.3%) and vegetation indices (23.4%) emerged as the most influential predictors of species distributions, followed by elevation (21.7%) and proximity to water (14.5%). Species richness, estimated using a stacked SDM approach, was highest in the northern and riparian zones of the park, identifying potential biodiversity hotspots. This study presents the first fine-scale SDMs for mammal species in Nyerere National Park, offering a valuable ecological baseline to support conservation planning and promote sustainable ecotourism development in Tanzania’s southern protected areas. Full article

Ultraviolet (UV) communication, characterized by non-line-of-sight (NLOS) scattering, holds substantial potential for enabling communication networking in unmanned aerial vehicle (UAV) formations within strong electromagnetic interference environments. This paper proposes a networking protocol for large-scale mobile ultraviolet communication networks (LSM-UVCN). In large-scale networks, the proposed protocol establishes multiple non-interfering transmission paths based on a connection matrix simultaneously, ensuring reliable space division multiplexing (SDM) and optimizing the utilization of network channel resources. To address frequent network topology changes in mobile scenarios, the protocol employs periodic maintenance of the connection matrix, significantly reducing the adverse impacts of node mobility on network performance. Simulation results demonstrate that the proposed protocol achieves superior performance in large-scale mobile UV communication networks. By dynamically adjusting the connection matrix update frequency, it adapts to varying node mobility intensities, effectively minimizing control overhead and data loss rates while enhancing network throughput. This work underscores the protocol’s adaptability to dynamic network environments, providing a robust solution for high-reliability communication requirements in complex electromagnetic scenarios, particularly for UAV swarm applications. The integration of SDM and adaptive matrix maintenance highlights its scalability and efficiency, positioning it as a viable technology for next-generation wireless communication systems in challenging operational conditions. Full article

In the world of solar technology, precisely extracting photovoltaic cell and panel parameters is key to efficient energy production. This paper presents a new metaheuristic algorithm for extracting parameters from photovoltaic cells using the functionality of the PID-based search algorithm (PSA). The research includes single-diode (SDM) and double-diode (DDM) models applied to RTC France, amorphous silicon (aSi), monocrystalline silicon (mSi), PVM 752 GaAs, and STM6-40 panels. Datasets from multijunction solar cells at three temperatures (41.5 °C, 51.3 °C, and 61.6 °C) were used. PSA performance was assessed using root mean square error (RMSE), mean bias error (MBE), and absolute error (AE). A strategy was introduced by refining PID parameters and relocating error calculations outside the main loop to enhance exploration and exploitation. A Lévy flight-based zero-output mechanism was integrated, enabling shorter extraction times and requiring a smaller population, while enhancing search diversity and mitigating local optima entrapment. PSA was compared against 26 top-performing algorithms. RTC France showed RMSE improvements of 0.67–2.10% in 3.35 s, while for the mSi model, PSA achieved up to 40.9% improvement in 5.57 s and 22.18% for PVM 752 in 8.52 s. PSA’s accuracy and efficiency make it a valuable tool for advancing renewable energy technologies. Full article

Pulse-width modulation (PWM) and pulse-density modulation (PDM) are widely used in applications where electrical energy is delivered in a pulsed manner. Typical examples include LED (light-emitting diode) control, DC motor control, switched-mode power supplies (SMPS), and electric heating control. However, the pulsed operation of power switches is often associated with significant electromagnetic interference (EMI). As an alternative, stochastic pulse-density modulation (SPDM), also referred to as stochastic signal density modulation (SSDM), can be considered. This technique distributes the energy of generated harmonics over a broader frequency spectrum, thereby reducing the amplitude of individual frequency components. As a result, unwanted frequencies become easier to filter out, mitigating EMI more effectively. Full article

The subway is crucial for urban operations, especially during holidays. Unlike traditional studies using smart card data, this research analyzes National Day holiday subway travel patterns with Hangzhou’s 2021 mobile payment data, covering 42 days from 6 September to 17 October for comprehensive comparison. Considering spatial passenger flow correlations, a Composite Weight (CW) matrix integrating network distance and time is defined and integrated into a Spatial Error Model (SEM), Spatial autoregressive model (SAR), and Spatial Durbin Model (SDM) to create CW-SEM, CW-SAR, and CW-SDM. The CW matrix innovatively considers network distance and time, overcoming traditional spatial weight matrix limitations to accurately and dynamically capture passenger flow spatial correlations. The results show the following: (1) Hangzhou saw 37% and 49% increases in average daily passenger flow during the extended holiday versus workdays and weekends, with holiday peak hour flow declining 16% compared to workdays but increasing 18% versus weekends, likely due to shifted travel purposes from commuting to tourism; (2) strong spatial passenger flow correlations existed in both workdays and weekends, attributed to urban functional zoning and transport network connectivity; (3) key factors such as population, social media activity, commercial facilities and transportation hubs show significant positive correlations with holiday passenger flow. Medical facility reveals significant negative correlations with holiday passenger flow. These findings highlight the need to incorporate spatial variations into major holiday subway travel studies for urban planning and traffic management insights. Full article

The accurate modeling of photovoltaic (PV) systems is crucial in optimizing energy efficiency and operational reliability. To address challenges in parameter estimation under dynamic conditions, a hybrid deep learning (DL)-based optimization scheme is proposed. It is hypothesized that combining the global search capabilities of the Whale Optimization Algorithm (WOA) with local refinement of Fishier Mantis Optimization (FMO), supported by long short-term memory (LSTM)-based predictions, enhances accuracy and robustness. The method was validated through simulations on single-, double-, and triple-diode models (SDM, DDM, and TDM) using MATLAB 2021a version. The hybrid model achieved the lowest root mean square error (RMSE) of 6.96 × 10⁻⁴ across all models, outperforming standard metaheuristics and showing strong stability over multiple runs. These findings confirm the method’s superior accuracy and efficiency for PV parameter extraction. Full article

As climate change continues to endanger a sustainable global condition, a growing literature investigates how to pursue green practices to fight its effects. Individuals are the essential starting point for such bottom-up attempts, with their attitudes towards sustainability driving pro-environmental behaviors (PEBs). Objectives: Based on the available relevant literature, this scoping review aims to delve into the processes underlying people’s sustainable decision-making (SDM) associated with PEBs. Methods: A scientific literature search was performed through (a) an active database search and (b) the identification of studies via reference and citation tracking. Results were screened and selected in Rayyan. Results: Included articles (n = 30) heterogeneously reported cognitive and neural aspects of SDM shaping PEBs. These proved to (a) recruit brain areas involved in mentalizing and moral cognition (likely because of their role in processing the interplay between personal and contextual factors rather than moral considerations in themselves); (b) undergo the same modulatory influences shaping other kinds of prosocial/cooperative behaviors; and (c) include brain areas involved in attentional/monitoring and emotional/motivational processes, alongside those consistently associated with decision-making processes. Conclusions: These results help interpret the available evidence on the neuro-cognitive bases of SDM while focusing on potential interventions to foster better practices and mitigate the adverse repercussions of climate change on human and global health. Full article

The tea mosquito bug, Helopeltis theivora Waterhouse, (Hemiptera: Miridae) is a significant sap-sucking pest in tropical tea plantations that causes substantial losses in tea production on Hainan Island, China. The morphological and ultrastructural characteristics of H. theivora antennae have not been elucidated. Here, we used several microscopy techniques (SDM, SEM, and TEM) to investigate the morphology as well as the setae and sensilla on the antennae of nymphs and adults of H. theivora. SDM observations indicated that the antennae of H. theivora were filamentous in shape and included four segments: scape, pedicel, flagellum I, and flagellum II. The length of the antenna was approximately twice that of the body and the setae were enriched in flagellum II. The SEM results showed that there were a total of six types of sensilla on the antenna of H. theivora, including the sensilla chaetica (SCh), sensilla trichoidea (ST), sensilla basiconica (SB), sensilla coeloconica (SCo), sensilla mammilliformia (SM), and Böhm’s bristles (BB). In particular, there were three subtypes (I, II, and III) of different lengths in SCh and SB, and two subtypes of straight (I) and curved (II) sensilla in ST. The TEM results indicated that diverse internal structures were present in SCh, ST, SB, and SCo, suggesting different functions and different sensory mechanisms of these four main sensilla in the orientation behavior of H. theivora on tea plants. These findings provide a theoretical basis for further exploration of the olfactory orientation of H. theivora in tropical tea plantations and pave the way for the development of semiochemical-based control options in the future. Full article

Aims: Soil compaction is one of the main challenges in agriculture, negatively affecting cotton growth (Gossypium hirsutum L.), nutrition, and productivity. This study evaluated the efficacy of plant growth-promoting bacteria (PGPB), Exiguobacterium sibiricum, and Pantoea vagans in mitigating the effects of different soil compaction levels (65%, 75%, 85%, and 95%) on cotton performance. Methods: Parameters such as plant height, stem diameter, number of leaves, shoot dry matter (SDM), and nutrient content in leaves, stems, and roots were assessed. The methodology included variance analysis and mean clustering to identify significant differences among treatments using R software. Results: The results indicated that PGPB inoculation improved plant growth and nutrition even under high compaction levels. Cotton height increased by up to 45% in compacted soils (95%), while stem diameter and SDM also showed significant gains. Foliar nutrient levels of N (37.2 g kg⁻¹), Ca, and Mg remained within the adequate range for cotton cultivation, reflecting the efficiency of PGPB in enhancing nutrient absorption. Under severe compaction, Ca accumulation dropped to 18.2 g kg⁻¹, highlighting the physical constraints imposed on the roots; however, the bacterial action mitigated this impact. Additionally, bacterial strains increased the availability of N and P in the soil due to their ability to fix nitrogen, solubilize phosphates, and produce exopolysaccharides that improve soil structure. Conclusions: In conclusion, inoculation with Exiguobacterium sibiricum and Pantoea vagans is an effective strategy to mitigate the impacts of soil compaction on cotton. These bacteria promote plant growth and nutrition and enhance the soil’s physical and biological properties. Full article

In order to improve the accuracy of pipe burst detection in water distribution networks (WDNs), a novel small sample diagnosis method (SSDM) based on the head loss ratio (HLR) method and deep transfer learning (DTL) method has been proposed. In this paper, the burst state was quickly detected through the limited data analysis of pressure monitoring points. The HLR method was introduced to enhance data features. DTL was introduced to improve the accuracy of small sample burst detection. The simulated data and real data were enhanced by HLR. Then, the model was trained and obtained through the DTL. The performance of the model was evaluated in both simulated and real scenarios. The results indicate that the leaked features can be improved by 350% by the HLR. The accuracy of SSDM reaches 99.56%. The SSDM has been successfully applied to the detection of real WDNs. The proposed method provides potential application value for detecting pipe bursts. Full article

Sterculia foetida L., commonly known as the Java olive, is a tropical tree species native to regions of East Africa, tropical Asia, and northern Australia. This study employs species distribution modeling (SDM) to predict the potential geographic distribution of S. foetida under current and future climate scenarios. Using 1425 occurrence data and 19 environmental variables, we applied an ensemble modelling approach of three algorithms: Boosting Regression Trees (BRT), Generalized Linear Model (GLM), and Random Forests (RF), to generate distribution maps. Our models showed high accuracy (mean AUC = 0.98) to indicate that S. foetida has a broad ecological niche, with high suitability in tropical and subtropical regions of north Australia (New Guinea and Papua), Southeast Asia (India, Thailand, Myanmar, Taiwan, Philippines, Malaysia, Sri Lanka), Oman and Yemen in the southwest of Asia, Central Africa (Guinea, Ghana, Nigeria, Congo, Kenya and Tanzania), the Greater and Lesser Antilles, Mesoamerica, and the north of South America (Colombia, Panama, Venezuela, Ecuador and Brazil). Indeed, the probability of occurrence of S. foetida positively correlates with the Maximum temperature of warmest month (bio5), Mean temperature of wettest quarter (bio8) and Precipitation of wettest month (bio13). The model results showed a suitability area of 4,744,653 km², representing 37.86% of the total study area, classified into Low (14.12%), Moderate (8.71%), and High suitability (15.02%). Furthermore, the study found that habitat suitability for S. foetida showed similar trends under both near future climate scenarios (SSP1-2.6 and SSP5-8.5 for 2041–2060), with a slight loss in potential distribution (0.24% and 0.25%, respectively) and moderate gains (1.98% and 2.12%). In the far future (2061–2080), the low scenario (SSP1-2.6) indicated a 0.29% loss and a 2.52% gain, while the high scenario (SSP5-8.5) showed a more dramatic increase in both loss (0.6%) and gain areas (3.79%). These findings are crucial for conservation planning and management, particularly in regions where S. foetida is considered invasive and could become problematic. The study underscores the importance of incorporating climate change projections in SDM to better understand species invasiveness dynamics and inform biodiversity conservation strategies. Full article