Search Results (43,138)

To reveal the regulatory effects of nitrogen and phosphorus interactions on grain-filling- and starch-synthesis-related enzymes, and grain weight of superior grains (SGs) and inferior grains (IGs) and taste quality, the japonica rice cultivar Shennong 265 was grown under field conditions with three nitrogen levels (210, 178.5, and 147 kg N ha⁻¹; N3, N2, and N1) and two phosphorus levels (105 and 73.5 kg P ha⁻¹; P2 and P1). At the N3 level, the yield of P1 was significantly lower (by 19.26%) compared to P2; at the N2 and N1 levels, P1 yielded higher than P2, peaking at N2P1. Spikelets per panicle showed P2 exceeding P1 at the same nitrogen level, with the highest for both SGs and IGs observed at N2P2, followed by N2P1. Reductions in nitrogen and phosphorus decreased the grain-filling rate but prolonged the duration for grain-filling. N2P1 maintained grain weight by extending the grain-filling duration across the early, middle, and late stages of IGs, and the middle and late stages of SGs. Increased nitrogen enhanced the activities of soluble starch synthase (SSS) and starch branching enzyme (SBE), whereas increased phosphorus inhibited these activities in SGs but enhanced them in IGs. Reduced nitrogen and phosphorus fertilizer diminished ADP glucose pyrophosphorylase (AGPP) and granule-bound starch synthase (GBSS) activities in SGs and IGs, inhibiting amylose accumulation while enhancing taste value. Compared with N3P2, the taste value of N2P1 increased significantly by 6.93%, attributed to a higher amylopectin/amylose ratio. N2P1 (178.5 kg N ha⁻¹ and 73.5 kg P ha⁻¹) optimized enzyme activity, starch composition, and grain filling, balancing both yield and taste, and thus demonstrated an effective fertilization strategy for stable rice production. Full article

The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation and improving the reliability of power systems and is a key component in the operational planning and efficient market. For many years, a conventional method has been used by using historical data as input parameters. With swift progress and improvement in technology, which shows more potential due to its accuracy, different methods can be applied depending on the identified model. To enhance the forecast of load, this paper introduces and proposes a framework developed on graph database technology to archive large amounts of data, which collects measured data from electrical substations in Pristina, Kosovo. The data includes electrical and weather parameters collected over a four-year timeframe. The proposed framework is designed to handle short-term load forecasting. Machine learning Linear Regression and deep learning Long Short-Term Memory algorithms are applied to multiple datasets and mean absolute error and root mean square error are calculated. The results show the promising performance and effectiveness of the proposed model, with high accuracy in load forecasting. Full article

Ural owls are one of the largest owls in Europe, exhibiting known aggressive behaviour toward other raptors. They are known to interact with nearly all sympatric owls and many diurnal raptors. To summarise these interactions, a literature search was undertaken in the Web of Sciences and Scopus databases using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology as well principal books on owl biology. The search revealed 22 relevant publications that (along with the book data) described the Ural owl’s relations with seven owls and six diurnal raptor species. The Ural owl is subordinate only to the largest predators like golden eagles and eagles, although only its chicks are known to be killed. Contrary to that, the Ural owls shape the distribution of numerous other species, mostly by strong competition (e.g., forcing tawny owls to breed in suboptimal habitats) or by predation (killing smaller owls and diurnal raptors). Their occurrence could be also protective for some species like boreal owls thanks to the removal of intermediate predators. The relations of Ural owls with goshawks are interesting, which seem to live in some balance—temporal avoidance of activity with frequent co-occurrence. Thanks to their association with old-growth forests and their impact on other predators in their territories, Ural owls act as keystone species in mountainous and boreal forests in Europe. Considering this ecosystem service, Ural owls should be effectively protected e.g., by designing forest-management-free zones around their nesting sites. Full article

The current study examines the neural mechanisms underlying facial recognition, focusing on how emotional expression and mouth display modulate event-related potential (ERP) waveforms. 42 participants categorized faces by gender in one of two experimental setups: one featuring full-face images and another with cropped faces presented against neutral gray backgrounds. The stimuli included 288 images balanced across gender, race/ethnicity, emotional expression (“Fearful”, “Happy”, “Neutral”), and mouth display (“closed mouth” vs. “open mouth with exposed teeth”). Results revealed that N170 amplitude was significantly greater for open-mouth (exposed teeth) conditions (p < 0.01), independent of emotional expression, and no interaction between emotional expression and mouth display was found. However, the P100 amplitude exhibited a significant interaction between these variables (p < 0.05). Monte Carlo simulations analyzing N170 latency differences showed that fearful faces elicited a faster response than happy and neutral faces, with a 2 ms delay unlikely to occur by chance (p < 0.01). While these findings challenge prior research suggesting that N170 is directly influenced by emotional expression, they also highlight the potential role of emotional intensity as an alternative explanation. This underscores the importance of further studies to disentangle these effects. This study highlights the critical need to control for mouth display when investigating emotional face processing. The results not only refine our understanding of the neural dynamics of face perception but also confirm that the brain processes fearful expressions more rapidly than happy or neutral ones. These insights offer valuable methodological considerations for future neuroimaging research on emotion perception. Full article

Hybrid and plant-based products are an emerging trend in food science. This study aimed to develop three patty prototypes (meat, hybrid, and plant-based) enhanced with vegetable fat replacement and broccoli extract using a soy allergen-free protein matrix treated with high hydrostatic pressure (HHP) and sous vide cooking to create sustainable and nutritious burger alternatives. The samples were evaluated for microbiological safety, proximal composition, physicochemical properties, sensory characteristics, and carbon footprint. The key findings revealed that the plant-based patties had the smallest carbon footprint (0.12 kg CO₂e), followed by the hybrid patties (0.87 kg CO₂e) and the meat patties (1.62 kg CO₂e). The hybrid patties showed increased hardness, cohesiveness, gumminess, and chewiness compared to the meat patties after sous vide treatment. This improvement likely results from synergies between the meat and plant proteins. Regarding the treatments, in all the samples, the highest hardness was observed after the combined HHP and sous vide treatment, an interesting consideration for future prototypes. Sensory analysis indicated that the plant-based and hybrid samples maintained appealing visual and odour characteristics through the treatments, while the meat patties lost the evaluator’s acceptance. Although further improvements in sensory attributes are needed, hybrid patties offer a promising balance of improved texture and intermediate carbon footprint, making them a viable alternative as sustainable, nutritious patties. Full article

Gochujang, a traditional Korean fermented red pepper paste, is celebrated for its unique spicy and fermented flavor. This natural, whole food offers several health benefits due to the bioactive compounds formed during fermentation and its diverse ingredients. These bioactive compounds have been shown to have anti-cancer properties and anti-inflammatory effects by reducing inflammatory cytokines and suppressing pathways associated with diseases such as colitis and hepatitis. Gochujang has also been shown to help prevent obesity by promoting weight loss, inhibiting fat accumulation, and improving lipid profiles. It has also been shown to aid in the prevention of diabetes by suppressing hepatic glucose production and improving insulin sensitivity. The influence of gochujang on the gut microbiota is remarkable, with the ability to increase beneficial bacteria, improve microbial balance, and alleviate metabolic disorders. The primary agents responsible for these effects are capsaicin, fermentation by-products, and other bioactive compounds. The fermentation process, driven by microorganisms, enhances the nutritional and functional properties of gochujang, strengthening its health-promoting potential. This paper provides a comprehensive review of gochujang’s historical background, production methods, the role of microorganisms in fermentation, and its functional properties, emphasizing its value as a functional food for overall health improvement. Full article

Foreign capital has dominated over half of the public–private partnership (PPP) projects in developing countries over the past three decades. As such, attracting and regulating foreign participation in water PPP projects presents a critical challenge for both practitioners and scholars. Using a dataset of 2024 water PPP projects from 1994 to 2021, this study investigates foreign participation and its fall in China’s water PPP projects. Our findings highlight three key points: First, the proportion of projects undertaken by foreign capital decreased from 100% to less than 0.5%, with Chinese domestic capital taking its place. Second, resource dependence on foreign capital and the local government’s need for control lead to four types of foreign participation: financing water plants under user-pays, financing and operating water utilities under government-pays, participating with mainly an O&M role, and nearly no participation. Third, a better balance between efficiency gains and control needs via cooperation with domestic capital by local governments had driven the decline in foreign participation. This study makes two key contributions: (1) it is one of the pioneer studies on systematically tracing the evolution of foreign participation in PPP projects, and (2) it explains the fall of foreign participation from a local government perspective, complementing market-based explanations. Full article

Genetic obesity results from loss-of-function mutations, including those affecting the leptin–melanocortin system, which regulates body weight. Pro-opiomelanocortin (POMC)-derived neurohormones act as ligands for melanocortin receptors (MCRs), regulating the leptin–melanocortin pathway through protein–protein interactions. Loss-of-function mutations in the genes encoding POMC, MC3R, and MC4R can lead to the dysregulation of energy expenditure and feeding balance, early-onset obesity, and developmental dysregulation. Recent studies have identified new genetic regulatory mechanisms and potential biomarker regions for the POMC gene and MC4R secondary messenger pathway associated with obesity. Recent advances in crystal structure studies have enhanced our understanding of the protein interactions in this pathway. This narrative review focuses on recent developments in two key areas related to POMC regulation and the leptin–melanocortin pathway: (1) genetic variations in and functions of POMC, and (2) MC3R and MC4R variants that lead to genetic obesity in humans. Understanding these novel mutations in POMC and MC4R/MC3R, as well as their structural and intracellular mechanisms, may help identify strategies for the treatment and diagnosis of obesity, particularly childhood obesity. Full article

Perovskite solar cells (PSCs) have emerged as a promising photovoltaic technology, yet their stability under environmental stressors remains a critical challenge. This study examines the substrate-dependent degradation mechanisms of perovskite films and evaluates the role of methylammonium chloride (MACl) incorporation. Devices fabricated on ITO and glass substrates exhibited markedly different stability behaviors under high-humidity conditions. ITO substrates delayed the phase transition from the black α-phase to the yellow δ-phase due to stronger substrate–film interactions and reduced defect densities, while glass substrates facilitated rapid degradation through moisture infiltration and grain boundary instability. MACl incorporation enhanced the initial crystallinity and optoelectronic properties of the perovskite films, as evidenced by superior power conversion efficiency and photon absorption. However, residual MACl under humid conditions introduced structural instability, particularly on glass substrates. To address these challenges, a fully coated ITO structure, referred to as the Island Type design, was proposed. This structure eliminates exposed glass regions, leveraging the stabilizing properties of ITO to suppress moisture infiltration and prolong device durability. The findings provide a comprehensive understanding of the interplay between substrate properties and material composition in PSC stability and highlight the potential of structural optimizations to balance efficiency and durability for commercial applications. Full article

With the widespread application of remote sensing technologies and UAV imagery in various fields, dense object detection has become a significant and challenging task in computer vision research. Existing end-to-end detection models, particularly those based on DETR, often face criticism due to their high computational demands, slow convergence rates, and inadequacy in managing dense, multi-scale objects. These challenges are especially acute in remote sensing applications, where accurate analysis of large-scale aerial and satellite imagery relies heavily on effective dense object detection. In this paper, we propose the DHQ-DETR framework, which addresses these issues by modeling bounding box offsets as distributions. DHQ-DETR incorporates the Distribution Focus Loss (DFL) to enhance residual learning, and introduces a High-Quality Query Selection (HQQS) module to effectively balance classification and regression tasks. Additionally, we propose an auxiliary detection head and a sample assignment strategy that complements the Hungarian algorithm to accelerate convergence. Our experimental results demonstrate the superior performance of DHQ-DETR, achieving an average precision (AP) of 53.7% on the COCO val2017 dataset, 54.3% on the DOTAv1.0, and 32.4% on Visdrone, underscoring its effectiveness for real-world dense object detection tasks. Full article

The brain acts as the body’s central command, overseeing diverse functions including thought, memory, speech, movement, and the regulation of various organs. When healthy, the brain functions seamlessly and automatically; however, disruptions can lead to serious conditions such as Alzheimer’s Disease, Brain Cancer, Stroke, and Epilepsy. Epilepsy, a neurological disorder marked by recurrent seizures, results from irregular electrical activity in the brain. These seizures, which can strain both patients and neurologists, are characterized by symptoms like the loss of awareness, unusual behavior, and confusion. This study presents an efficient EEG-based epileptic seizure detection framework utilizing a hybrid Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models approach to support automated and accurate diagnosis. Handling imbalanced EEG data, which can otherwise bias model outcomes and reduce predictive accuracy, is a key focus. Experimental results indicate that the proposed framework generally outperforms other Deep Learning and Machine Learning techniques with the highest accuracy at 99.13%. Likewise, an Explainable Artificial Intelligence (XAI) called SHAP (SHapley Additive exPlanations) is utilized to analyze the results and to improve the interpretability of the models from medical decision-making. This framework aligns with the objectives of the Medical Internet of Things (MIoT), advancing smart medical applications and services for effective epileptic seizure detection. Full article

Obesity, chronic pain, and aging are prevalent global challenges with profound implications for health and well-being. Central to these processes are adrenal hormones, particularly cortisol and dehydroepiandrosterone (DHEA), along with its sulfated form (DHEAS). Cortisol, essential for stress adaptation, can have adverse effects on pain perception and aging when dysregulated, while DHEA/S possess properties that may mitigate these effects. This review explores the roles of cortisol and DHEA/S in the contexts of obesity, acute and chronic pain, aging, and age-related diseases. We examine the hormonal balance, specifically the cortisol-to-DHEA ratio (CDR), as a key marker of stress system functionality and its impact on pain sensitivity, neurodegeneration, and physical decline. Elevated CDR and decreased DHEA/S levels are associated with worsened outcomes, including increased frailty, immune dysfunction, and the progression of age-related conditions such as osteoporosis and Alzheimer’s disease. This review synthesizes the current literature to highlight the complex interplay between these hormones and their broader implications for health. It aims to provide insights into potential future therapies to improve pain management and promote healthy weight and aging. By investigating these mechanisms, this work contributes to a deeper understanding of the physiological intersections between pain, aging, and the endocrine system. Full article

Light quality is an important variable affecting plant growth, so we aimed to explore the impact of light quality on plants under salt stress. The salt tolerance of pea (Pisum sativum L.) seedlings illuminated by LED red light and 4:1 of red/blue light in a hydroponic system was evaluated at three salinity levels (0, 50, and 100 mmol/L of NaCl) for their morphological and physiological parameters and their root growth characteristics in response to salt stress. Results demonstrated that, as salt stress intensified, the plant height, aboveground fresh/dry mass, root growth indices, and chlorophyll content of pea seedlings exhibited a decreasing trend, while the malondialdehyde (MDA) content and the activity of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) in leaves increased. Also, more sodium (Na⁺) but less potassium (K⁺) ions were detected due to the change in electrolyte balance. Compared with pea seedlings under no salt stress, the growth rate, plant height, and K⁺ ion content significantly increased with the red light treatments, but both lights did not affect the aboveground fresh/dry mass, chlorophyll content, or root growth index. Under medium salt stress (50 mmol/L), red light helped generate more chlorophylls by 17.06%, accelerate leaf electrolyte exudation by 23.84%, accumulate more K⁺ ions by 46.32%, and increase the K⁺/Na⁺ ratio by 45.45%. When pea seedlings were stressed by 100 mmol/L salinity stress, red light was able to maintain the leaf chlorophyll level by 114.66%, POD enzyme activity by 157.78%, MDA amount by 14.16%, leaf and stem electrolyte leakage rate by 38.76% and 21.80%, respectively, K⁺ ion content by 45.47%, and K⁺/Na⁺ ratio by 69.70%. In conclusion, the use of red light has proven to enhance the salt tolerance of pea seedlings in a hydroponic system, which can and should be a promising approach to prime pea seedlings for more salt tolerance. Full article

Southern leaf blight (SLB), caused by Bipolaris maydis, poses a significant threat to maize and popcorn production. To understand the molecular mechanisms underlying SLB resistance, we conducted a high-throughput proteomic analysis comparing SLB-resistant (L66) and SLB-susceptible (L51) popcorn genotypes at four and ten days after inoculation (DAI). A total of 717 proteins were identified, with 151 differentially accumulated proteins (DAPs) between the genotypes. Eighteen DAPs exhibited the same regulatory pattern in both the SLB-resistant and SLB-susceptible genotypes at four (R4/S4) and ten (R10/S10) DAI. The protein-protein interaction (PPI) network of differentially accumulated proteins (DAPs) linked to SLB resistance and susceptibility enriched specific metabolic pathways in the SLB response, including photosynthesis, ribosome, ascorbate and aldarate metabolism, glutathione metabolism, and carbon metabolism. Proteins such as photosystem II 11 kD protein (B4FRJ4, PSB27-1), which was up-regulated at both time points (R4/S4 and R10/S10), and 60S acidic ribosomal protein P0 (A0A1D6LEZ7, RPP0B), which was unique to the resistant genotype at both time points (R4 and R10), highlighted the importance of maintaining photosynthetic efficiency and protein synthesis during pathogen attack. Additionally, dehydroascorbate reductase like-3 (B4F817, DHAR3) was consistently up-regulated at both time points in resistant genotypes, emphasizing its role in redox balance and ROS detoxification. In contrast, glyceraldehyde-3-phosphate dehydrogenase (K7UGF5, GAPC2), a glycolytic enzyme, was unique to the susceptible genotype, suggesting its involvement in managing energy metabolism under stress conditions. Our findings suggest that resistance to SLB in popcorn involves a combination of enhanced photosynthetic repair, redox homeostasis, and ribosomal protein activity, providing new potential molecular targets, such as DHAR3 and RPP0B, for genetic improvement in SLB resistance. These results offer valuable insights into breeding programs aimed at developing SLB-resistant popcorn varieties. Full article

Path planning is of great research significance as it is key to affecting the efficiency and safety of mobile robot autonomous navigation task execution. The traditional gray wolf optimization algorithm is widely used in the field of path planning due to its simple structure, few parameters, and easy implementation, but the algorithm still suffers from the disadvantages of slow convergence, ease of falling into the local optimum, and difficulty in effectively balancing exploration and exploitation in practical applications. For this reason, this paper proposes a multi-strategy improved gray wolf optimization algorithm (MSIAR-GWO) based on reinforcement learning. First, a nonlinear convergence factor is introduced, and intelligent parameter configuration is performed based on reinforcement learning to solve the problem of high randomness and over-reliance on empirical values in the parameter selection process to more effectively coordinate the balance between local and global search capabilities. Secondly, an adaptive position-update strategy based on detour foraging and dynamic weights is introduced to adjust the weights according to changes in the adaptability of the leadership roles, increasing the guiding role of the dominant individual and accelerating the overall convergence speed of the algorithm. Furthermore, an artificial rabbit optimization algorithm bypass foraging strategy, by adding Brownian motion and Levy flight perturbation, improves the convergence accuracy and global optimization-seeking ability of the algorithm when dealing with complex problems. Finally, the elimination and relocation strategy based on stochastic center-of-gravity dynamic reverse learning is introduced for the inferior individuals in the population, which effectively maintains the diversity of the population and improves the convergence speed of the algorithm while avoiding falling into the local optimal solution effectively. In order to verify the effectiveness of the MSIAR-GWO algorithm, it is compared with a variety of commonly used swarm intelligence optimization algorithms in benchmark test functions and raster maps of different complexities in comparison experiments, and the results show that the MSIAR-GWO shows excellent stability, higher solution accuracy, and faster convergence speed in the majority of the benchmark-test-function solving. In the path planning experiments, the MSIAR-GWO algorithm is able to plan shorter and smoother paths, which further proves that the algorithm has excellent optimization-seeking ability and robustness. Full article