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Effects of hydrogen cyanamide and garlic extract on dormancy release, yield, and fruit quality of low-chill apple cultivars at Debre Tabor, northwestern Ethiopia
Abstract Incomplete dormancy release due to insufficient winter chilling is a major constraint to apple ( Malus domestica Borkh.) production in tropical highland environments. This study evaluated the effects of hydrogen cyanamide (1.5%), garlic extract (15%), and their combined application on bud phenology, reproductive performance, yield, and fruit quality of three low-chill apple cultivars (‘Anna’, ‘Dorsett Golden’, and ‘Princesa’) under Ethiopian highland conditions over two consecutive seasons (2023–2024). Data were analyzed using a linear mixed-effects model with repeated measures across the two growing seasons. Dormancy-breaking treatments significantly advanced bud break by 15–24 days and shortened flowering duration from 7 to 8 weeks in the control to 3–5 weeks in treated trees. The combined hydrogen cyanamide + garlic extract treatment produced the earliest bud break and the highest flowering percentage (up to 67%), fruit set (up to 19%), fruit number (up to 63 fruits tree⁻¹), and yield (5.0–6.0 kg tree⁻¹) across cultivars. Yield improvement was associated with increases in fruit number and fruit weight. Significant cultivar × treatment interactions ( P < 0.05) were observed for most phenological and reproductive traits, demonstrating differential cultivar responsiveness to dormancy-breaking agents. Conversely, total soluble solids and fruit firmness were not significantly affected by treatment, cultivar, or their interaction ( P > 0.05). These findings indicate that combining low-chill cultivars with dormancy-breaking treatments partially compensates for inadequate winter chilling and improves apple productivity under tropical highland conditions.
HO-1 and NLRP3-associated lung dysfunction and systemic inflammation in a noninfectious COVID-19 spike protein murine model
Ethane dry reforming for CO2 utilization and H2 generation
Abstract The dry reforming of ethane offers a promising pathway for the co-utilization of CO 2 , which are two abundant industrial byproducts to produce blue H 2 and CO, enabling sustainable routes toward carbon circularity. This study investigates the performance of CeO 2 supported metal catalysts and the effect of catalyst loading in the dry reforming process across a range of temperatures. Catalyst characterization included H 2 -TPR, NH 3 -TPD, CO 2 -TPD, H 2 -TPD, XRD and SEM/EDS mapping to correlate surface acidity, basicity, reducibility, and metal dispersion with catalytic performance. Among all formulations, 2% Rh/CeO 2 showed the highest ethane conversion of ~ 98.5%-91.2% with early reduction behavior, but suffered from minor byproduct of ~ 15 − 12% methane generated from ethane dissociation. Ni/CeO 2 catalysts, especially at 15% loading, demonstrated excellent balance between performance up to 80 − 52% conversion, CO 2 utilization ~ 70 − 48%, with ~ 99% selectivity toward H 2 and CO, and less than 1% formation of methane and ethylene byproducts. Increased Ni loading improved performance due to better reducibility and moderate surface acidity. These findings highlight CeO 2 role in enhancing metal dispersion, particularly Ni/CeO 2 catalyst offers a cost-effective alternative to noble metal of Rh/CeO 2 for dry reforming of ethane, enabling efficient CO 2 valorization for decarbonized energy systems.
Assessing indirect rebound effects of China’s household energy consumption with input-output and redistribution models
Prediction of chronic obstructive pulmonary disease using machine learning models
The effect of Roy adaptation model-based education on self-care and coping in patients with plaster casts: a single-blind randomized controlled trial
Similar systemic but differential vaginal soluble mediator levels in injectable contraceptive users and impact of study site and STI prevalence
Anti-obesity effects of heat-killed Apilactobacillus kosoi, a FLAB-derived postbiotic, in high-fat diet-induced obese mice
Abstract Apilactobacillus kosoi is a fructophilic lactic acid bacterium (FLAB) isolated from a Japanese fermented food, kôso liquid. As a postbiotic, heat-killed A. kosoi has previously been reported to exhibit immunomodulatory and antitumor activities; however, its effects on obesity remain unknown. In this study, we investigated the anti-obesity effects of heat-killed A. kosoi in a high-fat diet-induced obese mouse model. Heat-killed A. kosoi significantly suppressed body weight gain and energy intake, with a tendency toward reduced fat accumulation, without causing adverse changes in blood biochemical parameters. It also partially alleviated the high-fat diet-induced increase in the relative abundance of Bacillota, mitigated the reduction in serum IgA levels, and restored fecal concentrations of both formate and butyrate. Modest alterations in the expression of hepatic genes involved in fatty acid, cholesterol, and bile acid metabolism further suggested a contribution of hepatic metabolic regulation to its anti-obesity effects. Collectively, these findings demonstrate that heat-killed A. kosoi is the first FLAB-derived postbiotic reported to exhibit anti-obesity activity. The restoration of formate, together with butyrate, highlights a potential role for microbial metabolites in host energy metabolism and expands current understanding of the physiological functions of FLAB-derived postbiotics.
Green synthesis of gold nanoparticles from Lilium ciliatum leaf and flower extracts through phenolic composition, process optimization, and colloidal properties
Design and evaluation of IntuNav and EdgeNav navigation methods in desktop multi-browser virtual environments
Abstract Desktop virtual reality (Desktop VR) is increasingly used as a software platform for information exploration, productivity, and learning, particularly when immersive head-mounted displays are not feasible. In these systems, navigation serves as a fundamental software interaction mechanism that directly influences usability, efficiency, and technology acceptance. This research empirically evaluates two mouse-only, first-person navigation techniques (IntuNav and EdgeNav) implemented as software-level interaction models within a desktop Multi-Browser virtual environment. A controlled between-subjects experiment involved 111 participants engaging in structured information-seeking and content-creation tasks that reflect real-world software workflows. Objective performance metrics were gathered alongside validated subjective measures, including the System Usability Scale (SUS), Technology Acceptance Model (TAM), Igroup Presence Questionnaire (IPQ), and the raw NASA Task Load Index (NASA-TLX). The findings indicate that IntuNav markedly enhances task efficiency, usability, technology acceptance, and presence relative to EdgeNav, whereas perceived cognitive load shows no significant differences between the navigation models. The findings provide empirical evidence that navigation design is a crucial factor in software interaction in Desktop VR systems, highlighting practical implications for usability-focused software development in three-dimensional navigation.
A multidimensional epidemiological study of 905 canine leptospirosis cases, metropolitan France, 2020–2024
Radar detection of small UAVs in severe ground clutter using 2D spatial-temporal matched filtering
Abstract Based on the empirical characterization of radar signatures from three distinct Unmanned Aerial Vehicles (UAVs)—the DJI Mini 4 Pro, Mavic 3 Pro, and Phantom 4 Pro—this paper proposes a detection framework applicable to both Frequency-Modulated Continuous Wave (FMCW) and pulsed radar architectures. Unlike conventional approaches that rely on theoretical point-target models, we design a two-dimensional spatial-temporal matched filter. This approach addresses the challenge of detecting low Radar Cross-Section (RCS) targets in severe ground clutter, where standard Constant False Alarm Rate (CFAR) algorithms frequently suffer from threshold breakdown. By shifting the paradigm from amplitude thresholding to 2D pattern matching via Normalized Cross-Correlation (NCC), the proposed method integrates target energy across both fast-time (range) and slow-time dimensions. The study presents validation comprising both Monte Carlo simulations and outdoor field trials. Simulation results demonstrate sensitivity gain of 10 to 14 dB over standard CA-, GO-, SO-, and OS-CFAR methods in Rayleigh and Weibull clutter. Crucially, experimental validation on the outdoor field radar data, for the three small UAVs, confirms the robustness of the 2D matched filter, which consistently achieves a Precision-Recall Area Under the Curve (AUC) between 0.93 and 0.99, effectively suppressing false alarms even for low Signal-to-Clutter Ratio (SCR).
Genetic parser and deep residual bivariate Pascal-based energy-aware routing and scheduling for edge-enabled smart grids
Abstract Edge-enabled smart grid communication systems operate under tight energy budgets and fluctuating traffic conditions, making it essential to use routing and scheduling methods that control energy use without compromising delay or reliability. Many existing approaches treat routing and scheduling as separate tasks, which often leads to poor coordination—especially when traffic surges unexpectedly or when edge devices face uneven energy availability. In this work, a unified solution is developed that couples a Genetic Parser, used to generate energy-aware routing and slot assignments, with a deep residual model that leverages Bivariate Pascal statistics to anticipate short-term traffic and energy behavior. The Genetic Parser selects paths that distribute load efficiently, while the predictive module estimates the likelihood of delay increases or energy spikes so the scheduler can adjust in advance. Tests carried out on representative smart grid scenarios show clear gains: energy use drops by roughly 18–24%, network lifetime increases by about 27%, and end-to-end latency falls by 15–20% when compared with established methods. Packet delivery also remains consistently high, reaching 98.4% even when traffic conditions vary rapidly. These results indicate that combining evolutionary search with lightweight predictive modeling can improve both stability and overall efficiency in next-generation smart grid communication networks.