Search Results (18,220)

Background: Genetic diversity and genetic differentiation between Gossypium hirsutum-Gossypium barbadense introgression lines (ILs) and early-maturing upland cotton lines are critical for resolving the core breeding contradiction in Xinjiang cotton region: narrow genetic basis of early-maturing cultivars and late maturity of ILs with superior fiber quality. Xinjiang is one of the major cotton-producing regions in China, and breeding high-quality early-maturing upland cotton adapted to local ecological conditions is essential for improving cotton yield and quality. However, the genetic relationship and differentiation between the two types of cotton germplasm remain unclear, which hinders the efficient utilization of germplasm resources in breeding. Therefore, this study aimed to clarify the genetic diversity and differentiation between the two germplasm types and identify key candidate loci related to early maturity and fiber quality, providing support for cotton breeding. Results: Here, we used a 40K Single Nucleotide Polymorphism chip to genotype core cotton germplasm in northern Xinjiang, and analyzed their population structure, genetic diversity and functional SNP loci associated with early maturity and fiber quality. The tested materials were clearly divided into two subgroups (ILs and early-maturing lines). Genetic diversity analysis revealed a significantly narrow genetic basis in the early-maturing subgroup, while the IL subgroup had higher genetic diversity. Specifically, the early-maturing subgroup showed lower nucleotide diversity and polymorphism information content compared with the IL subgroup, indicating that the genetic variation of early-maturing cotton germplasm in northern Xinjiang is relatively limited. A total of 25 non-synonymous SNPs were identified, among which the c.A613G:p.T205A mutation in GH_D09G1484 (mRNA-decapping enzyme 1, DCP1) was a characteristic variation of early-maturing cotton, and a possible non-synonymous mutation in GH_A09G2400 (Heat shock transcription factor A6b, HSFA6B) was associated with fiber development. These two candidate genes were annotated to be involved in plant growth and development, further supporting their potential roles in regulating cotton early maturity and fiber quality. Conclusions: This study clarified the genetic differentiation between the two types of germplasms and identified key candidate loci for early maturity and fiber quality, providing precise molecular markers and theoretical support for breeding high-quality early-maturing upland cotton adapted to Xinjiang’s ecological conditions. The results also highlight the value of Gossypium hirsutum–Gossypium barbadense introgression lines in enriching the genetic basis of early-maturing cotton, which can be further utilized to solve the core breeding contradiction in the Xinjiang cotton region. Full article

Long non-coding RNAs (lncRNAs) are key regulators of gene expression and play critical roles in cancer-related signaling networks. Dysregulation of antagonistic lncRNAs may contribute to hepatocarcinogenesis and disease progression. This study investigated the clinical significance and predictive value of two biologically antagonistic lncRNAs, UFC1 and PTENP1, as circulating biomarkers for hepatocellular carcinoma (HCC) in an Egyptian cohort. Expression levels of these lncRNAs were quantified in 100 HCC patients and 100 age- and sex-matched healthy controls. UFC1 was significantly upregulated (~2.9-fold), while PTENP1 was markedly downregulated (~4-fold) in HCC patients, with a strong inverse correlation (r = −0.609, p < 0.001). Both lncRNAs demonstrated higher diagnostic accuracy compared to alpha-fetoprotein (AFP); combining them with AFP further enhanced overall performance. UFC1 expression was increased progressively with advancing fibrosis grade and Barcelona Clinic Liver Cancer (BCLC) stage, while PTENP1 levels diminished with BCLC stage. Logistic regression confirmed UFC1 as an independent risk factor and PTENP1 as a protective factor for HCC. In conclusion, the blood-based UFC1/PTENP1 panel exhibits promising diagnostic accuracy and is associated with disease severity, surpassing AFP. Their fibrosis-associated dysregulation suggests a role in early hepatocarcinogenesis. This antagonistic lncRNA signature represents a potential, non-invasive tool for HCC detection and risk stratification, meriting further clinical validation. Full article

This study investigates the screw withdrawal resistance (SWR) of hollow wood–plastic composite (WPC) door frames, which serve as moisture-resistant alternatives to traditional wood-based materials. The tested WPC, characterised by a density of 1.33 g/cm³ and a polymer-bound lignocellulosic filler, exhibits superior dimensional stability and low water absorption—under 4% after 24 h of immersion. The research focuses on how the unique chambered geometry of these industrial profiles affects the anchoring of 20 mm conical wood screws used to mount essential fittings such as hinges and lock catches. The SWR was determined using a universal testing machine in accordance with the modified EN 320 standards. Results indicate that the installation location within the profile significantly dictates load-bearing capacity: the band profile (lock catch) achieved an average SWR of 525.65 N, while the beam profile (hinge) averaged only 275.25 N. This performance gap arises because screws anchor only into internal “ribs” rather than the full material depth. Since these values are considerably lower than those of traditional particleboard (~1364–1775 N), the study highlights a critical need to optimise screw dimensions to ensure the structural stability and safety of hollow WPC door systems. Full article

Motion sickness (MS), or kinetosis, is a condition experienced by some individuals in response to rhythmic or irregular body motion. Multiple studies have explored its neurobiological mechanisms and countermeasures, with the sensory-conflict hypothesis remaining the most accepted explanation. Heart-rate variability (HRV) and electrocardiography provide complementary autonomic nervous system perspectives that may support MS assessments. From an applied viewpoint, reliable HRV markers could enable the early detection and continuous monitoring of MS in real-world contexts, such as autonomous vehicles, where passenger comfort and safety are critical, motivating contact-free cardiac sensing for unobtrusive monitoring. This systematic review examines the value of HRV indices in MS, conducted under PRISMA guidelines across PubMed, Scopus, and the Web of Science. The included studies were grouped into four categories based on the methods used to induce MS: mechanical stimulus, real trip, visual stimulus, and virtual reality. Aggregated findings indicate that frequency–domain metrics, particularly the low frequency (LF)/high frequency (HF) ratio, HF power, and mean heart rate (mHR), are most frequently reported in relation to MS. Overall, autonomic dysregulation likely contributes to MS susceptibility, but standardized protocols are needed to validate HRV as a reliable marker. Full article

To address the challenges of coordinated optimization in building integrated energy systems (IES) under the dual-carbon targets—characterized by strong multi-energy coupling, significant uncertainty in renewable generation, and stringent safety constraints—a novel safe deep reinforcement learning algorithm, Safe-DDPG, is proposed. Traditional deep reinforcement learning methods often suffer from high constraint-violation risk and limited policy reliability due to coupled objectives in building IES optimization. To overcome these limitations, a dual-channel critic architecture is designed to independently evaluate and decouple economic and safety objectives. In addition, a dynamic safety–penalty mechanism based on logarithmic barrier functions is introduced, together with an adaptive exploration strategy, enabling dynamic balancing between economic cost and constraint satisfaction according to system states during training. Experimental results demonstrate that, compared with mainstream algorithms, Safe-DDPG achieves substantial improvements across multiple key performance indicators: safety violations are reduced by up to 96.7%, average daily operating costs decrease by 18.5%, and cumulative rewards increase by more than 30%. Ablation studies further confirm the effectiveness and necessity of each core component. Two DRL methods from reference papers are reproduced, and their performance is compared with the proposed method in the existing experimental results, showing that the proposed method has significant advantages in reward value and economic cost. This work provides a safe, reliable, and efficient reinforcement-learning-based approach for optimization and scheduling of building energy systems under complex operational constraints. Full article

Diagnostic testing of foot-and-mouth disease virus (FMDV) currently utilizes reverse transcription quantitative PCR (RT-qPCR) to detect the presence of viral RNA and double antibody sandwich ELISAs (DAS-ELISAs) to determine viral serotype. Serotype identification is critical to support informed vaccine selection to combat outbreaks. While DAS-ELISAs are capable of serotype identification, the test suffers from low sensitivity and requires a viral isolate for successful detection. In this study, we developed FMDV-ONTAPS: an Oxford Nanopore Technologies Amplicon P1 Sequencing protocol involving reverse transcription-PCR to amplify P1 of the FMDV genome, and Nanopore sequencing of the amplicons to provide genetic data for serotype and subtype/topotype identification. FMDV isolates representing all seven serotypes were successfully sequenced with this method. Additionally, the protocol successfully provided serotype identification from a variety of specimen matrices obtained from experimentally infected animals that included milk, serum, oral and nasal swabs, tissue suspensions, vesicular fluid, and oral fluid. The limit of detection for FMDV cell culture isolates was comparable for both sequencing and RT-qPCR detection. RT-qPCR Cq values for clinical samples evaluated ranged from 8 to 28.21. Sequencing was successful for all samples except for a single tissue suspension sample (Cq of 28.21). Identification of FMDV serotype in clinical samples is critical for effective outbreak response, and Nanopore sequencing offers a timelier and more sensitive alternative to DAS-ELISAs. Full article

Mycterothrips glycines Okamoto (1911) (Thysanoptera: Thripidae) is rapidly increasing in fields, posing a new and potentially serious threat to soybean production in Northeast China. To clarify the population dynamics and screen effective insecticides against M. glycines, systematic monitoring and pesticide evaluation were conducted from 2024 to 2025. Occurrence dynamics were continuously monitored using yellow and blue sticky boards, while ten commonly used commercial insecticide formulations (thiamethoxam, clothianidin, sulfoxaflor, acetamiprid, imidacloprid, fenthion, pyridaben, abamectin, beta-cypermethrin, spinetoram) were hierarchically screened through laboratory bioassays, pot trials, and field spraying experiments. The results revealed a distinct ‘rise-and-fall’ occurrence pattern, with the initial, peak, and late occurrence periods occurring in mid-July, late July to early August, and mid-to-late August, respectively. Thiamethoxam and clothianidin exhibited the strongest toxicity against M. glycines (LC₅₀ values of 12.87 mg/L and 13.46 mg/L, respectively), achieving field control efficacies exceeding 85%, which were significantly superior to conventional agents such as imidacloprid and abamectin. The study identified the soybean flowering stage as the critical window for control, recommending preventive interventions when sticky trap monitoring indicates the initial population peak (around mid-July). This research fills the technical gaps regarding the occurrence dynamics of M. glycines and the lack of registered control products in China, providing essential support for precision monitoring and management of soybean thrips. Full article

Microalgae are photosynthetic microorganisms with high biotechnological potential, though optimising inorganic carbon supply remains a critical challenge to enhance growth, biomass quality, and carbon use efficiency. To address this, this study evaluated the impact of sodium bicarbonate supplementation (0, 0.5, 1.5, and 3.0 g L⁻¹) on Chlorella vulgaris growth, carbon dynamics, biochemical composition, and metabolism over 11 days. Higher carbon availability (3.0 g L⁻¹ NaHCO₃) increased the specific growth rate to 0.472 ± 0.004 d⁻¹, accelerated nitrogen removal (85% by day 4), enhanced phosphorus removal (up to 90% by the end of cultivation), and increased dissolved inorganic carbon uptake (93 ± 6 mg L⁻¹). Carbohydrate and lipid contents were not significantly affected by bicarbonate concentration, whereas protein and pigment levels were higher in non-supplemented conditions due to prolonged exponential growth. Bicarbonate supplementation enhanced MUFA content, improving biodiesel quality. Amino acid profiles were similar across conditions, with glutamic acid as the predominant amino acid (up to 17 mg g⁻¹ DW) and higher values under moderate bicarbonate supplementation (1.5 g L⁻¹). Overall, bicarbonate supplementation enhanced microalgal growth, nutrient removal efficiency, and fatty acid composition, highlighting its potential to improve carbon availability for C. vulgaris cultivation. Full article

This research investigates the optimization of surface integrity in powder-mixed electrical discharge machining (PMEDM) through the innovative use of Jatropha biodielectric fluid enhanced with titanium dioxide (TiO₂) nanoparticles. A comprehensive experimental framework was developed using design expert software (DOE) with Response Surface Methodology (RSM) to systematically analyze the machining of AISI D2 tool steel using copper electrodes. The study examined five critical process parameters, gap current (Ip), pulse-on duration (Ton), pulse-off time (Toff), gap voltage (V), and powder concentration, evaluating their combined effects on surface roughness (SR), surface crack density (SCD), and residual stress characteristics. Advanced characterization techniques including scanning electron microscopy (SEM) were employed to analyze surface topography and subsurface microstructural changes. The optimization process successfully identified optimal machining conditions of current = 9 A, Ton = 100 µs, Toff = 10 µs, and gap voltage = 65 V, achieving exceptional surface quality with a minimum surface roughness of 3.22 µm. Remarkably, these optimized parameters resulted in crack-free surfaces with zero surface crack density and minimal residual stress values across the 2θ range of 90° to 180°. To enhance predictive capabilities, supervised machine learning algorithms were implemented to model surface roughness behavior. Comparative analysis of classification algorithms demonstrated that Support Vector Machine (SVM), k-Nearest Neighbors (kNNs), and Gaussian Naïve Bayes achieved superior performance with F1-scores of 0.88 and prediction accuracies of 90%. The integration of sustainable Jatropha biodielectric with TiO₂ nanoparticles represents a significant advancement in environmentally conscious precision machining, while the machine learning approach establishes a robust framework for intelligent process optimization and quality prediction in advanced manufacturing applications. Full article

Rice (Oryza sativa L.) grain quality is a critical determinant of market value, consumer acceptance, and nutritional security. This multifaceted trait is governed by the dynamic interaction of genotype (G), environment (E), and management practices (M). In this review, we synthesize recent advances in understanding these multifaceted determinants. We first delineate the genetic architecture, emphasizing key genes and quantitative trait loci (QTLs) such as Wx, ALK, Chalk5, and the GS3/GW families, which control starch composition, gelatinization temperature, chalkiness, and grain dimensions, forming the foundational blueprint for quality potential. We examine how this genetic potential is influenced by environmental factors, focusing on the detrimental impacts of abiotic stresses, particularly high temperatures during grain filling and drought, which impair milling yield, increase chalkiness, and modify starch and protein profiles. Furthermore, we discuss how optimized agronomic strategies—including precision water management (e.g., alternate wetting and drying), balanced nitrogen fertilization, and targeted micronutrient (e.g., silicon) application—can mitigate these adverse effects and potentially improve specific quality parameters. Post-harvest handling is identified as the final determinant of product quality. We conclude that achieving high and stable rice quality under climate variability requires an integrated G × E × M approach. Prospects include next-generation breeding for climate-resilient quality, precision agronomy guided by real-time sensing, synergistic soil health management, and the integration of systems biology with digital agriculture to design sustainable, high-quality rice production systems. Full article

Hazardous compounds from used batteries pose a great threat to the environment. To prevent pollution and to recover critical materials from battery waste, efficient recycling is required. Until now, battery recycling has focused on the recovery of valuable metals from cathode materials, while organic fractions have often been neglected due to their low material value. New approaches to battery recycling are therefore necessary, where recycling methods based on supercritical carbon dioxide (SC-CO₂) extraction show great potential. In this work, a SC-CO₂ method was implemented to extract electrolyte solvents for the purification and recovery of a separator waste material (SWM) sorted out from lithium-ion battery (LIB)-based black mass. In addition, two other separation routes (ultrasonic washing and thermal treatment) were used for comparison. Based on the results from the three routes, mass balances revealed the gravimetric composition of the SWM, which includes separator, electrolyte, and electrode powder. The composition of electrolyte solvents was determined via Gas Chromatography-Mass Spectroscopy analysis. Furthermore, the polymeric separator was analyzed using Fourier Transform Infrared Spectroscopy, Thermogravimetric Analysis, and Differential Scanning Calorimetry analysis to evaluate the effects of SC-CO₂ extraction on the physicochemical properties. The recovery of electrolyte by the SC-CO₂ route is more efficient than the others, with extraction yields of 162 mg of electrolyte per gram of SWM. Moreover, no changes are observed in the analyzed properties of the polymeric separator material due to the SC-CO₂ extraction. Thus, the SC-CO₂ process proves to be a promising method for an efficient and sustainable recycling of electrolyte solvent and purifying of separator material from LIB waste. Full article

In classical reliability engineering, failure is a probabilistic structural failure based on lifetime distributions of Weibull models. However, in the control-critical mechanical systems, it is possible that functional failure of the system happens before material failure occurs as a result of control power loss. This paper proposes a Controllability–Reliability Coupling (CRC) model, which redefines the concept of reliability as the stabilizability in the face of progressive degradation. The actuators’ deterioration is modeled using the time-varying input effectiveness factor $\alpha \left(t\right)$, and the actuator is said to be in failure when the minimum singular value of the finite-horizon controllability Gramian becomes less than a stabilizability threshold $\epsilon$. The performance of the simulation indicates that the functional failure is a precursor of structural failure in several degradation conditions. A baseline comparison shows that the CRC metric forecasts loss of controllability at $T_{CRC}=17.0$ s, but the classical Weibull reliability never attains the structural failure threshold even in the time horizon of 20 s. The system retains margins of Lyapunov stability and H infinity robustness are not lost, and it is still stable and attenuates disturbances even when control authority is lost. In practical degradation scenarios, the forecasted CRC failure times are 21.5 s (linear wear), 13.1 s (accelerated fatigue), 23.7 s (intermittent faults), and 24.4 s (shock damage), whereas maintenance recovery abated functional failure completely. In a case study of an industrial robotic joint, at 27.0 s, functional collapse occurred, and at the same time, structural reliability was still above the failure threshold. The findings support the hypothesis that structural survival and functional controllability are distinct concepts. The proposed CRC framework is an approach to control-conscious reliability measure, which can detect early failures and offer proactive maintenance advice in the context of a cyber–physical system. Full article

Canopy fractional vegetation cover (FVCc) is a critical indicator for evaluating the effectiveness of ecological restoration, and its accurate estimation provides valuable data for regional ecological management. In this study, Sentinel-2 and MODIS data were integrated to develop an angular relationship model for MODIS reflectance, which was then used to estimate Sentinel-2 reflectance at a 45° viewing angle. Background reflectance at a 10 m spatial resolution was derived using a four-scale model, and total and shrub-herb fractional vegetation cover were estimated using a pixel dichotomy model. Finally, an empirical model tailored to the characteristics of the study area was developed to retrieve FVCc. Cross-validation results demonstrated that the multi-angle retrieval method proposed in this study achieved higher accuracy than the single-angle approach. The spatial distribution of FVCc in Changting County is characterized by higher values in peripheral areas and lower values in the central region. Temporal transitions among fractional vegetation cover classes were predominantly upward, indicating an overall trend of continuous improvement. These findings provide important technical support and a scientific basis for estimating and monitoring dynamic changes in forest canopy fractional vegetation cover. Full article

Urban green spaces (UGSs) provide critical ecosystem services (ESs) in rapidly urbanising cities but are increasingly threatened by land-use change, population growth, and socio-economic pressures. This study assessed spatial and temporal changes in UGS in Zomba City, Malawi, from 1998 to 2021 using geospatial and remote sensing methods. Landsat imagery from 1998, 2007, 2013, and 2021 was analysed through post-classification change detection to map land-use/land-cover (LULC) transitions, while the relationship between ward-level population density and vegetation condition was evaluated using the Normalised Difference Vegetation Index (NDVI). Results show a decline in total UGS cover from 60% in 1998 to 51% in 2021, primarily due to the expansion of built-up areas. Tree cover increased from 11% to 18%, with NDVI values rising from 0.700 to 0.947; these changes may reflect both natural vegetation growth and targeted restoration, indicating localised improvements in vegetation condition. An inverse relationship was observed between population density and NDVI, though some high-density wards exhibited NDVI gains associated with restoration initiatives. These findings underscore the role of both institutional and community efforts in sustaining urban vegetation and highlight the potential of ecological restoration to mitigate UGS loss and support ESs. Policymakers and planners should prioritise the protection, restoration, and equitable distribution of UGS, particularly in dense and underserved areas, as strategic urban greening enhances city resilience and human well-being. Full article

Background: This exploratory study investigates whether STEM (science, technology, engineering, and mathematics) students’ increasing reliance on off-campus resources (e.g., online platforms, private tutors) reflects an authentic preference for autonomous learning or a compensatory response to perceived deficiencies in on-campus instruction. Methodology: Using a mixed-methods design, data were collected from 118 engineering and science students. A model was developed to examine the relationship between the intensity of student criticism and their declared preference for off-campus learning. Findings: The model revealed a significant negative relationship between the intensity of criticism and the preference for off-campus instruction. This suggests that for highly critical students, external resources function primarily as a compensatory mechanism for “needs frustration” rather than a preferred alternative. The results imply that these students continue to value the frontal model but find its current implementation insufficient to meet their pedagogical needs. Conclusion: These findings challenge the assumption that digital trends signify a voluntary abandonment of the classroom. Instead, reliance on external resources is positioned as a reactive, compensatory strategy. Higher education institutions should prioritize revitalizing frontal instruction through enhanced clarity and focus to reduce dependency on off-campus platforms and restore the value of the campus experience. Full article