Effective decision-making in forest management relies on accurate and timely data, yet conventional manpower-based methods often fail to provide sufficient coverage. This study develops a cost-effective, deep learning–based monitoring technique for assessing replanted areas following timber harvesting, utilizing a small, field-verified dataset. Orthophotos of a two-year-old Larix kaempferi plantation were acquired using drones, and seedling locations were identified through field surveys. The Faster R-CNN model with a ResNet-50 backbone was trained under different layer freezing settings and with or without pretrained weights. To improve experimental accuracy, data augmentation, k-fold cross-validation, and hyperparameter random search were applied. Freezing some of the initial layers while fine-tuning the remaining layers with pretrained weights resulted in the best performance, achieving an F1 score of 0.87-surpassing the 0.84 score obtained without pretrained weights. These findings demonstrate that transfer learning and layer-freezing strategies effectively enhance seedling detection performance, even with limited field validation data. The proposed method offers a cost-effective approach to forest management data analysis by reducing reliance on traditional survey methods while simultaneously improving data collection efficiency and utilization.
Virus-host interactions have contributed to the acquisition of host-derived genetic material by viral genomes, although the mechanisms underlying such events remain incompletely understood. Here, we identified the S9 protein of squirrel monkey cytomegalovirus (Saimiriine herpesvirus 4; SBHV4) as a unique viral protein showing extensive similarity to mammalian major histocompatibility complex class I (MHC class I) molecules. SBHV4-S9 shared 60.6% amino acid identity across 86.3% of the full-length human HLA-A protein, representing the highest level of similarity between a viral protein and an MHC class I molecule identified in Virus-Host DB-based screening. Structural modeling demonstrated that SBHV4-S9 possesses an overall architecture highly similar to HLA-A, including the characteristic MHC class I domains. Comprehensive phylogenetic analyses of primate MHC class I-like genes, incorporating assessments of substitution saturation, potential recombination signals, and comparisons between complete sequences and non-peptide-binding regions, showed that SBHV4-S9 consistently clustered with Old World monkey MHC class I sequences, including those from the genus Cercopithecus. However, the complex evolutionary history of MHC class I genes prevented definitive identification of the donor lineage or acquisition mechanism. These findings identify SBHV4-S9 as an exceptional viral MHC class I-like protein and highlight the complexity of reconstructing ancient virus-host evolutionary events.
Cross-lingual speech recognition for real-time applications is constrained by the joint requirements of low latency, high recognition accuracy, and strict privacy over conversational audio. This research work addresses these constraints by designing an adaptive dual-stream neural encoder that explicitly models acoustic and linguistic information in parallel while operating in a streaming regime. The proposed architecture was developed with two coordinated streams: an acoustic encoder that processed log-Mel features in fixed-size chunks and a linguistic encoder that consumed partial token embeddings, with an adaptive gating module that fused both representations to minimize re-computation and buffering delay. A privacy-preserving data augmentation pipeline was further introduced, in which non-identifying signal transforms, feature-space perturbations, and cross-lingual masking were applied without storing raw sensitive speech or speaker metadata. The framework was trained and evaluated on multilingual corpora including CoVoST 2, Common Voice, and MuST-C, achieving relative word error rate reductions of approximately 19% on average and up to 22% in low-resource languages compared with a strong streaming Transformer baseline. Under streaming conditions, the dual-stream encoder attained an operating Real-Time Factor of 0.60 with end-to-end latency below 320 ms for typical utterances, while maintaining character error rates below 7% across the majority of evaluated languages. Ablation studies indicated that the adaptive gating contributed a relative WER improvement of about 20% and that privacy-preserving augmentation yielded a further gain of about 14% in noisy and domain-shifted test scenarios. This research work therefore establishes a practical pathway toward low-latency, privacy-aware, cross-lingual speech-to-text systems suitable for deployment in real-time multilingual services
Driven by global warming and thermal expansion of the oceans, ice loss from glaciers and ice sheets will continue to increase sea levels and the risk of storm-surge, threatening coastal regions worldwide. This study presents the first national-scale assessment of SLR/S impacts on land use and assets across Australia, covering 1,156 coastal sub-regions in six states and the Northern Territory under RCP4.5 and RCP8.5 scenarios. The analysis integrates physical and economic impacts across 10 aggregated land-use categories. By 2100, under RCP4.5, SLR/S is projected to affect 267.5 thousand properties and 2.0 million hectares (ha) of land, resulting in present-value damages of $855.1 billion, including $274.3 billion in property losses and $580.7 billion in land-use losses. Under RCP8.5, impacts increase to 719.6 thousand properties and 3.7 million ha, with total damages of $1,909 billion, comprising $705.7 billion in property losses and $1,203 billion in land-use losses. The results reveal substantial spatial heterogeneity in physical exposure and economic impacts across Australia. Average annual SLR/S damages are estimated to reduce national GDP by 1.05–1.39% under RCP4.5 and 2.26–3.0% under RCP8.5. These estimates are conservative, as they exclude additional losses to environmental assets, cultural heritage, and other infrastructure not captured in the assessment.
Diabetic foot ulcer (DFU), a severe complication of diabetes, is driven by hypoxia and impaired cell metabolism. The crosstalk between hypoxia and the newly defined copper-dependent cell death (cuproptosis) in DFU remains unknown. We aimed to identify key biomarkers at this intersection for diagnosis and therapy. We integrated transcriptomic data (GSE134431) with cuproptosis and hypoxia gene sets. Machine learning (XGBoost) screened for core genes, validated in an independent cohort (GSE80178). Diagnostic models were built and assessed via ROC curves and nomograms. Functional enrichment, regulatory networks, and drug predictions were performed bioinformatically. RT-qPCR validated findings in clinical DFU tissues. Thirty-one hub genes linking hypoxia and cuproptosis were identified. Three genes—BCL2, CA12, and CP—emerged as robust biomarkers. A diagnostic model based on them showed high accuracy (AUC > 0.7). They were functionally linked to apoptosis, spliceosome, and lysine degradation. BCL2 and CP were enriched in endothelial cells, correlating with DFU-associated angiogenesis defects. Drug prediction suggested ferulic acid (targeting CA12) and deferiprone (targeting CP) as potential therapeutics. RT-qPCR confirmed significant downregulation of BCL2 and CP in DFU samples. We unveil a novel molecular network coupling hypoxia and cuproptosis in DFU, and nominate BCL2, CA12, and CP as promising diagnostic biomarkers and therapeutic targets, offering new avenues for precise DFU management.
Organismal nutritional needs vary across time and context, with consequent impacts on foraging decisions. In collectively foraging social groups, this includes variation in nutritional needs among group members. In social symbioses, such as between ants and their farmed fungus in a leafcutter ant colony, nutritional decisions are being made for multiple species as well as multiple group members, and the optimal nutrient balance may vary accordingly. In this study, we examined how nutritional decisions are organized in this symbiotic mutualism by measuring the impact of changes in fungal volume and brood number on relative and absolute collection of protein and carbohydrates by colonies of the desert leafcutter ant Acromyrmex versicolor. Our results show that there was no change in relative collection of protein and carbohydrates or in total nutrient intake following an increase or decrease in the proportion of brood to adult ants. Decreasing the relative abundance of fungus, however, resulted in an increase in absolute nutrient collection, while relative nutrient collection remained constant. These results demonstrate that the fungus plays a distinct and central role in driving foraging decisions in this symbiosis, and that its nutritional needs shape the social organization of the colony in the context of foraging.
In this study, a new hierarchical micro/mesoporous catalyst is prepared via the synthesis of a metal-organic framework (ZIF-8/Ni) on mesoporous silica (MCM-41) and characterized by various analytical techniques including FT-IR, XRD, ICP, FESEM, EDX, TEM, TGA, and BET. The combination of MCM-41 and ZIF-8/Ni in a single catalytic system can provide a synergistic effect between the two porous structures, resulting in superior catalytic activity. The prepared catalyst was successfully applied to the synthesis of a series of spiro pyrimido[4,5-b]quinoline and pyrazolo[3,4-b]quinolone derivatives, several of which have not been previously reported. The studied reactions were performed using a water–ethanol solvent system under mild conditions, affording the desired products with high isolated yields without the need for column chromatography or tedious purification steps. Moreover, the catalyst exhibited excellent reusability and maintained high activity over several consecutive runs, highlighting its potential as a sustainable and reusable heterogeneous catalyst for environmentally friendly organic synthesis.
Neonatal mortality remains a major public health concern in Ethiopia, particularly in rural areas where access to healthcare is limited. This study investigated the association between birth interval, place of delivery, and distance to health facilities and neonatal mortality among mothers in the Hawela Lida district in Southern Ethiopia. A cross-sectional study was conducted between February and March 2025, enrolling 3526 mothers who had delivered at least one live-born infant within the previous 5 years. We included only the most recent live birth for each mother, resulting in 3526 mother–most recent live birth pairs for analysis. Neonatal death was defined as death of a live-born infant within the first 28 completed days of life. Multistage sampling was used to select participants across ten kebeles in the Hawela Lida district. Multilevel modified Poisson regression models with robust error variance were fitted to assess the associations of birth interval, place of delivery, and distance to health facilities with the prevalence of reported neonatal death, while adjusting for individual- and community-level covariates. Among 3526 mother–most recent live birth pairs, there were 133 neonatal deaths reported, resulting in a prevalence of reported neonatal death of 3.8% (95% CI 2.6–5.9). The prevalence was slightly higher among mothers living in rural areas than among those living in urban areas (4.0% vs 2.7%). In multilevel modified Poisson regression analysis, short birth interval (< 24 months) was associated with a higher prevalence of reported neonatal death (adjusted prevalence ratio (aPR) = 2.71; 95% CI 1.70–4.18). Home delivery of the index live birth was associated with more than twice the prevalence of reported neonatal death compared with facility delivery (aPR = 2.56; 95% CI 1.52–3.69). At the community level, residing far from the nearest health facility was associated with a higher prevalence of reported neonatal death (aPR = 5.48; 95% CI 2.99–12.17). Prevalence of neonatal death was notable in the Hawela Lida district of Southern Ethiopia, particularly among mothers living in rural communities. Short birth intervals, home delivery, and greater distance from health facilities were associated with a higher prevalence of reported neonatal death. These findings suggest the need to strengthen access to maternal and newborn health services and support further longitudinal research to better understand factors associated with neonatal death and inform effective interventions.
Sustained sport participation among undergraduates is a public-health priority because university life is a period in which independent health routines, social identities, academic pressure, and mental-health risks converge. In this study, sustained sport participation denotes the continuity and maintenance of sport involvement over time despite competing demands. Environmental sustainability is considered separately as a secondary campus-context theme rather than as part of the primary behavioral outcome. This study examined Theory of Planned Behavior pathways and mental-health indirect associations with sustained sport participation and used qualitative evidence to explain how peer support and institutional facility access shaped the everyday enactment of sport intentions. An explanatory sequential mixed-methods design was used at Hubei Polytechnic University, China. The quantitative phase comprised a cross-sectional survey of 386 undergraduates selected through stratified random sampling and analyzed using descriptive statistics, internal consistency testing, confirmatory factor analysis, structural equation modeling, and indirect-effect analysis. The subsequent qualitative phase comprised 20 semi-structured interviews selected for maximum variation. Integration was achieved by connecting survey results to interview sampling and questions and by comparing quantitative findings with qualitative themes in a joint display to develop integrated interpretations. Attitude, perceived behavioral control, and subjective norms were positively associated with behavioral intention, and behavioral intention was positively associated with sustained sport participation. Depression, anxiety, and stress showed statistically significant indirect associations between intention and participation. Peer support and facility access were not specified as latent predictors in the structural model; instead, their descriptive survey patterns and interview themes clarified the social and institutional conditions under which students described maintaining or interrupting participation. The findings support an interpretation of sustained sport participation as a behavior associated with cognitive, emotional, social, and institutional conditions. Because the quantitative phase was cross-sectional, the structural and indirect pathways should not be interpreted as evidence of temporal or causal effects. Universities may nevertheless use the convergent quantitative and qualitative evidence to inform peer-supported, accessible, inclusive, and mental-health-sensitive sport opportunities.
Hepatocellular carcinoma (HCC) remains a major global health challenge, with over 900,000 new cases annually and a pronounced male predominance. In pursuit of novel, low-toxicity therapeutics, this study isolated Atractylenolide III from the marine coral Tubipora musica and demonstrated measurable, dose-dependent cytotoxic activity against HepG2 liver cancer cells, achieving an IC50 value of 15.50 µg/mL and reducing cell viability to 25.0% at 25 µg/mL. To investigate potential molecular targets and propose hypothetical mechanism pathways, we employed a multi-layered computational strategy. Molecular docking across 76 cancer-associated proteins identified a strong binding affinity (-9.6 kcal/mol) with CD1b (PDB ID: 1GZP), a T-cell surface glycoprotein key to lipid antigen presentation and tumor immunology. Short-term molecular dynamics (MD) simulations were conducted to provide a preliminary structural-viability filter for this complex under physiological conditions. Pharmacophore modeling expanded the screening to 1,513 structurally related compounds, pinpointing ZINC64701878 as a superior candidate with enhanced binding affinity (-12.7 kcal/mol). Density functional theory (DFT) calculations supported its high reactivity, and pharmacokinetic profiling suggested favorable drug-likeness. Together, these findings propose a theoretical framework for marine-derived compounds as potential leads for HCC therapy. While the cytotoxicity assay provides initial phenotypic support, the primary contribution of this study lies in establishing a predictive computational-experimental framework to guide future in vivo and mechanistic investigations.
This exploratory cross-sectional study examined associations between a study-specific self-reported sports-participation category and balance measures in 65 healthy young adults, including 32 participants in the sports-participation group and 33 in the non-sports-participation group. Quiet-standing centre-of-pressure (COP) measures and a floor-marked Y-Balance Test Lower Quarter (YBT-LQ) were assessed, while Romberg-type relative change and inter-limb symmetry indices were treated as secondary descriptive measures. A covariate-adjusted MANCOVA of five representative outcomes showed an overall association with sports participation (P < 0.001). The sports-participation group had a higher YBT total score than the non-sports-participation group (94.20 ± 9.25 vs 85.96 ± 8.44%LL; P < 0.001; point-biserial r = 0.43). The adjusted YBT difference was 13.59%LL (95% CI 7.90 to 19.28; Holm-adjusted P < 0.001). Adjusted eyes-open bipedal COP velocity was lower in the sports-participation group than in the non-sports-participation group (3.53 vs 3.85 cm·s⁻1; difference − 0.32 cm·s⁻1, 95% CI − 0.55 to − 0.09; Holm-adjusted P = 0.023). An unconditional adjusted estimate for eyes-closed bipedal COP velocity was not interpreted because the homogeneity-of-regression-slopes assumption was violated. No statistically significant differences were detected for the bipedal or unipedal Romberg-type indices (P = 0.782 and P = 0.172) or for the static or dynamic symmetry indices (P = 0.984 and P = 0.376); small-to-moderate effects may have been missed. Descriptive PCA had borderline sampling adequacy (KMO = 0.51), and LASSO retained sports participation under the applied penalty but showed poor cross-validated performance (R2 = 0.075). These findings do not establish causation or clinical importance and should not be used for clinical decision-making or injury-risk screening.
Groundwater is the primary source of water in Madhesh Province, Nepal, yet the province lacks a reliable forecasting framework. This study develops a provincial-scale groundwater forecasting system by integrating satellite-derived groundwater storage (GWS), machine learning, and CMIP6 climate forcing. A Long Short-Term Memory (LSTM) model was trained using two decades of GRACE–GLDAS anomalies, observed climate data, and land use–land cover (LULC) information, and was forced with bias-corrected MIROC6 projections under the SSP5-8.5 scenario. Results reveal a persistent historical GWS decline of approximately 50 mm over 20 years (2.5 mm yr⁻¹), driven primarily by rainfall variability and intensified by urban expansion and surface water loss. Two LSTM setups were tested: a climate-driven model using precipitation and temperature, which achieved higher predictive accuracy but underrepresented human influences, and a multi-parameter model incorporating LULC, soil moisture, groundwater-irrigated area, and domestic demand, which captured more realistic depletion dynamics despite slightly lower statistical performance. Both the climate-driven and multi-parameter LSTM models indicate a monsoon-dependent, highly seasonal recharge with weakening peaks and continued decline through 2045. Furthermore, scenario-based demand sensitivity showed that mean GWS increased from 635.20 mm to 639.82 mm under demand − 10%, but declined to 623.91 mm under total demand + 30%, showing a mean 95% confidence interval width of 9.24 mm and a mean model spread of 7.78 mm, indicating stable projection responses across model structures. The bias-corrected Machine Learning-driven GWS and predictor dataset provides a reproducible basis for groundwater assessments and model benchmarking in data scare regions. These findings provide actionable insights for groundwater management, supporting climate-resilient water resource planning, sustainable irrigation practices, and policy development in Madhesh Province and similar data-scarce alluvial regions.
The rapid growth of multimedia communication and cloud-based image transmission has increased the demand for secure and computationally efficient image encryption techniques. However, many existing chaos-based image encryption schemes rely on partially adaptive key generation, limited inter-channel interaction, and globally uniform diffusion mechanisms, which may reduce their resistance to statistical, differential, and chosen-plaintext attacks. To address these limitations, this paper presents an integrated chaos-based color image encryption framework, namely Reversible Cross-Channel Coupling and Dynamic Diffusion (RC3D), which combines SHA-256-based plaintext-dependent key generation, Piecewise Linear Chaotic Map (PWLCM)-driven permutation, reversible cross-channel coupling, and adaptive block-wise diffusion within a unified encryption architecture. Unlike conventional approaches that process RGB channels independently or employ fixed diffusion strategies, the proposed framework strengthens inter-channel dependency while preserving exact invertibility and enhances local randomness through adaptive diffusion without increasing computational complexity. Experimental results demonstrate that the proposed framework achieves information entropy exceeding 7.999, absolute correlation coefficients below 0.01, NPCR values greater than 99.6%, and UACI values exceeding 33.4%, indicating excellent resistance against statistical and differential cryptanalysis. Furthermore, the encrypted images exhibit uniform histogram distributions, high key sensitivity, and efficient computational performance, demonstrating that the proposed RC3D framework provides a practical and effective solution for secure real-time multimedia communication.
A complex computer worm that was first discovered in 2010, Stuxnet targeted industrial control systems and led to physical sabotage of production processes all without being detected. Symantec estimates that over 100,000 computers were infected by the malware, with around 60% of infections occurring in Iran. The worm disrupted 900-1,000 uranium-enrichment centrifuges at Iran’s Natanz facility. In this paper, we propose a stochastic time-delayed compartment model to simulate the propagation process of the Stuxnet cyber-virus in critical industrial control systems. The total population of computing nodes is divided into susceptible, infected, and damaged compartments, denoted by S(t), I(t), and P(t). Additionally, removable storage media such as USB devices are classified into two infection states: susceptible and infected, represented by $$U_s(t)$$ and $$U_i(t)$$. To capture realistic transmission dynamics, the model accounts for both stochastic perturbations and time delays, which reflect environmental fluctuations and latent infection effects. The malware-free and endemic equilibria are identified, and a coupled stochastic invasion threshold, $$\mathcal {R}_{0}^{S}$$, is derived from the joint dynamics of infected computers and infected removable storage devices. The threshold includes both direct computer-to-computer transmission and the indirect computer–USB–computer transmission cycle and is used to characterize local malware extinction and initial invasion. Real epidemiological data are incorporated to parameterize the model and support the numerical simulations. In numerical computations, we use the stochastic Euler scheme, the stochastic fourth order Runge-Kutta scheme and an original nonstandard finite difference scheme that is positivity and boundedness preserving. The findings of the simulations validate theoretical results and provide a way in which stochasticity and delays affect malware propagation. These findings can be considered a significant move towards achieving prevention and defense measures against advanced cyber threats on industrial infrastructures.
Precision agriculture increasingly relies on data-driven methods to address the challenges of crop stress and drought monitoring under changing climatic conditions. Hence, a deep learning (DL) based innovative framework is required for assessing crop stress dynamics, aimed at improving agricultural productivity and ensuring regional food security, because conventional models often struggle to capture subtle spatial and spectral variations, resulting in limited accuracy and generalizability. To overcome these challenges, an innovative and adaptive DL-based approach has been proposed to utilize multi-temporal Sentinel-2 satellite images collected over Haldharmau village, Gonda District, Uttar Pradesh. The model effectively integrates local spectral and spatial details with broader contextual features, enabling crop classification and stress detection to support precision agriculture. The crop stress and drought maps have been generated with the help of a CNN+ViT model (i.e., A hybrid DL-based classification model) based classified images in conjunction with NDVI and NDWI images to evaluate drought-induced crop variability across 2023, 2024, and 2025. The monthly (January to June) stress maps derived from this process provide valuable insights into temporal patterns of crop exposure. The proposed framework offers quantitative insights into temporal and spatial patterns of crop vulnerability and resilience across semi‐arid agricultural landscapes. Therefore, the proposed framework advances precision agriculture modeling by integrating satellite images, deep learning, and phenological analysis, and is readily transferable to other drought-prone regions with analogous crop systems.
Drought is a major constraint to sugarcane biomass production, necessitating the identification of resilient high-energy genotypes. This study evaluated 20 interspecific hybrids along with four commercial checks for biomass and bioenergy potential under irrigated and drought conditions during the 2021 plant crop and 2022 ratoon crop. Significant genetic variation was observed for biomass-related traits and energy yield under drought stress. The top-performing genotypes, G1 (SA 14–161), G8 (SA 14–111), and G20 (SA 14–34), consistently maintained higher dry biomass and energy production under moisture stress, retaining approximately 80–95% of their dry biomass and producing about energy about 880–1000 GJ ha−1 under drought. Correlation analysis showed that dry matter, fresh biomass, and dry biomass were strongly and positively associated. Cluster analysis grouped the genotypes into three drought-response classes, with G1, G8, G20, G15, and G5 clustering among the most drought-tolerant genotypes. Stress Tolerance Index (STI), Mean Productivity Index (MPI), Abiotic Tolerance Index (ATI), and Gold_M were identified as the most informative indices for selecting drought-tolerant, high-biomass genotypes. Genotype-by-trait biplot and cumulative ranking consistently identified G1 as the best-performing genotype, followed by G8 and G20. These superior interspecific hybrids represent promising genetic resources for developing drought-tolerant high-bioenergy cane for subtropical environments.
Nepheline (NaAlSiO₄) and Combeite (Na₂Ca₂Si₃O₉) are silicates belonging to the alkaline silicate ceramic family, recognized for their remarkable structural and functional properties. The development of composites integrating these both phases makes it possible to combine the structural and chemical advantages of each, paving the way for the design of innovative biomaterials. In this study, a Nepheline-Combeite bioceramic was successfully synthesized using an alkali activation method from industrial by-products, derived from coal gangue (as the source of SiO₂) and limestone powder (as the source of CaCO3). The bioceramic was synthesized at room temperature using sodium hydroxide (NaOH) as the alkaline activator at different concentrations (2, 3, and 4 M) to evaluate the influence of alkali concentration on bioceramic formation. The mixtures were activated with an alkaline activator (NaOH) under magnetic stirring for 6 h, then heat-treated at 700 °C. Furthermore, the bioactivity of the synthesized bioceramics was evaluated by immersing them in simulated body fluid (SBF) and artificial saliva (AS) at 37 °C for 48 h. The structural, morphological, and optical properties of the obtained bioceramics were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy (RS), diffuse reflectance UV–visible spectroscopy (DRS), transmission electron microscopy (TEM), and selected area electron diffraction (SAED). The XRD analysis confirmed that NaOH concentration significantly affected bioceramic formation, with 4 M identified as the optimal concentration for the formation of the Nepheline–Combeite bioceramic. Furthermore, SEM observations revealed lamellar nepheline particles, while UV-Vis analysis showed an optical band gap of 3.02 eV with maximum absorption at 304 nm. The XRD analyses after only 48 h immersion in simulated media, complemented by scanning electron microscopy (SEM) observations, showed that these bioceramics rapidly promote the formation of a bone-like hydroxyapatite (Ca₁₀(PO₄)₆(OH)₂) layer, demonstrating their potential as biomaterials for bone regeneration. This study contributes to the United Nations Sustainable Development Goal 12 (Responsible Consumption and Production) through the valorization of industrial by-products into high-value bioceramics.
Thyroid cancer is affecting many people all around the world and is rising in incidence and prevalence. It is also highly treatable if diagnosed and addressed on time. This study aims to investigate the time intervals between symptom onset and seeking medical care, and also the time interval between diagnosis and treatment in thyroid cancer, and the factors influencing these intervals in Iran. This cross-sectional multicenter study was conducted in southern Iran on 189 adult patients with thyroid cancer. Data were collected using a checklist developed following a thorough literature review. Non-parametric tests (Mann–Whitney and Kruskal–Wallis) and regression analyses were used to assess associations between measured time intervals and demographic, psychosocial, and clinical variables. The median patient delay was 90 days (Q1–Q3: 30–365), and the median diagnosis-to-surgery interval was 28 days (Q1–Q3: 14–44). Patients with more prominent symptoms, such as dyspnea, dysphagia, neck swelling, and hoarseness, and those who searched their symptoms online showed shorter delays. Patients with depression and those in the age group above 40 had a significantly longer delay. People with higher education, dyspnea, married patients, and those who had family support underwent surgery more quickly. Patients who changed their doctor had longer delays. Significant variability in diagnostic and treatment intervals was observed in this regional cohort of thyroid cancer patients. Several modifiable patient-related and system-related factors associated with delays were noticed. Targeted interventions to optimize referral pathways, enhance patient awareness, and improve access to healthcare, especially for vulnerable populations, may reduce delays and improve timely surgical management.
Occupational fatigue is a major contributor to workplace hazards, injuries and health adversities in the emergency department (ED). To address the existing gap in the literature, the goal of the current study was to identify work system factors associated with occupational fatigue among Iranian ED nurses. This study adopted a sequential exploratory (Qual → Quan; two stages) mixed methods design. The function of this mixed methods design was “development”, in which the insights gained from a qualitative phase (n = 17) formed the foundation for a quantitative stage (n = 166) through sequential transformation of qualitative data into a conceptual model and a fatigue exposure survey. Analyses were performed via the SPSS v26, MAXQDA2018 and SmartPLS v3 software. Findings from the qualitative phase informed the development of a conceptual model of occupational fatigue, identifying twenty major themes across four levels of the work system. Using the developed survey, field study results demonstrated not only the system elements with the greatest association but also their interdependence and potential fatiguing mechanisms. The developed conceptual model and survey represent a preliminary, holistic framework for systematically identifying and assessing fatigue-related risk factors in Iranian emergency nursing, although further psychometric validation and evaluation across diverse healthcare settings are warranted.
The purpose of this study was to assess the relationship between baseline heart rate variability (HRV) and bioelectrical phase angle and hamstring recovery at 72 and 96 h after a DOMS-inducing protocol in physically active men. Following experimentally induced hamstring DOMS in 46 physically active male participants aged 20.78 ± 2.19 years, recovery outcomes including maximal isometric strength, active knee extension range of motion (AROM), pressure pain threshold, and perceived soreness were evaluated at baseline and at 48, 72, and 96 h. Associations between baseline physiological markers and changes in recovery outcomes were subsequently examined using correlation analyses. Statistical significance was set at p < 0.05. Changes in perceived soreness at 72 h showed moderate positive correlations with baseline HRV (r = 0.45), DFA-alpha 1 (r = 0.41), and stress index (r = 0.35), and moderate negative correlations with SDNN (r = -0.33) and RMSSD (r = -0.37). At 96 h, correlations with HRV (r = 0.33), DFA-alpha 1 (r = 0.38), SDNN (r = -0.29), and RMSSD (r = -0.34) remained significant (all p < 0.05). In addition, at 72 h post DOMS left limb ROM demonstrated a moderate positive correlation with DFA-alpha 1 (r = 0.32), while changes in right limb ROM were moderately positively correlated with phase angle (r = 0.31). Additionally, changes in left limb peak torque exhibited a moderate negative correlation with HRV (r = -0.32). Baseline HRV-derived indices, particularly SDNN and RMSSD, were associated with perceived soreness in the later stages of recovery from hamstring DOMS. On the other hand, relationships with objective recovery outcomes were less consistent. Phase angle showed limited and outcome-specific associations, suggesting it may be less sensitive for monitoring acute recovery after eccentric exercise. These results suggest that autonomic status may be involved in interindividual variability in the perceptual response to muscle damage.
