Search Results (150)

This paper proposes a spatial-functional two-dimensional hierarchical group decision-making (HGDM) spectrum management architecture for emergency communication unmanned aerial systems (EC-UAS). The architecture handles the highly dynamic topology, large node population, and differentiated task priorities that characterize EC-UAS. Using the spectrum management properties of EC-UAS, we develop a discrete-time closed-loop dynamic model of the architecture and design adaptive hierarchical iteration rules. We prove global stability of the model under the stated assumptions and analyze the convergence of the state error, deriving its theoretical upper bound and the relationship between convergence steps and accuracy. An input-to-state stability analysis further demonstrates that the system state error remains bounded under dynamic disturbances, with its magnitude scaling with the disturbance bound. Simulations verify the effectiveness of the architecture and the correctness of the theoretical analysis. Full article

Adult deep neck infection (DNI) is a potentially life-threatening condition associated with significant morbidity and mortality. Contemporary large-scale data characterizing long-term microbial evolution and clinical outcome trends in adult DNI remain limited. This study aimed to investigate temporal changes in bacterial pathogens, treatment strategies, and clinical outcomes of adult DNI across an 18-year period. This is a retrospective multicenter cohort study using the Chang Gung Research Database (CGRD) and a de-identified nationwide database from Taiwan’s largest medical system. Hospitalized adult patients (aged ≥18 years) with DNI from 2006 to 2023 were identified and stratified into two consecutive 9-year epochs (Epoch 1: 2006–2014, n = 5512; Epoch 2: 2015–2023, n = 6551). Demographics, comorbidities, treatment modalities, disease severity, and microbiological profiles were analyzed. Bacterial isolates were evaluated at genus and species levels, with methicillin-sensitive Staphylococcus aureus (MSSA) and methicillin-resistant S. aureus (MRSA) assessed separately. Among 12,063 adult DNI patients (Epoch 1: n = 5512; Epoch 2: n = 6551), antibiotic-only treatment increased (77.3% to 83.2%) and surgical intervention decreased (22.7% to 16.9%). Descending necrotizing mediastinitis decreased markedly (2.7% to 0.7%) and in-hospital mortality declined (7.7% to 6.4%). Poly-microbial infections increased substantially (45.3% to 53.7%). Among facultative anaerobic and aerobic isolates, the Streptococcus anginosus group (SAG) emerged as a clinically important pathogen. Among anaerobes, Prevotella displaced Peptostreptococcus as the dominant genus, with Peptostreptococcus micros (Parvimonas micra) and Prevotella buccae emerging as prominent species. Over 18 years, adult DNI in Taiwan demonstrated significant improvements in clinical outcomes, with marked reductions in mediastinitis and in-hospital mortality. Concurrently, poly-microbial infections increased, and the SAG, Peptostreptococcus micros (Parvimonas micra), Prevotella, and anaerobic organisms emerged as clinically important pathogens, underscoring the need for empiric antibiotic regimens providing broad aerobic and anaerobic coverage. Although microbiological profiles vary geographically, these findings provide a contemporary evidence base to guide empiric antibiotic selection, inform surgical decision-making, and identify high-risk adult patients with DNI. Full article

Background: Autism Spectrum Disorder (ASD) can be conceptualized not as a static condition but as a dynamic disorder of developmental regulation, in which neuroplastic potential is translated into functional gains only through structured, experience-dependent therapeutic input. Neuropsychomotor Therapy of Early Development (TNPEE) and Therapy in Aquatic Motor Activities (TAMA) have shown differential efficacy in children with ASD, but whether chronological age moderates the magnitude of therapeutic response—or whether the characteristics of the intervention itself are the stronger determinant of outcome—has not been systematically examined. Methods: This was a non-randomized, exploratory secondary stratified analysis of a multicenter 18-month longitudinal cohort (N = 77 children with ASD, age 4–12 years) allocated to three groups: TAMA only, TNPEE combined with TAMA (TAMA+TNPEE), or TNPEE only. Participants were stratified into a younger (≤6 years, n = 16; TAMA n = 8, TAMA+TNPEE n = 3, TNPEE n = 5) and an older (>6 years, n = 61; TAMA n = 18, TAMA+TNPEE n = 22, TNPEE n = 21) subgroup. Adaptive functioning was assessed with the Vineland Adaptive Behavior Scales, Second Edition (VABS-II) at baseline and 18 months. Between-stratum comparisons used the Mann–Whitney U test; Spearman rank correlations and ANCOVA models with Age × Treatment interaction terms were used to test age moderation within a neuroplasticity-recruitment framework. Results: In the TAMA-only group, younger children showed significantly smaller adaptive gains than older children across the Composite (2.22 ± 1.14 vs. 3.53 ± 0.76, p = 0.012), Communication (p = 0.041), and Socialization (p = 0.016) subscales, with significant positive Spearman correlations between age and adaptive gains (r = 0.397–0.437). Simple slope analysis confirmed a significant age effect within the TAMA group (b = 0.231, SE = 0.083, p = 0.010) but not in the TNPEE (b = 0.133, p = 0.157) or TAMA+TNPEE (b = 0.135, p = 0.198) groups. The formal Age × Treatment interaction term in the ANCOVA did not reach statistical significance (all p > 0.18). As this interaction test is the formal statistical test of differential age moderation, the simple-slope pattern above should be regarded as exploratory rather than confirmatory. Treatment group accounted for the large majority of variance in adaptive gains (partial η² = 0.78–0.95), whereas age explained only a small proportion of outcome variability. ASD severity level transitions were comparable across age strata in TNPEE-based groups. Conclusions: In this exploratory secondary analysis, TNPEE-based interventions were associated with adaptive gains that did not differ significantly by age across the 4–12 year age range, whereas aquatic therapy alone showed an age-dependent pattern favoring older children. These findings are hypothesis-generating and consistent with a neuroplasticity-recruitment model in which therapeutic architecture—rather than chronological age alone— may be associated with how effectively latent plastic potential is translated into developmental gains, although the non-significant Age × Treatment interaction and the small, imbalanced subgroups (including n = 3 in the youngest TAMA+TNPEE stratum) mean these findings require confirmation in adequately powered, prospectively designed studies, with implications for clinical decision-making regarding therapy timing and design. Full article

The discharge of metal-rich mining effluents and waste into marine environments represents a significant environmental challenge, with impacts extending from coastal to deep-sea ecosystems. This study presents a scoping review conducted in accordance with PRISMA-ScR guidelines to identify, classify, and synthesise evidence on the effects of large-scale mining waste disposal in coastal and marine settings. Searches of Web of Science, Scopus, and relevant grey literature sources yielded 147 eligible publications from an initial dataset of 563 records. The evidence reviewed enabled the identification of 81 distinct impacts systematically grouped into nine primary categories spanning physical, geotechnical, hydrodynamic, geochemical, biological, ecological, and socio-economic dimensions. Marine tailings deposits behave as dynamic systems that facilitate the transport of contaminants beyond disposal sites through processes such as plume dispersion, sediment resuspension, turbidity currents, and porewater exchange. Geochemical reactions and geotechnical failures can increase environmental vulnerability, a risk further exacerbated by ocean warming, acidification, and changing circulation patterns. Beyond highlighting the need for precautionary, integrated, and adaptive management approaches, this review provides the first comprehensive global inventory of impact categories and assessment factors associated with marine mining waste disposal, establishing a reference framework to guide future research, environmental management, regulatory evaluations, and industry decision-making. Full article

Three-dimensional instrumented gait analysis is widely used to guide surgical decision-making in children with cerebral palsy (CP), but its interpretation is time-consuming and prone to inter-rater variability. In this single-centre pilot study, we investigated whether a generative large language model (LLM) could consistently generate gait deviation findings and surgical procedure suggestions that align with expert judgement. Kinematic features for lower-limb joints across the gait cycle, stance, and swing were extracted from eight children with unilateral CP using the open-source GaitSharing Toolkit and a structured prompt, then submitted three times per patient to OpenAI’s GPT-5.5 model. The model assessed 28 kinematic deviations and 12 surgical procedure groups using majority voting. One gait analyst and two paediatric orthopaedic surgeons independently rated outputs on a 0–2 ordinal scale, blinded to all clinical information beyond the kinematic curves and diagnosis. Agreement was summarised descriptively as the percentage of the maximum attainable score with 95% confidence intervals (CIs), and quadratic-weighted Cohen’s kappa was used to quantify inter-surgeon agreement. Agreement with the gait expert was highest at the hip (90.6%) and lowest at the knee, particularly in the transverse plane (65.2%). For surgical procedures, agreement with the LLM reached 83.9% and 73.4% for the two surgeons, with the tibialis anterior procedure showing the lowest concordance. Inter-surgeon agreement was 79.2% (95% CI 71.9–85.4) with a kappa of 0.59 (0.47–0.70), indicating moderate agreement. The LLM showed high self-consistency (>90% across runs). These preliminary findings suggest that generative LLMs may be feasible as assistive tools in clinical gait analysis for deviation detection and future treatment planning and should be interpreted as hypothesis-generating, warranting confirmation in larger, more diverse cohorts. Full article

Dynamic scheduling systems often rely on structured domain knowledge for real-time optimization and decision making. However, much of this knowledge originates from unstructured text, such as operational manuals, maintenance logs, and expert records. Extracting machine readable decision categories from such raw corpora without manual intervention remains a significant challenge. To address this issue, we propose a framework using large language models (LLMs) that automatically discovers latent semantic dimensions from unstructured text without any predefined schema. The LLM performs unsupervised semantic clustering via hierarchical prompts, autonomously inducing concept categories. We validate our method on Traditional Chinese Medicine (TCM), a domain characterized by highly nested, multi-dimensional knowledge structures. This makes it a challenging and representative benchmark for testing the generalizability of knowledge discovery methods. From 12,163 TCM herb records (64,107 independent short sentences), the DeepSeek-V3.2 model with batch-wise clustering generates 458 raw labels. These labels are further merged into 22 broad semantic categories. The resulting groups can be interpreted as potential decision dimensions or state abstractions for knowledge driven optimization systems. For evaluation, we adopt a dual approach: (1) semantic cosine similarity computed by Qwen3-Embedding-8B shows that 19 concepts achieve an average internal coherence above 60%; (2) domain experts rate the reasonableness of sampled concepts on a 1 to 5 scale, yielding an average score of 4.64. Experimental results demonstrate that our method autonomously induces high quality, interpretable concept taxonomies from large-scale unstructured text. We further validate the transferability of the method on an aircraft assembly manual, where it induces 12 semantically coherent concepts (average example coherence 59.6%) that align well with a domain ontology. This work offers a transferable paradigm for integrating unstructured domain knowledge into intelligent scheduling and optimization frameworks, with potential implications for dynamic scheduling systems. Full article

Background: Upper-limb lymphedema after breast cancer treatment is associated with pain, functional limitations, and impaired quality of life. Although complex decongestive therapy (CDT) is standard conservative care, prospective evidence regarding factors associated with clinically meaningful response remains limited. We evaluated the short-term outcomes following Phase-I CDT and identified factors associated with clinically meaningful pain reduction. Methods: A prospective observational study was conducted in 94 women with breast cancer-related lymphedema undergoing a standardized 14-day Phase-I CDT program. Outcomes included limb circumference, pain intensity measured using the Visual Analogue Scale (VAS), and quality of life assessed with LYMQOL-Arm. Clinically meaningful improvement was defined a priori as a reduction of at least 2 points on the VAS (ΔVAS ≥ 2). Analyses included paired t-tests, Cohen’s d, multivariable logistic regression, analysis of covariance (ANCOVA), and receiver operating characteristic (ROC) analysis. Results: Significant reductions in limb circumference were observed across all measurement levels (3.08–5.83%; all p < 0.001). Pain intensity decreased from 5.53 ± 2.15 to 2.82 ± 1.41, with a mean reduction of 2.71 points (95% CI 2.32–3.11; p < 0.001) and a very large effect size (Cohen’s d = 1.40). All LYMQOL domains improved significantly. Higher baseline pain intensity was associated with a greater likelihood of achieving the predefined criterion for clinically meaningful improvement (OR 3.03; 95% CI 1.91–4.80), while older age was associated with reduced odds of response (OR 0.90; 95% CI 0.85–0.96). Baseline pain intensity demonstrated good discriminative performance (AUC 0.85). Circumference changes were not correlated with subjective improvement. Conclusions: Following the 14-day Phase-I CDT program, statistically significant reductions in total limb circumference, clinically meaningful pain reduction, and significant improvements in quality of life were observed. Exploratory analyses demonstrated an association between baseline pain intensity and the predefined responder outcome; however, this association is structurally influenced by the mathematical relationship between baseline VAS and the responder definition, baseline-dependent opportunity for improvement, and regression to the mean. Therefore, it should not be interpreted as evidence of an independent predictive effect and requires external validation before being considered for patient stratification or clinical decision-making. Because of the observational pre–post design without a control group, the observed changes cannot be attributed specifically to Phase-I CDT. Full article

Reliable medium–long-term wind energy projection is essential in the planning, financing, and operation of large-scale offshore wind development. This study classified projection methods into three categories: statistical/empirical and climate-signal-driven methods, dynamical models with reanalysis and regional downscaling, and machine/deep learning and hybrid methods for bias correction, downscaling, and direct data-driven projection. Then, this study reviewed the technical framework, representative studies, and comparative strengths and limitations. The main finding was that the state of the art increasingly converged on “dynamical simulation plus statistical or machine learning correction”. Next, seven main bottlenecks, along with the countermeasures, were systematically presented: (i) difficult data quality control and insufficient observational representativeness, especially offshore; (ii) divergent, even contradictory, conclusions for the same region across data sources and research groups; (iii) large uncertainty in extrapolating 10 m winds to the continually rising turbine hub height; (iv) difficulty in quantifying and communicating non-stationarity and uncertainty to decision-makers; (v) engineering conversion errors from projected “wind resource” to deliverable “electricity”; (vi) systematic biases in the marine atmospheric boundary layer, strong winds, and extreme conditions; and (vii) unresolved reliability, interpretability, and out-of-distribution generalization of AI models. Correspondingly, three mutually reinforcing strands of countermeasures were proposed: first, strengthening the observational and benchmarking foundation through unified, open, quality-controlled observation networks with data-provenance standards and shared reference datasets and intercomparison protocols; second, advancing physics–data integration and uncertainty quantification through hybrid and physics-informed correction, regime-specific bias correction of boundary-layer and extreme-wind errors, and probabilistic frameworks that delivered and clearly communicated credible intervals; and third, closing the resource-to-electricity gap by embedding power-curve convolution, wake-loss modeling, and availability and technology derating into the projection workflow, with the aim of improving medium–long-term wind energy projection accuracy. Full article

High-resolution remote sensing visual question answering (RS-VQA) requires models to identify question-relevant regions and reason over fine-grained visual evidence. However, existing vision–language models usually rely on fixed global image inputs, which may lose critical local details in ultra-high-resolution imagery and struggle with sparse informative regions, large object-scale variations, and complex spatial layouts. To address these challenges, this paper proposes a crop-tool-augmented active perception framework with reinforcement learning. The framework introduces structured tokens to explicitly organize the reasoning process into question understanding, cropping decision-making, local evidence acquisition and final answer generation. Based on this design, the model can actively determine whether a cropping operation is needed and select task-relevant regions for further inspection. To enable stable tool-use and multi-turn reasoning in a compact vision–language model, we construct teacher-guided cropping reasoning trajectories from high-resolution images, question–answer pairs, and annotated regions in the LRS-GRO dataset, and use them for cold-start supervised fine-tuning of Qwen2.5-VL-3B. Furthermore, we introduce Group Relative Policy Optimization to refine the model’s active perception policy. A region-aware reward function is designed by integrating output-format constraints, reference-region coverage, answer semantic consistency, and cropping penalties, which encourages compact and informative region selection while reducing redundant tool invocations. Experiments on VRSBench, MME-RealWorld-RS, XLRS-Bench, and LRS-VQA demonstrate that the proposed method achieves competitive overall performance compared with closed-source, open-source, and remote-sensing-specific vision–language models, and obtains the best or comparable results on most benchmarks. Ablation studies further verify the effectiveness of structured supervised fine-tuning, reinforcement learning optimization, and the proposed reward design. Full article

Background/Objectives: Chronic non-specific low back pain (CNLBP) is a leading cause of disability worldwide. Although several randomized trials have evaluated treatment effectiveness, less attention has been given to the responsiveness of outcome measures used to assess clinical change. This study aimed to evaluate the internal and external responsiveness of commonly used outcome measures in individuals with CNLBP. Methods: This study is a secondary analysis of a randomized controlled trial. Participants were analyzed as active and placebo groups and assessed at baseline, post-intervention, and follow-up. Internal responsiveness was evaluated using standardized mean differences (SMD) and standardized response means (SRM). External responsiveness was assessed using anchor-based approaches, including correlations with the Global Rating of Change Scale (GRCS) and receiver operating characteristic (ROC) curve analysis. Results: Outcome measures demonstrated moderate to high internal responsiveness, with large effect sizes observed for pain intensity (NRS) and quality of life (EQ-5D-3L). However, external responsiveness was limited, with all instruments presenting area under the curve (AUC) values below 0.70. The Bournemouth Questionnaire showed the highest discriminative performance among the instruments. Conclusions: The evaluated instruments were sensitive to detecting change at the group level but showed limited ability to discriminate clinically meaningful improvement at the individual level. These findings support the use of combined outcome measures to improve clinical interpretation and decision-making in CNLBP. Full article

Background/Objectives: No study has investigated the effect of the catechol-O-methyltransferase (COMT) Val158Met rs4680 polymorphism in cognitive and executive performance in migraine. The current study investigated the potential influence of the Val158Met rs4680 polymorphism in cognitive performance/executive function in women with migraine. Methods: One hundred and forty women with migraine (70 chronic and 70 episodic) and 70 healthy controls completed the following neurocognitive tests (D2 Attention test and Rey–Osterrieth Complex Figure) and executive functions (subtest “Digits D/R/I” of the Wechsler Adult Intelligence Scale WAIS-IV battery for, the 5-Digit test, the Symbol Search for and the Zoo Test) for evaluating selective attention, visual perception, working memory, mental inhibition, processing speed and planning/decision making, respectively. Thus, three genotypes (Val/Val, Val/Met, and Met/Met) of the Val158Met polymorphism were identified by polymerase chain reaction. The effect of group and Val158Met genotype in neurocognitive tests and executive functions was evaluated with multivariate analysis of covariance (MANCOVA). Results: The MANCOVA revealed a significant Val158Met polymorphism* group interaction on neurocognitive performance (Wilk’s λ = 0.393, F [76,688] = 2.425, p < 0.001, n2p = 0.208, 1 − β = 0.999), not influenced by age (Wilk’s λ = 0.920, F [19,174] = 0.743, p = 0.734, n2p = 0.035, 1 − β = 0.120), educational level (Wilk’s λ = 0.875, F [19,174] = 1.024, p = 0.440, n2p = 0.047, 1 − β = 0.190) and prophylactic medication (Wilk’s λ = 0.855, F [19,174]= 1.000, p = 0.467, n2p= 0.145, 1 − β = 0.686). Post hoc analyses revealed that women with chronic migraine with the Met/Met genotype exhibited domain-specific better performance (i.e., higher selective attention, visuospatial memory) and executive functioning (i.e., working memory, planning/decision making) than those women with chronic migraine carrying Val/Val or Val/Met genotypes. Conclusions: We found an association of the Met/Met genotype with neurocognitive performance/executive functioning, particularly in women with chronic migraine since women with chronic migraine carrying the Met/Met genotype showed domain-specific better cognitive performance/executive functioning than those with the Val allele. Future studies including large sample sizes from different geographic locations are needed to better generalizability and validity of the current results. Full article

To address the power quality deterioration caused by the large-scale integration of grid-following (GFL) electric vehicle charging stations, this paper proposes a comprehensive assessment method based on relative entropy distance fusion weighting and a dynamic ideal solution VIKOR algorithm. First, a multi-dimensional power quality evaluation system is constructed, focusing on key indicators such as voltage deviation, frequency deviation, three-phase imbalance, and harmonic distortion, to accommodate the operational characteristics of vehicle-to-grid (V2G) under grid-following and grid-forming (GFM) interaction scenarios. Building on this, the three-scale analytic hierarchy process (AHP) is employed to determine subjective weights, while the divergence-maximized entropy weight method is used to derive objective weights. The relative entropy distance model is then applied to achieve adaptive fusion of subjective and objective weights, resulting in an optimal combined weighting. Subsequently, a dynamic ideal solution mechanism is introduced into the VIKOR algorithm, where the range of the ideal solution is adjusted based on the indicator weights to enhance the discrimination of key indicators. By comprehensively calculating the group utility value, individual regret value, and compromise evaluation index, accurate ranking and performance assessment of different mitigation schemes are achieved. Using measured data from a vehicle-grid interaction demonstration base for analysis, the results demonstrate that the proposed method can effectively quantify the actual effects of various mitigation schemes, providing decision-making support for power grid safety and stability under high penetration of renewable energy and converter-interfaced generation. Full article

Energy efficiency retrofits are widely promoted for public buildings, yet evidence from large-scale real-world projects remains limited compared with simulation-based assessments. This study leverages measured pre- and post-retrofit operational data from 530 public building retrofit projects across 11 provinces/municipalities in China to quantify realized energy-saving performance and screening-level cost-effectiveness across building types and climate zones. Wilcoxon and Kruskal–Wallis tests were employed to ensure statistical rigor. Retrofit measures were grouped into seven categories (e.g., HVAC, lighting, envelope, monitoring/management), and a median-based four-quadrant framework was employed to characterize investment–savings profiles by climate zone and building function. Across the full sample, mean energy use intensity decreased by 19.1%, with 99.2% of projects achieving positive savings. Savings varied markedly by building type: commercial and hotels achieved the highest savings intensities (26.5–28.0 kWh/(m²·a)), while education and cultural buildings generally showed lower gains, with some projects having < 10 kWh/(m²·a). Technology performance exhibited distinct climate and building suitability. Envelope retrofits were most effective in the Cold and Hot Summer–Cold Winter zones (13.30–22.06 kWh/(m²·a)) but yielded limited benefits in the Hot Summer–Warm Winter zone (~1.73 kWh/(m²·a)). HVAC and lighting upgrades delivered comparatively stable savings across climates and building types and dominated retrofit portfolios. Based on these findings, we propose a tiered strategy: prioritizing HVAC and envelope upgrades for high-load sectors while focusing on low-cost optimizations for educational facilities to mitigate investment risks. The findings provide large-scale empirical evidence to support climate- and building-specific retrofit prioritization and investment decision-making under real-world operating conditions. Full article

Background: Hospital readmission among patients with diabetes remains a major challenge for healthcare systems, contributing to increased costs and adverse patient outcomes. Early identification of high-risk patients may support targeted interventions and improved care management. Objectives: This study aimed to develop and rigorously evaluate a machine learning framework for predicting 30-day hospital readmission in patients with diabetes using a large multi-institutional clinical dataset. Methods: The study utilized the Diabetes 130-US Hospitals dataset from the UCI Machine Learning Repository, comprising 101,766 hospital encounters. Data preprocessing included missing-value handling and feature engineering. Several machine learning models were evaluated, including Logistic Regression, Random Forest, XGBoost, and LightGBM, alongside a stacking ensemble model. Model performance was assessed using nested cross-validation (5 outer folds, 3 inner folds), probability calibration via Platt scaling, and statistical robustness through 1000 bootstrap resamples. Clinical utility was evaluated using decision curve analysis and clinical impact curves, while SHAP analysis was applied for model interpretability. Results: The stacking ensemble model achieved a nested cross-validated ROC–AUC of 0.664 and a calibrated AUC of 0.688, with a Brier score of 0.094. Risk stratification demonstrated a clear gradient between low- and high-risk groups, and decision curve analysis indicated positive clinical net benefit across relevant decision thresholds. Conclusions: The proposed machine learning framework provides a robust and clinically interpretable approach for predicting 30-day hospital readmission in diabetic patients, with potential utility for supporting clinical decision-making and care management. Full article

Background: Cemented and cementless fixation techniques in total hip arthroplasty (THA) each present distinct biomechanical properties and perioperative risk profiles. While cementless fixation has gained increasing popularity, large-scale nationally representative comparisons of perioperative outcomes between cemented and cementless elective THA remain limited. This study aimed to compare complication rates, healthcare utilization, and temporal trends between cemented and cementless elective THA using the National Inpatient Sample. Methods: A retrospective cohort study was conducted using the National Inpatient Sample database from 2016 to 2021. Adult patients undergoing elective primary total hip arthroplasty were identified using ICD-10-PCS codes and categorized into cemented and cementless fixation groups. Patient demographics, comorbidities, indications, postoperative complications, length of stay, hospital charges, and in-hospital mortality were compared. Multivariate logistic regression analysis was performed to evaluate the independent association between fixation type and postoperative complications while adjusting for demographic, clinical, and hospital-level variables. Results: A total of 81,668 elective THAs were identified, including 40,290 cemented (49.33%) and 41,378 cementless (50.67%) procedures. Cemented THA was associated with a shorter length of stay (2.09 ± 1.88 vs. 2.26 ± 2.47 days, p < 0.001) and lower total hospital charges ($65,584.53 ± 48,797.21 vs. $72,186.84 ± 49,860.20, p < 0.001). Unadjusted analyses demonstrated higher rates of acute kidney injury and sepsis in the cementless group. After multivariate adjustment, cemented fixation was associated with lower odds of acute kidney injury (OR 0.87, 95% CI 0.79–0.96, p = 0.004). However, cemented THA was associated with higher odds of postoperative delirium (OR 1.20, 95% CI 1.02–1.42, p = 0.030), blood transfusion (OR 1.27, 95% CI 1.17–1.37, p < 0.001), and periprosthetic fracture (OR 1.32, 95% CI 1.02–1.71, p = 0.035). Rates of myocardial infarction, pneumonia, venous thromboembolism, urinary tract infection, and in-hospital mortality were similar between groups. Temporal analysis demonstrated comparable utilization trends, with a decline in elective procedures during 2020–2021. Conclusions: In this nationwide analysis, cemented total hip arthroplasty was associated with lower risk of acute kidney injury, shorter length of stay, and lower hospital charges, but higher odds of postoperative delirium, blood transfusion, and periprosthetic fracture compared with cementless fixation. These findings highlight distinct perioperative risk profiles between fixation strategies and may assist surgeons in individualized decision-making for elective total hip arthroplasty. Full article