Search Results (1,838)

Studies on the use of LPG in maritime applications mostly evaluate emissions, engine performance, or system safety separately; approaches that integrate experimental findings with ship-level risks remain limited. This study aims to evaluate the trade-offs between the environmental advantages of LPG and energy performance and safety requirements within a common decision support framework. In the experimental phase, a single-cylinder gasoline–LPG spark-ignition engine was tested at five LPG mixture ratios and six load levels between 500–3000 W; specific fuel consumption, thermal efficiency, CO, CO₂, and HC were measured. Using legislation, the literature, and engineering evaluation, 38 failure types were identified from the experimental findings and prioritized using FMEA and entropy-weighted multi-criteria decision-making methods. The final ranking was obtained using the Borda method, and inter-method agreement and ranking stability were validated with sensitivity analyses. The results showed that increasing the LPG ratio reduced CO, CO₂, and HC emissions, but higher ratios increased fuel consumption and decreased thermal efficiency. Specific fuel consumption, gas detection error, and thermal efficiency were identified as the three most prioritized risks. The findings reveal that the emission benefits of LPG in maritime applications should be evaluated in conjunction with sensing, insulation, emergency stop reliability, and energy performance. This integrated approach provides a scientific basis for balanced and transparent fuel decisions. Full article

Cost control in engineering, procurement, and construction (EPC) public works is shaped by interacting drivers, nonlinear feedback, and qualitative judgments. Existing studies usually apply the three relevant method families separately: DEMATEL-ISM maps causal structure but does not propagate hesitation-aware expert judgments into cost trajectories; fuzzy systems represent uncertainty but commonly lack a verified causal hierarchy; and system dynamics (SDs) capture dynamic accumulation but often rely on crisp inputs. The resulting absence of a traceable causal screening to uncertainty to dynamic cost link is the specific gap addressed in this study. We therefore develop a transparent three-stage pipeline combining the Decision-Making Trial and Evaluation Laboratory–Interpretive Structural Modeling (DEMATEL-ISM) method, triangular intuitionistic fuzzy reasoning (TIFR), and SDs. DEMATEL-ISM identifies the causal hierarchy; TIFR represents membership, non-membership, and hesitation in design complexity and human–technology synergy judgments; and SDs evaluate stage-specific cost trajectories. Recalculation from the supplied 17 × 17 direct influence matrix produced a six-level hierarchy in which senior management decision-making capability and the level of integration occupy the two deepest driving levels. For the Urumqi Civic Center case, the baseline terminal cost absolute percentage error was 0.492%. A coordinated intervention scenario shifted the simulated terminal cost by CNY 12.1243 million (6.1%) relative to the baseline; this is a model-based scenario difference, not an observed project saving. Integration had the largest simulated effects on design and transportation costs, whereas senior management decision-making capability had the largest effects on procurement and construction costs. Security cost curves showed a complementary pattern between managerial capability and workers’ professional competence, but no statistical interaction effect is claimed. The framework is intended for within-case scenario comparison and intervention prioritization; multi-project and time-series validation remains necessary. Full article

Peak secant stiffness degradation in reinforced concrete (RC) columns governs how lateral stiffness is redistributed and where deformation concentrates during repeated earthquake loading. Fixed stiffness-reduction factors and prescribed degradation functions cannot simultaneously account for member properties and deformation demand. This study develops a hierarchical framework for predicting the deformation-dependent peak secant stiffness of rectangular RC columns. Separate specimen-level models estimate the first-reference peak secant stiffness, K₀, and the drift capacity, θ_u, from mechanical descriptors. A physics-guided cumulative sequence model then predicts the normalized stiffness-degradation path, with deformation demand normalized by the predicted drift capacity, θ_u. Non-negative degradation increments ensure bounded, monotonic stiffness loss. On the test set, the selected K₀ predictor achieved R² = 0.935, MAE = 4.098 kN/mm, and RMSE = 7.666 kN/mm; the selected θ_u predictor achieved R² = 0.927, MAE = 0.346 percentage points, and RMSE = 0.512 percentage points. With normalization based on predicted drift capacity, the degradation submodel achieved R² = 0.9272, MAE = 0.0542, and RMSE = 0.0788, substantially outperforming constant, linear, exponential, and power-law global functions. At frame level, the predicted $\hat{\mathrm{K}}$₀, $\hat{\mathsf{\theta }}$_u, and degradation ratio are incorporated into an equivalent secant-stiffness procedure. Column shear is obtained directly from the updated stiffness and interstory displacement, without a separate strength-degradation law. Comparisons with OpenSeesPy fiber-frame analyses of 12 two-, three-, and four-story frames yielded mean errors of 8.26–21.88% for base shear, 11.42–24.54% for story shear, and 10.19–23.51% for column shear; the mean absolute error in structural stiffness ratio ranged from 0.019 to 0.068. These comparisons establish a numerical consistency benchmark for the adopted frame configurations and modeling assumptions. The method is intended for stiffness-based peak-response assessment, not as a complete hysteretic constitutive model. Full article

Grouping vehicles with similar paths is important for traffic analysis, but camera-image trajectories are distorted by perspective, and unsupervised clustering does not directly reveal how many traffic movements should be expected. This paper presents Homography-Guided Semantic Maneuver Graph Trajectory Clustering (HG-SMG-TC), a model-selection framework that uses a lightweight homography to estimate intersection structure while retaining isotropically normalized camera coordinates for clustering. Entry and exit endpoint groups are consolidated into physical approach-level groups, and their supported origin–destination relationships form a maneuver graph. Bootstrap resampling converts this structure into an interval for the expected number of observed movements, which guides clustering model selection. The method is evaluated on 67,029 vehicle trajectories from five urban intersection scenes in the Traffic Node Video Dataset, using separate target-estimation, model-selection, and independent-test recording blocks. Independent polygon-rule reference labels cover 89.6–98.4% of test trajectories. HG-SMG-TC reduces mean target-count error from 3.20 for untargeted selection and 2.53 for the point-target variant to 2.13, while achieving an adjusted Rand index of 0.734 and normalized mutual information of 0.839. The results show that the proposed semantic maneuver prior improves target alignment and provides a reproducible way to guide unsupervised trajectory clustering, while retaining explicit trade-offs across evaluation metrics. Full article

Objectives: To derive sex-specific Relative Body Mass Index (rBMI) cut-offs for classifying adolescents into four adiposity categories defined by dual-energy X-ray absorptiometry (DXA)-based body fat percentage (BF%), quantify their stability, and evaluate their classification performance against current BMI%-based classification and previously published ROC-derived rBMI cut-offs. Materials and Methods: Data from 567 observations in adolescents aged 11–19 years were analyzed. Adiposity categories (normal, mildly elevated, moderately elevated, severely elevated) were defined by sex-specific BF% thresholds. Classification and Regression Tree models with inverse class-frequency weighting were fitted in two formulations: sex-stratified, and a pooled-threshold model with shared lower boundaries and sex-specific upper boundaries. Threshold stability was assessed by bootstrap resampling. Performance was estimated by repeated nested cross-validation, with the full derivation pipeline repeated within each training fold; the previously published ROC-derived thresholds were re-derived within each fold so that both approaches carried the same correction for optimism. Results: Sex-stratified thresholds were 103, 118, and 162 in males and 100, 118, and 176 in females; three of six agreed with the previously published ROC-derived values to within 0.5 units and a fourth to within 3.2 units. Five of the six published values fell within the corresponding bootstrap intervals, the exception being the female moderately/severely elevated boundary (160.0; 95% CI: 161.4–218.7). The male boundary separating normal from mildly elevated adiposity was identified in 76.0% of bootstrap resamples; estimating the lower boundaries from the pooled sample raised this to 99.9% and improved ordinal agreement among males (quadratic-weighted kappa 0.812 vs. 0.759). Out-of-fold accuracy was 0.697, 0.694, and 0.694 for the sex-stratified, pooled-threshold, and ROC-derived approaches, respectively, with all paired differences including zero in the overall sample. All three exceeded BMI% classification (accuracy 0.608), which identified only 15.5% of adolescents with mildly elevated adiposity and produced more than twice the proportion of errors spanning two or more categories (6.0% vs. 1.8–2.1%). Conclusions: rBMI cut-offs aligned more closely with DXA-defined adiposity than BMI% classification, particularly in the intermediate categories. Tree-derived and ROC-derived thresholds converged and performed equivalently, indicating that threshold location does not depend on the derivation procedure. A pooled-threshold formulation provided a simpler and more stable rule. These are derivation-stage estimates requiring external validation before clinical adoption. Full article

Background: Convolutional neural networks (CNNs) perform well in histopathological classification of oral squamous cell carcinoma (OSCC), but their robustness across distinct tissue domains remains insufficiently studied. This study assessed whether a CNN trained only on primary oral and oropharyngeal squamous cell carcinoma images remained transferable to an independent metastatic lymph node histopathology dataset. Methods: Three public datasets containing 14,760 OSCC/OPSCC and 8530 normal oral mucosa images were combined for model development. An ImageNet-pretrained EfficientNetB0 backbone was used as a fixed feature extractor with a task-specific binary classification head. Performance was first assessed on an independent internal testing subset and subsequently evaluated on 20,000 H&E-stained normal and metastatic lymph node patches from a separate public dataset. Results: The model achieved an internal testing accuracy of 91.48%, with 92.95% sensitivity, 88.92% specificity, 93.56% precision, a 93.25% F1-score, and a Youden’s J index of 0.819. External validation resulted in a substantial decrease in overall classification performance, with an accuracy of 56.91%, specificity of 33.32%, precision of 48.71%, F1-score of 63.37%, balanced accuracy of 61.99%, MCC of 0.279, and a Youden’s J index of 0.240. Nevertheless, sensitivity for metastatic tissue remained high at 90.66%, indicating a markedly asymmetric external error profile characterized predominantly by false-positive classifications. Conclusions: The marked performance decrease during external validation demonstrates the limitations of direct cross-domain transfer between substantially different histopathological environments. However, the preserved sensitivity suggests that some discriminative information remained transferable beyond the development domain. These findings support partial rather than universal cross-domain generalization and emphasize the importance of independent out-of-distribution evaluation when assessing deep learning robustness in computational pathology. Full article

Background/Objectives: Multiple chronic conditions (MCCs) are highly prevalent among older adults and require effective collaboration between the patient and caregiver. Mutuality, reflecting the quality of the dyadic relationship, is associated with better self-care and health outcomes. However, the Mutuality Scale (MS) has not been validated on patient–caregiver dyads managing MCCs living in a low–middle-income country (LMIC). Aim: This study seeks to evaluate the structural and convergent validity and reliability of the MS among patient–caregiver dyads managing MCCs living in a LMIC. Methods: A cross-sectional study was conducted on MCC patients and their caregiver recruited from community and outpatient settings. The MS, Self-care of Chronic Illness Inventory (SC-CII) and Caregiver Contribution to self-care Inventory (CC-SCCII) were used for measuring mutuality, patient self-care, and Caregiver Contribution (CC) to patient self-care, respectively. Confirmatory factor analysis (CFA) was performed separately for patients and caregivers to evaluate the original four-factor structure of the MS. Convergent validity was examined through correlations with self-care and CC to patient self-care. Reliability was evaluated using composite reliability and the Global Reliability Index for multidimensional scale. Results: A sample of 406 patient–caregiver dyads was examined. Patients had a mean age of 73.9 (±6.2) years. Caregivers had a mean age of 47.8 (±15.5) years. The four-factor structure was supported in both samples, with acceptable model fit (patients: Comparative Fit Index (CFI) = 0.953 and Root Mean Square Error of Approximation (RMSEA) = 0.078; caregiver CFI = 0.945 and RMSEA = 0.085. The second-order CFA supported a hierarchical structure. Patient and caregiver mutuality scores were strongly correlated (r = 0.778, p < 0.01). Higher mutuality was associated with better patient self-care (r = 0.276–0.479) and CC to self-care (r = 0.174–0.556). Reliability indices ranged from 0.70 to 0.91 for patients and 0.66 to 0.89 for caregivers. Conclusions: The findings support the validity and reliability of the MS for assessing mutuality in patients and their caregivers managing MCCs in an LMIC characterized by limited healthcare resources and formal support. Its use provides empirical support for assessing relationship quality and facilitating dyadic care within this vulnerable population. Future longitudinal studies should evaluate its predictive validity, responsiveness, and measurement invariance across different groups and between patient and caregiver. Full article

Background/Objectives: Burnout is an important occupational health concern among orthopedic and traumatology physicians. This study aimed to evaluate the individual dimensions of burnout and identify their demographic and occupational correlates among orthopedic and traumatology physicians practicing in Türkiye. Methods: This cross-sectional study used an anonymous online survey distributed through the Turkish Society of Orthopaedics and Traumatology electronic mailing group between October 2024 and June 2026. Burnout was assessed using the Turkish adaptation of the 22-item Maslach Burnout Inventory, comprising the Emotional Exhaustion (EE), Depersonalization (DP), and Personal Accomplishment (PA) subscales. Unadjusted group comparisons, Spearman correlation analyses, and separate multivariable linear regression models with HC3 heteroscedasticity-consistent standard errors were performed. Results: A total of 320 complete questionnaires were analyzed. The mean EE, DP, and PA scores were 20.17 ± 6.83, 7.40 ± 3.58, and 20.37 ± 4.19, respectively. The subscale scores were analyzed as continuous dimensions rather than classified using low-, moderate-, or high-burnout thresholds. In unadjusted comparisons, residents had higher EE and DP scores and lower PA scores than specialists. After adjustment, resident status remained associated with higher DP (B = 2.14, p = 0.010) and lower PA (B = −1.92, p = 0.018). Seeing more than 120 patients per day was associated with higher EE (B = 4.22, p = 0.023) and DP (B = 2.49, p = 0.018), while working more than 100 h per week was associated with higher EE (B = 4.47, p = 0.009). Individual predictor effect sizes and the explanatory power of the models were generally modest. Conclusions: Daily patient volume, weekly working hours, and professional status showed distinct associations with the individual dimensions of burnout. These findings suggest that workload organization and support for resident physicians warrant consideration. However, because of the cross-sectional design and voluntary non-probability sampling, the findings should not be interpreted as causal or nationally representative. Full article

This study evaluates adaptive spatial matrix filtering (AMF) combined with MUSIC for underwater acoustic direction-of-arrival estimation under strong out-of-sector interference and extends the method to coherent sources by incorporating forward spatial smoothing (FSS) into the AMF design. Simulations compared AMF-MUSIC with conventional MUSIC and continuous matrix filter (CMF)-MUSIC. For a 20-sensor array with targets at −2° and 1°, an interferer at 50°, an SNR of −5 dB, and an INR of 20 dB, both AMF-MUSIC and CMF-MUSIC resolved the targets under a common −25 dB stopband bound, but AMF-MUSIC produced a smoother out-of-sector background. Tightening the CMF bound to −40 dB reduced background peaks but degraded target resolution. In coherent-source simulations using a 25-sensor array, AMF-FSS-MUSIC resolved the targets for all tested subarray lengths when the angular separation was at least 4.5°, achieving resolution probabilities of at least 95%. A 900–1100 Hz broadband simulation maintained an approximately −15 dB stopband response and a passband-response error below −12 dB. For the SWellEx-96 narrowband data, AMF-MUSIC reduced the DOA-estimation RMSE from 11.16° to 5.18° and increased the mean spatial-spectrum SIR from −0.41 dB to 13.18 dB, while the broadband results qualitatively demonstrated interference suppression. These results indicate a favorable configuration-dependent suppression–fidelity tradeoff, while robustness to other coherent-source conditions and broader measured-data validation require further investigation. Full article

Rule-defined rankings often transform continuous marks, discrete judgments, trimming rules, caps, truncation, and tie-breaking variables into a single official order. When ranking margins are small, a formally valid outcome may nevertheless be sensitive to marginal changes in the recorded decision state. This article presents Score Cloud Analysis as a rule-aware statistical sensitivity-reporting method for such systems. The method represents the official score as a deterministic function of recorded inputs and recomputes scores and ranks under finite perturbations, rather than relying on local linear approximations. It defines deterministic diagnostics, including directional Group A/Group C (A/C) decision exposures and their aggregate contested-point exposure, total sensitivity exposure, fragility-to-margin ratios, Score Cloud overlap, and single-call rank sensitivity, and separates these from scenario-conditional Monte Carlo Rank Cloud frequencies. The method is illustrated using a synthetic Wushu Taolu case study because that setting contains majority decisions, trimmed rater marks, discrete difficulty values, Head Judge adjustments, and tie-break rules. The synthetic experiment is an internal-consistency stress test, not an empirical validation and not an estimate of judging-error rates. A small sensitivity study varies perturbation scale and intra-athlete dependence to show which conclusions are scenario-specific. The method separates procedural validity from local rank robustness and is transferable to other reconstructable, rule-based, rater-mediated ranking systems. Full article

Accurate remaining useful life (RUL) prediction of lithium-ion batteries is critical for reliable operation of the system. The performance evolution of lithium-ion batteries shows long-range dependence and capacity regeneration. However, existing performance evolution models fail to properly consider the joint effect of long-range dependence and capacity regeneration, and consequently have a deficiency in mechanism interpretability, which may limit the prediction accuracy of RUL. To address this gap, this paper separately characterizes the degradation and regeneration processes of the discharge capacity, and proposes a novel discharge capacity evolution model incorporating fractional Brownian motion and the Poisson capacity regeneration process. For the estimation problem of the model parameters caused by the Poisson regeneration, we approximately transform the proposed model to one with independent increments according to the weak convergence theorem, and then develop a maximum likelihood estimation method. From a limit perspective and using the theory of total probability, we derive the distribution of RUL, and provide the point estimation of RUL. Finally, we utilize the data set of lithium-ion batteries produced by NASA to verify the effectiveness of the proposed method, and the mean absolute errors of the proposed method are declined by at least 24% compared to the existing methods. Full article

In large-scale multiple-input multiple-output (MIMO) systems, inadequate pilots can necessitate pilot reuse, reducing channel-estimation accuracy, while too few received pilot observations can also lead to inaccurate interference-plus-noise covariance estimates. These estimation errors can further degrade the performance of downstream interference suppression and data detection. Learning-based methods have been developed for pilot assignment, channel estimation, and receiver processing, but these methods are often studied separately. This survey organizes recent studies according to where learning-based methods are applied in the signal-processing chain: pilot-domain mitigation, intelligent channel estimation with contaminated or limited pilots, and intelligent receiver processing with contaminated or limited pilots. We also classify the studies by learning method and compare them using the same set of evaluation criteria. Across the surveyed papers, performance is evaluated using different metrics. Many studies also lack evaluations under changing channel or system conditions and do not fully report implementation costs such as computational complexity, memory usage, and latency. Among the studies that satisfy our selection criteria, none directly investigates learning-based estimation of the interference-plus-noise covariance matrix for interference rejection combining (IRC) receivers when only limited pilot observations are available. Based on these findings, we propose a minimum set of benchmarking requirements and identify lightweight online adaptation, joint processing, learning-based covariance estimation for IRC receivers, and robust processing for large-array architectures as future research directions for emerging sixth-generation (6G) systems. Full article

In industrial robot trajectory planning, trajectory segmentation has crucial importance in distinguishing between different geometric primitives, such as straight lines and arcs. Separating these elements facilitates allocating optimized motion instructions, customized to each segment category. This distinction ensures both improved execution smoothness and better operational performance. For this purpose, the Bidirectional Long Short-Term Memory (Bi-LSTM) machine learning algorithm has been implemented to segment trajectories into linear and arc-shaped parts, for which dedicated robotic commands can be used. First, several Bi-LSTM models with different architectures were trained using a synthetic dataset containing different shapes with labelled segments. Then, a theoretical study was performed to evaluate the accuracy of recognizing different shape segments using a test dataset. Finally, the generated trajectories, which implemented the best machine learning model, were transferred into the RoboDK software to launch the robot. Two different methods were used to generate trajectories for the UR3 industrial robot. The original trajectory was generated using linear interpolation only, while the second was generated using the machine learning algorithm. The experimental results show significant differences in terms of the smoothness and velocity profiles between these two trajectory generation methods. By enabling automatic classification of trajectory segments into line and arc primitives using the Bi-LSTM-based approach, the execution time is reduced by up to 43.5% and the vibration amplitude by up to 27.4% at higher speeds around 250 mm/s. However, this came at the cost of reduced accuracy at high speed, with reproduction error reaching 1.4–1.9 mm versus 0.46–0.7 mm for linear interpolation. Full article

This research focuses on the relationship between position and time. A first-order integro-partial differential equation in time and the nth dimension of position is considered in the space $L_2\left(\Omega \right)\times C[0,T],T<1$, where $\Omega$ denotes the domain of integration with respect to position. The position-specific kernel is assumed to be a singular kernel from which many important kernels with physical and geometric significance can be derived, such as Carleman-, Cauchy-, logarithmic-, and Hilbert-type kernels. The time-specific kernel is assumed to be continuous. The integro-differential equation is transformed into a mixed equation, and many important special cases are derived, some of which have not been previously presented. Moreover, under certain assumptions, the existence and uniqueness properties of the proposed equation are studied, as well as convergence and relative errors. The variables are separated to yield a Fredholm integral equation of the second kind with a singular kernel and time-varying coefficients. As the potential kernel is in a general singular state, the Toeplitz matrix method—which is considered the best approach for solving singular integral equations as it can convert singular integrals into calculable ordinary integrals—is used to solve the Fredholm integral equation to obtain and study a linear algebraic system. Finally, for applications assuming special types of kernels, the numerical solutions and their related errors are calculated for each case. Solved cases show how strong, efficient, and straightforward the proposed method is. The results of this investigation show that the method converges quickly for all kernel types. Full article

Recursive evidence fusion gives an intrusion detection system temporal memory, but it also gives unreliable windows and erroneous review outcomes a path to influence later diagnoses. Existing drift-handling, open-set, conformal, continual-learning, and human-in-the-loop methods provide useful signals or update classifiers and memories; they do not, by themselves, specify when a post-classification evidential state may be written or replaced. We present RTEF-IDS, a protocol that separates current action, model-evidence admission, reviewed-feedback admission, and history replacement. The protocol retains the history-relative reliability principle from our previous work, instantiates it for singleton-plus-ignorance IDS evidence, and assigns operation-specific credentials. Reviewed windows make no base-state change, mapped-known feedback may be appended, and replacement requires persistent confirmation. On 33,384 frozen windows, 30% retrospective admission excludes 26.3% of held-out-or-misclassified mass while retaining 94.8% of known-correct evidence. Under paired imperfect feedback, retrospective replacement increases one-window future history-state agreement by 0.107 in the primary block and 0.129 in IoT-23 leave-scenario-out replay. Under a past-only rolling-budget gate within externally supplied frozen partitions, the corresponding increments are 0.001 and 0.000, indicating that the tested gate exposes few qualifying replacement opportunities; bounded external short streams show the same opportunity constraint. Independent second review reduces false authorization from 5.66 to 0.124 per 1000 first-stage reviewed windows under independent errors and from 34.27 to 0.181 under five-window correlated errors, with a corresponding increase in review demand and a reduction in admitted corrective feedback. A shared systematic label alias remains unresolved by the tested review arms. These results support explicit, auditable state-mutation control while identifying the causal-opportunity and feedback-provenance conditions under which it operates. Full article