Search Results (1,402)

Inspired by the hovering, diving, and cooperative hunting behaviors of the pied kingfisher, the Pied Kingfisher Optimizer (PKO) has demonstrated competitive performance in optimization tasks. However, it exhibits several phase-specific limitations, including uneven population distribution caused by random initialization, insufficient use of historical information during exploration, over-reliance on the global best during exploitation, and weakly guided perturbation in the symbiosis phase. To address these issues, this study proposes an Improved Pied Kingfisher Optimizer (IPKO), which incorporates biologically inspired adaptive strategies. Drawing inspiration from the kingfisher’s diverse perching, gaze adjustment during hovering, evasive diving after failed strikes, and territory shifting based on flock position, four mechanisms are developed. Specifically, sine chaotic opposition-based initialization enhances population diversity; adaptive directional search regulates the exploration–exploitation balance; stochastic perturbation-based information fusion improves the ability to escape local optima; and centroid-based adaptive boundary handling strengthens constraint adaptability. The performance of IPKO is evaluated on the CEC2017 benchmark suite (10, 30, 50, and 100 dimensions) and two real-world engineering problems. Experimental results show that IPKO achieves superior overall performance compared with eleven state-of-the-art algorithms, with statistical significance confirmed by the Friedman test and Holm’s post-hoc procedure. Ablation studies further verify the contribution of each strategy. In engineering applications such as cold chain logistics and dynamic multi-UAV cooperative path planning, the IPKO algorithm demonstrates superior solution quality, robustness, and constraint-handling capability compared with competing algorithms. These results demonstrate that IPKO is a robust and effective bio-inspired optimization approach for solving complex, high-dimensional constrained engineering problems. Full article

Service failures remain inevitable in tourism and hospitality, yet complaint behavior is often suppressed, particularly in non-routine, time-bound travel contexts. The Tourist Complaint Constraints (TCC) framework explains this silence through five tourism-specific constraints. However, it does not explicitly account for how platform-based and AI-mediated service environments reshape post-failure behavior. This paper revisits TCC and introduces TCC 2.0, a conceptual extension that reframes complaint constraints as structurally generated within platform-mediated recovery architectures. Drawing on justice theory and emerging research on AI-enabled service systems, the framework positions distributive, procedural, and interactional justice as central mediators linking complaint constraints to behavioral outcomes. It further incorporates platform/AI process constraints and algorithmic trust constraints as additional structural dimensions, while identifying recovery channel and failure magnitude as boundary conditions. A key contribution is the concept of platform-mediated silence, defined as a structurally induced form of non-complaining behavior shaped by constrained agency and recovery system design rather than satisfaction. The paper advances a set of propositions to guide empirical testing and future scale development in AI-mediated tourism contexts. By extending complaint behavior theory into digitally mediated service environments, TCC 2.0 offers a foundation for understanding how platform architectures shape customer voice, silence, and post-failure responses. Full article

Employees’ voice is an important source of organizational learning and adaptive change. As algorithmic management is increasingly applied across organizational management processes, an urgent practical question arises: Does it affect employees’ participation in organizational improvement through voice? To address this challenge, drawing on signaling theory, this study examines the differential effects of distinct dimensions of algorithmic management on voice, while also considering work locus of control as a key moderating variable. We collected one-to-one matched data from 351 employees and their supervisors in a large Chinese platform-based enterprise. We tested the hypothesized theoretical model using structural equation modeling and bootstrapping procedures. The results show that algorithmic feedback enhances employees’ felt responsibility for constructive change, which in turn promotes employees’ voice. In contrast, algorithmic directing, algorithmic scheduling, and algorithmic monitoring undermine employees’ felt responsibility for constructive change and thereby inhibit voice. In addition, work locus of control moderates these relationships: employees with an external work locus of control strengthen the negative effects of algorithmic directing, algorithmic scheduling, and algorithmic monitoring, whereas employees with an internal work locus of control strengthen the positive effect of algorithmic feedback. These findings deepen our understanding of how different dimensions of algorithmic management shape voice and offer practical insights for fostering voice in contexts characterized by algorithmic management. Full article

Artificial intelligence-based management systems are becoming increasingly embedded in labor processes, particularly in platform-mediated work. While existing research has shown that algorithmic management intensifies data-driven control, opacity, and performance monitoring, less attention has been paid to how algorithmic decision-making reshapes the institutional conditions of representation, negotiation, and accountability in employment relations. This article examines how AI-based management may reconfigure workplace conflict by translating managerial decisions into “system outputs” and narrowing the extent to which disputes remain institutionally addressable and negotiable. Drawing on a qualitative case study of platform-based motorcycle couriers in Türkiye, the analysis is based on semi-structured, decision-moment-focused interviews with 19 couriers and 5 representation actors. Rather than testing a full causal model or advancing a universal claim about algorithmic management, the article traces recurring processual linkages among the technicalization of decision-making, epistemic opacity, weakened addressability, and the thinning of representational intervention. The findings suggest that, in the Turkish platform courier context examined here, representationless governance appears as an empirically observable pattern where consequential algorithmic decisions intersect with limited transparency, fragmented appeal channels, income-sensitive sanctions, and constrained collective representation. In this configuration, decision-making remains procedurally dense yet substantively difficult to contest through identifiable, accountable, and negotiable channels. The article argues that the social sustainability of labor governance depends not only on efficiency, flexibility, or access to work, but also on whether decisions affecting workers’ livelihoods remain intelligible, contestable, attributable, and open to institutional negotiation. Full article

Sleep apnea is a prevalent sleep-related breathing disorder characterized by recurrent cessations or reductions in airflow during sleep. It significantly impacts the quality of life, yet current diagnostic methods like polysomnography (PSG) are expensive and uncomfortable, limiting accessibility and ease of use. We developed a novel non-contact biosensing system using secondary laser speckle pattern analysis and dedicated image processing algorithms for apnea-like breathing cessations. The proposed method was tested on $14$ healthy subjects with diverse body characteristics, aged 22–50 years (mean $33.1 \pm 9.3$ years) and body mass index (BMI) ranging from $19.6$ to $28.7$ kg/m² (mean $24.6 \pm 3.0$ kg/m²) at different ‘simulated’ sleeping positions (back-lying, stomach-lying and side-lying), using voluntary breath-holding protocols to simulate apnea-like cessations lasting 10–20 s (short duration) and 20–30 s (long duration). To evaluate the performance of the system without selection bias, two complementary five-fold cross-validation procedures were applied: a participant-level and a class-level stratification. Using class-wise stratification, the system achieved an overall accuracy of $87.0 \pm 3.0\%$ (95% CI: [85.3%, 88.7%]), long-cessation sensitivity of $91 \pm 12.4\%$$(95\% \mathrm{C}\mathrm{I}:[83.8\%,98.2\%\left]\right)$ and a short-cessation sensitivity of $88.0 \pm 11\%$$\left(95\% \mathrm{C}\mathrm{I}:[81.6\%,94.4\%]\right)$. The two-class classification strategy confirm the robustness of the approach, supporting the potential of secondary laser speckle pattern analysis as a low-cost, non-contact alternative for home-based sleep apnea screening. Full article

This paper proposes a computationally efficient algorithm for nonlinear optimal guidance with a predefined final flight path angle. Although numerous impact angle guidance methods based on optimal control theory exist, a lack of efficient calculation procedures remains for the exact nonlinear engagement model, leaving practical hardware implementation challenges for the end-user. A fixed-structure algorithm with deterministic computational burden is developed for real-time onboard integration. The performance and optimality of the algorithm are verified through a comparative study with established guidance laws. Unlike methods relying on line-of-sight rate or time-to-go estimations, the proposed approach uses a closed-feedback form based on standard navigation data. A closed-form solution is derived for the climb phase to the cruise altitude. Practical feasibility is demonstrated on a microcontroller-based onboard computer, with execution times analyzed for flight software compatibility. The robustness of the proposed framework is validated via high-fidelity hardware-in-the-loop tests for two distinct scenarios: a multi-phase cruise mission and a short-range ballistic trajectory subject to propulsion uncertainties. Results confirm high precision and accurate impact angles across vastly different flight regimes, ranging from low-altitude cruise to high-dynamic reentry. Full article

Gravity currents in open channels are important transport mechanisms that influence the propagation of saline plumes in rivers, reservoirs and waterways. Predicting the evolution of the current front in channels with varying geometry and bed roughness conditions remains a challenge due to the non-linear interactions between geometric confinement, buoyancy and hydraulic resistance. In the present study, an explainable machine learning (ML) framework is developed to predict the front propagation of saline gravity currents in a composite trapezoidal open-channel configuration. Eight ML algorithms were employed, combined with a group-aware validation procedure to ensure generalization. Model performance was assessed utilizing standard regression metrics. Among the tested ML models, the CatBoost algorithm achieved the highest predictive accuracy. Interpretation of the model was carried out with the Shapley Additive Explanation (SHAP) approach to quantify the contribution of governing variables including time, initial water depth, density difference and bed condition. The SHAP analysis reveals that the initial depth in the channel has a stronger impact on the front propagation than the density difference, reflecting the combined effects of buoyancy, geometric confinement and bed roughness. Bed roughness is also a contributing factor to propagation dynamics by modifying hydraulic resistance. The proposed ML-SHAP framework provides a robust and interpretable tool for gravity current evolution prediction in channels with complex geometry and varying bed roughness. It may further aid in rapid assessment of transport processes in hydraulic and environmental settings. Full article

Organizations are currently in a stage where the volume of financial transactions and data is constantly growing. The same goes for risks associated with the use of data for risk management and strategic decision-making. The likelihood of transactional errors generally increases with data volume and process complexity, while fraud, although less frequent, may have more severe financial, compliance, and reputational consequences for organizations. Continuous auditing practices and well-established enterprise risk management (ERM) processes, combined with AI-driven pattern recognition, trend analysis and segmentation, can enhance timely detection and proper investigation of suspicious transactions. In areas with large volumes of transactions, the audit sampling process may be a lengthy process and pose a detection risk. Using machine learning (ML) models to support critical business processes could prove effective in managing enterprise risk overall. The current study offers new perspectives on managing risk and assurance with ML model output for flagging possible risky transactions within ERP (SAP) systems data. The study population consists of 69,158 finalized billing records extracted from the SAP production environment of a private sector organization, which covers a six-month operational period. The dataset was divided into an 80/20 train–test split, yielding 55,326 training and 13,832 test instances across six classification categories. The study examines the ML methods’ outcomes from billing datasets and their applicability in enhancing audit, assurance, and ERM processes by evaluating output data results from two supervised classification algorithms—multinomial logistic regression (SoftMax regression) and XGBoost—against various criteria generally accepted as risky in audit engagements. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, and average precision (AP) from precision–recall curves. The results confirm that XGBoost achieves 99% overall accuracy with a macro F1-score of 0.965, outperforming logistic regression (macro F1 = 0.863), and that ML output allows early investigation and follow-up procedures to minimize the risk of fraud and errors and optimize risk management activities, thus strengthening internal control frameworks. Full article

Road safety barriers represent a core component of the road with relevant consequences on effective safety for users. Maintaining these components in adequate conditions, within the quality admissibility thresholds, in compliance with all economic and management constraints, is a primary need for road administrators. In this paper, the authors propose an original procedure to classify the state of efficiency of road safety barriers, at the network scale and relying on conventional administrative data, in an optimized BIM environment, to simplify evaluations and management procedures. Through purpose-built algorithms based on selected geometric and functional parameters of the different road barriers, the algorithm provides a preliminary classification of the various segments, evidencing attention class indicators, useful as preliminary alert signals and for anticipating detailed investigations that can ensure significant economic efficiencies. The method was tested on a 10 km long motorway segment in Italy, evidencing the potential advantages of such an innovative approach to support, as a final goal, a comprehensive infrastructure digital model for virtual inspections, evaluating road component “health” state and properly implementing maintenance strategies. This approach improves network-scale monitoring and maintenance-related activity prioritization phases for road safety barriers, leveraging administrative data. This methodology functions as a BIM-based asset screening tool, as it offers a digital decision support system that identifies critical segments, to optimize the allocation of physical resources and prioritize on-site inspections where they are most needed. Full article

Achieving resilient consensus in adversarial environments often requires extending the W-MSR algorithm to multi-hop communication. While the robustness guarantees of multi-hop W-MSR are now well understood, the problem of how to determine the minimal hop radius $h^{*}$ that ensures these guarantees has remained largely unaddressed. Existing work typically assumes a fixed h, leaving practitioners without a systematic way to balance robustness requirements against communication and computational cost. This paper introduces a new hop-selection framework that identifies the smallest communication horizon capable of satisfying the robustness assumptions underlying MW-MSR consensus. The framework combines exact robustness verification—when tractable—with a hierarchy of computationally efficient proxy tests based on local feasibility, normalized algebraic connectivity, and adversary-dilution criteria. These components provide a practical and scalable mechanism for establishing $h^{*}$ in both synchronous and bounded-delay asynchronous settings. Design-time and runtime procedures, complexity analysis, and validation on IEEE 14-, 30-, and 57-bus networks demonstrate that the proposed approach reliably detects resilience thresholds and substantially improves consensus behavior under stealthy and burst-type adversaries. The results show that systematic hop selection is essential for avoiding failure at small h while preventing unnecessary communication overhead at large h. The framework thus offers an implementable and deployment-oriented strategy for resilient distributed coordination in sparse and adversarial multi-agent networks. Full article

Pig weighing is an essential procedure for monitoring growth and animal health; however, conventional methods are often labor-intensive, costly, and potentially stressful. In this context, this study proposes a non-invasive approach for estimating the body weight of pigs during the growing stage based on computer vision and the YOLOv11 algorithm, enabling automatic segmentation and individual identification in multi-animal environments. The study used RGB images of 10 group-housed pigs captured throughout the growing phase, in which automatic dorsal segmentation was combined with individual identification through numerical markings. From the generated binary masks, the segmented dorsal area was extracted and used as a predictor variable in Linear Regression and a Multilayer Perceptron (MLP) Artificial Neural Network. The YOLOv11 model showed consistent performance in the segmentation task, achieving test-set metrics of Precision = 0.849, Recall = 0.886, mAP@0.50 = 0.936, and mAP@0.50–0.95 = 0.819, demonstrating good generalization capability in scenarios with intense animal interaction. In the weight prediction stage, Linear Regression and the MLP achieved high coefficients of determination (R² = 0.96 and 0.95, respectively) with low errors (RMSE = 1.52 kg and 1.63 kg; MAE = 1.20 kg and 1.25 kg), indicating a strong correlation between segmented dorsal area and actual body weight. Class-wise analysis revealed superior performance for classes 7 and 9, with R² values up to 0.98 and RMSE below 1.1 kg, whereas class 8 showed greater error dispersion, associated with higher morphological variability and a smaller number of available samples. These results demonstrate that the direct use of morphometric information extracted from segmented masks in 2D images constitutes a robust, accurate, and low-cost approach for automatic pig body-weight estimation. Moreover, this study is among the few addressing this task specifically during the growing stage, highlighting its potential for future deployment in embedded systems and intelligent monitoring platforms for precision pig farming, although further evaluation of computational efficiency and real-time performance is still required. Full article

High-frequency surface wave radar (HFSWR) often suffers from highly non-uniform, striped ionospheric clutter, which significantly degrades sea-surface target detection performance. To address this challenge, this paper proposes a reduced-dimension space-time adaptive processing (STAP) algorithm based on sparse representation. In this method, a dictionary is first constructed using the Doppler resolution and an appropriate angle interval as the frequency and angle grids, aiming to obtain fully orthogonal clutter atoms. Both the training sample and the cell under test are then sparsely represented over this dictionary to extract consistent clutter atoms. Due to discrepancies between the dictionary atoms and the actual clutter, low-power atoms are deemed unreliable and are discarded via a thresholding procedure. The remaining reliable atoms are used to construct a dimensionality-reduction matrix, thereby obtaining an accurate local clutter-plus-noise covariance matrix. Experimental results on measured data demonstrate that the proposed method effectively suppresses striped ionospheric clutter and enhances target detection performance. Full article

As global energy demand continues to grow, the inherent safety requirements for natural gas long-distance pipelines are becoming increasingly stringent. Therefore, accurately analyzing the trends in pipeline defects using multi-round internal inspection data is of great significance for enhancing pipeline inherent safety levels and reducing the risk of pipeline medium leakage. However, existing pipeline in-line inspection data alignment methods for long-distance multi-round pipeline data alignment suffer from cumbersome alignment procedures and low computational efficiency. This paper proposes an adaptive threshold dynamic time warping defect alignment method (Adaptive Dynamic Threshold-Dynamic Time Warping, ADTH-DTW) for rapidly matching multi-round in-line inspection data. A new multi-round in-line inspection data alignment framework based on valve-weld-defect is established. By integrating the DTW algorithm into each alignment stage, unnecessary manual effort is avoided, significantly improving data alignment efficiency. First, the ADTH method is used to clean redundant weld seam data in the in-line inspection data. By dynamically generating expected values and combining an intelligent point selection strategy, the method accurately identifies and removes interfering data. Additionally, valve chamber data is used to correct the overall mileage, providing a data foundation for subsequent defect alignment. Second, the dynamic time warping algorithm is used to align weld seam data and establish a data mapping table. Finally, relative displacement methods are employed to achieve defect matching. The validation results from three rounds of in-vehicle inspection data tested on-site indicate that the ADTH-DTW algorithm achieves an average 23.08% improvement in alignment accuracy compared to methods such as DTW, KL divergence, JS divergence, and linear interpolation, with computational efficiency nearly tripled. This effectively addresses the issue of incompatible computational efficiency and accuracy in existing data alignment algorithms, thereby enhancing the intrinsic safety level of natural gas long-distance pipelines. Full article

Liver transplantation (LT) is the definitive treatment for patients with end-stage liver disease. Since its inception in the 1960s, transplant medicine has undergone substantial advances in surgical technique, immunosuppression, organ preservation, and organ allocation policies. According to the 2023 WHO census, approximately 47,180 LT procedures occur worldwide each year, with living donors contributing to up to 23% of cases. Additional milestones include the expansion of transplant eligibility to patients with hilar cholangiocarcinoma and advanced colorectal liver metastasis, the incorporation of viscoelastic testing into perioperative blood management algorithms, and the increasing use of mechanical circulatory support for pre-transplant optimization. In parallel, medical training has evolved to meet the complexities associated with these high-risk procedures. Structured fellowship programs now provide focused expertise, and guide investigations to resolve complex clinical dilemmas. Experience accumulated over decades has improved clinicians’ ability to manage the expanding spectrum of comorbidities seen in contemporary transplant candidates. Key perioperative challenges include accurate assessment of fluid status, optimization of intravascular volume, management of vasoplegia, intraoperative renal replacement therapy, treatment of right-ventricular failure, and the mitigation of severe lactic acidosis. As transplant recipients increasingly present at older ages and with multiple comorbidities, perioperative management has become more demanding. One emerging strategy for select high-risk patients involves performing concurrent surgical procedures within a single operative session. This narrative review focuses on the intraoperative management of five variables that proved challenging during the first case of concurrent liver transplantation and off-pump coronary artery bypass surgery in our institution. Full article

SAG grinding mills represent critical energy-intensive operations in copper concentrators, accounting for 30%–50% of total plant energy consumption. The accurate prediction of mill power draw and production rate under varying operational conditions is essential for real-time control, production planning, and energy management. This study presents a comprehensive comparison of ML algorithms for modeling Production and Power in a Chilean copper mining industry. Deterministic and stochastic models were fitted and validated using industrial data from a Chilean copper operation. More representative models were re-estimated and subsequently evaluated under different operating regimes to examine their predictive performance under aggregated conditions of the feeding variables. This procedure allowed for the identification of the modeling approaches that provide the most robust performance across varying operational regimes. The results show that XGB achieved the best predictive performance, with test RMSE and R² values of 87.98 and 97.35% for SAG Production, and 431.11 and 95.11% for SAG Power, respectively. Stochastic approaches provided complementary uncertainty quantification, supporting risk-informed decision making under variable operating conditions. The analysis by operational regime indicates that XGB presents better fit in the Thick hydraulic regime, for both responses’ variables, which could be explained why a dense pulp operation provides more predictable grinding dynamics. The comparative analysis reveals trade-offs between model complexity, interpretability, computational requirements, and predictive performance, offering practical guidance for selecting appropriate modeling frameworks based on specific operational objectives and data availability in mineral processing applications. Full article