Search Results (1,214)

Unmanned Underwater Vehicles (UUVs) are used for ocean research and underwater exploration along with military applications. The underwater environment that UUVs operate in poses unique design constraints that govern the design of the electric propulsion system of UUVs. Such designs must be oriented around high torque generation, extending mission duration, and maintaining optimal temperatures. This paper reviews developments to address these parameters around UUV design. Concepts for motor designs, energy management, thermal management, control systems and charging infrastructure centered around the environmental constraints are presented. This paper adopts a sustainability lens throughout to highlight impacts that could be overlooked. Research gaps are identified in each area, including the reliance on simulation-only validation, the absence of cooling studies for transverse flux motors, and the limited work on renewable recharging during deployment. There has yet to be a review paper which overviews UUV electric propulsion with a scope as broad as this paper while featuring the environmental impacts and sustainability of the content covered. From the information in this paper, UUV electric propulsion research advances high performance and energy efficient designs often only in one technical dimension while sustainability aspects are also concurrently pushed. Full article

Advancing structural sustainability requires a rigorous evaluation of the economic and environmental trade-offs inherent in engineering design and material selection. To address this in sustainable construction, this paper presents a comparative study of the lateral-torsional buckling (LTB) resistance of glulam timber, structural steel, and reinforced concrete beams under fluctuations in material and labor costs and environmental carbon impacts. Two parallel series of multi-parameter cost optimizations are performed using discrete mixed-integer non-linear programming (MINLP) via the outer-approximation/equality-relaxation algorithm to analyze configurations with active LTB controls (laterally unrestrained beams) and without LTB controls (laterally restrained beams) across various discrete spans and imposed loads. The objective functions define the production-dependent material, power, labor, and CO₂ emission costs of the beams. The optimization results show that active LTB constraints force steel and concrete elements into sturdier profiles. Conversely, timber demonstrates a unique geometric behavior reversal under medium-to-high loads, shifting into tall, narrow sections. In the projected near future, when the global warming price CGW increases to a tenfold threshold, the combined self-manufacturing and production-induced CO₂ emission costs of beams on average could increase by 15.2% for timber, 35.8% for steel, and 50.2% for reinforced concrete. In this case, LTB-unrestrained configurations, compared to restrained beams, will cause total production costs to increase by up to nearly one-third for timber, more than double for steel, and by one-half for reinforced concrete. At higher CGW increases, the total costs expand further, reaching up to 38.1% for timber, 144.8% for steel, and 62.5% for reinforced concrete at a hundredfold escalation. Similar cost increases can likely be expected across the construction industry at large. The sector will have to adapt. Full article

Bezier curve-based optimization (BCO) provides a population-based search framework that generates candidate solutions along linear, quadratic, and cubic Bezier paths defined by control points. This paper presents IBCO, an improved BCO configuration that integrates three established controls: dimension- and bound-gated diversity feedback, reflective boundary repair, and stagnation-triggered differential or elite-guided perturbation. The contribution is an auditable integration and activation design, not a claim that these operators are individually new. Existing records comprise 30 runs on 26 classical instances, 29 evaluated CEC2017 functions (F1 and F3–F30; F2 excluded), 24 CEC2022 instances, 5 constrained engineering problems, and 26 high-dimensional classical instances. The CEC2017, CEC2022, and engineering profiles used fixed per-run realized evaluation counts within each documented profile; this does not imply identical candidate-evaluation sequences within every run. Classical and high-dimensional IBCO runs used 9030–9144 evaluations, versus 9030 for Original BCO; therefore, those profiles do not establish fixed-evaluation superiority. A retrospective, problem-blocked reanalysis of existing runs rejected the omnibus null of equal treatments in all seven comparison and ablation profiles at $\alpha =0.05$ (largest $p=0.0220$), but IBCO’s advantage over Original BCO did not exceed profile-level Nemenyi critical differences. Among 24 classical rank-1 results, only 4 were unique first places. Ablation records identify reflective repair as the most stable component but do not establish synergy among the complete configuration combining diversity feedback, reflective boundary repair, and stagnation-triggered perturbation. IBCO is therefore interpreted as a competitive, incremental BCO configuration relative to the implemented historical baselines, not as a scale-invariant, evaluation-efficient, or universally dominant optimizer. Full article

Turmeric (Curcuma longa L.) is one of the most important spice crops and a valuable medicinal plant, but it is seriously affected by various types of diseases such as fungal, bacterial, nematode and viral diseases. In this paper, a complete mathematical model of the turmeric plant disease dynamics is developed under a fractal-fractional model in this context, encompassing all four types of pathogens and associated treatment classes. The fractal-fractional Caputo derivative operator captures memory effects and, through its fractal exponent, a genuine deformation of the classical memory kernel, allowing the underlying biological dynamics to be represented more flexibly than under the classical integer-order derivative; we do not, however, claim that this kernel deformation corresponds to demonstrated self-similarity or spatial heterogeneity in the turmeric plant–pathogen system. We show the positivity and boundedness of the solutions, calculate the next-generation matrix approach-based basic reproduction number ${\mathcal{R}}_0$ and investigate the local and global stability of both disease-free and endemic equilibria by Lyapunov functionals. A sensitivity analysis of ${\mathcal{R}}_0$ is conducted to determine the most important parameters influencing disease transmission and control. The existence and uniqueness of solutions and Ulam-Hyers stability of solutions are established by fixed point theory. For the associated integer-order system, we formulate an optimal control problem is formulated with three time-dependent controls: the prevention effort ($u_1$), the enhancement of treatment ($u_2$), and the care management ($u_3$), and the optimality conditions are derived via Pontryagin’s maximum principle. Numerical simulations are conducted with three different fractal-fractional operators, namely Caputo, Caputo-Fabrizio and Atangana-Baleanu. A deep neural network is developed and trained to approximate the solution of the integer-order system. The third-layer deep neural network consists of neurons of sizes 80, 32, and 24, with activation functions of logistic sigmoid, radial basis and hyperbolic tangent, respectively, and is trained to approximate the system dynamics with the fourth-order Runge-Kutta method as a reference. The DNN is found to be very accurate in predicting the values with Nash-Sutcliffe Efficiency between 0.79 and 0.99 and Theil Inequality Coefficient around ${10}^{-2}$ in all 11 compartments, and hence proved capable of being a good surrogate modelling tool for the ODE systems. The present work contributes towards SDG 2 (Zero Hunger) and SDG 3 (Good Health and Well-being) by laying a mathematical basis for integrated disease management in turmeric cultivation for sustainable agriculture and food security. Full article

α-Cyclopentylmandelic acid (CPMA) is a chiral α-hydroxycarboxylic acid valuable in pharmaceutical synthesis and asymmetric catalysis. However, it remains challenging to prepare with high optical purity due to its unique steric structure. This study established a chiral resolution strategy using derivatization-assisted lipase-catalyzed kinetic resolution to efficiently prepare optically pure CPMA. We employed acetoxyacetyl chloride derivatization to introduce a diester structure into CPMA that lipase recognizes efficiently and identified recombinant Cal B as the optimal catalyst through enzyme screening. Theoretical calculations suggested that the activity difference resulting from the derivatization strategy stems from spatial recognition rather than electronic effects; the significant difference in binding affinity between the R- and S-substrates provides the driving force for subsequent time-dependent separation. By controlling the reaction time, the (R)- and (S)-CPMA enantiomers could be selectively prepared. This achieved ee values exceeding 99.5% for both enantiomers, with isolated yields over 38% for each. Cal B was immobilized on an ESR carrier and retained good catalytic activity after 12 reuse cycles. The resulting (R)-CPMA was successfully used to synthesize sofpironium bromide, thereby validating the practical feasibility of this process. This strategy provides an effective, environmentally friendly enzymatic route to highly sterically hindered chiral α-hydroxycarboxylic acids. Full article

A flexible part has no unique shape until it is constrained, which makes dimensional inspection difficult. Standard practice clamps it in a dedicated jig and probes it with a coordinate measuring machine or a range sensor. We replace the jig with virtual fixturing. From partial range views of the unfixtured part, the pipeline recovers a coarse pose between the scan and the CAD model using a robust geodesic bilateral curvature algorithm, deforms the model towards the scan by non-rigid registration, and computes deviations along the model surface normal to decide whether the part is in tolerance. On a prismatic part and a game-controller housing, the method is more accurate than optimal-step non-rigid ICP, radial basis function FEM, and coherent point drift, because the generated deformations come from an operator closely related to the proposed method’s own regularizer, those margins favor it by construction and are reported with that caveat. On a generated thin-shell test part the measurement uncertainty of the implementation is measured at about 0.039 mm, dominated by the registration rather than by the sensor. The pipeline is then exercised on a physically scanned injection-molded engine cover, a 612 mm part whose free-state residual against its nominal model is 4.62 mm at the verified global optimum. No independent coordinate-measuring-machine reference was available for that part, so this experiment is reported as a free-state residual and a controlled comparison with and without the feature set, not as a statement of absolute measurement accuracy. Full article

Indoleamine 2,3-dioxygenase 2 (IDO2) and tryptophan 2,3-dioxygenase (TDO2) are rate-limiting enzymes of the kynurenine pathway, well-characterized in mammals. Unlike all other vertebrates, chickens lack the IDO1 gene and exclusively rely on IDO2/TDO2 for tryptophan (Trp) catabolism; however, their biochemical features and physiological regulatory functions remain poorly defined. Here, we integrated dietary Trp intervention, multi-omics profiling, recombinant enzyme kinetics, and in vitro/inflammatory animal models to characterize chicken IDO2 and TDO2. Supplementary dietary Trp maintained circulating Trp homeostasis while activating hepatic kynurenine metabolism being associated with reduced PPAR signaling and altered lipid and energy balance. Hepatic combined IDO2/TDO2 catalytic activity increased markedly with Trp supplementation; this increase was accompanied by only modest transcriptional upregulation of kynurenine pathway genes (including IDO2, TDO2, and KYNU) and no major changes in their protein abundance, indicating post-translational functional modulation. Recombinant enzyme assays demonstrated optimal activity at chicken physiological pH of 7.2 and temperature of 42 °C; chicken IDO2 displayed measurable catalytic efficiency, which was lower than that of chicken TDO2, whereas its human ortholog showed no detectable activity under the same conditions. Furthermore, LPS and APEC infection significantly suppressed the combined total IDO2/TDO2 activity during inflammatory stress, revealing an avian-specific metabolic–immune regulatory pattern. This work defines the unique biochemical and physiological functions of avian IDO2 and TDO2, offering mechanistic references for poultry nutritional regulation and disease control. Full article

This paper studies a class of fully coupled forward–backward stochastic differential equations (FBSDEs in short) with Volterra delayed and anticipated terms. Such equations naturally arise in stochastic control problems involving memory effects and future-dependent terms. We establish the existence and uniqueness of adapted solutions by using the continuation method. We also discuss a linear quadratic optimal control problem with Volterra delayed terms and provide an explicit representation of the unique optimal control in terms of the solution to the associated FBSDE. Full article

Type 1 diabetes (T1D) is a chronic autoimmune disease in which autoreactive effector T cells destroy insulin-producing pancreatic β-cells, leading to impaired insulin production and long-term metabolic complications. This study develops a mathematical model of the interactions among pancreatic β-cells, autoreactive effector T cells, regulatory T cells (Tregs), and stem cell therapy, treating stem cell administration as an immunomodulatory and regenerative strategy that suppresses autoimmune activity and promotes β-cell recovery. The existence, uniqueness, positivity, and boundedness of solutions are established. For the auxiliary subsystem with $S_{E }=0$ and constant treatment input, the disease-free equilibrium and the threshold quantity $R_{0 }$ are derived, and local asymptotic stability of the DFE is established for $R_{0 }<1$. For the full model with $S_{E }>0$, the existence of a unique biologically feasible positive equilibrium is established, together with its local asymptotic stability under constant treatment input. An optimal control problem, formulated using Pontryagin’s Maximum Principle, is used to guide therapeutic dosing, and one-year numerical simulations compare two administration protocols: high-dose pulse injections and continuous infusion. Both reduce autoimmune activity and improve β-cell dynamics, with pulse administration producing stronger transient responses and continuous infusion producing smoother treatment-period dynamics, while both protocols approach similar long-term levels. A local sensitivity analysis identifies immune activation, β-cell destruction, and regulatory T-cell activity as the parameters most strongly shaping disease progression and treatment outcomes. By unifying stem cell therapy, stability analysis, sensitivity analysis, and optimal control, and directly comparing pulse and infusion protocols, this framework offers new insight for designing stem cell-based treatments for autoimmune diabetes. Full article

Wind erosion poses a serious threat to land stability and agricultural sustainability across global arid and semi-arid zones. This study was conducted in Tuoksun County, China’s sole county situated below sea level; its unique low-elevation landform generates distinctive wind–sand movement processes, forming a representative research platform for elucidating shelterbelt functional mechanisms under extreme arid environments. In this work, we systematically monitored wind field characteristics, shelterbelt windbreak performance, and soil wind erodibility and quantitatively analyzed the regulatory effects of different shelterbelt configurations on sand-fixing efficiency and soil anti-wind erosion capacity. The results revealed an obvious decoupling between wind velocity and wind direction frequency within the study area: the maximum wind speed (≈5.5 m s⁻¹) occurred in the NNW direction, whereas the W and WSW directions exhibited the highest wind occurrence frequency. Shelterbelt height showed an extremely significant positive correlation with wind speed reduction efficiency (r = 0.817 ***), while ambient wind speed was significantly negatively correlated with windproof benefit (r = −0.690 ***). Among forest types, F2 and PF achieved significantly higher efficiency and lower wind speeds than F1. Intact shelterbelts reached >85% efficiency, while degraded belts fell below 40%. Soil texture acted as the dominant factor controlling soil erodibility: clay, fine sand, and very fine sand increased soil wind erodibility, while coarse sand and gravel suppressed erodibility. Hierarchical clustering analysis of vertical wind speed profiles confirmed that shelterbelts can substantially reduce near-ground wind velocity; compared with shelterbelt types, spatial position (windward side, leeward side, and central zone) exerted a stronger influence on wind field regulation. This study elucidates the internal correlations among shelterbelt spatial configuration, sand-fixing efficiency, and soil wind erodibility, providing scientific support for the optimization of shelterbelt layout in low-elevation arid ecological regions. Full article

This paper addresses loop-wise PI/PID controller structure selection in a coupled two-tank hydraulic system. Existing studies generally prescribe controller architectures or compare predefined configurations after synthesis, leaving this problem insufficiently explored when each choice depends energetically on the neighboring loop. Although exact feedback linearization decouples the auxiliary tracking dynamics, the reconstructed physical pump inputs retain nonlinear hydraulic coupling. Consequently, reducing the control energy of one loop may increase the effort required by the other, creating conflicting local objectives that motivate a non-cooperative game-theoretic formulation. The plant is feedback-linearized, PI and PID controllers are parameterized by pole placement, and their assignment is posed as a finite static game in which each player minimizes pump energy cost. For the nominal symmetric configuration, (PID, PID) is the unique Nash equilibrium, although (PI, PI) yields a lower aggregate control energy cost (291.4134 vs. 291.8184), showing that unilateral optimality does not coincide with collective energy minimization. PID reduces settling time from 5.253 s to 3.422 s (34.9%) but increases maximum overshoot from 13.55% to 20.82% (53.7%). Sensitivity analyses show that tuning, leakage, coupling, and initial conditions can reverse best responses, while a $\pm 10\%$ parametric mismatch analysis preserves convergent tracking with small steady-state errors, although the equilibrium may change. Full article

Background/Objectives: Distinguishing invasive cervical cancer from high-grade cervical intraepithelial neoplasia (CIN) before histopathology remains difficult. We evaluated routine blood-based parameters for this distinction and developed and internally validated a parsimonious prediction model, testing its incremental value over age. Methods: In this retrospective diagnostic accuracy study with two-gate (case–control) sampling, we analyzed 137 unique patients with histopathologically confirmed cervical cancer and 238 with high-grade CIN after duplicate removal. A four-variable model (age, CEA, neutrophil-to-lymphocyte ratio (NLR), and hemoglobin) was developed and internally validated by bootstrap optimism correction, calibration, and decision-curve analysis, with age-matched and subgroup sensitivity analyses. Results: The strongest single discriminators were age (AUC 0.826), C-reactive protein (0.805), and albumin (0.772). The continuous model showed an apparent AUC of 0.859 (optimism-corrected 0.853; calibration slope 0.962; Brier 0.136). The gain over age alone was statistically significant but modest (ΔAUC +0.027; DeLong p = 0.015). After 1:1 age matching, age no longer discriminated (AUC 0.49), whereas the model retained moderate discrimination (AUC 0.686), with NLR independent. Performance was lower for CIN 3 versus early-stage cancer (AUC 0.759). Conclusions: The model may provide adjunctive risk-stratification information, but clinical implementation requires external validation in prospectively assembled cohorts; the findings are hypothesis-generating. Full article

People who benefit from valuable information do not always pass it on. We develop a delayed IHSCR information propagation model in which a beneficiary can either continue spreading the information or hold it after a behavioral decision lag. The key modeling distinction is that information acquisition and the subsequent sharing-or-holding decision are treated as separate behavioral stages, while information holders can also suppress active spreaders. The delayed transitions are written as outflow rates, so arbitrary nonnegative histories do not automatically preserve positivity. We therefore work with nonnegative-feasible histories and show that such histories exist near each positive equilibrium on any fixed finite interval. For zero delay, we derive the basic reproduction number, prove global asymptotic stability of the information-free equilibrium when $R_0<1$, and give Routh–Hurwitz conditions for local stability of the positive equilibrium. With the delay as a bifurcation parameter, the linearized system gives a transcendental characteristic equation and a quartic frequency equation. The critical delay is recovered from an atan2-based phase condition, and the transversality condition identifies the first spectral stability switch. For the stability-switching parameter set, an independent characteristic-root computation verifies a unique simple crossing, and a characteristic-matrix normal-form calculation gives a negative first Lyapunov coefficient, classifying the local Hopf bifurcation as supercritical with a locally orbitally stable periodic branch. We also prove the existence of a delayed optimal control on the nonnegative-feasible set and derive the optimality system, including advanced adjoint terms. At the baseline cost weights, the computed dynamic control gives a modestly higher net objective than a numerically optimized constant-control benchmark, and this ordering persists when the relative cost ratio $c_1/c_2$ is varied from 1 to 100. A two-parameter sensitivity analysis shows how the beneficiary-to-spreader and beneficiary-to-holder transition rates shift the first critical delay. Full article

The rapid advancement of autonomous agricultural systems (AASs) is transforming modern agriculture, where labor shortages, sustainability imperatives, and demands for precision farming are driving the adoption of intelligent agricultural platforms. Agricultural production environments present uniquely challenging conditions for autonomous agricultural systems, including unstructured and dynamically changing terrain, biologically variable targets, unpredictable illumination and weather conditions, and safe human–machine coexistence. This review systematically investigates three cornerstone technologies: multi-source perception, intelligent decision-making, and precision control. Furthermore, typical agricultural operations, including soil tillage, planting, irrigation and drainage, fertilization, plant protection, harvesting, and agricultural product processing, are reviewed to illustrate their applications. Based on representative operational scenarios, the research progress and application characteristics of intelligent equipment in environmental perception, operational optimization, and control execution are summarized. Specifically, multi-source perception is evolving from isolated sensor-based acquisition toward multimodal and deep learning-enabled semantic scene understanding. Intelligent decision-making has evolved from experience-driven approaches toward physics-informed, data-driven, and knowledge-enhanced frameworks for adaptive operational optimization. Precision control has progressed from conventional PID control toward adaptive, learning-based, and digital twin-enabled control strategies, achieving robust high-precision closed-loop regulation. However, several critical challenges persist: limited cross-domain generalization and robustness of perception models under environmental distribution shift, constrained interpretability and trustworthiness of data-driven decision systems, and insufficient adaptability of control architectures under multi-disturbance coupled field conditions. To address these gaps, future research should prioritize multi-source heterogeneous data fusion and standardization, collaborative control frameworks integrating mechanistic knowledge with data-driven learning, and explainable artificial intelligence combined with agricultural domain expertise—advancing toward genuinely autonomous, trustworthy, and resilient agricultural systems. Full article

Embedding an evolutionary algorithm (EA) into control structures offers an effective solution for specific control problems. Often, it predicts the best control values using a process model (PM). The primary limitation is its high computational time. Our work addresses optimal control problems (OCPs) with a final cost, using receding horizon control (RHC) with an EA as a predictor. This work is a continuation of a previous article, in which the EA predictor was replaced with a multilinear regression-based predictor. Our objective is to propose a predictor based on regression neural networks (RNNs) that emulates the behavior of the (EA, PM) couple. A number of closed-loop simulations using the existing EA controller produce sequences of optimal control values and corresponding state values, which are stored in a data structure. Datasets for each sampling period are derived from these data and are used to train RNN objects employing a unique RNN model. The model, which is an “optimizable” RNN plus the list of hyperparameters preset before optimization, is determined after a thorough analysis of possible candidates using a MATLAB R2025b application. The presented method of constructing an RNN predictor is the main contribution. Algorithms for (a) constructing the sequence of RNN objects and (b) simulating the closed loop are also proposed. A case study illustrates our method. The RNN predictor successfully emulated the (EA, PM) couple: (a) the control-loop dynamics were nearly identical; (b) the performance indices were essentially the same; and (c) the execution time of the controller significantly decreased from 38 to 0.054 s, demonstrating that RHC can be applied more broadly. Full article