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The aim of this work is to develop a beneficial methodology for improving the ecological and economic efficiency of chemico-technological systems (CTS). The problem is formulated as a control with a vector objective function that includes economic and environmental components. A practical approach to enhancing the environmental and economic efficiency of CTS is presented. Some approaches to accident prevention including the application of a problem-oriented dynamic model are introduced. Extended Ecological–Technological Portfolios have been developed. These Portfolios represent simplified visual models aiming to increase the environmental and economic efficiency of the CTS. Portfolios allow for the identification of dependencies between technological faults and ecological criteria and enable the investigation of the impact of the concrete chemico-technological process on the environment. Based on the Portfolios, decisions can be made for improving the economic–ecological efficiency of CTS and the prevention of accidents. Ecological–Technological Matrices, which provide a generalized characterization of technological breakdowns, have been developed. A strategy for adjusting technological constraints, using Matrices and vector criteria, has been proposed. Portfolios and Matrices can be applied in data preparation to solve certain artificial intelligence tasks for increasing the environmental and economic efficiency of potentially hazardous CTS. Some examples are given, presenting the industrial control of ammonia synthesis, methane conversion, and chemical absorption of CO₂. Full article

(This article belongs to the Special Issue Circular Economy in Process Engineering: Impact Assessment and Directions for Sustainability)

16 pages, 1104 KB

Open AccessFeature PaperArticle

Modeling the Presence of Humanoid Robots in Indoor Propagation Channels

by Adolphe D. J. Nseme, Larbi Talbi and Vincent A. Fono

Telecom 2026, 7(1), 17; https://doi.org/10.3390/telecom7010017 (registering DOI) - 2 Feb 2026

Abstract

The increasing deployment of humanoid robots in indoor environments such as smart factories, laboratories, offices, and hospitals poses new challenges to millimeter-wave wireless communication systems. Existing human body obstruction models, while effective at characterizing pedestrian-induced signal attenuation, are not designed to directly capture the structural geometry, material composition, and controlled mobility of humanoid robotic platforms. In this work, we first reproduce a well-established human-body-based propagation model under comparable indoor conditions and subsequently extend this hybrid framework to controlled humanoid-based scenarios by combining double knife-edge diffraction (DKED) with a modified street-canyon reflection model operating at 28 GHz. Compared to existing human-based studies, the proposed approach explicitly incorporates the material properties of the humanoid robot’s envelope through a calibrated correction factor and accounts for its controlled lateral movements. An indoor measurement campaign using three programmable humanoid robots was conducted to evaluate the model. Experimental results show that humanoid robots can reproduce attenuation trends and obstruction dynamics consistent with those reported in prior human-body blockage studies, while offering improved repeatability and greater experimental control. The proposed framework provides a practical and reproducible tool for modeling indoor millimeter-wave channels under controlled humanoid-based experimental conditions, in environments involving mobile robotic agents. Full article

23 pages, 10699 KB

Open AccessArticle

YOLOv11-IMP: Anchor-Free Multiscale Detection Model for Accurate Grape Yield Estimation in Precision Viticulture

by Shaoxiong Zheng, Xiaopei Yang, Peng Gao, Qingwen Guo, Jiahong Zhang, Shihong Chen and Yunchao Tang

Agronomy 2026, 16(3), 370; https://doi.org/10.3390/agronomy16030370 - 2 Feb 2026

Abstract

Estimating grape yields in viticulture is hindered by persistent challenges, including strong occlusion between grapes, irregular cluster morphologies, and fluctuating illumination throughout the growing season. This study introduces YOLOv11-IMP, an improved multiscale anchor-free detection framework extending YOLOv11, tailored to vineyard environments. Its architecture comprises five specialized components: (i) a viticulture-oriented backbone employing cross-stage partial fusion with depthwise convolutions for enriched feature extraction, (ii) a bifurcated neck enhanced by large-kernel attention to expand the receptive field coverage, (iii) a scale-adaptive anchor-free detection head for robust multiscale localization, (iv) a cross-modal processing module integrating visual features with auxiliary textual descriptors to enable fine-grained cluster-level yield estimation, and (v) aross multiple scales. This work evaluated YOLOv11-IMP on five grape varieties collecten augmented spatial pyramid pooling module that aggregates contextual information acd under diverse environmental conditions. The framework achieved 94.3% precision and 93.5% recall for cluster detection, with a mean absolute error (MAE) of 0.46 kg per vine. The robustness tests found less than 3.4% variation in accuracy across lighting and weather conditions. These results demonstrate that YOLOv11-IMP can deliver high-fidelity, real-time yield data, supporting decision-making for precision viticulture and sustainable agricultural management. Full article

(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)

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20 pages, 2553 KB

Open AccessArticle

Internal and External Landscape Features of 18 Parks in Hangzhou, China That Cool the Park and the Surrounding Urban Areas: Strategies for Other Cities

by Tao Ma, Mengxin Yang, Shaojie Zhang, Xiaofan Jiang and Wenbin Nie

Buildings 2026, 16(3), 630; https://doi.org/10.3390/buildings16030630 - 2 Feb 2026

Abstract

As one of China’s “New Four Furnaces”, the city of Hangzhou faces significant heat challenges exacerbated by rapid urbanization. Urban parks offer effective nature-based solutions, but optimizing their multi-dimensional cooling performance—encompassing cooling area (PCA), efficiency (PCE), intensity (PCI), and gradient (PCG)—remains a key challenge. This study quantitatively analyzed the internal and external landscape features of 18 parks in Hangzhou, revealing that park cooling performance is not simply a case of “bigger is better.” We found that parks with more complex shapes and irregular boundaries exhibited higher cooling efficiency per unit area (PCE) compared to larger parks with smooth, simple shapes, though sometimes at the expense of peak PCI. Furthermore, the surrounding built environment is critical: high building density within a 300 m buffer zone was found to significantly impede the spatial extent of the cooling effect (PCA). Based on these findings, we propose that to effectively mitigate urban heat, cities should (1) shift focus away from creating large, isolated parks with smooth boundaries; (2) prioritize a network of smaller, morphologically diverse parks with irregular edges that extend into the community; and (3) enhance each park’s cooling reach through strategies like green streets and tree-lined paths. These approaches offer tangible, actionable guidance for designing high-performance cooling green infrastructure in dense urban environments. Full article

(This article belongs to the Special Issue Advanced Research on Intelligent Building Construction and Management)

24 pages, 1091 KB

Open AccessArticle

Coordinated Multi-Intersection Traffic Signal Control Using a Policy-Regulated Deep Q-Network

by Lin Ma, Yan Liu, Yang Liu, Changxi Ma and Shanpu Wang

Sustainability 2026, 18(3), 1510; https://doi.org/10.3390/su18031510 - 2 Feb 2026

Abstract

Coordinated control across multiple signalized intersections is essential for mitigating congestion propagation in urban road networks. However, existing DQN-based approaches often suffer from unstable action switching, limited interpretability, and insufficient capability to model spatial spillback between adjacent intersections. To address these limitations, this study proposes a Policy-Regulated and Aligned Deep Q-Network (PRA-DQN) for cooperative multi-intersection signal control. A differentiable policy function is introduced and explicitly trained to align with the optimal Q-value-derived target distribution, yielding more stable and interpretable policy behavior. In addition, a cooperative reward structure integrating local delay, movement pressure, and upstream–downstream interactions enables agents to simultaneously optimize local efficiency and regional coordination. A parameter-sharing multi-agent framework further enhances scalability and learning consistency across intersections. Simulation experiments conducted on a 2 × 2 SUMO grid show that PRA-DQN consistently outperforms fixed-time, classical DQN, distributed DQN, and pressure/wave-based baselines. Compared with fixed-time control, PRA-DQN reduces maximum queue length by 21.17%, average queue length by 18.75%, and average waiting time by 17.71%. Moreover, relative to classical DQN coordination, PRA-DQN achieves an additional 7.53% reduction in average waiting time. These results confirm the effectiveness and superiority of the proposed method in suppressing congestion propagation and improving network-level traffic performance. The proposed PRA-DQN provides a practical and scalable basis for real-time deployment of coordinated signal control and can be readily extended to larger networks and time-varying demand conditions. Full article

(This article belongs to the Special Issue Sustainable Intelligent Transport Systems: AI-Driven Multi-Modal Fusion for Green Development)

23 pages, 871 KB

Open AccessArticle

TLOA: A Power-Adaptive Algorithm Based on Air–Ground Cooperative Jamming

by Wenpeng Wu, Zhenhua Wei, Haiyang You, Zhaoguang Zhang, Chenxi Li, Jianwei Zhan and Shan Zhao

Future Internet 2026, 18(2), 81; https://doi.org/10.3390/fi18020081 (registering DOI) - 2 Feb 2026

Abstract

Air–ground joint jamming enables three-dimensional, distributed jamming configurations, making it effective against air–ground communication networks with complex, dynamically adjustable links. Once the jamming layout is fixed, dynamic jamming power scheduling becomes essential to conserve energy and prolong jamming duration. However, existing methods suffer from poor applicability in such scenarios, primarily due to their sparse deployment and adversarial nature. To address this limitation, this paper develops a set of mathematical models and a dedicated algorithm for air–ground communication countermeasures. Specifically, we (1) randomly select communication nodes to determine the jammer operation sequence; (2) schedule the number of active jammers by sorting transmission path losses in ascending order; and (3) estimate jamming effects using electromagnetic wave propagation characteristics to adjust jamming power dynamically. This approach formally converts the original dynamic, stochastic jamming resource scheduling problem into a static, deterministic one via cognitive certainty of dynamic parameters and deterministic modeling of stochastic factors—enabling rapid adaptation to unknown, dynamic communication power strategies and resolving the coordination challenge in air–ground joint jamming. Experimental results demonstrate that the proposed Transmission Loss Ordering Algorithm (TLOA) extends the system operating duration by up to 41.6% compared to benchmark methods (e.g., genetic algorithm). Full article

(This article belongs to the Special Issue Adversarial Attacks and Cyber Security)

24 pages, 10544 KB

Open AccessArticle

Prediction of Added Resistance in Waves Using a Frequency-Domain Rankine Source Method: Middle-Field Formulation and Low-Speed Validation

by Seunghoon Oh, Se-Yun Hwang, Jae-Chul Lee, Soon-Sup Lee and Eun Soo Kim

J. Mar. Sci. Eng. 2026, 14(3), 296; https://doi.org/10.3390/jmse14030296 - 2 Feb 2026

Abstract

A three-dimensional frequency-domain ship-motion solver based on the Rankine source method is extended to predict added resistance in waves. Although middle-field formulations have been used mainly in time-domain Rankine panel methods, a middle-field evaluation is implemented here within a frequency-domain Rankine source framework and its validity is examined, including low-speed conditions where the enforcement of radiation conditions is challenging. To enhance robustness at low forward speeds, a hybrid radiation technique is incorporated. Convergence studies are carried out for the free-surface and radiation-boundary discretization, as well as for the control-surface resolution and the clearance distance, and practical numerical settings for added-resistance computations are established. The approach is first verified for Wigley III hulls by comparing motion RAOs and added resistance with published experimental and numerical results. It is then validated for the blunt KVLCC2 hull at the design speed and at low speeds (0 and 4 knots) against published measurements and calculations. Further validations are conducted for additional hull forms (Wigley I, KCS, S-175, and Series 60). The results indicate that the proposed frequency-domain Rankine source method with middle-field evaluation and hybrid radiation yields consistent predictions of motion responses and added resistance over a range of speeds and hull forms, while retaining computational efficiency. Full article

(This article belongs to the Section Ocean Engineering)

21 pages, 355 KB

Open AccessArticle

Certain Properties and Characterizations of Generalized Gould–Hopper-Based Hybrid Polynomials

by Waseem Ahmad Khan, Francesco Aldo Costabile, Can Kızılateş, Khidir Shaib Mohamed, Alawia Adam and Mona A. Mohamed

Mathematics 2026, 14(3), 541; https://doi.org/10.3390/math14030541 - 2 Feb 2026

Abstract

This study offers a comprehensive generalization of the Gould–Hopper polynomials and their Appell-type analogs. Employing the quasi-monomiality approach, we delineate fundamental analytical characteristics, including recurrence relations, associated multiplicative and differential operators, and governing differential equations. Additionally, we derive series representations and determinantal expressions for this newly defined polynomial family. Within this framework, several significant subclasses are introduced and examined, such as the generalized Gould–Hopper-based Appell polynomials. The formulation is further extended using fractional operator techniques to explore their intrinsic structural attributes. Moreover, we construct and investigate new families, namely, the generalized Gould–Hopper-based Bernoulli, Gould–Hopper-based Euler, and Gould–Hopper-based Genocchi polynomials, emphasizing their operational and algebraic properties. Collectively, these findings advance the theory of special functions and provide a foundation for potential applications in mathematical physics and the study of differential equations. Full article

(This article belongs to the Special Issue Polynomial Sequences and Their Applications, 2nd Edition)

24 pages, 21615 KB

Open AccessArticle

DL-AWI: Adaptive Full Waveform Inversion Using a Deep Twin Neural Network

by Chao Li and Yangkang Chen

Geosciences 2026, 16(2), 65; https://doi.org/10.3390/geosciences16020065 - 2 Feb 2026

Abstract

Full waveform inversion (FWI) iteratively improves the accuracy of the model by minimizing the discrepancies between the predicted and the observed data. However, FWI commonly suffers from cycle skipping when the initial model is poor, leading to an erroneous result. To mitigate this problem, we propose deep-learning-backed adaptive waveform inversion (DL-AWI), which introduces a deep twin neural network to precondition the waveforms and compare the ratio of two signals with a zero-lag spike, thereby enhancing the stability of the inversion process. DL-AWI can project the synthetic and observed signals into an extended latent space via several convolutional neural networks (CNNs) with shared weights, which can accelerate the data matching. Compared with classic FWI methods, the proposed DL-AWI provides a wider space for model updates, significantly decreasing the risk of being trapped in local minima. We use synthetic and field examples to validate its efficiency in subsurface model inversion, and the results show that DL-AWI is robust even when a poor initial model is provided. Full article

(This article belongs to the Special Issue Geophysical Inversion)

19 pages, 335 KB

Open AccessArticle

A Note on Truncated Exponential-Based Appell Polynomials via Fractional Operators

by Waseem Ahmad Khan, Francesco Aldo Costabile, Khidir Shaib Mohamed, Alawia Adam and Shahid Ahmad Wani

Axioms 2026, 15(2), 111; https://doi.org/10.3390/axioms15020111 - 2 Feb 2026

Abstract

In this work, we construct a new class of Appell-type polynomials generated through extended truncated and truncated exponential kernels, and we analyze their core algebraic and operational features. In particular, we establish a suitable recurrence scheme and obtain the associated multiplicative and differential operators. By confirming the quasi-monomial structure, we further deduce the governing differential equation for the proposed family. In addition, we present both a series expansion and a determinant formulation, providing complementary representations that are useful for symbolic manipulation and computation. As special cases, we introduce and study subfamilies arising from this setting, namely, extended truncated exponential versions of the Bernoulli, Euler, and Genocchi polynomials, and discuss their structural identities and operational behavior. Overall, these developments broaden the theory of special polynomials and furnish tools relevant to problems in mathematical physics and differential equations. Full article

(This article belongs to the Special Issue Advances in Classical and Applied Mathematics, 2nd Edition)

25 pages, 4447 KB

Open AccessArticle

Process–Microstructure–Property Characteristics of Aluminum Walls Fabricated by Hybrid Wire Arc Additive Manufacturing with Friction Stir Processing

by Ahmed Nabil Elalem and Xin Wu

Materials 2026, 19(3), 580; https://doi.org/10.3390/ma19030580 - 2 Feb 2026

Abstract

Wire Arc Additive Manufacturing (WAAM) is a cost-effective method for fabricating large aluminum components; however, it tends to suffer from heat accumulation and coarse anisotropic microstructures, which can limit the part’s performance. In this study, a wall is fabricated using a hybrid unified additive deformation manufacturing process (UAMFSP) method, which integrates friction stir processing (FSP) into WAAM, and is compared with a Metal Inert Gas (MIG)-based WAAM wall. Infrared (IR) thermography revealed progressive heat buildup in MIG walls, with peak layer temperatures of about 870 to 1000 °C. In contrast, in the UAMFSP process, heat was redistributed through mechanical stirring, maintaining more uniform sub-solidus profiles below approximately 400 °C. Also, optical microscopy and quantitative image analysis showed that MIG walls developed coarse, dendritic grains with a mean grain area of about 314 µm², whereas the UAMFSP produced refined, equiaxed grains with a mean grain area of about 10.9 µm². Microhardness measurement (Vickers HV0.2, 200 gf) confirmed that the UAMFSP process can improve the hardness by 45.8% compared to the MIG process (75.8 ± 7.7 HV vs. 52.0 ± 1.3 HV; p = 0.0027). In summary, the outcomes of this study introduce the UAMFSP process as a method for addressing the thermal and microstructural limitations of WAAM. These findings provide a framework for further extending hybrid additive–deformation strategies to thicker builds, alternative alloys, and service-relevant mechanical evaluations. Full article

(This article belongs to the Section Manufacturing Processes and Systems)

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19 pages, 8317 KB

Open AccessArticle

Systematic Design of Phononic Band Gap Crystals for Elastic Waves at the Specified Target Frequency via Topology Optimization

by Jingjie He, Zhiyuan Jia, Yuhao Bao and Xiaopeng Zhang

Materials 2026, 19(3), 581; https://doi.org/10.3390/ma19030581 - 2 Feb 2026

Abstract

Phononic band gap crystals are characterized by periodic scatterers embedded within a matrix, which enable precise modulation of acoustic or elastic waves. Conventional optimization prioritizes bandwidth maximization, yet practical engineering often requires band gaps at specified frequencies. This requirement creates a significant design challenge. To this end, we develop a topology optimization strategy capable of maximizing elastic wave band gaps around prescribed target frequencies. The approach utilizes Material-Field Series Expansion (MFSE) for unit cell representation and a gradient-free Kriging-based algorithm to tackle the complex optimization problems. This strategy is systematically applied to optimize the band gaps of out-of-plane, in-plane, and complete wave modes, and is further extended to more complex scenarios involving dual-target frequencies. A variety of numerical results demonstrate the method’s effectiveness in engineering phononic crystals for bespoke frequency specifications. Full article

(This article belongs to the Special Issue Advanced Materials in Acoustics and Vibration)

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34 pages, 3681 KB

Open AccessArticle

A Semi-Supervised Transformer with a Curriculum Training Pipeline for Remote Sensing Image Semantic Segmentation

by Peizhuo Liu, Hongbo Zhu, Xiaofei Mi, Yuke Meng, Huijie Zhao and Xingfa Gu

Remote Sens. 2026, 18(3), 480; https://doi.org/10.3390/rs18030480 - 2 Feb 2026

Abstract

Semantic segmentation of remote sensing images is crucial for geospatial applications but is severely hampered by the prohibitive cost of pixel-level annotations. Although semi-supervised learning (SSL) offers a solution by leveraging unlabeled data, its application to Vision Transformers (ViTs) often encounters overfitting and even training instability under extreme label scarcity. To tackle these challenges, we propose a Curriculum-based Self-supervised and Semi-supervised Pipeline (CSSP). The pipeline adopts a staged, easy-to-hard training strategy, commencing with in-domain pretraining for robust feature representation, followed by a carefully designed finetuning stage to prevent overfitting. The pipeline further integrates a novel Difficulty-Adaptive ClassMix (DA-ClassMix) augmentation that dynamically reinforces underperforming categories and a Progressive Intensity Adaptation (PIA) strategy that systematically escalates augmentation strength to maximize model generalization. Extensive evaluations on the Potsdam, Vaihingen, and Inria datasets demonstrate state-of-the-art performance. Notably, with only 1/32 of the labeled data on the Potsdam dataset, the CSSP reaches 82.16% mIoU, nearly matching the fully supervised result (82.24%). Furthermore, we extend the CSSP to a semi-supervised domain adaptation (SSDA) scenario, termed Cross-Domain CSSP (CDCSSP), which outperforms existing SSDA and unsupervised domain adaptation (UDA) methods. This work establishes a stable and highly effective framework for training ViT-based segmentation models with minimal annotation overhead. Full article

(This article belongs to the Special Issue New Insights in Remote Sensing Image Interpretation with Deep Learning)

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24 pages, 2893 KB

Open AccessArticle

Intelligent Control for Quadrotors Based on a Novel Method: TD3-ADRC

by Runyu Cai, Liang Zhang, Wutao Qin and Jie Yan

Drones 2026, 10(2), 110; https://doi.org/10.3390/drones10020110 - 2 Feb 2026

Abstract

To address the requirements for multi-channel decoupling and high-precision control in quadrotor UAV systems, this paper proposes a novel intelligent controller (TD3-ADRC) which integrates Active Disturbance Rejection Control (ADRC) with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Firstly, the dynamic model of the quadrotor is established. Secondly, a parameterized tanh function is introduced and applied to design the tracking differentiator, extended state observer, and nonlinear feedback control law. Then, the TD3 learning mechanism is incorporated to automatically learn and optimize controller parameters, thereby significantly enhancing the system’s disturbance rejection capability. Finally, simulation studies comparing conventional PID, ADRC, DDPG and the proposed TD3-ADRC algorithms are conducted in Simulink. In addition, a bench test system is developed using the PX4 flight controller. Experimental results show that, under complex environmental conditions, the proposed TD3-ADRC controller outperforms both conventional PID and linear ADRC methods in terms of reliability and adaptability, validating the effectiveness of the proposed control approach. Full article

(This article belongs to the Special Issue Advances in AI Large Models for Unmanned Aerial Vehicles)

21 pages, 1285 KB

Open AccessArticle

Template-Based Evaluation of Stable Diffusion via Attention Maps

by Haruno Fusa, Chonho Lee, Sakuei Onishi, Kanshin Fusa and Hiromitsu Shiina

Information 2026, 17(2), 149; https://doi.org/10.3390/info17020149 - 2 Feb 2026

Abstract

Text-to-image models such as Stable Diffusion (SD) require comprehensive, fine-grained, and high-precision methods for evaluating text–image alignment. A prior method, the text–image alignment metric (TIAM), employs a template-based approach for fine-grained, high-precision evaluation; however, it is restricted to objects and colors, limiting its comprehensiveness. This study extends the TIAM by incorporating attention maps and vision–language models to deliver a fine-grained and high-precision evaluation framework that goes beyond colors and objects to include attributes, actions, and positions. In our experiments, we analyze the evaluation scores of images generated by the proposed method and compare them with human judgments. The results demonstrate that the proposed method outperforms existing methods, exhibiting a stronger correlation with human judgments (r = 0.853,

p < 10^{- 48}

). In addition, we applied the proposed method to evaluate the generation abilities of three SD models (i.e., SD1.4, SD2, and SD3.5). Each experiment used over 900 images, totaling 9858 images across all experiments to ensure statistical significance. The results indicate that SD3.5 exhibits superior expressiveness compared with SD1.4 and SD2. Nevertheless, for more complex tasks such as multi-attribute generation or multi-action generation, limitations in text–image alignment remain evident. Full article

(This article belongs to the Section Artificial Intelligence)

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