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The present paper investigates the adoption of green manufacturing approaches among industrial enterprises in Slovakia, emphasizing the interplay between institutional pressures and enterprise-level resources. Based on a survey of 88 enterprises from energy- and material-intensive sectors, the study evaluates how regional context and enterprise size influence the adoption of green practices. Using logistic regression and the chi-squared test, the findings reveal minimal regional variation, suggesting strong isomorphic effects of harmonised European Union environmental regulations. In contrast, enterprise size significantly correlates with the adoption of complex green practices, confirming the relevance of the resource-based view. These results highlight the dominance of internal capabilities over regional factors in green transition pathways within small post-transition economies. The study contributes to cross-national theorising by showing how resource asymmetries, rather than institutional diversity, shape environmental behaviour in uniform regulatory environments. Specifically, the paper examines how institutional pressures, enterprise-level resources, stakeholders, and legitimacy influence the adoption of green manufacturing practices in Slovak industrial enterprises. The study draws on institutional theory, the resource-based view, stakeholder theory, and legitimacy theory to explore the relationship between enterprise size, regional location, and the adoption levels of green manufacturing. Full article

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18 pages, 2436 KiB

Open AccessArticle

Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows

by Rui Guo and Yongqiang Dai

Appl. Sci. 2025, 15(15), 8763; https://doi.org/10.3390/app15158763 (registering DOI) - 7 Aug 2025

Abstract

Subclinical mastitis in dairy cows poses a significant challenge to the dairy industry, leading to reduced milk yield, altered milk composition, compromised animal health, and substantial economic losses for dairy farmers. A model based on the XGBoost algorithm, optimized with an Improved GOOSE Optimization Algorithm (IGOOSE), is presented in this work as an innovative approach for predicting subclinical mastitis in order to overcome these problems. The Dairy Herd Improvement (DHI) records of 4154 cows served as the model’s original foundation. A total of 3232 samples with 21 characteristics made up the final dataset, following extensive data cleaning and preprocessing. To overcome the shortcomings of the original GOOSE algorithm in intricate, high-dimensional problem spaces, three significant enhancements were made. First, an elite inverse strategy was implemented to improve population initialization, enhancing the algorithm’s balance between global exploration and local exploitation. Second, an adaptive nonlinear control factor was added to increase the algorithm’s stability and convergence speed. Lastly, a golden sine strategy was adopted to reduce the risk of premature convergence to suboptimal solutions. According to experimental results, the IGOOSE-XGBoost model works better than other models in predicting subclinical mastitis, especially when it comes to recognizing somatic cell scores, which are important markers of the illness. This study provides a strong predictive framework for managing the health of dairy cows, allowing for the prompt identification and treatment of subclinical mastitis, which enhances the efficiency and quality of milk supply. Full article

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10 pages, 1591 KiB

Open AccessCommunication

Adsorptive Separation of Chlorobenzene and Chlorocyclohexane by Nonporous Adaptive Crystals of Perethylated Pillar[6]arene

by Sha Wu, Yuyue Chi, Qian Dong and Jiong Zhou

Molecules 2025, 30(15), 3312; https://doi.org/10.3390/molecules30153312 (registering DOI) - 7 Aug 2025

Abstract

The separation of chlorobenzene (CB) and chlorocyclohexane (CCH) using traditional industrial separation technologies (distillation, fractionation, and rectification) is a great challenge due to their close boiling points. Here, we report an innovative method for the separation of the mixture of CB and CCH by nonporous adaptive crystals (NACs) of perethylated pillar[6]arene (EtP6). NACs of EtP6 (EtP6α) can selectively adsorb CCH vapor from the vapor mixture of CB and CCH (v:v = 1:1) with a purity of 99.5%. Furthermore, EtP6α can be recycled for five times without a significant loss of performance. Full article

(This article belongs to the Special Issue Recent Advances in Supramolecular Chemistry)

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22 pages, 9279 KiB

Open AccessArticle

ORD-YOLO: A Ripeness Recognition Method for Citrus Fruits in Complex Environments

by Zhaobo Huang, Xianhui Li, Shitong Fan, Yang Liu, Huan Zou, Xiangchun He, Shuai Xu, Jianghua Zhao and Wenfeng Li

Agriculture 2025, 15(15), 1711; https://doi.org/10.3390/agriculture15151711 (registering DOI) - 7 Aug 2025

Abstract

With its unique climate and geographical advantages, Yunnan Province in China has become one of the country’s most important citrus-growing regions. However, the dense foliage and large fruit size of citrus trees often result in significant occlusion, and the fluctuating light intensity further complicates accurate assessment of fruit maturity. To address these challenges, this study proposes an improved model based on YOLOv8, named ORD-YOLO, for citrus fruit maturity detection. To enhance the model’s robustness in complex environments, several key improvements have been introduced. First, the standard convolution operations are replaced with Omni-Dimensional Dynamic Convolution (ODConv) to improve feature extraction capabilities. Second, the feature fusion process is optimized and inference speed is increased by integrating a Re-parameterizable Generalized Feature Pyramid Network (RepGFPN). Third, the detection head is redesigned using a Dynamic Head structure that leverages dynamic attention mechanisms to enhance key feature perception. Additionally, the loss function is optimized using InnerDIoU to improve object localization accuracy. Experimental results demonstrate that the enhanced ORD-YOLO model achieves a precision of 93.83%, a recall of 91.62%, and a mean Average Precision (mAP) of 96.92%, representing improvements of 4.66%, 3.3%, and 3%, respectively, over the original YOLOv8 model. ORD-YOLO not only maintains stable and accurate citrus fruit maturity recognition under complex backgrounds, but also significantly reduces misjudgment caused by manual assessments. Furthermore, the model enables real-time, non-destructive detection. When deployed on harvesting robots, it can substantially increase picking efficiency and reduce post-maturity fruit rot due to delayed harvesting. These advancements contribute meaningfully to the quality improvement, efficiency enhancement, and digital transformation of the citrus industry. Full article

(This article belongs to the Special Issue Application of Smart Technologies in Orchard Management)

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21 pages, 926 KiB

Open AccessArticle

Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools

by Edina Jenčová, Tatiana Gajdušková, Martin Jezný and Pavol Hudák

Appl. Sci. 2025, 15(15), 8760; https://doi.org/10.3390/app15158760 (registering DOI) - 7 Aug 2025

Abstract

The paper focuses on the identification of “bottlenecks” in the passenger handling process at the airports. In the current era of digital transformation and the emergence of Industry 4.0 and 5.0 concepts, optimizing passenger flows through data-driven tools is becoming an essential part of intelligent airport management. While many solutions focus on high-end software or AI-based systems, this study demonstrates the value of preparatory models built in widely accessible platforms such as Microsoft Excel. A simulation model was developed to analyze check-in and security screening, integrating discrete event simulation (DES), queueing theory, and elements of Monte Carlo simulation. The model enables the segmentation of the handling process into key events, including probabilistically generated arrivals and service durations. Although the model is built in a basic environment, it serves as a prototype platform for potential integration into broader digitalization strategies, offering a preparatory framework for future implementation in more sophisticated systems. Full article

(This article belongs to the Special Issue The Wide Range Use of Innovative Technologies in Industry 4.0/5.0 and IoT)

13 pages, 1135 KiB

Open AccessArticle

A Study on the Beneficiation of Very Fine Particle Rutile Ore Using Flotation

by Oyku Bilgin and Ilhan Ehsani

Minerals 2025, 15(8), 838; https://doi.org/10.3390/min15080838 (registering DOI) - 7 Aug 2025

Abstract

This study investigates the beneficiation of finely grinded rutile ore utilizing a combination of flocculation and flotation methods. Rutile, a Ti-bearing mineral with industrial significance, is often associated with heavy minerals found in coastal and metamorphic environments. A rutile ore sample from Azıtepe (Alaşehir, Türkiye) was reduced to −63 µm and enriched under varying pH conditions (2.5–12) using different reagent combinations and was used for our investigation of both flocculation and flotation processes using reagents such as Aero801(SIPX), Aero825, tannic acid (TA), and pomace oil. The best results were achieved at pH: 8 using Aero801(SIPX) and pomace oil during flocculation, and Aero801(SIPX), Aero825, and Aerofroth88 during flotation, yielding a concentrate with an 8.99% TiO₂ grade and an 89.5% recovery rate. Meanwhile, a 7.00% TiO₂ grade concentrate was obtained with a recovery rate of 71.92% at neutral pH. This study found that pH and reagent selection had an important effect on TiO₂ enrichment efficiency in fine size, low-grade rutile ores. Future research is recommended to investigate selective depressants and multi-stage cleaning to improve separation. Full article

(This article belongs to the Special Issue Particle–Bubble Interactions in the Flotation Process)

17 pages, 4935 KiB

Open AccessArticle

Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling

by Miao Peng, Sue Bai and Yang Lu

Appl. Sci. 2025, 15(15), 8759; https://doi.org/10.3390/app15158759 (registering DOI) - 7 Aug 2025

Abstract

Detecting steel defects is a vital process in industrial production, but traditional methods suffer from poor feature extraction and low detection accuracy. To address these issues, this research introduces an improved model, EB-YOLOv8, based on YOLOv8. First, the multi-scale attention mechanism EMA is integrated into the backbone and neck sections to reduce noise during gradient descent and enhance model stability by encoding global information and weighting model parameters. Second, the weighted fusion splicing module, Concat_BiFPN, is used in the neck network to facilitate multi-scale feature detection and fusion. This improves detection precision. The results show that the EB-YOLOv8 model increases detection accuracy on the NEU-DET dataset by 3.1%, reaching 80.2%, compared to YOLOv8. Additionally, the average precision on the Severstal steel defect dataset improves from 65.4% to 66.1%. Overall, the proposed model demonstrates superior recognition performance. Full article

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48 pages, 3035 KiB

Open AccessReview

A Review of Indian-Based Drones in the Agriculture Sector: Issues, Challenges, and Solutions

by Ranjit Singh and Saurabh Singh

Sensors 2025, 25(15), 4876; https://doi.org/10.3390/s25154876 (registering DOI) - 7 Aug 2025

Abstract

In the current era, Indian agriculture faces a significant demand for increased food production, which has led to the integration of advanced technologies to enhance efficiency and productivity. Drones have emerged as transformative tools for enhancing precision agriculture, reducing costs, and improving sustainability. This study provides a comprehensive review of drone adoption in Indian agriculture by examining its effects on precision farming, crop monitoring, and pesticide application. This research evaluates technological advancements, regulatory frameworks, infrastructure, farmers’ perceptions, and the financial accessibility of drone technology in the Indian agricultural context. Key findings indicate that, while drone adoption enhances efficiency and sustainability, challenges such as high costs, lack of training, and regulatory barriers hinder widespread implementation. This paper also explores the growing market for agricultural drones in India, highlighting key industry players and projected market growth. Furthermore, it addresses regional differences in adoption rates and emphasizes the increasing social acceptance of drones among Indian farmers. To bridge the gap between potential and practice, the study proposes several policy and institutional recommendations, including government-led financial incentives, training programs, and public–private partnerships to facilitate drone integration. Moreover, this review article also highlights technological advancements, such as AI and IoT, in agriculture. Finally, open issues and future research directions for drones are discussed. Full article

(This article belongs to the Section Smart Agriculture)

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23 pages, 2252 KiB

Open AccessFeature PaperArticle

The Influence of the Geometric Configuration of the Drive System on the Motion Dynamics of Jaw Crushers

by Emilian Mosnegutu, Claudia Tomozei, Oana Irimia, Vlad Ciubotariu, Diana Mirila, Mirela Panainte-Lehadus, Marcin Jasiński, Nicoleta Sporea and Ivona Camelia Petre

Processes 2025, 13(8), 2498; https://doi.org/10.3390/pr13082498 (registering DOI) - 7 Aug 2025

Abstract

This study presents a comparative analysis of two double-toggle drive systems for jaw crushers that are tension based and compression based (this refers to the way in which the connecting rod is mechanically stressed within the drive mechanism), with the objective of identifying the optimal configuration from both kinematic and functional perspectives. Jaw crushers play a critical role in the extractive industry, and their performance is strongly influenced by the geometry and positioning of the drive mechanism. A theoretical approach based on mathematical modeling and numerical simulation was applied to a real constructive model (SMD-117), assessing variations in the linear velocity of the moving links as a function of mechanism placement. The study employed Mathcad 15, Roberts Animator, and GIM (Graphical Interactive Mechanisms) 2025.4 software to perform calculations and simulate motion. Results revealed a sinusoidal velocity pattern with significant differences between the two systems: the tension-based drive achieves peak velocities at the beginning of the angular variation interval, while the compression-based system reaches its maximum toward the end. Link C consistently exhibits higher velocities than link E, indicating increased mechanical stress. Polar graphic analysis identified critical velocity angles, and simulations confirmed the model’s validity with a maximum error of just 1.79%. The findings emphasize the importance of selecting an appropriate drive system to enhance performance, durability, and energy efficiency, offering concrete recommendations for equipment design and operation. Full article

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

13 pages, 718 KiB

Open AccessArticle

Evaluation and Verification of Starch Decomposition by Microbial Hydrolytic Enzymes

by Makoto Takaya, Manzo Uchigasaki, Koji Itonaga and Koichi Ara

Water 2025, 17(15), 2354; https://doi.org/10.3390/w17152354 (registering DOI) - 7 Aug 2025

Abstract

This study investigates the Enzyme Biofilm Method (EBM), a biological wastewater treatment technology previously developed by the authors. EBM employs microbial-derived hydrolytic enzyme groups in the initial treatment stage to break down high-molecular-weight organic matter—such as starch, proteins, and fats—into low-molecular-weight compounds. These compounds enhance the growth of native microorganisms, promoting biofilm formation on carriers and improving treatment efficiency. Over the past decade, EBM has been practically applied in food factory wastewater facilities handling high organic loads. The enzyme groups used in EBM are derived from cultures of Bacillus mojavensis, Saccharomyces cariocanus, and Lacticaseibacillus paracasei. To clarify the system’s mechanism and ensure its practical viability, this study focused on starch—a prevalent and recalcitrant component of food wastewater—using two evaluation approaches. Verification 1: Field testing at a starch factory showed that adding enzyme groups to the equalization tank effectively reduced biological oxygen demand (BOD) through starch degradation. Verification 2: Laboratory experiments confirmed that the enzyme groups possess both amylase and maltase activities, sequentially breaking down starch into glucose. The resulting glucose supports microbial growth, facilitating biofilm formation and BOD reduction. These findings confirm EBM’s potential as a sustainable and effective solution for treating high-strength food industry wastewater. Full article

(This article belongs to the Special Issue Advanced Biological Wastewater Treatment and Nutrient Removal)

19 pages, 2573 KiB

Open AccessReview

A Review on Pipeline In-Line Inspection Technologies

by Qingmiao Ma, Weige Liang and Peiyi Zhou

Sensors 2025, 25(15), 4873; https://doi.org/10.3390/s25154873 (registering DOI) - 7 Aug 2025

Abstract

Pipelines, as critical infrastructure in energy transmission, municipal facilities, industrial production, and specialized equipment, are essential to national economic security and social stability. This paper systematically reviews the domestic and international research status of pipeline in-line inspection (ILI) technologies, with a focus on four major technological systems: electromagnetic, acoustic, optical, and robotic technologies. The operational principles, application scenarios, advantages, and limitations of each technology are analyzed in detail. Although existing technologies have achieved significant progress in defect detection accuracy and environmental adaptability, they still face challenges including insufficient adaptability to complex environments, the inherent trade-off between detection accuracy and efficiency, and high equipment costs. Future research directions are identified as follows: intelligent algorithm optimization for multi-physics collaborative detection, miniaturized and integrated design of inspection devices, and scenario-specific development for specialized environments. Through technological innovation and multidisciplinary integration, pipeline ILI technologies are expected to progressively realize efficient, precise, and low-cost lifecycle safety monitoring of pipelines. Full article

(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring and Nondestructive Evaluation: 2nd Edition)

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21 pages, 737 KiB

Open AccessArticle

RiscADA: RISC-V Extension for Optimized Control of External D/A and A/D Converters

by Cosmin-Andrei Popovici, Andrei Stan, Nicolae-Alexandru Botezatu and Vasile-Ion Manta

Electronics 2025, 14(15), 3152; https://doi.org/10.3390/electronics14153152 (registering DOI) - 7 Aug 2025

Abstract

The increasing interest shared by academia and industry in the development of RISC-V cores, extensions and accelerators becomes fructified by collaborative efforts, like the EU’s ChipsJU, which leverages the design of building blocks, IPs and cores based on RISC-V architecture. A domain capable of benefiting from the RISC-V extensibility is the control of external DACs and ADCs. The proposed solution is an open-source RISC-V extension for optimized control of external DACs and ADCs called RiscADA. The extension supports a parametrizable number of DACs and ADCs, is integrated as a coprocessor beside CVA6 in a SoC by using the CV-X-IF interface, deployed on a Kintex UltraScale+ FPGA and implements ISA extension instructions. After benchmarks with commercial solutions, the results show that CVA6 using RiscADA extension configures external DACs 38.6 × and 10.9× times faster than MicroBlaze V and simple CVA6, both using AXI SPI peripherals. The proposed extension achieves 5.35× and 3.05× times higher sample rates of external ADCs than the two configurations mentioned above. RiscADA extension performs digital signal conditioning 4.52× and 3.1× times faster than the MicroBlaze V and CVA6, both using AXI SPI peripherals. It computes statistics for external ADC readings (minimum, maximum, simple-moving average and over-threshold duration). Full article

(This article belongs to the Section Computer Science & Engineering)

15 pages, 1544 KiB

Open AccessArticle

Optimizing Scaled up Production and Purification of Recombinant Hydrophobin HFBI in Pichia pastoris

by Mason A. Kinkeade, Aurora L. Pagan and Bryan W. Berger

Microorganisms 2025, 13(8), 1845; https://doi.org/10.3390/microorganisms13081845 (registering DOI) - 7 Aug 2025

Abstract

Hydrophobins are small, surface-active protein biosurfactants secreted by filamentous fungi with potential applications in industries such as pharmaceuticals, sanitation, and biomaterials. Additionally, hydrophobins are known to stabilize enzymatic processing of biomass for improved catalytic efficiency. In this study, Pichia pastoris was used to recombinantly express hydrophobin HFBI from Trichoderma reesei, a well-characterized fungal system used industrially for bioethanol production. Iterative optimization was performed on both the induction and purification of HFBI, ultimately producing yields of 86.6 mg/L HFBI and elution concentrations of 48 μM HFBI determined pure by SDS-PAGE, over a five-day methanol-fed batch shake flask induction regiment followed by a single unit operation multimodal cation exchange purification. This final purified material represents an improvement over prior approaches to enable a wider range of potential applications for biosurfactants. Full article

(This article belongs to the Special Issue Advances in Microbial Surfactants: Production and Applications (2nd Edition))

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18 pages, 3363 KiB

Open AccessArticle

Spatial Heterogeneity of Heavy Metals in Arid Oasis Soils and Its Irrigation Input–Soil Nutrient Coupling Mechanism

by Jiang Liu, Chongbo Li, Jing Wang, Liangliang Li, Junling He and Funian Zhao

Sustainability 2025, 17(15), 7156; https://doi.org/10.3390/su17157156 (registering DOI) - 7 Aug 2025

Abstract

Soil environmental quality in arid oases is crucial for regional ecological security but faces multi-source heavy metal (HM) contamination risks. This study aimed to (1) characterize the spatial distribution of soil HMs (As, Cd, Cr, Cu, Hg, and Zn) in the Ka Shi gar oasis, Xinjiang, (2) quantify the driving effect of irrigation water, and (3) elucidate interactions between HMs, soil properties, and land use types. Using 591 soil and 12 irrigation water samples, spatial patterns were mapped via inverse distance weighting interpolation, with drivers and interactions analyzed through correlation and land use comparisons. Results revealed significant spatial heterogeneity in HMs with no consistent regional trend: As peaked in arable land (5.27–40.20 μg/g) influenced by parent material and agriculture, Cd posed high ecological risk in gardens (max 0.29 μg/g), and Zn reached exceptional levels (412.00 μg/g) in gardens linked to industry/fertilizers. Irrigation water impacts were HM-specific: water contributed to soil As enrichment, whereas high water Cr did not elevate soil Cr (indicating industrial dominance), and Cd/Cu showed no significant link. Interactions with soil properties were regulated by land use: in arable land, As correlated positively with EC/TN and negatively with pH; in gardens, HMs generally decreased with pH, enhancing mobility risk; in forests, SOM adsorption immobilized HMs; in construction land, Hg correlated with SOM/TP, suggesting industrial-organic synergy. This study advances understanding by demonstrating that HM enrichment arises from natural and anthropogenic factors, with the spatial heterogeneity of irrigation water’s driving effect critically regulated by land use type, providing a spatially explicit basis for targeted pollution control and sustainable oasis management. Full article

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18 pages, 3248 KiB

Open AccessArticle

Evaluation Model of Climatic Suitability for Olive Cultivation in Central Longnan, China

by Li Liu, Ying Na and Yun Ma

Atmosphere 2025, 16(8), 948; https://doi.org/10.3390/atmos16080948 (registering DOI) - 7 Aug 2025

Abstract

Longnan is the largest olive cultivation area in China. The unique microclimates in Longnan make it an ideal testing ground for climate-resilient cultivation strategies with broader applications across similar regions, yet predictive models linking weather to oil quality remain scarce. This study establishes a climate suitability evaluation model for olive cultivation in central Longnan based on meteorological data and olive quality data in the Fotanggou planting base. Four key climatic factors are identified: cumulative sunshine hours during the fruit coloring to ripening period, average temperature during the fruit coloring to harvesting period, number of cloudy and rainy days during the harvesting period, and relative humidity during the fruit setting to fruit enlargement period. Olive oil quality is graded into three levels (Excellent III, Good II, Fair I) based on acidity, linoleic acid, and peroxide value using K-means clustering. A climate suitability index is developed by integrating these factors, with weights determined via principal component analysis. The model is validated against an olive quality report from the Dabao planting base, showing an 80% match rate. From 1991 to 2023, 87.9% of years exhibit suitable or moderately suitable conditions, with 100% of years in the past decade (2014–2023) reaching “Good” or “Excellent” levels. This model provides a scientific basis for evaluating and predicting olive oil quality, supporting sustainable olive industry development in Longnan. This model provides policymakers and farmers with actionable insights to ensure the long-term sustainability of olive industry amid climate uncertainty. Full article

(This article belongs to the Special Issue Understanding and Forecasting Seasonal Weather and Climate Extreme Events)

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