Search Results (90)

Flocculants are indispensable in water and wastewater treatment, enabling the aggregation and removal of suspended particles, colloids, and emulsions. However, the conventional development and application of flocculants rely heavily on empirical methods, which are time-consuming, resource-intensive, and environmentally problematic due to issues such as sludge production and chemical residues. Recent advances in machine learning (ML) have opened transformative avenues for the design, optimization, and intelligent application of flocculants. This review systematically examines the integration of ML into flocculant research, covering algorithmic approaches, data-driven structure–property modeling, high-throughput formulation screening, and smart process control. ML models—including random forests, neural networks, and Gaussian processes—have successfully predicted flocculation performance, guided synthesis optimization, and enabled real-time dosing control. Applications extend to both synthetic and bioflocculants, with ML facilitating strain engineering, fermentation yield prediction, and polymer degradability assessments. Furthermore, the convergence of ML with IoT, digital twins, and life cycle assessment tools has accelerated the transition toward sustainable, adaptive, and low-impact treatment technologies. Despite its potential, challenges remain in data standardization, model interpretability, and real-world implementation. This review concludes by outlining strategic pathways for future research, including the development of open datasets, hybrid physics–ML frameworks, and interdisciplinary collaborations. By leveraging ML, the next generation of flocculant systems can be more effective, environmentally benign, and intelligently controlled, contributing to global water sustainability goals. Full article

Ensuring data integrity and adaptability in aircraft health monitoring (AHM) is vital for safety-critical aviation systems. Traditional digital twin (DT) and federated learning (FL) frameworks, while effective in enabling distributed, privacy-preserving fault detection, lack mechanisms to remove the influence of corrupted or adversarial data once these have been integrated into global models. This paper proposes a novel FL–DT–FU framework that combines digital twin-based subsystem modeling, federated learning for collaborative training, and federated unlearning (FU) to support the post hoc correction of compromised model contributions. The architecture enables real-time monitoring through local DTs, secure model aggregation via FL, and targeted rollback using gradient subtraction, re-aggregation, or constrained retraining. A comprehensive simulation environment is developed to assess the impact of sensor drift, label noise, and adversarial updates across a federated fleet of aircraft. The experimental results demonstrate that FU methods restore up to 95% of model accuracy degraded by data corruption, significantly reducing false negative rates in early fault detection. The proposed system further supports auditability through cryptographic logging, aligning with aviation regulatory standards. This study establishes federated unlearning as a critical enabler for resilient, correctable, and trustworthy AI in next-generation AHM systems. Full article

Twinning-induced plasticity (TWIP) steels represent a significant development in automotive steel production, characterized by advanced strength and ductility properties. The present study empirically investigated the effects of process parameters on the cutting process and surface quality of TWIP980 steel sheet by abrasive water jet (AWJ) cutting. The cutting experiments were conducted on 1.4 mm thick sheet metal using four different traverse speeds (50, 100, 200, and 400 mm/min) and four different water jet pressures (1500, 2000, 2500, and 3000 bar). Two different abrasive flow rates (300 and 600 g/min) were also utilized. The cut surfaces were characterized in three dimensions with an optical profilometer. The parameters of surface roughness, kerf width, taper angle, and material removal rate (MRR) were determined. Furthermore, microhardness measurements were conducted on the cut surfaces. The optimal surface quality and geometrical accuracy were achieved by applying a combination of parameters, including 3000 bar of pressure, a traverse rate of 400 mm/min, and an abrasive flow rate of 600 g/min. Concurrently, an effective cutting performance with increased MRR and reduced taper angles was achieved under these conditions. The observed increase in microhardness with increasing pressure is attributable to a hardening effect resulting from local plastic deformation. Full article

Neonatal calves possess an immature and naïve immune system and are reliant on the intake of maternal colostrum for the passive transfer of immunoglobulins. Maternal antibodies delivered to the calf via colostrum, are crucial to prevent calfhood diseases and death. Failure of transfer of passive immunity (FTPI) is a condition in which calves do not acquire enough maternal antibodies, mostly in the form of IgG, due to inadequate colostrum quality or delayed colostrum feeding. The diagnosis and risk factors for FTPI have been widely studied in dairy cattle; however, in beef calves, the research interest in the topic is relatively recent, and the most adequate diagnostic and preventative methods are still in development, making it difficult to define recommendations for the assessment and prevention of FTPI in cow–calf operations. The objective of this scoping review is to identify the published literature on best practices for colostrum management and transfer of passive immunity (TPI) in neonatal beef calves. The literature was searched using three electronic databases (CAB Direct, Scopus, and PubMed) for publications from 2003 to 2025. The search process was performed during the period from May to July 2023, and was repeated in January 2025. All screening processes were performed using Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia). A total of 800 studies were initially identified through database searches. After removing duplicates, 346 studies were screened based on their titles and abstracts, leading to the exclusion of 260 studies. The remaining 86 studies underwent full-text screening, and 58 studies were considered eligible for data extraction. Hand-searching the references from published review papers on the subject yielded an additional five studies, bringing the total to 63 included articles. The prevalence of FTPI has been estimated to be between 5.8% and 34.5% in beef calves. Factors studied related to colostrum management include quality and quantity of colostrum intake, the timing and method of colostrum feeding, and the microbial content of the colostrum. Studies on risk factors related to the calf include the topics calf sex, twin status, calf vigor, weight, month of birth, cortisol and epinephrine concentrations, and the administration of nonsteroidal anti-inflammatory drugs to calves after difficult calving. The dam-related risk factors studied include dam body condition score and udder conformation, breed, parity, genetics, prepartum vaccinations and nutrition, calving area and difficulty, and the administration of nonsteroidal anti-inflammatory drugs at C-section. Most importantly for beef systems, calves with low vigor and a weak suckling reflex are at high risk for FTPI; therefore, these calves should be given extra attention to ensure an adequate consumption of colostrum. While serum IgG levels of < 8 g/L or < 10 g/L have been suggested as cutoffs for the diagnosis of FTPI, 16 g/L and 24 g/L have emerged as cutoffs for adequate and optimal serum IgG levels in beef calves. Several field-ready diagnostics have been compared in various studies to the reference standards for measuring indicators of TPI in beef calves, where results often differ between models or manufacturers. Therefore, care must be taken when interpreting these results. Full article

Thermal processes with prolonged and variable delays pose considerable difficulties due to unpredictable system dynamics and external disturbances, often resulting in diminished control effectiveness. This work presents a hybrid control strategy that synthesizes deep reinforcement learning (DRL) strategies with nonlinear model predictive control (NMPC) to improve the robust control performance of a thermal process with a long time delay. In this approach, NMPC cost functions are formulated as learning functions to achieve control objectives in terms of thermal tracking and disturbance rejection, while an actor–critic (AC) reinforcement learning agent dynamically adjusts control actions through an adaptive policy based on the exploration and exploitation of real-time data about the thermal process. Unlike conventional NMPC approaches, the proposed framework removes the need for predefined terminal cost tuning and strict constraint formulations during the control execution at runtime, which are typically required to ensure robust stability. To assess performance, a comparative study was conducted evaluating NMPC against AC-based controllers built upon policy gradient algorithms such as the deep deterministic policy gradient (DDPG) and the twin delayed deep deterministic policy gradient (TD3). The proposed method was experimentally validated using a temperature control laboratory (TCLab) testbed featuring long and varying delays. Results demonstrate that while the NMPC–AC hybrid approach maintains tracking control performance comparable to NMPC, the proposed technique acquires adaptability while tracking and further strengthens robustness in the presence of uncertainties and disturbances under dynamic system conditions. These findings highlight the benefits of integrating DRL with NMPC to enhance reliability in thermal process control and optimize resource efficiency in thermal applications. Full article

This paper presents a methodology for correcting geometric and topological errors, specifically addressing fragmented and disconnected components in buildings (FDCB) in 3D models intended for urban digital twin (UDT). The proposed two-stage approach combines geometric refinement via duplicate vertex removal with topological refinement using a novel spatial partitioning-based Depth-First Search (DFS) algorithm for connected mesh clustering. This spatial partitioning-based DFS significantly improves upon traditional graph traversal methods like standard DFS, breadth-first search (BFS), and Union-Find for connectivity analysis. Experimental results demonstrate that the spatial DFS algorithm significantly improves computational speed, achieving processing times approximately seven times faster than standard DFS and 17 times faster than BFS. In addition, the proposed approach achieves a data size ratio of approximately 20% in the simplified mesh, compared to the 50–60% ratios typically observed with established techniques like Quadric Decimation and Vertex Clustering. This research enhances the quality and usability of 3D building models with FDCB issues for UDT applications. Full article

Polyvinyl chloride (PVC) recycling poses significant engineering challenges and opportunities, particularly regarding material integrity, energy efficiency, and integration into circular manufacturing systems. This systematic review evaluates recent advancements in mechanical innovations, tooling strategies, and intelligent technologies reshaping PVC recycling. An emphasis is placed on machinery-driven solutions—including high-efficiency shredders, granulators, extrusion moulders, and advanced sorting systems employing hyperspectral imaging and robotics. This review further explores chemical recycling technologies, such as pyrolysis, gasification, and supercritical fluid extraction, for managing contamination and additive removal. The integration of Industry 4.0 technologies, notably digital twins and artificial intelligence, is highlighted for its role in predictive maintenance, real-time quality assurance, and process optimisation. A combined PRISMA approach and ontological mapping are applied to classify technological pathways and lifecycle optimisation strategies. Critical engineering constraints—including thermal degradation, additive leaching, and feedstock heterogeneity—are examined alongside emerging innovations, like additive manufacturing and microwave-assisted depolymerisation, offering scalable, low-emission solutions. Regulatory instruments, such as REACH and Extended Producer Responsibility (EPR), are analysed for their influence on machinery compliance and design standards. Drawing from sustainable manufacturing frameworks, this study also promotes energy efficiency, eco-designs, and modular integration in recycling systems. This paper concludes by proposing a digitally optimized, machinery-integrated recycling model aligned with circular economy principles to support the development of future-ready PVC reprocessing infrastructures. This review serves as a comprehensive resource for researchers, practitioners, and policymakers, advancing sustainable polymer recycling. Full article

The crop yield can be affected by many factors, including various levels of weed presence. Therefore, we conducted a study to evaluate the effect of time of weed removal in combination with planting pattern and pre-emergence-applied herbicides on maize yield and yield components in 2015, 2016 and 2017. The experiments were designed in a split–split plot arrangements with three replications, which consisted of the two main plots (standard/conventional and twin-row planting pattern), two subplots (with and without pre-emergence herbicide application) and seven sub-subplots (seven weed removal timings). In the dry season of 2015, maize yield was much lower (413–9045 kg ha⁻¹) than in the wet 2016 seasons with yields of 5759–14,067 kg ha⁻¹ across both planting patterns. Yield and yield components were inversely correlated with the time of weed removal. The application of pre-emergence herbicides delayed the critical time for weed removal (CTWR), which ranged from V4 to V10 and from V3 to V11 for standard and twin-row planting patterns, respectively. Herbicides also protected various yield components, including 1000 seeds weight and number of seeds per cob. Full article

The automotive industry’s rapid expansion has made the development of lightweight, high-strength automotive steels essential for both energy efficiency and emission reduction. Among these materials, Fe-Mn-Al-C steel has drawn considerable interest due to its favorable combination of low density and high strength. This research examines the impact of Nb alloying (with Nb content of 0% and 0.5%) and solution treatment on the microstructure and mechanical properties of cold-rolled Fe-28Mn-10Al-C low-density steel. Various methods were employed, including Thermo-Calc thermodynamic simulations, the Olson–Cohen model, X-ray diffraction (XRD), metallographic microscopy, room-temperature tensile testing, and scanning electron microscopy (SEM). The findings demonstrate that Nb alloying significantly refines the austenite grain structure of the Fe-28Mn-10Al-C steel, improving both strength and ductility in comparison to the 0Nb steel. After solution treatment at 1050 °C for 30 min, the cold-rolling-induced defects are effectively removed, leading to a substantial increase in elongation at fracture (38.14–44.45%) and an ultimate tensile strength exceeding 900 MPa. As the solution treatment temperature increases, the austenite grains coarsen, and the number of twins increases, while yield strength and ultimate tensile strength decrease. However, there is a notable enhancement in ductility, with the material exhibiting a ductile fracture mechanism. These results offer valuable insights and a theoretical foundation for further improving the mechanical properties of Fe-Mn-Al-C low-density steels. Full article

In modern manufacturing environments, pollution management is critical as exposure to harmful substances can cause serious health issues. This study presents a two-stage computational fluid dynamic (CFD) model to estimate the distribution of pollutants in indoor production spaces. In the first stage, the Reynolds-averaged Navier–Stokes (RANS) method was used to simulate airflow and temperature. In the second stage, the Lagrangian method was applied for particle tracing. The model was applied to a theoretical acrylonitrile butadiene styrene (ABS) filament 3D printing process to evaluate the factors affecting the distribution of ultrafine particles (30 nm). Key parameters such as ventilation system effects, the presence of cooling fans and the print bed, and nozzle temperatures were considered. The results show that the highest flow velocities (1.97 × 10⁻⁶ m/s to 3.38 m/s) occur near the ventilation system’s inlet and outlet, accompanied by regions of high turbulent kinetic energy (0.66 m²/s²). These conditions promote dynamic airflow, facilitating particulate removal by reducing stagnant zones prone to pollutant buildup. The effect of cooling fans and thermal sources was investigated, showing limited contribution on particle removal. These findings emphasize the importance of digital twins for better worker safety and air quality in 3D printing environments. Full article

Background: In northern countries, spring requires the removal of large volumes of abrasive materials used in winter road maintenance. This sweeping process, crucial for safety and environmental protection, has traditionally relied on conventional mechanical brooms. Recent technological innovations, however, have introduced more efficient and environmentally friendly sweeping solutions; Methods: This study provides a comprehensive comparative analysis of the environmental and operational performance of these innovative sweeping systems versus conventional methods. Using simulation models based on real-world data and integrating fuel consumption models, the analysis replicates sweeping behaviors to assess both operational and environmental performance. A sensitivity analysis was conducted using these models, focusing on key parameters such as the collection rate, the number of trucks, the payload capacity, and the truck unloading duration; Results: The results show that the innovative sweeping system achieves an average 45% reduction in GHG emissions per kilometer compared to the conventional system, consistently demonstrating superior environmental efficiency across all resources configurations; Conclusions: These insights offer valuable guidance for service providers by identifying effective resource configurations that align with both environmental and operational objectives. The approach adopted in this study demonstrates the potential to develop decision-making support tools that balance operational and environmental pillars of sustainability, encouraging policy decision-makers to adopt greener procurement policies. Future research should explore the integration of advanced technologies such as IoT, AI-driven analytics, and digital twin systems, along with life cycle assessments, to further support sustainable logistics in road maintenance. Full article

Methylene blue (MB) is a dye hazardous pollutant widely used in several industrial processes that represents a relevant source of water pollution. Thus, the research of new systems to avoid their environmental dispersion represents an important goal. In this work, an efficient and sustainable nanocomposite material based on green gold nanoparticles for MB water remediation was developed. Starting from the reducing and stabilizing properties of some compounds naturally present in Lambrusco winery waste (grape marc) extracts, green gold nanoparticles (GM-AuNPs) were synthesized and deposited on a supporting membrane to create an easy and stable system for water MB decontamination. GM-AuNPs, with a specific plasmonic band at 535 nm, and the modified membrane were first characterized by UV–vis spectroscopy, X-ray diffraction (XRD), and electron microscopy. Transmission electron microscopy analysis revealed the presence of two breeds of crystalline shapes, triangular platelets and round-shaped penta-twinned nanoparticles, respectively. The crystalline nature of GM-AuNPs was also confirmed from XRD analysis. The photocatalytic performance of the modified membrane was evaluated under natural sunlight radiation, obtaining a complete disappearance of MB (100%) in 116 min. The photocatalytic process was described from a pseudo-first-order kinetic with a rate constant (k) equal to 0.044 ± 0.010 min⁻¹. The modified membrane demonstrated high stability since it was reused up to 20 cycles, without any treatment for 3 months, maintaining the same performance. The GM-AuNPs-based membrane was also tested with other water pollutants (methyl orange, 4-nitrophenol, and rhodamine B), revealing a high selectivity towards MB. Finally, the photocatalytic performance of GM-AuNPs-based membrane was also evaluated in real samples by using tap and pond water spiked with MB, obtaining a removal % of 99.6 ± 1.2% and 98.8 ± 1.9%, respectively. Full article

Extracorporeal Carbon Dioxide Removal (ECCO₂R) systems support patients with severe respiratory failure. Concurrent ambulation and physical therapy improve patient outcomes, but these procedures are limited by the complexity and size of the extracorporeal systems and rapid changes in patient metabolism and the acid–base balance. Here, we present the first prototype of a wearable ECCO₂R system capable of adjusting to a patient’s changing metabolic needs. Exhaust gas CO₂ (EGCO₂) partial pressure is used as an analog for blood CO₂ partial pressure (pCO₂). Twin blowers modulate sweep gas through the AL to achieve a desired target EGCO₂. The integrated system was tested in vitro for 24 h with water, under varying simulated metabolic conditions and target EGCO₂ values, and in a single test with whole blood. When challenged with changing inlet water pCO₂ levels in in vitro tests, the system adjusted the sweep gas to achieve target EGCO₂ within 1 min. Control runs with a fixed sweep gas (without negative feedback) demonstrated higher EGCO₂ levels when challenged with higher water flow rates. A single in vitro test with whole ovine blood confirmed functionality in blood. This is the first step toward wearable ECCO₂R systems that automatically respond to changing metabolism. Such devices would facilitate physical therapy and grant greater autonomy to patients. Full article

With the continuous development of actuator technology, the Electro-Mechanical Actuator (EMA) is gradually becoming the first choice in the aviation field. Permanent Magnet Synchronous Motor (PMSM) is one of the core components of EMA, and its healthy state determines the working performance of EMA. In this paper, the interturn short-circuit fault of PMSM is taken as the typical fault, and a new fault diagnosis framework is proposed based on a wide-kernel convolutional neural network (WCNN) and few-shot learning. Firstly, the wide convolution kernel is added as the first layer to extract short-time features while automatically learning deeply oriented features oriented to diagnosis and removing useless features. Then, the twin neural network is introduced to establish a wide kernel convolutional neural network, which can also achieve good diagnostic results under a few-shot learning framework. The effectiveness of the proposed method is verified by the general data set. The results show that the accuracy of few-shot learning is 9% higher than that of WCNN when the fault data are small. Finally, a fault test platform was built for EMA to collect three-phase current data under different fault states, and the collected data were used to complete the fault diagnosis of PMSM. With limited data, the accuracy of few-shot learning increased by 8% on average compared with WCNN, which has good engineering value. Full article

In this work emphasis was given to determine the evolution of the retained austenite phase fraction via X-ray diffractometry technique in the as-hardened AISI 440C martensitic stainless steel surface subjected to cavitation for increasing test times. Scanning electron microscopy results confirmed the preferential carbide phase removal along the prior/parent austenite grain boundaries for the first cavitation test times on the polished sample surface during the incubation period. Results suggest that the strain-induced martensitic transformation of the retained austenite would be assisted by the elastic deformation and intermittent relaxation action of the harder martensitic matrix on the austenite crystals through the interfaces between both phases. In addition, an estimation of the stacking fault energy value on the order of 15 mJ m⁻² for the retained austenite phase made it possible to infer that mechanical twinning and strain-induced martensite formation mechanisms could be effectively presented in the studied case. Finally, incubation period, maximum erosion rate, and erosion resistance on the order of 7.0 h, 0.30 mg h⁻¹, and 4.8 h μm⁻¹, respectively, were determined for the as-hardened AISI 440C MSS samples investigated here. Full article