Search Results (49)

The blending process in raw material yards is essential for maintaining precise material proportions in downstream production, directly influencing product quality and energy efficiency in industries such as steel and coal processing. However, stringent operational constraints, including silo capacity limits, discharge rates, equipment movement delays, and a strict no-empty-silo requirement, result in a strongly coupled, high-dimensional combinatorial scheduling problem. In this paper, we develop a mixed-integer nonlinear programming (MINLP) model to capture the complex dynamics of silo weight and equipment operations. The primary scientific contribution of this work lies in the theoretical discovery of a structural decoupling property within the complex MINLP. We analytically prove that by fixing the replenishment sequence, the intractable global problem can be rigorously decomposed into two subproblems: a linear programming (LP) problem for silo-filling cart scheduling and a shortest-path problem solvable via dynamic programming (DP) for reclaimer scheduling. Leveraging this decomposition, a two-stage metaheuristic algorithm is proposed, combining greedy initialization with multi-round simulated annealing enhanced by local search. Experimental validation using real industrial data demonstrates that the proposed method consistently outperforms the greedy algorithm. Crucially, while the commercial solver Gurobi struggles to converge within a practical 1800 s time limit, our approach yields comparable solution quality in mere seconds. Furthermore, robustness analysis under a 20% demand surge confirms the algorithm’s adaptive capability, maintaining the silo weight stability through re-optimization. This research provides a robust, computationally efficient solution for the blending process in raw material yards. Full article

Enhancing green innovation performance is crucial for manufacturing enterprises to achieve sustainable development. This paper employs the strategic tripod framework (organization, industry, institution) using the K-means clustering algorithm to identify types of manufacturing performed by listed companies in China’s Shanghai and Shenzhen markets and adopts the CART decision tree algorithm to analyze influencing factors of green innovation performance across different enterprise types. The study finds that manufacturing enterprises can be divided into three types, with significant differences in influencing factors of green innovation performance. From the perspective of internal drivers, the improvement in green innovation performance mainly relies on organizational resource endowments, among which R&D ability is particularly key. From the perspective of the external institutional environment, the driving logic of mimetic pressure shows differentiated characteristics between different enterprise groups and differentiated response strategies need to be formulated accordingly. In addition, when the overall impact of external factors is weak, the level of industrial structure still has a prominent promoting effect on green innovation performance. Based on the data-driven perspective, this paper identifies the influencing factors of green innovation performance of different types of manufacturing enterprises, which is helpful to improve the green innovation performance of manufacturing enterprises. Full article

Chimeric antigen receptor (CAR)-T and T-cell receptor (TCR)-engineered T-cell (TCR-T) therapies have revolutionized the treatment of hematological malignancies; however, their application to solid tumors remains a formidable challenge. The immunosuppressive tumor microenvironment, antigen heterogeneity, and manufacturing complexity limit the clinical efficacy and scalability of these treatment modalities. This review provides a comprehensive analysis of the current clinical development strategies for CAR-T and TCR-T cell therapies for solid tumors. Herein, we discuss recent breakthroughs and highlight the potential of TCR-T cell therapy. Furthermore, innovative approaches for enhancing CAR-T cell function in solid tumors (e.g., in vivo engineering; induced pluripotent stem cell-derived allogeneic CAR-T cells; armored CAR constructs; dual-antigen targeting; and combination regimens with checkpoint inhibitors, chemotherapy, radiotherapy, and oncolytic viruses) are explored. We also present trends in global patent activity, revealing a marked acceleration in CAR-T- and TCR-T-related innovations, with the United States and China leading with respect to application volumes. This field is increasingly characterized by multidisciplinary collaborations between academia and industry, driving the development of next-generation platforms, including messenger RNA-based and off-the-shelf cell therapies. Although no CAR-T product has been approved for solid tumors, these findings underscore the accelerating momentum and translational promise of adoptive cell therapies. Addressing the unique biological and logistical challenges of solid tumors is essential for realizing the full potential of these transformative immunotherapies. Full article

Stabilization and tracking problems for cart inverted pendulums under disturbances and uncertainties have posed significant challenges for control engineers. While various controllers have been designed for an inverted pendulum, they often overlook the calibration error of the pendulum angle in practical implementations, which degrades the control performance. Incorrect calibration of the pendulum angle in upright equilibrium position generates an offset of cart position errors. To solve this problem, an augmented model comprising integral cart position errors was first constructed. Afterwards, a sliding mode control was designed for this system based on a linear quadratic controller, to facilitate implementation. Additionally, a stepper motor was employed in the inverted pendulum to enhance the control performance and widen applicability in industrial settings. The effectiveness and performance of the proposed controller were validated by means of experimental studies, focusing on stabilization control and tracking control of a cart inverted pendulum actuated by a stepper motor. Full article

In the digital era, technological catch-up is inevitable for firms confronted with intensified competition, rapid technological advancements, and customers’ upgrade requirements. By strengthening their cooperation with external parties and integrating internal knowledge, firms can better absorb internal and external resources, accelerating their technological catch-up performance (TCP). This study mainly explores the influence of collaboration and knowledge networks on the TCP of firms based on machine learning algorithms. First, patent data from the Chinese AI industry from 2013 to 2022 were used to construct collaboration and knowledge networks. Then, the hierarchical clustering algorithm was applied to categorize firms based on six network characteristics. Finally, the classification and regression trees (CART) algorithm was employed to analyze the nonlinear relationship between dual networks and firm TCP. The findings show that firms exhibit distinct network configurations and that the drivers of TCP vary across firm groups. For firms lagging behind, prioritizing knowledge network integration proves more effective than expanding collaborations. Leading firms perform best when maintaining balanced collaboration strategies. This study contributes to both theory and practice by identifying the optimal mix of network characteristics and providing empirically grounded strategies for different firm types. Full article

This paper introduces a comprehensive and publicly accessible data set from an experimental study on an independent cart system powered by linear motors. The primary objective is to advance research in machine health monitoring, predictive maintenance, and stochastic modeling by providing the first data set of its kind. Vibration signals were collected using sensors placed along the track, alongside key system variables such as cart position, following error, speed, and set current. Experiments were conducted under a wide range of operating conditions, including different fault types, fault severities, cart speeds, and fault orientations, for both single-cart and multi-cart configurations. The data set captures the relationship between vibration signatures, system variables, and fault characteristics across diverse speed profiles. The data set includes inner race (IR) and outer race (OR) faults in both the top and bottom bearings, with fault severities of 0.25 mm, 0.5 mm, 1.0 mm, and 1.5 mm in width. Eight different types of experiments were performed, classified based on the number of carts used, the section of the guide rail traversed, and the type of movement exhibited. Each experiment was conducted at two distinct nominal speeds of 1000 mm/s and 2000 mm/s, with acquisition durations ranging from 30 s to 2 min. Many experiments included multiple realizations to ensure statistical reliability. Data were recorded at a sampling frequency of 50 kHz with a resolution of 24 bits. For single-cart experiments, 5 system variables were captured, while for three-cart experiments, 15 system variables were recorded along with nine vibration channels. The total data set is approximately 400 GB, offering an extensive resource for data-driven research. Independent cart systems present unique challenges such as non-synchronous operation, speed reversals, and modularity, with each cart containing multiple bearings. In industrial applications where hundreds of carts may operate simultaneously, monitoring a large number of bearings becomes highly complex, making fault identification and localization particularly difficult. Unlike conventional rotary systems, where bearings are fixed around a rotating shaft, independent cart systems involve bearings that both rotate and translate along the track. This fundamental difference makes existing data sets and methodologies inadequate, emphasizing the need for specialized research. By addressing this gap, this work provides a critical resource for benchmarking and developing novel algorithms for fault diagnosis, signal processing, and machine learning in industrial transport applications. The outcomes of this study lay the foundation for future research in the condition monitoring of linear motor-driven transport systems. Full article

In recent years, Internet of Things (IoT) systems are used in Industrial Internet of Things (IIoT) systems due to their widespread use in industrial sectors, providing convenience to users in SCADA systems, like other domains. In addition to the diverse technological advancements discussed, the inherent symmetry within the network structures of SCADA systems utilized in the IIoT echoes a fundamental balance sought in digital frameworks. However, along with the advantages of IIoT systems, there are also disadvantages, one major drawback being their vulnerability to attacks. It has been observed that advanced methods such as artificial intelligence, unlike traditional detection techniques, are more successful at detecting attacks on IIoT systems used in SCADA systems. The proposed model was developed to detect cyberattacks on SCADA systems using machine learning and deep learning models. The SCADA network traffic consists of over 7 million rows and has a size of 627 MB. Attack network traffic refers to the type of traffic aimed at causing damage to the system. The attack traffic in this study includes five different attacks. Normal traffic is the type of traffic that facilitates the system’s usual communication. Prepared network traffic is not a different type of traffic. Prepared network traffic, as named, is the state of the traffic dataset that has been made ready for analysis with models. The prepared network traffic was examined using eight machine learning models, including the CART, Decision Tree, KNN, Logistic Regression, Naive Bayes, Random Forest, SVM, and XGBoost models, as well as seven deep learning models, namely, CNN, GRU, LSTM, MLP, RNN, CNN-LSTM, and LSTM-CNN. During the evaluation of the models, performance parameters such as the accuracy, F-score, precision, and recall were considered, and the results are presented accordingly. Upon examining the dataset with various models, the highest outcomes were achieved using the MLP model. The investigation utilizing the MLP model resulted in an accuracy of 99.95%, a precision of 99.63%, a recall of 99.49%, and an F-score of 99.56%. These values were obtained with a batch-size combination of 100 and 20 epochs. By addressing cyberattack detection in SCADA systems used in the IIoT within a big data environment, the study encompasses a multidisciplinary approach, touching upon cybersecurity, big data analytics, AI, information security, and IoT-related concerns, all of which are focal points within the scope of the journal. This breadth and depth of coverage make the study highly relevant and aligned with the diverse interests of the journal. Full article

The by-product gases generated during steel manufacturing processes, including blast furnace gas, coke oven gas, and Linz–Donawitz gas, exhibit considerable variability in composition and supply. Consequently, achieving stable combustion control of these gases is critical for improving boiler efficiency. This study developed the advanced boiler combustion control model (ABCCM) by combining the random forest (RF) and classification and regression tree (CART) algorithms to optimize the combustion of steam power boilers using steel by-product gases. The ABCCM derives optimal combustion patterns in real time using the RF algorithm and minimizes fuel consumption through the CART algorithm, thereby optimizing the overall gross heat rate. The results demonstrate that the ABCCM achieves a 0.86% improvement in combustion efficiency and a 1.7% increase in power generation efficiency compared to manual control methods. Moreover, the model reduces the gross heat rate by 58.3 kcal/kWh, which translates into an estimated annual energy cost saving of USD 89.6 K. These improvements contribute considerably to reducing carbon emissions, with the ABCCM being able to optimize fuel utilization and minimize excess air supply, thus enhancing the overall sustainability of steelmaking operations. This study underscores the potential of the ABCCM to extend beyond the steel industry. Full article

Cotyledon color is one of the important indices for identifying faba bean variety purity and measuring processing quality. Therefore, an in-depth study of the genetic mechanism of cotyledon color is vital for promoting faba bean industry development. We used the yellow cotyledon variety Qingcan 16 and the green cotyledon variety Qingcan 17 as parent plants to construct hybrid combinations. F₁-, F₂-, BC₁F₁-, and BC₂F₁-generation single-plant cotyledon colors were counted to clarify cotyledon color inheritance. F₂-generation individuals were genotyped using ddRAD-Seq to construct a genetic linkage map and identify QTLs for cotyledon color. Green cotyledons were controlled by one pair of recessive nuclear genes. Using the screened 1991 SNP markers, a high-density linkage map was constructed, with a coverage length of 1476.95 cM and an average map distance of 0.96 cM. The green cotyledon trait was located using WinQTL Cart, and a vfGC candidate interval explaining 34.30 to 49.40% of the phenotypic variation was identified at LG02 (101.952 cM to 115.493 cM) and at LOD = 16.0, corresponding to chr1L 1,077,051,302 bp to 1,636,400,339 bp (559.35 Mb). The above interval contained 2021 genes, 20 of which were involved in photosynthesis, but no SGR or genes with similar functions were identified. However, the published faba bean vfSGR was located within the vfGC candidate interval, confirming that our localization interval was reliable. The above findings provided further clues for the fine localization of genes regulating green cotyledons and the development of molecular linked markers in faba bean. Full article

The paper presents a method for modeling and estimating the orientation of a launch cart in the magnetic suspension system of an innovative UAV catapult. The catapult consists of stationary tracks lined with neodymium magnets, generating a trough-shaped magnetic field. The cart levitates above the tracks, supported by four containers housing high-temperature YBCO superconductors cooled with liquid nitrogen. The Meissner effect, characterized by the expulsion of magnetic fields from superconductors, ensures stable hovering of the cart. The main research challenge was to determine the cart’s orientation relative to the tracks, with a focus on the pitch angle, which is critical for collision-free operation and system efficiency. A dedicated measurement stand equipped with Hall sensors and Time-of-Flight (ToF) distance sensors was developed. Hall sensors mounted on the cart’s supports captured magnetic field data at specific points. To model the tracks, the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology was employed—a structured framework consisting of six stages; from problem understanding and data preparation to model evaluation and deployment. This approach guided the analysis of data-driven models and facilitated accurate pitch angle estimation. Evaluation metrics, including mean squared error (MSE), were used to identify and select the optimal models. The final model achieved an MSE of 0.084°, demonstrating its effectiveness for precise orientation control. Full article

Gastronomic identity is a crucial segment of sustainable gastronomy and its successful positioning in the tourism market. As such, it calls for the creation of a suitable SusGastroIdentity scale that would identify influential factors. The research investigated the opinions of the employees in catering establishments in two tourist destinations in the Balkans: Fruška Gora Mountain, a tourist area in the Autonomous Province of Vojvodina in Serbia, and Jahorina Mountain, a tourist area in the Republic of Srpska entity in Bosnia and Herzegovina. The study involved 606 participants, 66% of whom work in à la carte restaurants. Of these participants, 68% hold operational roles in hospitality establishments, and 58.3% have over 5 years of experience in the hospitality industry. After conducting a survey using a questionnaire and performing appropriate statistical analysis of the responses, four factors of gastronomic identity and sustainable gastronomy were defined: geographic and cultural characteristics of gastronomy, gastro-tourism events, economic aspects of business operations, and commercial aspects of business operations. The present research has shown that employees in hospitality and tourism perceive geographic and cultural characteristics and the economic aspects of business as the significant factors of gastronomic identity that affect both the sustainability of gastronomy in tourism and the tourist destination itself. Full article

Background: The global pharmaceutical industry is crucial for providing medications but faces challenges in distributing products safely, especially in tropical and remote areas. Pharmaceuticals require careful transport control to maintain quality; therefore, manufacturers must adopt optimal distribution strategies to ensure product quality throughout the supply chain. The current research focused on creating a model to assess risk levels and predict risk categorization (low, moderate, and high) associated with thermal mapping across pharmaceutical transportation pathways. Methods: Data from a company for pharmaceutical logistics in Brazil were used. The data had 85,261 instances and six attributes (season, origin, destination, route, temperature, and temperature excursion). The dataset consisted of critical destinations, including the shipment time, cargo temperature, and route information. The classification algorithms (CART-Decision Tree, NB-Naive Bayes, and MP-Multilayer Perceptron) were used to build up a model of rules for predicting risk levels in thermal mapping routes; Results: The MP model presented the best performance, indicating a better application probability. The machine learning model is the basis for an automated risk prediction for routes of pharmaceutical transportation; Conclusions: the developed MP model might automatically predict risk during the distribution of pharmaceutical products, which might lead to optimizing time and costs. Full article

The children’s toy market is increasingly dominated by products that rely heavily on visual appeal. This article presents the development of ‘bumpi’, a cork toy specially developed for young children who experience visual impairments or blindness. Research was conducted about these children’s needs and the existing assistive products for them in the market. This research revealed that they often face developmental challenges, including delays in achieving key milestones such as crawling and walking, which happens because blind and visually impaired children are less confident to moving and exploring. A significant gap in the market for toys and assistive devices for blind young children was identified. Bumpi aims to fill such a gap. It is designed to stimulate and foster the earlier development of motor skills in children between one and five years old, leading to greater independence. This toy enhances sensory experiences through touch and sound to stimulate children’s urge to move. The toy set includes a puzzle-like mat, a toy cart that follows a predefined path, building blocks for constructing a ramp, and sensory balls that emit sounds when they move. Agglomerated cork, chosen for its unique properties such as lightness, durability and its hypoallergenic nature, is the primary material used. Furthermore, it is not only safe and comfortable for children to handle but also offers great stimulation to their senses. In addition, this is a sustainable material that offers several benefits for the toy industry. Full article

The automotive industry is currently transforming, primarily due to the rise of electric and hybrid vehicle technologies and the need to reduce vehicle mass and energy losses to decrease consumption, pollution, and raw material usage. Additionally, road surface manufacturers emphasize improving pavement durability and reducing rolling noise. This necessitates precise load condition definitions and drives the need for reliable wheel testing benches. Many current benches use abrasive-coated rollers or synthetic tapes, but devices capable of testing on actual road surfaces are rare. In this work, a novel device for testing tire-pavement interaction is proposed. The system features a cart moving along a closed-track platform, ensuring test repeatability and enabling structural durability tests on uneven surfaces with installed obstacles. The cart is equipped with a cantilever arm capable of supporting either a testing wheel with customizable dimensions and kinematic parameters or a tire integrated with a complete suspension system, moving along a customizable pavement surface. The system includes actuators and sensors for applying vertical loads and adjusting the alignment of the testing wheel (slip angle, camber angle, etc.), allowing the characterization of tire behavior such as wear, fatigue, rolling noise, and rolling resistance. Multibody simulations were performed to evaluate the bench’s feasibility in terms of kinematics, power requirements, and structural loads. Results confirmed how this novel test bench represents a promising advancement in tire testing capabilities, enabling comprehensive studies on tire performance, noise reduction, and the structural dynamics of vehicle subsystems. Full article

The frequent fluctuation of pork prices has seriously affected the sustainable development of the pork industry. The accurate prediction of pork prices can not only help pork practitioners make scientific decisions but also help them to avoid market risks, which is the only way to promote the healthy development of the pork industry. Therefore, to improve the prediction accuracy of pork prices, this paper first combines the Sparrow Search Algorithm (SSA) and traditional machine learning model, Classification and Regression Trees (CART), to establish an SSA-CART optimization model for predicting pork prices. Secondly, based on the Sichuan pork price data during the 12th Five-Year Plan period, the linear correlation between piglet, corn, fattening pig feed, and pork price was measured using the Pearson correlation coefficient. Thirdly, the MAE fitness value was calculated by combining the validation set and training set, and the hyperparameter “MinLeafSize” was optimized via the SSA. Finally, a comparative analysis of the prediction performance of the White Shark Optimizer (WSO)-CART model, CART model, and Simulated Annealing (SA)-CART model demonstrated that the SSA-CART model has the best prediction of pork price (compared with a single decision tree, R₂ increased by 9.236%), which is conducive to providing support for pork price prediction. The accurate prediction of pork prices with an optimized machine learning model is of great practical significance for stabilizing pig production, ensuring the sustainable growth of farmers’ income, and promoting sound economic development. Full article