Engine Components Research Articles

Corrosion characterization of engine connecting rods using fringe projection profilometry and unsupervised machine learning

The consequences associated with corrosion, a global industrial peril, cost an estimated $ 2.5 trillion annually to inspect, rectify, and prevent. In addition to significant economic losses, corrosion-induced failure of critical components in transport systems, like automobiles, may also lead to loss of human life. Hence, it is essential to eradicate corrosion in its early stages. The most vital automobile component is its engine, whose failure can cause fatal accidents. Regular quality inspection and maintenance by skilled personnel is essential to prevent this. Automating this task will address this domain’s personnel shortage while mitigating the risk of human error. To enable the performance of this task without the need for human intervention, we determine the morphological parameters affected by corrosion in automotive engine components, namely connecting rods, using fringe projection profilometry, a high-speed 3D profiling technique capable of achieving sub-millimeter accuracy. We then perform classification using k-means clustering into low, medium, and high corrosion bands, based on the parameters obtained from 3D imaging. The model was able to achieve a high accuracy of 88.57%. The accuracy was determined by considering the visual classification performed by a Material Science Expert as the ground truth.

Optimal design of structural engineering components using artificial neural network-assisted crayfish algorithm

Abstract Optimization techniques play a pivotal role in enhancing the performance of engineering components across various real-world applications. Traditional optimization methods are often augmented with exploitation-boosting techniques due to their inherent limitations. Recently, nature-inspired algorithms, known as metaheuristics (MHs), have emerged as efficient tools for solving complex optimization problems. However, these algorithms face challenges such as imbalance between exploration and exploitation phases, slow convergence, and local optima. Modifications incorporating oppositional techniques, hybridization, chaotic maps, and levy flights have been introduced to address these issues. This article explores the application of the recently developed crayfish optimization algorithm (COA), assisted by artificial neural networks (ANN), for engineering design optimization. The COA, inspired by crayfish foraging and migration behaviors, incorporates temperature-dependent strategies to balance exploration and exploitation phases. Additionally, ANN augmentation enhances the algorithm’s performance and accuracy. The COA method optimizes various engineering components, including cantilever beams, hydrostatic thrust bearings, three-bar trusses, diaphragm springs, and vehicle suspension systems. Results demonstrate the effectiveness of the COA in achieving superior optimization solutions compared to other algorithms, emphasizing its potential for diverse engineering applications.

Investigation of pad radius effects on multiaxial fretting fatigue behavior of al2024 alloy

Fretting fatigue, a critical phenomenon prevalent in engineering applications, occurs at the interface of contacting surfaces under cyclic loading, presenting significant challenges. The advent of Finite Element Analysis (FEA) has transformed the investigation of fretting fatigue by offering detailed insights into stress distributions and fatigue damage mechanisms. This study delves into the impact of pad radius on the fretting fatigue behavior of Al2024 alloy using FEA, with a focus on hot spot analysis and the Smith-Watson-Topper (SWT) criterion. The analysis aims to validate numerical results against analytical findings and explore the role of pad radius in determining contact behavior, fatigue life, and hot spot locations. By incorporating "fe-safe" software, computations are streamlined, facilitating efficient comparison of different fatigue initiation criteria. The results demonstrate that variations in pad radius significantly influence fretting fatigue behavior. Larger pad radii tend to distribute stresses more uniformly across the contact interface, resulting in reduced fatigue damage and longer fatigue life compared to smaller pad radii. Hot spot analysis reveals that smaller pad radii concentrate stresses at specific regions, accelerating fatigue damage initiation. The findings contribute to a deeper understanding of fretting fatigue mechanisms, shedding light on the importance of pad radius in designing more durable engineering components. Through professional scientific methodology, this study provides valuable insights into optimizing pad design to mitigate fretting fatigue and enhance the reliability of mechanical systems.

Predictive control method for mode transition process of multi-mode turbine engine based on onboard adaptive composite model

In order to achieve precise control of thrust and flow during the mode transition(MT) process of multi-mode turbine engine(MMTE) within the switching envelope and under the condition of engine performance degradation, research on multi-mode model predictive control method is conducted. A mode transition trajectory optimization method based on SQP is explored, and a small range of adjustable parameters that ensure the continuous thrust and flow is obtained. Based on this, a multi-mode onboard modeling approach based on the directional adjustment law scheduling of the variable geometry adjustment mechanism is proposed. Furthermore, an investigation into a predictive control method for MT process based on onboard adaptive composite model is conducted. The simulation results demonstrate that the proposed method exhibits a maximum thrust fluctuation of less than 0.66 % during MT process, under both normal and degraded engine component performance conditions, which is significantly superior to the conventional PID control of 2.8 %. Compared to the average single step calculation time of 7.24 ms using the SQP optimization method, the proposed approach only requires 0.204 ms. The methodology presented takes into consideration of control accuracy and computing efficiency, thereby offering promising prospects for engineering applications in MMTE MT control system design.

Transportation of Objects on an Inclined Plane Oscillating in the Longitudinal Direction Applying Dynamic Dry Friction Manipulations

A transportation system requires an asymmetry to achieve objects’ motion on an oscillating surface. Transportation methods based on vibrational techniques usually employ different types of asymmetries, such as temporal (time) asymmetry, kinematic asymmetry, wave asymmetry or power asymmetry. However, transporting an object on an inclined angle requires a relatively high net frictional force over each period of vibrational cycles due to the gravitational potential energy exerted on the object. This paper investigates the transportation of an object upward on an inclined plane that harmonically oscillates in its longitudinal direction. The novelty of this research is attributed to the upward motion of the object on the inclined plane, which is achieved by creating an additional asymmetry of the system through dry friction dynamic manipulations. For this reason, periodic dynamic dry friction manipulations have been employed to create the asymmetry of frictional conditions, resulting in a net frictional force that outweighs the gravitational force. A mathematical model has been developed using the Lagrange method, which describes the moving object’s motion. Moreover, the theoretical findings and results confirmed that the object’s velocity and direction can be controlled by dynamic dry friction manipulations. To demonstrate the technical feasibility of the proposed method, an experimental investigation was carried out where the results demonstrated that the control parameters significantly influence the characteristics of the directional motion of the moving object. This transportation method is beneficial for various modern industries engaged in transportation and manipulation tasks with objects spanning a broad range of sizes, including those operating at small scales for applications in lab-on-a-chip technology, micro-assembly lines, micro-feeder systems and other delicate component manipulation systems. The presented research advances the classical theories of vibrational transportation on inclined surfaces.

High Temperature Oxidation Behavior of Additive Manufactured Ti6Al4V Alloy with the Addition of Yttrium Oxide Nanoparticles

Titanium alloys face challenges of high temperature oxidation during the service period when used as aircraft engine components. In this paper, the effect of Y2O3 addition on the oxidation behavior and the microstructural change of the Ti6Al4V alloy fabricated by selective laser melting (SLM) was comprehensively studied. The results show that the surface of the Ti6Al4V alloy is a dense oxide layer composed of TiO2 and Al2O3 compounds. The thickness of the oxide layer of the Ti6Al4V increased from 59.55 μm to 139.15 μm. In contrast, with the addition of Y2O3, the thickness of the oxide layer increased from 35.73 μm to 80.34 μm. This indicates that the thickness of the oxide layer formation was a diffusion-controlled process and, therefore, the thickness of the oxide layer increased with an increase in temperature. The Ti6Al4V-1.0 wt.% Y2O3 alloy exhibits excellent oxidation resistance, and the thickness is significantly lower than that of the Ti6Al4V alloy. The oxidation kinetics of the Ti6Al4V and Ti6Al4V-1.0 wt.% Y2O3 alloys at 600 °C and 800 °C follows a parabolic rule, whereas the oxidation of the Ti6Al4V and Ti6Al4V-1.0 wt.% Y2O3 alloys at 1000 °C follows the linear law. The average microhardness values of Ti6Al4V samples after oxidation increased to 818.9 ± 20 HV0.5 with increasing temperature, and the average microhardness values of the Ti6Al4V-1.0 wt.% Y2O3 alloy increases until 800 °C and then decreases at 1000 °C. The addition of Y2O3 shows a significant improvement in the microhardness during the different temperatures after oxidation.

Multiaxial fatigue life prediction based on modular neural network pretrained with uniaxial fatigue data

PurposeData-driven models are increasingly being used to predict the fatigue life of many engineering components exposed to multiaxial loading. However, owing to their high data requirements, they are cost-prohibitive and underperforming for application scenarios with limited data. Therefore, it is essential to develop an advanced model with good applicability to small-sample problems for multiaxial fatigue life assessment.Design/methodology/approachDrawing inspiration from the modeling strategy of empirical multiaxial fatigue models, a modular neural network-based model is proposed with assembly of three sub-networks in series: the first two sub-networks undergo pretraining using uniaxial fatigue data and are then connected to a third sub-network trained on a few multiaxial fatigue data. Moreover, general material properties and necessary loading parameters are used as inputs in place of explicit damage parameters, ensuring the universality of the proposed model.FindingsBased on extensive experimental evaluations, it is demonstrated that the proposed model outperforms empirical models and conventional data-driven models in terms of prediction accuracy and data demand. It also holds good transferability across various multiaxial loading cases.Originality/valueThe proposed model explores a new avenue to incorporate uniaxial fatigue data into the data-driven modeling of multiaxial fatigue life, which can reduce the data requirement under the promise of maintaining good prediction accuracy.

Research Progress of Single-Photon Emitters Based on Two-Dimensional Materials

From quantum communications to quantum computing, single-photon emitters (SPEs) are essential components of numerous quantum technologies. Two-dimensional (2D) materials have especially been found to be highly attractive for the research into nanoscale light–matter interactions. In particular, localized photonic states at their surfaces have attracted great attention due to their enormous potential applications in quantum optics. Recently, SPEs have been achieved in various 2D materials, while the challenges still remain. This paper reviews the recent research progress on these SPEs based on various 2D materials, such as transition metal dichalcogenides (TMDs), hexagonal boron nitride (hBN), and twisted-angle 2D materials. Additionally, we summarized the strategies to create, position, enhance, and tune the emission wavelength of these emitters by introducing external fields into these 2D system. For example, pronounced enhancement of the SPEs’ properties can be achieved by coupling with external fields, such as the plasmonic field, and by locating in optical microcavities. Finally, this paper also discusses current challenges and offers perspectives that could further stimulate scientific research in this field. These emitters, due to their unique physical properties and integration potential, are highly appealing for applications in quantum information and communication, as well as other physical and technological fields.

Examining Linguistic Differences in Electronic Health Records for Diverse Patients With Diabetes: Natural Language Processing Analysis.

Individuals from minoritized racial and ethnic backgrounds experience pernicious and pervasive health disparities that have emerged, in part, from clinician bias. We used a natural language processing approach to examine whether linguistic markers in electronic health record (EHR) notes differ based on the race and ethnicity of the patient. To validate this methodological approach, we also assessed the extent to which clinicians perceive linguistic markers to be indicative of bias. In this cross-sectional study, we extracted EHR notes for patients who were aged 18 years or older; had more than 5 years of diabetes diagnosis codes; and received care between 2006 and 2014 from family physicians, general internists, or endocrinologists practicing in an urban, academic network of clinics. The race and ethnicity of patients were defined as White non-Hispanic, Black non-Hispanic, or Hispanic or Latino. We hypothesized that Sentiment Analysis and Social Cognition Engine (SEANCE) components (ie, negative adjectives, positive adjectives, joy words, fear and disgust words, politics words, respect words, trust verbs, and well-being words) and mean word count would be indicators of bias if racial differences emerged. We performed linear mixed effects analyses to examine the relationship between the outcomes of interest (the SEANCE components and word count) and patient race and ethnicity, controlling for patient age. To validate this approach, we asked clinicians to indicate the extent to which they thought variation in the use of SEANCE language domains for different racial and ethnic groups was reflective of bias in EHR notes. We examined EHR notes (n=12,905) of Black non-Hispanic, White non-Hispanic, and Hispanic or Latino patients (n=1562), who were seen by 281 physicians. A total of 27 clinicians participated in the validation study. In terms of bias, participants rated negative adjectives as 8.63 (SD 2.06), fear and disgust words as 8.11 (SD 2.15), and positive adjectives as 7.93 (SD 2.46) on a scale of 1 to 10, with 10 being extremely indicative of bias. Notes for Black non-Hispanic patients contained significantly more negative adjectives (coefficient 0.07, SE 0.02) and significantly more fear and disgust words (coefficient 0.007, SE 0.002) than those for White non-Hispanic patients. The notes for Hispanic or Latino patients included significantly fewer positive adjectives (coefficient -0.02, SE 0.007), trust verbs (coefficient -0.009, SE 0.004), and joy words (coefficient -0.03, SE 0.01) than those for White non-Hispanic patients. This approach may enable physicians and researchers to identify and mitigate bias in medical interactions, with the goal of reducing health disparities stemming from bias.

Quasi-static tensile properties of DMLS TI6AL4V(ELI) specimens exposed to two different heat treatment processes and tested at different elevated temperatures

The use of Ti6Al4V components in the aerospace industry has increased in the past years s. Jet engine components made of Ti6Al4V such as compressor blades and exhaust ducts, as well as landing gear, hydraulic systems, and many airframe parts, are exposed to different temperatures while in service, which may affect their mechanical properties. Compressor blades are exposed to temperatures up to 400 °C, exhaust ducts up to 300 °C, and hydraulic systems in the range of −50 °C–135 °C, while landing gears and many airframe structures are exposed to temperatures in the range of −50 °C–70 °C. Therefore, it is of great importance to determine their stability at different temperatures. The results of a study aimed at investigating the effect of elevated temperatures on the mechanical properties and microstructures of DMLS Ti6Al4V(ELI) alloy are presented and discussed in this paper. A DMLS EOSINT M290 machine was used to manufacture test specimens, which were then machined to comply with ASTM E8 for tensile tests. Thereafter, tensile tests were carried out at different test temperatures till fracture. Fractography was then used to study the characteristics of the fracture surfaces. The results obtained in this work showed that temperatures between 175 °C and 325 °C have a significant effect on the mechanical properties and microstructures of the alloy.

От гибридизации жизненного мира к гибридности научных проблем

The convergence of social, technological, and scientific domains in modern society has given rise to novel interdisciplinary approaches and problem-solving methodologies. This article addresses the specificity of modern technicization of the lifeworld, giving rise to transdisciplinary practices. The author adopts a socio-phenomenological interpretation of the lifeworld but deems it necessary to consider the technical and technological component within its structure. In the lifeworld, the social and the digital merge in the logic of hybridization, transforming into a unified cybersocio-physical environment. Hybrid digital-social phenomena emerge as a result of adapting high technologies to personalized human needs. The relevant agent of such adaptation becomes science, hybridizing scientific problems through transdisciplinary practices that involve non-scientists in scientific research and traverse traditional institutional boundaries. The primary experimental space for hybridizing technologies in the lifeworld becomes medicine, which seeks not only means to combat diseases but also tools to enhance the quality of life for patients, enabling them to maintain an active social life and their own personality. In conclusion, the hybridization of the lifeworld and scientific problems represents a significant paradigm shift in modern society. By recognizing and embracing the interplay between social, technological, and scientific domains, scientists can foster innovation and address complex challenges more effectively. Transdisciplinary practices serve as a bridge between different disciplines, enabling collaboration and synergy in the pursuit of knowledge and progress.

Indoor AR Navigation Framework Based on Geofencing and Image-Tracking with Accumulated Error Correction

This study presents a novel framework for improving indoor augmented reality (AR) navigation with modern smartphone technology, which is achieved by addressing two major challenges: managing large absolute coordinate spaces and reducing error accumulation in camera-based spatial tracking. Our contribution is significant in two ways. First, we integrate geofencing with indoor navigation by considering spatial tracking errors, timing for audio guidance, and dynamic 3D arrow visualization for effective local-to-global spatial coordinate transformation. This method achieves precise local positioning and seamlessly integrates with larger spatial contexts, overcoming the limitations of current AR systems. Second, we introduce a periodic image-based calibration approach to minimize the inherent error accumulation in camera-based tracking, enhancing accuracy over longer distances. Unlike prior studies focusing on individual technologies, our work explores the software architecture of indoor AR navigation by providing a comprehensive framework for its design and practical use. The practicality of our approach is validated through the implementation of a smartphone application at the Mineral Industry Museum of Akita University, highlighting the limitations of component technologies and demonstrating our framework’s effectiveness.

Low-velocity impact performance of 3D-printed PLA/Epoxy/Glass fibers of composite structures and latches

The integration of 3D printing into the synthesis process of polymeric composite structures can offer significant advantages in producing geometrically complex parts. Moreover, engineering components may be subjected to impact loads that can adversely affect their performance and, in some cases, lead to failure. Traditionally, fiber-reinforced composite structures have demonstrated improved responses to applied mechanical loads. In this study, we designed multi-material polymer-based composite structures to enhance structural responses to impact loads. Employing fused filament fabrication (FFF) and solution casting methods, we manufactured PLA lattice infilled with epoxy/Milled Glass Fibers (MGFs) of composite structures with varied MGFs content ranging from 0 to 7.5 vol%. The research provides a comprehensive analysis of the impact behavior of glass-fiber reinforced composite structures, specifically focusing on PLA lattice frames filled with epoxy resin and varying levels of MGFs. Through low-velocity impact tests, the study assesses key parameters such as peak force, peak absorbed energy, residual velocity, and displacement, providing insights into the materials' response to impact loading conditions. The findings highlight the effect of MGF reinforcement on enhancing the impact toughness and resistance to crack propagation, with higher MGF content leading to improved mechanical properties. The microstructural analysis demonstrates the morphology and interface bonding of the composite materials, elucidating the mechanisms. Finally, latches were designed, and their impact behavior was tested to examine the usability of the composite structure in real-life components. The results suggest that the incorporation of MGFs in 3D-printed composite latches significantly enhances their impact resistance, with potential implications for various engineering applications.

Secondary flexural behaviour of reinforced concrete beams strengthened with special-shaped rivet bonded steel plates and prestressed CFRP bars

Special-shaped rivet and glued steel plate reinforcement is an innovative structural reinforcement technology. A riveted glued steel plate primarily relies on the rivet to transfer shear. Prestressed carbon fibre-reinforced polymer (CFRP) reinforcement is a new technique for strengthening concrete structures. These studies focus on the ‘primary load’, overestimate the ultimate flexural load of the components by neglecting the ‘secondary load’ of the components in real engineering. In this study, the flexural properties of concrete beams strengthened using special-shaped rivet steel plates and CFRP prestressed bars were studied theoretically and experimentally. The cracking load, ultimate load capacity, and strain of the steel plate, steel bar and CFRP bars were recorded during the tests. In addition, the failure mode of the beams was observed, and the effect of secondary stress on the flexural capacity of the beams was analysed. The failure load of the beams strengthened by CFRP prestressed tendons increased by 67.8% and 46.1% for beams with primary and active secondary loads, respectively. The failure load of the special-shaped rivet and glued steel plate strengthened beam increased by 115.7% and 160.5%, for beams with primary and active secondary loads, respectively. Both methods have a strong guiding significance for engineering practice.

A computationally efficient thermo-mechanical model with temporal acceleration for prediction of residual stresses and deformations in WAAM

ABSTRACT The present paper introduces a novel temporal acceleration strategy for computationally efficient prediction of residual stresses and deformations in wire arc additive manufacturing (WAAM) components. It employs a semi-analytical approach, dividing temperature into the analytical and the complementary fields. The analytical field is obtained by a closed-form solution, and the complementary field is employed to solve the boundary conditions. A temporal acceleration factor for the heating period is applied. Meanwhile, the diffusion time in the analytical field is manipulated to guarantee accurate temperature prediction. Validation via WAAM experiments, including both a thin-wall structure and a practical engineering component with characteristic dimensions on the order of metres, indicates that the predicted stresses is 50 MPa lower than the experimental values. Moreover, the discrepancy of between the predicted deformations and the experimental measurements is less than 10%, demonstrating reasonable accuracy can be achieved with attractive computational efficiency.

Low-cost Nursery Media for Improving Germination of Chip Budded Seedlings for SSI

The quality and vigour of the chip budded seedlings are important components in SSI technology which in turn, depends on the nature of growing media. The most commonly used media for chip budded seedlings is pure coco peat which is a costlier one and availability is also restricted. Hence an alternate organic media to pure coco peat compressing low cost organic sources viz., Press mud, Coco peat, Coir pith, Town Compost and Vermicompost are essential to widespread this technology. Hence, an experiment was conducted at Sugarcane Research Station, Sirugamani in 2013-14 to find out suitable low cost media for producing vigorous seedlings of chip budded seedlings of Sugarcane. The experiment was conducted in RBD with 11 treatments comprising of different media with three replications. The treatments include Composted Coir pith :Vermicompost @2:1 ratio (T1), Composted coir pith + press mud @ 2:1 ratio (T2),Composted Coir pith :Vermicompost @3:1 ratio (T3), Composted coir pith + press mud @ 3:1 ratio (T4), Bagasse + Press mud @ 1:1 ratio (T5), Banana leaf compost: Press mud @1:2ratio (T6), Sugarcane trash Compost + press mud @ 1:1ratio (T7), Municipal Waste (Town Compost) + Sugarcane trash compost + Press mud @ 1:1 ratio (T7), Municipal waste (town Compost) + Sugarcane trash compost @ 1:1 ratio (T8), Composted Coir pith clone (Coco peat) (T9), Press mud alone (T10), Farm boon (composted press mud) (T11). Single budded chip buds from the healthy mother canes o TNAU Sugarcane Si 7 variety were prepared using bud chipper machine and the same was treated with 0.05% Carbendazim, Malathion 2% and Urea @ 1 for 15 minutes. The chip buds were shade dried for 2 hours and then planted in the pro trays filled with the media as per the treatments. The nursery media in different treatments were prepared using bud chipper machine and the same materials as per the treatments, half filed in the pro trays and the treated chip buds were planted in slanting position in the canes of the trays and then covered with the respective media. The trays were incubated for 5 days to induce sprouting and then kept under 50 % shade ned and water was sprinkled every day on the pro trays the grown up seedlings were observed for different growth parameters viz., Germination %, Survival percentage, Root and Shoot length, Shoot and root dry weight, dry matter production and vigour index.The results revealed that there was a significant difference on germination recorded at 20 DAS due to different nursery media tried. Higher germination of 66.93% was observed with T3 treatment (coir pith + Vermicompost @ 3: 1 ratio) It was on par with T6 treatment (Banana leaf Compost: Press mud % 1:2 ratio) and T10 treatment (Press mud alone). Regarding survival % recorded at 30 DAS, the higher revival % was recorded with T3 treatment. However, it was on par with T4, T5 & T10 treatments (Bagasse: press mud @ 1:2 ratio). The lower shoot length was recorded with T11 treatment where the nursery media of farm boon was used. There was no significant difference in influencing root length and number of leaves / seedling and root / shot ratio due to different treatments (nursery media) tried.

Smart urban management identification by integrating optical flow characteristics and HSV space

n smart cities, sanitation workers are the key to urban construction. Accurate targeting of sanitation workers can help managers better monitor and manage them. Pedestrian detection is the core and key component of object detection technology. The difficulty of pedestrian detection in the actual feature recognition is still how to quickly and accurately identify the identity in the complex video scene. To realize the effective detection of sanitation workers, the study designs an identity identification scheme conducive to the friendly management of smart urban management. Since the optical flow feature extraction method and HSV color space extraction method can not meet the actual detection efficiency, this study innovatively integrates the two methods to improve the detection accuracy of the mode. Meanwhile, the study also introduces PCA algorithm to identify the specific identity of sanitation workers. In the actual detection of sanitation workers, the identification rate of two sanitation workers is high, and the similarity is 98.61%. This technology greatly reduces the false detection rate of actual detection and improves the detection accuracy.

ADVANCING POS SYSTEMS FOR SEAMLESS RETAIL EXPERIENCES

This research paper explores the significance of advancing point of sale (POS) technologies in modern commerce, with a focus on their role in improving business operations and enhancing customer experiences. POS systems serve as critical tools for businesses across various industries, facilitating transactions, managing sales processes, and providing valuable insights into business performance. The paper delves into the evolution of POS systems, from traditional cash registers to sophisticated digital solutions, and examines the current state of POS technology, including hardware and software components. Moreover, it discusses recent trends and advancements in POS innovation, such as the integration of emerging technologies like artificial intelligence (AI) and Internet of Things (IoT), and their implications for businesses. Through empirical research, case studies, and expert analysis, the paper aims to provide insights into the benefits and challenges of adopting advanced POS technologies, as well as recommendations for businesses seeking to leverage these technologies to drive growth and improve operational efficiency in today's dynamic market place. Keywords- Point of Sale (POS) Systems, Advancing Technologies, Retail, Business Operations, Customer Experience, Emerging Technologies, Artificial Intelligence (AI), Internet of Things (IoT), Payment Processing, Inventory Management, Cloud-based POS, Customer Engagement, Digital Commerce, Innovation, Competitive Advantage.

The role of big data and IoT in logistics supply chain management of e-commerce

With the rapid development of social mode virtualization and electronic component technology, the application of data science and Internet of Things (IoT) technology in the field of e-commerce is gradually increasing. This study aims to explore how these emerging technologies can enhance the advantage of Chinese e-commerce companies in international competition. By comprehensively analyzing the massive data generated by online social networking and the application of IoT sensor technology in logistics and enterprise management, this paper proposes a decision support model based on data analysis. Research methods include data collection, data analysis and case studies. The results of the study show that data analytics and IoT technologies can effectively improve the efficiency of e-commerce operations and customer experience. The conclusion is that these technologies not only contribute to the domestic development of e-commerce enterprises, but also play a non-negligible role in international competition. This research has important implications for understanding the practical applications and potential of new technologies in the field of e-commerce.

Design and Fabrication of Ocean Plastic Waste Cleaner

An ocean cleaning device abstract could focus on various aspects, depending on the specific device or technology being described. With the escalating threat of ocean pollution, innovative solutions are imperative to mitigate its detrimental impacts on marine ecosystems and human livelihoods. This abstract presents a comprehensive overview of an advanced ocean cleaning device designed to tackle the burgeoning issue of marine debris. The device integrates cutting-edge technology, including [insert specific technologies], to efficiently remove various forms of pollution from coastal areas and open water on the severity of ocean pollution and the urgent need for effective cleanup technologies. It highlights the detrimental effects of marine debris on marine life, biodiversity, and coastal communities, emphasizing the importance of sustainable solutions. Here's a general abstract outline for an ocean cleaning device.This paper discusses the design principles, technological components, and operational strategies of the ocean plastic waste cleaning device. Additionally, it evaluates the device's performance through simulation models and field tests, demonstrating its effectiveness in reducing plastic pollution and contributing to sustainable ocean conservation efforts.Integrated sensors monitor water quality, temperature, and marine life, providing valuable data for environmental research and conservation efforts.Operators can remotely control the device and receive real-time data, enhancing operational efficiency and response times