Platform Structure Research Articles

Comprehensive evaluation of solar floating photovoltaic prospective in Saudi Arabia: Comparative experimental investigation and thermal performance analysis

Recently, floating photovoltaic systems have been regarded as a promising technology for producing clean energy by utilizing the surface of water bodies, such as lakes, rivers and oceans. This study introduces a comparative experimental study and energy performance evaluation of a 1.0 kW offshore floating photovoltaic (FPV) system and a nearby traditional ground-based PV system (GPV) installed in the eastern province of Saudi Arabia. The FPV system was deployed in the Arabian Gulf, 25 m off the coast, at an average depth of 1 to 1.5 m depending on tide, wave, and current intensity. The FPV system employs a strong novel eco-friendly platform structure made of recycled buoyant materials such as engineered plastic drums and wood. This system is anchored with tension cables and concrete blocks that can withstand the changing sea water conditions. The GPV system, on the other hand, was installed 150 m inland from the shore. Real-time monthly data monitoring and assessment indicated that FPV systems outperformed GPV systems in terms of lower PV panel surface temperatures, higher power output, and panel efficiency. Based on daily average back surface temperatures, FPV system temperature was decreased by 7.5 % to 21.34 % when compared to GPV. Moreover, the FPV system efficiency was also increased by 12.2 % when compared to the GPV system. This study aims to assist in promoting the applicability of solar floating photovoltaic systems to synergistically fulfill the requirements of sustainable electricity production for the arid costal community in Saudi Arabia and similar areas.

Channel structures of third-party platforms

The growing prominence of third-party (3P) platforms in the online retail sector has made the selection of an appropriate channel structure strategy a critical concern for manufacturers, 3P platforms, and retailers operating within this environment. We construct a Stackelberg game model with the manufacturer as the leader, evaluating four channel structures: (A) reselling and retailer agency selling, (B) agency selling and retailer agency selling, (C) reselling and agency selling, and (D) reselling, agency selling, and retailer agency selling. The supply chain can adopt two pricing strategies: uniform pricing (UP) and differential pricing (DP). Research indicates that expanding the number of channels may not always enhance profitability for suppliers and supply chain participants. Under strategy UP, the 3P platform opts for structure B, whereas the retailer favors structure C. Incorporating agency selling is advantageous for the manufacturer; however, structure A represents the least beneficial option for the supply chain. Under strategy DP, a substantial portion of the pareto optimal region is present. Intense competition and reduced agency fees lead both the supply chain as a whole and individual member to prefer structure A. The addition of a reselling or retailer agency channel boosts the manufacturer's profits, and benefits the 3P platform, while structure D emerges as the least favorable option for the retailer. Furthermore, under strategy DP, structure C is rendered obsolete, signifying the retailer's essential role in the supply chain to the advantage of all involved parties. Our study advances the scholarly understanding of sales models and platform economies by offering valuable insights into the decision-making processes of manufacturers, third-party (3P) platforms, and retailers regarding channel structure choices in a 3P platform environment.

Soft wearable electronics for evaluation of biological tissue mechanics

Flexible wearable devices designed to evaluate the biomechanical properties of deep tissues not only facilitate continuous and effective monitoring in basic performance but also exhibit significant potential in broader disease assessments. Recent advancements are highlighted in the structural and principled design of platforms capable of capturing various biomechanical signals. These advancements have led to enhanced testing capabilities concerning spatial scales and resolution modes at different depths. This review discusses the engineering of soft wearable devices for the biomechanical evaluation of deep tissue signals. It encompasses different measurement modes, device design and fabrication methods, integrated circuit (IC) integration schemes, and the characteristics of measurement depth and accuracy. The core discussion focuses on platform development, targeting different monitoring sites and platform structure design, ranging from linear strain gauges and conformal stretchable sensors to complex three-dimensional (3D) circuit-integrated stretchable arrays. We further explore various technologies associated with different measurement mechanisms and engineering designs, as well as the penetration depth and spatial resolution of these wearable sensors. The practical applications of these technologies are evident in the monitoring of deep tissue signals and changes in tissue characteristics. The results suggest that wearable biomechanical sensing systems hold substantial promise for applications in healthcare and research.

Becoming Yoga Bodies of Instagram: Women’s Agency, Platform Negotiations and Everyday Activism

Women’s yoga bodies have been overutilised in mainstream media through their stereotypical representation aligned with the market forces. Previous studies have shown the domination of similar visuals in yoga spaces on Instagram, following the algorithmic platform structures. Avoiding the dichotomy of subject–object bodies, this article brings out women participants’ living experiences of becoming yoga bodies on Instagram and their practices countering the dominant visual culture related to yoga bodies. The article argues that women use ‘everyday activism’ on Instagram to formulate their yoga body experiences. These findings are based on in-depth interviews with 17 women yoga practitioners active on Instagram. The analysis reveals the emergence of three main themes contributing to the ‘becoming of yoga bodies on Instagram’, namely (a) reclaiming the space through increased visibility, (b) training gazes towards women’s yoga bodies and (c) breaking the yoga body stereotype.

Dynamic Response Prediction Model for Jack-Up Platform Pile Legs Based on Random Forest Algorithm

Jack-up offshore platforms are widely used in many fields, and it is of great importance to quickly and accurately predict the dynamic response of platform pile leg structures in real time. The current analytical techniques are founded upon numerical modelling of the platform structure. Although these methods can be used to accurately analyze the dynamic response of the platform, they require a large quantity of computational resources and cannot meet the requirements of real-time prediction. A predictive model for the dynamic response of the pile leg of a jack-up platform based on the random forest algorithm is proposed. Firstly, a pile leg dynamic response database is established based on high-fidelity numerical model simulation calculations. The data are subjected to cleaning and dimensional reduction in order to facilitate the training of the random forest model. Cross-checking and Bayesian optimization algorithms are used for the selection of random forest parameters. The results show that the prediction model is capable of outputting response results for new environmental load inputs within a few milliseconds, and the prediction results remain highly accurate and perform well at extreme values.

Dynamic strain field estimation of fixed offshore support structures using limited acceleration measurements

The strain history affecting the fatigue life of fixed offshore support structures is one of the most important topics in structural monitoring. However, some critical fatigue locations are located below the seabed, and the installation of strain gauges at these locations entails significant maintenance costs. In contrast, acceleration which reflects the vibration characteristics of the structure is an easily accessible and more stable physical parameter. Therefore, this study presents a strain estimation scheme that uses limited acceleration measurements to estimate the dynamic strain field of the fixed offshore support structures. An adaptive displacement reconstruction method is used to obtain the displacements at the master degrees of freedom, then the global displacements are obtained by the evolutionary orthogonal response expansion method, and finally, these displacements are implemented into the numerical software by redefining the boundary conditions to obtain the dynamic strain field. The advantage of the proposed method over the traditional strain-solving forward problem is that it relies solely on measured acceleration and structural geometry information, without requiring displacement, strain, or modal information. The correctness of the proposed method is verified by a numerical jacket platform model with multi-harmonic and non-stationary random excitations. Furthermore, physical experiments under eccentric rotation and hammering excitations are performed to assess the applicability of the proposed method on a wind turbine model equipped with laser displacement and fibre bragg grating sensors as reference measurements. The results show that the proposed method can accurately predict the dynamic strains of jacket platform and wind turbine support structures, while the appropriate agreement between the estimated strains and the numerical results, and the optical measured response is observed, with maximum estimation errors of 1.80% and 4.63%, respectively. Summarily, the application of the approach enables a new type of strain monitoring applicable to fixed offshore support structures.

Capturing the Benefits of Autonomous Vehicles in Ride Hailing: The Role of Market Configuration

We develop an economic model of autonomous vehicle (AV) ride-hailing markets, in which uncertain aggregate demand is served with a combination of a fixed fleet of AVs and a flexible pool of human drivers (HVs). Dispatch efficiencies increase with scale because of density effects. We analyze market outcomes in this setting under four market configurations, defined by two dispatch platform structures (common platform versus independent platforms) and two levels of supply competition (monopoly AV versus competitive AV). A key result of our analysis is that the lower cost of AVs does not necessarily translate into lower prices; the price impact of AVs is ambiguous and depends critically on both the dispatch platform structure and the level of AV supply competition. In the extreme case, we show that if AVs and HVs operate on independent dispatch platforms, there is a monopoly AV supplier, and labor supply elasticity is sufficiently high, then prices are even higher than in a pure-HV market. Indeed, to guarantee consistently lower prices (relative to a pure HV market) in all scenarios and under all supply and density elasticities, a common dispatch platform between AVs and HVs is required. Furthermore, competitive AVs lead to lower prices than monopoly AVs in every such scenario. Our results illustrate the critical role that market configuration plays in realizing potential welfare gains from AVs. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.03112 .

A probabilistic time-dependent corrosion wastage model for offshore platforms based on reverse diffusion model

Corrosion wastage poses significant challenges to offshore platforms, making accurate corrosion depth prediction crucial for structural integrity and extending service life. This study addresses the limitations in measurement processes by integrating a reverse diffusion model into the traditional probabilistic corrosion wastage model, enabling comprehensive analysis of corrosion data. The reverse diffusion model effectively manages data uncertainty and noise, leading to a proposed probabilistic model that incorporates measurement uncertainty. This model not only mitigates data noise and errors but also improves the reliability and robustness of corrosion depth predictions for offshore platforms. Initially, statistical analysis of corrosion data from offshore mobile platforms confirmed that the corrosion rate and depth distribution of local components follow a Weibull distribution. The effectiveness of the reverse diffusion model was validated by comparing the probability distributions of original data with those processed using the model. Finally, leveraging the optimized data and an empirical corrosion model for ships, a probabilistic model for time-dependent corrosion wastage in offshore platforms was developed, and its reliability was assessed. The proposed model enhances the accuracy of corrosion predictions, contributing to better management of corrosion risks in offshore platform structures.

Surface PEGylation of Poly(Ethylenimine) dendrimers grafted magnetic nanoparticles as a pH and magnetic targeting responsive system for Cytarabine drug delivery

A pH and magnetically dual responsive platform was synthesized by PEGylation of poly (ethylenimine) dendrimers grafted magnetic nanoparticles for targeted cytarabine delivery. Characterization techniques confirmed the platform structure and properties. Optimal drug adsorption occurred at pH 8, with an adsorbent dosage of 0.01 g, and 60 min of contact time at 298 K. Pseudo-second-order kinetic and Langmuir isotherm models best fit the adsorption data. The thermodynamic data shows that drug adsorption is a favorable and spontaneous process. Drug release rates were 61.45 % and 21.92 % at pH 5.6 and 7.4 over 6 h. The release kinetics followed Korsmeyer-Peppas model, showing non-Fickian behavior.

The role of blockchain technology and smart contracts in enhancing the transparency of fragmented investments in overseas real estate

As the mobility of global capital is getting higher, more and more investors tend to invest in overseas real estate. However, the conventional model of investing in real estate possesses several disadvantages including high entry hurdles, low levels of market liquidity, and low transparency, particularly for retail investors. This article will seek to establish how blockchain technology as applied in smart contracts can help in the clarification of unclear fragmented investments in overseas property. First of all, this paper begins with the definition and examples of applying conventional and advanced blockchain technology and then describes the work of smart contracts and the comparison between regular contracts and smart contracts. The paper also evaluates the nature of fragmented investment in overseas property and its associated risks before suggesting the use of blockchain and smart contracts for a new institutional structure of the investment platform. This study demonstrates how the technologies can advance data sec, promote trust in transactions, and how investment can be automated. It can be concluded that blockchain technology, and smart contracts, can enhance Overseas Real Estate Investment by increasing transparency and decreasing the investment threshold as well as the instances of fraud. This study also presents an analysis of the potential difficulties that may be encountered in real-world applications and a recommendation of what may be done in subsequent studies. On one hand, this research offers a new perspective to the theoretical world, on the other hand, it offers practical problems and solutions to the practitioners who are a part of overseas real estate markets and businesses by steadily helping the overseas real estate markets to move in the fairer and more transparent direction.

Research on Model Reduction of AUV Underwater Support Platform Based on Digital Twin

Digital twin technology, as a data-driven and model-driven innovation means, plays a crucial role in the process of digital transformation and intelligent upgrading of the marine industry, helping the industry to move towards a new stage of more intelligent and efficient development. In order to solve the defects of the Autonomous Underwater Vehicle (AUV) underwater support platform structure deformation field, digital twin technology and model reduction technology are applied to an AUV underwater support platform, and a five-dimensional digital twin model of the AUV underwater support platform is studied, including five dimensions: physical world, digital world, twin data center, service application, and data connection. The digital twin of the subsea support platform is established by using the digital twin modeling technology. The POD method is used to calculate the deformation field matrix of the support structure of the subsea support platform under the 0–5 sea state, and the corresponding eigenvalues and eigenvectors are obtained. By intercepting the eigenvectors corresponding to the eigenvalues of the high energy proportion, the low-order equation is constructed, and the reduced-order model under each sea state can be quickly solved. The experimental results show that the model reduction technology can greatly shorten the model solving time, and the calculated results are highly consistent with the simulation results of the finite element full-order model, which can realize the rapid analysis of the deformation response of the subsea support platform structure, and provide a theoretical basis and technical support for the subsequent simulation, state evaluation, visual monitoring, and predictive maintenance.

Innovative load identification with Res-UNet: Integrating phase space reconstruction and physics-informed deep learning

This study developed a specialized load identification model for a specific ocean platform that integrates advanced deep learning techniques with the unique physical characteristics of marine engineering. Key achievements include high-dimensional mapping of data features using phase space reconstruction (PSR) techniques to enhance the predictive capacity of the model through chaos system theory; optimization of ResNet and UNet deep learning networks with attention mechanisms to boost performance; calculation of stiffness, mass, and damping matrices using finite element software; establishment of corresponding state-space equations; and integration of structural dynamics equations with the deep learning network loss function to significantly improve load identification accuracy. In addition, proprietary structural equations for ocean platform structures were developed, underscoring the uniqueness and applicability of the algorithm. The proposed physics-informed Res-UNet model exhibited excellent performance in identifying both periodic and random loads. These improvements were validated through training with finite element simulated data and subsequent experimental applications, demonstrating the effectiveness of the proposed innovative methods.

A Synergistically Regulated Cathode/Electrolyte Interphase With High Stability and Rapid Zn2+ Migration Toward Advanced Flexible Zinc-Ion Batteries.

Na3V2(PO4)3(NVP), as a representative sodium superionic conductor with a stable polyanion framework, is considered a cathode candidate for aqueous zinc-ion batteries attributed to their high discharge platform and open 3D structure. Nevertheless, the structural stability of NVP and the cathode-electrolyte interphase (CEI) layer formed on NVP can be deteriorated by the aqueous electrolyte to a certain extent, which will result in slow Zn2+ migration. To solve these problems, doping Si elements to NVP and adding sodium acetate (NaAc) to the electrolyte are utilized as a synergistic regulation route to enable a highly stable CEI with rapid Zn2+ migration. In this regard, Ac- competitively takes part in the solvation structure of Zn2+ in aqueous electrolyte, weakening the interaction between water and Zn2+, and meanwhile a highly stable CEI is formed to avoid structural damage and enable rapid Zn2+ migration. The NVPS/C@rGO electrode exhibits a notable capacity of 115.5mAhg-1 at a current density of 50mAg-1 in the mixed electrolyte (3M ZnOTF2+3M NaAc). Eventually, a collapsible "sandwich" soft pack battery is designed and fabricated and can be used to power small fans and LEDs, which proves the practical application of aqueous zinc-ion batteries in flexible batteries.

XR-based interactive visualization platform for real-time exploring dynamic earth science data

The transition from 2D planar displays to immersive holographic 3D environments has brought advancements in visualization technology. However, there remains a lack of effective interactive visualization tools for complex multi-dimensional structured or unstructured datasets in immersive space. To address this gap, we have developed MetIVA, a state-of-the-art multiscale interactive data visualization platform that leverages the Extended Reality (XR) and cloud rendering technology for immersive data exploration. In this paper, we firstly outline the historical development of scientific visualization and the recent shift towards 3D and higher-dimensional visualization, and then basically introduce the conceptual framework and platform structure of MetIVA, and finally present the evaluation results from recruited potential users. The results confirm that MetIVA is a powerful tool to accelerate data exploration and decision-making processes. Its interactive and intuitive features, along with ongoing optimization efforts, make it a valuable tool for researchers and practitioners in the field of Earth science.

On the Development of a Digital Data Management Platform for Battery Material and Processing Data

Research on battery materials and systems is broader than ever and a large amount of data is being generated. The data collected is often challenging to correlate or compare because they are not compiled in a structured way since no generally uniform data structure exists yet in the battery field. Besides, manufacturing steps and analysis parameters vary enormously from laboratory scale to industrial scale, from research institutions to consortia.Within the "DigiBatMat project" we have created a digital platform for battery material data and processing data that combines collected material data and knowledge within a defined structure based on taxonomies and ontologies. The structure of the digital platform was developed to cover the Battery LabFactory Braunschweig (BLB) pilot-scale production line for lithium-ion cells while being compatible with other battery production processes, for example at the laboratory bench scale. The platform’s underlying data structure is divided into an “electrode production” and “cell production” taxonomy, providing input modules for the most typical battery material and cell characterization methods. This approach offers a commercially available platform with a defined structure, which still allows flexibility for individual electrode and cell productions through option based input. Due to this structure, we aim to go beyond simple data storage by implementing functionalities that allow users to work with and analyze the data within the digital platform. Beside the functionalities we have implemented so far, which are shown here, we also aim to develop further functionalities that enable the identification of new correlations that are otherwise difficult or even impossible to obtain.A consistent data set is necessary to build up and validate such a digital platform. Hence, we specifically generated “reference” data and tested their management and analysis with the platform. We focus on three key use cases of collecting process data and characterizing electrodes and cells properties along the production process. In one of these key use cases we compare state-of-the-art cathodes with Ni-rich active materials, namely NMC811 and NCA. Data are collected from cells with such cathodes with varying loadings and electrode densities. Characterization of the electrodes includes, among other things, microscope data analysis of the microstructure and electrochemical characterization such as electrochemical impedance spectroscopy and rate capability. By showing how raw data can be imported into the database, processed and analyzed, we aim to demonstrate the possibilities and advantages of our digital platform.

The geological-tectonic structure and specific development of the Siberian platform in the context of the diamond prospecting

The geological-tectonic structure and specific development of the Siberian platform in the context of the diamond prospecting

메타버스기반 LLM을 활용한 생성형 AI의 구현

A metaverse is a virtual convergence environment that promotes inclusiveness and diversity by maintaining social relationships among users through avatars and engaging them in various cultural activities. Because everyday activities can be maintained the same in a metaverse environment, the transmission, exchange, and sharing of useful and accurate information among members in the metaverse environment has become important. Additionally, the desire and requirements of members to obtain the desired information through conversations with avatars in a metaverse are increasing; the number of these cases is also increasing. Accordingly, in this study, we redesigned and implemented the structure of the metaverse platform at Korea Polytechnics such that generative AI based on an LLM could be utilized on the metaverse platform. By presenting experimental examples according to the implementation, we confirmed that an AI-based service could be provided that configured the LLM and generated answers in the metaverse. In future research, we plan to apply various generative AI models to the metaverse platform, present a customized model through a comparative analysis, and continue research on implementing our LLM model.

Defect Detection of Topside Offshore Platform Structure Using Non-Destructive Testing Methods

Non-destructive testing on offshore rigs involves multiple sections. The topside platform structure, which consists of the main deck, cellar, and sub-cellar deck, is one of the most important sections. The purpose of this research is to use appropriate nondestructive testing (NDT) methods to assess the internal and surface defects as well as identify the material properties of the selected structure at the topside platform structure. Various NDT technologies can be utilized to detect faults on the topside of the offshore platform. The NDT techniques used in this study are ultrasonic, magnetic particle, and dye penetrant testing. To describe the material in the samples, a different method known as positive identification material is employed. Four tasks are involved in this process: identifying the material properties of each sample, identifying surface and interior faults, and comparing the two types of defects. The results of the analysis revealed that a 316L stainless steel specimen contained as much as 19.2% chromium, 11.8% nickel, and 3.0% molybdenum. Magnetic particle inspection was recommended for surface and subsurface inspection. In contrast, the ultrasonic testing examination yielded the best outcome for the topside offshore platform's interior problem.

Study on the hydraulic turbine modeling method based on IGSA-NN revision strategy

Abstract With the further increase of renewable energy such as wind power and photovoltaic in the power grid, the hydraulic turbine regulating system (HTRS) faces enormous challenges. As simulation modeling is the basis for studying the optimal control of HTRS, it is crucial to establish a high-precision model of HTRS. As the core component of the HTRS, the hydro-turbine contains extremely complex nonlinearity, which model accuracy directly affects the accuracy of the HTRS simulation platform. However, current modeling methods have certain disadvantages such as large modeling errors and making the structure of the HTRS simulation platform more complex. Therefore, this paper proposes a data modification strategy (IGSA-NN) based on the BP neural network (BPNN) and an improved gravity search algorithm (IGSA) and then achieves high-precision modeling of the hydro-turbine combining the BPNN and actual operation data. Finally, the accuracy of the hydro-turbine model is verified by the actual operation data, and the method to build the simulation platform of the HTRS which contains both real and virtual components is given. The research results have a certain guiding significance for hydro-turbine modeling.

Interactive learning spaces: the challenges and prospects of internationalizing medical education / Espacios de aprendizaje interactivo: dificultades y perspectivas de la internacionalización de la educación médica

The research aims to develop the structure of a streaming platform for conducting webinars for medical students studying at universities in China and abroad. The paper’s authors developed the plan and structure of the ChinaInterMed website with video materials and conducted a survey involving 17 rectors of universities in Beijing. Because of the survey, the authors determined that there were topics on international health systems in the curricula of the medical faculties of all 17 universities under study, courses in English in 10 universities (70.6%), topics on tropical diseases in 16 universities (94.1%) and seminars and practical classes with patients from different cultures in eight universities (47.1%). The results of the study will facilitate the use and integration of ICT in educational institutions, teaching medical students from an international point of view, raising general awareness of international communication, promoting equality in education, creating an effective institutional mechanism for internationalization, increasing the global competence of students, developing seminars, symposiums and workshops, and bringing international aspects to teaching and research work.