Integration Platform Research Articles

HomoeOmicsDB: A global reference platform for accelerating basic and application research in homeopathy

Homeopathy, a globally practiced alternative medicine, faces criticism due to the lack of molecular mechanisms elucidating its drug actions. To address this, researchers are examining the mechanistic action of homeopathic drugs by assessing the molecular markers of various disorders in real-time patients and/or disease models. However, there is a gap: the lack of an assembled view of publicly available molecular-level studies hampers the research on homeopathic drugs. For example, the information on the mechanistic action of Arnica montana tinctures/dilutions - anti-inflammatory, apoptotic, anti-migratory, and healing properties through gene expression and cellular assays – are scattered over published scientific literature. To bridge this gap and accelerate research, we developed the HomoeOmicsDB database (https://ciods.in/homoeomicsdb/), the first reference and molecular analysis platform for multi-omic research in homeopathy. Currently, HomoeOmicsDB hosts information on 65 homeopathic drugs' effects on the expression of around 600 genes. This information is curated from over 100 peer-reviewed published research articles involving more than 50 human and animal cell lines. For each drug, the database integrates the studied host system (body fluid, tissue, host cell/line, cell type), and its regulated expression of genes at RNA, protein, and post-translational modification levels. The information assembled in HomoeOmicsDB, might galvanize research on molecular mechanisms of other homeopathic drugs with similar clinico-physiological outcomes. Integrated with the DisGeNet database, HomoeOmicsDB entails this comparative analysis of homeopathic drug efficacy for multiple disorders in practice. This would subsequently embrace translational/re-purposing options for diseases with limited or no therapeutic alternatives. Together, HomoeOmicsDB aims to serve as a platform for reference and integration of multi-omic data relevant to homeopathic research. The evaluation of the database by the practitioners and researchers would foster greater acceptance and integration of homeopathic medicine.

Smart Response: Leveraging AI Analytics for Enhanced Disaster Resilience

The emergence of artificial intelligence and cloud computing has transformed modern disaster response capabilities, particularly by developing sophisticated real-time analytics systems for natural disaster management. This article examines the integration of AI-powered analytics platforms that synthesize data from multiple sources, including satellite imagery, meteorological stations, and social media streams, to enable rapid response during crises. This article demonstrates that cloud-based real-time analytics substantially enhance early warning systems and optimize resource allocation during disaster events while significantly improving response times compared to traditional methods. However, these technological advances raise important ethical considerations regarding data privacy and equity in access to digital resources. The findings suggest that AI-driven disaster response systems offer unprecedented capabilities in protecting communities. However, their implementation must be guided by robust frameworks that ensure equitable access and maintain privacy standards while maximizing public safety benefits.

The role of data integration and analysis platforms in contemporary society: an introduction

ABSTRACT This introduction to the special issue on data integration and analytics platforms provides an overview of the current importance of these platforms, with particular reference to the market leader, Palantir Technologies. In addition to the platformization of many areas of society through the use of data integration and analytics platforms, we argue that, where Palantir platforms are used, the process of ‘Palantirization’ can also be observed, because Palantir as a company is characterized not only by powerful platforms, but also by particular market entry techniques and, in particular, a decidedly political stance. This, we argue, is part of the package when Palantir's platforms were adopted.

Leveraging Social Media in The College Classroom

This paper explores the integration of social media platforms into higher education to enhance student engagement, foster collaboration, and promote meaningful learning experiences. Drawing upon a comprehensive review of existing literature, empirical evidence, and practical insights, the study investigates the implementation of various social media projects within educational contexts. The research examines the effectiveness of diverse social media initiatives, including infographics, memes, blog posts, TikTok-style public service announcements, and podcast scripts, in facilitating student learning and participation. Moreover, the paper identifies key challenges encountered during the integration process, such as technical hurdles, alignment with course objectives, and ethical considerations, and proposes strategies for addressing theses challenges. Additionally, the study explores promising future directions for refining the use of social media in education, including integrating emerging technologies, evolving pedagogical approaches, and prioritizing ethical considerations. Through a reflective analysis of successes, shortcomings, and lessons learned, this paper provides valuable insights for educators, instructional designers, and educational policymakers seeking to leverage social media effectively in higher education. Ultimately, the research contributes to a deeper understanding of the role of social media in promoting student engagement, facilitating learning, and shaping the future of education in a digital age. Keywords: social media, higher education, student engagement, educational technology, pedagogical approaches, digital literacy, social media integration

Big data for SMEs: A review of utilization strategies for market analysis and customer insight

This paper provides a comprehensive review of how small and medium-sized enterprises (SMEs) can leverage big data for market analysis and customer insights, offering strategies to enhance competitiveness in today’s data-driven economy. The study explores the role of big data in helping SMEs understand customer behavior, preferences, and trends, enabling them to make informed decisions and improve customer satisfaction. It highlights the practical challenges SMEs face in adopting big data solutions, including limited resources and technical expertise, and suggests scalable, cost-effective strategies for data collection, storage, and analysis. Key utilization strategies discussed include the integration of cloud-based platforms for data management, the use of data analytics tools to process customer feedback, and the application of machine learning algorithms to predict customer behavior. The paper also examines case studies where SMEs successfully implemented big data solutions to achieve significant market insights and improve business outcomes, such as enhanced marketing strategies, personalized customer experiences, and streamlined operations. The review concludes that, despite the challenges, SMEs can effectively harness the power of big data by adopting tailored strategies that align with their specific business needs and resources. By utilizing big data, SMEs can gain a competitive edge, improve customer engagement, and ultimately drive business growth. The paper emphasizes the importance of training, data literacy, and the need for partnerships with technology providers to ensure the successful integration of big data into business processes.

AI-based optimization of EM radiation estimates from GSM base stations using traffic data

The fast expansion of mobile networks has sparked worries regarding base station EM radiation's health impacts. Traffic load is commonly ignored when evaluating EM radiation levels using maximum power output. This study proposes utilizing AI and ML on real network traffic data to optimize GSM base station EM radiation estimations. We obtained EM radiation measurements and traffic data from selecting GSM base stations by location and configuration. To predict EM radiation levels, traffic patterns were used to train linear regression, random forests, and neural networks. Base stations were clustered by radiation profile using unsupervised learning. Considering regulatory restrictions and measurement feasibility, an optimization methodology was created to minimize EM radiation estimate inaccuracy. The results show better prediction accuracy than power-based estimations and high generalisability across base station types. Site-specific factors influenced daily EM radiation patterns after clustering. EM radiation levels can be monitored using traffic data and the optimized AI/ML model. This research helps telecom operators and regulators analyze EM radiation more accurately and efficiently. Future projects should include 5G and small cell network extensions and intelligent city platform integration. The suggested method develops data-driven, AI-powered Public Safety and mobile network trust solutions.

Spherical Nucleic Acid Probes on Floating-Gate Field-Effect Transistor Biosensors for Attomolar-Level Analyte Detection.

Field-effect transistor (FET) sensors are attractive for the label-free detection of target biomolecules, offering ultrahigh sensitivity and a rapid response. However, conventional methods for modifying biomolecular probes on sensors often involve intricate and time-consuming procedures that require specialized training. Herein, we propose a simple and versatile approach to functionalize floating-gate (FG) FET sensors by exploiting the strong binding ability of polyvalent interactions and the three-dimensional structure of densely functionalized spherical nucleic acids (SNAs). Crucially, the SNAs can be easily deposited onto a dielectric layer under mild conditions, ensuring stable immobilization of the probes. Further, the SNAs show efficient and robust immobilization on various dielectric layers including Y2O3, Ta2O5, and HfO2, forming conjugates that resist denaturation by various agents. By modifying the DNA sequence within the SNAs, we achieved highly sensitive FG-FET biosensors for DNA, adenosine triphosphate, and viral nucleic acids at the attomolar level. For clinical samples detection, unamplified enterovirus 71 RNA at levels as low as 0.13 copies μL-1 was detected within 100 s. Moreover, the sensor attained 100% accuracy for analyte detection in both positive and negative samples. Our findings provide a general and simple method for fabricating FET-based biochemical sensors and demonstrate that the SNA-modified FG-FET biosensor is a versatile and reliable integrated platform for ultrasensitive biomarker detection.

Lithographically defined cavities with high Quality and Purcell factors

Abstract We introduce and analyze a lithographically defined cavity (Li-cavity) incorporating a buried mesa defect in a planar vertical structure, which induces transverse photonic confinement while enabling compatibility with electrical injection. We systematically investigate the influence of key cavity parameters on optical behavior and fundamental properties using a comprehensive three-dimensional optical model. Our analysis reveals that the Li-cavity exhibits low optical scattering, resulting in high Q and Purcell factors, even at sub-wavelength dimensions. Furthermore, the Li-cavity supports single-mode operation within a broader aperture size range compared to conventional cavity designs, promising enhanced optical power of single-mode emission. Adequately designed structures demonstrate superior Q and Purcell factors at wavelength scale sizes,- outperforming conventional micropillar cavities. These improved properties and customizable device characteristics stem from a simple fabrication process that precisely controls the cavity size, shape, and optical mode. Superior characteristics, along with its adaptability to diverse materials, wavelengths, and photonic integration platforms, promise a broad range of applications and potential adaptation of this design to diverse photonic devices.

Assessing the impact of marine growth on the structural integrity of jacket platforms

ABSTRACT The marine organisms of the jacket platform will directly affect the surface roughness and hydrodynamic coefficient of the platform. Therefore, it is of great significance to effectively analyze the influence of the attachment of marine growth on the structural performance of the platform. Firstly, the distribution law of marine growth was established by the statistical data in Bohai Sea of China. Secondly, the hydrodynamic coefficients of the platform which were changed due to marine growth were determined, and the finite element model of the platform was established. Finally, the finite element simulation results of the platforms with attachment patterns of no marine growth, uniform marine growth, and non-uniform marine growth respectively were compared. It is shown that the calculation results of the platform with uniform marine growth were relatively conservative. Meanwhile, the attachment of marine biofouling produced a significant effect on the structural hydrodynamic load, etc. of the platform.

The Digitization in The Application in Literacy Teaching among Students from Primary School in Nanning City of Guangxi, China

This study explores the application and impact of digitization in literacy instruction for primary school students in Nanning, Guangxi, China. As digital technologies are increasingly integrated into educational practice, it is particularly important to understand their role in literacy instruction. Using a combination of qualitative and quantitative research methods, we examined the various digital tools and resources used by teachers and students and the effectiveness of these technologies in promoting literacy skills. The results show that the integration of digital platforms not only promotes personalized learning experiences but also enhances student engagement. In addition, we discuss the challenges faced by educators in adapting to these new approaches, including the need for professional development and adequate technological infrastructure. This study contributes to the ongoing discussion of educational innovation and highlights the importance of adapting literacy instruction strategies to the requirements of the digital age. Ultimately, our study highlights the need to embrace digitization as a key component of literacy education development in primary schools in Nanning and beyond.

Integrating artificial intelligence in strabismus management: current research landscape and future directions

Advancements in artificial intelligence (AI) are transforming strabismus management through improved screening, diagnosis, and surgical planning. Deep learning has notably enhanced diagnostic accuracy and optimized surgical outcomes. Despite these advancements, challenges such as the underrepresentation of diverse strabismus types and reliance on single-source data remain prevalent. Emphasizing the need for inclusive AI systems, future research should focus on expanding AI capabilities with large model technologies, integrating multimodal data to bridge existing gaps, and developing integrated management platforms to better accommodate diverse patient demographics and clinical scenarios.

Real-time prediction of structural responses in deep-water jacket platforms using Ada-STLNet: A comprehensive analysis with prototype monitoring data

Real-time prediction of the mechanical behaviors based on prototype monitoring data is crucial for ensuring the integrity and safety of deep-water jacket platforms. As complex nonlinear systems, these platforms’ response exhibit varying temporal trends, dynamic spatial correlations, and load dependencies. This makes accurate prediction of future axial force and bending moment responses a significant challenge. In this paper, a novel deep learning method called Adaptive Spatio-Temporal Load Network (Ada-STLNet) is proposed aiming to address the issue of multi-node axial force and bending moment response prediction of deep-water jacket platform. Our model utilizes an enhanced graph convolution (EGCN) and self-attention mechanism for efficient spatial correlation modeling in multi-node response, LSTM for long sequence temporal feature capture. It integrates spatial and temporal, along with load-aware modules to capture intricate patterns in structural responses. Additionally, a multi-gated coupling mechanism with two independent gating modules is designed to adaptively represent the complex dependencies of structural responses, ensuring model stability and robustness. Extensive experiments conducted on prototype structural monitoring data from a deep-water jacket platform in the South China Sea demonstrate that Ada-STLNet outperforms six traditional models, including HA, SVR, KNN, MLP, LSTM, and GRU, across various prediction steps. The proposed model exhibits superior accuracy, particularly in short-term predictions, achieving up to 62.80% improvement in MAE and 48.11% in RMSE for axial force predictions compared to the second-best model. Ablation studies confirm the effectiveness of each component within Ada-STLNet. This research highlights the potential of Ada-STLNet for reliable and accurate prediction of structural responses, contributing significantly to the field of marine engineering.

RIGTA: Interactive Robot as an Integration Platform for Engineering Education

RIGTA: Interactive Robot as an Integration Platform for Engineering Education

Data reflectivity and user reflexivity: New conceptual pathways for connecting structural approaches with user perspectives

Ongoing societal datafication and, most recently, the widely noticed launch of ChatGPT, continue to raise the “user question”: what role(s) does the user play in datafied, artificial, and automated environments? Recent technological advancements have begun to challenge fundamental assumptions in media and communication theory and, thus, urge scholars to (re-)visit and (re-)examine the interrelations, dynamics, and entanglements of (human) users with datafied environments. Two rapidly expanding but still largely distinct bodies of research are addressing these topics: critical data and platform studies, primarily focusing on structural approaches, and user-centered perspectives anchored in the tradition of audience research. Situated at the intersection of studying platform power and user agency, the aim of this special issue is to connect structural approaches with studies of lived user experiences through the lenses of reflectivity and reflexivity. We believe that linking platform and data perspectives dealing with questions of power, accountability, and governance on a societal level to users’ engagement with digital technologies and automated systems in an everyday context is a fruitful path when aiming for a comprehensive understanding of the societal implications of the widespread adoption and integration of digital platforms and intelligent technologies. The concepts of reflectivity and reflexivity provide a valuable conceptual starting point for a more integrated and holistic discussion of the complex user-data relations emerging in and from datafied environments. While the individual contributions in this special issue present their own take on the proposed concepts, the purpose of this introduction is to establish a broader theoretical foundation by outlining some overarching considerations on how these concepts can be understood and integrated.

PyINETA: Open-Source Platform for INADEQUATE-JRES Integration in NMR Metabolomics.

Robust annotation of compounds is a critical element in metabolomics. The 13C-detection NMR experiment incredible natural abundance double-quantum transfer experiment (INADEQUATE) stands out as a powerful tool for structural elucidation, but this valuable experiment is not often included in metabolomics studies. This is partly due to the lack of a community platform that provides structural information based on INADEQUATE. Also, it is often the case that a single study uses various NMR experiments synergistically to improve the quality of information or balance total NMR experiment time, but there is no public platform that can integrate the outputs of INADEQUATE with other NMR experiments. Here, we introduce PyINETA, a Python-based INADEQUATE network analysis. PyINETA is an open-source platform that provides structural information on molecules using INADEQUATE, conducts database searches using an INADEQUATE library, and integrates information on INADEQUATE and a complementary NMR experiment 13C J-resolved experiment (13C-JRES). 13C-JRES was chosen because of its ability to efficiently provide relative quantification in a study of the 13C-enriched samples. Those steps are carried out automatically, and PyINETA keeps track of all the pipeline parameters and outputs, ensuring the transparency of annotation in metabolomics. Our evaluation of PyINETA using a model mouse study showed that PyINETA successfully integrated INADEQUATE and 13C-JRES. The results showed that 13C-labeled amino acids that were fed to mice were transferred to different tissues and were transformed to other metabolites. The distribution of those compounds was tissue-specific, showing enrichment of specific metabolites in the liver, spleen, pancreas, muscle, or lung. PyINETA is freely available on NMRbox.

ChemPrice, a Python Package for Automated Chemical Price Search

AbstractThe open‐access movement in chemistry has led to a surge in publicly accessible data repositories such as PubChem and ChemSpider, housing more than 100 million chemical compounds. These repositories are invaluable for large‐scale virtual screening but lack crucial pricing information, a vital economic aspect influencing commercial decisions. Currently, these databases merely provide hyperlinks to vendor websites for pricing details, necessitating manual exploration due to the diverse structures of vendor websites, making automated data extraction impractical. To tackle this challenge, we introduce ChemPrice, a Python package that streamlines the collection of pricing data through the integration of ChemSpace, Mcule, and Molport platforms that provide up‐to‐date price information from over 100 suppliers. ChemPrice standardizes pricing units, offering consolidated and best‐pricing information for specified compounds. Its modular design facilitates integration into automated virtual screening workflows, and a user‐friendly web interface caters to those with limited coding skills, enabling easy compound list uploading and pricing data retrieval. This innovation addresses the complexity of collecting compound prices and enables integration into the virtual screening code workflows, providing a valuable resource for a wide community of users seeking comprehensive and up‐to‐date pricing information to enhance their decision‐making processes.

Reform and Practice of Cultivation Mode of Top Talents in Product Design Specialty under the Background of Industry-Teaching Integration

In the wave of globalization and informationization, the demand for top talents in the field of product design is increasingly urgent. This study delves into the reform path of cultivating top talent in product design against the backdrop of industry-teaching integration, addressing issues with weak practical teaching, lagging curriculum content, and a lack of practical experience among teachers in the field of product design education. The study clarifies the connotation of industry-teaching integration and its application value in product design education, and then, based on the theory of cultivation of top-notch talents, puts forward a multi-dimensional reform plan such as optimization of the curriculum system, strengthening of practical teaching, construction of the platform for industry-teaching integration and construction of the teaching staff, which provides valuable references to the educational reform of other related majors

The molecular architecture of the desmosomal outer dense plaque by integrative structural modeling.

Desmosomes mediate cell-cell adhesion and are prevalent in tissues under mechanical stress. However, their detailed structural characterization is not available. Here, we characterized the molecular architecture of the desmosomal outer dense plaque (ODP) using Bayesian integrative structural modeling via the Integrative Modeling Platform. Starting principally from the structural interpretation of a cryo-electron tomography (cryo-ET) map of the ODP, we integrated information from x-ray crystallography, an immuno-electron microscopy study, biochemical assays, in silico predictions of transmembrane and disordered regions, homology modeling, and stereochemistry information. The integrative structure was validated by information from imaging, tomography, and biochemical studies that were not used in modeling. The ODP resembles a densely packed cylinder with a plakophilin (PKP) layer and a plakoglobin (PG) layer; the desmosomal cadherins and PKP span these two layers. Our integrative approach allowed us to localize disordered regions, such as the N-terminus of PKP and the C-terminus of PG. We refined previous protein-protein interactions between desmosomal proteins and provided possible structural hypotheses for defective cell-cell adhesion in several diseases by mapping disease-related mutations on the structure. Finally, we point to features of the structure that could confer resilience to mechanical stress. Our model provides a basis for generating experimentally verifiable hypotheses on the structure and function of desmosomal proteins in normal and disease states.

A Study on Regulating the Residual Stress of Electroplated Cu by Manipulating the Nanotwin Directions.

Glass substrate, a new type of substrate with excellent mechanical and electrical properties of glass itself, has great potential to become an ideal platform for heterogeneous integration in chiplet systems for high-performance computing applications. The residual stress of the metal layer generated on the glass surface during the electroplating process is one of the major bottlenecks of glass packaging technologies, resulting in glass-metal layer delamination and glass breakage. This paper demonstrated for the first time a method to regulate the residual stress by manipulating the nanotwin directions of the electroplated Cu. The experimental results show that nanotwins with three different directions (non-directional, vertical, and horizontal) can be manipulated by controlling electroplating conditions (concentration of Cl- and gelatin, stirring speed). The orientations of non-directional, vertical, and horizontal nanotwinned Cu are non-oriented, 110 and 111, respectively. After electroplating, the 111-oriented nanotwinned Cu has the smallest residual stress (39.7 MPa). Annealing can significantly reduce the residual stress of nanotwinned Cu, which has been attributed to the decrease in the geometric necessity dislocation density. 110-oriented nanotwinned Cu had drastic recrystallization, while 111-oriented nanotwinned Cu and non-oriented nanotwinned Cu had only slight recrystallization. After annealing, the residual stress of 111-nt-Cu remains the lowest (29.1 MPa).

Mid-infrared arrayed waveguide gratings using a quantum cascade laser gain medium as core material

Mid-infrared photonics is a widely researched field with several applications, such as chemical sensing and spectroscopy. The development of photonic integrated circuits for the mid-infrared can enable the reduction in device size, weight, and power (SWaP) consumption. This paper demonstrates arrayed waveguide gratings working in the mid-infrared regime (5–5.4 µm). Our devices are fabricated on an InP-based quantum cascade laser platform with the gain medium as the waveguide core. To minimize the propagation losses caused by free carrier absorption and intersubband absorption in the unbiased QCL structure, we exposed the photonic chips to proton implantation. The performance of three sets of AWGs with different etch depths was characterized. The lowest waveguide losses were measured to be 2 dB/cm. The best performing 7×1 AWG and 13×1 AWG designs featured insertion losses of −2dB and −2.5dB, respectively. This study showcases the feasibility of applying such a platform for easy integration with active components like lasers and photodetectors, paving the path for on-chip mid-infrared applications.