Integration Platform Research Articles

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.

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.

Chinese Cinephiles in the Post-Pandemic Pandemic Era: Comparing the Transition from Traditional Theatres to OTT Platforms

The COVID-19 pandemic has reshaped film consumption patterns in China, driving a notable transition from traditional theatres to over-the-top (OTT) platforms. This study investigates the factors influencing post-pandemic cinephile preferences, particularly the interplay between OTT platforms and traditional cinema-going platforms. The surge in online streaming, boosted by pandemic-induced restrictions, has been critical with regional giants such as iQIYI restyling Chinese entertainment traditions. This research aims to uncover the determinants guiding cinephile choices by analysing demographic variables, technological advancements and consumption patterns. Through a survey in Tier II cities, data from 656 participants were analysed to determine the OTT platform and cinema usage. The findings reveal nuanced trends: females slightly favour OTT platforms, while males prefer traditional cinemas. Young adults aged 21 to 25 years are the most active users of both platforms, with education level slightly affecting usage. Smartphone usage is widespread, with 70% accessing OTT platforms via mobile. While 40% view OTT platforms as challenging traditional cinemas, 65% view OTT platforms in the future. Key factors influencing future cinema-going include OTT platform accessibility and time constraints—over 80% expressed satisfaction with their cinema experiences, with peak seasons significantly impacting ticket prices. Comedy, drama and action genres were most popular among cinemagoers, each with over 60% preference. These findings imply that the future of film consumption in China will likely be shaped by the balance and integration of OTT platforms and traditional cinema experiences.

Fermatean Fuzzy TOPSIS Method and Its Application in Ranking Business Intelligence-Based Strategies in Smart City Context

With the expansion of smart cities, the use of business intelligence (BI) has emerged as a crucial tool for resource optimization, increasing efficiency, and improving the citizens’ quality of life. BI enables companies to make better strategic decisions by analyzing vast amounts of urban data, helping them remain competitive in the dynamic smart city environment. This study utilizes content analysis and the Fermatean Fuzzy TOPSIS (FF-TOPSIS) method to rank the strategies based on business intelligence in the context of smart city. Initially, relevant criteria were identified through content analysis, and subsequently, five strategies were developed and ranked based on these criteria. The results revealed that the "Development of IOT-enabled smart networks (S2)" ranked highest, as it plays a significant role in optimizing resource management and enhancing urban service performance, thereby contributing greatly to the advancement of smart cities. "Process automation and the deployment of robotic systems (S5)" ranked second, as it enhances efficiency and reduces human errors. "Cloud platform integration for seamless access to data and services (S3)" also proved to be of considerable importance, ranking third, as it provides seamless access to data and services. " Artificial intelligence deployment for predictive analytics and process optimization (S4)" ranked fourth and was vital for predictive analytics and process optimization, while " Big data analytics for smart decision-making (S1)"—despite its importance—ranked fifth. Urban managers should prioritize the development of IOT networks to fully leverage their potential for resource management and efficiency gains. Following this, attention to process automation and AI integration can significantly enhance the quality of life for citizens and reduce urban costs.

Targeting online sales through last‐mile delivery platform integration

AbstractWe analyze channel integration between a last‐mile delivery platform and a general merchandise retailer in two distinct stages: (1) platform delivery access (PDA), where the retailer continues to offer standard delivery through its own website but directs customers to the platform's website for new same‐day delivery; and (2) integrated delivery access (IDA), where customers can continue to use same‐day delivery service at the delivery platform website but can purchase products in a single order with both same‐day and standard delivery options at the retailer's website. We perform a quasi‐experiment using consumer spending data from retailer, target, and delivery platform, Shipt. We find that PDA provides positive impacts to the delivery platform through increased sales. IDA, on the other hand, increases the retailer's online channel sales but does not impact the delivery platform's sales. Moreover, we find that the positive effects of PDA on the delivery platform's sales are stronger in markets where online grocery penetration is lower, indicating that the effects were likely driven by increased purchases for groceries. Finally, the positive effect of IDA on the retailer's online channel sales is stronger in markets where the retailer has a greater loyal customer base and online grocery penetration is lower.

TinyML-powered ensemble modeling for greenhouse climate control using XGBoost and LightGBM

The cultivation of crops in smart greenhouses is experiencing a profound transformation, fueled by cutting-edge technological advancements in environmental control that significantly improve efficiency, sustainability, and productivity. Nonetheless, the intricate and ever-changing dynamics of microclimate conditions pose challenges in customizing environments to satisfy the specific requirements of various plants. Accurate prediction of these microclimate parameters emerges as a promising solution to this challenge. This study explores the integration of machine learning and TinyML platforms to create a groundbreaking ensemble approach for effectively forecasting microclimate conditions. We obtained exceptional prediction accuracy for temperature (R2 = 0.9972) and humidity (R2 = 0.9976) using a stacking ensemble of XGBoost and LightGBM models. We used Optuna for accurate hyperparameter optimization and thoroughly examined the best possible input variable combinations as part of our meticulous model construction approach. The results of this study demonstrate the revolutionary potential of machine learning in greenhouse climate management, opening the door for data-driven, intelligent agricultural systems that maximize crop yields while reducing energy consumption.

Enhancing sound source localization and music teaching through integrated computational resource allocation

In contemporary educational and computational settings, the incorporation of cutting-edge technologies like sound source localization and personalized music teaching helps in offering an effective resource allocation strategies. Previous systems for sound localization and music teaching frequently lacked real-time flexibility and effective resource use, reducing their efficiency in dynamic learning settings and tasks involving computation. To overcome these shortcomings, the SoundLocMusicTeachRA (SLMTRA) algorithm is presented, a single, integrated platform made to maximize sound localization accuracy, improve music teaching efficiency, and enhance computational resource oversight. However, the existing study did not highlight the importance of computation resource allocation but this proposed algorithm will address it. SLMTRA uses a new Bagging ensemble approach incorporating Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), with hyperparameter tuning to enhance the effectiveness of the approach. These classifiers are trained utilizing sound localization datasets from recordings made with microphones, music teaching feedback datasets from data on student performance, and resource allocation datasets from metrics for computer utilization. Experimental findings indicate SLMTRA’s high accuracy in sound source localization, improved music teaching feedback capacities, as well as effective resource allocation tactics, guaranteeing the best performance of the system. The implementation of SLMTRA represents a noteworthy development in combining sound localization, music teaching, and resource allocation within a unified computational framework, offering a more flexible and effective system compared to previous methodologies.

A Survey on AI-Driven Mouse Behavior Analysis Applications and Solutions

The physiological similarities between mice and humans make them vital animal models in biological and medical research. This paper explores the application of artificial intelligence (AI) in analyzing mice behavior, emphasizing AI’s potential to identify and classify these behaviors. Traditional methods struggle to capture subtle behavioral features, whereas AI can automatically extract quantitative features from large datasets. Consequently, this study aims to leverage AI to enhance the efficiency and accuracy of mice behavior analysis. The paper reviews various applications of mice behavior analysis, categorizes deep learning tasks based on an AI pyramid, and summarizes AI methods for addressing these tasks. The findings indicate that AI technologies are increasingly applied in mice behavior analysis, including disease detection, assessment of external stimuli effects, social behavior analysis, and neurobehavioral assessment. The selection of AI methods is crucial and must align with specific applications. Despite AI’s promising potential in mice behavior analysis, challenges such as insufficient datasets and benchmarks remain. Furthermore, there is a need for a more integrated AI platform, along with standardized datasets and benchmarks, to support these analyses and further advance AI-driven mice behavior analysis.

Precision Cut Lung Slices: Emerging Tools for Preclinical and Translational Lung Research. An Official American Thoracic Society Workshop Report.

The urgent need for effective treatments for acute and chronic lung diseases underscores the significance of developing innovative preclinical human research tools. The 2023 ATS Workshop on Precision Cut Lung Slices (PCLS) brought together 35 experts to discuss and address the role of human tissue-derived PCLS as a unique tool for target and drug discovery and validation in pulmonary medicine. With increasing interest and usage, along with advancements in methods and technology, there is a growing need for consensus on PCLS methodology and readouts. The current document recommends standard reporting criteria and emphasizes the requirement for careful collection and integration of clinical metadata. We further discuss current clinically relevant readouts that can be applied to PCLS and highlight recent developments and future steps for implementing novel technologies for PCLS modeling and analysis. The collection and correlation of clinical metadata and multiomic analysis will further advent the integration of this preclinical platform into patient endotyping and the development of tailored therapies for lung disease patients.

Improving the Effectiveness of a Stock Simulation Trading Course via Blockchain and Social Networking: A Taiwanese Study

Online courses in higher education became prevalent during the COVID-19 pandemic; however, their application requires technology to be fully integrated into the curriculum. This study explores the integration of a blockchain-based platform in a private online stock simulation trading course during the COVID-19 pandemic. Using a pre–post experimental design with 142 college students, it assessed learning behaviors and outcomes. Students collaborated with teaching assistants via LINE groups, fostering discussion and engagement. They received cryptocurrency rewards, which enhanced motivation and connected the course to their career goals. The findings suggest that combining blockchain and social networking is an effective approach to improving online education. This contributes to the literature on educational technology and online learning by exploring the integration of blockchain and social networking in higher education, specifically within the context of stock simulation trading courses, and demonstrates its impact on student motivation and learning outcomes.

Big Data, Big Insights: Leveraging Data Analytics to Unravel Cardiovascular Exposome Complexities.

The exposome encompasses the full range of environmental exposures throughout a person's lifetime and plays an important role in cardiovascular health. Interactions with the social, natural, and built components of the exposome significantly impact cardiovascular disease prevalence and mortality. Robust data analytics, including machine learning and geospatial analysis, have advanced our understanding of how these factors converge to influence cardiovascular disease risk. The integration of multiomics platforms and advanced computational approaches enhances our ability to characterize the exposome, leading to targeted public health interventions and innovative risk reduction strategies aimed at improving cardiovascular health globally. These multiomics platforms that integrate factors such as genomics, epigenomics, clinical data, social factors, environmental factors, and wearable technology will characterize the exposome in greater detail concerning cardiovascular health. In this review, we aimed to elucidate the components of the exposome and discuss recent literature regarding their relationship to cardiovascular health.

The Three Main Logic of the Reform of Catering Major in Higher Vocational Colleges Integrating Curriculum Ideological and Political Education under the Perspective of Industry-University-Research Collaborative Education

In the context of the deepening of vocational education reform and integration of industry and education in the new era, the ideological and political construction of catering courses in higher vocational colleges has become an important path to improve the quality of education and cultivate compound talents. This paper takes the significance of the reform of catering majors in higher vocational colleges that integrates ideological and political courses as the starting point. This paper discusses the problems of the disconnection between ideological and political education and professional education, the lack of a school-enterprise cooperation practice platform, and the urgent need to improve the teaching staff in the current ideological and political course construction. This paper proposes strategies such as strengthening the integration of ideological and political education and professional education, deepening the construction of the practice platform of integration of industry and education, and strengthening teacher training and teaching staff construction, which provides some references for promoting the comprehensive development of catering majors in higher vocational colleges in China.

Decoding Silence in Digital Cross-Cultural Communication: Overcoming Misunderstandings in Global Teams

This study investigates the role of silence in digital cross-cultural communication, emphasizing its potential for misinterpretation due to cultural differences and the absence of non-verbal cues. Silence, often perceived differently across high- and low-context cultures, plays a crucial role in conveying emotions and intentions. In digital spaces, the lack of immediate feedback and non-verbal richness increases the complexity of silence interpretation. This research employs a comprehensive literature review to explore how silence is used in virtual communication platforms like Zoom and Slack and its impact on global teams. The study analyzes various existing research on silence in intercultural communication and digital communication tools. Key findings reveal that silence in high-context cultures, such as Japan and China, is seen as reflective and respectful, while in low-context cultures like the United States, it is often interpreted negatively, leading to miscommunication. Furthermore, the absence of non-verbal cues in digital environments exacerbates these misunderstandings. The study also explores the potential of emerging technologies like artificial intelligence (AI) to mitigate these communication barriers by detecting and interpreting silence more effectively. The results underscore the need for cultural sensitivity in virtual teams and highlight the limitations of current AI tools in understanding cultural nuances. This research contributes to the ongoing discourse on improving intercultural communication in digital settings, offering practical implications for global teams and suggesting future directions for AI integration in digital communication platforms.

Perspectives Toward an Integrative Structural Biology Pipeline With Atomic Force Microscopy TopographicImages.

After the recent double revolutions in structural biology, which include the use of direct detectors for cryo-electron microscopy resulting in a significant improvement in the expected resolution of large macromolecule structures, and the advent of AlphaFold which allows for near-accurate prediction of any protein structures, the field of structural biology is now pursuing more ambitious targets, including several MDa assemblies. But complex target systems cannot be tackled using a single biophysical technique. The field of integrative structural biology has emerged as a global solution. The aim is to integrate data from multiple complementary techniques to produce a final three-dimensional model that cannot be obtained from any single technique. The absence of atomic force microscopy data from integrative structural biology platforms is not necessarily due to its nm resolution, as opposed to Å resolution for x-ray crystallography, nuclear magnetic resonance, or electron microscopy. Rather a significant issue was that the AFM topographic data lacked interpretability. Fortunately, with the introduction of the AFM-Assembly pipeline and other similar tools, it is now possible to integrate AFM topographic data into integrative modeling platforms. The advantages of single molecule techniques, such as AFM, include the ability to confirm experimentally any assembled molecular models or to produce alternative conformations that mimic the inherent flexibility of large proteins or complexes. The review begins with a brief overview of the historical developments of AFM data in structural biology, followed by an examination of the strengths and limitations of AFM imaging, which have hindered its integration into modern modeling platforms. This review discusses the correction and improvement of AFM topographic images, as well as the principles behind the AFM-Assembly pipeline. It also presents and discusses a series of challenges that need to be addressed in order to improve the incorporation of AFM data into integrative modeling platform.

METHODS OF DEVELOPMENT OF PROFESSIONAL MOBILITY OF STUDENTS STUDYING IN THE FIELD OF SPECIAL PEDAGOGY

The development of professional mobility among students in the field of special pedagogy is crucial for fostering adaptable, internationally-minded educators. This abstract explores various methods designed to enhance students' professional mobility, including experiential learning, international internships, cross-cultural communication training, and the integration of digital platforms for virtual exchange. These methods aim to equip students with the skills needed to engage in diverse educational environments, promoting an inclusive and globally competent approach to special education. By focusing on active learning and international collaboration, these strategies encourage the development of skills that are essential for educators in an increasingly interconnected world.

Convolutional neural network augmented soft-sensor for autonomous microfluidic production of uniform bubbles

Microfluidics has emerged as a foundational process for creating highly uniform emulsions and bubbles. To enable integration of microfluidic platforms into industrial processes, achieving precise control over the size uniformity of microfluidic-generated bubbles and emulsions is crucial. Even if the external variables such as flow rates or pressures are kept constant, microfluidic processes can be easily disturbed by unknown factors that would substantially compromise the uniformity of resulting emulsions and bubbles. In this study, we introduce a two-step soft-sensor approach that combines a convolutional neural network (CNN) and an image recognition algorithm for feature extraction to detect both the flow regime and the size and uniformity of resulting bubbles. By using a CNN to detect flow regimes, our controller is able to restore the bubble-producing flow regime in response to disturbances. Beyond self-recovery, our controller actively adjusts to minimize errors, maintain setpoints, mitigate disturbances, and ensure system stability over extended periods. 99.2% of bubbles produced during an 8-hour period remain within 5% of the setpoint with our controller acting. By leveraging the soft sensor and artificial intelligence-assisted feedback control, our work presents a widely applicable approach for precise and automated control of microfluidics in diverse applications.

Can an OTT platform develop brand advocates via customer engagement? An integration of platform and message-based factors

Can an OTT platform develop brand advocates via customer engagement? An integration of platform and message-based factors

National biodiversity data infrastructures: ten essential functions for science, policy, and practice

Abstract Today, at the international level, powerful data portals are available to biodiversity researchers and policymakers, offering increasingly robust computing and network capacities and capable data services for internationally agreed-on standards. These accelerate individual and complex workflows to map data-driven research processes or even to make them possible for the first time. At the national level, however, and alongside these international developments, national infrastructures are needed to take on tasks that cannot be easily funded or addressed internationally. To avoid gaps, as well as redundancies in the research landscape, national tasks and responsibilities must be clearly defined to align efforts with core priorities. In the present article, we outline 10 essential functions of national biodiversity data infrastructures. They serve as key providers, facilitators, mediators, and platforms for effective biodiversity data management, integration, and analysis that require national efforts to foster biodiversity science, policy, and practice.

Data-Driven Environmental Risk Management and Sustainability Analytics

This paper explores the intersection of data-driven approaches and environmental risk management, emphasizing the critical role of technology in enhancing sustainability. It provides a systematic review of current literature on public-private partnerships, data quality challenges, and innovative methodologies such as machine learning and Internet of Things (IoT) applications for environmental monitoring. Key themes include the integration of interoperable data platforms, the implications of big data on climate change, and the importance of fostering government policies that promote data sharing for sustainability initiatives. The analysis highlights best practices and recommendations for leveraging advanced analytics and remote sensing technologies to assess and mitigate environmental risks. Ultimately, this research underscores the necessity of collaborative efforts among stakeholders to develop effective strategies for sustainable resource management.

Biocompatible Metal-Organic Framework-Based Fabric Composite as an Efficient Personal Protective Equipment for Particulate Matter-Induced Pulmonary Injury.

Efficient personal protection has emerged as a crucial approach for reducing pulmonary injury induced by particulate matter (PM). However, current personal protective equipments usually lack essential biosafety concerns and fail to own adsorbing/antioxidant/antibacterial function together, making it a challenge to develop an integrated platform with the above characteristics. Herein, a facile oxygen-free hydrothermal strategy is proposed to synthesize new copper-based metal-organic frameworks, Cu-HHTPs, (HHTP: 2,3,6,7,10,11-hexahydroxytriphenylene), with great adsorbing/antioxidant/antibacterial activity and high biosafety. The Cu-HHTPs can serve as an efficient additive incorporated with various fabrics including cellulose acetate (CA) membrane to achieve novel fabric composites, such as CA@Cu-HHTPs, with ideal scavenging outcome for the main components of PM. Evidenced by the animal experiments, CA@Cu-HHTPs can highly mitigate PM-induced adverse effects via adsorbing PM, scavenging ROS, and killing bacteria, leading to a significant reduction in lung permeability, inflammation and oxidative stress, and pulmonary infection. Last but not least, a two-week exposure of CA@Cu-HHTPs exhibits no obvious damage toward the animals by examining their long-term toxicity. Collectively, this study not only highlights the potential of Cu-HHTPs as attractive additives for the preparation of fabric composites, but also lays out a new concept toward the development of new-generation multifunctional personal protective equipment against PM.