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STREAMS guidelines: standards for technical reporting in environmental and host-associated microbiome studies
No full textThe interdisciplinary nature of microbiome research, coupled with the generation of complex multi-omics data, makes knowledge sharing challenging. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) guidelines provide a checklist for the reporting of study information, experimental design and analytical methods within a scientific manuscript on human microbiome research. Here, in this Consensus Statement, we present the standards for technical reporting in environmental and host-associated microbiome studies (STREAMS) guidelines. The guidelines expand on STORMS and include 67 items to support the reporting and review of environmental (for example, terrestrial, aquatic, atmospheric and engineered), synthetic and non-human host-associated microbiome studies in a standardized and machine-actionable manner. Based on input from 248 researchers spanning 28 countries, we provide detailed guidance, including comparisons with STORMS, and case studies that demonstrate the usage of the STREAMS guidelines. STREAMS, like STORMS, will be a living community resource updated by the Consortium with consensus-building input of the broader community
Smart Sensor Technologies
No full textIn the era of digital transformation and intelligent systems, smart sensors have emerged as a cornerstone technology, significantly enhancing the way data is collected, processed, and utilized across various industries. Traditional sensors were primarily responsible for converting physical stimuli-such as temperature, pressure, motion, light, or sound-into electrical signals for further processing. However, with increasing demands for more accurate, autonomous, and intelligent decision-making systems, traditional sensors have evolved into smart sensors-devices that combine sensing, signal processing, and communication capabilities within a single integrated package. Smart sensors represent a convergence of sensing elements, microprocessors, software algorithms, and communication technologies. These devices are designed not only to sense environmental parameters but also to analyze data locally, make decisions, and transmit processed information to other systems or devices, often in real time
Dimensional stability and bending properties of silver birch (Betula pendula Roth.) modified wood with sorbitol and different types of polycarboxylic acids
No full textWood modification with bio-based chemicals such as sorbitol–citric acid (SorCA) system is a low-cost and effective approach, while the low pH of the modifying agent reduces the mechanical properties of modified wood. Thus, this study was conducted to find alternative polycarboxylic acids with comparable functional groups, reactivity, and processing conditions. Silver beech wood (Betula pendula Roth.) was modified through in situ esterification approach using sorbitol in combination with citric acid (CA), tartaric acid (TA) or malic acid (MA). Wood samples were modified at 20% w/w of sorbitol–CA, –TA, and –MA at 1:3 molar ratio and cured at 140°C for 24 h. The SorCA modified samples showed the highest leaching resistance followed by SorMA and SorTA. The samples modified with SorTA and SorMA illustrated superior dimensional stability compared with SorCA. The modulus of elasticity (MOE) was slightly improved in all modified samples in comparison with unmodified birch. However, the modulus of rupture (MOR) was marginally decreased, particularly in the samples modified with SorCA and SorTA
Influence of gas-phase polycyclic aromatic hydrocarbons on molecular composition of α-pinene ozonolysis secondary organic aerosol particles
No full textPrevious studies have shown that the presence of gas-phase Polycyclic Aromatic Hydrocarbons (PAHs) during biogenic Secondary Organic Aerosol (SOA) formation can significantly enhance SOA formation, resulting in higher mass loadings, and alter properties of SOA particles. Compared to “pure” biogenic SOA, these particles have higher viscosity, lower bulk volatility, and are composed of a higher fraction of low-volatility compounds, i.e., oligomers. The in-situ aerosol mass spectrometry of α-pinene ozonolysis SOA particles indicates that while PAHs significantly enhance oligomer formation, the “extra mass” is dominated by α-pinene oxidation products. To understand the potential chemical mechanisms responsible for the increased SOA formation yields and formation of oligomers we utilized an ultrahigh resolution Orbitrap Elite mass spectrometer (resolving power 240,000 @ m/z 400). Multiple ionization methods were used to provide a comprehensive perspective of the molecular composition. Here, we present the differences in molecular composition resulting from the presence of three different PAHs during α-pinene SOA formation compared to a sample of pure α-pinene SOA. First, we report the presence of unique α-pinene + pyrene oligomers potentially responsible for low particle volatility observed in previous studies. We also report low-oxygen α-pinene oligomers only observable using atmospheric pressure photoionization. These oligomers are seemingly enhanced by the presence of benzo[a]anthracene, suggesting a potential role for certain PAHs in their formation. These results imply that our understanding of the oxidative reaction pathways may be incomplete, especially with regard to the interaction of biogenic and anthropogenic emissions. Copyright © 2025 American Association for Aerosol Research
Foreign institutional investors and corporate labor investment efficiency
No full textPurpose – This article examines the relationship between foreign institutional ownership and firms’ labor investment efficiency. Design/methodology/approach – A series of regression models are performed to examines the research question. Findings – We find that foreign institutional investors enhance firms’ labor investment outcomes primarily through mitigating asymmetric information and by strengthening internal governance. Specifically, the influence of foreign institutional investors on a firm’s labor investment efficiency is stronger when the firm faces greater labor adjustment frictions. This effect is more evident when foreign institutional investors are originated from countries or areas with stronger cultural connections to China, stronger governance quality, common law traditions or stronger bargaining power in the firms’ governance. Practical implications – Pragmatically, our research implies that opening capital markets to foreign investment, particularly by introducing institutional investors from other countries or regions, can help domestic firms to achieve higher efficiency of human resource allocation. Another implication of our research could be that foreign institutional investors, as one type of institutional investors, do play a significant role in improving the efficiency of firms’ human resource allocation. This is because, aside from the common effects of large shareholders in participating in corporate governance, the foreign institutional investors may bring about other things, such as the industrial practices prevalent in the setting of the common law system. Originality/value – The potential contributions of this paper are twofold. First, this paper documents the monitoring role of FIIs from a new perspective: firms’ labor investment decisions. Second, this paper enriches the literature on the determinants of firms’ labor investment decisions
An Uncertainty-aware Decision Support System: Integrating text narratives and conformal prediction for trustworthy accident code classification
No full textIt is imperative to assign accident classification codes to the Mine Safety and Health Administration (MSHA) accident data for effective data analysis and risk assessment. Although trained personnel are capable of performing this task, the manual process is both time-consuming and resource-intensive. Automating the classification process with machine learning (ML) algorithms promises to expedite code assignment. However, ML predictions typically lack uncertainty metrics. This study proposes an uncertainty-aware hierarchical classification framework that assists human experts in efficiently and accurately assigning accident codes. Several text representation techniques combined with different ML algorithms were employed within a hierarchical architecture to assign classification codes. Low-frequency codes were consolidated into a single category, with a primary classifier distinguishing between these and a secondary classifier further classifying the grouped categories. Regularized Adaptive Prediction Sets (RAPS) was integrated to quantify uncertainty. Highly confident predictions yielding single-class sets were automatically classified, whereas multi-class sets were flagged for manual review. Primary Classifier with XGBoost with word2vec text representation achieved the best performance, with 95.12% coverage, 37.02% single-class prediction sets at 96.11% accuracy, and an average prediction set size of 2.39. Whereas the secondary classifier, a logistic regression model with TF-IDF representation, yielded 96.19% coverage, an average set size of 1.80, and 53.66% single-class prediction sets with 98.90% accuracy. Additionally, sensitivity analysis determined that a 95% coverage guarantee offers the best trade-off between prediction set size and coverage. The framework effectively integrates conformal prediction to quantify uncertainty and aid human experts in improving the decision-making process in safety management. Although the framework is broadly applicable across different sectors, it needs to be retrained on domain-specific data for effective use
SLUG/ANNULAR FLOW BOILING HEAT TRANSFER EVENTS IN SMOOTH AND STRUCTURED RECTANGULAR MICROCHANNELS
No full textSince the introduction of transistors, integrated circuits, and silicon packages, the pursuit of higher computational power has driven the electronics industry toward continually shrinking chip sizes and increased power densities. As a result, advanced thermal management has become essential, especially as conventional air and single-phase liquid cooling methods approach their operational limits. Air cooling typically supports heat fluxes of 50–100 W/cm², while liquid cooling can manage up to 800 W/cm². However, with the rise of supercomputers and AI systems, next-generation devices now generate heat fluxes exceeding 1,000 W/cm². This trend makes two-phase flow cooling, particularly flow boiling in microchannels, a compelling solution for dissipating extreme heat within limited surface areas. Among two-phase regimes, slug and annular flow boiling are especially beneficial, as the formation of thin evaporating liquid films produces exceptionally high local heat transfer coefficients. These thin films enable superior heat flux performance compared to other boiling regimes, making them ideal for microscale high heat flux applications. Despite these advantages, challenges remain, such as the risk of dry-out zones as vapor slugs or annular regions expand, and the need to optimize liquid film thickness to maximize overall heat transfer efficiency.
This dissertation introduces novel methods to improve slug flow boiling heat transfer with FC-72 in high heat flux rectangular microchannels. It focuses on reducing dry-out by enhancing the wicking effect, which draws liquid into dry zones, and by optimizing liquid film thickness. Both the extension of liquid film length and the optimization of film thickness are extensively examined to maximize heat transfer in vapor slug and annular flow regimes.
A robust mathematical framework was established to develop an advanced evaporation phase change CFD model using Fluent and custom UDF code. The model, validated with experimental data, accurately delineated the liquid-vapor interface and calculated phase change rates based on mesh element volume fractions. This approach enabled dynamic tracking of vapor evolution, and benchmarking confirmed the CFD model’s reliability for further research stages.
A numerical study investigated how channel width and height affect slug and annular flow boiling in rectangular microchannels. Channel geometry strongly influences local heat flux, with four primary heat transfer mechanisms identified: microlayer evaporation, interline evaporation, transient conduction, and micro-convection. Simulations of five geometries revealed that wider channels form larger thin liquid films at the base, resulting in higher average heat fluxes during slug flow boiling. Measurements showed that wider channels support more evaporation, especially at interfaces with pronounced microlayer convection and interline evaporation, whereas increasing channel height reduces centerline heat flux. The analysis found that film thickness at the centerline is stable, but the bubble tip and rear regions become single-phase conduction zones with thicker films. Overall, wider channels outperform taller ones by sustaining higher heat transfer rates due to thinner average liquid films, offering practical insights for designing microchannels in phase change and boiling applications.
Numerical simulations were performed to optimize wicking and heat transfer in textured microchannels during slug flow boiling, using square pillar textures on the channel base. The study explored pillar spacing, pillar height, and base temperature. Increased pillar spacing enhanced wicking and reduced dry-out, but too much spacing weakened capillary forces. Taller pillars enhanced capillary action and liquid delivery to dry-out zones, although thicker films reduced heat transfer. Higher base temperatures accelerated evaporation but could shorten the liquid film and limit overall heat transfer. The research identified optimal pillar geometry and base temperature combinations for maximizing heat transfer, with base temperature being the most influential factor and channel height having minimal effect on wicking efficiency.
The results of this dissertation show significant advancement in microchannel slug flow boiling, from smooth to textured channel geometries as examined through detailed CFD simulations. Further research is necessary to identify boundary conditions that could further enhance heat transfer in slug flow boiling within textured channels
Advancing Intelligent Transportation through Data-Driven Traffic Monitoring, Real-Time Forecasting, and Stability-Aware Control Strategies
No full textIntelligent Transportation Systems (ITS) are increasingly shifting toward data-driven and artificial intelligence-based frameworks that integrate sensing, prediction, and control to enhance mobility efficiency, safety, and adaptability. This dissertation contributes to the development of next-generation ITS through three complementary studies focusing on flexible traffic monitoring, real-time traffic forecasting, and stability-aware vehicle control. The first study develops a multi-drone framework for wide-view corridor traffic analysis and origin-destination (OD) estimation. By coordinating multiple unmanned aerial vehicles, as demonstrated with two drones in this study, the framework enables continuous vehicle tracking across extended corridors. A deep learning-based pipeline combining vehicle detection and tracking automatically reconstructs vehicle trajectories and matches them across drone views. Field experiments conducted at a freeway weaving section achieved over 91% accuracy in OD flow estimation compared with ground-truth data, demonstrating the framework’s effectiveness for scalable and high-resolution traffic monitoring. The second study proposes a day-specific spatial-temporal graph convolutional network (Day-STGCN) for real-time traffic forecasting. Building upon the classical STGCN architecture, the model integrates rolling historical time-dependent features with day-type categorization to capture both short-term variations and long-term recurring patterns. Using real-world sensor data, the model significantly improves prediction accuracy over baseline graph neural networks, providing robust day-aware forecasting that reflects realistic daily traffic dynamics. The third study investigates cooperative adaptive cruise control (CACC) and its effects on traffic string stability under varying market penetration rates of connected and automated vehicles (CAVs). Analytical derivations establish explicit stability conditions linking controller parameters, time headway, and control intervals in both homogeneous and heterogeneous traffic flows. The results reveal that appropriately designed CACC controllers enhance string stability even in mixed environments with different types of vehicles. Collectively, these studies present an integrated framework that advances ITS through data-driven sensing, learning, and control, offering theoretical and practical foundations for more intelligent, responsive, and stable transportation systems
MATERIAL OPTIMIZATION OF THERMIONIC EMITTERS FOR OPERATION IN OXYGEN-CONTAINING ATMOSPHERES
No full textCurrently, no viable emitter materials exist for Hall-effect thrusters used in air-breathing electric propulsion systems (ABEP) operating in very low earth orbit (VLEO). The atmosphere at VLEO is primarily composed of atomic oxygen and nitrogen, with the oxygen having a catastrophic effect on emitter stability, leading to rapid failure. LaB6 is a rare-earth hexaboride emitter used in Hall-effect thrusters propelled by xenon or krypton and has better oxygen resistance than other common emitter materials. Modeling by others has suggested that optimizing mixed rare-earth hexaboride emitters may have potential for VLEO thrusters. Binary and mixed rare-earth hexaborides were synthesized based on their ability to form oxide films that will fail in tension as predicted by models utilizing the Pilling-Bedworth ratio and Ellingham diagrams. Testing conducted by the sponsor, Aerojet Rocketdyne, produced emitters with an order of magnitude higher operating lifetimes, but with accelerated material consumption. Even with improved lifetimes, further work is needed to design a propulsion system to meet the design requirements
Generative AI in Construction: Emerging Trends and Use Cases
No full textThe construction industry is beginning to explore generative artificial intelligence (AI) technologies to address complex data management challenges and inefficiencies within traditional workflows, particularly in design, planning, and project management. Although there is growing interest in generative AI in construction, the understanding of its trends and opportunities remains scattered. This paper investigates the current trends, applications, and research needs for generative AI in construction through a bibliometric analysis of 148 publications from Scopus and Google Scholar. The keyword co-occurrence map highlights several search clusters, with a strong focus on Building Information Modeling (BIM), digital twins, sustainability, construction safety, structural and architectural design, education, and project management. Generative AI technologies like Generative Adversarial Networks (GANs), large language models (LLMs), Generative Pre-trained Transformer (GPT), and diffusion models play critical roles in this research area. By pinpointing key research gaps, it underscores avenues for future investigation and empowers researchers to expand and refine the latest advancements in this dynamic field, propelling progress toward a more efficient, resilient, and sustainable built environment