Real-world Conditions Research Articles

Improved real-world UHPLC-MS/MS iohexol analysis; thawed samples, improved calibration curve, and additional matrices.

Plasma clearance of iohexol is used to measure glomerular filtration rate, for which a UHPLC-MS/MS analytical method was previously developed. In real-world conditions, samples may be thawed on arrival and sampled in unvalidated matrices, prompting the need for an improved validation. We aim to improve the method for iohexol determination in plasma with enhanced stability testing, optimized calibration curves, and partial validation in additional matrices. Stability testing was conducted up to 9 weeks at room temperature, 4°C, 37°C, and at 37°C with daily two-hour exposure to 55°C. Quintuplicate QC samples were analyzed on 3 days, comparing results from eight-point and two-point calibration curves. Matrix comparison was performed on quintuplicate QC samples in serum, heparin plasma, urine, EDTA whole blood, and heparin whole blood. The method improvements were all compliant with the requirements for bioanalytical methods issued by the US FDA and European Medicines Agency. Human EDTA plasma samples can be stored up to 9 weeks at room temperature, 4°C, 37°C, and 37°C with 55°C daily temperature spikes. The samples can be analyzed using a two-point calibration curve and are partial validated for serum-, heparin plasma-, urine-, EDTA whole blood-, and lithium whole blood iohexol samples.

Enhancing Dynamic Control and Stability Assessment of Cessna 172 Aircraft with a PID Controller for New Pilot Trainees

Abstract This research proposes a novel approach to enhance the dynamic control and stability assessment of the Cessna 172 aircraft for new pilot trainees by incorporating a proportional–integral–derivative (PID) controller. The PID controller is designed to improve the aircraft’s responsiveness to control inputs, reduce overshoot and settling time, and enhance overall stability. The study involves developing a mathematical model of the Cessna 172’s longitudinal dynamics, designing a PID controller, and conducting simulations to evaluate the performance of the PID-controlled aircraft. The evaluation focuses on key metrics such as stability, responsiveness, overshoot, and settling time. The results of the study demonstrate that the PID controller effectively enhances the dynamic control and stability of the Cessna 172, providing new pilot trainees with a safer and more efficient learning experience. The PID controller’s ability to mitigate the effects of pilot errors and disturbances contributes to improved flight performance and reduced risk of accidents. Future research directions include exploring the use of adaptive PID controllers, integrating PID controllers with other advanced flight control systems, and conducting flight tests to validate the performance of the PID-controlled Cessna 172 in real-world conditions.

Solar Stirling for Renewable Energy Multigeneration Systems

This study explores the feasibility and potential of integrating dish–Stirling systems (DSSs) into multigeneration energy systems, focusing on their ability to produce both thermal and electrical energy. By leveraging the concentrated solar power capabilities of DSSs, this research examines their performance relative to alternative solutions such as photovoltaic (PV) systems and solar heating. A 25 kW Stirling Energy Systems (SES) DSS served as the basis for the analysis. Simulations were performed for local 2022 weather conditions in Germany. The study employed a detailed modeling approach using the NREL System Advisor Model (SAM) to quantify the energy outputs and evaluate the system efficiencies. The results indicate that the DSS achieved an electrical efficiency of 25% and a combined efficiency of 78% when accounting for the maximum thermal energy generated. Seasonal analysis highlights the adaptability to fluctuating energy demands, with advantages in winter heating applications. Comparative evaluations revealed DSSs as a viable cogeneration alternative to standalone PV systems and solar heaters, offering reduced environmental impacts and enhanced energy efficiency. Future work will address real-world operational conditions, including thermal storage and multigeneration integration, positioning the DSS as a sustainable solution for renewable energy generation.

Advancing moisture management in activewear: a novel dynamic testing approach for dynamic breathability of sportswear

This study presents a novel dynamic hot plate test for examining the exothermic and endothermic behaviors of textiles, emphasizing the dynamic conditions akin to physical activities. The method surpasses previous standards, for example ISO 16533 and the dynamic hot plate test developed by Naylor, by integrating sweating and wind simulations, thus offering a more precise assessment of fabric performance and physical sensation while wearing activewear. The investigation involved five fabric samples made from four different fiber types: 100% Merino wool, 100% polyester, 100% cotton, and 100% viscose.. The experimental setup, consisting of a 90-minute test with relaxation, activity, and drying phases, highlighted significant fabric-specific responses. Notably, polyester exhibited the highest heat loss rate at 388 W/m² during the activity phase, underscoring its potential for moisture management and cooling. Conversely, Merino wool showed the lowest heat loss rate at 212 W/m², suggesting a more stable microclimate. The dynamic conditions induced marked changes in microclimate temperature, humidity, and fabric surface temperatures, particularly noticeable when wind was introduced. These findings emphasized the importance of dynamic testing in evaluating activewear fabrics, suggesting that materials such as polyester offer excellent cooling and moisture management, whereas natural fibers such as Merino wool may enhance wearer comfort by maintaining a more consistent microclimate. This research contributes to refining fabric performance testing standards by emphasizing the significance of real-world dynamic conditions.

Biosorption of Cr (III) from Polluted Water Using Pennisetum clandestinum Hochst (Kikuyo)

Given the abundance of kikuyu biomass resulting from the pruning of green areas, the aim of this study was to evaluate its use as a biosorbent (BK) for Cr (III) removal from polluted waters. The biomass was activated using H2SO4 (1.25%) and NaOH (3.25%). The characterization methods were Fourier-Transform Infrared Spectroscopy (FTIR), scanning electron microscopy/energy-dispersive spectroscopy (SEM/EDS), and Brunauer–Emmett–Teller (BET) analysis. Our results confirmed the presence of active groups on BK, such as –OH, -C=C-, -C=O, and -C-O-, with an increase of 1308.58% in specific surface area, as well as the presence of chromium on the biosorbent after adsorption process. The adsorption capacity (q) was tested in a jar test as a function of biomass granulometry, dose (BK), and the pH of the solution; the best response was 47.9 mg/g at a pH of 5.5, a biosorbent dose of 0.5 g/L, and a biosorbent size of 100 μm. The effect of pH was positive; by increasing the pH, the adsorption capacity increased. However, the effect of the biosorbent dose and size was negative, as when increasing the dose and granulometry, the adsorption capacity decreased. In addition, the kinetic process was studied, where the removal data were better fitted for the pseudo-second-order kinetic model, confirming that the adsorption mechanism was chemisorption. The adsorption capacity was 37.6 mg/g for industrial wastewater. The possibility of using kikuyu within the circular economy was demonstrated and suggests its application in continuous systems for real-world environmental conditions.

Electrochemical Direct Lithium Extraction: A Review of Electrodialysis and Capacitive Deionization Technologies

The rapid expansion of lithium-ion battery (LIB) markets for electric vehicles and renewable energy storage has exponentially increased lithium demand, driving research into sustainable extraction methods. Traditional lithium recovery from brine using evaporation ponds is resource intensive, consuming vast amounts of water and causing severe environmental issues. In response, Direct Lithium Extraction (DLE) technologies have emerged as more efficient, eco-friendly alternatives. This review explores two promising electrochemical DLE methods: Electrodialysis (ED) and Capacitive Deionization (CDI). ED employs ion-exchange membranes (IEMs), such as cation exchange membranes, to selectively transport lithium ions from sources like brine and seawater and achieves high recovery rates. IEMs utilize chemical and structural properties to enhance the selectivity of Li+ over competing ions like Mg2+ and Na+. However, ED faces challenges such as high energy consumption, membrane fouling, and reduced efficiency in ion-rich solutions. CDI uses electrostatic forces to adsorb lithium ions onto electrodes, offering low energy consumption and adaptability to varying lithium concentrations. Advanced variants, such as Membrane Capacitive Deionization (MCDI) and Flow Capacitive Deionization (FCDI), enhance ion selectivity and enable continuous operation. MCDI incorporates IEMs to reduce co-ion interference effects, while FCDI utilizes liquid electrodes to enhance scalability and operational flexibility. Advancements in electrode materials remain crucial to enhance selectivity and efficiency. Validating these methods at the pilot scale is crucial for assessing performance, scalability, and economic feasibility under real-world conditions. Future research should focus on reducing operational costs, developing more durable and selective electrodes, and creating integrated systems to enhance overall efficiency. By addressing these challenges, DLE technologies can provide sustainable solutions for lithium resource management, minimize environmental impact, and support a low-carbon future.

Hardware-in-the-Loop testing for low-inertia grids

Abstract Extensive testing of various control algorithms using comprehensive testing frameworks enables us to assess their effectiveness in managing system dynamics and maintaining grid stability. In order to validate the algorithms in the real world, testing of the developed applications in an empirical laboratory environment is essential and the testing parameters in the laboratory have to closely resemble real-world conditions. The fundamental aspect of such testing is to monitor and analyze the dynamics and interaction between the various components in synchronous mode, thereby assessing the system’s ability to operate the grid without violations. The experimental test setup and script-based testing explained in this paper helps to develop innovative control algorithms, with a key focus on medium voltage and low voltage grids with low rotating masses. The test platform makes extended use of a Typhoon Real-Time Simulator (RTS). Firstly, it is used for the simulation of the distributed energy resources and the overlying distribution grid as well as the implementation of control algorithms. Secondly, the RTS acts as a control and SCADA system for the whole testing platform. While the physical components provide measurements in regular intervals, the control algorithms and the energy management system are implemented directly in the Typhoon platform. The data acquisition is performed either via programmable logic controllers for the nodal measurements or through faster measurements such as current transformers. This ensures that the developed algorithms are empirically tested and optimized in close to real-world conditions. The control algorithms provide the set points for the individual components, and the results show the advantages of script-based testing in HIL testing to ensure deterministic and reliable operation of innovative grid components.

Activatable Photosensitizers: From Fundamental Principles to Advanced Designs.

Photodynamic therapy (PDT) is a promising treatment that uses light to excite photosensitizers in target tissue, producing reactive oxygen species for localized cell death. It is recognized as a minimally invasive, clinically approved cancer therapy with additional preclinical applications in arthritis, atherosclerosis, and infection control. A hallmark of ideal PDT is delivering disease-specific cytotoxicity while sparing healthy tissue. However, conventional photosensitizers often suffer from non-specific photoactivation, causing off-target toxicity. Activatable photosensitizers have emerged as more precise alternatives, offering controlled activation. Unlike traditional photosensitizers, they remain inert and photoinactive during circulation and off-target accumulation, minimizing collateral damage. These photosensitizers are designed to "turn on" in response to disease-specific biostimuli, enhancing therapeutic selectivity and reducing off-target effects. This review explores the principles of activatable photosensitizers, including quenching mechanisms stemming from activatable fluorescent probe and applied to activatable photosensitizers (RET, PeT, ICT, ACQ, AIE), as well as pathological biostimuli (pH, enzymes, redox conditions, cellular internalization), and bioresponsive constructs enabling quenching and activation. We also provide a critical assessment of unresolved challenges in aPS development, including limitations in targeting precision, selectivity under real-world conditions, and persistent issues and potential solutions (dual-lock, targeting moieties, biorthogonal chemistry and artificial receptors).

Forecasting the efficiency of agile projects using machine learning

The aim of the research is to address current challenges in the development of informa-tion systems and the integration of artificial intelligence technologies to optimize Agile meth-odologies. The study focuses on modeling and forecasting the efficiency of Agile processes, taking into account changing requirements over time and random deviations. Forecasting is understood as the process of estimating the future efficiency of Agile processes based on the analysis of time series and machine learning models. Efficiency forecasting is considered as time series forecasting, where parameters adapt depending on the system and data, allowing the model to generalize and take into account various factors affecting project outcomes. The study examines the selection of the optimal model for forecasting Agile process effi-ciency by comparing machine learning methods such as Support Vector Machines (SVM), Random Forest, and neural networks (CNN, LSTM). Comparative analysis showed that neu-ral networks (LSTM, Bidirectional LSTM, CNN) are more effective for predicting project suc-cess, demonstrating high accuracy and lower errors. The practical value of the research lies in identifying LSTM and other neural network architectures as effective tools for predicting project success. This can serve as a guideline for implementing effective management systems in real-world conditions. For example, the application of LSTM in large IT companies for forecasting the success of Agile projects has significantly improved planning accuracy and reduced risks. The research confirms that the integration of machine learning models, particularly LSTM and CNN, significantly improves the accuracy of forecasting and managing Agile pro-jects. The application of these technologies can greatly enhance project management effi-ciency in information systems.

Data-Driven Clustering of Plantar Thermal Patterns in Healthy Individuals: An Insole-Based Approach to Foot Health Monitoring

Monitoring plantar foot temperatures is essential for assessing foot health, particularly in individuals with diabetes at increased risk of complications. Traditional thermographic imaging measures foot temperatures in unshod individuals lying down, which may not reflect thermal characteristics of feet in shod, active, real-world conditions. These controlled settings limit understanding of dynamic foot temperatures during daily activities. Recent advancements in wearable technology, such as insole-based sensors, overcome these limitations by enabling continuous temperature monitoring. This study leverages a data-driven clustering approach, independent of pre-selected foot regions or models like the angiosome concept, to explore normative thermal patterns in shod feet with insole-based sensors. Data were collected from 27 healthy participants using insoles embedded with 21 temperature sensors. The data were analysed using clustering algorithms, including k-means, fuzzy c-means, OPTICS, and hierarchical clustering. The clustering algorithms showed a high degree of similarity, with variations primarily influenced by clustering granularity. Six primary thermal patterns were identified, with the “butterfly pattern” (elevated medial arch temperatures) predominant, representing 51.5% of the dataset, aligning with findings in thermographic studies. Other patterns, like the “medial arch + metatarsal area” pattern, were also observed, highlighting diverse yet consistent thermal distributions. This study shows that while normative thermal patterns observed in thermographic imaging are reflected in insole data, the temperature distribution within the shoe may better represent foot behaviour during everyday activities, particularly when enclosed in a shoe. Unlike thermal imaging, the proposed in-shoe system offers the potential to capture dynamic thermal variations during ambulatory activities, enabling richer insights into foot health in real-world conditions.

Summer urban synergistic effects of anthropogenic pollutants and low-molecular-weight biogenic volatile organic compounds on secondary organic aerosol presented by PM1.

Biogenic volatile organic compounds (BVOCs) are emitted by urban vegetation and can interact with anthropogenic pollutants to generate secondary organic aerosols (SOA) that are atmospheric pollutants in urban environments. In urban forests, SOA comprise up to 90% of all fine aerosols (particulate matter smaller than 1μm [PM1]) in the summer. PM1 can greatly affect urban air quality and public health. The formation of SOA is affected by both environmental conditions and the presence of light BVOCs-predominantly isoprene, pentene, butene, and 1,3-butadiene. These factors exhibit complex interactions and nonlinear relationships. In this study, high-frequency field observations were conducted in two urban forest sites in Shanghai during the summers of 2022 and 2023. Data were collected regarding the concentrations of light BVOCs; SOA; and the anthropogenic pollutants NOx, O3, and SO2, as well as solar radiation, temperature, and humidity. A model was developed to identify the synergistic effects of anthropogenic pollutants, meteorological factors, and BVOCs on SOA concentrations. Increases in short-term SOA concentrations were most strongly correlated with O3, which had a synergistic effect alongside NOx. The empirical analysis indicated that 0.144-0.585μg/m3 SOA is produced per μg/m3 of urban BVOCs but can be augmented by 0.072-0.491μg/m3 in the presence of O3, NOx, and SO2. However, long-term feedback mechanisms in urban forests contribute to the maintenance of stable SOA concentrations. The field data and models in this study provide a scientific basis for regulating atmospheric pollutants in urban forests under real-world conditions and offer intuitive and straightforward solutions for managing complex urban air pollution. Synopsis: Anthropogenic pollutants NOx, O3, and SO2 boost BVOCs' SOA production in summer urban forests. Short-term, pollutants are restrictive factors in SOA generation, but long-term forest SOA production exhibits negative feedback.

Light-enhanced Ion Transport and Fouling Resistance Properties of Metal/Semiconductor Heterojunction Nanochannel Membranes for Osmotic Energy Recovery in Real-world Conditions

Light-enhanced Ion Transport and Fouling Resistance Properties of Metal/Semiconductor Heterojunction Nanochannel Membranes for Osmotic Energy Recovery in Real-world Conditions

Abstract 22: Implementation of increased physical therapy intensity for improving walking across inpatient stroke rehabilitation units: Primary results of the Walk ‘n Watch multi-site stepped-wedge cluster randomized controlled trial

Introduction: Though clinical practice guidelines support high repetitions of walking after stroke, practice is slow to change with low levels of walking activity in stroke rehabilitation units. We undertook an implementation trial to change practice; we enabled entire stroke units to use the Walk ‘n Watch protocol and determined the effect of implementation on the 6 Minute Walk Test (6MWT) at hospital discharge. Methods: This 12-site clinical trial across 7 Canadian provinces used a stepped-wedge cluster design to randomize when each site switched from Usual Care to the Walk ‘n Watch protocol. At the start of the Walk ‘n Watch phase, we trained all front-line physical therapists on the unit with training workshops, manuals, hands-on practice, and videos. Each site was provided onboarding materials to address staff changes so therapists who did not attend the initial training could adopt the protocol. Each site also identified a ‘protocol champion’ to facilitate initial weekly huddles with therapists to discuss barriers to implementation. Therapists were trained to complete safety screening and to determine eligibility. The Walk ‘n Watch protocol focused on completing a minimum of 30-minutes of daily weight-bearing, walking-related activities that progressively increased in intensity informed by activity trackers measuring heart rate and step number. Blinded assessors completed the outcomes at baseline and 4-weeks later (near discharge). Primary analysis used a linear mixed-effects model adjusted for stratum, date of enrollment, age, sex and baseline 6MWT. Results: The total number of participants was 306 (162 Usual Care, 144 Walk ‘n Watch, 188 males/118 females) with a mean(SD) age of 68(13), 29(17) days since stroke, and a baseline 6MWT of 152(106) m. The improvement on the 6MWT was 43.6m (95%CI 12.7, 76.1) greater in the Walk ‘n Watch group compared to the Usual Care group. Further, the Walk ‘n Watch group improved quality of life (EQ5D), balance and mobility (Short Physical Performance Battery) and gait speed. Conclusions: The implementation trial design enabled the protocol to be tested under real-world conditions, involving all therapists on each unit to deliver the protocol. The trial had a deliberate aim to facilitate changes in practice that resulted in clinically meaningful improvements in walking and quality of life.

Modeling condensation of Methane-Water vapor mixtures in the Hedbäck Nozzle: A comparative study of 2D and 3D geometries

Modeling condensation of Methane-Water vapor mixtures in the Hedbäck Nozzle: A comparative study of 2D and 3D geometries

Vibration dataset of main journal bearings in internal combustion engines under diverse climatic and varying operating conditions.

The dataset includes vibration signatures from both healthy and faulty main journal bearings of an internal combustion engine, captured with a tri-axial accelerometer mounted on the bearings' housing. The engine was exposed to various climatic and operating conditions, including variations in temperature and humidity, and tested at different engine rotation speeds, following a standard, MIL-STD-810G. The data was collected in a state-of-the-art climatic and vibration chamber to simulate real-world environmental conditions. The dataset comprises more than 500 files, each recorded under a range of climatic and operational conditions, with root mean square (RMS) values included. This dataset provides a valuable and reliable benchmark for researchers and practitioners to validate their diagnostic algorithms and models against real engine conditions, as no such dataset is publicly available.

Preschool and Me: Educational-clinical linkage to improve health equity for children with developmental delays and disabilities from historically marginalized communities.

Societal and structural inequities have resulted in longstanding health care disparities among Black, Latino/a/e, and low-income preschool children with developmental delays and disabilities (PCw/DD), depriving them of educational and therapeutic services that improve future academic, economic, and health outcomes. To address this issue, we developed Preschool and Me (PreM), a community-clinical linkage (CCL) implemented within healthcare settings serving historically marginalized communities. This novel CCL, an educational-medical linkage model, aims to increase access to school-based services for PCw/DD. Combining key components of CCLs with a personalized medical-education care plan and remote navigator support, PreM targets multiple levels of influence impacting access to school-based therapeutic and educational services. We will utilize a hybrid effectiveness-implementation approach in two models of real-world service delivery conditions. Participants (n=320) will be randomized to either 6 months of PreM or a waitlist control arm beginning the intervention after a 6-month delay. Our specific aims are to test the effectiveness of PreM on access to school-based services as well as health service outcomes; examine mediators of intervention effects using a mixed-methods approach; and explore social determinants of health as potential moderators. We will simultaneously conduct an implementation evaluation. The results of this study have the potential to support effective implementation of CCL models within pediatric clinical settings serving historically marginalized communities which can be utilized to improve health outcomes for families and their children with a range of health conditions.

Modernizing load and emission factors for construction machinery based on real-world operation: Estimation of emission data in Korea.

The emissions inventory for non-road mobile machinery (NRMM) in Korea relies on laboratory engine tests, which do not accurately reflect the real-world emissions from construction machinery. Hence, standardized real-world test conditions suitable for the Korean environment were first designed based on the manufacturer's testing methods and previous research. Next, using portable emissions measurement systems (PEMS), data on exhaust emissions and load factor (LF) were collected and analyzed during real-world tests on three types of construction machinery (excavators, loaders, and forklifts) with high registration rates and emissions in Korean environment. The improved LFs, which reflect real-world tests, were significantly lower than the existing LF value (0.48), with values of 0.38-0.5 for excavators, 0.3-0.44 for loaders and 0.26 for forklifts. The emission factors (EFs) were higher than those currently used in Korea's emissions inventory, namely, the Clean Air Policy Support System (CAPSS). Based on the results, emissions were calculated and compared using an emission calculation formula. The relationship between LFs and EFs was also investigated to address the limitations of previous studies that focused solely on EF measurements. The combined improvements in EFs+LFs can lead to slightly higher emissions inventories. The improved LFs and EFs based on the results of real-world tests reported herein can enhance the accuracy of estimating emissions from construction machinery in Korea.

An efficient photovoltaic system with strong control capabilities that significantly increases performance under complex real-world PV meteorological conditions

An efficient photovoltaic system with strong control capabilities that significantly increases performance under complex real-world PV meteorological conditions

Developmental programming: Sex-specific effects of prenatal exposure to a real-life mixture of environmental chemicals on liver function and transcriptome in sheep.

Humans are chronically exposed to a mixture of environmental chemicals (ECs), many with metabolic and endocrine disrupting potential, contributing to non-communicable disease burden. Understanding the effects of chronic exposure to low-level mixtures of ECs requires an animal model that reflects real-world conditions, lags behind studies on single ECs. Biosolids, from wastewater treatment, offers a real-life model to investigate the developmental health risks from EC mixtures. Prenatal biosolids exposure studies have documented metabolic perturbations including heavier thyroid glands in male fetuses and reduced bodyweight in prepubertal male lambs followed by catchup growth. We hypothesized that maternal preconceptional and gestational exposure of sheep to biosolids programs sex-specific transcriptional and functional changes in the offspring liver. Ewes (F0) were grazed on either inorganic fertilizer (C) or biosolids-treated pastures (BTP) preconception till parturition. All lambs (n=15/group with male n=7/group and females n=8/group) were raised on Control pastures until euthanasia at 9.5 weeks. Next generation sequencing of liver RNA and DESeq2 was used to identify exposure-specific differentially expressed genes (DEG) and sex-differentially expressed genes (SDG). Liver function was assessed with markers of oxidative stress, triglyceride and fibrosis markers. Control lambs exhibited 647 SDGs confirming the inherent sexual dimorphism in hepatic gene expression. A sex-stratified analysis identified 10 DEG, mostly affecting metabolism, in male and none in female lambs. Biosolids exposure diminished the sexual dimorphism in hepatic gene expression barring 41 genes, potentially due to the increase in androgenic steroids found in F0 maternal circulation. Additionally, BTP male lambs showed elevated plasma triglyceride and a trend towards increased liver triglyceride concentrations. The identified effects of prenatal exposure to low-dose mixture of ECs via biosolids, in a precocial species paralleling human developmental patterns holds translational importance for understanding the sexually dimorphic origin of non-communicable diseases.

Vertical two-phase flow regimes in an annulus image dataset - Texas A&M university.

The Vertical Two-Phase Flow Regimes in an annulus Image Dataset, generated at Texas A&M University, presents an extensive collection of high-resolution images capturing various gas-liquid two-phase flow dynamics within a vertical flow setup. This dataset results from meticulous experimental work in the 140 ft Tower Lab, utilizing a combination of water and air flows to simulate real-world conditions and employing high-quality video recordings to document flow regime transitions. Designed to support research in fluid dynamics, machine vision, and computational modeling, the dataset offers valuable resources for developing machine vision models for accurate regime detection and differentiation, enhancing the fidelity of computational fluid dynamics simulations, and facilitating the estimation of critical flow parameters. Despite its comprehensive nature, the dataset notes limitations such as the absence of annular flow regime images and its exclusive focus on vertical flow conditions.