Comparison Of Methods Research Articles

Comparison of Statistical Signal Detection Methods in Adverse Events Following Immunization - China, 2011-2015.

The current study aims to assess the performance of data mining techniques in detecting safety signals for adverse events following immunization (AEFI) using routinely obtained data in China. Four different methods for detecting vaccine safety signals were evaluated. The AEFI data from 2011 to 2015 was collected for our study. We analyzed the data using four different methods to detect signals: the proportional reporting ratio (PRR), reporting odds ratio (ROR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS). Each method was evaluated at 1-3 thresholds for positivity. To assess the performance of these methods, we used the published signal rates as gold standards to determine the sensitivity and specificity. The number of identified signals varied from 602 for PRR1 (with a threshold of 1) to 127 for MGPS1. When considering the common reactions as the reference standard, the sensitivity ranged from 0.9% for MGPS1/2 to 38.2% for PRR1/2, and the specificity ranged from 85.2% for PRR1 and ROR1 to 96.7% for MGPS1. When considering the rare reactions as the reference standard, PRR1, PRR2, ROR1, ROR2, and BCPNN exhibited the highest sensitivity (73.3%), while MGPS1 exhibited the highest specificity (96.9%). For common reactions, the sensitivities were modest and the specificities were high. For rare reactions, both the sensitivities and specificities were high. Our study provides valuable insights into the selection of signal detection methods and thresholds for AEFI data in China.

Numerical analysis and experimental comparison of temperature-compensation method for large length–diameter ratio thermal mass flowmeter

Thermal mass flowmeters (TMFs) are inherently sensitive to temperature fluctuations, which can result in inaccurate measurements. This study focuses on a novel TMF named the large length–diameter ratio thermal mass flowmeter (LLRTMF), which was previously proposed for its insensitivity to the distribution of radial flow velocity and implemented via a variable-speed, variable-temperature experimental setup. The power characteristics of the LLRTMF are investigated over velocity and temperature ranges of 1.00–6.00 m/s and 283.0–319.0 K, respectively. The experimental results indicate that the maximum relative error of the experimental power consumption is 21.96 %. Additionally, a full-size numerical model of the LLRTMF is developed to simulate its power characteristics across wider velocity and temperature ranges of 0.25–16.00 m/s and 283.0–353.0 K, respectively. Experimental data are used to verify the accuracy of the simulation model. Using the numerical model, an empirical correlation equation between power and velocity is derived and polynomially fitted to establish a temperature-compensation equation. The results of the temperature compensation equations and experiment were compared. The maximum relative error of the absolute value was 20.84 %, i.e., less than the maximum error in the results of the experiment due to temperature change, confirming the validity of the formula.

A fuzzy Gaussian process regression function approach for forecasting problem

A fuzzy regression function approach is a fuzzy inference system method whose rules cannot be determined based on expert opinion, unlike a classical fuzzy inference system. In a fuzzy regression function approach, an input matrix consists of memberships obtained by the fuzzy clustering method and lagged variables of the time series. In the fuzzy regression function approach, the output vector corresponding to this input matrix is also created and the parameter estimation for the method is carried out with the ordinary least square method. As it is known, the ordinary least square method assumes that the data are linear. In addition, although it is very useful to include a priori information describing the formation of the data in the model, in most cases this information is not available. It is also inappropriate to use a model that does not accurately characterize the data. However, it is not appropriate to estimate parameters for nonlinear data using the ordinary least square method. One of the methods to be used in such a situation is the Gaussian process regression method. While the parameters of a selected basis function are fitted in the ordinary least squares regression method, how all measured data are related is determined in the Gaussian process regression. Besides, Gaussian process regression is a Bayesian approach, it can provide uncertainty measurements on forecasts. In this study, a fuzzy Gaussian process regression function is proposed. The contribution of this paper is to propose a new fuzzy inference system that can be used to solve nonlinear data by proposing a fuzzy Gaussian process regression function. The performance of the newly proposed method is evaluated based on the closing values of the Bitcoin and Crude oil time series. The performance comparison of the proposed method is evaluated with many different forecasting methods and it is concluded that the proposed method has superior forecasting performance.

A novel impact indentation technique with dynamic calibration method for measurement of dynamic mechanical properties

High strain rates and continuous real-time data acquisition are difficult to balance in existing impact indentation techniques. Meanwhile, the inaccuracy of the testing P-h curves limits the development of quantitative applications for dynamic testing of mechanical properties at micro region. This article develops a momentum exchange impact indentation testing method, which extends the testing range of strain rate to above 105 s−1, and at the same time avoids secondary impact. Impact indentation tests are conducted on single-crystal aluminum specimens with impact velocity from 76.9 to 176 mm/s. Based on calibrated data, the comparison of calculation methods for dynamic hardness is carried out. The dynamic elastic modulus is obtained by means of the Oliver-Pharr method, which increases from 71.9 to 89.4 GPa with increasing impact velocity. A multi-dimensional data analysis method is proposed based on the obtained experimental data to quantitatively study the damping and stiffness characteristics of materials. This testing method is particularly useful for quantitatively obtaining the micro region dynamic mechanical properties of solid materials at high strain rates.

Comparison of Classical and Green RPLC Methods in the Determination of pKa Values of Some Piperazine Group Antihistamines.

In this study, protonation constant values and liquid chromatographic behaviors of hydrophobic cyclizine, chlorcyclizine, hydroxyzine, cinnarizine, cetirizine, meclizine, and buclizine in some water-organic solvent binary mixtures were examined for the first time using classical and green reverse phase liquid chromatography methods. In the isocratic study, the relationship of the retention time and mobile phase pH in water-organic solvent binary mixtures containing acetonitrile (45, 50, 55, 60, 65, v/v), methanol (60, 65, 70, 75, v/v) and ethanol (45, 50, 55, 56, 59, 60, 62, 65, v/v) were determined at 37 °C. In the study, XBridge C18 and Gemini NX C18 columns suitable for the chemical properties of basic compounds were used. The obtained liquid chromatographic data were analyzed using the linear solvation energy relationship methodology and the SOLVER program. The aqueous protonation constant values of the investigated compounds were calculated using the linear relationship between the protonation constant data calculated in studied binary mixtures and some macroscopic constant values of the solvents used. The greenness of methods developed using three different solvents was evaluated with the Analytical Greenness Metric Approach, the Green Analytical Procedures Index, and the Green Solvent Selection Tool approaches.

Accuracy of template‐based guided dental implant placement—An in vitro comparison of different manufacturing methods

Accuracy of template‐based guided dental implant placement—An in vitro comparison of different manufacturing methods

Recognising small colour changes with unsupervised learning, comparison of methods

Colour differentiation is crucial in machine learning and computer vision. It is often used when identifying items and objects based on distinct colours. While common colours like blue, red, green, and yellow are easily distinguishable, some applications require recognising subtle colour variations. Such demands arise in sectors like agriculture, printing, healthcare, and packaging. This research employs prevalent unsupervised learning techniques to detect printed colours on paper, focusing on CMYK ink (saturation) levels necessary for recognition against a white background. The aim is to assess whether unsupervised clustering can identify colours within QR-Codes. One use-case for this research is usage of functional inks, ones that change colour based on environmental factors. Within QR-Codes they serve as low-cost IoT sensors. Results of this research indicate that K-means, C-means, Gaussian Mixture Model (GMM), Hierarchical clustering, and Spectral clustering perform well in recognising colour differences when CMYK saturation is 20% or higher in at least one channel. K-means stands out when saturation drops below 10%, although its accuracy diminishes significantly, especially for yellow or magenta channels. A saturation of at least 10% in one CMYK channel is needed for reliable colour detection using unsupervised learning. To handle ink densities below 5%, further research or alternative unsupervised methods may be necessary.

Methods for Indirect Treatment Comparison: Results from a Systematic Literature Review.

Health technology assessment (HTA) agencies express a clear preference for randomized controlled trials when assessing the comparative efficacy of two or more treatments. However, an indirect treatment comparison (ITC) is often necessary where a direct comparison is unavailable or, in some cases, not possible. Numerous ITC techniques are described in the literature. A systematic literature review (SLR) was conducted to identify all the relevant literature on existing ITC techniques, provide a comprehensive description of each technique and evaluate their strengths and limitations from an HTA perspective in order to develop guidance on the most appropriate method to use in different scenarios. Electronic database searches of Embase and PubMed, as well as grey literature searches, were conducted on 15 November 2021. Eligible articles were peer-reviewed papers that specifically described the methods used for different ITC techniques and were written in English. The review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 73 articles were included in the SLR, reporting on seven different ITC techniques. All reported techniques were forms of adjusted ITC. Network meta-analysis (NMA) was the most frequently described technique (in 79.5% of the included articles), followed by matching-adjusted indirect comparison (MAIC) (30.1%), network meta-regression (24.7%), the Bucher method (23.3%), simulated treatment comparison (STC) (21.9%), propensity score matching (4.1%) and inverse probability of treatment weighting (4.1%). The appropriate choice of ITC technique is critical and should be based on the feasibility of a connected network, the evidence of heterogeneity between and within studies, the overall number of relevant studies and the availability of individual patient-level data (IPD). MAIC and STC were found to be common techniques in the case of single-arm studies, which are increasingly being conducted in oncology and rare diseases, whilst the Bucher method and NMA provide suitable options where no IPD is available. ITCs can provide alternative evidence where direct comparative evidence may be missing. ITCs are currently considered by HTA agencies on a case-by-case basis; however, their acceptability remains low. Clearer international consensus and guidance on the methods to use for different ITC techniques is needed to improve the quality of ITCs submitted to HTA agencies. ITC techniques continue to evolve quickly, and more efficient techniques may become available in the future.

Comparison of manure application methods on nutrient and metal loss to snowmelt

Manure injection is well documented to reduce rain-driven nutrient losses, but the effect on snowmelt-driven losses is less studied. This study examined the surface application versus injection of liquid swine manure on phosphorus (P), nitrate‑nitrogen (nitrate-N), zinc (Zn), manganese (Mn), iron (Fe), magnesium (Mg), and calcium (Ca) release from soils to snowmelt. Manure was applied in the fall of 2021 to four replicated plots of surface-applied or injected treatments, and boxes were installed to collect snowmelt in each plot, including four unmanured (control) plots. In the spring of 2022, the snowmelt was collected from each box for 10 days, volume was recorded, and analyzed for dissolved reactive P (DRP), nitrate-N, Zn, Mn, Fe, Mg, and Ca concentrations, and pH. The mean daily snowmelt volume and snowmelt depth ranged from 2.9 to 25.2 L, and 2.7 to 23.3 mm, respectively. The mean snowmelt DRP concentrations for manure injected, surface-applied, and control treatments were 0.66 ± 0.07, 0.89 ± 0.07, and 0.65 ± 0.07 mg L−1, respectively. The mean DRP concentration did not vary among the manure application methods for the first 5 days of snowmelt. However, DRP was significantly higher in the manure surface-applied treatment, compared to both control and manure-injected treatments on days 7 and 8, compared to the control on day 6, and compared to the manure-injected on day 9. The effect of the manure application method on nitrate-N (3.27 to 3.60 mg L−1) and metal concentrations in the snowmelt was not significant. The cumulative loads (mean across application methods and sampling days in kg ha−1) of DRP (0.56), nitrate-N (4.03), and metals [Zn (0.11), Mn (0.01), Fe (0.18), Mg (8.03), Ca (21.70)] mobilized with snowmelt flooding did not significantly vary among the manure application methods. Results suggest that DRP, nitrate, and metal loads were primarily controlled by the snowmelt volume rather than their concentrations. Therefore, land management practices that reduce the volume of snowmelt discharge could be more effective for reducing the P, nitrate-N, and metal loss to snowmelt from manured soils.

Comparison of Different Methods for Ancient Ship Calm Water Resistance Estimation

The remains of ancient ships from various time periods are commonly found on land and under the sea in conditions that make it difficult to reconstruct their original form and structure. For this reason, the reconstruction should be supported by other data, such as data on similar ships, but also by certain assumptions. The results of the reconstruction are significant not only in a historical sense but are of exceptional importance when building floating replicas. Two ships, Nin 1 and Nin 2, today for promotional purposes known as Condurae Croaticae, were found in Nin (Croatia) at the end of the 1960s. They are about 8 to 10 m long, and tentatively dated to the 11th century AD, although there are indications that they could be dated two centuries later. Based on archaeological finds exhibited in the Museum of Nin Antiquities, hull line drawings were created, according to which two floating replicas were made at the end of the 1990s. Considering the problem of hogging that appeared in both ships, a new proposal for the reconstruction of the original hull lines was performed based on the available documentation. The aim of this paper is a systematic analysis of its calm water resistance. Based on the established credibility of experimental testing, a scale model (1:4 ratio) of the Nin 1 vessel is constructed and evaluated through towing tank experiments. The second approach, the CFD method, is a reliable numerical method for calm resistance estimation, but it is rarely used in the analysis of ancient ships. Finally, the widely used empirical Holtrop method is also applied, but it was developed for ships of larger dimensions and with large parts of flat bottoms and, therefore, the more appropriate Delft Hull Yacht Series method is also tested. The results obtained by applying the four mentioned methods are compared and discussed.

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images

Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images. Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics—the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)—to evaluate the agreement between the manual and semi-automatic methods. Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91. Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.

Comparison of DNA extraction methods on different sample matrices within the same terrestrial ecosystem

Metataxonomic studies of ecosystem microbiotas require the simultaneous processing of samples with contrasting physical and biochemical traits. However, there are no published studies of comparisons of different DNA extraction kits to characterize the microbiotas of the main components of terrestrial ecosystems. Here, and to our knowledge for the first time, five DNA extraction kits were used to investigate the composition and diversity of the microbiota of a subset of samples typically studied in terrestrial ecosystems such as bulk soil, rhizosphere soil, invertebrate taxa and mammalian feces. DNA extraction kit was associated with changes in the relative abundance of hundreds of ASVs, in the same samples, resulting in significant differences in alpha and beta diversity estimates of their microbiotas. Importantly, the impact of DNA extraction kit on sample diversity varies according to sample type, with mammalian feces and soil samples showing the most and least consistent diversity estimates across DNA extraction kits, respectively. We show that the MACHEREY–NAGEL NucleoSpin® Soil kit was associated with the highest alpha diversity estimates, providing the highest contribution to the overall sample diversity, as indicated by comparisons with computationally assembled reference communities, and is recommended to be used for any large-scale microbiota study of terrestrial ecosystems.

Comparison of Fourier optics-based methods for modeling coherence scanning interferometry

Comparison of Fourier optics-based methods for modeling coherence scanning interferometry

Comparison of Methods Predicting Advance Time in Furrow Irrigation

In the design of furrow irrigation, and in general in surface irrigation, the reliable estimation of the advance time at the furrow end (tL) is a key issue for improving the efficiency and uniformity of irrigation. In this study, three methods are used for estimating the tL, and their results are compared with the experimental data of fifteen different furrows from the international literature. These methods are as follows: (a) the Valiantzas equation, (b) the method presented by Walker and Skogerboe, based on solving the volume balance equation by the Newton–Raphson iterative procedure and (c) the method of Philip and Farrell. The first two methods assume that the infiltration is described by the Lewis–Kostiakov equation and the extended Lewis–Kostiakov equation, respectively, while in the case of the Philip and Farrell method, the infiltration is described by the Philip equation and the Lewis-Kostiakov equation. The results showed that in most cases of the first two methods, the absolute relative error value of the predicted time tL was less than 10%. The Philip and Farrell method using the Lewis–Kostiakov infiltration equation underestimates the time tL and fails especially in the case where the volume of the surface water is not negligible compared to the total volume of water entering the system. The Valiantzas method is recommended because it was simpler and easier to use and showed greater prediction accuracy of tL, resulting in better planning of irrigation systems and contributing to water saving, which is currently a big issue.

Hydraulic tortuosity of porous media: comparison of different modeling methods

Abstract Hydraulic tortuosity is a crucial parameter affecting the movement of fluid in porous media. Currently, researchers have used different methods to construct porous media models and studied the variation of hydraulic tortuosity with porosity. In this paper, we use Monte Carlo random particle, quartet structure generation set (QSGS), and CT-scan reconstruction to construct porous media models with different porosity. The finite element method is used to simulate the fluid passing through the models. The effectiveness of the QSGS algorithm in constructing porous media is verified. The hydraulic tortuosity of the three types of model is computed using the streamline length ratio method, and its variation law with porosity is explored. The results show that the change law of the three models is consistent. The law of power function change is satisfied between the two for all models, which means the increase in porosity causes a decrease in tortuosity. Different models are constructed to explore the effect of tortuosity on permeability. An increase in tortuosity results in a smaller permeability when other conditions are equal. This paper aims to provide effective methods for constructing porous media models and a reference for studying hydraulic tortuosity.

Cryptocurrency Adoption and Financial Innovation

The advent of cryptocurrencies and blockchain technology has sparked a revolutionary Shift in the financial sector. This study sets out on a wide-ranging investigation to understand the nuanced dynamics, repercussions, and potential future paths of this shifting environment in the UK and USA. The primary goals of the research are to examine how cryptocurrencies affect financial markets and conventional banking systems; to examine how blockchain technology might be used in the financial sector; to assess policy and regulatory considerations; and to predict and plan for the future. This research digs into how cryptocurrencies have revolutionized the banking and finance sectors. Analysis of adoption rates, market volatility, and integration methods sheds light on the changing position of cryptocurrencies in investment portfolios, reconfiguration of asset classes, and coping mechanisms of conventional financial institutions. When looking at the financial sector as a whole, the transformational potential of blockchain technology becomes clear. The advent of DeFi, smart contracts, and asset tokenization offers new prospects to improve financial transactions, increase transparency, and broaden participation in the investment market. The research analyzes cryptocurrencies and blockchain technology from a policy and regulatory perspective. The delicate balancing act between stimulating innovation and guaranteeing consumer protection, market integrity, and financial stability is highlighted by a comparison of the regulatory methods adopted in the United Kingdom and United States, as well as proposals from international organizations. The research identifies potential future paths for these technologies and their implications. Opportunities and challenges that will influence the future of finance emerge, with a focus on central bank digital currencies (CBDCs), sustainable blockchain solutions, and interdisciplinary collaborations. As this deep dive comes to a close, the transformational power of cryptocurrencies and blockchain technology is highlighted. It sheds light on the forces that are altering the structures of the world’s financial markets, conventional banking structures, and regulatory frameworks. The findings and critical assessment stress the need for well-considered choices, ethical innovation, and interdisciplinary cooperation in order to succeed in an ever-changing environment. To further democratize access, improve transparency, and reshape the economic fabric of our planet, the future of finance resides at the confluence of tradition and innovation, where cryptocurrencies and blockchain technology exist. Cryptocurrency adoption has catalyzed changes in consumer behavior and investment patterns. While some view digital currencies as speculative assets, others embrace them as alternative forms of money and store of value. This diversity of perspectives underscores the need for a nuanced understanding of cryptocurrency adoption and its implications for financial systems. The significance of this study lies in its contribution to our understanding of the evolving relationship between cryptocurrency adoption and financial innovation. By elucidating the drivers, challenges, and implications of this phenomenon, policymakers, industry stakeholders, and researchers can make informed decisions to harness the transformative potential of digital currencies while mitigating associated risks. Cryptocurrency adoption and the financial innovations it has spurred are transforming the way we think about money and financial services. While challenges remain, the potential benefits of a more decentralized, efficient, and inclusive financial system are significant. As the technology matures, regulatory frameworks adapt, and user confidence grows, cryptocurrency adoption is likely tocontinue its upward trajectory. Cryptocurrency adoption and its associated financial innovations have the potential to usher in a paradigm shift in the way we manage and exchange value.

A Comparison of Methods of Visual Census and Cryptobenthic Fish Collecting, an Integrative Approach to the Qualitative and Quantitative Composition of the Mediterranean Temperate Reef Fish Assemblages

The present research quantitatively compared the fish composition among two methods for non-cryptic benthic fish species and one method for cryptobenthic fish species for the first time for the Mediterranean temperate reef fish assemblage. A visual census of fishes was performed within a cylinder of 4 m radius and within a cylinder of 2 m radius, while the cryptobenthic fishes were collected using a square of 1 m2 with anesthetic. The data and material were collected at fifty sampling points. The visual census methods together recorded 31 species, and the square with anesthetic method recorded 18 species. The quantitative comparison of methods of visual census and cryptobenthic fish collecting showed significantly different species richness, total fish abundance, and fish assemblage structure among methods. The applied methods were highly complementary. The cylinder of 2 m radius is well suited for epibenthic fishes and the cylinder of 4 m radius is reliable for hyperbenthic and benthopelagic fishes. Therefore, each of the methods well covered one of three components of ichthyobenthos (hyperbenthic, epibenthic, and cryptobenthic fishes), and all three methods together provided a far more complete assessment of fish species composition than any individual census method for the Mediterranean littoral benthic fishes.

A gentle conditioning agent consisted of oppositely-charged-induced cellulose nanocrystal and cationic cellulose: Stability, conditioning and delivery

The concerns about the health and environmental effects of the conditioning agent in current hair care products have attracted attention to seeking out a gentle and biosafe alternative, especially from nature. Cationic cellulose is a gentle conditioning agent from natural resource, but is rarely used in emulsion due to its hydrophilicity. In this study, cellulose-based complexes were prepared by opposite-charged-induced cellulose nanocrystals and cationic cellulose to replace cationic surfactants as conditioning agent. We demonstrated that the presence of cellulose nanocrystals effectively improved the emulsifying property of cationic cellulose, thereby could be used to prepare stable silicone oil emulsion. More importantly, by analyzing the relationship between emulsion morphology and the deposition of silicone oil, it was proposed for the first time that emulsion flocculation was positively correlated with deposition of silicone oil. Based on this, a method for rapid comparison of deposition of silicone oil was creatively proposed using ultraviolet absorbance of emulsion. Besides, we found that the resulting emulsion could be used as platform for delivery of essential oil to control the release of essential oil. In summary, this study has developed a gentle and clean cellulose-based complexes for personal care, which is expected to produce safer and cleaner emulsion to reduce potential health risks.

Comparison of teaching methods for the emergence and maintenance of untaught relations in foreign language vocabulary acquisition: A systematic replication.

In a replication of Daly and K. Dounavi (2020), the researchers evaluated the effect of foreign tact and bidirectional intraverbal teaching on the emergence of untaught relations. Three university students learned three stimulus sets through three types of teaching: native-foreign intraverbal teaching (vocalizing Spanish words that refer to a Japanese textual stimulus), foreign-native intraverbal teaching (reversed relation of native-foreign condition), and foreign-tact teaching (tacting a picture in Spanish). The researchers used an adapted alternating-treatments design to assess the differential effect of each teaching condition on the emergence of untaught relations in a foreign language and collected data on response maintenance. The results replicated previous findings that native-foreign intraverbal and foreign-tact teachings were more effective than foreign-native intraverbal teaching despite previous reporting that the maintenance outcomes may be a result of carryover effects.

Benchmarking Guanidinium Organosulfonate Hydrogen-Bonded Frameworks for Structure Determination of Encapsulated Guests.

Single crystal X-ray diffraction (SCXRD) is arguably the most definitive method for molecular structure determination, but it is often challenged by compounds that are liquids or oils at room temperature or do not form crystals adequate for analysis. Our laboratory previously reported a simple, cost-effective, single-step crystallization method based on guanidinium organosulfonate (GS) hydrogen bonded frameworks for structure determination of a wide range of encapsulated guest molecules, including assignment of the absolute configuration of chiral centers. Herein, we expand on those results and report a head-to-head comparison of the GS method with adamantoid "molecular chaperones", which have been reported to be useful hosts for structure determination. Inclusion compounds limited to only two GS hosts are characterized by low R1 values and Flack parameters, infrequent disorder of the host and guest, and manageable disorder when it does exist. The structures of some target molecules that were not included or resolved using the adamantoid chaperones were successfully included and resolved by the GS hosts, and vice versa. Of the 32 guests attempted by the GS method, 31 inclusion compounds afforded successful guest structure solutions, a 97% success rate. The GS hosts and adamantoid chaperones are complementary with respect to guest inclusion, arguing that both should be employed in the arsenal of methods for structure determination. Furthermore, the low cost of organosulfonate host components promises an accessible route to molecular structure determination for a wide range of users.