Value Of Experiments Research Articles

Influence of flow nonuniformities and real gas effects on cylindrical shock wave convergence

Convergence of cylindrical shock in argon is studied both experimentally and numerically. Shock tube experiments are conducted, where a planar shock is first transformed to a cylindrical shape and then converged to its focal axis. Numerical simulations of the converging shock using equations of state for an ideal gas and a real gas (SESAME 5173 model) are conducted and compared. High temporal resolution data of cylindrical shock convergence is presented. When comparing the trajectories of the converging shock of initial shock Mach number (MS) of 4.63, the convergence exponent (α) in experiments is found to be 0.833. This α value in experiments is higher than the value obtained from computations with argon treated as an ideal gas but agrees well with the real gas computations. It is revealed that the form of convergence varies with different MS. An asymptotic approach of α toward the self-similar solution for high MS is attributed to an earlier transition of shock motion to self-similarity, while a significantly higher α observed at lower MS is attributed to the negative influence of upstream nonuniformities and weaker initiation of the shock. It is found that even before the shock reflection, real gas effects are significant enough to affect the convergence of the shock and limit the extreme conditions predicted by the ideal gas computations. For an MS of 4.63, the maximum temperature reached is 9250 K before reflection, leading to 0.12% of the argon gas undergoing the first stage of ionization.

Cobalt-doped iron-based Prussian blue analogue cubes anchored to phosphorus‑nitrogen-covered BN surfaces for enhancing the flame retardancy of epoxy coatings

Prussian blue analogue (PBA) is considered to be a very promising inorganic nano-flame retardant material due to its structure enriched with transition metal elements. Here, hexachlorocyclotriphosphazene (HCCP) and p-phenylenediamine (p-PDA) were first loaded onto the boron nitride surface (BN/PCP) via a polymerization reaction to impart richer flame retardant characteristics. Then, Co-Fe-based PBA nanoparticles were homogeneously anchored on the BN/PCP surface to obtain the BN/PCP@PBA hierarchical nano-flame retardant. It is worth mentioning that this novel composite flame retardant effectively combined the superior barrier properties of BN, fire retardancy of N, P elements and the accelerated carbon conversion effect of Co-Fe/PBA. The test results showed that the loading of PCP on the BN surface could effectively improve the thermal insulation performance of EP, while Co-Fe/PBA had good catalytic carbon formation effect at high temperature. Specifically, the backside of the BN/PCP@PBA filled EP sample displayed the lowest temperature during flame attack (164.1 °C) compared to the other specimens, proving its best thermal insulation. The BN/PCP@PBA/EP obtained the maximum expansion height and expansion rate values (27.6 mm and 21.07) in the furnace chamber experiments, which are crucial for its fire resistance. In addition, BN/PCP@PBA/EP retained the highest residual char (32.6 %) and achieved the lowest smoke density rating (36.9 %), demonstrating its high carbon conversion and fume suppression. Carbon residue analysis confirmed that the char layer of BN/PCP@PBA/EP was more complete and the metal oxides were uniformly embedded in the carbon residue skeleton, which ensured good flame protection for the substrate.

An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM

In recent years, with the application of Internet of Things (IoT) and cloud technology in smart industrialization, Industrial Internet of Things (IIoT) has become an emerging hot topic. The increasing amount of data and device numbers in IIoT poses significant challenges to its security issues, making anomaly detection particularly important. Existing methods for anomaly detection in the IIoT often fall short when dealing with data imbalance, and the huge amount of IIoT data makes feature selection challenging and computationally intensive. In this paper, we propose an optimal deep learning model for anomaly detection in IIoT. Firstly, by setting different thresholds of eXtreme Gradient Boosting (XGBoost) for feature selection, features with importance above the given threshold are retained, while those below are ignored. Different thresholds yield different numbers of features. This approach not only secures effective features but also reduces the feature dimensionality, thereby decreasing the consumption of computational resources. Secondly, an optimized loss function is designed to study its impact on model performance in terms of handling imbalanced data, highly similar categories, and model training. We select the optimal threshold and loss function, which are part of our optimal model, by comparing metrics such as accuracy, precision, recall, False Alarm Rate (FAR), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision–Recall Curve (AUC-PR) values. Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. Experiments are conducted using the UNSW-NB15 and NSL-KDD datasets. The proposed MIX_LSTM model can achieve 0.084 FAR, 0.984 AUC-ROC, and 0.988 AUC-PR values in the binary anomaly detection experiment on the UNSW-NB15 dataset. In the NSL-KDD dataset, it can achieve 0.028 FAR, 0.967 AUC-ROC, and 0.962 AUC-PR values. By comparing the evaluation indicators, the model shows good performance in detecting abnormal attacks in the Industrial Internet of Things compared with traditional deep learning models, machine learning models and existing technologies.

Angle-insensitive all-optical diodes in a large range of incidence by photonic heterostructure containing hyperbolic metamaterials

We theoretically and systematically investigate angle-insensitive all-optical diodes using photonic heterostructures composed of different one-dimensional photonic crystals containing hyperbolic metamaterials. Hyperbolic metamaterials comprise metals with high nonlinear coefficients and transparent dielectrics. The results indicate that the photonic heterostructure shows angle insensitivity in transmittance, transmission contrast, and the critical intensity of the threshold over a large range of incidence, achieving a high transmission contrast of approximately 0.93 between forward and backward incident light. This angle insensitivity originates from the phase-variation compensation effect in a basic unit composed of hyperbolic metamaterials and transparent dielectric materials under TM polarization. These results provide important reference values for future experiments.

Model-based experiments as epistemic evidence in paleoecology

Where ordinary experiments are impossible and observational data scarce and indirect—particularly in paleoecosystems—computational experiments are often our only means to learn about reality. There are good arguments to count such model-based predictions as evidence, testing hypotheses and updating our beliefs about the world. However, the epistemic weight of computational experiments depends on an adequate model representation of the target system, transparency about predictive uncertainty, and the avoidance of confirmation bias. I argue that mechanistic models are particularly suited for paleoecological predictions but that iterative uncertainty analyses should guide their development. Using a Bayesian framework I propose preregistration and blinded analysis as tools to strengthen the epistemic value of computational experiments. Here, a preregistration marks the boundary between exploratory model development, which establishes credence in the model, and predictive model application, which tests hypotheses. As good modeling practice I suggest clarifying epistemic goals at the outset of a project and accordingly choose methods to maximize the epistemic weight of the computational experiment.

Experimental investigation of the effects of aqueous ammonia and water mixtures on the efficiency and emissions of a compression ignition engine

In this study, different concentrations of aqueous ammonia and pure water were fumigated and aspirated into the cylinder from the engine intake manifold. Aqueous ammonia (NH4OH) with 5 %, 10 % and 15 % ammonia (NH3) concentration was used in the experiments. Both aqueous ammonia and pure water were converted into cold vapor using an ultrasonic evaporator and conveyed to the combustion chamber as fume. Experiments were carried out at a constant engine speed of 660 rpm. Torque values of 25 %, 50 %, and 100 % (386 Nm) were used in the experiments, respectively. As a result of the experiments, it was seen that as the ammonia percentage increased, the engine efficiency was negatively affected. At 100 % torque, when comparing the BTE value of pure diesel fuel with the BTE values of experiments with 5 %, 10 %, and 15 % ammonia addition, decreases of 0.09 %, 2.27 %, and 3.57 % were observed, respectively. In experiments conducted with pure water, although the thermal efficiency increased significantly as the torque ratio increased, it still could not reach the thermal efficiency of pure diesel fuel. On the other hand, it has been observed that water vapor improves NO and HC emissions. While the NOx value tends to increase with the increase in ammonia concentration at low and medium load values, the NOx value tends to decrease relatively as the ammonia concentration increases at 100 % torque value. The highest NOx emission value, 534 ppmvol, occurred in the experiments with 15 % NH4OH addition. It was observed that the NOx emission value of all experiments conducted with aqueous ammonia additive at 100 % torque was lower than pure diesel. In this study, the effects of NH4OH and H2O on engine performance and emissions were investigated.

Effects of rare earth modifying inclusions on the nucleation mechanism and mechanical properties of MC5 roller steel

The effect of Ce treatment on the inclusions and mechanical properties of MC5 roller steel after solidification and annealing process was investigated. The implementation was observed by thermodynamic calculation, Electron Probe Micro-Analyzer and high-temperature confocal laser scanning microscopy (HT-CLSM). The results showed that as rare earth content increase, the inclusions were effectively modified into cerium oxide (Ce-O) and cerium sulfide (Ce-O-S); Compared with 0 RE steel, eliminating the proportion of inclusions larger than 5 μm by 14% and 22%; the average size decreased to 3.60 μm and 2.30 μm. Subsequently. CLSM was used to observe the aggregation, separation and agglomeration behavior of MgAlO4 inclusions and alloy elements on the surface of molten steel. Moreover. the ultimate tensile strength and yield strength of MC5 roller steel with Ce treatment increased by 12.6% and 34%. The addition of rare earths could improve the of MC5 roller steel, it provides effective reference value for subsequent industrial experiments.

“This Is Not What I Normally Do”

This video essay, a product of the “Videographic Methods and Practices: Embodying the Video Essay” workshop (Bowdoin College, July 2023), is comprised of two sections, exploring constraint-based approaches to videographic scholarship. Part 1, “The Incredible Machine,” documents an attempt at recreating a 1990s Rube Goldberg-inspired computer game interface through the handling of various film clips arranged on a computer desktop. The deliberate avoidance of digital shortcuts highlights the value of playful experimentation within scholarly and artistic practices. Part 2, “The Five Obstructions,” presents five interviews conducted under randomly-assigned constraints, fostering unforeseen responses and creative insights. These ludic experiments demonstrate the potential of constraints to stimulate creativity and to provoke unconventional outputs. Emphasizing process over outcome, the video showcases the laborious yet rewarding nature of scholarly experimentation, echoing a broader shift towards embracing the creative-academic journey in videographic scholarship.

Co-parasitization effect of Anagyrus pseudococci (Girault) and Coccidoxenoides perminutus Girault (Hymenoptera: Encyrtidae) on the vine mealybug Planococcus ficus (Signoret) (Hemiptera: Pseudococcidae) and their intrinsic interspecific larval competition

BackgroundThe vine mealybug Planococcus ficus (Signoret) (Hemiptera: Pseudococcidae) is a cosmopolitan species that causes economic damage to grapevines, especially in Mediterranean countries, South Africa, North and South America and Europe. In this study, the co-parasitization effect of Anagyrus pseudococci (Girault) and Coccidoxenoides perminutus Girault (Hymenoptera: Encyrtidae) on the vine mealybug Planococcus ficus (Signoret) (Hemiptera: Pseudococcidae) and their intrinsic interspecific larval competition were investigated under laboratory conditions.ResultsIn non-choice experiments, the highest parasitism value (11.75 ± 0.87) was obtained on 2nd instar mealybug nymphs in containers with only C. perminutus, while the highest parasitism value (11.20 ± 0.59) in choice experiments was obtained on female mealybugs in containers with both parasitoids. It was determined that C. perminutus parasitized the 1st, 2nd and 3rd nymph stages, while A. pseudococci parasitized the 2nd, 3rd nymph and female stages of vine mealybug. These mean that mealybug populations can be controlled more effectively by parasitizing all mealybug stages in the environment when parasitoids are together. In intrinsic interspecific larval competition experiments on the mealybugs 2nd and 3rd nymph stages, where both parasitoids can parasitize, it was determined that C. perminutus generally won the competition, even though A. pseudococci had a three-day parasitism priority. The highest A. pseudococci emergence rate in competition trials occurred on 2nd instar mealybug nymphs when A. pseudococci had 72 h parasitism priority (36.09%). In addition, head capsule width and tibia lengths of A. pseudococci and C. perminutus adults obtained from intrinsic interspecific larval competition were measured, and it was determined that the head capsule width and tibia length of adult parasitoids generally increased as the host period progressed. In addition, it has been determined that there may be some differences in the measured characteristics of the adult parasitoids obtained due to intrinsic interspecific competition compared to the control.ConclusionsUsing these two parasitoids together in the biological control of vine mealybugs will allow parasitizing all mealybug nymph stages and females in the environment and provide more effective pest control.

Bismuth Ordering and Optical Anisotropy in GaAsBi Alloys

Reflectance anisotropy spectroscopy (RAS) is applied to investigate GaAsBi samples grown by molecular beam epitaxy on (001)‐oriented GaAs substrates with GaAs or InGaAs buffer layers, resulting in nearly lattice‐matched or compressive strain conditions, with Bi concentration in the alloy in the range 2–5%. These new samples allow to bridge the gap in the Bi concentration values of previous RAS experiments (C. Goletti et al., Appl. Phys. Lett. 2022, 120, 031902), confirming the [110]‐polarized Bi‐related anisotropy in optical spectra below 3 eV and the linear dependence of its amplitude on Bi concentration. The characterization of the grown GaAsBi samples by X‐Ray diffraction and transmission electron microscopy clearly demonstrates the presence of CuPt‐like ordering in the bulk. CuPt structure is the primary origin of the optical anisotropy measured by RAS and by polarized photoluminescence, due to the anisotropic strain produced in the bulk crystal lattice. The lineshape of the RAS spectra above 3 eV, with its overall and characteristic positive convexity, confirms this conclusion.

Boron powder injection experiments in WEST with a fully actively cooled, ITER grade, tungsten divertor

Reactor relevant fusion devices will use tungsten (W) for their plasma facing components (PFCs) due to its thermomechanical properties and low tritium retention. However, W introduces high-Z impurities into the plasma, degrading its performance. Different wall conditioning methods have been developed to address this issue, including coating of W PFCs with layers of low-Z material. Wall conditioning by boron (B) powder injection using an impurity powder dropper (IPD) is being studied in WEST. Two series of experiments were conducted since the installation of the new ITER grade full W divertor. During the first series in 2023 ∼ 1 g of B powder was injected in total at a maximum rate of ∼ 58 mg/s, both of which are three times greater than respective values in the initial WEST powder injection experiments. The second series of experiments included injection of B and BN powders for comparison of their effects on plasma performance. The presence of an instantaneous conditioning effect is suggested by visible spectroscopy measurements of low-Z impurity lines and a rollover of total radiated power past an injection rate of ∼ 20 mg/s was observed. Presence of B coating layer formation is supported by the evolution of the average radiance of visible lines of B, W and oxygen (O). To understand B transport, an interpretative modeling workflow is employed, utilizing the SOLEDGE-EIRENE fluid boundary plasma code and the Dust Injection Simulator (DIS) code. Parameters like B perpendicular diffusivity and recycling coefficients are varied to match experimental results to see if the initial assumption of B sticking to the PFCs immediately after the contact with the wall is adequate for correctly modelling its distribution on the PFCs.

Mechanism of grain boundary angle on solidification cracking in directed energy deposition Hastelloy X superalloys

Solidification cracking occurs only when the grain boundary (GB) angle (θ) exceeds a critical value. This value, known as the critical cracked GB angle (θ*), can be predicted from the grain coalescence theory based on GB-angle-dependent GB energy. However, the calculated value (θc*) is always less than the measured value in experiments (θe*), which is also confirmed in our directed energy deposition Hastelloy X superalloys. In addition to GB energy, there are evidences showing that GB angle can affect cracking by changing dendrite spacings. We show by experiments and phase field simulations that, same as GB energy, the dendrite spacings at GBs increase with GB angle, but its effect on solidification cracking sensitivity (SCS) is opposite to GB energy. Depending on their relative contributions, three ranges can be identified. In the first range of θ<θc*, both dendrite spacings and GB energy have negligible effects on dendrite coalescence, compared to the case inside a grain. In the second range of θc*<θ<θe*, dendrite spacings counteract the effect of high GB energy on SCS. It is exactly this effect that induces the gap in θ* between theory and experiments. In the third range of θ>θe*, GB energy plays a dominant role and leads to severe solidification cracking. After including the effect of dendrite spacings on SCS, we predict θ*=15° in directed energy deposition Hastelloy X superalloys, close to the experimental value of 18°. These new findings provide new insights for suppressing cracking by controlling the dendrite spacings near GBs.

Study on the mixed lubrication of rough planar extrusion considering surface texture

Surface texture plays a crucial role in fluid dynamic lubrication. The non-Newtonian thermal elastohydrodynamic lubrication problem involving rough surfaces with texture has not been investigated to date. In this paper, a model for non-Newtonian thermal elastohydrodynamic lubrication incorporating rough surfaces and texture morphology is developed, focusing on the problem of mixed lubrication in planar extrusion with texture. The model builds upon the Reynolds equation with flow factor introduced. It considers the effects of rough surface texture, thermal effects, and non-Newtonian effects. The Reynolds equation is numerically solved using the Semi-System method to calculate the oil film pressure in full film region and contact pressure in dry contact area. The DC-FFT algorithm is employed to calculate surface elastic deformation. Comparing the calculated friction coefficient of the present model with the measured values in literature experiments, the average error is only 6.94%. Furthermore, the study investigates the effects of texture, temperature, and non-Newtonian on interfacial lubrication performance under mixed lubrication conditions. It’s found that compared to untextured surface, the average film thickness of textured surface increased by a maximum of 10.8%, and the friction coefficient decreased by a maximum of 67.4%; Compared to Newtonian fluids, shear thinning fluids reduce temperature by 0.18%, and shear thickening fluids are more conducive to improving mixed lubrication performance. A stepped pit texture is designed based on the dynamic pressure mechanism of the texture, indicating that the circular stepped pit texture has the best load-bearing capacity improvement.

RHEOLOGICAL PROPERTIES OF LOW-TEMPERATURE GEL-FORMING COMPOSITIONS

In this work, the rheological properties of low-temperature versions of gel-forming compositions developed at the IPC SB RAS are studied. A composition on an inorganic basis and a composition comprising a polymer and inorganic components are intended to increase oil recovery from low temperature reservoirs. The measurements were carried using a Haake Viscotester iQ rheometer with a CC25 coaxial cylinder measuring system in oscillatory mode with controlled strain at a frequency of 1 Hz. During the oscillation test, the dependences of the elastic modulus G' and the viscosity modulus G'' on the strain amplitude were recorded, the ranges of linear viscoelasticity of the samples under study were established, and the strain value for kinetic experiments was chosen. At a given strain value (0.01), the kinetic dependences of G' were obtained and the dynamics of the rheological properties and the strength of the formed structure were evaluated. The time of gelation onset has been found. It was 2300 s at 38 °C, 3900 s at 30 °C, and 21000 s at 20 °C for the inorganic composition and 200 s at 70 °C, 570 s at 60 °C, and 1920 s at 50 °C for the polymer-inorganic composition. The time required to form a gel of maximum strength also was found to be increased with increasing temperature. It has been established that for all systems under study the time of gel formation was reduced with increasing temperature, while the strength of the formed structure also increased. A slight increase in the maximum value of the modulus G' (33490 and 32805 Pa) was observed for the composition on an inorganic basis at 38 and 30 °C respectively as compared with the maximum of the dependence obtained at 20 °C (24660 Pa). The major increase in the value of the modulus G' has been recorded for the composition on a polymer-inorganic basis with increase in temperature. In this case the value of the modulus G' increased from 8970 Pa at 50 °C to 11680 Pa at 60 °C and 14590 Pa at 70 °C, while the general form of the rheokinetic dependence for different compositions and temperatures remained the same. For citation: Kozhevnikov I.S., Bogoslovskii А.V. Rheological properties of low-temperature gel-forming compositions. ChemChemTech [Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol.]. 2024. V. 67. N 8. P. 22-28. DOI: 10.6060/ivkkt.20246708.1t.

Quantitative estimation of ethanol content in eribulin mesylate injection using headspace gas chromatographic with flame ionization detector [HS-GC-FID

A specific HS-GC method has been developed, optimized, and validated for the quantitative estimation of ethanol content in eribulin mesylate injection. Chromatographic separation was achieved on DB-624 column (30 m x 0.32 mm, 1.8 μm), consisting of 6% cyanopropyl and 94% polydimethylsiloxane as stationary phase and passing nitrogen carrier gas. The performance of the method was assessed by evaluating the specificity, linearity, precision, and accuracy of experiments. The correlation coefficient value of the linearity experiment was 0.9999. The average recoveries for the accuracy were in the range of 98.7 to 102.4%. The results proved that the method is suitable for the quantitative estimation of ethanol content in eribulin mesylate injection.

Feature selection based on contradictory state sequence for multi-scale interval valued decision table

In the context of selecting features for multi-scale interval valued decision table (MIVDT), conventional research approaches encounter difficulties including elevated data complexity, diminished computational efficiency, and limited model generalization capacity. To overcome these difficulties, feature selection methods based on contradictory state sequence (CSS) and fuzzy contradictory state sequence (FCSS) are proposed in this paper. Initially, MIVDT is established. According to the characteristics of interval value, a more thorough and impartial metric for assessing the inclusion degree is suggested, along with similarity relation based on the metric. Subsequently, the introduction of the first contradictory object allows for the delineation of contradictory state and fuzzy contradictory state, which serve to characterize the consistency of MIVDT. These two concepts are proposed to enhance the accuracy and efficiency of capturing key information in intricate data. Additionally, rapid feature selection algorithms are suggested, which rely on CSS and FCSS. In contrast to conventional feature selection approaches, the algorithms introduced in this study demonstrate superior computational efficiency and enhanced generalization capabilities when confronted with intricate datasets. In twelve open source datasets, the upper limit for the quantity of objects is 110341, and the average accuracy values of experiments under two parameter combinations are 99.54% and 97.08% respectively. The results of experiments demonstrate that the algorithms introduced are capable of efficiently identifying a novel feature subset from intricate datasets within a shorter timeframe.

Analysis of animal-to-human translation shows that only 5% of animal-tested therapeutic interventions obtain regulatory approval for human applications.

There is an ongoing debate about the value of animal experiments to inform medical practice, yet there are limited data on how well therapies developed in animal studies translate to humans. We aimed to assess 2 measures of translation across various biomedical fields: (1) The proportion of therapies which transition from animal studies to human application, including involved timeframes; and (2) the consistency between animal and human study results. Thus, we conducted an umbrella review, including English systematic reviews that evaluated the translation of therapies from animals to humans. Medline, Embase, and Web of Science Core Collection were searched from inception until August 1, 2023. We assessed the proportion of therapeutic interventions advancing to any human study, a randomized controlled trial (RCT), and regulatory approval. We meta-analyzed the concordance between animal and human studies. The risk of bias was probed using a 10-item checklist for systematic reviews. We included 122 articles, describing 54 distinct human diseases and 367 therapeutic interventions. Neurological diseases were the focus of 32% of reviews. The overall proportion of therapies progressing from animal studies was 50% to human studies, 40% to RCTs, and 5% to regulatory approval. Notably, our meta-analysis showed an 86% concordance between positive results in animal and clinical studies. The median transition times from animal studies were 5, 7, and 10 years to reach any human study, an RCT, and regulatory approval, respectively. We conclude that, contrary to widespread assertions, the rate of successful animal-to-human translation may be higher than previously reported. Nonetheless, the low rate of final approval indicates potential deficiencies in the design of both animal studies and early clinical trials. To ameliorate the efficacy of translating therapies from bench to bedside, we advocate for enhanced study design robustness and the reinforcement of generalizability.

Host ecotype and rearing environment are the main drivers of threespine stickleback gut microbiota diversity in a naturalistic experiment.

Host-microbiota interactions play a critical role in the hosts' biology, and thus, it is crucial to elucidate the mechanisms that shape gut microbial communities. We leveraged threespine stickleback fish (Gasterosteus aculeatus) as a model system to investigate the contribution of host and environmental factors to gut microbiota variation. These fish offer a unique opportunity for experiments in naturalistic conditions; we reared benthic and limnetic ecotypes from three different lakes in experimental ponds, allowing us to assess the relative effects of shared environment (pond), geographic origin (lake-of-origin), trophic ecology and genetics (ecotype) and biological sex on gut microbiota α- and β-diversity. Host ecotype had the strongest influence on α-diversity, with benthic fish exhibiting higher diversity than limnetic fish, followed by the rearing environment. β-diversity was primarily shaped by rearing environment, followed by host ecotype, indicating that environmental factors play a crucial role in determining gut microbiota composition. Furthermore, numerous bacterial orders were differentially abundant across ponds, underlining the substantial contribution of environmental factors to gut microbiota variation. Our study illustrates the complex interplay between environmental and host ecological or genetic factors in shaping the stickleback gut microbiota and highlights the value of experiments conducted under naturalistic conditions for understanding gut microbiota dynamics.

Exploring solar neutrino oscillation parameters with the liquid scintillator counter at Yemilab with a comparison to JUNO

We investigate the sensitivities of the liquid scintillator counter (LSC) at Yemilab and JUNO to solar neutrino oscillation parameters, focusing on θ12 and Δm212. We compare the potential of JUNO with LSC at Yemilab utilizing both reactor and solar data in determining those parameters. We find that the solar neutrino data of LSC at Yemilab is highly sensitive to θ12 enabling its determination with a higher precision compared to reactor experiments. Our study also reveals that if Δm212 is larger, with a value close to the best-fit value of KamLAND, JUNO reactor data will have about two times better precision than the reactor LSC at Yemilab. On the other hand, if Δm212 is smaller and closer to the best-fit value of solar neutrino experiments, the precision of the reactor LSC at Yemilab will be better than JUNO. Published by the American Physical Society 2024

DSFworld: A flexible and precise tool to analyze differential scanning fluorimetry data.

Differential scanning fluorimetry (DSF) is a method to determine the apparent melting temperature (Tma) of a purified protein. In DSF, the raw unfolding curves from which Tma is calculated vary widely in shape and complexity. However, the tools available for calculating Tma are only compatible with the simplest of DSF curves, hindering many otherwise straightforward applications of the technology. To overcome this limitation, we designed new mathematical models for Tma calculation that accommodate common forms of variation in DSF curves, including the number of transitions, the presence of high initial signal, and temperature-dependent signal decay. When tested these models against DSFbase, an open-source database of 6235 raw, real-life DSF curves, these models outperformed the existing standard approaches of sigmoid fitting and maximum of the first derivative. To make these models accessible, we created an open-source software and website, DSFworld (https://gestwickilab.shinyapps.io/dsfworld/). In addition to these improved fitting capabilities, DSFworld also includes features that overcome the practical limitations of many analysis workflows, including automatic reformatting of raw data exported from common qPCR instruments, labeling of data based on experimental variables, and flexible interactive plotting. We hope that DSFworld will enable more streamlined and accurate calculation of Tma values for DSF experiments.