Accurate Observations Research Articles

Subsidence detection in southwest Guangdong–Hong Kong–Macao Greater Bay Area using InSAR with GNSS corrected tropospheric delays

As one of the biggest bay areas in the world, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is subject to subsidence due to thick sedimentary. In this study, we combine 154 Sentinel-1 A/B images acquired between 2017 and 2022 with Global Navigation Satellite System (GNSS) observations to characterize the subsidence in southwestern GBA. Specifically, we use GNSS to establish the Iterative Tropospheric Decomposition (ITD) model for zenith tropospheric delay correction and the model is improved using random forest algorithm to alleviate errors caused by unreasonable interpolation methods, showing better performance than Generic Tropospheric Correction Online Service (GACOS) product. At GNSS stations with complete data, the improved model utilizing GNSS data achieves a Pearson correlation coefficient of 0.659, in contrast to a coefficient of only 0.434 using GACOS. The maximum subsidence rate detected is up to 11.32 cm/year in Zhuhai, accompanied by noticeable seasonal variations correlated with precipitation. In addition to the sediment consolidation and compaction, human activities such as agriculture, construction, and land reclamation further exacerbate subsidence in the GBA. This study enhances the utility of GNSS in improving tropospheric delay correction performance and providing more accurate observations of subsidence.

Automated Classification System Based on YOLO Architecture for Body Condition Score in Dairy Cows.

Body condition score (BCS) is a common tool used to assess the welfare of dairy cows and is based on scoring animals according to their external appearance. If the BCS of dairy cows deviates from the required value, it can lead to diseases caused by metabolic problems in the animal, increased medication costs, low productivity, and even the loss of dairy cows. BCS scores for dairy cows on farms are mostly determined by observation based on expert knowledge and experience. This study proposes an automatic classification system for BCS determination in dairy cows using the YOLOv8x deep learning architecture. In this study, firstly, an original dataset was prepared by dividing the BCS scale into five different classes of Emaciated, Poor, Good, Fat, and Obese for images of Holstein and Simmental cow breeds collected from different farms. In the experimental analyses performed on the dataset prepared in this study, the BCS values of 102 out of a total of 126 cow images in the test set were correctly classified using the proposed YOLOv8x deep learning architecture. Furthermore, an average accuracy of 0.81 was achieved for all BCS classes in Holstein and Simmental cows. In addition, the average area under the precision-recall curve was 0.87. In conclusion, the BCS classification system for dairy cows proposed in this study may allow for the accurate observation of animals with rapid declines in body condition. In addition, the BCS classification system can be used as a tool for production decision-makers in early lactation to reduce the negative energy balance.

The Formation of the Anthroposen - The Impact of the Preaching of the Risen Christ

The importance of the seeds of the Gospel being sown in cultural and religious treasures requires accurate observation. The preacher of the Gospel, in this case the Prophet Paul, who was rooted in Judeo-Christianity, was very aware of the religious cultural aspect as a bridge for planting the seeds of the Gospel to produce new humans. In the conditions that occurred in Acts 17 on his second missionary journey, the Areopagus was a platform for preaching about the resurrection of Christ to the people of Athens who were rational in philosophy but still limited in understanding. Namely understanding the relationship between humans and God. This article highlights these aspects by using a reflective grammatical approach to issues of human experience, expressions and actions, the diction of the unknown God and the importance of the risen Christ. The findings show that the presence of new social relationships is inevitable. This is traced based on Habermas' theory which emphasizes the function of communication in social relations.

Comparative Evaluation of Tear Strength and Tensile Strength of Different Types of Gingival Mask Materials: An In Vitro Study.

The implant-supported prosthetic treatment strategy is commonly chosen in modern dentistry to address tooth loss caused by a variety of conditions or dental defects. To achieve healthy and natural-looking results in implant dentistry, it is essential to replicate the peri-implant soft tissue.The gingival tissue that surrounds implants is quite accurately replicated by gingival masks. They facilitate more accurate prosthesis restoration design, enhance periodontal health, and promote oral cleanliness. Furthermore, gingival masks allow for the accurate observation of superstructure seating on implant analogs, which is essential for creating superstructures that fit perfectly. To evaluate the change in tear strength and tensile strength of three different gingival mask materials (esthetic mask auto mix, Gi-MaskandGingifast Rigid) available in the market at various time intervals. Total of 540 specimens were fabricated with 180 samples of each group. Changes in tensile strength and tear strength of three different gingival mask materials (esthetic mask auto mix, Gi-MaskandGingifast Rigid) at intervals of one day, three days, and seven days were measured by a universal testing machine. Statistical analysis was done using one-way ANOVA and Tukey Post Hoc test.We also performed correlation and regression analyses on tear and tensile strength. The null hypothesis, which is supported by these data, claims that there is no discernible variation in the tear strength and tensile strength of three distinct materials across various time intervals. Thus, the null hypothesis was rejected, and it was concluded that there was a significant change in the tear strength and tensile strength of these gingival mask materials at different time intervals. Esthetic mask auto mix has a high tear strength compared to Gi-Mask and gingifast rigid. Gi-Mask has the least tear strength among all three. Tensile strength decreases as time increases, but the Esthetic mask auto mixhas high strength compared to Gi-Mask and gingifast rigid. Selecting the right material for gingival masks is essential, taking into account the clinical scenario and the articulation time.Time influences gingival mask materials' tear strength and tensile strength, which impacts their performance and durability. Esthetic mask auto mix has a high tear and tensile strength compared to Gi-Mask and gingifast rigid.

Retrieving compressive sea ice strength in the Beaufort Sea using the inverse visco-plastic model

We apply a simplified 2d Visco-Plastic (VP) sea ice model with a spatially variable representation of the sea ice rheological parameters for retrieving maximum compressive sea ice strength from satellite and in situ observations. A set of Observing System Simulation Experiments (OSSEs) demonstrates feasibility of optimizing rheological parameter of the VP sea ice model through the variational data assimilation approach during the periods of strong sea ice convergence if accurate sea ice observations are available. Following this strategy, the developed variational data assimilation VP model was applied to the sea ice velocity (https://nsidc.org/data/nsidc-0116/versions/4), sea ice concentration (https://nsidc.org/data/) and CryoSat-2 sea ice thickness observations collected in the vicinity of three moorings in the Beaufort Sea during periods of intensive sea ice convergence. Ice velocities from moorings and atmospheric wind speed (NCEP-NCAR) were used as well. Our results show that conventional maximum compressive sea ice strength (Hibler, 1979) may depend on sea ice thickness or other parameters partly controlled by the sea ice thickness, which is driven by the seasonal cycle.

Study of changes in the pulsation period of six Cepheids variable

We study the period change of six Cepheids using 19376 accurate flux observations of the Solar Mass Ejection Imager (SMEI) onboard the Coriolis spacecraft. All observations for the six Cepheids have been derived as templates for each star, independent of the specific sites utilized to establish and update the O–C values. Sometimes, sinusoidal patterns are superimposed on the star’s O–C changes, which cannot be regarded as random fluctuations in the pulsation period. Random period changes were detected and computed using Eddington’s and Plakidis’s approaches. A comparison of the observed and predicted period change reveals a good agreement with some published models and a very substantial divergence with others. Between the reported period change and that estimated by the current technique, a linear fit with a correlation coefficient of 90.08 percent was obtained. The temporal rate of period change in Cepheid stars might be connected to how well these stars’ mass losses are known today.

Fast Loosely-Timed Deep Neural Network Models with Accurate Memory Contention

The emergence of data-intensive applications, such as Deep Neural Networks (DNN), exacerbates the well-known memory bottleneck in computer systems and demands early attention in the design flow. Electronic System-Level (ESL) design using SystemC Transaction Level Modeling (TLM) enables effective performance estimation, design space exploration (DSE), and gradual refinement. However, memory contention is often only detectable after detailed TLM-2.0 approximately-timed or cycle-accurate RTL models are developed. A memory bottleneck detected at such a late stage can severely limit the available design choices or even require costly redesign. In this work, we propose a novel TLM-2.0 loosely-timed contention-aware (LT-CA) modeling style that offers high-speed simulation close to traditional loosely-timed (LT) models, yet shows the same accuracy for memory contention as low-level approximately-timed (AT) models. Thus, our proposed LT-CA modeling breaks the speed/accuracy tradeoff between regular LT and AT models and offers fast and accurate observation and visualization of memory contention. Our extensible SystemC model generator automatically produces desired TLM-1 and TLM-2.0 models from a DNN architecture description for design space exploration focusing on memory contention. We demonstrate our approach with a real-world industry-strength DNN application, GoogLeNet. The experimental results show that the proposed LT-CA modeling is 46× faster in simulation than equivalent AT models with an average error of less than 1% in simulated time. Early detection of memory contentions also suggests that local memories close to computing cores can eliminate memory contention in such applications.

East African Megacity Air Quality: Rationale and Framework for a Measurement and Modeling Program

Abstract Air pollution in Africa is a significant public health issue responsible for 1.1 million premature deaths annually. Sub-Saharan Africa has the highest rate of population growth and urbanization of any region in the world, with substantial potential for future emission growth and worsening air quality. Accurate and extensive observations of meteorology and atmospheric composition have underpinned successful air pollution mitigation strategies in the Global North, yet Africa in general and East Africa in particular remain among the most sparsely observed regions in the world. This paper is based on the discussion of these issues during two international workshops, one held virtually in the United States in July 2021 and one in Kigali, Rwanda, in January 2023. The workshops were designed to develop a measurement, capacity building, and collaboration strategy to improve air quality-relevant measurements, modeling, and data availability in East Africa. This paper frames the relevant scientific needs and describes the requirements for training and infrastructure development for an integrated observing and modeling strategy that includes partnerships between East African scientists and organizations and their counterparts in the developed world.

Impact of astrophysical scatter on the epoch of reionization [H i]21 bispectrum

It is believed that the first star-forming galaxies are the main drivers of cosmic reionization. It is usually assumed that there is a one-to-one relationship between the star formation rate (SFR) inside a galaxy and the host halo mass in semi-analytical/numerical modeling of large-scale ionization maps during the epoch of reionization. However, more accurate simulations and observations suggest that the SFR and ionizing luminosity in galaxies may vary considerably even if the host halo mass is the same. This astrophysical scatter can introduce an additional non-Gaussianity in the [H i]21cm signal, which might not be captured adequately in the power spectrum. In this work, we have studied the impact of the scatter on the [H i]21cm bispectrum using semi-numerical simulations. We find that the scatter primarily affects small ionized regions, whereas the large ionized bubbles remain largely unaffected. Although, the fractional change in the [H i]21cm bispectra due to the scatter is found to be more than a factor of 10 at large scales (k 1 ≲ 1 Mpc-1) for z=7.4, it is found to be statistically insignificant. However, at small scales (k 1 ~ 2.55 Mpc-1), we have found the impact due to the scatter to be high in magnitude (|〈Δ B 〉/B no-scatter| ~ 1) and statistically significant (|〈Δ B〉/σ ΔB| ≳ 5) at neutral fraction, x̅HI ~ 0.8 for z=7.4. The impact due to scatter is found to be even more prominent (|〈Δ B 〉/B no-scatter| ≳ 10) at small scales for z=10 and x̅HI ~ 0.8, but with reduced statistical significance to some extent (|〈Δ B〉/σ ΔB| ~ 3), compared to z=7.4 at the same neutral fraction. We have also found that in the most optimistic scenario, SKA1-Low might be able to detect these signatures of astrophysical scatter, at ~ 3σ and ~ 5σ detection significance for x̅HI ~ 0.8 and 0.9 respectively, for the equilateral [H i]21cm bispectrum at z=7.4.

Probabilistic reconstruction of sea-level changes and their causes since 1900

Abstract. Coastal communities around the world are increasingly exposed to extreme events that have been exacerbated by rising sea levels. Sustainable adaptation strategies to cope with the associated threats require a comprehensive understanding of past and possible future changes. Yet, many coastlines lack accurate long-term sea-level observations. Here, we introduce a novel probabilistic near-global reconstruction of relative sea-level changes and their causes over the period from 1900 to 2021. The reconstruction is based on tide gauge records and incorporates prior knowledge about physical processes from ancillary observations and geophysical model outputs, allowing us, for the first time, to resolve individual processes and their uncertainties. We demonstrate good agreement between the reconstruction and satellite altimetry and tide gauges (if local vertical land motion is considered). Validation against steric height estimates based on independent temperature and salinity observations over their overlapping periods shows moderate to good agreement in terms of variability, though with larger reconstructed trends in three out of six regions. The linear long-term trend in the resulting global-mean sea-level (GMSL) record is 1.5 ± 0.19 mm yr−1 since 1900, a value consistent with central estimates from the 6th Assessment Report of the Intergovernmental Panel on Climate Change. Multidecadal trends in GMSL have varied; for instance, there were enhanced rates in the 1930s and near-zero rates in the 1960s, although a persistent acceleration (0.08 ± 0.04 mm yr−2) has occurred since then. As a result, most recent rates have exceeded 4 mm yr−1 since 2019. The largest regional rates (>10 mm yr−1) over the same period have been detected in coastal areas near western boundary currents and the larger tropical Indo-Pacific region. Barystatic mass changes due to ice-melt and terrestrial-water-storage variations have dominated the sea-level acceleration at global scales, but sterodynamic processes are the most crucial factor locally, particularly at low latitudes and away from major melt sources. These results demonstrate that the new reconstruction provides valuable insights into historical sea-level change and its contributing causes, complementing observational records in areas where they are sparse or absent. The Kalman smoother sea-level reconstruction dataset can be accessed at https://doi.org/10.5281/zenodo.10621070 (Dangendorf, 2024).

Using the Multiple-Sensor-Based Frost Observation System (MFOS) for Image Object Analysis and Model Prediction Evaluation in an Orchard

Accurate frost observations are crucial for developing and validating frost prediction models. In 2022, the multi-sensor-based automatic frost observation system (MFOS), including an RGB camera, a thermal infrared camera, a leaf wetness sensor (LWS), LED lighting, and three glass plates, was developed to replace the naked-eye observation of frost. The MFOS, herein installed and operated in an apple orchard, provides temporally high-resolution frost observations that show the onset, end, duration, persistence, and discontinuity of frost more clearly than conventional naked-eye observations. This study introduces recent additions to the MFOS and presents the results of its application to frost weather analysis and forecast evaluation in an orchard in South Korea. The NCAM’s Weather Research and Forecasting (WRF) model was employed as a weather forecast model. The main findings of this study are as follows: (1) The newly added image-based object detection capabilities of the MFOS helped with the extraction and quantitative comparison of surface temperature data for apples, leaves, and the LWS. (2) The resolution matching of the RGB and thermal infrared images was made successful by resizing the images, matching them according to horizontal movement, and conducting apple-centered averaging. (3) When applied to evaluate the frost-point predictions of the numerical weather model at one-hour intervals, the results showed that the MFOS could be used as a much more objective tool to verify the accuracy and characteristics of frost predictions compared to the naked-eye view. (4) Higher-resolution and realistic land-cover and vegetation representation are necessary to improve frost forecasts using numerical grid models based on land–atmosphere physics.

Interdisciplinary Applications of AI: Dermatology and Psychology

Artificial Intelligence (AI) is profoundly transforming interdisciplinary domains like dermatology and psychology, providing revolutionary possibilities in the areas of diagnosis, treatment, and patient care. AI-driven solutions in dermatology are improving diagnostic accuracy by analyzing medical photos to identify skin disorders such as melanoma with a level of precision comparable to that of professional dermatologists. These technologies utilize machine learning algorithms that have been trained on extensive datasets to detect minor patterns and anomalies in skin lesions. This enables the early detection of skin conditions and the creation of personalized treatment strategies. Moreover, artificial intelligence (AI) assists in optimizing the efficiency of workflows, minimizing diagnostic inaccuracies, and enhancing the availability of dermatological treatment, particularly in places with limited access to healthcare services. Within the field of psychology, artificial intelligence (AI) has a significant influence. It is utilized to assess individuals' mental well-being by analyzing their text and speech patterns, employing techniques such as natural language processing and sentiment analysis. Artificial intelligence algorithms have the capability to observe and analyze patient interactions, offering immediate and accurate observations regarding emotional states and possible psychological illnesses. The incorporation of AI enables more precise evaluations and prompt actions, enhancing conventional therapeutic methods. In addition, chatbots and virtual therapists that utilise artificial intelligence provide instant assistance and ongoing supervision, thereby extending the accessibility of mental health care. The integration of AI with dermatology and psychology not only improves diagnostic skills and treatment results but also highlights the significance of interdisciplinary collaboration in advancing healthcare. By using artificial intelligence (AI), these industries can utilize data-driven analysis to provide more accurate, tailored, and easily accessible healthcare, thereby enhancing patient results and quality of life.

Temporal-Difference Graph-Based Optimization for High-Quality Reconstruction of MODIS NDVI Data

The Normalized Difference Vegetation Index (NDVI) is a crucial remote-sensing metric for assessing land surface vegetation greenness, essential for various studies encompassing phenology, ecology, hydrology, etc. However, effective applications of NDVI data are hindered by data noise due to factors such as cloud contamination, posing challenges for accurate observation. In this study, we proposed a novel approach for employing a Temporal-Difference Graph (TDG) method to reconstruct low-quality pixels in NDVI data. Regarding spatio-temporal NDVI data as a time-varying graph signal, the developed method utilized an optimization algorithm to maximize the spatial smoothness of temporal differences while preserving the spatial NDVI pattern. This approach was further evaluated by reconstructing MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m Grid (MOD13Q1) products over Northwest China. Through quantitative comparison with a previous state-of-the-art method, the Savitzky–Golay (SG) filter method, the obtained results demonstrated the superior performance of the TDG method, and highly accurate results were achieved in both the temporal and spatial domains irrespective of noise types (positively-biased, negatively-biased, or linearly-interpolated noise). In addition, the TDG-based optimization approach shows great robustness to noise intensity within spatio-temporal NDVI data, suggesting promising prospects for its application to similar datasets.

Impact Analysis of Vegetation FVC Changes and Drivers in the Ring-Tarim Basin from 1993 to 2021

As an ecologically sensitive area with significant desertification problems, the Ring-Tarim Basin has a fragile ecological environment that is vulnerable to both natural and anthropogenic factors. Accurate long-term vegetation observations are ecologically, socially, and economically important for desertification control. In this study, based on the ground-measured data and the fractional vegetation cover (FVC) inversion dataset obtained by the image element dichotomy method, we used the methods of slope-trend analysis and multiple-regression residual analysis to analyze the spatial and temporal characteristics of the vegetation cover in the desertified area of the Ring-Tarim Basin. At the same time, we assessed the impacts of climate change and human activities on vegetation changes and the contribution of driving forces. The results showed that (1) The annual mean value of FVC in the growing season in the Ring-Tarim Basin generally showed a fluctuating and increasing trend during the period of 1993–2021; a decreasing trend during 1993–1999, with a change rate of −0.13 × 10−2a−1; and the fastest increasing trend during 2010–2021, with a change rate of 0.23 × 10−2a−1. (2) The effects of climate change and human activities on FVC changes in the growing season had great spatial heterogeneity. The areas where climate change and human activities had no significant effect on FVC changes in the growing season accounted for 86.25% and 77.91%, respectively, the areas where climate and human activities promoted FVC increase in the growing season accounted for 10.53% and 16.37%, respectively, and the areas where climate and human activities inhibited FVC increase in the growing season accounted for 3.22% and 5.72%, respectively. (3) About 76.9% of the FVC changes in the area around the Ring-Tarim Basin were caused by climate change and human activities. In addition to the eastern part of the study area, the vegetation cover of the oases in the west, north, and south generally showed an increasing trend, and the increasing area was proportional to the distribution density of the oasis cities. The trend of vegetation change in the area of the oasis and the fringes of the oasis was drastic. The contribution and inhibition of human activities to FVC, and the driving force of FVC change were greater than that of climate change. More than half of the area had an anthropogenic contribution of more than 60%, indicating that China’s ecological projects have had a significant effect on vegetation change in the extreme arid regions.

The Development of a Hailstone Disdrometer and Its Preliminary Observation in Aksu, Xinjiang

Hailfall is a severe local weather event that can cause great economic losses as well as the loss of people’s property; however, it is still difficult for domestic meteorological stations to comprehensively observe hail, and domestic independently developed hail observation instruments are still scarce. To help enable better automatic hail observations, a new independently developed hailstone disdrometer based on the acoustic principle, which can be used to measure the hailstone number and particle size and to calculate the corresponding equivalent liquid precipitation of hailstones, is proposed in this paper. The characteristics of hailstones were preliminarily analyzed using observation data from two hailstone disdrometers installed in Aksu, Xinjiang, where three hail events were observed via the hailstone disdrometer in the summer of 2023. By analyzing the development of deep convection clouds using the Fengyun 4A satellite-based cloud-top brightness temperature, and synoptic conditions based on the fifth-generation global climate reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (the ECMWF ERA5 dataset), the hail formation mechanism was investigated in detail for one hailfall event. Accurate hail observations are an important basis for understanding spatiotemporal hail variation. The hailstone disdrometer proposed in this study offers a useful approach for domestic hail observation to provide first-hand hail information for the inspection of weather modification effects and disaster prevention and reduction.

The incidence of periungual desquamation and thrombocytosis in Kawasaki disease and the importance of systematic observation in the subacute phase.

Periungual desquamation and thrombocytosis are characteristic of the subacute phase of Kawasaki disease (KD). However, accurate observations of periungual desquamation and thrombocytosis are lacking. This retrospective study included patients with acute-phase KD who received treatment at seven affiliated university hospitals in Korea between 2015 and 2017. Data were extracted from an anonymized registry established by the Korean Society of Kawasaki Disease. We investigated whether the findings of patients observed according to a set protocol until the subacute stage (group I) were different from those of patients observed without the use of a protocol (group II). A total of 879 patients with KD were included in the analysis. Periungual desquamation was observed in 85% and 12.7% of patients in groups I and II, respectively. Thrombocytosis was observed in 76.7% and 44.7% of patients in groups I and II, respectively. Furthermore, compared to the initial test, the platelet counts of patients increased 100% and 67.9% in group I and II, respectively. When incomplete KD was defined only by the main symptoms during the acute stage and the diagnostic criterion of periungual desquamation during the subacute stage was excluded, the significant difference in the incidence of incomplete KD between groups I and II was no longer apparent. Performing regular and detailed observations has resulted in a higher incidence of periungual desquamation and thrombocytosis during the subacute phase of KD than those reported in recent studies. This indicates that until now, we have been neglecting the observation of symptoms and signs during the subacute phase. Regular monitoring during this period can also aid in differentiating suspected cases of KD and facilitate appropriate follow-up of complications.

Aharonov-Bohm effect in Generalized Electrodynamics

The Aharonov-Bohm (AB) effect is considered in the context of Generalized Electrodynamics (GE) by Podolsky and Bopp. GE is the only extension to Maxwell electrodynamics that is locally U(1)-gauge invariant, admits linear field equations and contains higher-order derivatives of the vector potential. GE admits both massless and massive modes for the photon. We recover the ordinary quantum phase shift of the AB effect, derived in the context of Maxwell electrodynamics, for the massless mode of the photon in GE. The massive mode induces a correction factor to the AB phase shift depending on the photon mass. We study both the magnetic AB effect and its electric counterpart. In principle, accurate experimental observations of AB the phase shift could be used to constrain GE photon mass.

Distributionally robust origin–destination demand estimation

Gaining a good understanding of the travel demands of a city or region is extremely important for many transportation applications. For stochastic origin–destination (OD) estimation problems, an accurate distribution assumption or observation of OD estimates or data is usually desired but not always available. In this paper, we establish a novel two-stage OD estimation framework based on distributionally robust optimization (DRO) and quasi-sparsity property of large-scale OD demand matrices. The proposed two-stage Distributionally Robust Quasi-Sparsity OD estimation (DR-QSOD) model does not require an accurate or complete distribution assumption of estimates/data. Numerical results demonstrate that DR-QSOD model outperforms stochastic QSOD model in estimating OD demands when the distribution assumption of data is biased. This paper also discusses two different approaches to solve the DR-QSOD model as well as compares their computational efficiency. In addition, DR-QSOD model is shown to keep relatively high quasi-sparsity consistency, which also brings lots of meaningful practical insights.

Isochrone fitting of the open cluster M67 in the era of Gaia and improved model physics

ABSTRACT The Gaia mission has provided highly accurate observations that have significantly reduced the scatter in the colour–magnitude diagrams of open clusters. As a result of the improved isochrone sequence of the open cluster M67, we have created new stellar models that avoid commonly used simplifications in 1D stellar modelling, such as mass-independent core overshooting and a constant mixing length parameter. This has enabled us to deliver a precise isochrone specifically designed for M67, available for download. We follow a commonly used qualitative approach to adjust the input physics to match the well-defined colour–magnitude sequence, and we test the model-predicted masses against a known eclipsing binary system at the main sequence turnoff of the cluster. Despite using improvements in photometry and stellar physics we cannot match the masses of both binary components with the same theoretical isochrone. A $\chi ^{2}$-based isochrone fitting approach using our preferred input physics results in a cluster age of $3.95^{+ 0.16}_{- 0.15}$ Gyr.

Linear and nonlinear regression modelling of industrial dye adsorption using nanocellulose@chitosan nanocomposite beads

Nanocellulose@chitosan (nc@ch) composite beads were prepared via coagulation technique for the elimination of malachite green dye from aqueous solution. As malachite green dye is highly used in textile industries for dyeing purpose which after usage shows fatal effects to the ecosystems and human beings also. In this study the formulated nanocellulose@chitosan composite beads were characterized by Particle size analysis (PSA), Field emission scanning electron microscopy (FESEM), Fourier-transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analysis were done to evaluate nanoparticles size distribution, morphological behaviour, functional group entities and degree of crystallinity of prepared beads. The nanocomposite beads adsorption performance was investigated for malachite green (MG) dye and BET analysis were also recorded to know about porous behaviour of the nanocomposite beads. Maximum removal of malachite green (MG) dye was found to be 72.0 mg/g for 100 ppm initial dye concentration. For accurate observations linear and non-linear modelling was done to know about the best-fitted adsorption model during the removal mechanism of dye molecules, on evaluating it has been observed that Langmuir isotherm and Freundlich isotherm show best-fitted observation in the case of linear and non-linear isotherm respectively (R2 = 0.96 & R2 = 0.957). In the case of kinetic linear models, the data was well fitted with pseudo-second-order showing chemosorption mechanism (R2 = 0.999), and in the case of non-linear kinetic model pseudo first order showed good fit showing physisorption mechanism during adsorption (R2 = 0.999). The thermodynamic study showed positive values for ΔH° and ΔS° throughout the adsorption process respectively, implying an endothermic behaviour. In view of cost effectiveness, desorption or regeneration study was done and it was showed that after the 5th cycle, the removal tendency had decreased from 48 to 38 % for 20–100 ppm dye solution accordingly. Thus, nanocomposite beads prepared by the coagulation method seem to be a suitable candidate for dye removal from synthetic wastewater and may have potential to be used in small scale textile industries for real wastewater treatment.