Unique Methodology Research Articles

Mineral Resource Abundance: An Assessment Methodology for a Responsible Use of Mineral Raw Materials in Downstream Industries

The sustainability of mineral resources and, in particular, their abundance is a topic of growing interest. Nevertheless, the abundance of mineral raw materials is an extremely complex notion as it not only encompasses geological considerations but also environmental, technical, economic, and social constraints. In addition, to the best of our knowledge, no tools are currently available to allow a comprehensive evaluation of mineral raw material abundance. This research paper, therefore, aims to present an innovative and unique methodology to evaluate the abundance of non-energy mineral resources and determine a mineral abundance index (MAI). Based on a multicriteria analysis, MAI considers the natural abundance of a mineral raw material in the Earth’s crust and its availability on the market and integrates the influence of factors that could constrain or promote future market changes. This new index ranging from 0 (very scarce) to 100 (very abundant) aims to qualify the abundance of mineral resources in a simple and rapid manner based on published and reliable data. This new methodology could be a powerful decision-making support tool for any downstream industrials and end-users making use of mineral raw materials.

REGIONAL ECONOMY AND MARKETING OF TERRITORIES: THE METHODOLOGY OF INTERDISCIPLINARY RESEARCH

Interdisciplinary research allows to receive versatile and more profound scientific knowledge about a complex natural phenomenon or an ambiguous event of social life by using approaches and methods of diverse branches of knowledge. The main condition of using these approaches and methods of diverse branches of knowledge in one and the same research is the generality of the object and the subject, the core purpose. For example, studying the motives of labor behavior we may combine approaches and methods of economy, sociology, psychology; studying the causes of agression we may use knowledge in biochemistry of primate brain along with the rest. In every single case the researcher builds a unique and original methodology of research. Quite often interdisciplinary research are made in adjacent fields of knowledge (economic theory, labor economy, management theory). Fields of knowledge that can be called interdisciplinary are based on interdisciplinary research. Regional economy is an interdisciplinary branch of science. Its structure includes knowledge of economy, sociology, geography, geology, etc. In this article the idea of amplification the interdisciplinary branch of science “Regional economy” by methods of marketing the territories is proved. This way of regional econo

GeeSEBAL-MODIS: Continental-scale evapotranspiration based on the surface energy balance for South America

Monitoring actual evapotranspiration (ET) is critical for the accurate assessment of water availability and water resources management, especially in areas with dry climates and frequent droughts. The Surface Energy Balance Algorithm for Land (SEBAL) has been used over several land and climate conditions, and is able to estimate ET at field scale with high accuracy. However, model complexity and subjective parameterization have hindered its operationalization, until the recent development of the geeSEBAL model, which implements the SEBAL model on the Google Earth Engine platform. Here, we present a unique methodology for a continental-scale application of SEBAL, called geeSEBAL-MODIS, that employs novel land surface temperature normalization techniques, enabling the application of contextual ET models to very large scales. We introduce a dynamic ET dataset for the entire South American continent, between 2002 and 2021, at 500 m spatial and 8 days temporal resolution. The satellite-based data were compared against daily ET measured at 27 flux towers as well as water balance-based annual ET from 29 large river basins. geeSEBAL-MODIS data were also compared to eight state-of-the-art global ET datasets. At local scale, geeSEBAL-MODIS demonstrated a satisfactory performance (correlation (r) = 0.65, Kling-Gupta Efficiency (KGE) = 0.64, Mean Absolute Error (MAE) = 0.83 mm day−1 (24.7 %) and Root Mean Squared Error (RMSE) = 1.07 mm day−1 (31.8 %)), with negligible bias. At basin scale, geeSEBAL-MODIS generally underestimated ET (bias = -85 mm year−1, r = 0.65, KGE = 0.47, MAE = 107 mm year−1 (10.1 %) and RMSE = 137 mm year−1 (12.9 %)). Compared to other global datasets, geeSEBAL-MODIS demonstrated better performance over multiple South American biomes, climates and land cover types. The developed dataset also provides lower errors (local monthly RMSE = 23.0 mm month−1 and basin annual RMSE = 138 mm year−1) and when compared to the performance of the global datasets (local monthly RMSE between 23.9 and 30.1 mm month−1 and basin annual RMSE between 161 and 308 mm year−1). The analyses demonstrate that geeSEBAL-MODIS can be used as a tool for monitoring climate change and human-related impacts on ET. The geeSEBAL-MODIS model opens the path for high accuracy global ET monitoring at moderate to high resolution, supporting advances in water resources management around the globe.

Corticosteroid treatment prediction using chest X-ray and clinical data

Background and ObjectiveSevere courses of COVID-19 disease can lead to long-term complications. The post-acute phase of COVID-19 refers to the persistent or new symptoms. This problem is becoming more relevant with the increasing number of patients who have contracted COVID-19 and the emergence of new virus variants. In this case, preventive treatment with corticosteroids can be applied. However, not everyone benefits from the treatment, moreover, it can have severe side effects. Currently, no study would analyze who benefits from the treatment. MethodsThis work introduces a novel approach to the recommendation of Corticosteroid (CS) treatment for patients in the post-acute phase. We have used a novel combination of clinical data, including blood tests, spirometry, and X-ray images from 273 patients. These are very challenging to collect, especially from patients in the post-acute phase of COVID-19. To our knowledge, no similar dataset exists in the literature. Moreover, we have proposed a unique methodology that combines machine learning and deep learning models based on Vision Transformer (ViT) and InceptionNet, preprocessing techniques, and pretraining strategies to deal with the specific characteristics of our data. ResultsThe experiments have proved that combining clinical data with CXR images achieves 8% higher accuracy than independent analysis of CXR images. The proposed method reached 80.0% accuracy (78.7% balanced accuracy) and a ROC-AUC of 0.89. ConclusionsThe introduced system for CS treatment prediction using our neural network and learning algorithm is unique in this field of research. Here, we have shown the efficiency of using mixed data and proved it on real-world data. The paper also introduces the factors that could be used to predict long-term complications. Additionally, this system was deployed to the hospital environment as a recommendation tool, which admits the clinical application of the proposed methodology.

Hybrid model to improve reference evapotranspiration prediction: Integrating ANN and PSO

Reference evapotranspiration (ETo), one of the key elements of the hydrological cycle, is crucial for managing irrigation and drainage systems. In order to estimate monthly ETo, this study tested the prediction abilities of a unique hybrid methodology that coupled data pre-processing with a hybrid model composed of an artificial neural network (ANN) and particle swarm optimisation (PSO). In order to train and evaluate the model, monthly meteorological data were collected in Al-Kut City, Iraq, from 1990 to 2020. A range of statistical indicators were used to assess the model, including RMSE, NSE, and R2. The outcomes showed that the model, with a coefficient of determination of 0.93, is effective and has good simulation levels.
 

Biomimetic tail-to-head terpene cyclizations using the resorcin[4]arene capsule catalyst.

The tail-to-head terpene (THT) cyclization is a biochemical process that gives rise to many terpene natural product skeletons encountered in nature. Historically, it has been difficult to achieve THT synthetically without using an enzyme. In this protocol, a hexameric resorcin[4]arene capsule acts as an artificial enzyme mimic to carry out biomimetic THT cyclizations and related carbocationic rearrangements. The precursor molecule bears a leaving group (usually an alcohol or acetate group) and undergoes the THT reaction in the presence of the capsule catalyst and HCl as a cocatalyst. Careful control of several parameters (including water content, amount of HCl cocatalyst, temperature and solvent) is crucial to successfully carrying out the reaction. To facilitate the application of this unique capsule-catalysis methodology, we therefore developed a very detailed procedure that includes the preparation and analysis of all reaction components. In this protocol, we describe how to prepare two different terpenes: isolongifolene and presilphiperfolan-1β-ol. The two procedures differ in the water content required for efficient product formation, and thus exemplify the two common use cases of this methodology. The influence of other crucial reaction parameters and means of precisely controlling them are described. A commercially available substrate, nerol, can be used as simple test substrate to validate the reaction setup. Each synthetic procedure requires 5-7 d, including 1-5 h of hands-on time. The protocol applies to the synthesis of many complex terpene natural products that would otherwise be difficult to access in synthetically useful yields.

Assessment of Lifecourse Social Exposome in Brain Bank Decedents

AbstractBackgroundThere is increasing evidence that the social exposome impacts the development of Alzheimer’s disease and related dementias (ADRD). The social exposome includes factors such as neighborhood disadvantage, and is thought to impact brain health across the lifecourse, particularly during sensitive life stages such as early childhood. Residential histories are required to assess dosage and timing of exposures, yet are rarely categorized and linked to brain bank cohorts. Our objective was to characterize the lifecourse neighborhood disadvantage across two Alzheimer’s Disease Research Center (ADRC) brain banks.MethodAfter abstracting publicly available residential data, we constructed residential histories using validated metrics of neighborhood disadvantage, nationwide Area Deprivation percentile rankings (ADI), constructed for each census timeframe from 1910‐present, to measure lifecourse social exposome time‐concordantly aligned to participant life‐year. ADIs were linked for all available years between birth and death, ranging from age 0 to 103. Greater ADI values denote living in higher neighborhood disadvantage. These ADIs were linked to the residential histories for each decedent and used to calculate two sensitive life stages: ages 0‐19 and 40‐60. A repeated measures one‐way ANOVA determined variance in ADI both within and between these sensitive life stages.ResultThis sample contained 1309 decedents from two brain. To date, 1230 decedents have been linked to at least one ADI timepoint, with an average of 81% (71‐88%) of all life‐course years accounted for via ADI linkage. The mean ADI for the 0‐19 period was 23.5 (SD = 22.3, 5.0‐36.9), while the 40‐60 period was 22.0 (21.6, 5.0‐34.0). A repeated measures one‐way ANOVA found a statistically significant difference in ADI between the two windows of time.ConclusionTissue banked samples are not typically characterized on social determinants of health. Here we show that determining lifecourse social exposomes are feasible using publicly available data. To our knowledge, this is the first time such characterization has been successfully performed. Efforts to link decedents to ADI timepoints are still ongoing, which will reduce missingness, enhance measurement precision, and allow for comparison between all life stages. This work provides a unique methodology for assessing the social exposome in relation to ADRD brain pathology.

Envisaging AR travel revolution for visiting heritage sites: A mixed-method approach

Augmented Reality (AR) offers transformative potential for augmenting and replacing realities within a wide range of business and social applications. Within this, this study explores antecedents of behavioral adoption of AR mobile travel apps to visit a heritage site by substituting in-person and corporeal tourism experiences through a visualization technique that overlays 3D simulated architecture in front of a user. This research specifically investigates the collective impact of perceived app attributes of the AR service model, and critical characteristics of users in terms of their underlying psychological predispositions impacting their behavioral intention to use AR travel apps for heritage tourism in an emerging nation. In pursuance of this, we adopt a unique mixed-method research methodology where industry experts and travel enthusiasts are invited to delve into the phenomenon and identify facilitators of AR mobile app adoption for heritage tourism in India. Empirical findings find that experiential authenticity, service quality, and user innovativeness are the significant underlying factors expediting the use of AR mobile apps. Additionally, with a focus on the sustainability aspect of AR tourism, this study considers sustainability within travel. This construct is critical since it discerns how it affects the boundary conditions in the light of consumers' personality characteristics for determining intention to use AR apps, thereby providing insightful inferences for the tourism industry and research.

Experimental approach for assessing dissipated excess pore pressure-induced settlement

Upon dynamic loading, saturated soils lose their strengths and undergo deformations resulting in volumetric-induced settlements that vary according to the excess pore pressure generation and dissipation variations. Traditionally, these settlements have been evaluated using standard charts based on one soil type and its relative density (RD). To assess these settlements, this study established a unique experimental methodology based on two laboratory testings: triaxial simple shear and piezoelectric ring actuator technique. Fifty-seven tests were performed on Ottawa F65 sand under strain-controlled cyclic and post-cyclic conditions. A chart was generated, revealing a relationship between the dissipated energy from cyclic loading and volumetric strain ( εv ), based on the shear wave velocity as a controlling factor. This study was compared with previous studies to verify the compatibility of the proposed approach. Another novelty was revealed by studying εv variation with the dissipated pressure. This variation is presented in a post-seismic chart in which deformations are tracked based on the initial soil state and maximum excess pore pressure generation ratio ( Rumax) at the end of the loading phase. For each RD, the soil is divided between liquefied and non-liquefied states according to a specific Rumax ( Rumax-trigger point). The calculation of the volume compressibility coefficient is proven to serve as a liquefaction-triggering criterion identifying the liquefied state.

Evaluation of distribution of emerging mycotoxins in human tissues: applications of dispersive liquid–liquid microextraction and liquid chromatography-mass spectrometry

In this work, a complete study of the distribution of emerging mycotoxins in the human body has been carried out. Specifically, the presence of enniatins (A, A1, B, B1) and beauvericin has been monitored in brain, lung, kidney, fat, liver, and heart samples. A unique methodology based on solid–liquid extraction (SLE) followed by dispersive liquid–liquid microextraction (DLLME) was proposed for the six different matrices. Mycotoxin isolation was performed by adding ultrapure water, acetonitrile, and sodium chloride to the tissue sample for SLE, while the DLLME step was performed using chloroform as extraction solvent. Subsequently, the analysis was carried out by high-performance liquid chromatography coupled to tandem mass spectrometry (HPLC–MS/MS). The proposed method allowed limits of quantification (LOQs) to be obtained in a range of 0.001–0.150 ng g−1, depending on the tissue and mycotoxin. The precision was investigated intraday and interday, not exceeding of 9.8% of relative standard deviation. In addition, trueness studies achieved 75 to 115% at a mycotoxin concentration of 25 ng g−1 and from 82 to 118% at 5 ng g−1. The application of this methodology to 26 forensic autopsies demonstrated the bioaccumulation of emerging mycotoxins in the human body since all mycotoxins were detected in tissues. Enniatin B (ENNB) showed a high occurrence, being detected in 100% of liver (7 ± 13 ng g−1) and fat samples (0.2 ± 0.8 ng g−1). The lung had a high incidence of all emerging mycotoxins at low concentrations, while ENNB, ENNB1, and ENNA1 were not quantifiable in heart samples. Co-occurrence of mycotoxins was also investigated, and statistical tests were applied to evaluate the distribution of these mycotoxins in the human body.Graphical

An adaptive B-spline representation of topology optimization design for Additive Manufacturing

Topology optimization is a renowned structural optimization approach used to compute the optimal topology for the enhancement of structural performance. It has been in common practice in different engineering fields such as automobile and aerospace. However, there still exist gaps between topology optimization and its engineering operations, which considerably impedes topology optimization’s applications. One of these gaps is how to make (especially finite element-based) topology optimization results machine-readable, which means, how to transform these into computer-aided design (CAD) models, which are ready-to-use models for manufacturing. In the proposed work the authors adopted a unique methodology that plugs the gap between topology optimization and manufacturing. This methodology seamlessly integrates the structural analysis tool, boundary description tool and standard CAD geometry representation; equipped with the Hausdorff distance approach for 3D printing/additive manufacturing of optimal structures. Initially, we extract the skeleton of the optimal design (in the form of a points cloud) using the level set method, followed by an interpolation technique with shape preservation property, the extracted skeleton is made dense. Incorporating the shape and curvature information of the data set, an adaptive B-spline approximation is devised for fitting a smooth curve through the points cloud. This approach interprets topology optimization results as a parametric CAD model with minimum possible control points and minimum approximation error. The CAD-based optimal designs are then used for additive manufacturing. The numerical results exhibit in detail the validity and accuracy of the proposed method.

Exploring national digital transformation and Industry 4.0 policies through text mining: a comparative analysis including the Turkish case

Purpose This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking. Design/methodology/approach With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context. Findings Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT. Practical implications The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies. Originality/value This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

Феномен русскоязычной литературы народов Поволжья в современном научном дискурсе

For the first time, the article examines the primary methods employed in the study of Russian literature in the Volga Region (Mari and Tatar) in contemporary scientific discourse. Aim: to reveal the epistemiologic potential of the most significant literary approaches and a new theoretical thesaurus, formed from the experience of studying Russian literature in the Volga Region in the early 21 century. Research material — scientific works of leading specialists and philologists in the field of Russian literature (works of I.P. Karpov, I.A. Yenikeeva, Ya.G. Safiullina, K.K. Sultanova). It is stated that the anthropological approach was first applied in I.P. Karpov’s research on the works of Mari El prose writers and poets, writing in Russian. Russian scientist expands the horizons of Mari literature itself due to the fact that the works of Russian-speaking authors, on the one hand, are inscribed in the national Mari context and, on the other, are part of Russian literature. Compared to I.P. Karpov’s strategy of addressing the text, I.A. Enikeev, a researcher of Russian-language literature in Tatarstan, draws attention to the context and develops a unique methodology based on the interaction of factual and biographical principles in solving the problem of the status of Russian-language works in the modern historical and literary context. The novelty of the presented material lies in the systematization of methodological approaches to the problem of Russian-language literatures of the Finno-Ugric and Turkic peoples of the Volga region in a comparative aspect. The tendencies toward expanding the theoretical thesaurus of concepts characteristic of modern scientific discourse and updating the semantics of existing terms are revealed in the field of research on the Russian-language creativity of poets and prose writers of the Volga region.

Strategies for efficient resource management in federated cloud environments supporting Infrastructure as a Service (IaaS)

The increased market activity and visibility have resulted in a growing demand for additional computer resources to meet the needs of cloud clients. Delivering promised quality of service (QoS) while fulfilling diverse resource demands dynamically might be difficult for a single Cloud Service Provider (CSP). Federated cloud computing, also known as distributed cloud computing, is an established paradigm in which CSPs collaborate to aggregate unused resources, resulting in financial and QoS benefits. Notably, this strategy improves availability and reliability while overcoming individual CSP difficulties in preserving QoS amid fluctuations in resource demand. The federated cloud successfully leverages computing resources even during low-demand periods, demanding a comprehensive resource management strategy for Infrastructure as a Service (IaaS) inside participating CSPs. Such a strategy is crucial for preserving QoS, ensuring availability and dependability, and optimizing underutilized computing resources. This research presents the novel IaaS cloud design, revealing a unique methodology that reimagines traditional cloud systems. The proposed IaaS cloud framework investigates virtual machine migration and resource consolidation, building a strong foundation founded on IaaS principles and emphasizing the crucial role of virtualization. The technique introduces ground-breaking concepts such as Cloud User (CU) and Reputation Management, which are strengthened by specific algorithms that improve cloud service security and trust. Furthermore, the combination of Trust Manager (TM) and Broker Manager (BM) components strengthens SLA control and trust evaluation, aligning smoothly with IaaS standards to improve service quality and reliability. User Profiling, which is classified into private, social, and corporate profiles, provides a separate lens for successful cloud user management inside the IaaS landscape, allowing for customised service delivery. SMI and cutting-edge ranking algorithms, such as the Deep Q-based Algorithm, optimize cloud service provider selection and ranking—an important aspect of IaaS. The use of the Banker's algorithm and a comprehensive Service Level Agreement (SLA) Management plan provides efficient resource allocation, mirroring recognized IaaS standards. This research study not only throws light on these trailblazing techniques, but it also establishes a new standard for IaaS cloud design and resource management.

Federated Machine Learning Using The Internet Of Medical Things For Cardiac Disease Detection

Federated Machine Learning Using The Internet Of Medical Things For Cardiac Disease Detection

On the solutions of some nonlinear fractional partial differential equations using an innovative and direct procedure

In this article, a highly effective technique is implemented to obtain the approximate solutions of strongly nonlinear fractional order partial differential equations (NFPDEs). The findings of this study show the successful behavior of the fractional novel analytical method (FNAM), which can be used successfully for the solutions of common, severe NFPDEs. In the proposed method, the nonlinearity in each mathematical model is directly handled by using fractional Taylor series, which reduces the calculation effort. In this work, the method's strength is primarily demonstrated on NFPDEs, and the obtained results are displayed via graphs and tables. From the numerical simulations, it is evident that the suggested technique has greater accuracy despite smaller calculations. It is the most straightforward method for determining the formulaic solution to any type of NFPDE and is considered to be the unique numerical methodology.

Do oil droplets and chemical dispersants contribute to uptake of oil compounds and toxicity of crude oil dispersions in cold-water copepods?

ABSTRACT Accidental crude oil spills to the marine environment cause dispersion of oil into the water column through the actions of breaking waves, a process that can be facilitated using chemical dispersants. Oil dispersions contain dispersed micron-sized oil droplets and dissolved oil components, and the toxicity of oil dispersions has been assumed to be associated primarily with the latter. However, most hydrophobic, bioaccumulative and toxic crude oil components are retained within the droplets which may interact with marine filter-feeders. We here summarize the findings of 15 years of research using a unique methodology to generate controlled concentrations and droplet size distributions of dispersed crude oil to study effects on the filter-feeding cold-water copepod Calanus finmarchicus. We focus primarily on the contribution of chemical dispersants and micron-sized oil droplets to uptake and toxicity of oil compounds. Oil dispersion exposures cause PAH uptake and oil droplet accumulation on copepod body surfaces and inside their gastrointestinal tract, and exposures to high exposure (mg/L range) reduce feeding activity, causes reproductive impairments and mortality. These effects were slightly higher in the presence of chemical dispersants, possibly due to higher filtration of chemically dispersed droplets. For C. finmarchicus, dispersions containing oil droplets caused more severe toxic effects than filtered dispersions, thus, oil droplets contribute to the observed toxicity. The methodology for generating crude oil dispersion is a valuable tool to isolate impacts of crude oil microdroplets and can facilitate future research on oil dispersion toxicity and produce data to improve oil spill models.

GAGPT-2: A Geometric Attention-based GPT-2 Framework for Image Captioning in Hindi

Image captioning frameworks usually employ an encoder-decoder paradigm, with the encoder receiving abstract image feature vectors as input and the decoder for language modeling. Nowadays, most prominent architectures employ features from region proposals derived from object detection modules. In this work, we propose a novel architecture for image captioning. We employ the object detection module integrated with transformer architecture as an encoder and GPT-2 (Generative Pre-trained Transformer) as a decoder. The encoder utilizes the information of the spatial relationships among detected objects. We introduce a unique methodology for image caption generation in Hindi, which is widely spoken in South Asia and India and is the world’s third most spoken language as well as India’s official language. In terms of BLEU scores, the proposed approach’s performance is comparable to those of other baselines, and the results illustrate that the proposed approach outperforms the other baselines. The efficacy of the proposed approach in generating correct captions is further determined by human assessment in terms of adequacy and fluency.

Beeja Chatushtaya - A Special Unique Contribution of Sushruta Samhita

Beeja Chatushtaya or Chikitsa Chatustaya is an important unique methodology explained by Acharya Sushruta. It is the fundamental principle that establishes the foundation for the Sushruta Samhita. On the basis of this principle, the whole Shusruta Samhita has been developed. Beeja Chatushtaya includes Purusha (individual person), Vyadhi (diseases), Aushadha (medicine) and Kriyakaal (time of administration of treatment). Purusha is believed to be the combination of Panchamahabhuta and Atma. Karma Purusha provides Adhishtana and Ashraya for Vyadhi and Chikitsa. Every type of Shareerika, Manasika, and Agantuja Vedana are included in the Roga, Dukkha, or Vedana. Dukkha Nivrutti can be obtained through administration certain drugs and other procedures are used to implement certain Kriya or Chikitsa principles. In Beeja Chatushtaya Kriya, Swasthanuvartana chikitsa, Snehanadi karma, and Shastradi karma are all mentioned. Snehapana in Prabhatakaal, Swedana jeernaanna Kaal, Vamana in Purvahna, Virechana in Prataha Kaal, Asthapana in Madhyahna Kaal, and Anuvasana in Sayankaal (Pradosha Kaal after meal) are examples of Kaal. Kaal also refers to Avasthika Kaal, such as Langhana in Jwara's samavastha and Ghritapana in Jeerna jwara avastha. In this article, an attempt has been made to analyse the various measures and concepts of Beeja Chatushtaya described in a dispersed manner in Sushruta Samhita, which are collected, compiled and discussed.

The Bogs in a Forest–Steppe Region of Western Siberia: Plant Biomass and Net Primary Production (NPP)

Hereby, we report the new experimental data of plant biomass and biological productivity (net primary production, NPP) of wetland/peatland plant communities of oligotrophic bogs located on the same latitude, but at a distance of 1300 km west-east in the forest–steppe region of Western Siberia. The data were collected by implementing direct in situ measurements based on a unique methodology developed by authors. The study revealed that the values of net primary production (NPP) are basically constrained by the live fraction of plant biomass (or phytomass). It also revealed that the dead fraction of plant biomass (mortmass), along with the live fraction of biomass (phytomass) and net primary production (NPP), all major components of the carbon cycle, differ significantly at the two study sites. The values of dead biomass (mortmass), live biomass (phytomass) and net primary production (NPP) were found at significantly higher values in bog ecosystems studied in western regions than in those of the eastern regions.