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Industrial processes are constantly changing owing to increasing demand and technological growth throughout the world. These changes influence environmental, economic, and social pillars of sustainable development to a large extent. So, production needs to be carried via considering the viewpoint of sustainable development which should include multi-criteria decision-making in planning. Including multiple criteria in planning is significant as this inclusion lessens the damaging effects on different pillars of sustainable development. Also, environmental policies are usually framed such that carbon emission need not exceed certain limit. In this paper, an optimal production mix of process routes is determined while satisfying a predefined maximum limit on total carbon emission to obtain most satisfied solution (MSS) for multiple conflicting criteria. This novel approach provides an extension to TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) methodology, and an integrated TOPSIS-pinch analysis model is proposed to determine multi-criteria-prioritized factor for ranking of new process routes in carbon-constrained production planning. The obtained MSS provides a unique solution that maximizes benefit and minimizes risk in sustainable production planning. For Iron and Steel industry, energy consumption is observed to be reduced by 74.05 × 106 GJ in comparison to the production plan given by (Sinha and Chaturvedi in J Clean Product 171:312–321, 2018), proposed method facilitates to include other objectives such as operating cost and profit. The proposed methodology is generic and can be applied to other allocation networks, an example is also discussed to show its applicability in water allocation network synthesis.

Breast cancer is among the most prevalent type of cancer among women. Mastectomy is a common surgical option for breast cancer, which usually leads to significant functional impairment. Fatigue, anxiety and depression are reported by mastectomy patients. This study aims to investigate the effect of a multimodal exercise program on improving functional impairment, fatigue, anxiety and depression after mastectomy. A 40-year-old female presented with abnormal tissue growth, pain and nipple discharge at the right breast. She was diagnosed with invasive ductal carcinoma stage I and undertook six weeks of neoadjuvant chemotherapy followed by modified radical mastectomy (MRM). Postoperatively, patient had a functional impairment, fatigue, anxiety and depression. An eight-week multimodal exercise program comprising ROM exercises, strength and endurance training, breathing exercises and functional activities was given. DASH for assessing upper extremity functions, BFI for assessing the impact of fatigue on daily activities and HADS for assessing anxiety and depression. An eight-week multimodal exercise program effectively improves functional impairment, fatigue, anxiety and depression in mastectomy patients with rheumatoid arthritis.

Stress treatment of microspore embryogenesis leads to the reprogramming of male gametophyte development pathways, which generates haploid green plants. However, low green plant production efficiency is a major hindrance in the adoption of double haploid (DH) production platform for wheat breeding. The aim of the current study was to induce the expression of microspore embryogenesis associated genes by over expressing the RKD transcription factor using Transcription activator-like effector (TALE) proteins. The TALE protein can modulate the gene expression by recognizing and binding host plant promoter sequences. Taking into consideration, we have cloned and analysed RKD promoter from wheat and triticale and used for the custom synthesis of TaRKD-TALE protein. The isolated and purified protein was conjugated with R9 (Cys (Npys)-(D-Arg)9) cargo peptides and successfully transduced into wheat microspores. The expression of early-culture abundant (ECA1), RWP-RK domain-containing proteins (RKD1) and Tapetum determinant 1 (TPD1) marker genes in wheat linked to microspore embryogenesis was studied. The expression of all three (ECA1, TPD1 and RKD1) embryogenesis related marker genes in the TALE transduced microspore was significantly higher than the control. Microspore culture transduced by R9-TaRKD-TALE protein yielded a significantly higher number of embryo-like structures (ELS) and the total green plants in wheat cultivars AC Fielder. Conversely, the microspore cell death has not been significantly affected by the transduction of R9-TaRKD-TALE, while showing a higher recovery at 120 h of microspore culture. This work will be helpful to exploit the TALE protein to improve our understanding of DH production and other pathways related genes.

In this study, we assess the soil erosion susceptibility at the sub-basins scale using remote sensing data with the integration of the Geographical Information System (GIS) for the Netravati river basin in Western Ghats, India. The Cartosat digital elevation model was used for the calculation of morphometric parameters, and Landsat satellite images were used for land cover classification. The whole river basin was divided into five sub-basins for identifying the spatial variation of morphometric parameters and different land use categories. A weighted sum approach (WSA) was used for the ranking of each morphometric parameter and land use types. Then each sub-basin was assigned as very low, low, medium, high, and very high priority ranking. The final prioritization map based on the combined (morphometric and land cover) analysis showed erosion susceptibility is very high for sub-basin 4, high for sub-basin 2, medium for sub-basin 5, low for sub-basin 1, and very low for sub-basin 3. Further, the soil erosion susceptibility analysis has been done separately for morphometric and land cover at the sub-basins scale. The results of this study will be useful for the water resource manager and policymaker while considering the soil erosion-prone areas in the region, and the methodology can be adapted for other river basins.

Diabetes is a very common occurring disease, diagnosed by hyperglycemia. The established mode of diagnosis is the analysis of blood glucose level with the help of a hand-held glucometer. Nowadays, it is also known for affecting multi-organ functions, particularly the microvasculature of the cardiovascular system. In this work, an alternative diagnostic system based on the heart rate variability (HRV) analysis and artificial neural network (ANN) and support vector machine (SVM) have been proposed. The experiment and data recording has been performed on male Wister rats of 10–12 week of age and 200 ± 20 gm of weight. The digital lead-I electrocardiogram (ECG) data are recorded from control (n = 5) and Streptozotocin-induced diabetic rats (n = 5). Nine time-domain linear HRV parameters are computed from 60 s of ECG data epochs and used for the training and testing of backpropagation ANN and SVM. Total 526 (334 Control and 192 diabetics) such datasets are computed for the testing of ANN for the identification of the diabetic conditions. The ANN has been optimized for architecture 9:5:1 (Input: hidden: output neurons, respectively) with the optimized learning rate parameter at 0.02. With this network, a very good classification accuracy of 96.2% is achieved. While similar accuracy of 95.2% is attained using SVM. Owing to the successful implementation of HRV parameters based automated classifiers for diabetic conditions, a non-invasive, ECG based online prognostic system can be developed for accurate and non-invasive prediction of the diabetic condition.

It is a well-established fact that patients of Covid 19 have musculoskeletal dysfunctions in addition to cardiorespiratory and psychosocial problems. Musculoskeletal system is amongst the most common systems found to be affected in patients admitted in ICU for prolonged periods and some patients of Covid 19 do have to spend a reasonably long period in ICU’s. Prolonged hospital stay may be needed in Covid 19 patients having ARDS or pneumonia and symptoms of fever, cold and cough to monitor their recovery. This may result in musculoskeletal dysfunctions as demonstrated in some studies. Thus, treating the musculoskeletal system becomes a vital part of the regimen to be followed during the patients stay in the ICU which needs to be followed up even after the patient is shifted from the ICU by keeping a track of Physical Functional performance of the subjects after discharge. This experimental study was done on 150 Covid-19 positive subjects to see the long term effectiveness of a structured exercise protocol on the Physical Functional performance of Covid 19 patients. The results of the study were analysed statistically and both the outcome measures FIMS and POMA showed significant changes with p value

The climate change has a significant contribution in the uncertainty in the river flow. In this study, the uncertainty in the river flow of Pamba River Basin (PRB) in South India is investigated due to climate change impacts. In order to assess the hydrological impact in the basin for future preparedness and planning for sustainable use of water resources, an ensemble of five general circulation models (GCMs) and hydrological model SWAT (soil and Water Assessment Tool) were used. The objective of the present study was to understand the surface runoff change over the PRB in the near future (2016-2030) under representative concentration pathways (RCP) 4.5 and 8.5 of the downscaled ensemble GCM data. Furthermore, spatial runoff change at sub-basin scale and percentage runoff change at monthly scale in the PRB were assessed. Hence, to study the impact due to climate change, SWAT model was simulated with base period historical data (1984 – 2015) and future climate data (2016 – 2030), and then changes at spatial and monthly scale were plotted. The results shows that at basin scale, there is an overall increase in the mean runoff by the 1.4 % and 3.0% under RCP4.5 and RCP 8.5 scenarios respectively. At seasonal scale, winter shows a tremendous increase in the runoff with around 38% increase in both RCP 4.5 and RCP 8.5, followed by summer with 17.9% and 18.6% for RCP 4.5 and RCP 8.5 scenarios respectively. Notably, Monsoon witnesses a negative trend in both the scenarios with -18.6% and -15.5% runoff change from the base period. This study will be useful in future water resources management in the basin at micro-level due the spatial and temporal variations.Keywords: Climate change; SWAT model; GCMs; runoff change; spatial, and temporal change.

Climate change is considered as the important factor for change in water balancing components (WBCs) at the river basin scale. In this study, the Soil and Water Assessment Tool (SWAT) hydrological model is used for the assessment of WBCs for the Kalada river basin (KRB) in the Western Ghats, India. To assess the climate change impacts of near (2021 – 2040), mid (2041 – 2060), and far (2081 – 2100) future for moderate scenarios under representative concentration pathways (RCP) 4.5 and worse scenarios (RCP 8.5) were considered by using the present (2018) fixed land use. The multi-optimization techniques have been used for model calibration and verification of climatic data of the five General Circulation Models in the study area. The results indicated that the actual evapotranspiration (ET), surface runoff, and water yield are decreased (16 to 27%) in all-time slices for both RCP 4.5 and 8.5 emission scenarios but the decreasing trend is non-uniform. This is because of the decline of rainfall by 50 to 230 mm and the increase of temperature by 2 to 5 ℃ in the study area. Furthermore, results indicated that the wet season showed less decrease in comparison to winter and summer, but impacts are high because more than 80% of rain occurred in the monsoon season.Keywords: Water Balancing Components; Climate change; Surface runoff; GCMs; SWAT model.

In this study, the impacts of changes in land use/ land cover (LULC) and climate on surface runoff are investigated in the Kadalundi river basin, Western Ghats, India. Land Change Modeller (LCM) wa...

This study investigated the impacts of changes in land cover and climate on runoff and sediment yield in a river basin in India. Land Change Modeler was used to derive the future land cover and its...

Diabetes mellitus (DM) is a multifactorial disease characterized by hyperglycemia. The type 1 and type 2 DM are two different conditions with insulin deficiency and insulin resistance, respectively. It may cause atherosclerosis, stroke, myocardial infarction and other relevant complications. It also features neurological degeneration with autonomic dysfunction to meet metabolic demand. The autonomic balance controls the physiological variables that exhibit nonlinear dynamics. Thus, in current work, nonlinear heart rate variability (HRV) parameters in prognosis of diabetes using artificial neural network (ANN) and support vector machine (SVM) have been demonstrated. The digital lead-I electrocardiogram (ECG) was recorded from male Wister rats of 10–12 week of age and 200 ± 20 gm of weight from control (n = 5) as well as from Streptozotocin induced diabetic rats (n = 5). A total of 526 datasets were computed from the recorded ECG data for evaluating thirteen nonlinear HRV parameters and used for training and testing of ANN. Using these parameters as inputs, the classification accuracy of 86.3% was obtained with an ANN architecture (13:7:1) at learning rate of 0.01. While relatively better accuracy of 90.5% was observed with SVM to differentiate the diabetic and control subjects. The obtained results suggested that nonlinear HRV parameters show distinct changes due to diabetes and hence along with machine learning tools, these can be used for development of noninvasive low-cost real-time prognostic system in predicting diabetes using machine learning techniques.

Plants consistently synthesize and accumulate medically valuable secondary metabolites which can be isolated and clinically tested under in vitro conditions. An advancement with such important phytochemical production has been recognized and utilized as herbal drugs. Bioactive andrographolide (AGL; C20H30O5) isolated from Andrographis paniculate (AP) (Kalmegh) is a diterpenoid lactones having multifunctional medicinal properties including anti-manic, anti-inflammatory, liver, and lung protective. AGL is known for its immunostimulant activity against a variety of microbial infections thereby, regulating classical and alternative macrophage activation, Ag-specific antibody production during immune disorder therapy. In vitro studies with AGL found it to be effective against multiple tumors, neuronal disorders, diabetes, pneumonia, fibrosis, and other diverse therapeutic misadventures. Generally, virus-based diseases like ZIKA, influenza A virus subtype (H1NI), Ebola (EBOV), Dengue (DENV), and coronavirus (COVID-19) epidemics have greatly increased scientific interest and demands to develop more effective and economical immunomodulating drugs with minimal side effects. Trials and in vitro pharmacological studies with AGL and medicinally beneficial herbs might contribute to benefit the human population without using chemical-based synthetic drugs. In this review, we have discussed the possible role of AGL as a promising herbal-chemo remedy during human diseases, viral infections and as an immunity booster.

Mental calculation demanding tasks cause changes in the autonomic modulation of the cardiac function. Heart rate variability (HRV) is a parameter to understand the autonomic changes. To examine HRV in young individuals in the performance of two levels of arithmetic mental calculation tasks. We assessed 22 participants (18–25 years; both male and female) who performed two levels of mental arithmetic calculations, i.e., 15 easy and 15 calculative. Each task was for 10 s, for both easy and calculative and presented in random manner, through Superlab 4.5 (Cedrus Inc.) and electrocardiogram data were acquired by MP35 (Biopac Inc., Goleta, California, USA). Acqknowledge 4.1 and KUBIOS 2.1 software were used for HRV analysis. Decreased heart rate in calculative task as compared to easy task (p

Abiotic stresses, for example, water stress, salinity, chilling, high temperature, and nutrient imbalance may influence the molecular and physiological process of plants in declining crop production. The molecular and physiological changes that hinder crop growth are foremost concerned since it affects directly meeting the global food demand. Abiotic stress reveals a profound impact on plant proteins, including alteration in posttranscriptional, cellular localization, and at the posttranslational levels. Understanding and identifying the role of differentially expressed protein during abiotic stress will be comparatively easier to deal with plant signaling to stresses. A proteomics approach like identification of abiotic stress–specific biomarkers, study of the selected protein accumulation pattern in response to different stresses, and elucidation of functional protein pathways is most important. Currently, gel electrophoresis, in addition to high throughput mass spectrometry (LC-MS-MS, MALDI-TOF) coupled with bioinformatics, is a widely used proteomics approach in plant-protein identification. This chapter focuses on advanced quantitative proteomics approaches like isobaric tag for relative and absolute quantitation (Iraq) and inductively coupled plasma-mass spectrometry approaches used to find novel and differentially expressed proteins. The proteomics approach and findings will enhance our understanding of the cellular processes and the pathways associated with abiotic-stress responses to develop and improve important agronomical traits.

The agricultural is an important sector and shares a major fraction of any nations’ gross national product and, thus, has a key role in the development. Due to the green revolution, crop production has increased to a greater extent in the past decades and simulated to be continued in the coming years. But, at present, one of the challenging issues is food security and maintaining the equitable standard of living for the coming generations. In this regard, one of the major global environmental issues in front of us is human-induced climate change, which threatens our ability to meet the demand. In the past decades, the high growth rate of industrialization, urbanization, and anthropogenic activities has resulted in adverse climate changes, significantly affecting the global temperature, increased greenhouse gases, seasonal variation, and irregular precipitation patterns. According to the Intergovernmental Panel on Climate Change (IPCC) global warming prediction report, the global mean temperature will be increased by 1.0–3.5°C by 2100, and CO2 doubling would be increased temperature by 1.5–4.5°C. Global mean surface temperature anomalies were also recorded to be increasing consistently with time since 1880 to the year 2019; 2016 ranks as the warmest on record (NASA GISS). Thus, increase in temperature and CO2 can be a big challenge in front of farmers and result in a major threat to agriculture and food security. It has the potential to affect agriculture positively as well as negatively in terms of yield, depending upon the variations in various factors. The share of agriculture in global emissions, the need for further global mitigation efforts, and continued projected agricultural emissions growth in many countries all combined together to underline the necessity for stronger and more effective policies. Thus, this chapter is focused on the impact of climate change on the agricultural sector and strategies for mitigating climate change in the agriculture system. In this context, there is an urgent need to analyze the impact of climate change on agriculture productivity and project sustainable approaches to adapt the climate change and meet the future food demand.

Increase in the cost of process water and stringent environmental norms result in the requirement for water reducing technologies in process industries. Batch processes have been widely used in process industry (such as pharmaceuticals, fine and specialty chemicals, etc.) as batch processing is appropriate to produce a specialized low amount of production and is flexible to adjust according to the dynamism of the market. In recent years, significant research efforts are made to minimize resources such as energy and water in batch processes. Optimizing the storage requirement is an essential aspect while considering capital investment and space availability. This paper focuses on minimizing freshwater and storage requirement simultaneously. In this paper, a mathematical formulation is proposed to solve the proposed bi-objectives problem. The primary objective of this work is to generate a Pareto optimal front for these two objectives via using a ϵ-constraint method. The proposed linear programming formulation includes demand satisfaction, source availability and quality constraints. The trade-off between storage and resource requirements is captured through a Pareto optimal front. This Pareto optimal front facilitates decision-makers to select an appropriate operating point as per suitability.

Metals being one of the essential ingredients for plant growth and development play a dual role. At the optimum level, they intend to improve the plants nutritional level and yield while in access being toxic displays, direct and indirect adverse impacts resulting in lower quality and quantity. Nowadays due to industrialization and modernization, metal contamination in agricultural land (soil) is a foremost concern since it directly affects the human health. Most of the edible crops, including tubers, fruits, and vegetables, accumulate metals at the toxic levels since they are grown on metal-contaminated soil. Taking this into consideration, here we address different approaches in horticultural crops to mitigate the effect of metal stress. The use of various chemical immobilization materials such as organic, inorganic, and nanoparticles is generally used approaches. However, eco-friendly and cost-effective microbe-assisted bioremediation and phytoremediation employing hyperaccumulator plants to remove the specific metal contaminants from the soil and water are successful. Additionally, we will discuss metal uptake and transport mechanisms, including important genes involved in metal accumulation and detoxification. Such genes might be useful in future research by improving phytoremediation potential using genetic transformation to mitigate the metal stress in plants.