Updates on the pretreatment of lignocellulosic feedstocks for bioenergy production–a review

Lignocellulosic biomass is the most abundant renewable energy bioresources available today. Due to its recalcitrant structure, lignocellulosic feedstocks cannot be directly converted into fermentable sugars. Thus, an additional step known as the pretreatment is needed for efficient enzyme hydrolysis for the release of sugars. Various pretreatment technologies have been developed and examined for different biomass feedstocks. One of the major concerns of pretreatments is the degradation of sugars and formation of inhibitors during pretreatment. The inhibitor formation affects in the following steps after pretreatments such as enzymatic hydrolysis and fermentation for the release of different bioenergy products. The sugar degradation and formation of inhibitors depend on the types and conditions of pretreatment and types of biomass. This review covers the structure of lignocellulose, followed by the factors affecting pretreatment and challenges of pretreatment. This review further discusses diverse types of pretreatment technologies and different applications of pretreatment for producing biogas, biohydrogen, ethanol, and butanol

Challenges and Approaches in Green Data Center

Cloud computing is a fast evolving area of information and communication technologies (ICTs)that hascreated new environmental issues. Cloud computing technologies have a widerange ofapplications due to theirscalability, dependability, and trustworthiness, as well as their abilityto deliver high performance at a low cost.The cloud computing revolution is altering modern networking, offering both economic and technologicalbenefits as well as potential environmental benefits. These innovations have the potential to improve energyefficiency while simultaneously reducing carbon emissions and e-waste. These traits have thepotential tomakecloud computing more environmentally friendly. Green cloud computing is the science and practise of properlydesigning, manufacturing, using, and disposing of computers, servers,and associated subsystems like displays,printers, storage devices, and networking and communication systems while minimising or eliminatingenvironmental impact. The most significant reason for a data centre review is to understand capacity,dependability, durability,algorithmic efficiency, resource allocation, virtualization, power management, andother elements. The green cloud design aims to reduce data centre power consumption. The main advantageof green cloud computing architecture is that it ensures real-time performance whilereducing IDC’s energyconsumption (internet data center).This paper analyzed the difficultiesfaced by data centers such as capacityplanning and management, up-time and performance maintenance, energy efficiency and cost cutting, realtime monitoring and reporting. The solution for the identified problems with DCIM system is also presentedin this paper. Finally, it discusses the market report’s coverage of green data centres, green computingprinciples, andfuture research challenges. This comprehensive green cloud analysis study will assist nativegreen research fellows in learning about green cloud concerns and understanding future research challengesin the field

Vascular malformations: a hospital based study

Background: Vascular anomalies comprise a widely heterogeneous group of lesions. Diagnosis and management of these lesions present challenges to the surgeons, radiologists and histopathologists. Accurate classification of these lesions results in appropriate therapy. Aim of the study is to study the role of histopathology and histochemical stain in the diagnosis of vascular malformationMethods: The present study is a hospital based observational study on vascular malformations over a period of three years from 2016 to 2018 done in department of Pathology and Plastic Surgery at tertiary care centre, Visakhapatnam.Results: Out of 107 specimens of vascular anomalies received, 72 cases were vascular neoplasms, 35 were vascular malformations. Majority of the vascular malformations were seen in the cervicofacial region (43%) followed by extremities (37.1%). Most common malformation in the present study was arterio venous malformations (60%) followed by venous malformations (22.8%). Verhoeff’s VanGieson stain demonstrated discontinuity of internal elastic lamina in cases of arteriovenous malformation and helped in differentiating the lesions from capillary malformation and vascular tumors.Conclusions: Histopathology, special histochemical stains along with imaging features can be used to reduce diagnostic difficulties and in helping proper management of vascular malformations

Solar Power Prediction Using Machine Learning

This paper presents a machine learning-based approach for predicting solar power generation with high accuracy using a 99% AUC (Area Under the Curve) metric. The approach includes data collection, pre-processing, feature selection, model selection, training, evaluation, and deployment. High-quality data from multiple sources, including weather data, solar irradiance data, and historical solar power generation data, are collected and pre-processed to remove outliers, handle missing values, and normalize the data. Relevant features such as temperature, humidity, wind speed, and solar irradiance are selected for model training. Support Vector Machines (SVM), Random Forest, and Gradient Boosting are used as machine learning algorithms to produce accurate predictions. The models are trained on a large dataset of historical solar power generation data and other relevant features. The performance of the models is evaluated using AUC and other metrics such as precision, recall, and F1-score. The trained machine learning models are then deployed in a production environment, where they can be used to make real-time predictions about solar power generation. The results show that the proposed approach achieves a 99% AUC for solar power generation prediction, which can help energy companies better manage their solar power systems, reduce costs, and improve energy efficiency.Comment: 7 page

A multi-dimensional study to estimate the behaviour of the general public during COVID-19 pandemic

Background: The COVID-19 pandemic has had a significant impact on societies all over the world, leading to significant shifts in individual behavior as well as societal norms. The goal of this study was to provide an in-depth look at the many different aspects of public behavior during the COVID-19 pandemic. Methods: Demographic information, COVID-19 knowledge and awareness, prevention adherence, vaccination attitudes, and psychological well-being as a whole were all be gathered through the quantitative survey. The subjective meetings will give further bits of knowledge into the hidden inspirations, discernments, and difficulties faced by people in following general wellbeing rules. Results: To find patterns and correlations among the various variables, statistical methods like regression analysis, factor analysis, and clustering algorithms were used in the data analysis. The subjective information was investigated specifically, separating key topics and accounts that shed light on the subtleties of the public’s way of behaving during the pandemic. Conclusions: In the end, the goal of this multidimensional study was to help make decisions based on evidence and come up with plans to improve public health and lessen the impact of infectious disease outbreaks like COVID-19

Antimicrobial nature of specific compounds of Ampelomyces quisqualis identified from gas chromatography-mass spectrometry (GCMS) analysis and their mycoparasite nature against powdery mildew of grapes

Grapevine powdery mildew is the world's most important plant disease, and Ampelomyces frequently fight them. While it does not usually cause plant death, its major infections can result in significant production losses and severely impact wine quality. Fungicides are frequently used to control the disease, which can have long-term adverse effects on the ecosystem. As a result, alternative and environmentally friendly disease management approaches must be developed. The study aimed to reduce costly and toxic fungicide use by using Ampelomyces, a natural biofungicide, against various powdery mildew fungi. GC-MS analysis was also used to determine the antagonistic potential and efficacy of volatile organic chemicals produced by several Ampelomyces spp. against Erysiphe necator, which causes powdery mildew of grapes. The molecular characterization of A. quisqualis isolates based on using rDNA ITS region was also carried out and sequenced. GC-MS analysis identified various antimicrobial compounds, such as squalene (4.643%), octadecanoic acid (3.862%), tetradecanoic acid (3.600%), and 9,12-octadecadienoic acid (Z,Z) (1.451%). The least abundant compounds were 2-Hexadecanol, 1-Tricosanol, and 2-propenyl ester, with percentages of 0.485, 0.519, and 0.560, respectively. These bioactive compounds revealed by GC-MS analysis in crude extracts of A. quisqualis had a stronger antifungal and antibacterial activity against E. necator. As a result, using A. quisqualis to control the powdery mildew of grapes significantly reduced pathogen growth and disease incidence

ETMS: Efficient Traffic Management System for Congestion Detection and Alert using HAAR Cascade

Rapid social development has resulted in the emergence of a new major societal issue: urban traffic congestion, which many cities must address. In addition to making  it more difficult for people to get around town, traffic jams are a major source of the city's pollution crisis. In order to address the problems of automobile exhaust pollution and congestion, this paper uses the system dynamics approach to develop a model to study the urban traffic congestion system from the perspectives of trucks,private cars, bikes and public transportation. This project proposes a system for detecting vehicles and sending alerts when traffic levels rise to dangerous levels using Haar Cascade and Fuzzy Cognitive Maps (FCP). The proposed system uses Haar Cascade to detect moving vehicles, which are then classified using FCP. The system can make decisions based on partial or ambiguous information by utilising FCP, a soft computing technique, which allows it to learn from past actions. An algorithm for estimating traffic density is also used by the system to pinpoint active areas. In congested areas, the system will alert the driver if it anticipates a collision with another vehicle and also Experiments show that the proposed system is able to accurately detect vehicles and provide timely alerts to the driver, drastically lowering the probability of accidents occurring in heavily travelled areas. The importance of introducing such a system cannot be overstated in today's transportation system. It's a big deal for the future of intelligent urban planning and traffic control. Congestion relief, cleaner air, and increased security are just some of the long-term benefits that justify the high initial investment. To add, this system is adaptable to suburban and rural areas, which can also experience traffic congestion issues

Predictors for Gingival Index in Middle-Aged Asian Indians with Type 2 Diabetes from South India: A Cross-Sectional Observational Study

Asian Indians develop type 2 diabetes mellitus (T2DM) much earlier as compared to White Caucasians, due to unique phenotypic and genetic architecture. Periodontitis in T2DM patients is often a neglected clinical feature. This study was conducted to derive predictor variables for gingival index in middle-aged Asian Indians with T2DM in a semiurban population of Dravidian ethnicity from Tamil Nadu, India. T2DM patients (n=232, mean age: 50.6±10.4 years) with periodontitis (n=123, mean age: 54.3±2.4 years) and without periodontitis (n=109, mean age: 55.2±3.1 years) were recruited between 2014 and 2016 by purposive sampling method. Dental examinations for pocket depth (PD) and clinical attachment level (CAL) were performed and gingival index was calculated. Fasting venous blood samples were analysed for measures of glycaemia and cholesterol. Significant positive correlation (p<0.01) was observed for gingival index with glycosylated haemoglobin (HbA1c), pocket depth, presence of T2DM, and clinical attachment level. Stepwise multiple linear regression analysis derived increased pocket depth (p<0.01), elevated HbA1c (p<0.01), clinical attachment level (p<0.01), and presence of diabetes (p<0.01) as significant predictors (r2 value = 0.67) for increased gingival index in middle aged patients with T2DM. These variables significantly (p<0.01) predispose middle-aged T2DM patients to increased gingival index, thus warranting appropriate intervention

Traditional methods of purification (detoxification process) for Schedule E poisonous drugs

740-748Medicinal plants have different types of active phytochemicals, which are still in use, either in their crude form or after proper processing. Though most of the plant drugs are safe, few are poisonous and may cause immediate toxic effect or cumulative toxic effect for human health. There are 25 poisonous or toxic plants in Siddha texts listed in the Schedule E of Drugs and Cosmetics Act 1940. The concept of Suthimuraigal in Siddha not only covers the process of purification and detoxification of physical and chemical impurities but also minimizes the side effects and improves the potency/therapeutic efficacy of the purified drugs. The aim of this review is to perceive the importance of the Schedule E drugs through their immense uses to treat diseases and to flourish the knowledge of purification processes to detoxify the poisonous elements, thus enhancing and utilizing them in curing challenging diseases. The distinct purification methods mentioned in Ayurvedic journals have also been reviewed for possible information. Methods of Suthi are variable and some of the important Siddha Suthimuraigal are reviewed in this paper. The traditional methods of purification may combat the toxic effects like ulceration, swelling, giddiness, skin rashes, pruritis also, thus enhancing the efficacy of the drugs in healing various ailments. Since these poisonous plants have very high potential to treat diseases, the chemical changes which transpired during the Suthi are to be revealed in further studies such as quantitative and qualitative analysis after their purification before they are applied in medicines