ALDEHYDE EMISSIONS FROM TWO-STROKE AND FOUR-STROKE SPARK IGNITION ENGINES WITH CATALYTIC CONVERTER RUNING ON GASOHOL

Rad donosi rezultate ispitivanja emisije aldehida iz dvotaktnog i četverotaktnog jednocilindričnog motora na paljenje svjećicama koji koristi mješavinu benzina (80 vol. %) i alkohola (20 vol.); motor je prevučen bakrom (sloj debljine 300 μm na klipu i na unutarnjoj strani glave cilindra) i opremljen katalizatorom od spužvastog željeza. Rezultati su uspoređeni s konvencionalnim benzinskim motorom na paljenje svjećicama. Motor prevučen bakrom pokazuje smanjenje emisije aldehida u odnosu na konvencionalni motor za oba ispitna goriva. Katalitički pretvornik s ubrizgavanjem zraka značajno smanjuje štetne emisije kod oba ispitna goriva i kod obje konfiguracije motora.This paper reports aldehyde emissions from two-stroke and four-stroke, single cylinder spark ignition (SI) engines with gasohol (80 vol. % gasoline, 20 vol. % ethanol) having copper coated engine (copper-coated thickness, 300 μm) on piston crown and inner side of cylinder head) provided with catalytic converter with sponge iron as catalyst and compared with conventional SI engine with gasoline operation. Copper-coated engine showed reduction in aldehyde emissions when compared with conventional engine with both test fuels. Catalytic converter with air injection significantly reduced emissions with both test fuels on both configurations of the engine

AN APPLICATION OF THE PHOSPHORUS CONSISTENT RULE FOR ENVIRONMENTALLY ACCEPTABLE COST-EFFICIENT MANAGEMENT OF BROILER LITTER IN CROP PRODUCTION

We calculated the profitability of using broiler litter as a source of plant nutrients using the phosphorus consistent litter application rule. The cost saving by using litter is 37% over the use of chemical fertilizer alone to meet the nutrient needs of major crops grown in Alabama. In the optimal solution, only a few routes of all the possible routes developed were used for inter- and intra- county litter hauling. If litter is not adopted as the sole source of crop nutrients, the best environmental policy may be to pair the phosphorus consistent rule with taxes, marketable permits, and subsidies.Environmental Economics and Policy, Production Economics,

Dynamic Gaits and Control in Flexible Body Quadruped Robot

Legged robots are highly attractive for military purposes such as carrying heavy loads on uneven terrain for long durations because of the higher mobility they give on rough terrain compared to wheeled vehicles/robots. Existing state-of-the-art quadruped robots developed by Boston Dynamics such as LittleDog and BigDog do not have flexible bodies. It can be easily seen that the agility of quadruped animals such as dogs, cats, and deer etc. depend to a large extent on their ability to flex their bodies. However, simulation study on step climbing in 3D terrain quadruped robot locomotion with flexible body has not been reported in literature. This paper aims to study the effect of body flexibility on stability and energy efficiency in walking mode, trot mode and running (bounding) mode on step climbing

Biodegradation Of Synthetic Compounds By The Microorganisms Isolated From Different Regeions Of Telangana

The extensive use of synthetic compounds, including pesticides, has raised concerns about their environmental impact and potential risks to human health. Microbial biodegradation has emerged as a promising eco-friendly approach to mitigate the accumulation of these compounds in the environment. In this study, we investigated the biodegradation potential of found microorganisms isolated from pesticidetreated soils in different regions of Telangana State. 50 Soil samples were collected from agricultural areas in diverse regions, known for their significant pesticide usage. Enrichment cultures were prepared using these soil samples to isolate predominantly found 10 bacterial and 5 fungal genus capable of utilizing synthetic compounds as a carbon and energy source. The isolated microbial strains were characterized by morphological, physiological, and biochemical characteristics. Subsequently, the biodegradation potential of the isolated microorganisms was assessed through laboratory-scale degradation experiments. Commonly used pesticides were selected as model substrates for degradation studies. The degradation efficiency of the microorganisms was evaluated at different incubation periods (05, 10 and 15 days) to understand their ability to break down these synthetic compounds. The results demonstrated that the degradation of pesticides by bacteria and fungi was found significant after 15th day of incubation. The degradation of tested pesticides was initiated from the 5th day. At the end of 10th day there is an exponential degradation percentage. By 15th day the degradation percentage was approximately 1fold compared to 10th day degradation percentage. This investigation emphasizes the significance of harnessing the potential of bacteria and fungi to mitigate the environmental burden of synthetic compounds. The findings hold practical implications for developing eco-friendly and region-specific bioremediation strategies to combat pollution caused by synthetic compounds and promote environmental sustainability in the agricultural secto

Isolation And Characterization Of Microorganisms From Pesticide Treated Soils Of Different Regions Of Telangana

This investigation is carried out to identify the microorganisms for the pesticide treated soil samples collected from different places of Warangal District, Telangana. The identification was based on morphological, physiological and biochemical. Randomly five samples from one village haven selected. The type and amount of pesticide used by the farmers have been noted. With reference to the results of morphological, physiological and biochemical, we identified 10 bacterial genus namely, Pseudomonas, Bacillus, Streptomyces, Rhizobium, Klebsiella, Staphylococcus, Streptococcus, Azatobacter Azospirillum, Actinomycetes and 06 fungal genus namely,  Aspergillus, Penicillium, Trichoderma, Fusarium, Rhizopus and Vesicular Arbuscular Mycorrhiza have been predominantly identified from all the samples collected

Exploratory Boosted Feature Selection and Neural Network Framework for Depression Classification

Depression is a burdensome psychiatric disease common in low and middle income countries causing disability, morbidity and mortality in late life. In this study, we demonstrate a novel approach for detection of depression using clinical data obtained from the on-going Mysore Studies of Natal effects on Ageing and Health (MYNAH), in South India where the members have undergone a comprehensive assessment for cognitive function, mental health and cardiometabolic disorders. The proposed model is developed using machine learning approach for classification of depression using Meta-Cognitive Neural Network (McNN) classifier with Projection-based learning (PBL) to address the self-regulating principles like how, what and when to learn. XGBoost is used for feature selection on the available data of assessments with improved confidence. To improve the efficiency of McNN-PBL classifier the best parameters are found using Particle Swarm Optimization (PSO) algorithm. The results indicate that the McNNPBL classifier selects appropriate records to learn and remove repetitive records which improve the generalization performance. The study helps the clinician to identify the best parameters to analyze the patient

Advancements in Machine Learning for the Diagnosis of Chronic Kidney Disease

Chronic Kidney Disease (CKD) constitutes a significant global health issue, precipitating damage to the kidneys and stripping many individuals of their most productive years. Alarmingly, 40% of those affected by CKD remain oblivious to their condition, a stark contrast to many other diseases where early detection is more common. Unlike other conditions, CKD eludes cure unless identified promptly in its nascent stages. This research emphasizes the collection of critical indicators such as blood pressure and diabetes status to ascertain the presence of CKD in individuals. It proposes the employment of advanced machine learning techniques, including Random Forest, XGBoost, and Support Vector Machines, aiming to enhance early detection and thereby mitigate the disease's impact. Utilizing a CKD dataset, this study endeavors to predict the likelihood of CKD in individuals, offering a proactive approach to tackle this formidable health challenge

Acute Lymphoblastic Leukemia Blood Cells Prediction Using Deep Learning & Transfer Learning Technique

White blood cells called lymphocytes are the target of the blood malignancy known as acute lymphoblastic leukemia (ALL). In the domain of medical image analysis, deep learning and transfer learning methods have recently showcased significant promise, particularly in tasks such as identifying and categorizing various types of cancer. Using microscopic pictures, we suggest a deep learning and transfer learning-based method in this research work for predicting ALL blood cells. We use a pre-trained convolutional neural network (CNN) model to extract pertinent features from the microscopic images of blood cells during the feature extraction step. To accurately categorize the blood cells into leukemia and non- leukemia classes, a classification model is built using a transfer learning technique employing the collected features. We use a publicly accessible collection of microscopic blood cell pictures, which contains samples from both leukemia and non-leukemia, to assess the suggested method. Our experimental findings show that the suggested method successfully predicts ALL blood cells with high accuracy. The method enhances early ALL detection and diagnosis, which may result in better patient treatment outcomes. Future research will concentrate on larger and more varied datasets and investigate the viability of integrating it into clinical processes for real-time ALL prediction