CO2 fixation by seaweeds and their role in De-acidifying Ocean - An experimental approach

CO2 fixation by seaweeds and their role in De-acidifying Ocean - An experimental approac

Enhancing the Efficiency of Attack Detection System Using Feature selection and Feature Discretization Methods

Intrusion detection technologies have grown in popularity in recent years using machine learning. The variety of new security attacks are increasing, necessitating the development of effective and intelligent countermeasures. The existing intrusion detection system (IDS) uses Signature or Anomaly based detection systems with machine learning algorithms to detect malicious activities. The Signature-based detection rely only on signatures that have been pre-programmed into the systems, detect known attacks and cannot detect any new or unusual activity. The Anomaly based detection using supervised machine learning algorithm detects only known threats. To address this issue, the proposed model employs an unsupervised machine learning approach for detecting attacks. This approach combines the Sub Space Clustering and One Class Support Vector Machine algorithms and utilizes feature selection methods such as Chi-square, as well as Feature Discretization Methods like Equal Width Discretization to identify both known and undiscovered assaults. The results of the experiments using proposed model outperforms several of the existing system in terms of detection rate and accuracy and decrease in the computational time

Effect of cadmium stress on seed germination and seedling morpho-physiological growth parameters of barnyard millet (Echinochloa frumentacea Link)

Cadmium (Cd) is a heavy metal, which is seen in the contaminated soils and severely affects the growth and development of plants in recent years. The study on the seed germination and morpho-physiological growth characteristics of barnyard millet (Echinochloa frumentacea) cultivar CO (KV) 2 treated with different concentrations (50, 100, 150, 200, and 250 mg/kg of soil) of Cd were evaluated at 15th, 30th, and 45th day of interval. The findings of this research demonstrate that the maximum dosage of Cd (250 mg/kg of soil) affects the germination percentage (65%) of barnyard millet. Seedling vigor index has been negatively influences a drop in germination percentage. Increasing concentrations of Cd reveals the growth of root and shoot length and the quantity of fresh and dry weight affected. The phytotoxicity percentage of roots and shoots also increases with increasing concentrations of Cd, whereas the tolerance index level decreases with increasing concentrations of Cd. In root and shoot, the relative growth index was reduced in higher concentration of Cd. The relative water content remains high in the initial stages of leaf development and declines when the leaf matures. From this study, it was found that the increase in the concentration of Cd leads to decrease the germination percentage and morpho-physiological growth parameters as compared to control

Improving Performance of Quantum Heat Engines by Free Evolution

The efficiency of a quantum heat engine is maximum when the unitary strokes are adiabatic. On the other hand, this may not be always possible due to small energy gaps in the system, especially at the critical point where the gap vanishes. With the aim to achieve this adiabaticity, we modify one of the unitary strokes of the cycle by allowing the system to evolve freely with a particular Hamiltonian till a time so that the system reaches a less excited state. This will help in increasing the magnitude of the heat absorbed from the hot bath so that the work output and efficiency of the engine can be increased. We demonstrate this method using an integrable model and a non- integrable model as the working medium. In the case of a two spin system, the optimal value for the time till which the system needs to be freely evolved is calculated analytically in the adiabatic limit. The results show that implementing this modified stroke significantly improves the work output and efficiency of the engine, especially when it crosses the critical point.Comment: 8 pages, 8 figure

Geographical Mapping and Socio-Demographic Analysis of Out-Patient at A Tertiary Hospital in Chennai

There is a lack of comprehensive geographical mapping and socio-demographic analysis of outpatients at a Tertiary Hospital in Chennai. This knowledge gap hinders the understanding of distribution patterns and socio-demographic characteristics of patients visiting the hospital from different geographic locations. The study seeks to enhance healthcare delivery by identifying specific health needs, catchment zones, and areas for improvement in healthcare services. Therefore, the study aims to assess the geographical distribution of outpatients attending the institution, to identify catchment zones and investigate changes in the pattern of geographic distribution of outpatients and to assess the socio-demographic characteristics of outpatients. Geographical data, including addresses, and patient demographic data such as age and gender are collected from the Hospital's Electronic Health Records (EHR) department. The sampling technique employed is an entire population approach, where data is collected and analyzed from every patient attending the outpatient department. MS Excel, Power BI, and ArcGIS are used for data analysis. A total of 40,90,460 patients visited the Outpatient department from 2018 to 2022. Female patients accounted for approximately 57.96% of the total patient visits. Patients between the ages of 31-64 years are the most frequent visitors. General medicine is the most visited department, followed by general surgery and obstetrics & gynecology. The geographical distribution analysis identified Chennai, Kancheepuram, Tiruvallur, and Vellore as major catchment zones. There is a need for targeted outreach programs and resource allocation to improve healthcare services. The results emphasize the importance of tailoring healthcare to the specific needs of female patients and middle-aged adults. Strengthening the general medicine department and optimizing resource allocation based on patient demand can enhance service delivery. Continuous monitoring and analysis of patient data are essential for adapting healthcare strategies to evolving patient demographics and needs

The universally growing mode in the solar atmosphere: coronal heating by drift waves

The heating of the plasma in the solar atmosphere is discussed within both frameworks of fluid and kinetic drift wave theory. We show that the basic ingredient necessary for the heating is the presence of density gradients in the direction perpendicular to the magnetic field vector. Such density gradients are a source of free energy for the excitation of drift waves. We use only well established basic theory, verified experimentally in laboratory plasmas. Two mechanisms of the energy exchange and heating are shown to take place simultaneously: one due to the Landau effect in the direction parallel to the magnetic field, and another one, stochastic heating, in the perpendicular direction. The stochastic heating i) is due to the electrostatic nature of the waves, ii) is more effective on ions than on electrons, iii) acts predominantly in the perpendicular direction, iv) heats heavy ions more efficiently than lighter ions, and v) may easily provide a drift wave heating rate that is orders of magnitude above the value that is presently believed to be sufficient for the coronal heating, i.e., $\simeq 6 \cdot 10^{-5}$J/(m$^3$s) for active regions and $\simeq 8 \cdot 10^{-6}$J/(m$^3$s) for coronal holes. This heating acts naturally through well known effects that are, however, beyond the current standard models and theories.Comment: To appear in MNRA

Smart pools of data with ensembles for adaptive learning in dynamic data streams with class imbalance

Streaming data incorporates dynamicity due to a nonstationary environment where data samples may endure class imbalance and change in data distribution over the period causing concept drifts. In real-life applications learning in dynamic data streams, is vitally important and challenging. A combined solution to adapt to class imbalance and concept drifts in dynamic data streams is rarely addressed by researchers. With this motivation, the current communication presents the online ensemble model smart pools of data with ensembles for class imbalance adaptive learning (SPECIAL) to learn in skewed and drifting data streams. It employs an ageing-based G-mean maximization strategy to adapt to dynamicity in data streams. It employs smart data-pools with the local expertise ensemble to classify samples lying in the same data-pool. The empirical and statistical study on different evaluation metrics exhibits that SPECIAL is more adaptive to class imbalanced dynamic data streams than the state-of-the-art algorithms

Quadcopter based emergency medikit delivery system for hill stations

Nowadays UAV’s are more common for search, rescue and surveillance.  A quadcopter UAV system is proposed to be used for aerial transportation of medicine. A Quadcopter has a drive chassis having a propeller, ESC, motor, frames and battery. The quadcopter is supported with GPS to know the exact position. To know the exact location and delivering medicine to accident area, an audio and video system is used. The quad frame built from quality materials, which are reinforced and much more stronger, this reduces arm breakage. A set of two plastic propellers, one normal and one pusher (reverse) to rotate and lift up the quadcopter. The brushless out-runner will provide more power with its high efficiency, long run times. The ESC includes programmable motor braking, soft start for helicopters and planes, timing, throttle input range and low-voltage cutoff. Lithium-Polymer (Li-Po) battery for very lightweight, small size and durability without losing charging capacity. The whole quadcopter process can be monitored and controlled by a remote control system, quadcopter will capture the live video and current status can be seen visualized and provides information about all the other exact conditions in real-time

Large chorangiomas: a seven years study in a tertiary care obstetrics and gynaecology hospital

Background: Chorangioma is a benign vascular placental tumour. It is composed of fetal capillary proliferation within the chorionic villi supported by a variable stroma. Smaller lesions are incidental, are often missed and carry no clinical significance. Larger lesions are associated with feto-maternal complications and are infrequently sent for histopathological examination.Methods: The study was conducted at the department of pathology, at a tertiary care obstetrics and gynaecology hospital. The study was a retrospective study which covered 7 years. Paraffin embedded blocks of placental specimens containing mass were taken up for the study. Sections were stained with haematoxylin and eosin (H and E). The results are compared and correlated with clinicopathologic factors. The statistical data are analysed manually.Results: A total of seven cases were included in the study, 4 cases were primi gravida, 1 case each in second, third and fourth gravida. Pregnancy outcome was intrauterine death in 1 case, dead born in 1 case, alive healthy children in 5 cases, birth weight was normal in 4 children, low birth weight in 2 children and 1 was extremely low birth weight. Of the total of 7 placental specimens 3 showed extraplacental mass and rest 4 showed intraplacental mass. All cases showed solitary lesions and measured > 5cm (large). Histopathological examination of all 7 specimens showed features of chorangioma.Conclusions: Careful inspection of the placenta is necessary following all deliveries. Any suspicious lesions should be documented and evaluated by histopathological examination there by predicting feto maternal complications and help the clinicians in better management of the mother and child accordingly. Meagre documentation of such cases prompted us to present this series of 7 cases of large chorangiomas with a mixed fetal outcome