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Loneliness can be deemed as a social deficiency. Loneliness discloses the relationship between the desired and achieved a level of social interaction. Loneliness is not linked with social isolation, solitude, or aloneness. When low levels of social contact are desired, they may be experienced as positive. Loneliness is associated with mental illness. Loneliness is an emotionally unpleasant experience. It causes dissatisfaction, unhappiness, and depression. Anxiety, emptiness, boredom, restlessness, and marginality are the offshoot of loneliness. Divorce and the breakup of dating relationships are all associated with loneliness. Physical separation from family and friends drives one at risk for loneliness. Retirement, unemployment, and reduced satisfaction may also precipitate loneliness.

Behaviour modification refers to the techniques used to try and decrease or increase a particular type of behaviour or reaction. This might sound very technical, but it is used very frequently by all of us. Parents use this to teach their children right from wrong. Therapists use it to promote healthy behaviours in their patients. The purpose behind behaviour modification is not to understand why or how a particular behaviour started. Instead, it only focuses on changing behaviour, and there are different methods used to accomplish it.

The study aims to empirically test the relationship between types of campus adaptations across academic years of engineering undergraduate B. Tech students pursuing a four-year study at Indian Institute of Technology (IIT's) and National Institute of Technology (NIT's) in India. The Multivariate Analysis of Variance (Manova) test was run with SPSS vs. 21 to compare the student's campus adaptations of IIT's and NIT's by student's academic year level. Multistage random sampling with n = 1420 students were selected comprising of Multistage random sampling with n = 1420 students were selected comprising of first academic year (n = 160), second academic year (n = 273), third academic year (n = 460) and fourth academic year (n = 561). In academic adaptation, first year and second year students had negative outcomes with third and fourth year students having positive outcomes. In Social Adaptation, first year and fourth year students had negative outcomes with second and third year having positive outcomes. In physical -- psychological and institutional adaptation, only second year students had negative outcomes with first, third and fourth year had positive outcomes driving towards mental well-being and successful completion. Campus adaptations do vary across academic years influencing student's experiences at IIT's and NIT's.

The increasing computational demand for real-time mobile applications has led to the development of mobile edge computing (MEC), with support from unmanned aerial vehicles (UAVs), as a promising paradigm for constructing high-throughput line-of-sight links for ground users and pushing computational resources to network edges. Users can reduce processing latency and the load on their local computers by delegating tasks to the UAV in its role as an edge server. The coverage capacity of a single UAV is, however, very limited. Moreover, it will be easy to intercept the data that is transferred to the unmanned aerial vehicle. Thus, for UAV-assisted mobile edge computing, we proposed a transmission technique based on multi-agent deep reinforcement learning in this study. The recommended approach to maximize UAV deployment first applies the particle swarm optimization algorithm. Then, deep reinforcement learning is utilized to optimize the secure offloading to maximize the system utility and minimize the quantity of information eavesdropping, taking into consideration different user task types with diverse preferences for processing time and residual energy of computing equipment. The results of the simulation demonstrate that, in comparison to the single-agent strategy and the benchmark, the multi-agent approach can optimize offloading more successfully and produce higher system utility. [ABSTRACT FROM AUTHOR]

In this study, an efficient technique to improve and maintain the voltage levels of Cascaded H-Bridge Multi-Level Inverter (CHB-MLI) which utilizing PhotoVoltaic (PV) panel sources is presented. It is designed to make fifteen level output voltage and tested under the various levels of resistive loads. It produces staircase output with reduced component size, increases the output voltage level, reduces the voltage stress across the load/switches, reduces the Total Harmonic Distortion (THD) and increases the voltage gain. The switching pulses are generated using Phase Opposition Disposition (POD)-Pulse Width Modulation (PWM) technique. It is modeled and simulated using SIMULINK graphical programming environment in MATLAB. Results prove that the system provides lower THD than conventional systems.

Abnormal accumulation of acyl-CoA cholesterol acyltransferase-1 (ACAT-1) mediated cholesterol ester has been shown to contribute to cancer progression in various cancers including leukemia, glioma, breast, pancreatic and prostate cancers. However, the significance of ACAT-1 and cholesterol esters (CE) is relatively understudied in ovarian cancer. In this in vitro study, we assessed the expression and contribution of ACAT-1 in ovarian cancer progression. We observed a significant increase in the expression of ACAT-1 and CE levels in a panel of ovarian cancer cell lines (OC-314, SKOV-3 and IGROV-1) compared to primary ovarian epithelial cells (normal controls). To confirm the tumor promoting capacity of ACAT-1, we inhibited ACAT-1 expression and activity by treating our cell lines with an ACAT inhibitor, avasimibe, or by stable transfection with ACAT-1 specific short hairpin RNA (shRNA). We observed significant suppression of cell proliferation, migration and invasion in ACAT-1 knockdown ovarian cancer cell lines compared to their respective controls (cell lines transfected with scrambled shRNA). ACAT-1 inhibition enhanced apoptosis with a concurrent increase in caspases 3/7 activity and decreased mitochondrial membrane potential. Increased generation of reactive oxygen species (ROS) coupled with increased expression of p53 may be the mechanism(s) underlying pro-apoptotic action of ACAT-1 inhibition. Additionally, ACAT-1 inhibited ovarian cancer cell lines displayed enhanced chemosensitivity to cisplatin treatment. These results suggest ACAT-1 may be a potential new target for the treatment of ovarian cancer.

Profit prediction is a pivotal task in financial markets, empowering investors and traders to make informed decisions. In recent years, the advent of deep learning techniques has revolutionized the field of financial forecasting, offering the potential to extract intricate patterns and relationships from vast and complex datasets. This paper presents an innovative approach to profit prediction using Long Short-Term Memory (LSTM) networks, a specialized type of Recurrent Neural Network (RNN). LSTMs excel at capturing long term dependencies in sequential data, making them well-suited for modeling the dynamics of the financial markets. The core of the paper lies in the practical application of LSTM model architecture specially tailored for profit prediction. This includes defining the input layer, LSTM layers, fully connected layers and the output layer. The training and validation process is elucidated, covering data splitting, model training, validation techniques and hyper parameter tuning to enter ensure the model performance. The paper also explores the practical application of the LSTM-based profit prediction algorithm through a case study involving real-world financial data. Evaluation metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are employed to assess the algorithm's predictive accuracy and effectiveness. Additionally, the paper addresses risk assessment, a critical aspect of profit prediction in financial markets. It sheds light on the promising potential of LSTM-based profit prediction algorithms as a powerful tool for financial forecasting. It summarizes key findings, acknowledges limitations and challenges, and outlines future directions for improving the algorithm, including incorporating additional data sources and fine-tuning hyper parameters. The presented approach offers a significant advancement in the realm of profit prediction, enabling investors and traders to make more informed and data-driven decisions in an ever-evolving financial landscape. [ABSTRACT FROM AUTHOR]

Previously, Bithionol (BT) was shown to enhance the chemosensitivity of ovarian cancer cell lines to cisplatin treatment. In the present study, we focused on the anti-tumor potential of the BT-paclitaxel combination when added to a panel of ovarian cancer cell lines. This in vitro study aimed to 1) determine the optimum schedule for combination of BT and paclitaxel and 2) assess the nature and mechanism(s) underlying BT-paclitaxel interactions. The cytotoxic effects of both drugs either alone or in combination were assessed by presto-blue cell viability assay using six human ovarian cancer cell lines. Inhibitory concentrations to achieve 50% cell death (IC50) were determined for BT and paclitaxel in each cell line. Changes in levels of cleaved PARP, XIAP, bcl-2, bcl-xL, p21 and p27 were determined via immunoblot. Luminescent and colorimetric assays were used to determine caspases 3/7 and autotaxin (ATX) activity. Cellular reactive oxygen species (ROS) were measured by flow cytometry. Our results show that the efficacy of the BT-paclitaxel combination depends upon the concentrations and sequence of addition of paclitaxel and BT. Pretreatment with BT followed by paclitaxel resulted in antagonistic interactions whereas synergistic interactions were observed when both drugs were added simultaneously or when cells were pretreated with paclitaxel followed by BT. Synergistic interactions between BT and paclitaxel were attributed to increased ROS generation and enhanced apoptosis. Decreased expression of pro-survival factors (XIAP, bcl-2, bcl-xL) and increased expression of pro-apoptotic factors (caspases 3/7, PARP cleavage) was observed. Additionally, increased expression of key cell cycle regulators p21 and p27 was observed. These results show that BT and paclitaxel interacted synergistically at most drug ratios which, however, was highly dependent on the sequence of the addition of drugs. Our results suggest that BT-paclitaxel combination therapy may be effective in sensitizing ovarian cancer cells to paclitaxel treatment, thus mitigating some of the toxic effects associated with high doses of paclitaxel.

Nowadays, according to the Food and Agriculture Organization (FAO), agricultural pests cause severe loss of global crop production every year. In order to eliminate these harmful insect pests, smart agriculture is the best way for farmers to use artificial intelligence techniques integrated with modern information and communication technology. Thus, they can increase the productivity of their crops. The main limiting factor for cultivation, however, is pest and disease infections. It is not possible to eradicate these diseases, but they can be controlled and monitored to minimize their impact. Crop diseases can be rapidly diagnosed by using automated image identification systems. An accurate identification system requires the extraction of features from images. To identify the samples, we used three different classes of machine learning: a support vector machine (SVM), a random forest (RF), and an artificial neural network (ANN). In addition, our deep learning method outperforms other previous recognition methods, namely Single Shot Multi-Box Detector (SSD), which increases the accuracy and multi-directional way of pest detection. Our results contribute to the use of machine learning in agriculture literature and have implications for research in deep learning and practical implications. [ABSTRACT FROM AUTHOR]

The age of technology has introduced integrated wearables into the area of driving to prevent accidents. A smart device is fitted easily into any moving vehicle, which renders a convenient option regarding the activities of the drivers such as abnormality, health condition, and sleep information. This research study discusses two types of outcomes that are enabled according to the situations of the input provided to the BPNN (Back Propagation Neural Network). At first, when an anomaly is predicted within the vehicle, such as the unusual behaviour of the driver, then it is reported through the help of IoT sensors installed inside the vehicle. The BPNN communicates the relevant signal to the driver assistance system to gain control over the vehicle and safely stop it at an appropriate place on the road. The second type of outcome discussed is when an anomaly is explored on the road when a vehicle moves in a heedless way. In such cases, the nearby vehicles are alerted and in the case of an accident are reported, the signals are sent to the local police officials. These suggested instructions perhaps minimize the damage and decrease the rate of mortality every year because of road accidents. Appropriate actions are taken by the model in averting the occurrence of accidents using cloud AI. The performance of the proposed system is assessed based on its accuracy rate. [ABSTRACT FROM AUTHOR]