Miracle-dz/UniFAR-FTUs huggingface dataset:
Higher education decision making of Australian women with caring responsibilities

It is well established that women bear greater caring responsibilities than men, however little is known about how this care work influences the decision-making processes of female carers who are considering Higher Education (HE). These deliberations occur well before women commit to enrolling and frequently result in carers making decisions to delay or not pursue HE, with consequences for their own careers and the persistence of gender inequalities more broadly. A scoping review of academic literature published since 1980 which followed the PRISMA-ScR process for scoping reviews was conducted to answer the research question “What is the scope of the literature regarding the influence of caregiver responsibilities on Australian women’s tertiary education decision making processes? ” Studying the literature in one national context enabled the influences of personal as well as contextual influences to be identified. The results show that very little is known about how women who have caring responsibilities make decisions about whether or not to undertake HE studies. Important issues were identified such as the lack of a clear definition of carer and recognition of this cohort as an equity group. A complex array of personal, cultural and structural factors which may enable women’s HE decisions were identified, including the desire to achieve personal life goals, the encouragement and support of family members and supportive workplaces. However, constraints such as competing time demands, the continuing prevalence of traditional gendered expectations and idealised notions of HE students as unencumbered were also noted. No studies directly addressed decision-making processes, or how the many elements combined to influence female carers prior to enrolment. The paucity of research points to an urgent need for studies which will address the gaps in knowledge. This review points to new directions in research and decision-making theories to encompass the role of context and care in decisions, and provides some advice meanwhile to inform career practitioners, HE providers and career researchers.
Effects of exercise therapy on patients with poststroke cognitive impairment: A systematic review and meta-analysis

Objective To evaluate the effects of exercise therapy on patients with poststroke cognitive impairment and compare the differences in the effect of this method when compared with conventional measures, providing evidence for a more standardized and effective clinical application of exercise therapy. Methods A search was conducted using 7 electronic databases, including PubMed, CINAHL, Web of Science, CENTRAL, CNKI, Wanfang, SinoMed, and clinical trials registry platforms for randomized controlled trials concerning exercise therapy on patients with poststroke cognitive impairment. Two researchers independently screened the literature, evaluated the quality, and extracted information. Meta-analysis was carried out using Review Manager 5.4 software. Results There were 11 studies with 1,382 patients. Meta-analysis showed that exercise therapy could improve cognitive function [SMD = 0.67, 95% CI (0.31, 1.04), P = 0.0003], motor function [SMD = 1.81, 95% CI (0.41, 3.20), P = 0.01], and the activities of daily living [MD = 8.11, 95% CI (3.07, 13.16), P = 0.002] in patients with poststroke cognitive impairment. Conclusion Exercise therapy can not only improve cognitive function in patients with poststroke cognitive impairment but also improve motor function and the activities of daily living. Medical staff should prioritize the management of patients with poststroke cognitive impairment and carry out exercise therapy actively to improve the cognitive function of patients with stroke. Systematic review registration https://www.crd. york.ac. uk/prospero/, identifier: CRD42023397553.
Atmospheric And Oceanic Fluid Dynamics Fundamentals And Large Scale Circulation

Thank you very much for downloading atmospheric and oceanic fluid dynamics fundamentals and large scale circulation. Maybe you have knowledge that, people have look numerous times for their chosen readings like this atmospheric and oceanic fluid dynamics fundamentals and large scale circulation, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some malicious bugs inside their laptop.
Flight Control Design Using Observer-based Loop Transfer Recovery

This paper presents an output feedback flight control design method that is focused on designing a high-performance robust control. The architecture is similar to Linear Quadratic Gaussian controllers. When using full order observers, or Kalman filters, the stability robustness properties can typically degrade, and the system can be sensitive to errors in the plant model. The method presented here, called observer-based loop transfer recovery, asymptotically achieves positive real system behavior at certain loop break points during recovery. This paper presents design insight into using this method for flight control design problems, including systems using acceleration feedbacks that are nonminimum phase. During the design process the observer is artificially squared-up. This adds fictitious inputs to make the number of controls equal the number of measurements, and makes the observer design model minimum phase. This step is central to achieving the positive real behavior during recovery. To place the zeros in a desired location during plant squaring, a pole-placement algorithm is used. A design example using the X-45A Joint Unmanned Combat Air System dynamics is utilized to highlight the design approach.
Cascading failures coupled model of interdependent double layered public transit network

Taking urban public transit network as research perspective, this work introduces the influences of adjacent stations on definition of station initial load, the connected edge transit capacity, and the coupled capacity to modify traditional load-capacity cascading failures (CFs) model. Furthermore, we consider the coupled effect of lower layered public transit network on the CFs of upper layered public transit network, and construct CFs coupled model of double layered public transit network with “interdependent relationship”. Finally, taking Jinan city’s public transit network as example, we give the dynamics simulation analysis of CFs under different control parameters based on measurement indicator of station cascading failures ratio (abbreviated as CF) and the scale of time-step cascading failures (abbreviated as TCFl), get the influencing characteristics of various control parameters, and verify the feasibility of CFs coupled model of double layered public transit network.
A Remark on Oka's Coherence without Weierstrass' Preparation Theorem and the Oka Theory

The proofs of Oka's Coherence Theorems are based on Weierstrass' Preparation (division) Theorem. Here we observe that a Weak Coherence of Oka proved without Weierstrass' Preparation (division) Theorem, but only with \textit{power series expansions} is sufficient to prove Oka's J\^oku-Ik\^o and hence Cousin I, II, holomorphic extensions, and Levi's Problem, as far as the domain spaces are non-singular. The proof of the Weak Coherence of Oka is almost of linear algebra. We will present some new or simplified arguments in the proofs.
Role of Rituximab Addition to First-line Chemotherapy Regimens in Nodular Lymphocyte-predominant Hodgkin Lymphoma: A Study by Fondazione Italiana Linfomi

Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is a rare entity whose neoplastic cells retain a B-cell phenotype with expression of CD20. Radiotherapy is recommended for favorable stage IA disease while for other stages guidelines suggest therapeutic strategies similar to those used for classic HL. The role of rituximab, although quite widespread, is not completely elucidated. We retrospectively analyzed baseline characteristics of 308 consecutive patients with NLPHL diagnosed in 19 Italian centers from 2000 to 2018. With a median follow-up of 8.4 years (interquartile range: 4.5–12.4) for treated patients, median overall survival (OS) was not reached and estimated 5-year OS was 97.8% and 5-year progression-free survival (PFS) was 84.5%. Five-year cumulative incidence of histological transformation was 1.4%, 95% confidence interval (CI), 0.5%-3.8%. After adjusting for lymphocyte count, splenic involvement, bulky disease and B symptoms (fever, drenching night sweats, unintentional loss >10% of body weight within the preceding 6 months), patients with stage II or more showed superior PFS with immunochemotherapy in comparison to chemotherapy alone (hazard ratio = 0.4, 95% CI, 0.2-0.8; P = 0.015). Our data suggest an advantage of the use of rituximab combined with chemotherapy ± radiotherapy in the treatment of stage II–III–IV NLPHL.
Adaptive Position Prediction Based GNSS Spoofing Detection for Autonomous Vehicles

Automatic vehicles (AVs) have been a hot topic recently, but GNSS spoofing is becoming a technology that can be used manually and has a huge potential threat to AV security. This paper proposes a GNSS spoofing detection method based on vehicle position prediction and develops a prediction error model, which can dynamically adjust the detection threshold. The simulation results show that this adaptive detection threshold setting can keep the gap between the error distance and the detection threshold in a relatively stable range without spoofing, significantly reducing the possibility of false alarms. Besides, this position prediction method’s horizontal error (around 0.01m) is minor enough to satisfy defense demands against spoofing attacks. Overall, the analyses reveal that the spoofing detection method is effective in spoofing attacks with offset in different directions.
Need for AI in Transformer Diagnostics and Prognostics

SUMMARY & CONCLUSIONSIn this paper we present the Probabilistic Operations Warranted for Energy Reliability Evaluation and Diagnostics (POWERED) hybrid artificial intelligence (HAI)/ machine learning (ML) tool for diagnosing and predicting performance and remaining useful life (RUL) of electrical transformers to increase reliability and inform maintenance. POWERED incorporates electrical, thermal, and environmental factors that influence transformer degradation to perform predictive analytics and forecast their health and status (H&S) indicators (internal temperatures, dissolved gases) to predict impending failures and RUL more accurately. POWERED uses a rich and modular probabilistic modeling approach that holistically integrates various types of models including sub-symbolic (i.e., purely data-driven) models and symbolic (i.e., Bayesian and physics-based) models. POWERED’s predictive analytics are demonstrated on a rich data set collected over one year from an operational distribution transformer. The data set includes timestamped ambient temperatures, oil and hot spot winding temperatures, and electrical loading and concentrations of dissolved gases. We show how these diverse factors can be correlated and used both in real-time H&S monitoring and what-if analysis under extreme conditions.
Contemplate and Investigate a Network based Intrusion Detection System

With the expanding request of system and advances in the field of system, presently every association needs to have their own information organizer and secure it against phishing exercises. To protect an enterprise from such attacks, a variety of technologies can be deployed such as honeypot, firewall and intrusion detection/prevention systems. This study is more focused on Intrusion Detection System (IDS). In particular, this study has focused on snort, a lightweight open-source detection system, which is widely used these days. Further, Snort can also be utilized as IPS (Intrusion Prevention System). Here, a G-snort an interface is designed to establish standards and execute commands in snort. It offers all types of documents assistance needed for a greater grasp of the user. The developed system can further be used for Intrusion prevention also.
Detection of White Stem Borer Disease in Coffee Plantation using Autonomous Multi Terrain Robot

A number of nations’ economies rely heavily on the production of coffee. The coffee plant has experienced significant damage as a result of a White Stem Borer (WSB) bug. The WSB bores into the coffee stem and stays buried there until the plant fully falls. As it is challenging to diagnose the disease in the early stages of its infestation, this disease greatly distresses coffee growers. In order to find a way to prevent the significant yield loss, we built on a dataset of WSB-infected stems, identified 3 distinct features to classify infected coffee stems and distinguish them from healthy stems, and then trained YoloV5 deep learning model. We developed a model using augmented images from the existing image data after dividing it into training and testing sets. mAP, precision, and recall of 81.9, 89.7 and 73.8 percentages respectively were obtained, which would increase substantially with larger data size. The proposed method of utilising the bark’s characteristics for early detection of stem borer disease has not been explored so far. The image processing pipeline implemented is accompanied by the simulation of Arabica Coffee plantationlike environments. The simulations focus on the Jackal UGV as it is an ideal robot for the use case of autonomous deployment in mapping and navigation of the plantations. Functionalities such as Elevation Mapping, State Estimation and Localisation have been incorporated in tandem so that data captured by use of a depth camera can be used efficiently for operation.
A Skip Residual Features Aggregation-Based Methodology for Classification of 3D Lung Nodules

Lung cancer is one of the deadliest diseases caused by genetic and textural mutations leading to infinite proliferation of tissue cells, which can be diagnosed and prevented in advance by medical images, and deep learning provides an effective automatic diagnosis technique for this purpose. However, the diagnostic accuracy as well as the reduction of fault-diagnosis and misdiagnosis rates still need to be improved. To address this problem, a skip residual features aggregation-based methodology for classification of 3D Lung nodules is proposed. Through two pathways, residual fusion and dimensional stitching of features are achieved to improve the richness of deep feature information, suppress network degradation, and maintain feature continuity. Before training, the images are pre-processed with ROI region extraction, setting of window width window level, voxel normalization and data augmentation. Finally, the average precision in benign and malignant classification task is 86%, the recall reaches 87%, and the f1-score is 86%. The average precision in texture type of classification is 82%, the recall is 83%, and the f1-score is 82%.
Music Recommendation System using Hybrid Approach

Due to the expansion of the World Wide Web a lot of music data is now accessible online. It becomes a requirement to create a suggestion service in addition to conducting searches for user’s anticipated musical things. A Music Recommendation System using a hybrid approach is presented in this paper, which provides a personalized music recommendation service by using a combination of several factors. A hybrid approach is a powerful tool in the development of music recognition, as it can classify music into different genres based on the audio signal beats. This allows for easier categorization and understanding of the similarities between various music genres. Moreover, this approach could be further developed to improve the accuracy of automatic music classification. By using the hybrid approach, music recognition research can become more efficient and accurate, resulting in an advancement of the field. In a personalized music recommendation System, to integrate the results of the hybrid algorithm with the log files, this study has used the recommended method of collective filtering (CF). To make music recommendations to the user, this algorithm was created, based on their individual preferences. The CF recommendation algorithm takes as input the user's favorite songs and the log files Every user who uses a system for tailored music recommendations has their history recorded in the log file. The system that provides tailored music recommendations takes the user's history from the log file and suggests songs for each genre. Giving the user with positive human-computer interaction and top-notch recommendations is the primary goal of music recommendation. And to achieve this, the user interface allows users to play songs and simultaneously recommends songs based on the current song being played. The audio features and lyrics scores of each pair of songs are fed into the algorithm for analysis. The suggestions are stored using firebase storage, which ensures that the user gets reliable recommendations.
A Novel DenseNet-based Deep Reinforcement Framework for Portfolio Management

The objective of portfolio management is to realize portfolio optimization, i.e., maximizing the cumulative return of the portfolio over continuous trading periods. Using Artificial Intelligence algorithms, e.g., Deep Reinforcement Learning (DRL), to realize portfolio optimization is an emerging research trend. Jiang et al.’s Ensemble of Identical Independent Evaluators (EIIE) framework achieves at least a four-fold improvement in the indicator of final portfolio value. Their framework has high flexibility to allow us to replace components to achieve continuous improvement. In EIIE, the DRL agent uses neural networks to extract data features from historical data of assets and evaluate each asset’s potential growth. This paper introduces a novel network architecture called Dense Based EIIE (DBE), which is embedded in an DRL framework based on Convolutional Neural Network (CNN) and Densely Convoluted Neural Network (DenseNet) module. Compared to Jiang et al.’s strategy, our improved framework uses DenseNet to achieve the EIIE framework, further increasing profitability. In all three experiments carried out, our strategy outperforms Jiang et al.’s strategy and nine traditional strategies. Our strategy achieves at least a 17% improvement in cumulative return compared to other strategies. Furthermore, it achieves at least twice as much in Sharpe Ratio as other strategies.
An Implementation of a Socially Adaptive Blockchain-Based Transactive Energy System

Rapid growth of renewable resources (DERs) requires transactive energy as a key player in distribution network operations and energy management. Conventional centralized approaches can't handle the effective bidirectional sharing of information and energy within the local distribution network. Local balancing difficulties include using renewable resources efficiently, reducing energy costs regionally and nationally, and increasing prosumer financial rewards. This paper focuses on blockchain solutions for diverse stakeholders to actively participate in energy management and trade, followed by a blockchain use case implementation. The necessity for the cooperation of smart homes/buildings with renewable resources and shiftable loads through intelligent energy management systems (IEMS) is also covered in this paper. The overall conclusion and case study results suggest that this blockchain-based transactive energy platform is practicable in practice and helps improve the financial benefit of individual prosumers, which assures socially optimal distribution system performance.
A Naive Bayes Classifier Based Energy Scheduling for Home Energy Management System (HEMS)

With the increase of domestic energy consumption, an efficient energy management scheme for smart home is needed. Just as solar power can be used as a supplement to grid power in order to use smart appliances in case of grid unavailability or blackout. In this research, a load and power management system based on load priority and instant grid energy price has been proposed using Fuzzy logic algorithms. The home energy management systems (HEMS) modeled using Naive Bayes classifier shows that the loads can be shifted on or off based on grid availability, solar availability, pricing demand, urgency and loads can be changed their power source based on Interrupted, Non-interrupted, and Fixed appliances. This sort of technologies would be needed in Bangladesh whereas cost adopts grid energy price variation over time and demand. Simulation results reveal that the proposed protocol saves 47.1% ~ 48.47% of monthly electricity cost compare to the traditional scheme.
System Profit Assessment of a Solar-Integrated Deregulated Power System

The maximization of earnings from an electricity market is one of the most critical parts. The electricity generated by the stations is fixed. During the low peak hours, the energy is available in abundance. These excess units left from renewable energy sources like solar, windand the flow of water can be stored in storage units. One of the solutions used in recent times is the usage of Battery Energy Storage Systems (BESS). The leftover energy generated in the low peak hours can be saved and reused at the peak demand time. The energy stored in the units can be further used during high peak hours and can be utilized to gain further profits which can be the motto of an Electricity Market. With this electricity, greater income can be generated. Nowadays the usage of renewable sources for power generation to preserve non-renewable resources from extinction has emerged as a topic. This paper discusses a way by putting the renewable deliver at one of the load centers and locating the greatest component where the storing of electricity can take advantage of both renewable and non-renewable resources. The work mainly focuses onthe presence of regulated and deregulated systems. The main objective of this work is to find the considerable changes in system profit in a power system with regulated and deregulated conditions with the placement of Solar PV. The work is simulated using MATPOWER and the programming language is SQP (Sequential Quadratic Programming) which follows the Newton-Raphson method.
Using a High frequency LC Resonant Inverter for Ultrasonic Cleaning Applications

Nowadays, ultrasonic systems have many applications in various fields such as welding, cutting, imaging, and cleaning. One of the main requirements of the ultrasonic systems is using a resonant circuit to drive the transducer, properly. In this paper, a high frequency LC resonant inverter is used for driving an ultrasonic cleaner in its optimum frequency, which provides maximum power conversion, as well as optimum clearing performance. Also, zero voltage switching (ZVS) at MOSFETs turn-on times is properly achieved, which reduces the dominant switching losses and electromagnetic interference noise and improves the converter efficiency. Finally, a laboratory prototype circuit has been implemented and tested under different conditions to confirm the performance of the proposed inverter for ultrasonic cleaner systems.