Miracle-dz/UniFAR-FTUs huggingface dataset:
The Integrated Voxel Analysis Method (IVAM) to Diagnose Onset of Alzheimer’s Disease and Identify Brain Regions through Structural MRI Images

Magnetic Resonance Imaging (MRI) provides three-dimensional anatomical and physiological details of the human brain. We describe the Integrated Voxel Analysis Method (IVAM) which, through machine learning, classifies MRI images of brains afflicted with early Alzheimer’s Disease (AD). This fully automatic method uses an extra trees regressor model in which the feature vector input contains the intensities of voxels, whereby the effect of AD on a single voxel can be predicted. The resulting tree predicts based on the following two steps: a K-nearest neighbor (KNN) algorithm based on Euclidean distance with the feature vector to classify whole images based on their distribution of affected voxels and a voxel-by-voxel classification by the tree of every voxel in the image. An Ising model filter follows voxel-by-voxel tree-classification to remove artifacts and to facilitate clustering of classification results which identify significant voxel clusters affected by AD. We apply this method to T1-weighted MRI images obtained from the Open Access Series of Imaging Studies (OASIS) using images belonging to normal and early AD-afflicted individuals associated with a Client Dementia Rating (CDR) which we use as the target in the supervised learning. Furthermore, statistical analysis using a pre-labeled brain atlas automatically identifies significantly affected brain regions. While achieving 90% AD classification accuracy on 198 images in the OASIS dataset, the method reveals morphological differences caused by the onset of AD.
A Novel Distributed Cloud-Fog Based Framework for Energy Management of Networked Microgrids

Power grid resilience, reliability, and sustainability can be improved by decomposing the large-scale grids into the Networked Microgrids (NMGs). However, different MGs may have different roles and policies. Hence, in comparison with conventional networks, optimal energy management, as well as the grid reconfiguration of the NMGs is more completed and challenging. This article develops a three-layer cloud-fog computing architecture for energy management of reconfigurable NMGs considering dynamic thermal line rating (DLR) constraint. DLR can potentially affect the ampacity of feeders, especially in the islanding mode, when lines approach their maximum capacity. In order to avoid any feeder contingency in the off-grid (islanded) mode, the reconfiguration technique as well as the cloud fogs framework are employed to change the topology of the NMG network swiftly and release the line capacity. Finally, the proposed problem is formulated as a mixed-integer linear optimization problem considering DLR constraint. The proposed model is examined on a modified IEEE 69 bus test network. Results demonstrate the high performance and effectiveness of the developed model and also validate its reliability and economic assets.
Delineating Urban Functional Areas with Sina Weibo Check-in Data: A Matching Time Series Distance Based LST-SVM Multi-classifier Method

Delineating the distribution of different urban functional areas is a hot topic in urban studies. The accuracy of describing urban functional areas is particularly important. This paper aims to improve the estimated accuracy of delineating urban functional areas according to the assumption that social media activities in these buildings with similar functionality not only have similar spatiotemporal patterns but also are strictly correlated to the temporal information. We propose a novel Matching Time Series (MTS) method to calculate the distance of the time-series data and use this method to modify the least squares twin support vector machine (LST-SVM) Multi-classifier Method for classifying the building objects with similar functionality as a functional area. According to the time series dataset built based on Sina Weibo check-in data, we compare with the dynamic time warping (DTW) distance based k -medoids method from different aspects. The results show that the accuracy is improved from 29.96% to 82.68%, which verifies the superiority of our proposed approach in improving the estimated accuracy of delineating urban functional areas. By separating time series dataset into weekdays and weekends, we also obtain relatively high classification accuracy respectively, and it contributes to analyze the distribution of urban functional buildings more clearly.
Face-based Gender recognition Analysis for Nigerians Using CNN

Estimating human gender from faces in images is an important area of research as many applications rely on it. Facial recognition is a branch of biometrics that uses the face which is a physical trait to uniquely identify individuals. Gender recognition using face analysis is also an important task in computer vision as it helps in visual surveillance, intelligent user interfaces, demographic studies etc. The fundamental of gender recognition and other similar classification problem is broken into four stages i.e. the image to be examined to the pre – processing of the image, feature extraction and lastly classification. Several approaches including the deep learning approach which is a representation of the learning procedure that discover multiple levels of representations using neural network has been explored for gender recognition. This work is essential in creating a face-based recognition for gender analysis for Nigerians. The face database consists of over 6000 images of Nigerians with different variations. The created database was used to analyze gender by pre-processing the images, extracting necessary features and classification using the Convolutional Neural Network (CNN). An overall recognition accuracy of 98.72% was achieved demonstrating the feasibility and research potential in such direction.
Discrete Wavelet Denoising into MFCC for Noise Suppressive in Automatic Speech Recognition System

: Automatic Speech Recognition (ASR) is a challenging task and the most problematic issues being in presence of background noise and substantial variability in speech. Extracting the noise-robust features adjust for speech degradations due to noise effect retained popular issue in recent years. This paper presented a framework for wavelet denoising scheme and analysed the different wavelet families and proper thresholding rule into feature extraction to enhance the performance of ASR system. Gaussian Mixture Model-based Hidden Markov Model (GMM-HMM) and Deep Neural Network (DNN)-HMM are used as the speech recognizer. The recognition performance shows that the noise-robust features are obtained while combining with the wavelet transform denoising into Mel Frequency Cepstral Coefficient (MFCC) on Aurora2 database. The best accuracy is gained by cross entropy DNN-HMM training using denoising with Coiflet wavelet and Rigrsure threshold, which provides 97.54% in 10dB, 93.13% in 5dB, 75.63% in 0dB and 37.29% in −5dB .
Impaired cholesterol metabolism in the mice model of cystic fibrosis. A vicious circle?

Patients with cystic fibrosis (CF) have low cholesterol absorption and, despite enhanced endogenous biosynthesis, low serum cholesterol. Herein, we investigated cholesterol metabolism in a murine CF model in comparison to wild type (WT) testing serum and liver surrogate biomarkers together with the hepatic expression of genes involved in cholesterol metabolism. CF mice display lower sterols absorption and increased endogenous biosynthesis. Subsequently, we evaluated the effects of a cholesterol-supplemented diet on cholesterol metabolism in CF and WT mice. The supplementation in WT mice determines biochemical changes similar to humans. Instead, CF mice with supplementation did not show significant changes, except for serum phytosterols (−50%), liver cholesterol (+35%) and TNFα mRNA expression, that resulted 5-fold higher than in CF without supplementation. However, liver cholesterol in CF mice with supplementation resulted significantly lower compared to WT supplemented mice. This study shows that in CF mice there is a vicious circle in which the altered bile salts synthesis/secretion contribute to reduce cholesterol digestion/absorption. The consequence is the enhanced liver cholesterol biosynthesis that accumulates in the cell triggering inflammation.
1 Developing consensus on hospital prescribing indicators of potential harm for infants and children

Aims To develop a list of hospital based paediatric prescribing indicators that can be used to assess the impact of electronic prescribing or clinical decision support tools on paediatric prescribing errors. Background Medication errors are a major cause for concern in the NHS. Prescribing is part of the medication use process and is a complex task requiring an understanding of medicines, disease processes, and patient parameters. Systematic reviews have reported that medication errors occur in as many as 50% of hospital admissions and prescribing error rates in the UK hospitals vary between 9% and 15%. Prescribing for children is further complicated by the need to take into account weight, altered physiology and pharmacokinetics. Prescribing error rates of 13.1% have been reported in children with a potentially greater impact due to the nature of the patients. Electronic prescribing (EP) while relatively uncommon in UK hospitals is an important tool in reducing prescribing errors. EP systems have been shown to have a positive impact on prescribing errors, however methodologies vary and the reduction in harm is rarely investigated. A standard tool to allow an evaluation of the harm reduction is desirable and currently does not exist for the paediatric setting. Methods Two rounds of an electronic consensus method (eDelphi) were carried out with 21 expert panellists from the UK. Panellists were asked to score each prescribing indicator for its likelihood of occurrence and severity of outcome should the error occur. The scores were combined to produce a risk score and a median score for each indicator calculated. The degree of consensus between panellists was defined as the proportion that gave a risk score in the same category as the median. Indicators were included if a consensus of 80% or higher was achieved and were in the high risk categories. Results An expert panel consisting of 8 pharmacists and 13 paediatricians with a total of 437 years of clinical experience completed an exploratory round and two rounds of scoring. This identified 41 paediatric prescribing indicators with a high risk rating and greater than 80% consensus. The most common error type within the indicators was wrong dose (n=19) and the most common drug classes were antimicrobials (n=10) and cardiovascular (n=7). Conclusions A set of 41 paediatric prescribing indicators describing potential harm for the hospital setting have been identified by an expert panel. The indicators provide a standardised method of evaluation of prescribing data on both paper and electronic systems. They can also be used to assess implementation of clinical decision support systems or other quality improvement initiatives.
162. SIMULTANEOUS OPTIMIZATION OF ELECTRICAL INTERCONNECTION CONFIGURATION IN ONSHORE WIND FIELDS

The electrical interconnection cost of onshore wind fields (WF) is a part that should not be neglected in the design of WF. In order to minimize the investment and operational costs, this paper pro- poses an optimization formulation to find the optimal electrical interconnection configuration of wind tur- bines (WTs), simultaneously. This simultaneous minimization of total trenching length is done during а WF layout optimization by application of evolutional algorithm. In this paper, an algorithm is considered to comprehensively assess the optimal interconnection layout of the WTs. The interconnections are consid- erable fraction of the overall design cost of the WF. The optimal electrical interconnection design obtained by an algorithm with implemented Euclidean Minimum Spanning Tree method, corresponds to a lower cost and coupled with the technological developments can help policy makers and WF developers increase the use of onshore wind energy as a feasible unlimited renewable source of electrical energy
Estimating densities of larval Salmonflies (Pteronarcys californica) through multiple pass removal of post-emergent exuvia in Colorado rivers

Traditional methods of collecting and sorting benthic macroinvertebrate samples are useful for stream biomonitoring and ecological studies; however, these methods are time consuming, expensive, and require taxonomic expertise. Estimating larval densities through collection of post-emergent exuvia can be a practical and time efficient alternative. We evaluated the use of multiple pass depletion techniques of the post-emergent exuvia of Pteronarcys californica to estimate larval densities at ten sites in three Colorado rivers. Exuvia density was highly correlated with both final-instar larval density (R2 = 0.90) and total larval density (R2 = 0.88) and the multiple pass removal technique performed well. Exuvia surveys found P. californica at three low density sites where benthic sampling failed to detect it. At moderate and high density sites the exuvia surveys always produced lower density estimates than benthic surveys. Multiple pass depletion estimates of exuvia proved to be an accurate and efficient technique at estimating larval densities and provided an effective alternative for traditional benthic sampling when objectives are monitoring P. californica and detecting populations, especially at low density sites.
Emergency Support Unmanned Aerial Vehicle for Forest Fire Surveillance

The advances in autonomous technologies and microelectronics have increased the use of Autonomous Unmanned Aerial Vehicles (UAVs) in more critical applications, such as forest fire monitoring and fighting. In addition, implementing surveillance methods that provide rich information about the fires is considered a great tool for Emergency Response Teams (ERT). From this aspect and in collaboration with Telefónica Digital España, Dronitec S.L, and Divisek Systems, this paper presents a fire monitoring system based on perception algorithms, implemented on a UAV, to perform surveillance tasks allowing the monitoring of a specific area, in which several algorithms have been implemented to perform the tasks of autonomous take-off/landing, trajectory planning, and fire monitoring. This UAV is equipped with RGB and thermal cameras, temperature sensors, and communication modules in order to provide full information about the fire and the UAV itself, sending these data to the ground station in real time. The presented work is validated by performing several flights in a real environment, and the obtained results show the efficiency and the robustness of the proposed system, against different weather conditions.
AtTPR10 Containing Multiple ANK and TPR Domains Exhibits Chaperone Activity and Heat-Shock Dependent Structural Switching

Among the several tetratricopeptide (TPR) repeat-containing proteins encoded by the Arabidopsis thaliana genome, AtTPR10 exhibits an atypical structure with three TPR domain repeats at the C-terminus in addition to seven ankyrin (ANK) domain repeats at the N-terminus. However, the function of AtTPR10 remains elusive. Here, we investigated the biochemical function of AtTPR10. Bioinformatic analysis revealed that AtTPR10 expression is highly enhanced by heat shock compared with the other abiotic stresses, suggesting that AtTPR10 functions as a molecular chaperone to protect intracellular proteins from thermal stresses. Under the heat shock treatment, the chaperone activity of AtTPR10 increased significantly; this was accompanied by a structural switch from the low molecular weight (LMW) protein to a high molecular weight (HMW) complex. Analysis of two truncated fragments of AtTPR10 containing the TPR and ANK repeats showed that each domain exhibits a similar range of chaperone activity (approximately one-third of that of the native protein), suggesting that each domain cooperatively regulates the chaperone function of AtTPR10. Additionally, both truncated fragments of AtTPR10 underwent structural reconfiguration to form heat shock-dependent HMW complexes. Our results clearly demonstrate that AtTPR10 functions as a molecular chaperone in plants to protect intracellular targets from heat shock stress.
Understanding Visitors’ Experiences in Nature-based Tourism: A Case Study of Komodo National Park Indonesia

III TABLE OF CONTENTS. V LIST OF APPENDICES . VII LIST OF FIGURES . VIII LIST OF TABLES . IX LIST OF ABBREVIATIONS . X ATTESTATION OF AUTHORSHIP . XI ACKNOWLEDGEMENTS . XII CHAPTER 1: INTRODUCTION . 1 1.1 Background of the study . 1 1.2 Tourism in Indonesian National Parks . 6 1.3 Tourism in Komodo National Park . 8 1.4 Statement of the problem . 14 1.5 Research aim and objectives . 14 1.6 Significance of the study . 15 1.7 Structure of the thesis . 16 CHAPTER 2: LITERATURE REVIEW . 18 2.1 Tourism in the national park . 18 2.1.1 A brief history of the early establishment of national parks . 18 2.1.2 The role of tourism in the national parks . 20 2.1.3 Tourism in national park as nature-based tourism product . 21 2.2 Visitors’ experiences . 24 2.2.1 The benefit gained by visitors from tourism experiences . 27 2.3 ASEB Grid Analysis . 28 2.4 Summary . 30 CHAPTER 3: METHODOLOGY . 32 3.1 Research paradigm . 32 3.2 The rationale of qualitative research . 33 3.3 The rationale for semi-structured interviews . 34 3.4 Semi-structured interviews. 35 3.5 Photo elicitation . 36 3.6 Data sampling . 37 3.6. 1 Semi-structured interview sample . 37 3.6. 2 Photo collection sample . 39 3.7 Data analysis . 40 3.7. 1 Photo analysis . 41 3.8 Ethical considerations . 42 3.9 Trustworthiness of the research . 43 3.10 Limitations of the study . 44 3.11 Chapter conclusion . 44 CHAPTER 4: FINDINGS AND DISCUSSION . 46 4.1 Overall findings . 46 4.2 Activities . 49 4.2.1 Strengths (SA) . 50 4.2.2 Weakness (WA) . 53 4.2.3 Opportunities (OA) . 54
ETHNOBOTANICAL SURVEY OF MEDICINAL PLANTS COMMONLY USED BY TRIBAL PERSON OF TIKAMGARH DISTRICT MADHYAPRADESH.

Use of plants resources for fulfilment of various requirements of any community may be regarded as a part of a cultures traditional knowledge. The tribes of Madhya Pradesh are found to be rich in traditional knowledge system and are using their indigenous methods in treatment of different diseases. The present paper deals with the detail study of medicinal plants used by Tikamgarh district Madhya Pradesh using a quantitative consensus analysis. A total of 106 plants species belonging to 51 families used for medicinal and general health purposes were identified and included with relevant information. An informant consensus (FIC) analysis revealed a high level of homogeneity among the informants knowledge on various ethnomedicinal plants. High consensus factor was observed for dermatological disorder (0.64) and low in oral and dental problem (0.5). among different plant parts leaves were used in most of the cases for treatment of various diseases.
THE AFFORDANCES OF THE FLIPPED CLASSROOM APPROACH IN ENGLISH GRAMMAR INSTRUCTION

Recently, the flipped classroom instructional approach has received much attention from teachers of different subjects around the world. This new pedagogical model has been reported to be a potential method in the area of EFL teaching. In Vietnam, teaching English grammar has mainly focused on students’ acquisition of grammar rules without much practical application in real communicative situations. This case study aims at investigating the affordances perceived by students in a flipped English grammar class in order to help language teachers to harness the approach to enhance their students’ learning. The qualitative research design was adopted in the study. Thirty-four students majoring in the English language at a university in Vietnam attended a 10-week flipped grammar class. The instruments include semistructured interviews with ten students randomly selected from the participants. Thematic analysis was performed to address the qualitative data drawing on the Activity Theory framework. Twelve affordances of the flipped classroom approach in English grammar instruction such as being self-paced in learning, offering opportunities to voice opinions, saving time for in-class communicative activities and facilitating learning English grammar communicatively were identified. The results of the study offer valuable implications for the application of this model in teaching English as a foreign language, especially in the context of Vietnam.
Toxic sights: The spectacle of hazardous waste removal

This paper examines the geographies of hazardous waste removal. Over the past decade, studies of disposal have demonstrated the myriad ways in which things can never disappear – they can only be transformed, transmuted, combusted, combined or any other manner of material change. This paper aims to develop understandings of the material politics of disposal by considering the matter of representation. It does this ethnographically, by following a chemical stockpile though the process of removal from its storage site in Tanzania. In examining everyday disposal practices, this paper highlights the materialities of hazardous waste in ways that have been epistemologically side-lined. Locating the analysis at the intersection of matter and representation, the paper illustrates the centrality of paper-work, diagrams, photographs and standard operating procedures in performing removal. It argues that removal is achieved through a bureaucratic spectacle; a process which obscures lingering residues and compounds their toxic effects. By attending to chemicals through the mundane work of removal, this paper opens up different lines of inquiry for studies of waste, and enriches understandings of materiality by considering how visual representations make a difference.
microRNA-93, upregulated in serum of nasopharyngeal carcinoma patients, promotes tumor cell proliferation by targeting PDCD4

Deregulation of microRNAs (miRs) has been demonstrated to contribute to the development and malignant progression of nasopharyngeal carcinoma (NPC). Recently, miR-93 was reported to be significantly upregulated in NPC tissues and cell lines, and promote the proliferation, migration and invasion of NPC cells in vitro, as well as tumor growth in vivo. However, whether there is any clinical value of serum miR-93 expression in NPC still remains unclear. Therefore, the present study aimed to explore the clinical significance of serum miR-93 expression in NPC. A total of 85 serum samples from NPC patients and 30 from healthy controls were collected. Reverse transcription-quantitative polymerase chain reaction data demonstrated that the serum expression of miR-93 was significantly increased in NPC patients, when compared with those in healthy controls. Following receiving chemo-radiotherapy, the serum miR-93 levels were significantly decreased in NPC patients. Furthermore, the increased serum levels of miR-93 were significantly associated with advanced grade, clinical stage, lymph node metastasis, as well as worse 5-year overall survival of NPC patients. Furthermore, the serum miR-93 expression was demonstrated to be an independent factor for predicating the prognosis of NPC. In vitro experiments demonstrated that knockdown of miR-93 caused a decrease in NPC cell proliferation, whereas overexpression of miR-93 promoted NPC cell proliferation. PDCD4 was then identified as a direct target of miR-93 in NPC cells. Overexpression of PDCD4 significantly eliminated the promoting effects of miR-93 overexpression on NPC cell proliferation. Taken together, these findings suggest that the serum miR-93 expression could be used as a predicator for the clinical outcome of NPC patients, and suggest that miR-93 may also become a potential therapeutic target for NPC treatment.