Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Brain Tumor Segmentation and Classification using Machine and Deep Learning Techniques

One of the most common methodologies in medical study is to detect a brain tumor and its development from a magnetic resonance imaging (MRI) of the brain. As a result, manually segmenting brain tumor from MRI images is a time-consuming and difficult process. Furthermore, an automatic brain tumor categorization based on an MRI scan is non-invasive, eliminating the need for a sample and making the diagnosing procedure safer. The scientific community has been working tirelessly since the turn of the millennium and the late 1990s to develop an automated brain tumor segmentation and classification approach. As a result, there is a lot of literature on segmentation employing region growth, classical machine learning, and deep learning methods. Similarly, several tasks in the domain of brain tumor categorization into their various histological types have been completed, with outstanding performance outcomes. The goal of this study is to present a complete assessment of three newly suggested important segmentation and classification methods for the brain tumor, namely, region growth, shallow machine learning, and deep learning, taking into explanation state-of-the-art techniques and their performance. Technical issues such as the strengths and drawbacks of alternative methods, pre-and post-processing methodologies, feature extraction, datasets, and model performance of the evaluation metrics are also covered in the conventional works involved in this study.
Improved 2D laser slam graph optimization based on Cholesky decomposition

Laser slam usually needs to complete a back-end graph optimization at a fast speed in some specific scenes, such as sharp turns, fast motion, and limited calculation time. Aiming at these problems, this paper proposed a 2D laser slam back-end graph optimization combined with Cholesky decomposition to accelerate a linear solution process and further to achieve a purpose of accelerating graph optimization. In MATLAB simulation experiments, the rate of 2D laser slam back-end graph optimization combined with Cholesky decomposition increased 24%, compared to that of the traditional method without Cholesky decomposition. The result verified the effectiveness of the improved method.
On the Impact of Hardware Timing Errors on Stochastic Computing based Neural Networks

Stochastic computing (SC) with its stream-based, probabilistic number representation promises large area and power benefits as well as increased error tolerance compared to conventional binary computing. While SC is less precise, it is considered a promising option for implementing neural network inferencing in ultra-low-power edge devices. SC-based Neural Networks (SCNNs) typically combine stochastic and binary components for interfacing and to alleviate certain SC limitations. Moreover, ultra-low-power VLSI for edge computing is often less reliable due to noisy environments or deliberate power-reliability trade-offs. In this work, we present the first detailed investigation of the behavior of an SCNN and its individual components on hardware prone to timing errors. Our results show that robustness of SC is highly dependent on specific design choices and that biases in the error distributions may even cause SCNNs to perform worse under certain circumstances than comparable binary implementations. It shows that robustness should be treated as a design goal in SC rather than taken for granted.
Using Metamorphic Relations to Improve The Effectiveness of Automatically Generated Test Cases

Automated test case generation has helped to reduce the cost of testing. However, developing effective test oracles for these automatically generated test cases still remains a challenge. Metamorphic testing (MT) has become a well-known software testing approach over the years. This testing technique can effectively alleviate the oracle problem faced when testing using metamorphic relations (MRs) to determine whether a test case is passed or failed. In this work, we conduct an empirical study on an open source linear algebra library to evaluate whether MRs can be utilized to improve the fault detection effectiveness of automatically generated test cases. Our experiment suggests that MRs can help to improve the fault detection effectiveness of automatically generated test cases.
Weakly Supervised Part-Based Method for Combined Object Detection in Remote Sensing Imagery

Deep learning methods have reached considerable achievement on remote sensing object detection in recent years. However, most methods are designed for single object detection, such as vehicles and ships, and have limited detection capabilities for the combined object with large scale and complex part structure. In this article, we propose a part-based topology distillation network (PTDNet) for accurate and efficient combined object detection in remote sensing imagery. Specifically, a part-based feature module is designed to extract the key parts information of a combined object in a weakly supervised manner. Besides, to balance the accuracy and efficiency of the model, with considering the topology structure of multiple parts in combined objects, a lightweight network training method based on partial topological feature distillation is proposed to improve the model performance without additional parameters. Experiments show that the PTDNet outperforms the state of the art methods and achieves 65.4% mean average precision and 84.1% accuracy for combined object detection.
Trunk and Hip Muscle Activation during Yoga Poses in Untrained Individuals

Yoga has become a popular form of exercise for improving core strength and stability in individuals with Low Back Pain (LBP). Researchers have quantified muscle activation during core stabilization exercises, believing that exercises which require greater activation will benefit that needing improved core stabilization. Minimal attention has been directed toward muscle activation during yoga. The purpose of this study was to determine and compare the relative activation of core muscles during yoga to traditional back exercises. Surface Electro Myo Graphy (EMG) was used to quantify the relative activation of the Rectus Abdominis (RA), Abdominal Obliques (AO), Lumbar Extensors (LE), and Gluteus Maximus (GMX) during four yoga poses (Chair, High Plank, Upward-Facing Dog, and Dominant-Side Warrior 1). Data were expressed as 100% of a Maximum Voluntary Isometric Contraction (100% MVIC). Separate analyses of variance with repeated measures were used to compare muscle activity across each exercise. The sequentially rejective Bonferroni test was used for post hoc testing. EMG activity during the High Plank was moderate (28% MVIC) and high (44% MVIC) for the RA and AO, respectively. The AO had moderate (27% MVIC) activity during the Upward-Facing Dog. EMG activity during the Chair was moderate (38% MVIC) only for the LE. GMX activity was low (< 21% MVIC) during all the exercises. These findings suggest that certain yoga poses may be useful for improving core endurance and strength. Clinicians may use these data when developing and implementing an evidence-based core exercise program for individuals with LBP who prefer a yoga treatment strategy. next 12-month period. More than 15% (36.7 million) of adults in the US consistently practice yoga, an increase of over 16 million since 2012. Most importantly, 74% of those who engage in yoga are relatively new to the practice, becoming involved within the last five years. Significance is the emerging and growing use of yoga across ethnic dimensions. Non-Hispanic adults showed a 30% increase in use of yoga from 2002 to 2012; significant increases in use by Hispanic (5.1%) and African American (5.6%) adults has been reported as well [1].
Green supply chain management and export performance

Purpose The purpose of this paper is to examine the impact of green supply chain management (GSCM) on environmental performance (EP) and export performance in the context of a developing country, Jordan. In addition, the mediating effect of EP on the relationship between GSCM and export performance is investigated. Design/methodology/approach The study is based on survey data collected from 221 manufacturing companies in Jordan. The companies were selected from different industry types to ensure diversity. Validity and reliability analyses were performed using SPSS and Amos, and structural equation modeling was used to test the study hypotheses. Findings The results revealed that GSCM positively and significantly affects both EP and export performance. In addition, the results demonstrated that EP positively and significantly affects export performance. Further, it is also found that EP positively and significantly mediates the relationship between GSCM and export performance. Originality/value The current study is one of the first to investigate the impact of GSCM on export performance, especially in the context of a developing country. In addition, this study contributes to the existing literature by highlighting the mediating effect of EP on GSCM—export performance relationship. GSCM is an under-investigated area in Jordan. The results are expected to promote GSCM implementation among manufacturing companies in Jordan in order to achieve economic benefits by increasing their exports through improvements in EP.
Scholars' Mine Scholars' Mine Analysis of Simple Two-Capacitor Low-pass Filters Analysis of Simple Two-Capacitor Low-pass Filters

— The performance of typical low-pass capacitor fil- ters is limited by the mutual inductance between the input and output sides of the filter. This paper describes how two appropri- ately spaced capacitors can be used to construct a low-pass filter with significantly better high-frequency performance than a one- capacitor filter. Laboratory measurements and numerical simulations are used to quantify the mutual inductance and compare the performance of one- and two-capacitor low-pass filters.
Edinburgh Research Explorer Estrogen-induced chromatin decondensation and nuclear reorganization linked to regional epigenetic regulation in breast cancer

Background: Epigenetic changes are being increasingly recognized as a prominent feature of cancer. This occurs not only at individual genes, but also over larger chromosomal domains. To investigate this, we set out to identify large chromosomal domains of epigenetic dysregulation in breast cancers. Results: We identify large regions of coordinate down-regulation of gene expression, and other regions of coordinate activation, in breast cancers and show that these regions are linked to tumor subtype. In particular we show that a group of coordinately regulated regions are expressed in luminal, estrogen-receptor positive breast tumors and cell lines. For one of these regions of coordinate gene activation, we show that regional epigenetic regulation is accompanied by visible unfolding of large-scale chromatin structure and a repositioning of the region within the nucleus. In MCF7 cells, we show that this depends on the presence of estrogen. Conclusions: Our data suggest that the liganded estrogen receptor is linked to long-range changes in higher-order chromatin organization and epigenetic dysregulation in cancer. This may suggest that as well as drugs targeting histone modifications, it will be valuable to investigate the inhibition of protein complexes involved in chromatin folding in cancer cells.
Visceral Leishmaniasis Chemotherapy Cures Established Murine Combined with Antimony-Based Dendritic Cell-Based Immunotherapy

Dendritic cells (DCs) have been proposed to play a critical role as adjuvants in vaccination and immunotherapy. In this study we evaluated the combined effect of soluble Leishmania donovani Ag (SLDA)-pulsed syngeneic bone marrow-derived DC-based immunotherapy and antimony-based chemotherapy for the treatment of established murine visceral leishmaniasis. Three weekly injections of SLDA-pulsed DCs into L. donovani -infected mice reduced liver and splenic parasite burden signiﬁcantly, but could not clear parasite load from these organs completely. Strikingly, the conventional antileishmanial chemotherapy (sodium antimony gluconate) along with injections of SLDA-pulsed DCs resulted in complete clearance of parasites from both these organs. Repetitive in vitro stimulation of splenocytes from uninfected or L. donovani -infected mice with SLDA-pulsed DCs led to the emergence of CD4 (cid:1) T cells with characteristics of Th1 cells. Our data indicate that DC-based immunotherapy enhances the in vivo antileishmanial potential of antimony or vice versa. The Journal of Immunology, 2003, 170: 5625–5629.
A Higher-Order Eulerian-Lagrangian Localized Adjoint Method for Two-Dimensional Unsteady Advection-Diffusion Problems

We present a higher-order in-space characteristic method for the solution of the transient advection diﬀusion equations in two space dimensions. This method uses biquadratic trial and test functions within the framework of the Eulerian-Lagrangian localized Adjoint Methods (ELLAM). It therefore maintains the advantages of previous ELLAM schemes. Namely, it treats general boundary conditions naturally in a systematic manner, conserves mass, and symmetrizes the governing transport equations. Moreover, it generates accurate numerical solutions even if large time steps are used in the simulation. Numerical experiments are presented to illustrate the performance of this method and establish its order of convergence numerically.
Intratumoral heterogeneity in a p53 null mouse model of human breast cancer

Intratumoral heterogeneity correlates with clinical outcome and reflects the cellular complexity and dynamics within a tumor. Such heterogeneity is thought to contribute to radio- and chemoresistance since many treatments may only target certain tumor cell subpopulations. A better understanding of the functional interactions between various subpopulations of cells, therefore, may help in the development of effective cancer treatments. We identified a unique subpopulation of tumor cells expressing mesenchymal-like markers in a p53 null mouse model of basal-like breast cancer using fluorescence-activated cell sorting and microarray analysis. Both in vitro and in vivo experiments revealed the existence of crosstalk between these “mesenchymal-like” cells and tumor-initiating cells. Knockdown of genes encoding ligands upregulated in the mesenchymal cells and their corresponding receptors in the tumor-initiating cells resulted in reduced tumorigenicity and increased tumor latency. These studies illustrate the non-cell autonomous properties and importance of cooperativity between tumor subpopulations.
An Automatic Analysis System for High-Throughput Clostridium Difficile Toxin Activity Screening

Clostridium difficile infection (CDI) is an increasing global health threat and major worldwide cause of hospital-acquired diarrhea. The development of novel therapies to effectively treat this bacterial pathogen is an unmet clinical need. Here, we describe an image processing and classification algorithm that automatically identifies toxin-induced cytotoxicity to host cells based on characteristic morphological changes. This efficient and automatic algorithm can be incorporated into a screening platform to identify novel anti-toxin inhibitors of the C. difficile major virulence factors TcdA and TcdB, and contains the following steps: image enhancement, cell segmentation, and classification. We tested the algorithm on 504 images (containing 5096 cells) and achieved 93% sensitivity and 91% specificity, indicating that the proposed computational approach correctly classified most of the cells and provided reliable information for an effective screening platform. This algorithm achieved higher classification results compared to existing cell counter and analysis programs, scoring 92.6% accuracy. Compared to visual examination by a researcher, the algorithm significantly decreased classification time and identified toxin-induced cytotoxicity in an unbiased manner. Availability: Examples are available at home.agh. edu.pl/jaworek/CDI.
Carbon Disclosures, Carbon Performances and Green Reputation of Higher Educational Institutions in UK

Purpose – This study investigated the carbon emission disclosures (CED) and performance of UK Higher Educational Institutions (HEIs) and the associated impact on their environmental reputation. The paper argues that HEIs possess distinct characteristics that make comparisons with profit-oriented companies problematic and misleading. Design/methodology/approach – The green score published by the People & Planet organisation provided the population for this analysis. All universities with a score were entered into the initial sample. The association between green reputation, CED and carbon performance was examined using a robust least squared regression model. Findings – CED, carbon emissions and carbon audit were found to have highly significant determinant relationships with HEIs’ green reputation status at a 1% significance level while the presence of standalone sustainability reporting was found to have a very weak significance in determining an HEI’s environmental reputation. Research limitations/implications – The study investigated the impact of CED and other indicators of carbon performance by HEIs on their green reputation. The impact of CED and other carbon performance measures needs to have a clear relationship with reputation in order to motivate HEIs to act and disclose. Originality/value The study is distinct in investigating the impact of CED and carbon performance by UK HEIs on their environmental reputation. The study shows whether, and how, the HEI CED and carbon performances contribute towards the environmental reputation
Complex interactions of NO/cGMP/PKG systems on Ca 2 (cid:1) signaling in afferent arteriolar vascular smooth muscle

NO/cGMP/ PKG systems on Ca 2 (cid:1) signaling in afferent arteriolar vascular smooth muscle. is known about the effects of nitric oxide (NO) and the cyclic GMP (cGMP)/protein kinase G (PKG) system on Ca 2 (cid:1) signaling in vascular smooth muscle cells (VSMC) of resistance vessels in general and afferent arterioles in particular. We tested the hypotheses that cGMP-, Ca 2 (cid:1) -dependent big potassium channels (BK Ca 2 (cid:1) ) buffer the Ca 2 (cid:1) response to depolarization by high extracellular KCl and that NO inhibits adenosine diphosphoribose (ADPR) cyclase, thereby reducing the Ca 2 (cid:1) -induced Ca 2 (cid:1) release. We isolated rat afferent arterioles, utilizing the magnetized microsphere method, and measured cytosolic Ca 2 (cid:1) concentration ([Ca 2 (cid:1) ] i ) with fura-2, a preparation in which endothelial cells do not participate in [Ca 2 (cid:1) ] i responses. KCl (50 mM)-induced depolarization causes an immediate increase in [Ca 2 (cid:1) ] i of 151 nM. The blockers N (cid:2) -nitro- L -arginine methyl ester (of nitric oxide synthase), 1,2,4-oxodiazolo-[4,3- a ]quinoxalin-1-one (ODQ, of guanylyl cyclase), KT-5823 (of PKG activation), and iberiotoxin (IBX, of BK Ca 2 (cid:1) activity) do not alter the [Ca 2 (cid:1) ] i response to KCl, suggesting no discernible endogenous NO production under basal conditions. The NO donor sodium nitroprusside (SNP) reduces the [Ca 2 (cid:1) ] i response to 77 nM; IBX restores the response to control values. These data show that activation of BK Ca 2 (cid:1) in the presence of NO/cGMP provides a brake on KCl-induced [Ca 2 (cid:1) ] i responses. Experiments with the inhibitor of cyclic ADPR 8-bromo-cyclic ADPR (8-Br-cADPR) and SNP (cid:1) downstream inhibitors of PKG and BK Ca 2 (cid:1) suggest that NO inhibits ADPR cyclase in intact arterioles. When we pretreat afferent arterioles with 8-bromoguanosine 3 (cid:3) ,5 (cid:3) -cyclic monophosphate (8-Br-cGMP; 10 (cid:4) M), the response to KCl is 143 nM. However, in the presence of both IBX and 8-Br-cGMP, we observe a surprising doubling of the [Ca 2 (cid:1) ] i response to KCl. In summary, we present evidence for effects of the NO/cGMP/PKG system to reduce [Ca 2 (cid:1) ] i , via activation of BK Ca 2 (cid:1) and possibly by inhibition of ADPR cyclase, and to increase [Ca 2 (cid:1) ] i , by a mechanism(s) yet to be deﬁned.
SERP: Smart Edge-Assisted System for Real-Time Pain Monitoring

In the healthcare sector, there is a strong demand for accurate objective pain assessment as a key for eﬀective pain management. Real-time and accurate objective pain assessment help caregivers and hospital staﬀs decide the proper dosage of pain medication to be provided to a patient in a timely manner. The state-of-the-art automatic and objective pain assessment techniques in the literature can be classiﬁed into two main categories: physiological-based and behavioral-based. The ﬁrst-class monitors the changes in patients’ physiological data such as heart rate (HR), heart rate variability (HRV), Electrocardiography (ECG), Electromyography (EMG), Photoplethysmography (PPG) to identify autonomic nervous system reactions to pain, while the second class utilizes behavioral reactions to pain such as techniques using computer vision-based techniques by extracting features from patients’ head poses and facial expressions. Recent pain monitoring systems have recently gained attention on multi-modality meaning that they deploy a combination of both approaches to improve pain monitoring accuracy. Although such complex models are highly accurate in pain monitoring, they are more computationally intensive imposing feasibility limitations to implement them on wearable devices in terms of energy eﬃciency (battery life) as well as computation latency. A smart and self-aware system capable of adaptively making a decision at run-time in response to the changes in pain level and context can minimize energy consumption by dynamically oﬄoading tasks to the gateway devices at the edge layer. For this reason, in
Development of rapid charging system for EV battery

292 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: F02560376S19/19©BEIESP Abstract--The adaption of electric vehicle in worldwide is increasing in acceleration fashion while in Malaysia has been a slow hike, especially with the lack of infrastructure development. There are few conventional charging stations of EV are set up in shopping malls and hotels are not enough to initiate the EVs in the country. However, the charging awaiting time is quite longer could be about 5 to 6 hours which makes Malaysia is slowly falling out from EV trend. The development of quick charging system from this study might be considered as the development of electric mobility solutions. A prototype on-board charging system with three different charging modes: the slow charging mode is normally considered for residence, medium charging mode for office parking lots, and fast charging mode for charging station on road. The quick charging mode has been developed to charge the battery in 1.5 2.0 hours with maximum charging current 50 A with auto activated quick evaporative battery thermal management system. The performance of the quick charging system has shown that the battery to be charge up to 85% of its rated capacity by constant current mode rather than constant voltage, which has shorten the battery charging time by 16%. However, it might shorten the battery life about 5% due to the fast redox reaction of the electrochemistry of battery.