An Unsupervised Saliency-Guided Deep Convolutional Neural Network for Accurate Burn Mapping from Sentinel-1 SAR Data.

SAR data provide sufficient information for burned area detection in any weather condition, making it superior to optical data. In this study, we assess the potential of Sentinel-1 SAR images for precise forest-burned area mapping using deep convolutional neural networks (DCNN). Accurate mapping with DCNN techniques requires high quantity and quality training data. However, labeled ground truth might not be available in many cases or requires professional expertise to generate them via visual interpretation of aerial photography or field visits. To overcome this problem, we proposed an unsupervised method that derives DCNN training data from fuzzy c-means (FCM) clusters with the highest and lowest probability of being burned. Furthermore, a saliency-guided (SG) approach was deployed to reduce false detections and SAR image speckles. This method defines salient regions with a high probability of being burned. These regions are not affected by noise and can improve the model performance. The developed approach based on the SG-FCM-DCNN model was investigated to map the burned area of Rossomanno-Grottascura-Bellia, Italy. This method significantly improved the burn detection ability of non-saliency-guided models. Moreover, the proposed model achieved superior accuracy of 87.67% (i.e., more than 2% improvement) compared to other saliency-guided techniques, including SVM and DNN

Patient Satisfaction in Malaysia’s Busiest Outpatient Medical Care

This study aimed to explore factors associated with patient satisfaction of outpatient medical care in Malaysia. A cross-sectional exit survey was conducted among 340 outpatients aged between 13 and 80 years after successful clinical consultations and treatment acquirements using convenience sampling at the outpatient medical care of Tengku Ampuan Rahimah Hospital (HTAR), Malaysia, being the country’s busiest medical outpatient facility. A survey that consisted of sociodemography, socioeconomic, and health characteristics and the validated Short-Form Patient Satisfaction Questionnaire (PSQ-18) scale were used. Patient satisfaction was the highest in terms of service factors or tangible priorities, particularly “technical quality” and “accessibility and convenience,” but satisfaction was low in terms of service orientation of doctors, particularly the “time spent with doctor,” “interpersonal manners,” and “communication” during consultations. Gender, income level, and purpose of visit to the clinic were important correlates of patient satisfaction. Effort to improve service orientation among doctors through periodical professional development programs at hospital and national level is essential to boost the country’s health service satisfaction

Comparative study for load management of HBase and Cassandra distributed databases in big data

The advancement in cloud computing, the increasing size of databases and the emergence of big data have made traditional data management system to be insufficient solution to store and manage such large-scale data. Therefore, there has been an emergence of new mechanisms for data storage that can handle large-scale data. NoSQL databases are used to store and manage large amount of data. They are intended to be open source, distributed and horizontally scalable in order to provide high performance. Scalability is one of the important features of such systems, it means that by increasing the number of nodes, more requests can be served per unit of time. Distribution and scalability are always companied with load management, which provides load balancing of work among multiple nodes. Load management efficiency varies from system to another according to the used load balancing technique. In this study, HBase and Cassandra load management with scalability will be evaluated as they are the most popular NoSQL databases modeled based on Big Table. In particular,this paper will compare and analyze the load management for the distributed performance of HBase and Cassandra using standard benchmark tool named Yahoo! Cloud Serving Benchmark (YCSB). The experiments will measure the performance of database operations with a different number of connections using different numbers of operations, database size, and processing nodes. The experimental results showed that HBase can provide better performance as the number of connections increase in the presence of horizontal scalabilit

Family Context and Khat Chewing among Adult Yemeni Women: A Cross-Sectional Study

Khat chewing is associated with unfavourable health outcomes and family dysfunction. Few studies have addressed the factors associated with khat chewing among Yemeni women. However, the family and husband effects on chewing khat by women have not been addressed. This study aimed to determine the prevalence of khat chewing among Yemeni women and its associated factors, particularly husbands and family factors. A cross-sectional study was conducted among 692 adult Yemeni women in the city of Sana'a in Yemen using structured "face to face" interviews. Mean (±SD) age of women was 27.3 years (±6.10). The prevalence of chewing khat by women was 29.6%. Factors associated with chewing khat among women were chewing khat by husbands (OR = 1.8; 95% CI: 1.26, 2.53), being married (OR = 2.0; 95% CI: 1.20, 3.37), frequent family social gatherings (OR = 1.5; 95% CI: 1.06, 2.10), high family income (OR = 1.57; 95% CI: 1.12, 2.21), larger house (OR = 1.63; 95% CI: 1.16, 2.31), and age of women (OR = 0.64; 95% CI: 0.44, 0.92). It is concluded that khat chewing by women in this study was significantly associated with family factors and with khat chewing by their husbands. Urgent action is needed to control khat chewing particularly among women

Differential modulation of excitatory and inhibitory neurons during periodic stimulation

Non-invasive transcranial neuronal stimulation, in addition to deep brain stimulation, is seen as a promising therapeutic and diagnostic approach for an increasing number of neurological diseases such as epilepsy, cluster headaches, depression, specific type of blindness, and other central nervous system disfunctions. Improving its effectiveness and widening its range of use may strongly rely on development of proper stimulation protocols that are tailored to specific brain circuits and that are based on a deep knowledge of different neuron types response to stimulation. To this aim, we have performed a simulation study on the behavior of excitatory and inhibitory neurons subject to sinusoidal stimulation. Due to the intrinsic difference in membrane conductance properties of excitatory and inhibitory neurons, we show that their firing is differentially modulated by the wave parameters. We analyzed the behavior of the two neuronal types for a broad range of stimulus frequency and amplitude and demonstrated that, within a small-world network prototype, parameters tuning allow for a selective enhancement or suppression of the excitation/inhibition ratio

Chromosome Duplication in <i>Saccharomyces cerevisiae</i>

The accurate and complete replication of genomic DNA is essential for all life. In eukaryotic cells, the assembly of the multi-enzyme replisomes that perform replication is divided into stages that occur at distinct phases of the cell cycle. Replicative DNA helicases are loaded around origins of DNA replication exclusively during G 1 phase. The loaded helicases are then activated during S phase and associate with the replicative DNA polymerases and other accessory proteins. The function of the resulting replisomes is monitored by checkpoint proteins that protect arrested replisomes and inhibit new initiation when replication is inhibited. The replisome also coordinates nucleosome disassembly, assembly, and the establishment of sister chromatid cohesion. Finally, when two replisomes converge they are disassembled. Studies in Saccharomyces cerevisiae have led the way in our understanding of these processes. Here, we review our increasingly molecular understanding of these events and their regulation. Keywords: DNA replication; cell cycle; chromatin; chromosome duplication; genome stability; YeastBookNational Institutes of Health (U.S.) (Grant GM-052339

Recent trends in the use of electrical neuromodulation in Parkinson's disease

Purpose of Review: This review aims to survey recent trends in electrical forms of neuromodulation, with a specific application to Parkinson’s disease (PD). Emerging trends are identified, highlighting synergies in state-of-the-art neuromodulation strategies, with directions for future improvements in stimulation efficacy suggested. Recent Findings: Deep brain stimulation remains the most common and effective form of electrical stimulation for the treatment of PD. Evidence suggests that transcranial direct current stimulation (tDCS) most likely impacts the motor symptoms of the disease, with the most prominent results relating to rehabilitation. However, utility is limited due to its weak effects and high variability, with medication state a key confound for efficacy level. Recent innovations in transcranial alternating current stimulation (tACS) offer new areas for investigation. Summary: Our understanding of the mechanistic foundations of electrical current stimulation is advancing and as it does so, trends emerge which steer future clinical trials towards greater efficacy

Wet-ConViT: A Hybrid Convolutional–Transformer Model for Efficient Wetland Classification Using Satellite Data

Accurate and efficient classification of wetlands, as one of the most valuable ecological resources, using satellite remote sensing data is essential for effective environmental monitoring and sustainable land management. Deep learning models have recently shown significant promise for identifying wetland land cover; however, they are mostly constrained in practical issues regarding efficiency while gaining high accuracy with limited training ground truth samples. To address these limitations, in this study, a novel deep learning model, namely Wet-ConViT, is designed for the precise mapping of wetlands using multi-source satellite data, combining the strengths of multispectral Sentinel-2 and SAR Sentinel-1 datasets. Both capturing local information of convolution and the long-range feature extraction capabilities of transformers are considered within the proposed architecture. Specifically, the key to Wet-ConViT’s foundation is the multi-head convolutional attention (MHCA) module that integrates convolutional operations into a transformer attention mechanism. By leveraging convolutions, MHCA optimizes the efficiency of the original transformer self-attention mechanism. This resulted in high-precision land cover classification accuracy with a minimal computational complexity compared with other state-of-the-art models, including two convolutional neural networks (CNNs), two transformers, and two hybrid CNN–transformer models. In particular, Wet-ConViT demonstrated superior performance for classifying land cover with approximately 95% overall accuracy metrics, excelling the next best model, hybrid CoAtNet, by about 2%. The results highlighted the proposed architecture’s high precision and efficiency in terms of parameters, memory usage, and processing time. Wet-ConViT could be useful for practical wetland mapping tasks, where precision and computational efficiency are paramount

Integrating InSAR and deep-learning for modeling and predicting subsidence over the adjacent area of Lake Urmia, Iran

InSAR processing is vastly used for land deformation monitoring from the space. Machine learning methods are known as strong tools for data modeling as well as predicting. In this study, we are going to model and predict the future behavior of land subsidence by InSAR processing and leveraging deep learning methods over the lands in the vicinity of Lake Urmia (located in the northwest of Iran). Accordingly, Sentinel-1 data over 56 months from November 2014 to June 2019 and small baseline subsets (SBAS) InSAR methods were utilized. Several regions with a high rate of subsidence were identified (maximum monthly subsidence of 13.3 mm). Furthermore, environmental factors affecting subsidence were considered. Therefore, parameters such as rainfall, groundwater, and lake area variations were measured using TRMM, GRACE, and MODIS satellite data, respectively. In order to determine and assess the relation between land deformations and environmental variations, several machine learning methods were implemented. The environmental parameters were used as the input of models and ground deformations as the target to be predicted. Eventually, ground deformations were estimated using multi-layer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM) networks, in which each network had strengths and weaknesses on different occasions. Thus, by blending the forecast of the three models, a weighted ensemble was constructed, which outperformed the single models and reached the root mean square error (RMSE), mean absolute error (MAE), and standard deviation (SD) of 8.2 mm, 6.4 mm, and ±5.2 mm, respectively. The result indicated that although each single model had proper accuracy, an ensemble model can improve land deformation anticipation using the strength of networks in various conditions

Teaching staff professional development as a basis of total quality in university education

Abstract                                                                                                          The study aimed at shedding light on the renewed roles of  the teaching staff that are in line with the requirements  and essence of the status quo.  In addition to that, it also aimed to show the relationship between quality assurance and quality of the teaching staff with emphasis on developing teachers competencies that will eventually be reflected on the quality of the outcomes that decides realization of higher education quality. The study clarified quality requirements and its relationship to the roles of the teaching staff and it revealed that quality assurance requires quality of the teaching staff, being a key factor in achieving quality. The study also indicates that professional development is a major requirement that would lead to improve the inputs, processes and outcomes of the educational system without which quality would be hard to achieve