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General General

Progress of IoT Research Technologies and Applications Serving Hajj and Umrah.

In Arabian journal for science and engineering

The term IoT technology is associated with many fields, including scientific, commercial, industrial, health, transportation and other fields, which became a necessity of daily life across all segments of society. Artificial intelligence, the Internet of things, data science and big data are among the most prominent fields of technologies in the modern era of e-knowledge, which are increasingly being relied on in many areas of our life. This research is considered very special and urgent affecting around 20% of the humanities, i.e., Muslim people, whom try to be selected to perform Islamic rituals every year. Given this importance of applying and adapting technologies to serve Hajj and Umrah performers, it is necessary to conduct an analytical study for the technologies and their applications that have been applied in the Hajj and Umrah system. The surveyed studies have been classified, according to their targeted services (focus area), into four main branches, including studies of the two holy mosques and the holy sites, studies of the pre-arrival visitors, housing and services studies, and transportation and crowd management studies. The classified studies were analyzed according to research field, research instrument, statistical method, nature of statistical data, data collection tools, and research citations. The paper outcome opens novel research practical directions selected based on holistic overview of the current technologies analytical survey, which is essential to be considered to improve Hajj and Umrah services coping with today's technology, in addition to discussing other open issues that need further deep research.

Shambour Mohd Khaled, Gutub Adnan

2021-Jun-28

Hajj services, Holy mosques facilities, Islamic pilgrimage, Makkah research, Ritual studies, Technology applications

General General

Digital mapping of soil texture in ecoforest polygons in Quebec, Canada.

In PeerJ

Texture strongly influences the soil's fundamental functions in forest ecosystems. In response to the growing demand for information on soil properties for environmental modeling, more and more studies have been conducted over the past decade to assess the spatial variability of soil properties on a regional to global scale. These investigations rely on the acquisition and compilation of numerous soil field records and on the development of statistical methods and technology. Here, we used random forest machine learning algorithms to model and map particle size composition in ecoforest polygons for the entire area of managed forests in the province of Quebec, Canada. We compiled archived laboratory analyses of 29,570 mineral soil samples (17,901 sites) and a set of 33 covariates, including 22 variables related to climate, five related to soil characteristics, three to spatial position or spatial context, two to relief and topography, and one to vegetation. After five repeats of 5-fold cross-validation, results show that models that include two functionally independent values regarding particle size composition explain 60%, 34%, and 78% of the variance in sand, silt and clay fractions, respectively, with mean absolute errors ranging from 4.0% for the clay fraction to 9.5% for the sand fraction. The most important model variables are those observed in the field and those interpreted from aerial photography regarding soil characteristics, followed by those regarding elevation and climate. Our results compare favorably with those of previous soil texture mapping studies for the same territory, in which particle size composition was modeled mainly from rasterized climatic and topographic covariates. The map we provide should meet the needs of provincial forest managers, as it is compatible with the ecoforest map that constitutes the basis of information for forest management in Quebec, Canada.

Duchesne Louis, Ouimet Rock

2021

Forest inventory, Geostatistics, Machine learning, Photo interpretation, Random forest, Soil mapping, Soil particle size, Soil samples, Soil texture, Spatial data

General General

Predicting judging-perceiving of Myers-Briggs Type Indicator (MBTI) in online social forum.

In PeerJ

The Myers-Briggs Type Indicator (MBTI) is a well-known personality test that assigns a personality type to a user by using four traits dichotomies. For many years, people have used MBTI as an instrument to develop self-awareness and to guide their personal decisions. Previous researches have good successes in predicting Extraversion-Introversion (E/I), Sensing-Intuition (S/N) and Thinking-Feeling (T/F) dichotomies from textual data but struggled to do so with Judging-Perceiving (J/P) dichotomy. J/P dichotomy in MBTI is a non-separable part of MBTI that have significant inference on human behavior, perception and decision towards their surroundings. It is an assessment on how someone interacts with the world when making decision. This research was set out to evaluate the performance of the individual features and classifiers for J/P dichotomy in personality computing. At the end, data leakage was found in dataset originating from the Personality Forum Café, which was used in recent researches. The results obtained from the previous research on this dataset were suggested to be overly optimistic. Using the same settings, this research managed to outperform previous researches. Five machine learning algorithms were compared, and LightGBM model was recommended for the task of predicting J/P dichotomy in MBTI personality computing.

Choong En Jun, Varathan Kasturi Dewi

2021

Judging-Perceiving, Light Gradient Boosting, MBTI, Myers-Briggs Type Indicator, Natural Language Processing, Personality Computing

Radiology Radiology

YKT6, as a potential predictor of prognosis and immunotherapy response for oral squamous cell carcinoma, is related to cell invasion, metastasis, and CD8+ T cell infiltration.

In Oncoimmunology

Metastasis and immune suppression account for the poor prognosis of oral squamous cell carcinoma (OSCC). YKT6 is a member of the soluble NSF attachment protein receptor (SNARE) family, and the effect of YKT6 in OSCC remains elusive. The purpose of this study was to explore promising prognostic and immune therapeutic candidate biomarkers for OSCC and to understand the expression pattern, prognostic value, immune effects, and biological functions of YKT6. Genes correlated with tumor metastasis and CD8 + T cell levels were identified by weighted gene coexpression network analysis (WGCNA). Next, YKT6 was analyzed through differential expression, prognostic and machine learning analyses. The molecular and immune characteristics of YKT6 were analyzed in independent cohorts, clinical specimens, and in vitro. In addition, we investigated the role of YKT6 at the pan-cancer level. The results suggested that the red module in WGCNA, as a hub module, was associated with lymph node (LN) metastasis and CD8 + T cell infiltration. Upregulation of YKT6 was found in OSCC and linked to adverse prognosis. A nomogram model containing YKT6 expression and tumor stage was constructed for clinical practice. The aggressive and immune-inhibitory phenotypes showed YKT6 overexpression, and the effect of YKT6 on OSCC cell invasion and metastasis in vitro was observed. Moreover, the low expression of YKT6 was correlated with high CD8 + T cell levels and potential immunotherapy response in OSCC. Similar results were found at the pan-cancer level. In total, YKT6 is a promising candidate biomarker for prognosis, molecular, and immune characteristics in OSCC.

Yang Zongcheng, Yan Guangxing, Zheng Lixin, Gu Wenchao, Liu Fen, Chen Wei, Cui Xiujie, Wang Yue, Yang Yaling, Chen Xiyan, Fu Yue, Xu Xin

2021-Jun-23

CD8+ T cell, Oral squamous cell carcinoma, YKT6, immunotherapy, prognosis

General General

Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis.

In Biomedical optics express

Atopic dermatitis (AD) is a skin inflammatory disease affecting 10% of the population worldwide. Raster-scanning optoacoustic mesoscopy (RSOM) has recently shown promise in dermatological imaging. We conducted a comprehensive analysis using three machine-learning models, random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) for classifying healthy versus AD conditions, and sub-classifying different AD severities using RSOM images and clinical information. CNN model successfully differentiates healthy from AD patients with 97% accuracy. With limited data, RF achieved 65% accuracy in sub-classifying AD patients into mild versus moderate-severe cases. Identification of disease severities is vital in managing AD treatment.

Park Sojeong, Saw Shier Nee, Li Xiuting, Paknezhad Mahsa, Coppola Davide, Dinish U S, Ebrahim Attia Amalina Binite, Yew Yik Weng, Guan Thng Steven Tien, Lee Hwee Kuan, Olivo Malini

2021-Jun-01

General General

Diagnosis and staging of multiple myeloma using serum-based laser-induced breakdown spectroscopy combined with machine learning methods.

In Biomedical optics express

Diagnosis and staging of multiple myeloma (MM) have been achieved using serum-based laser-induced breakdown spectroscopy (LIBS) in combination with machine learning methods. 130 cases of serum samples collected from registered MM patients in different progressive stages and healthy controls were deposited onto standard quantitative filter papers and ablated with a Q-switched Nd:YAG laser. Emissions of Ca, Na, K, Mg, C, H, O, N and CN were selected for malignancy diagnosis and staging. Multivariate statistics and machine learning methods, including principal component analysis (PCA), k-nearest neighbor (kNN), support vector machine (SVM) and artificial neural network (ANN) classifiers, were used to build the discrimination models. The performances of the classifiers were optimized via 10-fold cross-validation and evaluated in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves (AUC). The kNN, SVM and ANN classifiers achieved comparable discrimination performances with accuracies of over 90% for both diagnosis and staging of MM. For diagnosis of MM, the classifiers achieved performances with AUC of ∼0.970, sensitivity of ∼0.930 and specificity of ∼0.910; for staging of MM, the corresponding values were AUC of ∼0.970, sensitivity of ∼0.910 and specificity of ∼0.930. These results show that the serum-based LIBS in combination with machine learning methods can serve as a fast, less invasive, cost-effective, and robust technique for diagnosis and staging of human malignancies.

Chen Xue, Zhang Yao, Li Xiaohui, Yang Ziheng, Liu Aichun, Yu Xin

2021-Jun-01