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Abstract Excessive glucocorticoid (GC) action is linked to various metabolic disorders. Recent findings suggest that disrupting skeletal GC signaling prevents bone loss and alleviates metabolic disorders in high-fat diet (HFD)-fed obese mice, underpinning the neglected contribution of skeletal GC action to obesity and related bone loss. Here, we show that the elevated expression of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1), the enzyme driving local GC activation, and GC signaling in osteoblasts, are associated with bone loss and obesity in HFD-fed male mice. Osteoblast-specific 11β-HSD1 knockout male mice exhibit resistance to HFD-induced bone loss and metabolic disorders. Mechanistically, elevated 11β-HSD1 restrains glucose uptake and osteogenic activity in osteoblast. Pharmacologically inhibiting osteoblastic 11β-HSD1 by using bone-targeted 11β-HSD1 inhibitor markedly promotes bone formation, ameliorates glucose handling and mitigated obesity in HFD-fed male mice. Taken together, our study demonstrates that osteoblastic 11β-HSD1 directly contributes to HFD-induced bone loss, glucose handling impairment and obesity.

Abstract Biometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique for protecting biometric data. Regarding recognition accuracy, feature representation plays a significant role in the performance and reliability of cancellable biometric systems. How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community, especially from researchers of cancellable biometrics. Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance, while the privacy of biometric data is protected. This survey informs the progress, trend and challenges of feature extraction and learning for cancellable biometrics, thus shedding light on the latest developments and future research of this area.

Abstract Exploring the nature of human intelligence and behavior is a longstanding pursuit in cognitive neuroscience, driven by the accumulation of knowledge, information, and data across various studies. However, achieving a unified and transparent interpretation of findings presents formidable challenges. In response, an explainable brain computing framework is proposed that employs the never‐ending learning paradigm, integrating evidence combination and fusion computing within a Knowledge‐Information‐Data (KID) architecture. The framework supports continuous brain cognition investigation, utilizing joint knowledge‐driven forward inference and data‐driven reverse inference, bolstered by the pre‐trained language modeling techniques and the human‐in‐the‐loop mechanisms. In particular, it incorporates internal evidence learning through multi‐task functional neuroimaging analyses and external evidence learning via topic modeling of published neuroimaging studies, all of which involve human interactions at different stages. Based on two case studies, the intricate uncertainty surrounding brain localization in human reasoning is revealed. The present study also highlights the potential of systematization to advance explainable brain computing, offering a finer‐grained understanding of brain activity patterns related to human intelligence.

The next generation phased array radio telescopes, such as the Square Kilometre Array (SKA) low frequency aperture array, suffer from RF interference (RFI) because of the large field of view of antenna element. The classical station beamformer used in SKA-low is resource efficient but cannot deal with the unknown sidelobe RFI. A real-time adaptive beamforming strategy is proposed for SKA-low station, which trades the capability of adaptive RFI nulling at an acceptably cost, it doesn’t require hardware redesign but only modifies the firmware accordingly. The proposed strategy uses a Parallel Least Mean Square (PLMS) algorithm, which has a computational complexity of 4N+2 and can be performed in parallel. Beam pattern and output SINR simulation results show deeply nulling performance to sidelobe RFI, as well as good mainlobe response similar to the classical beamformer. The convergence performance depends on the signal-and-interference environments and step size, wherein too large a step size leads to a non-optimal output SINR and too small a step size leads to slow convergence speed. FPGA implementation demonstrations are implemented and tested on a NI FPGA module, and test results demonstrate good real-time performance and low slice resource consumption.

This study explores the learning effects of color cues in video lectures and their underlying mechanisms. With the rapid growth of online education, lifelong learning, and blended learning, video lectures have become integral to teaching and learning. Color, a crucial element in visual design, directs attention, organizes content, and integrates information. Evaluating 78 college students, we assessed learning performance by comparing video content with no-color, single-color, and multi-color cues using eye-tracking technology and cognitive load scales. Results indicate that students viewing videos with color cues demonstrated better retention and transfer test performance, while absence or excess of color cues increased cognitive load. These findings have practical implications for video producers and provide a theoretical foundation for enhancing learners’ viewing experience and overall effectiveness. This study not only offers an in-depth analysis of color cue utilization in video lectures, highlighting their positive impact on learning outcomes but also introduces fresh perspectives for educational technology and cognitive psychology research. Future investigations should consider color cue effects in diverse cultural contexts and subject areas, exploring varied strategies to optimize the learning experience.

Abstract Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression. In particular, precision psychiatry is an emerging field that utilises advanced computational techniques to achieve a more individualised approach to mental health care. This survey provides an overview of the ways in which artificial intelligence is currently being used to support precision psychiatry. Advanced algorithms are being used to support all phases of the treatment cycle. These systems have the potential to identify individuals suffering from mental health conditions, allowing them to receive the care they need and tailor treatments to individual patients who are mostly to benefit. Additionally, unsupervised learning techniques are breaking down existing discrete diagnostic categories and highlighting the vast disease heterogeneity observed within depression diagnoses. Artificial intelligence also provides the opportunity to shift towards evidence-based treatment prescription, moving away from existing methods based on group averages. However, our analysis suggests there are several limitations currently inhibiting the progress of data-driven paradigms in care. Significantly, none of the surveyed articles demonstrate empirically improved patient outcomes over existing methods. Furthermore, greater consideration needs to be given to uncertainty quantification, model validation, constructing interdisciplinary teams of researchers, improved access to diverse data and standardised definitions within the field. Empirical validation of computer algorithms via randomised control trials which demonstrate measurable improvement to patient outcomes are the next step in progressing models to clinical implementation.

In drug discovery, selecting targeted molecules is crucial as the target could directly affect drug efficacy and the treatment outcomes. As a member of the CCN family, CTGF (also known as CCN2) is an essential regulator in the progression of various diseases, including fibrosis, cancer, neurological disorders, and eye diseases. Understanding the regulatory mechanisms of CTGF in different diseases may contribute to the discovery of novel drug candidates. Summarizing the CTGF-targeting and -inhibitory drugs is also beneficial for the analysis of the efficacy, applications, and limitations of these drugs in different disease models. Therefore, we reviewed the CTGF structure, the regulatory mechanisms in various diseases, and drug development in order to provide more references for future drug discovery.

ObjectiveThis study aimed to compare the clinical outcomes between oblique (OLIF) and transforaminal lumbar interbody fusion (TLIF) for patients with degenerative spondylolisthesis during a 2-year follow-up.MethodsPatients with symptomatic degenerative spondylolisthesis who underwent OLIF (OLIF group) or TLIF (TLIF group) were prospectively enrolled in the authors’ hospital and followed up for 2 years. The primary outcomes were treatment effects [changes in visual analog score (VAS) and Oswestry disability index (ODI) from baseline] at 2 years after surgery; these were compared between two groups. Patient characteristics, radiographic parameters, fusion status, and complication rates were also compared.ResultsIn total, 45 patients were eligible for the OLIF group and 47 patients for the TLIF group. The rates of follow-up were 89% and 87% at 2 years, respectively. The comparisons of primary outcomes demonstrated no different changes in VAS-leg (OLIF, 3.4 vs. TLIF, 2.7), VAS-back (OLIF, 2.5 vs. TLIF, 2.1), and ODI (OLIF, 26.8 vs. TLIF, 30). The fusion rates were 86.1% in the TLIF group and 92.5% in the OLIF group at 2 years (P = 0.365). The OLIF group had less estimated blood loss (median, 200 ml) than the TLIF group (median, 300 ml) (P

Abstract Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI’s impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Context-based challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.

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In large MOOC cohorts, the sheer variance and volume of discussion forum posts can make it difficult for instructors to distinguish nuanced emotion in students, such as engagement levels or stress, purely from textual data. Sentiment analysis has been used to build student behavioral models to understand emotion, however, more recent research suggests that separating sentiment and stress into different measures could improve approaches. Detecting stress in a MOOC corpus is challenging as students may use language that does not conform to standard definitions, but new techniques like TensiStrength provide more nuanced measures of stress by considering it as a spectrum. In this work, we introduce an ensemble method that extracts feature categories of engagement, semantics and sentiment from an AdelaideX student dataset. Stacked and voting methods are used to compare performance measures on how accurately these features can predict student grades. The stacked method performed best across all measures, with our Random Forest baseline further demonstrating that negative sentiment and stress had little impact on academic results. As a secondary analysis, we explored whether stress among student posts increased in 2020 compared to 2019 due to COVID-19, but found no significant change. Importantly, our model indicates that there may be a relationship between features, which warrants future research.

Online learning and teaching increased in 2020, driven by the COVID-19 pandemic. As many researchers attempted to understand the impact stress had on the emotional behaviours and academic performance of students, most studies explored these pre- and during-COVID behaviours in the context of brick and mortar institutions transitioning to online delivery. There is an opportunity to compare the experiences of students in the MOOC environment in this period, particularly in terms of the difference of engagement, semantics and sentiment/stress behaviours in 2019 and 2020. In this study, we use a dataset from AdelaideX between this time period to identify the most significant features that impact student outcomes. Where previous machine learning approaches used singular features such as student interaction or sentiment in discussion forum posts, we incorporate three feature categories of engagement, semantics and sentiment/stress in an ensemble model is based on voting and stacked methods to determining the relationship between them and academic performance. From our results, we discover that sentiment/stress played little part in academic performance and was relatively unchanged in online courses in this dataset between 2019 and 2020. We present two individual student cases to further contextualise our findings.

ObjectiveTo summarize the clinical features and bone complications in a patient from a large family with X-linked congenital adrenocortical hypoplasia (AHC) and evaluate the efficacy of different treatment regimens on the prognosis of secondary osteoporosis caused by AHC at a 5-year follow-up.MethodsA large family with AHC was recruited, and the causative gene mutation was identified by Sanger sequencing in the proband. Clinical features as well as radiological examinations and laboratory indices of osteoporosis secondary to AHC were analyzed in this study. Meanwhile, the proband was treated with classical antiresorptive drugs (bisphosphonates) for 2 years and switched to a vitamin K2 analogue for another 3 years, during which the efficacy of the drugs was evaluated.ResultsThe proband was identified as carrying a homozygous insertion mutation (p. Thr193GlyfsX13) in the NR0B1 (nuclear receptor subfamily 0, group B, member 1) gene, resulting in a premature stop codon due to a frameshift mutation. During treatment and follow-up, the proband did not respond well to bisphosphonate and developed atypical femoral fractures. Vitamin K2 improved clinical symptoms. In terms of bone mineral density (BMD), there is no evidence of any effect of vitamin K2 on the neck of femur, though some minor effects on spinal BMD cannot be excluded.ConclusionsSecondary osteoporosis induced by AHC deserves clinical attention. Unlike in primary osteoporosis, the curative effect of bisphosphonates was unsatisfactory and was more likely to cause atypical femoral fractures in long-term treatment. It is suggested that bone anabolic drugs may be better alternatives.

Abstract Streamflow (Q flow ) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Q flow (short-term) at Brisbane River and Teewah Creek, Australia. The CNN layers were used to extract the features of Q flow time-series, while the LSTM networks use these features from CNN for Q flow time series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models. Q flow prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-Weeks, and 9-Months, respectively. With the help of different performance metrics and graphical analysis visualization, the experimental results reveal that with small residual error between the actual and predicted Q flow , the CNN-LSTM model outperforms all the benchmarked conventional AI models as well as ensemble models for all the time intervals. With 84% of Q flow prediction error below the range of 0.05 m3 s−1, CNN-LSTM demonstrates a better performance compared to 80% and 66% for LSTM and DNN, respectively. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. Thus, CNN-LSTM has significant practical value in Q flow prediction.

ObjectiveThe aim of this study was to fully describe the clinical and genetic characteristics, including clinical manifestations, intact fibroblast growth factor 23 (iFGF23) levels, and presence of PHEX gene mutations, of 22 and 7 patients with familial and sporadic X-linked dominant hypophosphatemia (XLH), respectively.MethodsDemographic data, clinical features, biochemical indicators, and imaging data of 29 patients were collected. All 22 exons and exon–intron boundaries of the PHEX gene were amplified by polymerase chain reaction (PCR) and directly sequenced. The serum level of iFGF23 was measured in 15 of the patients.ResultsTwenty-nine patients (male/female: 13:16, juvenile/adult: 15:14) with XLH were included. The main symptoms were bowed lower extremities (89.7%), abnormal gait (89.7%), and short stature/growth retardation (78.6%). Hypophosphatemia with a high alkaline phosphatase level was the main biochemical feature and the median value of serum iFGF23 was 55.7 pg/ml (reference range: 16.1–42.2 pg/ml). Eight novel mutations in the PHEX gene were identified by Sanger sequencing, including two missense mutations (p. Gln682Leu and p. Phe312Ser), two deletions (c.350_356del and c.755_761del), one insertion (c.1985_1986insTGAC), and three splice mutations (c.1700+5G>C, c.1966-1G>T, and c.350-14_350-1del). Additionally, the recurrence rate after the first orthopedic surgery was 77.8% (7/9), and five of them had their first surgery before puberty.ConclusionOur study expanded the clinical phenotypes and gene mutation spectrum of XLH and provided a reference for the optimal timing of orthopedic surgeries.

Abstract This paper introduces a purposed Location-based Recommender System (LBRS) that combines sentiment analysis and topic modelling techniques to improve user profiling for enhancing recommendations of Points of Interest (POIs). Using additional feature extraction, we built user profiles froma Foursquare dataset to evaluate our model and provide recommendations based on user opinions toward venues. Our combined model performed favourably against the baseline models, with an overall improved accuracy of 0.67. The limitations were the use of one dataset and that user profiles were constructed using predicted emotions extracted as features from review data with topic modelling, rather than literal user emotions. Nevertheless, this provides a step forward in user profile and emotion scoring, contributing further to the development of LBRS in the Tourism domain.

Abstract In today’s society, happiness has attracted more and more attentions from researchers. It is interesting to study happiness from the perspective of data mining. In psychology domain, the application of data mining gradually becomes widespread and popular, which works from a novel data-driven viewpoint. Current researches in machine learning, especially in deep learning provide newresearchmethods for traditional psychology research and bring newideas. This paper presents an empirical study of learning based happiness predicition approaches and their prediction quality. Conducted on the data provided by the “China Comprehensive Social Survey (CGSS)” project, we report the experimental results of happiness prediction and explore the influencing factors of happiness. According to the four stages of factor analysis, feature engineering, model establishment and evaluation, this paper analyzes the factors affecting happiness and studies the effect of different ensembles for happiness prediction. Through experimental results, it is found that social attitudes (fairness), family variables (family capital), and individual variables (mental health, socioeconomic status, and social rank) have greater impacts on happiness than others. Moreover, among the happiness prediction models established by these five features, boosting shows the most effective in model fusion.

Abstract Major depressive disorder (MDD) is an issue that affects 350 million people worldwide. Traditional approaches have been to identify depressive symptoms in datasets, but recently, research is beginning to explore the association between psychosocial factors such as those on the quality of life scale and mental well-being, which will lead to earlier diagnosis and prediction of MDD. In this research, an ensemble binary classifier is proposed to analyse health survey data against ground truth from the SF-20 Quality of Life scales. The classifier aims to improve the performance of machine learning techniques on large datasets and identify depressed cases based on associations between items on the QoL scale and mental illness by increasing predictive performance. On the experimental evaluation on the National Health and Nutrition Examination Survey (NHANES), the classifier demonstrated an F1 score of 0.976 in the prediction, without any incorrectly identified depression instances. Only about 4% of instances had been mistakenly classified into depressed cases, with a significant accuracy of 95.4% comparing to the result from PHQ-9 mental screen inventory. The presented ensemble binary classifier performed comparably better than each baseline algorithm in all measures and all experiments. We trained the ensemble model on the processed NHANES dataset, tested and evaluated the results of its performance against mental screen inventory and discussed the comparable predictions. Finally, we provided future research directions.

ObjectiveTo evaluate the clinical features of sporadic Paget’s disease of bone (PDB) in China and further explore the underlying genetic abnormalities of the disease.MethodsClinical characteristics, biochemical indices, bone turnover markers and radiographic examinations of the patients were collected. Genomic DNA was extracted from peripheral blood and whole-exome sequencing was carried out to identify the potential pathogenic genes. The pathogenicity of the variants was thereafter investigated by bioinformatics analysis.ResultsA total of 50 patients (57.20 ± 15.52 years, male/female: 1.63: 1) with PDB were included and the mean onset age was 48.34 years (48.34 ± 17.24 years). 94.0% of the patients exhibited symptomatic patterns described as bone pain (86.0%), elevated skin temperature at the lesion site (26.0%), bone deformity (22.0%) and local swelling (18.0%). The most frequently involved lesion sites were pelvis (52.0%), femur (42.0%), tibia (28.0%), skull (28.0%) and spine (18.0%), respectively. Additionally, 40.0% of them accompanied with osteoarthritis, 14.0% with pathological fractures, and the misdiagnosis rate of PDB was as high as 36.0%. Serum level of alkaline phosphatase was significantly increased, with the mean value of 284.00 U/L (quartiles, 177.00-595.00 U/L). Two heterozygous missense mutations of SQSTM1 gene (c.1211T>C, M404T) and one novel heterozygous missense mutation in HNRNPA2B1 gene (c.989C>T, p. P330L) were identified in our study. Moreover, several potential disease-causing genes were detected and markedly enriched in the pathways of neurodegeneration (including WNT16, RYR3 and RYR1 genes) and amyotrophic lateral sclerosis (ALS, including NUP205, CAPN2, and NUP214 genes).ConclusionIn contrast to Western patients, Chinese patients have an earlier onset age, more severe symptoms, and lower frequency of SQSTM1 gene mutation (4.0%). Moreover, a novel heterozygous missense mutation in HNRNPA2B1 gene was identified in one male patient with isolated bone phenotype. As for other genetic factors, it was indicated that WNT16, RYR3, RYR1, NUP205, CAPN2 and NUP214 genes may be potential pathogenic genes, pathways of neurodegeneration and ALS may play a vital role in the pathogenesis of PDB.

Recently, the use of intelligent technologies in clinical decision making in the telehealth environment has begun to play a vital role in improving the quality of patients' lives and helping reduce the costs and workload involved in their daily healthcare. In this paper, an effective medical recommendation system that uses a fast Fourier transformation-coupled machine learning ensemble model is proposed for short-term disease risk prediction to provide chronic heart disease patients with appropriate recommendations about the need to take a medical test or not on the coming day based on analysing their medical data. The input sequence of sliding windows based on the patient's time series data are decomposed by using the fast Fourier transformation in order to extract the frequency information. A bagging-based ensemble model is utilized to predict the patient's condition one day in advance for producing the final recommendation. A combination of three classifiers-artificial neural network, least squares-support vector machine, and naive bayes-are used to construct an ensemble framework. A real-life time series telehealth data collected from chronic heart disease patients are utilized for experimental evaluation. The experimental results show that the proposed system yields a very good recommendation accuracy and offers an effective way to reduce the risk of incorrect recommendations as well as reduce the workload for heart disease patients in conducting body tests every day. The results conclusively ascertain that the proposed system is a promising tool for analyzing time series medical data and providing accurate and reliable recommendations to patients suffering from chronic heart diseases.

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.

Non-destructive tests working at lower microwave frequencies have large advantages of dielectric material penetrability, lower equipment cost, and lower implementation complexity. However, the resolution will become worse as the work frequencies become lower. Relying on designing the structure of high field confinement, this study realizes a simple complementary spiral resonators (CSRs)-based near-field probe for microwave non-destructive testing (NDT) and imaging around 390 MHz (λ = 769 mm) whereby very high resolution (λ/308, 2.5 mm) is achieved. By applying an ingenious structure where a short microstrip is connected to a microstrip ring to feed the CSR, the probe, that is a single-port microwave planar circuit, does not need any extra matching circuits, which has more application potential in sensor arraying compared with other microwave probes. The variation of the electric field distribution with the standoff distance (SOD) between the material under test and the probe are analyzed to reveal the operation mechanisms behind the improved sensitivity and resolution of the proposed probe. Besides, the detection abilities of the tiny defects in metal and non-metal materials are demonstrated by the related experiments. The smallest detectable crack and via in the non-metal materials and the metal materials are of a λ/1538 (0.5 mm) width, a λ/513 (1.5 mm) diameter, a λ/3846 (0.2 mm) width and a λ/513 (1.5 mm) diameter, respectively. Moreover, to further evaluate the performance of the proposed probe, the defects under skin layer in the multilayer composite materials and the defects under corrosion in the carbon steel are inspected and imaged. Due to lower work frequency, high resolution, outstanding detection abilities of tiny defects, and large potentials in sensor arraying, the proposed probe would be a good candidate for microwave NDT and imaging.

With the explosive growth of resources available through the Internet, information mismatching and overload have become a severe concern to users.Web users are commonly overwhelmed by huge volume of information and are faced with the challenge of finding the most relevant and reliable information in a timely manner. Personalised information gathering and recommender systems represent state-of-the-art tools for efficient selection of the most relevant and reliable information resources, and the interest in such systems has increased dramatically over the last few years. However, web personalization has not yet been well-exploited; difficulties arise while selecting resources through recommender systems from a technological and social perspective. Aiming to promote high quality research in order to overcome these challenges, this paper provides a comprehensive survey on the recent work and achievements in the areas of personalised web information gathering and recommender systems. The report covers concept-based techniques exploited in personalised information gathering and recommender systems.