Search Results (14)

Full thickness cartilage defects in cases of knee osteoarthritis are challenging in nature and are difficult to treat. The implantation of three-dimensional (3D) biofabricated grafts into the defect site can be a promising biological one-stage solution for such lesions that can avoid different disadvantages of the alternative surgical treatment options. In this study, the short-term clinical outcome of a novel surgical technique that uses a 3D bioprinted micronized adipose tissue (MAT) graft for knee cartilage defects is assessed and the degree of incorporation of such graft types is evaluated via arthroscopic and radiological analyses. Ten patients received 3D bioprinted grafts consisting of MAT with an allogenic hyaline cartilage matrix on a mold of polycaprolactone, with or without adjunct high tibial osteotomy, and they were monitored until 12 months postoperatively. Clinical outcomes were examined with patient-reported scoring instruments that consisted of the Western Ontario and McMaster Universities Arthritis Index (WOMAC) score and the Knee Injury and Osteoarthritis Outcome Score (KOOS). The graft incorporation was assessed using the Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) score. At 12 months follow-up, cartilage tissue biopsy samples were taken from patients and underwent histopathological examination. In the results, at final follow-up, the WOMAC and KOOS scores were 22.39 ± 7.7 and 79.16 ± 5.49, respectively. All scores were significantly increased at final follow-up (p < 0.0001). MOCART scores were also improved to a mean of 82.85 ± 11.49, 12 months after operation, and we observed a complete incorporation of the grafts with the surrounding cartilage. Together, this study suggests a novel regeneration technique for the treatment of knee osteoarthritis patients, with less rejection response and better efficacy. Full article

Being able to predict the churn rate is the key to success for the telecommunication industry. It is also important for the telecommunication industry to obtain a high profit. Thus, the challenge is to predict the churn percentage of customers with higher accuracy without comprising the profit. In this study, various types of learning strategies are investigated to address this challenge and build a churn predication model. Ensemble learning techniques (Adaboost, random forest (RF), extreme randomized tree (ERT), xgboost (XGB), gradient boosting (GBM), and bagging and stacking), traditional classification techniques (logistic regression (LR), decision tree (DT), and k-nearest neighbor (kNN), and artificial neural network (ANN)), and the deep learning convolutional neural network (CNN) technique have been tested to select the best model for building a customer churn prediction model. The evaluation of the proposed models was conducted using two pubic datasets: Southeast Asian telecom industry, and American telecom market. On both of the datasets, CNN and ANN returned better results than the other techniques. The accuracy obtained on the first dataset using CNN was 99% and using ANN was 98%, and on the second dataset it was 98% and 99%, respectively. Full article

With the widespread use of IoT applications, malware has become a difficult and sophisticated threat. Without robust security measures, a massive volume of confidential and classified data could be exposed to vulnerabilities through which hackers could do various illicit acts. As a result, improved network security mechanisms that can analyse network traffic and detect malicious traffic in real-time are required. In this paper, a novel optimized machine learning image-based IoT malware detection method is proposed using visual representation (i.e., images) of the network traffic. In this method, the ant colony optimizer (ACO)-based feature selection method was proposed to get a minimum number of features while improving the support vector machines (SVMs) classifier’s results (i.e., the malware detection results). Further, the PSO algorithm tuned the SVM parameters of the different kernel functions. Using a public dataset, the experimental results showed that the SVM linear function kernel is the best with an accuracy of 95.56%, recall of 96.43%, precision of 94.12%, and F1_score of 95.26%. Comparing with the literature, it was concluded that bio-inspired techniques, i.e., ACO and PSO, could be used to build an effective and lightweight machine-learning-based malware detection system for the IoT environment. Full article

Today’s digital world necessitates the adoption of encryption techniques to ensure secure peer-to-peer communication. The sole purpose of this paper is to conglomerate the fundamentals of Blockchain, AI (Artificial Intelligence) and DNA (Deoxyribonucleic Acid) encryption into one proposed scheme, KryptosChain, which is capable of providing a secure information exchange between a sender and his intended receiver. The scheme firstly suggests a DNA-based Huffman coding scheme, which alternatively allocates purines—Adenine (A) and Guanine (G), and pyrimidines—Thymine (T) and Cytosine (C) values, while following the complementary rule to higher and lower branches of the resultant Huffman tree. Inculcation of DNA concepts makes the Huffman coding scheme eight times stronger than the traditional counterpart based on binary—0 and 1 values. After the ciphertext is obtained, the proposed methodology next provides a Blockchain-inspired message exchange scheme that achieves all the principles of security and proves to be immune to common cryptographic attacks even without the deployment of any smart contract, or possessing any cryptocurrency or arriving at any consensus. Lastly, different classifiers were engaged to check the intrusion detection capability of KryptosChain on the NSL-KDD dataset and AI fundamentals. The detailed analysis of the proposed KryptosChain validates its capacity to fulfill its security goals and stands immune to cryptographic attacks. The intrusion possibility curbing concludes that the J84 classifier provides the highest accuracy of 95.84% among several others as discussed in the paper. Full article

The efficiency of photovoltaics (PVs) is related to cover material properties and light management in upper layers of the device. This article investigates new polyimide (PI) covers for PVs that enable light trapping through their induced surface texture. The latter is attained via a novel strategy that involves multi-directional rubbing followed by plasma exposure. Atomic force microscopy (AFM) is utilized to clarify the outcome of the proposed light-trapping approach. Since a deep clarification of either random or periodic surface morphology is responsible for the desired light capturing in solar cells, the elaborated texturing procedure generates a balance among both discussed aspects. Multidirectional surface abrasion with sand paper on pre-defined directions of the PI films reveals some relevant modifications regarding both surface morphology and the resulted degree of anisotropy. The illuminance experiments are performed to examine if the created surface texture is suitable for proper light propagation through the studied PI covers. The adhesion among the upper layers of the PV, namely the PI and transparent electrode, is evaluated. The correlation between the results of these analyses helps to identify not only adequate polymer shielding materials, but also to understand the chemical structure response to new design routes for light-trapping, which might significantly contribute to an enhanced conversion efficiency of the PV devices. Full article

Hepatocellular carcinoma (HCC) is a highly prevalent malignancy. It is a common type of cancer in Egypt due to chronic virus C infection (HCV). Currently, the frequently used lab test is serum α-fetoprotein. However, its diagnostic value is challenging due to its low sensitivity and specificity. Genetic biomarkers have recently provided new insights for cancer diagnostics. Herein, we quantified Lnc HULC and miR-122 gene expression to test their potential in diagnosis. Both biomarkers were tested in the sera of 60 HCC patients and 60 with chronic HCV using real-time RT-PCR. miR-122 was highly expressed in HCV patients with a significant difference from the HCC group (p = 0.004), which points towards its role in prognosis value as a predictor of HCC in patients with chronic HCV. HULC was more highly expressed in HCC patients than in the HCV group (p = 0.018), indicating its potential use in screening and the early diagnosis of HCC. The receiver operating characteristic (ROC) curve analysis showed their reliable sensitivity and specificity. Our results reveal that miR-122 can act as a prognostic tool for patients with chronic HCV. Furthermore, it is an early predictor of HCC. LncRNA HULC can be used as an early diagnostic tool for HCC. Full article

Blockchain has found many applications, apart from Bitcoin, in different fields and it has the potential to be very useful in the satellite communications and space industries. Decentralized and secure protocols for processing and manipulating space transactions of satellite swarms in the form of Space Digital Tokens (SDT) can be built using blockchain technology. Tokenizing space transactions using SDTs will open the door to different new blockchain-based solutions for the advancement of constellation-based satellite communications in the space industry. Developing blockchain solutions using smart contracts could be used in securely authenticating various P2P satellite communications and transactions within/between satellite swarms. To manage and secure these transactions, using the proposed SDT concept, this paper suggested a blockchain-based protocol called Proof of Space Transactions (PoST). This protocol was adopted to manage and authenticate the transactions of satellite constellations in a P2P connection. The PoST protocol was prototyped using the Ethereum blockchain and experimented with to evaluate its performance using four metrics: read latency, read throughput, transaction latency, and transaction throughput. The simulation results clarified the efficiency of the proposed PoST protocol in processing and verifying satellite transactions in a short time according to read and transaction latency results. Moreover, the security results showed that the proposed PoST protocol is secure and efficient in verifying satellite transactions according to true positive rate (TPR), true negative rate (TNR), and accuracy metrics. These findings may shape a real attempt to develop a new generation of Blockchain-based satellite constellation systems. Full article

The rising antimicrobial resistance is particularly alarming for Acinetobacter baumannii, calling for the discovery and evaluation of alternatives to treat A. baumannii infections. Some bacteriophages produce a structural protein that depolymerizes capsular exopolysaccharide. Such purified depolymerases are considered as novel antivirulence compounds. We identified and characterized a depolymerase (DpoMK34) from Acinetobacter phage vB_AbaP_PMK34 active against the clinical isolate A. baumannii MK34. In silico analysis reveals a modular protein displaying a conserved N-terminal domain for anchoring to the phage tail, and variable central and C-terminal domains for enzymatic activity and specificity. AlphaFold-Multimer predicts a trimeric protein adopting an elongated structure due to a long α-helix, an enzymatic β-helix domain and a hypervariable 4 amino acid hotspot in the most ultimate loop of the C-terminal domain. In contrast to the tail fiber of phage T3, this hypervariable hotspot appears unrelated with the primary receptor. The functional characterization of DpoMK34 revealed a mesophilic enzyme active up to 50 °C across a wide pH range (4 to 11) and specific for the capsule of A. baumannii MK34. Enzymatic degradation of the A. baumannii MK34 capsule causes a significant drop in phage adsorption from 95% to 9% after 5 min. Although lacking intrinsic antibacterial activity, DpoMK34 renders A. baumannii MK34 fully susceptible to serum killing in a serum concentration dependent manner. Unlike phage PMK34, DpoMK34 does not easily select for resistant mutants either against PMK34 or itself. In sum, DpoMK34 is a potential antivirulence compound that can be included in a depolymerase cocktail to control difficult to treat A. baumannii infections. Full article

The World Health Organization (WHO) has stated that the spread of the coronavirus (COVID-19) is on a global scale and that wearing a face mask at work is the only effective way to avoid becoming infected with the virus. The pandemic made governments worldwide stay under lock-downs to prevent virus transmissions. Reports show that wearing face masks would reduce the risk of transmission. With the rise in population in cities, there is a greater need for efficient city management in today’s world for reducing the impact of COVID-19 disease. For smart cities to prosper, significant improvements to occur in public transportation, roads, businesses, houses, city streets, and other facets of city life will have to be developed. The current public bus transportation system, such as it is, should be expanded with artificial intelligence. The autonomous mask detection and alert system are needed to find whether the person is wearing a face mask or not. This article presents a novel IoT-based face mask detection system in public transportation, especially buses. This system would collect real-time data via facial recognition. The main objective of the paper is to detect the presence of face masks in real-time video stream by utilizing deep learning, machine learning, and image processing techniques. To achieve this objective, a hybrid deep and machine learning model was designed and implemented. The model was evaluated using a new dataset in addition to public datasets. The results showed that the transformation of Convolution Neural Network (CNN) classifier has better performance over the Deep Neural Network (DNN) classifier; it has almost complete face-identification capabilities with respect to people’s presence in the case where they are wearing masks, with an error rate of only 1.1%. Overall, compared with the standard models, AlexNet, Mobinet, and You Only Look Once (YOLO), the proposed model showed a better performance. Moreover, the experiments showed that the proposed model can detect faces and masks accurately with low inference time and memory, thus meeting the IoT limited resources. Full article

The current advancement of robotics, especially in Cyber-Physical Systems (CPS), leads to a prominent combination between the mining industry and connected-embedded technologies. This progress has arisen in the form of state-of-the-art automated giant vehicles with Autonomous Haulage Systems (AHS) that can transport ore without human intervention. Like CPS, AHS enable autonomous and/or remote control of physical systems (e.g., mining trucks). Thus, similar to CPS, AHS are also susceptible to cyber attacks such as Wi-Fi De-Auth and GPS attacks. With the use of the AHS, several mining activities have been strengthened due to increasing the efficiency of operations. Such activities require ensuring accurate data collection from which precise information about the state of the mine should be generated in a timely and consistent manner. Consequently, the presence of secure and reliable communications is crucial in making AHS mines safer, productive, and sustainable. This paper aims to identify and discuss the relation between safety of AHS in the mining environment and both cybersecurity and communication as well as highlighting their challenges and open issues. We survey the literature that addressed this aim and discuss its pros and cons and then highlight some open issues. We conclude that addressing cybersecurity issues of AHS can ensure the safety of operations in the mining environment as well as providing reliable communication, which will lead to better safety. Additionally, it was found that new communication technologies, such 5G and LTE, could be adopted in AHS-based systems for mining, but further research is needed to considered related cybersecurity issues and attacks. Full article

The COVID-19 pandemic not only affected our health and medical systems but also has created large disruption of education systems at school and universities levels. According to the United Nation’s report, COVID-19 has influenced more than 1.6 billion learners from all over the world (190 countries or more). To tackle this problem, universities and colleges have implemented various technologically based platforms to replace the physical classrooms during the spread of Coronavirus. The effectiveness of these technologies and their educational impact on the educational sector has been the concern of researchers during the spread of the pandemic. Consequently, the current study is an attempt to explore the effect of Google Meet acceptance among Arab students during the pandemic in Oman, UAE, and Jordan. The perceived fear factor is integrated into a hybrid model that combines crucial factors in TAM (Technology acceptance Model) and VAM (Value-based Adoption Model). The integration embraces perceived fear factor with other important factors in TAM perceived ease of use (PEOU) and perceived usefulness (PU) on the one hand and technically influential factor of VAM, which are perceived technicality (PTE) and perceived enjoyment (PE) on the other hand. The data, collected from 475 participants (49% males and 51% females students), were analyzed using the partial least squares-structural equation modelling (PLS-SEM). The results have shown that TAM hypotheses of usefulness and easy to use have been supported. Similarly, the results have supported the hypotheses related to VAM factors of being technically useful and enjoying, which helps in reducing the atmosphere of fear that is created due to the spread of Coronavirus. Full article

Breast cancer is one of the most prevalent cancer types with a high mortality rate in women worldwide. This devastating cancer still represents a worldwide public health concern in terms of high morbidity and mortality rates. The diagnosis of breast abnormalities is challenging due to different types of tissues and textural variations in intensity. Hence, developing an accurate computer-aided system (CAD) is very important to distinguish normal from abnormal tissues and define the abnormal tissues as benign or malignant. The present study aims to enhance the accuracy of CAD systems and to reduce its computational complexity. This paper proposes a method for extracting a set of statistical features based on curvelet and wavelet sub-bands. Then the binary grey wolf optimizer (BGWO) is used as a feature selection technique aiming to choose the best set of features giving high performance. Using public dataset, Digital Database for Screening Mammography (DDSM), different experiments have been performed with and without using the BGWO algorithm. The random forest classifier with 10-fold cross-validation is used to achieve the classification task to evaluate the selected set of features’ capability. The obtained results showed that when the BGWO algorithm is used as a feature selection technique, only 30.7% of the total features can be used to detect whether a mammogram image is normal or abnormal with ROC area reaching 1.0 when the fusion of both curvelet and wavelet features were used. In addition, in case of diagnosing the mammogram images as benign or malignant, the results showed that using BGWO algorithm as a feature selection technique, only 38.5% of the total features can be used to do so with high ROC area result at 0.871. Full article

Large amounts of carbonated mud waste (CMW) require disposal during sugar manufacturing after the carbonation process. The lightweight of CMW enables its utilization as a partial replacement for the cement to reduce costs and CO₂ emissions. Here, various levels of CMW, namely, 0, 5, 10, 15, 20, and 25 wt.% were applied to produce composite cement samples with ordinary Portland cement (OPC) as a regular mix design series. Pure calcium oxide (CaO) nanoparticles were obtained after the calcination of CMW. The techniques of X-ray fluorescence spectrometers (XRF), Transmission electron microscope (TEM), Selected area diffraction (SAED), Scanning electron microscope (SEM), energy dixpersive X-ray (EDX), and dynamic light scattering (DLS) were used to characterize the obtained CaO nanoparticles. According to the compressive strength and bulk density results, 15 wt.% CMW was optimal for the mix design. The specific surface area increased from 27.8 to 134.8 m²/g when the CMW was calcined to 600 °C. The compressive strength of the sample containing 15% CMW was lower than the values of the other pastes containing 5% and 10% CMW at all of the curing times. The porosity factor of the hardened cement pastes released with a curing time of up to 28 days. Excessive CMW of up to 25 wt.% reduced the properties of OPC. Full article

Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%. Full article