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Superplasticiser (SP) is widely used in foam concrete industry to improve rheological properties since compaction and vibration adversely affect the stability of foam bubbles. This study aims to investigate the effect of polycarboxylate SP contents on the properties of foam concrete. Different water-cement ratios (w/c) were used, and the SP added to the mixture to adjust the spreadability. The density of 1500 kg/m3 was chosen for the production of foam concrete for semi-structural applications. Fresh, mechanical, transport and microstructure properties were analysed in this study. The results of this study showed that the content of w/c and SP had a significant impact on the performance of the foam concrete. Increasing the SP content enhanced the foam concrete’s mechanical and transport properties, but the best behaviour was through the use of 1.35% of SP. Smaller pore diameter, better pore distribution and higher portlandite peak intensity were achieved through the use of the SP. Overall, the superior behaviour of the foam concrete was achieved by the use of 1.35% polycarboxylate SP.

This study aimed to control lesser grain borer Rhizopertha dominica via using of Zingier officinale (ginger) and Helianthus annuus (sunflower) oils as protectant of Tritium spp. The inhibition rates were 38.00, 28.98, 22.67 and 4.60% respectively when the wheat treated by 0.5, 1, 1.5and 2% of sunflower oil while the inhibition rates were 35.50, 28.00, 7.25 and 3.25% respectively when the wheat treated by the same concentrations of Z. officinale oil. The damage assessments were decreased while increasing the concentration of the oils, the H. annuus oil is more protectant than Z . officinale oil in 2%. Approximately the two plant oils caused same damage percentage when the wheat treated by 1% during storage. To sum up, the results strengthen the possibility of using the plant oils for the management this insect. http://dx.doi.org/10.25130/tjps.23.2018.026

The substitution of La2O3 by Tb2O3 was studied for glass samples with a chemical composition described by: 75P2O5 + (25 − x) La2O3 + xTb2O3, where x = 5, 10, 15, 20 mol%. The mechanical properties were predicted via the Makishima–Mackenzie model. The study demonstrated that when increasing the Tb2O3 content from 5 to 20 mol%, the elastic moduli of the glasses decreased, while the dissociation energy, packing density, and micro-hardness increased. In addition, the radiation shielding properties were studied using the MCNP5 code, which was utilized to simulate the linear attenuation coefficient (LAC) of the investigated samples. Furthermore, the mass attenuation coefficients (MAC) of the glasses were determined. The highest MAC was reported for the sample with 20 mol% of Tb2O3 and decreased from 0.358 to 0.0515 cm2/g between 0.184 and 1.408 MeV. Furthermore, the effective atomic number (Zeff), equivalent atomic number (Zeq), exposure buildup factor (EBF), and the energy absorption buildup factor (EABF) of the glasses were calculated utilizing BXCOM software. The substitution of La2O3 by Tb2O3 was revealed to enhance the shielding features of the TLP samples.

The process parameters for dry reforming of methane (DRM) over Ni–W/Al2O3–MgO catalyst are optimized using response surface methodology (RSM). The Ni–W bimetallic catalyst is synthesized by co-precipitation method followed by impregnation. The catalysts are characterized by BET, XRD, FESEM, EDX and TEM; to study physicochemical properties, morphology, composition, crystallite size and deposited carbon. The effect of process parameters, i.e., reaction temperature (600oC–800 °C) and feed gas ratio (0.5–1.5) on the CH4, CO2 conversions and syngas ratio are studied. A temperature of 777.29 °C with CH4: CO2 of 1.11 at GHSV of 36,000 cm3gm.cat−1h−1, delivered the CH4 and CO2 conversions of 87.6% and 93.3%, respectively along with H2:CO of 1. The predicted process parameters were verified through actual experimental analysis at the optimized conditions, and results agreed with CCD of the RSM model with insignificant error. The MWCNT formed during DRM avoided catalyst deactivation and delivered stable performance over 12 h of reaction test at the optimized conditions.

Despite impressive advancements in Autonomous Driving Systems (ADS), navigation in complex road conditions remains a challenging problem. There is considerable evidence that evaluating the subjective risk level of various decisions can improve ADS' safety in both normal and complex driving scenarios. However, existing deep learning-based methods often fail to model the relationships between traffic participants and can suffer when faced with complex real-world scenarios. Besides, these methods lack transferability and explainability. To address these limitations, we propose a novel data-driven approach that uses scene-graphs as intermediate representations. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers for modeling the subjective risk of driving maneuvers. To train our model, we formulate this task as a supervised scene classification problem. We consider a typical use case to demonstrate our model's capabilities: lane changes. We show that our approach achieves a higher classification accuracy than the state-of-the-art approach on both large (96.4% vs. 91.2%) and small (91.8% vs. 71.2%) synthesized datasets, also illustrating that our approach can learn effectively even from smaller datasets. We also show that our model trained on a synthesized dataset achieves an average accuracy of 87.8% when tested on a real-world dataset compared to the 70.3% accuracy achieved by the state-of-the-art model trained on the same synthesized dataset, showing that our approach can more effectively transfer knowledge. Finally, we demonstrate that the use of spatial and temporal attention layers improves our model's performance by 2.7% and 0.7% respectively, and increases its explainability., 10 pages, 5 figures, 1 table

Cloud Computing is a robust model that allows users and organizations to purchase required services per their needs. The model offers many services such as storage, platforms for deployment, convenient access to web services, and so on. Load Balancing is a common issue in the cloud that makes it hard to maintain the performance of the applications adjacent to the Quality of Service (QoS) measurement and following the Service Level Agreement (SLA) document as required from the cloud providers to enterprises. Cloud providers struggle to distribute equal workload among the servers. An efficient LB technique should optimize and ensure high user satisfaction by utilizing the resources of VMs efficiently. This paper presents a comprehensive review of various Load Balancing techniques in a static, dynamic, and nature-inspired cloud environment to address the Data Center Response Time and overall performance. An analytical review of the algorithms is provided, and a research gap is concluded for the future research perspective in this domain. This research also provides a graphical representation of reviewed algorithms to highlight the operational flow. Additionally, this review presents a fault-tolerant framework and explores the other existing frameworks in the recent literature.

This paper proposes the salp swarm algorithm (SSA) combined with a backpropagation neural network (BPNN) to solve the software fault prediction (SFP) problem. The SFP problem is one of the well-known software engineering problems that are concerned with anticipating the software defects that are likely to appear during a software project or thereafter. In order to find the optimal BPNN parameters, a combination of SSA optimizer and BPNN named (SSA-BPNN) is proposed, so as to enhance prediction accuracy. The proposed method is evaluated against several datasets for the SFP problem. These datasets vary in both size and complexity. The results obtained are evaluated using a variety of performance measures (i.e., the AUC, Confusion Matrix, Sensitivity, Specificity, Accuracy, and Error Rate). The results obtained by SSA-BPNN are better than those obtained by the conventional BPNN over all of the datasets. The proposed method also has the ability to outperform several state-of-the-art methods over the same datasets in respect of most of the aforementioned performance measures. Therefore, the hybridization of SSA with BPNN is a valuable addition to the software engineering issues and can be utilized to achieve higher prediction accuracy for a variety of prediction problems.

Classification is a common problem in various fields of life, and the key challenging task in data mining. The primary objective of the classification process is to classify the given dataset in a defined class label for all data. Many research papers widely used classification in the medical sector. Various researches have been carried out to classify medical data using different techniques, such as High Order Neural Network (HONN) combined with Back-Propagation Neural Network (BPNN) as a learning algorithm. Due to the increased data complexity, the back-propagation algorithm faced problems of slow convergence. The Back-Propagation (BP) using the gradient descent technique has the possibility of getting stuck in local minima. So, the BP algorithm may cause difficulties in finding the global minima of the error function. This paper proposed a high-order Functional Link Neural Network (FLNN). The proposed FLNN model is integrated with a metaheuristic-based searching algorithm called Accelerated Particle Swarm Optimization (APSO). The performance of the proposed Accelerated Particle Swarm Optimization Functional Link Neural Network (APSOFLNN) model is validated with various medical datasets and compared to traditional techniques such as Accelerated Particle Swarm Optimization Functional Link Back-Propagation (APSOFLBP), and Artificial Bee Colony Functional Link Neural Network (ABCFLNN). The simulation results showed that the proposed APSOFLNN algorithm for the used benchmarked datasets shows good results in terms of the mean square error (MSE) and accuracy in comparison to the traditional techniques.

The aim of this study is to discover medical students’ ideas on the phenomenon of death; produce information on how these students interpret the value-related problems regarding death that they come across in different units of hospitals; and assess this data in ethical terms. This study included a qualitative research in which 12 focus group interviews were conducted with 92 fifth- and sixth-year medical students. Data obtained from interviews were assessed using a thematic content secondary analysis. The main themes were specified according to the medical students’ statements and were reviewed under the contexts of the “dying process”; “effects of death”; “attitude and behavior of health professionals”; “seeing a dead body/looking at a dead body”; “accepting death”; and “forms of expressions of death.” Medical students’ encounters with death in different units of hospitals leads them to question their values and familiarize themselves with the borders of their areas of profession.

This paper presents the technique of flex-and-flip manipulation. It is suitable for grasping thin, flexible linear objects lying on a flat surface. During the manipulation process, the object is first flexed by a robotic gripper whose fingers are placed on top of it, and later the increased internal energy of the object helps the gripper obtain a stable pinch grasp while the object flips into the space between the fingers. The dynamic interaction between the flexible object and the gripper is elaborated by analyzing how energy is exchanged. We also discuss the condition on friction to prevent loss of contact. Our flex-and-flip manipulation technique can be implemented with open-loop control and lends itself to underactuated, compliant finger mechanism. A set of experiments in robotic page turning performed with our customized hardware and software system demonstrates the effectiveness and robustness of the manipulation technique., 6 pages,8 figures, 2 tables

A neuro-cognitive inspired architecture based on the Hierarchical Temporal Memory (HTM) is proposed for anomaly detection and simultaneous data prediction in real-time for smart grid μPMU data. The key technical idea is that the HTM learns a sparse distributed temporal representation of sequential data that turns out to be very useful for anomaly detection and simultaneous data prediction in real-time. Our results show that the proposed HTM can predict anomalies within 83%-90% accuracy for three different application profiles, namely Standard, Reward Few False Positive, Reward Few False Negative for two different datasets. We show that the HTM is competitive to five state-of-the-art algorithms for anomaly detection. Moreover, for the multi-step prediction in the online setting, the same HTM achieves a low 0.0001 normalized mean square error, a low negative log-likelihood score of 1.5 and is also competitive to six state-of-the-art prediction algorithms. We demonstrate that the same HTM model can be used for both the tasks and can learn online in one-pass, in an unsupervised fashion and adapt to changing statistics. The other state-of-the-art algorithms are either less accurate or are limited to one of the tasks or cannot learn online in one-pass, and adapt to changing statistics.

The technical transformation and transfer of most services to digital platforms require that everyone has an electronic device connected to the Internet to assist them accomplish their tasks. Smartphones are one of the best options for everyone because of their small size and ease of transport, in addition to their high capabilities equivalent to a personal computer. It is necessary to identify these devices, usually by checking their IMEI (International Mobile Equipment Identity), to provide and manage several services like the cellular network service. On the other hand, criminals and counterfeiters can manipulate this identity to hide the device and prevent it from being tracked or to make high profits from selling substandard devices. Therefore, several recent proposals have emerged to create a strong fingerprint for use in device identification purposes. This paper reviews and discusses the existing methods to generate a device identity and defines their gaps. Also, it classifies the methods into four categories based on the used technique, namely PUF, machine learning, comparison approach, and sensor calibration. Additionally, it introduces the factors to consider when choosing the technique of device identification. It provides a list of possible attacks on each technology used in device identification methods.

Crypto ransomware is a type of malware that locks its victim’s file for ransom using an encryption algorithm. Its popularity has risen at an alarming rate among the cyber security community due to several successful worldwide attacks. The encryption employed had caused irreversible damage to the victim’s digital files, even when the victim chooses to pay the ransom. Therefore, this research proposes the Pre-Encryption Detection Algorithm (PEDA) that can detect crypto-ransomware at the pre-encryption stage, when no encryption has been done. PEDA provides two levels of detection; the first level of detection was before the ransomware can be activated using a signature comparison with a known crypto-ransomware’s signature. The signature was generated using SHA-256 (Secure Hashing Algorithm) that allowed fast and accurate comparison of the file content. The second level of detection used Learning Algorithm (LA) that can detect crypto-ransomware based on pre-encryption application program interface (API). The LA produced a 100% recall rate based on 80:20 ratios of training and testing, and 99.9% recall rate with a 10-fold cross-verification test. In addition, this research had also successfully identified fourteen important APIs that can differentiate between ransomware and goodware. Three APIs were present in most ransomware, but less in goodware; these APIs were NtProtectVirtualMemory, NtResumeThread, and NtTerminateProcess. Eleven APIs, on the other hand, were mostly present in goodware, but less in ransomware; these APIs were NtWriteVirtualMemory, UuidCreate, NtDelayExecution, NtSetInformationFile, NtWriteFile, CreateThread, NtReadVirtualMemory, VirtualFreeEx, CreateDirectoryW, VirtualProtectEx, and SetFilePointer.

While designing control for a class of underactuated mechanical systems (UMSs), the uncertainty and the prescribed nonholonomic tracking trajectories should be taken into consideration. Uncertainty considered in this article is time varying and bounded, and the bound of uncertainty is described using the fuzzy set theory, namely, fuzzy UMSs. An analytical dynamics-based view is taken in which the prescribed tracking trajectories are viewed as servo constraints which can be linear, nonlinear, holonomic, and nonholonomic. Using this view, a novel closed form solution of adaptive robust control is found with leakage type adaptive law to guarantee deterministic system performance, including uniform boundedness and uniform ultimate boundedness. In order to find the optimal dual gain parameters of the designed control, a two-player cooperative game is proposed for which the Pareto optimality can always be guaranteed. The effectiveness of the proposed control is shown through numerical simulation of a two-wheeled inverted pendulum vehicle.

Image denoising is a vital pre-processing phase, used to refine the image quality and make it more informative. Many image-denoising algorithms have been proposed with their own pros and cons. This paper presents a comprehensive study of the median filter and its different variants to reduce or remove the impulse noise from gray scale images. These filters are compared with respect to their functionality, time complexity and relative performance. For performance evaluation of the existing algorithms, extensive MATLAB based simulations have been carried out on a set of images. For benchmarking the relative performance, we have used Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), Universal Image Quality Index (UQI), Structural Similarity Index (SSIM) and Edge-strength Similarity (ESSIM) as quality assessment metrics. The Extended median filter (EMF) and Modified BDND are best in terms of relative statistical ratios and pleasant visual results where IAMF is having the best time complexity among existing algorithms.

DMR is a promising technique to utilize the rising greenhouse gases and produce an alternative energy source. The main hurdle in the commercialization of DMR is the catalyst deactivation. Presently, the effect of Tungsten (W) addition on Ni-based catalysts supported on mixed oxide (Al2O3–MgO) support is tested for DMR. The Ni–W bimetallic catalysts are synthesized via co-precipitation followed by the impregnation technique. An equimolar stream of feed (CH4:CO2) is used for DMR at 800 °C. The Ni–W catalyst with 4 wt% of W showed steady performance with elevated conversions of CH4 and CO2, even after 24 h of DMR. The freshly and spent catalysts are characterized by BET, XRD, FESEM, EDX, elemental mapping, TPR-H2, TPD-CO2, and XPS to confirm the elemental composition and the type of carbon formed on the catalysts. The activity of Ni catalyst declined due to formation of amorphous carbon-nanosheets, whereas Ni–W catalyst remained active due to formation of MWCNT.

The most popular defects that infect new born babies have close connection with heart. Approximately 1% percent of new born babies suffering from defects that are caused by heart. By observing Electrocardiogram "ECG" during gestation doctors can study the fetal heart activity that collected from mother abdominal and rectify number of defects which has been diagnosed in fetal heart. Many techniques like filters, BSS and even artificial intelligence used to extract and process fetal ECG signal. In case of twins gestation the problem would be more complicated because the recorded signal is a mixture of multi signals which are mother ECG, fetuses ECG and noises where every signal comes from different source in addition fetus have the same ECG signal features as well. In this study the concentration would be on BSS techniques specifically on Stone BSS method. A comparison has been made between Stone method and two other methods "EFICA and JADE". The results proved that Stone method has better performance comparing to the other BSS techniques

New novel biodegradable natural thermal insulation materials are manufactured from agro-waste materials such as date palm tree leaves and wheat straw fibers as a potential use in buildings’ insulat...

الملخص: أهداف البحث: دراسة العلاقة بين مقدار معامل زاوية ''كوب'' ومختلف كثافات العظم لدى المرضى الإناث المصابات بجنف العمود الفقري لدى اليافعات في السعودية، وقياس مدى كثافة العظام في مختلف مناطق أجسامهن. طرق البحث: استخدم البحث نموذج دراسة مقطعية كمية بعدد مناسب من المشاركات. شملت العينة المدخلة ٥٤ مريضة بجنف العمود الفقري لدى اليافعات تراوحت أعمارهن بين ١٠-٢٠ عاما. تم تسجيل معامل زاوية كوب ودرجات ''زد'' في مقياس امتصاص الأشعة السينية ثنائي البواعث لكل من عظمة الفخذ وعظمة الفقرة القطنية، وتم جمعها بين الأعوام ٢٠٠٨-٢٠١٨. النتائج: من بين ٥٤ مريضة، ٤١ منهن كان معامل زاوية كوب لديهن يتراوح بين ٤٠°-٧٠°، و١٣ بمعامل زاوية كوب ٧٠° مقارنة بالمرضى اللاتي معامل زاوية كوب 70°. The mean lumbar bone, right femur, and left femur BMDs were markedly higher in those with Cobb angles ≤70° compared with BMDs in those with Cobb angles >70°. Of the group with Cobb angles ≤70°, six (14.6%) and nine (22.0%) exhibited low BMD according to their lumbar and femoral Z-scores, respectively. Of the group with Cobb angles >70°, eight (61.5%) and nine (69.2%) exhibited low BMD according to their lumbar and femoral Z-scores, respectively. Conclusions: Female AIS patients with greater higher Cobb angles exhibited a significantly higher frequency of low BMDs.

Driving oxygen evolution reaction (OER) at extremely low overpotential and the blockage of oxygen gas inside the catalytic material leads to the deactivation of catalytic activity, therefore it is an essential step in electrochemical energy conversion systems, but still very challenging task. The clay minerals including bentonite and kaolinite are rich with plenty of active centers and favorable chemical composition for the catalysis applications but limited by the insulating properties, thus they cannot be used as an electrode material for the water splitting. The unique presence of clay minerals in the form of pencil graphite rod (PGR) and its attractive architecture enabled us to exploit advantageous features and use them as an in situ electrode for growth of metal oxide nanostructures for the electrolysis applications. The naturally inherent presence of SiO2 favors the catalytic properties and durability of the electrode whereas the MgO produces the abundant oxygen vacancies and Co3+ ions for OER process. Herein, we present a facile approach of using PGR as host substrate and co-catalyst for the loading of Co3O4, NiCo2O4 and NiO nanostructures and the modified electrode carried high porosity for easily bubbling of oxygen gas, plenty of intrinsic active centers coming from both clay minerals and metal oxides for excellent OER process. The fabricated electrode is physically well-characterized, and it has a natural ability to sustain a long term stability even at higher current densities and industrial electrolyzer conditions. The NiCo2O4/PGR, Co3O4/PGR, and NiO/PGR electrodes exhibit an overpotential of 234, 242 and 272 mV respectively at a current density of 100 mAcm−2 in 1.0 M KOH electrolytic solution. The presence of large number of oxygen vacancies through SiO2 and MgO, high Ni2+/Ni3+ and Co3+/Co2+ ratios, multi metal centers, large specific surface area, high pore volume, high electrochemical active surface area and fast charge transport within the NiCo2O4/PGR are the main reasons for its superfast OER kinetics. Thus, the proposed method of electrode design will pave a potential way for high performance electrochemical devices like metal air batteries, fuel cell and supercapacitors.

The influence of different synthesis routes and the heat treatments on the fundamental physical properties of the potassium bismuth ferrite oxide KBiFe2O5 (KBFO) is investigated. The polycrystalline KBFO is prepared using a solid-state reaction route followed by conventional heating (SSR) and the sol–gel method followed by conventional heating (SGC) and microwave heating (SGM). The monoclinic phase of the prepared sample is confirmed from the Rietveld refined X-ray diffraction analysis. XRD confirms crystallite size decrease from SSR (165 nm) to SGM (91 nm) sample. The surface morphology reveals that grain size varies from 4 µm (SSR) to 100 nm (SGM). The observed variation in Raman intensity and FWHM indicates the effect of different synthesis routes on the particle sizes of KBFO. The optical energy band gap increases from 1.59 eV (SSR) to 1.73 eV (SGM) with changes in the synthesis route and heat treatment. Temperature-dependent dielectric (25 °C to 560 °C) data at 1 MHz frequency shows a significant variation in dielectric value followed by a transition at 510 °C (TC) in all the prepared samples. The dielectric value of the SSR sample almost 50 times larger than the SGC and SGM samples of KBFO.

The 3D printing thixo-forming process is a method that utilizes semisolid thixotropic feedstock for layer extrusion by controlling feedstock viscosity below the complete melting temperature of metallic alloys to form a 3D structure. The process of producing metallic feedstock has a significant impact on alloy microstructure, which is critical for the material’s printability. This study aims to propose a semisolid heat treatment processing window of Pb-40% Sn alloy for use as feedstock billets for a variety of extruder nozzles (sizes 1–3 mm) during the 3D printing thixo-forming process. Firstly, Pb-40% Sn alloys were cold deformed to reduce the specimens’ diameters. Semisolid treatments for deformed specimens were performed at a variety of semisolid heating temperatures and three separate holding times (2, 5, and 10 min). The microstructure of semisolid feedstock specimens was then investigated. The temperature and time of the semisolid heating processing window of Pb- 40% Sn alloy were optimized to feedstock specimens and thixo-extruding processes with different printed output sizes. Therefore, the proposed semisolid heat treatment processing window was a promising approach to pave the way for the low-cost manufacturing of AM, which yields higher printing rates and improved scalability of printed lines. Additionally, this approach allows for a versatile manufacturing route for producing 3D metal parts with maximum density and intricate geometries as a result of the ability to tailor the microstructure.

Oil palm trunk (OPT) panel has high potential of being the substitution of wood due to the abundant of its availability in Malaysia and correspondingly resulted an increase in the export of palm oil in recent years. The properties of OPT has widely been studied including improvement on their strength and stability properties. In the present work, a hydrophobic coating was applied on compressed OPT panel prior to improve its surface quality. The hydrophobic properties were determined by water contact angle (WCA) and water sliding angle (WSA). The addition of SiO2 and chlorotrymethylsilane (CTMS) as a hydrophobic agent plays a critical role in enhancing the panel surface by generate roughness at nanoscales and lower its surface free energy which turn to higher WCA and smaller WSA values. Based on mechanical stability of hydrophobic compressed OPT, it shows that the WCAs of the surface panel remained almost constant and the coated surface remain non-wettable with the nanoscales features remain intact after performing scratch test.

There has been a recent rise in the uses and applications of passive seismic data, such as tomographic imaging, volcanic monitoring, and hydrocarbon exploration. Consequently, the sharp increase in passive seismic applications requires real-time event detection capabilities with high accuracy. Proper analysis of such events depends largely on the signal-to-noise ratio improvement through noise suppression techniques. Recent advances in the fields of signal processing and deep learning coupled with the available computational resources provide a great opportunity to address this challenge. In this work, a workflow is proposed where a residual deep neural network is customized and employed to detect passive seismic events. The automated detection is followed by a denoising step to extract the signal of interest from background noise using an IIR Wiener filter. The proposed method does not require any prior knowledge of the signal/noise, and therefore, it can work with various types of signals/noises. Another benefit of the proposed detection method is that the deep neural network is trained only on synthetic seismic data without the need to use real data in the training process. Nevertheless, it exhibits high accuracy in detecting and denoising events from real passive seismic data sets. In particular, field seismic data is recorded in northern Saudi Arabia and used to test the complete detection and denoising method. The detection method proved its capability of detecting events automatically in large data sets and in real time (due to off-line training).

This paper investigates the adhesive effect between the surface layer of sandwich panel with three different core surface configurations by using a peel simulation approach. The sandwich panels with cavity on the core structure can potentially be highly vulnerable to debonding phenomenon when subjected to extreme loading. The three-dimensional sandwich panels based on ASTM D1876 were developed with a finite element modelling and subjected to a constant loading magnitude of 100 MPa in two force directions of 90° and 180°. The result shows that the sandwich panel with a honeycomb core and a dimpled surface core showed increments in stress distributions at bonding area (85 % and 12.8 % at the force direction of 90°; 69.9 % and 11.6 % at the force direction of 180°) compared to that with a solid plate core. Nevertheless, the high shear stress was revealed at a large adhesive area, indicating the high potential for debonding to occur at the adhesive area between panel surface layers. This stress was directly proportional to the total deformation. The size of the adhesive area influences the bonding strength, but an extra reduction in the core density could lead to the early failure of the sandwich panel.

Sound-absorbing asphalts are particularly useful for reducing noise emissions from vehicular traffic. This solution is perfectly suited for urban areas, in fact the use of sound-absorbing asphalt represents a noise control measure with a negligible environmental impact. In the present work, the results of an experimental investigation on sound-absorbing asphalts were reported. First, the characteristics of the sound-absorbing asphalts used were experimentally found. Then, the measurements of the sound absorption coefficient of the asphalt specimens were investigated. In the final part, numerical simulation model with artificial neural networks of the acoustic coefficient were compared with the data obtained from the measurements. The neural network model showed good Pearson correlation coefficient values (0.894) which can be used with good accuracy to predict the sound absorption coefficient.

Purpose This paper aims to develop a framework for the selection of private partners in the housing industry of Saudi Arabia under the scheme of the partnership between the public and private sectors. Design/methodology/approach By investigating criteria from a comprehensive literature review and experts input through surveys, developing further surveys incorporating decision-making methods: analytic hierarchy process (AHP) and multi-attribute utility theory (MAUT) to construct a framework for selection based on weightages and utilities. Findings The results identified criteria categorized under four categories: financial (C1), technical (C2), managerial (C3) and safety/environment (C4) and their sub-criteria. The study found that the main criteria were relatively close to each other in importance based on the subjective input of the experts with the technical and safety/environment criteria tying equally with 27% followed by the managerial with 24% and trailed by the financial with 22%. Research limitations/implications The study and surveys were conducted for the Saudi market and the experts were within the country. Originality/value The study contributes to the Saudi housing initiative which is a part of the 2030 Vision and provides insight to international investors who would be willing to invest in the Saudi market; and to the literature as there is a notable lack of study on public-private partnership in housing in Saudi Arabia.

By successfully infecting certain numbers of nodes in an Internet of Things (IoT) platform, botmaster often relies on specific bots (infected nodes) to scan and gather information about the target nodes for onward propagation of the attack. Therefore, the bot’s processing capability to scan and executes the botmaster’s instructions relied on its memory capability. Hence, the bot’s memory efficiency determines the selection or the abandonment of the bot by the botmaster during the propagation of the attack. To defend against IoT botnets attack, there is need to accurately analyze the dynamic characteristics of the botnet propagation in a network. Although conventional IoT botnet propagation models considered certain characteristics of IoT wireless sensor networks (IoT-WSN) in analyzing the propagation time and the size of the botnet infection, the models do not consider the impact of memory-efficient bots on the size of the botnet infection. As such they are not applicable to defend the network against them. To complement this gap, this study proposed an IoT- Susceptible-Infectious-Abandon (IoT-SIA) model to analyze the impact of the memory-efficient bots on the size of the botnet infection. Consequently, based on the numerical simulation results, the memory-efficient bots have shown a direct impact on the size of the botnet infection and the propagation time of the attack.

Swarm-based nature-inspired optimization techniques are developing rapidly due to their universal acceptance and capability in solving various real-life challenges efficiently. Hybridization of swarm intelligence based optimization algorithms with multi-objective based solution techniques is creating a wide door in the field of optimization. The main focus of this work is, developing a hybrid Non-dominated Sorting Whale Optimization Algorithm (NSWOA), where swarm-based Whale Optimization Algorithm (WOA) is hybridized with multi-objective based, non-dominated sorting technique. This is done to develop an algorithm with efficient optima searching ability and faster computational speed. The application of NSWOA in optimizing the controller parameters of an islanded microgrid consisting of both static and dynamic load has been also described. SPSS software has been used to compare the performance of proposed NSWOA with non-dominated sorting genetic algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA) technique in optimizing the controller parameters of islanded microgrid model with multi-objective problem. It is obtained that, NSWOA requires an average of 4 iterations to reach the best optimum solution, which is less than other existing algorithms. Moreover, the computational time required by NSWOA is 2.9201 s, which proves that, it converges at a much faster rate compared to existing NSGA-II and SPEA algorithms.

Potassium-ion batteries (PIBs) are currently recognized as an emerging battery technology because of the rich resources and low cost of potassium. Nevertheless, investigations on exploiting suitable anode materials to meet stable potassium-ions storage remain to be a problem owing to the big radius size of potassium-ions. Herein, we designed N-doped carbon restricted CoP polyhedra embedded into reduced graphene oxide (rGO) sheet (CoP/NC@rGO) through coupling the function of ZIF-67 and rGO. For this composite, nano-scale CoP particles can be encapsulated by ZIF-67 derived carbon matrix, which significantly alleviates the volume change and promotes K+/e− transfer. Additionally, the combination of CoP/NC polyhedra and rGO nanosheets can build a fascinating 3D architecture to further improve the electronic conductivity of materials, as well as effectively preventing the aggregation of CoP/NC polyhedra. As a result, such composites can exhibit remarkable long-cycle performance, maintaining remarkable capacities of 177 mAh g−1 after 2800 cycles at 1 A g−1. This work provides a hopeful strategy that the combination of MOF-derived porous structures with rGO can effectively promote K+/e− transfer and improve the stability of electrode materials.

If machine failures can be detected preemptively, then maintenance and repairs can be performed more efficiently, reducing production costs. Many machine learning techniques for performing early failure detection using vibration data have been proposed; however, these methods are often power and data-hungry, susceptible to noise, and require large amounts of data preprocessing. Also, training is usually only performed once before inference, so they do not learn and adapt as the machine ages. In this article, we propose a method of performing online, real-time anomaly detection for predictive maintenance using hierarchical temporal memory (HTM). Inspired by the human neocortex, HTMs learn and adapt continuously and are robust to noise. Using the Numenta Anomaly Benchmark, we empirically demonstrate that our approach outperforms state-of-the-art algorithms at preemptively detecting real-world cases of bearing failures and simulated 3-D printer failures. Our approach achieves an average score of 64.71, surpassing state-of-the-art deep-learning (49.38) and statistical (61.06) methods.

In this paper, Crank-Nicolson differential quadrature method based on cubic exponential B-spline (CExpB-spline) functions is presented to approximate the 1D nonlinear hyperbolic Sine-Gordon equation (SGE). The time derivative is discretized by the usual forward difference scheme while the differential quadrature method (DQM) is used to integrate the spatial derivatives. The discretization of the problem gives systems of linear equations. Three numerical examples are chosen to investigate the efficiency and accuracy of the method. It is observed that the proposed method provides excellent results than the existing methods. The rate of convergence (ROC) of the present method is obtained numerically showing that the method is second-order accurate in space.

The ever increasing pressure to conserve the environment from global warming cannot be overemphasized. Emission from the inventory and production process contributes immensely to global warming and hence, the need to device a sustainable green inventory by the operational managers. In this paper, a multi-item multi-objective inventory model with back-ordered quantity incorporating green investment in order to save the environment is proposed. The model is formulated as a multi-objective fractional programming problem with four objectives: maximizing profit ratio to total back-ordered quantity, minimizing the holding cost in the system, minimizing total waste produced by the inventory system per cycle and minimizing the total penalty cost due to green investment. The constraints are included with budget limitation, space restrictions, a constraint on cost of ordering each item, environmental waste disposal restriction, cost of pollution control, electricity consumption cost during production and cost of greenhouse gas emission in the production process. The model effectiveness is illustrated numerically, and the solution obtained to give a useful suggestion to the decision-markers in the manufacturing sectors.

The scale of criminal networks (e.g. drug syndicates and terrorist networks) extends globally and poses national security threat to many nations as they also tend to be technologically advance (e.g. Dark Web and Silk Road cryptocurrency). Therefore, it is critical for law enforcement agencies to be equipped with the latest tools in criminal network analysis (CNA) to obtain key hidden links (relationships) within criminal networks to preempt and disrupt criminal network structures and activities. Current hidden or missing link predictive models that are based on Social Network Analysis models rely on ML techniques to improve the performance of the models in terms of predictive accuracy and computing power. Given the improvement in the recent performance of Deep Reinforcement Learning (DRL) techniques which could train ML models through self-generated dataset, DRL can be usefully applied to domains with relatively smaller dataset such as criminal networks. The objective of this study is to assess the comparative performance of a CNA hidden link prediction model developed using DRL techniques against classical ML models such as gradient boosting machine (GBM), random forest (RF) and support vector machine (SVM). The experiment results exhibit an improvement in the performance of the DRL model of about 7.4% over the next best performing classical RF model trained within 1500 iterations. The performance of these link prediction models can be scaled up with the parallel processing capabilities of graphical processing units (GPUs), to significantly improve the speed of training the model and the prediction of hidden links.

A novel PCF-based sensor has been presented in this paper to sense different chemicals and biocomponents. The proposed biochemical sensor (BCS) comprises a simple arrangement of rectangular holes. The competence of this BCS in detecting analytes is evaluated employing the full-vector finite element method (FEM). Performance metrics prove the competence of this BCS in sensing various analytes that have a refractive index in the range of 1.33–1.48. The proposed BCS shows ultralower values for both the bulk absorption and confinement loss. This BCS attains a maximum of about 95.82% relative sensitivity at 2.5 THz. Also, the dispersion for this sensor is only 0.12 ± 0.011 ps/THz/cm at the same point of interest. These results prove that the proposed BCS will play a key role in sensing toxic chemicals, illegal drugs, biocomponents, etc. Besides, the simple rectangle-based PCF structure ensures the feasibility of fabrication by practicing the existing fabrication strategies.

Nanostructured β-Ga2O3 nanobelts are fabricated by thermal evaporation method at a temperature around of 1120 °C. Subsequently, a sol-gel processed SnO2 layer is employed to coat β-Ga2O3 nanobelts. This simple deposition technique is confirmed to be a promising route to incorporate a coating material onto nanostructured films, resulting in an attractive approach to modify the functionality of gallium oxide. In comparison with the data reported in the literature, the sensing characteristics of SnO2-functionalized β-Ga2O3 nanobelts to analyte gas H2 in the concentration range (33–1000 ppm) and within the temperature range (room temperature – 200 °C) are found to be greatly improved.

Refereed/Peer-reviewed Watershed characteristics and their hydrological responses can have severe effects on the occurrence and extent of floods. Therefore, this study focuses on the integration of geospatial techniques and remote sensing data to identify watershed terrain characteristics and evaluate the influence of these characteristics on flood susceptibility in South Australia. Data from the Shuttle Radar Topography Mission (SRTM) and geologic and topographic maps and a geographic information system (GIS) were used to delineate drainage basins, measure morphometric parameters and link different parameters to evaluate the degree of flood vulnerability. Depending on their relations to the flood hazards, the morphometric parameters were categorized into two groups; then, a rank score was assigned to each group. Finally, the flood susceptibility of the basins was visualized, and the basins were classified into low, intermediate and high flood hazard areas. The results show that approximately 45.7, 44.7 and 9.7% of the study area is at risk of high, medium and low degrees of flooding, respectively. The results were validated through secondary data relating to historic floods. The causes of flooding were analysed using rainfall and road density data, while the consequences of flooding were verified by the population distribution across the study area. The findings of this study can be used to support decision-makers in planning and investing in mitigation measures, especially in highly susceptible areas of South Australian basins.

As the number of connected devices is exponentially increasing, security in Internet of Things (IoT) networks presents a major challenge. Accordingly, in this work we investigate the secrecy performance of multihop IoT networks assuming that each node is equipped with only two antennas, and can operate in both Half-Duplex (HD) and Full-Duplex (FD) modes. Moreover, we propose an FD Cooperative Jamming (CJ) scheme to provide higher security against randomly located eavesdroppers, where each information symbol is protected with two jamming signals by its two neighbouring nodes, one of which is the FD receiver. We demonstrate that under a total power constraint, the proposed FD-CJ scheme significantly outperforms the conventional FD Single Jamming (FD-SJ) approach, where only the receiving node acts as a jammer, especially when the number of hops is larger than two. Moreover, when the Channel State Information (CSI) is available at the transmitter, and transmit beamforming is applied, our results demonstrate that at low Signal-to-Noise Ratio (SNR), higher secrecy performance is obtained if the receiving node operates in HD and allocates both antennas for data reception, leaving only a single jammer active; while at high SNR, a significant secrecy enhancement can be achieved with FD jamming. Our proposed FD-CJ scheme is found to demonstrate a great resilience over multihop networks, as only a marginal performance loss is experienced as the number of hops increases. For each case, an integral closed-form expression is derived for the secrecy outage probability, and verified by Monte Carlo simulations.

Nowadays, distribution utilities expend large investments on Distributed System Automation (DSA) based on smart secondary substations at load, capacitor, and distributed generator points with installed automatic sectionalizing switches on their branches. This article addresses the optimal control and operation of distribution systems that minimize the wasted energy and introducing quantitative and qualitative power services to meet consumers' satisfaction. Simultaneous allocations of Distributed Generators (DGs) and Capacitor Banks (CBs) are handled at peak loading condition. Then, the DSA is optimally activated for optimal Distribution Network Reconfiguration (DNR), optimal DGs commitment, and optimal CBs switching for losses minimization in coordination with different loading conditions. Practical daily load variation is applied to simulate the dynamic operation of automated distribution systems. For achieving these targets, the Manta Ray Foraging Optimization Algorithm (MRFOA) is adopted. MRFOA is an effective and simple structure optimizer that emulates three various individual manta rays foraging organizations. The capability of the MRFOA is applied to the IEEE 33-bus, 69-bus and practical distribution network of 84-bus due to the Taiwan Power Company (TPC). A comparison with recent techniques has been conducted to prove the effectiveness of MRFOA. The accomplished results demonstrate that the proposed MRFOA has great effectiveness and robustness among other optimization techniques.

A sudden blow or jolt to the human brain called traumatic brain injury (TBI) is one of the most common injuries recorded in the health insurance claim. Generally, computed tomography (CT) or magnetic resonance imaging (MRI) is required to identify the trauma’s severity. Unfortunately, CT and MRI equipment are bulky, expensive, and not always available, limiting their use in TBI detection. Therefore, as an alternative, this study presents a novel classification architecture that can classify non-severe TBI patients from healthy subjects by using resting-state electroencephalogram (EEG) as the input. The proposed architecture employs a convolutional neural network (CNN), and error-correcting output codes support vector machine (ECOC-SVM) to perform automated feature extraction and multi-class classification. In this architecture, complex feature selection and extraction steps are avoided. The proposed architecture attained a high-performance classification accuracy of 99.76%, potentially being used as a classification approach to preventing healthcare insurance fraud. The proposed method is compared to existing studies in the literature. The outcome from the comparisons indicates that the proposed method has outperformed the benchmarked methods by presenting the highest classification accuracy and precision.

This paper implements mathematically rigorous extended trial function algorithm to address cubic–quartic optical solitons in birefringent fibers having Kudryashov’s law of nonlinear refractive index. Three special cases of the power-law nonlinearity parameter are taken into consideration. Bright and singular optical solitons emerge from this analytical scheme.

Due to its environment-friendly and replenishable characteristics, biodiesel has the potential to substitute fossil fuels as an alternative source of energy. Although biodiesel has many benefits to offer, manufacturing biodiesel on an industrial scale is uneconomical as a high cost of feedstock is required. A novel sulfonated and magnetic catalyst synthesised from a palm kernel shell (PMB-SO3H) was first introduced in this study for methyl ester or biodiesel production to reduce capital costs. The wasted palm kernel shell (PKS) biochar impregnated with ferrite Fe3O4 was synthesised with concentrated sulphuric acid through the sulfonation process. The SEM, EDX, FTIR, VSM and TGA characterization of the catalysts were presented. Then, the optimisation of biodiesel synthesis was catalysed by PMB-SO3H via the Response Surface Methodology (RSM). It was found that the maximum biodiesel yield of 90.2% was achieved under these optimum operating conditions: 65 °C, 102 min, methanol to oil ratio of 13:1 and the catalyst loading of 3.66 wt%. Overall, PMB-SO3H demonstrated acceptable catalysing capability on its first cycle, which subsequently showed a reduction of the reusability performance after 4 cycles. An important practical implication is that PMB-SO3H can be established as a promising heterogeneous catalyst by incorporating an iron layer which can substantially improve the catalyst separation performance in biodiesel production.

The cationic gemini surfactant PyO-3-12 was designed to include two dimethyl ammonium groups, one dodecyl tail, and 1-pyrenemethyl hexyl ether tail into the structure of the surfactant. The pyrenyl label ensured that the fluorescence of pyrene could be employed to probe the behavior of PyO-3-12 at the molecular level. The introduction of the oxygen atom in the β-position to pyrene was found to be critical for restoring the sensitivity of the pyrenyl label to the polarity of its environment. The properties of PyO-3-12 were characterized in water by surface tension and a fluorescence methodology that involved the global model-free analysis (MFA) of the pyrene monomer and excimer fluorescence decays to provide quantitative information about the state (unassociated-vs-aggregated) of PyO-3-12. The MFA was combined with a fluorescence quenching study with 2,6-dinitrotoluene to determine the size of the PyO-3-12 micelles. PyO-3-12 was found to behave like a typical gemini surfactant, exhibiting a critical micelle concentration (CMC) of 0.38 (±0.05) mM and an aggregation number (Nagg) equal to 23 (±2). Besides allowing PyO-3-12 to probe the polarity of its environment, the oxygen atom in the β-position next to pyrene brought some pyrenyl labels closer to the interface between the micellar interior and the aqueous phase, in a process that increased the effective volume of the hydrophobic part of PyO-3-12. This led to an increase in the packing parameter of PyO-3-12 and, consequently, an increase in Nagg compared to the Nagg value of 14 (±0.2) obtained for Py-3-12, a gemini surfactant, whose chemical structure was similar to that of PyO-3-12 but without the oxygen in the β-position to pyrene. The methodology described in this study to prepare and characterize pyrene-labeled surfactants is general and can be applied to study any pyrene-labeled surfactant and its interactions with oppositely charged macromolecules.

Today's smartphones are equipped with a large number of powerful value-added sensors and features, such as a low-power Bluetooth sensor, powerful embedded sensors, such as the digital compass, accelerometer, GPS sensors, Wi-Fi capabilities, microphone, humidity sensors, health tracking sensors, and a camera, etc. These value-added sensors have revolutionized the lives of the human being in many ways, such as tracking the health of the patients and the movement of doctors, tracking employees movement in large manufacturing units, monitoring the environment, etc. These embedded sensors could also be used for large-scale personal, group, and community sensing applications especially tracing the spread of certain diseases. Governments and regulators are turning to use these features to trace the people's thoughts to have symptoms of certain diseases or viruses, e.g., COVID-19. The outbreak of COVID-19 in December 2019, has seen a surge of the mobile applications for tracing, tracking, and isolating the persons showing COVID-19 symptoms to limit the spread of the disease to the larger community. The use of embedded sensors could disclose private information of the users, thus potentially bring a threat to the privacy and security of users. In this article, we analyzed a large set of smartphone applications that have been designed to contain the spread of the COVID-19 virus and bring the people back to normal life. Specifically, we have analyzed what type of permission these smartphone apps require, whether these permissions are necessary for the track and trace, how data from the user devices are transported to the analytic center, and analyzing the security measures these apps have deployed to ensure the privacy and security of users.

Embedding strong photonic stopbands into traditional optical fibre that can directly access and sense the outside environment is challenging, relying on tedious nano-processing steps that result in fragile thinned fibre. Ultrashort-pulsed laser filaments have recently provided a non-contact means of opening high-aspect ratio nano-holes inside of bulk transparent glasses. This method has been extended here to optical fibre, resulting in high density arrays of laser filamented holes penetrating transversely through the silica cladding and guiding core to provide high refractive index contrast Bragg gratings in the telecommunication band. The point‐by‐point fabrication was combined with post-chemical etching to engineer strong photonic stopbands directly inside of the compact and flexible fibre. Fibre Bragg gratings with sharply resolved π-shifts are presented for high resolution refractive index sensing from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{{{{{\rm{H}}}}}}}$$\end{document}nH = 1 to 1.67 as the nano-holes were readily wetted and filled with various solvents and oils through an intact fibre cladding., Engineered stop bands to sense an ambient environment can enable many applications. Here, the authors demonstrate well-controlled processes to open high-aspect ratio nanoholes through optical fibre for Bragg gratings in the telecomm spectrum and to enable high-resolution refractive index sensing

A novel intelligent hybrid model is proposed in this paper for accurate prediction and modeling of solar power plants using the wavelet transform package (WTP) and generative adversarial networks (GANs). In the proposed model, the wavelet transform is deployed for decomposing the solar energy signal into the sub-harmonics followed by the statistical feature selection analyses. The GAN model as a deep learning approach is proposed for learning each sub-frequency and predicting the future of the solar energy in the short-time window. Due to the high complexity of the solar irradiance data when training, an evolutionary algorithm based on dragonfly algorithm (DA) is suggested to train the generative and discriminator networks in the GAN. Moreover, a three-phase adaptive modification is suggested to enhance the search capabilities of the DA optimization. The efficiency and appropriate performance of the proposed model is examined and compared with the most successful models such as artificial neural networks (ANNs), support vector machine (SVM), time series, auto-regressive moving average (ARMA), and original GAN on several benchmarks for varied forecast time horizons. The simulation results on the datasets of two regions show the mean absolute percentage error (MAPE) of 0.0282 and 0.0262, when 1-pace forecast horizon, for the two regions which increase up to 0.0531 and 0.0631 for 6-pace forecast horizon. Moreover, the root mean absolute error (RMSE) is 0.0473 and 0.0479 for the two regions at 1-pace forecast horizon which increased up to 0.0895 and 0.0946 for 6-pace forecast horizon. These results show the more precise performance of the proposed forecast deep learning as well as the more optimal performance of the modified DA over the other algorithms shown in the results.

The presence of asphaltenes and polar compounds in a crude oil plays a major role in dictating the type of wettability it displays with oil reservoir rocks. The main objective of this study was to investigate the effect of the asphaltenes structure and other polar compounds in altering rock wettability. Five, natural crude oil samples were used with varying asphaltene (6.4 to 11.6), total nitrogen (0.029 to 0.097) and sulfur (2.259 to 4.234) contents, all in mass%. Crude oil characterization showed the asphaltene samples to consist of condensed hydrocarbon and sulfur aromatic molecules with one or two sulfur atoms per molecule. The weight-average numbers of fused aromatic rings for hydrocarbon molecules were 6 to 7 and for sulfur-containing molecules was an average of 8 aromatic rings, where the aromatic systems were substituted with short alkyl chains of less than 20 carbon atoms. Based on mass spectral evidence, the average empirical molecular structures of the asphaltenes were constructed and they were all found to be relatively similar. Entrained maltene species with 2 to 5 aromatic rings with longer alkyl chains were also found. Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) was used to characterize each crudes’ asphaltenes fractions. Three rock samples with mineral compositions of 100% dolomite (D100), 100% calcite (C100), and 63% calcite + 37% dolomite (C63D37) were used. Variation of the contact angle with time was measured using brine, as the external phase, and the five crudes on the rock samples. The interfacial tension (IFT) between brine and the five crudes were also measured. Among the five crude types, the three rocks show the strongest wetting tendency when contacted by type V oil. These were 129.2°, 109.2°, and 135.1° for D100, C100 and C63D37, respectively. Interestingly, wettability was not linearly correlated with asphaltene contents for all crude types. Wettability varied with the rock sample, nitrogen and sulfur contents, and crude’s non-asphaltenic composition. As a result, there is no strong evidence suggesting that the content nor the structure of the asphaltenes alone has a large effect on altering rock wettability.

TiO2 nanostructures have been one of the most explored metal oxides photocatalysts to apply for environmental remediation. However, its wide band gap results in the underutilization of sunlight for degradation of pollutants. In order to overcome this handicap, the synthesis of TiO2-based composite has brought extraordinary materials. In this study, we design and prepare TiO2/Nb2O5 heterostructures with different molar ratios by using peroxotitanium and peroxoniobium complex as precursors in aqueous solution. The TiO2 exists in the form of anatase while Nb2O5 is amorphous in the composite, leading to a special crystalline TiO2/amorphous Nb2O5 heterostructures. In particular, Nb element is also doped and Ti3+ ions are formed in the TiO2 lattice, leading to a reduced band gap. The unique TiO2/0.25Nb2O5 (Ti:Nb = 2:1) heterostructures can effectively suppress the recombination of photogenerated electrons and holes, and facilitate the charge transfer, resulting in the optimum photocatalytic performance. The nitrogen oxide removal efficiency by TiO2/0.25Nb2O5 is 77.23% in visible light, which is 3.8-folds and 7.0-folds higher than pure TiO2 and Nb2O5. Photocatalytic degradation of acetaminophen by TiO2/0.25Nb2O5 is 90.6% in visible light, which is approximately 2.5-folds higher than pure TiO2 and Nb2O5.

A low-cost hydrophobic mullite hollow fibre membrane (Hy-MHFM) fabricated via phase inversion/sintering technique followed by fluoroalkyl silane (FAS) grafting is presented in this study. The prepared CHFMs were characterized before and after the grafting step using different characterization techniques. The pore size of the CHFM surface was also determined using ImageJ software. The desalination performance of the grafted membrane was evaluated in direct contact membrane distillation (DCMD) using synthetic seawater of varying salt concentrations for 2 h at various feedwater temperatures. The outcome of the evaluations showed declines in the permeate flux of the membrane at increasing feed concentration, as well as increased flux with increased feed temperature. The long-term stability of the membrane was achieved at time 20 h, feed temperature 60 °C, and permeate temperature 10 °C, the membrane achieved a salt rejection performance of about 99.99 % and a water flux value of 22.51 kg/ m2 h.