### Recent Submissions

• #### Solar-powered direct contact membrane distillation system: performance and water cost evaluation

Fresh water is crucial for life, supporting human civilizations and ecosystems, and its production is one of the global issues. To cope with this issue, we evaluated the performance and cost of a solar-powered direct contact membrane distillation (DCMD) unit for fresh water production in Karachi, Pakistan. The solar water heating system (SWHS) was evaluated with the help of a system advisor model (SAM) tool. The evaluation of the DCMD unit was performed by solving the DCMD mathematical model through a numerical iterative method in MATLAB software®. For the SWHS, the simulation results showed that the highest average temperature of 55.05 ◦C and lowest average temperature of 44.26 ◦C were achieved in May and December, respectively. The capacity factor and solar fraction of the SWHS were found to be 27.9% and 87%, respectively. An exponential increase from 11.4 kg/m2 ·h to 23.23 kg/m2 ·h in permeate flux was observed when increasing the hot water temperatures from 44 ◦C to 56 ◦C. In the proposed system, a maximum of 279.82 L/day fresh water was produced in May and a minimum of 146.83 L/day in January. On average, the solar-powered DCMD system produced 217.66 L/day with a levelized water cost of 23.01 USD/m3
• #### Synthesis of Ce3+ substituted Ni-Co ferrites for high frequency and memory storage devices by sol-gel route

Cerium (Ce3+) substituted Ni-Co ferrites with composition Ni0.3Co0.7CexFe2−xO4 (x = 0.0–0.20, with step size 0.05) were synthesized by sol-gel method. Face-centered cubic (FCC) spinel structure was revealed by X-ray analysis. The crystalline size was calculated ranging between 17.1 and 18.8 nm, lattice constant showed a decreasing trend with increase of Ce3+ contents, furthermore, X-ray density was calculated between 5.30 and 5.69 g/cm3. The two characteristic spinel ferrites absorption bands were seen around 550 (cm−1) and 415 (cm−1) in Fourier transform infra-red (FTIR) spectroscopy. The microstructural and elemental studies were carried out by field emission transmission electron microscopy (FE-TEM) and energy dispersive X-ray (EDX) respectively, the average particle size was calculated around 21.83 nm. Magnetic studies were per- formed by vibrating sample magnetometer (VSM), which showed that saturation magnetization Ms and remanence Mr decreased with substitution up to x = 0.10 due to small magnetic moment of Ce3+ than Fe3+. The coercivity Hc increased with substitution up to 908.93 Oe at x = 0.05, then it decreased following the trend of anisotropy constant. The dielectric studies exhibited decrease in dielectric parameters with fre- quency due to decreasing polarization in material. The dielectric loss was significantly decreased in material at high frequency. The Cole-Cole interpretation exhibited conduction mechanism being caused by grain boundary density. These attributes of Ce3+ substituted Ni-Co ferrites suggest their possible use in memory storage, switching and high frequency devices like antenna and satellite systems.
• #### Cyberbullying detection in Urdu language using machine learning

Cyberbullying has become a significant problem with the surge in the use of social media. The most basic way to prevent cyberbullying on these social media platforms is to identify and remove offensive comments. However, it is hard for humans to read and remove all the comments manually. Current research work focuses on using machine learning to detect and eliminate cyberbullying. Although most of the work has been conducted on English texts to detect cyberbullying, limited to no work can be found in Urdu. This paper aims to detect cyberbullying from the users' comments posted in Urdu on Twitter using machine learning and Natural Language Processing (NLP) techniques. To the best of our knowledge, cyberbullying detection on Urdu text comments has not been performed due to the lack of a publicly available standard Urdu dataset. In this paper, we created a dataset of offensive user-generated Urdu comments from Twitter. The comments in the dataset are classified into five categories. n-gram techniques are used to extract features at character and word levels. Various supervised machine-learning techniques are applied to the dataset to detect cyberbullying. Evaluation metrics such as precision, recall, accuracy and F1 scores are used to analyse the performance of machine learning techniques.
• #### Microstructural response and wear behaviour of Ti-6Al-4V impregnated with Ni/Al2O3 + TiO2 nanostructured coating using an electric arc

Titanium alloys are known for their excellent corrosion resistance; however, low surface hardness results in poor wear resistance, which limits its potential application. This study employs a novel two-step process to embed a hard Ni coating containing a mixture of nanosized particles (Al2O3 and TiO2) into the surface of the Ti-6Al-4V alloy using an electric arc produced during the inert tungsten gas welding process. The surface of the sample was evaluated using Vickers Microhardness, Scanning electron microscopy, Energy dispersive spectroscopy and pin-on-plate wear testing. Microstructural analysis showed that impregnating the titanium surface with Ni/(Al2O3 and TiO2) nanomaterials resulted in the formation of a hard martensitic structure to a depth of approximately 2 mm below the surface. The changes observed are driven by modification of the surface chemistry and the presence of nickel, causing grain size reduction, solid solution strengthening and dispersion strengthening of the treated layer by the nanoparticles. The hardness of the treated layer increased by more than 180% when 40 nm Al2O3 and 30 nm TiO2 particles were embedded into the surface. Similarly, the wear resistance of the treated surface improved by 100%.
• #### A compact broadband Circularly Polarized wide-slot antenna with axial ratio bandwidth encompassing LTE 42 and LTE 43 standards of 5G mid-band

This study presents a compact broadband wide-slot antenna with broadband left-hand circular polarization compatible with both LTE 42 and LTE 43 standards of 5G mid-band applications. The proposed antenna is fabricated on an FR4 dielectric substrate with overall dimensions of , where is the free space wavelength at the resonant frequency of the antenna. The antenna ground plane is etched to form a square radiating slot with a pair of rectangular ground stubs that are diagonally placed inside the slot. On the other side of the antenna, the feed line is loaded by horizontal and vertical stubs to improve the coupling between the feed line and the square slot. To generate a circular polarization, the feeding stubs cooperate with the pair of rectangular ground stubs to excite the radiating slot of the antenna at two different feeding points whose currents have approximately equal amplitude and 90o phase shift. The measured impedance bandwidth (BW) of the proposed wide-slot antenna is 16.2% (580 MHz along the band 3.3-3.88 GHz), while the observed axial ratio bandwidth (ARBW) is 12.2% (440 MHz in the 3.4-3.84 GHz band). The measured gain values are found to be larger than 2.5 dB along both standards of the 5G mid-band applications.
• #### The kihara potential function parameters of methane, ethane, propane, and i-butane: The effects on clathrate hydrate structure determination

Gas hydrates, or clathrate hydrates, are solid crystalline compounds, which are formed by combination of water and gas and/or some volatile liquid molecules. Prediction of hydrate stability/dissociation/equilibrium conditions of natural gases is important in separation processes, gas storage, and in preventing blockage of gas transmission pipelines. In this study, initially, the different sets of the Kihara Potential Function Parameters, KPFP, reported in the literature were used to predict the experimental hydrate dissociation conditions of methane, ethane, propane and i-butane and mixtures of these four compounds. In most cases, however, based on these sets of KPFP, the hydrate structure cannot be predicted correctly. Consequently due to incorrect estimation of the hydrate structure, especially for natural gas mixture, the predicted hydrate dissociation conditions are found inaccurate. For overcoming this fault and by using a genetic algorithm, a new set of KPFP were optimized based on the new definition of the objective function considering hydrate structure. The results show good agreement with experimental data, both in the prediction of hydrate dissociation conditions and hydrate structure.
• #### Design of new nano-catalysts and digital basket reactor for oxidative desulfurization of fuel: experiments and modelling

This study was focused on developing a new catalyst using metal oxide (10 %Mn) over Nano- activated Carbon (Nano-AC) particles and designing a new reactor (digital basket reactor, DBR) for the sulfur removal from kerosene oil via oxidative desulfurization (ODS). The new homemade Nano-catalyst was prepared by utilizing impregnation process and was characterized by SEM, EDX, BET, and FTIR techniques. The performance of ODS process under moderate operating conditions was significantly enhanced by the application of the new catalyst and the new reactor. The results showed that 94 % of the sulfur could be achieved at oxidation temperature of 80 ºC, oxidation time of 35 min and agitation rate of 750 rpm. The reactivity of catalyst was examined after four consecutive ODS cycles under the optimal experimental parameters and the used catalyst showed excellent stability based on oxidation efficiency. The spent catalyst was treated by methanol, ethanol and iso-octane solvents for regenerated it, and the result proved that iso-octane carried out the maximum regeneration performance. An optimization method depending on minimizing the sum of the squared error among the experimental and model predicted data of ODS technology was employed to evaluate the optimal kinetic model parameters of the reaction system. The ODS process model was able to predict the results obtained experimentally for a wide range of conditions very well by absolute average errors<5 %.
• #### 6G wireless communication systems: applications, opportunities and challenges

As the technical specifications of the 5th Generation (5G) wireless communication standard are being wrapped up, there are growing efforts amongst researchers, industrialists, and standardisation bodies on the enabling technologies of a 6G standard or the so-called Beyond 5G (B5G) one. Although the 5G standard has presented several benefits, there are still some limitations within it. Such limitations have motivated the setting up of study groups to determine suitable technologies that should operate in the year 2030 and beyond, i.e., after 5G. Consequently, this Special Issue of Future Internet concerning what possibilities lie ahead for a 6G wireless network includes four high-quality research papers (three of which are review papers with over 412 referred sources and one regular research). This editorial piece summarises the major contributions of the articles and the Special Issue, outlining future directions for new research.
• #### Four-Element/Eight-Port MIMO Antenna System with Diversity and Desirable Radiation for Sub 6 GHz Modern 5G Smartphones

In this manuscript, a multiple-input multiple-output (MIMO) antenna array system with identical compact antenna elements providing wide radiation and diversity function is introduced for sub 6 GHz fifth-generation (5G) cellular applications. The introduced design contains four pairs of miniaturized square-loop resonators with dual-polarization and independently coupled T-shaped feed lines which have been placed symmetrically at the edge corners of the smartphone mainboard with an overall size of 75 mm × 150 mm. Therefore, in total, the introduced array design encompasses four pairs of horizontally and vertically polarized resonators. The elements are very compact and utilize at 3.6 GHz, a potential 5G candidate band. In order to improve the frequency bandwidth and radiation coverage, a square slot has been placed and excited under each loop resonator. Desirable isolation has been observed for the adjacent elements without any decoupling structures. Therefore, they can be considered self-isolated elements. The presented smartphone antenna not only exhibits desirable radiation but also supports different polarizations at various sides of the printed circuit board (PCB). It exhibits good bandwidth of 400 MHz (3.4-3.8 GHz), high-gain patterns, improved radiation coverage, and low ECC/TARC (better than 0.004 and -30 dB at 3.6 GHz, respectively). Experimental measurements were conducted on an array manufactured on a standard smartphone board. The simulated properties of this MIMO array are compared with the measurements, and it is found that they are in good agreement. Furthermore, the introduced smartphone array offers adequate efficiency in both the user interface and components integrated into the device. As a result, it could be suitable for 5G handheld devices.
• #### Novel machine learning experiments with artificially generated big data from small immunotherapy datasets

Big data and machine learning result in agile and robust healthcare by expanding raw data into useful patterns for data-enhanced decision support. The available datasets are mostly small and unbalanced, resulting in non-optimal classification when the algorithms are implemented. In this study, five novel machine learning experiments are conducted to address the challenges of small datasets by expanding these into big data and then utilising Random Forests. The experiments are based on personalised adaptable strategies for both balanced and unbalanced datasets. Multiple datasets from cryotherapy and immunotherapy are considered, however, hereby only immunotherapy is used. In the first experiment, artificially generated data is presented by increasing the observations of the dataset, each new data is four-time larger than the previous one, resulting in better classification. In the second experiment, the effect of volume on classification is considered based on the number of attributes. The attributes of each new dataset are built based on conditional probabilities. It did not make any difference, in obtained classification, when the number of attributes is increased to more than 879. In the third simulation experiment, classes of data are classified manually by dividing the data into a two-dimensional plane. This experiment is first performed on small data and then on expanded big data: by increasing observations, an accuracy of 73.68% is attained. In the fourth experiment, the visualisation of the enlarged data did not provide better insights. In the fifth experiment, the impact of correlations among datasets’ attributes on classification is observed, however, no improvements in performance are achieved. The experiments generally improved performance by comparing the classification results using the original and artificial data.
• #### Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective

Immunotherapy treatments can be essential sometimes and a waste of valuable resources in other cases, depending on the diagnosis results. Therefore, researchers in immunotherapy need to be updated on the current status of research by exploring: application domains e.g. warts, datasets e.g. immunotherapy, classifiers or algorithms e.g. kNN and software tools. The research objectives were: 1) to study the immunotherapy-related published literature from a supervised machine learning perspective. In addition, to reproduce immunotherapy classifiers reported in research papers. 2) To find gaps and challenges both in publications and practical work, which may be the basis for further research. Immunotherapy, diabetes, cryotherapy, exasens data and ”one unbalanced dataset” are explored. The results are compared with published literature. To address the found gaps in further research: novel experiments, unbalanced studies, focus on effectiveness and a new classifier algorithm are suggested.
• #### An integrated data- and capability-driven approach to the reconfiguration of agent-based production systems

Industry 4.0 promotes highly automated mechanisms for setting up and operating flexible manufacturing systems, using distributed control and data-driven machine intelligence. This paper presents an approach to reconfiguring distributed production systems based on complex product requirements, combining the capabilities of the available production resources. A method for both checking the “realisability” of a product by matching required operations and capabilities, and adapting resources is introduced. The reconfiguration is handled by a multi-agent system, which reflects the distributed nature of the production system and provides an intelligent interface to the user. This is all integrated with a self-adaptation technique for learning how to improve the performance of the production system as part of a reconfiguration. This technique is based on a machine learning algorithm that generalises from past experience on adjustments. The mechanisms of the proposed approach have been evaluated on a distributed robotic manufacturing system, demonstrating their efficacy. Nevertheless, the approach is general and it can be applied to other scenarios.
• #### Control of three-links robot arm based on fuzzy neural Petri nets

A fuzzy neural Petri Nets (FNPN) controller is utilized for controlling a three-links robot arm which considers a nonlinear dynamic system. The incorporation of the classical FNN with a Petri net (PN) has been suggested to produce a new representing system called FNPN structure to alleviate the computation burden. The motion equation of three links robot arm is derived from Lagrange’s equation. This equation has been incorporated with the motion equations of DC Servo motors which motivate the robot. For nonlinearity dynamic problems, this paper presents a direct adaptive control technique to control three links robot arm utilizing the FNPN controller. The computer simulation depicts that the present FNPN controller accomplished better performance with fast response and minimum error.
• #### The flow of lubricant as a mist in the piston assembly and crankcase of a fired gasoline engine

The tribological performance of the piston assembly of an automotive engine is highly influenced by the complex flow mechanisms that supply lubricant to the upper piston rings. As well as affecting friction and wear, the oil consumption and emissions of the engine are strongly influenced by these mechanisms. There is a significant body of work that seeks to model these flows effectively. However, these models are not able to fully describe the flow of lubricant through the piston assembly. Some experimental studies indicate that droplets of lubricant carried in the gas flows through the piston assembly may account for some of this. This work describes an investigation into the nature of lubricant misting in a fired gasoline engine. Previous work in a laboratory simulator showed that the tendency of a lubricant to form mist is dependent on the viscosity of the lubricant and the type and concentration of viscosity modifier. The higher surface area-to-volume ratio of the lubricant if more droplets are formed or if the droplets are smaller is hypothesised to increase the degradation rate of the lubricant. The key work in the investigation was to measure the size distribution of the droplets in the crankcase of a fired gasoline engine. Droplets were extracted from the crankcase and passed through a laser diffraction particle sizer. Three characteristic droplet size ranges were observed: Spray sized (250–1000 μm); Major mist (30–250 μm); and Minor mist (0.1–30 μm). Higher base oil viscosity tended to reduce the proportion of mist-sized droplets. The viscoelasticity contributed by a polymeric viscosity modifier reduced the proportion of mist droplets, especially at high load.
• #### Planning the Radiology Workforce for Cancer Diagnostics

Grothendieck's bound is used in the context of a single quantum system, in contrast to previous work which used it for multipartite entangled systems and the violation of Bell-like inequalities. Roughly speaking the Grothendieck theorem considers a 'classical' quadratic form ${\cal C}$ that uses complex numbers in the unit disc, and takes values less than 1. It then proves that if the complex numbers are replaced with vectors in the unit ball of the Hilbert space, then the 'quantum' quadratic form ${\cal Q}$ might take values greater than 1, up to the complex Grothendieck constant $k_\mathrm G$. The Grothendieck theorem is reformulated here in terms of arbitrary matrices (which are multiplied with appropriate normalisation prefactors), so that it is directly applicable to quantum quantities. The emphasis in the paper is in the 'Grothendieck region' $(1,k_\mathrm G)$, which is a classically forbidden region in the sense that ${\cal C}$ cannot take values in it. Necessary (but not sufficient) conditions for ${\cal Q}$ taking values in the Grothendieck region are given. Two examples that involve physical quantities in systems with six and 12-dimensional Hilbert space, are shown to lead to ${\cal Q}$ in the Grothendieck region $(1,k_\mathrm G)$. They involve projectors of the overlaps of novel generalised coherent states that resolve the identity and have a discrete isotropy.
The cost of electricity produced by thermal power plants in Republic of Djibouti is relatively high at about $0.32/ kWh. This is due to its dependence on imported oil coupled with fluctuating oil prices. Consequently, the customer pays a high electricity bill. However, Djibouti is endowed with indigenous renewable energy resources such as a good solar irradiance of 5.92 kWh/ m2 day, a potential geothermal energy estimated up to 1000 MW, and few sites with annual wind speed higher than 6 m/s. The goal of this paper is, therefore, to assess an economic evaluation of different grid connected hybrid renewable energy systems to a residential urban house located in Tadjourah city (11.7913◦ N, 42.8796◦ E) in the North-Eastern part of Djibouti to reduce the cost of electricity from the grid. To reach this objective, a powerful software tool called HOMER (Hybrid Optimization Model for Electric Renewables) has been used to find the optimum hybrid energy system using real wind and solar irradiation data. The results obtained from this study show that the best economical suited combination of hybrid renewable energy system is a PV-Wind grid connected system. This study shows also that potentially the indigenous renewable energy contribution, in Tadjourah, can be as much as 77 % with 47 % of solar and 30% of Wind energy. The Net Present Cost, the Levelized Cost of Energy, and the operating cost of the optimal HRES are$337, $0.002/kWh and$1,025/year, respectively. When compared with the average cost of grid-only connection of \$0.32/kWh, the optimal hybrid renewable energy system is more economical and will save 51 % of the cost that the customer must pay when using only the electricity from the grid.