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

The flexural performance of geopolymer concrete (GPC) T-beams reinforced longitudinally with GFRP bars under a four-point static bending test was investigated. Six full-scale simply supported T-beams were cast and tested; one control specimen was made with ordinary Portland cement concrete (OPCC), while the other five beams were made of geopolymer concrete. The G-GPC2 was designed to attain the same theoretical moment capacity as the G-OPCC6 control beam. The main parameters investigated were the reinforcement ratio of ρ_f/ρ_b= 0.75, 1.05, 1.12, 1.34 and 1.34 for G-GPC1, G-GPC2, G-GPC3, G-GPC4, and G-GPC5, respectively, and compressive strength of geopolymer concrete. Based on the results of the experiments, the ultimate strain of GPC did not show the same behaviour as that of OPCC, which affects the mode of failure. The beam capacity and deflection were, respectively, overestimated and underestimated using the ACI 440 2R-17 predictive equations.

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.

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.

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 %.

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.

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.

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 publication of the Delivery plan for tackling the COVID-10 backlog of elective care (NHSE/I, 2022:5) contained a number of ambitions, including that, by March 2024, 75% of patients who have been urgently referred by their GP for suspected cancer are diagnosed or have had cancer ruled out within 28 days. By March 2025, waits of longer than a year for elective care should be eliminated and 95% of patients needing a diagnostic test should receive it within six weeks. The report acknowledged the need to grow the workforce to achieve these ambitions and ensure a timely cancer diagnosis, while also proposing the use of digital technology and data systems to free up capacity. To assist West Yorkshire National Health Service (NHS) organisations to meet these ambitions, this report presents the findings of a ‘deep dive’ that focuses on the role of radiology in meeting the ambitions of providing timely cancer diagnosis. Aims 1. To understand current and projected demand for radiology expertise in cancer diagnosis in West Yorkshire. 2. To understand the current and projected radiology workforce in West Yorkshire and determine the gap between the projected radiology workforce and the required radiology workforce. 3. To identify possible solutions to assist in providing the radiology workforce required for West Yorkshire and explore their acceptability and potential impact. Methods A range of sources of data and methods were utilised. We examined publicly available quantitative data concerning cancer waiting times and diagnostic waiting times and activity and used this to forecast future cancer waiting times and diagnostic waiting times and activity. We examined data from Health Education England (HEE) regarding radiologists’ and radiographers’ workforce profile data for West Yorkshire, the number of radiologists completing training, and the number of radiographers graduating, and data submitted by West Yorkshire Trusts to HEE regarding their plans for growing their radiology and radiographer workforce. Interviews (N=15) conducted with radiology service managers, university academics and key strategic and operational stakeholders delivering radiology services were used to understand the current and future issues around strategic workforce planning, workforce changes and transformation, workforce roles and skills, training and education and service changes. A rapid review of the literature examining the impacts of artificial intelligence (AI) on the workload of radiology services was also undertaken. To put this work in context, we also reviewed relevant policy documents and reports. Alongside this, we consulted with the Yorkshire Imaging Collaborative (YIC) and the West Yorkshire Cancer Alliance (WYCA) and attended a series of workshops run by the Yorkshire Imaging Collaborative. Results Overall, the findings show that demand for radiology services is increasing and that both cancer waiting times and the waiting times for diagnostic tests increased, with a concurrent downward trend in activity that, if all else stays the same, is forecast to continue up to 2025. The cancer waiting times data indicate that patients were waiting longer and that their needs were not being met. Moreover, 3 the proportion of people treated within accepted cancer waiting times decreased both nationally and within the West Yorkshire region from 2013. This was exacerbated by COVID-19 which caused a further decrease nationally and for the West Yorkshire region. National data for waiting times for all diagnostic tests show a significant decline between 2006 and 2008, with a decrease in median waiting times from just under 6.0 weeks to approximately 2.0 weeks. Overall, waiting times remained stable until late 2020 when they started to rise with the longest median waiting times at just over 8.0 weeks in mid-2020. The total number of people waiting for radiology tests nationally is decreasing and is predicted to continue to do so, while in West Yorkshire the number of people waiting for radiology tests decreased until 2020 but has since been on an upward trend which is predicted to continue. Nationally, the total number of radiology tests is on an upward trend that is predicted to continue, while in West Yorkshire activity has been decreasing since well before COVID-19 and is predicted to continue to do so. Data examining the current and future workforce showed that the national figures for the total radiology and radiography workforce are small relative to other health professional groups. In West Yorkshire, 265 radiologists and 926 radiographers were employed, and staff turnover was generally low. Trusts’ forecasts for the number of radiologists and radiographers they believe they need suggest a 16% increase in the number of radiologists in post between March 2022 and March 2027 and a 25% increase in the number of radiographers in post. The numbers of radiographers and radiologists being trained in West Yorkshire suggest that this is feasible. Interview data identified a number of main themes and associated issues: delivering diagnostic cancer targets, strategic workforce planning, workforce roles and skills, service transformation, recruitment and retention, universities, artificial intelligence, collaboration, and international recruitment. Across all themes, some reoccurring issues were identified: a lack of staff, increased demands, a lack of capacity in terms of space and staff, a lack of strategic workforce planning with a focus on operational or financial plans. Respondents proposed potential solutions to some of the issues raised that included: new ways of working, upskilling, developing current and emerging roles, Community Diagnostic Centres (CDCs), greater collaboration between NHS Trusts, universities, CDCs, imaging academies and networks and the private sector, and the international recruitment of radiologists and radiographers to address workforce gaps. The rapid review findings helped to identify a number of potential benefits of use of AI in radiology, including contributing to improved workflow efficacy and efficiency of radiology services. However, this is dependent on the nature of the work and the AI function. As a result of faster AI reading, radiologists may be able to focus more on high-risk, complex reading tasks. AI can support automation of image segmentation and classification and aid the diagnostic confidence of less experienced radiologists. Respondents’ views on AI were mixed. There was acknowledgement that AI was already used to support radiology service delivery and both the benefits and problems associated were identified. The implications of AI for radiologists’ and radiographers’ roles were discussed in terms of changing work, AI being used to support or in some cases substitute radiologists and radiographers, and the need for the radiology workforce to adapt to the technological change whilst maintaining a caring service

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.