Sharifa's House

In Bangladesh, a microbanking system exclusively targeted at female ‘entrepeneurs’ has been combined with a streamlined domestic construction process, transforming the possibilities of affordable housing for the rural poor. Dr Adnan Morshed tells Sharifa's story; how a very modest loan from the Grameen Bank allowed Sharifa and her family to build a house and to realise what amounts to very much more than even a permanent home. Copyright © 2007 John Wiley & Sons, Ltd.     

Interface microstructure of diffusion bonded Ni3Al intermetallic alloy and austenitic stainless steel

The diffusion bonding of a Ni₃Al intermetallic alloy to an austenitic stainless steel has been carried out at temperatures 950, 1000 and 1050 °C. The influence of bonding temperature on the microstructural development and hardness across the joint region has been determined. The microvoids in the interface have been found to decrease with increasing bonding temperature. The intermetallic phase Al₃Ni has been detected at the Ni₃Al side of the diffusion couple. Diffusion of Cr and Fe from the stainless steel to the Ni₃Al alloy has been observed.     

Hybrid Paper–Plastic Microchip for Flexible and High‐Performance Point‐of‐Care Diagnostics

A low‐cost and easy‐to‐fabricate microchip remains a key challenge for the development of true point‐of‐care (POC) diagnostics. Cellulose paper and plastic are thin, light, flexible, and abundant raw materials, which make them excellent substrates for mass production of POC devices. Herein, a hybrid paper–plastic microchip (PPMC) is developed, which can be used for both single and multiplexed detection of different targets, providing flexibility in the design and fabrication of the microchip. The developed PPMC with printed electronics is evaluated for sensitive and reliable detection of a broad range of targets, such as liver and colon cancer protein biomarkers, intact Zika virus, and human papillomavirus nucleic acid amplicons. The presented approach allows a highly specific detection of the tested targets with detection limits as low as 10² ng mL^?1 for protein biomarkers, 10³ particle per milliliter for virus particles, and 10² copies per microliter for a target nucleic acid. This approach can potentially be considered for the development of inexpensive and stable POC microchip diagnostics and is suitable for the detection of a wide range of microbial infections and cancer biomarkers.     

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.     

Effective use of hydrogen sulfide and natural gas resources available in the Black Sea for hydrogen economy

In this article, we propose a novel system to effectively deploy an integrated fuel processing system for hydrogen sulfide and natural gas resources available in the Black Sea to be used for a quick transition to the hydrogen economy. In this regard, the proposed system utilizes offshore wind and offshore photovoltaic power plants to meet the electricity demand of the electrolyzer. A PEM electrolyzer unit generates hydrogen from hydrogen sulfide that is available in the Black Sea deep water. The generated hydrogen and sulfur gas from hydrogen sulfide are stored in high-pressure tanks for later use. Hydrogen is blended with natural gas, and the blend is utilized for industrial and residential applications. The investigated system is modeled with the Aspen Plus software, and hydrogen production, blending, and combustion processes are analyzed accordingly. With the hydrogen addition up to 20% in the blend, the carbon dioxide emissions of combustion decrease from 14.7 kmol/h to 11.7 kmol/h, when the annual cost of natural gas is reduced from 9 billion $ to 8.3 billion $. The energy and exergy efficiencies for the combustion process are increased from 84% to 97% and from 62% to 72%, respectively by a 20% by volume hydrogen addition into natural gas.     

Enhanced therapeutic efficacy of clotrimazole by delivery through poly(ethylene oxide)-block-poly(ε-caprolactone) copolymer-based micelles

Amphiphilic block copolymers have been the subject of great scientific interests due to their applications in various fields including nano drug delivery. Three amphiphilic block copolymers based on poly(ε-caprolactone) as a hydrophobic segment and methoxy poly(ethylene oxide) ( as a hydrophilic part were synthesized by the ring-opening polymerization of ε-caprolactone using MeO-PEO_5K as macroinitiator by varying initial feed ratios. The synthesized polymers were further explored for their drug delivery potential using clotrimazole as model hydrophobic drug. Drug-loaded micelles were characterized for shape, size, drug encapsulation efficiency, in vitro release, and thermal stability using atomic force microscope, zetasizer, UV–visible spectrophotometry, FTIR, differential scanning calorimetry, and thermogravimetric analysis. Clotrimazole loaded in micelles were also investigated for its antifungal activity through an in vitro assay and scanning electron microscopy. The antifungal activity of drug increased significantly by delivering through polymeric micelles. Current study provides insight into different factors that can be maneuvered to achieve a variety of desired properties of micelles for improved therapeutic efficacy of drugs like clotrimazole. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47769.     

Development of optimized triaxially electrospun titania nanofiber-in-nanotube core-shell structure

One dimensional (1D) nanostructures and its derivatives can be manipulated to serve special functions like hollow structure, and higher surface area. 1D TiO₂ nanotube-in-nanofibers (NF@NT) are developed through triaxial electrospinning followed by a calcination process. A blended solution of polyvinyl pyrrolidone and tetra-butyl titanate is used in outer and inner layers of nanofibers, respectively, while paraffin oil is used in the middle layer. The optimized triaxial nanofibers of 669.4 ± 52.43 nm are developed at 7.5 w/w% concentration, 28 kV applied voltage, and 24 cm spinning distance. TiO₂ NF@NT structure is obtained through calcination of optimized triaxial nanofibers at 550°C. Subsequently, the morphology of TiO₂ NF@NT and its uniform diameter distribution is confirmed through scanning electron microscopy. Fourier-transform infrared spectroscopy results indicates the formation of TiO₂ NF@NT. X-Rays diffraction pattern peaks also reveals the presence of both anatase and rutile crystalline phases. The presence of only titanium (Ti) and oxygen (O) elements in the TiO₂ NF@NT is confirmed through energy dispersive X-ray spectroscopy. Brunauer–Emmett–Teller analysis indicates that TiO₂ NF@NT has a higher specific surface area of ~141.68 m²/g compared with the solid TiO₂ nanofiber (~75.31 m²/g). This study can be adopted to develop TiO₂ NF@NT for wide range of application.     

Optimizing Traffic Signals in Smart Cities Based on Genetic Algorithm

Current traffic signals in Jordan suffer from severe congestion due to many factors, such as the considerable increase in the number of vehicles and the use of fixed timers, which still control existing traffic signals. This condition affects travel demand on the streets of Jordan. This study aims to improve an intelligent road traffic management system (IRTMS) derived from the human community-based genetic algorithm (HCBGA) to mitigate traffic signal congestion in Amman, Jordan’s capital city. The parameters considered for IRTMS are total time and waiting time, and fixed timers are still used for control. By contrast, the enhanced system, called enhanced-IRTMS (E-IRTMS), considers additional important parameters, namely, the speed performance index (SPI), speed reduction index (SRI), road congestion index (R_i), and congestion period, to enhance IRTMS decision. A significant reduction in congestion period was measured using E-IRTMS, improving by 13% compared with that measured using IRTMS. Meanwhile, the IRTMS result surpasses that of the current traffic signal system by approximately 83%. This finding demonstrates that the E-IRTMS based on HCBGA and with unfixed timers achieves shorter congestion period in terms of SPI, SRI, and R_i compared with IRTMS.