Solution-processed,flexible and broadband photodetector based on CsPbBr_3/PbSe quantum dot heterostructures

Due to their promising applications in foldable displays,optical communication equipment and environmental monitoring systems,flexible and broadband optoelectronic devices have gained extensive attention in recent years.Here,a flexible and broadband photodetector based on CsPbBr₃/PbSe quantum dot(QD) heterostructures is firstly presented.The integrated QD heterostructures possess consecutive detection range from ultraviolet(UV) to long-wave length infrared(LW-IR) regions with efficient light absorption and chemical stability,in comparison with the pristine PbSe QDs.Systematic material characterizations reveal the improved exciton dissociation,carrier transport and carrier lifetime of the QD heterostructures.Flexible photodetector Ag/CsPbBr₃/PbSe/Ag demonstrate a high responsivity of 7.17 A/W with a specific detectivity of 8.97 × 10¹² Jones under 25 μW/cm² 365 nm illumination at 5 V.Furthermore,it could maintain 91.2 %(or 94.9 %) of its initial performance even after bending for thousands of times(or exposing in ambient air for 4 weeks).More importantly,its re s ponse time is shortened more than three orders of magnitude as that of pristine PbSe QDs-based photodetectors.Therefore,it provides a feasible and promising method for the next-generation high-performance broadband photodetectors via constructing heterostructures of various QDs.     

M2 macrophage-targeted iron oxide nanoparticles for magnetic resonance image-guided magnetic hyperthermia therapy

Tumor-associated macrophages (TAMs) play an important role in tumor development and progression.In particular,M2 TAMs can promote tumor growth by facilitating tumor progression and malignant behav-iors.Selectively targeted elimination of M2 TAMs to inhibit tumor progression is of great significance for cancer treatment.Iron oxide nanoparticles based magnetic hyperthermia therapy (MHT) is a classical approach to destroy tumor tissue with deep penetration depth.In this study,we developed a typical M2 macrophage-targeted peptide (M2pep) functionalized superparamagnetic iron oxide nanoparticle(SPIO) for magnetic resonance imaging (MRI)-guided MHT in an orthotopic breast cancer mouse model,The obtained multifunctional SPIO-M2pep with a hydrodynamic diameter of 20 nm showed efficient targeting capability,high transverse relaxivity (149 mM-1 s-1) and satisfactory magnetic hyperthermia performance in vitro.In vivo studies demonstrated that the SPIO-M2pep based MRI can monitor the distri-bution of nanoparticles in tumor and indicate the suitable timing for MHT.The M2 macrophage-targeted MHT significantly reduced the tumor volume and the population of pro-tumoral M2 TAMs in tumor.In addition,the SPIO-M2pep based MHT can remodel the tumor immune microenvironment (TIME).The multifunctional SPIO-M2pep with M2 macrophage-targeting ability,high magnetic hyperthermia effi-ciency,MR imaging capability and effective role in remodeling the TIME hold great potential to improve clinical cancer therapy outcomes.     

Mapping past,current and future energy research trend in Pakistan: a scientometric assessment

This work describes the contribution of researchers in the field of the energy from Pakistan in the period 1990–2016. A scientometric approach was applied to analyze the scientific publications in the field using the Scopus Elsevier database. Different aspects of the publications were analyzed, such as publication type, major research areas, journals, citations, authorship pattern, affiliations as well as the keyword occurrence frequency. The present research trends are analyzed and future research directions are outlined. The impact factor, h-index and number of citations were used to investigate the strength of active institutes, authors, and journals in the field of the energy in Pakistan. From 1990 to 2016, 991 articles have been published by 2139 authors from 213 research institutes. The total number of citations and impact factor are 10,287 and 2301 respectively, corresponding to 10 citations per paper and an impact factor of 2.32 per publication. The research articles originate primarily from COMSATS, NUST, PIEAS, and PINSTECH. Pakistan has published 60% of publication with the collaboration of the foreign institutes, mainly from the United States, the United Kingdom, China and Malaysia. The core research activities in the field are mainly focused on resource assessment, energy policy, energy efficiency, feasibility study, energy economics, and performance assessment. The most productive journal, author, institution, are renewable & sustainable energy review, Shahbaz M., and COMSATS, respectively.     

Creatinine-based non-phospholipid vesicular carrier for improved oral bioavailability of Azithromycin

Context: Novel, safe, efficient and cost effective nano-carriers from renewable resources have got greater interest for enhancing solubility and bioavailability of hydrophobic dugs.

Objectives: This study reports the synthesis of a novel biocompatible non-phospholipid human metabolite "Creatinine" based niosomal delivery system for Azithromycin improved oral bioavailability.

Methods: Synthesized surfactant was characterized through spectroscopic and spectrometric techniques and then the potential for niosomal vesicle formation was evaluated using Azithromycin as model drug. Drug loaded vesicles were characterized for size, polydispersity index (PDI), shape, drug encapsulation efficiency (EE), in vitro release and drug–excipient interaction using zetasizer, atomic force microscope (AFM), LC–MS/MS and FTIR. The biocompatibility of surfactant was investigated through cells cytotoxicity, blood hemolysis and acute toxicity. Azithromycin encapsulated in niosomes was investigated for in vivo bioavailability in rabbits.

Results: The vesicles were spherical with 247?±?4.67?nm diameter hosting 73.29?±?3.51% of the drug. Surfactant was nontoxic against cell cultures and caused 5.80?±?0.51% hemolysis at 1000?µg/mL. It was also found safe in mice up to 2.5?g/kg body weight. Synthesized surfactant based niosomal vesicles revealed enhanced oral bioavailability of Azithromycin in rabbits.

Conclusions: The results of the present study confirm that the novel surfactant is highly biocompatible and the niosomal vesicles can be efficiently used for improving the oral bioavailability of poor water soluble drugs.     

Dry powders for the inhalation of ciprofloxacin or levofloxacin combined with a mucolytic agent for cystic fibrosis patients

Objective: This study aimed to design and characterize an inhalable dry powder of ciprofloxacin or levofloxacin combined with the mucolytics acetylcysteine and dornase alfa for the management of pulmonary infections in patients with cystic fibrosis.

Methods: Ball milling, homogenization in isopropyl alcohol and spray drying processes were used to prepare dry powders for inhalation. Physico-chemical characteristics of the dry powders were assessed via thermogravimetric analysis, differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FT-IR), X-ray diffractometry and scanning electron microscopy. The particle size distribution, dissolution rate and permeability across Calu-3 cell monolayers were analyzed. The aerodynamic parameters of dry powders were determined using the Andersen cascade impactor (ACI).

Results: After the micronization process, the particle sizes of the raw materials significantly decreased. X-ray and DSC results indicated that although ciprofloxacin showed no changes in its crystal structure, the structure of levofloxacin became amorphous after the micronization process. FT-IR spectra exhibited the characteristic peaks for ciprofloxacin and levofloxacin in all formulations. The dissolution rates of micro-homogenized and spray-dried ciprofloxacin were higher than that of untreated ciprofloxacin. ACI results showed that all formulations had a mass median aerodynamic diameter less than 5?μm; however, levofloxacin microparticles showed higher respirability than ciprofloxacin powders did. The permeability of levofloxacin was higher than those of the ciprofloxacin formulations.

Conclusion: Together, our study showed that these methods could suitably characterize antibiotic and mucolytic-containing dry powder inhalers.     

A Highly Efficient Algorithm for Phased-Array mmWave Massive MIMO Beamforming

With the rapid development of the mobile internet and the internet of things (IoT), the fifth generation (5G) mobile communication system is seeing explosive growth in data traffic. In addition, low-frequency spectrum resources are becoming increasingly scarce and there is now an urgent need to switch to higher frequency bands. Millimeter wave (mmWave) technology has several outstanding features—it is one of the most well-known 5G technologies and has the capacity to fulfil many of the requirements of future wireless networks. Importantly, it has an abundant resource spectrum, which can significantly increase the communication rate of a mobile communication system. As such, it is now considered a key technology for future mobile communications. MmWave communication technology also has a more open network architecture; it can deliver varied services and be applied in many scenarios. By contrast, traditional, all-digital precoding systems have the drawbacks of high computational complexity and higher power consumption. This paper examines the implementation of a new hybrid precoding system that significantly reduces both calculational complexity and energy consumption. The primary idea is to generate several sub-channels with equal gain by dividing the channel by the geometric mean decomposition (GMD). In this process, the objective function of the spectral efficiency is derived, then the basic tracking principle and least square (LS) techniques are deployed to design the proposed hybrid precoding. Simulation results show that the proposed algorithm significantly improves system performance and reduces computational complexity by more than 45% compared to traditional algorithms.     

A Framework for Systematic Classification of Assets for Security Testing

Over the last decade, a significant increase has been observed in the use of web-based Information systems that process sensitive information, e.g., personal, financial, medical. With this increased use, the security of such systems became a crucial aspect to ensure safety, integrity and authenticity of the data. To achieve the objectives of data safety, security testing is performed. However, with growth and diversity of information systems, it is challenging to apply security testing for each and every system. Therefore, it is important to classify the assets based on their required level of security using an appropriate technique. In this paper, we propose an asset security classification technique to classify the System Under Test (SUT) based on various factors such as system exposure, data criticality and security requirements. We perform an extensive evaluation of our technique on a sample of 451 information systems. Further, we use security testing on a sample extracted from the resulting prioritized systems to investigate the presence of vulnerabilities. Our technique achieved promising results of successfully assigning security levels to various assets in the tested environments and also found several vulnerabilities in them.     

Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists’ experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can negatively affect the performance of CNN. Therefore, an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance. In this process, a hierarchical CNN model is designed, in which the former model is non-regularized (due to dense architecture) and the later one is regularized, specifically designed for small histopathology images. Moreover, the regularized model is integrated with CNN’s basic architecture to reduce overfitting. Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training. The training and testing accuracies of the non-regularized CNN model are 98% & 78% with an imbalanced dataset and 96% & 81% with a balanced dataset, respectively. The regularized CNN model training and testing accuracies are 84% & 75% for an imbalanced dataset and 87% & 86% for a balanced dataset.     

Automated Patient Discomfort Detection Using Deep Learning

The Internet of Things (IoT) has been transformed almost all fields of life, but its impact on the healthcare sector has been notable. Various IoT-based sensors are used in the healthcare sector and offer quality and safe care to patients. This work presents a deep learning-based automated patient discomfort detection system in which patients’ discomfort is non-invasively detected. To do this, the overhead view patients’ data set has been recorded. For testing and evaluation purposes, we investigate the power of deep learning by choosing a Convolution Neural Network (CNN) based model. The model uses confidence maps and detects 18 different key points at various locations of the body of the patient. Applying association rules and part affinity fields, the detected key points are later converted into six main body organs. Furthermore, the distance of subsequent key points is measured using coordinates information. Finally, distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions. The accuracy of the proposed system is assessed on various test sequences. The experimental outcomes reveal the worth of the proposed system’ by obtaining a True Positive Rate of 98% with a 2% False Positive Rate.     

Super Compact UWB Monopole Antenna for Small IoT Devices

This article introduces a novel, ultrawideband (UWB) planar monopole antenna printed on Roger RT/5880 substrate in a compact size for small Internet of Things (IoT) applications. The total electrical dimensions of the proposed compact UWB antenna are 0.19 λ_o × 0.215 λ_o × 0.0196 λ_o with the overall physical sizes of 15 mm × 17 mm × 1.548 mm at the lower resonance frequency of 3.8 GHz. The planar monopole antenna is fed through the linearly tapered microstrip line on a partially structured ground plane to achieve optimum impedance matching for UWB operation. The proposed compact UWB antenna has an operation bandwidth of 9.53 GHz from 3.026 GHz up to 12.556 GHz at −10 dB return loss with a fractional bandwidth (FBW) of about 122%. The numerically computed and experimentally measured results agree well in between. A detailed time-domain analysis is additionally accomplished to verify the radiation efficiency of the proposed antenna design for the ultra-wideband signal propagation. The fabricated prototype of a compact UWB antenna exhibits an omnidirectional radiation pattern with the low peak measured gain required of 2.55 dBi at 10 GHz and promising radiation efficiency of 90%. The proposed compact planar antenna has technical potential to be utilized in UWB and IoT applications.