Highly Stretchable Polyurethane Porous Membranes with Adjustable Morphology for Advanced Lithium Metal Batteries.

A highly flexible, tunable morphology membrane with excellent thermal stability and ionic conductivity can endow lithium metal batteries with high power density and reduced dendrite growth. Herein, a porous Polyurethane (PU) membrane with an adjustable morphology was prepared by a simple nonsolvent-induced phase separation technique. The precise control of the final morphology of PU membranes can be achieved through appropriate selection of a nonsolvent, resulting a range of pore structures that vary from finger-like voids to sponge-like pores. The implementation of combinatorial DFT and experimental analysis has revealed that spongy PU porous membranes, especially PU-EtOH, show superior electrolyte wettability (472%), high porosity (75%), good mechanical flexibility, robust thermal dimensional stability (above 170 °C), and elevated ionic conductivity (1.38 mS cm-1) in comparison to the polypropylene (PP) separator. The use of PU-EtOH in Li//Li symmetric cell results in a prolonged lifespan of 800 h, surpasing the longevity of PU or PP cells. Moreover, when subjected to a high rate of 5 C, the LiFePO4/Li half-cell with a PU-EtOH porous membrane displayed better cycling performance (115.4 mAh g-1) compared to the PP separator (104.4 mAh g-1). Finally, the prepared PU porous membrane exhibits significant potential for improving the efficiency and safety of LMBs.

A systematic review of physiological signals based driver drowsiness detection systems.

Driving a vehicle is a complex, multidimensional, and potentially risky activity demanding full mobilization and utilization of physiological and cognitive abilities. Drowsiness, often caused by stress, fatigue, and illness declines cognitive capabilities that affect drivers' capability and cause many accidents. Drowsiness-related road accidents are associated with trauma, physical injuries, and fatalities, and often accompany economic loss. Drowsy-related crashes are most common in young people and night shift workers. Real-time and accurate driver drowsiness detection is necessary to bring down the drowsy driving accident rate. Many researchers endeavored for systems to detect drowsiness using different features related to vehicles, and drivers' behavior, as well as, physiological measures. Keeping in view the rising trend in the use of physiological measures, this study presents a comprehensive and systematic review of the recent techniques to detect driver drowsiness using physiological signals. Different sensors augmented with machine learning are utilized which subsequently yield better results. These techniques are analyzed with respect to several aspects such as data collection sensor, environment consideration like controlled or dynamic, experimental set up like real traffic or driving simulators, etc. Similarly, by investigating the type of sensors involved in experiments, this study discusses the advantages and disadvantages of existing studies and points out the research gaps. Perceptions and conceptions are made to provide future research directions for drowsiness detection techniques based on physiological signals.

Impact of geographical, meteorological, demographic, and economic indicators on the trend of COVID-19: A global evidence from 202 affected countries.

Research problem: Public health and the economy face immense problems because of pathogens in history globally. The outbreak of novel SARS-CoV-2 emerged in the form of coronavirus (COVID-19), which affected global health and the economy in almost all countries of the world. Study design: The objective of this research is to examine the trend of COVID-19, deaths, and transmission rates in 202 affected countries. The virus-affected countries were grouped according to their continent, meteorological indicators, demography, and income. This is quantitative research in which we have applied the Poisson regression method to assess how temperature, precipitation, population density, and income level impact COVID-19 cases and fatalities. This has been done by using a semi-parametric and additive polynomial model. Findings: The trend analysis depicts that COVID-19 cases per million were comparatively higher for two groups of countries i.e., (a) average temperature below 7.5 °C and (b) average temperature between 7.5 °C and 15 °C, up to the 729th day of the outbreak. However, COVID-19 cases per million were comparatively low in the countries having an average temperature between 22.5 °C and 30 °C. The day-wise trend was comparatively higher for the countries having average precipitation between (a) 1 mm and 750 mm and (b) 750 mm and 1500 mm up to the 729th day of the outbreak. The day-wise trend was comparatively higher for the countries having more than 1000 people per sq. km. Discussing the COVID-19 cases per million, the day-wise trend was higher for the HICs, followed by UMICs, LMICs, and LIC. Conclusion: The study highlights the need for targeted interventions and responses based on the specific circumstances and factors affecting each country, including their geographical location, temperature, precipitation levels, population density, and per capita income.

Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence.

A novel approach is presented in this study for the classification of lower limb disorders, with a specific emphasis on the knee, hip, and ankle. The research employs gait analysis and the extraction of PoseNet features from video data in order to effectively identify and categorize these disorders. The PoseNet algorithm facilitates the extraction of key body joint movements and positions from videos in a non-invasive and user-friendly manner, thereby offering a comprehensive representation of lower limb movements. The features that are extracted are subsequently standardized and employed as inputs for a range of machine learning algorithms, such as Random Forest, Extra Tree Classifier, Multilayer Perceptron, Artificial Neural Networks, and Convolutional Neural Networks. The models undergo training and testing processes using a dataset consisting of 174 real patients and normal individuals collected at the Tehsil Headquarter Hospital Sadiq Abad. The evaluation of their performance is conducted through the utilization of K-fold cross-validation. The findings exhibit a notable level of accuracy and precision in the classification of various lower limb disorders. Notably, the Artificial Neural Networks model achieves the highest accuracy rate of 98.84%. The proposed methodology exhibits potential in enhancing the diagnosis and treatment planning of lower limb disorders. It presents a non-invasive and efficient method of analyzing gait patterns and identifying particular conditions.

Hierarchical NiCo@NiOOH@CoMoO4 Core-Shell Heterostructure on Carbon Cloth for High-Performance Asymmetric Supercapacitors.

The fabrication of low-cost, effective, and highly integrated nanostructured materials through simple and reproducible methods for high-energy-density supercapacitors is highly desirable. Herein, an activated carbon cloth (ACC) is designed as the functional scaffold for supercapacitors and treated hydrothermally to deposit NiCo nanoneedles working as internal core, followed by a dip-dry coating of NiOOH nanoflakes core-shell and uniform hydrothermal deposition of CoMoO4 nanosheets serving as an external shell. The structured core-shell heterostructure ACC@NiCo@NiOOH@CoMoO4 electrode resulted in exceptional specific areal capacitance of 2920 mF cm-2 and exceptional cycling stability for 10 000 cycles. Moreover, the fabricated electrode is developed into an asymmetric supercapacitor which demonstrates excellent areal capacitance, energy density, and power density within the broad potential window of 1.7 V with a cycling life of 92.4% after 10 000 charge-discharge cycles, which reflects excellent cycle life. The distinctive core-shell structure, highly conductive substrate, and synergetic effect of coated material results in more electrochemical active sites and flanges for effective electrons and ion transportation. This unique technique provides a new perspective for cost-efficient supercapacitor applications.

Financial intermediation through risk sharing vs non-risk sharing contracts, role of credit risk, and sustainable production: evidence from leading countries in Islamic finance.

The asset side of Islamic banks has two different portfolios running side by side, namely risk-sharing (PLS) and non-risk sharing (non-PLS) financing. The segregation of PLS and non-PLS financing has gathered some attention recently owning to its relative importance for sustainable economic output. This study attempts to analyze the impact of decomposed Islamic financing modes (PLS and non-PLS) with a particular focus on their impact on real economic activity. In addition, we moderated the relationship with asset quality of aggregate Islamic banking sector. Quarterly data from 2014 to 2021 have been sourced from datasets of the Islamic financial service board (IFSB), the International Monetary Fund (IMF), World Bank, and Central banks' data streams. Eleven countries have been selected based on the highest local and global share in global Islamic financial assets. Panel data regression model has been used in this study. The findings indicate that PLS financing is a weaker driver to channelize funds. However, industrial production output is significantly affected by non-PLS financing. Further the results suggest, Islamic finance-output nexus found to have a stronger relationship in the presence of higher asset quality of Islamic banks. The results show that firms mostly rely on non-PLS financing, due to reduced asymmetry and higher transparency in non-PLS contracts compared to PLS modes. The results have implications for governing bodies of Islamic financial system in boosting risk-sharing contracts and firms to limit agency conflicts arising from fluctuating cost of financing.

Manganese and Cobalt-Free Ultrahigh-Ni-Rich Single-Crystal Cathode for High-Performance Lithium Batteries.

Current commercial nickel (Ni)-rich Mn, Co, and Al-containing cathodes are employed in high-energy-density lithium (Li) batteries all around the globe. The presence of Mn/Co in them brings out several problems, such as high toxicity, high cost, severe transition-metal dissolution, and quick surface degradation. Herein, a Mn/Co-free ultrahigh-Ni-rich single-crystal LiNi0.94Fe0.05Cu0.01O2 (SCNFCu) cathode with acceptable electrochemical performance is benchmarked against a Mn/Co-containing cathode. Despite having a slightly lower discharge capacity, the SCNFCu cathode retaining 77% of its capacity across 600 deep cycles in full-cell outperforms comparable to a high-Ni single-crystal LiNi0.9Mn0.05Co0.05O2 (SCNMC; 66%) cathode. It is shown that the stabilizing ions Fe/Cu in the SCNFCu cathode reduce structural disintegration, undesirable side reactions with the electrolyte, transition-metal dissolution, and active Li loss. This discovery provides a new extent for cathode material development for next-generation high-energy, Mn/Co-free Li batteries due to the compositional tuning flexibility and quick scalability of SCNFCu, which is comparable to the SCNMC cathode.

An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features.

Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient's respiration rate. However, it is crucial to consider a patient's medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage.

ESG performance, capital financing decisions, and audit quality: empirical evidence from Chinese state-owned enterprises.

We study the nexus between environmental, social, and governance (ESG) performance and corporate capital financing decisions. Further, we also analyze the effect of audit quality and type of ownership (state-owned enterprises (SOEs) vs non-state-owned enterprises (non-SOEs), local vs central SOEs in this relationship. By applying panel regression (fixed effects) on 6295 firm-year observations of Chinese A-listed enterprises data for 2010-2019, we conclude that firms' ESG information is crucial to their financing decisions. In particular, firms with superior ESG performance have lower debt financing. The findings suggest that enterprises with strong ESG performance have easy access to equity funding via stock markets. Further, this relationship is more pronounced in SOE compared to non-SOEs and in central SOEs compared to local SOEs. These results demonstrate that the market may promote desired social outcomes by rewarding ESG performance; however, we find no significant effect of audit quality in this relationship. Findings are robust to different sensitivity tests, including an alternative estimation, sysGMM regression to address endogeneity issues, and lagged regressions to address reverse causality.

Drivers of post-adoption of e-wallet among academics in Palestine: An extension of the expectation confirmation model.

E-wallet is one of the latest innovations in the field of payments. However, despite numerous studies on the adoption of e-finance systems, the post-adoption phase is largely neglected. In this paper, we use the extended Expectation Confirmation Model (ECM) to address this gap by focusing on the study of consumers' continuous intentions regarding the use of an e-wallet service. We conducted an electronic questionnaire-based survey among 503 e-wallet users in Palestine. Using structural equation modeling to analyze the conceptual model of the study, our results confirm that satisfaction, trust, and perceived usefulness have a significant impact on consumers' continuous intention regarding e-wallet. In addition, the study found that perceived security has an insignificant impact on consumer satisfaction. The study has several implications: E-wallet providers should improve their services in terms of performance, privacy, and security to ensure customer loyalty in this competitive industry.

Automatic User Preferences Selection of Smart Hearing Aid Using BioAid.

Noisy environments, changes and variations in the volume of speech, and non-face-to-face conversations impair the user experience with hearing aids. Generally, a hearing aid amplifies sounds so that a hearing-impaired person can listen, converse, and actively engage in daily activities. Presently, there are some sophisticated hearing aid algorithms available that operate on numerous frequency bands to not only amplify but also provide tuning and noise filtering to minimize background distractions. One of those is the BioAid assistive hearing system, which is an open-source, freely available downloadable app with twenty-four tuning settings. Critically, with this device, a person suffering with hearing loss must manually alter the settings/tuning of their hearing device when their surroundings and scene changes in order to attain a comfortable level of hearing. However, this manual switching among multiple tuning settings is inconvenient and cumbersome since the user is forced to switch to the state that best matches the scene every time the auditory environment changes. The goal of this study is to eliminate this manual switching and automate the BioAid with a scene classification algorithm so that the system automatically identifies the user-selected preferences based on adequate training. The aim of acoustic scene classification is to recognize the audio signature of one of the predefined scene classes that best represent the environment in which it was recorded. BioAid, an open-source biological inspired hearing aid algorithm, is used after conversion to Python. The proposed method consists of two main parts: classification of auditory scenes and selection of hearing aid tuning settings based on user experiences. The DCASE2017 dataset is utilized for scene classification. Among the many classifiers that were trained and tested, random forests have the highest accuracy of 99.7%. In the second part, clean speech audios from the LJ speech dataset are combined with scenes, and the user is asked to listen to the resulting audios and adjust the presets and subsets. A CSV file stores the selection of presets and subsets at which the user can hear clearly against the scenes. Various classifiers are trained on the dataset of user preferences. After training, clean speech audio was convolved with the scene and fed as input to the scene classifier that predicts the scene. The predicted scene was then fed as input to the preset classifier that predicts the user's choice for preset and subset. The BioAid is automatically tuned to the predicted selection. The accuracy of random forest in the prediction of presets and subsets was 100%. This proposed approach has great potential to eliminate the tedious manual switching of hearing assistive device parameters by allowing hearing-impaired individuals to actively participate in daily life by automatically adjusting hearing aid settings based on the acoustic scene.

Modulating the Energy Band Structure of the Mg-Doped Sr0.5Pr0.5Fe0.2Mg0.2Ti0.6O3-δ Electrolyte with Boosted Ionic Conductivity and Electrochemical Performance for Solid Oxide Fuel Cells.

Achieving fast ionic conductivity in the electrolyte at low operating temperatures while maintaining the stable and high electrochemical performance of solid oxide fuel cells (SOFCs) is challenging. Herein, we propose a new type of electrolyte based on perovskite Sr0.5Pr0.5Fe0.4Ti0.6O3-δ for low-temperature SOFCs. The ionic conducting behavior of the electrolyte is modulated using Mg doping, and three different Sr0.5Pr0.5Fe0.4-xMgxTi0.6O3-δ (x = 0, 0.1, and 0.2) samples are prepared. The synthesized Sr0.5Pr0.5Fe0.2Mg0.2Ti0.6O3-δ (SPFMg0.2T) proved to be an optimal electrolyte material, exhibiting a high ionic conductivity of 0.133 S cm-1 along with an attractive fuel cell performance of 0.83 W cm-2 at 520 °C. We proved that a proper amount of Mg doping (20%) contributes to the creation of an adequate number of oxygen vacancies, which facilitates the fast transport of the oxide ions. Considering its rapid oxide ion transport, the prepared SPFMg0.2T presented heterostructure characteristics in the form of an insulating core and superionic conduction via surface layers. In addition, the effect of Mg doping is intensively investigated to tune the band structure for the transport of charged species. Meanwhile, the concept of energy band alignment is employed to interpret the working principle of the proposed electrolyte. Moreover, the density functional theory is utilized to determine the perovskite structures of SrTiO3-δ and Sr0.5Pr0.5Fe0.4-xMgxTi0.6O3-δ (x = 0, 0.1, and 0.2) and their electronic states. Further, the SPFMg0.2T with 20% Mg doping exhibited low dissociation energy, which ensures the fast and high ionic conduction in the electrolyte. Inclusively, Sr0.5Pr0.5Fe0.4Ti0.6O3-δ is a promising electrolyte for SOFCs, and its performance can be efficiently boosted via Mg doping to modulate the energy band structure.

PTFE/EP Reinforced MOF/SiO2 Composite as a Superior Mechanically Robust Superhydrophobic Agent towards Corrosion Protection, Self-Cleaning and Anti-Icing.

Organic resin cross-linking ZIF-67/SiO2 superhydrophobic (SHPB) multilayer coating was successfully fabricated on metal substrate. The perfluoro-octyl-triethoxy silane (POTS) modified ZIF-67 and SiO2 coating was applied on primary coated polytetrafluoroethylene (PTFE) and epoxy resin (EP) via spray coating method. Here, we present that the robust superhydrophobicity can be realized by structuring surfaces at two different length scales, with a nanostructure design to provide water repellence and a microstructure design to provide durability. The as-fabricated multilayer coating displayed superior water-repellence (CA=167.4°), chemical robustness (pH=1-14) and mechanical durability undergoing 120th linear abrasion or 35th rotatory abrasion cycle. By applying different acidic and basic corrosive media and various weathering conditions, it can still maintain superior-hydrophobicity. To get a better insight of interaction between inhibitor molecules and metal surface, density functional theory (DFT) calculations were performed, showing lower energy gap and increased binding energy of ZPS/SiO2 /PTFE/EP (ZPS=ZIF-67+POTS) multilayer coating compared to the ZIF-67/SiO2 /PTFE/EP, thereby supporting the experimental findings. Additionally, such coatings may be useful for applications such as anti-corrosion, self-cleaning, and anti-icing multi-functionalities.

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning.

Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human's voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors' knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.

Non-Invasive Driver Drowsiness Detection System.

Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death. Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar. Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy). Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%. This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration.

Simplified Synthesis of Biomass-Derived Si/C Composites as Stable Anode Materials for Lithium-Ion Batteries.

Synthesis of silicon/carbon (Si/C) composites from biomass resources could enable the effective utilization of agricultural products in the battery industry with economical as well as environmental benefits. Herein, a simplified process was developed to synthesize Si/C from biomass, by using a low-cost agricultural byproduct "rice husk (RH)" as a model. This process includes the calcination of RH for SiO2 /C and the reduction of SiO2 /C by Al in molten salts at a moderate temperature. This process does not need the removal of carbon before thermal reduction of SiO2 , which is thought to be necessary to avoid the formation of SiC at elevated temperatures. Thus, carbon derived from biomass can be directly used for Si/C composites for anode materials. The resultant Si/C shows a high reversible capacity of 1309 mAh g-1 and long cycle life (300 cycles). This research advocates a new and simplified strategy for the synthesis of RH-based biomass-derived Si/C, which is beneficial for low-cost, environmentally friendly, and green energy storage applications.

Carbon nanostructure-reinforced SiCw/Si3N4 composite with enhanced thermal conductivity and mechanical properties.

Carbon nanostructures (CNS) as a kind of reinforcement material can remarkably enhance the mechanical and thermal properties of ceramics. This research presents an analysis of the influence of CNS on the thermal conductivity and mechanical properties of SiCw/Si3N4 composites. The SiCw/Si3N4 composites containing various types of CNS e.g. carbon nanofibers (CNF), multi-walled carbon nanotubes (MWCNT) and graphene nano-platelets (GNP) were fabricated by hot-press sintering. XRD analysis confirmed a complete transformation of α-Si3N4 to ß-Si3N4 and microstructural analysis shows a uniform distribution, as well as a pullout and bridging mechanism of CNS. The results revealed that the thermal conductivity and mechanical properties of SiCw/Si3N4 composites increased with the addition of CNS. Maximum values of fracture toughness (9.70 ± 0.8 MPa m1/2) and flexural strength (765 ± 58 MPa) have been achieved for the MWCNT-containing SiCw/Si3N4 composite, whereas the maximum values of Young's modulus (250 ± 3.8 GPa) and hardness (27.2 ± 0.9 GPa) have been achieved for the CNF-containing SiCw/Si3N4 composite. Moreover, thermal conductivity also improved with the addition of CNS and reached a maximum value of 110.6 W m-1 K-1 for the CNF-containing SiCw/Si3N4 composite. This work provides a useful approach for the fabrication of high-performance multifunctional composites for emerging engineering applications.

Enhanced thermal conductivity and mechanical properties of a GNP reinforced Si3N4 composite.

Graphene nanocomposites can significantly enhance the thermal conductivity and mechanical properties of ceramics at relatively low nano-filler addition. Herein, graphene nano-platelet reinforced Si3N4 (GNP/Si3N4) composites were prepared by hot press (HP) sintering using fluoride (AlF3, MgF2) sintering-additives. The microstructural properties revealed the enhanced crystallization degree and density of the GNP/Si3N4 composites with different concentrations of graphene nano-platelets (GNPs). These properties help to achieve a significantly improved thermal conductivity (from 82.42 to 137.47 W m-1 K-1) of the GNP/Si3N4 composites. The morphology of the composites shows a uniform distribution of GNP, whereas overlapping of GNPs (2 to 4 platelets) at the grain boundaries of Si3N4 was observed. The fracture toughness and Vickers hardness of the composites also increased with the increasing content of GNP. The toughening mechanism was similar in all composites with GNP addition in respect of pull out, crack deflection, crack branching and crack bridging.

Cystoid macular oedema after phacoemulsification with and without type 2 diabetes mellitus; a hospital-based clinical prospective trial in Karachi.

OBJECTIVE: To know the outcomes of cystoid macular oedema after phacoemulsification in patients with and without type 2 diabetes mellitus using optical coherence tomography. METHODS: This non-concurrent, clinical, prospective study was carried out at Al-Ibrahim Eye Hospital, Karachi, from January to August 2015. After phacoemulsification with injectable posterior chamber intraocular lens implantation, eyes of patients were analysed. The patients were divided into diabetic and non-diabetic groups visual acuity, optical coherence tomography and dilated fundus examination were performed preoperatively (baseline) and post-operative 1st week and 6th week. SPSS 20 was used for data analysis. RESULTS: Of the 100 subjects, there were 50(50%) each in diabetic and non-diabetic group. Subsequently, 14(14%) patients were lost to follow-up, and 86 eyes of 86(86%) patients were analysed. Of them, 37(43%) were male and 49(57%) were female. The mean age of participants was 52.21±7.43 years (range: 38-62years). The non-diabetic group had 41(47.7%) patients and the diabetic group had 45(52.3%). There was no clinically significant cystoid macular oedema in either group. Central foveal thickness > 43.94 µm was observed in 1(2.5%) eye in the non-diabetic group and in none in the diabetic group at 1st post-operative week. At the 6th post-operative week, none of eyes in the non-diabetic group and 2(4.44%) eyes of the diabetic group showed macular oedema. There was no statistically significant difference in mean foveal volume between both groups at 1st week (p=0.896) and 6th week (p=0.230). CONCLUSIONS: Cystoid macular oedema after phacoemulsification was equally present in both diabetics and non-diabetics without any retinopathy.