Functional nanoporous polyamide aerogels

Aerogels are low-density materials consisting of 3D assemblies of nanoparticles with high open porosities and surface areas. Inspired by the extraordinary mechanical strength of polymer crosslinked aerogels, our recent attention is focused on inexpensive multifunctional isocyanates reacting with a variety of aromatic, organometallic and inorganic monomers. Three such systems discussed here are: (A) Polymeric aerogels synthesized via a room temperature reaction of an aromatic triisocyanate with pyromellitic acid. Using solid-state CPMAS 13C and 15N NMR, it was found that the skeletal framework was a statistical co-polymer of polyamide, polyurea and polyimide. Stepwise pyrolytic decomposition followed by reactive etching of those components yielded microporous carbon aerogels with good gas sorption selectivities that may find application in CO2 capture and sequestration. (B) Ferrocene-polyamide aerogels prepared in one pot via reaction of an aromatic triisocyanate and ferrocene dicarboxylic acid. Upon pyrolysis (≥800 ⁰C / H2), monolithic Fe(0)-doped C-aerogels were obtained followed by quantitative transmetalation with noble metals (M: Au, Pt, Pd). The latter were demonstrated as heterogeneous catalysts in high yield reduction, oxidation and Heck coupling reactions. The monolithic catalysts were reused several times without loss of activity. (C) Polyureas formed via reaction of an aromatic isocyanate with several mineral acids, (H3BO3, H3PO4, H3PO3, H2SeO3, H6TeO6, H5IO6 and H3AuO3). The residual boron in the H3BO3 model system was very low (≤0.05 % w/w), leaving pure polyurea as product and ruling out any process, in analogy to that with carboxylic acids, for systematic incorporation of H3BO3 in the polymeric chains --Abstract, page v

Facial Beauty Prediction and Analysis based on Deep Convolutional Neural Network: A Review

Abstract: Facial attractiveness or facial beauty prediction (FBP) is a current study that has several potential usages. It is a key difficulty area in the computer vision domain because of the few public databases related to FBP and its experimental trials on the minor-scale database. Moreover, the evaluation of facial beauty is personalized in nature, with people having personalized favor of beauty. Deep learning techniques have displayed a significant ability in terms of analysis and feature representation. The previous studies focussed on scattered portions of facial beauty with fewer comparisons between diverse techniques. Thus, this article reviewed the recent research on computer prediction and analysis of face beauty based on deep convolution neural network DCNN. Furthermore, the provided possible lines of research and challenges in this article can help researchers in advancing the state – of- art in future work

A SON Solution for Sleeping Cell Detection Using Low-Dimensional Embedding of MDT Measurements

Automatic detection of cells which are in outage has been identified as one of the key use cases for Self Organizing Networks (SON) for emerging and future generations of cellular systems. A special case of cell outage, referred to as Sleeping Cell (SC) remains particularly challenging to detect in state of the art SON because in this case cell goes into outage or may perform poorly without triggering an alarm for Operation and Maintenance (O&M) entity. Consequently, no SON compensation function can be launched unless SC situation is detected via drive tests or through complaints registered by the affected customers. In this paper, we present a novel solution to address this problem that makes use of minimization of drive test (MDT) measurements recently standardized by 3GPP and NGMN. To overcome the processing complexity challenge, the MDT measurements are projected to a low-dimensional space using multidimensional scaling method. Then we apply state of the art k-nearest neighbor and local outlier factor based anomaly detection models together with pre-processed MDT measurements to profile the network behaviour and to detect SC. Our numerical results show that our proposed solution can automate the SC detection process with 93 accuracy

Smart Phones and Personal Listening Devices - Tinnitus & Hearing Impairment in Adolescent and Young Adult Earphone Users

OBJECTIVES To determine the frequency of tinnitus & hearing impairment in adolescent and young adult earphone users with smartphones and other personal listening devices.METHODOLOGY This prospective & descriptive study involving children with glue ears was conducted in the ENT Department of Medical Teaching Institute, Hayatabad Medical Complex, Peshawar, from Jan 1, 2022, to Sep 30, 2022. Personal listening device (PLD) users aged 12-25 years with complaints of tinnitus and hearing impairment were examined. Conductive hearing loss was excluded by audiological testing. The frequency of resultant hearing loss and tinnitus was calculated. The information obtained was analyzed using SPSS v 26.0 for windows. A Chi square test was performed to determine the significance of tinnitus & hearing impairment in earphones and other PLD users.RESULTS A total of 163 patients were included in the study. The patient’s age range was 12-25 years, with a mean age of 18.03 years and a standard deviation of ± 3.575. There were 117 males and 46 females. The male: female ratio was of 2.54:1.Tinnitus was present in 73% and Hearing impairment in 54.4% of the patients. The chi-square test and p-values determined showed that both tinnitus and hearing loss occurred in many patients using personal listening devices.CONCLUSION Tinnitus and hearing impairment occurs in a significant number of those who use personal listening devices. Their inappropriate use can lead to auditory system damage. It is recommended that PLD users undergo periodic audiological testing to detect early hearing loss and tinnitus to minimize damage to the ear

Effect of High Dose Ginger on Plasma Testosterone and Leutinising Hormone Levels in Male Rats after Lead Induced Toxicity

To study the effect of high doseginger on plasma testosterone and leutinisinghormone levels in male rats after lead inducedtoxicityMethods: In this quasi experimental study, 30adult male Sprague Dawley rats were divided in twoequal groups. Group A was given 0.3% lead acetatein drinking water and kept as lead control while theGroup B was given a dose of 1.5gm/kg body weightginger orally along with 0.3% lead for 42 consecutivedays. Rats were then sacrificed and serumtestosterone and LH levels were analyzed usingELISA technique. Data was expressed as mean±SD.P-values <0.05 were considered as statisticallysignificant.Results: At the end of 42 days, mean serumtestosterone level in Group A (control Group) was2.2667± 0.45617ng/ml as compared to Group B(Experimental Group) 2.2667 ± 0.45617ng/ml andshowed statistically insignificant change(p>0.05).Comparison of mean serum LH levels in Group A(5.3200 ± 0.72526ng/ml)revealed statisticallyinsignificant difference (p>0.05) as compared toGroup B (5.7467 ± 0.70190ng/ml).Conclusion: High dose ginger (>1gm/kg bodyweight) failed to enhance the suppressedtestosterone level due to lead toxicity in male rat

Novel Porous Polymer Compositions for the Synthesis of Monolithic Bimodal Microporous/Macroporous Carbon Compositions Useful for Selective CO₂ Sequestration

The present invention discloses novel porous polymeric compositions comprising random copolymers of amides, imides, ureas, and carbamic-anhydrides, useful for the synthesis of monolithic bimodal microporous/macroporous carbon aerogels. It also discloses methods for producing said microporous/macroporous carbon aerogels by the reaction of a polyisocyanate compound and a polycarboxylic acid compound, followed by pyrolytic carbonization, and by reactive etching with CO2 at elevated temperatures. Also disclosed are methods for using the microporous/macroporous carbon aerogels in the selective capture and sequestration of carbon dioxide

Study of Multi-Classification of Advanced Daily Life Activities on SHIMMER Sensor Dataset

Today the field of wireless sensors have the dominance in almost every person’s daily life. Therefore researchers are exasperating to make these sensors more dynamic, accurate and high performance computational devices as well as small in size, and also in the application area of these small sensors. The wearable sensors are the one type which are used to acquire a person’s behavioral characteristics. The applications of wearable sensors are healthcare, entertainment, fitness, security and military etc. Human activity recognition (HAR) is the one example, where data received from wearable sensors are further processed to identify the activities executed by the individuals. The HAR system can be used in fall detection, fall prevention and also in posture recognition. The recognition of activities is further divided into two categories, the un-supervised learning and the supervised learning. In this paper we first discussed some existing wearable sensors based HAR systems, then briefly described some classifiers (supervised learning) and then the methodology of how we applied the multiple classification techniques using a benchmark data set of the shimmer sensors placed on human body, to recognize the human activity. Our results shows that the methods are exceptionally accurate and efficient in comparison with other classification methods. We also compare the results and analyzed the accuracy of different classifiers