The Journey of Insulin: Leaving a Legacy as a Medical Student

N/

Combined Influence of Fly Ash and Recycled Coarse Aggregates on Strength and Economic Performance of Concrete

Recycled coarse aggregates (RCA) and fly ash (FA) are materials with least to very low global warming potential. Considering long term strength and durability, various studies have suggested to use RCA in concrete with FA. This research paper deals with the strength and economic performance of concrete made with individual and combined incorporation of FA and RCA. Nine different mixtures of concrete were prepared by varying the incorporation levels of RCA and FA. 0% RCA, 50% RCA and 100% RCA were used in concrete with three different levels of FA (0%FA, 20%FA, and 40%FA). The compressive strength of each mixture of concrete was determined at the age of 3, 28, 90 and 180 days. To evaluate economic performance cost of 1 m3 of each mixture of concrete was compared to that of the control mixture having 0% RCA and 0% FA. Results showed that RCA was detrimental to the compressive strength of concrete at all ages, whereas, FA reduced early strength but improved the strength at later ages of testing i.e. 90 and 180 days. FA plus RCA mixes also showed lower early age strength but gained higher strength than conventional concrete at the age of 180 days. RCA did not reduce the cost of concrete effectively. FA despite having a very high transportation cost, it reduced the cost of concrete efficiently. FA did not only reduce the cost of binder but also lower the demand of plasticizer by improving workability. Cost to strength ratio (CSR) analysis also indicated that FA significantly improve the combined economic and strength performance of RCA concrete mixes

Anthropometric Study of the Human Craniofacial Morphology among different castes of Punjab Pakistan

Background: It appears from the literature that there is a research vacuum in craniofacial anthropometric studies in Pakistani population. Therefore, this study was carried out to characterize the craniofacial parameters among different castes of the Punjab Pakistan.Methods: This cross-sectional study was conducted on population of the Punjab, Pakistan, with age 18-45 years in a normal healthy state and data was collected using a questionnaire. Anthropometric instruments such as standard spreading caliper, round ended caliper, vernier caliper and scale were used for the measurement of craniofacial parameters. Data was analyzed by using SPSS version 20.0 and MS Excel 16. Morphological anthropometry of face, head, nose and ears was observed and noted.Results: Hyperleptoprosopic face was most common one in the studied population. The dominant nose type was Leptorrhine while the most dominant head shape was Dolichocephalic. The average ear index was 50.42 and 51.19 of right and left ears, respectively.Conclusion: This data is a base for the anthropometric data bank of the Punjab province of Pakistan. This data is helpful in medico legal cases, forensic investigations, and in facial surgeries. This study is also important for anthropological and forensic research.Keywords: Anthropology, Anthropometry; Craniofacial; Morphology; Populatio

Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality-of-service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI and blockchain for agricultural supply-chain management with the purpose of ensuring traceability, transparency, tracking inventories and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAV, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements.Comment: 8 pages, 5 figures, 1 table. Accepted to IEEE Internet of Things Magazin

Get Your Foes Fooled: Proximal Gradient Split Learning for Defense Against Model Inversion Attacks on IoMT Data

The past decade has seen a rapid adoption of Artificial Intelligence (AI), specifically the deep learning networks, in Internet of Medical Things (IoMT) ecosystem. However, it has been shown recently that the deep learning networks can be exploited by adversarial attacks that not only make IoMT vulnerable to the data theft but also to the manipulation of medical diagnosis. The existing studies consider adding noise to the raw IoMT data or model parameters which not only reduces the overall performance concerning medical inferences but also is ineffective to the likes of deep leakage from gradients method. In this work, we propose proximal gradient split learning (PSGL) method for defense against the model inversion attacks. The proposed method intentionally attacks the IoMT data when undergoing the deep neural network training process at client side. We propose the use of proximal gradient method to recover gradient maps and a decision-level fusion strategy to improve the recognition performance. Extensive analysis show that the PGSL not only provides effective defense mechanism against the model inversion attacks but also helps in improving the recognition performance on publicly available datasets. We report 14.0 % , 17.9 % , and 36.9 % gains in accuracy over reconstructed and adversarial attacked images, respectively

Role of Flavonoids as Wound Healing Agent

Flavonoids are found as the most abundant bioactive compounds all around the world. It is found in a number of medicinal plants that are used as wound healing agents in traditional medicinal uses such as Buddleja globosa, Moringa oleifera, Lam, Butea monosperma, Parapiptadenia rigida and Ononis spinosa. Flavonoids nowadays are being used in different formulation and wound healing dressings. Inflammation, proliferation and reepithelialization are involved in wound healing. Most of the wound healing medicinal plants possess multiple flavonoids that act as synergistic effect or combined effect. This chapter briefly reviews the role of flavonoids as wound healing agent in traditional and modern medicine

Toward On-Device AI and Blockchain for 6G-Enabled Agricultural Supply Chain Management

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality of service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI, and blockchain for agricultural supply chain management with the purpose of ensuring traceability and transparency, tracking inventories, and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAVs, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G-enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements

Antioxidants: Natural Antibiotics

The aim of this current piece of writing is to draw the attention of readers and researchers toward the natural antioxidants that can take the place of synthetic antibiotics to avoid bacterial resistance and gastrotoxicity/nephrotoxicity. Antioxidants such as polyphenols, vitamins, and carotenoids are the organic compounds mainly extracted from natural sources and dominantly involved in boosting the defense system of organisms. The main public health-related issue over the globe is ever-growing bacterial resistance to synthetic antibiotics, which is being continuously reported during the last decade. Further, the pipeline of the development of new synthetic antibacterial agents to replace the resistant antibiotics in clinical set-up is gradually drying up. This scenario originated the concept to revive the interest toward natural antibacterial products due to their chemical diversity, which provide important therapeutic effect and make the microbes unable to copy them for creating resistance. Natural products, especially polyphenols had been seen in antioxidant, antibacterial, anticancer, anti-inflammation, and antiviral activities with encouraging results. In this chapter, we will focus over the role of natural antioxidants as antibacterial agents

An Aggregate MapReduce Data Block Placement Strategy for Wireless IoT Edge Nodes in Smart Grid

Big data analytics has simplified processing complexity of large dataset in a distributed environment. Many state-of-the-art platforms i.e. smart grid has adopted the processing structure of big data and manages a large volume of data through MapReduce paradigm at distribution ends. Thus, whenever a wireless IoT edge node bundles a sensor dataset into storage media, MapReduce agent performs analytics and generates output into the grid repository. This practice has efficiently reduced the consumption of resources in such a giant network and strengthens other components of the smart grid to perform data analytics through aggregate programming. However, it consumes an operational latency of accessing large dataset from a central repository. As we know that, smart grid processes I/O operations of multi-homing networks, therefore, it accesses large datasets for processing MapReduce jobs at wireless IoT edge nodes. As a result, aggregate MapReduce at wireless IoT edge node produces a network congestion and operational latency problem. To overcome this issue, we propose Wireless IoT Edge-enabled Block Replica Strategy (WIEBRS), that stores in-place, partition-based and multi-homing block replica to respective edge nodes. This reduces the delay latency of accessing datasets for aggregate MapReduce and increases the performance of the job in the smart grid. The simulation results show that WIEBRS effective decreases operational latency with an increment of aggregate MapReduce job performance in the smart grid