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101.
An overview of smart packaging technologies for monitoring safety and quality of meat and meat products
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Ishfaq Ahmed Hong Lin Long Zou Zhenxing Li Aaron L. Brody Ihsan Mabood Qazi Liangtao Lv Tushar Ramesh Pavase Mati Ullah Khan Sohaib Khan Lirui Sun 《Packaging Technology and Science》2018,31(7):449-471
The food packaging sector has experienced much development since its inception. In the past few decades, innovations in packaging sector have led to the development of smart packaging (SP) systems that carve a niche in a highly competitive food industry. SP systems have great potential for improving the shelf‐life, and safety of food products apart from their basic roles of protecting the products against unwanted biological, chemical, and physical damage and keeping them clean. Indicators and sensors, SP components, are used for real‐time monitoring of meat quality and subsequently inform the retailers and consumers about the freshness, microbiological, temperature, and shelf life status of the products. Barcodes and radio‐frequency identification tags are employed in meat packaging for real‐time information about the authenticity, and traceability of the products in the supply chain. Recently, innovations in SP technologies resulted in fast, sensitive, and effective detection, sensing, and record keeping of freshness, microbiological, and shelf life status of meat and meat products. The SP system shows promise for extensive utilization in the meat industry in response to the consumer appreciation for safe, and quality meat products, as well as their waste reduction notions. This paper gives an updated overview of ongoing scientific research, and recent technological advances that offer the perspectives of developing smart meat packaging systems that are capable of monitoring the physical, microbial, and chemical changes of the package contents from producer to the point of sale and even beyond, and remediating potential adverse reactions. 相似文献
102.
Ahsan Nazir Haojie Yu Li Wang Muhammad Haroon Raja Summe Ullah Shah Fahad Kaleem-ur-Rahman Naveed Tarig Elshaarani Amin Khan Muhammad Usman 《Journal of Materials Science》2018,53(12):8699-8719
With the development in the modern technologies such as telecommunication instruments and scientific electronic devices, large amount of the electromagnetic radiations are produced, which lead to harmful effect on the highly sensitive electronic devices as well as on the health of human beings. To minimize the effect of electromagnetic radiations produced by different technologies, more efficient shielding materials are required which must be cost-effective, lightweight and good corrosion resistive. In this review, we focused on the shielding materials based on composites of carbon nanotubes and graphene. The typical surface modification of carbon nanotubes and graphene to optimize their interactions with polymers matrix has also summarized. It was found that the composites based on these carbon fillers were more efficient for electromagnetic interference shielding due to their unique properties (i.e., superior electrical, mechanical and thermal) together with lightweight, easy processing. Hence, the carbon nanotubes and graphene-based composites are excellent shielding materials against the electromagnetic radiations. 相似文献
103.
Rajwali Khan Zulfiqar Clodoaldo Irineu Levartoski de Araujo Tahirzeb Khan Muneeb-Ur-Rahman Zia-Ur-Rehman Aurangzeb Khan Burhan Ullah Simbarashe Fashu 《Journal of Materials Science: Materials in Electronics》2018,29(12):9785-9795
We analysed the variation and effect of oxygen vacancies on the structural, dielectric and magnetic properties in case of Mn (4%) and Co (1, 2 and 4%) co-doped ZnO nanoparticles (NPs), synthesized by chemical precipitation route and annealed at 750 °C for 2 h. From the XRD, the calculated average crystallite size increased from15.30?±?0.73 nm to 16.71?±?012 nm, when Co content is increased from 1 to 4%. Enhancement of dopants (Mn, Co) introduced more and more oxygen vacancies to ZnO lattice confirmed from EDX and XPS. The high-temperature annealing leads to reduction of the dielectric properties due to enhancement in grain growth (large grain volume and lesser number of grain boundaries) with the incorporation of Co and Mn ions into the ZnO lattice. The electrical conductivity of the Mn doped and (Mn, Co) co-doped ZnO samples were enhanced due to increase in the volume of conducting grains and charge density (liberation of trapped charge carriers in oxygen vacancies and free charge carriers at higher frequencies). The Mn-doped and (Mn, Co) co-doped ZnO NPs show ferromagnetic (FM) behaviour. The saturation and remnant magnetizations (Ms and Mr) elevates from (0.235 to 1.489)?×?10?2 and (0.12 to 0.27)?×?10?2 emu/g while Coercivity (Hc) reduced from 97 to 36 Oe with enhancement in the concentration of dopants in ZnO matrix. Oxygen vacancies were found to be the main reason for room-temperature ferromagnetism (RTFM) in the doped and co-doped ZnO NPs. The results show that the enhanced dielectric and magnetic properties of Mn doped and (Mn, Co) co-doped ZnO is strongly correlated with the concentration of oxygen vacancies. The observed enhanced RTFM, dielectric properties and electrical conductivity makes TM doped ZnO nanoparticles suitable for spintronics, microelectronics and optoelectronics based applications. 相似文献
104.
Shafi Ullah Mohammad Shoaib Muhammad Imran Syed Wadood Ali Shah Imdad Ali 《Drug development and industrial pharmacy》2017,43(6):1011-1022
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. 相似文献
105.
Healthcare is a binding domain for the Internet of Things (IoT) to automate healthcare services for sharing and accumulation patient records at anytime from anywhere through the Internet. The current IP-based Internet architecture suffers from latency, mobility, location dependency, and security. The Named Data Networking (NDN) has been projected as a future internet architecture to cope with the limitations of IP-based Internet. However, the NDN infrastructure does not have a secure framework for IoT healthcare information. In this paper, we proposed a secure NDN framework for IoT-enabled Healthcare (IoTEH). In the proposed work, we adopt the services of Identity-Based Signcryption (IBS) cryptography under the security hardness Hyperelliptic Curve Cryptosystem (HCC) to secure the IoTEH information in NDN. The HCC provides the corresponding level of security using minimal computational and communicational resources as compared to bilinear pairing and Elliptic Curve Cryptosystem (ECC). For the efficiency of the proposed scheme, we simulated the security of the proposed solution using Automated Validation of Internet Security Protocols and Applications (AVISPA). Besides, we deployed the proposed scheme on the IoTEH in NDN infrastructure and compared it with the recent IBS schemes in terms of computation and communication overheads. The simulation results showed the superiority and improvement of the proposed framework against contemporary related works. 相似文献
106.
Sadeeq Jan Omer Bin Tauqeer Fazal Qudus Khan George Tsaramirsis Awais Ahmad Iftikhar Ahmad Imran Maqsood Niamat Ullah 《计算机、材料和连续体(英文)》2021,66(1):631-645
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. 相似文献
107.
Khalil Khan Rehan Ullah Khan Jehad Ali Irfan Uddin Sahib Khan Byeong-hee Roh 《计算机、材料和连续体(英文)》2021,68(3):3483-3498
Race classification is a long-standing challenge in the field of face image analysis. The investigation of salient facial features is an important task to avoid processing all face parts. Face segmentation strongly benefits several face analysis tasks, including ethnicity and race classification. We propose a race-classification algorithm using a prior face segmentation framework. A deep convolutional neural network (DCNN) was used to construct a face segmentation model. For training the DCNN, we label face images according to seven different classes, that is, nose, skin, hair, eyes, brows, back, and mouth. The DCNN model developed in the first phase was used to create segmentation results. The probabilistic classification method is used, and probability maps (PMs) are created for each semantic class. We investigated five salient facial features from among seven that help in race classification. Features are extracted from the PMs of five classes, and a new model is trained based on the DCNN. We assessed the performance of the proposed race classification method on four standard face datasets, reporting superior results compared with previous studies. 相似文献
108.
Rehman Ullah Khan Woei Sheng Wong Insaf Ullah Fahad Algarni Muhammad Inam Ul Haq Mohamad Hardyman bin Barawi Muhammad Asghar Khan 《计算机、材料和连续体(英文)》2022,71(2):2755-2772
The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based Attention Module (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition. This study has created 2071 videos for 19 dynamic signs. Two different experiments were conducted for dynamic signs, using CBAM-3DResNet implementing ‘Within Blocks’ and ‘Before Classifier’ methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time were recorded to evaluate the models’ efficiency. Results showed that CBAM-ResNet models had good performances in videos recognition tasks, with recognition rates of over 90% with little variations. CBAM-ResNet ‘Before Classifier’ is more efficient than ‘Within Blocks’ models of CBAM-ResNet. All experiment results indicated the CBAM-ResNet ‘Before Classifier’ efficiency in recognising Malaysian Sign Language and its worth of future research. 相似文献
109.
Sami Ullah Muhammad Ramzan Talib Toqir A. Rana Muhammad Kashif Hanif Muhammad Awais 《计算机、材料和连续体(英文)》2022,72(2):2323-2339
In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users’ emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’ emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain. 相似文献
110.
Zia Ullah Muhammad Ismail Mohmand Sadaqat ur Rehman Muhammad Zubair Maha Driss Wadii Boulila Rayan Sheikh Ibrahim Alwawi 《计算机、材料和连续体(英文)》2022,73(3):4465-4487
Facial expression recognition has been a hot topic for decades, but high intraclass variation makes it challenging. To overcome intraclass variation for visual recognition, we introduce a novel fusion methodology, in which the proposed model first extract features followed by feature fusion. Specifically, RestNet-50, VGG-19, and Inception-V3 is used to ensure feature learning followed by feature fusion. Finally, the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification. The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy. To evaluate the efficiency of the proposed model, we use two wild benchmark datasets Real-world Affective Faces Database (RAF-DB) and AffectNet for facial expression recognition. The proposed model classifies the emotions into seven different categories namely: happiness, anger, fear, disgust, sadness, surprise, and neutral. Furthermore, the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost, convergence and accuracy based on a standard problem specific to classification applications. 相似文献