SELWAK: A Secure and Efficient Lightweight and Anonymous Authentication and Key Establishment Scheme for IoT Based Vehicular Ad hoc Networks.

In recent decades, Vehicular Ad Hoc Networks (VANET) have emerged as a promising field that provides real-time communication between vehicles for comfortable driving and human safety. However, the Internet of Vehicles (IoV) platform faces some serious problems in the deployment of robust authentication mechanisms in resource-constrained environments and directly affects the efficiency of existing VANET schemes. Moreover, the security of the information becomes a critical issue over an open wireless access medium. In this paper, an efficient and secure lightweight anonymous mutual authentication and key establishment (SELWAK) for IoT-based VANETs is proposed. The proposed scheme requires two types of mutual authentication: V2V and V2R. In addition, SELWAK maintains secret keys for secure communication between Roadside Units (RSUs). The performance evaluation of SELWAK affirms that it is lightweight in terms of computational cost and communication overhead because SELWAK uses a bitwise Exclusive-OR operation and one-way hash functions. The formal and informal security analysis of SELWAK shows that it is robust against man-in-the-middle attacks, replay attacks, stolen verifier attacks, stolen OBU attacks, untraceability, impersonation attacks, and anonymity. Moreover, a formal security analysis is presented using the Real-or-Random (RoR) model.

A death, infection, and recovery (DIR) model to forecast the COVID-19 spread.

BACKGROUND: The SARS-Cov-2 virus (commonly known as COVID-19) has resulted in substantial casualties in many countries. The first case of COVID-19 was reported in China towards the end of 2019. Cases started to appear in several other countries (including Pakistan) by February 2020. To analyze the spreading pattern of the disease, several researchers used the Susceptible-Infectious-Recovered (SIR) model. However, the classical SIR model cannot predict the death rate. OBJECTIVE: In this article, we present a Death-Infection-Recovery (DIR) model to forecast the virus spread over a window of one (minimum) to fourteen (maximum) days. Our model captures the dynamic behavior of the virus and can assist authorities in making decisions on non-pharmaceutical interventions (NPI), like travel restrictions, lockdowns, etc. METHOD: The size of training dataset used was 134 days. The Auto Regressive Integrated Moving Average (ARIMA) model was implemented using XLSTAT (add-in for Microsoft Excel), whereas the SIR and the proposed DIR model was implemented using python programming language. We compared the performance of DIR model with the SIR model and the ARIMA model by computing the Percentage Error and Mean Absolute Percentage Error (MAPE). RESULTS: Experimental results demonstrate that the maximum% error in predicting the number of deaths, infections, and recoveries for a period of fourteen days using the DIR model is only 2.33%, using ARIMA model is 10.03% and using SIR model is 53.07%. CONCLUSION: This percentage of error obtained in forecasting using DIR model is significantly less than the% error of the compared models. Moreover, the MAPE of the DIR model is sufficiently below the two compared models that indicates its effectiveness.

Fingerprint Identification With Shallow Multifeature View Classifier.

This article presents an efficient fingerprint identification system that implements an initial classification for search-space reduction followed by minutiae neighbor-based feature encoding and matching. The current state-of-the-art fingerprint classification methods use a deep convolutional neural network (DCNN) to assign confidence for the classification prediction, and based on this prediction, the input fingerprint is matched with only the subset of the database that belongs to the predicted class. It can be observed for the DCNNs that as the architectures deepen, the farthest layers of the network learn more abstract information from the input images that result in higher prediction accuracies. However, the downside is that the DCNNs are data hungry and require lots of annotated (labeled) data to learn generalized network parameters for deeper layers. In this article, a shallow multifeature view CNN (SMV-CNN) fingerprint classifier is proposed that extracts: 1) fine-grained features from the input image and 2) abstract features from explicitly derived representations obtained from the input image. The multifeature views are fed to a fully connected neural network (NN) to compute a global classification prediction. The classification results show that the SMV-CNN demonstrated an improvement of 2.8% when compared to baseline CNN consisting of a single grayscale view on an open-source database. Moreover, in comparison with the state-of-the-art residual network (ResNet-50) image classification model, the proposed method performs comparably while being less complex and more efficient during training. The result of classification-based fingerprint identification has shown that the search space is reduced by over 50% without degradation of identification accuracies.

A review on the state-of-the-art privacy-preserving approaches in the e-health clouds.

Cloud computing is emerging as a new computing paradigm in the healthcare sector besides other business domains. Large numbers of health organizations have started shifting the electronic health information to the cloud environment. Introducing the cloud services in the health sector not only facilitates the exchange of electronic medical records among the hospitals and clinics, but also enables the cloud to act as a medical record storage center. Moreover, shifting to the cloud environment relieves the healthcare organizations of the tedious tasks of infrastructure management and also minimizes development and maintenance costs. Nonetheless, storing the patient health data in the third-party servers also entails serious threats to data privacy. Because of probable disclosure of medical records stored and exchanged in the cloud, the patients' privacy concerns should essentially be considered when designing the security and privacy mechanisms. Various approaches have been used to preserve the privacy of the health information in the cloud environment. This survey aims to encompass the state-of-the-art privacy-preserving approaches employed in the e-Health clouds. Moreover, the privacy-preserving approaches are classified into cryptographic and noncryptographic approaches and taxonomy of the approaches is also presented. Furthermore, the strengths and weaknesses of the presented approaches are reported and some open issues are highlighted.

Paradigm in aetiology and management of zygomatic complex fractures in Pakistan.

OBJECTIVE: The purpose of this study was to compare the outcome of wire osteosynthesis with bone plating in the management of zygomatic complex (ZMC) fractures. The rationale was to enable the oral and maxillofacial surgeon to select either of the treatment procedure with confidence. METHODS: In this quasi experimental study, Sixty five patients of ZMC fractures were randomly allocated in groups of bone plating and wire osteosynthesis. This was done through envelope draw method. Three points fixation with either of the treatment modality was undertaken. Titanium bone plating or stain less steel wire osteosynthesis was carried out at frontozygomatic suture, infra orbital rim and at zygomaticomaxillary buttress. RESULTS: In this quasi experimental study, three patients failed to follow up. There were 31 patients in each group. Infra orbital step defect was observed in one patient of wire osteosynthesis and two patients of bone plating. Mild facial deformity was observed in two patients of wire osteosynthesis while none in that of bone plating. CONCLUSION: There is insignificant difference of proportion of infra orbital step defect and facial deformity between wire osteosynthesis and bone plating.

Root resorption of permanent incisors during three months of active orthodontic treatment.

BACKGROUND: Root resorption is one of the most common and undesirable sequelea of orthodontic treatment. The aim of this study was to evaluate the amount of root resorption in permanent incisors during 3 month active period of fixed orthodontic appliance therapy using periapical radiographs. METHODS: Periapical radiographs of a total of 138 permanent teeth (n = 138, mandibular n1 = 52, maxillary n2 = 86) were evaluated for root resorption. All patients were treated with 3M MBT multi-bonded, pre-adjusted appliances with 0.022 inch slots. Initial levelling and alignment was achieved with 0.0175 inch co-axial wires. All four incisors (maxillary and mandibular) were measured for any change in root length. The change in root length between T0 (pre-treatment) and T1 (post-treatment) was measured in millimetres and expressed in terms of percentage of original root length. RESULTS: The mean pre treatment (T0) root length for the maxillary teeth (n1 = 62) was 19.27 +/- 2.86 mm and 20.01 +/- 2.57 mm for the mandibular teeth (n2 = 31). The post-treatment (T1) root length for the maxillary teeth was 18.96 +/- 2.85 mm and 19.49 +/- 2.4 mm for the mandibular teeth showing a mean resorption of 0.31 mm and 0.52 mm for the maxillary and mandibular teeth respectively. CONCLUSION: Root resorption was strongly correlated with active orthodontic appliance therapy with maxillary and mandibular incisors being most susceptible. It was found that root resorption can be detected even in the early levelling and alignment stages of orthodontic treatment.

Evaluation of tooth size discrepancy in different malocclusion groups.

BACKGROUND: Orthodontic diagnosis and treatment planning requires the patient's thorough history, extra and intra oral examination, analysis of diagnostic records comprising of orthodontic photographs, necessary radiographs and properly trimmed study casts. Tooth-size is the sum of mesio distal widths of the maxillary and mandibular teeth. For ideal occlusion, teeth in both arches should be proportional in size. If larger teeth in one jaw are occluded with smaller teeth in the other jaw, ideal occlusion is not achieved. Tooth size analysis was presented by Bolton in 1958. The ratio for anterior segment was derived to be 77.2 +/- 0.22% and 91.3 +/- 0.26% for the whole arch. METHODS: A quantitative study was carried out at the Armed Forces Institute of Dentistry (AFID), Rawalpindi, Pakistan, a tertiary care facility. Cross sectional data was gathered from the study casts of patients undergoing orthodontic treatment at AFID, after obtaining informed written consent from them. 135 out of 200 study casts were filtered based upon the inclusion and exclusion criteria. ANB angle and witt's appraisal were traced on corresponding lateral cephalograms. The mesio distal widths of all maxillary and mandibular teeth from right first molar to left first molar were calibrated with the use of a manual caliper. The readings were then used to compute the anterior and total Bolton ratios. RESULTS: Significantly higher mean anterior tooth ratios were found for Class II (p < 0.01) patients. All other ratios were within close range of Bolton's norms. CONCLUSIONS: Skeletal Class II patients showed a tendency towards higher mesiodistal widths of teeth in the mandibular anterior region.