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The widespread use of spatial location-based services not only provides considerable convenience, but also exposes the downsides of location privacy leakage. Most of the existing user-side location privacy protection techniques are limited to planar locations. However, the extensive use of aircraft, sensor equipment and acquisition devices with positioning functions promotes the urgency of protecting the privacy of 3D spatial locations. Therefore, this study suggests a local differential privacy protection approach for 3D spatial locations. A 3D spatial decomposition and Hilbert encoding method are designed to reduce the 3D location data into one-dimensional encoding. The optimized random response mechanism was utilized to perturb the dimensional-reduced location encoding, which not only achieves user-side location privacy protection but also improves the accuracy of aggregated data on the server-side. Experiments on the real spatial location datasets show that the suggested method can reduce spatial location service quality loss, maintain the availability of perturbed spatial location and improve the operation efficiency of the spatial location perturbation algorithm.

The emerging and promising paradigm of the Internet of Vehicles (IoV) employ vehicle-to-everything communication for facilitating vehicles to not only communicate with one another but also with the supporting roadside infrastructure, vulnerable pedestrians, and the backbone network in a bid to primarily address a number of safety-critical vehicular applications. Nevertheless, owing to the inherent characteristics of IoV networks, in particular, of being (a) highly dynamic in nature and which results in a continual change in the network topology and (b) non-deterministic owing to the intricate nature of its entities and their interrelationships, they are susceptible to a number of malicious attacks. Such kinds of attacks, if and when materialized, jeopardizes the entire IoV network, thereby putting human lives at risk. Whilst the cryptographic-based mechanisms are capable of mitigating the external attacks, the internal attacks are extremely hard to tackle. Trust, therefore, is an indispensable tool since it facilitates in the timely identification and eradication of malicious entities responsible for launching internal attacks in an IoV network. To date, there is no dataset pertinent to trust management in the context of IoV networks and the same has proven to be a bottleneck for conducting an in-depth research in this domain. The manuscript-at-hand, accordingly, presents a first of its kind trust-based IoV dataset encompassing 96,707 interactions amongst 79 vehicles at different time instances. The dataset involves nine salient trust parameters, i.e., packet delivery ratio, similarity, external similarity, internal similarity, familiarity, external familiarity, internal familiarity, reward/punishment, and context, which play a considerable role in ascertaining the trust of a vehicle within an IoV network.

Abstract Location-based services (LBS) are capable of providing location-based information retrieval, traffic navigation, entertainment services, emergency rescues, and several similar services primarily on the premise of the geographic location of users or mobile devices. However, in the process of introducing a new user experience, it is also easy to expose users’ specific location which can result in more private information leakage. Hence, the protection of location privacy remains one of the critical issues of the location-based services. Moreover, the areas where humans work and live have different location semantics and sensitivities according to their different social functions. Although the privacy protection of a user’s real location can be achieved by the perturbation algorithm, the attackers may employ the semantics information of the perturbed location to infer a user’s real location semantics in an attempt to spy on a user’s privacy to certain extent. In order to mitigate the above semantics inference attack, and further improve the quality of the location-based services, this paper hereby proposes a user side location perturbation and optimization algorithm based on geo-indistinguishability and location semantics. The perturbation area satisfying geo-indistinguishability is thus generated according to the planar Laplace mechanism and optimized by combining the semantics information and time characteristics of the location. The optimum perturbed location that is able to satisfy the minimum loss of location-based service quality is selected via a linear programming method, and can be employed to replace the real location of the user so as to prevent the leakage of the privacy. Experimental comparison of the actual road network and location semantics dataset manifests that the proposed method reduces approximately 37% perturbation distance in contrast to the other state-of-the-art methods, maintains considerably lower similarity of location semantics, and improves region counting query accuracy by a margin of around 40%.

The broad use of mobile intelligent terminals with locating functions encourages the rapid development of location-based services (LBS), which are widely used in a variety of industries such as social networking, transportation, finance, and entertainment. While enjoying the convenience brought by LBS, the threat of location privacy leakage should not be overlooked. In view of the complex encoding mechanism and low availability of the current location privacy protection methods, a local differential privacy location protection method based on optimized random response is proposed in this paper. A spatial decomposition and Hilbert encoding mechanism are designed based on the Hilbert curve, which can reduce the two-dimensional location data into one-dimensional Hilbert encoding results. A local differential privacy location perturbation method based on optimized random response is proposed to improve the availability of perturbed locations and the accuracy of data aggregation. Experiments on actual location datasets prove that the proposed method can provide better location data availability and operational efficiency based on realizing local differential privacy protection of users’ location.

The emerging yet promising paradigm of the Internet of Vehicles (IoV) has recently gained considerable attention from researchers from academia and industry. As an indispensable constituent of the futuristic smart cities, the underlying essence of the IoV is to facilitate vehicles to exchange safety-critical information with the other vehicles in their neighborhood, vulnerable pedestrians, supporting infrastructure, and the backbone network via vehicle-to-everything communication in a bid to enhance the road safety by mitigating the unwarranted road accidents via ensuring safer navigation together with guaranteeing the intelligent traffic flows. This requires that the safety-critical messages exchanged within an IoV network and the vehicles that disseminate the same are highly reliable (i.e., trustworthy); otherwise, the entire IoV network could be jeopardized. A state-of-the-art trust-based mechanism is, therefore, highly imperative for identifying and removing malicious vehicles from an IoV network. Accordingly, in this paper, a machine learning-based trust management mechanism, MESMERIC, has been proposed that takes into account the notions of direct trust (encompassing the trust attributes of interaction success rate, similarity, familiarity, and reward and punishment), indirect trust (involving confidence of a particular trustor on the neighboring nodes of a trustee, and the direct trust between the said neighboring nodes and the trustee), and context (comprising vehicle types and operating scenarios) in order to not only ascertain the trust of vehicles in an IoV network but to segregate the trustworthy vehicles from the untrustworthy ones by means of an optimal decision boundary. A comprehensive evaluation of the envisaged trust management mechanism has been carried out which demonstrates that it outperforms other state-of-the-art trust management mechanisms.

Objectives: A phase 1, double-blind, placebo-controlled trial was conducted to evaluate the safety, tolerability, and exploratory efficacy of repeat monthly doses of subcutaneous (SC) casirivimab and imdevimab (CAS+IMD) in uninfected adult volunteers. Methods: Participants were randomized (3:1) to SC CAS+IMD 1200 mg or placebo every 4 weeks for up to six doses. Primary and secondary end points evaluated safety, pharmacokinetics, and immunogenicity. Exploratory efficacy was evaluated by the incidence of COVID-19 or SARS-CoV-2 seroconversion. Results: In total, 969 participants received CAS+IMD. Repeat monthly dosing of SC CAS+IMD led to a 92.4% relative risk reduction in clinically defined COVID-19 compared with placebo (3/729 [0.4%] vs 13/240 [5.4%]; odds ratio 0.07 [95% CI 0.01-0.27]), and a 100% reduction in laboratory-confirmed COVID-19 (0/729 vs 10/240 [4.2%]; odds ratio 0.00). Development of anti-drug antibodies occurred in a small proportion of participants (

Abstract The rapid development of the mobile Internet coupled with the widespread use of intelligent terminals have intensified the digitization of personal information and accelerated the evolution of the era of big data. The sharing and publishing of various big data brings convenience and also increases the risk of personal privacy leakage. In order to reduce users’ privacy leakage that may be caused by data release, many privacy preserving data publishing methods have been proposed by scientists in both academia and industry in the recent years. However, non-numerical sensitive information has natural semantic relevance, and therefore, synonymous linkages may still exist and cause serious privacy disclosures in privacy protection methods based on an anonymous model. To address this issue, this paper proposes a privacy preserving dynamic data publishing method based on microaggregation. A series of indicators are accordingly designed to evaluate the synonymous linkages between the non-numerical sensitive values which in turn facilitate in improving the clustering effect of the microaggregation anonymous method. The dynamic update program is introduced into the proposed microaggregation method to realize the dynamic release and update of data. Experimental analysis suggests that the proposed method provides better privacy protection effect and availability of published data in contrast to the state-of-the-art methods.

ABSTRACT We conducted a post hoc analysis in seropositive patients who were negative or borderline for functional neutralizing antibodies (NAbs) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at baseline from a phase 1, 2, and 3 trial of casirivimab and imdevimab (CAS+IMD) treatment in hospitalized coronavirus disease 2019 (COVID-19) patients on low-flow or no supplemental oxygen prior to the emergence of Omicron-lineage variants. Patients were randomized to a single dose of 2.4 g CAS+IMD, 8.0 g CAS+IMD, or placebo. Patients seropositive for anti-SARS-CoV-2 antibodies at baseline were analyzed by their baseline neutralizing antibody status. At baseline, 20.6% (178/864) of seropositive patients were negative or borderline for neutralizing antibodies, indicating negative or very low functionally neutralizing anti-SARS-CoV-2 antibodies. CAS+IMD reduced viral load in patients who were negative or borderline for neutralizing antibodies versus placebo, but not in patients who were positive for neutralizing antibodies. In patients who were negative or borderline for neutralizing antibodies, we observed a trend in reduction of the proportion of patients who died or required mechanical ventilation, as well as in all-cause mortality, by day 29 with CAS+IMD versus placebo. The proportions of patients who died or required mechanical ventilation from days 1 to 29 were 19.1% in the placebo group and 10.9% in the CAS+IMD combined-dose group, and the proportions of patients who died (all-cause mortality) from days 1 to 29 were 16.2% in the placebo group and 9.1% in the CAS+IMD combined-dose group. In patients who were positive for neutralizing antibodies, no measurable harm or benefit was observed in either the proportion of patients who died or required mechanical ventilation or the proportion of patients who died (all-cause mortality). In hospitalized COVID-19 patients on low-flow or no supplemental oxygen, CAS+IMD reduced viral load, the risk of death or mechanical ventilation, and all-cause mortality in seropositive patients who were negative or borderline for neutralizing antibodies. IMPORTANCE The clinical benefit of CAS+IMD in hospitalized seronegative patients with COVID-19 has previously been demonstrated, although these studies observed no clinical benefit in seropositive patients. As the prevalence of SARS-CoV-2-seropositive individuals rises due to both vaccination and previous infection, it is important to understand whether there is a subset of hospitalized patients with COVID-19 with antibodies against SARS-CoV-2 who could benefit from anti-SARS-CoV-2 monoclonal antibody treatment. This post hoc analysis demonstrates that there is a subset of hospitalized seropositive patients with inadequate SARS-CoV-2-neutralizing antibodies (i.e., those who were negative or borderline for neutralizing antibodies) who may still benefit from CAS+IMD treatment if infected with a susceptible SARS-CoV-2 variant. Therefore, utilizing serostatus alone to guide treatment decisions for patients with COVID-19 may fail to identify those seropositive patients who could benefit from anti-SARS-CoV-2 monoclonal antibody therapies known to be effective against circulating strains, dependent upon how effectively their endogenous antibodies neutralize SARS-CoV-2.

Abstract Background This study aimed to assess the impact of 15 days before, 15 days during, and 15 days after the lockdown on the trends in the prevalence and mortality in 27 countries during COVID-19 pandemic. Methods Twenty-seven countries were randomly selected from the different continents. The information on the trends in the prevalence and mortality due to COVID-19 pandemic in 27 countries was obtained from World Health Organization and lockdown data were obtained from concerned countries and their ministries. The impact of lockdown for 15 days before, 15 days during, and 15 days after the lockdown on the prevalence and mortality due to the COVID-19 pandemic in 27 countries was analyzed. Results The findings showed that 15 days after the lockdown there was a trend toward a decline, but no significant decline in the mean prevalence and mean mortality rate due to the COVID-19 pandemic compared to 15 days before, and 15 days during the lockdown in 27 countries. The mean growth factor for number of cases was 1.18 and for mortality rate was 1.16. Conclusions The findings indicate that 15 days after the lockdown, daily cases of COVID-19 and the growth factor of the disease showed a declined trend, but there was no significant decline in the prevalence and mortality.

The novel coronavirus (Covid-19) infection outbreak has posed a major threat to the international health system and economy. This study is aimed at investigating the biological and epidemiological trends in the prevalence and mortality due to outbreaks of novel coronavirus (COVID-19) infections. The data on the global outbreak of COVID-19, were obtained from World Health Organization (WHO), Worldometer, Centers for Disease Control and Prevention (CDC), and research institutes. The information was also recorded from research documents published in global scientific journals indexed in Pub Med and Institute of Scientific Information (ISI) Web of Science on the trends in the prevalence and mortality due to COVID-19 infection outbreaks. The results show rising trends in the transmission, prevalence and mortality rate due to coronavirus COVID-19. During the period of December 29, 2019 through March 31, 2020, it has infected 750,890 people worldwide, resulting in 36,405 deaths with a mortality rate of 4.84%. The infections were more frequent among male gender over 60 years of age. The mean growth rate index for total number of cases from January 23 to March 31, 2020 was 1.20 and growth rate index for mortality rate was 1.12. There was a positive association between the prevalence and mortality rate (R2 = 0.996). The novel coronavirus COVID-19 is highly contagious and has affected a large number of people worldwide. It is still spreading with mutable prevalence and mortality outbreak trends. The global health officials have taken priority measures to prevent further outbreaks of this emerging pathogen across the globe. However, the rising number of cases and mortality risk estimates are demonstrating that enhanced public health mediations, good hygienic conditions, social distancing, and movement limitations may control the COVID-19 epidemics.

Centralized publishing of big location data provides great convenience for various location-based interactive queries and services. Privacy protection of users' location information is an indispensable issue in the security of big data applications. Partition publishing is an effective way to release statistical information of two-dimensional big location data. By combining with the differential privacy model, it can provide more accurate range counting query service on the premise of ensuring location privacy. In order to further improve the availability of location data subsequent to centralized publishing, this paper analyzes the primary noise sources of partition publishing and discusses the constraints among publishing errors, the spatial partition structure, and privacy budget allocation. An unbalanced quadtree partition algorithm based on regional uniformity is proposed. Accordingly, the gradient privacy budget allocation scheme and adjustment method are designed to ensure the effectiveness of the differential privacy model. Experimental comparison of the real-world datasets proves the advantages of the proposed algorithm in improving the querying accuracy of the published data.

Level of Trust can determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for measuring trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected. In applications of trust, trust relations among users need to be predicted. This process faces many challenges, such as the sparsity of user-specified trust relations, the context-awareness of trust and changes in trust values over time. In this paper, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs, classify them based on different factors, and propose some future directions for researchers interested in this field.

الأهداف: تهدف الدراسة إلى بيان مفهوم حق المساطحة، وتكييفه الفقهي ومشروعيته، وآفاقه المستقبلية، وانقضاؤه. المنهجية: استخدمت الدراسة المنهج النقلي في إيراد النصوص، والمنهج الاستقرائي بجمع المادة العلمية من أهم كتب الفقه في المذاهب الفقهية الأربعة، والمراجع الحديثة التي تتناولت موضوع الدراسة. والمنهج التحليلي بتحليل آراء الفقهاء في المسائل المعروضة في البحث. النتائج: توصلت الدراسة إلى أن حق المساطحة مشروع، ولا يتعارض مع مبادئ الشريعة الإسلامية، ويؤسس حق المساطحة لاستثمارات مالية واقتصادية فاعلة؛ لأنه حق قابل للتطور، ويمكن صياغته بما يتلاءم مع المستجدات المعاصرة، وبما يحقق الأهداف المنشودة منه كأسلوب استثماري متميز ومكمل للعقود القائمة كعقد الB.O.T، والإجارة المنتهية بالتمليك، ويتميز بصبغة قانونية، وخصائص تنفع العاقدين، وتغني الاقتصاد، ويتميز بسهولة ضبطه من ناحية حقوقية. التوصيات: توصي الدراسة بدراسة تطبيقات حق المساطحة في المملكة الأردنية الهاشمية، وإمكانية الإفادة منه في مجال الاستثمار في البنوك الإسلامية.

Modeling complex spatial and temporal dependencies in multivariate time series data is crucial for traffic forecasting. Graph convolutional networks have proved to be effective in predicting multivariate time series. Although a predefined graph structure can help the model converge to good results quickly, it also limits the further improvement of the model due to its stationary state. In addition, current methods may not converge on some datasets due to the graph structure of these datasets being difficult to learn. Motivated by this, we propose a novel model named Dynamic Correlation Graph Convolutional Network (DCGCN) in this paper. The model can construct adjacency matrices from input data using a correlation coefficient; thus, dynamic correlation graph convolution is used for capturing spatial dependencies. Meanwhile, gated temporal convolution is used for modeling temporal dependencies. Finally, we performed extensive experiments to evaluate the performance of our proposed method against ten existing well-recognized baseline methods using two original and four public datasets.

The rapid proliferation of the emerging yet promising notion of the Internet-of-Vehicles (IoV) has led to the development of a variety of conventional trust assessment schemes to tackle insider attackers. The primary reliance of these frameworks is on the accumulation of individual trust attributes. While aggregating these influential parameters, weights are often associated with each individual attribute to reflect its impact on the final trust score. It is of paramount importance that such weights be precise to lead to an accurate trust assessment. Moreover, the value of the minimum acceptable trust threshold employed for the identification of dishonest vehicles needs to be carefully defined to avoid delayed or erroneous detection. This paper employs an IoT data set from CRAWDAD by suitably transforming it into an IoV format. This data set encompasses information regarding 18,226 interactions among 76 nodes, both honest and dishonest. First, the influencing parameters (i.e., packet delivery ratio, familiarity, timeliness and interaction frequency) were computed, and two feature matrices were formed. The first matrix (FM1) takes into account all the pairwise individual parameters as individual features, whereas the second matrix (FM2) considers the average of all pairwise computations performed for each individual parameter as one feature. Subsequently, unsupervised learning is employed to achieve the ground truth prior to applying supervised machine learning algorithms for classification purposes. It is worth noting that Subspace KNN yielded a perfect precision, recall, and the F1-score equal to 1 for individual parametric scores, whereas Subspace Discriminant returned an ideal precision, recall, and the F1-score equal to 1 for mean parametric scores. It is also evident from extensive simulations that FM2 yielded more accurate classification results compared to FM1. Furthermore, decision boundaries among honest and dishonest vehicles have also been computed for respective feature matrices.

Maintaining oral hygiene is very important for a healthy life. Poor toothbrushing is one of the leading causes of tooth decay and other gum problems. Many people do not brush their teeth properly. There is very limited technology available to help in assessing the quality of toothbrushing. Human Activity Recognition (HAR) applications have seen a tremendous growth in recent years. In this work, we treat the adherence to standard toothbrushing practice as an activity recognition problem. We investigate this problem and collect experimental data using a brush-attached and a wearable sensor when the users brush their teeth. In this paper, we extend our previous dataset [1] for toothbrushing activity by including more experiments and adding a new sensor. We discuss and analyse the collection of the dataset. We use an Inertial Measurement Unit (IMU) sensor to collect the time-series data for toothbrushing activity. We recruited 22 healthy participants and collected the data in two different settings when they brushed their teeth in five different locations using both electric and manual brushes. In total, we have recorded 120 toothbrushing sessions using both brush-attached sensor and the wearable sensor.

Statistical partitioning and publishing is commonly used in location-based big data services to address queries such as the number of points of interest, available vehicles, traffic flows, infected patients, etc., within a certain range. Adding noise perturbation to the location-based statistical data according to the differential privacy model can reduce various risks caused by location privacy leakage while keeping the statistical characteristics of the published data. The traditional statistical partitioning and publishing methods realize the decomposition and indexing of 2D space from top to bottom. However, they can easily cause the over-partitioning or under-partitioning phenomenon, and therefore need multiple times of data scan. This paper proposes a grid clustering and differential privacy protection method for location-based statistical big data publishing scenarios. We implement location-based big data statistics in units of equal-sized grids and perform density classification on uniformly distributed grids by discrete wavelet transform. A bottom-up grid clustering algorithm is designed to perform on the blank and the uniform grids of the same density level based on neighborhood similarity. The Laplacian noise is incorporated into the clustering results according to the differential privacy model to form the published statistics. Experimental comparison of the real-world datasets manifests that the grid clustering and differential privacy publishing method proposed in this paper is superior to other existing partition publishing methods in terms of range querying accuracy and algorithm operating efficiency.

As a promising next-generation network architecture, named data networking (NDN) supports name-based routing and in-network caching to retrieve content in an efficient, fast, and reliable manner. Most of the studies on NDN have proposed innovative and efficient caching mechanisms and retrieval of content via efficient routing. However, very few studies have targeted addressing the vulnerabilities in NDN architecture, which a malicious node can exploit to perform a content poisoning attack (CPA). This potentially results in polluting the in-network caches, the routing of content, and consequently isolates the legitimate content in the network. In the past, several efforts have been made to propose the mitigation strategies for the content poisoning attack, but to the best of our knowledge, no specific work has been done to address an emerging attack-surface in NDN, which we call an interest flooding attack. Handling this attack-surface can potentially make content poisoning attack mitigation schemes more effective, secure, and robust. Hence, in this article, we propose the addition of a security mechanism in the CPA mitigation scheme that is, Name-Key Based Forwarding and Multipath Forwarding Based Inband Probe, in which we block the malicious face of compromised consumers by monitoring the Cache-Miss Ratio values and the Queue Capacity at the Edge Routers. The malicious face is blocked when the cache-miss ratio hits the threshold value, which is adjusted dynamically through monitoring the cache-miss ratio and queue capacity values. The experimental results show that we are successful in mitigating the vulnerability of the CPA mitigation scheme by detecting and blocking the flooding interface, at the cost of very little verification overhead at the NDN Routers.

Type 2 Diabetes (T2D) is a polygenic and multi-factorial complex disease. The significant challenge of finding genetic markers that could be used to predict T2D risk remains unresolved. Saudi Arabia is one of several ethnicities with a high prevalence rate of T2D. A large number of studies have been performed that explored genetic factors associated with T2D risk. In this work, we conducted a systematic review of published studies on T2D genetic markers in the Saudi population. Multiple databases were employed in the literature review, which focused on studies that either reported the association or non-association of a genetic marker with T2D risk. Using appropriate search terms, we collected, analyzed, and selected 428 articles published between the years of 2000-2018, Of these, 18 articles reported a total of 67 polymorphisms in 46 genes that are strongly linked to T2D in the Saudi population. Most of the relevant studies used genotyping as the primary methodology to identify genetic markers of T2D. Most of these studies were published between the years of 2012-2017. A total of 23 polymorphisms in 17 genes were found to be associated with T2D risk in the Saudi population: KCNJ11, PPARG2, IRS1, VDE, WFS1, JAZF1, CDKN2A/B, TCF7L2, KCNQ1, HNF4A, DUSP9, APOE, HNF1A, SLC30A8, APOC3, SNAP25, and ACE. [JBCGenetics 2018; 1(2.000): 57-65]

OBJECTIVES: To determine the factors responsible for insignificant decrease in pulmonary artery pressure immediately after percutaneous trans-mitral commissurotomy (PTMC) in patients of rheumatic mitral stenosis. METHODS: This cross-sectional study was conducted on patients undergoing PTMC at Cardiology Unit, Lady Reading Hospital, Peshawar, Pakistan from 11th February, 2016 to 28th February, 2018. Pulmonary artery pressure (PAP) was noted before and after PTMC through echocardiography. Data was analyzed with SPSS Version 20.0, categorical and continuous variables were described as frequencies/percentages and mean±SD respectively. Odds Ratio was determined for factors negatively affecting the fall in PAP. RESULTS: Out of 159 patients, 108 (67.9%) were females. Mean age was 25.38±10.67 years. PAP was insignificantly decreased in patients >30 years (p>0.05), symptoms for >5 years (p>0.05), left atrium diameter >4.5cm (p>0.05), atrial fibrillation (p>0.05), right ventricle diameter >2.5cm (p>0.05) and NYHA IV dyspnea (p>0.05). Odds Ratio for failure of significant decrease in PAP immediately post-PTMC was 1.68 for age more than 30 years, 1.10 for symptoms more than 5 years, 3.73 for LA diameter more than 4.5 cm, 2.31 for RV diameter more than 2.5 cm , 2.32 for history of atrial fibrillation and 6.71 for NYHA IV dyspnea. CONCLUSION: Factors which negatively affect the immediate fall in PAP post-PTMC are age >30years, duration of symptoms >5years, LA diameter >4.5cm, history of atrial fibrillation, RV diameter >2.5cm and NYHA IV dyspnea and hence are the poor predictors of successful PTMC while NYHA IV dyspnea has highest Odds for insignificant decrease.

ABSTRACT: OBJECTIVES: To find out the diagnostic role of platelet indices in newly diagnosed patients of myocardial infarction and compare it with normal individuals. METHODS: The platelet indices were measured in 100 newly diagnosed patients of myocardial infarction admitted in cardiology unit, Lady Reading Hospital (LRH) Peshawar, Pakistan and were compared with 100 normal subjects. Two milliliter of venous blood of each subject was collected in K3-EDTA tubes. Platelet indices were assayed by using Sysmex KX-21, Japan auto-analyzer in clinical laboratory of LRH within 10-20 minutes of collection of blood. The data was analyzed by using SPSS version 20. RESULTS: There were 85 males and 15 females in cases and 75 males and 25 females in control group. The mean ages in cases and control group were 50.87+11.02 47.31+12.09 respectively. Platelets count was significantly decreased by 19.93% (p

The promising advancements in the telecommunications and automotive sectors over the years have empowered drivers with highly innovative communication and sensing capabilities, in turn paving the way for the next-generation connected and autonomous vehicles. Today, vehicles communicate wirelessly with other vehicles and vulnerable pedestrians in their immediate vicinity to share timely safety-critical information primarily for collision mitigation. Furthermore, vehicles connect with the traffic management entities via their supporting network infrastructure to become more aware of any potential hazards on the roads and for guidance pertinent to their current and anticipated speeds and travelling course to ensure more efficient traffic flows. Therefore, a secure and low-latency communication is highly indispensable in order to meet the stringent performance requirements of such safety-critical vehicular applications. However, the heterogeneity of diverse radio access technologies and inflexibility in their deployment results in network fragmentation and inefficient resource utilization, and these, therefore, act as bottlenecks in realizing the aims for a highly efficient vehicular networking architecture. In order to overcome such sorts of bottlenecks, this article brings forth the current state-of-the-art in the context of intelligent transportation systems (ITS) and subsequently proposes a software-defined heterogeneous vehicular networking (SDHVNet) architecture for ensuring a highly agile networking infrastructure to ensure rapid network innovation on-demand. Finally, a number of potential architectural challenges and their probable solutions are discussed.

Objectives: This study aimed to assess the impact of the Gross Domestic Product (GDP), spending on Research and Development (R&D), the number of universities and scientific journals on the published research documents, citable documents, citations per document and H-index in environmental sciences in the Middle East countries. Materials and Methods: All the 16 Middle East countries were included in the study. Information regarding the GDP, spending on R&D, the total number of universities and indexed journals was collected. Total number of research documents (papers), citable documents, citations per document and H-index in environmental sciences during the period 1996-2011 was recorded. The study used the World Bank, SCI-mago/Scopus, Web of Science, Journal Citation Reports (Thomson Reuters) as the main sources of information. Results: The mean GDP per capita of all the Middle East countries amounted to 18 125.49±5386.28 US$, spending on R&D was 0.63±0.28 US$, the number of universities equaled 36.56±11.33 and mean ISI indexed journals amounted to 8.25±3.93. The mean number of research documents published in environmental sciences in the Middle East countries during the period 1996-2011 was 2202.12±883.98; citable documents: 2156.87±865.09; citations per document: 8.74±0.73; and the H-index: 35.37±6.17. There was a positive correlation between the money spent on R&D and citations per documents (r = 0.6, p = 0.01), H-Index (r = 0.6, p = 0.01); the number of universities and a total of research documents (r = 0.65, p = 0.006), citable documents (r = 0.65, p = 0.006), H-Index (r = 0.50, p = 0.04), as well as ISI indexed journals and total research documents (r = 0.94, p = 0.0001), citable documents (r = 0.94, p = 0.0001), H-Index (r = 0.73, p = 0.001). Conclusions: The Middle East countries which spend more on R&D and which have a large number of universities and ISI indexed journals are likely to produce more significant volume of research papers in the field of environmental science.

Objective: To evaluate the door-to-needle time for fibrinolytic therapy for acute myocardial infarction (AMI) and toidentify factors associated with a prolonged door-to-needle time. Methodology: This cross-sectional study was conducted at Cardiology Department, Lady Reading Hospital, Peshawarbetween 1st July and 15th September 2010. All patients having AMI, eligible for thrombolysis were included in the study.The time of onset of chest pain and arrival in the hospital and any reason for delay was determined by asking thepatients, the relatives and/or the attending nurse. Results: Out of 140 patients recruited, 60% (n=84) were males and mean age was 57.96 ± 13.55 years. The meandoor to needle time was 72.47± 50.85 minutes (range 25 – 305). Door to needle time of < 30 minutes was achieved in7.1% (10) patients, < 40 minutes in 21.4% (30) and < 50 minutes in 41.4% (58) patients. The main reason for delay instarting thrombolysis was logistic reasons in 42.9% (n=60) patients i.e. transfer from another hospital, non-availabilityof transfer staff from the casualty, unavailability of monitoring beds or non-availability of streptokinase in pharmacy.Other reasons were subtle ECG changes in 17.8% (n=25) cases, misinterpretation of symptoms in 21.5% (n=30),complete heart block needing pacemaker in 4.3% (n=6) , raised blood pressure in 4.3% (n=6) and arrival in oddtiming in 9.2% ( n=13) cases. Conclusion: Door to needle time of < 30 minutes was achieved in only a small minority of our patients. The mainreason for delay was logistics. Key words: Acute myocardial infarction, Door to needle time, Fibrinolytic therapy.

OBJECTIVES: This study aimed to compare the impact of Gross Domestic Product (GDP) per capita, spending on Research and Development (R&D), number of universities, and Indexed Scientific Journals on total number of research documents (papers), citations per document and Hirsch index (H-index) in various science and social science subjects among Asian countries. MATERIALS AND METHODS: In this study, 40 Asian countries were included. The information regarding Asian countries, their GDP per capita, spending on R&D, total number of universities and indexed scientific journals were collected. We recorded the bibliometric indicators, including total number of research documents, citations per document and H-index in various science and social sciences subjects during the period 1996-2011. The main sources for information were World Bank, SCI-mago/Scopus and Web of Science; Thomson Reuters. RESULTS: The mean per capita GDP for all the Asian countries is 14448.31±2854.40 US$, yearly per capita spending on R&D 0.64±0.16 US$, number of universities 72.37±18.32 and mean number of ISI indexed journal per country is 17.97±7.35. The mean of research documents published in various science and social science subjects among all the Asian countries during the period 1996-2011 is 158086.92±69204.09; citations per document 8.67±0.48; and H-index 122.8±19.21. Spending on R&D, number of universities and indexed journals have a positive correlation with number of published documents, citations per document and H-index in various science and social science subjects. However, there was no association between the per capita GDP and research outcomes. CONCLUSION: The Asian countries who spend more on R&D have a large number of universities and scientific indexed journals produced more in research outcomes including total number of research publication, citations per documents and H-index in various science and social science subjects.