Conflict in FATA and Governance

[Federally Administered Tribal Areas (FATA) is a distinct territory within Pakistan as far as its unique culture, traditions and governance structure is concerned. These tribal areas have generally been peaceful and calm throughout Pakistan's history but peace was ruined, as fallout of War on Terror and subsequent military operations since 2003-04. As Americans are themselves negotiating with Afghan Taliban, there can be no reason for Pakistan not to give peace a chance by starting a process of reorientation and reconciliation on its own territory. Besides, colonial-era body of laws that isolates the region from the rest of the country, giving it an ambiguous constitutional status and denying the political freedom and economic opportunity to its population, needs to be amended according to the aspirations of the people, at the earliest and in a comprehensive way. The longer the government delays implementing political, administrative, judicial and economic reforms, the more difficult it will be to stabilize the region.] </p

Potent antibacterial activity of MXene–functionalized graphene nanocomposites

Two dimensional (2D) nanomaterials display properties with significant biological utility (e.g., antimicrobial activity). In this study, MXene–functionalized graphene (FG) nanocomposites with Ti3C2Tx in varying ratios (FG : Ti3C2Tx, 25 : 75%, 50 : 50%, and 75 : 25%) were prepared and characterized via scanning electron microscopy, scanning electron microscopy-energy dispersive X-ray (SEM-EDX), high-resolution transmission electron microscopy (HRTEM), and zeta potential analysis. Their cytotoxicity was assessed using immortalized human keratinocytes (HaCaT) cells at three different timepoints, and antibacterial activity was assessed using Gram-positive Methicillin resistant Staphylococcus aureus, MRSA, and Gram-negative neuro-pathogenic Escherichia coli K1 (E. coli K1) in vitro. The nanomaterials and composites displayed potent antibacterial effects against both types of bacteria and low cytotoxicity against HaCaT cells at 200 μg mL−1, which is promising for their utilization for biomedical applications

HOGLA LEAF AS A POTENTIAL BIO-ADSORBENT FOR THE TREATMENT OF REACTIVE DYES IN TEXTILE EFFLUENTS

A new bio-adsorbent to remove reactive dyes from textile effluent was investigated in the present study. The adsorbent was the leaves of locally available hogla plant (Typha angustata). Initially, sunfix yellow, a reactive dye widely used in textile effluents, was used to check the removal efficiency in terms of contact time, pH of dye solution and adsorbent dosage. Complete removal (100%) of dye was achieved at adsorbent/dye ratio of 2300:1 at pH 10 with 180 minutes contact time. The adsorbent was then applied to deep colored, raw textile wastewater samples and it was found that 2.3 g of adsorbent was able to convert 100 mL of deep colored wastewater to transparent water at pH 10. Additionally, treatment by the adsorbent resulted in significant decreases in pH, BOD, COD, TS, TDS and TSS of wastewater, while improving the DO level

A migration aware scheduling technique for real-time aperiodic tasks over multiprocessor systems

Multi-processor systems consist of more than one processor and are mostly used for computationally intensive applications. Real-time systems are those systems that require completing execution of tasks within a pre-defined deadline. Traditionally, multiprocessor systems are given attention in periodic models, where tasks are executed at regular intervals of time. Gradually, as maturity in a multiprocessor design had increased; their usage has become very common for real-time systems to execute both periodic and aperiodic tasks. As the priority of an aperiodic task is usually but not essentially greater than the priority of a periodic task, they must be completed within the deadline. There is a lot of research works on multiprocessor systems with scheduling of periodic tasks, but the task scheduling is relatively remained unexplored for a mixed workload of both periodic and aperiodic tasks. Moreover, higher energy consumption is another main issue in multiprocessor systems. Although it could be reduced by using the energy-aware scheduling technique, the response time of aperiodic tasks still increases. In the literature, various techniques were suggested to decrease the energy consumption of these systems. However, the study on reducing the response time of aperiodic tasks is limited. In this paper, we propose a scheduling technique that: 1) executes aperiodic tasks at full speed and migrates periodic tasks to other processors if their deadline is earlier than aperiodic tasks-reduces the response time and 2) executes aperiodic tasks with lower speed by identifying appropriate processor speed without affecting the response time-reduces energy consumption. Through simulations, we demonstrate the efficiency of the proposed algorithm and we show that our algorithm also outperforms the well-known total bandwidth server algorithm

Role of the T cell in the genesis of angiotensin II–induced hypertension and vascular dysfunction

Hypertension promotes atherosclerosis and is a major source of morbidity and mortality. We show that mice lacking T and B cells (RAG-1−/− mice) have blunted hypertension and do not develop abnormalities of vascular function during angiotensin II infusion or desoxycorticosterone acetate (DOCA)–salt. Adoptive transfer of T, but not B, cells restored these abnormalities. Angiotensin II is known to stimulate reactive oxygen species production via the nicotinamide adenosine dinucleotide phosphate (NADPH) oxidase in several cells, including some immune cells. Accordingly, adoptive transfer of T cells lacking the angiotensin type I receptor or a functional NADPH oxidase resulted in blunted angiotensin II–dependent hypertension and decreased aortic superoxide production. Angiotensin II increased T cell markers of activation and tissue homing in wild-type, but not NADPH oxidase–deficient, mice. Angiotensin II markedly increased T cells in the perivascular adipose tissue (periadventitial fat) and, to a lesser extent the adventitia. These cells expressed high levels of CC chemokine receptor 5 and were commonly double negative (CD3+CD4−CD8−). This infiltration was associated with an increase in intercellular adhesion molecule-1 and RANTES in the aorta. Hypertension also increased T lymphocyte production of tumor necrosis factor (TNF) α, and treatment with the TNFα antagonist etanercept prevented the hypertension and increase in vascular superoxide caused by angiotensin II. These studies identify a previously undefined role for T cells in the genesis of hypertension and support a role of inflammation in the basis of this prevalent disease. T cells might represent a novel therapeutic target for the treatment of high blood pressure

Nitric Oxide Contributes to Vasomotor Tone in Hypertensive African Americans Treated With Nebivolol and Metoprolol

Endothelial dysfunction is more prevalent in African Americans (AAs) compared with whites. The authors hypothesized that nebivolol, a selective β1 -antagonist that stimulates nitric oxide (NO), will improve endothelial function in AAs with hypertension when compared with metoprolol. In a double-blind, randomized, crossover study, 19 AA hypertensive patients were randomized to a 12-week treatment period with either nebivolol 10 mg or metoprolol succinate 100 mg daily. Forearm blood flow (FBF) was measured using plethysmography at rest and after intra-arterial infusion of acetylcholine and sodium nitroprusside to estimate endothelium-dependent and independent vasodilation, respectively. Physiologic vasodilation was assessed during hand-grip exercise. Measurements were repeated after NO blockade with L-N(G) -monomethylarginine (L-NMMA) and after inhibition of endothelium-derived hyperpolarizing factor (EDHF) with tetraethylammonium chloride (TEA). NO blockade with L-NMMA produced a trend toward greater vasoconstriction during nebivolol compared with metoprolol treatment (21% vs 12% reduction in FBF, P=.06, respectively). This difference was more significant after combined administration of L-NMMA and TEA (P\u3c.001). Similarly, there was a contribution of NO to exercise-induced vasodilation during nebivolol but not during metoprolol treatment. There were significantly greater contributions of NO and EDHF to resting vasodilator tone and of NO to exercise-induced vasodilation with nebivolol compared with metoprolol in AAs with hypertension

A heuristic approach for finding similarity indexes of multivariate data sets

Multivariate data sets (MDSs), with enormous size and certain ratio of noise/outliers, are generated routinely in various application domains. A major issue, tightly coupled with these MDSs, is how to compute their similarity indexes with available resources in presence of noise/outliers - which is addressed with the development of both classical and non-metric based approaches. However, classical techniques are sensitive to outliers and most of the non-classical approaches are either problem/application specific or overlay complex. Therefore, the development of an efficient and reliable algorithm for MDSs, with minimum time and space complexity, is highly encouraged by the research community. In this paper, a non-metric based similarity measure algorithm, for MDSs, is presented that solves the aforementioned issues, particularly, noise and computational time, successfully. This technique finds the similarity indexes of noisy MDSs, of both equal and variable sizes, through utilizing minimum possible resources i.e., space and time. Experiments were conducted with both benchmark and real time MDSs for evaluating the proposed algorithm`s performance against its rival algorithms, which are traditional dynamic programming based and sequential similarity measure algorithms. Experimental results show that the proposed scheme performs exceptionally well, in terms of time and space, than its counterpart algorithms and effectively tolerates a considerable portion of noisy data

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

[EN] Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.This research work was funded by the Ministry of Education and the Deanship of Scientific Research, Najran University. Kingdom of Saudi Arabia, under code number NU/ESCI/19/001.Hussain, A.; Draz, U.; Ali, T.; Tariq, S.; Glowacz, A.; Irfan, M.; Antonino Daviu, JA.... (2020). Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach. Energies. 13(15):1-22. https://doi.org/10.3390/en13153930S1221315Lionetto, M. G., Guascito, M. 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Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices. Sensors, 14(3), 5687-5701. doi:10.3390/s140305687Ali, T., Draz, U., Yasin, S., Noureen, J., shaf, A., & Zardari, M. (2018). An Efficient Participant’s Selection Algorithm for Crowdsensing. International Journal of Advanced Computer Science and Applications, 9(1). doi:10.14569/ijacsa.2018.090154Ali, T., Noureen, J., Draz, U., Shaf, A., Yasin, S., & Ayaz, M. (2018). Participants Ranking Algorithm for Crowdsensing in Mobile Communication. ICST Transactions on Scalable Information Systems, 5(16), 154476. doi:10.4108/eai.13-4-2018.15447