Biochar for adsorption remediation of Ciprofloxacin in wastewater

Ciprofloxacin is one of the major pharmaceutical pollutants being fed into waterbodies around the world. It is consumed by humans and animals, passed into the environment mainly through hospital waste. Ciprofloxacin, an antibiotic could be very harmful for environmental, disturbing the natural balance and can further increase the problem of antibiotic resistance. This thesis aims to study biochar as an adsorbent to remove ciprofloxacin from wastewater. Nine samples of biochar from wood and sludge pyrolyzed at different temperatures were characterized using XRD, FTIR, TGA and BET using Nitrogen and Carbon dioxide. Finally, batch equilibrium method was performed to investigate the sorption of ciprofloxacin on biochar. The results showed overall promising protentional of biochar for sorption of Ciprofloxacin. Wood biochar pyrolyzed at higher temperature exhibited larger specific area and more micropore volume as compared to sludge samples of same temperature, hence better suited for the task. In batch adsorption, wood and sludge sample did not reach saturation point and showed lower sorption capacity from literature. Also, the sorption capacity trend which was expected to increase did not match with previous data for sludge sample. This could be due to lower adsorbent quantity and Ciprofloxacin concentration used and variation in biochar composition especially sludge. Future study with changing suggested variables like mass of biochar, concentration of Ciprofloxacin, and changing temperature etc will help fully understand its sorption capacity and eventually support in application of biochar in adsorption of ciprofloxacin

Indoleamine 2,3- Dioxygenase: A Novel Immunotherapeutic Target for Osteosarcoma

Introduction: Tumour-emitted molecules induce immunosuppression in the tumour microenvironment. An immunosuppressive enzyme, indoleamine 2,3-dioxygenase (IDO/IDO1), facilitates immune escape in several malignant tumours, including osteosarcoma. Upregulation of IDO establishes a tolerogenic environment in the tumour and the tumour-draining lymph nodes. IDO-induced downregulation of effector T-cells and upregulation of local regulatory T-cells creates immunosuppression and promotes metastasis. Observations: Osteosarcoma is the most common bone tumour characterised by immature bone formation by the tumour cells. Almost 20% of osteosarcoma patients present with pulmonary metastasis at the time of diagnosis. The improvement in therapeutic modalities for osteosarcoma has been in a stagnant phase for two decades. Therefore, the development of novel immunotherapeutic targets for osteosarcoma is emergent. High IDO expression is associated with metastasis and poor prognosis in osteosarcoma patients. Conclusion and Relevance: At present, only a few studies are available describing IDO’s role in osteosarcoma. This review describes the prospects of IDO not only as a prognostic marker but also as an immunotherapeutic target for osteosarcoma

Multi-Robot Path Planning for Persistent Monitoring in Stochastic and Adversarial Environments

In this thesis, we study multi-robot path planning problems for persistent monitoring tasks. The goal of such persistent monitoring tasks is to deploy a team of cooperating mobile robots in an environment to continually observe locations of interest in the environment. Robots patrol the environment in order to detect events arriving at the locations of the environment. The events stay at those locations for a certain amount of time before leaving and can only be detected if one of the robots visits the location of an event while the event is there. In order to detect all possible events arriving at a vertex, the maximum time spent by the robots between visits to that vertex should be less than the duration of the events arriving at that vertex. We consider the problem of finding the minimum number of robots to satisfy these revisit time constraints, also called latency constraints. The decision version of this problem is PSPACE-complete. We provide an O(log p) approximation algorithm for this problem where p is the ratio of the maximum and minimum latency constraints. We also present heuristic algorithms to solve the problem and show through simulations that a proposed orienteering-based heuristic algorithm gives better solutions than the approximation algorithm. We additionally provide an algorithm for the problem of minimizing the maximum weighted latency given a fixed number of robots. In case the event stay durations are not fixed but are drawn from a known distribution, we consider the problem of maximizing the expected number of detected events. We motivate randomized patrolling paths for such scenarios and use Markov chains to represent those random patrolling paths. We characterize the expected number of detected events as a function of the Markov chains used for patrolling and show that the objective function is submodular for randomly arriving events. We propose an approximation algorithm for the case where the event durations for all the vertices is a constant. We also propose a centralized and an online distributed algorithm to find the random patrolling policies for the robots. We also consider the case where the events are adversarial and can choose where and when to appear in order to maximize their chances of remaining undetected. The last problem we study in this thesis considers events triggered by a learning adversary. The adversary has a limited time to observe the patrolling policy before it decides when and where events should appear. We study the single robot version of this problem and model this problem as a multi-stage two player game. The adversary observes the patroller’s actions for a finite amount of time to learn the patroller’s strategy and then either chooses a location for the event to appear or reneges based on its confidence in the learned strategy. We characterize the expected payoffs for the players and propose a search algorithm to find a patrolling policy in such scenarios. We illustrate the trade off between hard to learn and hard to attack strategies through simulations

Robot Patrolling for Stochastic and Adversarial Events

In this thesis, we present and analyze two robot patrolling problems. The first problem discusses stochastic patrolling strategies in adversarial environments where intruders use the information about a patrolling path to increase chances of successful attacks on the environment. We use Markov chains to design the random patrolling paths on graphs. We present four different intruder models, each of which use the information about patrolling paths in a different manner. We characterize the expected rewards for those intruder models as a function of the Markov chain that is being used for patrolling. We show that minimizing the reward functions is a non convex constrained optimization problem in general. We then discuss the application of different numerical optimization methods to minimize the expected reward for any given type of intruder and propose a pattern search algorithm to determine a locally optimal patrolling strategy. We also show that for a certain type of intruder, a deterministic patrolling policy given by the orienteering tour of the graph is the optimal patrolling strategy. The second problem that we define and analyze is the Event Detection and Confirmation Problem in which the events arrive randomly on the vertices of a graph and stay active for a random amount of time. The events that stay longer than a certain amount of time are defined to be true events. The monitoring robot can traverse the graph to detect newly arrived events and can revisit these events in order to classify them as true events. The goal is to maximize the number of true events that are correctly classified by the robot. We show that the off-line version of the problem is NP-hard. We then consider a simple patrolling policy based on the TSP tour of the graph and characterize the probability of correctly classifying a true event. We investigate the problem when multiple robots follow the same path, and show that the optimal spacing between the robots in that case can be non uniform

Lexical Richness, a reliable measure of Intermediate L2 Learners’ current status of acquisition of English language

This article aims to explore the utility of the relationship between lexical richness and size as an indicator of acquisition status of English language of L2 learners of intermediate level, having rural background on the basis of their self -written output. 126 students’ essays were used to measure the lexical richness (126 students of Sem-I and 63 students of Sem-II) Lexical Frequency Profile was used to sort it out.  Its values discriminated students of different proficiency level and displayed L2 Learners vocabulary size in use. LFP result’s consistency and legitimacy was obtained by comparing its result with an independent and separate measure of vocabulary size, VLT. The result showed that lexical richness has a direct link with vocabulary size (receptive vocabulary) of L2 learners. It discusses the utility of the inference based on the lexical richness of L2’s written text for monitoring purpose of language acquisition process of L2 learners and to determine appropriate strategies for the desired growth of vocabulary size

A CROSS-SECTIONAL STUDY TO ASSESS THE PERVASIVENESS OF IRRESISTIBLE MALADIES IN THE FLOOD AFFECTED AREAS

Background: Natural disasters are calamitous occasions with air, geologic, and hydrologic birthplaces. Calamities incorporate quakes, volcanic emissions, avalanches, waves, floods and dry season. Pakistan confronted floods in 2010 that started following substantial rainstorm rains in the Khyber Pakhtunkhwa, Sindh, Punjab and Baluchistan areas of Pakistan. Objectives: To assess the pervasiveness of irresistible maladies in the flood influenced people. Patients and Methods: This cross-sectional investigation was led at Services Hospital, Lahore from October 2017 to May 2018. The information with respect to age, sex, training, occupation, land beginning and nature of illness were gotten from the patients going to flood alleviation therapeutic camp for human services and were broke down on SPSS. Results: Amid the investigation time frame, 8074 patients were inspected. Patients all things considered and both genders were incorporated. Male to female proportion was 1 to 1.01. The patients run from neonates to over 70 years old. The kids younger than ten years were 40.99%. Among the flood affected, the most well-known sicknesses in diminishing request of recurrence were looseness of the bowels, RTI, skin contamination, eye disease, ear disease and bone injury. Conclusion: Health education, clean water and environmental hygiene with proper and timely medical cover can reduce mortality and grimness. Keywords: Flood, Flood Affected, Disease Epidemic