CARLA+: An Evolution of the CARLA Simulator for Complex Environment Using a Probabilistic Graphical Model

In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant to the complex urban environment, specifically, highlighting the challenges that are unaddressed and are within the scope of collaborative autonomous driving. To this end, we model the dynamic urban environment leveraging a probabilistic graphical model (PGM). To develop the proposed solution, a realistic simulation environment is required. There are a number of simulators—CARLA (Car Learning to Act), one of the prominent ones, provides rich features and environment; however, it still fails on a few fronts, for example, it cannot fully capture the complexity of an urban environment. Moreover, the classical CARLA mainly relies on manual code and multiple conditional statements, and it provides no pre-defined way to do things automatically based on the dynamic simulation environment. Hence, there is an urgent need to extend the off-the-shelf CARLA with more sophisticated settings that can model the required dynamics. In this regard, we comprehensively design, develop, and implement an extension of a classical CARLA referred to as CARLA+ for the complex environment by integrating the PGM framework. It provides a unified framework to automate the behavior of different actors leveraging PGMs. Instead of manually catering to each condition, CARLA+ enables the user to automate the modeling of different dynamics of the environment. Therefore, to validate the proposed CARLA+, experiments with different settings are designed and conducted. The experimental results demonstrate that CARLA+ is flexible enough to allow users to model various scenarios, ranging from simple controlled models to complex models learned directly from real-world data. In the future, we plan to extend CARLA+ by allowing for more configurable parameters and more flexibility on the type of probabilistic networks and models one can choose. The open-source code of CARLA+ is made publicly available for researchers

Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving

Autonomous vehicles and smart roads are not new concepts and the undergoing development to empower the vehicles for higher levels of automation has achieved initial milestones. However, the transportation industry and relevant research communities still require making considerable efforts to create smart and intelligent roads for autonomous driving. To achieve the results of such efforts, the CCAM infrastructure is a game changer and plays a key role in achieving higher levels of autonomous driving. In this paper, we present a smart infrastructure and autonomous driving capabilities enhanced by CCAM infrastructure. Meaning thereby, we lay down the technical requirements of the CCAM infrastructure: identify the right set of the sensory infrastructure, their interfacing, integration platform, and necessary communication interfaces to be interconnected with upstream and downstream solution components. Then, we parameterize the road and network infrastructures (and automated vehicles) to be advanced and evaluated during the research work, under the very distinct scenarios and conditions. For validation, we demonstrate the machine learning algorithms in mobility applications such as traffic flow and mobile communication demands. Consequently, we train multiple linear regression models and achieve accuracy of over 94% for predicting aforementioned demands on a daily basis. This research therefore equips the readers with relevant technical information required for enhancing CCAM infrastructure. It also encourages and guides the relevant research communities to implement the CCAM infrastructure towards creating smart and intelligent roads for autonomous driving

The possible role of cerium oxide (CeO2) nanoparticles in prevention of neurobehavioral and neurochemical changes in 6-hydroxydopamineinduced parkinsonian disease

Cerium oxide nanoparticles (CeO2NPs) is an efficient neuroprotective agent and showed promising effects in some neurodegenerative diseases such as Alzheimer’s disease and multiple sclerosis. However, the implication of CeO2NPs in Parkinsonism remains to be investigated. The aim of this study was to assess the possible role of CeO2NPs as a neuroprotective agent against the development of behavioral and biochemical changes in rat model of Parkinson’s disease. Thirty rats were included and received left intrastriatal injection of either saline (controls, n = 10) or 6-hydroxy dopamine (6-OHDA) in untreated group (n = 10) and 10 rats were received intraperitoneal injection of low dose CeO2NPs two hours before surgery, and continued once daily for 6 weeks (preventive group). At the end of experimental period, rats were subjected to behavioral assessment and then killed for biochemical analysis of striatal dopamine levels, oxidative stress markers and caspase-3 activity. Results showed that CeO2NPs resulted in partial neuroprotection against disturbances in motor performance. It also partially decreased apoptosis and oxidative stress in preventive group, while it failed to increase striatal dopamine level as compared to untreated rats. The present study verified some neuroprotective effects of CeO2NPs in 6-OHDA-induced Parkinsonian rats through their antioxidant and anti apoptotic effects. Some of these effects persisted till the end of six weeks whereas others declined after three weeks. A larger dose may be needed to produce more valuable effects and to maintain protection for a longer period

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Probabilistic leak detection and quantification using multi-output Gaussian processes

A water distribution system WDS is often divided into smaller isolated and independent zones called district metering areas (DMA). A DMA can have anywhere from a few hundred to a few thousand properties. Normally only three locations within a district metering area are actively monitored for pressure or flow readings. These are the supply point pressure and flow and the critical point pressure which is the point of the lowest pressure in the DMA. As leakage rates are typically directly proportional to average pressures in the DMA, keeping the network pressure as low as possible while maintaining desired serviceability is an effective and widely used method for leak reduction. With advancement in technology this network pressure reduction is now done in real-time, where the network pressure is increased or decreased based on the demand. However, such real-time optimisation changes the DMA dynamics making it different from traditional unoptimised DMAs. We consider the problem of detecting and quantifying leaks in pressure optimised DMA, using only these three DMA-level hydraulic measurements. The DMA-level measurements represent the current aggregate water demand/consumption within the DMA. Detecting leaks at this point is challenging, particularly small leaks, as they do not produce a significant increase in the aggregated DMA-level measurements. Furthermore, the DMA-level data exhibits input signal dependence whereby both noise and leaks are dependent on the flow and pressure being measured, making leak detection task more difficult. To address this, we first propose a Gaussian process (GP) based approach that uses only the DMA-level flow to detect leaks (NSGP). We devise an additive diagonal noise covariance for the GP that is able to handle the input dependant noise observed in this setting. A parameterised mean step change function is used to detect and approximate leaks. As accurate leak data is often not available due to poor record keeping, we develop a detailed simulated model of a pressure optimised DMA and use it for analysing proposed leak detection methods. We show that active pressure optimisation changes the dynamics of a DMA. In light of the change in DMA dynamics, we proposed a domain specific, data driven, multi output gaussian process model, to detect and quantify leaks in pressure optimised DMAs (SMOGP). The novelty of the model is, firstly its ability to use all available information from a DMA to detect leaks, secondly the ability to model the pressure dependant leak process mathematically within the GP framework. We compare the performance of the proposed methods with the current state of the art leak detection method. We show that our proposed method out perform other approaches considerably both in terms of the accuracy of leak detection and leak magnitude estimation

Probabilistic leak detection and quantification using multi-output Gaussian processes

A water distribution system WDS is often divided into smaller isolated and independent zones called district metering areas (DMA). A DMA can have anywhere from a few hundred to a few thousand properties. Normally only three locations within a district metering area are actively monitored for pressure or flow readings. These are the supply point pressure and flow and the critical point pressure which is the point of the lowest pressure in the DMA. As leakage rates are typically directly proportional to average pressures in the DMA, keeping the network pressure as low as possible while maintaining desired serviceability is an effective and widely used method for leak reduction. With advancement in technology this network pressure reduction is now done in real-time, where the network pressure is increased or decreased based on the demand. However, such real-time optimisation changes the DMA dynamics making it different from traditional unoptimised DMAs. We consider the problem of detecting and quantifying leaks in pressure optimised DMA, using only these three DMA-level hydraulic measurements. The DMA-level measurements represent the current aggregate water demand/consumption within the DMA. Detecting leaks at this point is challenging, particularly small leaks, as they do not produce a significant increase in the aggregated DMA-level measurements. Furthermore, the DMA-level data exhibits input signal dependence whereby both noise and leaks are dependent on the flow and pressure being measured, making leak detection task more difficult. To address this, we first propose a Gaussian process (GP) based approach that uses only the DMA-level flow to detect leaks (NSGP). We devise an additive diagonal noise covariance for the GP that is able to handle the input dependant noise observed in this setting. A parameterised mean step change function is used to detect and approximate leaks. As accurate leak data is often not available due to poor record keeping, we develop a detailed simulated model of a pressure optimised DMA and use it for analysing proposed leak detection methods. We show that active pressure optimisation changes the dynamics of a DMA. In light of the change in DMA dynamics, we proposed a domain specific, data driven, multi output gaussian process model, to detect and quantify leaks in pressure optimised DMAs (SMOGP). The novelty of the model is, firstly its ability to use all available information from a DMA to detect leaks, secondly the ability to model the pressure dependant leak process mathematically within the GP framework. We compare the performance of the proposed methods with the current state of the art leak detection method. We show that our proposed method out perform other approaches considerably both in terms of the accuracy of leak detection and leak magnitude estimation

A noise scaled semi parametric gaussian process model for real time water network leak detection in the presence of heteroscedasticity

The timely detection of leaks in water distribution systems is critical to the sustainable provision of clean water to consumers. Increasingly, water companies are deploying remote sensors to measure water flow in real-time in order to detect such leaks. However, in practice, for typical District Metering Zones (DMZ), financial constraints limit the number of deployable real time flow sensors/meters to one or two, thus constraining leak detection to be based on the aggregated flow being monitored at these point. Such aggregated flow data typically exhibits input signal dependence whereby both noise and leaks are dependent on the flow being measured. This limited monitoring and input signal dependence make conventional approaches based on simple thresholds unreliable for real time leak detection. To address this, we propose a Gaussian process (GP) model with an additive diagonal noise covariance that is able to handle the input dependent noise observed in this setting. A parameterised mean step change function is used to detect leaks and to estimate their size. Using prior water distribution systems (WDS) knowledge we dynamically bound and discretize the detection parameters of the step change mean function, reducing and pruning the parameter search space considerably. We evaluate the proposed noise scaled GP (NSGP) against both the latest research work on GP based fault detection methods and the current state of the art and applied leak detection approaches in water distribution systems. We show that our proposed method out performs other approaches, on real water network data with synthetically generated time varying leaks, with a detection accuracy of 99%, almost zero false positive detections and the lowest root mean

Mining object-oriented software execution traces to discover patterns for automated testing

With the evolution of new software technologies, the requirements for automated testing are becoming more and more stringent. With increasing size of software projects, manual testing is becoming less efficient. For automated testing one of the most important question is, what to focus upon while testing? For a large number of functions along with large number of possible call sequences, it is very hard to generate test cases that cover all possible paths of control flow. By finding patterns in the calling sequences we will be able to identify more defects by focusing our testing efforts on those patterns. In this paper, we have described our work on tracing call sequences using Aspect Oriented Programming methodology and discovering those patterns in call sequences using data mining techniques