Evaluation of the Feasibility of an Intelligent Transport System for an Urban Area

The Intelligent Transportation System is one of the burgeoning inventions that uses new technology to solve a variety of issues. Its compatibility with real-world issues in developing nations like India, such as traffic congestion, infrastructure demand, high traffic loads, and non-lane traffic systems. It is critical to assess a technology's potential in order to determine its viability. The goal of this article is to determine the utility cost ratio of implementation so that it may be evaluated without changing the existing infrastructure design. The end result is a utility cost analysis approach that takes social, economic, and environmental issues into account. As a result, the analysis is quickly examined so that the technology may be applied according to its appropriateness. Keywords: Investments, Congestion, Intelligent Transportation System (ITS), Benefits, Traffic.

Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning

In this study, we aim to bridge the gap between the theoretical understanding of attacks against collaborative machine learning workflows and their practical ramifications by considering the effects of model architecture, learning setting and hyperparameters on the resilience against attacks. We refer to such mitigations as model adaptation. Through extensive experimentation on both, benchmark and real-life datasets, we establish a more practical threat model for collaborative learning scenarios. In particular, we evaluate the impact of model adaptation by implementing a range of attacks belonging to the broader categories of model inversion and membership inference. Our experiments yield two noteworthy outcomes: they demonstrate the difficulty of actually conducting successful attacks under realistic settings when model adaptation is employed and they highlight the challenge inherent in successfully combining model adaptation and formal privacy-preserving techniques to retain the optimal balance between model utility and attack resilience.

Confidential machine learning on untrusted platforms: a survey

With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address these concerns. In this survey, we summarize notable studies in this emerging area of research. With a unified framework, we highlight the critical challenges and innovations in outsourcing machine learning confidentially. We focus on the cryptographic approaches for confidential machine learning (CML), primarily on model training, while also covering other directions such as perturbation-based approaches and CML in the hardware-assisted computing environment. The discussion will take a holistic way to consider a rich context of the related threat models, security assumptions, design principles, and associated trade-offs amongst data utility, cost, and confidentiality.

Economic Evaluation of Combined Heat and Power Integrated With Food Waste-Based Ethanol Production

The concern of food waste (FW) impact on the environment, societies, and economies, has triggered many researchers to find alternative ways to utilize these materials. FW can be high in glucose and other sugars (depending upon the food used) and has the potential to be converted into value-added products such as ethanol. Ethanol is an organic material that has a high demand from different industries for products such as fuel, beverages, pharmaceuticals, and other industrial applications. FW fermentation to produce ethanol may be a promising method, and might results in positive impacts on economies. However, it is a challenge for the product price to compete with that of corn ethanol due to low yield and the inconsistency of FW composition. Thus, to increase the profitability, a conventional fermentation plant integrated with a combined heat and power (CHP) system might be a great combination, and was analyzed in this study. Solid waste stream from the process can be converted into energy and could reduce the utility cost. Therefore, the main focus of this study is to evaluate the economic impact of this integrated system by estimating the minimum selling price (MSP) using techno-economic analysis (TEA) and compare to conventional plants without CHP. Results from this analysis showed that the MSE value for this integrated system was $1.88 per gallon ($0.50 per liter). This study suggests that an integrated system with CHP was found to be more economical and attractive to be implemented on a commercial scale.

Robust adaptive fault estimation for multizone building VAV terminal units

In this paper, we propose a novel bilinear observer- based robust fault detection, isolation and adaptive fault estimation methodology to precisely estimate a class of actuator faults, namely bias in damper position and lock-in-place faults, in Variable-Air-Volume (VAV) terminal units of Heating Ventilation and Air-Conditioning (HVAC) systems. The proposed adaptive fault estimator is robust in the sense that the fault estimates are not affected by the unmeasured disturbance variable and that the effects of measurement noises on fault estimates are attenuated. The fault estimation algorithm with the integrated building control system improves occupants comfort and reduces the operation, maintenance, and utility cost, thereby reducing the impact on the environment. The effectiveness of the methodology for adaptive estimation of multiple or single VAV damper faults is successfully demonstrated through different simulation scenarios with SIMBAD (SIMulator of Building And Devices), which is being used in industries for testing and validation of building energy management systems.

Robust adaptive fault estimation for multizone building VAV terminal units

In this paper, we propose a novel bilinear observer- based robust fault detection, isolation and adaptive fault estimation methodology to precisely estimate a class of actuator faults, namely bias in damper position and lock-in-place faults, in Variable-Air-Volume (VAV) terminal units of Heating Ventilation and Air-Conditioning (HVAC) systems. The proposed adaptive fault estimator is robust in the sense that the fault estimates are not affected by the unmeasured disturbance variable and that the effects of measurement noises on fault estimates are attenuated. The fault estimation algorithm with the integrated building control system improves occupants comfort and reduces the operation, maintenance, and utility cost, thereby reducing the impact on the environment. The effectiveness of the methodology for adaptive estimation of multiple or single VAV damper faults is successfully demonstrated through different simulation scenarios with SIMBAD (SIMulator of Building And Devices), which is being used in industries for testing and validation of building energy management systems.

Resource Recommender for Cloud-Edge Engineering

The interaction between artificial intelligence (AI), edge, and cloud is a fast-evolving realm in which pushing computation close to the data sources is increasingly adopted. Captured data may be processed locally (i.e., on the edge) or remotely in the clouds where abundant resources are available. While many emerging applications are processed in situ due primarily to their data intensiveness and short-latency requirement, the capacity of edge resources remains limited. As a result, the collaborative use of edge and cloud resources is of great practical importance. Such collaborative use should take into account data privacy, high latency and high bandwidth consumption, and the cost of cloud usage. In this paper, we address the problem of resource allocation for data processing jobs in the edge-cloud environment to optimize cost efficiency. To this end, we develop Cost Efficient Cloud Bursting Scheduler and Recommender (CECBS-R) as an AI-assisted resource allocation framework. In particular, CECBS-R incorporates machine learning techniques such as multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks. In addition to preserving privacy due to employing edge resources, the edge utility cost plus public cloud billing cycles are adopted for scheduling, and jobs are profiled in the cloud-edge environment to facilitate scheduling through resource recommendations. These recommendations are outputted by the MLP neural network and LSTM for runtime estimation and resource recommendation, respectively. CECBS-R is trained with the scheduling outputs of Facebook and grid workload traces. The experimental results based on unseen workloads show that CECBS-R recommendations achieve a ∼65% cost saving in comparison to an online cost-efficient scheduler (BOS), resource management service (RMS), and an adaptive scheduling algorithm with QoS satisfaction (AsQ).

Comparison of various methods for Group B Streptococcus Identification and Cost Comparison

Objectives To verify the utility, cost and feasibility of various methods for Group (Gp) B Streptococcus (GBS) identification; To elucidate the prevalence and resistance patterns of GBS in a clinic population. Design and Methods Isolates were collected from pregnant patients by culturing lower vaginal swabs (LVS) and rectal swabs (RS) from May to September 2015 at Sangre Grande Hospital, Trinidad. These were screened in Carrot Broth (CB), Gram stained and isolated on Blood Agar (BA) and Streptococcus Selective Agar (SSA) simultaneously. Identification was done simultaneously with the Microscan Autoscan® and Streptex® – Streptococcal Grouping kit, to establish concordance. The Microscan Autoscan® panel identified various Streptococcus spp. and Streptex® identified Lancefield Gps A-G. Antimicrobial susceptibility was determined with the Microscan Autoscan® for Gp B Streptococcus only. Discordant isolate identifications between Microscan and Streptex were retained for further analysis. Gram staining was also carried out on negative CB. The total cost of identification of isolates was calculated in Trinidad and Tobago dollars. Results 36 LVS & RS samples were collected: 16 Gp B, 1 Gp C, 11 Gp D & 8 with no Streptococci Gp identification. Prevalence of Gp B Streptococci: 44.4%. Concordance between CB and other methods was 86.1%. Sensitivity: 100%; CI (72% – 100%), Specificity: 80%; CI (59% – 93%). Accuracy: 86.1%; CI (70% – 96%). Microscan Autoscan® and Streptex® identified 100% of isolates correctly. Penicillin resistance was 12.5%, Vancomycin and Clindamycin sensitivity were 100% each. The costs for isolation media plates were – SSA: $ 26 per plate and BA: $18 per plate. Streptococcus identification and sensitivity using Microscan Autoscan® Panel 33: $114 per isolate (with blood agar). Streptococcus identification using Streptex®: $193 per isolate (with blood agar) and Carrot Broth: $49 per isolate. Key Words: Group B Streptococci, Carrot Broth, Microscan, Streptex, SSA, Cost

COVID-19 impact on cultural and natural Pan-European thematic routes

Although the definitive effect is not measurable yet, it is evident that the hospitality and tourism sectors have endured the greatest pressure in the coronavirus pandemic crisis. This paper presents the analysis of the impact of the crisis on the thematic tourism routes. The emphasis is placed on investigating the impacts on market structure and seasonality, being the external dimensions, and on employment and cost-controlled measures as instruments of the internal management dimension. The case study analysis employed is based on the empirical examples of Pan-European thematic routes titled "Roman Emperors & Danube Wine Route" (RER & DWR) and "Via Dinarica Route" (VDR). The paper also discusses models of various scenarios for business recovery and further development. The findings show that COVID-19 has had a minimal impact of -2% on employment in the thematic routes and that massive cost control measures have been predominantly aimed at fixed operational costs. Thematic routes have experienced a decrease in operating time of up to 50%, and at the same time are undergoing market restructuring, with domestic and regional guests being the leading segments. Research further shows that the most needed form of government support through crisis mitigation measures is destination promotion support, followed by wage support and utility cost reduction.

Sustainable green campus in NEPAL: 3E analysis

ABSTRACT In today’s world, where global warming is one of the greatest human challenges, sustainable energy generation is becoming increasingly relevant. The use of green and clean energy sources is the best way to minimize CO2, CO, NOX and other emissions of conventional energy usage. Solar photovoltaic (PV) systems are more beneficial and an exciting application to set up an eco-friendly green educational campus. In this regard, the potential sites within Tribhuvan University, Institute of Engineering, Purwanchal Campus, Dharan city, Nepal are analysed for grid-tied solar PV power plant installation to meet the 100% energy demand of the campus using energy, economic and environment-friendly analysis. The daily, monthly and annual load and solar irradiance data of past years of the campus have been analysed to estimate the solar PV plant’s capacity and system performance using PVSYST V7.0 software analysis tools .The simulation results show that 110 kWp of solar PV power plant will be sufficient for the entire campus to qualify for the first fully green-powered campus in Nepal, which corresponds to fulfill 66.4 MWh/year daytime energy demand out of total 161 MWh/year energy consumption of the campus with a capacity to generate a total of 181.5 MWh/year energy from the designed solar PV system. The result also shows that 115.1 MWh/year of surplus energy produced from the PV power plant can be injected into the utility grid to yield considerable savings in utility cost. On the basis of these results, campus authorities and stakeholders may commit to investing and implementing of this project to ensure that the campus is completely green.