Miracle-dz/UniFAR-FTUs huggingface dataset:
INDIAN AIR QUALITY PREDICTION AND ANALYSIS USING MACHINE LEARNING

---------------------------------------------------------------------***---------------------------------------------------------------------Abstract - We read the air quality of India by using machine literacy to prognosticate the air quality indicator of a given area. Air quality indicator of India is a standard measure used to indicate the contaminant (so2, no2, rspm, spm.etc.) situations over a period. We developed a model to prognosticate the air quality indicator grounded on literal data of former times and prognosticating over a particular forthcoming time as aGradient decent boosted multivariable retrogression problem. we ameliorate the effectiveness of the model by applying cost Estimation for our prophetic Problem. Our model will be able for successfully prognosticating the air quality indicator of a total county or any state or any bounded region handed with the literal data of contaminant attention. In our model by enforcing the proposed parameter- reducing phrasings, we achieved better performance than the standard retrogression models. our model has 96 delicacy on prognosticating the current available dataset on prognosticating the air quality indicator of whole India, also we use AHP MCDM fashion to find of order of preference by similarity to ideal result.
Working Paper Series Cover new

This paper examines the usefulness of survey-based information on inflation expectations of households in the analysis of inflation dynamics in India. As household inflation expectations do not satisfy the statistical properties of rationality and unbiasedness, hybrid versions of New Keynesian Phillips Curve (NKPC) are used to study whether survey-based measures of inflation expectations can be used as proxy for forward looking expectations to predict inflation in India. While both 3-months ahead and 1-year ahead household inflation expectations emerge statistically significant in explaining and predicting inflation, effectively they work as substitutes of backward looking expectations given that household expectations are found to be adaptive. When transmission of inflation expectations to inflation is assessed through wage dynamics, no robust evidence is found for expectations induced wage pressures influencing CPI inflation. Shortterm food and fuel shocks explain significant part of variations in inflation expectations of households. Notwithstanding limited evidence on spillover of inflation expectations of households to wages and prices, the high degree of observed inflation persistence and significant sensitivity of inflation expectations to food and fuel shocks warrant sustained emphasis of monetary policy on wellanchored inflation expectations. JEL Classification: E52, E70
Security Considerations for Next-Generation Operating Systems for Cyber-Physical Systems

Cyber-physical systems (CPSs) are increasingly targeted in highprofile cyber attacks. Examples of such attacks include Stuxnet, which targeted nuclear centrifuges; Crashoverride, and Triton, which targeted power grids; and the Mirai botnet, which targeted internet-of-things (IoT) devices such as cameras to carry out a large-scale distributed denial-of-service (DDoS) attack. Such attacks demonstrate the importance of securing current and future cyber-physical systems. Therefore, next-generation operating systems (OSes) for CPS need to be designed to provide security features necessary, as well as be secure in and of themselves. CPSs are designed with one of three broad classes of OSes: (a) bare-metal applications with effectively no operating system, (b) embedded systems executing on impoverished platforms running an embedded or real-time operating system (RTOS) such as FreeRTOS, or (c) more performant platforms running generalpurpose OSes such as Linux, sometimes tuned for real-time performance such as through the PREEMPT_RT patch. In cases (a) and (b), the OS, if any, is very minimal to facilitate improved resource utilization in real-time or latency-sensitive applications, especially running on impoverished hardware platforms. In such OSes, security is often overlooked, and many important security features (e.g. process/kernel memory isolation) are notably absent. In case (c), the general-purpose OS inherits many of the security-related features that are critical in enterprise and generalpurpose applications, such as virtual memory and address-space layout randomization (ASLR). However, the highly complex nature of general-purpose OSes can be problematic in the development of CPSs, as they are highly non-deterministic and difficult to formally reason about for cyber-physical applications, which often have real-time constraints. These issues motivate the need for a next generation OS that is highly capable, predictable and deterministic for real-time performance, but also secure in the face of many of the next generation of cyber threats. In order to design such a next-generation OS, it is necessary to first reflect on the types of threats that CPSs face, including the attacker intentions and types of effects that can be achieved, as well as the type of access that attackers have. While threat models are not the same for all CPSs, it is important to understand how the threat models for CPSs compare to general-purpose or enterprise computing environments. We discuss these issues next (Sec. 2),
In-Stream Monitoring of PIT-tagged Wild Spring/Summer Chinook Salmon Juveniles in Valley Creek, Idaho

—Recent advances in passive integrated transponder (PIT) tag technology have allowed the development of in-stream fish-monitoring systems. We installed two such systems in Valley Creek near its confluence with the Salmon River in summer 2002. In the summers of 2003–2005 we collected and PIT tagged wild spring/ summer Chinook salmon parr Oncorhynchus tshawytscha in natal rearing areas upstream from the monitors. Although subsequent detection numbers between fall 2003 and spring 2006 were low and variable, they were sufficient to determine timing and estimate survival. We defined migrational groups by period of detection: late summer and fall (August–October), winter (November–February), and the following spring (March–June). Combining 3 years of data, the mean proportions of fish detected during these three respective detection periods were 60.6, 27.7, and 11.7%. Mean probability estimates of survival from Valley Creek to Lower Granite or Little Goose Dams were 9.2, 23.4, and 40.8% for the respective late summer and fall, winter, and spring periods. Estimated overall mean probabilities of survival were 46.6% from tagging as parr to movement into the mouth of Valley Creek and 17.3% from Valley Creek to Lower Granite Dam. The overall mean parr-to-smolt survival estimate from tagging to arrival at Lower Granite Dam was 9.0%. The unexpectedly high proportion of fish migrating in winter has important implications for fish monitoring studies that use rotary screw or scoop traps: these traps are generally inoperable during winter near most natal rearing areas and thus may result in biased estimates of fish population status and migration timing. Advancements in technologies and methodologies to instream PIT-tag monitoring systems will improve data quality to assist recovery planning for threatened and endangered fish species.
The Lie Deflator – The effect of polygraph test feedback on subsequent (dis)honesty

Despite its controversial status, the lie detection test is still a popular organizational instrument for credibility assessment. Due to its popularity, we examined the effect of the lie-detection test feedback on subsequent moral behavior. In three studies, participants could cheat to increase their monetary payoff in two consecutive phases. Between these two phases the participants underwent a mock polygraph test and were randomly given Deception Indicated (DI) or No Deception Indicated (NDI) assigned feedback. Then, participants engaged in the second phase of the task and their level of dishonesty was measured. Study 1 showed that both NDI and DI feedback (but not the control) reduced cheating behavior on the subsequent task. However, Study 2 showed that the mere presence of the lie-detection test (without feedback) did not produce the same effect. When the role of the lie detector as a moral reminder was cancelled out in Study 3, feedback had no effect on the magnitude of cheating behavior. However, cheaters who were given NDI feedback exhibited a lower level of physiological arousal than cheaters who were given DI feedback. These results suggest that lie detection tests can be used to promote honesty in the field, and that, while feedback type does not affect the magnitude of cheating, NDI may allow people to feel better about cheating.
City Logistics Policy Toolkit : A Study of Three Latin American Cities

The ultimate development objective of this project is to help cities make better urban freight policy choices to minimize externalities while providing adequate flow of goods within the city. In this report, the authors present the final results of three-stage methodology employed to: 1) identify logistics critical areas; 2) derive a granular characterization of retail and logistics activities in those areas; and 3) identify and assess tailored urban freight policy choices. The authors apply this methodology to three case study cities. The report outlines the different data collection protocols and analytical methods employed in each stage of the research, and summarizes the main findings.
Design and Control of a 1-DOF Robotic Lower-Limb System Driven by Novel Single Pneumatic Artificial Muscle

This study determines the practicality and feasibility of the application of pneumatic artificial muscles (PAMs) in a pneumatic therapy robotic system. The novel mechanism consists of a single actuated pneumatic artificial muscle (single-PAM) robotic lower limb that is driven by only one PAM combined with a torsion spring. Unlike most of previous studies, which used dual-actuated pneumatic artificial muscles (dual-PAMs) to drive joints, this design aims to develop a novel single-PAM for a one degree-of-freedom (1-DOF) robotic lower-limb system with the advantage of a mechanism for developing a multi-axial therapy robotic system. The lower limb robotic assisting system uses the stretching/contraction characteristics of a single-PAM and the torsion spring designed by the mechanism to realize joint position control. The joint is driven by a single-PAM controlled by a proportional pressure valve, a designed 1-DOF lower-limb robotic system, and an experimental prototype system similar to human lower limbs are established. However, the non-linear behavior, high hysteresis, low damping and time-variant characteristics for a PAM with a torsion spring still limits its controllability. In order to control the system, a fuzzy sliding mode controller (FSMC) is used to control the path tracking for the PAM for the first time. This control method prevents approximation errors, disturbances, un-modeled dynamics and ensures positioning performance for the whole system. Consequently, from the various experimental results, the control response designed by the joint torsion spring mechanism can also obtain the control response like the design of the double-PAMs mechanism, which proves that the innovative single-PAM with torsion spring mechanism design in this study can reduce the size of the overall aid mechanism and reduce the manufacturing cost, can also improve the portability and convenience required for the wearable accessory, and is more suitable for the portable rehabilitation aid system architecture.
Performance Oriented Error Correction for Robust Neural Networks

When deep neural networks (DNNs) are implemented in hardware, their weights usually need to be stored in nonvolatile memory devices. As noise accumulates in the stored weights, the DNN’s performance will degrade. This work studies how to use error correcting codes (ECCs) to protect the weights. Different from classic error correction in data storage, the optimization objective is to optimize the DNN’s performance after error correction, instead of minimizing the Uncorrectable Bit Error Rate (UBER) in the protected bits. That is, the error correction scheme is performance-oriented. A main challenge is that a DNN often has millions to hundreds of millions of weights, causing a large redundancy overhead for ECCs, and the relationship between the weights and the DNN’s performance is highly complex. To address the challenge, we propose a Selective Protection (SP) scheme, which chooses only a subset of important bits for ECC protection. The scheme can be evaluated based on the redundancyperformance tradeoff as follows. Let ktotal denote the total number of bits used to represent the neural network’s weights. Let kpro denote the number of bits we protect with ECCs. Let the ECCs be (n, k) linear codes, where n denotes the codeword length and k denotes the number of information bits. Then the number of parity-check bits is n−k k · kpro. We normalize it by ktotal, and call it redundancy r, namely, r = kpro(n−k) ktotalk . As for the performance of the neural network, for classification tasks (which this work focuses on), it usually refers to the classification accuracy, namely, the probability that the inputs are classified correctly. To determine which important bits to protect and achieve an optimized tradeoff between ECC’s redundancy and DNN’s performance, we present an algorithm based on deep reinforcement learning (DRL). We compare the performance of our algorithm to a natural baseline scheme, where all layers of the neural network receive the same level of protection from ECCs. Experimental results verify that our proposed algorithm achieves substantially better performance. For example, when the neural network is ResNet-18 [1] and its weights are represented by bits using the IEEE-754 standard (i.e., the single-precision floating-point format), and when BER is 1% and BCH codes are used, the baseline scheme’s classification accuracy drops very quickly once its redundancy r is below the threshold 0.04525. In comparison, our algorithm can decrease the corresponding threshold to 0.03879, which represents a reduction of 14.3% in the redundancy requirement. If the ECC approaches the Shannon capacity, this reduction can be further enlarged to 25.7%.
Methods for efficient modelling of progressive failure in laminated fibre-reinforced composites

To meet increasing demands on reduced CO$_2$ emissions, the automotive industry is currently very active in research to reduce vehicle weight by incorporating laminated composites (primarily carbon fibre-reinforced polymers) into structural components. Historically, composite materials have mainly been used in the aerospace industry, whereby CAE-based design and development tools for composite structures have been developed primarily to the specific needs and requirements in this industry. In general, the crashworthiness of aerospace structures is only assessed to a small extent compared to that of automotive structures. Consequently, no suitable numerical simulation tools, capable of assessing the crashworthiness of composite automotive structures, have been developed. The fracture process of laminated composites is more complicated than that of metals, the dominant class of materials used in automotive crash protection systems today. Thus, numerical models developed for metals cannot be used to accurately predict the crashworthiness of composite structures. Instead, high-fidelity models that can resolve the complicated fracture process must be used. However, these models require excessive computational times, making industrial crash simulations infeasible. It is therefore crucial to develop computationally efficient numerical tools, which are able to accurately predict the crashworthiness performance of composite structures. In this thesis, I will present a route towards full-scale vehicle crash simulations using a computationally efficient adaptive method. The method is based on an equivalent single-layer shell model which, during the analysis, is adaptively transformed to a high-fidelity model in areas where higher accuracy is required. This way, the increased computational cost, associated with the analysis of progressive damage in laminated composites, can be limited both in time and to the pertinent areas of the model. The adaptive modelling method can successfully reproduce the same level of accuracy as a high-fidelity model, at lower computational cost. Consequently, this method can help to enable computationally efficient crash simulations of laminated structures, which in the long run will allow composite materials to have a widespread use in future automotive vehicles.
Exposure to a second language in infancy alters speech production

Abstract We evaluated the impact of exposure to a second language on infants’ emerging speech production skills. We compared speech produced by three groups of 12-month-old infants while they interacted with interlocutors who spoke to them in Spanish and English: monolingual English-learning infants who had previously received 5 hours of exposure to a second language (Spanish), English- and Spanish-learning simultaneous bilinguals, and monolingual English-learning infants without any exposure to Spanish. Our results showed that the monolingual English-learning infants with short-term exposure to Spanish and the bilingual infants, but not the monolingual English-learning infants without exposure to Spanish, flexibly matched the prosody of their babbling to that of a Spanish- or English-speaking interlocutor. Our findings demonstrate the nature and extent of benefits for language learning from early exposure to two languages. We discuss the implications of these findings for language organization in infants learning two languages.
Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals

Measures of predictability in physiological signals based on entropy metrics have been widely used in the application domain of medical assessment and clinical diagnosis. In this paper, we propose a new entropy-based pattern learning by a combination of singular spectrum analysis (SSA) and entropy measures for assessment of physiological signals. Physiological signals are first represented as a series of SSA components, and then well-established entropy measures are extracted from the resulting SSA components that can help to facilitate the features extraction from physiological signals. The entropy measures of notable SSA components are used to form input features and fed into pattern classifier. To demonstrate its validity, applicability, and versatility, the proposed entropy-based pattern learning is used to perform medical assessments with three kinds of classical physiological signals, that is, electroencephalogram (EEG), electromyogram (EMG), and RR-interval signals. Experiments demonstrate that in all cases, the proposed entropy-based pattern learning can effectively capture specific biosignal patterns of physiological signals and achieve excellent identification performances for the assessments of EEG, EMG, and RR-interval signals. Besides, through the comparison of the identification performances for entropy-based pattern learning based on the physiological signals themselves and the SSA components, it is concluded that the discriminating power of entropy-based pattern learning based on the SSA components is much stronger than that based on the physiological signals themselves. Since it can be easily extended to any other physiological signal analysis, the proposed entropy-based pattern learning may use as an efficient approach to reveal biosignal patterns for medical assessment of physiological signals.
Analysis and characterization of the choline-utilizing microcompartment in uropathogenic E. coli 536

Bacterial microcompartments (MCPs) are subcellular organelles that are widespread among bacteria. MCPs consist of metabolic enzymes encapsulated within a protein shell and their function is to increase the efficiency of metabolic processes in part by sequestering toxic and volatile intermediates. MCPs are found in about 20% of bacteria. In several cases, MCP production is correlated to bacterial pathogenesis. In addition, because of their metabolic role in the human gut microbiome, MCPs are linked to cancer and heart disease. Only three of ten MCP types have been well-studied. Recently, a new type of MCP that is used to metabolize choline under anaerobic conditions and is linked to cardiovascular disease was discovered in Proteus and Desulfovibrio. The genes for the choline-utilizing (cut) MCP were also found in pathogenicity island II of uropathogenic Escherichia coli 536 through bioinformatics analyses. The main focus of this thesis is to describe biochemical and genetic studies carried out to increase our understanding of the E. coli 536 cut MCP. Our results show that E. coli 536 does indeed produce cut MCPs and that the cut genes are found in two adjacent operons containing three and thirteen genes, respectively. The main 13-gene cut operon contains genes responsible for anaerobic choline degradation and MCP formation, and the smaller cut operon contains three regulatory proteins that control transcription of the main cut operon. In addition, our studies show that the global regulatory systems, ArcAB, Fnr, and CRP (which are encoded outside the cut locus) all positively regulate transcription of the main cut operon. Overall, the findings reported here provide insights into the function and regulation of the cut MCP of E. coli 536. This further expands our understanding of bacterial MCPs and provides new knowledge that might be applied to their future use in nanotechnology and biomedicine.
Tribological Behaviour of Neem Oil with and without Graphene Nanoplatelets Using Four-Ball Tester

The present work was aimed to study the friction and wear behaviour of graphene nanoplatelets (GNPs) under extreme pressure conditions as an anti-weld additive for neem oil. The effect of neem oil, blended with various loading of GNPs on the friction and wear characteristics has been investigated. From the experimental results, it was found that 1 wt. % of GNPs in neem oil showed the least coefficient of friction and smoother wear scar diameter. The extreme pressure test was performed on neem oil with and without GNPs as per ASTM standards. The extreme pressure test results indicated the improvement in seizure load of neem oil by 27.8% at 0.5 wt. % of GNPs as compared to pure neem oil. Optical microscopy of worn steel ball surface revealed the pit formation and the formation of wedge cutting edge in GNPs modified neem oil.
Dynamic Basic Activity Sequence Matching Method in Abnormal Driving Pattern Detection Using Smartphone Sensors

In this work, we present a novel method, namely dynamic basic activity sequence matching (DAS), a combination of machine learning methods and flexible threshold based methods for distinguishing normal and abnormal driving patterns. Indeed, DAS relies on the activity detection module (ADM) presented in our previous work to analyze each driving pattern as a sequence of basic activities—stopping (S), going straight (G), turning left (L), and turning right (R). In fact, the threshold value and other parameters like the duration of long and short activities are iteratively induced from the collected dataset. Hence, DAS is flexible and independent of driving contexts such as vehicle modes and road conditions. Experimental results, on the dataset collected from numerous motorcyclists, show the outperformance of our proposed method against dynamic time warping and the two popular machine learning methods—random forest and neural network—in distinguishing the normal and abnormal driving patterns. Moreover, we propose an efficient framework composing of two phases: in the first phase, the normal and abnormal driving patterns are distinguished by relying on DAS. In the second phase, the detected abnormal patterns are further classified into various specific abnormal driving patterns—weaving, sudden braking, etc. This fusion framework again achieves the highest overall accuracy of 97.94%.