Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Active Control Mitigation Method for Aircraft Generator Arc Faults

Modern more electric aircraft (MEA) are increasingly reliant on robust power generation systems to drive basic aircraft systems, such as propulsion and control surface actuation, among others. These MEA loads require a low impedance and high bandwidth generator source. In turn, generation capacity and capability increases, and with it, the available current in the event of an arc fault. Such a fault may result in a catastrophic release of energy and the potential loss or extensive damage to the aircraft. In this paper, parallel arc faults for a type of aircraft generator are simulated, and a new mitigation method is introduced. This novel method makes use of available generator feedback control structures to greatly reduce the duration of the arc fault, thereby minimizing fault energy. The method is compared via computer simulations to two other feasible methods, one that turns off the generator's exciter and one that opens the exciter circuit. Further, since fault detection is not instantaneous, the paper also investigates the consequences of delay in fault detection by numerically evaluating the amount of energy released as a function of delay. The findings are that the novel method can significantly and robustly ameliorate the deleterious effects of delay, and therefore it is potentially a practical solution for implementation in MEA.
RL-SPIHT: Reinforcement Learning-Based Adaptive Selection of Compression Ratios for 1-D SPIHT Algorithm

In recent years, deep learning technologies have been actively used in various applications. In particular, networks trained using reinforcement learning (RL) are widely exploited for auxiliary tasks in various multimedia frameworks, including image restoration, image compression, and computer vision. Discrete wavelet transform (DWT) and set partitioning in hierarchical trees (SPIHT) are the representative lightweight compression methods that are most widely used for the purposes of frame memory compression and LCD overdrive. In precedent research, in order to improve the compression efficiency of DWT-SPIHT algorithms, the relative complexity of DWT coefficients is quantified, and when compressing DWT coefficients with the SPIHT algorithm, the compression ratio (CR) is adaptively allocated to the compression block according to the numerically expressed complexity. However, the SPIHT algorithm has the characteristic of resource limitation, resulting in the occurrence of remaining blocks, which cannot take advantage of allocating the adaptive CR. Moreover, since the equation expressing the block complexity that determines the CR of each block is obtained through machine learning-based linear regression, it lacks the capability to deal with a wide range of real-world images. To compensate for these drawbacks, this paper optimizes the compression efficiency of the 1-D DWT-SPIHT algorithm using the RL-based episodic auxiliary task. In detail, the proposed method optimally adjusts the proportion of CRs, which are adaptively selected for each block according to the DWT coefficient, through the episodic model trained with the RL algorithm. Consequently, the proposed method achieves an average improvement in peak signal to noise ratio (PSNR) of 2.18dB compared to the baseline 1-D DWT-SPIHT with the fixed compression ratio and 0.68dB compared to the precedent research.
Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.
Disrupting Continuity of Apple's Wireless Ecosystem Security: New Tracking, DoS, and MitM Attacks on iOS and macOS Through Bluetooth Low Energy, AWDL, and Wi-Fi

Apple controls one of the largest mobile ecosystems, with 1.5 billion active devices worldwide, and offers twelve proprietary wireless Continuity services. Previous works have unveiled several security and privacy issues in the involved protocols. These works extensively studied AirDrop while the coverage of the remaining vast Continuity service space is still low. To facilitate the cumbersome reverse-engineering process, we describe the ﬁrst guide on how to approach a structured analysis of the involved protocols using several vantage points available on macOS. Also, we develop a toolkit to automate parts of this otherwise manual process. Based on this guide, we analyze the full protocol stacks involved in three Continuity services, in particular, Handoff (HO), Universal Clipboard (UC), and Wi-Fi Password Sharing (PWS). We discover several vulnerabilities spanning from Bluetooth Low Energy (BLE) advertisements to Apple’s proprietary authentication protocols. These ﬂaws allow for device tracking via HO’s mDNS responses, a denial-of-service (DoS) attack on HO and UC, a DoS attack on PWS that prevents Wi-Fi password entry, and a machine-in-the-middle (MitM) attack on PWS that connects a target to an attacker-controlled Wi-Fi network. Our PoC implementations demonstrate that the attacks can be mounted using affordable off-the-shelf hardware ($20 micro:bit and a Wi-Fi card). Finally, we suggest practical mitigations and share our ﬁndings with Apple, who have started to release ﬁxes through iOS and macOS updates.
Berth Planning and Disruption Recovery: A Simulation Approach

With the increasing amount of container freight transport and the increasing size of container vessels, for the Port of Antwerp, the second largest container port in Europe, a critical task is port planning. A simulation model provides the means to gain proper insight in the effect of future expansions. Macomi, a company specialized in simulation and optimisation, has been working on a simulation model to aid the Port of Antwerp in their port planning. One important issue in this simulation model is how the berth allocation of vessels is handled. Berth allocation is the problem of assigning vessels a time and location at the quay wall where the vessel can be loaded and unloaded. In this thesis, the aim is to develop decision models for both the preliminary berth planning and the real-time recovery of this plan during simulation. For the first part, a cyclic baseline berth allocation plan is created which takes into account the tidal dependencies vessels have when entering the port of Antwerp. This preliminary berth plan is used as a basis for the simulation model as the arrival times are based on this plan. However, during the simulation disruptions might occur; vessels can arrive earlier or later or take longer to load and unload. To deal with these disruptions a real-time disruption management decision model is proposed which aims to solve all disruptions while staying as close to the theoretical berth plan as possible. Using the proposed models, several experiments have been conducted regarding the influence of uncertainty, occupation and robustness on the quality of the solutions that the decision models found. Regarding occupancy rates, results show that a tipping point exists where the recovery model has more difficulty to find a good solution. Results also show that when the expected occupation of a terminal is higher, adding robustness has more effect and is therefore more important. The decision models presented in this thesis have been implemented in the Macomi port simulation model and have been demonstrated to the Port of Antwerp. Both parties have expressed their satisfaction with the models.
12 Aversive Racism and Contemporary Bias

In the United States, the 1960s and early 1970s were characterized by significant societal changes. The Civil Rights Movement and social, political, and moral forces stimulated these changes to address racism by White Americans toward Black Americans and achieve the nation’s historical egalitarian ideals. With the Civil Rights legislation and other federal mandates, it was no longer simply immoral to discriminate against Blacks; it was now also illegal. Surveys and national polls revealed significant reductions in overt expressions of prejudice among Whites toward Blacks (Dovidio & Gartner, 2004). This unprecedented change in race relations in the United States changed the nature of racial attitudes, from blatant to subtle, and consequently the study of prejudice in psychology (Dovidio, 2001). In other countries, similar normative changes have reduced blatant expressions of prejudice while more subtle, yet equally pernicious, forms of bias persist (see Pettigrew & Meertens, 1995). This chapter reviews the development of theory about contemporary forms of racism – focusing primarily on aversive racism – tracing the evolution of this perspective, describing key empirical evidence, and identifying productive avenues for future research. We begin by reviewing relations among different theories of subtle contemporary racism and discussing work on implicit prejudice and its relationship to aversive racism. We then consider the implications of aversive racism for interventions to reduce bias and identify promising new directions for research on contemporary racism, in general, and aversive racism, in particular.
Effect of Mobile Phone Utilization in Predicting Drought Progression in Arid and Semi-Arid Lands: A Case of Kinna Ward in Isiolo County, Kenya

- In the event of natural disaster occurrences such as hurricanes, earthquakes, floods, pandemic and epidemic diseases, phone utilization aspects such as calls, SMS, airtime, and mobile money transaction have been widely used to monitor behavioral change and prevalence trend of the affected population. However, little is known on the ability of the meta data produced by network operators if could be utilized to monitor and predict drought progression in arid and semi-arid areas in Kenya. The gap necessitated the conveyance of this evaluation to give more insight on the possibility of utilizing the phone generated records in monitoring drought progression. The study was a panel data conducted on January, 2020, June, 2020 and November, 2020 on 233 phone users sampled from a population of 4345 phone owners in Kinna ward, Isiolo County, Kenya. The three drought phases: normal, emergency, and recovery were developed and projected based on the area standard precipitation index derived from 2009-2019 precipitation values recorded by the Kenya meteorological department. Daily information on calls made and received, SMSs send and received, airtime purchased, and money send and received through mobile money as well as villages visited during the three phases were recorded accordingly. The study used fixed effects panel regression model to reveal the fixed effect of phone utilization aspects on drought progression phases and found out that the phone utilization aspects: received calls, call length, money transaction records and locations significantly influenced drought progression from normal, to emergency to recovery. The study concludes that telecommunication data which can be cheaply obtained has a potential to be employed in drought monitoring in regions with restricted ability to gather information like in ASALs regions of Kenya.
The Impact of Globalization on European Airline Market

Airline industry is very important for modern society as the biggest player in the globalization process by connecting regions, promoting global trade and tourism, facilitating economic and social development. However, here is a lack of research on relationship between globalization and airline industry in Europe. It remains unclear how to measure the impact of globalization on performance of airline companies and industry. The article aims at investigation of the impact of globalization on operational and financial performance of European airlines before pandemics. The authors applied a nonexperimental quantitative research design to analyze the relationship between independent globalization variables (level of globalization in Europe, globalization opportunity, globalization threat) and dependent airlines’ operational and financial performance indicators. Research is done using secondary data from annual reports of 19 European airlines members of European Common Aviation Area (ECAA). The panel data analysis was applied for 2007–2017 with multiple regression analysis using STATA. The results show that globalization exerts a significant positive effect on operational performance. On financial performance only revenue per passenger kilometers is positively influenced by globalization. Globalization affects low-cost airlines and full-service airlines performance differently.
On the robustness of minimum norm interpolators and regularized empirical risk minimizers

This article develops a general theory for minimum norm interpolating estimators and regularized empirical risk minimizers (RERM) in linear models in the presence of additive, potentially adversarial, errors. In particular, no conditions on the errors are imposed. A quantitative bound for the prediction error is given, relating it to the Rademacher complexity of the covariates, the norm of the minimum norm interpolator of the errors and the size of the subdifferential around the true parameter. The general theory is illustrated for Gaussian features and several norms: The $\ell_1$, $\ell_2$, group Lasso and nuclear norms. In case of sparsity or low-rank inducing norms, minimum norm interpolators and RERM yield a prediction error of the order of the average noise level, provided that the overparameterization is at least a logarithmic factor larger than the number of samples and that, in case of RERM, the regularization parameter is small enough. Lower bounds that show near optimality of the results complement the analysis.
CEG_A_304854 133..144

1Department of Clinical Medicine, University of Oslo, Oslo, Norway; 2Norwegian Competence Centre for Functional Gastrointestinal Disorders, Department of Medicine, Haukeland University Hospital, Bergen, Norway; 3Department of Medicine, Haukeland University Hospital, Bergen, Norway; 4Department of Clinical Medicine, University of Bergen, Bergen, Norway; 5Hormone Laboratory, Haukeland University Hospital, Bergen, Norway; 6Department of Transplantation Medicine, Section for Gastroenterology, Oslo University Hospital, Oslo, Norway; 7Centre for Nuclear Medicine and PET, Department of Radiology, Haukeland University Hospital, Bergen, Norway; 8National Centre for Ultrasound in Gastroenterology, Haukeland University Hospital, Bergen, Norway Objective: Gastric emptying measurements are mandatory in gastroparesis diagnostics, but the association between delayed emptying and symptoms is questionable. It is imperative to find biomarkers better correlated to symptom generation. Hence, we examined the association between symptom severity and gastrointestinal motility measured by wireless motility capsule. Patients and Methods: In this prospective single-centre study, patients with gastroparesis symptoms were simultaneously investigated with gastric emptying scintigraphy and wireless motility capsule, measuring regional transit times and contractility parameters. Symptom severity was assessed with the Patient Assessment of Upper Gastrointestinal Symptom Severity Index (PAGI-SYM), including the Gastroparesis Cardinal Symptom Index (GCSI). Results: We included 107 patients (70% women). In the whole patient group, nausea correlated with the gastric (rs = −0.31, p = 0.007), small bowel (rs = −0.41, p < 0.001) and colonic (rs = −0.33, p = 0.012) motility indices. In patients with idiopathic etiology, nausea correlated with small bowel motility index (rs = −0.81, p < 0.001) and mean stomach pressure (rs = −0.64, p = 0.013). We also found negative correlations between total GCSI score and maximum pressure of the small bowel (rs = −0.77, p < 0.001) and colon (rs = −0.74, p = 0.002). In diabetes patients, total PAGI-SYM score correlated with colonic motility index (rs = −0.34, p = 0.012), and mean pressure of the colon correlated with upper abdominal pain (rs = −0.37, p = 0.007). We found no association between symptoms, gastric emptying nor any other transit times. Conclusion: In patients with gastroparesis symptoms, we found that symptom severity was associated with intestinal hypomotility. Based on these results, gastroparesis diagnostics should also include an evaluation of the small bowel and colon.
Impacts of Recreational SCUBA Diving on a Natural Area in Puerto Vallarta, Mexico

Despite the protection status of the marine area in Los Arcos, it has experienced reef deterioration associated with tourism activities. Therefore, this study aimed to analyze SCUBA divers’ impacts by quantifying and typifying their contact with the seabed, and to determine if the diving leader’s performance is related to the behavior of the divers being guided. The number and type of impacts on the seabed were counted and categorized as intentional and non-intentional. A total of 9811 contacts were made by 101 divers. The highest frequency type of non-intentional contact identified was “lifting sediment”, following by non-intentional—fins. The “hands” intentional contact type was found to occur most often. The highest frequency of total contacts per diver was “very high” (31.7%). By analyzing the profile of the divers related to their certification level, number of dives, SCUBA center attended, and leader, it was possible to establish a relationship between the number of contacts made by the leader and the number of contacts made by the divers guided by each leader. Our findings suggest the need for a joint approach of key players toward good practices to highlight the important role of diving leaders on their clients’ behavior, and thus preserve the ecological and aesthetic qualities of the dive site.
Optimal Earth-Moon Trajectory Design using Constant and Variable Low Thrust

For preparing Korean lunar missions, optimal Earth-Moon transfer trajectory is designed using continuous low thrust. Using both constant and variable low thrusting method, "End-to-End" mission analysis is made from beginning of the Earth departure to the final lunar arrival. Spacecraft's equations of motion is expressed using N-body dynamics including the gravitational effects due to the Earth, Moon, Sun and also with Earth's J2 effects. Planets' exact locations are computed accurately with JPL's DE405 ephemeris. As a results, optimal thrust steering angle's characteristics are discovered which showed almost tangential direction burns at the near of central planets. Also, it is confirmed that variable low thrusting method is more efficient than constant thrusting method, and can save about 5% of fuel consumption. Presented algorithm and various results will give numerous insights into the future Korea's Lunar missions using low thrust engines. Also, it is expected to be used as a basis of more detailed mission analyzing tool.
Linking Conservation, Community Knowledge, and Adaptation to Extreme Climatic Events: A Case Study in Gorongosa National Park, Mozambique

This study is based on ethnographic research that analyzes how traditional knowledge and local beliefs on biodiversity conservation relates to the local ability to adapt and be resilient to climatic changes in two communities around Gorongosa National Park, Mozambique: Nhanfisse in the buffer zone and Muanandimae in the core area. A total of 78 semi-structured interviews with heads of households were conducted. We found that both communities carried out practices and held beliefs associated with conservation, such as protecting trees and animal species considered sacred or perceived as beneficial for human life in terms of water provision and agricultural production. In addition to traditional ceremonies that respond to extreme climatic events such as drought and flood, other adaptation strategies used by the communities include moving to neighboring areas in search of better living conditions and using forest products in times of scarcity. We discuss that the management of the park should be agreed on, in a shared way, between local communities and conservation agents to ensure that these areas continue to perform the ecological, subsistence, and spiritual functions required. Our research results contribute to a better understanding of local adaptation dynamics towards extreme climatic events and improvement of management strategies.
Childhood Antisocial Behavior: A Neurodevelopmental Problem.

Early-onset disruptive, aggressive, and antisocial behavior is persistent, can become increasingly serious as children grow older, and is difficult to change. In 2007, our group proposed a theoretical model highlighting the interplay between neurobiological deficits and cognitive and emotional functioning as mediators of the link between genetic influences and early social adversity, on the one hand, and antisocial behavioral problems in childhood, on the other. In this article, we review the post-2007 evidence relevant to this model. We discuss research on genetics/epigenetics, stress/arousal regulation, and emotion and executive functioning in support of the argument that antisocial children, especially those who persist in engaging in antisocial behavior as they grow older, have a range of neuropsychological characteristics that are important in explaining individual differences in the severity and persistence of antisocial behavior. Current clinical practice tends not to acknowledge these individual neuropsychological risks factors or to target them for intervention. We argue that aggressive and disruptive behavior in childhood should be regarded as a neurodevelopmental problem and that intervening at the level of mediating neuropsychological processes represents a promising way forward in tackling these serious behavioral problems. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.