Miracle-dz/UniFAR-FTUs huggingface dataset:
Maritime Transportation: Between AVs and tankers, there is (not) the middle of the sea

The maritime transportation of crude oil by tankers represents a major aspect of global trade, as crude oil is refined to all sorts of different fuel products. The crude oil shipping market is a decentralized system based on traditional concepts with multiple stakeholders involved (e.g. ship owners, charterers, ship brokers, etc.). Most recently (March 2021), the blockage of the Suez Canal by a vessel showed that this system can be strongly affected by external factors. Literature on crude oil shipping either addresses approaches for very specific problems, therefore not applicable to other scenarios, or more general aspects, mainly by focusing on analytical methods. Alternatively, we present in this Master’s thesis a general simulation framework in order to analyze, model and simulate the crude oil shipping market in its entirety. For this work, we strongly collaborated with the commercial ship management group, The Signal Group, who provided us access to their valuable knowledge, software and data. The simulation framework describes efficiency in crude oil shipping as combination of incomparable metrics addressing resources in time, emission and cost. Based on that, a concept for preferences is defined where these metrics are considered simultaneously, accounting for mixed strategies during simulation. The mathematical model and implementation of the framework cover multiple different aspects, e.g. graphs representing the transportation network, demands and vessels, etc. By running simulations over time, dynamic aspects of the system could be analyzed, such as congestion at canals. This dynamic interaction and dependence of the vessels on each other enables a new perspective for the usually static scheduling and ship routing problems. The results of the simulations based on different preferences provide first insights on the relations between the chosen metrics for efficiency. The implemented simulation framework provides a solid foundation for further refinements and extensions in the future, especially due to its modularity. Ultimately, this should lead to a better understanding of efficiency in crude oil shipping and its optimization.
Impacts of invasive plant species on soil biodiversity: a case study of dog-strangling vine (Vincetoxicum rossicum) in a Canadian National Park

Abstract: Soil microbial diversity is expected to be altered by the establishment of invasive plant species, such as dog-strangling vine (DSV) [Vincetoxicum rossicum (Apocynaceae)]. However, in urban ecosystems where DSV invasion is high, there is little research evaluating the impacts of DSV and other anthropogenic disturbances on microbial diversity. Our study was based in Rouge National Urban Park, Canada, where we used terminal restriction fragment length polymorphism data to evaluate (i) if DSV has a detectable impact on soil bacterial community composition and (ii) if these impacts occur independently of other anthropogenic change or soil characteristics. Variation in soil bacterial communities was greatly reduced in DSV-invaded sites vs. less-invaded sites. The degree of DSV invasion independently explained 23.8% of variation in bacterial community composition: a value similar to the explanatory power of proximity to roadways (which explained 22.6% of the variation in community composition), and considerably greater than soil parameters (pH, moisture, carbon, and nitrogen concentrations) which explained only between 6.0% and 10.0% of variation in bacterial community composition. Our findings indicate that DSV influences soil bacterial community composition independent of other anthropogenic disturbances and soil parameters, with potential impacts on multiple facets of plant–soil interactions and plant invasion dynamics.
Overview of recent progress in condition monitoring for insulated gate bipolar transistor modules: Detection, estimation, and prediction

The insulated gate bipolar transistor (IGBT) is one of the most fragile components in power electronics converters. In order to improve the reliability of IGBTs, various measurements are taken according to the condition monitoring (CM) technique. Traditional CM techniques include the measurement and estimation of the device operation conditions. Recently, emerging techniques have been developed, not only for the detection and estimation but also for the prognostics of IGBTs with the condition data. In this paper, a review is performed on the recent progress in the CM techniques for IGBTs. First, some emerging electrical and thermal measurements are reviewed. Based on the sensed data, the health indicator estimation techniques are summarised. Moreover, for the emerging prognostics and health management applications, some remaining using lifetime (RUL) prediction methods are reviewed. Finally, the research gaps and directions are discussed for the CM in IGBT applications.
Differentiation of ultraviolet/visible photons from near infrared photons by MoS2/GaN/Si-based photodetector

Conventional photodetectors (PDs) generally exhibit a unipolar photoresponse within their responsive spectral range. Different from the traditional PDs, we report here a broadband PD based on the MoS2/GaN/Si heterojunction that shows a unique phenomenon of wavelength selectivity through photocurrent polarity inversion. Overall, the device can differentiate the photons of the ultraviolet (UV)/visible region from that of the near infrared (NIR) region. This polarity inversion is explained with the help of the band diagram and the wavelength dependent photothermoelectric (PTE) effect in MoS2. The vertical transport characteristics of the MoS2/GaN/Si device exhibit a high spectral response in a broad range of wavelengths (300–1100 nm) in a self-biased mode. The maximum response of the device is found to be 23.81 A/W at a wavelength of 995 nm. Our results demonstrate a route for the development of PDs without filter that possess a lot of potential for the futuristic photonic devices.
How will Brexit affect the patterns of European agricultural and food exports

We analyse how Brexit will affect European agrifood trade using a structural gravity model and by comparing the present deal to four alternative scenarios on the UK’s trade policy. European Union (EU) countries will suffer marginal drops in imports, exports and real income, with stronger effects for Ireland. EU exports to the UK will drop by 10 per cent. European products will be redirected to both intra-EU and extra-EU markets and sold at slightly lower prices. Meat products will suffer the largest losses, Ireland being particularly vulnerable due to strong interconnections with British production chains and increased costs for accessing EU and extra-EU markets.
Combustion, performances, and emissions characteristics of Hermetia illucens larvae oil in a direct injection compression ignition engine

ABSTRACT Hermetia illucens larvae oil (HILO) is among biofuel feedstock from insects that has high potential to reduce dependency on petroleum resources. The present paper is motivated by the need to critically examine the effect of HILO mixed with diesel fuel (DF) on combustion, engine performance, and emission characteristics of a single cylinder direct injection (DI) compression ignition (CI) engine. The experiment was performed at a constant speed of 1500 rpm under various engine loads. The results revealed that the in-cylinder pressure, heat release rate (HRR), and the ignition delay (ID) were reduced by an average of 3.32%, 12.89%, and 4.36%, respectively. The brake specific fuel consumption (BSFC) and exhaust gas temperature (EGT) increased considerably at all engine loads. The brake thermal efficiency (BTE) was discovered to be lower by 11.47% compared to DF. The finding also shows that carbon monoxide (CO), carbon dioxide (CO2), and unburned hydrocarbon (UHC) emissions increased with the addition of HILO. The nitrogen oxides (NOx) emission reduced by 19.80% compared to DF at all the engine loads. Overall, this study concluded the potential of HILO in CI engine as a promising renewable and environmentally friendly resource for the better earth.
Machine learning based approach for detecting Distributed Denial of Service attack

All elements of the IT industry are expanding, including bandwidth, storage, processing speed. As a result, there are now more cyber threats and attacks, necessitating a creative and predictive security approach that employs cutting-edge technology to combat the danger. The trends will be monitored, and adequate analysis from various sets of data will be utilized to build a model that is based on the information available. Distributed Denial of Service (DDoS) is one of the most prevalent dangers and attacks wreaking havoc on internet-connected computer equipment. This study compares the performance of several machine learning-based classifiers for detecting DDoS assaults before they occur. This experiment made use of data from the benchmark KDD-Cup-1999 DDoS attack. To choose essential characteristics in the context of DDoS detection, I created three distinct types of feature selection techniques. The findings revealed that feature selection approaches can assist domain specialists in understanding the intrusion system’s hidden key patterns and features during DDoS detection. DNN-based deep learning and semi-supervised learning method were also compared with the ML-based classifiers output. The suggested model learns to recognize regular network traffic to detect ICMP, TCP, and UDP DDoS traffic as it arrives. Experiments show that machine learning algorithms may correctly classify the traffic into regular and DDoS. This discovery has long-term implications in a variety of sectors, including national defence, financial institutions, healthcare, and other businesses where sophisticated intrusion detection techniques are required. In the future, I would want to apply similar approaches to a variety of datasets.
Cationization preferences of small molecules from brain tissue

The composition of lipids in brain is important for its metabolism and function. Lysophosphatidylcholine 16:0 (LPC 16:0) are derived from phosphatidylcholine 36:2 (PC 36:2) which both are membrane lipids and have main functions in signal transduction across biological membranes. The metabolites creatine and glutamine are essential for the function of the brain. Creatine is physiologically important for the neurons and glutamine increases energy for the brain. The aim of this thesis project is to determine how lipids LPC and PC and metabolites creatine and glutamine in brain tissue form adducts in electrospray ionization. Lipids and metabolites were extracted from the brain tissue by mixing it with 90% MeOH and centrifuged for 10 min at 16 000x at 4 °C. 50 µl of tissue extract were mixed with 50 µl of either NaCl or KCl at concentrations ranged from 16-500 µM. The molecules were first ionized by electrospray ionization. The ions were then transported through the mass analyzer Orbitrap Velos Pro in order to be separated based on their mass to charge ratio. procedure
The effects of the Malawi Farm Input Subsidy Program on household per-capita consumption convergence

ABSTRACT The Malawi Farm Input Subsidy Program (FISP) was praised for turning the country's food deficit into a surplus after inception in 2005. It is, however, not clear whether these food security gains spill over to equity in distribution of welfare. We examine the effects of the FISP on per-capita consumption convergence, on 2251 households interviewed in 2010–2013 Malawi Integrated Household Panel Survey. The analysis employs a Lewbel method of instrumental variables to account for non-random selection of beneficiaries into the programme. The results reveal that FISP helps relatively poor farmers increase household per-capita consumption towards converging to that of the relatively rich. This convergence is robust only among small but not large farmers. Past studies that evaluated the FISP while not paying attention to the welfare equity gains in household per-capita consumption, may have underestimated its benefits. Therefore, policy should support the programme with an additional objective of reducing inequality, beyond the primary aim of enhanced food security. Considering that the effects of FISP are limited to small farmers, alternative interventions such as inputs for credit should be made available to large farmers. This will allow FISP to induce widespread welfare gains.
Importance of Image Enhancement and CDF for Fault Assessment of Photovoltaic Module Using IR Thermal Image

Infrared thermography is the science of measuring the infrared energy emitted by an object, translating it to apparent temperature variance, and displaying the result as an infrared image. Significantly, acquiring thermal images delivers distinctive levels of temperature differences in solar panels that correspond to their health status, which is beneficial for the early detection of defects. The proposed algorithm aims to analyze the thermal solar panel images. The acquired thermal solar panel images were segmented into solar cell sizes to provide more detailed information by region or cell area instead of the entire solar panel. This paper uses both the image histogram information and its corresponding cumulative distribution function (CDF), useful for image analysis. The acquired thermal solar panel images are enhanced using grayscale, histogram equalization, and adaptive histogram equalization to represent a domain that is easier to analyze. The experimental results reveal that the extraction results of thermal images provide better histogram and CDF features. Furthermore, the proposed scheme includes the convolutional neural network (CNN) for classifying the enhanced images, which shows that a 97% accuracy of classification was achieved. The proposed scheme could promote different thermal image applications—for example, non-physical visual recognition and fault detection analysis.
Incomplete Multi-View Clustering With Reconstructed Views

As one category of important incomplete multi-view clustering methods, subspace based methods seek the common latent representation of incomplete multi-view data by matrix factorization and then partition the latent representation to get clustering results. However, these methods ignore missing views in the process of matrix factorization, which makes the connection of different views be exploited inadequately. This paper proposes Incomplete Multi-view Clustering with Reconstructed Views (IMCRV), which utilizes the incomplete examples sufficiently. In IMCRV, the missing views of incomplete examples are reconstructed and the reconstructed views are also used to seek the common latent representation. IMCRV also involves the Laplacian regularization to preserve the global property of the latent representation. The gradient descent method with the multiplicative update rule is employed to solve the objective function of IMCRV. The corresponding iterative algorithm is developed and the convergence of the algorithm is proved. IMCRV is compared with many state-of-the-art incomplete multi-view clustering methods under different Incomplete Example Rates (IER) on public multi-view datasets. The experimental results demonstrate the superior effectiveness of IMCRV.
Nanotechnology enabled management approaches against the Covid-19 pandemic

COVID-19 has been the causative virus for one of the biggest pandemic in human history and is still a major threat to human health after more than a year of its first incidence. Since its outbreak, it fueled a worldwide demand for the following: i) rapid, predictive and sensitive diagnostic tool ii) specific vaccine candidate and targeted prevention iii) effective anti SARS CoV2 therapy and personalized medicine. Different fields of science and technology have contributed in this battle against coronavirus. Nanotechnology has also played a crucial role in making technologically advanced equipment and devices, breaking bottlenecks in facing the pandemic. Deeper insights regarding interface between nanotechnology and coronavirus can further direct the scientists and researchers towards a new era for the impeccable design and repurposing of drugs, vaccines and detection method for COVID-19. Entering the era of Nanotechnology-SARS CoV2 interface will not only be critical in counteracting COVID-19 but also be vital in the management of future pandemics. Hence, our review systematically correlated the SARS-CoV2 structure, life cycle and proteomic antigenicity with the potential drugs, vaccines, diagnostic and management strategies with a special emphasis on the existing and promising role of nanotechnology. This review offers a reference for future studies and guide researchers for the translation of nanotechnological concepts into effective theranostic and management strategies and support our fight against the current and the future coronavirus pandemics. *Correspondence to: Verma RS, Prof., Head, Stem cell and Molecular Biology Laboratory, Bhupat and Jyoti Mehta school of Biosciences, Department of Biotechnology, IIT-Madras Chennai-600036, Tamil Nadu, India Tel: 044 2257 4109; Email: vermars@iitm.ac. in
High Psychological Burden of Young Family Physicians Early in the COVID-19 Pandemic: Results from an Exploratory Survey

Background Previous pandemics such as SARS in 2002 revealed an especially high psychological burden of frontline healthcare professionals in hospitals directly involved in the treatment of confirmed cases. As family physicians are among the first exposed to the SARS-CoV-2 virus, assessing their psychological burden and needs for support is important. Methods A web-based survey was distributed among family physicians of a German university teaching practice network. The questionnaire consisted of validated instruments to evaluate symptoms of psychological distress, i.e., the patient health questionnaire (PHQ-9), generalized anxiety disorder 7 (GAD-7), peritraumatic distress inventory (PDI), and insomnia severity index (ISI). Additional self-developed items addressed sociodemographic and professional characteristics including professional duties and protective equipment available. Effects between the symptoms were examined using bivariate Pearson correlation. Results The participation rate was 31 % (36 of 116) with 33 valid cases. About 47 % (n = 17) of the respondents were 56 years old or more. 66.7 % (n = 22) physicians reported moderate to severe depressive symptoms (PHQ-9 ≥ 10) and 51.5 % (n = 17) moderate to severe signs of anxiety (GAD-7 _ 10). Physicians with depressive symptoms showed more signs of anxiety (p <.001) and insomnia (p <.001). There is a negative correlation between age and severity of depressive symptoms (p =.041) and insomnia (p =.002). The regression analysis showed that younger participants were more likely to show depressive symptoms (β = –.358, p =.046), even after correcting for gender (R2 =.402). Conclusions Depressive symptoms were frequent among family physicians, especially in younger physicians who were also more likely to report symptoms of insomnia. The symptoms of psychological distress are unlikely to be explained by a lack of protective equipment, because the majority had sufficient supplies. © 2021, Deutscher Arzte-Verlag GmbH. All rights reserved.
Nuchal Translucency Thickness Measurement in Fetal Ultrasound Images to Analyze Down Syndrome

Down syndrome (DS) is a chromosomal condition associated with intellectual disability and developmental delay in infants. The most salient pre-natal marker to identify DS during the early stages of gestation is the Nuchal translucency (NT) thickness. Accurate NT measurement from ultrasound (US) images becomes challenging due to the presence of speckle noise, weak edges and other artifacts. This paper proposes a semi-automated approach for early identification and calibration of NT thickness by estimating the distance between the edges of NT. A comparative study is performed on six traditional filters to determine the effective denoising filter for US datasets. This paper also presents an evaluation of two popular segmentation algorithms, the Active Contour and Chan-Vese-based segmentation. The study reveals that Wiener filter outperformed other filters based on the performance metrics and achieved peak signal noise ratio of 42.88 dB and mean squared error of −3.45. The prime feature NT region has been segmented effectively using enhanced double region-based active contour segmentation and obtained Dice Similarity Index (DSI- 84%) and Jaccard Index (76%). This technique has been validated by using Bland Altman’s plot to determine the correlation between clinical and proposed method.
On the Design of Discrete Apertures for High-Efficiency Wireless Power Transfer

This communication deals with the design of discrete apertures (DAs) for wireless power transfer (WPT) within the Fresnel region and provides some design guidelines to enhance WPT efficiency. In fact, although the aperture excitations optimization problem, given two specific continuous or DAs, has been already researched, the effect of antenna arrays design choices (such as the number of antenna elements or their spacings) on the WPT efficiency has not been investigated yet. In order to do that, WPT efficiency formulation has been theoretically analyzed to understand the different antenna parameters contributions to the power transfer efficiency (PTE). After that, the problem of the array excitation optimization in the sense of maximum PTE has been revisited including cases with or without perfect impedance matching. Finally, different array designs and relative achievable PTEs have been compared systematically, and it is shown that arrays with interelement spacing in the order of <inline-formula> <tex-math notation="LaTeX">$0.3\lambda $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$0.4\lambda $ </tex-math></inline-formula> (tightly coupled arrays) provide the largest PTE (also for oblique WPT cases) when the array excitation is calculated taking into account the impedance mismatching.
Investigating the Influence of Personalised Gamification on Mobile Survey User Experience

Gamification in healthcare has shown to increase user motivation and treatment adherence. Personalisation strategies have the potential to create sustainable health change and user motivation. In striving for personalisation, surveys are often used as an easy tool to collect information about the user. But lengthy surveys are often tedious and demotivating, provoking bad respondent behaviour which results in a loss of data quality. Gamified surveys can enhance respondent behaviour but are labour-intensive to develop. This study explores the effect of a re-usable mobile survey application, using personalised gamification, on user experience. In an A/B study setting with 28 participants, The Hexad Player Type Framework is used to determine the player type of the user and personalise the survey accordingly. Results have shown that the overall user experience of the gamified application is higher than the traditional survey and 37.5% of gamified users perceived the duration of the survey as shorter than the actual time, compared to 20% of the respondents of the traditional survey. No significant difference in data quality has been detected, as loss in data quality remained limited in both versions. Future work should explore the influence of more elaborate game elements in a larger population.