Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges and Opportunities in the Sustainable Radioactive Waste Management

Nuclear power plants are worldwide operated. While some countries consider phasing out nuclear power, others are considering building new power plants. Controversial debates have always arisen over the sustainability of nuclear energy, and radioactive waste has been the main topic. The possibility of long term management of radioactive waste considering the population behavioural and demographic characteristics is of particular importance in this study. The aim of the study was to examine if demographic factors and behavioural factors influence the sustainable management of radioactive waste, more precisely their final disposal. In particular, we analysed the relationship between each variable and the acceptance of the final disposal of radioactive waste and the location of the disposal facility. To find the answer of the present research study, we set the acceptance of the final disposal of radioactive waste and the location of the disposal facility as dependent variable and two demographic variables and two behavioural variables as independent variables. Statistical correlations were performed between the selected variables by using SPSS Statistics to establish the relationship between the factors at the individual level and the acceptance of the final disposal of radioactive waste in the context of sustainable development. Questionnaires were used as research tool. Based on this investigation, it was found that there is no correlation between perceived risks and the acceptance of the final disposal of radioactive waste and the location of the disposal facility. Likewise, this study shows that individuals’ acceptance of radioactive waste is based on individual behaviour, but it is also influenced by the demographic characteristics.
Exploring Truss Maintenance in Fully Dynamic Graphs: A Mixed Structure-Based Approach

Graphs are widely employed in complex system modeling, VLSI design, and social analysis. Mining cohesive subgraphs is a fundamental problem in graph analysis, while implementing cohesive subgraphs requires analysts to not only ensure cohesiveness but also consider the computational intractability. Among a variety of diverse cohesive structures, <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="luo-ieq1-3174594.gif"/></alternatives></inline-formula>-truss exhibits a perfect trade-off between structure tightness and computational efficiency. In a <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="luo-ieq2-3174594.gif"/></alternatives></inline-formula>-truss, each edge is present in at least <inline-formula><tex-math notation="LaTeX">$k-2$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>k</mml:mi><mml:mo>-</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="luo-ieq3-3174594.gif"/></alternatives></inline-formula> triangles. This study aims to contribute to this growing area of truss maintenance in fully dynamic graphs by avoiding expensive re-computation. Specifically, we consider the challenging scenario of batch processing of edge and vertex insertion/deletion and propose efficient algorithms that can maintain the trusses by only searching a very small range of affected edges. Also, our algorithms allow parallel implementations to further improve the efficiency of maintenance. Extensive experiments on both real-world static and temporal graphs illustrate the efficiency and scalability of our algorithms.
Numerical performance analysis of quartz tuning fork-based force sensors

Quartz tuning fork-based force sensors where one prong is immobilized onto a holder while the other one is allowed to oscillate freely (‘qPlus’ configuration) are in widespread use for high-resolution scanning probe microscopy applications. Due to the small size of the tuning forks (≈3 mm) and the complexity of the sensor assemblies, the reliable and repeatable manufacturing of the sensors has been challenging. In this paper, we investigate the contribution of the amount and location of the epoxy glue used to attach the tuning fork to its holder on the sensor’s performance. Towards this end, we use finite element analysis to model the entire sensor assembly and to perform static and dynamic numerical simulations. Our analysis reveals that increasing the thickness of the epoxy layer between prong and holder results in a decrease of the sensor’s spring constant, eigenfrequency, and quality factor while showing an increasing deviation from oscillation in its primary modal shape. Adding epoxy at the sides of the tuning fork also leads to a degradation of the quality factor even though in this case, spring constant and eigenfrequency rise in tandem with a lessening of the deviation from its ideal modal shape.
Screw Rotor Profiles of Variable Lead Vacuum and Multiphase Machines and Their Calculation Models

Twin screw machines are mechanically simple and reliable, having only two moving parts, while their manufacturing cost is low and, as a result of optimization of their rotor proportions and profiles, their efficiency is high. Consequently, they are now used widely both as multiphase and vacuum pumps. This study gives the results of an investigation of how their performance is affected by changing the rotor lead, i.e. the axial step along the rotor wrap, from a constant value to a variable one. A number of different configurations have been considered and it is shown that, for machines with a large wrap angle, a variable lead results in greater displacement and improved efficiency.
Perencanaan Injeksi Kimia Untuk Meningkatkan Perolehan Minyak Menggunakan Surfactant-Polymer Flooding

Seiring dengan bertambahnya masa produksi suatu lapangan minyak, produktivitasnya akan semakin berkurang. Hal ini disebabkan bertambahnya jumlah minyak yang telah diproduksikan dari reservoir, yang sangat berpengaruh terhadap berkurangnya energi reservoir alamiah (tekanan reservoir) yang diperlukan untuk mengalirkan minyak ke dalam sumur produksi. Untuk dapat memproduksikan minyak setelah energi alamiah reservoir berkurang maka diperlukan teknologi pengurasan minyak tahap lanjut (Enhanced Oil Recovery - EOR). Untuk memilih salah satu teknologi EOR tersebut yang akan di implementasikan di suatu reservoir diperlukan pekerjaan awal yang disebut Screening EOR dengan menggunakan standart evaluasi yang normal digunakan di kalangan industri minyak. Injeksi kimia adalah salah satu metode pengurasan minyak tahap lanjut dengan menambahkan zat-zat kimia ke dalam reservoir dengan jalan injeksi. Penambahan zat-zat kimia ini bertujuan untuk merubah sifat fisik dari batuan terhadap fluida reservoirnya, seperti menurunkan tegangan antar muka dan menaikan viskositas. Injeksi kimia ini memiliki prospek yang baik pada reservoir-reservoir yang telah sukses dilakukan injeksi air (waterflooding), selain itu injeksi kimia ini memiliki nilai ekonomis yang tinggi. Disamping itu, terdapat beberapa faktor yang dapat mempengaruhi keberhasilan dari injeksi kimia ini yaitu temperatur, jenis reservoir dan permeabilitas. Injeksi kimia menggunakan Surfactant-Polymer pada penelitian ini dapat meningkatkan efisiensi pendesakan dan efisiensi penyapuan sehingga perolehan minyaknya meningkat sekitar 12,89% setelah waterflooding dan/atau 68,27% dari jumlah cadangan minyak mula-mula (OOIP) di reservoir.
Strengthening the HF/E Value Proposition

A frequently reported experience by Human Factors/Ergonomics (HF/E) practitioners is that HF/E is included too late in the design process, at a time where most main decisions have been made, thereby limiting the opportunities for improving aspects of performance and occupational health. The Capability Approach to structured development of capabilities can be a step towards remedying this challenge by connecting the HF/E contribution to the company strategy. This paper introduces the approach and provides a case example to demonstrate its application. Main experiences with using the approach are that it contributes to a holistic understanding of how one can enhance targeted (sub)system performance, alignment of key actors across organisational levels, functions and disciplines, and timely integration of activities to support delivery of such performance. Further, we offer some perspectives on its potentials for broader use by HF/E practitioners and researchers.
An Economic Decision-Making Model for Drugs Using Big Data and Convolution Neural Network in Healthcare

Incorporating health big data (HBD) and observational real-world patient-level genomic and clinical data in several subpopulations into drug economic analysis of precision medicine has the potential to be beneficial ion various ways. However, health economists encounter practical and operational difficulties when using HBD in this framework. On an individual patient level, the “big data” age represents an exciting opportunity to use strong new sources of information to reduce clinical and drug economic uncertainty. Health big data is a very important resource in the field of medicine. It is an irresistible trend to rationally utilize the advantages of health big data to serve medical care and clinical medicine. Health big data will play an irreplaceable role in pharmaceutical research and development, disease diagnosis and treatment, health risk factor analysis, public health emergency management, residents’ health management, and precision medicine. However, the application of health big data in the field of medicine also faces some challenges, such as irrational drug use in primary medical and health institutions. This paper builds a drug economic decision-making model based on health big data and uses a convolutional neural network to study the model. This research is conducted to realize the understanding of drug demand, which is conducive to the country’s understanding of drugs. It also allows people to better seek medical treatment. Artificial intelligence’s new advancements in the big data era have prepared the way for future rational drug modeling and evaluation that will have a huge influence on drug discovery methods which ultimately have better impact on public health.
The effect of static magnetic field on bone mineral content in aging rats

Introduction: Aging is defined as a consequence of progressive accumulation of metabolic waste, which results in development of various disorders in the structure and function of cells over a number of years. It is followed by loss of bone tissue, where bones become less firm. The cycle of bone remodeling with age becomes longer, and the degree of bone mineralization decreases. Static magnetic fields (SMF) are stable magnetic fields that can be natural or artificial. Their moderate intensity (1mT-1T) affects physiological processes, cells, genetic material, behavior and development. So far, numerous studies have shown different effects of static magnetic fields on cell cultures, experimental animals, and the human population. Aim: Aim of this study was to determine the effect of static magnetic field (SMF) on bone mineral content (BMC) values in aging rats. Material and methods: Male Wistar rats, 3 years old, were used in the experiment. A total of 18 animals were divided into two groups: exposed (experimental group) and unexposed (control group). Nine animals from the experimental group were exposed to 30mT intensity static magnetic field for 10 weeks, while nine control rats were not exposed to the static magnetic field. Bone mineral content was measured by DXA (Dual-Energy X-Ray Absorptiometry. Results: Based on the experiment, it was found that after the exposure to the static magnetic field, in the experimental group, there is a statistically significant increase in the value of BMC in the trunk region and ribs region. In all other regions of interest: head, legs, pelvic bone, spine, total BMC - there were no statistically significant changes in BMC values. Conclusion: In the conducted experiment, a higher increase in the value of BMC was found in animals exposed to the SMF, compared to animals that were not exposed to the static magnetic field.
A review of combustion control strategies in diesel HCCI engines

ABSTRACT Homogenous charge compression ignition (HCCI) has attracted much attention recently owing to its potential for significantly reducing NOx and particulate emissions whilst achieving high thermal efficiency. Commencement of the combustion in a HCCI engine is not the result of the ignition of fuel by spark or fuel injection; it is the result of auto-ignition of the entire fuel–air mixture. This becomes the sensitive parameter (auto-ignition temperature) to control combustion. Research and development of HCCI diesel engines has been pursued along three main technical routes, depending on the mixture preparation process involved. First, injecting the fuel into the intake air – similar to a conventional port fuel injection SI engine – or early and late direct injection into the cylinder. The second approach is low temperature combustion by using a high exhaust gas recirculation rate coupled with a different injection strategy, thus improving premixing. The third technique is modifying the fuel characteristics. Thus, alcohol, hydrogen natural gas, ethers and n-heptane/iso-octane have been studied. It has been established using the above techniques will result in stable combustion even at higher engine loads. Using alternate fuels like alcohol, hydrogen natural gas and ethers showed significant improvements in fuel economy and reductions in NOx and particulate matter.
Affective Distancing Associated with Second Language Use Influences Response to Health Information

Health care delivery depends on effective provider–patient communication. An important issue is whether and how this communication differs for second language (SL) patients. While understanding health information can be impaired by limited English proficiency, we examined a potential benefit of SL use. SL users may be “affectively distanced”, with weaker emotional reactions to content presented in a foreign versus native language (NL). This distancing may have important implications for understanding, and for making decisions and judgements about health information to the extent these processes involve affective responses. For example, patients may respond to diagnostic test results indicating risk of illness with less intense negative affect if the information is presented in their SL. Language differences in affective response may in turn attenuate risk perception for SL versus NL users, with perceived risk being lower while the objective risk associated with test results increases, as predicted by the ‘risk as feelings’ view of risk perception, where perceived risk is based on affective response to the information. On the other hand, risk perception may be more calibrated with objective risk for SL users to the extent that affective distancing encourages SL users to rely on deliberative rather than affective-based, intuitive processes related to risk perception. SL use may also influence attitudes toward and intentions to perform behaviors that address risk because these processes are driven in part by risk perception and memory for the risk information. These processes may also depend on numeracy, defined as the ability to make sense of and rationalize numbers, because it influences risk perception. We tested these predictions in the context of a simulated Electronic Health Record (EHR) patient portal, in which participants were presented diagnostic test results in English from fictional patients. Native English speakers (n = 25), and native Mandarin speakers with higher numeracy (n = 25) and lower numeracy (n = 28) participated in the study. Consistent with the ‘affective distancing’ effect, SL participants with either higher or lower numeracy demonstrated a flatter slope for positive and negative affective responses to the test results compared to NL participants. Moreover, SL participants reported greater perceived risk than NL participants did as objective risk rose. A similar pattern occurred for attitudes toward and intentions to perform behaviors that addressed this risk, especially for treatment health behaviors. On the other hand, language did not influence memory for risk-related information. Our findings extend the affective distancing effect associated with SL use to the health domain and show that this effect influences risk perception and behavioral intentions beyond memory recall and numeracy skills.
NEW APPROACHES TO DISTRIBUTED CONTROL OF SATELLITE FORMATION FLYING

We present two control design techniques for formations composed of satellites. The proposed methods allow significant reduction in complexity associated with distributed structured controller design. One approach relies on solving a single local linear quadratic regulator problem and constructs a global stabilizing distributed control structure by exploiting properties of the solution. The second technique uses a system decomposition approach within a linear matrix inequality formulation and considers more general H2 and H1 performance indices. Its performance is compared with centralized and fully decentralized designs in a satellite formation scenario. An overview of possible extensions, current and future research directions is provided as well.
Postpartum Depression Detection: Concept Analysis

Postpartum depression is one of the health problems that may occur without being recognized which may lead to a serious adverse effect for mother and her child. Postpartum depression remain underestimated and unrecognized as many women are discharged without being detected as having PPD or even uneducated in order to seek for help when needed. Postpartum depression has a negative impact on both mother and the child wellbeing. Early detection is essential for mothers’ and infants’ safety. Postpartum depression is defined as “a disorder that is often unrecognized and undertreated”. The aim of this paper is to go through an analysis for postpartum depression detection and get a clear picture about the definition, attributes, antecedents, consequences and its empirical References. Method: a review of the literature was conducted. Articles from 2013 to 2020 were included. Walker and Avant framework was used for concept analysis. Results: the search have found four attributes: time, risk factors, symptoms, and outcome. Also, the search have found the following antecedents: knowledge, screening and care. The consequences are access to mental health care and drawing the treatment line process. Conclusion: this analysis will help in discovering the suitable methods for detecting the postpartum depression to achieve high quality care for mothers and infants.
Modeling, Optimization and Power Efficiency Comparison of High-speed Inter-chip Electrical and Optical Interconnect Architectures in Nanometer CMOS Technologies

Modeling, Optimization and Power Efficiency Comparison of High-speed Inter-chip Electrical and Optical Interconnect Architectures in Nanometer CMOS Technologies. (December 2010) Arun Palaniappan, B.E. , Anna University Chair of Advisory Committee: Dr. Samuel Palermo Inter-chip input-output (I/O) communication bandwidth demand, which rapidly scaled with integrated circuit scaling, has leveraged equalization techniques to operate reliably on band-limited channels at additional power and area complexity. Highbandwidth inter-chip optical interconnect architectures have the potential to address this increasing I/O bandwidth. Considering future tera-scale systems, power dissipation of the high-speed I/O link becomes a significant concern. This work presents a design flow for the power optimization and comparison of high-speed electrical and optical links at a given data rate and channel type in 90 nm and 45 nm CMOS technologies. The electrical I/O design framework combines statistical link analysis techniques, which are used to determine the link margins at a given bit-error rate (BER), with circuit power estimates based on normalized transistor parameters extracted with a constant current density methodology to predict the power-optimum equalization architecture, circuit style, and transmit swing at a given data rate and process node for three different channels. The transmitter output swing is scaled to operate the link at
Generalized Space Shift Keying for Ambient Backscatter Communication

We consider a generalized space shift keying (GSSK)-enabled multiple-input multiple-output (MIMO) ambient backscatter communication (ABC) system. We propose a scheme to exploit the multiple antenna structure of the system to achieve a lower error-rate performance than conventional ABC systems. Furthermore, we present a novel low complexity energy-based maximum likelihood (EML) GSSK detector, which does not require the perfect knowledge of the ambient source’s signal, unlike the conventional ABC receivers. To gain insights into the performance of the proposed scheme, we derive the exact pairwise error probability (PEP) of the EML detector and further obtain an upper bound on the probability of error. We also derive a simple asymptotic PEP expression as the number of antennas of the reader becomes large. Finally, we derive a simple, asymptotic PEP at the reader when the noise variance approaches zero, i.e., under the large signal-to-interference ratio regime. We validate our analysis through Monte Carlo simulations and show a small performance loss due to the approximations.
Micro-autopilot for research and development

The Australian Defence Science and Technology Organisation has been developing an autopilot framework for more than a decade in support of research and development for Unmanned Aerial Systems. This paper reviews the specific requirements on avionics for research and development purposes, and compares in house development achievements with commercial and open-source avionics solutions to date.
Unsupervised Single-Scene Semantic Segmentation for Earth Observation

Earth observation data have huge potential to enrich our knowledge about our planet. An important step in many Earth observation tasks is semantic segmentation. Generally, a large number of pixelwise labeled images are required to train deep models for supervised semantic segmentation. On the contrary, strong intersensor and geographic variations impede the availability of annotated training data in Earth observation. In practice, most Earth observation tasks use only the target scene without assuming availability of any additional scene, labeled or unlabeled. Keeping in mind such constraints, we propose a semantic segmentation method that learns to segment from a single scene, without using any annotation. Earth observation scenes are generally larger than those encountered in typical computer vision datasets. Exploiting this, the proposed method samples smaller unlabeled patches from the scene. For each patch, an alternate view is generated by simple transformations, e.g., addition of noise. Both views are then processed through a two-stream network and weights are iteratively refined using deep clustering, spatial consistency, and contrastive learning in the pixel space. The proposed model automatically segregates the major classes present in the scene and produces the segmentation map. Extensive experiments on four Earth observation datasets collected by different sensors show the effectiveness of the proposed method. Implementation is available at https://gitlab.lrz. de/ai4eo/cd/-/tree/main/unsupContrastiveSemanticSeg.