Miracle-dz/UniFAR-FTUs huggingface dataset:
Micro-Doppler Signatures - Review, Challenges, and Perspectives

The micro-Doppler signature is a distinctive characteristic of the observed micro-Doppler effect in an object. The “signature” is commonly used to refer to the characteristic expression of an object or a process. When examining the micro-Doppler effect, the distinctive micro-Doppler characteristic, i.e., the micro-Doppler signature of an object, allows the recognition of an object's identity through its movement. To extract micro-Doppler signature of an object by radar, a simple continuous wave (CW) radar is good enough. However, wide-band coherent Doppler radars, which use wide frequency bandwidth to gain high range resolution and measure both the range and Doppler information, are more useful and desirable in the radar micro-Doppler signature research. In this chapter, we review current progress in the extraction, processing, analysis, and applications of radar micro-Doppler signatures, and discuss the challenges and perspectives in radar micro-Doppler signature research.
Neurodevelopmental Outcome in Very Preterm Infants Randomised to Receive Two Different Standardised, Concentrated Parenteral Nutrition Regimens

We have previously shown that increasing parenteral protein (target: 3.8 versus 2.8 g/kg/d) and energy (12% versus 10% glucose; 3.8 versus 2.8 g/kg/d) intake using a Standardised, Concentrated with Added Macronutrients Parenteral (SCAMP) nutrition regimen ameliorates early head growth failure in very-preterm infants (VPIs). We hypothesised that the SCAMP nutrition regimen would also improve neurodevelopmental outcome. The original double-blind randomised, controlled study (ISRCTN: 76597892) received ethical approval. VPIs were randomised to either start SCAMP or remain on the control regimen. The consent process included neurodevelopmental assessments (Bayley III), all of which were performed (blinded) at 2–3.5 years of corrected gestational age. Bayley III assessments were performed for 38/60 SCAMP survivors and 41/63 control survivors at means of (sd) 29.2 (3.7) and 20.0 (3.9) months, respectively. Motor, cognitive, language, and combined scores were all higher in the SCAMP intervention group, but none of the differences were statistically significant. Nutrient intake and biochemical monitoring data confirmed that protein/energy ratios were maintained in the SCAMP intervention group without increasing the incidence of hyperglycaemia, insulin treatment, or the derangement of plasma mineral/electrolyte levels. This study did not show a statistically significant improvement in neurodevelopmental outcome when administering higher parenteral protein/energy intakes despite optimal energy and mineral intakes.
The Effect of Exercise on Pulsatility Index of Uterine Arteries and Pain in Primary Dysmenorrhea

Background: Primary dysmenorrhea (PD) is one of the most common diseases in women of reproductive age. Our aim was to examine whether a twice-weekly thirty-minute exercise intervention could result in a difference in the pulsatility index (PI) of the uterine arteries (UAs) and level of menstrual pain in patients with PD. Methods: In our prospective observational trial, the observation period included one spontaneous menstrual cycle and the consecutive time of the next menstruation of all participants, aged 18–44, with no extensive sports experience. In total, 73 volunteers were enrolled: 38 persons in the intervention group (IG) and 35 in the control group (CG). The intervention program was accompanied by music, performed in groups under the supervision of a qualified instructor in Hungary. The primary outcome was the difference between the IG and CG regarding the PI values of UAs at the 1st and the 2nd ultrasound (US) Doppler flowmetry. The secondary outcome was the difference between the IG and CG regarding the PI of UAs and menstrual pain measured by using the Numeric Rating Scale and adherence to the intervention. Statistical tests such as an independent-samples t-test, chi-square test, Mann–Whitney test and analysis of covariance (ANCOVA) were used during the analyses. Results: Examining the mean of the PI of UAs in the IG and the CG at the 1st and the 2nd US measurement, a significant difference was found in the change in the measured value (Z = −2.545; p = 0.011). The IG showed a significantly higher increase in the mean of the PI of UAs (Median = 0.825) than the CG (Median = 0.130). The difference in the PI of the UAs of the IG and the CG is not related to the level of pain in any group (p = 0.336) and not related to the whole sample (p = 0.354); furthermore, the level of pain did not significantly differ between the two groups. Conclusions: Our study is the first to document the significant effects of mild-to-moderate exercise training on the change in the PI of the UAs in individuals with PD. The IG had a reduced blood flow due to circulatory redistribution after exercise. The level of menstrual pain of primary dysmenorrhea patients is independent of the level of blood circulation regarding the PI of the UAs. Randomized controlled studies with more participants and a longer research period are needed to confirm our findings regarding the association between regular exercise and the PI of UAs. The study was registered at clinicaltrials.gov: NCT04618172.
Implications of climatic variability and climate change on water resources availability and water resources management in West Africa

This paper discusses the implications of climatic variability and change for water resources systems in West Africa, with particular reference to hydrological systems, water resources availability, and water resources management. The paper in particular discusses issues related to rainfall trends, seasonal variations in river flows, mean annual trends in river flows and discharges, and local variations and extremes of river discharges. The paper also discusses the climate change implications for groundwater variations, water resources availability and climatic events (including droughts and floods). The results show that a lot of spatial and temporal variations occur regionally and locally, but in general there have been downward trends in rainfall and increases in water deficits and drought events. The paper then discusses the response strategies needed to adapt to the climate change impacts on water resources, and emphasizes the need for both water supply-demand adaptations, details of which are discussed in the paper. The paper finally discusses the mechanisms needed for the implementation of the adaptation measures. Such mechanisms include: (a) building capacity and manpower development of water institutions in the region for producing hydrological and hydroclimatological data; (b) public participation and the involvement of stakeholders; (c) the establishment of both national and regional cooperation; and (d) the need for climate and other environmental data collection and monitoring.
The Why, When, What, and How About Predictive Continuous Integration: A Simulation-Based Investigation

Continuous Integration (CI) enables developers to detect defects early and thus reduce lead time. However, the high frequency and long duration of executing CI have a detrimental effect on this practice. Existing studies have focused on using CI outcome predictors to reduce frequency. Since there is no reported project using predictive CI, it is difficult to evaluate its economic impact. This research aims to investigate predictive CI from a process perspective, including why and when to adopt predictors, what predictors to be used, and how to practice predictive CI in real projects. We innovatively employ Software Process Simulation to simulate a predictive CI process with a Discrete-Event Simulation (DES) model and conduct simulation-based experiments. We develop the Rollback-based Identification of Defective Commits (RIDEC) method to account for the negative effects of false predictions in simulations. Experimental results show that: 1) using predictive CI generally improves the effectiveness of CI, reducing time costs by up to 36.8% and the average waiting time before executing CI by 90.5%; 2) the time-saving varies across projects, with higher commit frequency projects benefiting more; and 3) predictor performance does not strongly correlate with time savings, but the precision of both failed and passed predictions should be paid more attention. Simulation-based evaluation helps identify overlooked aspects in existing research. Predictive CI saves time and resources, but improved prediction performance has limited cost-saving benefits. The primary value of predictive CI lies in providing accurate and quick feedback to developers, aligning with the goal of CI.
Influence of Automatic Speech Recognition and Understanding on Flight Efficiency and Throughput – A Human-in-the-Loop Study

Modern Controller Working Positions (CWP) require manual input into digital flight strips or aircraft radar labels. Erroneous or missing input from air traffic controllers (ATCos) can lead to inefficiencies and potential safety issues. Automatic speech recognition and understanding (ASRU) support is a method to improve the accuracy of digital system inputs. This paper investigates the effectiveness of ASRU support on ATCos radar label maintenance, with the aim of determining the impact on of Air Traffic Management efficiency. To that aim, a human-in-the-loop study was carried out at DLR Braunschweig. The results reveal (i) that the verbal ATCo transmissions analyzed by ASRU achieved a robust command recognition rate of 92.5% with a command error rate of 2.4%; (ii) within the baseline conditions seven actual safety-critical situations were observed, whereas with ASRU support only four happened; (iii) through ASRU support the number of wrong or missing radar label entries were reduced by the factor of two; and (iv) ATCos’ situational awareness improved while the perceived mental workload decreased. These results indicate that more mental spare capacity was available with ASRU support, which improves the planning process and increases traffic flow.
Enabling Digital Air Traffic Controller Assistant through Human-Autonomy Teaming Design

The air traffic management domain has started to reap the benefits of Artificial Intelligence (AI) to support human controllers. However, the controller working position as the interface for managing air traffic is still limited to passively providing information, leaving the bulk of the tasks for human controllers, especially tasks involving decision-making. To meet the growing demands of air traffic, one way is to increase the level of automation in air traffic control systems. This requires a completely new task distribution for human controllers in combination with AI-based systems. Our paper will give an overview of our work related to the introduction of a digital Air Traffic Controller (ATCO) into an en-route controller working position. The digital ATCO is designed with the capabilities to perform several time-consuming tasks autonomously. The system is based on a meaningful distribution of tasks between a human and a digital ATCO. Several supporting tools and a Human-Autonomy Teaming (HAT) interface are described, which are able to handle different modes of HAT operations. Lastly, we describe a proof of concept setup of the system integrated in a controller working position that was presented at the AIRSPACE WORLD 2023.
Reliability Estimation of Retraction Mechanism Kinematic Accuracy under Small Sample

Due to intricate operating conditions, including structural clearances and assembly deviations, the acquisition of test data of landing gear retraction mechanism is limited, posing challenges for reliability analysis. To solve the problem, a Bayesian-based reliability analysis method by fusing prior and test data is proposed, focusing on the mechanism kinematic accuracy under small-sample conditions. Firstly, a dynamic simulation model is established to collect prior data, and retraction tests are conducted to obtain test data. Then, based on Bayesian theory, the motion accuracy parameter estimation model integrating prior and test samples is established. To obtain accurate hyper parameters, the prior samples are expanded using neural network. Finally, taking the retraction mechanism as the research object, the kinematic accuracy reliability is quantified, and the impact of uncertainty factors is analyzed in depth. The results show that the proposed method is superior to the classical interval estimation method in stability and effectively mitigates the impact of uncertainty factors.
Information-Theoretic Pseudosignatures and Byzantine Agreement for t ≥ n/3

Byzantine agreement means achieving reliable broadcast on a point-to-point network of n processors, of which up to t may be maliciously faulty. A well-known result by Pease, Shostak, and Lamport says that perfect Byzantine agreement is only possible if t < n/3. In contrast, so-called authenticated protocols achieve Byzantine agreement for any t based on computational assumptions, typically the existence of a digital signature scheme, an assumption equivalent to the existence of one-way functions. The “folklore” belief based on these two results is that computational assumptions are necessary to achieve Byzantine agreement for t ≥ n/3. We present a protocol that refutes this folklore belief, i.e., it achieves Byzantine agreement for any t in an information-theoretic setting. It does not, however, contradict the precise impossibility result: More than one difference exists between the model in that proof and the model of the existing authenticated protocols, and we only remove the computational assumption. Our protocol is based on a new information-theoretically secure authentication scheme with many of the properties of digital signatures; we call it pseudosignatures. Our construction of pseudosignatures generalizes a scheme by Chaum and Roijakkers.
and Oxygen Metabolism in Children Developmental Changes of Cerebral Blood Flow

BACKGROUND AND PURPOSE: Normal values for cerebral blood ﬂow (CBF) and metabolism in adults are well established, but not for children. Our goal, therefore, was to clarify functional developmental changes of the brain in children in relation to CBF and oxygen metabolism. METHODS: We measured regional CBF (rCBF), regional cerebral metabolic rate for oxygen (rCMRO 2 ), and regional oxygen extraction fraction (rOEF), using positron emission tomography (PET). We performed 30 PET studies in 24 children ages 10 days to 16 years (nine boys, 15 girls), using a steady inhalation method with C 15 O 2 , 15 O 2 , and 15 CO in order to measure rCBF, rCMRO 2 , and rOEF, respectively. Regions of interest were set in the primary cerebral areas (sensorimotor, visual, temporal, and parietal cortex), cerebral association areas (frontal and visual association), basal ganglia (lenticular and thalamus), and posterior fossa (brain stem and cerebellar cortex). Subjects were grouped by age ( (cid:44) 1, 1 to (cid:44) 3, 3 to (cid:44) 8, and (cid:36) 8 years), and the absolute values of the parameters were compared with those obtained from 10 healthy adults. RESULTS: rCBF and rCMRO 2 were lower in the neonatal period than in older children and adults, and increased signiﬁcantly during early childhood. rCBF was higher as compared with adults, peaking around age 7, whereas rCMRO 2 was relatively high, with the last area to increase being the frontal association cortex. Both rCBF and rCMRO 2 reached adult values during adolescence. No difference in rCBF was observed between the basal ganglia and the primary cerebral cortex; however, it was prominent in the occipital lobe in every age bracket. No signiﬁcant changes in rOEF were found during childhood. CONCLUSION: The dynamic changes of rCBF
Sequencing Educational Content Using Diversity Aware Bandits

One important function of e-learning systems is to sequence learning material for students. E-learning systems use data, such as demographics, past performance, preferences, skillset, etc. to construct an accurate model of each student so that the sequencing of educational content can be personalized. Some of these student features are“shallow”traits which seldom change (e.g. age, race, gender) while others are “deep” traits that are more volatile (e.g. performance, goals, interests). In this work, we explore how reasoning about this diversity of student features can enhance the sequencing of educational content in an e-learning environment. By modeling the sequencing process as a Reinforcement Learning (RL) problem, we introduce Diversity Aware Bandit for Sequencing Educational Content (DABSEC), a novel contextual multi-armed bandit algorithm that leverages the dynamics within user features to cluster similar users together when making sequencing recommendations.
Modifications of collagen and chromatin in ECM-related disease

PLOD2 (procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2) hydroxylates lysine residues in collagen telopeptides and is essential for collagen pyridinoline cross-link formation. PLOD2 expression and subsequent pyridinoline cross-links are increased in fibrotic pathologies by transforming growth factor Beta-1 (TGFβ1). In this report we examined the molecular processes underlying TGFβ1-induced PLOD2 expression. We found that binding of the TGFβ1 pathway related transcription factors SMAD3 and SP1-mediated TGFβ1 enhanced PLOD2 expression and could be correlated to an increase of acetylated histone H3 and H4 at the PLOD2 promoter. Interestingly, the classical co-activators of SMAD3 complexes, p300 and CBP, were not responsible for the enhanced H3 and H4 acetylation. Depletion of SMAD3 reduced PLOD2 acetylated H3 and H4, indicating that another as of yet unidentified histone acetyltransferase binds to SMAD3 at PLOD2 . Assessing histone methylation marks at the PLOD2 promoter depicted an increase of the active histone mark H3K79me2, a decrease of the repressive H4K20me3 mark, but no role for the generally strong transcription-related modifications: H3K4me3, H3K9me3 and H3K27me3. Collectively, our findings reveal that TGFβ1 induces a SP1-and SMAD3-dependent recruitment of histone modifying enzymes to the PLOD2 promoter other than the currently known TGFβ1 downstream co-activators and epigenetic modifications. This also suggests that additional activation strategies are used downstream of the TGFβ1 pathway, and hence their unraveling could be of great importance to fully understand TGFβ1 activation of genes.
Endovascular Therapy Thromboembolic Complications of Use of Abciximab for Mediation of

Summary: We describe four cases in which abciximab was used as a thrombolytic rescue agent in the setting of thrombotic events complicating interventional neuroradiologic procedures. After IV administration of abciximab, the thrombus resolved within 30 min in three cases, whereas no thrombolysis occurred in the fourth case despite the addition of intraarterial tissue plasminogen activator. Further evaluation of glycoprotein IIb-IIIa inhibitors in patients with cerebral thromboembolic events is necessary to prove clinical efﬁcacy.
Monitoring ground deformation in the Hangjiahu Plain using InSAR technique

Affected by over-exploration of groundwater for a long time, the Hangjiahu Plain in Zhejiang province, southeast of China, has suffering serious ground subsidence during the past several decades. In this paper, we investigate the time series InSAR technique for the generation of subsidence maps over this plain. 25 Radarsat-2 images acquired from Jan 2012 to Nov 2014 are used. The results show that serious subsidence has taken place in the north and southeast of Jiaxing, the east and north of Huzhou, and the north of Hangzhou. Meanwhile some rebound occurs in the east of Jiaxing and the southeast of Huzhou. The results are compared with 35 levelling measurements. The standard deviation of the error between the two data is 3.01mm, which demonstrate that time series InSAR technique has good accuracy for subsidence monitoring.
Early Prediction of Student Performance in a Health Data Science MOOC

Massive Open Online Courses (MOOCs) make high-quality learning accessible to students from all over the world. On the other hand, they are known to exhibit low student performance and high dropout rates. Early prediction of student performance in MOOCs can help teachers intervene in time in order to improve learners’ future performance. This is particularly important in healthcare courses, given the acute shortages of healthcare staff and the urgent need to train data-literate experts in the healthcare field. In this paper, we analysed a health data science MOOC taken by over 3,000 students. We developed a novel three-step pipeline to predict student performance in the early stages of the course. In the first step, we inferred the transitions between students’ low-level actions from their clickstream interactions. In the second step, the transitions were fed into Artificial Neural Network (ANN) that predicted student performance. In the final step, we used two explanation methods to interpret the ANN result. Using this approach, we were able to predict learners’ final performance in the course with an AUC ranging from 83% to 91%. We found that students who interacted predominately with lab, project, and discussion materials outperformed students who interacted predominately with lectures and quizzes. We used the DiCE coun-terfactual method to automatically suggest simple changes to the learning behaviour of low-and moderate-performance students in the course that could potentially improve their performance. Our method can be used by instructors to help identify and support struggling students during the course.
Bumblebee responses to variation in pollinator‐attracting traits of Vicia faba flowers

Abstract Adaptations that attract pollinators to flowers are central to the reproductive success of insect‐pollinated plants, including crops. Understanding the influence of these non‐rewarding traits on pollinator preference is important for our future food security by maintaining sufficient crop pollination. We have identified substantial variation in flower shape, petal size, corolla‐tube length, petal spot size and floral volatile compounds among a panel of 30 genetically distinct lines of Vicia faba. Using this variation, we found that Bombus terrestris was able to distinguish between natural variation in petal spot size, floral volatile emissions and corolla‐tube length. Foragers showed some innate preference for spotted flowers over non‐spotted flowers and preferred shorter corolla‐tube lengths over longer tubes. Our results suggest that some floral traits may have significant potential to enhance pollinator attraction to V. faba crops, particularly if paired with optimised rewards.