Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal trajectories for two UAVs in localization of multiple RF sources

This paper is concerned with optimal trajectory control for two unmanned aerial vehicles (UAVs) in a multisource localization environment. The received signal strength (RSS) at the UAVs in specified time intervals permits passive differential RSS (DRSS)-based localization of multiple radio frequency (RF) sources with unknown transmit powers. A steering algorithm is proposed to update the UAV waypoints in order to minimize the summation of the uncertainty of the source locations. The UAV paths are optimized by maximizing the determinant of the Fisher Information Matrix (FIM). The FIM is approximated at successive waypoints using the estimated locations of the sources. In addition to maximizing the localization accuracy, the objectives of the proposed trajectory control are to minimize the number of UAVs, the mission time and the path length. As the DRSS is a non-linear measurement, an extended Kalman filter (EKF), which is a non-linear filtering technique, is considered in this paper. The efficiency of the approach is depicted through simulations.
Views and Perceptions of People Aged 55+ on the Vaccination Programme for Older Adults in the UK: A Qualitative Study

Vaccination reduces the risks related to infectious disease, especially among more vulnerable groups, such as older adults. The vaccines available to older adults in the UK through the government-funded programme currently include influenza, pneumococcal, shingles and COVID-19 vaccines. The purpose of the programme is disease prevention and improving wellbeing among the ageing population. Yet, the target population’s views of the programme remain unknown. This paper aims to increase the understanding of older adults’ perceptions of the vaccination programme available in the UK. A total of 13 online focus groups (56 informants) were carried out for this qualitative study. The findings indicate that getting vaccinated involves personal decision-making processes, which are influenced by previous experiences and interpersonal interactions. Factors related to the wider community and culture are less prominent in explaining vaccination decisions. However, opportunistic vaccination offers, a lack of information and a lack of opportunities to discuss vaccines, especially with healthcare professionals, are prominent factors. The study provides in-depth data about the rationale behind older adults’ vaccination decisions in the UK. We recommend that the provision of information and opportunities to discuss vaccines and infectious disease be improved to enable older adults’ to make better informed decisions regarding the vaccines available to them.
Nonsingular $H$-tensor and its criteria

$H$-tensor is a new developed concept in tensor analysis and it is an extension of $H$-matrix and $M$-tensor. Based on the spectral theory of nonnegative tensors, several equivalent conditions of nonsingular $H$-tensors are established in the literature. However, these conditions can not be used as a criteria to identify nonsingular $H$-tensors as they are hard to verify. In this paper, based on the diagonal product dominance and $S$ diagonal product dominance of a tensor, we establish some new implementable criteria in identifying nonsingular $H$-tensors. The positive definiteness of nonsingular $H$-tensors with positive diagonal entries is also discussed in this paper. The obtained results extend the corresponding conclusions for nonsingular $H$-matrices and improve the existing results for nonsingular $H$-tensors.
Evaluation of the Impact of some Plant Extracts against Streptococcus Spp. Isolated from Dental Decay Infection

This study aimed to isolate and identify the Streptococcus spp. bacteria from patients with dental caries infection. Furthermore, the antibacterial activity of new plant extracts on isolated Streptococcus spp. was also evaluated. A total of 150 samples were obtained at random from people of various ages and genders who were suffering from dental caries infection. Four different culture media were used for isolation: nutrient agar, MacConkey agar, blood agar, and Streptococcus selection agar. The identification of bacterial isolates was distinguished by macroscopic examination and the VITEK 2 system. Only 120 cases (80%) showed positive culture, and these were distributed as follows: Streptococcus sanguinis had the highest rate and accounted for 38.33%, and Streptococcus pseudoporcinus represented only 29.16%. A 20% decline was reported for Streptococcus salivarius, with the lowest proportion being 12.5% for Staphylococcus warneri. The MIC for 16 tested plant extracts ranged from 0.97 to 125 µg/mL, whereas the MBC values ranged from 3.9 to 500 µg/mL. Imipenem was a positive control, with MIC values ranging from 3.9 µg/mL to 15.6 µg/mL; the MBCs varied from 31.2 to 125 µg/mL against all isolated species. Among the isolated bacterial species from tooth decay, Streptococcus sanguinis had the highest rate of isolated bacteria and accounted for 38.33%, while Staphylococcus warneri had the lowest percentage at 12.5%. Sargassum, Proskia, and Cicer arietinum were three distinct extracts that demonstrated superior antibacterial activity against all of the tested bacterial species. Their MIC values ranged from 0.97 µg/mL to 15.6 µg/mL, and the MBC values were between 3.9 µg/mL and 31.2 µg/mL.
T2-Low Asthma: A Discussed but Still Orphan Disease

Asthma affects 10% of the worldwide population; about 5% of cases are severe with the need for target therapies such as biologics. All the biologics approved for asthma hit the T2 pathway of inflammation. T2-high asthma is classified as allergic and non-allergic, whereas T2-low asthma can be further defined as paucigranulocytic asthma, Type 1 and Type-17 inflammation and the neutrophilic form that accounts for 20–30% of all patients with asthma. Neutrophilic asthma’s prevalence is even higher in patients with severe or refractory asthma. We searched Medline and PubMed archives from the past ten years for articles with the subsequent titles: “neutrophilic asthma”, “non-type 2 asthma” and “paucigranulocytic asthma”. We identified 177 articles; 49 were considered relevant by the title and 33 by the reading of the abstract. Most of these articles are reviews (n = 19); only 6 are clinical trials. No study identified an effective treatment. We used the literature reported by these articles to search for further biologic treatments that target pathways different from T2. We identified 177 articles, 93 of which were considered relevant for the review and included in the present article. In conclusion, T2-low asthma remains poorly investigated in terms of biomarkers, especially as a therapeutic orphan disease.
Multinuclear Magnetic Resonance Spectroscopy at Ultra-High-Field: Assessing Human Cerebral Metabolism in Healthy and Diseased States

The brain is a highly energetic organ. Although the brain can consume metabolic substrates, such as lactate, glycogen, and ketone bodies, the energy metabolism in a healthy adult brain mainly relies on glucose provided via blood. The cerebral metabolism of glucose produces energy and a wide variety of intermediate metabolites. Since cerebral metabolic alterations have been repeatedly implicated in several brain disorders, understanding changes in metabolite levels and corresponding cell-specific neurotransmitter fluxes through different substrate utilization may highlight the underlying mechanisms that can be exploited to diagnose or treat various brain disorders. Magnetic resonance spectroscopy (MRS) is a noninvasive tool to measure tissue metabolism in vivo. 1H-MRS is widely applied in research at clinical field strengths (≤3T) to measure mostly high abundant metabolites. In addition, X-nuclei MRS including, 13C, 2H, 17O, and 31P, are also very promising. Exploiting the higher sensitivity at ultra-high-field (>4T; UHF) strengths enables obtaining unique insights into different aspects of the substrate metabolism towards measuring cell-specific metabolic fluxes in vivo. This review provides an overview about the potential role of multinuclear MRS (1H, 13C, 2H, 17O, and 31P) at UHF to assess the cerebral metabolism and the metabolic insights obtained by applying these techniques in both healthy and diseased states.
Face Recognition Based on Supervised Neighborhood Preserving Projections

Neighborhood Preserving Projections(NPP) is a method for unsupervised linear dimensionality reduction, which can preserve the local neighborhood structure on the data manifold. However, when NPP is applied to the classification tasks, it has some limitations such as ignorance of the class labels information. This paper proposes a novel subspace method named Supervised Neighborhood Preserving Projections(SNPP) for face recognition, in which local geometric relations are preserved according to prior class-label information. It gains a perfect approximation of face manifold and enhances its discriminant power in a feature space. Experiments on ORL face database demonstrate the effectiveness of the method.
A brief survey of the discrete logarithm problem

The Discrete Logarithm Problem (DLP) has been the subject of interest among many mathematicians and cryptographers in recent times because of its computational di¢ culty. For the former, the enormity of the mathematics involved and the intellectual challenge that it entails are certainly motivating factors; for the latter, its usefulness in the eld of cryptography. Cryptosystems rest their security on some assumptions that certain mathematical problems are di¢ cult to solve. The ElGamal cryptosystem, for instance, is considered secure because of the computational assumption that it is di¢ cult to solve the Discrete Logarithm Problem. The di¢ culty of the DLP lies in the fact that it has a one-way property. Its computational complexity is roughly measured by the computing time of the algorithm used to solve this mathematical problem. This paper is a brief survey of some of the best known algorithms for solving the DLP, examines their computing time, and considers the DLP over two particular nite groups.
Classifying the unknown: Insect identification with deep hierarchical Bayesian learning

Classifying insect species involves a tedious process of identifying distinctive morphological insect characters by taxonomic experts. Machine learning can harness the power of computers to potentially create an accurate and efficient method for performing this task at scale, given that its analytical processing can be more sensitive to subtle physical differences in insects, which experts may not perceive. However, existing machine learning methods are designed to only classify insect samples into described species, thus failing to identify samples from undescribed species. We propose a novel deep hierarchical Bayesian model for insect classification, given the taxonomic hierarchy inherent in insects. This model can classify samples of both described and undescribed species; described samples are assigned a species while undescribed samples are assigned a genus, which is a pivotal advancement over just identifying them as outliers. We demonstrated this proof of concept on a new database containing paired insect image and DNA barcode data from four insect orders, including 1040 species, which far exceeds the number of species used in existing work. A quarter of the species were excluded from the training set to simulate undescribed species. With the proposed classification framework using combined image and DNA data in the model, species classification accuracy for described species was 96.66% and genus classification accuracy for undescribed species was 81.39%. Including both data sources in the model resulted in significant improvement over including image data only (39.11% accuracy for described species and 35.88% genus accuracy for undescribed species), and modest improvement over including DNA data only (73.39% genus accuracy for undescribed species). Unlike current machine learning methods, the proposed deep hierarchical Bayesian learning approach can simultaneously classify samples of both described and undescribed species, a functionality that could become instrumental in biodiversity monitoring across the globe. This framework can be customized for any taxonomic classification problem for which image and DNA data can be obtained, thus making it relevant for use across all biological kingdoms.
Does the intracoronary pressure differ according to two types (diffuse or focal) of coronary spasm?

BACKGROUND Several reports show that two types of coronary vasospasm (diffuse and focal spasm) are associated with the severity or prognosis of coronary spasm in patients with vasospastic angina (VSA). It is unclear whether intracoronary pressure differs between the two spasm types. AIM To investigate such relationships using a pressure wire during the spasm provocation test (SPT) in patients with VSA. METHODS Eighty-seven patients with VSA (average age: 67 years; 50 men, 37 women) underwent SPT. During the SPT, a pressure wire was advanced into the distal portion of the right coronary artery and left anterior descending coronary artery, and the ratio of the intracoronary pressure to the aortic pressure (Pd/Pa) was continuously monitored. An SPT was performed using acetylcholine (ACh), and the presence of coronary spasm was defined as the presence of > 90% arterial narrowing in response to an ACh infusion, with the usual chest symptoms and/or ischemic ECG changes. Focal spasm was defined as total or subtotal spasm within one segment of the AHA classification, while diffuse spasm was defined as > 90% spasm with two or more segments. RESULTS Among 87 patients, the frequencies of metabolic syndrome and having coronary atherosclerosis were higher in the focal group (n = 33) than in the diffuse spasm group (n = 54, P < 0.05). In the vessel analyses, in these 134 spastic segments, diffuse and focal spasms were detected in 100 and 34 vessels, respectively. The Pd/Pa at baseline was similar in both groups (diffuse: 0.96 ± 0.05, focal: 0.95 ± 0.05, P = 0.35); however, the Pd/Pa during coronary spasm was lower in focal spastic vessels (0.66 ± 0.20) than in diffuse spastic vessels (0.76 ± 0.11, P < 0.01), and the reduction in Pd/Pa during an SPT was also lower in focal spastic vessels (-0.29 ± 0.20) than in diffuse spastic vessels (-0.18 ± 0.11, P < 0.01). The presence of focal spasm was a significant factor responsible for reduction in Pd/Pa during SPT. CONCLUSION These findings suggest that focal spasm may be more severe than diffuse spasm, judging from the intracoronary pressure during coronary spasm.
On-Orbit Absolute Calibration of the Global Precipitation Measurement Microwave Imager

AbstractThe Global Precipitation Measurement (GPM) Core Observatory was launched on 27 February 2014. One of the principal instruments on the spacecraft is the GPM Microwave Imager (GMI). This paper describes the absolute calibration of the GMI antenna temperature (TA) and the earth brightness temperature (TB). The deep-space observations taken on 20 May 2014, supplemented by nadir-viewing data, are used for the TA calibration. Data from two backlobe maneuvers are used to determine the primary reflector’s cold-space spillover, which is required to convert the TA into TB. The calibrated GMI observations are compared to predictions from an ocean radiative transfer model (RTM) using collocated WindSat ocean retrievals as input. The mean difference when averaged globally over 13 months does not exceed 0.1 K for any of the nine channels from 11 to 89 GHz. The RTM comparisons also show that there are no significant solar intrusion errors in the GMI hot load. The absolute accuracy of the GMI instrument is define.
Radar Jamming Decision-Making in Cognitive Electronic Warfare: A Review

With the increasingly complex electromagnetic environment and the intelligent development of radar, the jammer, as opposed to radar, urgently needs to improve its ability to recognize threat targets and make jamming decisions. In this article, we first introduce the concepts and systems of cognitive electronic warfare (CEW) and summarize its research status. Through analysis of the existing CEW systems, we propose a CEW model suitable for cluster confrontation scenarios. Then, for the radar jamming decision-making (RJDM), namely, a crucial part of CEW, we discuss the advantages, disadvantages, and applications of the traditional methods and analyze the machine-learning-based methods, including Markov decision processing, the newest Q-learning, deep Q-learning (DQN), double DQN (DDQN), asynchronous advantage actor-critic (A3C) algorithms, and their improved algorithms. We build radar adversarial models and verify the effectiveness of reinforcement learning (RL) algorithm and the superiority of deep RL by simulating both the underlying Q-learning and DQN algorithms. Finally, the research trends of CEW are discussed.
Use and Misuse of the Langevin and Fokker-Planck Formalisms. The Brillouin and Classical Nucleation Theory Paradoxes

There has been a recent explosion in the application of nonlinear stochastic differential equations in various applied fields. Often, proposed solutions rely on passage to the corresponding deterministic Fokker-Planck equations. However, substitution of nonlinear stochastic differential equations by deterministic Fokker-Plank equations is problematic. The purpose of this paper is to elucidate the logic of such substitution for two non-trivial stochastic nonlinear problems that result in obvious paradoxes.
Envelope-Detection-Based Accurate Small-Signal Modeling of Series Resonant Converters

Resonant converters have many desirable characteristics, such as high efficiency, low component count, and low electromagnetic interference production. The resonance phenomenon that enables this also makes their design and control a challenging process. Attempts have been made to determine the small-signal control-to-output dynamics for resonant converters; however, they either involve complex mathematics or numerical solutions, which do not provide much insights and make it difficult to design controllers. This article uses fundamental harmonic approximation to model the different stages of a series resonant converter. Herein, the small-signal perturbations in control variable, i.e., switching frequency, result in perturbations in inductor current envelope that is analyzed in the Laplace domain. This current envelope is then used to analytically derive the control-to-output transfer function for the series resonant converter. The modeling process is verified and validated by comparing the analytical frequency response with that obtained from Simulink simulation and experimental results. For wide output voltage application designs, the system performance and stability vary with the changing operating point. An example demonstrating the utility of the developed model in analyzing such systems is also presented.
Investigation of Optimal Ratio of Males to Females in Fireﬂy Algorithm

We have proposed a ﬁreﬂy algorithm that distinguishes be-tween males and females. In this algorithm, both males and females exist. In this study, we investigate the features of our proposed algorithm by changing the parameters and the percentage of females. We compare our proposed ﬁreﬂy algo-rithm with the conventional ﬁreﬂy algorithm using four well-known test functions. Numerical experiments indicate that our proposed ﬁreﬂy algorithm is superior to the conventional ﬁreﬂy algorithm under some conditions.
I got played by my best friend in my own home: survivor testimonies of sibling sexual abuse

ABSTRACT Sibling sexual abuse may be the most prevalent type of intrafamilial sexual abuse, however, the dearth in empirical knowledge lends to its great obscurity and lack of awareness, prevention and intervention by clinicians, researchers and families alike. The current study fills this knowledge gap through an inductive analysis with written testimonies of 28 survivors of sibling sexual abuse. Findings indicate familial and external circumstances surrounding the abuse that contribute to its occurrence. Three main themes emerge from the analysis: (1) Survivor reflections on their perceptions at the time of the abuse, (2) Responses to disclosure of abuse both in childhood and in adulthood and, (3) Survivors’ reflections upon their perceptions and feelings concerning SSA. The analysis indicates the critical need to listen to the voices of survivors and put an end to the marginalisation and minimisation of their experiences especially when attempting to make waves in practice, policy and prevention. PRACTICE IMPACT STATEMENT Survivors of sexual abuse, especially SSA, rarely disclose the abuse and even more rarely receive the proper interventions to allow them to cope with these experiences. The current study is thus an important step forward in terms of listening to the lived experiences of survivors and presenting to the scientific audience the invaluable information that they have bravely shared which will invariably further the research, awareness and policy.
Storm time dynamics of ring current protons: Implications for the long‐term energy budget in the inner magnetosphere

Our investigation of the long‐term ring current proton pressure evolution in Earth's inner magnetosphere based on Van Allen Probes data shows drastically different behavior of the low‐ and high‐ energy components of the ring current proton population with respect to the SYM‐H index variation. We found that while the low‐energy component of the protons (<80 keV) is strongly governed by convective timescales and is very well correlated with the absolute value of SYM‐H index, the high‐energy component (>100 keV) varies on much longer timescales and shows either no correlation or anticorrelation with the absolute value of SYM‐H index. Our study also shows that the contributions of the low‐ and high‐ energy protons to the inner magnetosphere energy content are comparable. Thus, our results conclusively demonstrate that proton dynamics, and as a result the energy budget in the inner magnetosphere, do not vary strictly on storm time timescales as those are defined by the SYM‐H index.
Theoretical Analysis and Experimental Verification of the Effect of Surface Tension on Morphology of Nanofibers in Bubble Electrospinning

Bubble electrospinning is a novel and powerful approach for mass production of nanofibers. In this work, a nonlinear mathematical model is established for analyzing the effect of the surface tension of polymer solution on the diameter of bubble-electrospun nanofibers. The validity of the model is then evaluated using 36wt% Polyvinylpyrrolidone (PVP) solutions with different surface tension in bubble electrospinning process. The experimental data agree well with the model prediction, suggesting that the analytical model exhibits high accuracy which enables it to be used for the electrospinning process. The potential application of nanofibers to explosion-proof vest is outlined.
Multievent moment-tensor inversion for ill-conditioned geometries

ABSTRACTMoment-tensor inversion under single monitoring well geometries becomes unstable due to the singularity of the inversion matrix. But microseismic events observed during hydraulic fracturing commonly show clusters of events with similar source mechanisms despite differences in the origin time and the magnitude. If the events with similar source mechanisms can be grouped and inverted for a single common moment tensor, the singularity can be eliminated. We have developed a normalized multievent moment-tensor inversion (NME-MTI) method, which does the MTI simultaneously for multiple events, to test the feasibility of this multievent approach. First, the scalar moment for each event was estimated using the far-field low-frequency level at each receiver. Then, the displacements measured at the receivers were normalized by the scalar moment and used to invert for the common moment tensor simultaneously for all the events in the group. We introduced a gradient search method to minimize the overall misfit .