Miracle-dz/UniFAR-FTUs huggingface dataset:
Poor Oral Health Increases the Physical Pain, Psychological Discomfort and Social Incapacity of Individuals with Oncohaematological Diseases

The objective of this study was to evaluate the relationship between oral health status and its impact on quality of life, and to suggest dental management strategies in patients undergoing antineoplastic therapy for oncohematological diseases. A retrospective study including 33 individuals (age 9-79 yr) was conducted. It was observed that the dimensions related to physical pain, psychological discomfort, and social incapacity had statistically significant values. The most frequently performed dental treatments were periodontal treatment (45.45 %), dental restoration (36.36 %), tooth extraction (33.33 %), and endodontic treatment (24.24 %). Thus, poor oral health directly affects the quality of life. Dental management should consider the aspects of the disease and antineoplastic treatment while aiming for safe and effective dental care.
State of the Art for Microhaplotypes

In recent years, the number of publications on microhaplotypes has averaged more than a dozen papers annually. Many have contributed to a significant increase in the number of highly polymorphic microhaplotype loci. This increase allows microhaplotypes to be very informative in four main areas of forensic uses of DNA: individualization, ancestry inference, kinship analysis, and mixture deconvolution. The random match Probability (RMP) can be as small as 10−100 for a large panel of microhaplotypes. It is possible to measure the heterozygosity of an MH as the effective number of alleles (Ae). Ae > 7.5 exists for African populations and >4.5 exists for Native American populations for a smaller panel of two dozen selected microhaplotypes. Using STRUCTURE, at least 10 different ancestral clusters can be defined by microhaplotypes. The Ae for a locus is also identical to the Paternity Index (PI), the measure of how informative a locus will be in parentage testing. High Ae loci can also be useful in missing persons cases. Finally, high Ae microhaplotypes allow the near certainty of seeing multiple additional alleles in a mixture of two or more individuals in a DNA sample. In summary, a panel of higher Ae microhaplotypes can outperform the standard CODIS markers.
Kernel-Based Random Vector Functional-Link Network for Fast Learning of Spatiotemporal Dynamic Processes

Distributed parameter systems widely exist in many industrial thermal processes. Estimation of their temperature distribution in the entire operating area is not easy as the dynamics are time/space coupled, and there are only a few sensors available for measurement. In this paper, an effective spatiotemporal model is proposed for prediction of the temperature distribution. After the dominant spatial basis functions are obtained by the Karhunen–Loève method under the time/space separation, a kernel-based random vector functional-link network is developed for learning unknown temporal dynamics. After time/space synthesis, the spatiotemporal model can be constructed to effectively estimate the temperature distribution in high learning speed. The generalization performance of this model is discussed using Rademacher complexity. Simulations on two typical industrial thermal processes show that the proposed method has superior model performance than neural networks and least square support vector machine.
Visual Construction Algorithm of Attack Path Based on Medical Sensor Networks

The age of IOT, the robustness of basic network has been a hot issue in industry and academia. To improve the security of THE basic network, researchers have proposed solutions from different perspectives, such as firewall based on management policy, intrusion detection system, and vulnerability scanner. However, the existing solutions cannot meet the requirements of attack intention prediction. In this paper, a construction and prediction algorithm based on the network attack path is proposed to mitigate the threats of attackers. The proposed algorithm adopts analysis integration results generated by the device of network security deployment to predict the objects of probabilistic attack targets. Based on the prediction results, the attack path is demonstrated, which provides assistant decision-making suggestions for network users. To evaluate the effectiveness, the prototype of the proposed algorithm is implemented and deployed into the actual sensor network. The experimental results indicate the vulnerability of network is accurately analyzed via the proposed algorithm.
Design and Implementation of Real Time Data Center Access Interface Based on Big Data Technology

Big-data Platform has important applications in the power distribution system. To export data in the Real-Time Data Platform to Big Data Platform, this paper designed PostgreSQL-based access scheme. Firstly, this paper designed the overall structure, then gave design idea and main features of the FDW module, proposed customized program for the grid system to estimate the size of external tables and the SQL resolution scheme. This assessment scheme provided good support for Big Data Platform to get data from the underlying Real-Time Data Platform in a SQL way.
Past and future climate change effects on the thermal regime and oxygen solubility of four peri-alpine lakes

Abstract. Long-term effects of climate change on lakes globally will include a substantial modification in the thermal regime and the oxygen solubility of lakes, resulting in the alteration of ecosystem processes, habitats, and concentrations of critical substances. Recent efforts have led to the development of long-term model projections of climate change effects on lake thermal regimes and oxygen solubility. However, such projections are hardly ever confronted with observations extending over multiple decades. Furthermore, global-scale forcing parameters in lake models present several limitations, such as the need of significant downscaling. In this study, the effects of climate change on thermal regime and oxygen solubility were analyzed in the four largest French peri-alpine lakes over 1850–2100. We tested several one-dimensional (1D) lake models' robustness for long-term variations based on up to 63 years of limnological data collected by the French Observatory of LAkes (OLA). Here, we evaluate the possibility of forcing mechanistic models by following the long-term evolution of shortwave radiation and air temperature while providing realistic seasonal trends for the other variables for which local-scale downscaling often lacks accuracy. Based on this approach, MyLake, forced by air temperatures and shortwave radiations, predicted accurately the variations in the lake thermal regime over the last 4 to 6 decades, with RMSE < 1.95 ∘C. Over the previous 3 decades, water temperatures have increased by 0.46 ∘C per decade (±0.02 ∘C) in the epilimnion and 0.33 ∘C per decade (±0.06 ∘C) in the hypolimnion. Concomitantly and due to thermal change, O2 solubility has decreased by −0.104 mg L−1 per decade (±0.005 mg L−1) and −0.096 mg L−1 per decade (±0.011 mg L−1) in the epilimnion and hypolimnion, respectively. Based on the shared socio-economic pathway SSP370 of the Intergovernmental Panel on Climate Change (IPCC), peri-alpine lakes could face an increase of 3.80 ∘C (±0.20 ∘C) in the next 70 years, accompanied by a decline of 1.0 mg L−1 (±0.1 mg L−1) of O2 solubility. Together, these results highlight a critical alteration in lake thermal and oxygen conditions in the coming decades, and a need for a better integration of long-term lake observatories data and lake models to anticipate climate effects on lake thermal regimes and habitats.
Bringing Sociology of Law Back into Pierre Bourdieu's Sociology: Elements of Bourdieu's Sociology of Law and Dispute Transformation

The academic response to Bourdieu's sociology of law has mainly followed his Weberian focus on the role of legal professionals in state transformations. However, rereading Bourdieu's “The Force of Law” through the lens of its references and relating it to the sociology of law “of the moment” (i.e. that of the 1980s), it becomes clear that Bourdieu's sociology of law is more sophisticated than has generally been acknowledged. In this article, we reread Bourdieu's article with a specific focus on the hitherto overlooked parts that elucidate dispute transformation. We unpack one of Bourdieu's most central sources, Felstiner et al. (1981), by rereading it in the light of Bourdieu's sociological tools. Emphasizing Bourdieu's implicit points about the pre-dispute phase accentuates how habitual dispositions and forms of capital have an impact on the possibilities available to citizens to transform a justiciable problem into a legal dispute.
Enhancement of the MQW-RSOA's Small-Signal Modulation Bandwidth by Inductive Peaking

We present a small-signal model of the multiple quantum well (MQW) reflective semiconductor optical amplifier (RSOA) with the accompanying parasitic circuit, followed by a numerical simulation of its modulation response. We analyze the corresponding <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>3 dB bandwidth's dependence on the bias current, optical power of the input signal, and the RSOA's active region length. Material, electrical and optical parameters, and the overall design of MQW RSOA are derived bottom-up from the fundamental principles. It is shown that the modulation response, resulting from either intrinsic or parasitic-like model (including transport effects), usually provides high <inline-formula><tex-math notation="LaTeX">$-3$</tex-math></inline-formula> dB bandwidth, which is clamped by the chip's parasitics, leading to a relatively poor external modulation bandwidth. This issue can be overcome by an advanced design of the RSOA structure, as the one proposed in this paper and with the optimized bonding, which may improve the external modulation bandwidth significantly through the inductive peaking effect (IPE). Moreover, IPE's efficiency increases if the intrinsic or parasitic-like modulation bandwidth is in the proximity of the parasitic circuit resonant frequency.
Soft Sensor of 4-CBA Concentration Using Deep Belief Networks with Continuous Restricted Boltzmann Machine

In order to improve the quality of industrial products and effectively reduce the cost of raw materials, the prediction of 4-CBA concentration in industrial PTA oxidation process is very important. A soft sensor model based on the deep belief network (DBN) and BP neural network is proposed to predict the 4-CBA concentration in the PTA industrial production process. The deep belief network is composed of a continuous restricted Boltzmann machine. Unsupervised learning method is used to train the deep belief network in which the weights are updated by the error between training data and reconstruction data. The outputs of the deep belief network are the inputs of the BP neural network. The simulation results by use of real industrial data show that the soft sensor of 4-CBA concentration based on DBN-BP neural network has excellent prediction accuracy.
Conductive Gas Plasma Treatment Augments Tumor Toxicity of Ringer’s Lactate Solutions in a Model of Peritoneal Carcinomatosis

Reactive species generated by medical gas plasma technology can be enriched in liquids for use in oncology targeting disseminated malignancies, such as metastatic colorectal cancer. Notwithstanding, reactive species quantities depend on the treatment mode, and we recently showed gas plasma exposure in conductive modes to be superior for cancer tissue treatment. However, evidence is lacking that such a conductive mode also equips gas plasma-treated liquids to confer augmented intraperitoneal anticancer activity. To this end, employing atmospheric pressure argon plasma jet kINPen-treated Ringer’s lactate (oxRilac) in a CT26-model of colorectal peritoneal carcinomatosis, we tested repeated intraabdominal injection of such remotely or conductively oxidized liquid for antitumor control and immunomodulation. Enhanced reactive species formation in conductive mode correlated with reduced tumor burden in vivo, emphasizing the advantage of conduction over the free mode for plasma-conditioned liquids. Interestingly, the infiltration of lymphocytes into the tumors was equally enhanced by both treatments. However, significantly lower levels of interleukin (IL)4 and IL13 and increased levels of IL2 argue for a shift in intratumoral T-helper cell subpopulations correlating with disease control. In conclusion, our data argue for using conductively over remotely prepared plasma-treated liquids for anticancer treatment.
CODE-WSN: A Formal Modelling Tool for Congestion Detection on Wireless Sensor Networks

Wireless sensor networks (WSNs) have recently been attracting much attention from not only the research community but also the industry. One of the important constraints which must be seriously considered when designing a WSN is the congestion control, since it may cause damaged or even lost packets during the transmission. Hence, this paper proposes a tool, known as CODE-WSN (Congestion Detection on Wireless Sensor Networks) to detect possible congestion occurrence on a WSN design. The major difference between this tool and the others is that CODE-WSN exploits the strength of formal modelling approaches. In CODE-WSN, a WSN is modelled by the formal modelling language Petri Net (PN), to allow one to inspect and verify congestion potential. In addition, two types of PN, including Place/Transition Nets and Coloured Petri Nets, are supported for modelling in CODE-WSN, thus readers can observe the advantages and the disadvantages as well of each PN kind. Notably, this tool also presents some ways to overcome the issue of state space explosion which seemingly the most practically painful problem on formal modelling aspect.
Distribution patterns of dinoflagellate communities along the Songhua River

Background Dinoflagellates have the potential to pose severe ecological and economic damages to aquatic ecosystems. It is therefore largely needed to understand the causes and consequences of distribution patterns of dinoflagellate communities in order to manage potential environmental problems. However, a majority of studies have focused on marine ecosystems, while the geographical distribution patterns of dinoflagellate communities and associated determinants in freshwater ecosystems remain unexplored, particularly in running water ecosystems such as rivers and streams. Methods Here we utilized multiple linear regression analysis and combined information on species composition recovered by high-throughput sequencing and spatial and environmental variables to analyze the distribution patterns of dinoflagellate communities along the Songhua River. Results After high-throughput sequencing, a total of 490 operational taxonomic units (OTUs) were assigned to dinoflagellates, covering seven orders, 13 families and 22 genera. Although the sample sites were grouped into three distinctive clusters with significant difference (p < 0.05) in environmental variables, OTUs-based dinoflagellate communities among the three clusters showed no significant difference (p > 0.05). Among all 24 environmental factors, two environmental variables, including NO3-N and total dissolved solids (TDS), were selected as the significantly influential factors (p < 0.05) on the distribution patterns of dinoflagellate communities based on forward selection. The redundancy analysis (RDA) model showed that only a small proportion of community variation (6.1%) could be explained by both environmental (NO3-N and TDS) and dispersal predictors (watercourse distance) along the River. Variance partitioning revealed a larger contribution of local environmental factors (5.85%) than dispersal (0.50%) to the total variation of dinoflagellate communities. Discussion Our findings indicated that in addition to the two quantifiable processes in this study (species sorting and dispersal), more unquantifiable stochastic processes such as temporal extinction and colonization events due to rainfall may be responsible for the observed geographical distribution of the dinoflagellate community along the Songhua River. Results obtained in this study suggested that deeper investigations covering different seasons are needed to understand the causes and consequences of geographical distribution patterns of dinoflagellate biodiversity in river ecosystems.
Learning Environment Properties in Partially Observable Monte Carlo Planning

We tackle the problem of learning state-variable relationships in Partially Observable Markov Decision Processes to improve planning performance on mobile robots. The proposed approach extends Partially Observable Monte Carlo Planning (POMCP) and represents state-variable relationships with Markov Random Fields. A ROS-based implementation of the approach is proposed and evaluated in rocksample, a standard benchmark for probabilistic planning under uncertainty. Experiments have been performed in simulation with Gazebo. Results show that the proposed approach allows to effectively learn state-variable probabilistic constraints on ROS-based robotic platforms and to use them in subsequent episodes to outperform standard POMCP.
A Semi-Blind Based Channel Estimator for Pilot Contaminated One-Bit Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is a breakthrough technology equipped with a large number of antennas and radio frequency (RF) chains, which increases complexity and circuit power consumption at the RF front-ends. Power utilisation of analog-to-digital converters is a major concern in RF chains, so one-bit massive MIMO systems are seen as one of the potential solutions to this problem. Further, channel state information (CSI) accuracy at the base station is one of the crucial needs for realising the benefits of one-bit massive MIMO. The existing pilot based estimators demand additional pilots for enhancing the CSI accuracy which in turn reduces the spectral efficiency of the system. To address this limitation, we propose an iterative semi-blind based channel estimator for one-bit massive MIMO systems in a pilot contaminated scenario. The proposed algorithm consists of two stages namely, initialisation and iteration. The initial channel estimate is obtained from pilot based initialisation stage, which is further refined in iteration stage with the help of both pilot and a few data symbols. The proposed semi-blind algorithm improves estimation accuracy with minimum number of pilot symbols. Through simulations, we show that the proposed scheme achieves a considerable improvement in mean square error and bit error rate against the existing pilot based estimators at the cost of a nominal increase in computational complexity. Moreover, the proposed algorithm attains convergence in two iterations for all the considered scenarios. The proposed estimator is spectral and power-efficient in comparison to the pilot based algorithms. To the best of our knowledge, it is the first attempt of channel estimation in pilot contaminated one-bit massive MIMO systems.
Novel Deep-Water Tidal Meter for Offshore Aquaculture Infrastructures

This paper presents a tidal current meter that is based on the inertial acceleration principle for offshore infrastructures in deep water. Focusing on the marine installations of the aquaculture industry, we studied the forces of tides at a depth of 15 m by measuring the acceleration. In addition, we used a commercial MEMS triaxial accelerometer to record the acceleration values. A prototype of the tidal measurement unit was developed and tested at a real offshore aquaculture infrastructure in Gran Canaria, which is one of the Canary Islands in the Atlantic Ocean. The proposed tidal measurement unit was used as a recorder to assess the complexity of measuring the frequency of tidal currents in the short (10 min), medium (one day) and long term (one week). The acquired data were studied in detail, in both the time and frequency domains, to determine the frequency of the forces that were involved. Finally, the complexity of the frequency measurements from the captured data was analyzed in terms of sampling ratio and recording duration, from the point of view of using our proposed measurement unit as an ultra-low-power embedded system. The proposed device was tested for more than 180 days using a lithium-ion battery. This working period was three times greater than the best alternative in the literature because of the ultra-low-power design of the on-board embedded system. The measurement accuracy error was lower than 1% and the resolution was 0.01 cm/s for the 0.8 m/s velocity scale. This performance was similar to the best Doppler solution that was found in the literature.
A Two-Stage Content Pushing Service Based on Virtual MIMO in LTE-V Networks

Prefetching based caching technology has attracted much attention owing to its potential for achieving more effective transmission and lower latency. In this paper, we propose to take advantage of prefetching based caching technology in long term evolution based vehicle (LTE-V) networks to actively push content items to vehicle user equipments (VUEs), which are labeled as new vehicle user equipments (NVEs) and old vehicle user equipments (OVEs) based on whether there exist any prior records or not. Meanwhile, roadside unit (RSU) can provide information pushing service, which consists of a user recognition stage and a content pushing stage. Specifically, by grouping vehicles into cooperative coalitions, a virtual multiple-input-multiple-output (MIMO) system can be formed to increase data rate. To maximize the average data rate of users, we propose an Average Channel Gain based Pushing (ACGP) scheme, which selects suitable cooperative partners for NVEs. Numerical results corroborate the analysis and demonstrate the performance of the proposed strategy.
Residual Blocks PointNet: A novel faster PointNet framework for segmentation and estimated pose

Given recent advances in Segmentation of Convolutional Neural Networks (CNNs), this paper aims to propose a more efficient structure which directly consumes point clouds for segmentation and estimated pose. More specifically, a novel Residual Blocks PointNet is proposed providing a fast framework taking point sets as input and predicting 3D object part segmentation and 3D pose. The network of the proposed structure has been established composed of two subnet works: a key branch for 3D object part segmentation and the other branch for spatial transform to predict a 3D affine matrix. The major branch contains more residual blocks, which encapsulate shortcut connects with specified layer numbers, growth rate and conv (1*1)-bn-relu structure. The key point is the decrease of each level of network computing and the reuse of feature maps. The other is a parallel classification network for estimated pose with share portion weight except 3 groups of full connected layers. Empirically, Residual Blocks PointNet shows faster rate of convergence and acceptable performance.
Finite Element Approximation of Electromagnetic Fields Using Nonfitting Meshes for Geophysics

We analyze the use of nonfitting meshes for simulating the propagation of electromagnetic waves inside the earth with applications to borehole logging. We avoid the use of parameter homogenization and employ standard edge finite element basis functions. For our geophysical applications, we consider a 3D Maxwell's system with piecewise constant conductivity and globally constant permittivity and permeability. The model is analyzed and discretized using both the E- and H-formulations. Our main contribution is to develop a sharp error estimate for both the electric and magnetic fields. In the presence of singularities, our estimate shows that the magnetic field approximation is converging faster than the electric field approximation. As a result, we conclude that error estimates available in the literature are sharp with respect to the electric field error but provide pessimistic convergence rates for the magnetic field in our geophysical applications. Another surprising consequence of our analysis is that n.