Miracle-dz/UniFAR-FTUs huggingface dataset:
Purified high‐sulfur coal as a fuel for direct carbon solid oxide fuel cells

The use of low‐rank coal in a clean and efficient manner is a major challenge facing the current coal technology. A high‐sulfur coal with 4.5 wt% sulfur is chosen to examine the compatibility of the pristine coal and the purified contrast with a solid oxide fuel cell (SOFC) with nickel cermet anodes. Desulfurization of the pristine coal is performed by molten caustic leaching method with a removal ratio of 80%. Analyses of the physicochemical properties of coal samples indicate that the purified coal has a more favorable structure and higher Boudouard reactivity, which is suitable as a fuel for fuel cells. The assessment of electrochemical performance reveals that the purification treatment not only makes the peak power density of SOFCs improve from 115 to 221 mW cm−2 at 900°C but also extends their durability from 1.7 to 11.2 hours under a current density of 50 mA cm−2 at 850°C with a fuel availability increasing from 6.25% to 40%. The postmortem analyses show that far less deposited carbon and nickel sulfide are observed on the anode surface. The fuel‐based investigation reveals that the purified coal is a promising fuel for direct carbon fuel cells.
A Novel Cooperative Cache Policy for Wireless Networks

Mobile edge caching is an emerging approach to manage high mobile data traffic in fifth-generation wireless networks that reduces content access latency and offloading data traffic of backhaul links. This paper proposes a novel cooperative caching policy based on long short-term memory (LSTM) neural networks considering the characteristics between the features of the heterogeneous layers and the user moving speed. Specifically, LSTM is applied to predict content popularity. Size-weighted content popularity is utilised to balance the impact of the predicted content popularity and content size. We also consider the moving speeds of mobile users and introduce a two-level caching architecture consisting of several small base stations (SBSs) and macro base stations (MBSs). To avoid content requests of fast-moving users affecting the content popularity distribution of the SBS since fast-moving users frequently handover among SBSs, fast-moving users are served by MBSs no matter which SBS they are in. SBSs serve low-speed users, and SBSs in the same cluster can communicate with one another. The simulation results show that compared to common cache methods, for example, the least frequently used and least recently used methods, our proposed policy is at least 8.9% lower and 6.8% higher in terms of the average content access latency and offloading ratio, respectively.
Fault Detection and Identification for Nonlinear Process Based on Inertia-Based KEPCA and a New Combined Monitoring Index

In the present study, we introduce a new approach for the nonlinear monitoring process based on kernel entropy principal component analysis (KEPCA) and the notion of inertia. KEPCA plays double roles. First, it reduces the data in the high-dimensional space. Second, it constructs the model. Before data reduction, KEPCA transforms input data into high-dimensional feature space based on a nonlinear kernel function and automatically determines the number of principal components (PCs) based on the computation of the inertia. The retained PCs express the maximum inertia entropy of data in the feature space. Then, we use the Parzen window estimator to compute the upper control limit (UCL) for inertia-based KEPCA instead of the Gaussian assumption. Our second contribution concerns a new combined index based on the monitoring indices T2 and SPE in order to simplify the detection task of the fault and prevent any confusion. The proposed approaches have been applied to process fault detection and diagnosis for the well-known benchmark Tennessee Eastman process (TE). Results were performing.
A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

IPv6 routing protocol for low-power and lossy networks (RPL) has been developed as a routing agent in low-power and lossy networks (LLN), where nodes’ resource constraint nature is challenging. This protocol operates at the network layer and can create routing and optimally distribute routing information between nodes. RPL is a low-power, high-throughput IPv6 routing protocol that uses distance vectors. Each sensor-to-wire network router has a collection of fixed parents and a preferred parent on the path to the Destination-oriented directed acyclic graph (DODAG) graph’s root in steady-state. Each router part of the graph sends DODAG information object (DIO) control messages and specifies its rank within the graph, indicating its position within the network relative to the root. When a node receives a DIO message, it determines its network rank, which must be higher than all its parents’ rank, and then continues sending DIO messages using the trickle timer. As a result, DODAG begins at the root and eventually extends to encompass the whole network. This paper is the first review to study intrusion detection systems in the RPL protocol based on machine learning (ML) techniques to the best of our knowledge. The complexity of the new attack models identified for RPL and the efficiency of ML in intelligent and collaborative threats detection, and the issues of deploying ML in challenging LLN environments underscore the importance of research in this area. The analysis is done using research sources of “Google Scholar,” “Crossref,” “Scopus,” and “Web of Science” resources. The evaluations are assessed for studies from 2016 to 2021. The results are illustrated with tables and figures.
Speech Recognition Using RFID Tattoos (Extended Abstract)

This paper presents a radio-frequency (RF) based assistive technology for voice impairments (i.e., dysphonia), which occurs in an estimated 1% of the global population. We specifically focus on acquired voice disorders where users continue to be able to make facial and lip gestures associated with speech. Despite the rich literature on assistive technologies in this space, there remains a gap for a solution that neither requires external infrastructure in the environment, battery-powered sensors on skin or body-worn manual input devices. We present RFTattoo, which to our knowledge is the first wireless speech recognition system for voice impairments using batteryless and flexible RFID tattoos. We design specialized wafer-thin tattoos attached around the user's face and easily hidden by makeup. We build models that process signal variations from these tattoos to a portable RFID reader to recognize various facial gestures corresponding to distinct classes of sounds. We then develop natural language processing models that infer meaningful words and sentences based on the observed series of gestures. A detailed user study with 10 users reveals 86% accuracy in reconstructing the top-100 words in the English language, even without the users making any sounds.
Travel Trajectory Frequent Pattern Mining Based on Differential Privacy Protection

Now, many application services based on location data have brought a lot of convenience to people’s daily life. However, publishing location data may divulge individual sensitive information. Because the location records about location data may be discrete in the database, some existing privacy protection schemes are difficult to protect location data in data mining. In this paper, we propose a travel trajectory data record privacy protection scheme (TMDP) based on differential privacy mechanism, which employs the structure of a trajectory graph model on location database and frequent subgraph mining based on weighted graph. Time series is introduced into the location data; the weighted trajectory model is designed to obtain the travel trajectory graph database. We upgrade the mining of location data to the mining of frequent trajectory graphs, which can discover the relationship of location data from the database and protect location data mined. In particular, to improve the identification efficiency of frequent trajectory graphs, we design a weighted trajectory graph support calculation algorithm based on canonical code and subgraph structure. Moreover, to improve the data utility under the premise of protecting user privacy, we propose double processes of adding noises to the subgraph mining process by the Laplace mechanism and selecting final data by the exponential mechanism. Through formal privacy analysis, we prove that our TMDP framework satisfies ε -differential privacy. Compared with the other schemes, the experiments show that the data availability of the proposed scheme is higher and the privacy protection of the scheme is effective.
Modeling Trajectories with Neural Ordinary Differential Equations

Recent advances in location-acquisition techniques have generated massive spatial trajectory data. Recurrent Neural Networks (RNNs) are modern tools for modeling such trajectory data. After revisiting RNN-based methods for trajectory modeling, we expose two common critical drawbacks in the existing uses. First, RNNs are discrete-time models that only update the hidden states upon the arrival of new observations, which makes them an awkward fit for learning real-world trajectories with continuous-time dynamics. Second, real-world trajectories are never perfectly accurate due to unexpected sensor noise. Most RNN-based approaches are deterministic and thereby vulnerable to such noise. To tackle these challenges, we devise a novel method entitled TrajODE for more natural modeling of trajectories. It combines the continuous-time characteristic of Neural Ordinary Differential Equations (ODE) with the robustness of stochastic latent spaces. Extensive experiments on the task of trajectory classification demonstrate the superiority of our framework against the RNN counterparts.
What's the Context? Implicit and Explicit Assumptions in Model-Based Goal Recognition

Every model involves assumptions. While some are standard to all models that simulate intelligent decision-making (e.g., discrete/continuous, static/dynamic), goal recognition is well known also to involve choices about the observed agent: is it aware of being observed? cooperative or adversarial? In this paper, we examine not only these but the many other assumptions made in the context of model-based goal recognition. By exploring their meaning, the relationships between them and the confusions that can arise, we demonstrate their importance, shed light on the way trends emerge in AI, and suggest a novel means for researchers to uncover suitable avenues for future work.
Morphology-Tuned Phase Transitions of Horseshoe Shaped BaTiO3 Nanomaterials under High Pressure

Exploring new physical properties of nanomaterials with special morphology have been important topics in nanoscience and nanotechnology. Here we report a morphology-tuned structural phase transition under high pressure in the horseshoe shaped BaTiO3 nanomaterials with an average diameter of 26 ± 4 nm. A direct structural phase transition from the tetragonal to the cubic phase without local rhombohedral distortion was observed at about 7.7 GPa by in situ high-pressure X-ray diffraction and Raman spectroscopy, which is clearly different from the phase transition processes of the BaTiO3 bulks and nanoparticles. Additionally, bulk modulus of the tetragonal and cubic phases were determined to be 125.0 and 211.7 GPa, respectively, obviously smaller than the estimated values for BaTiO3 nanoparticles with the same grain size. Further analysis shows that the unique phase transition process and the enhanced structural stability of the tetragonal horseshoe shaped BaTiO3 nanomaterials, may be attributed to the simila.
Evaluation of Different Depth Injections of Reticulated Hyaluronic Acid Fillers with Different Concentration

Background: Aging of the face is characterized by different phenomena happening at more or less the same time, these issues can be addressed both surgically and non surgically, with nonsurgical cosmetic medicine being exponentially more popular than surgical intervention. In this article, authors evaluate efficacy, safety and patient’s satisfaction of a 3 different reticulated HA filler of 15 mg/ml, 20 mg/ml and 25 mg/ml of non animal origin, produced via bacterial fermentation injected in different areas and different plane of the face. Material and methods: Between January 2017 and June 2017, 25 patients (22 female and 3 male), ranging between 27 and 58 years old, seeking facial aesthetic medical treatments were enrolled in the present study. Three different HA filler formulation were used based on the anatomical area to treat: a 15 mg/ml formulation was used to treat crow’s feet and glabella’s wrinkle with superficial/intradermic injections, a 20 mg/ml nasolabial groove and peri-oral wrinkles filling with deep dermal plane injections, a 25 mg/ml to perform non surgical reshape of the nose, malar and chin augmentation with deep bolus injections, next or under the periosteum. Results: Every patient was treated in a single stage injection. In 7 patients an ecchymosis occurred on site injection and was self resolved within 10 days, others complications did not occurred. Patient’s satisfaction form revealed a rating of 7,84/10 after the first month, a 7,2/10 after 3 months and 6,16/10 after 6 months. Conclusions: The HA fillers used in this paper showed a good profile of safety and a different lasting results based not only on the concentration of HA per vial but also due to site injection, deep versus superficial.
Natural history of mental health competence from childhood to adolescence

Background Mental health competence (MHC) involves psychosocial capabilities such as regulating emotions, interacting well with peers and caring for others, and predicts a range of health and social outcomes. This study examines the course of MHC from childhood to adolescence and patterning by gender and disadvantage, in Australian and UK contexts. Methods Data: Longitudinal Study of Australian Children (n=4983) and the Millennium Cohort Study (n=18 296). Measures: A measure capturing key aspects of MHC was derived summing items from the parent-reported Strengths and Difficulties Questionnaire, assessed at 4–5 years, 6–7 years, 10–11 years and 14–15 years. Analysis: Proportions of children with high MHC (scores ≥23 of range 8–24) were estimated by age and country. Random-effects models were used to define MHC trajectories according to baseline MHC and change over time. Sociodemographic patterns were described. Results The prevalence of high MHC steadily increased from 4 years to 15 years (from 13.6% to 15.8% and 20.6% to 26.2% in Australia and the UK, respectively). Examination of trajectories revealed that pathways of some children diverge from this normative MHC progression. For example, 7% and 9% of children in Australia and the UK, respectively, had a low starting point and decreased further in MHC by mid-adolescence. At all ages, and over time, MHC was lower for boys compared with girls and for children from disadvantaged compared with advantaged family backgrounds. Conclusions Approaches to promoting MHC require a sustained focus from the early years through to adolescence, with more intensive approaches likely needed to support disadvantaged groups and boys.
Characteristics of child welfare investigations reported by healthcare professionals in Ontario: secondary analysis of a regional database

Objectives This study examines the characteristics and outcomes of child welfare investigations reported by hospital-based and community-based healthcare professionals. Methods A sample of 7590 child maltreatment-related investigations from the Ontario Incidence Study of Reported Child Abuse and Neglect-2018, a cross-sectional study, was analysed. Bivariate analyses compared characteristics of hospital and community healthcare-reported investigations. Chi-square automatic interaction detector analyses were used to predict the most influential factors in the decision to provide a family with services following a child welfare investigation from each referral source. Results Community healthcare-reported investigations were more likely to have a primary concern of physical abuse while hospital-reported investigations were more likely to be focused on assessing risk of future maltreatment. Hospital-reported investigations were more likely to involve noted primary caregiver (eg, mental health issues, alcohol/drug abuse, victim of intimate partner violence (IPV)) and household risk factors. The most significant predictor of service provision following an investigation was having a caregiver who was identified as a victim of IPV in hospital-reported investigations (χ2=30.237, df=1, adj. p<0.001) and having a caregiver for whom few social supports was noted in community healthcare-reported investigations (χ2=18.892, df=1, adj. p<0.001). Conclusion Healthcare professionals likely interact with children who are at high risk for maltreatment. This study’s findings highlight the important role that healthcare professionals play in child maltreatment identification, which may differ across hospital-based and community-based settings and has implications for future collaborations between the healthcare and child welfare systems.
Persistent neuromuscular junction transmission defects in adults with spinal muscular atrophy treated with nusinersen

Objective Spinal muscular atrophy (SMA) is a motor neuron disease caused by low levels of survival motor neuron (SMN) protein. Prior work in models and patients has demonstrated electrophysiological and morphological defects at the neuromuscular junction (NMJ). Therapeutic development has resulted in clinically available therapies to increase SMN protein levels in patients and improve muscle function. Here we aimed to investigate the effect of SMN restoration (via nusinersen) on NMJ transmission in adults with SMA. Methods Participants undergoing nusinersen treatment underwent 3 Hz repetitive nerve stimulation (RNS) of the spinal accessory nerve to assess compound muscle action potential amplitude decrement. Maximum voluntary isometric contraction (MVICT), Revised Upper Limb Module (RULM), and 6 min walk test (6MWT) were assessed for correlations with decrement. Results Data from 13 ambulatory (7 men/6 women, mean age 40±11 years) and 11 non-ambulatory (3 men/8 women, mean age 38±12 years) participants were analysed. Cross-sectional analyses of RNS decrement were similar at 14 months of nusinersen (−14.2%±11.5%, n=17) vs baseline (−11.9%±8.3%, n=15) (unpaired t-test, p=0.5202). Longitudinal comparison of decrement in eight participants showed no change at 14 months (−13.9%±6.7%) vs baseline (−16.9%±13.4%) (paired t-test, p=0.5863). Decrement showed strong correlations with measures of MVICT, RULM and 6MWT but not age or disease duration. Conclusion Adults with SMA had significant NMJ transmission defects that were not corrected with 14 months of nusinersen treatment. NMJ defects were negatively associated with physical function, and thus may represent a promising target for additive or combinatorial treatments.
Expression of ANCR in nasopharyngeal carcinoma patients and its clinical significance

Abstract Anti-differentiation non-coding RNA (ANCR), a long non-coding RNA, is involved in the development, progression and metastasis of various human cancers. However, its clinical significance in nasopharyngeal carcinoma (NPC) still remains unknown. This study aimed to investigate ANCR expression and its clinical significance in NPC. Totally, 96 NPC tissues and 24 non-cancerous nasopharyngeal mucosa tissues were used. The levels of ANCR were determined by qRT-PCR. Relationship of ANCR with patient clinical characteristics, disease-free survival and overall survival (OS) was evaluated. ANCR expression was increased in NPC tissues compared to non-cancerous nasopharyngeal mucosae. ANCR expression was significantly related to lymph node metastasis, clinical stage, and tumor differentiation (P < .05). Kaplan-Meier survival analysis revealed that high level of ANCR expression was significantly associated with poor disease-free survival but not with OS in NPC patients. Univariate analysis showed a significant association between increased ANCR expression and adverse OS (P < .05), but multivariate analysis suggested that ANCR could not be used as an independent prognostic factor for NPC patients. ANCR is involved in the development and progression of NPC, but whether it can be used as an effective therapeutic target for NPC needs further study.
The Effect of Algorithms on Dose Distribution in Inhomogeneous Phantom: Monaco Treatment Planning System versus Monte Carlo Simulation

Background: The aim of this study is to evaluate the dose calculation algorithms commonly used in TPS by using MC simulation in the highly different inhomogeneous region and in the small fields and to provide the following uniquely new information in the study of correction algorithm. Materials and Methods: We compared the dose distribution obtained by Monaco TPS for small fields. Results: When we examine lung medium, for four different fields, we can see that the algorithms begin to differ. In both the lung and bone environment, the percentage differences decrease as the field size increases. In areas less than or equal to 3x3 cm2, there are serious differences between the algorithms. The CC algorithm calculates a low dose value as the photon passes from the lung environment to water environment. We can also see that this algorithm measures a low dose value in voxel as the photon passes from the water medium to the bone medium. In the transition from the water environment to the bone environment or from the bone environment to the water environment, the results of the CC algorithm are not close to MC simulation. Conclusion: The effect of the algorithms used in TPS on dose distribution is very strong, especially in environment with high electron density variation and in applications such as Stereotactic Body Radiotherapy and Intensity Modulated Radiotherapy where small fields are used.
Adjuvant ovarian suppression for premenopausal hormone receptor-positive breast cancer

Supplemental Digital Content is available in the text Abstract Background: Ovarian function suppressor (OFS) plus either tamoxifen (TAM) or aromatase inhibitor (AI) could improve the survival outcome for premenopausal hormone receptor-positive (HR+) breast cancer. However, the optimal OFS-based regimen and medication duration remain uncertain. This article aims to systematically evaluate the OFS-based adjuvant endocrine therapy for premenopausal breast cancer. Methods: We searched several public databases from January 1980 to November 2020. A random model was adopted in this meta-analysis. We used the hazard ratio (HR) with a 95% confidence interval (CI) for the statistical analysis of efficacy. The primary outcome measures included overall survival and disease-free survival. Results: A total of 32 articles with 37,224 cases were included in this network meta-analysis. OFS+TAM improved 5-year disease-free survival (HR –0.09, 95% CI –0.16 to –0.01) and 5-year overall survival (HR –0.18, 95% CI –0.33 to –0.03) compared with TAM monotherapy. For OFS+AI, although the 5-year disease-free survival was improved (HR –0.18, 95% CI –0.29 to –0.08), the 5-year overall survival was not improved (HR –0.13, 95% CI –0.43 to 0.18). In subgroup analysis, both OFS+AI and OFS+TAM showed a protective effect in stage I–III patients compared with stage I–II patients. For the course of therapy, OFS+TAM for 2-years could achieve clinical benefit and the best course of therapy of OFS+AI still waits for further study. Conclusions: OFS+TAM might be a better option than OFS+AI for premenopausal intensive adjuvant endocrine therapy. Stage III patients are more suitable for the OFS-based therapy. For the medication duration, the 2-years course of OFS+TAM could be effective. This analysis provides helpful information for selecting therapeutic regimen in intensive adjuvant endocrine therapy and identifying the target population.