Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid methods of landslide hazard mapping : Fiji case study

A landslide hazard probability map can help planners (1) prepare for, and/or mitigate against, the effects of landsliding on communities and infrastructure, and (2) avoid or minimise the risks associated with new developments. The aims of the project were to establish, by means of studies in a few test areas, a generic method by which remote sensing and data analysis using a geographic information system (GIS) could provide a provisional landslide hazard zonation map. The provision of basic hazard information is an underpinning theme of the UN’s International Decade for Natural Disaster Reduction (IDNDR). It is an essential requirement for disaster preparedness and mitigation planning. This report forms part of BGS project 92/7 (R5554) ‘Rapid assessment of landslip hazards’ Carried out under the ODA/BGS Technology Development and Research Programme as part of the British Government’s provision of aid to developing countries. It provides a detailed technical account of work undertaken in a test area in Viti Levu in collaboration with Fiji Mineral Resources Department. The study represents a demonstration of a methodology that is applicable to many developing countries. The underlying principle is that relationships between past landsliding events, interpreted from remote sensing, and factors such as the geology, relief, soils etc provide the basis for modelling where future landslides are most likely to occur. This is achieved using a GIS by ‘weighting’ each class of each variable (e.g. each lithology ‘class’ of the variable ‘geology’) according to the proportion of landslides occurring within it compared to the regional average. Combinations of variables, produced by summing the weights in individual classes, provide ‘models’ of landslide probability. The approach is empirical but has the advantage of potentially being able to provide regional scale hazard maps over large areas quickly and cheaply; this is unlikely to be achieved using conventional ground-based geotechnical methods. In Fiji, landslides are usually triggered by intense rain storms commonly associated with tropical cyclones. However, the regional distribution of landslides has not been mapped nor is it known how far geology and landscape influence the location and severity of landsliding events. The report discusses the remote sensing and GIS methodology, and describes the results of the pilot study over an area of 713 km2 in south east Viti Levu. The landslide model uses geology, elevation, slope angle, slope aspect, soil type, and forest cover as inputs. The resulting provisional landslide hazard zonation map, divided into high, medium and low zones of landslide hazard probability, suggests that whilst rainfall is the immediate cause, others controls do exert a significant influence. It is recommended that consideration be given in Fiji to implementing the techniques as part of a national strategic plan for landslide hazard zonation mapping.
Unexpected Inheritance Patterns in a Large Cohort of Patients with a Suspected Ciliopathy

Ciliopathies are rare genetic disorders caused by dysfunction of the primary or motile cilia. Their mode of inheritance is mostly autosomal recessive with biallelic pathogenic variants inherited from the parents. However, exceptions exist such as uniparental disomy (UPD) or the appearance of a de novo pathogenic variant in trans of an inherited pathogenic variant. These two genetic mechanisms are expected to be extremely rare, and few data are available in the literature, especially regarding ciliopathies. In this study, we investigated 940 individuals (812 families) with a suspected ciliopathy by Sanger sequencing, high-throughput sequencing and/or SNP array analysis and performed a literature review of UPD and de novo variants in ciliopathies. In a large cohort of 623 individuals (511 families) with a molecular diagnosis of ciliopathy (mainly Bardet-Biedl syndrome and Alström syndrome), we identified five UPD, revealing an inherited pathogenic variant and five pathogenic variants of de novo appearance (in trans of another pathogenic variant). Moreover, from these ten cases, we reported 15 different pathogenic variants of which five are novel. We demonstrated a relatively high prevalence of UPD and de novo variants in a large cohort of ciliopathies and highlighted the importance of identifying such rare genetic events, especially for genetic counseling.
Implementation of Presidential Regulation Number 87 of 2017 about Strengthening Student’s Character Education

The purpose of this study is to describe and analyze the implementation of Presidential Regulation Number 87 of 2017 concerning strengthening student character education at SMA Negeri 3 Kayuagung, Ogan Komering Ilir Regency, as well as the enabling and impeding factors for the implementation of that regulation at Kayuagung 3 Public High School, Ogan Komering Ilir Regen. This study is qualitative in nature. The study’s findings indicate that: 1) internal communication at SMA Negeri 3 Kayuagung, as well as communication with the school’s supervisor and committee and other connected agencies, all happened as planned; 2) There is already a clear standard operating procedure (SOP), and the majority of policy implementers are familiar with its procedures; 3) All policy implementers have a high commitment to the implementation of Presidential Regulation No. 87 of 2017 concerning Strengthening Character Education, which is not all teachers’ ability to apply character education to the subjects they teach.
A Pattern Mining-Based False Data Injection Attack Detector for Industrial Cyber-Physical Systems

The implication of cyber-physical systems into industrial processes has introduced some security breaches due to the lack of security mechanisms. This article aims to come up with a novel methodology to detect false data injection attacks on cyber-physical systems. To reach this goal, we propose an efficient anomaly-based approach for detecting false data injection attacks against industrial cyber-physical systems. Particularly, we use sequential pattern mining techniques, which are commonly used for learning most important patterns of a system. In our case, the frequent pattern learning algorithm is used to create a database corresponding to the normal operation of the system, then, this database is fed into an attack detection algorithm in order to alert the user whenever an attack is occurring. The extensive simulations prove that our attack detection approach is able to detect attacks with a great accuracy and that this methodology could work even for large scale systems.
Functionality and quality of life of patients with knee osteoarthritis after platelet-rich therapy

Objective: To evaluate the functional capacity and quality of life of patients with knee osteoarthritis submitted to platelet-rich plasma injection. Methods: Observational cross-sectional study carried out with 31 subjects seen at the orthopedics outpatient clinic of a university hospital in a municipality in northwestern Paraná, from 2016 to 2017. For data collection, a sociodemographic questionnaire was used, the Short Form36 and Lequesne's Algofunctional Questionnaire. For data analysis, descriptive statistics were used, with measures of dispersion, interquartile range and non-parametric Wilcoxon test. A 95% confidence level was established, and associations with p < 0.05 were considered statistically significant. All analyzes were performed using the R statistical environment. Results: Most patients were female, with a mean age of 59.8 years (SD ± 12.2). In the pre-intervention with platelet-rich plasma, all subjects were classified in the extremely severe category. There was a significant improvement in all eight Short Form-36 domains, as well as pain reduction and functional recovery in the 31 patients. Conclusion: Platelet-rich therapy through injection in patients with knee osteoarthritis improved quality of life and reduced pain with increased operational capacity.
INVESTIGATION OF EQUATION OF STATE OF POROUS EARTH MEDIA

Abstract : Earlier experimental work has been extended to evaluate the effect of moisture on the Hugoniot of playa. For engineering applications the Hugoniot of moist playa can be predicted with sufficient accuracy from the Hugoniot of dry playa and water and the assumption of pressure equilibrium. Isentropic release data were obtained for moist and dry playa. The steep release curve (in the P-V plane) from high pressure implies an irreversible phase change. Some low pressure data in the elastic-plastic region are presented. A theoretical discussion of various forms of the Mie-Gruneisen equation and the physical basis of asymptotic statistical models is presented. Shock stability is reviewed. Phase transitions in which delta V 0 are classified according to the signs of the slopes of the coexistence curves. Relative slopes of Hugoniots and isentropes in the mixed phase region are calculated. The results of the theoretical discussion are applied to transitions in bismuth, iron, and quartz. Agreement of values of dP/dT deduced from shock data and measured directly are good for bismuth and poor for quartz and iron. Calculations of spherical shock propagation in a hypothetical medium that undergoes a phase change are presented. The calculations show qualitatively some types of pulse shapes that may be expected in a transforming medium. It is concluded that the proper treatment of phase changes is an outstanding problem in predicting equations of state for earth materials.
Location Optimization Based on Improved 3D DV-HOP Algorithm in Wireless Sensor Networks

The Distance Vector Hop (DV-HOP) localization algorithm, as a non ranging node localization algorithm, its operation is simple, does not require additional measurement equipment support, with low cost. But wireless sensor network is generally used in three-dimensional complex environment. In response to the low positioning accuracy of this algorithm in an environment with randomly distributed nodes, research is conducted to optimize and improve it. Firstly, by combining multiple communication radii, the hops’ quantity in nodes is decimalized to diminish errors from the hops’ quantity. Then, it calculates the average jump distance of anchor nodes, sets a hop threshold, and eliminates anchor nodes with significant errors. It calculates the range existing anchor nodes and unknown nodes through taking the median. Finally, a particle swarm optimization algorithm was used instead of the maximum likelihood estimation method to obtain the optimized Hop distance correction multi communication radius Particle swarm optimization algorithm DV-HOP (HCMSPSO-DV-HOP) localization algorithm. The improved 3D DV-HOP algorithm was applied to a simulation environment, and through experiments, it was found that the algorithm’s average elapsed time was 0.49 seconds; The average positioning accuracy is 96.40%. Compared to other localization algorithms, the localization algorithm designed by the research institute has good localization performance. It provides technical support for WSN applications in various fields.
ER translocon inhibitor ipomoeassin F inhibits triple-negative breast cancer growth via blocking ER molecular chaperones

Triple-negative breast cancer (TNBC) is an aggressive type of breast cancer where no effective therapy has been developed. Here, we report that the natural product ER translocon inhibitor ipomoeassin F is a selective inhibitor of TNBC cell growth. A proteomic analysis of TNBC cells revealed that ipomoeassin F significantly reduced the levels of ER molecular chaperones, including PDIA6 and PDIA4, and induced ER stress, unfolded protein response (UPR) and autophagy in TNBC cells. Mechanistically, ipomoeassin F, as an inhibitor of Sec61α-containing ER translocon, blocks ER translocation of PDIA6, inducing its proteasomal degradation. Silencing of PDIA6 or PDIA4 by RNA interferences or treatment with a small molecule inhibitor of the protein disulfide isomerases in TNBC cells successfully recapitulated the ipomoeassin F phenotypes, including the induction of ER stress, UPR and autophagy, suggesting that the reduction of PDIAs is the key mediator of the pharmacological effects of ipomoeassin F. Moreover, ipomoeassin F significantly suppressed TNBC growth in a mouse tumor xenograft model, with a marked reduction in PDIA6 and PDIA4 levels in the tumor samples. Our study demonstrates that Sec61α-containing ER translocon and PDIAs are potential drug targets for TNBC and suggests that ipomoeassin F could serve as a lead for developing ER translocon-targeted therapy for TNBC.
Rebooting One’s Professional Work: The Case of French Anesthesiologists Using Hypnosis

Individuals deeply socialized into professional cultures tend to strongly resist breaking from their professions’ core cultural tenets. When these individuals face external pressure (e.g., via new technology or regulation), they typically turn to peers for guidance in such involuntary reinventions of their work. But it is unclear how some professionals may voluntarily break from deeply ingrained views. Through our study of French anesthesiologists who practice hypnosis, we aim to better understand this little-explored phenomenon. Adopting hypnosis, a technique that many anesthesiologists consider subjective and even magical, contradicted a core tenet of their profession: the need to only use techniques validated by rigorous scientific-based research. Drawing on interviews and observations, we analyze how these anesthesiologists were able to change their views and reinvent their work. We find that turning inward to oneself (focusing on their own direct experiences of clients) and turning outward to clients (relying on relations with clients) played critical roles in anesthesiologists’ ability to shift their views and adopt hypnosis. Through this process, these anesthesiologists embarked on a voluntary internal transformation, or reboot, whereby they profoundly reassessed their work, onboarded people in adjacent professions to accept their own reinvention, and countered isolation from their peers. Overall, we show a pathway to such reinvention that entails turning inward and outward (rather than to peers), a result that diverges significantly from prior understandings of professionals’ transformations.
Teleconnections linking worldwide climate anomalies : scientific basis and societal impact

Part I. Introduction: 1. Introduction Michael H. Glantz 2. General characteristics of El Nino-southern oscillation Kevin E. Trenberth Part II. Regional case studies of teleconnections: physical aspects: 3. Brazil's climate anomalies and ENSO Pao-Shin Chu 4. Australasia R. J. Allan 5. West Africa Peter J. Lamb and Randy A. Peppler 6. The Asian snow cover-monsoon-ENSO connection T. P. Barnett, L. Dumenil, U. Schlese, E. Roeckner and M. Latif 7. Teleconnections in global rainfall anomalies: seasonal to inter-decadal time scales K. M. Lau and P. J. Sheu Part III. Scientific Basis for Teleconnections: 8. El Nino and QBO influences on tropical cyclone activity William M. Gray and John D. Sheaffer 9. The rudimentary theory of atmospheric teleconnections associated with ENSO Joseph J. Tribbia 10. Observational aspects of ENSO cycle teleconnections Eugene M. Rasmusson 11. Forecasting El Nino with a geophysical model Mark A. Cane 12. Use of statistical methods in the search for teleconnections: past, present and future Barbara G. Brown and Richard W. Katz Part IV. Regional Impacts of Climate Anomalies: Environmental and Societal Impacts: 13. Impact of ENSO events on the southeastern Pacific region with special reference to the interaction of fishing and climate variability Romulo Jordan S 14. ENSO, monsson and drought in India George Kiladia and Suresh K. Sinha 15. The shrimp fishery in the Gulf of Mexico: relation to climatic variability and global atmospheric patterns Marie E. White and Mary W. Downton 16. Teleconnections and health Neville Nicholls Part V. Implications for ENSO forecasts: 17. Teleconnections and their implications for long-range forecasts Neville Nicholls and Richard W. Katz Index.
An Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data

Outlier detection is an important task in the field of data mining and a highly active area of research in machine learning. In industrial automation, datasets are often high-dimensional, meaning an effort to study all dimensions directly leads to data sparsity, thus causing outliers to be masked by noise effects in high-dimensional spaces. The “curse of dimensionality” phenomenon renders many conventional outlier detection methods ineffective. This paper proposes a new outlier detection algorithm called EOEH (Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data). First, random secondary subsampling is performed on the data, and detectors are run on various small-scale sub-samples to provide diverse detection results. Results are then aggregated to reduce the global variance and enhance the robustness of the algorithm. Subsequently, information entropy is utilized to construct a dimension-space weighting method that can discern the influential factors within different dimensional spaces. This method generates weighted subspaces and dimensions for data objects, reducing the impact of noise created by high-dimensional data and improving high-dimensional data detection performance. Finally, this study offers a design for a new high-precision local outlier factor (HPLOF) detector that amplifies the differentiation between normal and outlier data, thereby improving the detection performance of the algorithm. The feasibility of this algorithm is validated through experiments that used both simulated and UCI datasets. In comparison to popular outlier detection algorithms, our algorithm demonstrates a superior detection performance and runtime efficiency. Compared with the current popular, common algorithms, the EOEH algorithm improves the detection performance by 6% on average. In terms of running time for high-dimensional data, EOEH is 20% faster than the current popular algorithms.
NOMA- and MRC-Enabled Framework in Drone-Relayed Vehicular Networks: Height/Trajectory Optimization and Performance Analysis

In this article, we present a drone-relayed vehicular networking architecture, which aims to improve the achievable data rate of cell-edge vehicles in rural highway scenarios. Specifically, we first incorporate the decode-and-forward (DF) relay protocol with the nonorthogonal multiple access (NOMA) and maximum ratio combining (MRC) techniques, based on which an NOMA- and MRC-Enabled framework is proposed. Next, to fully exploit the advantages of the proposed framework, we separately formulate the total achievable data rate maximization and energy consumption minimization problems by jointly considering the height and 2-D trajectory optimization of relaying drone. The formulated energy consumption minimization problem is transformed into a trajectory optimization problem with obstacle avoidance constraints. Then, for the total achievable data rate maximization problem, we utilize the golden section method to design a height optimization scheme with polynomial complexity. Afterward, we improve the particle swarm optimization (PSO) algorithm, and present an effective 2-D optimization scheme. In addition, the performance superiority of the proposed NOMA- and MRC-Enabled framework is analyzed theoretically. Finally, simulation results verify the efficacy of the proposed height and trajectory optimization schemes. For instance, by using the NOMA and MRC techniques, the total achievable data rate can be improved by 24.4%. Moreover, within the same running time, a shorter trajectory can be obtained by adopting our presented trajectory optimization scheme in comparison with the current works.
Sim2Real Transfer of Reinforcement Learning for Concentric Tube Robots

Concentric Tube Robots (CTRs) are promising for minimally invasive interventions due to their miniature diameter, high dexterity, and compliance with soft tissue. CTRs comprise individual pre-curved tubes usually composed of NiTi and are arranged concentrically. As each tube is relatively rotated and translated, the backbone elongates, twists, and bends with a dexterity that is advantageous for confined spaces. Tube interactions, unmodelled phenomena, and inaccurate tube parameter estimation make physical modeling of CTRs challenging, complicating in turn kinematics and control. Deep reinforcement learning (RL) has been investigated as a solution. However, hardware validation has remained a challenge due to differences between the simulation and hardware domains. With simulation-only data, in this work, domain randomization is proposed as a strategy for translation to hardware of a simulation policy with no additionally acquired physical training data. The differences in simulation and hardware forward kinematics accuracy and precision are characterized by errors of <inline-formula><tex-math notation="LaTeX">$14.74 \pm 8.87$</tex-math></inline-formula> mm or <inline-formula><tex-math notation="LaTeX">$26.61 \pm 17.00$</tex-math></inline-formula>% robot length. We showcase that the proposed domain randomization approach reduces errors by 56% in mean errors as compared to no domain randomization. Furthermore, we demonstrate path following capability in hardware with a line path with resulting errors of <inline-formula><tex-math notation="LaTeX">$4.37 \pm 2.39$</tex-math></inline-formula> mm or <inline-formula><tex-math notation="LaTeX">$5.61 \pm 3.11$</tex-math></inline-formula>% robot length.
Synthesis and characterization by experimental and theory research suitable on the CdS and CdO materials

In this paper, we have compared and analyzed experimental and theoretical research on A2B6 types semiconductor materials CdS and CdO. The XRD spectra of the samples were examined using an atomic force microscope and X-ray diffraction microscopy. During film deposition, the temperature of the crucible with a source (CdS) varied in the range Tsourse ≈ 800 ÷ 850°C, and the substrate temperature was maintained within the range Tp ≈ 250 ÷ 270°C. In this case, to ensure the reproducibility of the structures, a shutter was used, with the help of which the CdS deposition time was set, which ensured that the film thickness was the same from experiment to experiment.