Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic algorithm-based optimisation method for product family design with multi-level commonality

An increasing number of companies design a family of product variants simultaneously in order to introduce adequate product variety in the competitive market in a cost-effective way while shortening product lead-times, instead of designing one product at a time. The key to using this approach successfully is to achieve the right trade-off between product family commonality and performance of individual product variants. This paper considers multi-level commonality in product family design in the sense that the feature or component can be common only among some product variants. This differs from the two extremes of being totally common throughout the family or being completely different from one product variant to another. A product family design model is proposed as a multi-objective optimisation. A commonality index is introduced to evaluate the family commonality in the presence of multiple levels. A multi-objective genetic algorithm is developed for simultaneous design of a family of product variants. Optimal decisions include which design variables should be common among which product variants. Computational experiments are conducted using the design of a family of welded beams to demonstrate the effectiveness of the product family design method proposed in this paper.
Governor Vessel Moxibustion Therapy Improves Microbiota Structure in Ankylosing Spondylitis Patients

Background Clinical studies have shown that ankylosing spondylitis (AS) could be significantly improved by Governor Vessel moxibustion (GVM) therapy. Objective Study whether GVM therapy alleviates the clinical symptoms of AS by modulating intestinal microbiota. Methods A total of 9 AS patients and 9 paired healthy individuals were enrolled, and GVM therapy was provided to the AS patients. Stool, urine, and saliva samples from the healthy individuals and the AS patients before and after GVM therapy were collected, and 16S rRNA gene sequencing was performed for microbiota analysis. Results We found that GVM therapy can significantly alleviate the symptoms of AS, such as diarrhea, abdominal pain, and bloating. GVM therapy also decreased the abundances of Bacteroides and Prevotella while increasing the abundances of beneficial bacteria, such as Lactobacillus, in the gut microbiota of the AS patients. The analyses for AS clinical data and microbial abundances in AS patients revealed their multiple significant correlations (P < 0.01); for example, an unclassified crystal was positively correlated with AF12 and Delftia, monocyte had a negative correlation with Scardovia, and human leukocyte antigen-B27 was negatively correlated with Catenibacterium, Coprococcus, and Oscillospira. Conclusions Overall, these findings demonstrate that GVM therapy can alleviate AS clinical symptoms, and at the same time, it improves the microbial structure of microbiota in AS patients. This trial is registered with Chinese Clinical Trial Registry ChiCTR2100051907.
Contribution to deterioration modeling and residual life estimation based on condition monitoring data

Predictive maintenance plays a crucial role in maintaining continuous production systems since it can help to reduce unnecessary intervention actions and avoid unplanned breakdowns. Indeed, compared to the widely used condition-based maintenance (CBM), the predictive maintenance implements an additional prognostics stage. The maintenance actions are then planned based on the prediction of future deterioration states and residual life of the system. In the framework of the European FP7 project SUPREME (Sustainable PREdictive Maintenance for manufacturing Equipment), this thesis concentrates on the development of stochastic deterioration models and the associated remaining useful life (RUL) estimation methods in order to be adapted in the project application cases. Specifically, the thesis research work is divided in two main parts. The first one gives a comprehensive review of the deterioration models and RUL estimation methods existing in the literature. By analyzing their advantages and disadvantages, an adaption of the state of the art approaches is then implemented for the problem considered in the SUPREME project and for the data acquired from a project's test bench. Some practical implementation aspects, such as the issue of delivering the proper RUL information to the maintenance decision module are also detailed in this part. The second part is dedicated to the development of innovative contributions beyond the state-of-the-are in order to develop enhanced deterioration models and RUL estimation methods to solve original prognostics issues raised in the SUPREME project. Specifically, to overcome the co-existence problem of several deterioration modes, the concept of the "multi-branch" models is introduced. It refers to the deterioration models consisting of different branches in which each one represent a deterioration mode. In the framework of this thesis, two multi-branch model types are presented corresponding to the discrete and continuous cases of the systems' health state. In the discrete case, the so-called Multi-branch Hidden Markov Model (Mb-HMM) and the Multi-branch Hidden semi-Markov model (Mb-HsMM) are constructed based on the Markov and semi-Markov models. Concerning the continuous health state case, the Jump Markov Linear System (JMLS) is implemented. For each model, a two-phase framework is carried out for both the diagnostics and prognostics purposes. Through numerical simulations and a case study, we show that the multi-branch models can help to take into account the co-existence problem of multiple deterioration modes, and hence give better performances in RUL estimation compared to the ones obtained by standard "single branch" models.
Large-signal circuit model for simulation of injection-laser modulation dynamics

A new large-signal circuit model of a heterojunction injection laser is presented. The model includes the current/voltage characteristics of the semiconductor heterojunction and the electro-optical dynamics of the active layer. It enables the laser and its electrical drive circuit to be analysed in a unified manner. The model can be easily implemented on general-purpose nonlinear circuit analysis programs, and does not require specialised laser analysis software. The model is illustrated with a number of simulated laser response characteristics.
A Hybrid and Self-Adaptive Differential Evolution Algorithm for the Multi-Depot Vehicle Routing Problem in Egg Distribution

This paper presents the Hybrid and Self-Adaptive Differential Evolution algorithms (HSADE) to solve an egg distribution problem in Thailand. We introduce and formalize a model for a multi-product, multi-depot vehicle routing problem with a time window, a heterogeneous fleet and inventory restrictions. The goal of the problem is to minimize the total cost. The multiple products comprise customers’ demands with different egg sizes. This paper presents a Mixed Integer Linear Programming (MILP) model, an initial solution-based constructive heuristic, a new self-adaptive mutation strategy, and a neighborhood search structure with the probability to improve DE. The two measurements of criteria are the heuristic performance (HP) compared with the solution obtained by MILP and the relative improvement (RI) of the solution compared with Thailand’s current egg distribution practice. The computational results show that the performance of HSADE is better than the current practice, and HSADE can provide on average a 14.13% improvement in total cost. Additionally, our proposed algorithm can be applied to similar agriculture logistics in Thailand and worldwide.
Factors promoting academic credit attainment in an urban full-service community school initiative

Abstract Students who attend alternative high schools are considered at risk of not graduating. To graduate from high school, students need to obtain the appropriate number of credits. In this study, factors related to the attainment of credit by students in an alternative school during 2015–2019 were examined. Five factors had an effect on the number of credits students in the alternative school earned: the duration of students’ active enrollment, participation in service programs, ongoing participation in service programs, student attendance rate, and home language. However, behavioral referrals, gender, race, ethnicity, and grade level had no effect on credit attainment. Implications for alternative high school policies and practice are also discussed.
Robust Estimation of Linear Fixed Effects Panel Data Models with an Application to the Exporter Productivity Premium

Summary In empirical studies often the values of some variables for some observations are much larger or smaller than the values for the other observations in the sample. These extreme observations, or outliers, often have a large impact on the results of statistical analyses. Conclusions based on a sample with and without these units may differ drastically. While applied researchers are usually aware of this the detection of outliers and their appropriate treatment is often dealt with in a rather sloppy manner. One reason for this habit seems to be the lack of availability of appropriate canned programs for robust methods that can be used in the presence of outliers. Our paper intents to improve on this situation by presenting a highly robust method for estimation of the popular linear fixed effects panel data model and to supply Stata code for it. In an application from the field of the micro-econometrics of international firm activities we demonstrate that outliers can indeed drive results.
Metal Pollution and Bioaccumulation in the Nhue-Day River Basin, Vietnam: Potential Ecological and Human Health Risks

(1) Background: Metal pollution in the Nhue-Day River basin has impacted approximately 12 million people. However, none of the previous studies considered the entire basin’s environmental and health risks. Thus, this research aims to fill knowledge gaps and reduce risks. (2) Methods: Sediment and fish samples from the basin were analyzed to determine the levels of Zn, Cu, Pb, and Cd pollution and their potential ecological (EF, modified Pollution Index—mPI, and expanded, modified potential ecological risk index—emRI) and human health risks (THQ, HI, and TR indices). (3) Results: Metal levels in sediment exceeded Canadian aquatic life protection guidelines, indicating moderate to severe contamination (EFs: 1.3–58.5 and mPIs: 4–39). Compared to the new proposed ecological risk threshold, all river sites and Site 1 for ponds had elevated metal levels; and these posed a very high ecological risk in spring (emRI > 4.5), with Cd being the most hazardous. Lead levels in all fish tissues surpassed Vietnamese and EU food regulations. In agreement with THQ, EWI (Zn, Cu) and EMI (Cd) were both less than 2.5% of the PTWI and PTMI, respectively. However, HI values of 0.67–1.26 suggested a moderate health risk. Carcinogenic risk (TR > 10−6; estimated for Pb) was detected in several localities for Common carp and Tilapia during the warm season. (4) Conclusions: Metals had a negative impact on the basin’s ecosystem, with Cd being the most dangerous. Because of lead, consumption of Common carp and Tilapia from the basin may pose both non-carcinogenic and carcinogenic health concerns.
Short term unsteady wind loading on a low-rise building

This paper presents an extensive analysis of the short term, unsteady wind loading on a low-rise building. The building is located in a rural environment and only the specific situation of wind flow orthogonal to the long face of the structure is considered. The data is analysed using conventional analysis and less traditional methods such as conditional sampling and wavelet analysis. The nature of the flow field over the building is found to be highly unsteady and complex. Fluctuating pressures on the windward wall are shown to a large extent to be caused by the fluctuations in the upstream flow, whereas extreme pressures on the roof are as a result of high intensity small scale flow structures. On the roof of the building a significant amount of energy is shown to exist at frequencies above 1 Hz.
Effects of IMU Sensor-to-Segment Misalignment and Orientation Error on 3-D Knee Joint Angle Estimation

Accurate joint angle estimation using magneto-inertial measurement units (IMU) is a significant challenge for human movement analysis outside of the lab. Sensor-to-segment misalignment and sensor orientation errors are key factors in joint angle estimation. However, the effects of these factors on 3-D knee joint angle estimation accuracy are not well understood. This paper presents a general model to investigate the effects of sensor-to-segment misalignment and IMU orientation error on 3-D knee joint angle estimation accuracy. An analytical model was built to quantify the effects of sensor-to-segment misalignment and IMU orientation errors for 3-D knee joint angle estimation. Precise outcomes were quantified via simulation analysis and experimental testing in which fourteen healthy subjects performed drop landing and cutting tasks while wearing IMUs and optical motion capture markers for ground truth estimation. Sensor-to-segment misalignment accounted for 52% of the total knee abduction angle error and 40% of the knee internal rotation angle error but did not significantly affect knee flexion angle error. Conversely, IMU orientation error accounted for 55% of the knee flexion angle error but did not significantly affect knee abduction or internal rotation angle errors. These results indicate that sensor-to-segment misalignment significantly affects knee abduction and internal rotation angle estimation while IMU orientation error significantly affects knee flexion angle estimation. This study could serve to inform the development of accurate IMU-based 3-D knee joint angle algorithms for human movement analysis.
Imaging Rhizosphere CO2 and O2 Concentration to Localize Respiration Hotspots Linked to Root Type and Soil Moisture Dynamics

PurposeRhizosphere respiration strongly affects CO2 concentration within vegetated soils and resulting fluxes to the atmosphere. Respiration in the rhizosphere exhibits high spatiotemporal variability that may be linked to root type, but also to small-scale variation of soil water content altering gas transport dynamics in the soil. We address spatiotemporal dynamics of CO2 and O2 concentration in the rhizosphere via non-invasive in-situ imaging.MethodsOptodes sensitive to CO2 and O2 were applied to non-invasively measure in-situ rhizosphere CO2 and O2 concentration of white lupine (Lupinus albus) grown in slab-shaped glass rhizotrons. We monitored CO2 concentration over the course of 16 days at constant water content and also performed a drying-rewetting experiment to explore sensitivity of CO2 and O2 concentration to soil moisture changes. ResultsHotspots of respiration formed around cluster roots and CO2 concentration locally increased to > 20 % pCO2 (CO2 partial pressure). After rewetting the soil, cluster roots consumed available O2 significantly faster compared to non-cluster lateral roots. In wet soil, CO2 accumulation zones extended up to 9.5 mm from the root surface compared to 0.3-1 mm in dry soil.ConclusionResults from this imaging experiment indicate that respiratory activity differs substantially within the root system of a plant individual and that cluster roots are hotspots of respiration. As rhizosphere CO2 and O2 concentration was strongly sensitive to soil water content and its variation, we recommend monitoring the soil water content prior and during the measurement of rhizosphere respiration.
Power MOSFET dynamic large-signal model

A large-signal dynamic circuit model for the double-diffused vertical power MOSFET is reported. The model is physically based and its parameters are readily determined from static curve tracer measurements and small-signal measurements using simple data-reduction techniques. The equivalent circuit can be implemented in general purpose nonlinear circuit analysis computer programs, such as SPICE 2, for use in the analysis and design of switching circuits. The accuracy of the model is verified by the excellent correlation obtained between detailed simulated and measured switching characteristics on an experimental high-speed switching circuit.
A Context-Aware Access Control Model for Pervasive Computing Environments

In pervasive computing environments, a user can access resources and services from any where and at any time; thus a key security challenge in these environments is the design of an effective access control model which is aware of context modifications. Changes in context may trigger changes in authorizations. In this paper, we propose a new context-aware access control model based on role-based access control model for pervasive computing environments. We assign roles to users dynamically based on the long-term context information and tune active role's permissions according to the short-term context information of the users and environment.
Langevin power curve analysis for numerical wind energy converter models with new insights on high frequency power performance

Based on the Langevin equation it has been proposed to obtain power curves for wind turbines from high frequency data of wind speed measurements u(t) and power output P (t). The two parts of the Langevin approach, power curve and drift field, give a comprehensive description of the conversion dynamic over the whole operating range of the wind turbine. The method deals with high frequent data instead of 10 min means. It is therefore possible to gain a reliable power curve already from a small amount of data per wind speed. Furthermore, the method is able to visualize multiple fixed points, which is e.g. characteristic for the transition from partial to full load or in case the conversion process deviates from the standard procedures. In order to gain a deeper knowledge it is essential that the method works not only for measured data but also for numerical wind turbine models and synthetic wind fields. Here, we characterize the dynamics of a detailed numerical wind turbine model and calculate the Langevin power curve for different data samplings. We show, how to get reliable results from synthetic data and verify the applicability of the method for field measurements with ultra-sonic, cup and Lidar measurements. The independence of the fixed points on site specific turbulence effects is also confirmed with the numerical model. Furthermore, we demonstrate the potential of the Langevin approach to detect failures in the conversion process and thus show the potential of the Langevin approach for a condition monitoring system.
The geographical dimension of productivity in Great Britain, 2011–18: the sources of the London productivity advantage

ABSTRACT The UK government has committed to ‘levelling up’ regional economic performance. Through deriving geographically disaggregated estimates of total factor productivity from plant-level data, we show that the productivity advantage of London is far greater than differences between other regions. Evidence is then provided on the extent to which differences in multinational ownership, trade involvement, enterprise structure, plant age, research and development, subsidization, size, and industrial structure explain the London productivity advantage. Less than half can be explained by these characteristics, which suggests that they should not be the main focus of policy to reduce spatial productivity differentials.
GaInAsP/InP integrated laser with butt-jointed built-in distributed-Bragg-reflection waveguide

A novel GaInAsP/InP integrated laser with butt-jointed built-in distributed-Bragg-reflection waveguide (BJB-DBR integrated laser for short) is proposed and demonstrated. In this structure, it is found theoretically that an efficient coupling of 98% between the active and butt-jointed external waveguides is available by matching the propagation constants and the field profiles of both waveguides, which gives relatively larger fabrication tolerance. Prototype BJB-DBR integrated lasers with emitting wavelength of 1.55 µm were fabricated, and single-longitudinal-mode operation was obtained at room temperature.
NONLINEAR PAVEMENT DISTRESS MODEL USING DYNAMIC VEHICLE LOADS

The relationships between axle weight and pavement damage are traditionally based on data collected from road tests. The goal of the research presented in this paper is to use available field data to develop nonlinear dynamic simulation techniques for predicting pavement performance. The study provides information on pertinent issues through models of vehicle dynamic loads and their effect on selected pavement structures. The model structure allows estimates of the cracking and failure of rigid pavement slabs and joints as a result of repeated applications of the dynamic loads simulated for specific gross vehicle loads and tractor-trailer configurations. A preliminary pavement consumption model is proposed that gives reduced equivalent pavement thickness as a function of number of vehicle passes.
Analysis of the Moodle application instrument with the exam browser to measure online learning outcomes in digital communication simulation subjects in class X Multimedia at SMKN 1 Lamongan

This study was used to determine the feasibility of the instrument before being used for further research. This study uses a quasi-experimental research design. In this study, to calculate the validation results from three validators who are experts in their fields using the arithmetic average, the validated instruments are: (1) the validation questions get 87.51% results; (2) validation of attitude assessment got 90.27% results; (3) validation of student skills assessment got 92.71% results; (4) the validation of the assessment of the Learning Implementation Plan (RPP) got 92.16% results; (5) validation of the syllabus assessment got 86.95% results; (6) Moodle assessment validation with browser exam got 87.25% results; (7) the validation of the student learning module assessment got 84.71% results; (8) the validity of the items is declared valid; (9) the reliability of the items is declared reliable; (10) the differentiating power of the items has very good criteria; and (11) the level of difficulty of the items showed that of the 40 items tested, the percentage was 35% (14 questions) in the medium category, and 65% (26 questions) in the easy category. From the calculation results that have been mentioned, the Moodle application instrument with a browser exam to measure online learning outcomes is declared feasible and can be used for further research.
Waveguide Technique for the Calibration of Miniature Implantable Electric-Field Probes for Use in Microwave-Bioeffects Studies

A new S-band waveguide technique has been developed for the calibration of miniature probes used in determining electric fields in biological tissues at 2.45 GHz. A section of waveguide is filled with tissue-equivalent liquid separated from the air-filled waveguide by a very thin (0.25-mm) planar dielectric spacer. The probe response is measured as a function of position on each side of the spacer and extrapolated to the interface. The ratio of probe response in air to that in test liquid is then determined assuming continuity of tangential E-field across the spacer. In the water-glycerol solution modelling wet tissue, the probes are 3.0+-0.6 times more sensitive to E/sup 2/ than in air. A wide variety of both wet and dry tissues may be simulated using liquids of different dielectric properties--a check on the properties is provided by comparing the measured depth of penetration of the wave in the liquid with the calculated value. Problems using the probes in biological tissues are also discussed.