Miracle-dz/UniFAR-FTUs huggingface dataset:
PINN-Based Method for Predicting Flow Field Distribution of the Tight Reservoir after Fracturing

The physical-informed neural network (PINN) model can greatly improve the ability to fit nonlinear data with the incorporation of prior knowledge, which endows traditional neural networks with interpretability. Considering the seepage law in the tight reservoir after hydraulic fracturing, a model based on PINN and two-dimensional seepage physical equations was proposed, which can effectively predict the flow field distribution of the tight reservoir after fracturing. Firstly, the dataset was obtained based on physical and numerical models of the tight reservoirs developed by volume fracturing. Furthermore, coupling the neural networks and the two-dimensional unsteady seepage equation, a PINN model was developed to predict the flow field distribution of the tight reservoir. Finally, a systematic study was performed concerning the noise corruption levels, training iterations, and training sample size that affect the prediction results of PINN models. Besides, a comparison between PINN and traditional deep neural networks (DNN) was presented. The results show that the DNN model was not only sensitive to noisy data but also more vulnerable to overfitting as the training iterations increase. In addition, the prediction accuracy cannot be guaranteed when the samples are inadequate (<500). In contrast, the PINN model was less affected by noise and training iterations and thus indicates greater stability. Moreover, the PINN model outperforms the DNN model in the case of inadequate samples attributing to prior knowledge. This study confirms that the adopted PINN model can provide algorithmic support for the accurate prediction of flow field distribution of the tight reservoirs.
The Effect of Shifts in Medium Types on the Growth and Morphology of Spirulina Platensis (Arthrospira Platensis)

In order to reduce the cultural cost and increase the biomass of Spirulina platensis cultures, two basic culture media were used in the present study, the Zarrouk (Z9) and Provasoli's enriched Seawater (PES) media. They were re-modified into five media by adding varied concentrations of NaCl, NaHCO3 and seawater (salinity). S. platensis could grow well in all of these media. The Z9 medium provided the best growth rate at the end of culture and the PES medium provided the best initial growth curve slope. After the media were shifted from PES to Z9, the long algal filaments fragmented to form many short filaments within 5 days of culture. Those short filaments then straightened into less spiral forms due to the ingrowths of active cell division after 7 days of culture. This media shifted batch culture is simple to operate with the lowest cost and the highest biomass in laboratory scale culture.
Finite-time bounded control for quadrotors with extended dissipative performance using a switched system approach

In order to deal with the problem of attitude tracking control for quadrotors subject to time-varying inertia and external disturbances, a finite-time bounded switched linear parameter-varying (LPV) control method is presented. The attitude dynamics is described by two subsystems, where the inner angular-velocity system is used to track the desired angular velocities that are generated by the outer attitude-angle system. The angular-velocity system of quadrotors is modeled as a switched LPV model, where a family of linear models is developed to approximate the original nonlinear system and the LPV method is applied to model the time-varying inertia. Specially, a mode-dependent persistent dwell-time (MPDT) switching logic, which is more general than the typically used dwell-time (DT) or average dwell-time (ADT) switching logic, is adopted to govern the switching behaviors among these linear models. A state-feedback controller for the switched LPV error-tracking system is designed, which ensures both finite-time boundedness and extended dissipative performance. Finally, the developed theoretical results are verified by numerical simulation.
Lumber drying of Pinus: Geostatistics applied to drying kilns

This study aimed to evaluate the spatial distribution of the air circulation speed and the final moisture content of pine lumber after kiln drying by using geostatistics. Two kilns acting on boards of 27 mm and 42 mm were evaluated. Air circulation speed and final moisture content were collected in different regions of the kilns. There was no significant difference for air circulation speed and final moisture content between the front and rear regions of the equipment. In the horizontal axis (Y-axis), air circulation speed averages did not differ statistically in both situations, but higher values for this variable were obtained from the spaces between the piles. Final moisture content tended to increase when closer to the door opening. In the vertical axis (Z-axis), the lower part tended to reach higher air circulation speed values. Thus, the spatial behavior influences the air circulation speed and final moisture content during lumber drying. Besides, the variograms presented the same tendency in comparison to the data obtained in a conventional manner, which indicates that geostatistics can be used to represent variables in kilns during the drying process.
Predictability and Accuracy of the Short-Form Fonseca Anamnestic Index in Relation to the Modified Helkimo Index for the Diagnosis of Temporomandibular Disorders: A Cross-Sectional Study

Aim: The aim of this study was to evaluate the predictability and accuracy of the Short-Form Fonseca Anamnestic Index (SFAI) in relation to the modified Helkimo Index for the diagnosis of temporomandibular disorders (TMDs). Materials and Methods: A cross-sectional, prospective, and analytical predictive study was conducted in 240 students of a public institute of higher technological education in Ica, Peru during the months of February to May 2018. The SFAI of 10 closed questions and the Helkimo Index modified by Maglione (gold standard) were used as instruments to diagnose TMDs. For the analysis of concordance between both instruments, Cohen’s Kappa Index was applied. To evaluate the association according to gender and age group, Pearson’s chi-square test was used. For validity of the SFAI in relation to accuracy, sensitivity and specificity were calculated, and they were verified by receiver operating characteristics (ROC) to determine the best cutoff points (area under the curve [AUC]) considering a P value < 0.05. Regarding the predictability of the SFAI, the positive and negative predictive value was calculated by applying Bayes’ theorem. Results: The SFAI was highly significantly associated with the modified Helkimo Index according to gender (P < 0.001), age group (P < 0.001), and overall (P < 0.001) moderate overall agreement (k = 0.416; 95% confidence interval [CI] = 0.287–0.545); better concordance was obtained in individuals older than 20 years (k = 0.490, CI = 0.302–0.679) and women (k = 0.565, CI = 0.371–0.759). The ROC curve analysis of the SFAI showed good accuracy (0.852, CI = 0.800–0.905) and was highly significant (P < 0.001), with an optimal cutoff point of 17.5 and good sensitivity (80.10%) and specificity (74.36%). In addition, a very good positive predictive value (PPV) (94.15%) and a fair negative predictive value (NPV) (42.02%) were obtained. Conclusions: Although there was moderate concordance between the SFAI and the modified Helkimo Index for the diagnosis of TMDs, the SFAI obtained good accuracy in the overall analysis of sensitivity and specificity. In addition, it demonstrated a high predictive efficacy for detecting positive TMD cases, whereas its ability to rule out positive cases was fair.
Fractional Order PID Design based on Novel Improved Slime Mould Algorithm

Abstract This study attempts to maintain the terminal voltage level of an automatic voltage regulator (AVR) and control the speed of a direct current (DC) motor using a fractional order proportional integral derivative (FOPID) controller. The best parameters of the controller have been adjusted using a novel meta-heuristic algorithm called opposition-based hybrid slime mold with simulated annealing algorithm. The proposed algorithm aims to improve the original slime mold algorithm in terms of exploitation and exploration using simulated annealing and opposition-based learning, respectively. A time domain objective function was adopted as performance index to design the FOPID-based AVR and DC motor systems. The initial performance evaluation was carried out using unimodal and multimodal benchmark functions. The results confirmed the superior exploration and exploitation capabilities of the developed algorithm compared to the other state-of-the-art optimization algorithms. The performance of the proposed algorithm has also been assessed through statistical tests, time domain and frequency domain simulations along with robustness and disturbance rejection analyses for both DC motor and AVR systems. The proposed algorithm has shown superior capabilities for the respective systems compared to the other state-of-the-art optimization algorithms used for the same purpose.
Constraint Programming-Based Heuristics for the Multi-Depot Vehicle Routing Problem with a Rolling Planning Horizon

Der Transportmarkt ist sowohl durch einem intensiven Kostenwettbewerb als auch durch hohe Erwartungen der Kunden an den Service gepragt. Die vorliegende Dissertation stellt zwei auf Constraint Programming basierende heuristische Frameworks vor, die eine Reoptimierung bereits geplanter Touren zu festgelegten Zeitpunkten erlauben und so eine Reaktion auf die gesteigerte Wettbewerbsdynamik und den Kostendruck ermoglichen.
Experimental observations of mill operation parameters on kinematic of the tumbling mill contents

The present paper aims to investigate the key points of kinematic of contents inside the mill (load orientation), namely the shoulder, toe, impact, and pool angles. The kinematic of the mill contents in wet grinding is influenced by a range of parameters such as: media charge level (ball filling), slurry filling, and mill speed. In this work the influence of these operating parameters was investigated using a pilot mill (1000 × 500 mm). To this end, a Copper ore (–1000 μ m) was used to prepare slurry at 50% solids concentration by mass. The tests covered a range of slurry filling (U ) from 0 to 2.92 with ball filling 24% and 30% of mill volume and 6 different speeds between 60% and 85% of critical speed. It is observed as the mill speed increases, the more the impact angle will decrease. Toe angle increases in the range 15–25 degrees by formation of the pool in mill and exertion of the floating (buoyancy) forces to the particles inside the mill. The more the mill speed and the slurry filling increase, the more the shoulder angle will increase. The increase in slurry volume leads to the elevation of the pool level and therefore, the pool angle will exponentially decrease.
Moving Consumers from Free to Fee in Platform-Based Markets: An Empirical Study of Multiplayer Online Battle Arena Games

Companies in platform-based business markets have widely embraced freemium business models, in which profit is primarily determined by a minority of paying customers. However, the key challenge of these models is transitioning participants from free users to paying consumers. To encourage paid consumption, companies often rely on product differentiation such as providing consumers who pay for products or services with enhanced features. Product differentiation can be broadly classified into two categories: taste differentiation and quality differentiation. The authors demonstrate that extending the magnitude of taste differentiation is an effective differentiation strategy. Quality differentiation, however, is a double-edged sword and should be used with care. Increasing product differentiation leads to greater perceived value of the service, but undermines fairness perceptions.
Non-linear thermo-mechanical buckling approach for composite laminated marine structures

This paper describes the behaviour of large-length ships built using fibre-reinforced plastic composite materials under fire and large deformations. A novel methodology is proposed in order to analyse inelastic buckling and post-buckling of steel structures due to thermal loading and also extend this to the phenomenon regarded as pyrolysis that is natural in composite structures. The methodology is validated against the literature. A model of a container-ship where a fire scenario located in the engine room is used to demonstrate the methodology against a real case scenario. The section of the cargo hold closest to the engine room is structurally analysed, focusing in the integrity of the bulkhead and the supports of the TEU containers.
Dialogue-Based Evaluation as a Creative Climate Indicator: Evidence from the Pharmaceutical Industry

This paper examines how different forms of performance evaluation relate to aspects of the creative climate in a major pharmaceutical company. The study was based on a large employee-attitude survey that was distributed to all company employees. The study analyses survey results from 5,333 employees at five R&D sites. The results indicate that management’s evaluation of employees (either dialogue-based or control-based) relates to the type of motivation (intrinsic or extrinsic) that drives employees, to their style of thinking (value-focused thinking) and on their attitudes to organizational creativity. The paper then discusses implications of these findings for HRM.
Comparison of Actinobacteria communities from human‐impacted and pristine karst caves

Actinobacteria are important cave inhabitants, but knowledge of how anthropization and anthropization‐related visual marks affect this community on cave walls is lacking. We compared Actinobacteria communities among four French limestone caves (Mouflon, Reille, Rouffignac, and Lascaux) ranging from pristine to anthropized, and within Lascaux Cave between marked (wall visual marks) and unmarked areas in different rooms (Sas‐1, Passage, Apse, and Diaclase). In addition to the 16S rRNA gene marker, 441 bp fragments of the hsp65 gene were used and an hsp65‐related taxonomic database was constructed for the identification of Actinobacteria to the species level by Illumina‐MiSeq analysis. The hsp65 marker revealed higher resolution for species and higher richness (99% operational taxonomic units cutoff) versus the 16S rRNA gene; however, more taxa were identified at higher taxonomic ranks. Actinobacteria communities varied between Mouflon and Reille caves (both pristine), and Rouffignac and Lascaux (both anthropized). Rouffignac displayed high diversity of Nocardia, suggesting human inputs, and Lascaux exhibited high Mycobacterium relative abundance, whereas Gaiellales were typical in pristine caves and the Diaclase (least affected area of Lascaux Cave). Within Lascaux, Pseudonocardiaceae dominated on unmarked walls and Streptomycetaceae (especially Streptomyces mirabilis) on marked walls, indicating a possible role in mark formation. A new taxonomic database was developed. Although not all Actinobacteria species were represented, the use of the hsp65 marker enabled species‐level variations of the Actinobacteria community to be documented based on the extent of anthropogenic pressure. This approach proved effective when comparing different limestone caves or specific conditions within one cave.
From teacher professional development to teacher personal-professional growth: the case of expert STEM teachers

ABSTRACT Following Self-Determination Theory, scholars theorize that professional well-being is essential for teachers’ satisfaction and personal-professional growth. However, teacher professional development programs (TPDPs) primarily focus on promoting student achievement and only partially and indirectly address teachers’ professional well-being. The lack of teachers’ voices in this discrepancy is noticeable. This study aimed at confronting teachers’ perceptions about their professional well-being needs with how actual practices in effective TPDPs address these needs. Well-being components (competence, relatedness, autonomy, and aspirations) were thematically identified within 20 interviews with expert STEM teachers. The findings show that teachers attribute importance to their professional well-being in all components. In particular, aspirations for personal-professional growth, pointed out by teachers as the most important, were totally ignored by effective TPDPs. The findings emphasize the need to establish well-being as an additional characteristic of effective TPDPs. This additional characteristic may have practical and theoretical implications for designing and assessing TPDPs’ effectiveness.
Defining inkjet printing conditions of superconducting cuprate films through machine learning

The design and optimization of new processing approaches for the development of rare earth cuprate (REBCO) high temperature superconductors is required to increase their cost-effective fabrication and promote market implementation. The exploration of a broad range of parameters enabled by these methods is the ideal scenario for a new set of high-throughput experimentation (HTE) and data-driven tools based on machine learning (ML) algorithms that are envisaged to speed up this optimization in a low-cost and efficient manner compatible with industrialization. In this work, we developed a data-driven methodology that allows us to analyze and optimize the inkjet printing (IJP) deposition process of REBCO precursor solutions. A dataset containing 231 samples was used to build ML models. Linear and tree-based (Random Forest, AdaBoost and Gradient Boosting) regression algorithms were compared, reaching performances above 87%. Model interpretation using Shapley Additive Explanations (SHAP) revealed the most important variables for each study. We could determine that to ensure homogeneous CSD films of 1 micron thickness without cracks after the pyrolysis, we need average drop volumes of 190–210 pl, and no. of drops between 5000 and 6000, delivering a total volume deposited close to 1 μl.
Compensation for land use deprivation in mining: an analysis of the laws and practices relating to land use deprivation compensation in Ghana’s mining sector

ABSTRACT This article examines the laws and valuation methods that valuers apply in assessing compensation for land use deprivation in Ghana’s mining sector using two mining companies as case studies. We argue that analysing the valuation techniques applied in assessing compensation for land use deprivation in the context of current legal provisions facilitates a better understanding of the issues fuelling community dissatisfaction with compensation awards. Data produced through document analysis and interviews with 39 farmers, government officials, chiefs, private valuers, and officials of Newmont Goldcorp’s Ahafo Mine and Asanko Gold Ghana Limited were analysed in the context of the current legal framework for expropriation. Interview transcripts were coded based on themes and analysed using the constant comparison method. The findings reveal that the valuation techniques for assessing compensation for land use deprivation disregard fundamental compensation principles outlined under law. The study also exposes the weaknesses in the valuation techniques in assessing compensation for farmlands that mining may impair permanently. Towards ensuring fairness in compensating mining-impacted farmers, this study recommends additional legal directives to streamline the methods for assessing compensation for land use deprivation. The consequences of the limitations in the valuation methods on expropriated farmers are also discussed.
Risk Communication During COVID-19 Pandemic: Impacting Women in Bangladesh

Risk communication during COVID-19 is essential to have support, but it is challenging in developing countries due to a lack of communication setup. It is more difficult for the low-income, marginal communities, and specifically, women in developing countries. To understand this, particularly during the COVID-19 pandemic, we conducted a qualitative study among N = 37 women (urban 20, rural = 17) across Bangladesh that presents the risk communication factors related to social and financial challenges. It reveals that the majority of the urban communities lack communication with local authorities, where urban low-income communities are the worst sufferers. Due to that, the majority of the urban participants could not get financial support, whereas the rural participants received such support for having communications with local authorities during the pandemic. However, access to technology helped some participants share and receive pandemic-related information about risk communication, and the adoption of financial technology helped to get emergency financial support through risk communication. Moreover, this work is expected to understand the role of risk communication during the COVID-19 pandemic among women in Bangladesh.
Size-based isolation and detection of renal carcinoma cells from whole blood

Renal cell carcinoma (RCC) is a tumour type with an indolent growth pattern and rather vague symptoms. The present study developed a platform for liquid biopsy of RCC based upon the isolation of circulating tumour cells (CTCs). Founded on the observation that RCC tumour cells are considerably larger than leucocytes, the present study employed a microfluidics-based system for isolation of RCC CTCs from whole blood. Using this system, it was revealed that 66% of spiked-in RCC tumour cells could be retrieved using this approach. Furthermore, it was demonstrated that these cells could be molecularly detected with digital PCR using RCC-specific genes down to one tumour cell, whilst avoiding detection in samples lacking tumour cells. Finally, subtype specific transcripts were identified to distinguish the different subtypes of RCC, which were then validated in patient tumours. The present study established a novel workflow for the isolation of RCC CTCs from whole blood, with the potential to detect these cells irrespective of subtype.
Approach Review of Obstacle Detection for Intelligent Vehicle

Depending on the different sensors,the algorithm technology of obstacle detection for intelligent vehicles is summarized,which are based on stereo vision,laser radar,color machine vision and so on.In addition,the writer pointed out that the obstacle can not be detected based on only one kind of sensor,many kinds of information from different sensors must be employed.So the research focus in obstacle detection for intelligent vehicles is located at multi-sensors data fusion.The above methods related to the obstacle detection are introduced in details,the achievements of some research institutes are introduced for providing some reference to the development of domestic technology in this field.