Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixture effects of co-formulants and two plant protection products in a liver cell line

Plant protection products (PPPs) consist of one or more active substances and several co-formulants. Active substances provide the functionality of the PPP and are consequently evaluated according to standard test methods set by legal data requirements before approval, whereas co-formulants' toxicity is not as comprehensively assessed. However, in some cases mixture effects of active substances and co-formulants might result in increased or different forms of toxicity. In a proof-of-concept study we hence built on previously published results of Zahn et al. (2018[38]) on the mixture toxicity of Priori Xtra® and Adexar® to specifically investigate the influence of co-formulants on the toxicity of these commonly used fungicides. Products, their respective active substances in combination as well as some co-formulants were applied to human hepatoma cell line (HepaRG) in several dilutions. Cell viability analysis, mRNA expression, abundance of xenobiotic metabolizing enzymes and intracellular concentrations of active substances determined by LC-MS/MS analyses demonstrated that the toxicity of the PPPs is influenced by the presence of co-formulants in vitro. PPPs were more cytotoxic than the mix of their active substances. Gene expression profiles of cells treated with the PPPs were similar to those treated with their respective mixture combinations with marked differences. Co-formulants can cause gene expression changes on their own. LC-MS/MS analyses revealed higher intracellular concentrations of active substances in cells treated with PPPs compared to those treated with the respective active substances' mix. Proteomic data showed co-formulants can induce ABC transporters and CYP enzymes. Co-formulants can contribute to the observed increased toxicity of PPPs compared to their active substances in combination due to kinetic interactions, necessitating a more comprehensive evaluation approach.
Impact of the Texas-Wide Premedical Mentoring Program during the COVID-19 pandemic

Abstract The COVID-19 pandemic disrupted the usual processes and support systems related to applying to medical school in the United States. The Texas-Wide Premedical Mentoring Program (TPMP) was established to pair medical student mentors in Texas with medical school applicants attending Texas colleges and universities. Our objective was to demonstrate the effect of the TPMP on application preparedness and self-reported mental health outcomes of program participants. A survey was developed to understand the program’s impact on both mentees and mentors. Participants were sent a survey link 3 months after the TPMP launch. In total, 313 participants, comprising 62% premedical student mentees and 38% medical student mentors, completed the survey. Mentees reported a significantly positive effect of the program on anxiety, uncertainty of acceptance, connection to medicine, and making the road to medical school seem less impossible. After participation, mentees felt less alone and reported a positive impact on their perception of the application process. The TPMP positively impacted the mental wellness of both mentees and mentors, and about 80% of mentors felt more fulfilled despite not participating in clinical duties in light of suspensions. In conclusion, program participation was associated with decreasing application knowledge gaps, easing anxiety, and providing alliance for mentees. The TPMP had a similarly positive influence on the mental wellness of mentees and mentors as well as contributed to medical student mentors’ sense of fulfillment.
Hyperspectral image classification using deep convolutional neural network and stochastic relaxation labeling

Abstract. Convolutional neural networks (CNNs) have shown tremendous success for hyperspectral image classification in recent years. CNNs are capable of capturing multi-scale spectral–spatial characteristics of hyperspectral image pixels leading to good classification results. Despite the good accuracy, most of the classifiers misclassify some pixels and generate noisy classification maps. A deep CNN and Markov random field (MRF)-based two-stage classification framework is developed for hyperspectral images. The input image is first classified with the help of a deep CNN classifier. The results provided by CNN are further refined by applying stochastic relaxation labeling using MRF on the first-stage classification map to produce a refined classification map with better accuracy. This two-stage classification approach is particularly helpful if smaller misclassified regions are generated during the first-stage classification. Experiments are performed on one satellite-borne and three airborne hyperspectral images: Dioni, Indian Pines, Pavia University, and Salinas. The results show that the proposed method yields good classification accuracy and smoothed classification maps. The refinement by MRF relaxation improved the overall classification accuracy of the first-stage classifier by more than 2% for all the images. The overall classification accuracy in terms of κ coefficient is obtained as 0.9844, 0.9678, 0.9843, and 0.9841 for Dioni, Indian Pines, Pavia University, and Salinas images, respectively, which is comparable or better than several existing methods.
Robust Fuzzy K-Means Clustering With Shrunk Patterns Learning

Fuzzy K-Means (FKM) clustering regards each cluster as a fuzzy set and assigns each sample to multiple clusters with a certain degree of membership. However, conventional FKM methods perform clustering on the basis of original data directly where the intrinsic structure of data may be corrupted by the noise or other factors. According, the performance of these methods would be challenged. In this paper, we present a novel fuzzy K-Means clustering model to conduct clustering tasks on the flexible manifold. Technically, we perform fuzzy clustering based on the shrunk patterns which have desired manifold structure. The shrunk patterns can be viewed as an approximation to the original data; and a penalty term is employed to measure the mismatch between them. Moreover, we integrate the learning of shrunk patterns and the learning of membership degree between shrunk patterns and clusters into a unified framework. Consider traditional projected FKM methods usually project samples into a linear subspace, which is overstrict. We further extend the proposed model for projected FKM clustering to find a suitable subspace to fit the non-linear manifold structure of data, reduce the interference of the noise and redundant features and gather homogeneous samples together simultaneously. Two alternating iterative algorithms are derived to solve these models, respectively. Extensive experimental results demonstrate the feasibility and effectiveness of our proposed clustering algorithms.
A micro particle launching apparatus based on mode-division-multiplexing technology

We propose and demonstrate a trapped yeast cell being launched away from the fiber tip with a certain speed to a certain position without moving the optical fiber in a single fiber optical trapping system. We excite both LP01 mode and LP11 mode beams in a same normal communication fiber core to generate the optical scattering force and trapping force by molding the fiber tip into a special tapered-tip shape. A yeast cell of 6μm diameter is trapped and then being launched away. We construct the optical launching well by controlling the power of double mode beams. Besides that, we also built a physical model to analyze the micro particle dynamic behavior characteristics during the launching moment. This micro particle directional launching function expanded new features of fiber optical tweezers based on the normal communication fiber, providing for the possibility of more practical applications in the biomedical research fields.
CAD/CAM IN DENTAL RESTORATIONS: AN OVERVIEW

In the last two decades, exciting new developments in dental materials and computer technology have led to the success of dental computer aided design/computer aided manufacturing (CAD/CAM) technology. CAD/CAM is proving to be a valuable image enhancer, production booster and profit generator. Highly sophisticated chair side and laboratory CAD/CAM systems have been introduced. This article provides an overview of various CAD/CAM systems.
Transient stability simulation of power system including wind generator by PSCAD/EMTDC

Induction machines are mostly used as generators in wind power stations. Since induction machines also have a stability problem, it is important to analyze the transient stability of the power system including wind power stations. This paper presents simulation analyses of transient stability of a power system including an induction generator performed by PSCAD/EMTDC.
A Behavior-Based Malware Spreading Model for Vehicle-to-Vehicle Communications in VANET Networks

Network attacking using malware has become very popular on the Internet and in many other networks, namely Vehicular Ad-hoc Network (VANET) networks. It is required to have the model describing the malware spreading based on factors, which directly affect this process to limit its influences. In this paper, we propose a mathematical model called SEIR-S (Susceptible–Exposed–Infectious–Recovered–Susceptible) based on the characteristics of the VANET network and the well-known disease-spreading model SIR (Susceptible–Infectious–Recovered). We take into account possible behaviors of malware and provide the corresponding states to vehicles: Susceptible (S), Exposed (E), Infectious (I), Recovered (R). We evaluate the basic reproduction number R0 of the model and perform a stability analysis of the proposed model. The results show that, when R0 < 1, the malware spreading will gradually decrease, and, when R0 > 1, that spreading cannot be extinguished. We also point out the condition that we can control the endemic in the VANET network. In addition, the correctness of the proposed model is verified using both numerical analysis and agent-based simulation on NetLogo.
Swept Tool Envelope and Machining Potential Field for 5-Axis Sculptured Surface Machining

This paper presents the machining potential field (MPF) and the explicit solutions of swept envelopes for 5-axis sculptured surface machining. The properties of the swept envelopes are analyzed. The results help us realize the geometric matching in tool-tip machining and understand the geometric machinability in tool-side machining. The complement of the swept envelope, which represents the in-process workpiece, is also addressed. It finally presents the swept envelope applications in tool orientation determination, machining interference avoidance, and run-time simulation. When a solid object moves, it creates a three-dimensional swept volume. This swept volume defines a space where is ever occupied by the object during its motion. Anything inside this occupied space is collided with the object, while anything outside the space is collision-free. The swept envelope defines the boundary between the collision and collision-free space. Therefore, the swept envelope plays an important role in a wide variety of geometric applications such as geometric construction, robot workspace configuration, and collision detection (1, 2). Swept envelope computation is usually difficult because of the nonlinearity of the problem. It is generally impossible to obtain closed form expressions for the swept envelope created by objects with arbitrary geometric shapes (1, 3). Fortunately the majority of tool geometry for robots and NC machining is axial symmetry and defined by surfaces of revolution. These tools usually can be decomposed into geometric primitives, such as spheres, tori, cylinders, and cones (4). Due to their simplicity, the swept envelopes of the geometric primitives can be easily determined and explicitly presented. The swept envelopes of the original tools can then be integrated by the swept envelopes of their primitives (5). This paper details the determination of the swept envelope of axial symmetric tools, and its applications. It first analyzes tool geometry and its decomposition of geometric primitives. Then it discusses the tool position and the tool motion. Based on the tool geometry and motion, the explicit solution of the swept envelope is derived. It further analyzes the properties of the swept envelope. The results deduce us how to match the tools with the part geometry, and reveals the machinability of the geometry. The construction of the complement of the swept envelope is also discussed. After illustrating examples in computer applications, we conclude in the final section.
Optimization of Warehouse Location and Supplies Allocation for Emergency Rescue under Joint Government–Enterprise Cooperation Considering Disaster Victims’ Distress Perception

The location and allocation of emergency supplies are an important part of emergency rescue work. The existing emergency location and allocation process is inefficient, costly, and neglects the psychology of victims. To improve the emergency relief work and solve the current problems, this paper introduces the victims’ pain perception cost into the model, takes the lowest cost of the whole emergency rescue system as the goal, constructs a government–enterprise joint emergency material location–allocation model, and uses the simulated annealing algorithm to solve the model. This paper takes the 2008 Wenchuan earthquake in Sichuan Province as the background and verifies the validity and rationality of the model through a comparative analysis of case simulations. The results show that the model and algorithm can effectively solve the emergency supplies location–allocation problem considering the victims’ pain perception, reflecting the idea of human-centered sustainable development and providing support for building a sustainable emergency relief system.
Langsajarot Learning Community as a Response to Local Education Needs in the Pandemic Era

During the Covid-19 pandemic era, the world of education experienced a transformation from traditional to digital learning. Not all schools are ready to organize online learning, especially in villages. Langsajarot community in Magersari Village was formed as the response to the local education needs during this Covid-19 outbreak. Langsajarot community was formed to learn and prepare the children to adapt to digital learning. This study aims to obtain a detailed description of the learning management of the Langsajarot community so it can prepare the children for digital learning. Observation, documentation, and in-depth interview about the founding and learning activity of Langsajarot were carried out to obtain the research data. Observation and documentation were done for three months more. Founders, members, and the parents of members were interviewed. Data analysis was carried out through data condensation, data display, verification, and conclusion. The result shows that Langsajarot was formed to fill the free time of the children using positive activities and in managing the community during the pandemic era, there are three important factors. First, Langsajarot uses a constructivist approach to build the children’s motivation. Second, Langsajarot uses an engagement strategy to build the interest and focus in learning, therefore, although the community is nonformal they have many attendees. Third, Langsajarot introduces and trains the member about how to use the internet as learning resources and make their learning video and channel.
Flexible Modeling and Dynamic Design of the Auto Jack

For evaluating the stress state of the jack more precise, the technology of flexible modeling was applied to structure design of the jack. The simulation model with rigid body of the components were set up by AutoFlex module, and the jack’s dynamic curves in static loads were acquired through flexible simulation, then the dynamic characteristic and stress state of the jack which in working process was understand. Based on that, some basis was offered for improvement and reliability design of the jack.
Battery Model Identification Approach for Electric Forklift Application

Electric forklifts are extremely important for the world’s logistics and industry. Lead acid batteries are the most common energy storage system for electric forklifts; however, to ensure more energy efficiency and less environmental pollution, they are starting to use lithium batteries. All lithium batteries need a battery management system (BMS) for safety, long life cycle and better efficiency. This system is capable to estimate the battery state of charge, state of health and state of function, but those cannot be measured directly and must be estimated indirectly using battery models. Consequently, accurate battery models are essential for implementation of advance BMS and enhance its accuracy. This work presents a comparison between four different models, four different types of optimizers algorithms and seven different experiment designs. The purpose is defining the best model, with the best optimizer, and the best experiment design for battery parameter estimation. This best model is intended for a state of charge estimation on a battery applied on an electric forklift. The nonlinear grey box model with the nonlinear least square method presented a better result for this purpose. This model was estimated with the best experiment design which was defined considering the fit to validation data, the parameter standard deviation and the output variance. With this approach, it was possible to reach more than 80% of fit in different validation data, a non-biased and little prediction error and a good one-step ahead result.
Development of performance indicators for Occupational Health and Safety: a constructivist multicriteria approach for PPE

Abstract Paper aims This study aims to develop indicators, using the Multi-Criteria Decision Aid - Constructivist methodology (MCDA-C), to assess the occupational prevention activities in a psychiatric hospital. Originality The application to the health sector of MCDA-C, in a case study during the supply of medicines to patients. Research method We use the ProKnow-C. Then, MCDA-C was applied in a case study. Main findings Four interest areas were defined, then the critical situations were identified to construct 126 specific indicators and finally the intervention strategies were defined. 26 indicators focused on PPE, 4 lagging and 22 leading. The model enabled the mapping of performance indicator levels. For that, 48 recommendations, and intervention strategies for each situation were defined. Implications for theory and practice The use of specific indicators, obtained through a participatory process, allowed the identification of accurate and reliable metrics to measure the actual performance of activities, critical situations, and strategies.
Investigation on Dewatering of a Deep Shaft in Strong Permeable Sandy Pebble Strata on the Bank of the Yellow River

This paper reports the dewatering scheme of a deep excavation in sandy pebble strata. The excavation is in high permeability strata and is close to the Yellow River, making the dewatering difficult during construction. At present, few researchers have specially studied the dewatering scheme of deep excavations in strong permeable strata near the water resource. Field pumping test was conducted before the excavation activity, and the permeability coefficient of the strata was obtained by reverse analysis. According to the characteristics of the project, the dewatering scheme of “ waterproof curtain + base grouting + pumping ” was proposed. The influence of vertical waterproof curtain and base grouting on dewatering was analyzed by numerical simulation. In the construction process, the field water table and ground settlement were measured. The results show that (1) the groundwater table versus permeability coefficient curve shows three different stages and (2) the dewatering scheme of “ waterproof curtain + base grouting + pumping ” is effective for deep excavation in strong permeable strata.
Utilization of Biotechnology, Neurotransmitter and Cytogenetic Indices in Selecting Pigeon Breeds

The DNA polymorphism LDH-A and DRD4 gene, some neurotransmitter and cytogenetic indices of three sexed pigeon breeds; non-racing pigeons (wild rock), racing long and short distances pigeons (e.g: Gan aarden) and (e.g: Gansen) have been performed. The long-distance pigeon demonstrated the highest levels of neurotransmitters in brain tissues. Long distance male pigeons have a unique LDH-A polymorphism, so A changed to G, which means that changes in gene expression resulted in differences in amino acid structure K(lys) was converted to E(glu) in the LDH-A gene only in long distance male pigeons compared to other breeds. In addition to this, the DRD4 polymorphism of long distance male pigeons in which A changed to C and A changed to T indicates the presence of a unique diversity in this breed. This means that changes in gene expression resulted in overall amino acid structure differences only in long distance male pigeons when compared to other breeds, where R(arg) and L(leu) were converted to S(ser) and F(phe). Neurotransmitters are synthesised in the body from these amino acids. It concluded that measurements would be useful indices for the differentiation, genetic characterization, provide a foundation for conservation programs of the breeding and selecting racing pigeon breeders.
Noise Reduction of Elastic Structure Paste Viscoelastic Material Based on Complex Radius Vector of Virtual Source

It is hard to theoretically calculate the noise reduction characteristics for underwater complex structure of paste viscoelastic material. This paper introduces complex radius vector inside radiator based on the advantages of singular integral provided by traditional virtual source superposition method to calculate the radiated sound field from finite cylindrical shells. This method adapts truncation regularization filter to overcome the effect of background noise to enhance the calculation accuracy and then use impedance transmission theory to calculate the noise reduction characteristics of the cylindrical shells paste viscoelastic material. Numerical simulations show the calculation accuracy is greatly dependent on the size and position of the distributed virtual sources. There exists an optimal complex radius to minimize the error.
Energy-Constrained Computation Offloading in Space-Air-Ground Integrated Networks Using Distributionally Robust Optimization

With the rapid development of connecting massive devices to the Internet, especially for remote areas without cellular network infrastructures, space-air-ground integrated networks (SAGINs) emerge and offload computation-intensive tasks. In this paper, we consider a SAGIN, where multiple low-earth-orbit (LEO) satellites providing connections to the cloud server, an unmanned aerial vehicle (UAV), and nearby base stations (BSs) providing edge computing services are included. The UAV flies along a fixed trajectory to collect tasks generated by Internet of Things (IoT) devices, and forwards these tasks to a BS or the cloud server for further processing. To facilitate efficient processing, the UAV needs to decide where to offload as well as the proportion of offloaded tasks. However, in practice, due to the variability of environment and actual demand, the amount of arrival tasks is uncertain. If the deterministic optimization is utilized to develop offloading strategy, unnecessary system overhead or higher task drop rate may occur, which severely damages the system robustness. To address this issue, we characterize the uncertainty with a data-driven approach, and formulate a distributionally robust optimization problem to minimize the expected energy-constrained system latency under the worst-case probability distribution. Furthermore, the distributionally robust latency optimization algorithm is proposed to reach the sub-optimal solution. Finally, we perform simulations on the real-world data set, and compare with other benchmark schemes to verify the efficiency and robustness of our proposed algorithm.