Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural-Adaptive Control of Single-Master–Multiple-Slaves Teleoperation for Coordinated Multiple Mobile Manipulators With Time-Varying Communication Delays and Input Uncertainties

In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.
Analysis of steady state micro-droplet evaporation to enhance heat dissipation from tiny surfaces.

Experimental investigations and numerical predictions of steady state microdroplet evaporation experiments are presented. Steady state droplet evaporation experiments are conducted to understand (1) Droplet contact line influence on evaporation rate efficiency, (2) Droplet contact angle correlation to evaporation rate and (3) Substrate cooling. Experiments are performed on a polymer substrate with a moat like trench (laser patterned) to control droplet contact line dynamics. A bottom-up methodology is implemented for droplet formation on the patterned substrate. Droplet evaporation rates on substrate temperatures 22≤ ∆TSubstrate ≤75 and contact angles 80°≤ Θ ≤110° are measured. For a pinned microdroplet (CCR), volumetric infuse rate influences droplet contact angle. Results illustrate droplet contact line impact on evaporation rate. Moreover, these results coincide with previously published results and affirm that evaporation rate efficiency reduces with contact line depinning. Additionally, from all the analyzed experimental cases, evaporation rate scales proportional to the microdroplet contact angle (i.e. ṁLG ∝ θ). In conclusion, these experiments shed new light on steady state evaporation of a microdroplet and its corresponding observations. Vital research findings can be used to enhance heat dissipation from tiny surfaces.
Neuropsychological Measures of Attention and Impulse Control among 8-Year-Old Children Exposed Prenatally to Organochlorines

Background: We previously reported associations between organochlorines and behaviors related to attention deficit hyperactivity disorder among boys and girls at 8 years of age using a teacher’s rating scale for a birth cohort in New Bedford, Massachusetts (USA). Objectives: Our goal was to corroborate these findings using neuropsychological measures of inattentive and impulsive behaviors. Methods: We investigated the association between cord serum polychlorinated biphenyls (PCBs) and p,p´-dichlorodiphenyl dichloroethylene (p,p´-DDE) and attention and impulse control using a Continuous Performance Test (CPT) and components of the Wechsler Intelligence Scale for Children, 3rd edition (WISC-III). Participants came from a prospective cohort of children born during 1993–1998 to mothers residing near a PCB-contaminated harbor in New Bedford. Median (range) cord serum levels for the sum of four prevalent PCBs [congeners 118, 138, 153, and 180 (ΣPCB4)] and p,p´-DDE were 0.19 (0.01–2.59) and 0.31 (0–14.93) ng/g serum, respectively. Results: We detected associations between PCBs and neuropsychological deficits for 578 and 584 children with CPT and WISC-III measures, respectively, but only among boys. For example, boys with higher exposure to ΣPCB4 had a higher rate of CPT errors of omission [rate ratio for the exposure interquartile range (IQR) = 1.12; 95% confidence interval (CI): 0.98, 1.27] and slower WISC-III Processing Speed (change in score for the IQR = –2.0; 95% CI: –3.5, –0.4). Weaker associations were found for p,p´-DDE. For girls, associations were in the opposite direction for the CPT and null for the WISC-III. Conclusions: These results support an association between organochlorines (mainly PCBs) and neuropsychological measures of attention among boys only. Sex-specific effects should be considered in studies of organochlorines and neurodevelopment.
Overexpression of Nrf2 Protects against Microcystin-Induced Hepatotoxicity in Mice

Oxidative stress and glutathione (GSH) depletion are implicated in mycocystin hepatotoxicity. To investigate the role of nuclear factor erythroid 2-related factor 2 (Nrf2) in microcystin-induced liver injury, Nrf2-null, wild-type, and Keap1-hepatocyte knockout (Keap1-HKO) mice were treated with microcystin (50 μg/kg, i.p.). Blood and liver samples were collected 8 h thereafter. Microcystin increased serum alanine aminotransferase and aspartate aminotransferase activities, and caused extensive inflammation and necrosis in Nrf2-null and wild-type mice, but not in Keap1-HKO mice. Oxidative stress and inflammation are implicated in microcystin-induced hepatotoxicity, as evidenced by increased lipid peroxidation and increased expression of pro-inflammatory genes, such as neutrophil-specific chemokines mKC and MIP-2, and pro-inflammatory cytokines IL-1β and IL-6. The increased expression of these pro-inflammatory genes was attenuated in Keap1-HKO mice. Nrf2 and Nqo1 mRNA and protein were higher in Keap1-HKO mice at constitutive levels and after microcystin. To further investigate the mechanism of the protection, hepatic GSH and the mRNA of GSH-related enzymes were determined. Microcystin markedly depleted liver GSH by 60–70% in Nrf2 and WT mice but only 35% in Keap1-HKO mice. The mRNAs of GSH conjugation and peroxide reduction enzymes, such as Gstα1, Gstα4, Gstμ, and Gpx2 were higher in livers of Keap1-HKO mice, together with higher expression of the rate-limiting enzyme for GSH synthesis (Gclc). Organic anion transport polypeptides were increased by microcystin with the most increase in Keap1-HKO mice. In conclusion, this study demonstrates that higher basal levels of Nrf2 and GSH-related genes in Keap1-HKO mice prevented microcystin-induced oxidative stress and liver injury.
Role of Motivation in the Relationship Between Depression, Self-care, and Glycemic Control in Adults With Type 2 Diabetes

Purpose The mechanism by which depression influences health outcomes in persons with diabetes is uncertain. The purpose of this study was to test whether depression is related to self-care behavior via social motivation and indirectly related to glycemic control via self-care behavior. Methods Patients with diabetes were recruited from an outpatient clinic. Information gathered pertained to demographics, depression, and diabetes knowledge (information); diabetes fatalism (personal motivation); social support (social motivation); and diabetes self-care (behavior). Hemoglobin A1C values were extracted from the patient medical record. Structural equation models tested the predicted pathways. Results Higher levels of depressive symptoms were significantly related to having less social support and decreased performance of diabetes self-care behavior. In addition, when depressive symptoms were included in the model, fatalistic attitudes were no longer associated with behavioral performance. Conclusions Among adults with diabetes, depression impedes the adoption of effective self-management behaviors (including physical activity, appropriate dietary behavior, foot care, and appropriate self-monitoring of blood glucose behavior) through a decrease in social motivation.
The Effect of Electrical Impedance Matching on the Electromechanical Characteristics of Sandwiched Piezoelectric Ultrasonic Transducers

For achieving the power maximum transmission, the electrical impedance matching (EIM) for piezoelectric ultrasonic transducers is highly required. In this paper, the effect of EIM networks on the electromechanical characteristics of sandwiched piezoelectric ultrasonic transducers is investigated in time and frequency domains, based on the PSpice model of single sandwiched piezoelectric ultrasonic transducer. The above-mentioned EIM networks include, series capacitance and parallel inductance (I type) and series inductance and parallel capacitance (II type). It is shown that when I and II type EIM networks are used, the resonance and anti-resonance frequencies and the received signal tailing are decreased; II type makes the electro-acoustic power ratio and the signal tailing smaller whereas it makes the electro-acoustic gain ratio larger at resonance frequency. In addition, I type makes the effective electromechanical coupling coefficient increase and II type makes it decrease; II type make the power spectral density at resonance frequency more dramatically increased. Specially, the electro-acoustic power ratio has maximum value near anti-resonance frequency, while the electro-acoustic gain ratio has maximum value near resonance frequency. It can be found that the theoretically analyzed results have good consistency with the measured ones.
Association of E26 Transformation Specific Sequence 1 Variants with Rheumatoid Arthritis in Chinese Han Population

Objective E26 transformation specific sequence 1 (ETS-1) belongs to the ETS family of transcription factors that regulate the expression of various immune-related genes. Increasing evidence indicates that ETS-1 could contribute to the pathogenesis of autoimmune disease. Recent research has provided evidence that ETS-1 might correlate with rheumatoid arthritis (RA), but it's not clearly defined. In this study, we aimed to identify whether polymorphisms of ETS-1 play a role in Rheumatoid arthritis (RA) susceptibility and development in Chinese Han population. Methods Four single nucleotide polymorphisms (SNPs) within ETS-1 were selected based on HapMap data and previous associated studies. Whole blood and serum samples were obtained from 158 patients with RA and 192 healthy subjects. Genotyping was performed with polymerase chain reaction-high resolution melting (PCR-HRM) assay and the data was analyzed using SPSS17.0. Results A significantly positive correlation was observed between the SNP rs73013527 of ETS-1 and RA susceptibility, DAS28 and CRP (P<0.001, P = 0.001, and P = 0.028, respectively). Carriers of the haplotype CCT or TCT for rs4937333, rs11221332 and rs73013527 were associated with decreased risk of RA as compared to controls. No statistical significant difference was observed in the distribution of rs10893872, rs4937333 and rs11221332 genotypes between RA patients and controls. Conclusions Our data further supports that ETS-1 has a relevant role in the pathogenesis and development of RA. Allele T of rs73013527 plays a protective role in occurrence of RA but a risk factor in the high disease activity. Rs10893872, rs11221332 and rs4937333 are not associated with RA susceptibility and clinical features.
Triaging Patient Complaints: Monte Carlo Cross-Validation of Six Machine Learning Classifiers

Background Unsolicited patient complaints can be a useful service recovery tool for health care organizations. Some patient complaints contain information that may necessitate further action on the part of the health care organization and/or the health care professional. Current approaches depend on the manual processing of patient complaints, which can be costly, slow, and challenging in terms of scalability. Objective The aim of this study was to evaluate automatic patient triage, which can potentially improve response time and provide much-needed scale, thereby enhancing opportunities to encourage physicians to self-regulate. Methods We implemented a comparison of several well-known machine learning classifiers to detect whether a complaint was associated with a physician or his/her medical practice. We compared these classifiers using a real-life dataset containing 14,335 patient complaints associated with 768 physicians that was extracted from patient complaints collected by the Patient Advocacy Reporting System developed at Vanderbilt University and associated institutions. We conducted a 10-splits Monte Carlo cross-validation to validate our results. Results We achieved an accuracy of 82% and F-score of 81% in correctly classifying patient complaints with sensitivity and specificity of 0.76 and 0.87, respectively. Conclusions We demonstrate that natural language processing methods based on modeling patient complaint text can be effective in identifying those patient complaints requiring physician action.
SimStudent : Building an Intelligent Tutoring System by Tutoring a Synthetic Student

SimStudent is a machine-learning agent that has been developed to help novice authors to build Intelligent Tutoring Systems (ITS) without heavy programming. Integrated into an existing suite of software tools called CTAT (Cognitive Tutor Authoring Tools), SimStudent helps authors to create an expert model for ITS by “teaching” SimStudent how to solve problems. There are two ways for the author to teach SimStudent: by demonstrating solutions or by tutoring SimStudent. In the former approach, the author demonstrates solution steps, and SimStudent attempts to induce underlying domain principles by generalizing those demonstrations. In the latter approach, the author gives SimStudent problems to solve, and provides feedback on the steps performed by SimStudent, and also gives “hints” for steps that SimStudent cannot perform (where the hints consist of demonstrating that particular step). To evaluate which authoring strategy better facilitates authoring, we conducted two studies. The first measured the quality of the expert model; here the authoring processes were simulated by using pre-recorded solutions for demonstrations as well as using an existing Cognitive Tutor for tutoring SimStudent. The second study measured the time for authoring; here a human author was actually using SimStudent to author a Cognitive Tutor. The results show the following: (1) Authoring by demonstration requires more time than authoring by tutoring, because when authoring by demonstration, the author frequently needs to test the quality of the expert model to determine if more demonstration is needed. In contrast, when authoring by tutoring, this decision making can be done naturally by observing SimStudent’s performance during tutoring. (2) The expert model generated with authoring by tutoring tends to be more precise than authoring by demonstration. That is, when applied to solve problems, the expert model generated by tutoring makes more correct steps and/or fewer incorrect ones.
Requirements Elicitation from BPMN Models

When building a software system, it is crucial to understand the actual needs and the interfering constraints that apply in the surrounding environment. Elicitation of requirements is all about learning the environment and discovering the needs of users and other stakeholders. One of the primary sources for requirement elicitation is the system (processes, organization, environment and legacy systems) currently being used. The system is often captured in the form of graphical models, which are an important source of information for requirements elicitation. BPMN models are gaining popularity and are frequently used to model systems. Despite the fact that they are a valuable source of knowledge, they are rarely used as a source for eliciting requirements. One reason for this is the lack of concrete and comprehensive guidelines that would assist a systematic requirements elicitation from such models. This thesis presents a method for eliciting functional requirements from BPMN models. The method covers all components of a requirement and gives guidelines where in the BPMN model the information about the components can be found. It also provides a set of questions to be asked from domain experts to make sure that the requirement specification is complete, consistent, bounded and on the required level of granularity. The method was applied on a case study and it was proved that the method is applicable and provides a structured approach to eliciting requirements. The method elicited more requirements than the method previously used by the case organization, and the elicited requirements were also of better quality. The method took considerably less time to apply, it gave better control over the elicitation process, it was easier to evaluate the needed effort, and it enabled to better plan the process. The structured approach makes it easier to delegate work, and there are less situations where something might be overlooked.
Brain tumor image segmentation based on convolution neural network

Automatic segmentation and early diagnosis of brain tumor is a challenging problem in computer vision and it can provide possibility for pre-operative planning, and solve the problem such as low accurateness and time-consuming in traditional manual segmentation. Under the mentioned problems above, this paper put forward a new method: Based on traditional convolutional neural networks (CNNs), a new architecture model is proposed for automatic brain tumor segmentation, which combines multi-modality images. The newly designed CNNs model automatically learns useful features from multi-modality images to combine multi-modality information. Experiment results show that the proposed model is more accurate than traditional methods and can provide reliable information for clinic treatments.
Scan rectification for structured light range sensors with rolling shutters

Structured light range sensors, such as the Microsoft Kinect, have recently become popular as perception devices for computer vision and robotic systems. These sensors use CMOS imaging chips with electronic rolling shutters (ERS). When using such a sensor on a moving platform, both the image, and the depth map, will exhibit geometric distortions. We introduce an algorithm that can suppress such distortions, by rectifying the 3D point clouds from the range sensor. This is done by first estimating the time continuous 3D camera trajectory, and then transforming the 3D points to where they would have been, if the camera had been stationary. To ensure that image and range data are synchronous, the camera trajectory is computed from KLT tracks on the structured-light frames, after suppressing the structured-light pattern. We evaluate our rectification, by measuring angles between the visible sides of a cube, before and after rectification. We also measure how much better the 3D point clouds can be aligned after rectification. The obtained improvement is also related to the actual rotational velocity, measured using a MEMS gyroscope.
Genetic variants in IFIH1 play opposite roles in the pathogenesis of psoriasis and chronic periodontitis

The IFIH1 gene is a key factor connecting environmental and genetic factors in the pathogenesis of immune‐related diseases. We aimed to investigate whether it has effects on psoriasis, chronic periodontitis and skin test‐positive penicillin allergy and to confirm whether these diseases have shared molecular mechanisms originating from shared genetics. Two common variants in IFIH1 were genotyped in 561 patients with psoriasis, 421 patients with chronic periodontitis, 175 patients with skin test‐positive penicillin allergy and 1100 shared controls. Then, case–control study was used to analyse the association between IFIH1 and the three diseases. The allele distributions of rs1990760 and rs3747517 in the Chinese population are much different from the European population. The A allele of rs1990760 (OR = 1.30, P = 5.4 × 10−3) and A‐G (rs1990760/rs3747517) haplotype (OR = 1.31, P = 3.8 × 10−3) were highly associated with the risk of psoriasis. However, the A allele of rs1990760 (OR = 0.73, P = 7.8 × 10−3) and A‐G haplotype (OR = 0.71, P = 4.5 × 10−3) were identified as protective factors for chronic periodontitis. IFIH1 affects several immune‐related diseases, including psoriasis and chronic periodontitis, and provides a molecular link between genetic susceptibility, viral infections and immune‐related diseases. Moreover, we also confirm the hypothesis that shared molecular mechanisms from common genetic variants contribute to a spectrum of immune‐related diseases.
Knowledge engineering techniques for automated planning

Formulating knowledge for use in AI Planning engines is currently some-thing of an ad-hoc process, where the skills of knowledge engineers and the tools they use may signiﬁcantly inﬂuence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when formulating knowledge into planning domain languages such as PDDL. Also, there is little published research to inform engineers on which method and tools to use in order to effectively engineer a new planning domain model. This is of growing importance, as domain independent planning engines are now being used in a wide range of applications, with the consequence that op-erational problem encodings and domain models have to be developed in a standard language. In particular, at the difﬁcult stage of domain knowledge formulation, changing a statement of the requirements into something for-mal - a PDDL domain model - is still somewhat of an ad hoc process, usually conducted by a team of AI experts using text editors. On the other hand, the use of tools such as itSIMPLE or GIPO, with which experts gen-erate a high level diagrammatic description and automatically generate the domain model, have not yet been proven to be more effective than hand coding. The major contribution of this thesis is the evaluation of knowledge en-gineering tools and techniques involved in the formulation of knowledge. To support this, we introduce and encode a new planning domain called Road Trafﬁc Accidents (RTA), and discuss a set of requirements that we have derived, in consultation with stakeholders and analysis of accident management manuals, for the planning part of the management task. We then use and evaluate three separate strategies for knowledge formulation, encoding domain models from a textual, structural description of require-ments using (i) the traditional method of a PDDL expert and text editor (ii) a leading planning GUI with built in UML modelling tools (iii) an object-based notation inspired by formal methods. We evaluate these three ap-proaches using process and product metrics. The results give insights into the strengths and weaknesses of the approaches, highlight lessons learned regarding knowledge encoding, and point to important lines of research for knowledge engineering for planning. In addition, we discuss a range of state-of-the-art modelling tools to ﬁnd the types of features that the knowledge engineering tools possess. These features have also been used for evaluating the methods used. We bench-mark our evaluation approach by comparing it with the method used in the previous International Competition for Knowledge Engineering for Plan-ning & Scheduling (ICKEPS). We conclude by providing a set of guide-lines for building future knowledge engineering tools.