Miracle-dz/UniFAR-FTUs huggingface dataset:
What Recovery Means to Postpartum Women in Treatment for Opioid Use Disorder

Introduction: Opioid overdose has become a leading cause of pregnancy-associated deaths, particularly in the 1st year postpartum, highlighting the need to better understand how to promote recovery for postpartum women. This mixed-methods study aims to investigate how postpartum women receiving medication for opioid use disorder (MOUD) define recovery and factors associated with recovery progression or inhibition. Methods: Women receiving MOUD 2–6 months postpartum were recruited from an outpatient perinatal addiction clinic. Participants completed electronic measures including the Brief Assessment of Recovery Capital (BARC-10, total score range: 6–60) and semistructured individual interviews. Substance Abuse and Mental Health Services Administration (SAMHSA)'s recovery framework served as the conceptual model for interview guide development. Descriptive statistics were generated for survey responses. A qualitative descriptive approach was used to analyze and report the interview data. Results: On average, participants (n = 8) were 28.6 years old and taking 19.5 mg/day buprenorphine (range 8–24). Fifty percent identified as white and 37.5% as black. All participants identified as currently in recovery, with mean BARC-10 score 52.5 (standard deviation 4.8). Recovery goals included no use of drugs or alcohol (62.5%), being a better partner/spouse (87.5%), and improving finances (87.5%). Interviews generated themes including recovery as transformative, building resilience, and transforming one's health, relationships, and environment through recovery. Conclusions: Participants defined recovery as a dynamic transformative process, including nonabstinence-based goals consistent with SAMHSA domains coupled with reduced substance use. Central to recovery for our postpartum participants was the sense of self reinforced throughout their recovery journey. Women highlighted the key role of MOUD in their recovery process. Findings underscore the need for individualized treatment for postpartum women with opioid use disorder based on their personal goals and will inform development of a validated, gender-informed measure of patient-reported recovery outcomes tailored for this population.
Finite Mixture of the Hidden Markov Model for Driving Style Analysis

Analyzing driving style is useful for developing intelligent vehicles. Previous studies usually consider the statistical features (e.g., the means and standard deviations of brake pressure) of the measured driving data or manually define the number of patterns divided by behavior semantics to characterize driving styles. In this paper, we propose a driving style analysis to describe the personalized driving styles from time-series driving data without specifying the levels in advance but by estimating them from the data. First, range, range rate, and acceleration are selected as three feature variables to describe car-following scenarios. Then, the car-following data are normalized to reduce the scale influence of different variables on the segmentation results. The hidden Markov model (HMM) and the finite mixture of the hidden Markov model (MHMM) are adopted to extract behavior semantics. Compared with the HMM, the MHMM can identify the heterogeneity of data and then provide more reasonable primitive driving patterns. Based on the results, this study uses the K-means clustering to label all the driving patterns semantically and identifies a total of 75 different driving patterns. We use the normalized frequency distributions to describe personalized driving behavior characteristics, and similarity evaluations of driving styles are applied using the Kolmogorov–Smirnov test. The proposed approach in this paper is useful for exploring the characteristics of driving habits.
A predictive maintenance system for wireless sensor networks: a machine learning approach

Predictive maintenance system (PdM) is a new concept that helps system operators evaluate the current status of their systems, and it also assists in predicting the future quality of these systems and scheduling maintenance action. This paper proposes a PdM model that utilizes machine learning to predict the system’s operational status after M active steps based on L previous observations implemented by a feedforward neural network (FFNN). We use quantization and encoding schemes to reduce the complexity of the system. We apply the proposed model to build a PdM system for wireless sensors networks (WSNs), where our concern is to predict the state of the system as far as the quality of data transfer is concerned. The FFNN provides a forward prediction of the operational status of the network after M consecutive time steps in the future, based on the previous L readings of quality of service (QoS) requirements of WSN. We also demonstrate the relation between complexity and accuracy. We found that larger M leads to higher complexity and larger prediction error, where larger L entails higher complexity and smaller prediction error. We also investigate how quantization and encoding can reduce complexity to implement a real-time PdM system.
Teachers’ Perceptions of Integrated Reading-Writing Instruction for Emergent Bilinguals

Abstract Integrated Reading-Writing Instruction (IRWI) is an approach for English Language Arts (ELA) instruction that has gained much attention since the adoption of college and career standards. Previous studies have shown that connecting reading and writing during instruction positively affects students’ literacy outcomes as related cognitive processes are engaged in the construction of meaning. This qualitative study of five 4th grade elementary ELA classrooms serving diverse students, including emergent bilinguals, explored the transition from the previous form of writing instruction to IRWI, or analytical text-based writing in elementary classrooms when implementing college and career standards. The findings indicated that IRWI introduced several affordances for the five teachers’ instruction, such as an explicit connection between reading and writing, and for their emergent bilingual students, overall improvement of reading comprehension and preparation for more college-like writing. Also, participating teachers reported several disadvantages for their emergent bilingual students stemming from how IRWI was implemented in the district. These disadvantages included the absence of creativity and genre variety for writing instruction, writing that was dependent upon English reading comprehension, and difficulties with teaching how to write evidence and elaboration.
Solar Radiation Components on a Horizontal Surface in a Tropical Coastal City of Salvador

Renewable energy must be prioritized by humankind, mainly if there is an expected increase of 50% in energy consumption by 2030 and climate change scenarios are also confirmed. Urban areas consume 70% of the available energy on the planet. Brazil, the largest country in South America, concentrates more than 85% of its population in urban areas, facing a challenge to increase the renewable power plants in its energy matrix. This work presents the solar radiation components behavior for the city of Salvador to contribute with initiatives for the use of solar energy resource. Firstly, a radiometric platform was implemented to obtain direct measurements of global (EG) and diffuse (EDF) components of incoming solar radiation at the surface. The knowledge of EDF is an important requirement to support photovoltaic system projects, and there is no information on direct measurements of this component in the State of Bahia. The diffuse radiation measurement device (DRMD) was designed and built for this purpose. The measurements of solar radiation components performed in this research were submitted to a specific quality control, statistically analyzed and used to evaluate the performance of different empirical models to represent the behavior of EDF in the tropical coastal city. The results demonstrate the potential to explore solar energy in the city of Salvador, with annual values of sunshine higher than 2200 h year−1 and average daily intensities of EG and EDF equal to 18.7 MJm−2day−1 and 7.35 MJm−2day−1, respectively. The analysis of the diurnal cycle shows that EG in summer reaches a maximum of 4.2 MJm−2hour−1 and in the rainy season it reaches a minimum of 3.7 MJm−2hour−1, both at noon, and in summer the EDFh is 35% of the EGh and it is 46% in the rainy season.
A randomized feasibility study evaluating temozolomide circadian medicine in patients with glioma.

Background Gliomas are the most common primary brain tumor in adults. Current treatments involve surgery, radiation, and temozolomide (TMZ) chemotherapy; however, prognosis remains poor and new approaches are required. Circadian medicine aims to maximize treatment efficacy and/or minimize toxicity by timed delivery of medications in accordance with the daily rhythms of the patient. We published a retrospective study showing greater anti-tumor efficacy for the morning, relative to the evening, administration of TMZ in patients with glioblastoma. We conducted this prospective randomized trial to determine the feasibility, and potential clinical impact, of TMZ chronotherapy in patients with gliomas (NCT02781792). Methods Adult patients with gliomas (WHO grade II-IV) were enrolled prior to initiation of monthly TMZ therapy and were randomized to receive TMZ either in the morning (AM) before 10 am or in the evening (PM) after 8 pm. Pill diaries were recorded to measure compliance and FACT-Br quality of life (QoL) surveys were completed throughout treatment. Study compliance, adverse events (AE), and overall survival were compared between the two arms. Results A total of 35 evaluable patients, including 21 with GBM, were analyzed (18 AM patients and 17 PM patients). Compliance data demonstrated the feasibility of timed TMZ dosing. There were no significant differences in AEs, QoL, or survival between the arms. Conclusions Chronotherapy with TMZ is feasible. A larger study is needed to validate the effect of chronotherapy on clinical efficacy.
Fuzzy Time Decision and Model-based Torque Coordinating Control of Shifting Process for Dry Dual Clutch Transmission

(cid:3)Based on the five-speed dry dual clutch transmission(DCT) which have been independently developed, the issue of torque coordinating control between engine and twin clutches is been discussed from the system level in order to adequately reflect the driver’s intension and improve the shift quality of DCT, calculation formulas of engine speed and clutch delivered torque during the shifting process are been induced also. Considering the characteristic of physical configuration, five degrees-of-freedom(DOF) shifting dynamics equations of DCT are been established, after simplifying two-DOF model in the stage of slip-friction and one-DOF model in the stage of in-gear stable operation are been respectively attained. According to the requirements of the shift quality, the quantitative control objectives of shifting process are been proposed. Based on the analysis of torque and speed characteristics of engine and twin clutches during the shifting process, the fuzzy time decision and the model-based torque coordinating control strategy are been produced. Simulation model of DCT shifting control is been established on the Matlab/Simulink software platform, upshifting process and downshifting process are been simulated and analyzed under different driving intension. Real vehicle chassis dynamometer test has also been done. Simulation and experiment results show that the proposed model-based torque coordinating control strategy can not only effectively reflects the driver shift intention and improve the shift quality of DCT, but also be adaptable to the various shifting conditions and has strong robustness.
Research on Fault Location Based on Wavelet Technology in Transmission Lines

The method that applies the wavelet transform and combines traveling wave to analyze the transient process of power system fault is analyzed to find the position of fault.In the process of fault,the traveling wave provides the resolution of high frequency component of fault,the zero-mode resolves some assured and special faults and the line-mode analyzes all types of faults.At last,the MatLab validates the reliability of this method in all typical power system faults.The experimental result shows that the way can certainly judge the type and position of fault.
Anticipating Dynamic Loads in Handling Objects

In this paper we review a set of studies showing that when people pick up and move an object they continually adjust their grip force in order to stabilize the object in the hand. These grip force adjustments occur simultaneously with or slightly ahead of fluctuations in load forces and torques related to moving the object. They may therefore be seen as anticipatory and we argue that a key line of research in manipulation should be to understand the integration of sensory motor information to build an internal model of the object and the effector system to support such anticipation.
Nonconventional Hydrocolloids’ Technological and Functional Potential for Food Applications

This review aims to study the alternatives to conventional industrial starches, describing uncommon sources along with their technological characteristics, processing, and performance on food products. Minor components remaining after extraction play an important role in starch performance despite their low percentage, as happens with tuber starches, where minerals may affect gelatinization. This feature can be leveraged in favor of the different needs of the food industry, with diversified applications in the market being considered in the manufacture of both plant and animal-based products with different sensory attributes. Hydrocolloids, different from starch, may also modify the technological outcome of the amylaceous fraction; therefore, combinations should be considered, as advantages and disadvantages linked to biological origin, consumer perception, or technological performance may arise. Among water-based system modifiers, starches and nonstarch hydrocolloids are particularly interesting, as their use reaches millions of sales in a multiplicity of specialties, including nonfood businesses, and could promote a diversified scheme that may address current monocrop production drawbacks for the future sustainability of the food system.
The Development and Application Effects of Convergence Program for Field Trip and STEAM Education related Geology

ABSTRACT In this study, a theme-centered STEAM program converged with field trip was developed in the ‘The stratum and fossils’ unit of elementary science. The objectives were to find the influences on academic achievement, creative problem solving abilities and scientific attitude of elementary school students. The STEAM education program was composed of content which can be applied effectively by converging field trip and STEAM elements. As the results applied the STEAM program to elementary school students, the experimental group improved effectively in the academic achievement, creative problem solving abilities and scientific attitude compared to the control group, which took the theoretical lesson. In conclusion, it is necessary to develop systematically and continuously programs converging STEAM and field trip in various units of science subject which field trip is possible. Key words : geology, STEAM program, field trip, academic achievement, creative problem-solving ability, scientific attitude
Area Coverage with Multiple Capacity-Constrained Robots

The area coverage problem is the task of efficiently servicing a given two-dimensional surface using sensors mounted on robots. We present a novel formulation for generating coverage routes for multiple capacity-constrained robots, where capacity can be specified in terms of battery life or flight time. Traversing the environment incurs demands on the robot resources, which have capacity limits. The central aspect of our approach is transforming the area coverage problem into a line coverage problem (i.e., coverage of linear features), and then generating routes that minimize the total cost of travel while respecting the capacity constraints. We define two modes of travel: (1) servicing and (2) deadheading, which correspond to whether a robot is performing task-specific actions or not. Our formulation allows separate and asymmetric travel costs and demands for the two modes. Furthermore, the cells computed from cell decomposition, aimed at minimizing the number of turns, are not required to be monotone polygons. We develop new procedures for cell decomposition and generation of service tracks that can handle non-monotone polygons with or without holes. We establish the efficacy of our algorithms on a ground robot dataset with 25 indoor environments and an aerial robot dataset with 300 outdoor environments. The algorithm generates solutions whose costs are 10% lower on average than state-of-the-art methods. We demonstrate our algorithm in experiments with aerial robots.
Dual motor control for backlash reduction

Within the EU FP-6 project SMErobotTM a new type of high-performance robots has been developed by ABB Robotics, the Robotics Lab at Lund University and Gudel AG, Switzerland. The new design is based on a parallel configuration of the robot's joints (parallel robots). The main novelty of that concept is its completely new parallel kinematic structure, which allows to exploit all the advantages in terms of performance and cost of parallel kinematics, e.g. having only axial forces in the arm links. Consequently, this new type of robot can be used in many applications, such as laser, water and plasma jet cutting, gluing, assembly and machining. However, the actuator and drive-line of the robot are based on the 'Rack-and-pinion principle'. This leads to some difficulties concerning the positioning accuracy and the stiffness, as flexibility and backlash will occur in both the ordinary gearbox and in the mechanical connection to the rail. To fulfill the requirements on the robot, these effects need to be eliminated. The approach of the present work is to reduce these effects by using two motors for each cart instead of the conventional use of only one. Furthermore also position measurements along the rails are used in addition to the motor angle positions. For this configuration, a nonlinear model is derived and the effects of the additional motor on the system performance and stability are analyzed. Furthermore, several nonlinear, smooth switching control laws for the operation of the system are presented. The theoretical results are furthermore verified with an experimental setup representing a simplified implementation of the dual motor driven robot.
Successful Use of Salvage Bacteriophage Therapy for a Recalcitrant MRSA Knee and Hip Prosthetic Joint Infection

Prosthetic joint infections are a serious complication of joint replacement surgery due to the significant morbidity and financial burden that is associated with conventional treatments. When patients fail the gold standard two-stage revision surgery, very limited, well-defined standardized approaches are available. Herein, we discuss the case of a sixty-four-year-old woman who had a recalcitrant MRSA prosthetic joint infection of her knee and hip that failed repeated conventional surgical and medical treatments. Only after receiving intraoperative and intravenous bacteriophage therapy was the patient able to achieve cure of her prosthetic joint infections, as demonstrated by the lack of clinical recurrence and sterility of intraoperative cultures while off antibiotics. This case reinforces that bacteriophage therapy holds promise in the treatment of prosthetic joint infections and more specifically in complicated cases who have failed conventional surgical and medical interventions.
Reliability Assessment and Strengthening of a School Wooden Roof Structure of in Harbin

Through the reliability assessment and strengthening project of wood-roofs on a teaching building of a school in Harbin,some common damage phenomenon and corresponding causes of long-term service wood structures are summarized,such as decaying of wood members,cracking of wood,defectiveness of the former design,and mistakes of the repairing.The inspection of wood strength and strength grade evaluation of wood member are introduced;and the feasibility to use the wood-roof continually is confirmed by strength and deflection checking calculation for the main wood members,and security evaluation of wood roof.Finally corresponding reinforcement enhancement measures are proposed,such as ventilating,replacement and strengthening of members,filling of crack,addition of support,setting of fire zone,rectifying of mistakes of repairing,and so on.
Prediction models with graph kernel regularization for network data

Traditional regression methods typically consider only covariate information and assume that the observations are mutually independent samples. However, samples usually come from individuals connected by a network in many modern applications. We present a risk minimization formulation for learning from both covariates and network structure in the context of graph kernel regularization. The formulation involves a loss function with a penalty term. This penalty can be used not only to encourage similarity between linked nodes but also lead to improvement over traditional regression models. Furthermore, the penalty can be used with many loss-based predictive methods, such as linear regression with squared loss and logistic regression with log-likelihood loss. Simulations to evaluate the performance of this model in the cases of low dimensions and high dimensions show that our proposed approach outperforms all other benchmarks. We verify this for uniform graph, nonuniform graph, balanced-sample, and unbalanced-sample datasets. The approach was applied to predicting the response values on a ‘follow’ social network of Tencent Weibo users and on two citation networks (Cora and CiteSeer). Each instance verifies that the proposed method combining covariate information and link structure with the graph kernel regularization can improve predictive performance.
CORPORATE SOCIAL RESPONSIBILITY, FINANCING CONSTRAINTS AND CORPORATE SUSTAINABLE DEVELOPMENT

Based on the resource-based theory and stakeholder theory, this paper uses the data on 540 A-share listed companies in Shanghai and Shenzhen stock exchanges from 2014 to 2019 to carry out an empirical test of the relationship between corporate social responsibility, financing constraints and corporate sustainable development by using Stata14 software. The author explains the internal influence mechanism of corporate social responsibility on corporate sustainable development. The results show that corporate social responsibility can inhibit financing constraints and promote the sustainable development of enterprises in the Chinese context. Financing constraints restrain sustainable development of enterprises. Financing constraints play an intermediary role in corporate social responsibility and sustainable development. The conclusion of the study provides new management insights for the practice of corporate social responsibility in China.